DIGITS-CNN/cars/lr-investigations/exponential/1e-2/0.99/caffe_output.log

I0407 21:56:36.364892 23673 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-215634-6741/solver.prototxt
I0407 21:56:36.365115 23673 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 21:56:36.365124 23673 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 21:56:36.365221 23673 caffe.cpp:218] Using GPUs 1
I0407 21:56:36.395257 23673 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0407 21:56:36.676272 23673 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99990147
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 21:56:36.677296 23673 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 21:56:36.677866 23673 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 21:56:36.677881 23673 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 21:56:36.678038 23673 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 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 21:56:36.678125 23673 layer_factory.hpp:77] Creating layer train-data
I0407 21:56:36.679617 23673 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0407 21:56:36.679826 23673 net.cpp:84] Creating Layer train-data
I0407 21:56:36.679836 23673 net.cpp:380] train-data -> data
I0407 21:56:36.679855 23673 net.cpp:380] train-data -> label
I0407 21:56:36.679867 23673 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:56:36.685935 23673 data_layer.cpp:45] output data size: 128,3,227,227
I0407 21:56:36.806490 23673 net.cpp:122] Setting up train-data
I0407 21:56:36.806512 23673 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 21:56:36.806519 23673 net.cpp:129] Top shape: 128 (128)
I0407 21:56:36.806521 23673 net.cpp:137] Memory required for data: 79149056
I0407 21:56:36.806531 23673 layer_factory.hpp:77] Creating layer conv1
I0407 21:56:36.806552 23673 net.cpp:84] Creating Layer conv1
I0407 21:56:36.806558 23673 net.cpp:406] conv1 <- data
I0407 21:56:36.806569 23673 net.cpp:380] conv1 -> conv1
I0407 21:56:37.381981 23673 net.cpp:122] Setting up conv1
I0407 21:56:37.382002 23673 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:37.382006 23673 net.cpp:137] Memory required for data: 227833856
I0407 21:56:37.382025 23673 layer_factory.hpp:77] Creating layer relu1
I0407 21:56:37.382036 23673 net.cpp:84] Creating Layer relu1
I0407 21:56:37.382040 23673 net.cpp:406] relu1 <- conv1
I0407 21:56:37.382046 23673 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:56:37.382333 23673 net.cpp:122] Setting up relu1
I0407 21:56:37.382342 23673 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:37.382345 23673 net.cpp:137] Memory required for data: 376518656
I0407 21:56:37.382349 23673 layer_factory.hpp:77] Creating layer norm1
I0407 21:56:37.382359 23673 net.cpp:84] Creating Layer norm1
I0407 21:56:37.382362 23673 net.cpp:406] norm1 <- conv1
I0407 21:56:37.382387 23673 net.cpp:380] norm1 -> norm1
I0407 21:56:37.382825 23673 net.cpp:122] Setting up norm1
I0407 21:56:37.382834 23673 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:37.382838 23673 net.cpp:137] Memory required for data: 525203456
I0407 21:56:37.382843 23673 layer_factory.hpp:77] Creating layer pool1
I0407 21:56:37.382851 23673 net.cpp:84] Creating Layer pool1
I0407 21:56:37.382854 23673 net.cpp:406] pool1 <- norm1
I0407 21:56:37.382860 23673 net.cpp:380] pool1 -> pool1
I0407 21:56:37.382897 23673 net.cpp:122] Setting up pool1
I0407 21:56:37.382903 23673 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 21:56:37.382907 23673 net.cpp:137] Memory required for data: 561035264
I0407 21:56:37.382910 23673 layer_factory.hpp:77] Creating layer conv2
I0407 21:56:37.382920 23673 net.cpp:84] Creating Layer conv2
I0407 21:56:37.382925 23673 net.cpp:406] conv2 <- pool1
I0407 21:56:37.382930 23673 net.cpp:380] conv2 -> conv2
I0407 21:56:37.389679 23673 net.cpp:122] Setting up conv2
I0407 21:56:37.389693 23673 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:37.389696 23673 net.cpp:137] Memory required for data: 656586752
I0407 21:56:37.389704 23673 layer_factory.hpp:77] Creating layer relu2
I0407 21:56:37.389714 23673 net.cpp:84] Creating Layer relu2
I0407 21:56:37.389716 23673 net.cpp:406] relu2 <- conv2
I0407 21:56:37.389722 23673 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:56:37.390226 23673 net.cpp:122] Setting up relu2
I0407 21:56:37.390237 23673 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:37.390240 23673 net.cpp:137] Memory required for data: 752138240
I0407 21:56:37.390244 23673 layer_factory.hpp:77] Creating layer norm2
I0407 21:56:37.390251 23673 net.cpp:84] Creating Layer norm2
I0407 21:56:37.390255 23673 net.cpp:406] norm2 <- conv2
I0407 21:56:37.390261 23673 net.cpp:380] norm2 -> norm2
I0407 21:56:37.390621 23673 net.cpp:122] Setting up norm2
I0407 21:56:37.390630 23673 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:37.390633 23673 net.cpp:137] Memory required for data: 847689728
I0407 21:56:37.390636 23673 layer_factory.hpp:77] Creating layer pool2
I0407 21:56:37.390645 23673 net.cpp:84] Creating Layer pool2
I0407 21:56:37.390650 23673 net.cpp:406] pool2 <- norm2
I0407 21:56:37.390655 23673 net.cpp:380] pool2 -> pool2
I0407 21:56:37.390683 23673 net.cpp:122] Setting up pool2
I0407 21:56:37.390689 23673 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:37.390693 23673 net.cpp:137] Memory required for data: 869840896
I0407 21:56:37.390697 23673 layer_factory.hpp:77] Creating layer conv3
I0407 21:56:37.390705 23673 net.cpp:84] Creating Layer conv3
I0407 21:56:37.390708 23673 net.cpp:406] conv3 <- pool2
I0407 21:56:37.390713 23673 net.cpp:380] conv3 -> conv3
I0407 21:56:37.400655 23673 net.cpp:122] Setting up conv3
I0407 21:56:37.400666 23673 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:37.400671 23673 net.cpp:137] Memory required for data: 903067648
I0407 21:56:37.400681 23673 layer_factory.hpp:77] Creating layer relu3
I0407 21:56:37.400687 23673 net.cpp:84] Creating Layer relu3
I0407 21:56:37.400691 23673 net.cpp:406] relu3 <- conv3
I0407 21:56:37.400696 23673 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:56:37.401188 23673 net.cpp:122] Setting up relu3
I0407 21:56:37.401197 23673 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:37.401201 23673 net.cpp:137] Memory required for data: 936294400
I0407 21:56:37.401204 23673 layer_factory.hpp:77] Creating layer conv4
I0407 21:56:37.401214 23673 net.cpp:84] Creating Layer conv4
I0407 21:56:37.401218 23673 net.cpp:406] conv4 <- conv3
I0407 21:56:37.401226 23673 net.cpp:380] conv4 -> conv4
I0407 21:56:37.411550 23673 net.cpp:122] Setting up conv4
I0407 21:56:37.411563 23673 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:37.411568 23673 net.cpp:137] Memory required for data: 969521152
I0407 21:56:37.411576 23673 layer_factory.hpp:77] Creating layer relu4
I0407 21:56:37.411583 23673 net.cpp:84] Creating Layer relu4
I0407 21:56:37.411604 23673 net.cpp:406] relu4 <- conv4
I0407 21:56:37.411609 23673 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:56:37.411952 23673 net.cpp:122] Setting up relu4
I0407 21:56:37.411959 23673 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:37.411963 23673 net.cpp:137] Memory required for data: 1002747904
I0407 21:56:37.411967 23673 layer_factory.hpp:77] Creating layer conv5
I0407 21:56:37.411978 23673 net.cpp:84] Creating Layer conv5
I0407 21:56:37.411980 23673 net.cpp:406] conv5 <- conv4
I0407 21:56:37.411988 23673 net.cpp:380] conv5 -> conv5
I0407 21:56:37.420325 23673 net.cpp:122] Setting up conv5
I0407 21:56:37.420336 23673 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:37.420341 23673 net.cpp:137] Memory required for data: 1024899072
I0407 21:56:37.420351 23673 layer_factory.hpp:77] Creating layer relu5
I0407 21:56:37.420358 23673 net.cpp:84] Creating Layer relu5
I0407 21:56:37.420361 23673 net.cpp:406] relu5 <- conv5
I0407 21:56:37.420367 23673 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:56:37.420856 23673 net.cpp:122] Setting up relu5
I0407 21:56:37.420866 23673 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:37.420869 23673 net.cpp:137] Memory required for data: 1047050240
I0407 21:56:37.420872 23673 layer_factory.hpp:77] Creating layer pool5
I0407 21:56:37.420879 23673 net.cpp:84] Creating Layer pool5
I0407 21:56:37.420883 23673 net.cpp:406] pool5 <- conv5
I0407 21:56:37.420889 23673 net.cpp:380] pool5 -> pool5
I0407 21:56:37.420926 23673 net.cpp:122] Setting up pool5
I0407 21:56:37.420933 23673 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 21:56:37.420935 23673 net.cpp:137] Memory required for data: 1051768832
I0407 21:56:37.420938 23673 layer_factory.hpp:77] Creating layer fc6
I0407 21:56:37.420949 23673 net.cpp:84] Creating Layer fc6
I0407 21:56:37.420953 23673 net.cpp:406] fc6 <- pool5
I0407 21:56:37.420958 23673 net.cpp:380] fc6 -> fc6
I0407 21:56:37.782258 23673 net.cpp:122] Setting up fc6
I0407 21:56:37.782280 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:37.782286 23673 net.cpp:137] Memory required for data: 1053865984
I0407 21:56:37.782299 23673 layer_factory.hpp:77] Creating layer relu6
I0407 21:56:37.782310 23673 net.cpp:84] Creating Layer relu6
I0407 21:56:37.782316 23673 net.cpp:406] relu6 <- fc6
I0407 21:56:37.782325 23673 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:56:37.784193 23673 net.cpp:122] Setting up relu6
I0407 21:56:37.784207 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:37.784212 23673 net.cpp:137] Memory required for data: 1055963136
I0407 21:56:37.784217 23673 layer_factory.hpp:77] Creating layer drop6
I0407 21:56:37.784226 23673 net.cpp:84] Creating Layer drop6
I0407 21:56:37.784231 23673 net.cpp:406] drop6 <- fc6
I0407 21:56:37.784238 23673 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:56:37.784276 23673 net.cpp:122] Setting up drop6
I0407 21:56:37.784284 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:37.784289 23673 net.cpp:137] Memory required for data: 1058060288
I0407 21:56:37.784294 23673 layer_factory.hpp:77] Creating layer fc7
I0407 21:56:37.784307 23673 net.cpp:84] Creating Layer fc7
I0407 21:56:37.784312 23673 net.cpp:406] fc7 <- fc6
I0407 21:56:37.784319 23673 net.cpp:380] fc7 -> fc7
I0407 21:56:38.016695 23673 net.cpp:122] Setting up fc7
I0407 21:56:38.016716 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:38.016719 23673 net.cpp:137] Memory required for data: 1060157440
I0407 21:56:38.016728 23673 layer_factory.hpp:77] Creating layer relu7
I0407 21:56:38.016737 23673 net.cpp:84] Creating Layer relu7
I0407 21:56:38.016741 23673 net.cpp:406] relu7 <- fc7
I0407 21:56:38.016747 23673 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:56:38.017369 23673 net.cpp:122] Setting up relu7
I0407 21:56:38.017379 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:38.017382 23673 net.cpp:137] Memory required for data: 1062254592
I0407 21:56:38.017385 23673 layer_factory.hpp:77] Creating layer drop7
I0407 21:56:38.017392 23673 net.cpp:84] Creating Layer drop7
I0407 21:56:38.017416 23673 net.cpp:406] drop7 <- fc7
I0407 21:56:38.017421 23673 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:56:38.017446 23673 net.cpp:122] Setting up drop7
I0407 21:56:38.017452 23673 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:38.017454 23673 net.cpp:137] Memory required for data: 1064351744
I0407 21:56:38.017458 23673 layer_factory.hpp:77] Creating layer fc8
I0407 21:56:38.017467 23673 net.cpp:84] Creating Layer fc8
I0407 21:56:38.017470 23673 net.cpp:406] fc8 <- fc7
I0407 21:56:38.017477 23673 net.cpp:380] fc8 -> fc8
I0407 21:56:38.025106 23673 net.cpp:122] Setting up fc8
I0407 21:56:38.025115 23673 net.cpp:129] Top shape: 128 196 (25088)
I0407 21:56:38.025120 23673 net.cpp:137] Memory required for data: 1064452096
I0407 21:56:38.025125 23673 layer_factory.hpp:77] Creating layer loss
I0407 21:56:38.025133 23673 net.cpp:84] Creating Layer loss
I0407 21:56:38.025137 23673 net.cpp:406] loss <- fc8
I0407 21:56:38.025141 23673 net.cpp:406] loss <- label
I0407 21:56:38.025147 23673 net.cpp:380] loss -> loss
I0407 21:56:38.025156 23673 layer_factory.hpp:77] Creating layer loss
I0407 21:56:38.025749 23673 net.cpp:122] Setting up loss
I0407 21:56:38.025758 23673 net.cpp:129] Top shape: (1)
I0407 21:56:38.025761 23673 net.cpp:132]     with loss weight 1
I0407 21:56:38.025779 23673 net.cpp:137] Memory required for data: 1064452100
I0407 21:56:38.025782 23673 net.cpp:198] loss needs backward computation.
I0407 21:56:38.025790 23673 net.cpp:198] fc8 needs backward computation.
I0407 21:56:38.025794 23673 net.cpp:198] drop7 needs backward computation.
I0407 21:56:38.025797 23673 net.cpp:198] relu7 needs backward computation.
I0407 21:56:38.025800 23673 net.cpp:198] fc7 needs backward computation.
I0407 21:56:38.025804 23673 net.cpp:198] drop6 needs backward computation.
I0407 21:56:38.025807 23673 net.cpp:198] relu6 needs backward computation.
I0407 21:56:38.025810 23673 net.cpp:198] fc6 needs backward computation.
I0407 21:56:38.025815 23673 net.cpp:198] pool5 needs backward computation.
I0407 21:56:38.025817 23673 net.cpp:198] relu5 needs backward computation.
I0407 21:56:38.025821 23673 net.cpp:198] conv5 needs backward computation.
I0407 21:56:38.025825 23673 net.cpp:198] relu4 needs backward computation.
I0407 21:56:38.025828 23673 net.cpp:198] conv4 needs backward computation.
I0407 21:56:38.025831 23673 net.cpp:198] relu3 needs backward computation.
I0407 21:56:38.025835 23673 net.cpp:198] conv3 needs backward computation.
I0407 21:56:38.025840 23673 net.cpp:198] pool2 needs backward computation.
I0407 21:56:38.025842 23673 net.cpp:198] norm2 needs backward computation.
I0407 21:56:38.025846 23673 net.cpp:198] relu2 needs backward computation.
I0407 21:56:38.025849 23673 net.cpp:198] conv2 needs backward computation.
I0407 21:56:38.025853 23673 net.cpp:198] pool1 needs backward computation.
I0407 21:56:38.025856 23673 net.cpp:198] norm1 needs backward computation.
I0407 21:56:38.025861 23673 net.cpp:198] relu1 needs backward computation.
I0407 21:56:38.025863 23673 net.cpp:198] conv1 needs backward computation.
I0407 21:56:38.025867 23673 net.cpp:200] train-data does not need backward computation.
I0407 21:56:38.025871 23673 net.cpp:242] This network produces output loss
I0407 21:56:38.025885 23673 net.cpp:255] Network initialization done.
I0407 21:56:38.026327 23673 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 21:56:38.026357 23673 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 21:56:38.026494 23673 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 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 21:56:38.026589 23673 layer_factory.hpp:77] Creating layer val-data
I0407 21:56:38.028141 23673 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0407 21:56:38.028537 23673 net.cpp:84] Creating Layer val-data
I0407 21:56:38.028548 23673 net.cpp:380] val-data -> data
I0407 21:56:38.028555 23673 net.cpp:380] val-data -> label
I0407 21:56:38.028563 23673 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:56:38.033105 23673 data_layer.cpp:45] output data size: 32,3,227,227
I0407 21:56:38.063024 23673 net.cpp:122] Setting up val-data
I0407 21:56:38.063046 23673 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 21:56:38.063050 23673 net.cpp:129] Top shape: 32 (32)
I0407 21:56:38.063055 23673 net.cpp:137] Memory required for data: 19787264
I0407 21:56:38.063060 23673 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 21:56:38.063072 23673 net.cpp:84] Creating Layer label_val-data_1_split
I0407 21:56:38.063076 23673 net.cpp:406] label_val-data_1_split <- label
I0407 21:56:38.063083 23673 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 21:56:38.063093 23673 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 21:56:38.063155 23673 net.cpp:122] Setting up label_val-data_1_split
I0407 21:56:38.063163 23673 net.cpp:129] Top shape: 32 (32)
I0407 21:56:38.063165 23673 net.cpp:129] Top shape: 32 (32)
I0407 21:56:38.063169 23673 net.cpp:137] Memory required for data: 19787520
I0407 21:56:38.063172 23673 layer_factory.hpp:77] Creating layer conv1
I0407 21:56:38.063184 23673 net.cpp:84] Creating Layer conv1
I0407 21:56:38.063187 23673 net.cpp:406] conv1 <- data
I0407 21:56:38.063194 23673 net.cpp:380] conv1 -> conv1
I0407 21:56:38.065093 23673 net.cpp:122] Setting up conv1
I0407 21:56:38.065102 23673 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:38.065106 23673 net.cpp:137] Memory required for data: 56958720
I0407 21:56:38.065116 23673 layer_factory.hpp:77] Creating layer relu1
I0407 21:56:38.065122 23673 net.cpp:84] Creating Layer relu1
I0407 21:56:38.065126 23673 net.cpp:406] relu1 <- conv1
I0407 21:56:38.065131 23673 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:56:38.065420 23673 net.cpp:122] Setting up relu1
I0407 21:56:38.065428 23673 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:38.065431 23673 net.cpp:137] Memory required for data: 94129920
I0407 21:56:38.065435 23673 layer_factory.hpp:77] Creating layer norm1
I0407 21:56:38.065443 23673 net.cpp:84] Creating Layer norm1
I0407 21:56:38.065448 23673 net.cpp:406] norm1 <- conv1
I0407 21:56:38.065452 23673 net.cpp:380] norm1 -> norm1
I0407 21:56:38.065909 23673 net.cpp:122] Setting up norm1
I0407 21:56:38.065919 23673 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:38.065922 23673 net.cpp:137] Memory required for data: 131301120
I0407 21:56:38.065927 23673 layer_factory.hpp:77] Creating layer pool1
I0407 21:56:38.065932 23673 net.cpp:84] Creating Layer pool1
I0407 21:56:38.065937 23673 net.cpp:406] pool1 <- norm1
I0407 21:56:38.065941 23673 net.cpp:380] pool1 -> pool1
I0407 21:56:38.065989 23673 net.cpp:122] Setting up pool1
I0407 21:56:38.065995 23673 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 21:56:38.065999 23673 net.cpp:137] Memory required for data: 140259072
I0407 21:56:38.066002 23673 layer_factory.hpp:77] Creating layer conv2
I0407 21:56:38.066010 23673 net.cpp:84] Creating Layer conv2
I0407 21:56:38.066013 23673 net.cpp:406] conv2 <- pool1
I0407 21:56:38.066037 23673 net.cpp:380] conv2 -> conv2
I0407 21:56:38.073166 23673 net.cpp:122] Setting up conv2
I0407 21:56:38.073179 23673 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:38.073182 23673 net.cpp:137] Memory required for data: 164146944
I0407 21:56:38.073191 23673 layer_factory.hpp:77] Creating layer relu2
I0407 21:56:38.073199 23673 net.cpp:84] Creating Layer relu2
I0407 21:56:38.073204 23673 net.cpp:406] relu2 <- conv2
I0407 21:56:38.073208 23673 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:56:38.073712 23673 net.cpp:122] Setting up relu2
I0407 21:56:38.073722 23673 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:38.073726 23673 net.cpp:137] Memory required for data: 188034816
I0407 21:56:38.073729 23673 layer_factory.hpp:77] Creating layer norm2
I0407 21:56:38.073740 23673 net.cpp:84] Creating Layer norm2
I0407 21:56:38.073743 23673 net.cpp:406] norm2 <- conv2
I0407 21:56:38.073750 23673 net.cpp:380] norm2 -> norm2
I0407 21:56:38.074317 23673 net.cpp:122] Setting up norm2
I0407 21:56:38.074327 23673 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:38.074331 23673 net.cpp:137] Memory required for data: 211922688
I0407 21:56:38.074334 23673 layer_factory.hpp:77] Creating layer pool2
I0407 21:56:38.074340 23673 net.cpp:84] Creating Layer pool2
I0407 21:56:38.074344 23673 net.cpp:406] pool2 <- norm2
I0407 21:56:38.074350 23673 net.cpp:380] pool2 -> pool2
I0407 21:56:38.074381 23673 net.cpp:122] Setting up pool2
I0407 21:56:38.074386 23673 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:38.074389 23673 net.cpp:137] Memory required for data: 217460480
I0407 21:56:38.074393 23673 layer_factory.hpp:77] Creating layer conv3
I0407 21:56:38.074404 23673 net.cpp:84] Creating Layer conv3
I0407 21:56:38.074407 23673 net.cpp:406] conv3 <- pool2
I0407 21:56:38.074417 23673 net.cpp:380] conv3 -> conv3
I0407 21:56:38.085281 23673 net.cpp:122] Setting up conv3
I0407 21:56:38.085295 23673 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:38.085299 23673 net.cpp:137] Memory required for data: 225767168
I0407 21:56:38.085311 23673 layer_factory.hpp:77] Creating layer relu3
I0407 21:56:38.085319 23673 net.cpp:84] Creating Layer relu3
I0407 21:56:38.085323 23673 net.cpp:406] relu3 <- conv3
I0407 21:56:38.085328 23673 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:56:38.085840 23673 net.cpp:122] Setting up relu3
I0407 21:56:38.085851 23673 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:38.085855 23673 net.cpp:137] Memory required for data: 234073856
I0407 21:56:38.085858 23673 layer_factory.hpp:77] Creating layer conv4
I0407 21:56:38.085870 23673 net.cpp:84] Creating Layer conv4
I0407 21:56:38.085873 23673 net.cpp:406] conv4 <- conv3
I0407 21:56:38.085880 23673 net.cpp:380] conv4 -> conv4
I0407 21:56:38.095295 23673 net.cpp:122] Setting up conv4
I0407 21:56:38.095306 23673 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:38.095310 23673 net.cpp:137] Memory required for data: 242380544
I0407 21:56:38.095317 23673 layer_factory.hpp:77] Creating layer relu4
I0407 21:56:38.095327 23673 net.cpp:84] Creating Layer relu4
I0407 21:56:38.095332 23673 net.cpp:406] relu4 <- conv4
I0407 21:56:38.095337 23673 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:56:38.095679 23673 net.cpp:122] Setting up relu4
I0407 21:56:38.095687 23673 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:38.095692 23673 net.cpp:137] Memory required for data: 250687232
I0407 21:56:38.095696 23673 layer_factory.hpp:77] Creating layer conv5
I0407 21:56:38.095706 23673 net.cpp:84] Creating Layer conv5
I0407 21:56:38.095710 23673 net.cpp:406] conv5 <- conv4
I0407 21:56:38.095715 23673 net.cpp:380] conv5 -> conv5
I0407 21:56:38.104161 23673 net.cpp:122] Setting up conv5
I0407 21:56:38.104172 23673 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:38.104176 23673 net.cpp:137] Memory required for data: 256225024
I0407 21:56:38.104187 23673 layer_factory.hpp:77] Creating layer relu5
I0407 21:56:38.104193 23673 net.cpp:84] Creating Layer relu5
I0407 21:56:38.104197 23673 net.cpp:406] relu5 <- conv5
I0407 21:56:38.104221 23673 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:56:38.104715 23673 net.cpp:122] Setting up relu5
I0407 21:56:38.104725 23673 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:38.104729 23673 net.cpp:137] Memory required for data: 261762816
I0407 21:56:38.104732 23673 layer_factory.hpp:77] Creating layer pool5
I0407 21:56:38.104743 23673 net.cpp:84] Creating Layer pool5
I0407 21:56:38.104748 23673 net.cpp:406] pool5 <- conv5
I0407 21:56:38.104753 23673 net.cpp:380] pool5 -> pool5
I0407 21:56:38.104789 23673 net.cpp:122] Setting up pool5
I0407 21:56:38.104795 23673 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 21:56:38.104799 23673 net.cpp:137] Memory required for data: 262942464
I0407 21:56:38.104804 23673 layer_factory.hpp:77] Creating layer fc6
I0407 21:56:38.104810 23673 net.cpp:84] Creating Layer fc6
I0407 21:56:38.104813 23673 net.cpp:406] fc6 <- pool5
I0407 21:56:38.104820 23673 net.cpp:380] fc6 -> fc6
I0407 21:56:38.456493 23673 net.cpp:122] Setting up fc6
I0407 21:56:38.456514 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.456517 23673 net.cpp:137] Memory required for data: 263466752
I0407 21:56:38.456526 23673 layer_factory.hpp:77] Creating layer relu6
I0407 21:56:38.456535 23673 net.cpp:84] Creating Layer relu6
I0407 21:56:38.456539 23673 net.cpp:406] relu6 <- fc6
I0407 21:56:38.456545 23673 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:56:38.457394 23673 net.cpp:122] Setting up relu6
I0407 21:56:38.457404 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.457407 23673 net.cpp:137] Memory required for data: 263991040
I0407 21:56:38.457412 23673 layer_factory.hpp:77] Creating layer drop6
I0407 21:56:38.457418 23673 net.cpp:84] Creating Layer drop6
I0407 21:56:38.457422 23673 net.cpp:406] drop6 <- fc6
I0407 21:56:38.457427 23673 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:56:38.457456 23673 net.cpp:122] Setting up drop6
I0407 21:56:38.457461 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.457465 23673 net.cpp:137] Memory required for data: 264515328
I0407 21:56:38.457468 23673 layer_factory.hpp:77] Creating layer fc7
I0407 21:56:38.457476 23673 net.cpp:84] Creating Layer fc7
I0407 21:56:38.457479 23673 net.cpp:406] fc7 <- fc6
I0407 21:56:38.457484 23673 net.cpp:380] fc7 -> fc7
I0407 21:56:38.613785 23673 net.cpp:122] Setting up fc7
I0407 21:56:38.613806 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.613809 23673 net.cpp:137] Memory required for data: 265039616
I0407 21:56:38.613818 23673 layer_factory.hpp:77] Creating layer relu7
I0407 21:56:38.613827 23673 net.cpp:84] Creating Layer relu7
I0407 21:56:38.613832 23673 net.cpp:406] relu7 <- fc7
I0407 21:56:38.613838 23673 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:56:38.614269 23673 net.cpp:122] Setting up relu7
I0407 21:56:38.614277 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.614282 23673 net.cpp:137] Memory required for data: 265563904
I0407 21:56:38.614286 23673 layer_factory.hpp:77] Creating layer drop7
I0407 21:56:38.614293 23673 net.cpp:84] Creating Layer drop7
I0407 21:56:38.614297 23673 net.cpp:406] drop7 <- fc7
I0407 21:56:38.614302 23673 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:56:38.614326 23673 net.cpp:122] Setting up drop7
I0407 21:56:38.614332 23673 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:38.614336 23673 net.cpp:137] Memory required for data: 266088192
I0407 21:56:38.614338 23673 layer_factory.hpp:77] Creating layer fc8
I0407 21:56:38.614346 23673 net.cpp:84] Creating Layer fc8
I0407 21:56:38.614351 23673 net.cpp:406] fc8 <- fc7
I0407 21:56:38.614356 23673 net.cpp:380] fc8 -> fc8
I0407 21:56:38.622051 23673 net.cpp:122] Setting up fc8
I0407 21:56:38.622059 23673 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:38.622063 23673 net.cpp:137] Memory required for data: 266113280
I0407 21:56:38.622069 23673 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 21:56:38.622077 23673 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 21:56:38.622081 23673 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 21:56:38.622104 23673 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 21:56:38.622112 23673 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 21:56:38.622146 23673 net.cpp:122] Setting up fc8_fc8_0_split
I0407 21:56:38.622151 23673 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:38.622155 23673 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:38.622159 23673 net.cpp:137] Memory required for data: 266163456
I0407 21:56:38.622162 23673 layer_factory.hpp:77] Creating layer accuracy
I0407 21:56:38.622169 23673 net.cpp:84] Creating Layer accuracy
I0407 21:56:38.622174 23673 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 21:56:38.622177 23673 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 21:56:38.622184 23673 net.cpp:380] accuracy -> accuracy
I0407 21:56:38.622190 23673 net.cpp:122] Setting up accuracy
I0407 21:56:38.622195 23673 net.cpp:129] Top shape: (1)
I0407 21:56:38.622197 23673 net.cpp:137] Memory required for data: 266163460
I0407 21:56:38.622200 23673 layer_factory.hpp:77] Creating layer loss
I0407 21:56:38.622206 23673 net.cpp:84] Creating Layer loss
I0407 21:56:38.622210 23673 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 21:56:38.622213 23673 net.cpp:406] loss <- label_val-data_1_split_1
I0407 21:56:38.622218 23673 net.cpp:380] loss -> loss
I0407 21:56:38.622225 23673 layer_factory.hpp:77] Creating layer loss
I0407 21:56:38.622819 23673 net.cpp:122] Setting up loss
I0407 21:56:38.622828 23673 net.cpp:129] Top shape: (1)
I0407 21:56:38.622831 23673 net.cpp:132]     with loss weight 1
I0407 21:56:38.622840 23673 net.cpp:137] Memory required for data: 266163464
I0407 21:56:38.622844 23673 net.cpp:198] loss needs backward computation.
I0407 21:56:38.622849 23673 net.cpp:200] accuracy does not need backward computation.
I0407 21:56:38.622853 23673 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 21:56:38.622857 23673 net.cpp:198] fc8 needs backward computation.
I0407 21:56:38.622861 23673 net.cpp:198] drop7 needs backward computation.
I0407 21:56:38.622864 23673 net.cpp:198] relu7 needs backward computation.
I0407 21:56:38.622867 23673 net.cpp:198] fc7 needs backward computation.
I0407 21:56:38.622871 23673 net.cpp:198] drop6 needs backward computation.
I0407 21:56:38.622874 23673 net.cpp:198] relu6 needs backward computation.
I0407 21:56:38.622879 23673 net.cpp:198] fc6 needs backward computation.
I0407 21:56:38.622882 23673 net.cpp:198] pool5 needs backward computation.
I0407 21:56:38.622886 23673 net.cpp:198] relu5 needs backward computation.
I0407 21:56:38.622890 23673 net.cpp:198] conv5 needs backward computation.
I0407 21:56:38.622895 23673 net.cpp:198] relu4 needs backward computation.
I0407 21:56:38.622897 23673 net.cpp:198] conv4 needs backward computation.
I0407 21:56:38.622901 23673 net.cpp:198] relu3 needs backward computation.
I0407 21:56:38.622905 23673 net.cpp:198] conv3 needs backward computation.
I0407 21:56:38.622910 23673 net.cpp:198] pool2 needs backward computation.
I0407 21:56:38.622915 23673 net.cpp:198] norm2 needs backward computation.
I0407 21:56:38.622920 23673 net.cpp:198] relu2 needs backward computation.
I0407 21:56:38.622923 23673 net.cpp:198] conv2 needs backward computation.
I0407 21:56:38.622927 23673 net.cpp:198] pool1 needs backward computation.
I0407 21:56:38.622931 23673 net.cpp:198] norm1 needs backward computation.
I0407 21:56:38.622934 23673 net.cpp:198] relu1 needs backward computation.
I0407 21:56:38.622938 23673 net.cpp:198] conv1 needs backward computation.
I0407 21:56:38.622941 23673 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 21:56:38.622946 23673 net.cpp:200] val-data does not need backward computation.
I0407 21:56:38.622949 23673 net.cpp:242] This network produces output accuracy
I0407 21:56:38.622953 23673 net.cpp:242] This network produces output loss
I0407 21:56:38.622968 23673 net.cpp:255] Network initialization done.
I0407 21:56:38.623034 23673 solver.cpp:56] Solver scaffolding done.
I0407 21:56:38.623454 23673 caffe.cpp:248] Starting Optimization
I0407 21:56:38.623463 23673 solver.cpp:272] Solving
I0407 21:56:38.623476 23673 solver.cpp:273] Learning Rate Policy: exp
I0407 21:56:38.627288 23673 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 21:56:38.627298 23673 net.cpp:676] Ignoring source layer train-data
I0407 21:56:38.720505 23673 blocking_queue.cpp:49] Waiting for data
I0407 21:56:43.042217 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:56:43.087146 23673 solver.cpp:397]     Test net output #0: accuracy = 0.00306373
I0407 21:56:43.087195 23673 solver.cpp:397]     Test net output #1: loss = 5.27606 (* 1 = 5.27606 loss)
I0407 21:56:43.182868 23673 solver.cpp:218] Iteration 0 (1.74956e+36 iter/s, 4.55921s/12 iters), loss = 5.29002
I0407 21:56:43.184388 23673 solver.cpp:237]     Train net output #0: loss = 5.29002 (* 1 = 5.29002 loss)
I0407 21:56:43.184409 23673 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 21:56:47.166393 23673 solver.cpp:218] Iteration 12 (3.01367 iter/s, 3.98186s/12 iters), loss = 5.27431
I0407 21:56:47.166445 23673 solver.cpp:237]     Train net output #0: loss = 5.27431 (* 1 = 5.27431 loss)
I0407 21:56:47.166456 23673 sgd_solver.cpp:105] Iteration 12, lr = 0.00998818
I0407 21:56:52.030967 23673 solver.cpp:218] Iteration 24 (2.46692 iter/s, 4.86436s/12 iters), loss = 5.28889
I0407 21:56:52.031015 23673 solver.cpp:237]     Train net output #0: loss = 5.28889 (* 1 = 5.28889 loss)
I0407 21:56:52.031028 23673 sgd_solver.cpp:105] Iteration 24, lr = 0.00997638
I0407 21:56:56.934319 23673 solver.cpp:218] Iteration 36 (2.44741 iter/s, 4.90314s/12 iters), loss = 5.29434
I0407 21:56:56.934365 23673 solver.cpp:237]     Train net output #0: loss = 5.29434 (* 1 = 5.29434 loss)
I0407 21:56:56.934374 23673 sgd_solver.cpp:105] Iteration 36, lr = 0.00996459
I0407 21:57:01.873780 23673 solver.cpp:218] Iteration 48 (2.42952 iter/s, 4.93925s/12 iters), loss = 5.3048
I0407 21:57:01.873837 23673 solver.cpp:237]     Train net output #0: loss = 5.3048 (* 1 = 5.3048 loss)
I0407 21:57:01.873849 23673 sgd_solver.cpp:105] Iteration 48, lr = 0.00995282
I0407 21:57:06.853536 23673 solver.cpp:218] Iteration 60 (2.40987 iter/s, 4.97953s/12 iters), loss = 5.29247
I0407 21:57:06.853765 23673 solver.cpp:237]     Train net output #0: loss = 5.29247 (* 1 = 5.29247 loss)
I0407 21:57:06.853797 23673 sgd_solver.cpp:105] Iteration 60, lr = 0.00994106
I0407 21:57:11.844830 23673 solver.cpp:218] Iteration 72 (2.40438 iter/s, 4.9909s/12 iters), loss = 5.2996
I0407 21:57:11.844887 23673 solver.cpp:237]     Train net output #0: loss = 5.2996 (* 1 = 5.2996 loss)
I0407 21:57:11.844899 23673 sgd_solver.cpp:105] Iteration 72, lr = 0.00992931
I0407 21:57:16.868747 23673 solver.cpp:218] Iteration 84 (2.38868 iter/s, 5.02369s/12 iters), loss = 5.30185
I0407 21:57:16.868798 23673 solver.cpp:237]     Train net output #0: loss = 5.30185 (* 1 = 5.30185 loss)
I0407 21:57:16.868810 23673 sgd_solver.cpp:105] Iteration 84, lr = 0.00991757
I0407 21:57:21.777052 23673 solver.cpp:218] Iteration 96 (2.44495 iter/s, 4.90808s/12 iters), loss = 5.31926
I0407 21:57:21.777104 23673 solver.cpp:237]     Train net output #0: loss = 5.31926 (* 1 = 5.31926 loss)
I0407 21:57:21.777117 23673 sgd_solver.cpp:105] Iteration 96, lr = 0.00990586
I0407 21:57:23.501546 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:23.860872 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 21:57:26.923511 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 21:57:30.374620 23673 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 21:57:30.374646 23673 net.cpp:676] Ignoring source layer train-data
I0407 21:57:34.766472 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:34.843257 23673 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0407 21:57:34.843307 23673 solver.cpp:397]     Test net output #1: loss = 5.28986 (* 1 = 5.28986 loss)
I0407 21:57:36.667896 23673 solver.cpp:218] Iteration 108 (0.805894 iter/s, 14.8903s/12 iters), loss = 5.31861
I0407 21:57:36.667954 23673 solver.cpp:237]     Train net output #0: loss = 5.31861 (* 1 = 5.31861 loss)
I0407 21:57:36.667968 23673 sgd_solver.cpp:105] Iteration 108, lr = 0.00989415
I0407 21:57:41.661607 23673 solver.cpp:218] Iteration 120 (2.40313 iter/s, 4.99348s/12 iters), loss = 5.28317
I0407 21:57:41.661759 23673 solver.cpp:237]     Train net output #0: loss = 5.28317 (* 1 = 5.28317 loss)
I0407 21:57:41.661773 23673 sgd_solver.cpp:105] Iteration 120, lr = 0.00988246
I0407 21:57:46.652010 23673 solver.cpp:218] Iteration 132 (2.40477 iter/s, 4.99009s/12 iters), loss = 5.25356
I0407 21:57:46.652058 23673 solver.cpp:237]     Train net output #0: loss = 5.25356 (* 1 = 5.25356 loss)
I0407 21:57:46.652071 23673 sgd_solver.cpp:105] Iteration 132, lr = 0.00987078
I0407 21:57:51.662017 23673 solver.cpp:218] Iteration 144 (2.39531 iter/s, 5.00979s/12 iters), loss = 5.3105
I0407 21:57:51.662065 23673 solver.cpp:237]     Train net output #0: loss = 5.3105 (* 1 = 5.3105 loss)
I0407 21:57:51.662076 23673 sgd_solver.cpp:105] Iteration 144, lr = 0.00985912
I0407 21:57:56.692351 23673 solver.cpp:218] Iteration 156 (2.38563 iter/s, 5.03011s/12 iters), loss = 5.24762
I0407 21:57:56.692396 23673 solver.cpp:237]     Train net output #0: loss = 5.24762 (* 1 = 5.24762 loss)
I0407 21:57:56.692406 23673 sgd_solver.cpp:105] Iteration 156, lr = 0.00984747
I0407 21:58:01.667374 23673 solver.cpp:218] Iteration 168 (2.41215 iter/s, 4.97481s/12 iters), loss = 5.23787
I0407 21:58:01.667415 23673 solver.cpp:237]     Train net output #0: loss = 5.23787 (* 1 = 5.23787 loss)
I0407 21:58:01.667426 23673 sgd_solver.cpp:105] Iteration 168, lr = 0.00983583
I0407 21:58:07.052482 23673 solver.cpp:218] Iteration 180 (2.22846 iter/s, 5.38488s/12 iters), loss = 5.15948
I0407 21:58:07.052537 23673 solver.cpp:237]     Train net output #0: loss = 5.15948 (* 1 = 5.15948 loss)
I0407 21:58:07.052551 23673 sgd_solver.cpp:105] Iteration 180, lr = 0.00982421
I0407 21:58:12.336282 23673 solver.cpp:218] Iteration 192 (2.27119 iter/s, 5.28356s/12 iters), loss = 5.24681
I0407 21:58:12.336376 23673 solver.cpp:237]     Train net output #0: loss = 5.24681 (* 1 = 5.24681 loss)
I0407 21:58:12.336386 23673 sgd_solver.cpp:105] Iteration 192, lr = 0.0098126
I0407 21:58:16.170331 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:16.889189 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 21:58:19.843729 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 21:58:22.144641 23673 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 21:58:22.144662 23673 net.cpp:676] Ignoring source layer train-data
I0407 21:58:26.489969 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:26.612943 23673 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0407 21:58:26.612994 23673 solver.cpp:397]     Test net output #1: loss = 5.20483 (* 1 = 5.20483 loss)
I0407 21:58:26.704030 23673 solver.cpp:218] Iteration 204 (0.835237 iter/s, 14.3672s/12 iters), loss = 5.13684
I0407 21:58:26.704085 23673 solver.cpp:237]     Train net output #0: loss = 5.13684 (* 1 = 5.13684 loss)
I0407 21:58:26.704097 23673 sgd_solver.cpp:105] Iteration 204, lr = 0.009801
I0407 21:58:31.080279 23673 solver.cpp:218] Iteration 216 (2.74221 iter/s, 4.37604s/12 iters), loss = 5.17204
I0407 21:58:31.080327 23673 solver.cpp:237]     Train net output #0: loss = 5.17204 (* 1 = 5.17204 loss)
I0407 21:58:31.080338 23673 sgd_solver.cpp:105] Iteration 216, lr = 0.00978942
I0407 21:58:36.059645 23673 solver.cpp:218] Iteration 228 (2.41005 iter/s, 4.97914s/12 iters), loss = 5.20493
I0407 21:58:36.059700 23673 solver.cpp:237]     Train net output #0: loss = 5.20493 (* 1 = 5.20493 loss)
I0407 21:58:36.059711 23673 sgd_solver.cpp:105] Iteration 228, lr = 0.00977785
I0407 21:58:41.279723 23673 solver.cpp:218] Iteration 240 (2.29892 iter/s, 5.21984s/12 iters), loss = 5.22543
I0407 21:58:41.279772 23673 solver.cpp:237]     Train net output #0: loss = 5.22543 (* 1 = 5.22543 loss)
I0407 21:58:41.279783 23673 sgd_solver.cpp:105] Iteration 240, lr = 0.0097663
I0407 21:58:46.369488 23673 solver.cpp:218] Iteration 252 (2.35778 iter/s, 5.08954s/12 iters), loss = 5.13605
I0407 21:58:46.369587 23673 solver.cpp:237]     Train net output #0: loss = 5.13605 (* 1 = 5.13605 loss)
I0407 21:58:46.369597 23673 sgd_solver.cpp:105] Iteration 252, lr = 0.00975476
I0407 21:58:51.388101 23673 solver.cpp:218] Iteration 264 (2.39123 iter/s, 5.01834s/12 iters), loss = 5.25369
I0407 21:58:51.388156 23673 solver.cpp:237]     Train net output #0: loss = 5.25369 (* 1 = 5.25369 loss)
I0407 21:58:51.388170 23673 sgd_solver.cpp:105] Iteration 264, lr = 0.00974323
I0407 21:58:56.469434 23673 solver.cpp:218] Iteration 276 (2.3617 iter/s, 5.08109s/12 iters), loss = 5.20717
I0407 21:58:56.469491 23673 solver.cpp:237]     Train net output #0: loss = 5.20717 (* 1 = 5.20717 loss)
I0407 21:58:56.469503 23673 sgd_solver.cpp:105] Iteration 276, lr = 0.00973172
I0407 21:59:01.481992 23673 solver.cpp:218] Iteration 288 (2.3941 iter/s, 5.01233s/12 iters), loss = 5.08744
I0407 21:59:01.482036 23673 solver.cpp:237]     Train net output #0: loss = 5.08744 (* 1 = 5.08744 loss)
I0407 21:59:01.482045 23673 sgd_solver.cpp:105] Iteration 288, lr = 0.00972022
I0407 21:59:06.502712 23673 solver.cpp:218] Iteration 300 (2.3902 iter/s, 5.0205s/12 iters), loss = 5.18047
I0407 21:59:06.502763 23673 solver.cpp:237]     Train net output #0: loss = 5.18047 (* 1 = 5.18047 loss)
I0407 21:59:06.502776 23673 sgd_solver.cpp:105] Iteration 300, lr = 0.00970873
I0407 21:59:07.515949 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:08.614754 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 21:59:11.561811 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 21:59:13.846679 23673 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 21:59:13.846704 23673 net.cpp:676] Ignoring source layer train-data
I0407 21:59:18.286324 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:18.444336 23673 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0407 21:59:18.444380 23673 solver.cpp:397]     Test net output #1: loss = 5.15469 (* 1 = 5.15469 loss)
I0407 21:59:20.440160 23673 solver.cpp:218] Iteration 312 (0.861022 iter/s, 13.9369s/12 iters), loss = 5.13532
I0407 21:59:20.440210 23673 solver.cpp:237]     Train net output #0: loss = 5.13532 (* 1 = 5.13532 loss)
I0407 21:59:20.440220 23673 sgd_solver.cpp:105] Iteration 312, lr = 0.00969726
I0407 21:59:25.436688 23673 solver.cpp:218] Iteration 324 (2.40178 iter/s, 4.9963s/12 iters), loss = 5.18628
I0407 21:59:25.436738 23673 solver.cpp:237]     Train net output #0: loss = 5.18628 (* 1 = 5.18628 loss)
I0407 21:59:25.436749 23673 sgd_solver.cpp:105] Iteration 324, lr = 0.0096858
I0407 21:59:30.447798 23673 solver.cpp:218] Iteration 336 (2.39479 iter/s, 5.01088s/12 iters), loss = 5.1465
I0407 21:59:30.447845 23673 solver.cpp:237]     Train net output #0: loss = 5.1465 (* 1 = 5.1465 loss)
I0407 21:59:30.447856 23673 sgd_solver.cpp:105] Iteration 336, lr = 0.00967435
I0407 21:59:35.432197 23673 solver.cpp:218] Iteration 348 (2.40762 iter/s, 4.98417s/12 iters), loss = 5.12079
I0407 21:59:35.432247 23673 solver.cpp:237]     Train net output #0: loss = 5.12079 (* 1 = 5.12079 loss)
I0407 21:59:35.432258 23673 sgd_solver.cpp:105] Iteration 348, lr = 0.00966292
I0407 21:59:40.501835 23673 solver.cpp:218] Iteration 360 (2.36714 iter/s, 5.06941s/12 iters), loss = 5.17407
I0407 21:59:40.501874 23673 solver.cpp:237]     Train net output #0: loss = 5.17407 (* 1 = 5.17407 loss)
I0407 21:59:40.501883 23673 sgd_solver.cpp:105] Iteration 360, lr = 0.0096515
I0407 21:59:45.538745 23673 solver.cpp:218] Iteration 372 (2.38252 iter/s, 5.03669s/12 iters), loss = 5.10898
I0407 21:59:45.538792 23673 solver.cpp:237]     Train net output #0: loss = 5.10898 (* 1 = 5.10898 loss)
I0407 21:59:45.538803 23673 sgd_solver.cpp:105] Iteration 372, lr = 0.0096401
I0407 21:59:50.652194 23673 solver.cpp:218] Iteration 384 (2.34686 iter/s, 5.11322s/12 iters), loss = 5.08271
I0407 21:59:50.652338 23673 solver.cpp:237]     Train net output #0: loss = 5.08271 (* 1 = 5.08271 loss)
I0407 21:59:50.652351 23673 sgd_solver.cpp:105] Iteration 384, lr = 0.00962871
I0407 21:59:55.752672 23673 solver.cpp:218] Iteration 396 (2.35287 iter/s, 5.10015s/12 iters), loss = 5.07951
I0407 21:59:55.752727 23673 solver.cpp:237]     Train net output #0: loss = 5.07951 (* 1 = 5.07951 loss)
I0407 21:59:55.752739 23673 sgd_solver.cpp:105] Iteration 396, lr = 0.00961733
I0407 21:59:58.965939 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:00.397931 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 22:00:03.347818 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 22:00:05.633965 23673 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 22:00:05.633986 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:00:09.906111 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:10.109853 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 22:00:10.109902 23673 solver.cpp:397]     Test net output #1: loss = 5.09848 (* 1 = 5.09848 loss)
I0407 22:00:10.200992 23673 solver.cpp:218] Iteration 408 (0.830577 iter/s, 14.4478s/12 iters), loss = 5.17172
I0407 22:00:10.201038 23673 solver.cpp:237]     Train net output #0: loss = 5.17172 (* 1 = 5.17172 loss)
I0407 22:00:10.201048 23673 sgd_solver.cpp:105] Iteration 408, lr = 0.00960597
I0407 22:00:14.508584 23673 solver.cpp:218] Iteration 420 (2.78591 iter/s, 4.30739s/12 iters), loss = 5.13016
I0407 22:00:14.508628 23673 solver.cpp:237]     Train net output #0: loss = 5.13016 (* 1 = 5.13016 loss)
I0407 22:00:14.508638 23673 sgd_solver.cpp:105] Iteration 420, lr = 0.00959461
I0407 22:00:19.495308 23673 solver.cpp:218] Iteration 432 (2.4065 iter/s, 4.9865s/12 iters), loss = 5.13662
I0407 22:00:19.495352 23673 solver.cpp:237]     Train net output #0: loss = 5.13662 (* 1 = 5.13662 loss)
I0407 22:00:19.495362 23673 sgd_solver.cpp:105] Iteration 432, lr = 0.00958328
I0407 22:00:24.520870 23673 solver.cpp:218] Iteration 444 (2.3879 iter/s, 5.02534s/12 iters), loss = 5.07287
I0407 22:00:24.520959 23673 solver.cpp:237]     Train net output #0: loss = 5.07287 (* 1 = 5.07287 loss)
I0407 22:00:24.520969 23673 sgd_solver.cpp:105] Iteration 444, lr = 0.00957195
I0407 22:00:29.495390 23673 solver.cpp:218] Iteration 456 (2.41242 iter/s, 4.97425s/12 iters), loss = 5.09011
I0407 22:00:29.495436 23673 solver.cpp:237]     Train net output #0: loss = 5.09011 (* 1 = 5.09011 loss)
I0407 22:00:29.495445 23673 sgd_solver.cpp:105] Iteration 456, lr = 0.00956064
I0407 22:00:34.518591 23673 solver.cpp:218] Iteration 468 (2.38902 iter/s, 5.02298s/12 iters), loss = 5.10187
I0407 22:00:34.518636 23673 solver.cpp:237]     Train net output #0: loss = 5.10187 (* 1 = 5.10187 loss)
I0407 22:00:34.518644 23673 sgd_solver.cpp:105] Iteration 468, lr = 0.00954934
I0407 22:00:39.580722 23673 solver.cpp:218] Iteration 480 (2.37065 iter/s, 5.0619s/12 iters), loss = 5.02779
I0407 22:00:39.580777 23673 solver.cpp:237]     Train net output #0: loss = 5.02779 (* 1 = 5.02779 loss)
I0407 22:00:39.580790 23673 sgd_solver.cpp:105] Iteration 480, lr = 0.00953806
I0407 22:00:44.598134 23673 solver.cpp:218] Iteration 492 (2.39178 iter/s, 5.01718s/12 iters), loss = 5.06634
I0407 22:00:44.598191 23673 solver.cpp:237]     Train net output #0: loss = 5.06634 (* 1 = 5.06634 loss)
I0407 22:00:44.598202 23673 sgd_solver.cpp:105] Iteration 492, lr = 0.00952679
I0407 22:00:49.656487 23673 solver.cpp:218] Iteration 504 (2.37243 iter/s, 5.05811s/12 iters), loss = 5.0899
I0407 22:00:49.656536 23673 solver.cpp:237]     Train net output #0: loss = 5.0899 (* 1 = 5.0899 loss)
I0407 22:00:49.656549 23673 sgd_solver.cpp:105] Iteration 504, lr = 0.00951553
I0407 22:00:49.903225 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:51.700913 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 22:00:57.322207 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 22:00:59.649554 23673 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 22:00:59.649581 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:01:03.908774 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:04.146924 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0407 22:01:04.146973 23673 solver.cpp:397]     Test net output #1: loss = 5.03284 (* 1 = 5.03284 loss)
I0407 22:01:05.969785 23673 solver.cpp:218] Iteration 516 (0.735623 iter/s, 16.3127s/12 iters), loss = 5.00781
I0407 22:01:05.969838 23673 solver.cpp:237]     Train net output #0: loss = 5.00781 (* 1 = 5.00781 loss)
I0407 22:01:05.969851 23673 sgd_solver.cpp:105] Iteration 516, lr = 0.00950429
I0407 22:01:11.025851 23673 solver.cpp:218] Iteration 528 (2.37349 iter/s, 5.05584s/12 iters), loss = 5.07707
I0407 22:01:11.025895 23673 solver.cpp:237]     Train net output #0: loss = 5.07707 (* 1 = 5.07707 loss)
I0407 22:01:11.025907 23673 sgd_solver.cpp:105] Iteration 528, lr = 0.00949306
I0407 22:01:16.057947 23673 solver.cpp:218] Iteration 540 (2.3848 iter/s, 5.03188s/12 iters), loss = 5.03665
I0407 22:01:16.058005 23673 solver.cpp:237]     Train net output #0: loss = 5.03665 (* 1 = 5.03665 loss)
I0407 22:01:16.058013 23673 sgd_solver.cpp:105] Iteration 540, lr = 0.00948184
I0407 22:01:21.099918 23673 solver.cpp:218] Iteration 552 (2.38013 iter/s, 5.04174s/12 iters), loss = 5.083
I0407 22:01:21.099969 23673 solver.cpp:237]     Train net output #0: loss = 5.083 (* 1 = 5.083 loss)
I0407 22:01:21.099982 23673 sgd_solver.cpp:105] Iteration 552, lr = 0.00947063
I0407 22:01:26.324512 23673 solver.cpp:218] Iteration 564 (2.29693 iter/s, 5.22436s/12 iters), loss = 4.99145
I0407 22:01:26.324560 23673 solver.cpp:237]     Train net output #0: loss = 4.99145 (* 1 = 4.99145 loss)
I0407 22:01:26.324570 23673 sgd_solver.cpp:105] Iteration 564, lr = 0.00945944
I0407 22:01:31.333266 23673 solver.cpp:218] Iteration 576 (2.39591 iter/s, 5.00853s/12 iters), loss = 4.97568
I0407 22:01:31.333400 23673 solver.cpp:237]     Train net output #0: loss = 4.97568 (* 1 = 4.97568 loss)
I0407 22:01:31.333417 23673 sgd_solver.cpp:105] Iteration 576, lr = 0.00944826
I0407 22:01:36.246150 23673 solver.cpp:218] Iteration 588 (2.4427 iter/s, 4.91259s/12 iters), loss = 4.87745
I0407 22:01:36.246191 23673 solver.cpp:237]     Train net output #0: loss = 4.87745 (* 1 = 4.87745 loss)
I0407 22:01:36.246201 23673 sgd_solver.cpp:105] Iteration 588, lr = 0.0094371
I0407 22:01:41.172363 23673 solver.cpp:218] Iteration 600 (2.43605 iter/s, 4.926s/12 iters), loss = 5.05832
I0407 22:01:41.172411 23673 solver.cpp:237]     Train net output #0: loss = 5.05832 (* 1 = 5.05832 loss)
I0407 22:01:41.172422 23673 sgd_solver.cpp:105] Iteration 600, lr = 0.00942595
I0407 22:01:43.519995 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:45.740607 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 22:01:48.737026 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 22:01:51.066030 23673 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 22:01:51.066056 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:01:55.206990 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:55.492528 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0407 22:01:55.492578 23673 solver.cpp:397]     Test net output #1: loss = 4.98713 (* 1 = 4.98713 loss)
I0407 22:01:55.583765 23673 solver.cpp:218] Iteration 612 (0.832705 iter/s, 14.4109s/12 iters), loss = 4.99757
I0407 22:01:55.583817 23673 solver.cpp:237]     Train net output #0: loss = 4.99757 (* 1 = 4.99757 loss)
I0407 22:01:55.583829 23673 sgd_solver.cpp:105] Iteration 612, lr = 0.00941481
I0407 22:01:59.931967 23673 solver.cpp:218] Iteration 624 (2.75989 iter/s, 4.348s/12 iters), loss = 4.96052
I0407 22:01:59.932019 23673 solver.cpp:237]     Train net output #0: loss = 4.96052 (* 1 = 4.96052 loss)
I0407 22:01:59.932030 23673 sgd_solver.cpp:105] Iteration 624, lr = 0.00940368
I0407 22:02:04.971573 23673 solver.cpp:218] Iteration 636 (2.38125 iter/s, 5.03938s/12 iters), loss = 4.82169
I0407 22:02:04.971699 23673 solver.cpp:237]     Train net output #0: loss = 4.82169 (* 1 = 4.82169 loss)
I0407 22:02:04.971714 23673 sgd_solver.cpp:105] Iteration 636, lr = 0.00939257
I0407 22:02:09.963601 23673 solver.cpp:218] Iteration 648 (2.40398 iter/s, 4.99173s/12 iters), loss = 5.03702
I0407 22:02:09.963654 23673 solver.cpp:237]     Train net output #0: loss = 5.03702 (* 1 = 5.03702 loss)
I0407 22:02:09.963666 23673 sgd_solver.cpp:105] Iteration 648, lr = 0.00938147
I0407 22:02:15.145054 23673 solver.cpp:218] Iteration 660 (2.31606 iter/s, 5.18122s/12 iters), loss = 4.93725
I0407 22:02:15.145113 23673 solver.cpp:237]     Train net output #0: loss = 4.93725 (* 1 = 4.93725 loss)
I0407 22:02:15.145128 23673 sgd_solver.cpp:105] Iteration 660, lr = 0.00937039
I0407 22:02:20.131109 23673 solver.cpp:218] Iteration 672 (2.40683 iter/s, 4.98582s/12 iters), loss = 4.90497
I0407 22:02:20.131160 23673 solver.cpp:237]     Train net output #0: loss = 4.90497 (* 1 = 4.90497 loss)
I0407 22:02:20.131170 23673 sgd_solver.cpp:105] Iteration 672, lr = 0.00935931
I0407 22:02:25.167201 23673 solver.cpp:218] Iteration 684 (2.38291 iter/s, 5.03586s/12 iters), loss = 4.77132
I0407 22:02:25.167250 23673 solver.cpp:237]     Train net output #0: loss = 4.77132 (* 1 = 4.77132 loss)
I0407 22:02:25.167260 23673 sgd_solver.cpp:105] Iteration 684, lr = 0.00934825
I0407 22:02:25.977191 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:02:30.276525 23673 solver.cpp:218] Iteration 696 (2.34875 iter/s, 5.1091s/12 iters), loss = 4.90747
I0407 22:02:30.276566 23673 solver.cpp:237]     Train net output #0: loss = 4.90747 (* 1 = 4.90747 loss)
I0407 22:02:30.276577 23673 sgd_solver.cpp:105] Iteration 696, lr = 0.00933721
I0407 22:02:34.958429 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:35.344980 23673 solver.cpp:218] Iteration 708 (2.36769 iter/s, 5.06824s/12 iters), loss = 4.93135
I0407 22:02:35.345094 23673 solver.cpp:237]     Train net output #0: loss = 4.93135 (* 1 = 4.93135 loss)
I0407 22:02:35.345108 23673 sgd_solver.cpp:105] Iteration 708, lr = 0.00932617
I0407 22:02:37.390935 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 22:02:40.451826 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 22:02:42.766505 23673 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 22:02:42.766525 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:02:46.919201 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:47.239871 23673 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0407 22:02:47.239920 23673 solver.cpp:397]     Test net output #1: loss = 4.91919 (* 1 = 4.91919 loss)
I0407 22:02:49.238842 23673 solver.cpp:218] Iteration 720 (0.863726 iter/s, 13.8933s/12 iters), loss = 4.89855
I0407 22:02:49.238898 23673 solver.cpp:237]     Train net output #0: loss = 4.89855 (* 1 = 4.89855 loss)
I0407 22:02:49.238909 23673 sgd_solver.cpp:105] Iteration 720, lr = 0.00931515
I0407 22:02:54.586464 23673 solver.cpp:218] Iteration 732 (2.24409 iter/s, 5.34739s/12 iters), loss = 4.77419
I0407 22:02:54.586503 23673 solver.cpp:237]     Train net output #0: loss = 4.77419 (* 1 = 4.77419 loss)
I0407 22:02:54.586511 23673 sgd_solver.cpp:105] Iteration 732, lr = 0.00930415
I0407 22:02:59.706064 23673 solver.cpp:218] Iteration 744 (2.34403 iter/s, 5.11938s/12 iters), loss = 4.89559
I0407 22:02:59.706110 23673 solver.cpp:237]     Train net output #0: loss = 4.89559 (* 1 = 4.89559 loss)
I0407 22:02:59.706120 23673 sgd_solver.cpp:105] Iteration 744, lr = 0.00929315
I0407 22:03:04.711055 23673 solver.cpp:218] Iteration 756 (2.39771 iter/s, 5.00477s/12 iters), loss = 5.00385
I0407 22:03:04.711103 23673 solver.cpp:237]     Train net output #0: loss = 5.00385 (* 1 = 5.00385 loss)
I0407 22:03:04.711114 23673 sgd_solver.cpp:105] Iteration 756, lr = 0.00928217
I0407 22:03:09.620085 23673 solver.cpp:218] Iteration 768 (2.44458 iter/s, 4.90881s/12 iters), loss = 4.8122
I0407 22:03:09.620205 23673 solver.cpp:237]     Train net output #0: loss = 4.8122 (* 1 = 4.8122 loss)
I0407 22:03:09.620218 23673 sgd_solver.cpp:105] Iteration 768, lr = 0.0092712
I0407 22:03:14.618906 23673 solver.cpp:218] Iteration 780 (2.4007 iter/s, 4.99853s/12 iters), loss = 4.85319
I0407 22:03:14.618948 23673 solver.cpp:237]     Train net output #0: loss = 4.85319 (* 1 = 4.85319 loss)
I0407 22:03:14.618958 23673 sgd_solver.cpp:105] Iteration 780, lr = 0.00926025
I0407 22:03:19.616230 23673 solver.cpp:218] Iteration 792 (2.40139 iter/s, 4.99711s/12 iters), loss = 4.62438
I0407 22:03:19.616261 23673 solver.cpp:237]     Train net output #0: loss = 4.62438 (* 1 = 4.62438 loss)
I0407 22:03:19.616271 23673 sgd_solver.cpp:105] Iteration 792, lr = 0.0092493
I0407 22:03:24.677809 23673 solver.cpp:218] Iteration 804 (2.3709 iter/s, 5.06137s/12 iters), loss = 4.81705
I0407 22:03:24.677855 23673 solver.cpp:237]     Train net output #0: loss = 4.81705 (* 1 = 4.81705 loss)
I0407 22:03:24.677867 23673 sgd_solver.cpp:105] Iteration 804, lr = 0.00923837
I0407 22:03:26.437096 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:29.231016 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 22:03:32.314083 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 22:03:34.629091 23673 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 22:03:34.629114 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:03:38.717520 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:39.073168 23673 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0407 22:03:39.073218 23673 solver.cpp:397]     Test net output #1: loss = 4.81953 (* 1 = 4.81953 loss)
I0407 22:03:39.164633 23673 solver.cpp:218] Iteration 816 (0.828368 iter/s, 14.4863s/12 iters), loss = 4.90009
I0407 22:03:39.164680 23673 solver.cpp:237]     Train net output #0: loss = 4.90009 (* 1 = 4.90009 loss)
I0407 22:03:39.164691 23673 sgd_solver.cpp:105] Iteration 816, lr = 0.00922746
I0407 22:03:43.565249 23673 solver.cpp:218] Iteration 828 (2.72702 iter/s, 4.40041s/12 iters), loss = 4.92229
I0407 22:03:43.565359 23673 solver.cpp:237]     Train net output #0: loss = 4.92229 (* 1 = 4.92229 loss)
I0407 22:03:43.565373 23673 sgd_solver.cpp:105] Iteration 828, lr = 0.00921655
I0407 22:03:48.420486 23673 solver.cpp:218] Iteration 840 (2.4717 iter/s, 4.85497s/12 iters), loss = 4.68485
I0407 22:03:48.420533 23673 solver.cpp:237]     Train net output #0: loss = 4.68485 (* 1 = 4.68485 loss)
I0407 22:03:48.420545 23673 sgd_solver.cpp:105] Iteration 840, lr = 0.00920566
I0407 22:03:53.401693 23673 solver.cpp:218] Iteration 852 (2.40916 iter/s, 4.98099s/12 iters), loss = 4.75053
I0407 22:03:53.401748 23673 solver.cpp:237]     Train net output #0: loss = 4.75053 (* 1 = 4.75053 loss)
I0407 22:03:53.401760 23673 sgd_solver.cpp:105] Iteration 852, lr = 0.00919478
I0407 22:03:58.411015 23673 solver.cpp:218] Iteration 864 (2.39564 iter/s, 5.00909s/12 iters), loss = 4.73179
I0407 22:03:58.411067 23673 solver.cpp:237]     Train net output #0: loss = 4.73179 (* 1 = 4.73179 loss)
I0407 22:03:58.411078 23673 sgd_solver.cpp:105] Iteration 864, lr = 0.00918392
I0407 22:04:03.690582 23673 solver.cpp:218] Iteration 876 (2.27301 iter/s, 5.27934s/12 iters), loss = 4.69752
I0407 22:04:03.690631 23673 solver.cpp:237]     Train net output #0: loss = 4.69752 (* 1 = 4.69752 loss)
I0407 22:04:03.690644 23673 sgd_solver.cpp:105] Iteration 876, lr = 0.00917307
I0407 22:04:08.913000 23673 solver.cpp:218] Iteration 888 (2.29789 iter/s, 5.22219s/12 iters), loss = 4.60307
I0407 22:04:08.913056 23673 solver.cpp:237]     Train net output #0: loss = 4.60307 (* 1 = 4.60307 loss)
I0407 22:04:08.913069 23673 sgd_solver.cpp:105] Iteration 888, lr = 0.00916223
I0407 22:04:14.099617 23673 solver.cpp:218] Iteration 900 (2.31375 iter/s, 5.18638s/12 iters), loss = 4.75401
I0407 22:04:14.099792 23673 solver.cpp:237]     Train net output #0: loss = 4.75401 (* 1 = 4.75401 loss)
I0407 22:04:14.099809 23673 sgd_solver.cpp:105] Iteration 900, lr = 0.0091514
I0407 22:04:18.317723 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:19.474754 23673 solver.cpp:218] Iteration 912 (2.23264 iter/s, 5.37479s/12 iters), loss = 4.51286
I0407 22:04:19.474790 23673 solver.cpp:237]     Train net output #0: loss = 4.51286 (* 1 = 4.51286 loss)
I0407 22:04:19.474799 23673 sgd_solver.cpp:105] Iteration 912, lr = 0.00914059
I0407 22:04:21.742367 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 22:04:24.696581 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 22:04:29.558840 23673 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 22:04:29.558866 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:04:33.758172 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:34.160044 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0407 22:04:34.160095 23673 solver.cpp:397]     Test net output #1: loss = 4.80752 (* 1 = 4.80752 loss)
I0407 22:04:36.079795 23673 solver.cpp:218] Iteration 924 (0.722697 iter/s, 16.6045s/12 iters), loss = 4.72687
I0407 22:04:36.079843 23673 solver.cpp:237]     Train net output #0: loss = 4.72687 (* 1 = 4.72687 loss)
I0407 22:04:36.079855 23673 sgd_solver.cpp:105] Iteration 924, lr = 0.00912979
I0407 22:04:41.066298 23673 solver.cpp:218] Iteration 936 (2.4066 iter/s, 4.98628s/12 iters), loss = 4.68527
I0407 22:04:41.066352 23673 solver.cpp:237]     Train net output #0: loss = 4.68527 (* 1 = 4.68527 loss)
I0407 22:04:41.066365 23673 sgd_solver.cpp:105] Iteration 936, lr = 0.009119
I0407 22:04:46.143324 23673 solver.cpp:218] Iteration 948 (2.3637 iter/s, 5.0768s/12 iters), loss = 4.58337
I0407 22:04:46.143473 23673 solver.cpp:237]     Train net output #0: loss = 4.58337 (* 1 = 4.58337 loss)
I0407 22:04:46.143488 23673 sgd_solver.cpp:105] Iteration 948, lr = 0.00910822
I0407 22:04:51.199252 23673 solver.cpp:218] Iteration 960 (2.3736 iter/s, 5.05561s/12 iters), loss = 4.5346
I0407 22:04:51.199301 23673 solver.cpp:237]     Train net output #0: loss = 4.5346 (* 1 = 4.5346 loss)
I0407 22:04:51.199313 23673 sgd_solver.cpp:105] Iteration 960, lr = 0.00909746
I0407 22:04:56.289444 23673 solver.cpp:218] Iteration 972 (2.35758 iter/s, 5.08997s/12 iters), loss = 4.48475
I0407 22:04:56.289492 23673 solver.cpp:237]     Train net output #0: loss = 4.48475 (* 1 = 4.48475 loss)
I0407 22:04:56.289503 23673 sgd_solver.cpp:105] Iteration 972, lr = 0.00908671
I0407 22:05:01.297835 23673 solver.cpp:218] Iteration 984 (2.39608 iter/s, 5.00817s/12 iters), loss = 4.30539
I0407 22:05:01.297880 23673 solver.cpp:237]     Train net output #0: loss = 4.30539 (* 1 = 4.30539 loss)
I0407 22:05:01.297891 23673 sgd_solver.cpp:105] Iteration 984, lr = 0.00907597
I0407 22:05:06.468609 23673 solver.cpp:218] Iteration 996 (2.32083 iter/s, 5.17056s/12 iters), loss = 4.47152
I0407 22:05:06.468644 23673 solver.cpp:237]     Train net output #0: loss = 4.47152 (* 1 = 4.47152 loss)
I0407 22:05:06.468653 23673 sgd_solver.cpp:105] Iteration 996, lr = 0.00906525
I0407 22:05:11.924449 23673 solver.cpp:218] Iteration 1008 (2.19957 iter/s, 5.45562s/12 iters), loss = 4.54033
I0407 22:05:11.924491 23673 solver.cpp:237]     Train net output #0: loss = 4.54033 (* 1 = 4.54033 loss)
I0407 22:05:11.924500 23673 sgd_solver.cpp:105] Iteration 1008, lr = 0.00905453
I0407 22:05:13.066136 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:16.918043 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 22:05:21.500929 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 22:05:25.275084 23673 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 22:05:25.275102 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:05:29.211298 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:29.645314 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0582108
I0407 22:05:29.645349 23673 solver.cpp:397]     Test net output #1: loss = 4.54078 (* 1 = 4.54078 loss)
I0407 22:05:29.733922 23673 solver.cpp:218] Iteration 1020 (0.673822 iter/s, 17.8089s/12 iters), loss = 4.3574
I0407 22:05:29.733978 23673 solver.cpp:237]     Train net output #0: loss = 4.3574 (* 1 = 4.3574 loss)
I0407 22:05:29.733986 23673 sgd_solver.cpp:105] Iteration 1020, lr = 0.00904383
I0407 22:05:34.023010 23673 solver.cpp:218] Iteration 1032 (2.79793 iter/s, 4.28888s/12 iters), loss = 4.44446
I0407 22:05:34.023057 23673 solver.cpp:237]     Train net output #0: loss = 4.44446 (* 1 = 4.44446 loss)
I0407 22:05:34.023068 23673 sgd_solver.cpp:105] Iteration 1032, lr = 0.00903315
I0407 22:05:39.009115 23673 solver.cpp:218] Iteration 1044 (2.4068 iter/s, 4.98588s/12 iters), loss = 4.34081
I0407 22:05:39.009160 23673 solver.cpp:237]     Train net output #0: loss = 4.34081 (* 1 = 4.34081 loss)
I0407 22:05:39.009168 23673 sgd_solver.cpp:105] Iteration 1044, lr = 0.00902247
I0407 22:05:44.008869 23673 solver.cpp:218] Iteration 1056 (2.40022 iter/s, 4.99954s/12 iters), loss = 4.49058
I0407 22:05:44.008924 23673 solver.cpp:237]     Train net output #0: loss = 4.49058 (* 1 = 4.49058 loss)
I0407 22:05:44.008936 23673 sgd_solver.cpp:105] Iteration 1056, lr = 0.00901181
I0407 22:05:49.056615 23673 solver.cpp:218] Iteration 1068 (2.37741 iter/s, 5.04752s/12 iters), loss = 4.64789
I0407 22:05:49.060887 23673 solver.cpp:237]     Train net output #0: loss = 4.64789 (* 1 = 4.64789 loss)
I0407 22:05:49.060904 23673 sgd_solver.cpp:105] Iteration 1068, lr = 0.00900116
I0407 22:05:54.087749 23673 solver.cpp:218] Iteration 1080 (2.38725 iter/s, 5.0267s/12 iters), loss = 4.3995
I0407 22:05:54.087790 23673 solver.cpp:237]     Train net output #0: loss = 4.3995 (* 1 = 4.3995 loss)
I0407 22:05:54.087798 23673 sgd_solver.cpp:105] Iteration 1080, lr = 0.00899053
I0407 22:05:59.156257 23673 solver.cpp:218] Iteration 1092 (2.36766 iter/s, 5.0683s/12 iters), loss = 4.40228
I0407 22:05:59.156297 23673 solver.cpp:237]     Train net output #0: loss = 4.40228 (* 1 = 4.40228 loss)
I0407 22:05:59.156307 23673 sgd_solver.cpp:105] Iteration 1092, lr = 0.0089799
I0407 22:06:04.235697 23673 solver.cpp:218] Iteration 1104 (2.36256 iter/s, 5.07923s/12 iters), loss = 4.36355
I0407 22:06:04.235744 23673 solver.cpp:237]     Train net output #0: loss = 4.36355 (* 1 = 4.36355 loss)
I0407 22:06:04.235756 23673 sgd_solver.cpp:105] Iteration 1104, lr = 0.00896929
I0407 22:06:07.591742 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:09.482831 23673 solver.cpp:218] Iteration 1116 (2.28706 iter/s, 5.2469s/12 iters), loss = 4.37152
I0407 22:06:09.482885 23673 solver.cpp:237]     Train net output #0: loss = 4.37152 (* 1 = 4.37152 loss)
I0407 22:06:09.482897 23673 sgd_solver.cpp:105] Iteration 1116, lr = 0.00895869
I0407 22:06:11.536376 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 22:06:14.604773 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 22:06:16.906592 23673 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 22:06:16.906611 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:06:20.902673 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:21.383416 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0631127
I0407 22:06:21.383481 23673 solver.cpp:397]     Test net output #1: loss = 4.42848 (* 1 = 4.42848 loss)
I0407 22:06:23.232192 23673 solver.cpp:218] Iteration 1128 (0.872799 iter/s, 13.7489s/12 iters), loss = 4.40529
I0407 22:06:23.232239 23673 solver.cpp:237]     Train net output #0: loss = 4.40529 (* 1 = 4.40529 loss)
I0407 22:06:23.232249 23673 sgd_solver.cpp:105] Iteration 1128, lr = 0.0089481
I0407 22:06:28.349690 23673 solver.cpp:218] Iteration 1140 (2.345 iter/s, 5.11727s/12 iters), loss = 4.31024
I0407 22:06:28.349742 23673 solver.cpp:237]     Train net output #0: loss = 4.31024 (* 1 = 4.31024 loss)
I0407 22:06:28.349753 23673 sgd_solver.cpp:105] Iteration 1140, lr = 0.00893753
I0407 22:06:33.657089 23673 solver.cpp:218] Iteration 1152 (2.26109 iter/s, 5.30717s/12 iters), loss = 4.1737
I0407 22:06:33.657142 23673 solver.cpp:237]     Train net output #0: loss = 4.1737 (* 1 = 4.1737 loss)
I0407 22:06:33.657153 23673 sgd_solver.cpp:105] Iteration 1152, lr = 0.00892697
I0407 22:06:38.866791 23673 solver.cpp:218] Iteration 1164 (2.3035 iter/s, 5.20947s/12 iters), loss = 4.33327
I0407 22:06:38.866830 23673 solver.cpp:237]     Train net output #0: loss = 4.33327 (* 1 = 4.33327 loss)
I0407 22:06:38.866838 23673 sgd_solver.cpp:105] Iteration 1164, lr = 0.00891642
I0407 22:06:43.989686 23673 solver.cpp:218] Iteration 1176 (2.34252 iter/s, 5.12268s/12 iters), loss = 4.31619
I0407 22:06:43.989729 23673 solver.cpp:237]     Train net output #0: loss = 4.31619 (* 1 = 4.31619 loss)
I0407 22:06:43.989738 23673 sgd_solver.cpp:105] Iteration 1176, lr = 0.00890588
I0407 22:06:49.045915 23673 solver.cpp:218] Iteration 1188 (2.37341 iter/s, 5.05602s/12 iters), loss = 4.22972
I0407 22:06:49.045970 23673 solver.cpp:237]     Train net output #0: loss = 4.22972 (* 1 = 4.22972 loss)
I0407 22:06:49.045980 23673 sgd_solver.cpp:105] Iteration 1188, lr = 0.00889536
I0407 22:06:54.215201 23673 solver.cpp:218] Iteration 1200 (2.32151 iter/s, 5.16906s/12 iters), loss = 4.44619
I0407 22:06:54.215389 23673 solver.cpp:237]     Train net output #0: loss = 4.44619 (* 1 = 4.44619 loss)
I0407 22:06:54.215402 23673 sgd_solver.cpp:105] Iteration 1200, lr = 0.00888485
I0407 22:06:59.324811 23673 solver.cpp:218] Iteration 1212 (2.34868 iter/s, 5.10926s/12 iters), loss = 4.07184
I0407 22:06:59.324847 23673 solver.cpp:237]     Train net output #0: loss = 4.07184 (* 1 = 4.07184 loss)
I0407 22:06:59.324856 23673 sgd_solver.cpp:105] Iteration 1212, lr = 0.00887435
I0407 22:06:59.602377 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:03.915834 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 22:07:09.021040 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 22:07:11.730304 23673 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 22:07:11.730329 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:07:15.708912 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:16.221175 23673 solver.cpp:397]     Test net output #0: accuracy = 0.0827206
I0407 22:07:16.221225 23673 solver.cpp:397]     Test net output #1: loss = 4.36216 (* 1 = 4.36216 loss)
I0407 22:07:16.312711 23673 solver.cpp:218] Iteration 1224 (0.706409 iter/s, 16.9873s/12 iters), loss = 4.41684
I0407 22:07:16.312757 23673 solver.cpp:237]     Train net output #0: loss = 4.41684 (* 1 = 4.41684 loss)
I0407 22:07:16.312768 23673 sgd_solver.cpp:105] Iteration 1224, lr = 0.00886386
I0407 22:07:20.569988 23673 solver.cpp:218] Iteration 1236 (2.81883 iter/s, 4.25709s/12 iters), loss = 4.23493
I0407 22:07:20.570039 23673 solver.cpp:237]     Train net output #0: loss = 4.23493 (* 1 = 4.23493 loss)
I0407 22:07:20.570050 23673 sgd_solver.cpp:105] Iteration 1236, lr = 0.00885339
I0407 22:07:25.591588 23673 solver.cpp:218] Iteration 1248 (2.38978 iter/s, 5.02138s/12 iters), loss = 4.03193
I0407 22:07:25.591714 23673 solver.cpp:237]     Train net output #0: loss = 4.03193 (* 1 = 4.03193 loss)
I0407 22:07:25.591727 23673 sgd_solver.cpp:105] Iteration 1248, lr = 0.00884293
I0407 22:07:30.639899 23673 solver.cpp:218] Iteration 1260 (2.37717 iter/s, 5.04802s/12 iters), loss = 4.15227
I0407 22:07:30.639953 23673 solver.cpp:237]     Train net output #0: loss = 4.15227 (* 1 = 4.15227 loss)
I0407 22:07:30.639966 23673 sgd_solver.cpp:105] Iteration 1260, lr = 0.00883248
I0407 22:07:35.634824 23673 solver.cpp:218] Iteration 1272 (2.40255 iter/s, 4.9947s/12 iters), loss = 3.90676
I0407 22:07:35.634876 23673 solver.cpp:237]     Train net output #0: loss = 3.90676 (* 1 = 3.90676 loss)
I0407 22:07:35.634886 23673 sgd_solver.cpp:105] Iteration 1272, lr = 0.00882204
I0407 22:07:40.633052 23673 solver.cpp:218] Iteration 1284 (2.40096 iter/s, 4.99801s/12 iters), loss = 4.07134
I0407 22:07:40.633097 23673 solver.cpp:237]     Train net output #0: loss = 4.07134 (* 1 = 4.07134 loss)
I0407 22:07:40.633108 23673 sgd_solver.cpp:105] Iteration 1284, lr = 0.00881162
I0407 22:07:45.706708 23673 solver.cpp:218] Iteration 1296 (2.36526 iter/s, 5.07344s/12 iters), loss = 3.86791
I0407 22:07:45.706763 23673 solver.cpp:237]     Train net output #0: loss = 3.86791 (* 1 = 3.86791 loss)
I0407 22:07:45.706779 23673 sgd_solver.cpp:105] Iteration 1296, lr = 0.0088012
I0407 22:07:51.038132 23673 solver.cpp:218] Iteration 1308 (2.2509 iter/s, 5.3312s/12 iters), loss = 3.98068
I0407 22:07:51.038174 23673 solver.cpp:237]     Train net output #0: loss = 3.98068 (* 1 = 3.98068 loss)
I0407 22:07:51.038184 23673 sgd_solver.cpp:105] Iteration 1308, lr = 0.0087908
I0407 22:07:53.584787 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:56.034616 23673 solver.cpp:218] Iteration 1320 (2.40179 iter/s, 4.99627s/12 iters), loss = 3.95295
I0407 22:07:56.034770 23673 solver.cpp:237]     Train net output #0: loss = 3.95295 (* 1 = 3.95295 loss)
I0407 22:07:56.034785 23673 sgd_solver.cpp:105] Iteration 1320, lr = 0.00878042
I0407 22:07:58.079277 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 22:08:02.254097 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 22:08:07.062549 23673 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 22:08:07.062577 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:08:11.045135 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:11.610291 23673 solver.cpp:397]     Test net output #0: accuracy = 0.114583
I0407 22:08:11.610342 23673 solver.cpp:397]     Test net output #1: loss = 4.10392 (* 1 = 4.10392 loss)
I0407 22:08:13.412981 23673 solver.cpp:218] Iteration 1332 (0.690542 iter/s, 17.3777s/12 iters), loss = 3.84778
I0407 22:08:13.413046 23673 solver.cpp:237]     Train net output #0: loss = 3.84778 (* 1 = 3.84778 loss)
I0407 22:08:13.413058 23673 sgd_solver.cpp:105] Iteration 1332, lr = 0.00877004
I0407 22:08:18.832835 23673 solver.cpp:218] Iteration 1344 (2.21418 iter/s, 5.41962s/12 iters), loss = 4.01944
I0407 22:08:18.832870 23673 solver.cpp:237]     Train net output #0: loss = 4.01944 (* 1 = 4.01944 loss)
I0407 22:08:18.832880 23673 sgd_solver.cpp:105] Iteration 1344, lr = 0.00875968
I0407 22:08:24.099601 23673 solver.cpp:218] Iteration 1356 (2.27853 iter/s, 5.26655s/12 iters), loss = 4.02132
I0407 22:08:24.099660 23673 solver.cpp:237]     Train net output #0: loss = 4.02132 (* 1 = 4.02132 loss)
I0407 22:08:24.099675 23673 sgd_solver.cpp:105] Iteration 1356, lr = 0.00874932
I0407 22:08:29.262571 23673 solver.cpp:218] Iteration 1368 (2.32435 iter/s, 5.16274s/12 iters), loss = 3.82388
I0407 22:08:29.265668 23673 solver.cpp:237]     Train net output #0: loss = 3.82388 (* 1 = 3.82388 loss)
I0407 22:08:29.265681 23673 sgd_solver.cpp:105] Iteration 1368, lr = 0.00873899
I0407 22:08:30.484704 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:08:34.345319 23673 solver.cpp:218] Iteration 1380 (2.36245 iter/s, 5.07947s/12 iters), loss = 3.84068
I0407 22:08:34.345367 23673 solver.cpp:237]     Train net output #0: loss = 3.84068 (* 1 = 3.84068 loss)
I0407 22:08:34.345378 23673 sgd_solver.cpp:105] Iteration 1380, lr = 0.00872866
I0407 22:08:39.438167 23673 solver.cpp:218] Iteration 1392 (2.35636 iter/s, 5.09261s/12 iters), loss = 3.92615
I0407 22:08:39.438230 23673 solver.cpp:237]     Train net output #0: loss = 3.92615 (* 1 = 3.92615 loss)
I0407 22:08:39.438242 23673 sgd_solver.cpp:105] Iteration 1392, lr = 0.00871835
I0407 22:08:44.549154 23673 solver.cpp:218] Iteration 1404 (2.34799 iter/s, 5.11075s/12 iters), loss = 3.89455
I0407 22:08:44.549211 23673 solver.cpp:237]     Train net output #0: loss = 3.89455 (* 1 = 3.89455 loss)
I0407 22:08:44.549224 23673 sgd_solver.cpp:105] Iteration 1404, lr = 0.00870804
I0407 22:08:49.436530 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:49.815369 23673 solver.cpp:218] Iteration 1416 (2.27878 iter/s, 5.26599s/12 iters), loss = 3.73814
I0407 22:08:49.815418 23673 solver.cpp:237]     Train net output #0: loss = 3.73814 (* 1 = 3.73814 loss)
I0407 22:08:49.815429 23673 sgd_solver.cpp:105] Iteration 1416, lr = 0.00869775
I0407 22:08:54.821766 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 22:08:57.824978 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 22:09:00.145413 23673 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 22:09:00.145524 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:09:04.036437 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:04.627983 23673 solver.cpp:397]     Test net output #0: accuracy = 0.11826
I0407 22:09:04.628034 23673 solver.cpp:397]     Test net output #1: loss = 3.95968 (* 1 = 3.95968 loss)
I0407 22:09:04.719767 23673 solver.cpp:218] Iteration 1428 (0.80516 iter/s, 14.9039s/12 iters), loss = 3.67194
I0407 22:09:04.719820 23673 solver.cpp:237]     Train net output #0: loss = 3.67194 (* 1 = 3.67194 loss)
I0407 22:09:04.719831 23673 sgd_solver.cpp:105] Iteration 1428, lr = 0.00868747
I0407 22:09:09.177867 23673 solver.cpp:218] Iteration 1440 (2.69185 iter/s, 4.4579s/12 iters), loss = 3.86244
I0407 22:09:09.177914 23673 solver.cpp:237]     Train net output #0: loss = 3.86244 (* 1 = 3.86244 loss)
I0407 22:09:09.177925 23673 sgd_solver.cpp:105] Iteration 1440, lr = 0.00867721
I0407 22:09:14.452765 23673 solver.cpp:218] Iteration 1452 (2.27502 iter/s, 5.27468s/12 iters), loss = 3.89432
I0407 22:09:14.452806 23673 solver.cpp:237]     Train net output #0: loss = 3.89432 (* 1 = 3.89432 loss)
I0407 22:09:14.452816 23673 sgd_solver.cpp:105] Iteration 1452, lr = 0.00866696
I0407 22:09:19.686739 23673 solver.cpp:218] Iteration 1464 (2.29281 iter/s, 5.23375s/12 iters), loss = 3.77781
I0407 22:09:19.686789 23673 solver.cpp:237]     Train net output #0: loss = 3.77781 (* 1 = 3.77781 loss)
I0407 22:09:19.686800 23673 sgd_solver.cpp:105] Iteration 1464, lr = 0.00865671
I0407 22:09:24.988958 23673 solver.cpp:218] Iteration 1476 (2.2633 iter/s, 5.30199s/12 iters), loss = 3.60105
I0407 22:09:24.989006 23673 solver.cpp:237]     Train net output #0: loss = 3.60105 (* 1 = 3.60105 loss)
I0407 22:09:24.989015 23673 sgd_solver.cpp:105] Iteration 1476, lr = 0.00864648
I0407 22:09:30.273097 23673 solver.cpp:218] Iteration 1488 (2.27105 iter/s, 5.2839s/12 iters), loss = 3.78944
I0407 22:09:30.273197 23673 solver.cpp:237]     Train net output #0: loss = 3.78944 (* 1 = 3.78944 loss)
I0407 22:09:30.273211 23673 sgd_solver.cpp:105] Iteration 1488, lr = 0.00863627
I0407 22:09:35.534519 23673 solver.cpp:218] Iteration 1500 (2.28087 iter/s, 5.26115s/12 iters), loss = 3.27436
I0407 22:09:35.534569 23673 solver.cpp:237]     Train net output #0: loss = 3.27436 (* 1 = 3.27436 loss)
I0407 22:09:35.534581 23673 sgd_solver.cpp:105] Iteration 1500, lr = 0.00862606
I0407 22:09:40.664033 23673 solver.cpp:218] Iteration 1512 (2.33951 iter/s, 5.12929s/12 iters), loss = 3.60214
I0407 22:09:40.664094 23673 solver.cpp:237]     Train net output #0: loss = 3.60214 (* 1 = 3.60214 loss)
I0407 22:09:40.664106 23673 sgd_solver.cpp:105] Iteration 1512, lr = 0.00861587
I0407 22:09:42.518558 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:45.833148 23673 solver.cpp:218] Iteration 1524 (2.32159 iter/s, 5.16888s/12 iters), loss = 3.51796
I0407 22:09:45.833190 23673 solver.cpp:237]     Train net output #0: loss = 3.51796 (* 1 = 3.51796 loss)
I0407 22:09:45.833201 23673 sgd_solver.cpp:105] Iteration 1524, lr = 0.00860569
I0407 22:09:47.874891 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 22:09:51.711802 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 22:09:54.036870 23673 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 22:09:54.036895 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:09:57.828215 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:58.466341 23673 solver.cpp:397]     Test net output #0: accuracy = 0.143995
I0407 22:09:58.466392 23673 solver.cpp:397]     Test net output #1: loss = 3.78794 (* 1 = 3.78794 loss)
I0407 22:10:00.446319 23673 solver.cpp:218] Iteration 1536 (0.821206 iter/s, 14.6127s/12 iters), loss = 3.40414
I0407 22:10:00.446465 23673 solver.cpp:237]     Train net output #0: loss = 3.40414 (* 1 = 3.40414 loss)
I0407 22:10:00.446478 23673 sgd_solver.cpp:105] Iteration 1536, lr = 0.00859552
I0407 22:10:05.595360 23673 solver.cpp:218] Iteration 1548 (2.33067 iter/s, 5.14872s/12 iters), loss = 3.1715
I0407 22:10:05.595408 23673 solver.cpp:237]     Train net output #0: loss = 3.1715 (* 1 = 3.1715 loss)
I0407 22:10:05.595422 23673 sgd_solver.cpp:105] Iteration 1548, lr = 0.00858536
I0407 22:10:10.690383 23673 solver.cpp:218] Iteration 1560 (2.35534 iter/s, 5.0948s/12 iters), loss = 3.55946
I0407 22:10:10.690433 23673 solver.cpp:237]     Train net output #0: loss = 3.55946 (* 1 = 3.55946 loss)
I0407 22:10:10.690444 23673 sgd_solver.cpp:105] Iteration 1560, lr = 0.00857522
I0407 22:10:15.852505 23673 solver.cpp:218] Iteration 1572 (2.32473 iter/s, 5.1619s/12 iters), loss = 3.7873
I0407 22:10:15.852558 23673 solver.cpp:237]     Train net output #0: loss = 3.7873 (* 1 = 3.7873 loss)
I0407 22:10:15.852571 23673 sgd_solver.cpp:105] Iteration 1572, lr = 0.00856508
I0407 22:10:20.913864 23673 solver.cpp:218] Iteration 1584 (2.37101 iter/s, 5.06113s/12 iters), loss = 3.51933
I0407 22:10:20.913918 23673 solver.cpp:237]     Train net output #0: loss = 3.51933 (* 1 = 3.51933 loss)
I0407 22:10:20.913930 23673 sgd_solver.cpp:105] Iteration 1584, lr = 0.00855496
I0407 22:10:26.069773 23673 solver.cpp:218] Iteration 1596 (2.32753 iter/s, 5.15568s/12 iters), loss = 3.62207
I0407 22:10:26.069828 23673 solver.cpp:237]     Train net output #0: loss = 3.62207 (* 1 = 3.62207 loss)
I0407 22:10:26.069840 23673 sgd_solver.cpp:105] Iteration 1596, lr = 0.00854485
I0407 22:10:31.171725 23673 solver.cpp:218] Iteration 1608 (2.35214 iter/s, 5.10173s/12 iters), loss = 3.37716
I0407 22:10:31.171833 23673 solver.cpp:237]     Train net output #0: loss = 3.37716 (* 1 = 3.37716 loss)
I0407 22:10:31.171845 23673 sgd_solver.cpp:105] Iteration 1608, lr = 0.00853476
I0407 22:10:35.222003 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:36.345003 23673 solver.cpp:218] Iteration 1620 (2.31974 iter/s, 5.173s/12 iters), loss = 3.55597
I0407 22:10:36.345062 23673 solver.cpp:237]     Train net output #0: loss = 3.55597 (* 1 = 3.55597 loss)
I0407 22:10:36.345073 23673 sgd_solver.cpp:105] Iteration 1620, lr = 0.00852467
I0407 22:10:41.020815 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 22:10:46.521113 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 22:10:48.830533 23673 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 22:10:48.830554 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:10:52.633893 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:53.306007 23673 solver.cpp:397]     Test net output #0: accuracy = 0.129902
I0407 22:10:53.306052 23673 solver.cpp:397]     Test net output #1: loss = 3.84627 (* 1 = 3.84627 loss)
I0407 22:10:53.397253 23673 solver.cpp:218] Iteration 1632 (0.703744 iter/s, 17.0516s/12 iters), loss = 3.40244
I0407 22:10:53.397305 23673 solver.cpp:237]     Train net output #0: loss = 3.40244 (* 1 = 3.40244 loss)
I0407 22:10:53.397316 23673 sgd_solver.cpp:105] Iteration 1632, lr = 0.0085146
I0407 22:10:57.981524 23673 solver.cpp:218] Iteration 1644 (2.61777 iter/s, 4.58406s/12 iters), loss = 3.59637
I0407 22:10:57.981576 23673 solver.cpp:237]     Train net output #0: loss = 3.59637 (* 1 = 3.59637 loss)
I0407 22:10:57.981588 23673 sgd_solver.cpp:105] Iteration 1644, lr = 0.00850454
I0407 22:11:03.126242 23673 solver.cpp:218] Iteration 1656 (2.33259 iter/s, 5.14449s/12 iters), loss = 3.40473
I0407 22:11:03.126370 23673 solver.cpp:237]     Train net output #0: loss = 3.40473 (* 1 = 3.40473 loss)
I0407 22:11:03.126381 23673 sgd_solver.cpp:105] Iteration 1656, lr = 0.00849449
I0407 22:11:08.201942 23673 solver.cpp:218] Iteration 1668 (2.36434 iter/s, 5.07541s/12 iters), loss = 3.10173
I0407 22:11:08.201999 23673 solver.cpp:237]     Train net output #0: loss = 3.10173 (* 1 = 3.10173 loss)
I0407 22:11:08.202008 23673 sgd_solver.cpp:105] Iteration 1668, lr = 0.00848445
I0407 22:11:13.482067 23673 solver.cpp:218] Iteration 1680 (2.27278 iter/s, 5.27989s/12 iters), loss = 3.42093
I0407 22:11:13.482111 23673 solver.cpp:237]     Train net output #0: loss = 3.42093 (* 1 = 3.42093 loss)
I0407 22:11:13.482120 23673 sgd_solver.cpp:105] Iteration 1680, lr = 0.00847442
I0407 22:11:18.620010 23673 solver.cpp:218] Iteration 1692 (2.33566 iter/s, 5.13773s/12 iters), loss = 3.35783
I0407 22:11:18.620050 23673 solver.cpp:237]     Train net output #0: loss = 3.35783 (* 1 = 3.35783 loss)
I0407 22:11:18.620060 23673 sgd_solver.cpp:105] Iteration 1692, lr = 0.00846441
I0407 22:11:23.629979 23673 solver.cpp:218] Iteration 1704 (2.39533 iter/s, 5.00975s/12 iters), loss = 3.09615
I0407 22:11:23.630023 23673 solver.cpp:237]     Train net output #0: loss = 3.09615 (* 1 = 3.09615 loss)
I0407 22:11:23.630031 23673 sgd_solver.cpp:105] Iteration 1704, lr = 0.00845441
I0407 22:11:28.730959 23673 solver.cpp:218] Iteration 1716 (2.35259 iter/s, 5.10076s/12 iters), loss = 3.31927
I0407 22:11:28.731017 23673 solver.cpp:237]     Train net output #0: loss = 3.31927 (* 1 = 3.31927 loss)
I0407 22:11:28.731029 23673 sgd_solver.cpp:105] Iteration 1716, lr = 0.00844442
I0407 22:11:29.798019 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:33.788151 23673 solver.cpp:218] Iteration 1728 (2.37296 iter/s, 5.05697s/12 iters), loss = 3.24218
I0407 22:11:33.788233 23673 solver.cpp:237]     Train net output #0: loss = 3.24218 (* 1 = 3.24218 loss)
I0407 22:11:33.788247 23673 sgd_solver.cpp:105] Iteration 1728, lr = 0.00843444
I0407 22:11:35.846745 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 22:11:45.130303 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 22:11:47.525425 23673 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 22:11:47.525447 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:11:51.370719 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:52.079021 23673 solver.cpp:397]     Test net output #0: accuracy = 0.170343
I0407 22:11:52.079066 23673 solver.cpp:397]     Test net output #1: loss = 3.61265 (* 1 = 3.61265 loss)
I0407 22:11:54.057054 23673 solver.cpp:218] Iteration 1740 (0.592061 iter/s, 20.2682s/12 iters), loss = 3.14903
I0407 22:11:54.057106 23673 solver.cpp:237]     Train net output #0: loss = 3.14903 (* 1 = 3.14903 loss)
I0407 22:11:54.057116 23673 sgd_solver.cpp:105] Iteration 1740, lr = 0.00842447
I0407 22:11:59.150133 23673 solver.cpp:218] Iteration 1752 (2.35624 iter/s, 5.09286s/12 iters), loss = 3.02479
I0407 22:11:59.150180 23673 solver.cpp:237]     Train net output #0: loss = 3.02479 (* 1 = 3.02479 loss)
I0407 22:11:59.150192 23673 sgd_solver.cpp:105] Iteration 1752, lr = 0.00841452
I0407 22:12:04.353596 23673 solver.cpp:218] Iteration 1764 (2.30625 iter/s, 5.20324s/12 iters), loss = 3.29917
I0407 22:12:04.353674 23673 solver.cpp:237]     Train net output #0: loss = 3.29917 (* 1 = 3.29917 loss)
I0407 22:12:04.353686 23673 sgd_solver.cpp:105] Iteration 1764, lr = 0.00840457
I0407 22:12:09.877313 23673 solver.cpp:218] Iteration 1776 (2.17255 iter/s, 5.52345s/12 iters), loss = 3.46472
I0407 22:12:09.877363 23673 solver.cpp:237]     Train net output #0: loss = 3.46472 (* 1 = 3.46472 loss)
I0407 22:12:09.877375 23673 sgd_solver.cpp:105] Iteration 1776, lr = 0.00839464
I0407 22:12:15.085985 23673 solver.cpp:218] Iteration 1788 (2.30395 iter/s, 5.20845s/12 iters), loss = 3.28356
I0407 22:12:15.086036 23673 solver.cpp:237]     Train net output #0: loss = 3.28356 (* 1 = 3.28356 loss)
I0407 22:12:15.086048 23673 sgd_solver.cpp:105] Iteration 1788, lr = 0.00838472
I0407 22:12:20.159348 23673 solver.cpp:218] Iteration 1800 (2.3654 iter/s, 5.07314s/12 iters), loss = 3.26223
I0407 22:12:20.159401 23673 solver.cpp:237]     Train net output #0: loss = 3.26223 (* 1 = 3.26223 loss)
I0407 22:12:20.159413 23673 sgd_solver.cpp:105] Iteration 1800, lr = 0.00837481
I0407 22:12:25.245820 23673 solver.cpp:218] Iteration 1812 (2.3593 iter/s, 5.08625s/12 iters), loss = 3.14785
I0407 22:12:25.245867 23673 solver.cpp:237]     Train net output #0: loss = 3.14785 (* 1 = 3.14785 loss)
I0407 22:12:25.245877 23673 sgd_solver.cpp:105] Iteration 1812, lr = 0.00836492
I0407 22:12:28.497313 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:30.273370 23673 solver.cpp:218] Iteration 1824 (2.38695 iter/s, 5.02734s/12 iters), loss = 3.11305
I0407 22:12:30.273422 23673 solver.cpp:237]     Train net output #0: loss = 3.11305 (* 1 = 3.11305 loss)
I0407 22:12:30.273437 23673 sgd_solver.cpp:105] Iteration 1824, lr = 0.00835503
I0407 22:12:34.937638 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 22:12:39.356735 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 22:12:42.200219 23673 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 22:12:42.200242 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:12:45.921334 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:46.671835 23673 solver.cpp:397]     Test net output #0: accuracy = 0.186275
I0407 22:12:46.671885 23673 solver.cpp:397]     Test net output #1: loss = 3.62662 (* 1 = 3.62662 loss)
I0407 22:12:46.763022 23673 solver.cpp:218] Iteration 1836 (0.727755 iter/s, 16.4891s/12 iters), loss = 3.21696
I0407 22:12:46.763075 23673 solver.cpp:237]     Train net output #0: loss = 3.21696 (* 1 = 3.21696 loss)
I0407 22:12:46.763087 23673 sgd_solver.cpp:105] Iteration 1836, lr = 0.00834516
I0407 22:12:51.104022 23673 solver.cpp:218] Iteration 1848 (2.76447 iter/s, 4.3408s/12 iters), loss = 2.91917
I0407 22:12:51.104066 23673 solver.cpp:237]     Train net output #0: loss = 2.91917 (* 1 = 2.91917 loss)
I0407 22:12:51.104079 23673 sgd_solver.cpp:105] Iteration 1848, lr = 0.0083353
I0407 22:12:56.182224 23673 solver.cpp:218] Iteration 1860 (2.36314 iter/s, 5.07798s/12 iters), loss = 3.11619
I0407 22:12:56.182272 23673 solver.cpp:237]     Train net output #0: loss = 3.11619 (* 1 = 3.11619 loss)
I0407 22:12:56.182286 23673 sgd_solver.cpp:105] Iteration 1860, lr = 0.00832545
I0407 22:13:01.357868 23673 solver.cpp:218] Iteration 1872 (2.31865 iter/s, 5.17542s/12 iters), loss = 2.94947
I0407 22:13:01.357923 23673 solver.cpp:237]     Train net output #0: loss = 2.94947 (* 1 = 2.94947 loss)
I0407 22:13:01.357936 23673 sgd_solver.cpp:105] Iteration 1872, lr = 0.00831561
I0407 22:13:06.511346 23673 solver.cpp:218] Iteration 1884 (2.32863 iter/s, 5.15325s/12 iters), loss = 3.19943
I0407 22:13:06.511430 23673 solver.cpp:237]     Train net output #0: loss = 3.19943 (* 1 = 3.19943 loss)
I0407 22:13:06.511443 23673 sgd_solver.cpp:105] Iteration 1884, lr = 0.00830578
I0407 22:13:11.662509 23673 solver.cpp:218] Iteration 1896 (2.32969 iter/s, 5.1509s/12 iters), loss = 3.28377
I0407 22:13:11.662562 23673 solver.cpp:237]     Train net output #0: loss = 3.28377 (* 1 = 3.28377 loss)
I0407 22:13:11.662575 23673 sgd_solver.cpp:105] Iteration 1896, lr = 0.00829597
I0407 22:13:16.833907 23673 solver.cpp:218] Iteration 1908 (2.32056 iter/s, 5.17117s/12 iters), loss = 2.97967
I0407 22:13:16.833978 23673 solver.cpp:237]     Train net output #0: loss = 2.97967 (* 1 = 2.97967 loss)
I0407 22:13:16.833992 23673 sgd_solver.cpp:105] Iteration 1908, lr = 0.00828617
I0407 22:13:22.026257 23673 solver.cpp:218] Iteration 1920 (2.3112 iter/s, 5.19212s/12 iters), loss = 3.09817
I0407 22:13:22.026307 23673 solver.cpp:237]     Train net output #0: loss = 3.09817 (* 1 = 3.09817 loss)
I0407 22:13:22.026319 23673 sgd_solver.cpp:105] Iteration 1920, lr = 0.00827637
I0407 22:13:22.347321 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:27.136258 23673 solver.cpp:218] Iteration 1932 (2.34844 iter/s, 5.10978s/12 iters), loss = 3.18618
I0407 22:13:27.136310 23673 solver.cpp:237]     Train net output #0: loss = 3.18618 (* 1 = 3.18618 loss)
I0407 22:13:27.136322 23673 sgd_solver.cpp:105] Iteration 1932, lr = 0.00826659
I0407 22:13:29.248430 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 22:13:36.594502 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 22:13:39.042798 23673 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 22:13:39.042824 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:13:42.733100 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:43.520561 23673 solver.cpp:397]     Test net output #0: accuracy = 0.204657
I0407 22:13:43.520608 23673 solver.cpp:397]     Test net output #1: loss = 3.495 (* 1 = 3.495 loss)
I0407 22:13:45.453001 23673 solver.cpp:218] Iteration 1944 (0.655161 iter/s, 18.3161s/12 iters), loss = 2.76734
I0407 22:13:45.453043 23673 solver.cpp:237]     Train net output #0: loss = 2.76734 (* 1 = 2.76734 loss)
I0407 22:13:45.453052 23673 sgd_solver.cpp:105] Iteration 1944, lr = 0.00825683
I0407 22:13:50.745009 23673 solver.cpp:218] Iteration 1956 (2.26767 iter/s, 5.29178s/12 iters), loss = 2.83377
I0407 22:13:50.745061 23673 solver.cpp:237]     Train net output #0: loss = 2.83377 (* 1 = 2.83377 loss)
I0407 22:13:50.745074 23673 sgd_solver.cpp:105] Iteration 1956, lr = 0.00824707
I0407 22:13:55.883762 23673 solver.cpp:218] Iteration 1968 (2.3353 iter/s, 5.13853s/12 iters), loss = 2.83272
I0407 22:13:55.883810 23673 solver.cpp:237]     Train net output #0: loss = 2.83272 (* 1 = 2.83272 loss)
I0407 22:13:55.883819 23673 sgd_solver.cpp:105] Iteration 1968, lr = 0.00823732
I0407 22:14:01.395717 23673 solver.cpp:218] Iteration 1980 (2.17718 iter/s, 5.51172s/12 iters), loss = 2.9452
I0407 22:14:01.395758 23673 solver.cpp:237]     Train net output #0: loss = 2.9452 (* 1 = 2.9452 loss)
I0407 22:14:01.395767 23673 sgd_solver.cpp:105] Iteration 1980, lr = 0.00822759
I0407 22:14:06.893167 23673 solver.cpp:218] Iteration 1992 (2.18292 iter/s, 5.49722s/12 iters), loss = 2.67579
I0407 22:14:06.893246 23673 solver.cpp:237]     Train net output #0: loss = 2.67579 (* 1 = 2.67579 loss)
I0407 22:14:06.893255 23673 sgd_solver.cpp:105] Iteration 1992, lr = 0.00821787
I0407 22:14:11.945365 23673 solver.cpp:218] Iteration 2004 (2.37532 iter/s, 5.05194s/12 iters), loss = 2.46936
I0407 22:14:11.945418 23673 solver.cpp:237]     Train net output #0: loss = 2.46936 (* 1 = 2.46936 loss)
I0407 22:14:11.945430 23673 sgd_solver.cpp:105] Iteration 2004, lr = 0.00820816
I0407 22:14:17.082609 23673 solver.cpp:218] Iteration 2016 (2.33599 iter/s, 5.13702s/12 iters), loss = 2.86115
I0407 22:14:17.082653 23673 solver.cpp:237]     Train net output #0: loss = 2.86115 (* 1 = 2.86115 loss)
I0407 22:14:17.082662 23673 sgd_solver.cpp:105] Iteration 2016, lr = 0.00819846
I0407 22:14:19.702008 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:22.173983 23673 solver.cpp:218] Iteration 2028 (2.35703 iter/s, 5.09116s/12 iters), loss = 2.62497
I0407 22:14:22.174021 23673 solver.cpp:237]     Train net output #0: loss = 2.62497 (* 1 = 2.62497 loss)
I0407 22:14:22.174031 23673 sgd_solver.cpp:105] Iteration 2028, lr = 0.00818877
I0407 22:14:26.803316 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 22:14:31.693454 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 22:14:34.019977 23673 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 22:14:34.020004 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:14:37.670058 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:38.500842 23673 solver.cpp:397]     Test net output #0: accuracy = 0.226103
I0407 22:14:38.500892 23673 solver.cpp:397]     Test net output #1: loss = 3.36485 (* 1 = 3.36485 loss)
I0407 22:14:38.592597 23673 solver.cpp:218] Iteration 2040 (0.730903 iter/s, 16.418s/12 iters), loss = 2.95772
I0407 22:14:38.592641 23673 solver.cpp:237]     Train net output #0: loss = 2.95772 (* 1 = 2.95772 loss)
I0407 22:14:38.592653 23673 sgd_solver.cpp:105] Iteration 2040, lr = 0.00817909
I0407 22:14:42.768543 23673 solver.cpp:218] Iteration 2052 (2.87373 iter/s, 4.17576s/12 iters), loss = 2.794
I0407 22:14:42.768601 23673 solver.cpp:237]     Train net output #0: loss = 2.794 (* 1 = 2.794 loss)
I0407 22:14:42.768616 23673 sgd_solver.cpp:105] Iteration 2052, lr = 0.00816943
I0407 22:14:44.345557 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:14:47.708922 23673 solver.cpp:218] Iteration 2064 (2.42907 iter/s, 4.94015s/12 iters), loss = 2.86157
I0407 22:14:47.708977 23673 solver.cpp:237]     Train net output #0: loss = 2.86157 (* 1 = 2.86157 loss)
I0407 22:14:47.708992 23673 sgd_solver.cpp:105] Iteration 2064, lr = 0.00815977
I0407 22:14:52.698560 23673 solver.cpp:218] Iteration 2076 (2.40509 iter/s, 4.98941s/12 iters), loss = 2.7559
I0407 22:14:52.698611 23673 solver.cpp:237]     Train net output #0: loss = 2.7559 (* 1 = 2.7559 loss)
I0407 22:14:52.698623 23673 sgd_solver.cpp:105] Iteration 2076, lr = 0.00815013
I0407 22:14:57.775991 23673 solver.cpp:218] Iteration 2088 (2.3635 iter/s, 5.07721s/12 iters), loss = 3.00213
I0407 22:14:57.776042 23673 solver.cpp:237]     Train net output #0: loss = 3.00213 (* 1 = 3.00213 loss)
I0407 22:14:57.776054 23673 sgd_solver.cpp:105] Iteration 2088, lr = 0.0081405
I0407 22:15:02.878194 23673 solver.cpp:218] Iteration 2100 (2.35203 iter/s, 5.10198s/12 iters), loss = 2.76666
I0407 22:15:02.878239 23673 solver.cpp:237]     Train net output #0: loss = 2.76666 (* 1 = 2.76666 loss)
I0407 22:15:02.878248 23673 sgd_solver.cpp:105] Iteration 2100, lr = 0.00813088
I0407 22:15:08.150535 23673 solver.cpp:218] Iteration 2112 (2.27613 iter/s, 5.27212s/12 iters), loss = 2.96685
I0407 22:15:08.150607 23673 solver.cpp:237]     Train net output #0: loss = 2.96685 (* 1 = 2.96685 loss)
I0407 22:15:08.150617 23673 sgd_solver.cpp:105] Iteration 2112, lr = 0.00812127
I0407 22:15:12.825068 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:13.140869 23673 solver.cpp:218] Iteration 2124 (2.40476 iter/s, 4.99009s/12 iters), loss = 2.60363
I0407 22:15:13.140913 23673 solver.cpp:237]     Train net output #0: loss = 2.60363 (* 1 = 2.60363 loss)
I0407 22:15:13.140923 23673 sgd_solver.cpp:105] Iteration 2124, lr = 0.00811168
I0407 22:15:18.264346 23673 solver.cpp:218] Iteration 2136 (2.34226 iter/s, 5.12326s/12 iters), loss = 2.68822
I0407 22:15:18.264398 23673 solver.cpp:237]     Train net output #0: loss = 2.68822 (* 1 = 2.68822 loss)
I0407 22:15:18.264410 23673 sgd_solver.cpp:105] Iteration 2136, lr = 0.00810209
I0407 22:15:20.335908 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 22:15:24.968070 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 22:15:27.312256 23673 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 22:15:27.312284 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:15:30.857630 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:31.725620 23673 solver.cpp:397]     Test net output #0: accuracy = 0.252451
I0407 22:15:31.725669 23673 solver.cpp:397]     Test net output #1: loss = 3.21553 (* 1 = 3.21553 loss)
I0407 22:15:33.726979 23673 solver.cpp:218] Iteration 2148 (0.776092 iter/s, 15.4621s/12 iters), loss = 2.47292
I0407 22:15:33.727027 23673 solver.cpp:237]     Train net output #0: loss = 2.47292 (* 1 = 2.47292 loss)
I0407 22:15:33.727038 23673 sgd_solver.cpp:105] Iteration 2148, lr = 0.00809252
I0407 22:15:39.136615 23673 solver.cpp:218] Iteration 2160 (2.21836 iter/s, 5.4094s/12 iters), loss = 2.85904
I0407 22:15:39.136736 23673 solver.cpp:237]     Train net output #0: loss = 2.85904 (* 1 = 2.85904 loss)
I0407 22:15:39.136750 23673 sgd_solver.cpp:105] Iteration 2160, lr = 0.00808295
I0407 22:15:44.317795 23673 solver.cpp:218] Iteration 2172 (2.31621 iter/s, 5.18088s/12 iters), loss = 2.55976
I0407 22:15:44.317850 23673 solver.cpp:237]     Train net output #0: loss = 2.55976 (* 1 = 2.55976 loss)
I0407 22:15:44.317863 23673 sgd_solver.cpp:105] Iteration 2172, lr = 0.0080734
I0407 22:15:49.508340 23673 solver.cpp:218] Iteration 2184 (2.312 iter/s, 5.19031s/12 iters), loss = 2.80627
I0407 22:15:49.508399 23673 solver.cpp:237]     Train net output #0: loss = 2.80627 (* 1 = 2.80627 loss)
I0407 22:15:49.508412 23673 sgd_solver.cpp:105] Iteration 2184, lr = 0.00806386
I0407 22:15:55.008579 23673 solver.cpp:218] Iteration 2196 (2.18182 iter/s, 5.5s/12 iters), loss = 2.66681
I0407 22:15:55.008618 23673 solver.cpp:237]     Train net output #0: loss = 2.66681 (* 1 = 2.66681 loss)
I0407 22:15:55.008627 23673 sgd_solver.cpp:105] Iteration 2196, lr = 0.00805433
I0407 22:16:00.357722 23673 solver.cpp:218] Iteration 2208 (2.24344 iter/s, 5.34892s/12 iters), loss = 2.502
I0407 22:16:00.357774 23673 solver.cpp:237]     Train net output #0: loss = 2.502 (* 1 = 2.502 loss)
I0407 22:16:00.357785 23673 sgd_solver.cpp:105] Iteration 2208, lr = 0.00804482
I0407 22:16:05.530596 23673 solver.cpp:218] Iteration 2220 (2.3199 iter/s, 5.17265s/12 iters), loss = 2.36398
I0407 22:16:05.530648 23673 solver.cpp:237]     Train net output #0: loss = 2.36398 (* 1 = 2.36398 loss)
I0407 22:16:05.530658 23673 sgd_solver.cpp:105] Iteration 2220, lr = 0.00803531
I0407 22:16:07.352185 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:10.638469 23673 solver.cpp:218] Iteration 2232 (2.34942 iter/s, 5.10764s/12 iters), loss = 2.63228
I0407 22:16:10.655921 23673 solver.cpp:237]     Train net output #0: loss = 2.63228 (* 1 = 2.63228 loss)
I0407 22:16:10.655936 23673 sgd_solver.cpp:105] Iteration 2232, lr = 0.00802581
I0407 22:16:15.327852 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 22:16:20.013352 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 22:16:22.430148 23673 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 22:16:22.430179 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:16:26.046835 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:26.972385 23673 solver.cpp:397]     Test net output #0: accuracy = 0.259804
I0407 22:16:26.972421 23673 solver.cpp:397]     Test net output #1: loss = 3.13007 (* 1 = 3.13007 loss)
I0407 22:16:27.063762 23673 solver.cpp:218] Iteration 2244 (0.731381 iter/s, 16.4073s/12 iters), loss = 2.60012
I0407 22:16:27.063804 23673 solver.cpp:237]     Train net output #0: loss = 2.60012 (* 1 = 2.60012 loss)
I0407 22:16:27.063814 23673 sgd_solver.cpp:105] Iteration 2244, lr = 0.00801633
I0407 22:16:31.297441 23673 solver.cpp:218] Iteration 2256 (2.83454 iter/s, 4.23349s/12 iters), loss = 2.44962
I0407 22:16:31.297484 23673 solver.cpp:237]     Train net output #0: loss = 2.44962 (* 1 = 2.44962 loss)
I0407 22:16:31.297493 23673 sgd_solver.cpp:105] Iteration 2256, lr = 0.00800686
I0407 22:16:36.300002 23673 solver.cpp:218] Iteration 2268 (2.39888 iter/s, 5.00234s/12 iters), loss = 2.50488
I0407 22:16:36.300061 23673 solver.cpp:237]     Train net output #0: loss = 2.50488 (* 1 = 2.50488 loss)
I0407 22:16:36.300076 23673 sgd_solver.cpp:105] Iteration 2268, lr = 0.0079974
I0407 22:16:41.580446 23673 solver.cpp:218] Iteration 2280 (2.27264 iter/s, 5.28021s/12 iters), loss = 2.21417
I0407 22:16:41.580554 23673 solver.cpp:237]     Train net output #0: loss = 2.21417 (* 1 = 2.21417 loss)
I0407 22:16:41.580567 23673 sgd_solver.cpp:105] Iteration 2280, lr = 0.00798795
I0407 22:16:47.102453 23673 solver.cpp:218] Iteration 2292 (2.17324 iter/s, 5.52172s/12 iters), loss = 2.41745
I0407 22:16:47.102499 23673 solver.cpp:237]     Train net output #0: loss = 2.41745 (* 1 = 2.41745 loss)
I0407 22:16:47.102509 23673 sgd_solver.cpp:105] Iteration 2292, lr = 0.00797851
I0407 22:16:52.238560 23673 solver.cpp:218] Iteration 2304 (2.3365 iter/s, 5.13588s/12 iters), loss = 2.34782
I0407 22:16:52.238608 23673 solver.cpp:237]     Train net output #0: loss = 2.34782 (* 1 = 2.34782 loss)
I0407 22:16:52.238620 23673 sgd_solver.cpp:105] Iteration 2304, lr = 0.00796908
I0407 22:16:57.456076 23673 solver.cpp:218] Iteration 2316 (2.30004 iter/s, 5.21729s/12 iters), loss = 2.46142
I0407 22:16:57.456120 23673 solver.cpp:237]     Train net output #0: loss = 2.46142 (* 1 = 2.46142 loss)
I0407 22:16:57.456130 23673 sgd_solver.cpp:105] Iteration 2316, lr = 0.00795966
I0407 22:17:01.803594 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:02.963342 23673 solver.cpp:218] Iteration 2328 (2.17903 iter/s, 5.50703s/12 iters), loss = 2.25591
I0407 22:17:02.963397 23673 solver.cpp:237]     Train net output #0: loss = 2.25591 (* 1 = 2.25591 loss)
I0407 22:17:02.963409 23673 sgd_solver.cpp:105] Iteration 2328, lr = 0.00795026
I0407 22:17:08.367202 23673 solver.cpp:218] Iteration 2340 (2.22073 iter/s, 5.40362s/12 iters), loss = 2.12586
I0407 22:17:08.367256 23673 solver.cpp:237]     Train net output #0: loss = 2.12586 (* 1 = 2.12586 loss)
I0407 22:17:08.367269 23673 sgd_solver.cpp:105] Iteration 2340, lr = 0.00794086
I0407 22:17:10.551589 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 22:17:17.560112 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 22:17:20.679625 23673 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 22:17:20.679651 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:17:24.229388 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:25.245620 23673 solver.cpp:397]     Test net output #0: accuracy = 0.284314
I0407 22:17:25.245669 23673 solver.cpp:397]     Test net output #1: loss = 3.06286 (* 1 = 3.06286 loss)
I0407 22:17:27.186507 23673 solver.cpp:218] Iteration 2352 (0.637665 iter/s, 18.8186s/12 iters), loss = 2.56837
I0407 22:17:27.186558 23673 solver.cpp:237]     Train net output #0: loss = 2.56837 (* 1 = 2.56837 loss)
I0407 22:17:27.186568 23673 sgd_solver.cpp:105] Iteration 2352, lr = 0.00793148
I0407 22:17:32.229562 23673 solver.cpp:218] Iteration 2364 (2.37962 iter/s, 5.04283s/12 iters), loss = 2.37422
I0407 22:17:32.229621 23673 solver.cpp:237]     Train net output #0: loss = 2.37422 (* 1 = 2.37422 loss)
I0407 22:17:32.229633 23673 sgd_solver.cpp:105] Iteration 2364, lr = 0.00792211
I0407 22:17:37.514521 23673 solver.cpp:218] Iteration 2376 (2.2707 iter/s, 5.28472s/12 iters), loss = 2.31007
I0407 22:17:37.514576 23673 solver.cpp:237]     Train net output #0: loss = 2.31007 (* 1 = 2.31007 loss)
I0407 22:17:37.514588 23673 sgd_solver.cpp:105] Iteration 2376, lr = 0.00791274
I0407 22:17:42.562512 23673 solver.cpp:218] Iteration 2388 (2.37729 iter/s, 5.04777s/12 iters), loss = 2.47549
I0407 22:17:42.562552 23673 solver.cpp:237]     Train net output #0: loss = 2.47549 (* 1 = 2.47549 loss)
I0407 22:17:42.562561 23673 sgd_solver.cpp:105] Iteration 2388, lr = 0.00790339
I0407 22:17:47.658535 23673 solver.cpp:218] Iteration 2400 (2.35488 iter/s, 5.09581s/12 iters), loss = 2.12602
I0407 22:17:47.658845 23673 solver.cpp:237]     Train net output #0: loss = 2.12602 (* 1 = 2.12602 loss)
I0407 22:17:47.658856 23673 sgd_solver.cpp:105] Iteration 2400, lr = 0.00789405
I0407 22:17:52.710844 23673 solver.cpp:218] Iteration 2412 (2.37538 iter/s, 5.05183s/12 iters), loss = 2.17311
I0407 22:17:52.710897 23673 solver.cpp:237]     Train net output #0: loss = 2.17311 (* 1 = 2.17311 loss)
I0407 22:17:52.710909 23673 sgd_solver.cpp:105] Iteration 2412, lr = 0.00788473
I0407 22:17:57.778069 23673 solver.cpp:218] Iteration 2424 (2.36827 iter/s, 5.067s/12 iters), loss = 2.59616
I0407 22:17:57.778115 23673 solver.cpp:237]     Train net output #0: loss = 2.59616 (* 1 = 2.59616 loss)
I0407 22:17:57.778127 23673 sgd_solver.cpp:105] Iteration 2424, lr = 0.00787541
I0407 22:17:58.850898 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:02.835027 23673 solver.cpp:218] Iteration 2436 (2.37307 iter/s, 5.05674s/12 iters), loss = 2.28139
I0407 22:18:02.835069 23673 solver.cpp:237]     Train net output #0: loss = 2.28139 (* 1 = 2.28139 loss)
I0407 22:18:02.835080 23673 sgd_solver.cpp:105] Iteration 2436, lr = 0.0078661
I0407 22:18:07.585827 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 22:18:10.932289 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 22:18:13.304824 23673 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 22:18:13.304849 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:18:16.798020 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:17.794304 23673 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0407 22:18:17.794414 23673 solver.cpp:397]     Test net output #1: loss = 3.12738 (* 1 = 3.12738 loss)
I0407 22:18:17.885807 23673 solver.cpp:218] Iteration 2448 (0.797329 iter/s, 15.0503s/12 iters), loss = 2.27955
I0407 22:18:17.885854 23673 solver.cpp:237]     Train net output #0: loss = 2.27955 (* 1 = 2.27955 loss)
I0407 22:18:17.885864 23673 sgd_solver.cpp:105] Iteration 2448, lr = 0.00785681
I0407 22:18:22.217820 23673 solver.cpp:218] Iteration 2460 (2.77021 iter/s, 4.33181s/12 iters), loss = 2.01722
I0407 22:18:22.217876 23673 solver.cpp:237]     Train net output #0: loss = 2.01722 (* 1 = 2.01722 loss)
I0407 22:18:22.217888 23673 sgd_solver.cpp:105] Iteration 2460, lr = 0.00784752
I0407 22:18:27.325333 23673 solver.cpp:218] Iteration 2472 (2.34959 iter/s, 5.10728s/12 iters), loss = 2.34017
I0407 22:18:27.325381 23673 solver.cpp:237]     Train net output #0: loss = 2.34017 (* 1 = 2.34017 loss)
I0407 22:18:27.325392 23673 sgd_solver.cpp:105] Iteration 2472, lr = 0.00783825
I0407 22:18:32.665055 23673 solver.cpp:218] Iteration 2484 (2.2474 iter/s, 5.33949s/12 iters), loss = 2.08265
I0407 22:18:32.665109 23673 solver.cpp:237]     Train net output #0: loss = 2.08265 (* 1 = 2.08265 loss)
I0407 22:18:32.665122 23673 sgd_solver.cpp:105] Iteration 2484, lr = 0.00782899
I0407 22:18:37.728669 23673 solver.cpp:218] Iteration 2496 (2.36995 iter/s, 5.06339s/12 iters), loss = 2.40368
I0407 22:18:37.728713 23673 solver.cpp:237]     Train net output #0: loss = 2.40368 (* 1 = 2.40368 loss)
I0407 22:18:37.728724 23673 sgd_solver.cpp:105] Iteration 2496, lr = 0.00781974
I0407 22:18:43.232964 23673 solver.cpp:218] Iteration 2508 (2.18021 iter/s, 5.50405s/12 iters), loss = 1.98511
I0407 22:18:43.233029 23673 solver.cpp:237]     Train net output #0: loss = 1.98511 (* 1 = 1.98511 loss)
I0407 22:18:43.233044 23673 sgd_solver.cpp:105] Iteration 2508, lr = 0.0078105
I0407 22:18:48.737694 23673 solver.cpp:218] Iteration 2520 (2.18004 iter/s, 5.50448s/12 iters), loss = 1.96654
I0407 22:18:48.737834 23673 solver.cpp:237]     Train net output #0: loss = 1.96654 (* 1 = 1.96654 loss)
I0407 22:18:48.737848 23673 sgd_solver.cpp:105] Iteration 2520, lr = 0.00780127
I0407 22:18:51.973524 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:53.792528 23673 solver.cpp:218] Iteration 2532 (2.37411 iter/s, 5.05452s/12 iters), loss = 2.40244
I0407 22:18:53.792584 23673 solver.cpp:237]     Train net output #0: loss = 2.40244 (* 1 = 2.40244 loss)
I0407 22:18:53.792596 23673 sgd_solver.cpp:105] Iteration 2532, lr = 0.00779205
I0407 22:18:58.842181 23673 solver.cpp:218] Iteration 2544 (2.37651 iter/s, 5.04942s/12 iters), loss = 2.3547
I0407 22:18:58.842231 23673 solver.cpp:237]     Train net output #0: loss = 2.3547 (* 1 = 2.3547 loss)
I0407 22:18:58.842243 23673 sgd_solver.cpp:105] Iteration 2544, lr = 0.00778284
I0407 22:19:01.006417 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 22:19:09.194352 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 22:19:12.377218 23673 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 22:19:12.377239 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:19:15.821352 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:16.943943 23673 solver.cpp:397]     Test net output #0: accuracy = 0.284314
I0407 22:19:16.943994 23673 solver.cpp:397]     Test net output #1: loss = 3.13806 (* 1 = 3.13806 loss)
I0407 22:19:18.950093 23673 solver.cpp:218] Iteration 2556 (0.596801 iter/s, 20.1072s/12 iters), loss = 2.24839
I0407 22:19:18.953747 23673 solver.cpp:237]     Train net output #0: loss = 2.24839 (* 1 = 2.24839 loss)
I0407 22:19:18.953758 23673 sgd_solver.cpp:105] Iteration 2556, lr = 0.00777364
I0407 22:19:24.153012 23673 solver.cpp:218] Iteration 2568 (2.3081 iter/s, 5.19909s/12 iters), loss = 1.89148
I0407 22:19:24.153065 23673 solver.cpp:237]     Train net output #0: loss = 1.89148 (* 1 = 1.89148 loss)
I0407 22:19:24.153077 23673 sgd_solver.cpp:105] Iteration 2568, lr = 0.00776446
I0407 22:19:29.624913 23673 solver.cpp:218] Iteration 2580 (2.19312 iter/s, 5.47166s/12 iters), loss = 1.97796
I0407 22:19:29.624963 23673 solver.cpp:237]     Train net output #0: loss = 1.97796 (* 1 = 1.97796 loss)
I0407 22:19:29.624979 23673 sgd_solver.cpp:105] Iteration 2580, lr = 0.00775528
I0407 22:19:34.661934 23673 solver.cpp:218] Iteration 2592 (2.38246 iter/s, 5.0368s/12 iters), loss = 2.31022
I0407 22:19:34.661991 23673 solver.cpp:237]     Train net output #0: loss = 2.31022 (* 1 = 2.31022 loss)
I0407 22:19:34.662003 23673 sgd_solver.cpp:105] Iteration 2592, lr = 0.00774612
I0407 22:19:39.871570 23673 solver.cpp:218] Iteration 2604 (2.30353 iter/s, 5.2094s/12 iters), loss = 2.40099
I0407 22:19:39.871620 23673 solver.cpp:237]     Train net output #0: loss = 2.40099 (* 1 = 2.40099 loss)
I0407 22:19:39.871634 23673 sgd_solver.cpp:105] Iteration 2604, lr = 0.00773697
I0407 22:19:45.404964 23673 solver.cpp:218] Iteration 2616 (2.16874 iter/s, 5.53315s/12 iters), loss = 2.31446
I0407 22:19:45.405019 23673 solver.cpp:237]     Train net output #0: loss = 2.31446 (* 1 = 2.31446 loss)
I0407 22:19:45.405030 23673 sgd_solver.cpp:105] Iteration 2616, lr = 0.00772782
I0407 22:19:50.931871 23673 solver.cpp:218] Iteration 2628 (2.17129 iter/s, 5.52666s/12 iters), loss = 2.01284
I0407 22:19:50.931999 23673 solver.cpp:237]     Train net output #0: loss = 2.01284 (* 1 = 2.01284 loss)
I0407 22:19:50.932013 23673 sgd_solver.cpp:105] Iteration 2628, lr = 0.00771869
I0407 22:19:51.422209 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:56.465459 23673 solver.cpp:218] Iteration 2640 (2.1687 iter/s, 5.53328s/12 iters), loss = 2.6192
I0407 22:19:56.465503 23673 solver.cpp:237]     Train net output #0: loss = 2.6192 (* 1 = 2.6192 loss)
I0407 22:19:56.465513 23673 sgd_solver.cpp:105] Iteration 2640, lr = 0.00770957
I0407 22:20:01.461757 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 22:20:08.934579 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 22:20:11.715752 23673 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 22:20:11.715780 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:20:15.119843 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:16.180083 23673 solver.cpp:397]     Test net output #0: accuracy = 0.292279
I0407 22:20:16.180121 23673 solver.cpp:397]     Test net output #1: loss = 3.08091 (* 1 = 3.08091 loss)
I0407 22:20:16.271553 23673 solver.cpp:218] Iteration 2652 (0.605895 iter/s, 19.8054s/12 iters), loss = 2.08969
I0407 22:20:16.271602 23673 solver.cpp:237]     Train net output #0: loss = 2.08969 (* 1 = 2.08969 loss)
I0407 22:20:16.271611 23673 sgd_solver.cpp:105] Iteration 2652, lr = 0.00770046
I0407 22:20:20.500905 23673 solver.cpp:218] Iteration 2664 (2.83744 iter/s, 4.22916s/12 iters), loss = 2.28596
I0407 22:20:20.500941 23673 solver.cpp:237]     Train net output #0: loss = 2.28596 (* 1 = 2.28596 loss)
I0407 22:20:20.500949 23673 sgd_solver.cpp:105] Iteration 2664, lr = 0.00769136
I0407 22:20:25.483335 23673 solver.cpp:218] Iteration 2676 (2.40857 iter/s, 4.98222s/12 iters), loss = 2.1419
I0407 22:20:25.483484 23673 solver.cpp:237]     Train net output #0: loss = 2.1419 (* 1 = 2.1419 loss)
I0407 22:20:25.483497 23673 sgd_solver.cpp:105] Iteration 2676, lr = 0.00768227
I0407 22:20:30.525909 23673 solver.cpp:218] Iteration 2688 (2.37989 iter/s, 5.04225s/12 iters), loss = 1.98835
I0407 22:20:30.525981 23673 solver.cpp:237]     Train net output #0: loss = 1.98835 (* 1 = 1.98835 loss)
I0407 22:20:30.525995 23673 sgd_solver.cpp:105] Iteration 2688, lr = 0.00767319
I0407 22:20:35.592653 23673 solver.cpp:218] Iteration 2700 (2.36849 iter/s, 5.06651s/12 iters), loss = 1.90642
I0407 22:20:35.592708 23673 solver.cpp:237]     Train net output #0: loss = 1.90642 (* 1 = 1.90642 loss)
I0407 22:20:35.592720 23673 sgd_solver.cpp:105] Iteration 2700, lr = 0.00766413
I0407 22:20:40.603179 23673 solver.cpp:218] Iteration 2712 (2.39507 iter/s, 5.01029s/12 iters), loss = 1.65366
I0407 22:20:40.603232 23673 solver.cpp:237]     Train net output #0: loss = 1.65366 (* 1 = 1.65366 loss)
I0407 22:20:40.603245 23673 sgd_solver.cpp:105] Iteration 2712, lr = 0.00765507
I0407 22:20:45.658640 23673 solver.cpp:218] Iteration 2724 (2.37378 iter/s, 5.05523s/12 iters), loss = 1.77608
I0407 22:20:45.658695 23673 solver.cpp:237]     Train net output #0: loss = 1.77608 (* 1 = 1.77608 loss)
I0407 22:20:45.658707 23673 sgd_solver.cpp:105] Iteration 2724, lr = 0.00764602
I0407 22:20:48.281873 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:50.699247 23673 solver.cpp:218] Iteration 2736 (2.38077 iter/s, 5.04038s/12 iters), loss = 1.86153
I0407 22:20:50.699297 23673 solver.cpp:237]     Train net output #0: loss = 1.86153 (* 1 = 1.86153 loss)
I0407 22:20:50.699308 23673 sgd_solver.cpp:105] Iteration 2736, lr = 0.00763699
I0407 22:20:55.734787 23673 solver.cpp:218] Iteration 2748 (2.38317 iter/s, 5.03531s/12 iters), loss = 2.09083
I0407 22:20:55.734910 23673 solver.cpp:237]     Train net output #0: loss = 2.09083 (* 1 = 2.09083 loss)
I0407 22:20:55.734923 23673 sgd_solver.cpp:105] Iteration 2748, lr = 0.00762796
I0407 22:20:57.795639 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 22:21:02.759968 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 22:21:05.087728 23673 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 22:21:05.087754 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:21:08.201715 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:21:08.437824 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:09.545349 23673 solver.cpp:397]     Test net output #0: accuracy = 0.283701
I0407 22:21:09.545390 23673 solver.cpp:397]     Test net output #1: loss = 3.06711 (* 1 = 3.06711 loss)
I0407 22:21:11.508666 23673 solver.cpp:218] Iteration 2760 (0.760782 iter/s, 15.7732s/12 iters), loss = 1.82121
I0407 22:21:11.508723 23673 solver.cpp:237]     Train net output #0: loss = 1.82121 (* 1 = 1.82121 loss)
I0407 22:21:11.508735 23673 sgd_solver.cpp:105] Iteration 2760, lr = 0.00761895
I0407 22:21:16.613274 23673 solver.cpp:218] Iteration 2772 (2.35093 iter/s, 5.10437s/12 iters), loss = 2.25539
I0407 22:21:16.613327 23673 solver.cpp:237]     Train net output #0: loss = 2.25539 (* 1 = 2.25539 loss)
I0407 22:21:16.613338 23673 sgd_solver.cpp:105] Iteration 2772, lr = 0.00760995
I0407 22:21:21.681020 23673 solver.cpp:218] Iteration 2784 (2.36802 iter/s, 5.06752s/12 iters), loss = 2.02282
I0407 22:21:21.681073 23673 solver.cpp:237]     Train net output #0: loss = 2.02282 (* 1 = 2.02282 loss)
I0407 22:21:21.681085 23673 sgd_solver.cpp:105] Iteration 2784, lr = 0.00760095
I0407 22:21:26.741855 23673 solver.cpp:218] Iteration 2796 (2.37126 iter/s, 5.06061s/12 iters), loss = 2.06505
I0407 22:21:26.742000 23673 solver.cpp:237]     Train net output #0: loss = 2.06505 (* 1 = 2.06505 loss)
I0407 22:21:26.742013 23673 sgd_solver.cpp:105] Iteration 2796, lr = 0.00759197
I0407 22:21:31.774524 23673 solver.cpp:218] Iteration 2808 (2.38457 iter/s, 5.03235s/12 iters), loss = 1.89639
I0407 22:21:31.774582 23673 solver.cpp:237]     Train net output #0: loss = 1.89639 (* 1 = 1.89639 loss)
I0407 22:21:31.774595 23673 sgd_solver.cpp:105] Iteration 2808, lr = 0.007583
I0407 22:21:36.837996 23673 solver.cpp:218] Iteration 2820 (2.37002 iter/s, 5.06324s/12 iters), loss = 1.62236
I0407 22:21:36.838049 23673 solver.cpp:237]     Train net output #0: loss = 1.62236 (* 1 = 1.62236 loss)
I0407 22:21:36.838060 23673 sgd_solver.cpp:105] Iteration 2820, lr = 0.00757404
I0407 22:21:41.976550 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:42.286388 23673 solver.cpp:218] Iteration 2832 (2.20258 iter/s, 5.44815s/12 iters), loss = 1.74672
I0407 22:21:42.286443 23673 solver.cpp:237]     Train net output #0: loss = 1.74672 (* 1 = 1.74672 loss)
I0407 22:21:42.286455 23673 sgd_solver.cpp:105] Iteration 2832, lr = 0.00756509
I0407 22:21:47.493280 23673 solver.cpp:218] Iteration 2844 (2.30474 iter/s, 5.20666s/12 iters), loss = 1.91931
I0407 22:21:47.493331 23673 solver.cpp:237]     Train net output #0: loss = 1.91931 (* 1 = 1.91931 loss)
I0407 22:21:47.493343 23673 sgd_solver.cpp:105] Iteration 2844, lr = 0.00755615
I0407 22:21:52.062184 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 22:21:56.923662 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 22:21:59.249097 23673 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 22:21:59.249123 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:22:02.537824 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:03.680668 23673 solver.cpp:397]     Test net output #0: accuracy = 0.297181
I0407 22:22:03.680716 23673 solver.cpp:397]     Test net output #1: loss = 2.97826 (* 1 = 2.97826 loss)
I0407 22:22:03.772374 23673 solver.cpp:218] Iteration 2856 (0.737168 iter/s, 16.2785s/12 iters), loss = 1.8312
I0407 22:22:03.772423 23673 solver.cpp:237]     Train net output #0: loss = 1.8312 (* 1 = 1.8312 loss)
I0407 22:22:03.772435 23673 sgd_solver.cpp:105] Iteration 2856, lr = 0.00754722
I0407 22:22:08.197705 23673 solver.cpp:218] Iteration 2868 (2.71178 iter/s, 4.42513s/12 iters), loss = 1.70884
I0407 22:22:08.197742 23673 solver.cpp:237]     Train net output #0: loss = 1.70884 (* 1 = 1.70884 loss)
I0407 22:22:08.197751 23673 sgd_solver.cpp:105] Iteration 2868, lr = 0.0075383
I0407 22:22:13.303968 23673 solver.cpp:218] Iteration 2880 (2.35015 iter/s, 5.10605s/12 iters), loss = 1.62305
I0407 22:22:13.304008 23673 solver.cpp:237]     Train net output #0: loss = 1.62305 (* 1 = 1.62305 loss)
I0407 22:22:13.304016 23673 sgd_solver.cpp:105] Iteration 2880, lr = 0.0075294
I0407 22:22:18.293992 23673 solver.cpp:218] Iteration 2892 (2.4049 iter/s, 4.98981s/12 iters), loss = 1.98865
I0407 22:22:18.294039 23673 solver.cpp:237]     Train net output #0: loss = 1.98865 (* 1 = 1.98865 loss)
I0407 22:22:18.294049 23673 sgd_solver.cpp:105] Iteration 2892, lr = 0.0075205
I0407 22:22:23.383945 23673 solver.cpp:218] Iteration 2904 (2.35769 iter/s, 5.08973s/12 iters), loss = 1.66176
I0407 22:22:23.383985 23673 solver.cpp:237]     Train net output #0: loss = 1.66176 (* 1 = 1.66176 loss)
I0407 22:22:23.383994 23673 sgd_solver.cpp:105] Iteration 2904, lr = 0.00751161
I0407 22:22:28.517309 23673 solver.cpp:218] Iteration 2916 (2.33775 iter/s, 5.13315s/12 iters), loss = 1.762
I0407 22:22:28.517380 23673 solver.cpp:237]     Train net output #0: loss = 1.762 (* 1 = 1.762 loss)
I0407 22:22:28.517390 23673 sgd_solver.cpp:105] Iteration 2916, lr = 0.00750274
I0407 22:22:33.635454 23673 solver.cpp:218] Iteration 2928 (2.34471 iter/s, 5.1179s/12 iters), loss = 1.77419
I0407 22:22:33.635499 23673 solver.cpp:237]     Train net output #0: loss = 1.77419 (* 1 = 1.77419 loss)
I0407 22:22:33.635509 23673 sgd_solver.cpp:105] Iteration 2928, lr = 0.00749387
I0407 22:22:35.545096 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:38.830840 23673 solver.cpp:218] Iteration 2940 (2.30984 iter/s, 5.19517s/12 iters), loss = 1.50095
I0407 22:22:38.830883 23673 solver.cpp:237]     Train net output #0: loss = 1.50095 (* 1 = 1.50095 loss)
I0407 22:22:38.830893 23673 sgd_solver.cpp:105] Iteration 2940, lr = 0.00748501
I0407 22:22:43.915138 23673 solver.cpp:218] Iteration 2952 (2.36031 iter/s, 5.08408s/12 iters), loss = 1.906
I0407 22:22:43.915191 23673 solver.cpp:237]     Train net output #0: loss = 1.906 (* 1 = 1.906 loss)
I0407 22:22:43.915205 23673 sgd_solver.cpp:105] Iteration 2952, lr = 0.00747617
I0407 22:22:46.097334 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 22:22:51.200815 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 22:22:53.527551 23673 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 22:22:53.527580 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:22:56.803936 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:57.992295 23673 solver.cpp:397]     Test net output #0: accuracy = 0.335172
I0407 22:22:57.992326 23673 solver.cpp:397]     Test net output #1: loss = 2.89882 (* 1 = 2.89882 loss)
I0407 22:22:59.979199 23673 solver.cpp:218] Iteration 2964 (0.747036 iter/s, 16.0635s/12 iters), loss = 1.44791
I0407 22:22:59.979321 23673 solver.cpp:237]     Train net output #0: loss = 1.44791 (* 1 = 1.44791 loss)
I0407 22:22:59.979331 23673 sgd_solver.cpp:105] Iteration 2964, lr = 0.00746734
I0407 22:23:05.287787 23673 solver.cpp:218] Iteration 2976 (2.26061 iter/s, 5.30829s/12 iters), loss = 1.63648
I0407 22:23:05.287827 23673 solver.cpp:237]     Train net output #0: loss = 1.63648 (* 1 = 1.63648 loss)
I0407 22:23:05.287837 23673 sgd_solver.cpp:105] Iteration 2976, lr = 0.00745851
I0407 22:23:10.357185 23673 solver.cpp:218] Iteration 2988 (2.36724 iter/s, 5.06918s/12 iters), loss = 1.71525
I0407 22:23:10.357232 23673 solver.cpp:237]     Train net output #0: loss = 1.71525 (* 1 = 1.71525 loss)
I0407 22:23:10.357244 23673 sgd_solver.cpp:105] Iteration 2988, lr = 0.0074497
I0407 22:23:15.471073 23673 solver.cpp:218] Iteration 3000 (2.34665 iter/s, 5.11366s/12 iters), loss = 1.90417
I0407 22:23:15.471125 23673 solver.cpp:237]     Train net output #0: loss = 1.90417 (* 1 = 1.90417 loss)
I0407 22:23:15.471139 23673 sgd_solver.cpp:105] Iteration 3000, lr = 0.00744089
I0407 22:23:20.586282 23673 solver.cpp:218] Iteration 3012 (2.34605 iter/s, 5.11498s/12 iters), loss = 1.53533
I0407 22:23:20.586338 23673 solver.cpp:237]     Train net output #0: loss = 1.53533 (* 1 = 1.53533 loss)
I0407 22:23:20.586351 23673 sgd_solver.cpp:105] Iteration 3012, lr = 0.0074321
I0407 22:23:25.669756 23673 solver.cpp:218] Iteration 3024 (2.3607 iter/s, 5.08324s/12 iters), loss = 1.57173
I0407 22:23:25.669811 23673 solver.cpp:237]     Train net output #0: loss = 1.57173 (* 1 = 1.57173 loss)
I0407 22:23:25.669823 23673 sgd_solver.cpp:105] Iteration 3024, lr = 0.00742332
I0407 22:23:29.737737 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:30.753428 23673 solver.cpp:218] Iteration 3036 (2.36061 iter/s, 5.08344s/12 iters), loss = 1.53153
I0407 22:23:30.753540 23673 solver.cpp:237]     Train net output #0: loss = 1.53153 (* 1 = 1.53153 loss)
I0407 22:23:30.753553 23673 sgd_solver.cpp:105] Iteration 3036, lr = 0.00741455
I0407 22:23:35.835132 23673 solver.cpp:218] Iteration 3048 (2.36154 iter/s, 5.08142s/12 iters), loss = 1.88705
I0407 22:23:35.835180 23673 solver.cpp:237]     Train net output #0: loss = 1.88705 (* 1 = 1.88705 loss)
I0407 22:23:35.835192 23673 sgd_solver.cpp:105] Iteration 3048, lr = 0.00740579
I0407 22:23:40.734092 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 22:23:45.241933 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 22:23:50.163646 23673 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 22:23:50.163673 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:23:53.419903 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:54.642875 23673 solver.cpp:397]     Test net output #0: accuracy = 0.314338
I0407 22:23:54.642917 23673 solver.cpp:397]     Test net output #1: loss = 3.02528 (* 1 = 3.02528 loss)
I0407 22:23:54.734294 23673 solver.cpp:218] Iteration 3060 (0.634971 iter/s, 18.8985s/12 iters), loss = 1.74523
I0407 22:23:54.734352 23673 solver.cpp:237]     Train net output #0: loss = 1.74523 (* 1 = 1.74523 loss)
I0407 22:23:54.734364 23673 sgd_solver.cpp:105] Iteration 3060, lr = 0.00739703
I0407 22:23:59.178925 23673 solver.cpp:218] Iteration 3072 (2.70002 iter/s, 4.44441s/12 iters), loss = 1.71137
I0407 22:23:59.178982 23673 solver.cpp:237]     Train net output #0: loss = 1.71137 (* 1 = 1.71137 loss)
I0407 22:23:59.178997 23673 sgd_solver.cpp:105] Iteration 3072, lr = 0.00738829
I0407 22:24:04.513705 23673 solver.cpp:218] Iteration 3084 (2.24949 iter/s, 5.33453s/12 iters), loss = 1.68953
I0407 22:24:04.513833 23673 solver.cpp:237]     Train net output #0: loss = 1.68953 (* 1 = 1.68953 loss)
I0407 22:24:04.513844 23673 sgd_solver.cpp:105] Iteration 3084, lr = 0.00737956
I0407 22:24:09.543498 23673 solver.cpp:218] Iteration 3096 (2.38592 iter/s, 5.0295s/12 iters), loss = 1.73892
I0407 22:24:09.543546 23673 solver.cpp:237]     Train net output #0: loss = 1.73892 (* 1 = 1.73892 loss)
I0407 22:24:09.543555 23673 sgd_solver.cpp:105] Iteration 3096, lr = 0.00737084
I0407 22:24:14.609349 23673 solver.cpp:218] Iteration 3108 (2.36891 iter/s, 5.06563s/12 iters), loss = 1.74452
I0407 22:24:14.609390 23673 solver.cpp:237]     Train net output #0: loss = 1.74452 (* 1 = 1.74452 loss)
I0407 22:24:14.609400 23673 sgd_solver.cpp:105] Iteration 3108, lr = 0.00736213
I0407 22:24:19.727365 23673 solver.cpp:218] Iteration 3120 (2.34476 iter/s, 5.1178s/12 iters), loss = 1.42196
I0407 22:24:19.727404 23673 solver.cpp:237]     Train net output #0: loss = 1.42196 (* 1 = 1.42196 loss)
I0407 22:24:19.727413 23673 sgd_solver.cpp:105] Iteration 3120, lr = 0.00735343
I0407 22:24:25.064255 23673 solver.cpp:218] Iteration 3132 (2.24859 iter/s, 5.33667s/12 iters), loss = 1.69879
I0407 22:24:25.064306 23673 solver.cpp:237]     Train net output #0: loss = 1.69879 (* 1 = 1.69879 loss)
I0407 22:24:25.064316 23673 sgd_solver.cpp:105] Iteration 3132, lr = 0.00734474
I0407 22:24:26.191352 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:30.234233 23673 solver.cpp:218] Iteration 3144 (2.32119 iter/s, 5.16975s/12 iters), loss = 1.47621
I0407 22:24:30.234282 23673 solver.cpp:237]     Train net output #0: loss = 1.47621 (* 1 = 1.47621 loss)
I0407 22:24:30.234295 23673 sgd_solver.cpp:105] Iteration 3144, lr = 0.00733606
I0407 22:24:35.407177 23673 solver.cpp:218] Iteration 3156 (2.31986 iter/s, 5.17272s/12 iters), loss = 1.95945
I0407 22:24:35.407289 23673 solver.cpp:237]     Train net output #0: loss = 1.95945 (* 1 = 1.95945 loss)
I0407 22:24:35.407301 23673 sgd_solver.cpp:105] Iteration 3156, lr = 0.0073274
I0407 22:24:37.474335 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 22:24:43.270203 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 22:24:51.736855 23673 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 22:24:51.736883 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:24:54.895615 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:56.168380 23673 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0407 22:24:56.168416 23673 solver.cpp:397]     Test net output #1: loss = 3.03066 (* 1 = 3.03066 loss)
I0407 22:24:58.271333 23673 solver.cpp:218] Iteration 3168 (0.524858 iter/s, 22.8633s/12 iters), loss = 1.28666
I0407 22:24:58.271378 23673 solver.cpp:237]     Train net output #0: loss = 1.28666 (* 1 = 1.28666 loss)
I0407 22:24:58.271386 23673 sgd_solver.cpp:105] Iteration 3168, lr = 0.00731874
I0407 22:25:03.464493 23673 solver.cpp:218] Iteration 3180 (2.31083 iter/s, 5.19293s/12 iters), loss = 1.29858
I0407 22:25:03.464550 23673 solver.cpp:237]     Train net output #0: loss = 1.29858 (* 1 = 1.29858 loss)
I0407 22:25:03.464563 23673 sgd_solver.cpp:105] Iteration 3180, lr = 0.00731009
I0407 22:25:08.501834 23673 solver.cpp:218] Iteration 3192 (2.38232 iter/s, 5.03711s/12 iters), loss = 1.88738
I0407 22:25:08.501992 23673 solver.cpp:237]     Train net output #0: loss = 1.88738 (* 1 = 1.88738 loss)
I0407 22:25:08.502007 23673 sgd_solver.cpp:105] Iteration 3192, lr = 0.00730145
I0407 22:25:13.594415 23673 solver.cpp:218] Iteration 3204 (2.35652 iter/s, 5.09225s/12 iters), loss = 1.55279
I0407 22:25:13.594463 23673 solver.cpp:237]     Train net output #0: loss = 1.55279 (* 1 = 1.55279 loss)
I0407 22:25:13.594475 23673 sgd_solver.cpp:105] Iteration 3204, lr = 0.00729282
I0407 22:25:18.717530 23673 solver.cpp:218] Iteration 3216 (2.34243 iter/s, 5.12289s/12 iters), loss = 1.39978
I0407 22:25:18.717582 23673 solver.cpp:237]     Train net output #0: loss = 1.39978 (* 1 = 1.39978 loss)
I0407 22:25:18.717597 23673 sgd_solver.cpp:105] Iteration 3216, lr = 0.0072842
I0407 22:25:23.943738 23673 solver.cpp:218] Iteration 3228 (2.29623 iter/s, 5.22596s/12 iters), loss = 1.32041
I0407 22:25:23.943797 23673 solver.cpp:237]     Train net output #0: loss = 1.32041 (* 1 = 1.32041 loss)
I0407 22:25:23.943809 23673 sgd_solver.cpp:105] Iteration 3228, lr = 0.0072756
I0407 22:25:27.193194 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:28.955416 23673 solver.cpp:218] Iteration 3240 (2.39452 iter/s, 5.01145s/12 iters), loss = 1.52058
I0407 22:25:28.955466 23673 solver.cpp:237]     Train net output #0: loss = 1.52058 (* 1 = 1.52058 loss)
I0407 22:25:28.955479 23673 sgd_solver.cpp:105] Iteration 3240, lr = 0.007267
I0407 22:25:34.238332 23673 solver.cpp:218] Iteration 3252 (2.27157 iter/s, 5.28268s/12 iters), loss = 1.54464
I0407 22:25:34.238379 23673 solver.cpp:237]     Train net output #0: loss = 1.54464 (* 1 = 1.54464 loss)
I0407 22:25:34.238390 23673 sgd_solver.cpp:105] Iteration 3252, lr = 0.00725841
I0407 22:25:39.261132 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 22:25:50.122423 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 22:25:57.761286 23673 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 22:25:57.761315 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:26:00.925315 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:02.228812 23673 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0407 22:26:02.228861 23673 solver.cpp:397]     Test net output #1: loss = 3.10076 (* 1 = 3.10076 loss)
I0407 22:26:02.320463 23673 solver.cpp:218] Iteration 3264 (0.427332 iter/s, 28.0812s/12 iters), loss = 1.50905
I0407 22:26:02.320509 23673 solver.cpp:237]     Train net output #0: loss = 1.50905 (* 1 = 1.50905 loss)
I0407 22:26:02.320520 23673 sgd_solver.cpp:105] Iteration 3264, lr = 0.00724984
I0407 22:26:06.884003 23673 solver.cpp:218] Iteration 3276 (2.62966 iter/s, 4.56333s/12 iters), loss = 1.78439
I0407 22:26:06.884058 23673 solver.cpp:237]     Train net output #0: loss = 1.78439 (* 1 = 1.78439 loss)
I0407 22:26:06.884071 23673 sgd_solver.cpp:105] Iteration 3276, lr = 0.00724127
I0407 22:26:11.919028 23673 solver.cpp:218] Iteration 3288 (2.38341 iter/s, 5.03479s/12 iters), loss = 1.7721
I0407 22:26:11.919121 23673 solver.cpp:237]     Train net output #0: loss = 1.7721 (* 1 = 1.7721 loss)
I0407 22:26:11.919134 23673 sgd_solver.cpp:105] Iteration 3288, lr = 0.00723271
I0407 22:26:16.969942 23673 solver.cpp:218] Iteration 3300 (2.37593 iter/s, 5.05065s/12 iters), loss = 1.30094
I0407 22:26:16.970011 23673 solver.cpp:237]     Train net output #0: loss = 1.30094 (* 1 = 1.30094 loss)
I0407 22:26:16.970023 23673 sgd_solver.cpp:105] Iteration 3300, lr = 0.00722417
I0407 22:26:22.152477 23673 solver.cpp:218] Iteration 3312 (2.31558 iter/s, 5.18229s/12 iters), loss = 1.56297
I0407 22:26:22.152519 23673 solver.cpp:237]     Train net output #0: loss = 1.56297 (* 1 = 1.56297 loss)
I0407 22:26:22.152530 23673 sgd_solver.cpp:105] Iteration 3312, lr = 0.00721563
I0407 22:26:27.122016 23673 solver.cpp:218] Iteration 3324 (2.41481 iter/s, 4.96933s/12 iters), loss = 1.16414
I0407 22:26:27.122063 23673 solver.cpp:237]     Train net output #0: loss = 1.16414 (* 1 = 1.16414 loss)
I0407 22:26:27.122076 23673 sgd_solver.cpp:105] Iteration 3324, lr = 0.0072071
I0407 22:26:32.164459 23673 solver.cpp:218] Iteration 3336 (2.3799 iter/s, 5.04222s/12 iters), loss = 1.50422
I0407 22:26:32.164513 23673 solver.cpp:237]     Train net output #0: loss = 1.50422 (* 1 = 1.50422 loss)
I0407 22:26:32.164525 23673 sgd_solver.cpp:105] Iteration 3336, lr = 0.00719859
I0407 22:26:32.640929 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:37.148229 23673 solver.cpp:218] Iteration 3348 (2.40793 iter/s, 4.98354s/12 iters), loss = 1.65238
I0407 22:26:37.148284 23673 solver.cpp:237]     Train net output #0: loss = 1.65238 (* 1 = 1.65238 loss)
I0407 22:26:37.148296 23673 sgd_solver.cpp:105] Iteration 3348, lr = 0.00719008
I0407 22:26:42.284114 23673 solver.cpp:218] Iteration 3360 (2.3366 iter/s, 5.13566s/12 iters), loss = 1.58698
I0407 22:26:42.284394 23673 solver.cpp:237]     Train net output #0: loss = 1.58698 (* 1 = 1.58698 loss)
I0407 22:26:42.284406 23673 sgd_solver.cpp:105] Iteration 3360, lr = 0.00718158
I0407 22:26:44.535082 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 22:26:51.705760 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 22:26:57.389045 23673 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 22:26:57.389070 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:27:00.489647 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:01.826937 23673 solver.cpp:397]     Test net output #0: accuracy = 0.297181
I0407 22:27:01.826987 23673 solver.cpp:397]     Test net output #1: loss = 3.11607 (* 1 = 3.11607 loss)
I0407 22:27:03.727018 23673 solver.cpp:218] Iteration 3372 (0.559651 iter/s, 21.4419s/12 iters), loss = 1.45104
I0407 22:27:03.727089 23673 solver.cpp:237]     Train net output #0: loss = 1.45104 (* 1 = 1.45104 loss)
I0407 22:27:03.727104 23673 sgd_solver.cpp:105] Iteration 3372, lr = 0.0071731
I0407 22:27:08.828014 23673 solver.cpp:218] Iteration 3384 (2.3526 iter/s, 5.10075s/12 iters), loss = 1.46973
I0407 22:27:08.828078 23673 solver.cpp:237]     Train net output #0: loss = 1.46973 (* 1 = 1.46973 loss)
I0407 22:27:08.828090 23673 sgd_solver.cpp:105] Iteration 3384, lr = 0.00716462
I0407 22:27:13.974488 23673 solver.cpp:218] Iteration 3396 (2.3318 iter/s, 5.14624s/12 iters), loss = 1.73515
I0407 22:27:13.974575 23673 solver.cpp:237]     Train net output #0: loss = 1.73515 (* 1 = 1.73515 loss)
I0407 22:27:13.974587 23673 sgd_solver.cpp:105] Iteration 3396, lr = 0.00715615
I0407 22:27:19.032661 23673 solver.cpp:218] Iteration 3408 (2.37252 iter/s, 5.05791s/12 iters), loss = 1.53298
I0407 22:27:19.032717 23673 solver.cpp:237]     Train net output #0: loss = 1.53298 (* 1 = 1.53298 loss)
I0407 22:27:19.032728 23673 sgd_solver.cpp:105] Iteration 3408, lr = 0.0071477
I0407 22:27:24.168543 23673 solver.cpp:218] Iteration 3420 (2.33661 iter/s, 5.13565s/12 iters), loss = 1.12671
I0407 22:27:24.168592 23673 solver.cpp:237]     Train net output #0: loss = 1.12671 (* 1 = 1.12671 loss)
I0407 22:27:24.168604 23673 sgd_solver.cpp:105] Iteration 3420, lr = 0.00713925
I0407 22:27:29.180366 23673 solver.cpp:218] Iteration 3432 (2.39445 iter/s, 5.0116s/12 iters), loss = 1.38235
I0407 22:27:29.180418 23673 solver.cpp:237]     Train net output #0: loss = 1.38235 (* 1 = 1.38235 loss)
I0407 22:27:29.180431 23673 sgd_solver.cpp:105] Iteration 3432, lr = 0.00713082
I0407 22:27:31.834270 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:34.246888 23673 solver.cpp:218] Iteration 3444 (2.36859 iter/s, 5.0663s/12 iters), loss = 1.12519
I0407 22:27:34.246937 23673 solver.cpp:237]     Train net output #0: loss = 1.12519 (* 1 = 1.12519 loss)
I0407 22:27:34.246948 23673 sgd_solver.cpp:105] Iteration 3444, lr = 0.00712239
I0407 22:27:39.351212 23673 solver.cpp:218] Iteration 3456 (2.35105 iter/s, 5.1041s/12 iters), loss = 1.21929
I0407 22:27:39.351259 23673 solver.cpp:237]     Train net output #0: loss = 1.21929 (* 1 = 1.21929 loss)
I0407 22:27:39.351271 23673 sgd_solver.cpp:105] Iteration 3456, lr = 0.00711397
I0407 22:27:44.046818 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 22:27:50.278908 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 22:27:58.870927 23673 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 22:27:58.870955 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:27:59.301970 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:28:01.910111 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:03.295233 23673 solver.cpp:397]     Test net output #0: accuracy = 0.313726
I0407 22:28:03.295280 23673 solver.cpp:397]     Test net output #1: loss = 2.95018 (* 1 = 2.95018 loss)
I0407 22:28:03.383574 23673 solver.cpp:218] Iteration 3468 (0.499344 iter/s, 24.0315s/12 iters), loss = 1.43592
I0407 22:28:03.383625 23673 solver.cpp:237]     Train net output #0: loss = 1.43592 (* 1 = 1.43592 loss)
I0407 22:28:03.383637 23673 sgd_solver.cpp:105] Iteration 3468, lr = 0.00710557
I0407 22:28:07.518774 23673 solver.cpp:218] Iteration 3480 (2.90205 iter/s, 4.135s/12 iters), loss = 1.21484
I0407 22:28:07.518826 23673 solver.cpp:237]     Train net output #0: loss = 1.21484 (* 1 = 1.21484 loss)
I0407 22:28:07.518838 23673 sgd_solver.cpp:105] Iteration 3480, lr = 0.00709717
I0407 22:28:12.592893 23673 solver.cpp:218] Iteration 3492 (2.36505 iter/s, 5.0739s/12 iters), loss = 1.41319
I0407 22:28:12.592941 23673 solver.cpp:237]     Train net output #0: loss = 1.41319 (* 1 = 1.41319 loss)
I0407 22:28:12.592952 23673 sgd_solver.cpp:105] Iteration 3492, lr = 0.00708878
I0407 22:28:17.690248 23673 solver.cpp:218] Iteration 3504 (2.35426 iter/s, 5.09713s/12 iters), loss = 1.30981
I0407 22:28:17.690366 23673 solver.cpp:237]     Train net output #0: loss = 1.30981 (* 1 = 1.30981 loss)
I0407 22:28:17.690380 23673 sgd_solver.cpp:105] Iteration 3504, lr = 0.00708041
I0407 22:28:22.785055 23673 solver.cpp:218] Iteration 3516 (2.35547 iter/s, 5.09452s/12 iters), loss = 1.05286
I0407 22:28:22.785107 23673 solver.cpp:237]     Train net output #0: loss = 1.05286 (* 1 = 1.05286 loss)
I0407 22:28:22.785120 23673 sgd_solver.cpp:105] Iteration 3516, lr = 0.00707204
I0407 22:28:27.870843 23673 solver.cpp:218] Iteration 3528 (2.35962 iter/s, 5.08556s/12 iters), loss = 1.32253
I0407 22:28:27.870898 23673 solver.cpp:237]     Train net output #0: loss = 1.32253 (* 1 = 1.32253 loss)
I0407 22:28:27.870909 23673 sgd_solver.cpp:105] Iteration 3528, lr = 0.00706368
I0407 22:28:32.745085 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:33.017040 23673 solver.cpp:218] Iteration 3540 (2.33192 iter/s, 5.14597s/12 iters), loss = 1.1432
I0407 22:28:33.017087 23673 solver.cpp:237]     Train net output #0: loss = 1.1432 (* 1 = 1.1432 loss)
I0407 22:28:33.017100 23673 sgd_solver.cpp:105] Iteration 3540, lr = 0.00705534
I0407 22:28:38.101790 23673 solver.cpp:218] Iteration 3552 (2.3601 iter/s, 5.08452s/12 iters), loss = 1.4571
I0407 22:28:38.101850 23673 solver.cpp:237]     Train net output #0: loss = 1.4571 (* 1 = 1.4571 loss)
I0407 22:28:38.101867 23673 sgd_solver.cpp:105] Iteration 3552, lr = 0.007047
I0407 22:28:43.240396 23673 solver.cpp:218] Iteration 3564 (2.33537 iter/s, 5.13837s/12 iters), loss = 1.41141
I0407 22:28:43.240453 23673 solver.cpp:237]     Train net output #0: loss = 1.41141 (* 1 = 1.41141 loss)
I0407 22:28:43.240468 23673 sgd_solver.cpp:105] Iteration 3564, lr = 0.00703867
I0407 22:28:45.350138 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 22:28:54.496280 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 22:28:58.668875 23673 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 22:28:58.668898 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:29:01.714972 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:03.130367 23673 solver.cpp:397]     Test net output #0: accuracy = 0.324755
I0407 22:29:03.130417 23673 solver.cpp:397]     Test net output #1: loss = 3.0171 (* 1 = 3.0171 loss)
I0407 22:29:04.947080 23673 solver.cpp:218] Iteration 3576 (0.552844 iter/s, 21.7059s/12 iters), loss = 1.44788
I0407 22:29:04.947127 23673 solver.cpp:237]     Train net output #0: loss = 1.44788 (* 1 = 1.44788 loss)
I0407 22:29:04.947139 23673 sgd_solver.cpp:105] Iteration 3576, lr = 0.00703035
I0407 22:29:10.005990 23673 solver.cpp:218] Iteration 3588 (2.37216 iter/s, 5.05869s/12 iters), loss = 1.28184
I0407 22:29:10.006040 23673 solver.cpp:237]     Train net output #0: loss = 1.28184 (* 1 = 1.28184 loss)
I0407 22:29:10.006053 23673 sgd_solver.cpp:105] Iteration 3588, lr = 0.00702205
I0407 22:29:14.967839 23673 solver.cpp:218] Iteration 3600 (2.41856 iter/s, 4.96163s/12 iters), loss = 1.24944
I0407 22:29:14.967883 23673 solver.cpp:237]     Train net output #0: loss = 1.24944 (* 1 = 1.24944 loss)
I0407 22:29:14.967893 23673 sgd_solver.cpp:105] Iteration 3600, lr = 0.00701375
I0407 22:29:19.919575 23673 solver.cpp:218] Iteration 3612 (2.42349 iter/s, 4.95153s/12 iters), loss = 1.22555
I0407 22:29:19.919607 23673 solver.cpp:237]     Train net output #0: loss = 1.22555 (* 1 = 1.22555 loss)
I0407 22:29:19.919616 23673 sgd_solver.cpp:105] Iteration 3612, lr = 0.00700546
I0407 22:29:24.854028 23673 solver.cpp:218] Iteration 3624 (2.43198 iter/s, 4.93425s/12 iters), loss = 1.44958
I0407 22:29:24.854147 23673 solver.cpp:237]     Train net output #0: loss = 1.44958 (* 1 = 1.44958 loss)
I0407 22:29:24.854161 23673 sgd_solver.cpp:105] Iteration 3624, lr = 0.00699718
I0407 22:29:29.808537 23673 solver.cpp:218] Iteration 3636 (2.42218 iter/s, 4.95422s/12 iters), loss = 1.40502
I0407 22:29:29.808584 23673 solver.cpp:237]     Train net output #0: loss = 1.40502 (* 1 = 1.40502 loss)
I0407 22:29:29.808598 23673 sgd_solver.cpp:105] Iteration 3636, lr = 0.00698891
I0407 22:29:31.911983 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:35.109280 23673 solver.cpp:218] Iteration 3648 (2.26393 iter/s, 5.30052s/12 iters), loss = 1.02085
I0407 22:29:35.109326 23673 solver.cpp:237]     Train net output #0: loss = 1.02085 (* 1 = 1.02085 loss)
I0407 22:29:35.109338 23673 sgd_solver.cpp:105] Iteration 3648, lr = 0.00698066
I0407 22:29:40.090135 23673 solver.cpp:218] Iteration 3660 (2.40933 iter/s, 4.98063s/12 iters), loss = 1.0423
I0407 22:29:40.090191 23673 solver.cpp:237]     Train net output #0: loss = 1.0423 (* 1 = 1.0423 loss)
I0407 22:29:40.090204 23673 sgd_solver.cpp:105] Iteration 3660, lr = 0.00697241
I0407 22:29:44.982239 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 22:29:49.981377 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 22:29:54.080184 23673 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 22:29:54.080209 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:29:57.424962 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:58.947571 23673 solver.cpp:397]     Test net output #0: accuracy = 0.309436
I0407 22:29:58.947619 23673 solver.cpp:397]     Test net output #1: loss = 3.09424 (* 1 = 3.09424 loss)
I0407 22:29:59.038970 23673 solver.cpp:218] Iteration 3672 (0.633307 iter/s, 18.9482s/12 iters), loss = 1.01447
I0407 22:29:59.039026 23673 solver.cpp:237]     Train net output #0: loss = 1.01447 (* 1 = 1.01447 loss)
I0407 22:29:59.039036 23673 sgd_solver.cpp:105] Iteration 3672, lr = 0.00696417
I0407 22:30:03.216612 23673 solver.cpp:218] Iteration 3684 (2.87257 iter/s, 4.17744s/12 iters), loss = 1.28516
I0407 22:30:03.216666 23673 solver.cpp:237]     Train net output #0: loss = 1.28516 (* 1 = 1.28516 loss)
I0407 22:30:03.216681 23673 sgd_solver.cpp:105] Iteration 3684, lr = 0.00695594
I0407 22:30:08.228610 23673 solver.cpp:218] Iteration 3696 (2.39436 iter/s, 5.01177s/12 iters), loss = 1.21135
I0407 22:30:08.228669 23673 solver.cpp:237]     Train net output #0: loss = 1.21135 (* 1 = 1.21135 loss)
I0407 22:30:08.228682 23673 sgd_solver.cpp:105] Iteration 3696, lr = 0.00694772
I0407 22:30:13.341711 23673 solver.cpp:218] Iteration 3708 (2.34702 iter/s, 5.11287s/12 iters), loss = 1.10377
I0407 22:30:13.341768 23673 solver.cpp:237]     Train net output #0: loss = 1.10377 (* 1 = 1.10377 loss)
I0407 22:30:13.341779 23673 sgd_solver.cpp:105] Iteration 3708, lr = 0.00693951
I0407 22:30:18.449503 23673 solver.cpp:218] Iteration 3720 (2.34946 iter/s, 5.10756s/12 iters), loss = 1.32872
I0407 22:30:18.449544 23673 solver.cpp:237]     Train net output #0: loss = 1.32872 (* 1 = 1.32872 loss)
I0407 22:30:18.449553 23673 sgd_solver.cpp:105] Iteration 3720, lr = 0.00693131
I0407 22:30:23.547030 23673 solver.cpp:218] Iteration 3732 (2.35418 iter/s, 5.09731s/12 iters), loss = 1.11069
I0407 22:30:23.547078 23673 solver.cpp:237]     Train net output #0: loss = 1.11069 (* 1 = 1.11069 loss)
I0407 22:30:23.547087 23673 sgd_solver.cpp:105] Iteration 3732, lr = 0.00692312
I0407 22:30:27.643797 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:28.671375 23673 solver.cpp:218] Iteration 3744 (2.34187 iter/s, 5.12412s/12 iters), loss = 0.945905
I0407 22:30:28.671428 23673 solver.cpp:237]     Train net output #0: loss = 0.945905 (* 1 = 0.945905 loss)
I0407 22:30:28.671442 23673 sgd_solver.cpp:105] Iteration 3744, lr = 0.00691494
I0407 22:30:33.859812 23673 solver.cpp:218] Iteration 3756 (2.31294 iter/s, 5.18821s/12 iters), loss = 1.04121
I0407 22:30:33.859848 23673 solver.cpp:237]     Train net output #0: loss = 1.04121 (* 1 = 1.04121 loss)
I0407 22:30:33.859856 23673 sgd_solver.cpp:105] Iteration 3756, lr = 0.00690677
I0407 22:30:39.020581 23673 solver.cpp:218] Iteration 3768 (2.32533 iter/s, 5.16055s/12 iters), loss = 1.123
I0407 22:30:39.020625 23673 solver.cpp:237]     Train net output #0: loss = 1.123 (* 1 = 1.123 loss)
I0407 22:30:39.020635 23673 sgd_solver.cpp:105] Iteration 3768, lr = 0.0068986
I0407 22:30:41.088694 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 22:30:45.387542 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 22:30:54.888326 23673 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 22:30:54.888355 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:30:58.001636 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:59.550994 23673 solver.cpp:397]     Test net output #0: accuracy = 0.328431
I0407 22:30:59.551043 23673 solver.cpp:397]     Test net output #1: loss = 2.99749 (* 1 = 2.99749 loss)
I0407 22:31:01.448757 23673 solver.cpp:218] Iteration 3780 (0.53506 iter/s, 22.4274s/12 iters), loss = 1.0378
I0407 22:31:01.448799 23673 solver.cpp:237]     Train net output #0: loss = 1.0378 (* 1 = 1.0378 loss)
I0407 22:31:01.448808 23673 sgd_solver.cpp:105] Iteration 3780, lr = 0.00689045
I0407 22:31:06.523015 23673 solver.cpp:218] Iteration 3792 (2.36498 iter/s, 5.07404s/12 iters), loss = 1.27912
I0407 22:31:06.523061 23673 solver.cpp:237]     Train net output #0: loss = 1.27912 (* 1 = 1.27912 loss)
I0407 22:31:06.523070 23673 sgd_solver.cpp:105] Iteration 3792, lr = 0.00688231
I0407 22:31:11.624270 23673 solver.cpp:218] Iteration 3804 (2.35246 iter/s, 5.10103s/12 iters), loss = 1.10299
I0407 22:31:11.624320 23673 solver.cpp:237]     Train net output #0: loss = 1.10299 (* 1 = 1.10299 loss)
I0407 22:31:11.624333 23673 sgd_solver.cpp:105] Iteration 3804, lr = 0.00687418
I0407 22:31:16.727470 23673 solver.cpp:218] Iteration 3816 (2.35157 iter/s, 5.10297s/12 iters), loss = 1.09288
I0407 22:31:16.727522 23673 solver.cpp:237]     Train net output #0: loss = 1.09288 (* 1 = 1.09288 loss)
I0407 22:31:16.727535 23673 sgd_solver.cpp:105] Iteration 3816, lr = 0.00686605
I0407 22:31:21.849263 23673 solver.cpp:218] Iteration 3828 (2.34303 iter/s, 5.12156s/12 iters), loss = 1.12529
I0407 22:31:21.849313 23673 solver.cpp:237]     Train net output #0: loss = 1.12529 (* 1 = 1.12529 loss)
I0407 22:31:21.849325 23673 sgd_solver.cpp:105] Iteration 3828, lr = 0.00685794
I0407 22:31:26.987907 23673 solver.cpp:218] Iteration 3840 (2.33535 iter/s, 5.13842s/12 iters), loss = 0.948897
I0407 22:31:26.987951 23673 solver.cpp:237]     Train net output #0: loss = 0.948897 (* 1 = 0.948897 loss)
I0407 22:31:26.987960 23673 sgd_solver.cpp:105] Iteration 3840, lr = 0.00684984
I0407 22:31:28.126168 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:32.091156 23673 solver.cpp:218] Iteration 3852 (2.35154 iter/s, 5.10303s/12 iters), loss = 0.870748
I0407 22:31:32.091202 23673 solver.cpp:237]     Train net output #0: loss = 0.870748 (* 1 = 0.870748 loss)
I0407 22:31:32.091212 23673 sgd_solver.cpp:105] Iteration 3852, lr = 0.00684174
I0407 22:31:37.051218 23673 solver.cpp:218] Iteration 3864 (2.41943 iter/s, 4.95984s/12 iters), loss = 1.16978
I0407 22:31:37.051266 23673 solver.cpp:237]     Train net output #0: loss = 1.16978 (* 1 = 1.16978 loss)
I0407 22:31:37.051278 23673 sgd_solver.cpp:105] Iteration 3864, lr = 0.00683366
I0407 22:31:41.627436 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 22:31:44.578377 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 22:31:46.893746 23673 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 22:31:46.893776 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:31:49.827217 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:51.401855 23673 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0407 22:31:51.401904 23673 solver.cpp:397]     Test net output #1: loss = 3.03007 (* 1 = 3.03007 loss)
I0407 22:31:51.493234 23673 solver.cpp:218] Iteration 3876 (0.830939 iter/s, 14.4415s/12 iters), loss = 0.925296
I0407 22:31:51.493284 23673 solver.cpp:237]     Train net output #0: loss = 0.925296 (* 1 = 0.925296 loss)
I0407 22:31:51.493296 23673 sgd_solver.cpp:105] Iteration 3876, lr = 0.00682558
I0407 22:31:55.734012 23673 solver.cpp:218] Iteration 3888 (2.8298 iter/s, 4.24058s/12 iters), loss = 1.29869
I0407 22:31:55.734063 23673 solver.cpp:237]     Train net output #0: loss = 1.29869 (* 1 = 1.29869 loss)
I0407 22:31:55.734076 23673 sgd_solver.cpp:105] Iteration 3888, lr = 0.00681752
I0407 22:32:00.935847 23673 solver.cpp:218] Iteration 3900 (2.30698 iter/s, 5.2016s/12 iters), loss = 0.998382
I0407 22:32:00.935973 23673 solver.cpp:237]     Train net output #0: loss = 0.998382 (* 1 = 0.998382 loss)
I0407 22:32:00.935986 23673 sgd_solver.cpp:105] Iteration 3900, lr = 0.00680946
I0407 22:32:05.883416 23673 solver.cpp:218] Iteration 3912 (2.42558 iter/s, 4.94727s/12 iters), loss = 1.2557
I0407 22:32:05.883474 23673 solver.cpp:237]     Train net output #0: loss = 1.2557 (* 1 = 1.2557 loss)
I0407 22:32:05.883486 23673 sgd_solver.cpp:105] Iteration 3912, lr = 0.00680141
I0407 22:32:11.147297 23673 solver.cpp:218] Iteration 3924 (2.27979 iter/s, 5.26365s/12 iters), loss = 1.08113
I0407 22:32:11.147337 23673 solver.cpp:237]     Train net output #0: loss = 1.08113 (* 1 = 1.08113 loss)
I0407 22:32:11.147346 23673 sgd_solver.cpp:105] Iteration 3924, lr = 0.00679338
I0407 22:32:16.407371 23673 solver.cpp:218] Iteration 3936 (2.28144 iter/s, 5.25984s/12 iters), loss = 1.02381
I0407 22:32:16.407428 23673 solver.cpp:237]     Train net output #0: loss = 1.02381 (* 1 = 1.02381 loss)
I0407 22:32:16.407440 23673 sgd_solver.cpp:105] Iteration 3936, lr = 0.00678535
I0407 22:32:19.845736 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:21.505597 23673 solver.cpp:218] Iteration 3948 (2.35387 iter/s, 5.098s/12 iters), loss = 1.16812
I0407 22:32:21.505641 23673 solver.cpp:237]     Train net output #0: loss = 1.16812 (* 1 = 1.16812 loss)
I0407 22:32:21.505650 23673 sgd_solver.cpp:105] Iteration 3948, lr = 0.00677733
I0407 22:32:26.619099 23673 solver.cpp:218] Iteration 3960 (2.34683 iter/s, 5.11328s/12 iters), loss = 0.946512
I0407 22:32:26.619156 23673 solver.cpp:237]     Train net output #0: loss = 0.946512 (* 1 = 0.946512 loss)
I0407 22:32:26.619170 23673 sgd_solver.cpp:105] Iteration 3960, lr = 0.00676932
I0407 22:32:31.666568 23673 solver.cpp:218] Iteration 3972 (2.37753 iter/s, 5.04725s/12 iters), loss = 1.36826
I0407 22:32:31.666707 23673 solver.cpp:237]     Train net output #0: loss = 1.36826 (* 1 = 1.36826 loss)
I0407 22:32:31.666721 23673 sgd_solver.cpp:105] Iteration 3972, lr = 0.00676132
I0407 22:32:33.720649 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 22:32:39.381170 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 22:32:41.694911 23673 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 22:32:41.694938 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:32:44.752447 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:46.345386 23673 solver.cpp:397]     Test net output #0: accuracy = 0.341912
I0407 22:32:46.345428 23673 solver.cpp:397]     Test net output #1: loss = 3.03449 (* 1 = 3.03449 loss)
I0407 22:32:48.331871 23673 solver.cpp:218] Iteration 3984 (0.720088 iter/s, 16.6646s/12 iters), loss = 1.11844
I0407 22:32:48.331923 23673 solver.cpp:237]     Train net output #0: loss = 1.11844 (* 1 = 1.11844 loss)
I0407 22:32:48.331936 23673 sgd_solver.cpp:105] Iteration 3984, lr = 0.00675333
I0407 22:32:53.794631 23673 solver.cpp:218] Iteration 3996 (2.19679 iter/s, 5.46252s/12 iters), loss = 0.941721
I0407 22:32:53.794674 23673 solver.cpp:237]     Train net output #0: loss = 0.941721 (* 1 = 0.941721 loss)
I0407 22:32:53.794683 23673 sgd_solver.cpp:105] Iteration 3996, lr = 0.00674535
I0407 22:32:58.812480 23673 solver.cpp:218] Iteration 4008 (2.39157 iter/s, 5.01763s/12 iters), loss = 0.88353
I0407 22:32:58.812520 23673 solver.cpp:237]     Train net output #0: loss = 0.88353 (* 1 = 0.88353 loss)
I0407 22:32:58.812530 23673 sgd_solver.cpp:105] Iteration 4008, lr = 0.00673738
I0407 22:33:03.970459 23673 solver.cpp:218] Iteration 4020 (2.32659 iter/s, 5.15776s/12 iters), loss = 1.05532
I0407 22:33:03.970535 23673 solver.cpp:237]     Train net output #0: loss = 1.05532 (* 1 = 1.05532 loss)
I0407 22:33:03.970544 23673 sgd_solver.cpp:105] Iteration 4020, lr = 0.00672942
I0407 22:33:08.997408 23673 solver.cpp:218] Iteration 4032 (2.38725 iter/s, 5.0267s/12 iters), loss = 1.24307
I0407 22:33:08.997455 23673 solver.cpp:237]     Train net output #0: loss = 1.24307 (* 1 = 1.24307 loss)
I0407 22:33:08.997467 23673 sgd_solver.cpp:105] Iteration 4032, lr = 0.00672147
I0407 22:33:14.019765 23673 solver.cpp:218] Iteration 4044 (2.38942 iter/s, 5.02214s/12 iters), loss = 0.913905
I0407 22:33:14.019815 23673 solver.cpp:237]     Train net output #0: loss = 0.913905 (* 1 = 0.913905 loss)
I0407 22:33:14.019826 23673 sgd_solver.cpp:105] Iteration 4044, lr = 0.00671353
I0407 22:33:14.515882 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:19.052430 23673 solver.cpp:218] Iteration 4056 (2.38453 iter/s, 5.03244s/12 iters), loss = 1.07104
I0407 22:33:19.052472 23673 solver.cpp:237]     Train net output #0: loss = 1.07104 (* 1 = 1.07104 loss)
I0407 22:33:19.052482 23673 sgd_solver.cpp:105] Iteration 4056, lr = 0.00670559
I0407 22:33:24.365672 23673 solver.cpp:218] Iteration 4068 (2.25861 iter/s, 5.31301s/12 iters), loss = 0.99628
I0407 22:33:24.365731 23673 solver.cpp:237]     Train net output #0: loss = 0.99628 (* 1 = 0.99628 loss)
I0407 22:33:24.365743 23673 sgd_solver.cpp:105] Iteration 4068, lr = 0.00669767
I0407 22:33:29.199779 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 22:33:32.386755 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 22:33:34.785408 23673 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 22:33:34.785518 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:33:37.639706 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:39.257809 23673 solver.cpp:397]     Test net output #0: accuracy = 0.344976
I0407 22:33:39.257853 23673 solver.cpp:397]     Test net output #1: loss = 3.06588 (* 1 = 3.06588 loss)
I0407 22:33:39.352788 23673 solver.cpp:218] Iteration 4080 (0.800717 iter/s, 14.9866s/12 iters), loss = 1.02864
I0407 22:33:39.352854 23673 solver.cpp:237]     Train net output #0: loss = 1.02864 (* 1 = 1.02864 loss)
I0407 22:33:39.352869 23673 sgd_solver.cpp:105] Iteration 4080, lr = 0.00668975
I0407 22:33:43.615579 23673 solver.cpp:218] Iteration 4092 (2.8152 iter/s, 4.26258s/12 iters), loss = 0.801335
I0407 22:33:43.615633 23673 solver.cpp:237]     Train net output #0: loss = 0.801335 (* 1 = 0.801335 loss)
I0407 22:33:43.615644 23673 sgd_solver.cpp:105] Iteration 4092, lr = 0.00668185
I0407 22:33:48.665840 23673 solver.cpp:218] Iteration 4104 (2.37622 iter/s, 5.05003s/12 iters), loss = 0.965487
I0407 22:33:48.665876 23673 solver.cpp:237]     Train net output #0: loss = 0.965487 (* 1 = 0.965487 loss)
I0407 22:33:48.665885 23673 sgd_solver.cpp:105] Iteration 4104, lr = 0.00667395
I0407 22:33:53.785022 23673 solver.cpp:218] Iteration 4116 (2.34422 iter/s, 5.11897s/12 iters), loss = 1.0686
I0407 22:33:53.785063 23673 solver.cpp:237]     Train net output #0: loss = 1.0686 (* 1 = 1.0686 loss)
I0407 22:33:53.785073 23673 sgd_solver.cpp:105] Iteration 4116, lr = 0.00666607
I0407 22:33:58.894110 23673 solver.cpp:218] Iteration 4128 (2.34886 iter/s, 5.10887s/12 iters), loss = 0.969625
I0407 22:33:58.894162 23673 solver.cpp:237]     Train net output #0: loss = 0.969625 (* 1 = 0.969625 loss)
I0407 22:33:58.894176 23673 sgd_solver.cpp:105] Iteration 4128, lr = 0.00665819
I0407 22:34:04.056819 23673 solver.cpp:218] Iteration 4140 (2.32447 iter/s, 5.16248s/12 iters), loss = 0.931388
I0407 22:34:04.056875 23673 solver.cpp:237]     Train net output #0: loss = 0.931388 (* 1 = 0.931388 loss)
I0407 22:34:04.056888 23673 sgd_solver.cpp:105] Iteration 4140, lr = 0.00665032
I0407 22:34:06.776911 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:09.216400 23673 solver.cpp:218] Iteration 4152 (2.32587 iter/s, 5.15935s/12 iters), loss = 1.17056
I0407 22:34:09.216445 23673 solver.cpp:237]     Train net output #0: loss = 1.17056 (* 1 = 1.17056 loss)
I0407 22:34:09.216456 23673 sgd_solver.cpp:105] Iteration 4152, lr = 0.00664246
I0407 22:34:10.889719 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:34:14.337253 23673 solver.cpp:218] Iteration 4164 (2.34346 iter/s, 5.12063s/12 iters), loss = 1.14509
I0407 22:34:14.337306 23673 solver.cpp:237]     Train net output #0: loss = 1.14509 (* 1 = 1.14509 loss)
I0407 22:34:14.337318 23673 sgd_solver.cpp:105] Iteration 4164, lr = 0.00663461
I0407 22:34:19.487870 23673 solver.cpp:218] Iteration 4176 (2.32992 iter/s, 5.15038s/12 iters), loss = 1.13337
I0407 22:34:19.487924 23673 solver.cpp:237]     Train net output #0: loss = 1.13337 (* 1 = 1.13337 loss)
I0407 22:34:19.487936 23673 sgd_solver.cpp:105] Iteration 4176, lr = 0.00662677
I0407 22:34:21.691076 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 22:34:26.821240 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 22:34:29.157687 23673 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 22:34:29.157711 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:34:31.961077 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:33.766067 23673 solver.cpp:397]     Test net output #0: accuracy = 0.365196
I0407 22:34:33.766103 23673 solver.cpp:397]     Test net output #1: loss = 2.94328 (* 1 = 2.94328 loss)
I0407 22:34:35.692790 23673 solver.cpp:218] Iteration 4188 (0.740542 iter/s, 16.2043s/12 iters), loss = 1.11174
I0407 22:34:35.692842 23673 solver.cpp:237]     Train net output #0: loss = 1.11174 (* 1 = 1.11174 loss)
I0407 22:34:35.692852 23673 sgd_solver.cpp:105] Iteration 4188, lr = 0.00661894
I0407 22:34:40.794757 23673 solver.cpp:218] Iteration 4200 (2.35214 iter/s, 5.10173s/12 iters), loss = 1.01808
I0407 22:34:40.794906 23673 solver.cpp:237]     Train net output #0: loss = 1.01808 (* 1 = 1.01808 loss)
I0407 22:34:40.794921 23673 sgd_solver.cpp:105] Iteration 4200, lr = 0.00661112
I0407 22:34:45.975608 23673 solver.cpp:218] Iteration 4212 (2.31636 iter/s, 5.18053s/12 iters), loss = 0.951026
I0407 22:34:45.975656 23673 solver.cpp:237]     Train net output #0: loss = 0.951026 (* 1 = 0.951026 loss)
I0407 22:34:45.975667 23673 sgd_solver.cpp:105] Iteration 4212, lr = 0.00660331
I0407 22:34:51.068639 23673 solver.cpp:218] Iteration 4224 (2.35627 iter/s, 5.09281s/12 iters), loss = 0.940716
I0407 22:34:51.068686 23673 solver.cpp:237]     Train net output #0: loss = 0.940716 (* 1 = 0.940716 loss)
I0407 22:34:51.068697 23673 sgd_solver.cpp:105] Iteration 4224, lr = 0.00659551
I0407 22:34:56.181275 23673 solver.cpp:218] Iteration 4236 (2.34723 iter/s, 5.11241s/12 iters), loss = 0.936439
I0407 22:34:56.181340 23673 solver.cpp:237]     Train net output #0: loss = 0.936439 (* 1 = 0.936439 loss)
I0407 22:34:56.181354 23673 sgd_solver.cpp:105] Iteration 4236, lr = 0.00658771
I0407 22:35:01.024623 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:01.261307 23673 solver.cpp:218] Iteration 4248 (2.3623 iter/s, 5.07979s/12 iters), loss = 0.935679
I0407 22:35:01.261363 23673 solver.cpp:237]     Train net output #0: loss = 0.935679 (* 1 = 0.935679 loss)
I0407 22:35:01.261375 23673 sgd_solver.cpp:105] Iteration 4248, lr = 0.00657993
I0407 22:35:06.336205 23673 solver.cpp:218] Iteration 4260 (2.36469 iter/s, 5.07467s/12 iters), loss = 0.978754
I0407 22:35:06.336261 23673 solver.cpp:237]     Train net output #0: loss = 0.978754 (* 1 = 0.978754 loss)
I0407 22:35:06.336274 23673 sgd_solver.cpp:105] Iteration 4260, lr = 0.00657215
I0407 22:35:11.368453 23673 solver.cpp:218] Iteration 4272 (2.38473 iter/s, 5.03202s/12 iters), loss = 0.851716
I0407 22:35:11.368584 23673 solver.cpp:237]     Train net output #0: loss = 0.851716 (* 1 = 0.851716 loss)
I0407 22:35:11.368602 23673 sgd_solver.cpp:105] Iteration 4272, lr = 0.00656439
I0407 22:35:16.114295 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 22:35:19.130522 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 22:35:21.458024 23673 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 22:35:21.458050 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:35:24.232352 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:25.931041 23673 solver.cpp:397]     Test net output #0: accuracy = 0.360907
I0407 22:35:25.931092 23673 solver.cpp:397]     Test net output #1: loss = 2.94266 (* 1 = 2.94266 loss)
I0407 22:35:26.022470 23673 solver.cpp:218] Iteration 4284 (0.818922 iter/s, 14.6534s/12 iters), loss = 0.734185
I0407 22:35:26.022518 23673 solver.cpp:237]     Train net output #0: loss = 0.734185 (* 1 = 0.734185 loss)
I0407 22:35:26.022529 23673 sgd_solver.cpp:105] Iteration 4284, lr = 0.00655663
I0407 22:35:30.647604 23673 solver.cpp:218] Iteration 4296 (2.59464 iter/s, 4.62493s/12 iters), loss = 0.789141
I0407 22:35:30.647653 23673 solver.cpp:237]     Train net output #0: loss = 0.789141 (* 1 = 0.789141 loss)
I0407 22:35:30.647665 23673 sgd_solver.cpp:105] Iteration 4296, lr = 0.00654888
I0407 22:35:36.164639 23673 solver.cpp:218] Iteration 4308 (2.17517 iter/s, 5.5168s/12 iters), loss = 0.682877
I0407 22:35:36.164686 23673 solver.cpp:237]     Train net output #0: loss = 0.682877 (* 1 = 0.682877 loss)
I0407 22:35:36.164711 23673 sgd_solver.cpp:105] Iteration 4308, lr = 0.00654114
I0407 22:35:41.609839 23673 solver.cpp:218] Iteration 4320 (2.20387 iter/s, 5.44497s/12 iters), loss = 1.05989
I0407 22:35:41.609952 23673 solver.cpp:237]     Train net output #0: loss = 1.05989 (* 1 = 1.05989 loss)
I0407 22:35:41.609977 23673 sgd_solver.cpp:105] Iteration 4320, lr = 0.00653341
I0407 22:35:46.668589 23673 solver.cpp:218] Iteration 4332 (2.37226 iter/s, 5.05847s/12 iters), loss = 0.761345
I0407 22:35:46.668643 23673 solver.cpp:237]     Train net output #0: loss = 0.761345 (* 1 = 0.761345 loss)
I0407 22:35:46.668655 23673 sgd_solver.cpp:105] Iteration 4332, lr = 0.00652569
I0407 22:35:51.667412 23673 solver.cpp:218] Iteration 4344 (2.40067 iter/s, 4.9986s/12 iters), loss = 1.00677
I0407 22:35:51.667459 23673 solver.cpp:237]     Train net output #0: loss = 1.00677 (* 1 = 1.00677 loss)
I0407 22:35:51.667470 23673 sgd_solver.cpp:105] Iteration 4344, lr = 0.00651798
I0407 22:35:53.610584 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:56.699388 23673 solver.cpp:218] Iteration 4356 (2.38485 iter/s, 5.03175s/12 iters), loss = 0.980423
I0407 22:35:56.699438 23673 solver.cpp:237]     Train net output #0: loss = 0.980423 (* 1 = 0.980423 loss)
I0407 22:35:56.699450 23673 sgd_solver.cpp:105] Iteration 4356, lr = 0.00651028
I0407 22:36:01.872651 23673 solver.cpp:218] Iteration 4368 (2.31972 iter/s, 5.17304s/12 iters), loss = 0.967595
I0407 22:36:01.872694 23673 solver.cpp:237]     Train net output #0: loss = 0.967595 (* 1 = 0.967595 loss)
I0407 22:36:01.872705 23673 sgd_solver.cpp:105] Iteration 4368, lr = 0.00650259
I0407 22:36:07.223124 23673 solver.cpp:218] Iteration 4380 (2.24289 iter/s, 5.35024s/12 iters), loss = 0.845914
I0407 22:36:07.223179 23673 solver.cpp:237]     Train net output #0: loss = 0.845914 (* 1 = 0.845914 loss)
I0407 22:36:07.223192 23673 sgd_solver.cpp:105] Iteration 4380, lr = 0.0064949
I0407 22:36:09.375653 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 22:36:12.374981 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 22:36:16.996337 23673 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 22:36:16.996366 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:36:19.713241 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:21.457479 23673 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0407 22:36:21.457527 23673 solver.cpp:397]     Test net output #1: loss = 3.0443 (* 1 = 3.0443 loss)
I0407 22:36:23.463205 23673 solver.cpp:218] Iteration 4392 (0.738939 iter/s, 16.2395s/12 iters), loss = 0.892488
I0407 22:36:23.463254 23673 solver.cpp:237]     Train net output #0: loss = 0.892488 (* 1 = 0.892488 loss)
I0407 22:36:23.463264 23673 sgd_solver.cpp:105] Iteration 4392, lr = 0.00648723
I0407 22:36:28.624330 23673 solver.cpp:218] Iteration 4404 (2.32518 iter/s, 5.1609s/12 iters), loss = 0.79459
I0407 22:36:28.624382 23673 solver.cpp:237]     Train net output #0: loss = 0.79459 (* 1 = 0.79459 loss)
I0407 22:36:28.624394 23673 sgd_solver.cpp:105] Iteration 4404, lr = 0.00647956
I0407 22:36:33.678697 23673 solver.cpp:218] Iteration 4416 (2.37429 iter/s, 5.05415s/12 iters), loss = 0.775037
I0407 22:36:33.678745 23673 solver.cpp:237]     Train net output #0: loss = 0.775037 (* 1 = 0.775037 loss)
I0407 22:36:33.678756 23673 sgd_solver.cpp:105] Iteration 4416, lr = 0.00647191
I0407 22:36:39.360127 23673 solver.cpp:218] Iteration 4428 (2.11223 iter/s, 5.68119s/12 iters), loss = 0.608584
I0407 22:36:39.360177 23673 solver.cpp:237]     Train net output #0: loss = 0.608584 (* 1 = 0.608584 loss)
I0407 22:36:39.360189 23673 sgd_solver.cpp:105] Iteration 4428, lr = 0.00646426
I0407 22:36:44.464253 23673 solver.cpp:218] Iteration 4440 (2.35114 iter/s, 5.1039s/12 iters), loss = 1.06865
I0407 22:36:44.464365 23673 solver.cpp:237]     Train net output #0: loss = 1.06865 (* 1 = 1.06865 loss)
I0407 22:36:44.464377 23673 sgd_solver.cpp:105] Iteration 4440, lr = 0.00645662
I0407 22:36:48.610621 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:49.578668 23673 solver.cpp:218] Iteration 4452 (2.34644 iter/s, 5.11413s/12 iters), loss = 0.539245
I0407 22:36:49.578723 23673 solver.cpp:237]     Train net output #0: loss = 0.539245 (* 1 = 0.539245 loss)
I0407 22:36:49.578735 23673 sgd_solver.cpp:105] Iteration 4452, lr = 0.00644899
I0407 22:36:54.900373 23673 solver.cpp:218] Iteration 4464 (2.25502 iter/s, 5.32147s/12 iters), loss = 0.681997
I0407 22:36:54.900420 23673 solver.cpp:237]     Train net output #0: loss = 0.681997 (* 1 = 0.681997 loss)
I0407 22:36:54.900429 23673 sgd_solver.cpp:105] Iteration 4464, lr = 0.00644137
I0407 22:37:00.356642 23673 solver.cpp:218] Iteration 4476 (2.1994 iter/s, 5.45603s/12 iters), loss = 0.549058
I0407 22:37:00.356699 23673 solver.cpp:237]     Train net output #0: loss = 0.549058 (* 1 = 0.549058 loss)
I0407 22:37:00.356712 23673 sgd_solver.cpp:105] Iteration 4476, lr = 0.00643376
I0407 22:37:05.073704 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 22:37:12.027833 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 22:37:16.080041 23673 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 22:37:16.080121 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:37:18.770972 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:20.546378 23673 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0407 22:37:20.546429 23673 solver.cpp:397]     Test net output #1: loss = 3.04804 (* 1 = 3.04804 loss)
I0407 22:37:20.637732 23673 solver.cpp:218] Iteration 4488 (0.591705 iter/s, 20.2804s/12 iters), loss = 0.674301
I0407 22:37:20.637784 23673 solver.cpp:237]     Train net output #0: loss = 0.674301 (* 1 = 0.674301 loss)
I0407 22:37:20.637795 23673 sgd_solver.cpp:105] Iteration 4488, lr = 0.00642616
I0407 22:37:25.142655 23673 solver.cpp:218] Iteration 4500 (2.66387 iter/s, 4.50472s/12 iters), loss = 0.978929
I0407 22:37:25.142705 23673 solver.cpp:237]     Train net output #0: loss = 0.978929 (* 1 = 0.978929 loss)
I0407 22:37:25.142717 23673 sgd_solver.cpp:105] Iteration 4500, lr = 0.00641856
I0407 22:37:30.212816 23673 solver.cpp:218] Iteration 4512 (2.36689 iter/s, 5.06994s/12 iters), loss = 0.664458
I0407 22:37:30.212867 23673 solver.cpp:237]     Train net output #0: loss = 0.664458 (* 1 = 0.664458 loss)
I0407 22:37:30.212878 23673 sgd_solver.cpp:105] Iteration 4512, lr = 0.00641098
I0407 22:37:35.752579 23673 solver.cpp:218] Iteration 4524 (2.16625 iter/s, 5.53953s/12 iters), loss = 0.759205
I0407 22:37:35.752625 23673 solver.cpp:237]     Train net output #0: loss = 0.759205 (* 1 = 0.759205 loss)
I0407 22:37:35.752636 23673 sgd_solver.cpp:105] Iteration 4524, lr = 0.0064034
I0407 22:37:40.968376 23673 solver.cpp:218] Iteration 4536 (2.3008 iter/s, 5.21557s/12 iters), loss = 0.67534
I0407 22:37:40.968431 23673 solver.cpp:237]     Train net output #0: loss = 0.67534 (* 1 = 0.67534 loss)
I0407 22:37:40.968443 23673 sgd_solver.cpp:105] Iteration 4536, lr = 0.00639583
I0407 22:37:46.116672 23673 solver.cpp:218] Iteration 4548 (2.33098 iter/s, 5.14806s/12 iters), loss = 0.745576
I0407 22:37:46.126049 23673 solver.cpp:237]     Train net output #0: loss = 0.745576 (* 1 = 0.745576 loss)
I0407 22:37:46.126065 23673 sgd_solver.cpp:105] Iteration 4548, lr = 0.00638828
I0407 22:37:47.441110 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:51.348101 23673 solver.cpp:218] Iteration 4560 (2.29802 iter/s, 5.22188s/12 iters), loss = 0.753765
I0407 22:37:51.348153 23673 solver.cpp:237]     Train net output #0: loss = 0.753765 (* 1 = 0.753765 loss)
I0407 22:37:51.348165 23673 sgd_solver.cpp:105] Iteration 4560, lr = 0.00638073
I0407 22:37:56.607739 23673 solver.cpp:218] Iteration 4572 (2.28163 iter/s, 5.2594s/12 iters), loss = 0.592325
I0407 22:37:56.607800 23673 solver.cpp:237]     Train net output #0: loss = 0.592325 (* 1 = 0.592325 loss)
I0407 22:37:56.607811 23673 sgd_solver.cpp:105] Iteration 4572, lr = 0.00637319
I0407 22:38:01.753345 23673 solver.cpp:218] Iteration 4584 (2.3322 iter/s, 5.14537s/12 iters), loss = 0.524445
I0407 22:38:01.753398 23673 solver.cpp:237]     Train net output #0: loss = 0.524445 (* 1 = 0.524445 loss)
I0407 22:38:01.753409 23673 sgd_solver.cpp:105] Iteration 4584, lr = 0.00636566
I0407 22:38:03.862866 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 22:38:07.808413 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 22:38:12.045951 23673 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 22:38:12.045995 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:38:14.616627 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:16.477699 23673 solver.cpp:397]     Test net output #0: accuracy = 0.381127
I0407 22:38:16.477859 23673 solver.cpp:397]     Test net output #1: loss = 3.00484 (* 1 = 3.00484 loss)
I0407 22:38:18.361519 23673 solver.cpp:218] Iteration 4596 (0.722562 iter/s, 16.6076s/12 iters), loss = 0.630246
I0407 22:38:18.361578 23673 solver.cpp:237]     Train net output #0: loss = 0.630246 (* 1 = 0.630246 loss)
I0407 22:38:18.361590 23673 sgd_solver.cpp:105] Iteration 4596, lr = 0.00635814
I0407 22:38:23.424295 23673 solver.cpp:218] Iteration 4608 (2.37035 iter/s, 5.06255s/12 iters), loss = 0.912095
I0407 22:38:23.424336 23673 solver.cpp:237]     Train net output #0: loss = 0.912095 (* 1 = 0.912095 loss)
I0407 22:38:23.424346 23673 sgd_solver.cpp:105] Iteration 4608, lr = 0.00635062
I0407 22:38:28.586939 23673 solver.cpp:218] Iteration 4620 (2.32449 iter/s, 5.16242s/12 iters), loss = 0.601239
I0407 22:38:28.586993 23673 solver.cpp:237]     Train net output #0: loss = 0.601239 (* 1 = 0.601239 loss)
I0407 22:38:28.587007 23673 sgd_solver.cpp:105] Iteration 4620, lr = 0.00634312
I0407 22:38:33.625289 23673 solver.cpp:218] Iteration 4632 (2.38184 iter/s, 5.03812s/12 iters), loss = 0.508715
I0407 22:38:33.625341 23673 solver.cpp:237]     Train net output #0: loss = 0.508715 (* 1 = 0.508715 loss)
I0407 22:38:33.625355 23673 sgd_solver.cpp:105] Iteration 4632, lr = 0.00633562
I0407 22:38:38.973779 23673 solver.cpp:218] Iteration 4644 (2.24372 iter/s, 5.34826s/12 iters), loss = 0.977364
I0407 22:38:38.973817 23673 solver.cpp:237]     Train net output #0: loss = 0.977364 (* 1 = 0.977364 loss)
I0407 22:38:38.973826 23673 sgd_solver.cpp:105] Iteration 4644, lr = 0.00632813
I0407 22:38:42.517117 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:44.272822 23673 solver.cpp:218] Iteration 4656 (2.26466 iter/s, 5.29882s/12 iters), loss = 0.910479
I0407 22:38:44.272876 23673 solver.cpp:237]     Train net output #0: loss = 0.910479 (* 1 = 0.910479 loss)
I0407 22:38:44.272888 23673 sgd_solver.cpp:105] Iteration 4656, lr = 0.00632066
I0407 22:38:49.673354 23673 solver.cpp:218] Iteration 4668 (2.2221 iter/s, 5.40029s/12 iters), loss = 0.630513
I0407 22:38:49.673466 23673 solver.cpp:237]     Train net output #0: loss = 0.630513 (* 1 = 0.630513 loss)
I0407 22:38:49.673480 23673 sgd_solver.cpp:105] Iteration 4668, lr = 0.00631319
I0407 22:38:54.815207 23673 solver.cpp:218] Iteration 4680 (2.33392 iter/s, 5.14157s/12 iters), loss = 0.651857
I0407 22:38:54.815254 23673 solver.cpp:237]     Train net output #0: loss = 0.651857 (* 1 = 0.651857 loss)
I0407 22:38:54.815264 23673 sgd_solver.cpp:105] Iteration 4680, lr = 0.00630573
I0407 22:38:59.371285 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 22:39:04.984697 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 22:39:07.730209 23673 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 22:39:07.730235 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:39:10.340095 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:12.198200 23673 solver.cpp:397]     Test net output #0: accuracy = 0.371324
I0407 22:39:12.198248 23673 solver.cpp:397]     Test net output #1: loss = 3.05722 (* 1 = 3.05722 loss)
I0407 22:39:12.289726 23673 solver.cpp:218] Iteration 4692 (0.686738 iter/s, 17.4739s/12 iters), loss = 0.849303
I0407 22:39:12.289778 23673 solver.cpp:237]     Train net output #0: loss = 0.849303 (* 1 = 0.849303 loss)
I0407 22:39:12.289789 23673 sgd_solver.cpp:105] Iteration 4692, lr = 0.00629828
I0407 22:39:17.170657 23673 solver.cpp:218] Iteration 4704 (2.45866 iter/s, 4.88071s/12 iters), loss = 0.794684
I0407 22:39:17.170701 23673 solver.cpp:237]     Train net output #0: loss = 0.794684 (* 1 = 0.794684 loss)
I0407 22:39:17.170712 23673 sgd_solver.cpp:105] Iteration 4704, lr = 0.00629083
I0407 22:39:22.723932 23673 solver.cpp:218] Iteration 4716 (2.16098 iter/s, 5.55303s/12 iters), loss = 0.574322
I0407 22:39:22.724076 23673 solver.cpp:237]     Train net output #0: loss = 0.574322 (* 1 = 0.574322 loss)
I0407 22:39:22.724093 23673 sgd_solver.cpp:105] Iteration 4716, lr = 0.0062834
I0407 22:39:28.038451 23673 solver.cpp:218] Iteration 4728 (2.2581 iter/s, 5.3142s/12 iters), loss = 0.751721
I0407 22:39:28.038497 23673 solver.cpp:237]     Train net output #0: loss = 0.751721 (* 1 = 0.751721 loss)
I0407 22:39:28.038508 23673 sgd_solver.cpp:105] Iteration 4728, lr = 0.00627598
I0407 22:39:33.175913 23673 solver.cpp:218] Iteration 4740 (2.33588 iter/s, 5.13724s/12 iters), loss = 0.543426
I0407 22:39:33.175951 23673 solver.cpp:237]     Train net output #0: loss = 0.543426 (* 1 = 0.543426 loss)
I0407 22:39:33.175959 23673 sgd_solver.cpp:105] Iteration 4740, lr = 0.00626856
I0407 22:39:38.292624 23673 solver.cpp:218] Iteration 4752 (2.34536 iter/s, 5.1165s/12 iters), loss = 0.497719
I0407 22:39:38.292676 23673 solver.cpp:237]     Train net output #0: loss = 0.497719 (* 1 = 0.497719 loss)
I0407 22:39:38.292688 23673 sgd_solver.cpp:105] Iteration 4752, lr = 0.00626115
I0407 22:39:38.838248 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:43.403471 23673 solver.cpp:218] Iteration 4764 (2.34805 iter/s, 5.11062s/12 iters), loss = 0.504132
I0407 22:39:43.403529 23673 solver.cpp:237]     Train net output #0: loss = 0.504132 (* 1 = 0.504132 loss)
I0407 22:39:43.403542 23673 sgd_solver.cpp:105] Iteration 4764, lr = 0.00625375
I0407 22:39:48.480011 23673 solver.cpp:218] Iteration 4776 (2.36392 iter/s, 5.07631s/12 iters), loss = 0.522259
I0407 22:39:48.480067 23673 solver.cpp:237]     Train net output #0: loss = 0.522259 (* 1 = 0.522259 loss)
I0407 22:39:48.480079 23673 sgd_solver.cpp:105] Iteration 4776, lr = 0.00624636
I0407 22:39:53.603734 23673 solver.cpp:218] Iteration 4788 (2.34215 iter/s, 5.12349s/12 iters), loss = 0.680392
I0407 22:39:53.603838 23673 solver.cpp:237]     Train net output #0: loss = 0.680392 (* 1 = 0.680392 loss)
I0407 22:39:53.603850 23673 sgd_solver.cpp:105] Iteration 4788, lr = 0.00623898
I0407 22:39:55.641384 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 22:39:58.946306 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 22:40:02.341886 23673 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 22:40:02.341913 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:40:04.900559 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:06.803401 23673 solver.cpp:397]     Test net output #0: accuracy = 0.387868
I0407 22:40:06.803450 23673 solver.cpp:397]     Test net output #1: loss = 3.01223 (* 1 = 3.01223 loss)
I0407 22:40:08.683809 23673 solver.cpp:218] Iteration 4800 (0.795783 iter/s, 15.0795s/12 iters), loss = 0.687298
I0407 22:40:08.683856 23673 solver.cpp:237]     Train net output #0: loss = 0.687298 (* 1 = 0.687298 loss)
I0407 22:40:08.683867 23673 sgd_solver.cpp:105] Iteration 4800, lr = 0.00623161
I0407 22:40:13.997248 23673 solver.cpp:218] Iteration 4812 (2.25852 iter/s, 5.31321s/12 iters), loss = 0.5824
I0407 22:40:13.997303 23673 solver.cpp:237]     Train net output #0: loss = 0.5824 (* 1 = 0.5824 loss)
I0407 22:40:13.997316 23673 sgd_solver.cpp:105] Iteration 4812, lr = 0.00622425
I0407 22:40:19.490329 23673 solver.cpp:218] Iteration 4824 (2.18466 iter/s, 5.49284s/12 iters), loss = 0.855713
I0407 22:40:19.490375 23673 solver.cpp:237]     Train net output #0: loss = 0.855713 (* 1 = 0.855713 loss)
I0407 22:40:19.490386 23673 sgd_solver.cpp:105] Iteration 4824, lr = 0.00621689
I0407 22:40:24.748942 23673 solver.cpp:218] Iteration 4836 (2.28207 iter/s, 5.25839s/12 iters), loss = 0.642904
I0407 22:40:24.749065 23673 solver.cpp:237]     Train net output #0: loss = 0.642904 (* 1 = 0.642904 loss)
I0407 22:40:24.749078 23673 sgd_solver.cpp:105] Iteration 4836, lr = 0.00620954
I0407 22:40:26.833170 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:40:29.870471 23673 solver.cpp:218] Iteration 4848 (2.34318 iter/s, 5.12124s/12 iters), loss = 0.752162
I0407 22:40:29.870513 23673 solver.cpp:237]     Train net output #0: loss = 0.752162 (* 1 = 0.752162 loss)
I0407 22:40:29.870522 23673 sgd_solver.cpp:105] Iteration 4848, lr = 0.00620221
I0407 22:40:32.572352 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:35.048787 23673 solver.cpp:218] Iteration 4860 (2.31745 iter/s, 5.1781s/12 iters), loss = 0.472429
I0407 22:40:35.048826 23673 solver.cpp:237]     Train net output #0: loss = 0.472429 (* 1 = 0.472429 loss)
I0407 22:40:35.048836 23673 sgd_solver.cpp:105] Iteration 4860, lr = 0.00619488
I0407 22:40:40.329208 23673 solver.cpp:218] Iteration 4872 (2.27264 iter/s, 5.2802s/12 iters), loss = 0.74669
I0407 22:40:40.329263 23673 solver.cpp:237]     Train net output #0: loss = 0.74669 (* 1 = 0.74669 loss)
I0407 22:40:40.329277 23673 sgd_solver.cpp:105] Iteration 4872, lr = 0.00618756
I0407 22:40:45.571856 23673 solver.cpp:218] Iteration 4884 (2.28902 iter/s, 5.24241s/12 iters), loss = 0.382371
I0407 22:40:45.571908 23673 solver.cpp:237]     Train net output #0: loss = 0.382371 (* 1 = 0.382371 loss)
I0407 22:40:45.571920 23673 sgd_solver.cpp:105] Iteration 4884, lr = 0.00618025
I0407 22:40:50.263481 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 22:40:54.245676 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 22:40:59.518647 23673 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 22:40:59.518707 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:41:01.975458 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:03.947198 23673 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0407 22:41:03.947247 23673 solver.cpp:397]     Test net output #1: loss = 3.016 (* 1 = 3.016 loss)
I0407 22:41:04.038741 23673 solver.cpp:218] Iteration 4896 (0.64983 iter/s, 18.4664s/12 iters), loss = 0.611236
I0407 22:41:04.038794 23673 solver.cpp:237]     Train net output #0: loss = 0.611236 (* 1 = 0.611236 loss)
I0407 22:41:04.038805 23673 sgd_solver.cpp:105] Iteration 4896, lr = 0.00617294
I0407 22:41:08.526548 23673 solver.cpp:218] Iteration 4908 (2.67401 iter/s, 4.48764s/12 iters), loss = 0.481163
I0407 22:41:08.526594 23673 solver.cpp:237]     Train net output #0: loss = 0.481163 (* 1 = 0.481163 loss)
I0407 22:41:08.526607 23673 sgd_solver.cpp:105] Iteration 4908, lr = 0.00616565
I0407 22:41:13.628686 23673 solver.cpp:218] Iteration 4920 (2.35204 iter/s, 5.10196s/12 iters), loss = 0.731651
I0407 22:41:13.628743 23673 solver.cpp:237]     Train net output #0: loss = 0.731651 (* 1 = 0.731651 loss)
I0407 22:41:13.628756 23673 sgd_solver.cpp:105] Iteration 4920, lr = 0.00615836
I0407 22:41:18.770048 23673 solver.cpp:218] Iteration 4932 (2.33409 iter/s, 5.14118s/12 iters), loss = 0.601572
I0407 22:41:18.770085 23673 solver.cpp:237]     Train net output #0: loss = 0.601572 (* 1 = 0.601572 loss)
I0407 22:41:18.770094 23673 sgd_solver.cpp:105] Iteration 4932, lr = 0.00615109
I0407 22:41:23.903100 23673 solver.cpp:218] Iteration 4944 (2.33787 iter/s, 5.13288s/12 iters), loss = 0.677044
I0407 22:41:23.903144 23673 solver.cpp:237]     Train net output #0: loss = 0.677044 (* 1 = 0.677044 loss)
I0407 22:41:23.903156 23673 sgd_solver.cpp:105] Iteration 4944, lr = 0.00614382
I0407 22:41:28.805536 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:29.014493 23673 solver.cpp:218] Iteration 4956 (2.34778 iter/s, 5.11121s/12 iters), loss = 0.367749
I0407 22:41:29.014547 23673 solver.cpp:237]     Train net output #0: loss = 0.367749 (* 1 = 0.367749 loss)
I0407 22:41:29.014559 23673 sgd_solver.cpp:105] Iteration 4956, lr = 0.00613656
I0407 22:41:34.193559 23673 solver.cpp:218] Iteration 4968 (2.3171 iter/s, 5.17888s/12 iters), loss = 0.747932
I0407 22:41:34.193693 23673 solver.cpp:237]     Train net output #0: loss = 0.747932 (* 1 = 0.747932 loss)
I0407 22:41:34.193706 23673 sgd_solver.cpp:105] Iteration 4968, lr = 0.00612931
I0407 22:41:39.207691 23673 solver.cpp:218] Iteration 4980 (2.39336 iter/s, 5.01387s/12 iters), loss = 0.726671
I0407 22:41:39.207746 23673 solver.cpp:237]     Train net output #0: loss = 0.726671 (* 1 = 0.726671 loss)
I0407 22:41:39.207759 23673 sgd_solver.cpp:105] Iteration 4980, lr = 0.00612206
I0407 22:41:44.327844 23673 solver.cpp:218] Iteration 4992 (2.34377 iter/s, 5.11996s/12 iters), loss = 0.540761
I0407 22:41:44.327898 23673 solver.cpp:237]     Train net output #0: loss = 0.540761 (* 1 = 0.540761 loss)
I0407 22:41:44.327910 23673 sgd_solver.cpp:105] Iteration 4992, lr = 0.00611483
I0407 22:41:46.435096 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 22:41:51.721155 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 22:41:54.065433 23673 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 22:41:54.065459 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:41:56.560379 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:58.559798 23673 solver.cpp:397]     Test net output #0: accuracy = 0.377451
I0407 22:41:58.559847 23673 solver.cpp:397]     Test net output #1: loss = 3.05756 (* 1 = 3.05756 loss)
I0407 22:42:00.535274 23673 solver.cpp:218] Iteration 5004 (0.740422 iter/s, 16.207s/12 iters), loss = 0.663276
I0407 22:42:00.535328 23673 solver.cpp:237]     Train net output #0: loss = 0.663276 (* 1 = 0.663276 loss)
I0407 22:42:00.535339 23673 sgd_solver.cpp:105] Iteration 5004, lr = 0.0061076
I0407 22:42:05.610554 23673 solver.cpp:218] Iteration 5016 (2.36449 iter/s, 5.07509s/12 iters), loss = 0.676496
I0407 22:42:05.610653 23673 solver.cpp:237]     Train net output #0: loss = 0.676496 (* 1 = 0.676496 loss)
I0407 22:42:05.610666 23673 sgd_solver.cpp:105] Iteration 5016, lr = 0.00610038
I0407 22:42:10.713718 23673 solver.cpp:218] Iteration 5028 (2.35159 iter/s, 5.10293s/12 iters), loss = 0.421423
I0407 22:42:10.713774 23673 solver.cpp:237]     Train net output #0: loss = 0.421423 (* 1 = 0.421423 loss)
I0407 22:42:10.713788 23673 sgd_solver.cpp:105] Iteration 5028, lr = 0.00609318
I0407 22:42:15.746600 23673 solver.cpp:218] Iteration 5040 (2.38441 iter/s, 5.0327s/12 iters), loss = 0.553635
I0407 22:42:15.746647 23673 solver.cpp:237]     Train net output #0: loss = 0.553635 (* 1 = 0.553635 loss)
I0407 22:42:15.746659 23673 sgd_solver.cpp:105] Iteration 5040, lr = 0.00608598
I0407 22:42:20.706199 23673 solver.cpp:218] Iteration 5052 (2.41964 iter/s, 4.95943s/12 iters), loss = 0.457772
I0407 22:42:20.706244 23673 solver.cpp:237]     Train net output #0: loss = 0.457772 (* 1 = 0.457772 loss)
I0407 22:42:20.706254 23673 sgd_solver.cpp:105] Iteration 5052, lr = 0.00607878
I0407 22:42:22.657606 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:25.799088 23673 solver.cpp:218] Iteration 5064 (2.35631 iter/s, 5.09271s/12 iters), loss = 0.508789
I0407 22:42:25.799132 23673 solver.cpp:237]     Train net output #0: loss = 0.508789 (* 1 = 0.508789 loss)
I0407 22:42:25.799141 23673 sgd_solver.cpp:105] Iteration 5064, lr = 0.0060716
I0407 22:42:30.797117 23673 solver.cpp:218] Iteration 5076 (2.40103 iter/s, 4.99785s/12 iters), loss = 0.61289
I0407 22:42:30.797160 23673 solver.cpp:237]     Train net output #0: loss = 0.61289 (* 1 = 0.61289 loss)
I0407 22:42:30.797169 23673 sgd_solver.cpp:105] Iteration 5076, lr = 0.00606443
I0407 22:42:35.801970 23673 solver.cpp:218] Iteration 5088 (2.39776 iter/s, 5.00466s/12 iters), loss = 0.428715
I0407 22:42:35.802045 23673 solver.cpp:237]     Train net output #0: loss = 0.428715 (* 1 = 0.428715 loss)
I0407 22:42:35.802057 23673 sgd_solver.cpp:105] Iteration 5088, lr = 0.00605726
I0407 22:42:40.387598 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 22:42:46.334774 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 22:42:51.241573 23673 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 22:42:51.241597 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:42:53.694962 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:55.718497 23673 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0407 22:42:55.718540 23673 solver.cpp:397]     Test net output #1: loss = 3.13582 (* 1 = 3.13582 loss)
I0407 22:42:55.809700 23673 solver.cpp:218] Iteration 5100 (0.599785 iter/s, 20.0072s/12 iters), loss = 0.578556
I0407 22:42:55.809736 23673 solver.cpp:237]     Train net output #0: loss = 0.578556 (* 1 = 0.578556 loss)
I0407 22:42:55.809746 23673 sgd_solver.cpp:105] Iteration 5100, lr = 0.0060501
I0407 22:43:00.030073 23673 solver.cpp:218] Iteration 5112 (2.84345 iter/s, 4.22022s/12 iters), loss = 0.581513
I0407 22:43:00.030118 23673 solver.cpp:237]     Train net output #0: loss = 0.581513 (* 1 = 0.581513 loss)
I0407 22:43:00.030129 23673 sgd_solver.cpp:105] Iteration 5112, lr = 0.00604295
I0407 22:43:05.380672 23673 solver.cpp:218] Iteration 5124 (2.24282 iter/s, 5.35041s/12 iters), loss = 0.482719
I0407 22:43:05.380713 23673 solver.cpp:237]     Train net output #0: loss = 0.482719 (* 1 = 0.482719 loss)
I0407 22:43:05.380722 23673 sgd_solver.cpp:105] Iteration 5124, lr = 0.00603581
I0407 22:43:10.925992 23673 solver.cpp:218] Iteration 5136 (2.16406 iter/s, 5.54513s/12 iters), loss = 0.471126
I0407 22:43:10.926112 23673 solver.cpp:237]     Train net output #0: loss = 0.471126 (* 1 = 0.471126 loss)
I0407 22:43:10.926124 23673 sgd_solver.cpp:105] Iteration 5136, lr = 0.00602868
I0407 22:43:16.408563 23673 solver.cpp:218] Iteration 5148 (2.18886 iter/s, 5.48231s/12 iters), loss = 0.558202
I0407 22:43:16.408614 23673 solver.cpp:237]     Train net output #0: loss = 0.558202 (* 1 = 0.558202 loss)
I0407 22:43:16.408625 23673 sgd_solver.cpp:105] Iteration 5148, lr = 0.00602156
I0407 22:43:20.605777 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:21.524794 23673 solver.cpp:218] Iteration 5160 (2.34556 iter/s, 5.11604s/12 iters), loss = 0.487785
I0407 22:43:21.524845 23673 solver.cpp:237]     Train net output #0: loss = 0.487785 (* 1 = 0.487785 loss)
I0407 22:43:21.524857 23673 sgd_solver.cpp:105] Iteration 5160, lr = 0.00601444
I0407 22:43:26.581692 23673 solver.cpp:218] Iteration 5172 (2.37309 iter/s, 5.0567s/12 iters), loss = 0.513661
I0407 22:43:26.581748 23673 solver.cpp:237]     Train net output #0: loss = 0.513661 (* 1 = 0.513661 loss)
I0407 22:43:26.581758 23673 sgd_solver.cpp:105] Iteration 5172, lr = 0.00600733
I0407 22:43:31.806849 23673 solver.cpp:218] Iteration 5184 (2.29667 iter/s, 5.22496s/12 iters), loss = 0.443739
I0407 22:43:31.806891 23673 solver.cpp:237]     Train net output #0: loss = 0.443739 (* 1 = 0.443739 loss)
I0407 22:43:31.806901 23673 sgd_solver.cpp:105] Iteration 5184, lr = 0.00600024
I0407 22:43:37.318637 23673 solver.cpp:218] Iteration 5196 (2.17723 iter/s, 5.5116s/12 iters), loss = 0.402819
I0407 22:43:37.318686 23673 solver.cpp:237]     Train net output #0: loss = 0.402819 (* 1 = 0.402819 loss)
I0407 22:43:37.318697 23673 sgd_solver.cpp:105] Iteration 5196, lr = 0.00599314
I0407 22:43:39.596987 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 22:43:42.638571 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 22:43:45.028600 23673 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 22:43:45.028628 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:43:47.530706 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:49.696656 23673 solver.cpp:397]     Test net output #0: accuracy = 0.38174
I0407 22:43:49.696699 23673 solver.cpp:397]     Test net output #1: loss = 3.13115 (* 1 = 3.13115 loss)
I0407 22:43:51.693588 23673 solver.cpp:218] Iteration 5208 (0.834811 iter/s, 14.3745s/12 iters), loss = 0.608102
I0407 22:43:51.693672 23673 solver.cpp:237]     Train net output #0: loss = 0.608102 (* 1 = 0.608102 loss)
I0407 22:43:51.693691 23673 sgd_solver.cpp:105] Iteration 5208, lr = 0.00598606
I0407 22:43:56.910832 23673 solver.cpp:218] Iteration 5220 (2.30016 iter/s, 5.21703s/12 iters), loss = 0.550116
I0407 22:43:56.910876 23673 solver.cpp:237]     Train net output #0: loss = 0.550116 (* 1 = 0.550116 loss)
I0407 22:43:56.910887 23673 sgd_solver.cpp:105] Iteration 5220, lr = 0.00597899
I0407 22:44:01.971045 23673 solver.cpp:218] Iteration 5232 (2.37153 iter/s, 5.06002s/12 iters), loss = 0.528228
I0407 22:44:01.971101 23673 solver.cpp:237]     Train net output #0: loss = 0.528228 (* 1 = 0.528228 loss)
I0407 22:44:01.971112 23673 sgd_solver.cpp:105] Iteration 5232, lr = 0.00597192
I0407 22:44:07.304225 23673 solver.cpp:218] Iteration 5244 (2.25015 iter/s, 5.33298s/12 iters), loss = 0.508869
I0407 22:44:07.304280 23673 solver.cpp:237]     Train net output #0: loss = 0.508869 (* 1 = 0.508869 loss)
I0407 22:44:07.304291 23673 sgd_solver.cpp:105] Iteration 5244, lr = 0.00596487
I0407 22:44:12.825397 23673 solver.cpp:218] Iteration 5256 (2.17353 iter/s, 5.52097s/12 iters), loss = 0.479492
I0407 22:44:12.825521 23673 solver.cpp:237]     Train net output #0: loss = 0.479492 (* 1 = 0.479492 loss)
I0407 22:44:12.825536 23673 sgd_solver.cpp:105] Iteration 5256, lr = 0.00595782
I0407 22:44:14.254164 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:18.332973 23673 solver.cpp:218] Iteration 5268 (2.17893 iter/s, 5.5073s/12 iters), loss = 0.643354
I0407 22:44:18.333031 23673 solver.cpp:237]     Train net output #0: loss = 0.643354 (* 1 = 0.643354 loss)
I0407 22:44:18.333043 23673 sgd_solver.cpp:105] Iteration 5268, lr = 0.00595078
I0407 22:44:23.871976 23673 solver.cpp:218] Iteration 5280 (2.16654 iter/s, 5.5388s/12 iters), loss = 0.582776
I0407 22:44:23.872025 23673 solver.cpp:237]     Train net output #0: loss = 0.582776 (* 1 = 0.582776 loss)
I0407 22:44:23.872036 23673 sgd_solver.cpp:105] Iteration 5280, lr = 0.00594375
I0407 22:44:29.393095 23673 solver.cpp:218] Iteration 5292 (2.17355 iter/s, 5.52091s/12 iters), loss = 0.4629
I0407 22:44:29.393146 23673 solver.cpp:237]     Train net output #0: loss = 0.4629 (* 1 = 0.4629 loss)
I0407 22:44:29.393157 23673 sgd_solver.cpp:105] Iteration 5292, lr = 0.00593672
I0407 22:44:34.396185 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 22:44:37.422484 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 22:44:41.333173 23673 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 22:44:41.333199 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:44:43.680351 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:45.779875 23673 solver.cpp:397]     Test net output #0: accuracy = 0.397672
I0407 22:44:45.779927 23673 solver.cpp:397]     Test net output #1: loss = 2.96488 (* 1 = 2.96488 loss)
I0407 22:44:45.871322 23673 solver.cpp:218] Iteration 5304 (0.728255 iter/s, 16.4778s/12 iters), loss = 0.501838
I0407 22:44:45.871366 23673 solver.cpp:237]     Train net output #0: loss = 0.501838 (* 1 = 0.501838 loss)
I0407 22:44:45.871376 23673 sgd_solver.cpp:105] Iteration 5304, lr = 0.00592971
I0407 22:44:50.209380 23673 solver.cpp:218] Iteration 5316 (2.76633 iter/s, 4.33788s/12 iters), loss = 0.507754
I0407 22:44:50.209446 23673 solver.cpp:237]     Train net output #0: loss = 0.507754 (* 1 = 0.507754 loss)
I0407 22:44:50.209463 23673 sgd_solver.cpp:105] Iteration 5316, lr = 0.0059227
I0407 22:44:55.300499 23673 solver.cpp:218] Iteration 5328 (2.35714 iter/s, 5.09092s/12 iters), loss = 0.416805
I0407 22:44:55.300544 23673 solver.cpp:237]     Train net output #0: loss = 0.416805 (* 1 = 0.416805 loss)
I0407 22:44:55.300554 23673 sgd_solver.cpp:105] Iteration 5328, lr = 0.0059157
I0407 22:45:00.225433 23673 solver.cpp:218] Iteration 5340 (2.43667 iter/s, 4.92475s/12 iters), loss = 0.478783
I0407 22:45:00.225484 23673 solver.cpp:237]     Train net output #0: loss = 0.478783 (* 1 = 0.478783 loss)
I0407 22:45:00.225497 23673 sgd_solver.cpp:105] Iteration 5340, lr = 0.00590871
I0407 22:45:05.281608 23673 solver.cpp:218] Iteration 5352 (2.37343 iter/s, 5.05598s/12 iters), loss = 0.575504
I0407 22:45:05.281656 23673 solver.cpp:237]     Train net output #0: loss = 0.575504 (* 1 = 0.575504 loss)
I0407 22:45:05.281666 23673 sgd_solver.cpp:105] Iteration 5352, lr = 0.00590173
I0407 22:45:09.049379 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:10.773983 23673 solver.cpp:218] Iteration 5364 (2.18493 iter/s, 5.49216s/12 iters), loss = 0.533216
I0407 22:45:10.774039 23673 solver.cpp:237]     Train net output #0: loss = 0.533216 (* 1 = 0.533216 loss)
I0407 22:45:10.774051 23673 sgd_solver.cpp:105] Iteration 5364, lr = 0.00589476
I0407 22:45:16.184850 23673 solver.cpp:218] Iteration 5376 (2.21784 iter/s, 5.41066s/12 iters), loss = 0.55977
I0407 22:45:16.184974 23673 solver.cpp:237]     Train net output #0: loss = 0.55977 (* 1 = 0.55977 loss)
I0407 22:45:16.184984 23673 sgd_solver.cpp:105] Iteration 5376, lr = 0.00588779
I0407 22:45:21.245271 23673 solver.cpp:218] Iteration 5388 (2.37147 iter/s, 5.06015s/12 iters), loss = 0.539675
I0407 22:45:21.245323 23673 solver.cpp:237]     Train net output #0: loss = 0.539675 (* 1 = 0.539675 loss)
I0407 22:45:21.245334 23673 sgd_solver.cpp:105] Iteration 5388, lr = 0.00588083
I0407 22:45:26.233844 23673 solver.cpp:218] Iteration 5400 (2.4056 iter/s, 4.98837s/12 iters), loss = 0.436037
I0407 22:45:26.233902 23673 solver.cpp:237]     Train net output #0: loss = 0.436037 (* 1 = 0.436037 loss)
I0407 22:45:26.233916 23673 sgd_solver.cpp:105] Iteration 5400, lr = 0.00587388
I0407 22:45:28.430538 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 22:45:31.382566 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 22:45:33.737066 23673 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 22:45:33.737092 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:45:36.057543 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:38.193642 23673 solver.cpp:397]     Test net output #0: accuracy = 0.383578
I0407 22:45:38.193691 23673 solver.cpp:397]     Test net output #1: loss = 3.15454 (* 1 = 3.15454 loss)
I0407 22:45:40.212530 23673 solver.cpp:218] Iteration 5412 (0.858477 iter/s, 13.9783s/12 iters), loss = 0.385207
I0407 22:45:40.212589 23673 solver.cpp:237]     Train net output #0: loss = 0.385207 (* 1 = 0.385207 loss)
I0407 22:45:40.212601 23673 sgd_solver.cpp:105] Iteration 5412, lr = 0.00586694
I0407 22:45:45.281904 23673 solver.cpp:218] Iteration 5424 (2.36725 iter/s, 5.06917s/12 iters), loss = 0.774517
I0407 22:45:45.281951 23673 solver.cpp:237]     Train net output #0: loss = 0.774517 (* 1 = 0.774517 loss)
I0407 22:45:45.281976 23673 sgd_solver.cpp:105] Iteration 5424, lr = 0.00586001
I0407 22:45:50.358573 23673 solver.cpp:218] Iteration 5436 (2.36384 iter/s, 5.07648s/12 iters), loss = 0.366468
I0407 22:45:50.358685 23673 solver.cpp:237]     Train net output #0: loss = 0.366468 (* 1 = 0.366468 loss)
I0407 22:45:50.358700 23673 sgd_solver.cpp:105] Iteration 5436, lr = 0.00585308
I0407 22:45:55.447274 23673 solver.cpp:218] Iteration 5448 (2.35829 iter/s, 5.08844s/12 iters), loss = 0.374619
I0407 22:45:55.447332 23673 solver.cpp:237]     Train net output #0: loss = 0.374619 (* 1 = 0.374619 loss)
I0407 22:45:55.447345 23673 sgd_solver.cpp:105] Iteration 5448, lr = 0.00584617
I0407 22:46:00.859822 23673 solver.cpp:218] Iteration 5460 (2.21716 iter/s, 5.41233s/12 iters), loss = 0.628335
I0407 22:46:00.859879 23673 solver.cpp:237]     Train net output #0: loss = 0.628335 (* 1 = 0.628335 loss)
I0407 22:46:00.859892 23673 sgd_solver.cpp:105] Iteration 5460, lr = 0.00583926
I0407 22:46:01.421283 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:05.923763 23673 solver.cpp:218] Iteration 5472 (2.36979 iter/s, 5.06374s/12 iters), loss = 0.523545
I0407 22:46:05.923820 23673 solver.cpp:237]     Train net output #0: loss = 0.523545 (* 1 = 0.523545 loss)
I0407 22:46:05.923832 23673 sgd_solver.cpp:105] Iteration 5472, lr = 0.00583236
I0407 22:46:11.038614 23673 solver.cpp:218] Iteration 5484 (2.34621 iter/s, 5.11464s/12 iters), loss = 0.508037
I0407 22:46:11.038671 23673 solver.cpp:237]     Train net output #0: loss = 0.508037 (* 1 = 0.508037 loss)
I0407 22:46:11.038682 23673 sgd_solver.cpp:105] Iteration 5484, lr = 0.00582547
I0407 22:46:16.118587 23673 solver.cpp:218] Iteration 5496 (2.36231 iter/s, 5.07977s/12 iters), loss = 0.531823
I0407 22:46:16.118640 23673 solver.cpp:237]     Train net output #0: loss = 0.531823 (* 1 = 0.531823 loss)
I0407 22:46:16.118651 23673 sgd_solver.cpp:105] Iteration 5496, lr = 0.00581858
I0407 22:46:20.725937 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 22:46:23.669185 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 22:46:25.981492 23673 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 22:46:25.981518 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:46:28.257195 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:30.440711 23673 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0407 22:46:30.440760 23673 solver.cpp:397]     Test net output #1: loss = 3.1126 (* 1 = 3.1126 loss)
I0407 22:46:30.532122 23673 solver.cpp:218] Iteration 5508 (0.832577 iter/s, 14.4131s/12 iters), loss = 0.53988
I0407 22:46:30.532176 23673 solver.cpp:237]     Train net output #0: loss = 0.53988 (* 1 = 0.53988 loss)
I0407 22:46:30.532188 23673 sgd_solver.cpp:105] Iteration 5508, lr = 0.00581171
I0407 22:46:34.932876 23673 solver.cpp:218] Iteration 5520 (2.72692 iter/s, 4.40057s/12 iters), loss = 0.629333
I0407 22:46:34.932927 23673 solver.cpp:237]     Train net output #0: loss = 0.629333 (* 1 = 0.629333 loss)
I0407 22:46:34.932941 23673 sgd_solver.cpp:105] Iteration 5520, lr = 0.00580484
I0407 22:46:37.496479 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:46:40.040994 23673 solver.cpp:218] Iteration 5532 (2.34929 iter/s, 5.10792s/12 iters), loss = 0.622639
I0407 22:46:40.041040 23673 solver.cpp:237]     Train net output #0: loss = 0.622639 (* 1 = 0.622639 loss)
I0407 22:46:40.041051 23673 sgd_solver.cpp:105] Iteration 5532, lr = 0.00579798
I0407 22:46:45.132447 23673 solver.cpp:218] Iteration 5544 (2.35698 iter/s, 5.09126s/12 iters), loss = 0.513239
I0407 22:46:45.132483 23673 solver.cpp:237]     Train net output #0: loss = 0.513239 (* 1 = 0.513239 loss)
I0407 22:46:45.132493 23673 sgd_solver.cpp:105] Iteration 5544, lr = 0.00579113
I0407 22:46:50.279917 23673 solver.cpp:218] Iteration 5556 (2.33133 iter/s, 5.14728s/12 iters), loss = 0.536228
I0407 22:46:50.279963 23673 solver.cpp:237]     Train net output #0: loss = 0.536228 (* 1 = 0.536228 loss)
I0407 22:46:50.279974 23673 sgd_solver.cpp:105] Iteration 5556, lr = 0.00578429
I0407 22:46:53.050098 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:55.414453 23673 solver.cpp:218] Iteration 5568 (2.3372 iter/s, 5.13434s/12 iters), loss = 0.454606
I0407 22:46:55.414502 23673 solver.cpp:237]     Train net output #0: loss = 0.454606 (* 1 = 0.454606 loss)
I0407 22:46:55.414515 23673 sgd_solver.cpp:105] Iteration 5568, lr = 0.00577745
I0407 22:47:00.618656 23673 solver.cpp:218] Iteration 5580 (2.30592 iter/s, 5.20401s/12 iters), loss = 0.422172
I0407 22:47:00.618695 23673 solver.cpp:237]     Train net output #0: loss = 0.422172 (* 1 = 0.422172 loss)
I0407 22:47:00.618705 23673 sgd_solver.cpp:105] Iteration 5580, lr = 0.00577062
I0407 22:47:05.743815 23673 solver.cpp:218] Iteration 5592 (2.34148 iter/s, 5.12497s/12 iters), loss = 0.75693
I0407 22:47:05.743862 23673 solver.cpp:237]     Train net output #0: loss = 0.75693 (* 1 = 0.75693 loss)
I0407 22:47:05.743872 23673 sgd_solver.cpp:105] Iteration 5592, lr = 0.00576381
I0407 22:47:10.793754 23673 solver.cpp:218] Iteration 5604 (2.37636 iter/s, 5.04974s/12 iters), loss = 0.403569
I0407 22:47:10.793808 23673 solver.cpp:237]     Train net output #0: loss = 0.403569 (* 1 = 0.403569 loss)
I0407 22:47:10.793820 23673 sgd_solver.cpp:105] Iteration 5604, lr = 0.00575699
I0407 22:47:12.856156 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 22:47:16.936203 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 22:47:19.288066 23673 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 22:47:19.288094 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:47:21.584007 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:23.836328 23673 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0407 22:47:23.836464 23673 solver.cpp:397]     Test net output #1: loss = 3.15931 (* 1 = 3.15931 loss)
I0407 22:47:25.845037 23673 solver.cpp:218] Iteration 5616 (0.797299 iter/s, 15.0508s/12 iters), loss = 0.425607
I0407 22:47:25.845093 23673 solver.cpp:237]     Train net output #0: loss = 0.425607 (* 1 = 0.425607 loss)
I0407 22:47:25.845104 23673 sgd_solver.cpp:105] Iteration 5616, lr = 0.00575019
I0407 22:47:31.115873 23673 solver.cpp:218] Iteration 5628 (2.27677 iter/s, 5.27062s/12 iters), loss = 0.367853
I0407 22:47:31.115931 23673 solver.cpp:237]     Train net output #0: loss = 0.367853 (* 1 = 0.367853 loss)
I0407 22:47:31.115943 23673 sgd_solver.cpp:105] Iteration 5628, lr = 0.0057434
I0407 22:47:36.114490 23673 solver.cpp:218] Iteration 5640 (2.40076 iter/s, 4.99842s/12 iters), loss = 0.419675
I0407 22:47:36.114531 23673 solver.cpp:237]     Train net output #0: loss = 0.419675 (* 1 = 0.419675 loss)
I0407 22:47:36.114539 23673 sgd_solver.cpp:105] Iteration 5640, lr = 0.00573661
I0407 22:47:41.246680 23673 solver.cpp:218] Iteration 5652 (2.33827 iter/s, 5.132s/12 iters), loss = 0.238172
I0407 22:47:41.246723 23673 solver.cpp:237]     Train net output #0: loss = 0.238172 (* 1 = 0.238172 loss)
I0407 22:47:41.246734 23673 sgd_solver.cpp:105] Iteration 5652, lr = 0.00572983
I0407 22:47:46.197834 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:46.376920 23673 solver.cpp:218] Iteration 5664 (2.33916 iter/s, 5.13005s/12 iters), loss = 0.489024
I0407 22:47:46.376974 23673 solver.cpp:237]     Train net output #0: loss = 0.489024 (* 1 = 0.489024 loss)
I0407 22:47:46.376988 23673 sgd_solver.cpp:105] Iteration 5664, lr = 0.00572306
I0407 22:47:51.702484 23673 solver.cpp:218] Iteration 5676 (2.25337 iter/s, 5.32536s/12 iters), loss = 0.639075
I0407 22:47:51.702531 23673 solver.cpp:237]     Train net output #0: loss = 0.639075 (* 1 = 0.639075 loss)
I0407 22:47:51.702543 23673 sgd_solver.cpp:105] Iteration 5676, lr = 0.0057163
I0407 22:47:56.806468 23673 solver.cpp:218] Iteration 5688 (2.3512 iter/s, 5.10378s/12 iters), loss = 0.513047
I0407 22:47:56.806563 23673 solver.cpp:237]     Train net output #0: loss = 0.513047 (* 1 = 0.513047 loss)
I0407 22:47:56.806576 23673 sgd_solver.cpp:105] Iteration 5688, lr = 0.00570954
I0407 22:48:01.845527 23673 solver.cpp:218] Iteration 5700 (2.38151 iter/s, 5.03882s/12 iters), loss = 0.319407
I0407 22:48:01.845578 23673 solver.cpp:237]     Train net output #0: loss = 0.319407 (* 1 = 0.319407 loss)
I0407 22:48:01.845590 23673 sgd_solver.cpp:105] Iteration 5700, lr = 0.0057028
I0407 22:48:06.470105 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 22:48:09.505340 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 22:48:11.843921 23673 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 22:48:11.843947 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:48:14.077327 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:16.330772 23673 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0407 22:48:16.330822 23673 solver.cpp:397]     Test net output #1: loss = 3.12561 (* 1 = 3.12561 loss)
I0407 22:48:16.422089 23673 solver.cpp:218] Iteration 5712 (0.823265 iter/s, 14.5761s/12 iters), loss = 0.505359
I0407 22:48:16.422138 23673 solver.cpp:237]     Train net output #0: loss = 0.505359 (* 1 = 0.505359 loss)
I0407 22:48:16.422149 23673 sgd_solver.cpp:105] Iteration 5712, lr = 0.00569606
I0407 22:48:21.016842 23673 solver.cpp:218] Iteration 5724 (2.61178 iter/s, 4.59456s/12 iters), loss = 0.383393
I0407 22:48:21.016897 23673 solver.cpp:237]     Train net output #0: loss = 0.383393 (* 1 = 0.383393 loss)
I0407 22:48:21.016909 23673 sgd_solver.cpp:105] Iteration 5724, lr = 0.00568933
I0407 22:48:26.308791 23673 solver.cpp:218] Iteration 5736 (2.26768 iter/s, 5.29174s/12 iters), loss = 0.413877
I0407 22:48:26.308836 23673 solver.cpp:237]     Train net output #0: loss = 0.413877 (* 1 = 0.413877 loss)
I0407 22:48:26.308848 23673 sgd_solver.cpp:105] Iteration 5736, lr = 0.0056826
I0407 22:48:31.479934 23673 solver.cpp:218] Iteration 5748 (2.32066 iter/s, 5.17094s/12 iters), loss = 0.545295
I0407 22:48:31.480057 23673 solver.cpp:237]     Train net output #0: loss = 0.545295 (* 1 = 0.545295 loss)
I0407 22:48:31.480070 23673 sgd_solver.cpp:105] Iteration 5748, lr = 0.00567589
I0407 22:48:36.784691 23673 solver.cpp:218] Iteration 5760 (2.26224 iter/s, 5.30448s/12 iters), loss = 0.507455
I0407 22:48:36.784750 23673 solver.cpp:237]     Train net output #0: loss = 0.507455 (* 1 = 0.507455 loss)
I0407 22:48:36.784765 23673 sgd_solver.cpp:105] Iteration 5760, lr = 0.00566918
I0407 22:48:38.938802 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:41.945719 23673 solver.cpp:218] Iteration 5772 (2.32521 iter/s, 5.16082s/12 iters), loss = 0.236098
I0407 22:48:41.945765 23673 solver.cpp:237]     Train net output #0: loss = 0.236098 (* 1 = 0.236098 loss)
I0407 22:48:41.945776 23673 sgd_solver.cpp:105] Iteration 5772, lr = 0.00566248
I0407 22:48:47.059521 23673 solver.cpp:218] Iteration 5784 (2.34668 iter/s, 5.1136s/12 iters), loss = 0.403266
I0407 22:48:47.059574 23673 solver.cpp:237]     Train net output #0: loss = 0.403266 (* 1 = 0.403266 loss)
I0407 22:48:47.059587 23673 sgd_solver.cpp:105] Iteration 5784, lr = 0.00565579
I0407 22:48:52.187373 23673 solver.cpp:218] Iteration 5796 (2.34026 iter/s, 5.12764s/12 iters), loss = 0.505418
I0407 22:48:52.187431 23673 solver.cpp:237]     Train net output #0: loss = 0.505418 (* 1 = 0.505418 loss)
I0407 22:48:52.187443 23673 sgd_solver.cpp:105] Iteration 5796, lr = 0.00564911
I0407 22:48:57.269527 23673 solver.cpp:218] Iteration 5808 (2.3613 iter/s, 5.08195s/12 iters), loss = 0.545905
I0407 22:48:57.269567 23673 solver.cpp:237]     Train net output #0: loss = 0.545905 (* 1 = 0.545905 loss)
I0407 22:48:57.269577 23673 sgd_solver.cpp:105] Iteration 5808, lr = 0.00564243
I0407 22:48:59.447887 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 22:49:02.422734 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 22:49:04.729907 23673 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 22:49:04.729929 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:49:06.911475 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:09.206818 23673 solver.cpp:397]     Test net output #0: accuracy = 0.383578
I0407 22:49:09.206857 23673 solver.cpp:397]     Test net output #1: loss = 3.15516 (* 1 = 3.15516 loss)
I0407 22:49:11.024411 23673 solver.cpp:218] Iteration 5820 (0.872444 iter/s, 13.7545s/12 iters), loss = 0.485616
I0407 22:49:11.024451 23673 solver.cpp:237]     Train net output #0: loss = 0.485616 (* 1 = 0.485616 loss)
I0407 22:49:11.024461 23673 sgd_solver.cpp:105] Iteration 5820, lr = 0.00563576
I0407 22:49:16.101222 23673 solver.cpp:218] Iteration 5832 (2.36378 iter/s, 5.07661s/12 iters), loss = 0.512632
I0407 22:49:16.101279 23673 solver.cpp:237]     Train net output #0: loss = 0.512632 (* 1 = 0.512632 loss)
I0407 22:49:16.101292 23673 sgd_solver.cpp:105] Iteration 5832, lr = 0.0056291
I0407 22:49:21.204216 23673 solver.cpp:218] Iteration 5844 (2.35166 iter/s, 5.10278s/12 iters), loss = 0.351153
I0407 22:49:21.204267 23673 solver.cpp:237]     Train net output #0: loss = 0.351153 (* 1 = 0.351153 loss)
I0407 22:49:21.204277 23673 sgd_solver.cpp:105] Iteration 5844, lr = 0.00562245
I0407 22:49:26.600899 23673 solver.cpp:218] Iteration 5856 (2.22368 iter/s, 5.39647s/12 iters), loss = 0.295288
I0407 22:49:26.600950 23673 solver.cpp:237]     Train net output #0: loss = 0.295288 (* 1 = 0.295288 loss)
I0407 22:49:26.600962 23673 sgd_solver.cpp:105] Iteration 5856, lr = 0.00561581
I0407 22:49:31.115998 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:31.961457 23673 solver.cpp:218] Iteration 5868 (2.23866 iter/s, 5.36035s/12 iters), loss = 0.399765
I0407 22:49:31.961503 23673 solver.cpp:237]     Train net output #0: loss = 0.399765 (* 1 = 0.399765 loss)
I0407 22:49:31.961514 23673 sgd_solver.cpp:105] Iteration 5868, lr = 0.00560917
I0407 22:49:36.975689 23673 solver.cpp:218] Iteration 5880 (2.39328 iter/s, 5.01404s/12 iters), loss = 0.517163
I0407 22:49:36.975805 23673 solver.cpp:237]     Train net output #0: loss = 0.517163 (* 1 = 0.517163 loss)
I0407 22:49:36.975816 23673 sgd_solver.cpp:105] Iteration 5880, lr = 0.00560254
I0407 22:49:42.048811 23673 solver.cpp:218] Iteration 5892 (2.36553 iter/s, 5.07285s/12 iters), loss = 0.378885
I0407 22:49:42.048859 23673 solver.cpp:237]     Train net output #0: loss = 0.378885 (* 1 = 0.378885 loss)
I0407 22:49:42.048872 23673 sgd_solver.cpp:105] Iteration 5892, lr = 0.00559592
I0407 22:49:47.076496 23673 solver.cpp:218] Iteration 5904 (2.38688 iter/s, 5.02748s/12 iters), loss = 0.431868
I0407 22:49:47.076550 23673 solver.cpp:237]     Train net output #0: loss = 0.431868 (* 1 = 0.431868 loss)
I0407 22:49:47.076563 23673 sgd_solver.cpp:105] Iteration 5904, lr = 0.00558931
I0407 22:49:51.965667 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 22:49:55.220352 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 22:49:57.543735 23673 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 22:49:57.543762 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:49:59.788784 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:02.232060 23673 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0407 22:50:02.232103 23673 solver.cpp:397]     Test net output #1: loss = 3.09801 (* 1 = 3.09801 loss)
I0407 22:50:02.323704 23673 solver.cpp:218] Iteration 5916 (0.787054 iter/s, 15.2467s/12 iters), loss = 0.328902
I0407 22:50:02.323745 23673 solver.cpp:237]     Train net output #0: loss = 0.328902 (* 1 = 0.328902 loss)
I0407 22:50:02.323755 23673 sgd_solver.cpp:105] Iteration 5916, lr = 0.00558271
I0407 22:50:06.780438 23673 solver.cpp:218] Iteration 5928 (2.69266 iter/s, 4.45655s/12 iters), loss = 0.268474
I0407 22:50:06.780493 23673 solver.cpp:237]     Train net output #0: loss = 0.268474 (* 1 = 0.268474 loss)
I0407 22:50:06.780504 23673 sgd_solver.cpp:105] Iteration 5928, lr = 0.00557611
I0407 22:50:12.184980 23673 solver.cpp:218] Iteration 5940 (2.22044 iter/s, 5.40433s/12 iters), loss = 0.447216
I0407 22:50:12.185060 23673 solver.cpp:237]     Train net output #0: loss = 0.447216 (* 1 = 0.447216 loss)
I0407 22:50:12.185071 23673 sgd_solver.cpp:105] Iteration 5940, lr = 0.00556952
I0407 22:50:17.236721 23673 solver.cpp:218] Iteration 5952 (2.37553 iter/s, 5.05151s/12 iters), loss = 0.350745
I0407 22:50:17.236760 23673 solver.cpp:237]     Train net output #0: loss = 0.350745 (* 1 = 0.350745 loss)
I0407 22:50:17.236769 23673 sgd_solver.cpp:105] Iteration 5952, lr = 0.00556294
I0407 22:50:22.516779 23673 solver.cpp:218] Iteration 5964 (2.27279 iter/s, 5.27986s/12 iters), loss = 0.389845
I0407 22:50:22.516839 23673 solver.cpp:237]     Train net output #0: loss = 0.389845 (* 1 = 0.389845 loss)
I0407 22:50:22.516855 23673 sgd_solver.cpp:105] Iteration 5964, lr = 0.00555637
I0407 22:50:23.844664 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:27.629382 23673 solver.cpp:218] Iteration 5976 (2.34724 iter/s, 5.11239s/12 iters), loss = 0.431547
I0407 22:50:27.629429 23673 solver.cpp:237]     Train net output #0: loss = 0.431547 (* 1 = 0.431547 loss)
I0407 22:50:27.629441 23673 sgd_solver.cpp:105] Iteration 5976, lr = 0.0055498
I0407 22:50:32.725519 23673 solver.cpp:218] Iteration 5988 (2.35482 iter/s, 5.09594s/12 iters), loss = 0.359758
I0407 22:50:32.725566 23673 solver.cpp:237]     Train net output #0: loss = 0.359758 (* 1 = 0.359758 loss)
I0407 22:50:32.725577 23673 sgd_solver.cpp:105] Iteration 5988, lr = 0.00554324
I0407 22:50:37.785986 23673 solver.cpp:218] Iteration 6000 (2.37142 iter/s, 5.06026s/12 iters), loss = 0.405498
I0407 22:50:37.786044 23673 solver.cpp:237]     Train net output #0: loss = 0.405498 (* 1 = 0.405498 loss)
I0407 22:50:37.786058 23673 sgd_solver.cpp:105] Iteration 6000, lr = 0.00553669
I0407 22:50:42.867772 23673 solver.cpp:218] Iteration 6012 (2.36147 iter/s, 5.08158s/12 iters), loss = 0.374134
I0407 22:50:42.867868 23673 solver.cpp:237]     Train net output #0: loss = 0.374134 (* 1 = 0.374134 loss)
I0407 22:50:42.867878 23673 sgd_solver.cpp:105] Iteration 6012, lr = 0.00553015
I0407 22:50:44.883631 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 22:50:47.852880 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 22:50:51.929128 23673 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 22:50:51.929152 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:50:53.978631 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:56.430701 23673 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0407 22:50:56.430739 23673 solver.cpp:397]     Test net output #1: loss = 3.10412 (* 1 = 3.10412 loss)
I0407 22:50:58.353909 23673 solver.cpp:218] Iteration 6024 (0.774913 iter/s, 15.4856s/12 iters), loss = 0.489515
I0407 22:50:58.353951 23673 solver.cpp:237]     Train net output #0: loss = 0.489515 (* 1 = 0.489515 loss)
I0407 22:50:58.353969 23673 sgd_solver.cpp:105] Iteration 6024, lr = 0.00552361
I0407 22:51:03.375481 23673 solver.cpp:218] Iteration 6036 (2.38978 iter/s, 5.02137s/12 iters), loss = 0.228991
I0407 22:51:03.375531 23673 solver.cpp:237]     Train net output #0: loss = 0.228991 (* 1 = 0.228991 loss)
I0407 22:51:03.375542 23673 sgd_solver.cpp:105] Iteration 6036, lr = 0.00551709
I0407 22:51:08.446206 23673 solver.cpp:218] Iteration 6048 (2.36662 iter/s, 5.07053s/12 iters), loss = 0.256993
I0407 22:51:08.446247 23673 solver.cpp:237]     Train net output #0: loss = 0.256993 (* 1 = 0.256993 loss)
I0407 22:51:08.446256 23673 sgd_solver.cpp:105] Iteration 6048, lr = 0.00551057
I0407 22:51:13.505558 23673 solver.cpp:218] Iteration 6060 (2.37194 iter/s, 5.05916s/12 iters), loss = 0.325446
I0407 22:51:13.505630 23673 solver.cpp:237]     Train net output #0: loss = 0.325446 (* 1 = 0.325446 loss)
I0407 22:51:13.505641 23673 sgd_solver.cpp:105] Iteration 6060, lr = 0.00550406
I0407 22:51:17.016238 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:18.581784 23673 solver.cpp:218] Iteration 6072 (2.36407 iter/s, 5.076s/12 iters), loss = 0.483867
I0407 22:51:18.581830 23673 solver.cpp:237]     Train net output #0: loss = 0.483867 (* 1 = 0.483867 loss)
I0407 22:51:18.581840 23673 sgd_solver.cpp:105] Iteration 6072, lr = 0.00549755
I0407 22:51:24.061378 23673 solver.cpp:218] Iteration 6084 (2.19003 iter/s, 5.47938s/12 iters), loss = 0.364256
I0407 22:51:24.061421 23673 solver.cpp:237]     Train net output #0: loss = 0.364256 (* 1 = 0.364256 loss)
I0407 22:51:24.061430 23673 sgd_solver.cpp:105] Iteration 6084, lr = 0.00549106
I0407 22:51:29.408099 23673 solver.cpp:218] Iteration 6096 (2.24445 iter/s, 5.34651s/12 iters), loss = 0.34089
I0407 22:51:29.408144 23673 solver.cpp:237]     Train net output #0: loss = 0.34089 (* 1 = 0.34089 loss)
I0407 22:51:29.408156 23673 sgd_solver.cpp:105] Iteration 6096, lr = 0.00548457
I0407 22:51:34.924008 23673 solver.cpp:218] Iteration 6108 (2.17561 iter/s, 5.5157s/12 iters), loss = 0.346168
I0407 22:51:34.924062 23673 solver.cpp:237]     Train net output #0: loss = 0.346168 (* 1 = 0.346168 loss)
I0407 22:51:34.924077 23673 sgd_solver.cpp:105] Iteration 6108, lr = 0.00547809
I0407 22:51:39.854722 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 22:51:44.150876 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 22:51:48.746294 23673 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 22:51:48.746320 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:51:50.819880 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:53.229784 23673 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0407 22:51:53.229820 23673 solver.cpp:397]     Test net output #1: loss = 3.15334 (* 1 = 3.15334 loss)
I0407 22:51:53.321441 23673 solver.cpp:218] Iteration 6120 (0.652285 iter/s, 18.3969s/12 iters), loss = 0.290375
I0407 22:51:53.321482 23673 solver.cpp:237]     Train net output #0: loss = 0.290375 (* 1 = 0.290375 loss)
I0407 22:51:53.321491 23673 sgd_solver.cpp:105] Iteration 6120, lr = 0.00547161
I0407 22:51:57.676206 23673 solver.cpp:218] Iteration 6132 (2.75572 iter/s, 4.35458s/12 iters), loss = 0.325843
I0407 22:51:57.676260 23673 solver.cpp:237]     Train net output #0: loss = 0.325843 (* 1 = 0.325843 loss)
I0407 22:51:57.676272 23673 sgd_solver.cpp:105] Iteration 6132, lr = 0.00546515
I0407 22:52:02.763180 23673 solver.cpp:218] Iteration 6144 (2.35906 iter/s, 5.08676s/12 iters), loss = 0.332183
I0407 22:52:02.763244 23673 solver.cpp:237]     Train net output #0: loss = 0.332183 (* 1 = 0.332183 loss)
I0407 22:52:02.763260 23673 sgd_solver.cpp:105] Iteration 6144, lr = 0.00545869
I0407 22:52:07.699460 23673 solver.cpp:218] Iteration 6156 (2.43109 iter/s, 4.93606s/12 iters), loss = 0.601704
I0407 22:52:07.699515 23673 solver.cpp:237]     Train net output #0: loss = 0.601704 (* 1 = 0.601704 loss)
I0407 22:52:07.699528 23673 sgd_solver.cpp:105] Iteration 6156, lr = 0.00545224
I0407 22:52:12.792317 23673 solver.cpp:218] Iteration 6168 (2.35634 iter/s, 5.09264s/12 iters), loss = 0.321145
I0407 22:52:12.792371 23673 solver.cpp:237]     Train net output #0: loss = 0.321145 (* 1 = 0.321145 loss)
I0407 22:52:12.792383 23673 sgd_solver.cpp:105] Iteration 6168, lr = 0.0054458
I0407 22:52:13.393234 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:17.931668 23673 solver.cpp:218] Iteration 6180 (2.33502 iter/s, 5.13914s/12 iters), loss = 0.370388
I0407 22:52:17.931766 23673 solver.cpp:237]     Train net output #0: loss = 0.370388 (* 1 = 0.370388 loss)
I0407 22:52:17.931777 23673 sgd_solver.cpp:105] Iteration 6180, lr = 0.00543936
I0407 22:52:23.022089 23673 solver.cpp:218] Iteration 6192 (2.35749 iter/s, 5.09016s/12 iters), loss = 0.378975
I0407 22:52:23.022141 23673 solver.cpp:237]     Train net output #0: loss = 0.378975 (* 1 = 0.378975 loss)
I0407 22:52:23.022152 23673 sgd_solver.cpp:105] Iteration 6192, lr = 0.00543293
I0407 22:52:28.119503 23673 solver.cpp:218] Iteration 6204 (2.35423 iter/s, 5.09721s/12 iters), loss = 0.429114
I0407 22:52:28.119546 23673 solver.cpp:237]     Train net output #0: loss = 0.429114 (* 1 = 0.429114 loss)
I0407 22:52:28.119556 23673 sgd_solver.cpp:105] Iteration 6204, lr = 0.00542651
I0407 22:52:33.243250 23673 solver.cpp:218] Iteration 6216 (2.34213 iter/s, 5.12354s/12 iters), loss = 0.281007
I0407 22:52:33.243309 23673 solver.cpp:237]     Train net output #0: loss = 0.281007 (* 1 = 0.281007 loss)
I0407 22:52:33.243321 23673 sgd_solver.cpp:105] Iteration 6216, lr = 0.0054201
I0407 22:52:35.380771 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 22:52:42.273397 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 22:52:47.697518 23673 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 22:52:47.697543 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:52:49.727952 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:51.005651 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:52:52.179177 23673 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0407 22:52:52.179216 23673 solver.cpp:397]     Test net output #1: loss = 3.12422 (* 1 = 3.12422 loss)
I0407 22:52:54.042909 23673 solver.cpp:218] Iteration 6228 (0.576951 iter/s, 20.799s/12 iters), loss = 0.220968
I0407 22:52:54.042955 23673 solver.cpp:237]     Train net output #0: loss = 0.220968 (* 1 = 0.220968 loss)
I0407 22:52:54.042966 23673 sgd_solver.cpp:105] Iteration 6228, lr = 0.0054137
I0407 22:52:59.134734 23673 solver.cpp:218] Iteration 6240 (2.35681 iter/s, 5.09162s/12 iters), loss = 0.356742
I0407 22:52:59.134778 23673 solver.cpp:237]     Train net output #0: loss = 0.356742 (* 1 = 0.356742 loss)
I0407 22:52:59.134788 23673 sgd_solver.cpp:105] Iteration 6240, lr = 0.0054073
I0407 22:53:04.183861 23673 solver.cpp:218] Iteration 6252 (2.37674 iter/s, 5.04893s/12 iters), loss = 0.376241
I0407 22:53:04.183902 23673 solver.cpp:237]     Train net output #0: loss = 0.376241 (* 1 = 0.376241 loss)
I0407 22:53:04.183914 23673 sgd_solver.cpp:105] Iteration 6252, lr = 0.00540091
I0407 22:53:09.296792 23673 solver.cpp:218] Iteration 6264 (2.34708 iter/s, 5.11273s/12 iters), loss = 0.319341
I0407 22:53:09.296846 23673 solver.cpp:237]     Train net output #0: loss = 0.319341 (* 1 = 0.319341 loss)
I0407 22:53:09.296859 23673 sgd_solver.cpp:105] Iteration 6264, lr = 0.00539453
I0407 22:53:12.201012 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:14.664011 23673 solver.cpp:218] Iteration 6276 (2.23589 iter/s, 5.367s/12 iters), loss = 0.363225
I0407 22:53:14.664057 23673 solver.cpp:237]     Train net output #0: loss = 0.363225 (* 1 = 0.363225 loss)
I0407 22:53:14.664067 23673 sgd_solver.cpp:105] Iteration 6276, lr = 0.00538815
I0407 22:53:20.133460 23673 solver.cpp:218] Iteration 6288 (2.19409 iter/s, 5.46923s/12 iters), loss = 0.342467
I0407 22:53:20.133531 23673 solver.cpp:237]     Train net output #0: loss = 0.342467 (* 1 = 0.342467 loss)
I0407 22:53:20.133540 23673 sgd_solver.cpp:105] Iteration 6288, lr = 0.00538178
I0407 22:53:25.222715 23673 solver.cpp:218] Iteration 6300 (2.35801 iter/s, 5.08903s/12 iters), loss = 0.372968
I0407 22:53:25.222765 23673 solver.cpp:237]     Train net output #0: loss = 0.372968 (* 1 = 0.372968 loss)
I0407 22:53:25.222774 23673 sgd_solver.cpp:105] Iteration 6300, lr = 0.00537543
I0407 22:53:30.259819 23673 solver.cpp:218] Iteration 6312 (2.38242 iter/s, 5.03689s/12 iters), loss = 0.303949
I0407 22:53:30.259861 23673 solver.cpp:237]     Train net output #0: loss = 0.303949 (* 1 = 0.303949 loss)
I0407 22:53:30.259871 23673 sgd_solver.cpp:105] Iteration 6312, lr = 0.00536907
I0407 22:53:34.876739 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 22:53:40.157480 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 22:53:42.693208 23673 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 22:53:42.693238 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:53:44.682263 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:47.171306 23673 solver.cpp:397]     Test net output #0: accuracy = 0.398284
I0407 22:53:47.171356 23673 solver.cpp:397]     Test net output #1: loss = 3.13932 (* 1 = 3.13932 loss)
I0407 22:53:47.262884 23673 solver.cpp:218] Iteration 6324 (0.705778 iter/s, 17.0025s/12 iters), loss = 0.21941
I0407 22:53:47.262939 23673 solver.cpp:237]     Train net output #0: loss = 0.21941 (* 1 = 0.21941 loss)
I0407 22:53:47.262951 23673 sgd_solver.cpp:105] Iteration 6324, lr = 0.00536273
I0407 22:53:51.543536 23673 solver.cpp:218] Iteration 6336 (2.80344 iter/s, 4.28046s/12 iters), loss = 0.422834
I0407 22:53:51.543604 23673 solver.cpp:237]     Train net output #0: loss = 0.422834 (* 1 = 0.422834 loss)
I0407 22:53:51.543614 23673 sgd_solver.cpp:105] Iteration 6336, lr = 0.00535639
I0407 22:53:56.552464 23673 solver.cpp:218] Iteration 6348 (2.39583 iter/s, 5.0087s/12 iters), loss = 0.349196
I0407 22:53:56.552510 23673 solver.cpp:237]     Train net output #0: loss = 0.349196 (* 1 = 0.349196 loss)
I0407 22:53:56.552521 23673 sgd_solver.cpp:105] Iteration 6348, lr = 0.00535006
I0407 22:54:01.645823 23673 solver.cpp:218] Iteration 6360 (2.3561 iter/s, 5.09316s/12 iters), loss = 0.278238
I0407 22:54:01.645865 23673 solver.cpp:237]     Train net output #0: loss = 0.278238 (* 1 = 0.278238 loss)
I0407 22:54:01.645874 23673 sgd_solver.cpp:105] Iteration 6360, lr = 0.00534374
I0407 22:54:06.646937 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:06.802018 23673 solver.cpp:218] Iteration 6372 (2.32739 iter/s, 5.15599s/12 iters), loss = 0.375996
I0407 22:54:06.802058 23673 solver.cpp:237]     Train net output #0: loss = 0.375996 (* 1 = 0.375996 loss)
I0407 22:54:06.802067 23673 sgd_solver.cpp:105] Iteration 6372, lr = 0.00533743
I0407 22:54:12.464076 23673 solver.cpp:218] Iteration 6384 (2.11945 iter/s, 5.66184s/12 iters), loss = 0.434877
I0407 22:54:12.464128 23673 solver.cpp:237]     Train net output #0: loss = 0.434877 (* 1 = 0.434877 loss)
I0407 22:54:12.464140 23673 sgd_solver.cpp:105] Iteration 6384, lr = 0.00533112
I0407 22:54:17.794364 23673 solver.cpp:218] Iteration 6396 (2.25138 iter/s, 5.33007s/12 iters), loss = 0.254869
I0407 22:54:17.794411 23673 solver.cpp:237]     Train net output #0: loss = 0.254869 (* 1 = 0.254869 loss)
I0407 22:54:17.794427 23673 sgd_solver.cpp:105] Iteration 6396, lr = 0.00532482
I0407 22:54:22.861769 23673 solver.cpp:218] Iteration 6408 (2.36817 iter/s, 5.0672s/12 iters), loss = 0.332238
I0407 22:54:22.861865 23673 solver.cpp:237]     Train net output #0: loss = 0.332238 (* 1 = 0.332238 loss)
I0407 22:54:22.861876 23673 sgd_solver.cpp:105] Iteration 6408, lr = 0.00531853
I0407 22:54:28.248448 23673 solver.cpp:218] Iteration 6420 (2.22783 iter/s, 5.38642s/12 iters), loss = 0.368036
I0407 22:54:28.248493 23673 solver.cpp:237]     Train net output #0: loss = 0.368036 (* 1 = 0.368036 loss)
I0407 22:54:28.248507 23673 sgd_solver.cpp:105] Iteration 6420, lr = 0.00531224
I0407 22:54:30.513255 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 22:54:34.247182 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 22:54:36.574715 23673 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 22:54:36.574743 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:54:38.528038 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:41.114014 23673 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0407 22:54:41.114060 23673 solver.cpp:397]     Test net output #1: loss = 3.03721 (* 1 = 3.03721 loss)
I0407 22:54:42.926709 23673 solver.cpp:218] Iteration 6432 (0.817562 iter/s, 14.6778s/12 iters), loss = 0.306451
I0407 22:54:42.926755 23673 solver.cpp:237]     Train net output #0: loss = 0.306451 (* 1 = 0.306451 loss)
I0407 22:54:42.926767 23673 sgd_solver.cpp:105] Iteration 6432, lr = 0.00530596
I0407 22:54:47.937263 23673 solver.cpp:218] Iteration 6444 (2.39504 iter/s, 5.01034s/12 iters), loss = 0.311133
I0407 22:54:47.937325 23673 solver.cpp:237]     Train net output #0: loss = 0.311133 (* 1 = 0.311133 loss)
I0407 22:54:47.937337 23673 sgd_solver.cpp:105] Iteration 6444, lr = 0.00529969
I0407 22:54:53.125285 23673 solver.cpp:218] Iteration 6456 (2.31312 iter/s, 5.18779s/12 iters), loss = 0.310253
I0407 22:54:53.125377 23673 solver.cpp:237]     Train net output #0: loss = 0.310253 (* 1 = 0.310253 loss)
I0407 22:54:53.125391 23673 sgd_solver.cpp:105] Iteration 6456, lr = 0.00529343
I0407 22:54:58.281380 23673 solver.cpp:218] Iteration 6468 (2.32746 iter/s, 5.15584s/12 iters), loss = 0.467715
I0407 22:54:58.281440 23673 solver.cpp:237]     Train net output #0: loss = 0.467715 (* 1 = 0.467715 loss)
I0407 22:54:58.281451 23673 sgd_solver.cpp:105] Iteration 6468, lr = 0.00528718
I0407 22:55:00.266207 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:03.425925 23673 solver.cpp:218] Iteration 6480 (2.33267 iter/s, 5.14432s/12 iters), loss = 0.311095
I0407 22:55:03.425992 23673 solver.cpp:237]     Train net output #0: loss = 0.311095 (* 1 = 0.311095 loss)
I0407 22:55:03.426002 23673 sgd_solver.cpp:105] Iteration 6480, lr = 0.00528093
I0407 22:55:08.698861 23673 solver.cpp:218] Iteration 6492 (2.27587 iter/s, 5.2727s/12 iters), loss = 0.332551
I0407 22:55:08.698917 23673 solver.cpp:237]     Train net output #0: loss = 0.332551 (* 1 = 0.332551 loss)
I0407 22:55:08.698928 23673 sgd_solver.cpp:105] Iteration 6492, lr = 0.00527469
I0407 22:55:14.211683 23673 solver.cpp:218] Iteration 6504 (2.17683 iter/s, 5.5126s/12 iters), loss = 0.339227
I0407 22:55:14.211733 23673 solver.cpp:237]     Train net output #0: loss = 0.339227 (* 1 = 0.339227 loss)
I0407 22:55:14.211745 23673 sgd_solver.cpp:105] Iteration 6504, lr = 0.00526846
I0407 22:55:19.749534 23673 solver.cpp:218] Iteration 6516 (2.16699 iter/s, 5.53763s/12 iters), loss = 0.20099
I0407 22:55:19.749580 23673 solver.cpp:237]     Train net output #0: loss = 0.20099 (* 1 = 0.20099 loss)
I0407 22:55:19.749591 23673 sgd_solver.cpp:105] Iteration 6516, lr = 0.00526223
I0407 22:55:24.666749 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 22:55:27.695643 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 22:55:30.008271 23673 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 22:55:30.008296 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:55:31.909055 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:34.492071 23673 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0407 22:55:34.492116 23673 solver.cpp:397]     Test net output #1: loss = 3.05562 (* 1 = 3.05562 loss)
I0407 22:55:34.583665 23673 solver.cpp:218] Iteration 6528 (0.808972 iter/s, 14.8336s/12 iters), loss = 0.456931
I0407 22:55:34.583721 23673 solver.cpp:237]     Train net output #0: loss = 0.456931 (* 1 = 0.456931 loss)
I0407 22:55:34.583734 23673 sgd_solver.cpp:105] Iteration 6528, lr = 0.00525601
I0407 22:55:38.998121 23673 solver.cpp:218] Iteration 6540 (2.71847 iter/s, 4.41425s/12 iters), loss = 0.261815
I0407 22:55:38.998175 23673 solver.cpp:237]     Train net output #0: loss = 0.261815 (* 1 = 0.261815 loss)
I0407 22:55:38.998188 23673 sgd_solver.cpp:105] Iteration 6540, lr = 0.0052498
I0407 22:55:44.155174 23673 solver.cpp:218] Iteration 6552 (2.32701 iter/s, 5.15682s/12 iters), loss = 0.204952
I0407 22:55:44.155225 23673 solver.cpp:237]     Train net output #0: loss = 0.204951 (* 1 = 0.204951 loss)
I0407 22:55:44.155236 23673 sgd_solver.cpp:105] Iteration 6552, lr = 0.0052436
I0407 22:55:49.288233 23673 solver.cpp:218] Iteration 6564 (2.33788 iter/s, 5.13285s/12 iters), loss = 0.278702
I0407 22:55:49.288292 23673 solver.cpp:237]     Train net output #0: loss = 0.278702 (* 1 = 0.278702 loss)
I0407 22:55:49.288309 23673 sgd_solver.cpp:105] Iteration 6564, lr = 0.0052374
I0407 22:55:53.640599 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:54.408989 23673 solver.cpp:218] Iteration 6576 (2.3435 iter/s, 5.12054s/12 iters), loss = 0.390311
I0407 22:55:54.409037 23673 solver.cpp:237]     Train net output #0: loss = 0.390311 (* 1 = 0.390311 loss)
I0407 22:55:54.409050 23673 sgd_solver.cpp:105] Iteration 6576, lr = 0.00523121
I0407 22:55:59.481379 23673 solver.cpp:218] Iteration 6588 (2.36584 iter/s, 5.07218s/12 iters), loss = 0.214206
I0407 22:55:59.481464 23673 solver.cpp:237]     Train net output #0: loss = 0.214206 (* 1 = 0.214206 loss)
I0407 22:55:59.481477 23673 sgd_solver.cpp:105] Iteration 6588, lr = 0.00522503
I0407 22:56:04.541595 23673 solver.cpp:218] Iteration 6600 (2.37155 iter/s, 5.05997s/12 iters), loss = 0.341912
I0407 22:56:04.541648 23673 solver.cpp:237]     Train net output #0: loss = 0.341912 (* 1 = 0.341912 loss)
I0407 22:56:04.541661 23673 sgd_solver.cpp:105] Iteration 6600, lr = 0.00521886
I0407 22:56:09.543640 23673 solver.cpp:218] Iteration 6612 (2.39912 iter/s, 5.00184s/12 iters), loss = 0.360174
I0407 22:56:09.543692 23673 solver.cpp:237]     Train net output #0: loss = 0.360174 (* 1 = 0.360174 loss)
I0407 22:56:09.543705 23673 sgd_solver.cpp:105] Iteration 6612, lr = 0.00521269
I0407 22:56:14.662449 23673 solver.cpp:218] Iteration 6624 (2.34439 iter/s, 5.1186s/12 iters), loss = 0.256395
I0407 22:56:14.662503 23673 solver.cpp:237]     Train net output #0: loss = 0.256395 (* 1 = 0.256395 loss)
I0407 22:56:14.662515 23673 sgd_solver.cpp:105] Iteration 6624, lr = 0.00520653
I0407 22:56:16.743230 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 22:56:19.854549 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 22:56:25.151602 23673 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 22:56:25.151628 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:56:26.985170 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:29.589735 23673 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0407 22:56:29.589870 23673 solver.cpp:397]     Test net output #1: loss = 3.03397 (* 1 = 3.03397 loss)
I0407 22:56:31.395449 23673 solver.cpp:218] Iteration 6636 (0.717169 iter/s, 16.7324s/12 iters), loss = 0.205399
I0407 22:56:31.395509 23673 solver.cpp:237]     Train net output #0: loss = 0.205399 (* 1 = 0.205399 loss)
I0407 22:56:31.395521 23673 sgd_solver.cpp:105] Iteration 6636, lr = 0.00520038
I0407 22:56:36.368191 23673 solver.cpp:218] Iteration 6648 (2.41326 iter/s, 4.97253s/12 iters), loss = 0.351782
I0407 22:56:36.368237 23673 solver.cpp:237]     Train net output #0: loss = 0.351782 (* 1 = 0.351782 loss)
I0407 22:56:36.368248 23673 sgd_solver.cpp:105] Iteration 6648, lr = 0.00519423
I0407 22:56:41.316531 23673 solver.cpp:218] Iteration 6660 (2.42516 iter/s, 4.94813s/12 iters), loss = 0.297979
I0407 22:56:41.316597 23673 solver.cpp:237]     Train net output #0: loss = 0.297979 (* 1 = 0.297979 loss)
I0407 22:56:41.316610 23673 sgd_solver.cpp:105] Iteration 6660, lr = 0.00518809
I0407 22:56:46.353449 23673 solver.cpp:218] Iteration 6672 (2.38252 iter/s, 5.03669s/12 iters), loss = 0.306085
I0407 22:56:46.353514 23673 solver.cpp:237]     Train net output #0: loss = 0.306085 (* 1 = 0.306085 loss)
I0407 22:56:46.353526 23673 sgd_solver.cpp:105] Iteration 6672, lr = 0.00518196
I0407 22:56:47.698179 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:51.335685 23673 solver.cpp:218] Iteration 6684 (2.40867 iter/s, 4.98201s/12 iters), loss = 0.424573
I0407 22:56:51.335738 23673 solver.cpp:237]     Train net output #0: loss = 0.424573 (* 1 = 0.424573 loss)
I0407 22:56:51.335752 23673 sgd_solver.cpp:105] Iteration 6684, lr = 0.00517584
I0407 22:56:56.335480 23673 solver.cpp:218] Iteration 6696 (2.4002 iter/s, 4.99958s/12 iters), loss = 0.207807
I0407 22:56:56.335541 23673 solver.cpp:237]     Train net output #0: loss = 0.207807 (* 1 = 0.207807 loss)
I0407 22:56:56.335553 23673 sgd_solver.cpp:105] Iteration 6696, lr = 0.00516972
I0407 22:57:01.357137 23673 solver.cpp:218] Iteration 6708 (2.38975 iter/s, 5.02144s/12 iters), loss = 0.214655
I0407 22:57:01.357255 23673 solver.cpp:237]     Train net output #0: loss = 0.214655 (* 1 = 0.214655 loss)
I0407 22:57:01.357268 23673 sgd_solver.cpp:105] Iteration 6708, lr = 0.00516362
I0407 22:57:06.297510 23673 solver.cpp:218] Iteration 6720 (2.4291 iter/s, 4.9401s/12 iters), loss = 0.379533
I0407 22:57:06.297560 23673 solver.cpp:237]     Train net output #0: loss = 0.379533 (* 1 = 0.379533 loss)
I0407 22:57:06.297572 23673 sgd_solver.cpp:105] Iteration 6720, lr = 0.00515751
I0407 22:57:10.842613 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 22:57:13.946355 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 22:57:16.318012 23673 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 22:57:16.318037 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:57:18.141433 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:20.786736 23673 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0407 22:57:20.786777 23673 solver.cpp:397]     Test net output #1: loss = 3.13409 (* 1 = 3.13409 loss)
I0407 22:57:20.878087 23673 solver.cpp:218] Iteration 6732 (0.823041 iter/s, 14.5801s/12 iters), loss = 0.400757
I0407 22:57:20.878141 23673 solver.cpp:237]     Train net output #0: loss = 0.400757 (* 1 = 0.400757 loss)
I0407 22:57:20.878151 23673 sgd_solver.cpp:105] Iteration 6732, lr = 0.00515142
I0407 22:57:25.256793 23673 solver.cpp:218] Iteration 6744 (2.74066 iter/s, 4.37851s/12 iters), loss = 0.257316
I0407 22:57:25.256842 23673 solver.cpp:237]     Train net output #0: loss = 0.257316 (* 1 = 0.257316 loss)
I0407 22:57:25.256852 23673 sgd_solver.cpp:105] Iteration 6744, lr = 0.00514533
I0407 22:57:30.398581 23673 solver.cpp:218] Iteration 6756 (2.33392 iter/s, 5.14157s/12 iters), loss = 0.210393
I0407 22:57:30.398636 23673 solver.cpp:237]     Train net output #0: loss = 0.210393 (* 1 = 0.210393 loss)
I0407 22:57:30.398650 23673 sgd_solver.cpp:105] Iteration 6756, lr = 0.00513925
I0407 22:57:35.545240 23673 solver.cpp:218] Iteration 6768 (2.33171 iter/s, 5.14644s/12 iters), loss = 0.269796
I0407 22:57:35.545346 23673 solver.cpp:237]     Train net output #0: loss = 0.269796 (* 1 = 0.269796 loss)
I0407 22:57:35.545357 23673 sgd_solver.cpp:105] Iteration 6768, lr = 0.00513318
I0407 22:57:39.254456 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:40.749083 23673 solver.cpp:218] Iteration 6780 (2.30611 iter/s, 5.20357s/12 iters), loss = 0.240236
I0407 22:57:40.749140 23673 solver.cpp:237]     Train net output #0: loss = 0.240235 (* 1 = 0.240235 loss)
I0407 22:57:40.749151 23673 sgd_solver.cpp:105] Iteration 6780, lr = 0.00512711
I0407 22:57:45.955169 23673 solver.cpp:218] Iteration 6792 (2.30509 iter/s, 5.20587s/12 iters), loss = 0.284097
I0407 22:57:45.955217 23673 solver.cpp:237]     Train net output #0: loss = 0.284097 (* 1 = 0.284097 loss)
I0407 22:57:45.955229 23673 sgd_solver.cpp:105] Iteration 6792, lr = 0.00512105
I0407 22:57:50.924618 23673 solver.cpp:218] Iteration 6804 (2.41486 iter/s, 4.96924s/12 iters), loss = 0.221761
I0407 22:57:50.924672 23673 solver.cpp:237]     Train net output #0: loss = 0.221761 (* 1 = 0.221761 loss)
I0407 22:57:50.924685 23673 sgd_solver.cpp:105] Iteration 6804, lr = 0.005115
I0407 22:57:55.977864 23673 solver.cpp:218] Iteration 6816 (2.37481 iter/s, 5.05303s/12 iters), loss = 0.100522
I0407 22:57:55.977922 23673 solver.cpp:237]     Train net output #0: loss = 0.100522 (* 1 = 0.100522 loss)
I0407 22:57:55.977934 23673 sgd_solver.cpp:105] Iteration 6816, lr = 0.00510896
I0407 22:58:01.287778 23673 solver.cpp:218] Iteration 6828 (2.26002 iter/s, 5.30969s/12 iters), loss = 0.242382
I0407 22:58:01.287834 23673 solver.cpp:237]     Train net output #0: loss = 0.242382 (* 1 = 0.242382 loss)
I0407 22:58:01.287847 23673 sgd_solver.cpp:105] Iteration 6828, lr = 0.00510292
I0407 22:58:03.367710 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 22:58:07.799401 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 22:58:12.285506 23673 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 22:58:12.285533 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:58:14.207283 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:17.053033 23673 solver.cpp:397]     Test net output #0: accuracy = 0.408701
I0407 22:58:17.053083 23673 solver.cpp:397]     Test net output #1: loss = 3.11441 (* 1 = 3.11441 loss)
I0407 22:58:19.406304 23673 solver.cpp:218] Iteration 6840 (0.662328 iter/s, 18.1179s/12 iters), loss = 0.407451
I0407 22:58:19.406369 23673 solver.cpp:237]     Train net output #0: loss = 0.407451 (* 1 = 0.407451 loss)
I0407 22:58:19.406383 23673 sgd_solver.cpp:105] Iteration 6840, lr = 0.00509689
I0407 22:58:24.515427 23673 solver.cpp:218] Iteration 6852 (2.34884 iter/s, 5.1089s/12 iters), loss = 0.294337
I0407 22:58:24.515476 23673 solver.cpp:237]     Train net output #0: loss = 0.294337 (* 1 = 0.294337 loss)
I0407 22:58:24.515489 23673 sgd_solver.cpp:105] Iteration 6852, lr = 0.00509087
I0407 22:58:30.006368 23673 solver.cpp:218] Iteration 6864 (2.18551 iter/s, 5.49072s/12 iters), loss = 0.102293
I0407 22:58:30.006412 23673 solver.cpp:237]     Train net output #0: loss = 0.102293 (* 1 = 0.102293 loss)
I0407 22:58:30.006422 23673 sgd_solver.cpp:105] Iteration 6864, lr = 0.00508485
I0407 22:58:35.090629 23673 solver.cpp:218] Iteration 6876 (2.36032 iter/s, 5.08405s/12 iters), loss = 0.145511
I0407 22:58:35.090683 23673 solver.cpp:237]     Train net output #0: loss = 0.145511 (* 1 = 0.145511 loss)
I0407 22:58:35.090695 23673 sgd_solver.cpp:105] Iteration 6876, lr = 0.00507884
I0407 22:58:35.701318 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:40.132710 23673 solver.cpp:218] Iteration 6888 (2.38007 iter/s, 5.04187s/12 iters), loss = 0.464449
I0407 22:58:40.132845 23673 solver.cpp:237]     Train net output #0: loss = 0.464449 (* 1 = 0.464449 loss)
I0407 22:58:40.132855 23673 sgd_solver.cpp:105] Iteration 6888, lr = 0.00507284
I0407 22:58:45.308996 23673 solver.cpp:218] Iteration 6900 (2.3184 iter/s, 5.17599s/12 iters), loss = 0.276537
I0407 22:58:45.309047 23673 solver.cpp:237]     Train net output #0: loss = 0.276537 (* 1 = 0.276537 loss)
I0407 22:58:45.309058 23673 sgd_solver.cpp:105] Iteration 6900, lr = 0.00506685
I0407 22:58:50.502319 23673 solver.cpp:218] Iteration 6912 (2.31075 iter/s, 5.19311s/12 iters), loss = 0.200132
I0407 22:58:50.502367 23673 solver.cpp:237]     Train net output #0: loss = 0.200132 (* 1 = 0.200132 loss)
I0407 22:58:50.502378 23673 sgd_solver.cpp:105] Iteration 6912, lr = 0.00506086
I0407 22:58:55.641970 23673 solver.cpp:218] Iteration 6924 (2.33489 iter/s, 5.13943s/12 iters), loss = 0.213472
I0407 22:58:55.642015 23673 solver.cpp:237]     Train net output #0: loss = 0.213472 (* 1 = 0.213472 loss)
I0407 22:58:55.642024 23673 sgd_solver.cpp:105] Iteration 6924, lr = 0.00505488
I0407 22:59:00.323814 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 22:59:05.492579 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 22:59:09.372309 23673 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 22:59:09.372336 23673 net.cpp:676] Ignoring source layer train-data
I0407 22:59:10.034965 23673 blocking_queue.cpp:49] Waiting for data
I0407 22:59:11.112108 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:13.910195 23673 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0407 22:59:13.910248 23673 solver.cpp:397]     Test net output #1: loss = 3.18117 (* 1 = 3.18117 loss)
I0407 22:59:14.001979 23673 solver.cpp:218] Iteration 6936 (0.653616 iter/s, 18.3594s/12 iters), loss = 0.262531
I0407 22:59:14.002043 23673 solver.cpp:237]     Train net output #0: loss = 0.262531 (* 1 = 0.262531 loss)
I0407 22:59:14.002055 23673 sgd_solver.cpp:105] Iteration 6936, lr = 0.00504891
I0407 22:59:18.482507 23673 solver.cpp:218] Iteration 6948 (2.67838 iter/s, 4.48032s/12 iters), loss = 0.238523
I0407 22:59:18.482565 23673 solver.cpp:237]     Train net output #0: loss = 0.238523 (* 1 = 0.238523 loss)
I0407 22:59:18.482576 23673 sgd_solver.cpp:105] Iteration 6948, lr = 0.00504294
I0407 22:59:23.712867 23673 solver.cpp:218] Iteration 6960 (2.29439 iter/s, 5.23014s/12 iters), loss = 0.330089
I0407 22:59:23.712913 23673 solver.cpp:237]     Train net output #0: loss = 0.330089 (* 1 = 0.330089 loss)
I0407 22:59:23.712924 23673 sgd_solver.cpp:105] Iteration 6960, lr = 0.00503698
I0407 22:59:29.177151 23673 solver.cpp:218] Iteration 6972 (2.19617 iter/s, 5.46406s/12 iters), loss = 0.311399
I0407 22:59:29.177204 23673 solver.cpp:237]     Train net output #0: loss = 0.311399 (* 1 = 0.311399 loss)
I0407 22:59:29.177217 23673 sgd_solver.cpp:105] Iteration 6972, lr = 0.00503103
I0407 22:59:31.977986 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:34.391299 23673 solver.cpp:218] Iteration 6984 (2.30153 iter/s, 5.21393s/12 iters), loss = 0.370624
I0407 22:59:34.391350 23673 solver.cpp:237]     Train net output #0: loss = 0.370624 (* 1 = 0.370624 loss)
I0407 22:59:34.391362 23673 sgd_solver.cpp:105] Iteration 6984, lr = 0.00502508
I0407 22:59:39.465247 23673 solver.cpp:218] Iteration 6996 (2.36512 iter/s, 5.07373s/12 iters), loss = 0.179895
I0407 22:59:39.465304 23673 solver.cpp:237]     Train net output #0: loss = 0.179895 (* 1 = 0.179895 loss)
I0407 22:59:39.465317 23673 sgd_solver.cpp:105] Iteration 6996, lr = 0.00501915
I0407 22:59:44.532778 23673 solver.cpp:218] Iteration 7008 (2.36812 iter/s, 5.06731s/12 iters), loss = 0.177861
I0407 22:59:44.535429 23673 solver.cpp:237]     Train net output #0: loss = 0.177861 (* 1 = 0.177861 loss)
I0407 22:59:44.535441 23673 sgd_solver.cpp:105] Iteration 7008, lr = 0.00501322
I0407 22:59:49.827200 23673 solver.cpp:218] Iteration 7020 (2.26774 iter/s, 5.29161s/12 iters), loss = 0.349114
I0407 22:59:49.827253 23673 solver.cpp:237]     Train net output #0: loss = 0.349114 (* 1 = 0.349114 loss)
I0407 22:59:49.827266 23673 sgd_solver.cpp:105] Iteration 7020, lr = 0.00500729
I0407 22:59:55.209786 23673 solver.cpp:218] Iteration 7032 (2.2295 iter/s, 5.38237s/12 iters), loss = 0.229989
I0407 22:59:55.209827 23673 solver.cpp:237]     Train net output #0: loss = 0.229988 (* 1 = 0.229988 loss)
I0407 22:59:55.209836 23673 sgd_solver.cpp:105] Iteration 7032, lr = 0.00500137
I0407 22:59:57.460559 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 23:00:01.311204 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 23:00:04.174201 23673 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 23:00:04.174226 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:00:05.994887 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:08.983916 23673 solver.cpp:397]     Test net output #0: accuracy = 0.410539
I0407 23:00:08.983971 23673 solver.cpp:397]     Test net output #1: loss = 3.18401 (* 1 = 3.18401 loss)
I0407 23:00:10.734263 23673 solver.cpp:218] Iteration 7044 (0.772999 iter/s, 15.524s/12 iters), loss = 0.264682
I0407 23:00:10.734311 23673 solver.cpp:237]     Train net output #0: loss = 0.264682 (* 1 = 0.264682 loss)
I0407 23:00:10.734323 23673 sgd_solver.cpp:105] Iteration 7044, lr = 0.00499546
I0407 23:00:16.098942 23673 solver.cpp:218] Iteration 7056 (2.23695 iter/s, 5.36446s/12 iters), loss = 0.448337
I0407 23:00:16.099030 23673 solver.cpp:237]     Train net output #0: loss = 0.448337 (* 1 = 0.448337 loss)
I0407 23:00:16.099042 23673 sgd_solver.cpp:105] Iteration 7056, lr = 0.00498956
I0407 23:00:21.561209 23673 solver.cpp:218] Iteration 7068 (2.197 iter/s, 5.462s/12 iters), loss = 0.316392
I0407 23:00:21.561260 23673 solver.cpp:237]     Train net output #0: loss = 0.316392 (* 1 = 0.316392 loss)
I0407 23:00:21.561272 23673 sgd_solver.cpp:105] Iteration 7068, lr = 0.00498367
I0407 23:00:26.771216 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:26.889380 23673 solver.cpp:218] Iteration 7080 (2.25227 iter/s, 5.32795s/12 iters), loss = 0.24302
I0407 23:00:26.889432 23673 solver.cpp:237]     Train net output #0: loss = 0.24302 (* 1 = 0.24302 loss)
I0407 23:00:26.889444 23673 sgd_solver.cpp:105] Iteration 7080, lr = 0.00497778
I0407 23:00:32.428299 23673 solver.cpp:218] Iteration 7092 (2.16658 iter/s, 5.53869s/12 iters), loss = 0.0772631
I0407 23:00:32.428344 23673 solver.cpp:237]     Train net output #0: loss = 0.077263 (* 1 = 0.077263 loss)
I0407 23:00:32.428352 23673 sgd_solver.cpp:105] Iteration 7092, lr = 0.00497189
I0407 23:00:37.655598 23673 solver.cpp:218] Iteration 7104 (2.29573 iter/s, 5.22709s/12 iters), loss = 0.108408
I0407 23:00:37.655647 23673 solver.cpp:237]     Train net output #0: loss = 0.108408 (* 1 = 0.108408 loss)
I0407 23:00:37.655658 23673 sgd_solver.cpp:105] Iteration 7104, lr = 0.00496602
I0407 23:00:42.985006 23673 solver.cpp:218] Iteration 7116 (2.25175 iter/s, 5.32918s/12 iters), loss = 0.2041
I0407 23:00:42.985060 23673 solver.cpp:237]     Train net output #0: loss = 0.2041 (* 1 = 0.2041 loss)
I0407 23:00:42.985072 23673 sgd_solver.cpp:105] Iteration 7116, lr = 0.00496015
I0407 23:00:48.386798 23673 solver.cpp:218] Iteration 7128 (2.22158 iter/s, 5.40156s/12 iters), loss = 0.250186
I0407 23:00:48.387265 23673 solver.cpp:237]     Train net output #0: loss = 0.250186 (* 1 = 0.250186 loss)
I0407 23:00:48.387284 23673 sgd_solver.cpp:105] Iteration 7128, lr = 0.00495429
I0407 23:00:52.992646 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 23:00:56.137476 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 23:00:59.904292 23673 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 23:00:59.904320 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:01:01.596717 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:04.486928 23673 solver.cpp:397]     Test net output #0: accuracy = 0.414216
I0407 23:01:04.486972 23673 solver.cpp:397]     Test net output #1: loss = 3.07464 (* 1 = 3.07464 loss)
I0407 23:01:04.576707 23673 solver.cpp:218] Iteration 7140 (0.741246 iter/s, 16.189s/12 iters), loss = 0.201107
I0407 23:01:04.576752 23673 solver.cpp:237]     Train net output #0: loss = 0.201107 (* 1 = 0.201107 loss)
I0407 23:01:04.576766 23673 sgd_solver.cpp:105] Iteration 7140, lr = 0.00494843
I0407 23:01:09.077392 23673 solver.cpp:218] Iteration 7152 (2.66638 iter/s, 4.50049s/12 iters), loss = 0.419005
I0407 23:01:09.077440 23673 solver.cpp:237]     Train net output #0: loss = 0.419005 (* 1 = 0.419005 loss)
I0407 23:01:09.077448 23673 sgd_solver.cpp:105] Iteration 7152, lr = 0.00494259
I0407 23:01:14.150385 23673 solver.cpp:218] Iteration 7164 (2.36557 iter/s, 5.07278s/12 iters), loss = 0.261352
I0407 23:01:14.150445 23673 solver.cpp:237]     Train net output #0: loss = 0.261352 (* 1 = 0.261352 loss)
I0407 23:01:14.150458 23673 sgd_solver.cpp:105] Iteration 7164, lr = 0.00493675
I0407 23:01:19.331627 23673 solver.cpp:218] Iteration 7176 (2.31615 iter/s, 5.18102s/12 iters), loss = 0.160109
I0407 23:01:19.331732 23673 solver.cpp:237]     Train net output #0: loss = 0.160109 (* 1 = 0.160109 loss)
I0407 23:01:19.331743 23673 sgd_solver.cpp:105] Iteration 7176, lr = 0.00493091
I0407 23:01:21.509323 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:24.452327 23673 solver.cpp:218] Iteration 7188 (2.34355 iter/s, 5.12043s/12 iters), loss = 0.239023
I0407 23:01:24.452376 23673 solver.cpp:237]     Train net output #0: loss = 0.239023 (* 1 = 0.239023 loss)
I0407 23:01:24.452389 23673 sgd_solver.cpp:105] Iteration 7188, lr = 0.00492509
I0407 23:01:29.490753 23673 solver.cpp:218] Iteration 7200 (2.3818 iter/s, 5.03821s/12 iters), loss = 0.162993
I0407 23:01:29.490806 23673 solver.cpp:237]     Train net output #0: loss = 0.162993 (* 1 = 0.162993 loss)
I0407 23:01:29.490818 23673 sgd_solver.cpp:105] Iteration 7200, lr = 0.00491927
I0407 23:01:34.791757 23673 solver.cpp:218] Iteration 7212 (2.26382 iter/s, 5.30078s/12 iters), loss = 0.0764142
I0407 23:01:34.791808 23673 solver.cpp:237]     Train net output #0: loss = 0.0764141 (* 1 = 0.0764141 loss)
I0407 23:01:34.791821 23673 sgd_solver.cpp:105] Iteration 7212, lr = 0.00491345
I0407 23:01:39.692943 23673 solver.cpp:218] Iteration 7224 (2.4485 iter/s, 4.90097s/12 iters), loss = 0.222525
I0407 23:01:39.692993 23673 solver.cpp:237]     Train net output #0: loss = 0.222525 (* 1 = 0.222525 loss)
I0407 23:01:39.693004 23673 sgd_solver.cpp:105] Iteration 7224, lr = 0.00490765
I0407 23:01:44.723965 23673 solver.cpp:218] Iteration 7236 (2.3853 iter/s, 5.03081s/12 iters), loss = 0.223598
I0407 23:01:44.724018 23673 solver.cpp:237]     Train net output #0: loss = 0.223598 (* 1 = 0.223598 loss)
I0407 23:01:44.724030 23673 sgd_solver.cpp:105] Iteration 7236, lr = 0.00490185
I0407 23:01:46.812469 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 23:01:51.031378 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 23:01:54.651818 23673 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 23:01:54.651840 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:01:56.237746 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:59.083881 23673 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0407 23:01:59.083930 23673 solver.cpp:397]     Test net output #1: loss = 3.02706 (* 1 = 3.02706 loss)
I0407 23:02:01.101789 23673 solver.cpp:218] Iteration 7248 (0.732723 iter/s, 16.3773s/12 iters), loss = 0.199614
I0407 23:02:01.101837 23673 solver.cpp:237]     Train net output #0: loss = 0.199613 (* 1 = 0.199613 loss)
I0407 23:02:01.101848 23673 sgd_solver.cpp:105] Iteration 7248, lr = 0.00489606
I0407 23:02:06.292949 23673 solver.cpp:218] Iteration 7260 (2.31172 iter/s, 5.19094s/12 iters), loss = 0.113134
I0407 23:02:06.293005 23673 solver.cpp:237]     Train net output #0: loss = 0.113134 (* 1 = 0.113134 loss)
I0407 23:02:06.293018 23673 sgd_solver.cpp:105] Iteration 7260, lr = 0.00489027
I0407 23:02:11.257081 23673 solver.cpp:218] Iteration 7272 (2.41745 iter/s, 4.96391s/12 iters), loss = 0.19668
I0407 23:02:11.257136 23673 solver.cpp:237]     Train net output #0: loss = 0.19668 (* 1 = 0.19668 loss)
I0407 23:02:11.257149 23673 sgd_solver.cpp:105] Iteration 7272, lr = 0.00488449
I0407 23:02:15.619187 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:16.372440 23673 solver.cpp:218] Iteration 7284 (2.34598 iter/s, 5.11514s/12 iters), loss = 0.317686
I0407 23:02:16.372491 23673 solver.cpp:237]     Train net output #0: loss = 0.317686 (* 1 = 0.317686 loss)
I0407 23:02:16.372503 23673 sgd_solver.cpp:105] Iteration 7284, lr = 0.00487872
I0407 23:02:21.504079 23673 solver.cpp:218] Iteration 7296 (2.33853 iter/s, 5.13142s/12 iters), loss = 0.186202
I0407 23:02:21.504166 23673 solver.cpp:237]     Train net output #0: loss = 0.186202 (* 1 = 0.186202 loss)
I0407 23:02:21.504179 23673 sgd_solver.cpp:105] Iteration 7296, lr = 0.00487295
I0407 23:02:26.536689 23673 solver.cpp:218] Iteration 7308 (2.38457 iter/s, 5.03236s/12 iters), loss = 0.260728
I0407 23:02:26.536744 23673 solver.cpp:237]     Train net output #0: loss = 0.260728 (* 1 = 0.260728 loss)
I0407 23:02:26.536757 23673 sgd_solver.cpp:105] Iteration 7308, lr = 0.0048672
I0407 23:02:33.041671 23673 solver.cpp:218] Iteration 7320 (1.84481 iter/s, 6.50472s/12 iters), loss = 0.220251
I0407 23:02:33.041730 23673 solver.cpp:237]     Train net output #0: loss = 0.220251 (* 1 = 0.220251 loss)
I0407 23:02:33.041743 23673 sgd_solver.cpp:105] Iteration 7320, lr = 0.00486145
I0407 23:02:38.109076 23673 solver.cpp:218] Iteration 7332 (2.36818 iter/s, 5.06718s/12 iters), loss = 0.159954
I0407 23:02:38.109125 23673 solver.cpp:237]     Train net output #0: loss = 0.159954 (* 1 = 0.159954 loss)
I0407 23:02:38.109136 23673 sgd_solver.cpp:105] Iteration 7332, lr = 0.0048557
I0407 23:02:42.739413 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 23:02:47.194885 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 23:02:50.199039 23673 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 23:02:50.199064 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:02:52.284318 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:55.607890 23673 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0407 23:02:55.607940 23673 solver.cpp:397]     Test net output #1: loss = 3.07101 (* 1 = 3.07101 loss)
I0407 23:02:55.699273 23673 solver.cpp:218] Iteration 7344 (0.682221 iter/s, 17.5896s/12 iters), loss = 0.227751
I0407 23:02:55.699326 23673 solver.cpp:237]     Train net output #0: loss = 0.227751 (* 1 = 0.227751 loss)
I0407 23:02:55.699350 23673 sgd_solver.cpp:105] Iteration 7344, lr = 0.00484996
I0407 23:03:00.349258 23673 solver.cpp:218] Iteration 7356 (2.58077 iter/s, 4.64978s/12 iters), loss = 0.212196
I0407 23:03:00.349308 23673 solver.cpp:237]     Train net output #0: loss = 0.212196 (* 1 = 0.212196 loss)
I0407 23:03:00.349319 23673 sgd_solver.cpp:105] Iteration 7356, lr = 0.00484423
I0407 23:03:05.674940 23673 solver.cpp:218] Iteration 7368 (2.25332 iter/s, 5.32546s/12 iters), loss = 0.252905
I0407 23:03:05.674989 23673 solver.cpp:237]     Train net output #0: loss = 0.252905 (* 1 = 0.252905 loss)
I0407 23:03:05.675000 23673 sgd_solver.cpp:105] Iteration 7368, lr = 0.00483851
I0407 23:03:10.804284 23673 solver.cpp:218] Iteration 7380 (2.33958 iter/s, 5.12913s/12 iters), loss = 0.274873
I0407 23:03:10.804330 23673 solver.cpp:237]     Train net output #0: loss = 0.274873 (* 1 = 0.274873 loss)
I0407 23:03:10.804340 23673 sgd_solver.cpp:105] Iteration 7380, lr = 0.00483279
I0407 23:03:12.215046 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:15.978577 23673 solver.cpp:218] Iteration 7392 (2.31925 iter/s, 5.17408s/12 iters), loss = 0.251772
I0407 23:03:15.978618 23673 solver.cpp:237]     Train net output #0: loss = 0.251772 (* 1 = 0.251772 loss)
I0407 23:03:15.978629 23673 sgd_solver.cpp:105] Iteration 7392, lr = 0.00482708
I0407 23:03:21.071723 23673 solver.cpp:218] Iteration 7404 (2.35621 iter/s, 5.09294s/12 iters), loss = 0.153496
I0407 23:03:21.071777 23673 solver.cpp:237]     Train net output #0: loss = 0.153496 (* 1 = 0.153496 loss)
I0407 23:03:21.071789 23673 sgd_solver.cpp:105] Iteration 7404, lr = 0.00482137
I0407 23:03:26.023914 23673 solver.cpp:218] Iteration 7416 (2.42327 iter/s, 4.95198s/12 iters), loss = 0.137478
I0407 23:03:26.024019 23673 solver.cpp:237]     Train net output #0: loss = 0.137478 (* 1 = 0.137478 loss)
I0407 23:03:26.024029 23673 sgd_solver.cpp:105] Iteration 7416, lr = 0.00481568
I0407 23:03:31.126225 23673 solver.cpp:218] Iteration 7428 (2.352 iter/s, 5.10204s/12 iters), loss = 0.212868
I0407 23:03:31.126274 23673 solver.cpp:237]     Train net output #0: loss = 0.212868 (* 1 = 0.212868 loss)
I0407 23:03:31.126286 23673 sgd_solver.cpp:105] Iteration 7428, lr = 0.00480999
I0407 23:03:36.351161 23673 solver.cpp:218] Iteration 7440 (2.29678 iter/s, 5.22472s/12 iters), loss = 0.317629
I0407 23:03:36.351208 23673 solver.cpp:237]     Train net output #0: loss = 0.317629 (* 1 = 0.317629 loss)
I0407 23:03:36.351220 23673 sgd_solver.cpp:105] Iteration 7440, lr = 0.0048043
I0407 23:03:38.415690 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 23:03:42.728821 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 23:03:45.046169 23673 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 23:03:45.046195 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:03:46.591100 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:49.512403 23673 solver.cpp:397]     Test net output #0: accuracy = 0.425245
I0407 23:03:49.512454 23673 solver.cpp:397]     Test net output #1: loss = 3.11482 (* 1 = 3.11482 loss)
I0407 23:03:51.531739 23673 solver.cpp:218] Iteration 7452 (0.79051 iter/s, 15.1801s/12 iters), loss = 0.103015
I0407 23:03:51.531790 23673 solver.cpp:237]     Train net output #0: loss = 0.103015 (* 1 = 0.103015 loss)
I0407 23:03:51.531802 23673 sgd_solver.cpp:105] Iteration 7452, lr = 0.00479863
I0407 23:03:56.976142 23673 solver.cpp:218] Iteration 7464 (2.20419 iter/s, 5.44418s/12 iters), loss = 0.164859
I0407 23:03:56.976227 23673 solver.cpp:237]     Train net output #0: loss = 0.164859 (* 1 = 0.164859 loss)
I0407 23:03:56.976239 23673 sgd_solver.cpp:105] Iteration 7464, lr = 0.00479296
I0407 23:04:02.085988 23673 solver.cpp:218] Iteration 7476 (2.34852 iter/s, 5.1096s/12 iters), loss = 0.324079
I0407 23:04:02.086032 23673 solver.cpp:237]     Train net output #0: loss = 0.324079 (* 1 = 0.324079 loss)
I0407 23:04:02.086042 23673 sgd_solver.cpp:105] Iteration 7476, lr = 0.00478729
I0407 23:04:05.698264 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:07.194453 23673 solver.cpp:218] Iteration 7488 (2.34914 iter/s, 5.10826s/12 iters), loss = 0.342168
I0407 23:04:07.194504 23673 solver.cpp:237]     Train net output #0: loss = 0.342167 (* 1 = 0.342167 loss)
I0407 23:04:07.194515 23673 sgd_solver.cpp:105] Iteration 7488, lr = 0.00478163
I0407 23:04:12.569633 23673 solver.cpp:218] Iteration 7500 (2.23258 iter/s, 5.37495s/12 iters), loss = 0.188815
I0407 23:04:12.569692 23673 solver.cpp:237]     Train net output #0: loss = 0.188815 (* 1 = 0.188815 loss)
I0407 23:04:12.569705 23673 sgd_solver.cpp:105] Iteration 7500, lr = 0.00477598
I0407 23:04:17.769351 23673 solver.cpp:218] Iteration 7512 (2.30792 iter/s, 5.19949s/12 iters), loss = 0.341131
I0407 23:04:17.769402 23673 solver.cpp:237]     Train net output #0: loss = 0.341131 (* 1 = 0.341131 loss)
I0407 23:04:17.769415 23673 sgd_solver.cpp:105] Iteration 7512, lr = 0.00477034
I0407 23:04:22.939733 23673 solver.cpp:218] Iteration 7524 (2.32101 iter/s, 5.17016s/12 iters), loss = 0.237506
I0407 23:04:22.939783 23673 solver.cpp:237]     Train net output #0: loss = 0.237506 (* 1 = 0.237506 loss)
I0407 23:04:22.939795 23673 sgd_solver.cpp:105] Iteration 7524, lr = 0.0047647
I0407 23:04:28.218845 23673 solver.cpp:218] Iteration 7536 (2.27321 iter/s, 5.27889s/12 iters), loss = 0.277435
I0407 23:04:28.219017 23673 solver.cpp:237]     Train net output #0: loss = 0.277435 (* 1 = 0.277435 loss)
I0407 23:04:28.219034 23673 sgd_solver.cpp:105] Iteration 7536, lr = 0.00475907
I0407 23:04:32.829273 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 23:04:36.649066 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 23:04:41.005503 23673 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 23:04:41.005532 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:04:42.612310 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:45.574105 23673 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0407 23:04:45.574157 23673 solver.cpp:397]     Test net output #1: loss = 3.16236 (* 1 = 3.16236 loss)
I0407 23:04:45.665477 23673 solver.cpp:218] Iteration 7548 (0.68784 iter/s, 17.4459s/12 iters), loss = 0.202363
I0407 23:04:45.665551 23673 solver.cpp:237]     Train net output #0: loss = 0.202363 (* 1 = 0.202363 loss)
I0407 23:04:45.665570 23673 sgd_solver.cpp:105] Iteration 7548, lr = 0.00475345
I0407 23:04:49.912681 23673 solver.cpp:218] Iteration 7560 (2.82553 iter/s, 4.24699s/12 iters), loss = 0.131414
I0407 23:04:49.912744 23673 solver.cpp:237]     Train net output #0: loss = 0.131414 (* 1 = 0.131414 loss)
I0407 23:04:49.912755 23673 sgd_solver.cpp:105] Iteration 7560, lr = 0.00474783
I0407 23:04:54.868377 23673 solver.cpp:218] Iteration 7572 (2.42157 iter/s, 4.95547s/12 iters), loss = 0.122241
I0407 23:04:54.868436 23673 solver.cpp:237]     Train net output #0: loss = 0.122241 (* 1 = 0.122241 loss)
I0407 23:04:54.868451 23673 sgd_solver.cpp:105] Iteration 7572, lr = 0.00474222
I0407 23:05:00.236580 23673 solver.cpp:218] Iteration 7584 (2.23548 iter/s, 5.36797s/12 iters), loss = 0.360773
I0407 23:05:00.236688 23673 solver.cpp:237]     Train net output #0: loss = 0.360773 (* 1 = 0.360773 loss)
I0407 23:05:00.236701 23673 sgd_solver.cpp:105] Iteration 7584, lr = 0.00473662
I0407 23:05:00.896196 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:05.361918 23673 solver.cpp:218] Iteration 7596 (2.34143 iter/s, 5.12506s/12 iters), loss = 0.200789
I0407 23:05:05.361990 23673 solver.cpp:237]     Train net output #0: loss = 0.200789 (* 1 = 0.200789 loss)
I0407 23:05:05.362000 23673 sgd_solver.cpp:105] Iteration 7596, lr = 0.00473102
I0407 23:05:10.585005 23673 solver.cpp:218] Iteration 7608 (2.2976 iter/s, 5.22285s/12 iters), loss = 0.142475
I0407 23:05:10.585040 23673 solver.cpp:237]     Train net output #0: loss = 0.142475 (* 1 = 0.142475 loss)
I0407 23:05:10.585049 23673 sgd_solver.cpp:105] Iteration 7608, lr = 0.00472543
I0407 23:05:16.072607 23673 solver.cpp:218] Iteration 7620 (2.18683 iter/s, 5.48738s/12 iters), loss = 0.124939
I0407 23:05:16.072662 23673 solver.cpp:237]     Train net output #0: loss = 0.124939 (* 1 = 0.124939 loss)
I0407 23:05:16.072674 23673 sgd_solver.cpp:105] Iteration 7620, lr = 0.00471985
I0407 23:05:19.231326 23673 blocking_queue.cpp:49] Waiting for data
I0407 23:05:21.982802 23673 solver.cpp:218] Iteration 7632 (2.03048 iter/s, 5.90994s/12 iters), loss = 0.139938
I0407 23:05:21.982858 23673 solver.cpp:237]     Train net output #0: loss = 0.139938 (* 1 = 0.139938 loss)
I0407 23:05:21.982870 23673 sgd_solver.cpp:105] Iteration 7632, lr = 0.00471427
I0407 23:05:27.543254 23673 solver.cpp:218] Iteration 7644 (2.15819 iter/s, 5.56022s/12 iters), loss = 0.183379
I0407 23:05:27.543295 23673 solver.cpp:237]     Train net output #0: loss = 0.183379 (* 1 = 0.183379 loss)
I0407 23:05:27.543305 23673 sgd_solver.cpp:105] Iteration 7644, lr = 0.0047087
I0407 23:05:29.785449 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 23:05:36.704139 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 23:05:39.037719 23673 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 23:05:39.037746 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:05:40.520707 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:43.662514 23673 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0407 23:05:43.662565 23673 solver.cpp:397]     Test net output #1: loss = 3.17801 (* 1 = 3.17801 loss)
I0407 23:05:45.665872 23673 solver.cpp:218] Iteration 7656 (0.662178 iter/s, 18.122s/12 iters), loss = 0.318765
I0407 23:05:45.665913 23673 solver.cpp:237]     Train net output #0: loss = 0.318765 (* 1 = 0.318765 loss)
I0407 23:05:45.665923 23673 sgd_solver.cpp:105] Iteration 7656, lr = 0.00470313
I0407 23:05:50.669677 23673 solver.cpp:218] Iteration 7668 (2.39827 iter/s, 5.0036s/12 iters), loss = 0.311606
I0407 23:05:50.669718 23673 solver.cpp:237]     Train net output #0: loss = 0.311606 (* 1 = 0.311606 loss)
I0407 23:05:50.669728 23673 sgd_solver.cpp:105] Iteration 7668, lr = 0.00469758
I0407 23:05:55.732067 23673 solver.cpp:218] Iteration 7680 (2.37052 iter/s, 5.06218s/12 iters), loss = 0.0950024
I0407 23:05:55.732127 23673 solver.cpp:237]     Train net output #0: loss = 0.0950023 (* 1 = 0.0950023 loss)
I0407 23:05:55.732141 23673 sgd_solver.cpp:105] Iteration 7680, lr = 0.00469203
I0407 23:05:58.585382 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:00.830200 23673 solver.cpp:218] Iteration 7692 (2.35391 iter/s, 5.0979s/12 iters), loss = 0.1774
I0407 23:06:00.830257 23673 solver.cpp:237]     Train net output #0: loss = 0.1774 (* 1 = 0.1774 loss)
I0407 23:06:00.830271 23673 sgd_solver.cpp:105] Iteration 7692, lr = 0.00468648
I0407 23:06:05.780391 23673 solver.cpp:218] Iteration 7704 (2.42426 iter/s, 4.94997s/12 iters), loss = 0.298985
I0407 23:06:05.780453 23673 solver.cpp:237]     Train net output #0: loss = 0.298985 (* 1 = 0.298985 loss)
I0407 23:06:05.780467 23673 sgd_solver.cpp:105] Iteration 7704, lr = 0.00468094
I0407 23:06:10.877645 23673 solver.cpp:218] Iteration 7716 (2.35431 iter/s, 5.09703s/12 iters), loss = 0.116858
I0407 23:06:10.877738 23673 solver.cpp:237]     Train net output #0: loss = 0.116858 (* 1 = 0.116858 loss)
I0407 23:06:10.877748 23673 sgd_solver.cpp:105] Iteration 7716, lr = 0.00467541
I0407 23:06:15.985041 23673 solver.cpp:218] Iteration 7728 (2.34965 iter/s, 5.10713s/12 iters), loss = 0.112754
I0407 23:06:15.985088 23673 solver.cpp:237]     Train net output #0: loss = 0.112754 (* 1 = 0.112754 loss)
I0407 23:06:15.985098 23673 sgd_solver.cpp:105] Iteration 7728, lr = 0.00466989
I0407 23:06:21.024899 23673 solver.cpp:218] Iteration 7740 (2.38112 iter/s, 5.03965s/12 iters), loss = 0.231401
I0407 23:06:21.024952 23673 solver.cpp:237]     Train net output #0: loss = 0.231401 (* 1 = 0.231401 loss)
I0407 23:06:21.024964 23673 sgd_solver.cpp:105] Iteration 7740, lr = 0.00466437
I0407 23:06:25.541358 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 23:06:30.272863 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 23:06:32.921861 23673 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 23:06:32.921885 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:06:34.352450 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:37.405658 23673 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0407 23:06:37.405711 23673 solver.cpp:397]     Test net output #1: loss = 3.01813 (* 1 = 3.01813 loss)
I0407 23:06:37.497416 23673 solver.cpp:218] Iteration 7752 (0.728512 iter/s, 16.4719s/12 iters), loss = 0.222668
I0407 23:06:37.497464 23673 solver.cpp:237]     Train net output #0: loss = 0.222668 (* 1 = 0.222668 loss)
I0407 23:06:37.497473 23673 sgd_solver.cpp:105] Iteration 7752, lr = 0.00465886
I0407 23:06:41.808473 23673 solver.cpp:218] Iteration 7764 (2.78366 iter/s, 4.31086s/12 iters), loss = 0.202909
I0407 23:06:41.809195 23673 solver.cpp:237]     Train net output #0: loss = 0.202909 (* 1 = 0.202909 loss)
I0407 23:06:41.809209 23673 sgd_solver.cpp:105] Iteration 7764, lr = 0.00465335
I0407 23:06:46.857939 23673 solver.cpp:218] Iteration 7776 (2.37691 iter/s, 5.04858s/12 iters), loss = 0.147739
I0407 23:06:46.858018 23673 solver.cpp:237]     Train net output #0: loss = 0.147739 (* 1 = 0.147739 loss)
I0407 23:06:46.858031 23673 sgd_solver.cpp:105] Iteration 7776, lr = 0.00464785
I0407 23:06:51.945050 23673 solver.cpp:218] Iteration 7788 (2.35901 iter/s, 5.08688s/12 iters), loss = 0.123449
I0407 23:06:51.945097 23673 solver.cpp:237]     Train net output #0: loss = 0.123448 (* 1 = 0.123448 loss)
I0407 23:06:51.945111 23673 sgd_solver.cpp:105] Iteration 7788, lr = 0.00464236
I0407 23:06:51.956301 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:56.964627 23673 solver.cpp:218] Iteration 7800 (2.39074 iter/s, 5.01937s/12 iters), loss = 0.379349
I0407 23:06:56.964676 23673 solver.cpp:237]     Train net output #0: loss = 0.379349 (* 1 = 0.379349 loss)
I0407 23:06:56.964689 23673 sgd_solver.cpp:105] Iteration 7800, lr = 0.00463688
I0407 23:07:02.384183 23673 solver.cpp:218] Iteration 7812 (2.2143 iter/s, 5.41933s/12 iters), loss = 0.221702
I0407 23:07:02.384233 23673 solver.cpp:237]     Train net output #0: loss = 0.221702 (* 1 = 0.221702 loss)
I0407 23:07:02.384244 23673 sgd_solver.cpp:105] Iteration 7812, lr = 0.0046314
I0407 23:07:07.583107 23673 solver.cpp:218] Iteration 7824 (2.30827 iter/s, 5.19871s/12 iters), loss = 0.225673
I0407 23:07:07.583153 23673 solver.cpp:237]     Train net output #0: loss = 0.225673 (* 1 = 0.225673 loss)
I0407 23:07:07.583163 23673 sgd_solver.cpp:105] Iteration 7824, lr = 0.00462592
I0407 23:07:12.614207 23673 solver.cpp:218] Iteration 7836 (2.38526 iter/s, 5.03089s/12 iters), loss = 0.195456
I0407 23:07:12.614302 23673 solver.cpp:237]     Train net output #0: loss = 0.195456 (* 1 = 0.195456 loss)
I0407 23:07:12.614313 23673 sgd_solver.cpp:105] Iteration 7836, lr = 0.00462046
I0407 23:07:18.029695 23673 solver.cpp:218] Iteration 7848 (2.21598 iter/s, 5.41522s/12 iters), loss = 0.165
I0407 23:07:18.029736 23673 solver.cpp:237]     Train net output #0: loss = 0.164999 (* 1 = 0.164999 loss)
I0407 23:07:18.029745 23673 sgd_solver.cpp:105] Iteration 7848, lr = 0.004615
I0407 23:07:20.280259 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 23:07:23.333112 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 23:07:25.650247 23673 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 23:07:25.650274 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:07:27.035599 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:30.118921 23673 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0407 23:07:30.118971 23673 solver.cpp:397]     Test net output #1: loss = 3.13068 (* 1 = 3.13068 loss)
I0407 23:07:32.096037 23673 solver.cpp:218] Iteration 7860 (0.853129 iter/s, 14.0659s/12 iters), loss = 0.248674
I0407 23:07:32.096096 23673 solver.cpp:237]     Train net output #0: loss = 0.248674 (* 1 = 0.248674 loss)
I0407 23:07:32.096107 23673 sgd_solver.cpp:105] Iteration 7860, lr = 0.00460954
I0407 23:07:37.129400 23673 solver.cpp:218] Iteration 7872 (2.3842 iter/s, 5.03314s/12 iters), loss = 0.298403
I0407 23:07:37.129453 23673 solver.cpp:237]     Train net output #0: loss = 0.298403 (* 1 = 0.298403 loss)
I0407 23:07:37.129465 23673 sgd_solver.cpp:105] Iteration 7872, lr = 0.0046041
I0407 23:07:42.234040 23673 solver.cpp:218] Iteration 7884 (2.3509 iter/s, 5.10442s/12 iters), loss = 0.225998
I0407 23:07:42.234094 23673 solver.cpp:237]     Train net output #0: loss = 0.225998 (* 1 = 0.225998 loss)
I0407 23:07:42.234107 23673 sgd_solver.cpp:105] Iteration 7884, lr = 0.00459866
I0407 23:07:44.446321 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:47.545070 23673 solver.cpp:218] Iteration 7896 (2.25954 iter/s, 5.31081s/12 iters), loss = 0.0888889
I0407 23:07:47.545112 23673 solver.cpp:237]     Train net output #0: loss = 0.0888888 (* 1 = 0.0888888 loss)
I0407 23:07:47.545121 23673 sgd_solver.cpp:105] Iteration 7896, lr = 0.00459322
I0407 23:07:53.081677 23673 solver.cpp:218] Iteration 7908 (2.16748 iter/s, 5.53638s/12 iters), loss = 0.157983
I0407 23:07:53.081723 23673 solver.cpp:237]     Train net output #0: loss = 0.157983 (* 1 = 0.157983 loss)
I0407 23:07:53.081732 23673 sgd_solver.cpp:105] Iteration 7908, lr = 0.00458779
I0407 23:07:58.570644 23673 solver.cpp:218] Iteration 7920 (2.18629 iter/s, 5.48874s/12 iters), loss = 0.185352
I0407 23:07:58.570689 23673 solver.cpp:237]     Train net output #0: loss = 0.185352 (* 1 = 0.185352 loss)
I0407 23:07:58.570699 23673 sgd_solver.cpp:105] Iteration 7920, lr = 0.00458237
I0407 23:08:03.516319 23673 solver.cpp:218] Iteration 7932 (2.42646 iter/s, 4.94547s/12 iters), loss = 0.26738
I0407 23:08:03.516368 23673 solver.cpp:237]     Train net output #0: loss = 0.26738 (* 1 = 0.26738 loss)
I0407 23:08:03.516381 23673 sgd_solver.cpp:105] Iteration 7932, lr = 0.00457696
I0407 23:08:08.681298 23673 solver.cpp:218] Iteration 7944 (2.32344 iter/s, 5.16476s/12 iters), loss = 0.129747
I0407 23:08:08.681347 23673 solver.cpp:237]     Train net output #0: loss = 0.129747 (* 1 = 0.129747 loss)
I0407 23:08:08.681360 23673 sgd_solver.cpp:105] Iteration 7944, lr = 0.00457155
I0407 23:08:13.315112 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 23:08:16.272279 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 23:08:23.497937 23673 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 23:08:23.497983 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:08:24.848930 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:27.972802 23673 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0407 23:08:27.972842 23673 solver.cpp:397]     Test net output #1: loss = 3.21921 (* 1 = 3.21921 loss)
I0407 23:08:28.061487 23673 solver.cpp:218] Iteration 7956 (0.61921 iter/s, 19.3795s/12 iters), loss = 0.132999
I0407 23:08:28.061534 23673 solver.cpp:237]     Train net output #0: loss = 0.132999 (* 1 = 0.132999 loss)
I0407 23:08:28.061543 23673 sgd_solver.cpp:105] Iteration 7956, lr = 0.00456615
I0407 23:08:32.520058 23673 solver.cpp:218] Iteration 7968 (2.69156 iter/s, 4.45837s/12 iters), loss = 0.211931
I0407 23:08:32.520107 23673 solver.cpp:237]     Train net output #0: loss = 0.211931 (* 1 = 0.211931 loss)
I0407 23:08:32.520117 23673 sgd_solver.cpp:105] Iteration 7968, lr = 0.00456075
I0407 23:08:37.673815 23673 solver.cpp:218] Iteration 7980 (2.32849 iter/s, 5.15354s/12 iters), loss = 0.11834
I0407 23:08:37.673853 23673 solver.cpp:237]     Train net output #0: loss = 0.11834 (* 1 = 0.11834 loss)
I0407 23:08:37.673861 23673 sgd_solver.cpp:105] Iteration 7980, lr = 0.00455536
I0407 23:08:41.899216 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:42.631676 23673 solver.cpp:218] Iteration 7992 (2.4205 iter/s, 4.95766s/12 iters), loss = 0.229713
I0407 23:08:42.631721 23673 solver.cpp:237]     Train net output #0: loss = 0.229713 (* 1 = 0.229713 loss)
I0407 23:08:42.631732 23673 sgd_solver.cpp:105] Iteration 7992, lr = 0.00454998
I0407 23:08:47.758033 23673 solver.cpp:218] Iteration 8004 (2.34094 iter/s, 5.12614s/12 iters), loss = 0.0644083
I0407 23:08:47.758149 23673 solver.cpp:237]     Train net output #0: loss = 0.0644081 (* 1 = 0.0644081 loss)
I0407 23:08:47.758162 23673 sgd_solver.cpp:105] Iteration 8004, lr = 0.0045446
I0407 23:08:52.910864 23673 solver.cpp:218] Iteration 8016 (2.32895 iter/s, 5.15254s/12 iters), loss = 0.205196
I0407 23:08:52.910921 23673 solver.cpp:237]     Train net output #0: loss = 0.205196 (* 1 = 0.205196 loss)
I0407 23:08:52.910933 23673 sgd_solver.cpp:105] Iteration 8016, lr = 0.00453923
I0407 23:08:57.977766 23673 solver.cpp:218] Iteration 8028 (2.36842 iter/s, 5.06668s/12 iters), loss = 0.121694
I0407 23:08:57.977831 23673 solver.cpp:237]     Train net output #0: loss = 0.121694 (* 1 = 0.121694 loss)
I0407 23:08:57.977850 23673 sgd_solver.cpp:105] Iteration 8028, lr = 0.00453387
I0407 23:09:03.077723 23673 solver.cpp:218] Iteration 8040 (2.35307 iter/s, 5.09973s/12 iters), loss = 0.135087
I0407 23:09:03.077773 23673 solver.cpp:237]     Train net output #0: loss = 0.135087 (* 1 = 0.135087 loss)
I0407 23:09:03.077785 23673 sgd_solver.cpp:105] Iteration 8040, lr = 0.00452851
I0407 23:09:08.210888 23673 solver.cpp:218] Iteration 8052 (2.33784 iter/s, 5.13295s/12 iters), loss = 0.07128
I0407 23:09:08.210943 23673 solver.cpp:237]     Train net output #0: loss = 0.0712798 (* 1 = 0.0712798 loss)
I0407 23:09:08.210955 23673 sgd_solver.cpp:105] Iteration 8052, lr = 0.00452316
I0407 23:09:10.283479 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 23:09:15.344324 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 23:09:21.568683 23673 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 23:09:21.568740 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:09:22.804548 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:25.984834 23673 solver.cpp:397]     Test net output #0: accuracy = 0.460784
I0407 23:09:25.984872 23673 solver.cpp:397]     Test net output #1: loss = 3.14564 (* 1 = 3.14564 loss)
I0407 23:09:27.996718 23673 solver.cpp:218] Iteration 8064 (0.606515 iter/s, 19.7852s/12 iters), loss = 0.157395
I0407 23:09:27.996771 23673 solver.cpp:237]     Train net output #0: loss = 0.157395 (* 1 = 0.157395 loss)
I0407 23:09:27.996780 23673 sgd_solver.cpp:105] Iteration 8064, lr = 0.00451781
I0407 23:09:33.284883 23673 solver.cpp:218] Iteration 8076 (2.26932 iter/s, 5.28794s/12 iters), loss = 0.126004
I0407 23:09:33.284932 23673 solver.cpp:237]     Train net output #0: loss = 0.126004 (* 1 = 0.126004 loss)
I0407 23:09:33.284945 23673 sgd_solver.cpp:105] Iteration 8076, lr = 0.00451248
I0407 23:09:38.511337 23673 solver.cpp:218] Iteration 8088 (2.29611 iter/s, 5.22624s/12 iters), loss = 0.213329
I0407 23:09:38.511375 23673 solver.cpp:237]     Train net output #0: loss = 0.213329 (* 1 = 0.213329 loss)
I0407 23:09:38.511384 23673 sgd_solver.cpp:105] Iteration 8088, lr = 0.00450714
I0407 23:09:40.009523 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:43.856685 23673 solver.cpp:218] Iteration 8100 (2.24503 iter/s, 5.34513s/12 iters), loss = 0.186246
I0407 23:09:43.856734 23673 solver.cpp:237]     Train net output #0: loss = 0.186246 (* 1 = 0.186246 loss)
I0407 23:09:43.856746 23673 sgd_solver.cpp:105] Iteration 8100, lr = 0.00450182
I0407 23:09:49.182688 23673 solver.cpp:218] Iteration 8112 (2.25319 iter/s, 5.32578s/12 iters), loss = 0.17032
I0407 23:09:49.182745 23673 solver.cpp:237]     Train net output #0: loss = 0.17032 (* 1 = 0.17032 loss)
I0407 23:09:49.182758 23673 sgd_solver.cpp:105] Iteration 8112, lr = 0.0044965
I0407 23:09:54.308246 23673 solver.cpp:218] Iteration 8124 (2.34131 iter/s, 5.12534s/12 iters), loss = 0.300321
I0407 23:09:54.308315 23673 solver.cpp:237]     Train net output #0: loss = 0.300321 (* 1 = 0.300321 loss)
I0407 23:09:54.308324 23673 sgd_solver.cpp:105] Iteration 8124, lr = 0.00449118
I0407 23:09:59.531440 23673 solver.cpp:218] Iteration 8136 (2.29755 iter/s, 5.22295s/12 iters), loss = 0.0788153
I0407 23:09:59.531494 23673 solver.cpp:237]     Train net output #0: loss = 0.0788151 (* 1 = 0.0788151 loss)
I0407 23:09:59.531507 23673 sgd_solver.cpp:105] Iteration 8136, lr = 0.00448588
I0407 23:10:04.572331 23673 solver.cpp:218] Iteration 8148 (2.38063 iter/s, 5.04067s/12 iters), loss = 0.22455
I0407 23:10:04.572384 23673 solver.cpp:237]     Train net output #0: loss = 0.22455 (* 1 = 0.22455 loss)
I0407 23:10:04.572397 23673 sgd_solver.cpp:105] Iteration 8148, lr = 0.00448058
I0407 23:10:09.172875 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 23:10:15.721556 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 23:10:19.716773 23673 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 23:10:19.716799 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:10:20.964892 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:24.241784 23673 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0407 23:10:24.241829 23673 solver.cpp:397]     Test net output #1: loss = 3.14224 (* 1 = 3.14224 loss)
I0407 23:10:24.333092 23673 solver.cpp:218] Iteration 8160 (0.607284 iter/s, 19.7601s/12 iters), loss = 0.163085
I0407 23:10:24.333235 23673 solver.cpp:237]     Train net output #0: loss = 0.163085 (* 1 = 0.163085 loss)
I0407 23:10:24.333248 23673 sgd_solver.cpp:105] Iteration 8160, lr = 0.00447528
I0407 23:10:28.935060 23673 solver.cpp:218] Iteration 8172 (2.60775 iter/s, 4.60167s/12 iters), loss = 0.172748
I0407 23:10:28.935111 23673 solver.cpp:237]     Train net output #0: loss = 0.172748 (* 1 = 0.172748 loss)
I0407 23:10:28.935123 23673 sgd_solver.cpp:105] Iteration 8172, lr = 0.00446999
I0407 23:10:34.036409 23673 solver.cpp:218] Iteration 8184 (2.35242 iter/s, 5.10113s/12 iters), loss = 0.117476
I0407 23:10:34.036466 23673 solver.cpp:237]     Train net output #0: loss = 0.117475 (* 1 = 0.117475 loss)
I0407 23:10:34.036480 23673 sgd_solver.cpp:105] Iteration 8184, lr = 0.00446471
I0407 23:10:37.860359 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:39.357609 23673 solver.cpp:218] Iteration 8196 (2.25523 iter/s, 5.32097s/12 iters), loss = 0.102963
I0407 23:10:39.357659 23673 solver.cpp:237]     Train net output #0: loss = 0.102963 (* 1 = 0.102963 loss)
I0407 23:10:39.357671 23673 sgd_solver.cpp:105] Iteration 8196, lr = 0.00445944
I0407 23:10:44.506764 23673 solver.cpp:218] Iteration 8208 (2.33058 iter/s, 5.14894s/12 iters), loss = 0.174891
I0407 23:10:44.506817 23673 solver.cpp:237]     Train net output #0: loss = 0.174891 (* 1 = 0.174891 loss)
I0407 23:10:44.506829 23673 sgd_solver.cpp:105] Iteration 8208, lr = 0.00445417
I0407 23:10:49.569815 23673 solver.cpp:218] Iteration 8220 (2.37021 iter/s, 5.06284s/12 iters), loss = 0.141939
I0407 23:10:49.569860 23673 solver.cpp:237]     Train net output #0: loss = 0.141938 (* 1 = 0.141938 loss)
I0407 23:10:49.569869 23673 sgd_solver.cpp:105] Iteration 8220, lr = 0.0044489
I0407 23:10:54.526098 23673 solver.cpp:218] Iteration 8232 (2.42127 iter/s, 4.95608s/12 iters), loss = 0.229808
I0407 23:10:54.526211 23673 solver.cpp:237]     Train net output #0: loss = 0.229808 (* 1 = 0.229808 loss)
I0407 23:10:54.526221 23673 sgd_solver.cpp:105] Iteration 8232, lr = 0.00444365
I0407 23:10:59.646004 23673 solver.cpp:218] Iteration 8244 (2.34392 iter/s, 5.11963s/12 iters), loss = 0.332101
I0407 23:10:59.646055 23673 solver.cpp:237]     Train net output #0: loss = 0.332101 (* 1 = 0.332101 loss)
I0407 23:10:59.646067 23673 sgd_solver.cpp:105] Iteration 8244, lr = 0.00443839
I0407 23:11:04.745328 23673 solver.cpp:218] Iteration 8256 (2.35335 iter/s, 5.09911s/12 iters), loss = 0.15694
I0407 23:11:04.745379 23673 solver.cpp:237]     Train net output #0: loss = 0.15694 (* 1 = 0.15694 loss)
I0407 23:11:04.745391 23673 sgd_solver.cpp:105] Iteration 8256, lr = 0.00443315
I0407 23:11:06.809248 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 23:11:13.231417 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 23:11:15.568928 23673 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 23:11:15.568953 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:11:16.803608 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:20.034759 23673 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0407 23:11:20.034807 23673 solver.cpp:397]     Test net output #1: loss = 3.14857 (* 1 = 3.14857 loss)
I0407 23:11:22.158797 23673 solver.cpp:218] Iteration 8268 (0.689145 iter/s, 17.4129s/12 iters), loss = 0.136119
I0407 23:11:22.158840 23673 solver.cpp:237]     Train net output #0: loss = 0.136118 (* 1 = 0.136118 loss)
I0407 23:11:22.158850 23673 sgd_solver.cpp:105] Iteration 8268, lr = 0.00442791
I0407 23:11:27.161847 23673 solver.cpp:218] Iteration 8280 (2.39864 iter/s, 5.00284s/12 iters), loss = 0.135881
I0407 23:11:27.161988 23673 solver.cpp:237]     Train net output #0: loss = 0.135881 (* 1 = 0.135881 loss)
I0407 23:11:27.162003 23673 sgd_solver.cpp:105] Iteration 8280, lr = 0.00442268
I0407 23:11:32.332607 23673 solver.cpp:218] Iteration 8292 (2.32088 iter/s, 5.17046s/12 iters), loss = 0.15775
I0407 23:11:32.332657 23673 solver.cpp:237]     Train net output #0: loss = 0.15775 (* 1 = 0.15775 loss)
I0407 23:11:32.332669 23673 sgd_solver.cpp:105] Iteration 8292, lr = 0.00441745
I0407 23:11:32.983206 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:37.554455 23673 solver.cpp:218] Iteration 8304 (2.29813 iter/s, 5.22163s/12 iters), loss = 0.110295
I0407 23:11:37.554497 23673 solver.cpp:237]     Train net output #0: loss = 0.110295 (* 1 = 0.110295 loss)
I0407 23:11:37.554507 23673 sgd_solver.cpp:105] Iteration 8304, lr = 0.00441223
I0407 23:11:40.668427 23673 blocking_queue.cpp:49] Waiting for data
I0407 23:11:42.833410 23673 solver.cpp:218] Iteration 8316 (2.27327 iter/s, 5.27874s/12 iters), loss = 0.08802
I0407 23:11:42.833464 23673 solver.cpp:237]     Train net output #0: loss = 0.0880198 (* 1 = 0.0880198 loss)
I0407 23:11:42.833477 23673 sgd_solver.cpp:105] Iteration 8316, lr = 0.00440702
I0407 23:11:47.928097 23673 solver.cpp:218] Iteration 8328 (2.3555 iter/s, 5.09447s/12 iters), loss = 0.196169
I0407 23:11:47.928148 23673 solver.cpp:237]     Train net output #0: loss = 0.196169 (* 1 = 0.196169 loss)
I0407 23:11:47.928161 23673 sgd_solver.cpp:105] Iteration 8328, lr = 0.00440181
I0407 23:11:53.117342 23673 solver.cpp:218] Iteration 8340 (2.31257 iter/s, 5.18902s/12 iters), loss = 0.205661
I0407 23:11:53.117396 23673 solver.cpp:237]     Train net output #0: loss = 0.205661 (* 1 = 0.205661 loss)
I0407 23:11:53.117408 23673 sgd_solver.cpp:105] Iteration 8340, lr = 0.00439661
I0407 23:11:58.219849 23673 solver.cpp:218] Iteration 8352 (2.35189 iter/s, 5.10228s/12 iters), loss = 0.100056
I0407 23:11:58.219972 23673 solver.cpp:237]     Train net output #0: loss = 0.100056 (* 1 = 0.100056 loss)
I0407 23:11:58.219986 23673 sgd_solver.cpp:105] Iteration 8352, lr = 0.00439141
I0407 23:12:02.860139 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 23:12:05.848069 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 23:12:08.176095 23673 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 23:12:08.176122 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:12:09.357275 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:12.646163 23673 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0407 23:12:12.646214 23673 solver.cpp:397]     Test net output #1: loss = 3.16731 (* 1 = 3.16731 loss)
I0407 23:12:12.737502 23673 solver.cpp:218] Iteration 8364 (0.826613 iter/s, 14.5171s/12 iters), loss = 0.19166
I0407 23:12:12.737555 23673 solver.cpp:237]     Train net output #0: loss = 0.19166 (* 1 = 0.19166 loss)
I0407 23:12:12.737567 23673 sgd_solver.cpp:105] Iteration 8364, lr = 0.00438623
I0407 23:12:17.399935 23673 solver.cpp:218] Iteration 8376 (2.57388 iter/s, 4.66223s/12 iters), loss = 0.158605
I0407 23:12:17.399981 23673 solver.cpp:237]     Train net output #0: loss = 0.158605 (* 1 = 0.158605 loss)
I0407 23:12:17.399993 23673 sgd_solver.cpp:105] Iteration 8376, lr = 0.00438104
I0407 23:12:22.710884 23673 solver.cpp:218] Iteration 8388 (2.25958 iter/s, 5.31073s/12 iters), loss = 0.225954
I0407 23:12:22.710933 23673 solver.cpp:237]     Train net output #0: loss = 0.225954 (* 1 = 0.225954 loss)
I0407 23:12:22.710945 23673 sgd_solver.cpp:105] Iteration 8388, lr = 0.00437587
I0407 23:12:25.544078 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:27.740348 23673 solver.cpp:218] Iteration 8400 (2.38604 iter/s, 5.02925s/12 iters), loss = 0.0465599
I0407 23:12:27.740399 23673 solver.cpp:237]     Train net output #0: loss = 0.0465598 (* 1 = 0.0465598 loss)
I0407 23:12:27.740413 23673 sgd_solver.cpp:105] Iteration 8400, lr = 0.00437069
I0407 23:12:32.786665 23673 solver.cpp:218] Iteration 8412 (2.37807 iter/s, 5.0461s/12 iters), loss = 0.279343
I0407 23:12:32.786813 23673 solver.cpp:237]     Train net output #0: loss = 0.279343 (* 1 = 0.279343 loss)
I0407 23:12:32.786828 23673 sgd_solver.cpp:105] Iteration 8412, lr = 0.00436553
I0407 23:12:38.007792 23673 solver.cpp:218] Iteration 8424 (2.29849 iter/s, 5.22081s/12 iters), loss = 0.228779
I0407 23:12:38.007839 23673 solver.cpp:237]     Train net output #0: loss = 0.228778 (* 1 = 0.228778 loss)
I0407 23:12:38.007849 23673 sgd_solver.cpp:105] Iteration 8424, lr = 0.00436037
I0407 23:12:43.331195 23673 solver.cpp:218] Iteration 8436 (2.25429 iter/s, 5.32318s/12 iters), loss = 0.142052
I0407 23:12:43.331245 23673 solver.cpp:237]     Train net output #0: loss = 0.142052 (* 1 = 0.142052 loss)
I0407 23:12:43.331255 23673 sgd_solver.cpp:105] Iteration 8436, lr = 0.00435522
I0407 23:12:48.436458 23673 solver.cpp:218] Iteration 8448 (2.35061 iter/s, 5.10505s/12 iters), loss = 0.120266
I0407 23:12:48.436504 23673 solver.cpp:237]     Train net output #0: loss = 0.120266 (* 1 = 0.120266 loss)
I0407 23:12:48.436513 23673 sgd_solver.cpp:105] Iteration 8448, lr = 0.00435007
I0407 23:12:53.479579 23673 solver.cpp:218] Iteration 8460 (2.37958 iter/s, 5.04291s/12 iters), loss = 0.157382
I0407 23:12:53.479614 23673 solver.cpp:237]     Train net output #0: loss = 0.157382 (* 1 = 0.157382 loss)
I0407 23:12:53.479624 23673 sgd_solver.cpp:105] Iteration 8460, lr = 0.00434493
I0407 23:12:55.526703 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 23:12:58.513684 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 23:13:00.842248 23673 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 23:13:00.842273 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:13:02.000752 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:05.393474 23673 solver.cpp:397]     Test net output #0: accuracy = 0.429534
I0407 23:13:05.393611 23673 solver.cpp:397]     Test net output #1: loss = 3.24494 (* 1 = 3.24494 loss)
I0407 23:13:07.477238 23673 solver.cpp:218] Iteration 8472 (0.857315 iter/s, 13.9972s/12 iters), loss = 0.150495
I0407 23:13:07.477277 23673 solver.cpp:237]     Train net output #0: loss = 0.150495 (* 1 = 0.150495 loss)
I0407 23:13:07.477286 23673 sgd_solver.cpp:105] Iteration 8472, lr = 0.0043398
I0407 23:13:12.586793 23673 solver.cpp:218] Iteration 8484 (2.34864 iter/s, 5.10935s/12 iters), loss = 0.0890262
I0407 23:13:12.586844 23673 solver.cpp:237]     Train net output #0: loss = 0.089026 (* 1 = 0.089026 loss)
I0407 23:13:12.586856 23673 sgd_solver.cpp:105] Iteration 8484, lr = 0.00433467
I0407 23:13:17.692864 23673 solver.cpp:218] Iteration 8496 (2.35024 iter/s, 5.10585s/12 iters), loss = 0.14622
I0407 23:13:17.692915 23673 solver.cpp:237]     Train net output #0: loss = 0.14622 (* 1 = 0.14622 loss)
I0407 23:13:17.692929 23673 sgd_solver.cpp:105] Iteration 8496, lr = 0.00432955
I0407 23:13:17.740067 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:23.075242 23673 solver.cpp:218] Iteration 8508 (2.22959 iter/s, 5.38215s/12 iters), loss = 0.128871
I0407 23:13:23.075290 23673 solver.cpp:237]     Train net output #0: loss = 0.128871 (* 1 = 0.128871 loss)
I0407 23:13:23.075301 23673 sgd_solver.cpp:105] Iteration 8508, lr = 0.00432443
I0407 23:13:28.607074 23673 solver.cpp:218] Iteration 8520 (2.16935 iter/s, 5.5316s/12 iters), loss = 0.16616
I0407 23:13:28.607131 23673 solver.cpp:237]     Train net output #0: loss = 0.16616 (* 1 = 0.16616 loss)
I0407 23:13:28.607143 23673 sgd_solver.cpp:105] Iteration 8520, lr = 0.00431932
I0407 23:13:33.781440 23673 solver.cpp:218] Iteration 8532 (2.31923 iter/s, 5.17414s/12 iters), loss = 0.0966823
I0407 23:13:33.781500 23673 solver.cpp:237]     Train net output #0: loss = 0.0966821 (* 1 = 0.0966821 loss)
I0407 23:13:33.781514 23673 sgd_solver.cpp:105] Iteration 8532, lr = 0.00431422
I0407 23:13:38.782979 23673 solver.cpp:218] Iteration 8544 (2.39937 iter/s, 5.00132s/12 iters), loss = 0.273994
I0407 23:13:38.784435 23673 solver.cpp:237]     Train net output #0: loss = 0.273994 (* 1 = 0.273994 loss)
I0407 23:13:38.784449 23673 sgd_solver.cpp:105] Iteration 8544, lr = 0.00430912
I0407 23:13:44.029059 23673 solver.cpp:218] Iteration 8556 (2.28813 iter/s, 5.24445s/12 iters), loss = 0.0656031
I0407 23:13:44.029116 23673 solver.cpp:237]     Train net output #0: loss = 0.0656029 (* 1 = 0.0656029 loss)
I0407 23:13:44.029129 23673 sgd_solver.cpp:105] Iteration 8556, lr = 0.00430403
I0407 23:13:48.984915 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 23:13:52.092633 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 23:13:54.412904 23673 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 23:13:54.412925 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:13:55.472107 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:58.860033 23673 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0407 23:13:58.860083 23673 solver.cpp:397]     Test net output #1: loss = 3.11927 (* 1 = 3.11927 loss)
I0407 23:13:58.950240 23673 solver.cpp:218] Iteration 8568 (0.804254 iter/s, 14.9207s/12 iters), loss = 0.0701313
I0407 23:13:58.950294 23673 solver.cpp:237]     Train net output #0: loss = 0.0701311 (* 1 = 0.0701311 loss)
I0407 23:13:58.950304 23673 sgd_solver.cpp:105] Iteration 8568, lr = 0.00429894
I0407 23:14:03.251619 23673 solver.cpp:218] Iteration 8580 (2.78993 iter/s, 4.30119s/12 iters), loss = 0.138455
I0407 23:14:03.251658 23673 solver.cpp:237]     Train net output #0: loss = 0.138455 (* 1 = 0.138455 loss)
I0407 23:14:03.251667 23673 sgd_solver.cpp:105] Iteration 8580, lr = 0.00429386
I0407 23:14:08.680928 23673 solver.cpp:218] Iteration 8592 (2.21031 iter/s, 5.42909s/12 iters), loss = 0.134602
I0407 23:14:08.680969 23673 solver.cpp:237]     Train net output #0: loss = 0.134602 (* 1 = 0.134602 loss)
I0407 23:14:08.680979 23673 sgd_solver.cpp:105] Iteration 8592, lr = 0.00428879
I0407 23:14:11.102528 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:14.120236 23673 solver.cpp:218] Iteration 8604 (2.20625 iter/s, 5.43909s/12 iters), loss = 0.0904113
I0407 23:14:14.120285 23673 solver.cpp:237]     Train net output #0: loss = 0.0904111 (* 1 = 0.0904111 loss)
I0407 23:14:14.120296 23673 sgd_solver.cpp:105] Iteration 8604, lr = 0.00428372
I0407 23:14:19.132220 23673 solver.cpp:218] Iteration 8616 (2.39436 iter/s, 5.01177s/12 iters), loss = 0.0738635
I0407 23:14:19.132272 23673 solver.cpp:237]     Train net output #0: loss = 0.0738634 (* 1 = 0.0738634 loss)
I0407 23:14:19.132284 23673 sgd_solver.cpp:105] Iteration 8616, lr = 0.00427866
I0407 23:14:24.115968 23673 solver.cpp:218] Iteration 8628 (2.40793 iter/s, 4.98354s/12 iters), loss = 0.153574
I0407 23:14:24.116020 23673 solver.cpp:237]     Train net output #0: loss = 0.153573 (* 1 = 0.153573 loss)
I0407 23:14:24.116032 23673 sgd_solver.cpp:105] Iteration 8628, lr = 0.0042736
I0407 23:14:29.194309 23673 solver.cpp:218] Iteration 8640 (2.36308 iter/s, 5.07812s/12 iters), loss = 0.144961
I0407 23:14:29.194355 23673 solver.cpp:237]     Train net output #0: loss = 0.144961 (* 1 = 0.144961 loss)
I0407 23:14:29.194363 23673 sgd_solver.cpp:105] Iteration 8640, lr = 0.00426855
I0407 23:14:34.349980 23673 solver.cpp:218] Iteration 8652 (2.32764 iter/s, 5.15544s/12 iters), loss = 0.11623
I0407 23:14:34.350025 23673 solver.cpp:237]     Train net output #0: loss = 0.11623 (* 1 = 0.11623 loss)
I0407 23:14:34.350035 23673 sgd_solver.cpp:105] Iteration 8652, lr = 0.00426351
I0407 23:14:39.498694 23673 solver.cpp:218] Iteration 8664 (2.33078 iter/s, 5.1485s/12 iters), loss = 0.0809609
I0407 23:14:39.498744 23673 solver.cpp:237]     Train net output #0: loss = 0.0809607 (* 1 = 0.0809607 loss)
I0407 23:14:39.498754 23673 sgd_solver.cpp:105] Iteration 8664, lr = 0.00425847
I0407 23:14:41.592067 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 23:14:44.562461 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 23:14:46.868947 23673 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 23:14:46.868975 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:14:47.946465 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:51.609083 23673 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0407 23:14:51.609128 23673 solver.cpp:397]     Test net output #1: loss = 3.0716 (* 1 = 3.0716 loss)
I0407 23:14:53.466470 23673 solver.cpp:218] Iteration 8676 (0.85915 iter/s, 13.9673s/12 iters), loss = 0.112653
I0407 23:14:53.466514 23673 solver.cpp:237]     Train net output #0: loss = 0.112653 (* 1 = 0.112653 loss)
I0407 23:14:53.466523 23673 sgd_solver.cpp:105] Iteration 8676, lr = 0.00425344
I0407 23:14:58.640543 23673 solver.cpp:218] Iteration 8688 (2.31935 iter/s, 5.17387s/12 iters), loss = 0.147595
I0407 23:14:58.640595 23673 solver.cpp:237]     Train net output #0: loss = 0.147594 (* 1 = 0.147594 loss)
I0407 23:14:58.640607 23673 sgd_solver.cpp:105] Iteration 8688, lr = 0.00424841
I0407 23:15:03.051808 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:03.734501 23673 solver.cpp:218] Iteration 8700 (2.35583 iter/s, 5.09375s/12 iters), loss = 0.124958
I0407 23:15:03.734558 23673 solver.cpp:237]     Train net output #0: loss = 0.124958 (* 1 = 0.124958 loss)
I0407 23:15:03.734570 23673 sgd_solver.cpp:105] Iteration 8700, lr = 0.00424339
I0407 23:15:08.743692 23673 solver.cpp:218] Iteration 8712 (2.39569 iter/s, 5.00899s/12 iters), loss = 0.129073
I0407 23:15:08.743736 23673 solver.cpp:237]     Train net output #0: loss = 0.129073 (* 1 = 0.129073 loss)
I0407 23:15:08.743746 23673 sgd_solver.cpp:105] Iteration 8712, lr = 0.00423838
I0407 23:15:13.780877 23673 solver.cpp:218] Iteration 8724 (2.38237 iter/s, 5.037s/12 iters), loss = 0.103328
I0407 23:15:13.780942 23673 solver.cpp:237]     Train net output #0: loss = 0.103328 (* 1 = 0.103328 loss)
I0407 23:15:13.780952 23673 sgd_solver.cpp:105] Iteration 8724, lr = 0.00423337
I0407 23:15:18.702204 23673 solver.cpp:218] Iteration 8736 (2.43847 iter/s, 4.92111s/12 iters), loss = 0.128239
I0407 23:15:18.702257 23673 solver.cpp:237]     Train net output #0: loss = 0.128239 (* 1 = 0.128239 loss)
I0407 23:15:18.702270 23673 sgd_solver.cpp:105] Iteration 8736, lr = 0.00422836
I0407 23:15:23.757200 23673 solver.cpp:218] Iteration 8748 (2.37398 iter/s, 5.0548s/12 iters), loss = 0.289811
I0407 23:15:23.757249 23673 solver.cpp:237]     Train net output #0: loss = 0.289811 (* 1 = 0.289811 loss)
I0407 23:15:23.757261 23673 sgd_solver.cpp:105] Iteration 8748, lr = 0.00422337
I0407 23:15:28.837536 23673 solver.cpp:218] Iteration 8760 (2.36214 iter/s, 5.08014s/12 iters), loss = 0.140588
I0407 23:15:28.837575 23673 solver.cpp:237]     Train net output #0: loss = 0.140588 (* 1 = 0.140588 loss)
I0407 23:15:28.837584 23673 sgd_solver.cpp:105] Iteration 8760, lr = 0.00421838
I0407 23:15:33.403525 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 23:15:36.445777 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 23:15:38.748358 23673 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 23:15:38.748379 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:15:39.649379 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:43.185240 23673 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0407 23:15:43.185271 23673 solver.cpp:397]     Test net output #1: loss = 3.10627 (* 1 = 3.10627 loss)
I0407 23:15:43.276433 23673 solver.cpp:218] Iteration 8772 (0.831114 iter/s, 14.4384s/12 iters), loss = 0.0762339
I0407 23:15:43.276475 23673 solver.cpp:237]     Train net output #0: loss = 0.0762337 (* 1 = 0.0762337 loss)
I0407 23:15:43.276484 23673 sgd_solver.cpp:105] Iteration 8772, lr = 0.00421339
I0407 23:15:47.502032 23673 solver.cpp:218] Iteration 8784 (2.83995 iter/s, 4.22542s/12 iters), loss = 0.20574
I0407 23:15:47.502159 23673 solver.cpp:237]     Train net output #0: loss = 0.205739 (* 1 = 0.205739 loss)
I0407 23:15:47.502171 23673 sgd_solver.cpp:105] Iteration 8784, lr = 0.00420841
I0407 23:15:52.612886 23673 solver.cpp:218] Iteration 8796 (2.34807 iter/s, 5.11058s/12 iters), loss = 0.190834
I0407 23:15:52.612936 23673 solver.cpp:237]     Train net output #0: loss = 0.190834 (* 1 = 0.190834 loss)
I0407 23:15:52.612947 23673 sgd_solver.cpp:105] Iteration 8796, lr = 0.00420344
I0407 23:15:54.198966 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:57.829244 23673 solver.cpp:218] Iteration 8808 (2.30054 iter/s, 5.21616s/12 iters), loss = 0.155338
I0407 23:15:57.829293 23673 solver.cpp:237]     Train net output #0: loss = 0.155337 (* 1 = 0.155337 loss)
I0407 23:15:57.829305 23673 sgd_solver.cpp:105] Iteration 8808, lr = 0.00419847
I0407 23:16:02.942237 23673 solver.cpp:218] Iteration 8820 (2.34706 iter/s, 5.11279s/12 iters), loss = 0.0879409
I0407 23:16:02.942297 23673 solver.cpp:237]     Train net output #0: loss = 0.0879407 (* 1 = 0.0879407 loss)
I0407 23:16:02.942312 23673 sgd_solver.cpp:105] Iteration 8820, lr = 0.00419351
I0407 23:16:08.115681 23673 solver.cpp:218] Iteration 8832 (2.31963 iter/s, 5.17323s/12 iters), loss = 0.0972831
I0407 23:16:08.115731 23673 solver.cpp:237]     Train net output #0: loss = 0.0972829 (* 1 = 0.0972829 loss)
I0407 23:16:08.115744 23673 sgd_solver.cpp:105] Iteration 8832, lr = 0.00418856
I0407 23:16:13.179694 23673 solver.cpp:218] Iteration 8844 (2.36976 iter/s, 5.06381s/12 iters), loss = 0.133455
I0407 23:16:13.179747 23673 solver.cpp:237]     Train net output #0: loss = 0.133455 (* 1 = 0.133455 loss)
I0407 23:16:13.179759 23673 sgd_solver.cpp:105] Iteration 8844, lr = 0.00418361
I0407 23:16:18.287817 23673 solver.cpp:218] Iteration 8856 (2.3493 iter/s, 5.10791s/12 iters), loss = 0.0928284
I0407 23:16:18.287941 23673 solver.cpp:237]     Train net output #0: loss = 0.0928281 (* 1 = 0.0928281 loss)
I0407 23:16:18.287955 23673 sgd_solver.cpp:105] Iteration 8856, lr = 0.00417866
I0407 23:16:23.364876 23673 solver.cpp:218] Iteration 8868 (2.3637 iter/s, 5.07679s/12 iters), loss = 0.0987345
I0407 23:16:23.364926 23673 solver.cpp:237]     Train net output #0: loss = 0.0987342 (* 1 = 0.0987342 loss)
I0407 23:16:23.364938 23673 sgd_solver.cpp:105] Iteration 8868, lr = 0.00417373
I0407 23:16:25.429646 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 23:16:28.469274 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 23:16:30.767704 23673 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 23:16:30.767729 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:16:31.929356 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:35.583026 23673 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0407 23:16:35.583082 23673 solver.cpp:397]     Test net output #1: loss = 3.21331 (* 1 = 3.21331 loss)
I0407 23:16:37.600741 23673 solver.cpp:218] Iteration 8880 (0.842968 iter/s, 14.2354s/12 iters), loss = 0.0589438
I0407 23:16:37.600793 23673 solver.cpp:237]     Train net output #0: loss = 0.0589435 (* 1 = 0.0589435 loss)
I0407 23:16:37.600805 23673 sgd_solver.cpp:105] Iteration 8880, lr = 0.00416879
I0407 23:16:42.783324 23673 solver.cpp:218] Iteration 8892 (2.31554 iter/s, 5.18237s/12 iters), loss = 0.11053
I0407 23:16:42.783382 23673 solver.cpp:237]     Train net output #0: loss = 0.11053 (* 1 = 0.11053 loss)
I0407 23:16:42.783396 23673 sgd_solver.cpp:105] Iteration 8892, lr = 0.00416387
I0407 23:16:46.751081 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:48.299368 23673 solver.cpp:218] Iteration 8904 (2.17556 iter/s, 5.51582s/12 iters), loss = 0.0707729
I0407 23:16:48.299510 23673 solver.cpp:237]     Train net output #0: loss = 0.0707727 (* 1 = 0.0707727 loss)
I0407 23:16:48.299520 23673 sgd_solver.cpp:105] Iteration 8904, lr = 0.00415895
I0407 23:16:53.340350 23673 solver.cpp:218] Iteration 8916 (2.38063 iter/s, 5.04069s/12 iters), loss = 0.163494
I0407 23:16:53.340401 23673 solver.cpp:237]     Train net output #0: loss = 0.163493 (* 1 = 0.163493 loss)
I0407 23:16:53.340413 23673 sgd_solver.cpp:105] Iteration 8916, lr = 0.00415403
I0407 23:16:58.281723 23673 solver.cpp:218] Iteration 8928 (2.42858 iter/s, 4.94117s/12 iters), loss = 0.19839
I0407 23:16:58.281780 23673 solver.cpp:237]     Train net output #0: loss = 0.19839 (* 1 = 0.19839 loss)
I0407 23:16:58.281791 23673 sgd_solver.cpp:105] Iteration 8928, lr = 0.00414912
I0407 23:17:03.080821 23673 solver.cpp:218] Iteration 8940 (2.50057 iter/s, 4.7989s/12 iters), loss = 0.283444
I0407 23:17:03.080868 23673 solver.cpp:237]     Train net output #0: loss = 0.283444 (* 1 = 0.283444 loss)
I0407 23:17:03.080880 23673 sgd_solver.cpp:105] Iteration 8940, lr = 0.00414422
I0407 23:17:08.152637 23673 solver.cpp:218] Iteration 8952 (2.36611 iter/s, 5.07161s/12 iters), loss = 0.196053
I0407 23:17:08.152685 23673 solver.cpp:237]     Train net output #0: loss = 0.196053 (* 1 = 0.196053 loss)
I0407 23:17:08.152696 23673 sgd_solver.cpp:105] Iteration 8952, lr = 0.00413932
I0407 23:17:13.126133 23673 solver.cpp:218] Iteration 8964 (2.41289 iter/s, 4.97329s/12 iters), loss = 0.0942649
I0407 23:17:13.126188 23673 solver.cpp:237]     Train net output #0: loss = 0.0942647 (* 1 = 0.0942647 loss)
I0407 23:17:13.126199 23673 sgd_solver.cpp:105] Iteration 8964, lr = 0.00413443
I0407 23:17:17.725518 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 23:17:20.894266 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 23:17:23.219174 23673 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 23:17:23.219200 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:17:24.181277 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:27.691299 23673 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 23:17:27.691351 23673 solver.cpp:397]     Test net output #1: loss = 3.1224 (* 1 = 3.1224 loss)
I0407 23:17:27.782773 23673 solver.cpp:218] Iteration 8976 (0.818768 iter/s, 14.6562s/12 iters), loss = 0.0756663
I0407 23:17:27.782838 23673 solver.cpp:237]     Train net output #0: loss = 0.0756661 (* 1 = 0.0756661 loss)
I0407 23:17:27.782855 23673 sgd_solver.cpp:105] Iteration 8976, lr = 0.00412955
I0407 23:17:32.072860 23673 solver.cpp:218] Iteration 8988 (2.79727 iter/s, 4.28989s/12 iters), loss = 0.138925
I0407 23:17:32.072917 23673 solver.cpp:237]     Train net output #0: loss = 0.138925 (* 1 = 0.138925 loss)
I0407 23:17:32.072929 23673 sgd_solver.cpp:105] Iteration 8988, lr = 0.00412467
I0407 23:17:35.405908 23673 blocking_queue.cpp:49] Waiting for data
I0407 23:17:37.151108 23673 solver.cpp:218] Iteration 9000 (2.36312 iter/s, 5.07804s/12 iters), loss = 0.0514634
I0407 23:17:37.151157 23673 solver.cpp:237]     Train net output #0: loss = 0.0514632 (* 1 = 0.0514632 loss)
I0407 23:17:37.151166 23673 sgd_solver.cpp:105] Iteration 9000, lr = 0.00411979
I0407 23:17:37.855862 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:42.216176 23673 solver.cpp:218] Iteration 9012 (2.36926 iter/s, 5.06487s/12 iters), loss = 0.13372
I0407 23:17:42.216226 23673 solver.cpp:237]     Train net output #0: loss = 0.13372 (* 1 = 0.13372 loss)
I0407 23:17:42.216235 23673 sgd_solver.cpp:105] Iteration 9012, lr = 0.00411493
I0407 23:17:47.223601 23673 solver.cpp:218] Iteration 9024 (2.39654 iter/s, 5.00722s/12 iters), loss = 0.184898
I0407 23:17:47.223655 23673 solver.cpp:237]     Train net output #0: loss = 0.184898 (* 1 = 0.184898 loss)
I0407 23:17:47.223667 23673 sgd_solver.cpp:105] Iteration 9024, lr = 0.00411006
I0407 23:17:52.282310 23673 solver.cpp:218] Iteration 9036 (2.37224 iter/s, 5.0585s/12 iters), loss = 0.218241
I0407 23:17:52.282438 23673 solver.cpp:237]     Train net output #0: loss = 0.218241 (* 1 = 0.218241 loss)
I0407 23:17:52.282447 23673 sgd_solver.cpp:105] Iteration 9036, lr = 0.00410521
I0407 23:17:57.380527 23673 solver.cpp:218] Iteration 9048 (2.3539 iter/s, 5.09793s/12 iters), loss = 0.132126
I0407 23:17:57.380585 23673 solver.cpp:237]     Train net output #0: loss = 0.132126 (* 1 = 0.132126 loss)
I0407 23:17:57.380599 23673 sgd_solver.cpp:105] Iteration 9048, lr = 0.00410036
I0407 23:18:02.496250 23673 solver.cpp:218] Iteration 9060 (2.3458 iter/s, 5.11552s/12 iters), loss = 0.115809
I0407 23:18:02.496299 23673 solver.cpp:237]     Train net output #0: loss = 0.115809 (* 1 = 0.115809 loss)
I0407 23:18:02.496311 23673 sgd_solver.cpp:105] Iteration 9060, lr = 0.00409551
I0407 23:18:08.223935 23673 solver.cpp:218] Iteration 9072 (2.09517 iter/s, 5.72746s/12 iters), loss = 0.112893
I0407 23:18:08.223996 23673 solver.cpp:237]     Train net output #0: loss = 0.112893 (* 1 = 0.112893 loss)
I0407 23:18:08.224009 23673 sgd_solver.cpp:105] Iteration 9072, lr = 0.00409067
I0407 23:18:10.338248 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 23:18:13.415144 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 23:18:15.744987 23673 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 23:18:15.745015 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:18:16.650970 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:20.211954 23673 solver.cpp:397]     Test net output #0: accuracy = 0.457108
I0407 23:18:20.212016 23673 solver.cpp:397]     Test net output #1: loss = 3.07237 (* 1 = 3.07237 loss)
I0407 23:18:22.224398 23673 solver.cpp:218] Iteration 9084 (0.857142 iter/s, 14s/12 iters), loss = 0.0488291
I0407 23:18:22.224442 23673 solver.cpp:237]     Train net output #0: loss = 0.0488289 (* 1 = 0.0488289 loss)
I0407 23:18:22.224452 23673 sgd_solver.cpp:105] Iteration 9084, lr = 0.00408584
I0407 23:18:27.741235 23673 solver.cpp:218] Iteration 9096 (2.17524 iter/s, 5.51663s/12 iters), loss = 0.0517813
I0407 23:18:27.741325 23673 solver.cpp:237]     Train net output #0: loss = 0.051781 (* 1 = 0.051781 loss)
I0407 23:18:27.741335 23673 sgd_solver.cpp:105] Iteration 9096, lr = 0.00408101
I0407 23:18:30.958300 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:33.071009 23673 solver.cpp:218] Iteration 9108 (2.25161 iter/s, 5.32952s/12 iters), loss = 0.189858
I0407 23:18:33.071061 23673 solver.cpp:237]     Train net output #0: loss = 0.189857 (* 1 = 0.189857 loss)
I0407 23:18:33.071072 23673 sgd_solver.cpp:105] Iteration 9108, lr = 0.00407619
I0407 23:18:38.269922 23673 solver.cpp:218] Iteration 9120 (2.30827 iter/s, 5.1987s/12 iters), loss = 0.217262
I0407 23:18:38.269982 23673 solver.cpp:237]     Train net output #0: loss = 0.217262 (* 1 = 0.217262 loss)
I0407 23:18:38.269992 23673 sgd_solver.cpp:105] Iteration 9120, lr = 0.00407137
I0407 23:18:43.507773 23673 solver.cpp:218] Iteration 9132 (2.29111 iter/s, 5.23763s/12 iters), loss = 0.158232
I0407 23:18:43.507824 23673 solver.cpp:237]     Train net output #0: loss = 0.158232 (* 1 = 0.158232 loss)
I0407 23:18:43.507835 23673 sgd_solver.cpp:105] Iteration 9132, lr = 0.00406656
I0407 23:18:48.590728 23673 solver.cpp:218] Iteration 9144 (2.36093 iter/s, 5.08274s/12 iters), loss = 0.0834881
I0407 23:18:48.590776 23673 solver.cpp:237]     Train net output #0: loss = 0.0834878 (* 1 = 0.0834878 loss)
I0407 23:18:48.590787 23673 sgd_solver.cpp:105] Iteration 9144, lr = 0.00406175
I0407 23:18:53.615940 23673 solver.cpp:218] Iteration 9156 (2.38806 iter/s, 5.02501s/12 iters), loss = 0.0939696
I0407 23:18:53.615988 23673 solver.cpp:237]     Train net output #0: loss = 0.0939693 (* 1 = 0.0939693 loss)
I0407 23:18:53.615999 23673 sgd_solver.cpp:105] Iteration 9156, lr = 0.00405695
I0407 23:18:58.870415 23673 solver.cpp:218] Iteration 9168 (2.28386 iter/s, 5.25427s/12 iters), loss = 0.0378972
I0407 23:18:58.870558 23673 solver.cpp:237]     Train net output #0: loss = 0.037897 (* 1 = 0.037897 loss)
I0407 23:18:58.870573 23673 sgd_solver.cpp:105] Iteration 9168, lr = 0.00405216
I0407 23:19:03.485626 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 23:19:07.637996 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 23:19:09.996364 23673 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 23:19:09.996387 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:19:10.838724 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:14.426990 23673 solver.cpp:397]     Test net output #0: accuracy = 0.457721
I0407 23:19:14.427035 23673 solver.cpp:397]     Test net output #1: loss = 3.04143 (* 1 = 3.04143 loss)
I0407 23:19:14.518497 23673 solver.cpp:218] Iteration 9180 (0.766896 iter/s, 15.6475s/12 iters), loss = 0.156404
I0407 23:19:14.518548 23673 solver.cpp:237]     Train net output #0: loss = 0.156404 (* 1 = 0.156404 loss)
I0407 23:19:14.518559 23673 sgd_solver.cpp:105] Iteration 9180, lr = 0.00404737
I0407 23:19:19.110052 23673 solver.cpp:218] Iteration 9192 (2.6136 iter/s, 4.59137s/12 iters), loss = 0.313898
I0407 23:19:19.110097 23673 solver.cpp:237]     Train net output #0: loss = 0.313897 (* 1 = 0.313897 loss)
I0407 23:19:19.110107 23673 sgd_solver.cpp:105] Iteration 9192, lr = 0.00404259
I0407 23:19:24.335984 23673 solver.cpp:218] Iteration 9204 (2.29633 iter/s, 5.22572s/12 iters), loss = 0.0524873
I0407 23:19:24.336038 23673 solver.cpp:237]     Train net output #0: loss = 0.052487 (* 1 = 0.052487 loss)
I0407 23:19:24.336050 23673 sgd_solver.cpp:105] Iteration 9204, lr = 0.00403781
I0407 23:19:24.415395 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:29.333209 23673 solver.cpp:218] Iteration 9216 (2.40143 iter/s, 4.99702s/12 iters), loss = 0.107177
I0407 23:19:29.333326 23673 solver.cpp:237]     Train net output #0: loss = 0.107177 (* 1 = 0.107177 loss)
I0407 23:19:29.333340 23673 sgd_solver.cpp:105] Iteration 9216, lr = 0.00403304
I0407 23:19:34.472832 23673 solver.cpp:218] Iteration 9228 (2.33492 iter/s, 5.13935s/12 iters), loss = 0.0695427
I0407 23:19:34.472880 23673 solver.cpp:237]     Train net output #0: loss = 0.0695424 (* 1 = 0.0695424 loss)
I0407 23:19:34.472890 23673 sgd_solver.cpp:105] Iteration 9228, lr = 0.00402827
I0407 23:19:39.636521 23673 solver.cpp:218] Iteration 9240 (2.32401 iter/s, 5.16348s/12 iters), loss = 0.0633787
I0407 23:19:39.636574 23673 solver.cpp:237]     Train net output #0: loss = 0.0633784 (* 1 = 0.0633784 loss)
I0407 23:19:39.636584 23673 sgd_solver.cpp:105] Iteration 9240, lr = 0.00402351
I0407 23:19:44.792663 23673 solver.cpp:218] Iteration 9252 (2.32742 iter/s, 5.15593s/12 iters), loss = 0.121552
I0407 23:19:44.792719 23673 solver.cpp:237]     Train net output #0: loss = 0.121552 (* 1 = 0.121552 loss)
I0407 23:19:44.792732 23673 sgd_solver.cpp:105] Iteration 9252, lr = 0.00401876
I0407 23:19:49.878540 23673 solver.cpp:218] Iteration 9264 (2.35957 iter/s, 5.08567s/12 iters), loss = 0.105038
I0407 23:19:49.878585 23673 solver.cpp:237]     Train net output #0: loss = 0.105038 (* 1 = 0.105038 loss)
I0407 23:19:49.878597 23673 sgd_solver.cpp:105] Iteration 9264, lr = 0.00401401
I0407 23:19:55.011505 23673 solver.cpp:218] Iteration 9276 (2.33792 iter/s, 5.13276s/12 iters), loss = 0.157004
I0407 23:19:55.011556 23673 solver.cpp:237]     Train net output #0: loss = 0.157004 (* 1 = 0.157004 loss)
I0407 23:19:55.011569 23673 sgd_solver.cpp:105] Iteration 9276, lr = 0.00400927
I0407 23:19:57.079190 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 23:20:00.100250 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 23:20:02.720995 23673 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 23:20:02.721022 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:20:03.616326 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:07.379395 23673 solver.cpp:397]     Test net output #0: accuracy = 0.457108
I0407 23:20:07.379436 23673 solver.cpp:397]     Test net output #1: loss = 3.25668 (* 1 = 3.25668 loss)
I0407 23:20:09.377079 23673 solver.cpp:218] Iteration 9288 (0.835358 iter/s, 14.3651s/12 iters), loss = 0.0704898
I0407 23:20:09.377136 23673 solver.cpp:237]     Train net output #0: loss = 0.0704895 (* 1 = 0.0704895 loss)
I0407 23:20:09.377149 23673 sgd_solver.cpp:105] Iteration 9288, lr = 0.00400453
I0407 23:20:14.402276 23673 solver.cpp:218] Iteration 9300 (2.38807 iter/s, 5.02498s/12 iters), loss = 0.0635856
I0407 23:20:14.402334 23673 solver.cpp:237]     Train net output #0: loss = 0.0635854 (* 1 = 0.0635854 loss)
I0407 23:20:14.402346 23673 sgd_solver.cpp:105] Iteration 9300, lr = 0.0039998
I0407 23:20:16.665374 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:19.484146 23673 solver.cpp:218] Iteration 9312 (2.36144 iter/s, 5.08165s/12 iters), loss = 0.0769626
I0407 23:20:19.484200 23673 solver.cpp:237]     Train net output #0: loss = 0.0769623 (* 1 = 0.0769623 loss)
I0407 23:20:19.484211 23673 sgd_solver.cpp:105] Iteration 9312, lr = 0.00399507
I0407 23:20:24.585029 23673 solver.cpp:218] Iteration 9324 (2.35263 iter/s, 5.10067s/12 iters), loss = 0.0920527
I0407 23:20:24.585086 23673 solver.cpp:237]     Train net output #0: loss = 0.0920524 (* 1 = 0.0920524 loss)
I0407 23:20:24.585099 23673 sgd_solver.cpp:105] Iteration 9324, lr = 0.00399035
I0407 23:20:29.560559 23673 solver.cpp:218] Iteration 9336 (2.41191 iter/s, 4.97532s/12 iters), loss = 0.0968961
I0407 23:20:29.560611 23673 solver.cpp:237]     Train net output #0: loss = 0.0968958 (* 1 = 0.0968958 loss)
I0407 23:20:29.560622 23673 sgd_solver.cpp:105] Iteration 9336, lr = 0.00398563
I0407 23:20:34.932693 23673 solver.cpp:218] Iteration 9348 (2.23384 iter/s, 5.37191s/12 iters), loss = 0.0653665
I0407 23:20:34.932821 23673 solver.cpp:237]     Train net output #0: loss = 0.0653663 (* 1 = 0.0653663 loss)
I0407 23:20:34.932834 23673 sgd_solver.cpp:105] Iteration 9348, lr = 0.00398092
I0407 23:20:40.428221 23673 solver.cpp:218] Iteration 9360 (2.18371 iter/s, 5.49524s/12 iters), loss = 0.0489999
I0407 23:20:40.428259 23673 solver.cpp:237]     Train net output #0: loss = 0.0489996 (* 1 = 0.0489996 loss)
I0407 23:20:40.428269 23673 sgd_solver.cpp:105] Iteration 9360, lr = 0.00397622
I0407 23:20:45.597064 23673 solver.cpp:218] Iteration 9372 (2.32169 iter/s, 5.16865s/12 iters), loss = 0.0852792
I0407 23:20:45.597102 23673 solver.cpp:237]     Train net output #0: loss = 0.0852789 (* 1 = 0.0852789 loss)
I0407 23:20:45.597110 23673 sgd_solver.cpp:105] Iteration 9372, lr = 0.00397152
I0407 23:20:50.284320 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 23:20:53.302642 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 23:20:56.497793 23673 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 23:20:56.497822 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:20:57.257791 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:00.927469 23673 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 23:21:00.927518 23673 solver.cpp:397]     Test net output #1: loss = 3.11849 (* 1 = 3.11849 loss)
I0407 23:21:01.018798 23673 solver.cpp:218] Iteration 9384 (0.778147 iter/s, 15.4212s/12 iters), loss = 0.133325
I0407 23:21:01.018841 23673 solver.cpp:237]     Train net output #0: loss = 0.133325 (* 1 = 0.133325 loss)
I0407 23:21:01.018851 23673 sgd_solver.cpp:105] Iteration 9384, lr = 0.00396683
I0407 23:21:05.582599 23673 solver.cpp:218] Iteration 9396 (2.62949 iter/s, 4.56361s/12 iters), loss = 0.0343185
I0407 23:21:05.582717 23673 solver.cpp:237]     Train net output #0: loss = 0.0343182 (* 1 = 0.0343182 loss)
I0407 23:21:05.582729 23673 sgd_solver.cpp:105] Iteration 9396, lr = 0.00396214
I0407 23:21:10.191931 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:10.859290 23673 solver.cpp:218] Iteration 9408 (2.27427 iter/s, 5.27641s/12 iters), loss = 0.250631
I0407 23:21:10.859339 23673 solver.cpp:237]     Train net output #0: loss = 0.25063 (* 1 = 0.25063 loss)
I0407 23:21:10.859349 23673 sgd_solver.cpp:105] Iteration 9408, lr = 0.00395746
I0407 23:21:15.950624 23673 solver.cpp:218] Iteration 9420 (2.35704 iter/s, 5.09113s/12 iters), loss = 0.167101
I0407 23:21:15.950665 23673 solver.cpp:237]     Train net output #0: loss = 0.167101 (* 1 = 0.167101 loss)
I0407 23:21:15.950675 23673 sgd_solver.cpp:105] Iteration 9420, lr = 0.00395278
I0407 23:21:21.070313 23673 solver.cpp:218] Iteration 9432 (2.34398 iter/s, 5.11949s/12 iters), loss = 0.0965757
I0407 23:21:21.070360 23673 solver.cpp:237]     Train net output #0: loss = 0.0965754 (* 1 = 0.0965754 loss)
I0407 23:21:21.070370 23673 sgd_solver.cpp:105] Iteration 9432, lr = 0.00394811
I0407 23:21:26.140599 23673 solver.cpp:218] Iteration 9444 (2.36683 iter/s, 5.07008s/12 iters), loss = 0.0783833
I0407 23:21:26.140645 23673 solver.cpp:237]     Train net output #0: loss = 0.078383 (* 1 = 0.078383 loss)
I0407 23:21:26.140655 23673 sgd_solver.cpp:105] Iteration 9444, lr = 0.00394345
I0407 23:21:31.346215 23673 solver.cpp:218] Iteration 9456 (2.30529 iter/s, 5.20541s/12 iters), loss = 0.0612541
I0407 23:21:31.346254 23673 solver.cpp:237]     Train net output #0: loss = 0.0612538 (* 1 = 0.0612538 loss)
I0407 23:21:31.346263 23673 sgd_solver.cpp:105] Iteration 9456, lr = 0.00393879
I0407 23:21:36.419215 23673 solver.cpp:218] Iteration 9468 (2.36556 iter/s, 5.07281s/12 iters), loss = 0.0807772
I0407 23:21:36.419314 23673 solver.cpp:237]     Train net output #0: loss = 0.0807769 (* 1 = 0.0807769 loss)
I0407 23:21:36.419327 23673 sgd_solver.cpp:105] Iteration 9468, lr = 0.00393413
I0407 23:21:41.866461 23673 solver.cpp:218] Iteration 9480 (2.20306 iter/s, 5.44698s/12 iters), loss = 0.114585
I0407 23:21:41.866513 23673 solver.cpp:237]     Train net output #0: loss = 0.114585 (* 1 = 0.114585 loss)
I0407 23:21:41.866524 23673 sgd_solver.cpp:105] Iteration 9480, lr = 0.00392948
I0407 23:21:44.120573 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 23:21:47.240216 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 23:21:51.503000 23673 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 23:21:51.503028 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:21:52.228113 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:55.940320 23673 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 23:21:55.940371 23673 solver.cpp:397]     Test net output #1: loss = 3.12717 (* 1 = 3.12717 loss)
I0407 23:21:58.372967 23673 solver.cpp:218] Iteration 9492 (0.72701 iter/s, 16.506s/12 iters), loss = 0.108913
I0407 23:21:58.373019 23673 solver.cpp:237]     Train net output #0: loss = 0.108913 (* 1 = 0.108913 loss)
I0407 23:21:58.373031 23673 sgd_solver.cpp:105] Iteration 9492, lr = 0.00392484
I0407 23:22:04.258661 23673 solver.cpp:218] Iteration 9504 (2.03892 iter/s, 5.88546s/12 iters), loss = 0.126183
I0407 23:22:04.258718 23673 solver.cpp:237]     Train net output #0: loss = 0.126182 (* 1 = 0.126182 loss)
I0407 23:22:04.258730 23673 sgd_solver.cpp:105] Iteration 9504, lr = 0.0039202
I0407 23:22:05.868232 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:09.334650 23673 solver.cpp:218] Iteration 9516 (2.36417 iter/s, 5.07577s/12 iters), loss = 0.133341
I0407 23:22:09.334772 23673 solver.cpp:237]     Train net output #0: loss = 0.133341 (* 1 = 0.133341 loss)
I0407 23:22:09.334785 23673 sgd_solver.cpp:105] Iteration 9516, lr = 0.00391557
I0407 23:22:14.467453 23673 solver.cpp:218] Iteration 9528 (2.33803 iter/s, 5.13252s/12 iters), loss = 0.113791
I0407 23:22:14.467501 23673 solver.cpp:237]     Train net output #0: loss = 0.113791 (* 1 = 0.113791 loss)
I0407 23:22:14.467511 23673 sgd_solver.cpp:105] Iteration 9528, lr = 0.00391094
I0407 23:22:19.479969 23673 solver.cpp:218] Iteration 9540 (2.39411 iter/s, 5.01231s/12 iters), loss = 0.0459199
I0407 23:22:19.480021 23673 solver.cpp:237]     Train net output #0: loss = 0.0459196 (* 1 = 0.0459196 loss)
I0407 23:22:19.480033 23673 sgd_solver.cpp:105] Iteration 9540, lr = 0.00390632
I0407 23:22:24.495282 23673 solver.cpp:218] Iteration 9552 (2.39277 iter/s, 5.0151s/12 iters), loss = 0.155654
I0407 23:22:24.495334 23673 solver.cpp:237]     Train net output #0: loss = 0.155654 (* 1 = 0.155654 loss)
I0407 23:22:24.495347 23673 sgd_solver.cpp:105] Iteration 9552, lr = 0.00390171
I0407 23:22:29.504912 23673 solver.cpp:218] Iteration 9564 (2.39549 iter/s, 5.00942s/12 iters), loss = 0.0607887
I0407 23:22:29.504958 23673 solver.cpp:237]     Train net output #0: loss = 0.0607884 (* 1 = 0.0607884 loss)
I0407 23:22:29.504969 23673 sgd_solver.cpp:105] Iteration 9564, lr = 0.0038971
I0407 23:22:34.667438 23673 solver.cpp:218] Iteration 9576 (2.32454 iter/s, 5.16232s/12 iters), loss = 0.0474158
I0407 23:22:34.667490 23673 solver.cpp:237]     Train net output #0: loss = 0.0474155 (* 1 = 0.0474155 loss)
I0407 23:22:34.667502 23673 sgd_solver.cpp:105] Iteration 9576, lr = 0.00389249
I0407 23:22:39.383114 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 23:22:42.429730 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 23:22:46.849349 23673 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 23:22:46.849375 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:22:47.556082 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:51.341935 23673 solver.cpp:397]     Test net output #0: accuracy = 0.459559
I0407 23:22:51.341987 23673 solver.cpp:397]     Test net output #1: loss = 3.22732 (* 1 = 3.22732 loss)
I0407 23:22:51.433151 23673 solver.cpp:218] Iteration 9588 (0.715769 iter/s, 16.7652s/12 iters), loss = 0.136714
I0407 23:22:51.433184 23673 solver.cpp:237]     Train net output #0: loss = 0.136713 (* 1 = 0.136713 loss)
I0407 23:22:51.433192 23673 sgd_solver.cpp:105] Iteration 9588, lr = 0.00388789
I0407 23:22:56.049052 23673 solver.cpp:218] Iteration 9600 (2.59981 iter/s, 4.61572s/12 iters), loss = 0.0491747
I0407 23:22:56.049104 23673 solver.cpp:237]     Train net output #0: loss = 0.0491744 (* 1 = 0.0491744 loss)
I0407 23:22:56.049118 23673 sgd_solver.cpp:105] Iteration 9600, lr = 0.0038833
I0407 23:23:00.010787 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:01.446234 23673 solver.cpp:218] Iteration 9612 (2.22347 iter/s, 5.39696s/12 iters), loss = 0.0969214
I0407 23:23:01.446290 23673 solver.cpp:237]     Train net output #0: loss = 0.0969212 (* 1 = 0.0969212 loss)
I0407 23:23:01.446301 23673 sgd_solver.cpp:105] Iteration 9612, lr = 0.00387871
I0407 23:23:06.766760 23673 solver.cpp:218] Iteration 9624 (2.25551 iter/s, 5.32031s/12 iters), loss = 0.0941694
I0407 23:23:06.766805 23673 solver.cpp:237]     Train net output #0: loss = 0.0941691 (* 1 = 0.0941691 loss)
I0407 23:23:06.766816 23673 sgd_solver.cpp:105] Iteration 9624, lr = 0.00387412
I0407 23:23:11.922597 23673 solver.cpp:218] Iteration 9636 (2.32755 iter/s, 5.15563s/12 iters), loss = 0.176353
I0407 23:23:11.922703 23673 solver.cpp:237]     Train net output #0: loss = 0.176353 (* 1 = 0.176353 loss)
I0407 23:23:11.922713 23673 sgd_solver.cpp:105] Iteration 9636, lr = 0.00386955
I0407 23:23:17.009747 23673 solver.cpp:218] Iteration 9648 (2.35901 iter/s, 5.08688s/12 iters), loss = 0.225566
I0407 23:23:17.009804 23673 solver.cpp:237]     Train net output #0: loss = 0.225566 (* 1 = 0.225566 loss)
I0407 23:23:17.009817 23673 sgd_solver.cpp:105] Iteration 9648, lr = 0.00386497
I0407 23:23:21.983280 23673 solver.cpp:218] Iteration 9660 (2.41287 iter/s, 4.97332s/12 iters), loss = 0.0687485
I0407 23:23:21.983331 23673 solver.cpp:237]     Train net output #0: loss = 0.0687483 (* 1 = 0.0687483 loss)
I0407 23:23:21.983343 23673 sgd_solver.cpp:105] Iteration 9660, lr = 0.00386041
I0407 23:23:27.189457 23673 solver.cpp:218] Iteration 9672 (2.30505 iter/s, 5.20596s/12 iters), loss = 0.139547
I0407 23:23:27.189507 23673 solver.cpp:237]     Train net output #0: loss = 0.139547 (* 1 = 0.139547 loss)
I0407 23:23:27.189522 23673 sgd_solver.cpp:105] Iteration 9672, lr = 0.00385584
I0407 23:23:32.701223 23673 solver.cpp:218] Iteration 9684 (2.17725 iter/s, 5.51154s/12 iters), loss = 0.115391
I0407 23:23:32.701274 23673 solver.cpp:237]     Train net output #0: loss = 0.11539 (* 1 = 0.11539 loss)
I0407 23:23:32.701287 23673 sgd_solver.cpp:105] Iteration 9684, lr = 0.00385129
I0407 23:23:35.002410 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 23:23:39.819417 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 23:23:46.114143 23673 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 23:23:46.115896 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:23:46.732198 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:49.544095 23673 blocking_queue.cpp:49] Waiting for data
I0407 23:23:50.532527 23673 solver.cpp:397]     Test net output #0: accuracy = 0.460784
I0407 23:23:50.532562 23673 solver.cpp:397]     Test net output #1: loss = 3.0444 (* 1 = 3.0444 loss)
I0407 23:23:52.518599 23673 solver.cpp:218] Iteration 9696 (0.605549 iter/s, 19.8167s/12 iters), loss = 0.175833
I0407 23:23:52.518641 23673 solver.cpp:237]     Train net output #0: loss = 0.175833 (* 1 = 0.175833 loss)
I0407 23:23:52.518651 23673 sgd_solver.cpp:105] Iteration 9696, lr = 0.00384674
I0407 23:23:58.016832 23673 solver.cpp:218] Iteration 9708 (2.18261 iter/s, 5.49801s/12 iters), loss = 0.0219269
I0407 23:23:58.016889 23673 solver.cpp:237]     Train net output #0: loss = 0.0219267 (* 1 = 0.0219267 loss)
I0407 23:23:58.016901 23673 sgd_solver.cpp:105] Iteration 9708, lr = 0.00384219
I0407 23:23:58.851511 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:03.505184 23673 solver.cpp:218] Iteration 9720 (2.18654 iter/s, 5.48812s/12 iters), loss = 0.0498005
I0407 23:24:03.505239 23673 solver.cpp:237]     Train net output #0: loss = 0.0498002 (* 1 = 0.0498002 loss)
I0407 23:24:03.505250 23673 sgd_solver.cpp:105] Iteration 9720, lr = 0.00383765
I0407 23:24:09.019515 23673 solver.cpp:218] Iteration 9732 (2.17624 iter/s, 5.51411s/12 iters), loss = 0.0901195
I0407 23:24:09.019559 23673 solver.cpp:237]     Train net output #0: loss = 0.0901192 (* 1 = 0.0901192 loss)
I0407 23:24:09.019569 23673 sgd_solver.cpp:105] Iteration 9732, lr = 0.00383312
I0407 23:24:14.526710 23673 solver.cpp:218] Iteration 9744 (2.17905 iter/s, 5.50698s/12 iters), loss = 0.109038
I0407 23:24:14.526747 23673 solver.cpp:237]     Train net output #0: loss = 0.109038 (* 1 = 0.109038 loss)
I0407 23:24:14.526757 23673 sgd_solver.cpp:105] Iteration 9744, lr = 0.00382859
I0407 23:24:19.875933 23673 solver.cpp:218] Iteration 9756 (2.2434 iter/s, 5.34902s/12 iters), loss = 0.266349
I0407 23:24:19.876045 23673 solver.cpp:237]     Train net output #0: loss = 0.266348 (* 1 = 0.266348 loss)
I0407 23:24:19.876060 23673 sgd_solver.cpp:105] Iteration 9756, lr = 0.00382406
I0407 23:24:25.046890 23673 solver.cpp:218] Iteration 9768 (2.32077 iter/s, 5.17069s/12 iters), loss = 0.107951
I0407 23:24:25.046931 23673 solver.cpp:237]     Train net output #0: loss = 0.107951 (* 1 = 0.107951 loss)
I0407 23:24:25.046939 23673 sgd_solver.cpp:105] Iteration 9768, lr = 0.00381954
I0407 23:24:30.148715 23673 solver.cpp:218] Iteration 9780 (2.35219 iter/s, 5.10162s/12 iters), loss = 0.119994
I0407 23:24:30.148770 23673 solver.cpp:237]     Train net output #0: loss = 0.119994 (* 1 = 0.119994 loss)
I0407 23:24:30.148782 23673 sgd_solver.cpp:105] Iteration 9780, lr = 0.00381503
I0407 23:24:34.718173 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 23:24:38.378813 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 23:24:42.049564 23673 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 23:24:42.049592 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:24:42.640435 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:46.556082 23673 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0407 23:24:46.556133 23673 solver.cpp:397]     Test net output #1: loss = 3.18877 (* 1 = 3.18877 loss)
I0407 23:24:46.647552 23673 solver.cpp:218] Iteration 9792 (0.727348 iter/s, 16.4983s/12 iters), loss = 0.0690062
I0407 23:24:46.647604 23673 solver.cpp:237]     Train net output #0: loss = 0.069006 (* 1 = 0.069006 loss)
I0407 23:24:46.647617 23673 sgd_solver.cpp:105] Iteration 9792, lr = 0.00381052
I0407 23:24:51.120633 23673 solver.cpp:218] Iteration 9804 (2.68283 iter/s, 4.47289s/12 iters), loss = 0.191844
I0407 23:24:51.120733 23673 solver.cpp:237]     Train net output #0: loss = 0.191843 (* 1 = 0.191843 loss)
I0407 23:24:51.120743 23673 sgd_solver.cpp:105] Iteration 9804, lr = 0.00380602
I0407 23:24:54.128984 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:56.223966 23673 solver.cpp:218] Iteration 9816 (2.35153 iter/s, 5.10307s/12 iters), loss = 0.128989
I0407 23:24:56.224023 23673 solver.cpp:237]     Train net output #0: loss = 0.128988 (* 1 = 0.128988 loss)
I0407 23:24:56.224037 23673 sgd_solver.cpp:105] Iteration 9816, lr = 0.00380152
I0407 23:25:01.320930 23673 solver.cpp:218] Iteration 9828 (2.35444 iter/s, 5.09674s/12 iters), loss = 0.124763
I0407 23:25:01.320988 23673 solver.cpp:237]     Train net output #0: loss = 0.124763 (* 1 = 0.124763 loss)
I0407 23:25:01.320999 23673 sgd_solver.cpp:105] Iteration 9828, lr = 0.00379703
I0407 23:25:06.397399 23673 solver.cpp:218] Iteration 9840 (2.36395 iter/s, 5.07625s/12 iters), loss = 0.13631
I0407 23:25:06.397449 23673 solver.cpp:237]     Train net output #0: loss = 0.13631 (* 1 = 0.13631 loss)
I0407 23:25:06.397460 23673 sgd_solver.cpp:105] Iteration 9840, lr = 0.00379254
I0407 23:25:11.490846 23673 solver.cpp:218] Iteration 9852 (2.35606 iter/s, 5.09324s/12 iters), loss = 0.0389735
I0407 23:25:11.490900 23673 solver.cpp:237]     Train net output #0: loss = 0.0389732 (* 1 = 0.0389732 loss)
I0407 23:25:11.490912 23673 sgd_solver.cpp:105] Iteration 9852, lr = 0.00378806
I0407 23:25:16.572553 23673 solver.cpp:218] Iteration 9864 (2.36151 iter/s, 5.08149s/12 iters), loss = 0.183684
I0407 23:25:16.572609 23673 solver.cpp:237]     Train net output #0: loss = 0.183684 (* 1 = 0.183684 loss)
I0407 23:25:16.572621 23673 sgd_solver.cpp:105] Iteration 9864, lr = 0.00378359
I0407 23:25:21.654520 23673 solver.cpp:218] Iteration 9876 (2.36139 iter/s, 5.08175s/12 iters), loss = 0.00916722
I0407 23:25:21.654600 23673 solver.cpp:237]     Train net output #0: loss = 0.00916699 (* 1 = 0.00916699 loss)
I0407 23:25:21.654613 23673 sgd_solver.cpp:105] Iteration 9876, lr = 0.00377911
I0407 23:25:26.648372 23673 solver.cpp:218] Iteration 9888 (2.40307 iter/s, 4.99361s/12 iters), loss = 0.102931
I0407 23:25:26.648427 23673 solver.cpp:237]     Train net output #0: loss = 0.102931 (* 1 = 0.102931 loss)
I0407 23:25:26.648439 23673 sgd_solver.cpp:105] Iteration 9888, lr = 0.00377465
I0407 23:25:28.725455 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 23:25:34.973577 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 23:25:38.709838 23673 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 23:25:38.709865 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:25:39.279958 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:43.171025 23673 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0407 23:25:43.171072 23673 solver.cpp:397]     Test net output #1: loss = 3.27942 (* 1 = 3.27942 loss)
I0407 23:25:45.118811 23673 solver.cpp:218] Iteration 9900 (0.649708 iter/s, 18.4698s/12 iters), loss = 0.115426
I0407 23:25:45.118856 23673 solver.cpp:237]     Train net output #0: loss = 0.115425 (* 1 = 0.115425 loss)
I0407 23:25:45.118865 23673 sgd_solver.cpp:105] Iteration 9900, lr = 0.00377019
I0407 23:25:50.218678 23673 solver.cpp:218] Iteration 9912 (2.3531 iter/s, 5.09965s/12 iters), loss = 0.0806156
I0407 23:25:50.218727 23673 solver.cpp:237]     Train net output #0: loss = 0.0806153 (* 1 = 0.0806153 loss)
I0407 23:25:50.218736 23673 sgd_solver.cpp:105] Iteration 9912, lr = 0.00376573
I0407 23:25:50.316783 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:55.423213 23673 solver.cpp:218] Iteration 9924 (2.30578 iter/s, 5.20431s/12 iters), loss = 0.143227
I0407 23:25:55.423318 23673 solver.cpp:237]     Train net output #0: loss = 0.143226 (* 1 = 0.143226 loss)
I0407 23:25:55.423329 23673 sgd_solver.cpp:105] Iteration 9924, lr = 0.00376128
I0407 23:26:00.608971 23673 solver.cpp:218] Iteration 9936 (2.31415 iter/s, 5.18549s/12 iters), loss = 0.147971
I0407 23:26:00.609030 23673 solver.cpp:237]     Train net output #0: loss = 0.14797 (* 1 = 0.14797 loss)
I0407 23:26:00.609042 23673 sgd_solver.cpp:105] Iteration 9936, lr = 0.00375684
I0407 23:26:05.694849 23673 solver.cpp:218] Iteration 9948 (2.35958 iter/s, 5.08566s/12 iters), loss = 0.0445552
I0407 23:26:05.694900 23673 solver.cpp:237]     Train net output #0: loss = 0.0445549 (* 1 = 0.0445549 loss)
I0407 23:26:05.694911 23673 sgd_solver.cpp:105] Iteration 9948, lr = 0.0037524
I0407 23:26:10.799263 23673 solver.cpp:218] Iteration 9960 (2.35101 iter/s, 5.1042s/12 iters), loss = 0.11423
I0407 23:26:10.799310 23673 solver.cpp:237]     Train net output #0: loss = 0.11423 (* 1 = 0.11423 loss)
I0407 23:26:10.799319 23673 sgd_solver.cpp:105] Iteration 9960, lr = 0.00374796
I0407 23:26:15.930577 23673 solver.cpp:218] Iteration 9972 (2.33868 iter/s, 5.13111s/12 iters), loss = 0.103403
I0407 23:26:15.930615 23673 solver.cpp:237]     Train net output #0: loss = 0.103403 (* 1 = 0.103403 loss)
I0407 23:26:15.930622 23673 sgd_solver.cpp:105] Iteration 9972, lr = 0.00374354
I0407 23:26:21.086956 23673 solver.cpp:218] Iteration 9984 (2.32731 iter/s, 5.15617s/12 iters), loss = 0.0550655
I0407 23:26:21.087002 23673 solver.cpp:237]     Train net output #0: loss = 0.0550653 (* 1 = 0.0550653 loss)
I0407 23:26:21.087011 23673 sgd_solver.cpp:105] Iteration 9984, lr = 0.00373911
I0407 23:26:25.965713 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 23:26:29.762269 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 23:26:35.285786 23673 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 23:26:35.285815 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:26:35.770643 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:39.855986 23673 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 23:26:39.856034 23673 solver.cpp:397]     Test net output #1: loss = 3.14034 (* 1 = 3.14034 loss)
I0407 23:26:39.947553 23673 solver.cpp:218] Iteration 9996 (0.636268 iter/s, 18.86s/12 iters), loss = 0.117575
I0407 23:26:39.947625 23673 solver.cpp:237]     Train net output #0: loss = 0.117574 (* 1 = 0.117574 loss)
I0407 23:26:39.947641 23673 sgd_solver.cpp:105] Iteration 9996, lr = 0.00373469
I0407 23:26:44.235484 23673 solver.cpp:218] Iteration 10008 (2.79869 iter/s, 4.28772s/12 iters), loss = 0.127021
I0407 23:26:44.235530 23673 solver.cpp:237]     Train net output #0: loss = 0.127021 (* 1 = 0.127021 loss)
I0407 23:26:44.235541 23673 sgd_solver.cpp:105] Iteration 10008, lr = 0.00373028
I0407 23:26:46.603682 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:49.424059 23673 solver.cpp:218] Iteration 10020 (2.31287 iter/s, 5.18835s/12 iters), loss = 0.138874
I0407 23:26:49.424115 23673 solver.cpp:237]     Train net output #0: loss = 0.138874 (* 1 = 0.138874 loss)
I0407 23:26:49.424127 23673 sgd_solver.cpp:105] Iteration 10020, lr = 0.00372587
I0407 23:26:54.510860 23673 solver.cpp:218] Iteration 10032 (2.35915 iter/s, 5.08658s/12 iters), loss = 0.0491051
I0407 23:26:54.510902 23673 solver.cpp:237]     Train net output #0: loss = 0.0491049 (* 1 = 0.0491049 loss)
I0407 23:26:54.510911 23673 sgd_solver.cpp:105] Iteration 10032, lr = 0.00372147
I0407 23:26:59.622198 23673 solver.cpp:218] Iteration 10044 (2.34782 iter/s, 5.11113s/12 iters), loss = 0.0216121
I0407 23:26:59.622339 23673 solver.cpp:237]     Train net output #0: loss = 0.0216119 (* 1 = 0.0216119 loss)
I0407 23:26:59.622352 23673 sgd_solver.cpp:105] Iteration 10044, lr = 0.00371707
I0407 23:27:04.947186 23673 solver.cpp:218] Iteration 10056 (2.25365 iter/s, 5.32469s/12 iters), loss = 0.123722
I0407 23:27:04.947225 23673 solver.cpp:237]     Train net output #0: loss = 0.123722 (* 1 = 0.123722 loss)
I0407 23:27:04.947234 23673 sgd_solver.cpp:105] Iteration 10056, lr = 0.00371268
I0407 23:27:10.341863 23673 solver.cpp:218] Iteration 10068 (2.2245 iter/s, 5.39446s/12 iters), loss = 0.0785977
I0407 23:27:10.341909 23673 solver.cpp:237]     Train net output #0: loss = 0.0785975 (* 1 = 0.0785975 loss)
I0407 23:27:10.341920 23673 sgd_solver.cpp:105] Iteration 10068, lr = 0.00370829
I0407 23:27:15.288719 23673 solver.cpp:218] Iteration 10080 (2.42588 iter/s, 4.94666s/12 iters), loss = 0.120008
I0407 23:27:15.288761 23673 solver.cpp:237]     Train net output #0: loss = 0.120008 (* 1 = 0.120008 loss)
I0407 23:27:15.288770 23673 sgd_solver.cpp:105] Iteration 10080, lr = 0.00370391
I0407 23:27:20.394894 23673 solver.cpp:218] Iteration 10092 (2.35019 iter/s, 5.10597s/12 iters), loss = 0.100504
I0407 23:27:20.394939 23673 solver.cpp:237]     Train net output #0: loss = 0.100504 (* 1 = 0.100504 loss)
I0407 23:27:20.394949 23673 sgd_solver.cpp:105] Iteration 10092, lr = 0.00369953
I0407 23:27:22.412142 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 23:27:27.847314 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 23:27:40.147420 23673 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 23:27:40.147521 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:27:40.633297 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:44.617646 23673 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0407 23:27:44.617681 23673 solver.cpp:397]     Test net output #1: loss = 3.11758 (* 1 = 3.11758 loss)
I0407 23:27:46.622176 23673 solver.cpp:218] Iteration 10104 (0.457553 iter/s, 26.2265s/12 iters), loss = 0.0644831
I0407 23:27:46.622217 23673 solver.cpp:237]     Train net output #0: loss = 0.0644829 (* 1 = 0.0644829 loss)
I0407 23:27:46.622226 23673 sgd_solver.cpp:105] Iteration 10104, lr = 0.00369516
I0407 23:27:51.189345 23677 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:51.827693 23673 solver.cpp:218] Iteration 10116 (2.30534 iter/s, 5.20531s/12 iters), loss = 0.0843735
I0407 23:27:51.827740 23673 solver.cpp:237]     Train net output #0: loss = 0.0843733 (* 1 = 0.0843733 loss)
I0407 23:27:51.827752 23673 sgd_solver.cpp:105] Iteration 10116, lr = 0.0036908
I0407 23:27:57.102475 23673 solver.cpp:218] Iteration 10128 (2.27507 iter/s, 5.27455s/12 iters), loss = 0.223904
I0407 23:27:57.102524 23673 solver.cpp:237]     Train net output #0: loss = 0.223903 (* 1 = 0.223903 loss)
I0407 23:27:57.102535 23673 sgd_solver.cpp:105] Iteration 10128, lr = 0.00368643
I0407 23:28:02.263638 23673 solver.cpp:218] Iteration 10140 (2.32515 iter/s, 5.16095s/12 iters), loss = 0.178737
I0407 23:28:02.263684 23673 solver.cpp:237]     Train net output #0: loss = 0.178736 (* 1 = 0.178736 loss)
I0407 23:28:02.263697 23673 sgd_solver.cpp:105] Iteration 10140, lr = 0.00368208
I0407 23:28:07.332509 23673 solver.cpp:218] Iteration 10152 (2.36749 iter/s, 5.06866s/12 iters), loss = 0.0660207
I0407 23:28:07.332546 23673 solver.cpp:237]     Train net output #0: loss = 0.0660204 (* 1 = 0.0660204 loss)
I0407 23:28:07.332556 23673 sgd_solver.cpp:105] Iteration 10152, lr = 0.00367773
I0407 23:28:12.429327 23673 solver.cpp:218] Iteration 10164 (2.35451 iter/s, 5.09661s/12 iters), loss = 0.0631081
I0407 23:28:12.429476 23673 solver.cpp:237]     Train net output #0: loss = 0.0631079 (* 1 = 0.0631079 loss)
I0407 23:28:12.429489 23673 sgd_solver.cpp:105] Iteration 10164, lr = 0.00367338
I0407 23:28:17.488503 23673 solver.cpp:218] Iteration 10176 (2.37207 iter/s, 5.05888s/12 iters), loss = 0.0733068
I0407 23:28:17.488541 23673 solver.cpp:237]     Train net output #0: loss = 0.0733066 (* 1 = 0.0733066 loss)
I0407 23:28:17.488550 23673 sgd_solver.cpp:105] Iteration 10176, lr = 0.00366904
I0407 23:28:22.567410 23673 solver.cpp:218] Iteration 10188 (2.36281 iter/s, 5.07871s/12 iters), loss = 0.0503836
I0407 23:28:22.567456 23673 solver.cpp:237]     Train net output #0: loss = 0.0503834 (* 1 = 0.0503834 loss)
I0407 23:28:22.567467 23673 sgd_solver.cpp:105] Iteration 10188, lr = 0.0036647
I0407 23:28:27.287384 23673 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 23:28:33.551002 23673 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 23:28:38.215843 23673 solver.cpp:310] Iteration 10200, loss = 0.0516681
I0407 23:28:38.215873 23673 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 23:28:38.215878 23673 net.cpp:676] Ignoring source layer train-data
I0407 23:28:38.631624 23679 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:42.650635 23673 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 23:28:42.650761 23673 solver.cpp:397]     Test net output #1: loss = 3.2498 (* 1 = 3.2498 loss)
I0407 23:28:42.650776 23673 solver.cpp:315] Optimization Done.
I0407 23:28:42.650784 23673 caffe.cpp:259] Optimization Done.