DIGITS-CNN/cars/lr-investigations/exponential/1e-2/0.95/caffe_output.log

I0407 21:56:14.818867 23658 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-215613-3028/solver.prototxt
I0407 21:56:14.820785 23658 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 21:56:14.820801 23658 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 21:56:14.820948 23658 caffe.cpp:218] Using GPUs 0
I0407 21:56:14.850378 23658 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0407 21:56:15.147306 23658 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: 0.99949723
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 21:56:15.148005 23658 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 21:56:15.148581 23658 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 21:56:15.148598 23658 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 21:56:15.148742 23658 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 21:56:15.148841 23658 layer_factory.hpp:77] Creating layer train-data
I0407 21:56:15.286978 23658 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0407 21:56:15.335000 23658 net.cpp:84] Creating Layer train-data
I0407 21:56:15.335022 23658 net.cpp:380] train-data -> data
I0407 21:56:15.335050 23658 net.cpp:380] train-data -> label
I0407 21:56:15.335064 23658 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:56:15.341223 23658 data_layer.cpp:45] output data size: 128,3,227,227
I0407 21:56:15.478703 23658 net.cpp:122] Setting up train-data
I0407 21:56:15.478729 23658 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 21:56:15.478734 23658 net.cpp:129] Top shape: 128 (128)
I0407 21:56:15.478739 23658 net.cpp:137] Memory required for data: 79149056
I0407 21:56:15.478749 23658 layer_factory.hpp:77] Creating layer conv1
I0407 21:56:15.478770 23658 net.cpp:84] Creating Layer conv1
I0407 21:56:15.478776 23658 net.cpp:406] conv1 <- data
I0407 21:56:15.478788 23658 net.cpp:380] conv1 -> conv1
I0407 21:56:16.039898 23658 net.cpp:122] Setting up conv1
I0407 21:56:16.039919 23658 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:16.039923 23658 net.cpp:137] Memory required for data: 227833856
I0407 21:56:16.039942 23658 layer_factory.hpp:77] Creating layer relu1
I0407 21:56:16.039952 23658 net.cpp:84] Creating Layer relu1
I0407 21:56:16.039958 23658 net.cpp:406] relu1 <- conv1
I0407 21:56:16.039963 23658 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:56:16.040243 23658 net.cpp:122] Setting up relu1
I0407 21:56:16.040251 23658 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:16.040256 23658 net.cpp:137] Memory required for data: 376518656
I0407 21:56:16.040259 23658 layer_factory.hpp:77] Creating layer norm1
I0407 21:56:16.040267 23658 net.cpp:84] Creating Layer norm1
I0407 21:56:16.040271 23658 net.cpp:406] norm1 <- conv1
I0407 21:56:16.040295 23658 net.cpp:380] norm1 -> norm1
I0407 21:56:16.040741 23658 net.cpp:122] Setting up norm1
I0407 21:56:16.040751 23658 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:56:16.040755 23658 net.cpp:137] Memory required for data: 525203456
I0407 21:56:16.040758 23658 layer_factory.hpp:77] Creating layer pool1
I0407 21:56:16.040766 23658 net.cpp:84] Creating Layer pool1
I0407 21:56:16.040769 23658 net.cpp:406] pool1 <- norm1
I0407 21:56:16.040774 23658 net.cpp:380] pool1 -> pool1
I0407 21:56:16.040809 23658 net.cpp:122] Setting up pool1
I0407 21:56:16.040815 23658 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 21:56:16.040818 23658 net.cpp:137] Memory required for data: 561035264
I0407 21:56:16.040822 23658 layer_factory.hpp:77] Creating layer conv2
I0407 21:56:16.040832 23658 net.cpp:84] Creating Layer conv2
I0407 21:56:16.040835 23658 net.cpp:406] conv2 <- pool1
I0407 21:56:16.040841 23658 net.cpp:380] conv2 -> conv2
I0407 21:56:16.047812 23658 net.cpp:122] Setting up conv2
I0407 21:56:16.047828 23658 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:16.047832 23658 net.cpp:137] Memory required for data: 656586752
I0407 21:56:16.047840 23658 layer_factory.hpp:77] Creating layer relu2
I0407 21:56:16.047847 23658 net.cpp:84] Creating Layer relu2
I0407 21:56:16.047850 23658 net.cpp:406] relu2 <- conv2
I0407 21:56:16.047855 23658 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:56:16.048348 23658 net.cpp:122] Setting up relu2
I0407 21:56:16.048358 23658 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:16.048362 23658 net.cpp:137] Memory required for data: 752138240
I0407 21:56:16.048364 23658 layer_factory.hpp:77] Creating layer norm2
I0407 21:56:16.048372 23658 net.cpp:84] Creating Layer norm2
I0407 21:56:16.048375 23658 net.cpp:406] norm2 <- conv2
I0407 21:56:16.048382 23658 net.cpp:380] norm2 -> norm2
I0407 21:56:16.048732 23658 net.cpp:122] Setting up norm2
I0407 21:56:16.048741 23658 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:56:16.048744 23658 net.cpp:137] Memory required for data: 847689728
I0407 21:56:16.048748 23658 layer_factory.hpp:77] Creating layer pool2
I0407 21:56:16.048755 23658 net.cpp:84] Creating Layer pool2
I0407 21:56:16.048759 23658 net.cpp:406] pool2 <- norm2
I0407 21:56:16.048764 23658 net.cpp:380] pool2 -> pool2
I0407 21:56:16.048794 23658 net.cpp:122] Setting up pool2
I0407 21:56:16.048800 23658 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:16.048804 23658 net.cpp:137] Memory required for data: 869840896
I0407 21:56:16.048806 23658 layer_factory.hpp:77] Creating layer conv3
I0407 21:56:16.048815 23658 net.cpp:84] Creating Layer conv3
I0407 21:56:16.048818 23658 net.cpp:406] conv3 <- pool2
I0407 21:56:16.048825 23658 net.cpp:380] conv3 -> conv3
I0407 21:56:16.058775 23658 net.cpp:122] Setting up conv3
I0407 21:56:16.058785 23658 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:16.058789 23658 net.cpp:137] Memory required for data: 903067648
I0407 21:56:16.058796 23658 layer_factory.hpp:77] Creating layer relu3
I0407 21:56:16.058802 23658 net.cpp:84] Creating Layer relu3
I0407 21:56:16.058806 23658 net.cpp:406] relu3 <- conv3
I0407 21:56:16.058815 23658 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:56:16.059304 23658 net.cpp:122] Setting up relu3
I0407 21:56:16.059314 23658 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:16.059317 23658 net.cpp:137] Memory required for data: 936294400
I0407 21:56:16.059320 23658 layer_factory.hpp:77] Creating layer conv4
I0407 21:56:16.059330 23658 net.cpp:84] Creating Layer conv4
I0407 21:56:16.059334 23658 net.cpp:406] conv4 <- conv3
I0407 21:56:16.059340 23658 net.cpp:380] conv4 -> conv4
I0407 21:56:16.069701 23658 net.cpp:122] Setting up conv4
I0407 21:56:16.069713 23658 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:16.069717 23658 net.cpp:137] Memory required for data: 969521152
I0407 21:56:16.069725 23658 layer_factory.hpp:77] Creating layer relu4
I0407 21:56:16.069730 23658 net.cpp:84] Creating Layer relu4
I0407 21:56:16.069751 23658 net.cpp:406] relu4 <- conv4
I0407 21:56:16.069757 23658 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:56:16.070102 23658 net.cpp:122] Setting up relu4
I0407 21:56:16.070111 23658 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:56:16.070116 23658 net.cpp:137] Memory required for data: 1002747904
I0407 21:56:16.070119 23658 layer_factory.hpp:77] Creating layer conv5
I0407 21:56:16.070128 23658 net.cpp:84] Creating Layer conv5
I0407 21:56:16.070132 23658 net.cpp:406] conv5 <- conv4
I0407 21:56:16.070137 23658 net.cpp:380] conv5 -> conv5
I0407 21:56:16.078464 23658 net.cpp:122] Setting up conv5
I0407 21:56:16.078475 23658 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:16.078480 23658 net.cpp:137] Memory required for data: 1024899072
I0407 21:56:16.078488 23658 layer_factory.hpp:77] Creating layer relu5
I0407 21:56:16.078495 23658 net.cpp:84] Creating Layer relu5
I0407 21:56:16.078498 23658 net.cpp:406] relu5 <- conv5
I0407 21:56:16.078505 23658 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:56:16.078990 23658 net.cpp:122] Setting up relu5
I0407 21:56:16.078999 23658 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:56:16.079003 23658 net.cpp:137] Memory required for data: 1047050240
I0407 21:56:16.079007 23658 layer_factory.hpp:77] Creating layer pool5
I0407 21:56:16.079015 23658 net.cpp:84] Creating Layer pool5
I0407 21:56:16.079018 23658 net.cpp:406] pool5 <- conv5
I0407 21:56:16.079023 23658 net.cpp:380] pool5 -> pool5
I0407 21:56:16.079061 23658 net.cpp:122] Setting up pool5
I0407 21:56:16.079066 23658 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 21:56:16.079068 23658 net.cpp:137] Memory required for data: 1051768832
I0407 21:56:16.079072 23658 layer_factory.hpp:77] Creating layer fc6
I0407 21:56:16.079082 23658 net.cpp:84] Creating Layer fc6
I0407 21:56:16.079084 23658 net.cpp:406] fc6 <- pool5
I0407 21:56:16.079089 23658 net.cpp:380] fc6 -> fc6
I0407 21:56:16.430938 23658 net.cpp:122] Setting up fc6
I0407 21:56:16.430956 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.430960 23658 net.cpp:137] Memory required for data: 1053865984
I0407 21:56:16.430969 23658 layer_factory.hpp:77] Creating layer relu6
I0407 21:56:16.430979 23658 net.cpp:84] Creating Layer relu6
I0407 21:56:16.430984 23658 net.cpp:406] relu6 <- fc6
I0407 21:56:16.430990 23658 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:56:16.431602 23658 net.cpp:122] Setting up relu6
I0407 21:56:16.431612 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.431614 23658 net.cpp:137] Memory required for data: 1055963136
I0407 21:56:16.431618 23658 layer_factory.hpp:77] Creating layer drop6
I0407 21:56:16.431625 23658 net.cpp:84] Creating Layer drop6
I0407 21:56:16.431629 23658 net.cpp:406] drop6 <- fc6
I0407 21:56:16.431635 23658 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:56:16.431661 23658 net.cpp:122] Setting up drop6
I0407 21:56:16.431666 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.431669 23658 net.cpp:137] Memory required for data: 1058060288
I0407 21:56:16.431672 23658 layer_factory.hpp:77] Creating layer fc7
I0407 21:56:16.431681 23658 net.cpp:84] Creating Layer fc7
I0407 21:56:16.431685 23658 net.cpp:406] fc7 <- fc6
I0407 21:56:16.431691 23658 net.cpp:380] fc7 -> fc7
I0407 21:56:16.588145 23658 net.cpp:122] Setting up fc7
I0407 21:56:16.588163 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.588167 23658 net.cpp:137] Memory required for data: 1060157440
I0407 21:56:16.588176 23658 layer_factory.hpp:77] Creating layer relu7
I0407 21:56:16.588186 23658 net.cpp:84] Creating Layer relu7
I0407 21:56:16.588191 23658 net.cpp:406] relu7 <- fc7
I0407 21:56:16.588196 23658 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:56:16.588814 23658 net.cpp:122] Setting up relu7
I0407 21:56:16.588824 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.588827 23658 net.cpp:137] Memory required for data: 1062254592
I0407 21:56:16.588830 23658 layer_factory.hpp:77] Creating layer drop7
I0407 21:56:16.588837 23658 net.cpp:84] Creating Layer drop7
I0407 21:56:16.588860 23658 net.cpp:406] drop7 <- fc7
I0407 21:56:16.588865 23658 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:56:16.588889 23658 net.cpp:122] Setting up drop7
I0407 21:56:16.588896 23658 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:56:16.588898 23658 net.cpp:137] Memory required for data: 1064351744
I0407 21:56:16.588901 23658 layer_factory.hpp:77] Creating layer fc8
I0407 21:56:16.588908 23658 net.cpp:84] Creating Layer fc8
I0407 21:56:16.588912 23658 net.cpp:406] fc8 <- fc7
I0407 21:56:16.588917 23658 net.cpp:380] fc8 -> fc8
I0407 21:56:16.596531 23658 net.cpp:122] Setting up fc8
I0407 21:56:16.596540 23658 net.cpp:129] Top shape: 128 196 (25088)
I0407 21:56:16.596544 23658 net.cpp:137] Memory required for data: 1064452096
I0407 21:56:16.596549 23658 layer_factory.hpp:77] Creating layer loss
I0407 21:56:16.596556 23658 net.cpp:84] Creating Layer loss
I0407 21:56:16.596560 23658 net.cpp:406] loss <- fc8
I0407 21:56:16.596565 23658 net.cpp:406] loss <- label
I0407 21:56:16.596571 23658 net.cpp:380] loss -> loss
I0407 21:56:16.596580 23658 layer_factory.hpp:77] Creating layer loss
I0407 21:56:16.597169 23658 net.cpp:122] Setting up loss
I0407 21:56:16.597178 23658 net.cpp:129] Top shape: (1)
I0407 21:56:16.597182 23658 net.cpp:132]     with loss weight 1
I0407 21:56:16.597199 23658 net.cpp:137] Memory required for data: 1064452100
I0407 21:56:16.597203 23658 net.cpp:198] loss needs backward computation.
I0407 21:56:16.597209 23658 net.cpp:198] fc8 needs backward computation.
I0407 21:56:16.597213 23658 net.cpp:198] drop7 needs backward computation.
I0407 21:56:16.597216 23658 net.cpp:198] relu7 needs backward computation.
I0407 21:56:16.597220 23658 net.cpp:198] fc7 needs backward computation.
I0407 21:56:16.597224 23658 net.cpp:198] drop6 needs backward computation.
I0407 21:56:16.597229 23658 net.cpp:198] relu6 needs backward computation.
I0407 21:56:16.597231 23658 net.cpp:198] fc6 needs backward computation.
I0407 21:56:16.597235 23658 net.cpp:198] pool5 needs backward computation.
I0407 21:56:16.597239 23658 net.cpp:198] relu5 needs backward computation.
I0407 21:56:16.597242 23658 net.cpp:198] conv5 needs backward computation.
I0407 21:56:16.597245 23658 net.cpp:198] relu4 needs backward computation.
I0407 21:56:16.597249 23658 net.cpp:198] conv4 needs backward computation.
I0407 21:56:16.597252 23658 net.cpp:198] relu3 needs backward computation.
I0407 21:56:16.597256 23658 net.cpp:198] conv3 needs backward computation.
I0407 21:56:16.597259 23658 net.cpp:198] pool2 needs backward computation.
I0407 21:56:16.597263 23658 net.cpp:198] norm2 needs backward computation.
I0407 21:56:16.597266 23658 net.cpp:198] relu2 needs backward computation.
I0407 21:56:16.597270 23658 net.cpp:198] conv2 needs backward computation.
I0407 21:56:16.597273 23658 net.cpp:198] pool1 needs backward computation.
I0407 21:56:16.597277 23658 net.cpp:198] norm1 needs backward computation.
I0407 21:56:16.597280 23658 net.cpp:198] relu1 needs backward computation.
I0407 21:56:16.597283 23658 net.cpp:198] conv1 needs backward computation.
I0407 21:56:16.597287 23658 net.cpp:200] train-data does not need backward computation.
I0407 21:56:16.597291 23658 net.cpp:242] This network produces output loss
I0407 21:56:16.597306 23658 net.cpp:255] Network initialization done.
I0407 21:56:16.597772 23658 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 21:56:16.597805 23658 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 21:56:16.597949 23658 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 21:56:16.598053 23658 layer_factory.hpp:77] Creating layer val-data
I0407 21:56:16.601140 23658 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0407 21:56:16.601651 23658 net.cpp:84] Creating Layer val-data
I0407 21:56:16.601660 23658 net.cpp:380] val-data -> data
I0407 21:56:16.601670 23658 net.cpp:380] val-data -> label
I0407 21:56:16.601675 23658 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:56:16.605679 23658 data_layer.cpp:45] output data size: 32,3,227,227
I0407 21:56:16.635514 23658 net.cpp:122] Setting up val-data
I0407 21:56:16.635531 23658 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 21:56:16.635535 23658 net.cpp:129] Top shape: 32 (32)
I0407 21:56:16.635540 23658 net.cpp:137] Memory required for data: 19787264
I0407 21:56:16.635546 23658 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 21:56:16.635558 23658 net.cpp:84] Creating Layer label_val-data_1_split
I0407 21:56:16.635562 23658 net.cpp:406] label_val-data_1_split <- label
I0407 21:56:16.635569 23658 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 21:56:16.635578 23658 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 21:56:16.635619 23658 net.cpp:122] Setting up label_val-data_1_split
I0407 21:56:16.635624 23658 net.cpp:129] Top shape: 32 (32)
I0407 21:56:16.635628 23658 net.cpp:129] Top shape: 32 (32)
I0407 21:56:16.635632 23658 net.cpp:137] Memory required for data: 19787520
I0407 21:56:16.635634 23658 layer_factory.hpp:77] Creating layer conv1
I0407 21:56:16.635645 23658 net.cpp:84] Creating Layer conv1
I0407 21:56:16.635648 23658 net.cpp:406] conv1 <- data
I0407 21:56:16.635653 23658 net.cpp:380] conv1 -> conv1
I0407 21:56:16.637728 23658 net.cpp:122] Setting up conv1
I0407 21:56:16.637738 23658 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:16.637742 23658 net.cpp:137] Memory required for data: 56958720
I0407 21:56:16.637753 23658 layer_factory.hpp:77] Creating layer relu1
I0407 21:56:16.637758 23658 net.cpp:84] Creating Layer relu1
I0407 21:56:16.637763 23658 net.cpp:406] relu1 <- conv1
I0407 21:56:16.637768 23658 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:56:16.638139 23658 net.cpp:122] Setting up relu1
I0407 21:56:16.638147 23658 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:16.638150 23658 net.cpp:137] Memory required for data: 94129920
I0407 21:56:16.638154 23658 layer_factory.hpp:77] Creating layer norm1
I0407 21:56:16.638162 23658 net.cpp:84] Creating Layer norm1
I0407 21:56:16.638166 23658 net.cpp:406] norm1 <- conv1
I0407 21:56:16.638171 23658 net.cpp:380] norm1 -> norm1
I0407 21:56:16.638648 23658 net.cpp:122] Setting up norm1
I0407 21:56:16.638656 23658 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:56:16.638660 23658 net.cpp:137] Memory required for data: 131301120
I0407 21:56:16.638664 23658 layer_factory.hpp:77] Creating layer pool1
I0407 21:56:16.638670 23658 net.cpp:84] Creating Layer pool1
I0407 21:56:16.638674 23658 net.cpp:406] pool1 <- norm1
I0407 21:56:16.638679 23658 net.cpp:380] pool1 -> pool1
I0407 21:56:16.638706 23658 net.cpp:122] Setting up pool1
I0407 21:56:16.638711 23658 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 21:56:16.638715 23658 net.cpp:137] Memory required for data: 140259072
I0407 21:56:16.638718 23658 layer_factory.hpp:77] Creating layer conv2
I0407 21:56:16.638725 23658 net.cpp:84] Creating Layer conv2
I0407 21:56:16.638729 23658 net.cpp:406] conv2 <- pool1
I0407 21:56:16.638752 23658 net.cpp:380] conv2 -> conv2
I0407 21:56:16.647732 23658 net.cpp:122] Setting up conv2
I0407 21:56:16.647744 23658 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:16.647748 23658 net.cpp:137] Memory required for data: 164146944
I0407 21:56:16.647758 23658 layer_factory.hpp:77] Creating layer relu2
I0407 21:56:16.647764 23658 net.cpp:84] Creating Layer relu2
I0407 21:56:16.647768 23658 net.cpp:406] relu2 <- conv2
I0407 21:56:16.647774 23658 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:56:16.648277 23658 net.cpp:122] Setting up relu2
I0407 21:56:16.648285 23658 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:16.648288 23658 net.cpp:137] Memory required for data: 188034816
I0407 21:56:16.648293 23658 layer_factory.hpp:77] Creating layer norm2
I0407 21:56:16.648301 23658 net.cpp:84] Creating Layer norm2
I0407 21:56:16.648305 23658 net.cpp:406] norm2 <- conv2
I0407 21:56:16.648311 23658 net.cpp:380] norm2 -> norm2
I0407 21:56:16.648834 23658 net.cpp:122] Setting up norm2
I0407 21:56:16.648844 23658 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:56:16.648847 23658 net.cpp:137] Memory required for data: 211922688
I0407 21:56:16.648851 23658 layer_factory.hpp:77] Creating layer pool2
I0407 21:56:16.648859 23658 net.cpp:84] Creating Layer pool2
I0407 21:56:16.648862 23658 net.cpp:406] pool2 <- norm2
I0407 21:56:16.648867 23658 net.cpp:380] pool2 -> pool2
I0407 21:56:16.648897 23658 net.cpp:122] Setting up pool2
I0407 21:56:16.648902 23658 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:16.648905 23658 net.cpp:137] Memory required for data: 217460480
I0407 21:56:16.648908 23658 layer_factory.hpp:77] Creating layer conv3
I0407 21:56:16.648917 23658 net.cpp:84] Creating Layer conv3
I0407 21:56:16.648921 23658 net.cpp:406] conv3 <- pool2
I0407 21:56:16.648927 23658 net.cpp:380] conv3 -> conv3
I0407 21:56:16.660539 23658 net.cpp:122] Setting up conv3
I0407 21:56:16.660554 23658 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:16.660557 23658 net.cpp:137] Memory required for data: 225767168
I0407 21:56:16.660567 23658 layer_factory.hpp:77] Creating layer relu3
I0407 21:56:16.660575 23658 net.cpp:84] Creating Layer relu3
I0407 21:56:16.660579 23658 net.cpp:406] relu3 <- conv3
I0407 21:56:16.660584 23658 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:56:16.661128 23658 net.cpp:122] Setting up relu3
I0407 21:56:16.661136 23658 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:16.661139 23658 net.cpp:137] Memory required for data: 234073856
I0407 21:56:16.661144 23658 layer_factory.hpp:77] Creating layer conv4
I0407 21:56:16.661154 23658 net.cpp:84] Creating Layer conv4
I0407 21:56:16.661159 23658 net.cpp:406] conv4 <- conv3
I0407 21:56:16.661165 23658 net.cpp:380] conv4 -> conv4
I0407 21:56:16.670584 23658 net.cpp:122] Setting up conv4
I0407 21:56:16.670596 23658 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:16.670599 23658 net.cpp:137] Memory required for data: 242380544
I0407 21:56:16.670606 23658 layer_factory.hpp:77] Creating layer relu4
I0407 21:56:16.670614 23658 net.cpp:84] Creating Layer relu4
I0407 21:56:16.670617 23658 net.cpp:406] relu4 <- conv4
I0407 21:56:16.670622 23658 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:56:16.670964 23658 net.cpp:122] Setting up relu4
I0407 21:56:16.670971 23658 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:56:16.670975 23658 net.cpp:137] Memory required for data: 250687232
I0407 21:56:16.670979 23658 layer_factory.hpp:77] Creating layer conv5
I0407 21:56:16.670987 23658 net.cpp:84] Creating Layer conv5
I0407 21:56:16.670991 23658 net.cpp:406] conv5 <- conv4
I0407 21:56:16.670998 23658 net.cpp:380] conv5 -> conv5
I0407 21:56:16.679507 23658 net.cpp:122] Setting up conv5
I0407 21:56:16.679519 23658 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:16.679522 23658 net.cpp:137] Memory required for data: 256225024
I0407 21:56:16.679534 23658 layer_factory.hpp:77] Creating layer relu5
I0407 21:56:16.679541 23658 net.cpp:84] Creating Layer relu5
I0407 21:56:16.679545 23658 net.cpp:406] relu5 <- conv5
I0407 21:56:16.679567 23658 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:56:16.680061 23658 net.cpp:122] Setting up relu5
I0407 21:56:16.680070 23658 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:56:16.680074 23658 net.cpp:137] Memory required for data: 261762816
I0407 21:56:16.680078 23658 layer_factory.hpp:77] Creating layer pool5
I0407 21:56:16.680088 23658 net.cpp:84] Creating Layer pool5
I0407 21:56:16.680091 23658 net.cpp:406] pool5 <- conv5
I0407 21:56:16.680097 23658 net.cpp:380] pool5 -> pool5
I0407 21:56:16.680135 23658 net.cpp:122] Setting up pool5
I0407 21:56:16.680140 23658 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 21:56:16.680145 23658 net.cpp:137] Memory required for data: 262942464
I0407 21:56:16.680147 23658 layer_factory.hpp:77] Creating layer fc6
I0407 21:56:16.680155 23658 net.cpp:84] Creating Layer fc6
I0407 21:56:16.680158 23658 net.cpp:406] fc6 <- pool5
I0407 21:56:16.680163 23658 net.cpp:380] fc6 -> fc6
I0407 21:56:17.032145 23658 net.cpp:122] Setting up fc6
I0407 21:56:17.032163 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.032167 23658 net.cpp:137] Memory required for data: 263466752
I0407 21:56:17.032177 23658 layer_factory.hpp:77] Creating layer relu6
I0407 21:56:17.032186 23658 net.cpp:84] Creating Layer relu6
I0407 21:56:17.032191 23658 net.cpp:406] relu6 <- fc6
I0407 21:56:17.032197 23658 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:56:17.033046 23658 net.cpp:122] Setting up relu6
I0407 21:56:17.033054 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.033058 23658 net.cpp:137] Memory required for data: 263991040
I0407 21:56:17.033061 23658 layer_factory.hpp:77] Creating layer drop6
I0407 21:56:17.033071 23658 net.cpp:84] Creating Layer drop6
I0407 21:56:17.033074 23658 net.cpp:406] drop6 <- fc6
I0407 21:56:17.033079 23658 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:56:17.033105 23658 net.cpp:122] Setting up drop6
I0407 21:56:17.033110 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.033113 23658 net.cpp:137] Memory required for data: 264515328
I0407 21:56:17.033116 23658 layer_factory.hpp:77] Creating layer fc7
I0407 21:56:17.033123 23658 net.cpp:84] Creating Layer fc7
I0407 21:56:17.033126 23658 net.cpp:406] fc7 <- fc6
I0407 21:56:17.033133 23658 net.cpp:380] fc7 -> fc7
I0407 21:56:17.189707 23658 net.cpp:122] Setting up fc7
I0407 21:56:17.189728 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.189731 23658 net.cpp:137] Memory required for data: 265039616
I0407 21:56:17.189741 23658 layer_factory.hpp:77] Creating layer relu7
I0407 21:56:17.189749 23658 net.cpp:84] Creating Layer relu7
I0407 21:56:17.189754 23658 net.cpp:406] relu7 <- fc7
I0407 21:56:17.189759 23658 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:56:17.190194 23658 net.cpp:122] Setting up relu7
I0407 21:56:17.190202 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.190205 23658 net.cpp:137] Memory required for data: 265563904
I0407 21:56:17.190209 23658 layer_factory.hpp:77] Creating layer drop7
I0407 21:56:17.190215 23658 net.cpp:84] Creating Layer drop7
I0407 21:56:17.190219 23658 net.cpp:406] drop7 <- fc7
I0407 21:56:17.190225 23658 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:56:17.190248 23658 net.cpp:122] Setting up drop7
I0407 21:56:17.190254 23658 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:56:17.190258 23658 net.cpp:137] Memory required for data: 266088192
I0407 21:56:17.190260 23658 layer_factory.hpp:77] Creating layer fc8
I0407 21:56:17.190268 23658 net.cpp:84] Creating Layer fc8
I0407 21:56:17.190271 23658 net.cpp:406] fc8 <- fc7
I0407 21:56:17.190277 23658 net.cpp:380] fc8 -> fc8
I0407 21:56:17.197948 23658 net.cpp:122] Setting up fc8
I0407 21:56:17.197964 23658 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:17.197968 23658 net.cpp:137] Memory required for data: 266113280
I0407 21:56:17.197973 23658 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 21:56:17.197980 23658 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 21:56:17.197983 23658 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 21:56:17.198009 23658 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 21:56:17.198015 23658 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 21:56:17.198045 23658 net.cpp:122] Setting up fc8_fc8_0_split
I0407 21:56:17.198050 23658 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:17.198055 23658 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:56:17.198057 23658 net.cpp:137] Memory required for data: 266163456
I0407 21:56:17.198060 23658 layer_factory.hpp:77] Creating layer accuracy
I0407 21:56:17.198068 23658 net.cpp:84] Creating Layer accuracy
I0407 21:56:17.198071 23658 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 21:56:17.198076 23658 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 21:56:17.198081 23658 net.cpp:380] accuracy -> accuracy
I0407 21:56:17.198088 23658 net.cpp:122] Setting up accuracy
I0407 21:56:17.198091 23658 net.cpp:129] Top shape: (1)
I0407 21:56:17.198094 23658 net.cpp:137] Memory required for data: 266163460
I0407 21:56:17.198097 23658 layer_factory.hpp:77] Creating layer loss
I0407 21:56:17.198103 23658 net.cpp:84] Creating Layer loss
I0407 21:56:17.198107 23658 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 21:56:17.198110 23658 net.cpp:406] loss <- label_val-data_1_split_1
I0407 21:56:17.198114 23658 net.cpp:380] loss -> loss
I0407 21:56:17.198120 23658 layer_factory.hpp:77] Creating layer loss
I0407 21:56:17.198716 23658 net.cpp:122] Setting up loss
I0407 21:56:17.198724 23658 net.cpp:129] Top shape: (1)
I0407 21:56:17.198729 23658 net.cpp:132]     with loss weight 1
I0407 21:56:17.198738 23658 net.cpp:137] Memory required for data: 266163464
I0407 21:56:17.198742 23658 net.cpp:198] loss needs backward computation.
I0407 21:56:17.198747 23658 net.cpp:200] accuracy does not need backward computation.
I0407 21:56:17.198751 23658 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 21:56:17.198755 23658 net.cpp:198] fc8 needs backward computation.
I0407 21:56:17.198757 23658 net.cpp:198] drop7 needs backward computation.
I0407 21:56:17.198760 23658 net.cpp:198] relu7 needs backward computation.
I0407 21:56:17.198763 23658 net.cpp:198] fc7 needs backward computation.
I0407 21:56:17.198767 23658 net.cpp:198] drop6 needs backward computation.
I0407 21:56:17.198770 23658 net.cpp:198] relu6 needs backward computation.
I0407 21:56:17.198773 23658 net.cpp:198] fc6 needs backward computation.
I0407 21:56:17.198777 23658 net.cpp:198] pool5 needs backward computation.
I0407 21:56:17.198781 23658 net.cpp:198] relu5 needs backward computation.
I0407 21:56:17.198784 23658 net.cpp:198] conv5 needs backward computation.
I0407 21:56:17.198788 23658 net.cpp:198] relu4 needs backward computation.
I0407 21:56:17.198791 23658 net.cpp:198] conv4 needs backward computation.
I0407 21:56:17.198794 23658 net.cpp:198] relu3 needs backward computation.
I0407 21:56:17.198798 23658 net.cpp:198] conv3 needs backward computation.
I0407 21:56:17.198801 23658 net.cpp:198] pool2 needs backward computation.
I0407 21:56:17.198805 23658 net.cpp:198] norm2 needs backward computation.
I0407 21:56:17.198808 23658 net.cpp:198] relu2 needs backward computation.
I0407 21:56:17.198812 23658 net.cpp:198] conv2 needs backward computation.
I0407 21:56:17.198815 23658 net.cpp:198] pool1 needs backward computation.
I0407 21:56:17.198818 23658 net.cpp:198] norm1 needs backward computation.
I0407 21:56:17.198822 23658 net.cpp:198] relu1 needs backward computation.
I0407 21:56:17.198825 23658 net.cpp:198] conv1 needs backward computation.
I0407 21:56:17.198829 23658 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 21:56:17.198832 23658 net.cpp:200] val-data does not need backward computation.
I0407 21:56:17.198837 23658 net.cpp:242] This network produces output accuracy
I0407 21:56:17.198839 23658 net.cpp:242] This network produces output loss
I0407 21:56:17.198856 23658 net.cpp:255] Network initialization done.
I0407 21:56:17.198947 23658 solver.cpp:56] Solver scaffolding done.
I0407 21:56:17.199371 23658 caffe.cpp:248] Starting Optimization
I0407 21:56:17.199379 23658 solver.cpp:272] Solving
I0407 21:56:17.199391 23658 solver.cpp:273] Learning Rate Policy: exp
I0407 21:56:17.200747 23658 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 21:56:17.200757 23658 net.cpp:676] Ignoring source layer train-data
I0407 21:56:17.311674 23658 blocking_queue.cpp:49] Waiting for data
I0407 21:56:21.612509 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:56:21.657120 23658 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0407 21:56:21.657166 23658 solver.cpp:397]     Test net output #1: loss = 5.27933 (* 1 = 5.27933 loss)
I0407 21:56:21.754987 23658 solver.cpp:218] Iteration 0 (-5.34664e-30 iter/s, 4.55541s/12 iters), loss = 5.28167
I0407 21:56:21.756503 23658 solver.cpp:237]     Train net output #0: loss = 5.28167 (* 1 = 5.28167 loss)
I0407 21:56:21.756520 23658 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 21:56:25.883499 23658 solver.cpp:218] Iteration 12 (2.90779 iter/s, 4.12684s/12 iters), loss = 5.29463
I0407 21:56:25.883543 23658 solver.cpp:237]     Train net output #0: loss = 5.29463 (* 1 = 5.29463 loss)
I0407 21:56:25.883553 23658 sgd_solver.cpp:105] Iteration 12, lr = 0.00993983
I0407 21:56:30.904953 23658 solver.cpp:218] Iteration 24 (2.38985 iter/s, 5.02123s/12 iters), loss = 5.28869
I0407 21:56:30.904994 23658 solver.cpp:237]     Train net output #0: loss = 5.28869 (* 1 = 5.28869 loss)
I0407 21:56:30.905005 23658 sgd_solver.cpp:105] Iteration 24, lr = 0.00988003
I0407 21:56:35.908035 23658 solver.cpp:218] Iteration 36 (2.39863 iter/s, 5.00286s/12 iters), loss = 5.30106
I0407 21:56:35.908077 23658 solver.cpp:237]     Train net output #0: loss = 5.30106 (* 1 = 5.30106 loss)
I0407 21:56:35.908089 23658 sgd_solver.cpp:105] Iteration 36, lr = 0.00982059
I0407 21:56:41.244961 23658 solver.cpp:218] Iteration 48 (2.24858 iter/s, 5.33669s/12 iters), loss = 5.31373
I0407 21:56:41.245005 23658 solver.cpp:237]     Train net output #0: loss = 5.31373 (* 1 = 5.31373 loss)
I0407 21:56:41.245018 23658 sgd_solver.cpp:105] Iteration 48, lr = 0.0097615
I0407 21:56:46.558019 23658 solver.cpp:218] Iteration 60 (2.25869 iter/s, 5.31283s/12 iters), loss = 5.29973
I0407 21:56:46.558207 23658 solver.cpp:237]     Train net output #0: loss = 5.29973 (* 1 = 5.29973 loss)
I0407 21:56:46.558218 23658 sgd_solver.cpp:105] Iteration 60, lr = 0.00970277
I0407 21:56:51.924973 23658 solver.cpp:218] Iteration 72 (2.23606 iter/s, 5.36657s/12 iters), loss = 5.29145
I0407 21:56:51.925020 23658 solver.cpp:237]     Train net output #0: loss = 5.29145 (* 1 = 5.29145 loss)
I0407 21:56:51.925032 23658 sgd_solver.cpp:105] Iteration 72, lr = 0.00964439
I0407 21:56:57.374503 23658 solver.cpp:218] Iteration 84 (2.20212 iter/s, 5.44929s/12 iters), loss = 5.29258
I0407 21:56:57.374541 23658 solver.cpp:237]     Train net output #0: loss = 5.29258 (* 1 = 5.29258 loss)
I0407 21:56:57.374550 23658 sgd_solver.cpp:105] Iteration 84, lr = 0.00958637
I0407 21:57:02.519932 23658 solver.cpp:218] Iteration 96 (2.33227 iter/s, 5.1452s/12 iters), loss = 5.31723
I0407 21:57:02.519979 23658 solver.cpp:237]     Train net output #0: loss = 5.31723 (* 1 = 5.31723 loss)
I0407 21:57:02.519990 23658 sgd_solver.cpp:105] Iteration 96, lr = 0.00952869
I0407 21:57:04.302155 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:04.612977 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 21:57:07.643770 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 21:57:09.942056 23658 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 21:57:09.942075 23658 net.cpp:676] Ignoring source layer train-data
I0407 21:57:14.542642 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:14.619271 23658 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0407 21:57:14.619318 23658 solver.cpp:397]     Test net output #1: loss = 5.2893 (* 1 = 5.2893 loss)
I0407 21:57:16.597142 23658 solver.cpp:218] Iteration 108 (0.852474 iter/s, 14.0767s/12 iters), loss = 5.31168
I0407 21:57:16.598039 23658 solver.cpp:237]     Train net output #0: loss = 5.31168 (* 1 = 5.31168 loss)
I0407 21:57:16.598053 23658 sgd_solver.cpp:105] Iteration 108, lr = 0.00947136
I0407 21:57:21.621028 23658 solver.cpp:218] Iteration 120 (2.3891 iter/s, 5.02281s/12 iters), loss = 5.28125
I0407 21:57:21.621071 23658 solver.cpp:237]     Train net output #0: loss = 5.28125 (* 1 = 5.28125 loss)
I0407 21:57:21.621080 23658 sgd_solver.cpp:105] Iteration 120, lr = 0.00941438
I0407 21:57:26.626212 23658 solver.cpp:218] Iteration 132 (2.39762 iter/s, 5.00495s/12 iters), loss = 5.2403
I0407 21:57:26.626262 23658 solver.cpp:237]     Train net output #0: loss = 5.2403 (* 1 = 5.2403 loss)
I0407 21:57:26.626276 23658 sgd_solver.cpp:105] Iteration 132, lr = 0.00935774
I0407 21:57:31.621738 23658 solver.cpp:218] Iteration 144 (2.40226 iter/s, 4.99529s/12 iters), loss = 5.30658
I0407 21:57:31.621793 23658 solver.cpp:237]     Train net output #0: loss = 5.30658 (* 1 = 5.30658 loss)
I0407 21:57:31.621805 23658 sgd_solver.cpp:105] Iteration 144, lr = 0.00930144
I0407 21:57:36.769577 23658 solver.cpp:218] Iteration 156 (2.33118 iter/s, 5.1476s/12 iters), loss = 5.23766
I0407 21:57:36.769623 23658 solver.cpp:237]     Train net output #0: loss = 5.23766 (* 1 = 5.23766 loss)
I0407 21:57:36.769635 23658 sgd_solver.cpp:105] Iteration 156, lr = 0.00924547
I0407 21:57:41.731873 23658 solver.cpp:218] Iteration 168 (2.41835 iter/s, 4.96206s/12 iters), loss = 5.23451
I0407 21:57:41.731926 23658 solver.cpp:237]     Train net output #0: loss = 5.23451 (* 1 = 5.23451 loss)
I0407 21:57:41.731940 23658 sgd_solver.cpp:105] Iteration 168, lr = 0.00918985
I0407 21:57:46.713500 23658 solver.cpp:218] Iteration 180 (2.40897 iter/s, 4.98139s/12 iters), loss = 5.16472
I0407 21:57:46.713577 23658 solver.cpp:237]     Train net output #0: loss = 5.16472 (* 1 = 5.16472 loss)
I0407 21:57:46.713587 23658 sgd_solver.cpp:105] Iteration 180, lr = 0.00913456
I0407 21:57:51.697314 23658 solver.cpp:218] Iteration 192 (2.40792 iter/s, 4.98355s/12 iters), loss = 5.22671
I0407 21:57:51.697361 23658 solver.cpp:237]     Train net output #0: loss = 5.22671 (* 1 = 5.22671 loss)
I0407 21:57:51.697373 23658 sgd_solver.cpp:105] Iteration 192, lr = 0.0090796
I0407 21:57:55.525914 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:56.210467 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 21:57:59.216727 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 21:58:03.150154 23658 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 21:58:03.150179 23658 net.cpp:676] Ignoring source layer train-data
I0407 21:58:07.488489 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:07.611182 23658 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0407 21:58:07.611223 23658 solver.cpp:397]     Test net output #1: loss = 5.19461 (* 1 = 5.19461 loss)
I0407 21:58:07.700997 23658 solver.cpp:218] Iteration 204 (0.749856 iter/s, 16.0031s/12 iters), loss = 5.1174
I0407 21:58:07.701050 23658 solver.cpp:237]     Train net output #0: loss = 5.1174 (* 1 = 5.1174 loss)
I0407 21:58:07.701059 23658 sgd_solver.cpp:105] Iteration 204, lr = 0.00902497
I0407 21:58:12.137787 23658 solver.cpp:218] Iteration 216 (2.70479 iter/s, 4.43658s/12 iters), loss = 5.14829
I0407 21:58:12.137825 23658 solver.cpp:237]     Train net output #0: loss = 5.14829 (* 1 = 5.14829 loss)
I0407 21:58:12.137832 23658 sgd_solver.cpp:105] Iteration 216, lr = 0.00897067
I0407 21:58:17.128638 23658 solver.cpp:218] Iteration 228 (2.40451 iter/s, 4.99063s/12 iters), loss = 5.1989
I0407 21:58:17.128758 23658 solver.cpp:237]     Train net output #0: loss = 5.1989 (* 1 = 5.1989 loss)
I0407 21:58:17.128772 23658 sgd_solver.cpp:105] Iteration 228, lr = 0.0089167
I0407 21:58:22.032639 23658 solver.cpp:218] Iteration 240 (2.44713 iter/s, 4.90371s/12 iters), loss = 5.19757
I0407 21:58:22.032677 23658 solver.cpp:237]     Train net output #0: loss = 5.19757 (* 1 = 5.19757 loss)
I0407 21:58:22.032685 23658 sgd_solver.cpp:105] Iteration 240, lr = 0.00886305
I0407 21:58:27.039263 23658 solver.cpp:218] Iteration 252 (2.39693 iter/s, 5.00639s/12 iters), loss = 5.15089
I0407 21:58:27.039319 23658 solver.cpp:237]     Train net output #0: loss = 5.15089 (* 1 = 5.15089 loss)
I0407 21:58:27.039330 23658 sgd_solver.cpp:105] Iteration 252, lr = 0.00880973
I0407 21:58:31.999867 23658 solver.cpp:218] Iteration 264 (2.41918 iter/s, 4.96037s/12 iters), loss = 5.23863
I0407 21:58:31.999913 23658 solver.cpp:237]     Train net output #0: loss = 5.23863 (* 1 = 5.23863 loss)
I0407 21:58:31.999922 23658 sgd_solver.cpp:105] Iteration 264, lr = 0.00875672
I0407 21:58:36.972963 23658 solver.cpp:218] Iteration 276 (2.41309 iter/s, 4.97287s/12 iters), loss = 5.1736
I0407 21:58:36.973016 23658 solver.cpp:237]     Train net output #0: loss = 5.1736 (* 1 = 5.1736 loss)
I0407 21:58:36.973026 23658 sgd_solver.cpp:105] Iteration 276, lr = 0.00870404
I0407 21:58:41.883184 23658 solver.cpp:218] Iteration 288 (2.444 iter/s, 4.90999s/12 iters), loss = 5.05561
I0407 21:58:41.883231 23658 solver.cpp:237]     Train net output #0: loss = 5.05561 (* 1 = 5.05561 loss)
I0407 21:58:41.883241 23658 sgd_solver.cpp:105] Iteration 288, lr = 0.00865167
I0407 21:58:47.029734 23658 solver.cpp:218] Iteration 300 (2.33177 iter/s, 5.14631s/12 iters), loss = 5.16688
I0407 21:58:47.029778 23658 solver.cpp:237]     Train net output #0: loss = 5.16688 (* 1 = 5.16688 loss)
I0407 21:58:47.029788 23658 sgd_solver.cpp:105] Iteration 300, lr = 0.00859962
I0407 21:58:48.008745 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:49.046669 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 21:58:51.993199 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 21:58:56.141083 23658 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 21:58:56.141108 23658 net.cpp:676] Ignoring source layer train-data
I0407 21:59:00.536193 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:00.701720 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0407 21:59:00.701763 23658 solver.cpp:397]     Test net output #1: loss = 5.13218 (* 1 = 5.13218 loss)
I0407 21:59:02.693909 23658 solver.cpp:218] Iteration 312 (0.766108 iter/s, 15.6636s/12 iters), loss = 5.11227
I0407 21:59:02.693950 23658 solver.cpp:237]     Train net output #0: loss = 5.11227 (* 1 = 5.11227 loss)
I0407 21:59:02.693971 23658 sgd_solver.cpp:105] Iteration 312, lr = 0.00854788
I0407 21:59:07.819352 23658 solver.cpp:218] Iteration 324 (2.34139 iter/s, 5.12517s/12 iters), loss = 5.15344
I0407 21:59:07.819393 23658 solver.cpp:237]     Train net output #0: loss = 5.15344 (* 1 = 5.15344 loss)
I0407 21:59:07.819402 23658 sgd_solver.cpp:105] Iteration 324, lr = 0.00849645
I0407 21:59:13.018746 23658 solver.cpp:218] Iteration 336 (2.30806 iter/s, 5.19917s/12 iters), loss = 5.07645
I0407 21:59:13.018784 23658 solver.cpp:237]     Train net output #0: loss = 5.07645 (* 1 = 5.07645 loss)
I0407 21:59:13.018793 23658 sgd_solver.cpp:105] Iteration 336, lr = 0.00844533
I0407 21:59:18.041833 23658 solver.cpp:218] Iteration 348 (2.38908 iter/s, 5.02286s/12 iters), loss = 5.06271
I0407 21:59:18.041942 23658 solver.cpp:237]     Train net output #0: loss = 5.06271 (* 1 = 5.06271 loss)
I0407 21:59:18.041970 23658 sgd_solver.cpp:105] Iteration 348, lr = 0.00839452
I0407 21:59:22.988317 23658 solver.cpp:218] Iteration 360 (2.4261 iter/s, 4.9462s/12 iters), loss = 5.13908
I0407 21:59:22.988359 23658 solver.cpp:237]     Train net output #0: loss = 5.13908 (* 1 = 5.13908 loss)
I0407 21:59:22.988369 23658 sgd_solver.cpp:105] Iteration 360, lr = 0.00834401
I0407 21:59:28.002351 23658 solver.cpp:218] Iteration 372 (2.39339 iter/s, 5.01381s/12 iters), loss = 5.10989
I0407 21:59:28.002391 23658 solver.cpp:237]     Train net output #0: loss = 5.10989 (* 1 = 5.10989 loss)
I0407 21:59:28.002400 23658 sgd_solver.cpp:105] Iteration 372, lr = 0.00829381
I0407 21:59:33.004834 23658 solver.cpp:218] Iteration 384 (2.39891 iter/s, 5.00226s/12 iters), loss = 5.11957
I0407 21:59:33.004875 23658 solver.cpp:237]     Train net output #0: loss = 5.11957 (* 1 = 5.11957 loss)
I0407 21:59:33.004884 23658 sgd_solver.cpp:105] Iteration 384, lr = 0.00824391
I0407 21:59:38.040099 23658 solver.cpp:218] Iteration 396 (2.38329 iter/s, 5.03505s/12 iters), loss = 5.01952
I0407 21:59:38.040136 23658 solver.cpp:237]     Train net output #0: loss = 5.01952 (* 1 = 5.01952 loss)
I0407 21:59:38.040148 23658 sgd_solver.cpp:105] Iteration 396, lr = 0.00819431
I0407 21:59:41.164405 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:42.585028 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 21:59:47.037541 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 21:59:50.787129 23658 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 21:59:50.787242 23658 net.cpp:676] Ignoring source layer train-data
I0407 21:59:55.110311 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:55.321326 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0407 21:59:55.321375 23658 solver.cpp:397]     Test net output #1: loss = 5.09326 (* 1 = 5.09326 loss)
I0407 21:59:55.411715 23658 solver.cpp:218] Iteration 408 (0.690807 iter/s, 17.371s/12 iters), loss = 5.15603
I0407 21:59:55.411767 23658 solver.cpp:237]     Train net output #0: loss = 5.15603 (* 1 = 5.15603 loss)
I0407 21:59:55.411777 23658 sgd_solver.cpp:105] Iteration 408, lr = 0.00814501
I0407 21:59:59.775023 23658 solver.cpp:218] Iteration 420 (2.75034 iter/s, 4.3631s/12 iters), loss = 5.13169
I0407 21:59:59.775074 23658 solver.cpp:237]     Train net output #0: loss = 5.13169 (* 1 = 5.13169 loss)
I0407 21:59:59.775086 23658 sgd_solver.cpp:105] Iteration 420, lr = 0.008096
I0407 22:00:04.775350 23658 solver.cpp:218] Iteration 432 (2.39995 iter/s, 5.00011s/12 iters), loss = 5.08883
I0407 22:00:04.775390 23658 solver.cpp:237]     Train net output #0: loss = 5.08883 (* 1 = 5.08883 loss)
I0407 22:00:04.775399 23658 sgd_solver.cpp:105] Iteration 432, lr = 0.00804729
I0407 22:00:09.710961 23658 solver.cpp:218] Iteration 444 (2.43142 iter/s, 4.93539s/12 iters), loss = 4.99804
I0407 22:00:09.711004 23658 solver.cpp:237]     Train net output #0: loss = 4.99804 (* 1 = 4.99804 loss)
I0407 22:00:09.711014 23658 sgd_solver.cpp:105] Iteration 444, lr = 0.00799888
I0407 22:00:14.656476 23658 solver.cpp:218] Iteration 456 (2.42655 iter/s, 4.94529s/12 iters), loss = 5.11806
I0407 22:00:14.656520 23658 solver.cpp:237]     Train net output #0: loss = 5.11806 (* 1 = 5.11806 loss)
I0407 22:00:14.656529 23658 sgd_solver.cpp:105] Iteration 456, lr = 0.00795075
I0407 22:00:19.624858 23658 solver.cpp:218] Iteration 468 (2.41538 iter/s, 4.96816s/12 iters), loss = 5.07001
I0407 22:00:19.624905 23658 solver.cpp:237]     Train net output #0: loss = 5.07001 (* 1 = 5.07001 loss)
I0407 22:00:19.624914 23658 sgd_solver.cpp:105] Iteration 468, lr = 0.00790292
I0407 22:00:24.698158 23658 solver.cpp:218] Iteration 480 (2.36543 iter/s, 5.07306s/12 iters), loss = 5.00541
I0407 22:00:24.698251 23658 solver.cpp:237]     Train net output #0: loss = 5.00541 (* 1 = 5.00541 loss)
I0407 22:00:24.698262 23658 sgd_solver.cpp:105] Iteration 480, lr = 0.00785537
I0407 22:00:29.805646 23658 solver.cpp:218] Iteration 492 (2.34962 iter/s, 5.10722s/12 iters), loss = 5.02332
I0407 22:00:29.805694 23658 solver.cpp:237]     Train net output #0: loss = 5.02332 (* 1 = 5.02332 loss)
I0407 22:00:29.805706 23658 sgd_solver.cpp:105] Iteration 492, lr = 0.00780811
I0407 22:00:34.763063 23658 solver.cpp:218] Iteration 504 (2.42072 iter/s, 4.95719s/12 iters), loss = 5.09284
I0407 22:00:34.763108 23658 solver.cpp:237]     Train net output #0: loss = 5.09284 (* 1 = 5.09284 loss)
I0407 22:00:34.763118 23658 sgd_solver.cpp:105] Iteration 504, lr = 0.00776113
I0407 22:00:35.009732 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:36.789564 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 22:00:39.793076 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 22:00:46.117362 23658 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 22:00:46.117383 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:00:50.402443 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:50.647295 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0407 22:00:50.647343 23658 solver.cpp:397]     Test net output #1: loss = 5.0267 (* 1 = 5.0267 loss)
I0407 22:00:52.626185 23658 solver.cpp:218] Iteration 516 (0.671799 iter/s, 17.8625s/12 iters), loss = 4.93263
I0407 22:00:52.626255 23658 solver.cpp:237]     Train net output #0: loss = 4.93263 (* 1 = 4.93263 loss)
I0407 22:00:52.626272 23658 sgd_solver.cpp:105] Iteration 516, lr = 0.00771443
I0407 22:00:57.628576 23658 solver.cpp:218] Iteration 528 (2.39897 iter/s, 5.00214s/12 iters), loss = 5.06861
I0407 22:00:57.628734 23658 solver.cpp:237]     Train net output #0: loss = 5.06861 (* 1 = 5.06861 loss)
I0407 22:00:57.628755 23658 sgd_solver.cpp:105] Iteration 528, lr = 0.00766802
I0407 22:01:02.628998 23658 solver.cpp:218] Iteration 540 (2.39995 iter/s, 5.0001s/12 iters), loss = 4.94024
I0407 22:01:02.629036 23658 solver.cpp:237]     Train net output #0: loss = 4.94024 (* 1 = 4.94024 loss)
I0407 22:01:02.629045 23658 sgd_solver.cpp:105] Iteration 540, lr = 0.00762188
I0407 22:01:07.674553 23658 solver.cpp:218] Iteration 552 (2.37843 iter/s, 5.04534s/12 iters), loss = 5.02246
I0407 22:01:07.674593 23658 solver.cpp:237]     Train net output #0: loss = 5.02246 (* 1 = 5.02246 loss)
I0407 22:01:07.674602 23658 sgd_solver.cpp:105] Iteration 552, lr = 0.00757603
I0407 22:01:12.650161 23658 solver.cpp:218] Iteration 564 (2.41187 iter/s, 4.9754s/12 iters), loss = 4.95741
I0407 22:01:12.650210 23658 solver.cpp:237]     Train net output #0: loss = 4.95741 (* 1 = 4.95741 loss)
I0407 22:01:12.650221 23658 sgd_solver.cpp:105] Iteration 564, lr = 0.00753045
I0407 22:01:17.694591 23658 solver.cpp:218] Iteration 576 (2.37897 iter/s, 5.04421s/12 iters), loss = 4.99298
I0407 22:01:17.694636 23658 solver.cpp:237]     Train net output #0: loss = 4.99298 (* 1 = 4.99298 loss)
I0407 22:01:17.694648 23658 sgd_solver.cpp:105] Iteration 576, lr = 0.00748514
I0407 22:01:22.672519 23658 solver.cpp:218] Iteration 588 (2.41074 iter/s, 4.97771s/12 iters), loss = 4.8668
I0407 22:01:22.672562 23658 solver.cpp:237]     Train net output #0: loss = 4.8668 (* 1 = 4.8668 loss)
I0407 22:01:22.672572 23658 sgd_solver.cpp:105] Iteration 588, lr = 0.0074401
I0407 22:01:27.692523 23658 solver.cpp:218] Iteration 600 (2.39054 iter/s, 5.01979s/12 iters), loss = 4.98392
I0407 22:01:27.692646 23658 solver.cpp:237]     Train net output #0: loss = 4.98392 (* 1 = 4.98392 loss)
I0407 22:01:27.692658 23658 sgd_solver.cpp:105] Iteration 600, lr = 0.00739534
I0407 22:01:30.071053 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:32.224128 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 22:01:35.327822 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 22:01:38.933147 23658 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 22:01:38.933173 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:01:43.109758 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:43.394475 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0407 22:01:43.394526 23658 solver.cpp:397]     Test net output #1: loss = 4.98536 (* 1 = 4.98536 loss)
I0407 22:01:43.483292 23658 solver.cpp:218] Iteration 612 (0.759967 iter/s, 15.7902s/12 iters), loss = 4.92865
I0407 22:01:43.483343 23658 solver.cpp:237]     Train net output #0: loss = 4.92865 (* 1 = 4.92865 loss)
I0407 22:01:43.483355 23658 sgd_solver.cpp:105] Iteration 612, lr = 0.00735085
I0407 22:01:47.838567 23658 solver.cpp:218] Iteration 624 (2.7554 iter/s, 4.35508s/12 iters), loss = 4.91757
I0407 22:01:47.838611 23658 solver.cpp:237]     Train net output #0: loss = 4.91757 (* 1 = 4.91757 loss)
I0407 22:01:47.838624 23658 sgd_solver.cpp:105] Iteration 624, lr = 0.00730662
I0407 22:01:52.842234 23658 solver.cpp:218] Iteration 636 (2.39834 iter/s, 5.00346s/12 iters), loss = 4.80932
I0407 22:01:52.842286 23658 solver.cpp:237]     Train net output #0: loss = 4.80932 (* 1 = 4.80932 loss)
I0407 22:01:52.842298 23658 sgd_solver.cpp:105] Iteration 636, lr = 0.00726266
I0407 22:01:57.766403 23658 solver.cpp:218] Iteration 648 (2.43707 iter/s, 4.92395s/12 iters), loss = 5.04729
I0407 22:01:57.766535 23658 solver.cpp:237]     Train net output #0: loss = 5.04729 (* 1 = 5.04729 loss)
I0407 22:01:57.766549 23658 sgd_solver.cpp:105] Iteration 648, lr = 0.00721896
I0407 22:02:02.743408 23658 solver.cpp:218] Iteration 660 (2.41123 iter/s, 4.97671s/12 iters), loss = 4.93925
I0407 22:02:02.743458 23658 solver.cpp:237]     Train net output #0: loss = 4.93925 (* 1 = 4.93925 loss)
I0407 22:02:02.743468 23658 sgd_solver.cpp:105] Iteration 660, lr = 0.00717553
I0407 22:02:07.773912 23658 solver.cpp:218] Iteration 672 (2.38555 iter/s, 5.03029s/12 iters), loss = 4.92814
I0407 22:02:07.773972 23658 solver.cpp:237]     Train net output #0: loss = 4.92814 (* 1 = 4.92814 loss)
I0407 22:02:07.773984 23658 sgd_solver.cpp:105] Iteration 672, lr = 0.00713236
I0407 22:02:12.772812 23658 solver.cpp:218] Iteration 684 (2.40063 iter/s, 4.99869s/12 iters), loss = 4.73744
I0407 22:02:12.772850 23658 solver.cpp:237]     Train net output #0: loss = 4.73744 (* 1 = 4.73744 loss)
I0407 22:02:12.772859 23658 sgd_solver.cpp:105] Iteration 684, lr = 0.00708945
I0407 22:02:13.556880 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:02:17.769249 23658 solver.cpp:218] Iteration 696 (2.40181 iter/s, 4.99623s/12 iters), loss = 4.82502
I0407 22:02:17.769306 23658 solver.cpp:237]     Train net output #0: loss = 4.82502 (* 1 = 4.82502 loss)
I0407 22:02:17.769320 23658 sgd_solver.cpp:105] Iteration 696, lr = 0.00704679
I0407 22:02:22.427212 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:22.804361 23658 solver.cpp:218] Iteration 708 (2.38337 iter/s, 5.03489s/12 iters), loss = 4.97044
I0407 22:02:22.804404 23658 solver.cpp:237]     Train net output #0: loss = 4.97044 (* 1 = 4.97044 loss)
I0407 22:02:22.804414 23658 sgd_solver.cpp:105] Iteration 708, lr = 0.0070044
I0407 22:02:24.842067 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 22:02:27.809306 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 22:02:31.764813 23658 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 22:02:31.764838 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:02:35.933660 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:36.254046 23658 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0407 22:02:36.254096 23658 solver.cpp:397]     Test net output #1: loss = 4.89371 (* 1 = 4.89371 loss)
I0407 22:02:38.244551 23658 solver.cpp:218] Iteration 720 (0.777219 iter/s, 15.4397s/12 iters), loss = 5.04468
I0407 22:02:38.244606 23658 solver.cpp:237]     Train net output #0: loss = 5.04468 (* 1 = 5.04468 loss)
I0407 22:02:38.244617 23658 sgd_solver.cpp:105] Iteration 720, lr = 0.00696225
I0407 22:02:43.505234 23658 solver.cpp:218] Iteration 732 (2.28117 iter/s, 5.26046s/12 iters), loss = 4.65762
I0407 22:02:43.505283 23658 solver.cpp:237]     Train net output #0: loss = 4.65762 (* 1 = 4.65762 loss)
I0407 22:02:43.505295 23658 sgd_solver.cpp:105] Iteration 732, lr = 0.00692036
I0407 22:02:48.611716 23658 solver.cpp:218] Iteration 744 (2.35005 iter/s, 5.10627s/12 iters), loss = 4.91566
I0407 22:02:48.611768 23658 solver.cpp:237]     Train net output #0: loss = 4.91566 (* 1 = 4.91566 loss)
I0407 22:02:48.611780 23658 sgd_solver.cpp:105] Iteration 744, lr = 0.00687873
I0407 22:02:53.788892 23658 solver.cpp:218] Iteration 756 (2.31797 iter/s, 5.17695s/12 iters), loss = 4.93049
I0407 22:02:53.788944 23658 solver.cpp:237]     Train net output #0: loss = 4.93049 (* 1 = 4.93049 loss)
I0407 22:02:53.788955 23658 sgd_solver.cpp:105] Iteration 756, lr = 0.00683734
I0407 22:02:58.713932 23658 solver.cpp:218] Iteration 768 (2.43663 iter/s, 4.92483s/12 iters), loss = 4.83981
I0407 22:02:58.714044 23658 solver.cpp:237]     Train net output #0: loss = 4.83981 (* 1 = 4.83981 loss)
I0407 22:02:58.714053 23658 sgd_solver.cpp:105] Iteration 768, lr = 0.0067962
I0407 22:03:03.740406 23658 solver.cpp:218] Iteration 780 (2.38749 iter/s, 5.0262s/12 iters), loss = 4.88295
I0407 22:03:03.740453 23658 solver.cpp:237]     Train net output #0: loss = 4.88295 (* 1 = 4.88295 loss)
I0407 22:03:03.740464 23658 sgd_solver.cpp:105] Iteration 780, lr = 0.00675532
I0407 22:03:08.767128 23658 solver.cpp:218] Iteration 792 (2.38734 iter/s, 5.02651s/12 iters), loss = 4.69713
I0407 22:03:08.767174 23658 solver.cpp:237]     Train net output #0: loss = 4.69713 (* 1 = 4.69713 loss)
I0407 22:03:08.767185 23658 sgd_solver.cpp:105] Iteration 792, lr = 0.00671467
I0407 22:03:13.786796 23658 solver.cpp:218] Iteration 804 (2.39069 iter/s, 5.01946s/12 iters), loss = 4.73543
I0407 22:03:13.786842 23658 solver.cpp:237]     Train net output #0: loss = 4.73543 (* 1 = 4.73543 loss)
I0407 22:03:13.786854 23658 sgd_solver.cpp:105] Iteration 804, lr = 0.00667427
I0407 22:03:15.539912 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:18.292135 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 22:03:21.902838 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 22:03:25.559152 23658 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 22:03:25.559177 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:03:29.711670 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:30.066711 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0398284
I0407 22:03:30.066759 23658 solver.cpp:397]     Test net output #1: loss = 4.81729 (* 1 = 4.81729 loss)
I0407 22:03:30.156936 23658 solver.cpp:218] Iteration 816 (0.733066 iter/s, 16.3696s/12 iters), loss = 4.86118
I0407 22:03:30.156986 23658 solver.cpp:237]     Train net output #0: loss = 4.86118 (* 1 = 4.86118 loss)
I0407 22:03:30.156997 23658 sgd_solver.cpp:105] Iteration 816, lr = 0.00663412
I0407 22:03:34.631054 23658 solver.cpp:218] Iteration 828 (2.68221 iter/s, 4.47392s/12 iters), loss = 4.88271
I0407 22:03:34.631103 23658 solver.cpp:237]     Train net output #0: loss = 4.88271 (* 1 = 4.88271 loss)
I0407 22:03:34.631115 23658 sgd_solver.cpp:105] Iteration 828, lr = 0.0065942
I0407 22:03:39.634269 23658 solver.cpp:218] Iteration 840 (2.39855 iter/s, 5.00301s/12 iters), loss = 4.51513
I0407 22:03:39.634307 23658 solver.cpp:237]     Train net output #0: loss = 4.51513 (* 1 = 4.51513 loss)
I0407 22:03:39.634316 23658 sgd_solver.cpp:105] Iteration 840, lr = 0.00655453
I0407 22:03:44.602349 23658 solver.cpp:218] Iteration 852 (2.41552 iter/s, 4.96788s/12 iters), loss = 4.71625
I0407 22:03:44.602397 23658 solver.cpp:237]     Train net output #0: loss = 4.71625 (* 1 = 4.71625 loss)
I0407 22:03:44.602408 23658 sgd_solver.cpp:105] Iteration 852, lr = 0.00651509
I0407 22:03:49.592067 23658 solver.cpp:218] Iteration 864 (2.40504 iter/s, 4.98952s/12 iters), loss = 4.6272
I0407 22:03:49.592103 23658 solver.cpp:237]     Train net output #0: loss = 4.6272 (* 1 = 4.6272 loss)
I0407 22:03:49.592113 23658 sgd_solver.cpp:105] Iteration 864, lr = 0.00647589
I0407 22:03:54.580976 23658 solver.cpp:218] Iteration 876 (2.40543 iter/s, 4.98871s/12 iters), loss = 4.63453
I0407 22:03:54.581020 23658 solver.cpp:237]     Train net output #0: loss = 4.63453 (* 1 = 4.63453 loss)
I0407 22:03:54.581029 23658 sgd_solver.cpp:105] Iteration 876, lr = 0.00643693
I0407 22:03:59.629570 23658 solver.cpp:218] Iteration 888 (2.37699 iter/s, 5.04839s/12 iters), loss = 4.73374
I0407 22:03:59.629611 23658 solver.cpp:237]     Train net output #0: loss = 4.73374 (* 1 = 4.73374 loss)
I0407 22:03:59.629621 23658 sgd_solver.cpp:105] Iteration 888, lr = 0.0063982
I0407 22:04:04.589849 23658 solver.cpp:218] Iteration 900 (2.41932 iter/s, 4.96008s/12 iters), loss = 4.76228
I0407 22:04:04.589998 23658 solver.cpp:237]     Train net output #0: loss = 4.76228 (* 1 = 4.76228 loss)
I0407 22:04:04.590013 23658 sgd_solver.cpp:105] Iteration 900, lr = 0.00635971
I0407 22:04:08.538455 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:09.642346 23658 solver.cpp:218] Iteration 912 (2.37521 iter/s, 5.05219s/12 iters), loss = 4.36852
I0407 22:04:09.642387 23658 solver.cpp:237]     Train net output #0: loss = 4.36852 (* 1 = 4.36852 loss)
I0407 22:04:09.642396 23658 sgd_solver.cpp:105] Iteration 912, lr = 0.00632145
I0407 22:04:11.659884 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 22:04:14.934913 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 22:04:18.587085 23658 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 22:04:18.587114 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:04:22.644552 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:23.046438 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0514706
I0407 22:04:23.046487 23658 solver.cpp:397]     Test net output #1: loss = 4.64799 (* 1 = 4.64799 loss)
I0407 22:04:24.985219 23658 solver.cpp:218] Iteration 924 (0.782148 iter/s, 15.3424s/12 iters), loss = 4.63565
I0407 22:04:24.985263 23658 solver.cpp:237]     Train net output #0: loss = 4.63565 (* 1 = 4.63565 loss)
I0407 22:04:24.985273 23658 sgd_solver.cpp:105] Iteration 924, lr = 0.00628341
I0407 22:04:29.975080 23658 solver.cpp:218] Iteration 936 (2.40498 iter/s, 4.98965s/12 iters), loss = 4.53507
I0407 22:04:29.975121 23658 solver.cpp:237]     Train net output #0: loss = 4.53507 (* 1 = 4.53507 loss)
I0407 22:04:29.975131 23658 sgd_solver.cpp:105] Iteration 936, lr = 0.00624561
I0407 22:04:34.993301 23658 solver.cpp:218] Iteration 948 (2.39138 iter/s, 5.01802s/12 iters), loss = 4.56456
I0407 22:04:34.993399 23658 solver.cpp:237]     Train net output #0: loss = 4.56456 (* 1 = 4.56456 loss)
I0407 22:04:34.993408 23658 sgd_solver.cpp:105] Iteration 948, lr = 0.00620803
I0407 22:04:40.058162 23658 solver.cpp:218] Iteration 960 (2.36939 iter/s, 5.0646s/12 iters), loss = 4.50858
I0407 22:04:40.058213 23658 solver.cpp:237]     Train net output #0: loss = 4.50858 (* 1 = 4.50858 loss)
I0407 22:04:40.058225 23658 sgd_solver.cpp:105] Iteration 960, lr = 0.00617068
I0407 22:04:45.064702 23658 solver.cpp:218] Iteration 972 (2.39697 iter/s, 5.00633s/12 iters), loss = 4.61284
I0407 22:04:45.064743 23658 solver.cpp:237]     Train net output #0: loss = 4.61284 (* 1 = 4.61284 loss)
I0407 22:04:45.064754 23658 sgd_solver.cpp:105] Iteration 972, lr = 0.00613355
I0407 22:04:50.032152 23658 solver.cpp:218] Iteration 984 (2.41582 iter/s, 4.96725s/12 iters), loss = 4.44683
I0407 22:04:50.032193 23658 solver.cpp:237]     Train net output #0: loss = 4.44683 (* 1 = 4.44683 loss)
I0407 22:04:50.032203 23658 sgd_solver.cpp:105] Iteration 984, lr = 0.00609665
I0407 22:04:55.037765 23658 solver.cpp:218] Iteration 996 (2.3974 iter/s, 5.00542s/12 iters), loss = 4.38344
I0407 22:04:55.037792 23658 solver.cpp:237]     Train net output #0: loss = 4.38344 (* 1 = 4.38344 loss)
I0407 22:04:55.037801 23658 sgd_solver.cpp:105] Iteration 996, lr = 0.00605997
I0407 22:05:00.073243 23658 solver.cpp:218] Iteration 1008 (2.38318 iter/s, 5.03529s/12 iters), loss = 4.51121
I0407 22:05:00.073299 23658 solver.cpp:237]     Train net output #0: loss = 4.51121 (* 1 = 4.51121 loss)
I0407 22:05:00.073312 23658 sgd_solver.cpp:105] Iteration 1008, lr = 0.00602351
I0407 22:05:01.102970 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:04.656044 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 22:05:07.628787 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 22:05:11.650130 23658 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 22:05:11.650151 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:05:15.684088 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:16.115527 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0502451
I0407 22:05:16.115576 23658 solver.cpp:397]     Test net output #1: loss = 4.56858 (* 1 = 4.56858 loss)
I0407 22:05:16.204557 23658 solver.cpp:218] Iteration 1020 (0.743919 iter/s, 16.1308s/12 iters), loss = 4.46301
I0407 22:05:16.204613 23658 solver.cpp:237]     Train net output #0: loss = 4.46301 (* 1 = 4.46301 loss)
I0407 22:05:16.204625 23658 sgd_solver.cpp:105] Iteration 1020, lr = 0.00598727
I0407 22:05:20.430318 23658 solver.cpp:218] Iteration 1032 (2.83985 iter/s, 4.22557s/12 iters), loss = 4.48578
I0407 22:05:20.430361 23658 solver.cpp:237]     Train net output #0: loss = 4.48578 (* 1 = 4.48578 loss)
I0407 22:05:20.430372 23658 sgd_solver.cpp:105] Iteration 1032, lr = 0.00595125
I0407 22:05:25.389348 23658 solver.cpp:218] Iteration 1044 (2.41993 iter/s, 4.95883s/12 iters), loss = 4.45094
I0407 22:05:25.389396 23658 solver.cpp:237]     Train net output #0: loss = 4.45094 (* 1 = 4.45094 loss)
I0407 22:05:25.389408 23658 sgd_solver.cpp:105] Iteration 1044, lr = 0.00591544
I0407 22:05:30.271538 23658 solver.cpp:218] Iteration 1056 (2.45801 iter/s, 4.88199s/12 iters), loss = 4.49522
I0407 22:05:30.271589 23658 solver.cpp:237]     Train net output #0: loss = 4.49522 (* 1 = 4.49522 loss)
I0407 22:05:30.271600 23658 sgd_solver.cpp:105] Iteration 1056, lr = 0.00587985
I0407 22:05:35.189267 23658 solver.cpp:218] Iteration 1068 (2.44025 iter/s, 4.91753s/12 iters), loss = 4.45647
I0407 22:05:35.189306 23658 solver.cpp:237]     Train net output #0: loss = 4.45647 (* 1 = 4.45647 loss)
I0407 22:05:35.189316 23658 sgd_solver.cpp:105] Iteration 1068, lr = 0.00584448
I0407 22:05:40.181001 23658 solver.cpp:218] Iteration 1080 (2.40407 iter/s, 4.99154s/12 iters), loss = 4.48553
I0407 22:05:40.181114 23658 solver.cpp:237]     Train net output #0: loss = 4.48553 (* 1 = 4.48553 loss)
I0407 22:05:40.181128 23658 sgd_solver.cpp:105] Iteration 1080, lr = 0.00580931
I0407 22:05:45.237828 23658 solver.cpp:218] Iteration 1092 (2.37315 iter/s, 5.05656s/12 iters), loss = 4.45088
I0407 22:05:45.237869 23658 solver.cpp:237]     Train net output #0: loss = 4.45088 (* 1 = 4.45088 loss)
I0407 22:05:45.237877 23658 sgd_solver.cpp:105] Iteration 1092, lr = 0.00577436
I0407 22:05:50.300782 23658 solver.cpp:218] Iteration 1104 (2.37025 iter/s, 5.06275s/12 iters), loss = 4.43736
I0407 22:05:50.300830 23658 solver.cpp:237]     Train net output #0: loss = 4.43736 (* 1 = 4.43736 loss)
I0407 22:05:50.300843 23658 sgd_solver.cpp:105] Iteration 1104, lr = 0.00573962
I0407 22:05:53.419754 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:55.252365 23658 solver.cpp:218] Iteration 1116 (2.42358 iter/s, 4.95136s/12 iters), loss = 4.33475
I0407 22:05:55.252434 23658 solver.cpp:237]     Train net output #0: loss = 4.33475 (* 1 = 4.33475 loss)
I0407 22:05:55.252450 23658 sgd_solver.cpp:105] Iteration 1116, lr = 0.00570509
I0407 22:05:57.328768 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 22:06:00.357661 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 22:06:04.678937 23658 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 22:06:04.678966 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:06:08.758729 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:09.235770 23658 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0407 22:06:09.235818 23658 solver.cpp:397]     Test net output #1: loss = 4.40931 (* 1 = 4.40931 loss)
I0407 22:06:11.325748 23658 solver.cpp:218] Iteration 1128 (0.7466 iter/s, 16.0729s/12 iters), loss = 4.37737
I0407 22:06:11.325898 23658 solver.cpp:237]     Train net output #0: loss = 4.37737 (* 1 = 4.37737 loss)
I0407 22:06:11.325912 23658 sgd_solver.cpp:105] Iteration 1128, lr = 0.00567076
I0407 22:06:16.471463 23658 solver.cpp:218] Iteration 1140 (2.33217 iter/s, 5.14541s/12 iters), loss = 4.3523
I0407 22:06:16.471513 23658 solver.cpp:237]     Train net output #0: loss = 4.3523 (* 1 = 4.3523 loss)
I0407 22:06:16.471524 23658 sgd_solver.cpp:105] Iteration 1140, lr = 0.00563664
I0407 22:06:21.432719 23658 solver.cpp:218] Iteration 1152 (2.41884 iter/s, 4.96105s/12 iters), loss = 4.08633
I0407 22:06:21.432772 23658 solver.cpp:237]     Train net output #0: loss = 4.08633 (* 1 = 4.08633 loss)
I0407 22:06:21.432787 23658 sgd_solver.cpp:105] Iteration 1152, lr = 0.00560273
I0407 22:06:26.431815 23658 solver.cpp:218] Iteration 1164 (2.40053 iter/s, 4.99889s/12 iters), loss = 4.26338
I0407 22:06:26.431867 23658 solver.cpp:237]     Train net output #0: loss = 4.26338 (* 1 = 4.26338 loss)
I0407 22:06:26.431879 23658 sgd_solver.cpp:105] Iteration 1164, lr = 0.00556902
I0407 22:06:31.391988 23658 solver.cpp:218] Iteration 1176 (2.41937 iter/s, 4.95997s/12 iters), loss = 4.46624
I0407 22:06:31.392031 23658 solver.cpp:237]     Train net output #0: loss = 4.46624 (* 1 = 4.46624 loss)
I0407 22:06:31.392041 23658 sgd_solver.cpp:105] Iteration 1176, lr = 0.00553551
I0407 22:06:36.398425 23658 solver.cpp:218] Iteration 1188 (2.39701 iter/s, 5.00624s/12 iters), loss = 4.21252
I0407 22:06:36.398466 23658 solver.cpp:237]     Train net output #0: loss = 4.21252 (* 1 = 4.21252 loss)
I0407 22:06:36.398476 23658 sgd_solver.cpp:105] Iteration 1188, lr = 0.00550221
I0407 22:06:41.361780 23658 solver.cpp:218] Iteration 1200 (2.41781 iter/s, 4.96316s/12 iters), loss = 4.33594
I0407 22:06:41.361878 23658 solver.cpp:237]     Train net output #0: loss = 4.33594 (* 1 = 4.33594 loss)
I0407 22:06:41.361888 23658 sgd_solver.cpp:105] Iteration 1200, lr = 0.00546911
I0407 22:06:46.420290 23658 solver.cpp:218] Iteration 1212 (2.37236 iter/s, 5.05826s/12 iters), loss = 4.19442
I0407 22:06:46.420344 23658 solver.cpp:237]     Train net output #0: loss = 4.19442 (* 1 = 4.19442 loss)
I0407 22:06:46.420357 23658 sgd_solver.cpp:105] Iteration 1212, lr = 0.0054362
I0407 22:06:46.697670 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:50.993616 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 22:06:55.445021 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 22:06:59.078864 23658 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 22:06:59.078891 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:07:02.930621 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:03.441896 23658 solver.cpp:397]     Test net output #0: accuracy = 0.09375
I0407 22:07:03.441947 23658 solver.cpp:397]     Test net output #1: loss = 4.22089 (* 1 = 4.22089 loss)
I0407 22:07:03.533143 23658 solver.cpp:218] Iteration 1224 (0.70125 iter/s, 17.1123s/12 iters), loss = 4.30145
I0407 22:07:03.533206 23658 solver.cpp:237]     Train net output #0: loss = 4.30145 (* 1 = 4.30145 loss)
I0407 22:07:03.533219 23658 sgd_solver.cpp:105] Iteration 1224, lr = 0.00540349
I0407 22:07:07.869566 23658 solver.cpp:218] Iteration 1236 (2.76738 iter/s, 4.33624s/12 iters), loss = 4.27533
I0407 22:07:07.869611 23658 solver.cpp:237]     Train net output #0: loss = 4.27533 (* 1 = 4.27533 loss)
I0407 22:07:07.869623 23658 sgd_solver.cpp:105] Iteration 1236, lr = 0.00537098
I0407 22:07:12.947743 23658 solver.cpp:218] Iteration 1248 (2.36314 iter/s, 5.07798s/12 iters), loss = 4.03808
I0407 22:07:12.947839 23658 solver.cpp:237]     Train net output #0: loss = 4.03808 (* 1 = 4.03808 loss)
I0407 22:07:12.947847 23658 sgd_solver.cpp:105] Iteration 1248, lr = 0.00533867
I0407 22:07:18.008111 23658 solver.cpp:218] Iteration 1260 (2.37148 iter/s, 5.06012s/12 iters), loss = 4.01404
I0407 22:07:18.008159 23658 solver.cpp:237]     Train net output #0: loss = 4.01404 (* 1 = 4.01404 loss)
I0407 22:07:18.008172 23658 sgd_solver.cpp:105] Iteration 1260, lr = 0.00530655
I0407 22:07:22.999441 23658 solver.cpp:218] Iteration 1272 (2.40426 iter/s, 4.99113s/12 iters), loss = 4.08594
I0407 22:07:22.999495 23658 solver.cpp:237]     Train net output #0: loss = 4.08594 (* 1 = 4.08594 loss)
I0407 22:07:22.999506 23658 sgd_solver.cpp:105] Iteration 1272, lr = 0.00527462
I0407 22:07:28.051103 23658 solver.cpp:218] Iteration 1284 (2.37555 iter/s, 5.05146s/12 iters), loss = 4.14272
I0407 22:07:28.051154 23658 solver.cpp:237]     Train net output #0: loss = 4.14272 (* 1 = 4.14272 loss)
I0407 22:07:28.051168 23658 sgd_solver.cpp:105] Iteration 1284, lr = 0.00524289
I0407 22:07:33.072410 23658 solver.cpp:218] Iteration 1296 (2.38991 iter/s, 5.02111s/12 iters), loss = 3.99218
I0407 22:07:33.072449 23658 solver.cpp:237]     Train net output #0: loss = 3.99218 (* 1 = 3.99218 loss)
I0407 22:07:33.072459 23658 sgd_solver.cpp:105] Iteration 1296, lr = 0.00521134
I0407 22:07:38.094815 23658 solver.cpp:218] Iteration 1308 (2.38938 iter/s, 5.02221s/12 iters), loss = 4.17023
I0407 22:07:38.094852 23658 solver.cpp:237]     Train net output #0: loss = 4.17023 (* 1 = 4.17023 loss)
I0407 22:07:38.094861 23658 sgd_solver.cpp:105] Iteration 1308, lr = 0.00517999
I0407 22:07:40.653422 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:43.160701 23658 solver.cpp:218] Iteration 1320 (2.36887 iter/s, 5.0657s/12 iters), loss = 3.99938
I0407 22:07:43.160823 23658 solver.cpp:237]     Train net output #0: loss = 3.99938 (* 1 = 3.99938 loss)
I0407 22:07:43.160832 23658 sgd_solver.cpp:105] Iteration 1320, lr = 0.00514882
I0407 22:07:45.188753 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 22:07:48.090613 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 22:07:51.864159 23658 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 22:07:51.864184 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:07:55.847965 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:56.418989 23658 solver.cpp:397]     Test net output #0: accuracy = 0.109069
I0407 22:07:56.419039 23658 solver.cpp:397]     Test net output #1: loss = 4.05931 (* 1 = 4.05931 loss)
I0407 22:07:58.370270 23658 solver.cpp:218] Iteration 1332 (0.789006 iter/s, 15.209s/12 iters), loss = 3.887
I0407 22:07:58.370326 23658 solver.cpp:237]     Train net output #0: loss = 3.887 (* 1 = 3.887 loss)
I0407 22:07:58.370337 23658 sgd_solver.cpp:105] Iteration 1332, lr = 0.00511784
I0407 22:08:03.432709 23658 solver.cpp:218] Iteration 1344 (2.37049 iter/s, 5.06224s/12 iters), loss = 3.99908
I0407 22:08:03.432749 23658 solver.cpp:237]     Train net output #0: loss = 3.99908 (* 1 = 3.99908 loss)
I0407 22:08:03.432758 23658 sgd_solver.cpp:105] Iteration 1344, lr = 0.00508705
I0407 22:08:08.539952 23658 solver.cpp:218] Iteration 1356 (2.34969 iter/s, 5.10705s/12 iters), loss = 3.87883
I0407 22:08:08.539996 23658 solver.cpp:237]     Train net output #0: loss = 3.87883 (* 1 = 3.87883 loss)
I0407 22:08:08.540007 23658 sgd_solver.cpp:105] Iteration 1356, lr = 0.00505645
I0407 22:08:13.642187 23658 solver.cpp:218] Iteration 1368 (2.352 iter/s, 5.10204s/12 iters), loss = 3.87851
I0407 22:08:13.642285 23658 solver.cpp:237]     Train net output #0: loss = 3.87851 (* 1 = 3.87851 loss)
I0407 22:08:13.642295 23658 sgd_solver.cpp:105] Iteration 1368, lr = 0.00502602
I0407 22:08:14.832660 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:08:18.821796 23658 solver.cpp:218] Iteration 1380 (2.31689 iter/s, 5.17936s/12 iters), loss = 3.68622
I0407 22:08:18.821846 23658 solver.cpp:237]     Train net output #0: loss = 3.68622 (* 1 = 3.68622 loss)
I0407 22:08:18.821857 23658 sgd_solver.cpp:105] Iteration 1380, lr = 0.00499579
I0407 22:08:24.107370 23658 solver.cpp:218] Iteration 1392 (2.27042 iter/s, 5.28537s/12 iters), loss = 4.03061
I0407 22:08:24.107407 23658 solver.cpp:237]     Train net output #0: loss = 4.03061 (* 1 = 4.03061 loss)
I0407 22:08:24.107417 23658 sgd_solver.cpp:105] Iteration 1392, lr = 0.00496573
I0407 22:08:29.298645 23658 solver.cpp:218] Iteration 1404 (2.31165 iter/s, 5.19109s/12 iters), loss = 3.89543
I0407 22:08:29.298678 23658 solver.cpp:237]     Train net output #0: loss = 3.89543 (* 1 = 3.89543 loss)
I0407 22:08:29.298686 23658 sgd_solver.cpp:105] Iteration 1404, lr = 0.00493585
I0407 22:08:34.157896 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:34.513057 23658 solver.cpp:218] Iteration 1416 (2.3014 iter/s, 5.21422s/12 iters), loss = 3.54477
I0407 22:08:34.513101 23658 solver.cpp:237]     Train net output #0: loss = 3.54477 (* 1 = 3.54477 loss)
I0407 22:08:34.513110 23658 sgd_solver.cpp:105] Iteration 1416, lr = 0.00490616
I0407 22:08:39.199371 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 22:08:44.813252 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 22:08:54.058524 23658 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 22:08:54.058552 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:08:57.889451 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:58.480067 23658 solver.cpp:397]     Test net output #0: accuracy = 0.132353
I0407 22:08:58.480103 23658 solver.cpp:397]     Test net output #1: loss = 3.94971 (* 1 = 3.94971 loss)
I0407 22:08:58.569941 23658 solver.cpp:218] Iteration 1428 (0.498833 iter/s, 24.0562s/12 iters), loss = 3.87759
I0407 22:08:58.570008 23658 solver.cpp:237]     Train net output #0: loss = 3.87759 (* 1 = 3.87759 loss)
I0407 22:08:58.570019 23658 sgd_solver.cpp:105] Iteration 1428, lr = 0.00487664
I0407 22:09:02.794122 23658 solver.cpp:218] Iteration 1440 (2.84092 iter/s, 4.22399s/12 iters), loss = 3.81331
I0407 22:09:02.794167 23658 solver.cpp:237]     Train net output #0: loss = 3.81331 (* 1 = 3.81331 loss)
I0407 22:09:02.794176 23658 sgd_solver.cpp:105] Iteration 1440, lr = 0.0048473
I0407 22:09:07.954813 23658 solver.cpp:218] Iteration 1452 (2.32536 iter/s, 5.16049s/12 iters), loss = 3.86491
I0407 22:09:07.954864 23658 solver.cpp:237]     Train net output #0: loss = 3.86491 (* 1 = 3.86491 loss)
I0407 22:09:07.954876 23658 sgd_solver.cpp:105] Iteration 1452, lr = 0.00481813
I0407 22:09:13.054376 23658 solver.cpp:218] Iteration 1464 (2.35324 iter/s, 5.09936s/12 iters), loss = 3.7091
I0407 22:09:13.054425 23658 solver.cpp:237]     Train net output #0: loss = 3.7091 (* 1 = 3.7091 loss)
I0407 22:09:13.054435 23658 sgd_solver.cpp:105] Iteration 1464, lr = 0.00478914
I0407 22:09:18.356504 23658 solver.cpp:218] Iteration 1476 (2.26333 iter/s, 5.30192s/12 iters), loss = 3.82671
I0407 22:09:18.356612 23658 solver.cpp:237]     Train net output #0: loss = 3.82671 (* 1 = 3.82671 loss)
I0407 22:09:18.356624 23658 sgd_solver.cpp:105] Iteration 1476, lr = 0.00476033
I0407 22:09:23.408531 23658 solver.cpp:218] Iteration 1488 (2.37541 iter/s, 5.05177s/12 iters), loss = 3.96829
I0407 22:09:23.408581 23658 solver.cpp:237]     Train net output #0: loss = 3.96829 (* 1 = 3.96829 loss)
I0407 22:09:23.408591 23658 sgd_solver.cpp:105] Iteration 1488, lr = 0.00473169
I0407 22:09:28.493912 23658 solver.cpp:218] Iteration 1500 (2.3598 iter/s, 5.08518s/12 iters), loss = 3.41325
I0407 22:09:28.493966 23658 solver.cpp:237]     Train net output #0: loss = 3.41325 (* 1 = 3.41325 loss)
I0407 22:09:28.493978 23658 sgd_solver.cpp:105] Iteration 1500, lr = 0.00470322
I0407 22:09:33.711055 23658 solver.cpp:218] Iteration 1512 (2.3002 iter/s, 5.21694s/12 iters), loss = 3.5976
I0407 22:09:33.711109 23658 solver.cpp:237]     Train net output #0: loss = 3.5976 (* 1 = 3.5976 loss)
I0407 22:09:33.711122 23658 sgd_solver.cpp:105] Iteration 1512, lr = 0.00467492
I0407 22:09:35.437111 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:38.689633 23658 solver.cpp:218] Iteration 1524 (2.41042 iter/s, 4.97838s/12 iters), loss = 3.60055
I0407 22:09:38.689680 23658 solver.cpp:237]     Train net output #0: loss = 3.60055 (* 1 = 3.60055 loss)
I0407 22:09:38.689692 23658 sgd_solver.cpp:105] Iteration 1524, lr = 0.0046468
I0407 22:09:40.709339 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 22:09:45.363226 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 22:09:49.959441 23658 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 22:09:49.959550 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:09:53.790311 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:54.430336 23658 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0407 22:09:54.430385 23658 solver.cpp:397]     Test net output #1: loss = 3.78874 (* 1 = 3.78874 loss)
I0407 22:09:56.321274 23658 solver.cpp:218] Iteration 1536 (0.680616 iter/s, 17.6311s/12 iters), loss = 3.792
I0407 22:09:56.321324 23658 solver.cpp:237]     Train net output #0: loss = 3.792 (* 1 = 3.792 loss)
I0407 22:09:56.321336 23658 sgd_solver.cpp:105] Iteration 1536, lr = 0.00461884
I0407 22:10:01.239022 23658 solver.cpp:218] Iteration 1548 (2.44024 iter/s, 4.91755s/12 iters), loss = 3.53265
I0407 22:10:01.239068 23658 solver.cpp:237]     Train net output #0: loss = 3.53265 (* 1 = 3.53265 loss)
I0407 22:10:01.239079 23658 sgd_solver.cpp:105] Iteration 1548, lr = 0.00459105
I0407 22:10:06.446835 23658 solver.cpp:218] Iteration 1560 (2.30432 iter/s, 5.20761s/12 iters), loss = 3.61234
I0407 22:10:06.446877 23658 solver.cpp:237]     Train net output #0: loss = 3.61234 (* 1 = 3.61234 loss)
I0407 22:10:06.446887 23658 sgd_solver.cpp:105] Iteration 1560, lr = 0.00456343
I0407 22:10:11.554706 23658 solver.cpp:218] Iteration 1572 (2.34941 iter/s, 5.10767s/12 iters), loss = 3.54255
I0407 22:10:11.554752 23658 solver.cpp:237]     Train net output #0: loss = 3.54255 (* 1 = 3.54255 loss)
I0407 22:10:11.554764 23658 sgd_solver.cpp:105] Iteration 1572, lr = 0.00453597
I0407 22:10:16.570509 23658 solver.cpp:218] Iteration 1584 (2.39253 iter/s, 5.01561s/12 iters), loss = 3.52339
I0407 22:10:16.570559 23658 solver.cpp:237]     Train net output #0: loss = 3.52339 (* 1 = 3.52339 loss)
I0407 22:10:16.570572 23658 sgd_solver.cpp:105] Iteration 1584, lr = 0.00450868
I0407 22:10:21.650574 23658 solver.cpp:218] Iteration 1596 (2.36227 iter/s, 5.07987s/12 iters), loss = 3.62598
I0407 22:10:21.650683 23658 solver.cpp:237]     Train net output #0: loss = 3.62598 (* 1 = 3.62598 loss)
I0407 22:10:21.650696 23658 sgd_solver.cpp:105] Iteration 1596, lr = 0.00448155
I0407 22:10:26.634160 23658 solver.cpp:218] Iteration 1608 (2.40803 iter/s, 4.98333s/12 iters), loss = 3.58414
I0407 22:10:26.634205 23658 solver.cpp:237]     Train net output #0: loss = 3.58414 (* 1 = 3.58414 loss)
I0407 22:10:26.634217 23658 sgd_solver.cpp:105] Iteration 1608, lr = 0.00445459
I0407 22:10:30.567617 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:31.674639 23658 solver.cpp:218] Iteration 1620 (2.38082 iter/s, 5.04029s/12 iters), loss = 3.52847
I0407 22:10:31.674682 23658 solver.cpp:237]     Train net output #0: loss = 3.52847 (* 1 = 3.52847 loss)
I0407 22:10:31.674692 23658 sgd_solver.cpp:105] Iteration 1620, lr = 0.00442779
I0407 22:10:36.253988 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 22:10:40.593981 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 22:10:45.945791 23658 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 22:10:45.945817 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:10:49.723503 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:50.394446 23658 solver.cpp:397]     Test net output #0: accuracy = 0.158701
I0407 22:10:50.394495 23658 solver.cpp:397]     Test net output #1: loss = 3.70495 (* 1 = 3.70495 loss)
I0407 22:10:50.485751 23658 solver.cpp:218] Iteration 1632 (0.63794 iter/s, 18.8105s/12 iters), loss = 3.50309
I0407 22:10:50.485805 23658 solver.cpp:237]     Train net output #0: loss = 3.50309 (* 1 = 3.50309 loss)
I0407 22:10:50.485816 23658 sgd_solver.cpp:105] Iteration 1632, lr = 0.00440115
I0407 22:10:54.820793 23658 solver.cpp:218] Iteration 1644 (2.76826 iter/s, 4.33486s/12 iters), loss = 3.50135
I0407 22:10:54.820945 23658 solver.cpp:237]     Train net output #0: loss = 3.50135 (* 1 = 3.50135 loss)
I0407 22:10:54.820960 23658 sgd_solver.cpp:105] Iteration 1644, lr = 0.00437467
I0407 22:10:59.731822 23658 solver.cpp:218] Iteration 1656 (2.44363 iter/s, 4.91073s/12 iters), loss = 3.57644
I0407 22:10:59.731860 23658 solver.cpp:237]     Train net output #0: loss = 3.57644 (* 1 = 3.57644 loss)
I0407 22:10:59.731869 23658 sgd_solver.cpp:105] Iteration 1656, lr = 0.00434835
I0407 22:11:04.743325 23658 solver.cpp:218] Iteration 1668 (2.39458 iter/s, 5.01131s/12 iters), loss = 3.24523
I0407 22:11:04.743366 23658 solver.cpp:237]     Train net output #0: loss = 3.24523 (* 1 = 3.24523 loss)
I0407 22:11:04.743376 23658 sgd_solver.cpp:105] Iteration 1668, lr = 0.00432219
I0407 22:11:10.065778 23658 solver.cpp:218] Iteration 1680 (2.25469 iter/s, 5.32225s/12 iters), loss = 3.20501
I0407 22:11:10.065820 23658 solver.cpp:237]     Train net output #0: loss = 3.20501 (* 1 = 3.20501 loss)
I0407 22:11:10.065829 23658 sgd_solver.cpp:105] Iteration 1680, lr = 0.00429618
I0407 22:11:15.114310 23658 solver.cpp:218] Iteration 1692 (2.37702 iter/s, 5.04834s/12 iters), loss = 3.3108
I0407 22:11:15.114352 23658 solver.cpp:237]     Train net output #0: loss = 3.3108 (* 1 = 3.3108 loss)
I0407 22:11:15.114369 23658 sgd_solver.cpp:105] Iteration 1692, lr = 0.00427034
I0407 22:11:20.094427 23658 solver.cpp:218] Iteration 1704 (2.40968 iter/s, 4.97992s/12 iters), loss = 3.13584
I0407 22:11:20.094482 23658 solver.cpp:237]     Train net output #0: loss = 3.13584 (* 1 = 3.13584 loss)
I0407 22:11:20.094496 23658 sgd_solver.cpp:105] Iteration 1704, lr = 0.00424464
I0407 22:11:25.172861 23658 solver.cpp:218] Iteration 1716 (2.36303 iter/s, 5.07822s/12 iters), loss = 3.14752
I0407 22:11:25.172952 23658 solver.cpp:237]     Train net output #0: loss = 3.14752 (* 1 = 3.14752 loss)
I0407 22:11:25.172966 23658 sgd_solver.cpp:105] Iteration 1716, lr = 0.00421911
I0407 22:11:26.219415 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:30.121184 23658 solver.cpp:218] Iteration 1728 (2.42518 iter/s, 4.94809s/12 iters), loss = 3.37614
I0407 22:11:30.121229 23658 solver.cpp:237]     Train net output #0: loss = 3.37614 (* 1 = 3.37614 loss)
I0407 22:11:30.121238 23658 sgd_solver.cpp:105] Iteration 1728, lr = 0.00419372
I0407 22:11:32.122963 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 22:11:40.210388 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 22:11:45.546757 23658 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 22:11:45.546780 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:11:49.351768 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:50.058010 23658 solver.cpp:397]     Test net output #0: accuracy = 0.178309
I0407 22:11:50.058053 23658 solver.cpp:397]     Test net output #1: loss = 3.54509 (* 1 = 3.54509 loss)
I0407 22:11:52.031565 23658 solver.cpp:218] Iteration 1740 (0.547702 iter/s, 21.9097s/12 iters), loss = 3.25214
I0407 22:11:52.031620 23658 solver.cpp:237]     Train net output #0: loss = 3.25214 (* 1 = 3.25214 loss)
I0407 22:11:52.031632 23658 sgd_solver.cpp:105] Iteration 1740, lr = 0.00416849
I0407 22:11:56.951653 23658 solver.cpp:218] Iteration 1752 (2.43908 iter/s, 4.91988s/12 iters), loss = 3.09006
I0407 22:11:56.951804 23658 solver.cpp:237]     Train net output #0: loss = 3.09006 (* 1 = 3.09006 loss)
I0407 22:11:56.951817 23658 sgd_solver.cpp:105] Iteration 1752, lr = 0.00414341
I0407 22:12:02.280371 23658 solver.cpp:218] Iteration 1764 (2.25208 iter/s, 5.32841s/12 iters), loss = 3.15292
I0407 22:12:02.280417 23658 solver.cpp:237]     Train net output #0: loss = 3.15292 (* 1 = 3.15292 loss)
I0407 22:12:02.280426 23658 sgd_solver.cpp:105] Iteration 1764, lr = 0.00411848
I0407 22:12:07.399678 23658 solver.cpp:218] Iteration 1776 (2.34416 iter/s, 5.1191s/12 iters), loss = 3.17101
I0407 22:12:07.399729 23658 solver.cpp:237]     Train net output #0: loss = 3.17101 (* 1 = 3.17101 loss)
I0407 22:12:07.399741 23658 sgd_solver.cpp:105] Iteration 1776, lr = 0.0040937
I0407 22:12:12.581218 23658 solver.cpp:218] Iteration 1788 (2.31601 iter/s, 5.18133s/12 iters), loss = 3.11783
I0407 22:12:12.581265 23658 solver.cpp:237]     Train net output #0: loss = 3.11783 (* 1 = 3.11783 loss)
I0407 22:12:12.581274 23658 sgd_solver.cpp:105] Iteration 1788, lr = 0.00406907
I0407 22:12:17.734747 23658 solver.cpp:218] Iteration 1800 (2.32859 iter/s, 5.15333s/12 iters), loss = 3.35998
I0407 22:12:17.734794 23658 solver.cpp:237]     Train net output #0: loss = 3.35998 (* 1 = 3.35998 loss)
I0407 22:12:17.734804 23658 sgd_solver.cpp:105] Iteration 1800, lr = 0.00404459
I0407 22:12:23.168864 23658 solver.cpp:218] Iteration 1812 (2.20836 iter/s, 5.43391s/12 iters), loss = 3.05119
I0407 22:12:23.168917 23658 solver.cpp:237]     Train net output #0: loss = 3.05119 (* 1 = 3.05119 loss)
I0407 22:12:23.168929 23658 sgd_solver.cpp:105] Iteration 1812, lr = 0.00402026
I0407 22:12:26.418990 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:28.277216 23658 solver.cpp:218] Iteration 1824 (2.34919 iter/s, 5.10814s/12 iters), loss = 3.35333
I0407 22:12:28.277386 23658 solver.cpp:237]     Train net output #0: loss = 3.35333 (* 1 = 3.35333 loss)
I0407 22:12:28.277403 23658 sgd_solver.cpp:105] Iteration 1824, lr = 0.00399607
I0407 22:12:32.826615 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 22:12:36.892258 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 22:12:40.547667 23658 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 22:12:40.547693 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:12:44.265767 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:45.018170 23658 solver.cpp:397]     Test net output #0: accuracy = 0.211397
I0407 22:12:45.018213 23658 solver.cpp:397]     Test net output #1: loss = 3.35078 (* 1 = 3.35078 loss)
I0407 22:12:45.109756 23658 solver.cpp:218] Iteration 1836 (0.712932 iter/s, 16.8319s/12 iters), loss = 2.98844
I0407 22:12:45.109799 23658 solver.cpp:237]     Train net output #0: loss = 2.98844 (* 1 = 2.98844 loss)
I0407 22:12:45.109808 23658 sgd_solver.cpp:105] Iteration 1836, lr = 0.00397203
I0407 22:12:49.642694 23658 solver.cpp:218] Iteration 1848 (2.6474 iter/s, 4.53276s/12 iters), loss = 3.18605
I0407 22:12:49.642737 23658 solver.cpp:237]     Train net output #0: loss = 3.18605 (* 1 = 3.18605 loss)
I0407 22:12:49.642746 23658 sgd_solver.cpp:105] Iteration 1848, lr = 0.00394813
I0407 22:12:54.820659 23658 solver.cpp:218] Iteration 1860 (2.3176 iter/s, 5.17777s/12 iters), loss = 3.04395
I0407 22:12:54.820698 23658 solver.cpp:237]     Train net output #0: loss = 3.04395 (* 1 = 3.04395 loss)
I0407 22:12:54.820706 23658 sgd_solver.cpp:105] Iteration 1860, lr = 0.00392437
I0407 22:12:59.835944 23658 solver.cpp:218] Iteration 1872 (2.39278 iter/s, 5.01509s/12 iters), loss = 2.91361
I0407 22:12:59.836016 23658 solver.cpp:237]     Train net output #0: loss = 2.91361 (* 1 = 2.91361 loss)
I0407 22:12:59.836026 23658 sgd_solver.cpp:105] Iteration 1872, lr = 0.00390076
I0407 22:13:05.127149 23658 solver.cpp:218] Iteration 1884 (2.26801 iter/s, 5.29098s/12 iters), loss = 3.06357
I0407 22:13:05.127188 23658 solver.cpp:237]     Train net output #0: loss = 3.06357 (* 1 = 3.06357 loss)
I0407 22:13:05.127197 23658 sgd_solver.cpp:105] Iteration 1884, lr = 0.00387729
I0407 22:13:10.535004 23658 solver.cpp:218] Iteration 1896 (2.21908 iter/s, 5.40765s/12 iters), loss = 3.23004
I0407 22:13:10.535059 23658 solver.cpp:237]     Train net output #0: loss = 3.23004 (* 1 = 3.23004 loss)
I0407 22:13:10.535071 23658 sgd_solver.cpp:105] Iteration 1896, lr = 0.00385397
I0407 22:13:15.585530 23658 solver.cpp:218] Iteration 1908 (2.37609 iter/s, 5.05031s/12 iters), loss = 2.77661
I0407 22:13:15.585592 23658 solver.cpp:237]     Train net output #0: loss = 2.77661 (* 1 = 2.77661 loss)
I0407 22:13:15.585606 23658 sgd_solver.cpp:105] Iteration 1908, lr = 0.00383078
I0407 22:13:20.612288 23658 solver.cpp:218] Iteration 1920 (2.38732 iter/s, 5.02655s/12 iters), loss = 2.81363
I0407 22:13:20.612335 23658 solver.cpp:237]     Train net output #0: loss = 2.81363 (* 1 = 2.81363 loss)
I0407 22:13:20.612345 23658 sgd_solver.cpp:105] Iteration 1920, lr = 0.00380773
I0407 22:13:20.930200 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:25.690280 23658 solver.cpp:218] Iteration 1932 (2.36323 iter/s, 5.07779s/12 iters), loss = 2.90215
I0407 22:13:25.690320 23658 solver.cpp:237]     Train net output #0: loss = 2.90215 (* 1 = 2.90215 loss)
I0407 22:13:25.690333 23658 sgd_solver.cpp:105] Iteration 1932, lr = 0.00378482
I0407 22:13:27.893110 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 22:13:31.446300 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 22:13:36.997288 23658 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 22:13:36.997308 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:13:40.754230 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:41.542335 23658 solver.cpp:397]     Test net output #0: accuracy = 0.186275
I0407 22:13:41.542384 23658 solver.cpp:397]     Test net output #1: loss = 3.51667 (* 1 = 3.51667 loss)
I0407 22:13:43.471565 23658 solver.cpp:218] Iteration 1944 (0.674887 iter/s, 17.7807s/12 iters), loss = 3.03137
I0407 22:13:43.471613 23658 solver.cpp:237]     Train net output #0: loss = 3.03137 (* 1 = 3.03137 loss)
I0407 22:13:43.471624 23658 sgd_solver.cpp:105] Iteration 1944, lr = 0.00376205
I0407 22:13:48.477319 23658 solver.cpp:218] Iteration 1956 (2.39734 iter/s, 5.00555s/12 iters), loss = 3.17206
I0407 22:13:48.477367 23658 solver.cpp:237]     Train net output #0: loss = 3.17206 (* 1 = 3.17206 loss)
I0407 22:13:48.477380 23658 sgd_solver.cpp:105] Iteration 1956, lr = 0.00373941
I0407 22:13:53.713256 23658 solver.cpp:218] Iteration 1968 (2.29194 iter/s, 5.23573s/12 iters), loss = 2.94786
I0407 22:13:53.713306 23658 solver.cpp:237]     Train net output #0: loss = 2.94786 (* 1 = 2.94786 loss)
I0407 22:13:53.713318 23658 sgd_solver.cpp:105] Iteration 1968, lr = 0.00371692
I0407 22:13:58.792980 23658 solver.cpp:218] Iteration 1980 (2.36243 iter/s, 5.07952s/12 iters), loss = 3.08748
I0407 22:13:58.793035 23658 solver.cpp:237]     Train net output #0: loss = 3.08748 (* 1 = 3.08748 loss)
I0407 22:13:58.793046 23658 sgd_solver.cpp:105] Iteration 1980, lr = 0.00369455
I0407 22:14:03.796803 23658 solver.cpp:218] Iteration 1992 (2.39826 iter/s, 5.00362s/12 iters), loss = 2.99717
I0407 22:14:03.797117 23658 solver.cpp:237]     Train net output #0: loss = 2.99717 (* 1 = 2.99717 loss)
I0407 22:14:03.797130 23658 sgd_solver.cpp:105] Iteration 1992, lr = 0.00367233
I0407 22:14:09.171367 23658 solver.cpp:218] Iteration 2004 (2.23294 iter/s, 5.37409s/12 iters), loss = 2.6945
I0407 22:14:09.171425 23658 solver.cpp:237]     Train net output #0: loss = 2.6945 (* 1 = 2.6945 loss)
I0407 22:14:09.171438 23658 sgd_solver.cpp:105] Iteration 2004, lr = 0.00365023
I0407 22:14:14.375093 23658 solver.cpp:218] Iteration 2016 (2.30614 iter/s, 5.20351s/12 iters), loss = 2.70136
I0407 22:14:14.375145 23658 solver.cpp:237]     Train net output #0: loss = 2.70136 (* 1 = 2.70136 loss)
I0407 22:14:14.375159 23658 sgd_solver.cpp:105] Iteration 2016, lr = 0.00362827
I0407 22:14:16.880827 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:19.273763 23658 solver.cpp:218] Iteration 2028 (2.44974 iter/s, 4.89847s/12 iters), loss = 2.52627
I0407 22:14:19.273816 23658 solver.cpp:237]     Train net output #0: loss = 2.52627 (* 1 = 2.52627 loss)
I0407 22:14:19.273828 23658 sgd_solver.cpp:105] Iteration 2028, lr = 0.00360644
I0407 22:14:24.013873 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 22:14:26.990922 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 22:14:31.185530 23658 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 22:14:31.185557 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:14:34.860611 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:35.691886 23658 solver.cpp:397]     Test net output #0: accuracy = 0.221201
I0407 22:14:35.691936 23658 solver.cpp:397]     Test net output #1: loss = 3.356 (* 1 = 3.356 loss)
I0407 22:14:35.781811 23658 solver.cpp:218] Iteration 2040 (0.726941 iter/s, 16.5075s/12 iters), loss = 2.94853
I0407 22:14:35.781862 23658 solver.cpp:237]     Train net output #0: loss = 2.94853 (* 1 = 2.94853 loss)
I0407 22:14:35.781874 23658 sgd_solver.cpp:105] Iteration 2040, lr = 0.00358474
I0407 22:14:40.370425 23658 solver.cpp:218] Iteration 2052 (2.61528 iter/s, 4.58842s/12 iters), loss = 2.81711
I0407 22:14:40.370477 23658 solver.cpp:237]     Train net output #0: loss = 2.81711 (* 1 = 2.81711 loss)
I0407 22:14:40.370489 23658 sgd_solver.cpp:105] Iteration 2052, lr = 0.00356317
I0407 22:14:41.999569 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:14:45.428355 23658 solver.cpp:218] Iteration 2064 (2.37261 iter/s, 5.05773s/12 iters), loss = 2.75406
I0407 22:14:45.428404 23658 solver.cpp:237]     Train net output #0: loss = 2.75406 (* 1 = 2.75406 loss)
I0407 22:14:45.428416 23658 sgd_solver.cpp:105] Iteration 2064, lr = 0.00354174
I0407 22:14:50.451388 23658 solver.cpp:218] Iteration 2076 (2.38909 iter/s, 5.02283s/12 iters), loss = 2.98742
I0407 22:14:50.451437 23658 solver.cpp:237]     Train net output #0: loss = 2.98742 (* 1 = 2.98742 loss)
I0407 22:14:50.451447 23658 sgd_solver.cpp:105] Iteration 2076, lr = 0.00352043
I0407 22:14:55.530783 23658 solver.cpp:218] Iteration 2088 (2.36258 iter/s, 5.07919s/12 iters), loss = 2.85451
I0407 22:14:55.530833 23658 solver.cpp:237]     Train net output #0: loss = 2.85451 (* 1 = 2.85451 loss)
I0407 22:14:55.530844 23658 sgd_solver.cpp:105] Iteration 2088, lr = 0.00349925
I0407 22:15:00.626231 23658 solver.cpp:218] Iteration 2100 (2.35514 iter/s, 5.09524s/12 iters), loss = 2.65079
I0407 22:15:00.626283 23658 solver.cpp:237]     Train net output #0: loss = 2.65079 (* 1 = 2.65079 loss)
I0407 22:15:00.626294 23658 sgd_solver.cpp:105] Iteration 2100, lr = 0.00347819
I0407 22:15:05.598467 23658 solver.cpp:218] Iteration 2112 (2.4135 iter/s, 4.97204s/12 iters), loss = 2.69092
I0407 22:15:05.598562 23658 solver.cpp:237]     Train net output #0: loss = 2.69092 (* 1 = 2.69092 loss)
I0407 22:15:05.598572 23658 sgd_solver.cpp:105] Iteration 2112, lr = 0.00345727
I0407 22:15:10.355234 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:10.674075 23658 solver.cpp:218] Iteration 2124 (2.36436 iter/s, 5.07536s/12 iters), loss = 2.37749
I0407 22:15:10.674121 23658 solver.cpp:237]     Train net output #0: loss = 2.37749 (* 1 = 2.37749 loss)
I0407 22:15:10.674134 23658 sgd_solver.cpp:105] Iteration 2124, lr = 0.00343646
I0407 22:15:15.795900 23658 solver.cpp:218] Iteration 2136 (2.34301 iter/s, 5.12162s/12 iters), loss = 2.39148
I0407 22:15:15.795955 23658 solver.cpp:237]     Train net output #0: loss = 2.39148 (* 1 = 2.39148 loss)
I0407 22:15:15.795966 23658 sgd_solver.cpp:105] Iteration 2136, lr = 0.00341579
I0407 22:15:17.843277 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 22:15:21.064445 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 22:15:25.040437 23658 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 22:15:25.040465 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:15:28.818465 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:29.686596 23658 solver.cpp:397]     Test net output #0: accuracy = 0.249387
I0407 22:15:29.686643 23658 solver.cpp:397]     Test net output #1: loss = 3.16686 (* 1 = 3.16686 loss)
I0407 22:15:31.624492 23658 solver.cpp:218] Iteration 2148 (0.758146 iter/s, 15.8281s/12 iters), loss = 2.41204
I0407 22:15:31.624541 23658 solver.cpp:237]     Train net output #0: loss = 2.41204 (* 1 = 2.41204 loss)
I0407 22:15:31.624553 23658 sgd_solver.cpp:105] Iteration 2148, lr = 0.00339524
I0407 22:15:37.091483 23658 solver.cpp:218] Iteration 2160 (2.19508 iter/s, 5.46678s/12 iters), loss = 2.71098
I0407 22:15:37.091641 23658 solver.cpp:237]     Train net output #0: loss = 2.71098 (* 1 = 2.71098 loss)
I0407 22:15:37.091655 23658 sgd_solver.cpp:105] Iteration 2160, lr = 0.00337481
I0407 22:15:42.389088 23658 solver.cpp:218] Iteration 2172 (2.26531 iter/s, 5.2973s/12 iters), loss = 2.58455
I0407 22:15:42.389130 23658 solver.cpp:237]     Train net output #0: loss = 2.58455 (* 1 = 2.58455 loss)
I0407 22:15:42.389140 23658 sgd_solver.cpp:105] Iteration 2172, lr = 0.00335451
I0407 22:15:47.505869 23658 solver.cpp:218] Iteration 2184 (2.34531 iter/s, 5.11658s/12 iters), loss = 2.69314
I0407 22:15:47.505914 23658 solver.cpp:237]     Train net output #0: loss = 2.69314 (* 1 = 2.69314 loss)
I0407 22:15:47.505923 23658 sgd_solver.cpp:105] Iteration 2184, lr = 0.00333432
I0407 22:15:52.495985 23658 solver.cpp:218] Iteration 2196 (2.40485 iter/s, 4.98992s/12 iters), loss = 2.23057
I0407 22:15:52.496031 23658 solver.cpp:237]     Train net output #0: loss = 2.23057 (* 1 = 2.23057 loss)
I0407 22:15:52.496042 23658 sgd_solver.cpp:105] Iteration 2196, lr = 0.00331426
I0407 22:15:57.636763 23658 solver.cpp:218] Iteration 2208 (2.33437 iter/s, 5.14058s/12 iters), loss = 2.45949
I0407 22:15:57.636812 23658 solver.cpp:237]     Train net output #0: loss = 2.45949 (* 1 = 2.45949 loss)
I0407 22:15:57.636823 23658 sgd_solver.cpp:105] Iteration 2208, lr = 0.00329432
I0407 22:16:02.699399 23658 solver.cpp:218] Iteration 2220 (2.3704 iter/s, 5.06243s/12 iters), loss = 2.25176
I0407 22:16:02.699453 23658 solver.cpp:237]     Train net output #0: loss = 2.25176 (* 1 = 2.25176 loss)
I0407 22:16:02.699465 23658 sgd_solver.cpp:105] Iteration 2220, lr = 0.0032745
I0407 22:16:04.510694 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:07.751854 23658 solver.cpp:218] Iteration 2232 (2.37518 iter/s, 5.05225s/12 iters), loss = 2.54045
I0407 22:16:07.751969 23658 solver.cpp:237]     Train net output #0: loss = 2.54045 (* 1 = 2.54045 loss)
I0407 22:16:07.751982 23658 sgd_solver.cpp:105] Iteration 2232, lr = 0.0032548
I0407 22:16:12.363276 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 22:16:15.360484 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 22:16:19.233168 23658 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 22:16:19.233194 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:16:22.959378 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:23.867028 23658 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0407 22:16:23.867071 23658 solver.cpp:397]     Test net output #1: loss = 3.11187 (* 1 = 3.11187 loss)
I0407 22:16:23.956692 23658 solver.cpp:218] Iteration 2244 (0.740546 iter/s, 16.2043s/12 iters), loss = 2.55322
I0407 22:16:23.956740 23658 solver.cpp:237]     Train net output #0: loss = 2.55322 (* 1 = 2.55322 loss)
I0407 22:16:23.956750 23658 sgd_solver.cpp:105] Iteration 2244, lr = 0.00323522
I0407 22:16:28.514657 23658 solver.cpp:218] Iteration 2256 (2.63286 iter/s, 4.55778s/12 iters), loss = 2.10078
I0407 22:16:28.514694 23658 solver.cpp:237]     Train net output #0: loss = 2.10078 (* 1 = 2.10078 loss)
I0407 22:16:28.514703 23658 sgd_solver.cpp:105] Iteration 2256, lr = 0.00321575
I0407 22:16:33.633316 23658 solver.cpp:218] Iteration 2268 (2.34445 iter/s, 5.11846s/12 iters), loss = 2.18172
I0407 22:16:33.633370 23658 solver.cpp:237]     Train net output #0: loss = 2.18172 (* 1 = 2.18172 loss)
I0407 22:16:33.633383 23658 sgd_solver.cpp:105] Iteration 2268, lr = 0.00319641
I0407 22:16:39.123153 23658 solver.cpp:218] Iteration 2280 (2.18594 iter/s, 5.48962s/12 iters), loss = 2.51494
I0407 22:16:39.123286 23658 solver.cpp:237]     Train net output #0: loss = 2.51494 (* 1 = 2.51494 loss)
I0407 22:16:39.123297 23658 sgd_solver.cpp:105] Iteration 2280, lr = 0.00317717
I0407 22:16:44.342958 23658 solver.cpp:218] Iteration 2292 (2.29906 iter/s, 5.21952s/12 iters), loss = 2.23681
I0407 22:16:44.343009 23658 solver.cpp:237]     Train net output #0: loss = 2.23681 (* 1 = 2.23681 loss)
I0407 22:16:44.343021 23658 sgd_solver.cpp:105] Iteration 2292, lr = 0.00315806
I0407 22:16:49.370311 23658 solver.cpp:218] Iteration 2304 (2.38704 iter/s, 5.02714s/12 iters), loss = 2.5956
I0407 22:16:49.370363 23658 solver.cpp:237]     Train net output #0: loss = 2.5956 (* 1 = 2.5956 loss)
I0407 22:16:49.370375 23658 sgd_solver.cpp:105] Iteration 2304, lr = 0.00313906
I0407 22:16:54.445823 23658 solver.cpp:218] Iteration 2316 (2.36439 iter/s, 5.0753s/12 iters), loss = 2.23811
I0407 22:16:54.445879 23658 solver.cpp:237]     Train net output #0: loss = 2.23811 (* 1 = 2.23811 loss)
I0407 22:16:54.445891 23658 sgd_solver.cpp:105] Iteration 2316, lr = 0.00312017
I0407 22:16:58.686165 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:59.839104 23658 solver.cpp:218] Iteration 2328 (2.22508 iter/s, 5.39307s/12 iters), loss = 2.16299
I0407 22:16:59.839155 23658 solver.cpp:237]     Train net output #0: loss = 2.16299 (* 1 = 2.16299 loss)
I0407 22:16:59.839167 23658 sgd_solver.cpp:105] Iteration 2328, lr = 0.0031014
I0407 22:17:04.868944 23658 solver.cpp:218] Iteration 2340 (2.38586 iter/s, 5.02963s/12 iters), loss = 2.21376
I0407 22:17:04.869009 23658 solver.cpp:237]     Train net output #0: loss = 2.21376 (* 1 = 2.21376 loss)
I0407 22:17:04.869019 23658 sgd_solver.cpp:105] Iteration 2340, lr = 0.00308274
I0407 22:17:06.946502 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 22:17:09.945047 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 22:17:15.802922 23658 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 22:17:15.802950 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:17:19.399264 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:20.452450 23658 solver.cpp:397]     Test net output #0: accuracy = 0.284926
I0407 22:17:20.452497 23658 solver.cpp:397]     Test net output #1: loss = 3.09356 (* 1 = 3.09356 loss)
I0407 22:17:22.304685 23658 solver.cpp:218] Iteration 2352 (0.688263 iter/s, 17.4352s/12 iters), loss = 2.60025
I0407 22:17:22.304738 23658 solver.cpp:237]     Train net output #0: loss = 2.60025 (* 1 = 2.60025 loss)
I0407 22:17:22.304750 23658 sgd_solver.cpp:105] Iteration 2352, lr = 0.00306419
I0407 22:17:27.793293 23658 solver.cpp:218] Iteration 2364 (2.18644 iter/s, 5.48838s/12 iters), loss = 2.13675
I0407 22:17:27.793337 23658 solver.cpp:237]     Train net output #0: loss = 2.13675 (* 1 = 2.13675 loss)
I0407 22:17:27.793346 23658 sgd_solver.cpp:105] Iteration 2364, lr = 0.00304576
I0407 22:17:33.179270 23658 solver.cpp:218] Iteration 2376 (2.22809 iter/s, 5.38577s/12 iters), loss = 2.272
I0407 22:17:33.179316 23658 solver.cpp:237]     Train net output #0: loss = 2.272 (* 1 = 2.272 loss)
I0407 22:17:33.179327 23658 sgd_solver.cpp:105] Iteration 2376, lr = 0.00302743
I0407 22:17:38.183535 23658 solver.cpp:218] Iteration 2388 (2.39805 iter/s, 5.00407s/12 iters), loss = 2.28976
I0407 22:17:38.183583 23658 solver.cpp:237]     Train net output #0: loss = 2.28976 (* 1 = 2.28976 loss)
I0407 22:17:38.183593 23658 sgd_solver.cpp:105] Iteration 2388, lr = 0.00300922
I0407 22:17:43.276288 23658 solver.cpp:218] Iteration 2400 (2.35638 iter/s, 5.09255s/12 iters), loss = 2.07661
I0407 22:17:43.276371 23658 solver.cpp:237]     Train net output #0: loss = 2.07661 (* 1 = 2.07661 loss)
I0407 22:17:43.276383 23658 sgd_solver.cpp:105] Iteration 2400, lr = 0.00299111
I0407 22:17:48.351199 23658 solver.cpp:218] Iteration 2412 (2.36468 iter/s, 5.07468s/12 iters), loss = 1.85269
I0407 22:17:48.351245 23658 solver.cpp:237]     Train net output #0: loss = 1.85269 (* 1 = 1.85269 loss)
I0407 22:17:48.351256 23658 sgd_solver.cpp:105] Iteration 2412, lr = 0.00297312
I0407 22:17:53.355829 23658 solver.cpp:218] Iteration 2424 (2.39788 iter/s, 5.00443s/12 iters), loss = 2.36893
I0407 22:17:53.355882 23658 solver.cpp:237]     Train net output #0: loss = 2.36893 (* 1 = 2.36893 loss)
I0407 22:17:53.355895 23658 sgd_solver.cpp:105] Iteration 2424, lr = 0.00295523
I0407 22:17:54.462464 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:58.638635 23658 solver.cpp:218] Iteration 2436 (2.27161 iter/s, 5.2826s/12 iters), loss = 1.99456
I0407 22:17:58.638680 23658 solver.cpp:237]     Train net output #0: loss = 1.99456 (* 1 = 1.99456 loss)
I0407 22:17:58.638691 23658 sgd_solver.cpp:105] Iteration 2436, lr = 0.00293745
I0407 22:18:03.260253 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 22:18:06.246667 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 22:18:08.876212 23658 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 22:18:08.876240 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:18:12.544010 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:13.533869 23658 solver.cpp:397]     Test net output #0: accuracy = 0.313113
I0407 22:18:13.533969 23658 solver.cpp:397]     Test net output #1: loss = 2.86234 (* 1 = 2.86234 loss)
I0407 22:18:13.623483 23658 solver.cpp:218] Iteration 2448 (0.800834 iter/s, 14.9844s/12 iters), loss = 1.94349
I0407 22:18:13.623522 23658 solver.cpp:237]     Train net output #0: loss = 1.94349 (* 1 = 1.94349 loss)
I0407 22:18:13.623531 23658 sgd_solver.cpp:105] Iteration 2448, lr = 0.00291977
I0407 22:18:17.826157 23658 solver.cpp:218] Iteration 2460 (2.85544 iter/s, 4.2025s/12 iters), loss = 1.96095
I0407 22:18:17.826197 23658 solver.cpp:237]     Train net output #0: loss = 1.96095 (* 1 = 1.96095 loss)
I0407 22:18:17.826210 23658 sgd_solver.cpp:105] Iteration 2460, lr = 0.00290221
I0407 22:18:22.841697 23658 solver.cpp:218] Iteration 2472 (2.39266 iter/s, 5.01534s/12 iters), loss = 2.18395
I0407 22:18:22.841756 23658 solver.cpp:237]     Train net output #0: loss = 2.18395 (* 1 = 2.18395 loss)
I0407 22:18:22.841769 23658 sgd_solver.cpp:105] Iteration 2472, lr = 0.00288475
I0407 22:18:27.944669 23658 solver.cpp:218] Iteration 2484 (2.35167 iter/s, 5.10275s/12 iters), loss = 2.16185
I0407 22:18:27.944725 23658 solver.cpp:237]     Train net output #0: loss = 2.16185 (* 1 = 2.16185 loss)
I0407 22:18:27.944737 23658 sgd_solver.cpp:105] Iteration 2484, lr = 0.00286739
I0407 22:18:32.986683 23658 solver.cpp:218] Iteration 2496 (2.3801 iter/s, 5.0418s/12 iters), loss = 1.86661
I0407 22:18:32.986734 23658 solver.cpp:237]     Train net output #0: loss = 1.86661 (* 1 = 1.86661 loss)
I0407 22:18:32.986745 23658 sgd_solver.cpp:105] Iteration 2496, lr = 0.00285014
I0407 22:18:38.236222 23658 solver.cpp:218] Iteration 2508 (2.28601 iter/s, 5.24933s/12 iters), loss = 2.08623
I0407 22:18:38.236258 23658 solver.cpp:237]     Train net output #0: loss = 2.08623 (* 1 = 2.08623 loss)
I0407 22:18:38.236266 23658 sgd_solver.cpp:105] Iteration 2508, lr = 0.00283299
I0407 22:18:43.466745 23658 solver.cpp:218] Iteration 2520 (2.29431 iter/s, 5.23033s/12 iters), loss = 2.11124
I0407 22:18:43.466789 23658 solver.cpp:237]     Train net output #0: loss = 2.11124 (* 1 = 2.11124 loss)
I0407 22:18:43.466797 23658 sgd_solver.cpp:105] Iteration 2520, lr = 0.00281595
I0407 22:18:46.644359 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:48.497932 23658 solver.cpp:218] Iteration 2532 (2.38522 iter/s, 5.03098s/12 iters), loss = 1.95612
I0407 22:18:48.497998 23658 solver.cpp:237]     Train net output #0: loss = 1.95612 (* 1 = 1.95612 loss)
I0407 22:18:48.498010 23658 sgd_solver.cpp:105] Iteration 2532, lr = 0.002799
I0407 22:18:53.551767 23658 solver.cpp:218] Iteration 2544 (2.37454 iter/s, 5.05361s/12 iters), loss = 2.24017
I0407 22:18:53.551820 23658 solver.cpp:237]     Train net output #0: loss = 2.24017 (* 1 = 2.24017 loss)
I0407 22:18:53.551829 23658 sgd_solver.cpp:105] Iteration 2544, lr = 0.00278216
I0407 22:18:55.549971 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 22:19:03.713392 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 22:19:09.657776 23658 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 22:19:09.657799 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:19:13.140507 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:14.162055 23658 solver.cpp:397]     Test net output #0: accuracy = 0.295343
I0407 22:19:14.162102 23658 solver.cpp:397]     Test net output #1: loss = 3.04009 (* 1 = 3.04009 loss)
I0407 22:19:16.050213 23658 solver.cpp:218] Iteration 2556 (0.533386 iter/s, 22.4978s/12 iters), loss = 2.19576
I0407 22:19:16.050263 23658 solver.cpp:237]     Train net output #0: loss = 2.19576 (* 1 = 2.19576 loss)
I0407 22:19:16.050276 23658 sgd_solver.cpp:105] Iteration 2556, lr = 0.00276542
I0407 22:19:21.041620 23658 solver.cpp:218] Iteration 2568 (2.40423 iter/s, 4.99121s/12 iters), loss = 2.06314
I0407 22:19:21.041749 23658 solver.cpp:237]     Train net output #0: loss = 2.06314 (* 1 = 2.06314 loss)
I0407 22:19:21.041762 23658 sgd_solver.cpp:105] Iteration 2568, lr = 0.00274879
I0407 22:19:25.991027 23658 solver.cpp:218] Iteration 2580 (2.42466 iter/s, 4.94914s/12 iters), loss = 1.74344
I0407 22:19:25.991062 23658 solver.cpp:237]     Train net output #0: loss = 1.74344 (* 1 = 1.74344 loss)
I0407 22:19:25.991070 23658 sgd_solver.cpp:105] Iteration 2580, lr = 0.00273225
I0407 22:19:31.024690 23658 solver.cpp:218] Iteration 2592 (2.38404 iter/s, 5.03347s/12 iters), loss = 2.08537
I0407 22:19:31.024736 23658 solver.cpp:237]     Train net output #0: loss = 2.08537 (* 1 = 2.08537 loss)
I0407 22:19:31.024745 23658 sgd_solver.cpp:105] Iteration 2592, lr = 0.00271581
I0407 22:19:36.044401 23658 solver.cpp:218] Iteration 2604 (2.39067 iter/s, 5.01951s/12 iters), loss = 2.27447
I0407 22:19:36.044452 23658 solver.cpp:237]     Train net output #0: loss = 2.27447 (* 1 = 2.27447 loss)
I0407 22:19:36.044464 23658 sgd_solver.cpp:105] Iteration 2604, lr = 0.00269947
I0407 22:19:41.008072 23658 solver.cpp:218] Iteration 2616 (2.41766 iter/s, 4.96347s/12 iters), loss = 1.84834
I0407 22:19:41.008118 23658 solver.cpp:237]     Train net output #0: loss = 1.84834 (* 1 = 1.84834 loss)
I0407 22:19:41.008127 23658 sgd_solver.cpp:105] Iteration 2616, lr = 0.00268323
I0407 22:19:46.046770 23658 solver.cpp:218] Iteration 2628 (2.38166 iter/s, 5.03849s/12 iters), loss = 1.72607
I0407 22:19:46.046820 23658 solver.cpp:237]     Train net output #0: loss = 1.72607 (* 1 = 1.72607 loss)
I0407 22:19:46.046831 23658 sgd_solver.cpp:105] Iteration 2628, lr = 0.00266708
I0407 22:19:46.454402 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:51.072139 23658 solver.cpp:218] Iteration 2640 (2.38798 iter/s, 5.02516s/12 iters), loss = 1.93122
I0407 22:19:51.072232 23658 solver.cpp:237]     Train net output #0: loss = 1.93122 (* 1 = 1.93122 loss)
I0407 22:19:51.072245 23658 sgd_solver.cpp:105] Iteration 2640, lr = 0.00265104
I0407 22:19:55.700464 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 22:20:02.429522 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 22:20:08.796378 23658 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 22:20:08.796408 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:20:12.202407 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:13.275928 23658 solver.cpp:397]     Test net output #0: accuracy = 0.286152
I0407 22:20:13.275971 23658 solver.cpp:397]     Test net output #1: loss = 2.99151 (* 1 = 2.99151 loss)
I0407 22:20:13.365639 23658 solver.cpp:218] Iteration 2652 (0.538291 iter/s, 22.2928s/12 iters), loss = 2.02292
I0407 22:20:13.365689 23658 solver.cpp:237]     Train net output #0: loss = 2.02292 (* 1 = 2.02292 loss)
I0407 22:20:13.365700 23658 sgd_solver.cpp:105] Iteration 2652, lr = 0.00263509
I0407 22:20:17.620568 23658 solver.cpp:218] Iteration 2664 (2.82038 iter/s, 4.25475s/12 iters), loss = 1.9119
I0407 22:20:17.620611 23658 solver.cpp:237]     Train net output #0: loss = 1.9119 (* 1 = 1.9119 loss)
I0407 22:20:17.620621 23658 sgd_solver.cpp:105] Iteration 2664, lr = 0.00261923
I0407 22:20:22.650288 23658 solver.cpp:218] Iteration 2676 (2.38591 iter/s, 5.02952s/12 iters), loss = 2.00074
I0407 22:20:22.650408 23658 solver.cpp:237]     Train net output #0: loss = 2.00074 (* 1 = 2.00074 loss)
I0407 22:20:22.650420 23658 sgd_solver.cpp:105] Iteration 2676, lr = 0.00260348
I0407 22:20:27.714514 23658 solver.cpp:218] Iteration 2688 (2.36969 iter/s, 5.06395s/12 iters), loss = 1.90665
I0407 22:20:27.714570 23658 solver.cpp:237]     Train net output #0: loss = 1.90665 (* 1 = 1.90665 loss)
I0407 22:20:27.714581 23658 sgd_solver.cpp:105] Iteration 2688, lr = 0.00258781
I0407 22:20:32.698004 23658 solver.cpp:218] Iteration 2700 (2.40805 iter/s, 4.98328s/12 iters), loss = 1.73931
I0407 22:20:32.698060 23658 solver.cpp:237]     Train net output #0: loss = 1.73931 (* 1 = 1.73931 loss)
I0407 22:20:32.698071 23658 sgd_solver.cpp:105] Iteration 2700, lr = 0.00257224
I0407 22:20:37.774145 23658 solver.cpp:218] Iteration 2712 (2.3641 iter/s, 5.07593s/12 iters), loss = 1.66005
I0407 22:20:37.774196 23658 solver.cpp:237]     Train net output #0: loss = 1.66005 (* 1 = 1.66005 loss)
I0407 22:20:37.774206 23658 sgd_solver.cpp:105] Iteration 2712, lr = 0.00255677
I0407 22:20:43.162134 23658 solver.cpp:218] Iteration 2724 (2.22726 iter/s, 5.38778s/12 iters), loss = 1.79456
I0407 22:20:43.162192 23658 solver.cpp:237]     Train net output #0: loss = 1.79456 (* 1 = 1.79456 loss)
I0407 22:20:43.162204 23658 sgd_solver.cpp:105] Iteration 2724, lr = 0.00254138
I0407 22:20:45.960361 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:48.354010 23658 solver.cpp:218] Iteration 2736 (2.31142 iter/s, 5.19162s/12 iters), loss = 1.34168
I0407 22:20:48.354070 23658 solver.cpp:237]     Train net output #0: loss = 1.34168 (* 1 = 1.34168 loss)
I0407 22:20:48.354081 23658 sgd_solver.cpp:105] Iteration 2736, lr = 0.00252609
I0407 22:20:53.313024 23658 solver.cpp:218] Iteration 2748 (2.41994 iter/s, 4.95881s/12 iters), loss = 1.90733
I0407 22:20:53.313151 23658 solver.cpp:237]     Train net output #0: loss = 1.90733 (* 1 = 1.90733 loss)
I0407 22:20:53.313164 23658 sgd_solver.cpp:105] Iteration 2748, lr = 0.00251089
I0407 22:20:55.337056 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 22:20:58.334477 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 22:21:02.552350 23658 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 22:21:02.552378 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:21:05.752173 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:21:05.987808 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:07.090592 23658 solver.cpp:397]     Test net output #0: accuracy = 0.304534
I0407 22:21:07.090641 23658 solver.cpp:397]     Test net output #1: loss = 2.9341 (* 1 = 2.9341 loss)
I0407 22:21:09.055037 23658 solver.cpp:218] Iteration 2760 (0.762319 iter/s, 15.7414s/12 iters), loss = 1.59913
I0407 22:21:09.055094 23658 solver.cpp:237]     Train net output #0: loss = 1.59913 (* 1 = 1.59913 loss)
I0407 22:21:09.055106 23658 sgd_solver.cpp:105] Iteration 2760, lr = 0.00249579
I0407 22:21:14.374470 23658 solver.cpp:218] Iteration 2772 (2.25597 iter/s, 5.31922s/12 iters), loss = 1.66782
I0407 22:21:14.374526 23658 solver.cpp:237]     Train net output #0: loss = 1.66782 (* 1 = 1.66782 loss)
I0407 22:21:14.374536 23658 sgd_solver.cpp:105] Iteration 2772, lr = 0.00248077
I0407 22:21:19.410341 23658 solver.cpp:218] Iteration 2784 (2.38301 iter/s, 5.03565s/12 iters), loss = 1.73388
I0407 22:21:19.410395 23658 solver.cpp:237]     Train net output #0: loss = 1.73388 (* 1 = 1.73388 loss)
I0407 22:21:19.410408 23658 sgd_solver.cpp:105] Iteration 2784, lr = 0.00246585
I0407 22:21:24.462633 23658 solver.cpp:218] Iteration 2796 (2.37526 iter/s, 5.05209s/12 iters), loss = 1.80646
I0407 22:21:24.462782 23658 solver.cpp:237]     Train net output #0: loss = 1.80646 (* 1 = 1.80646 loss)
I0407 22:21:24.462795 23658 sgd_solver.cpp:105] Iteration 2796, lr = 0.00245101
I0407 22:21:29.534557 23658 solver.cpp:218] Iteration 2808 (2.3661 iter/s, 5.07163s/12 iters), loss = 1.54151
I0407 22:21:29.534600 23658 solver.cpp:237]     Train net output #0: loss = 1.54151 (* 1 = 1.54151 loss)
I0407 22:21:29.534610 23658 sgd_solver.cpp:105] Iteration 2808, lr = 0.00243626
I0407 22:21:34.500860 23658 solver.cpp:218] Iteration 2820 (2.41638 iter/s, 4.96611s/12 iters), loss = 1.69056
I0407 22:21:34.500910 23658 solver.cpp:237]     Train net output #0: loss = 1.69056 (* 1 = 1.69056 loss)
I0407 22:21:34.500921 23658 sgd_solver.cpp:105] Iteration 2820, lr = 0.00242161
I0407 22:21:39.219699 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:39.514008 23658 solver.cpp:218] Iteration 2832 (2.3938 iter/s, 5.01295s/12 iters), loss = 1.43708
I0407 22:21:39.514060 23658 solver.cpp:237]     Train net output #0: loss = 1.43708 (* 1 = 1.43708 loss)
I0407 22:21:39.514070 23658 sgd_solver.cpp:105] Iteration 2832, lr = 0.00240704
I0407 22:21:44.500180 23658 solver.cpp:218] Iteration 2844 (2.40675 iter/s, 4.98598s/12 iters), loss = 1.58996
I0407 22:21:44.500226 23658 solver.cpp:237]     Train net output #0: loss = 1.58996 (* 1 = 1.58996 loss)
I0407 22:21:44.500236 23658 sgd_solver.cpp:105] Iteration 2844, lr = 0.00239255
I0407 22:21:49.231595 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 22:21:52.233029 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 22:21:56.233881 23658 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 22:21:56.233984 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:21:59.661813 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:00.827299 23658 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0407 22:22:00.827347 23658 solver.cpp:397]     Test net output #1: loss = 2.83709 (* 1 = 2.83709 loss)
I0407 22:22:00.917309 23658 solver.cpp:218] Iteration 2856 (0.730967 iter/s, 16.4166s/12 iters), loss = 1.69363
I0407 22:22:00.917357 23658 solver.cpp:237]     Train net output #0: loss = 1.69363 (* 1 = 1.69363 loss)
I0407 22:22:00.917368 23658 sgd_solver.cpp:105] Iteration 2856, lr = 0.00237816
I0407 22:22:05.457159 23658 solver.cpp:218] Iteration 2868 (2.64337 iter/s, 4.53966s/12 iters), loss = 1.56888
I0407 22:22:05.457208 23658 solver.cpp:237]     Train net output #0: loss = 1.56888 (* 1 = 1.56888 loss)
I0407 22:22:05.457218 23658 sgd_solver.cpp:105] Iteration 2868, lr = 0.00236385
I0407 22:22:10.695458 23658 solver.cpp:218] Iteration 2880 (2.29091 iter/s, 5.23809s/12 iters), loss = 1.72476
I0407 22:22:10.695509 23658 solver.cpp:237]     Train net output #0: loss = 1.72476 (* 1 = 1.72476 loss)
I0407 22:22:10.695520 23658 sgd_solver.cpp:105] Iteration 2880, lr = 0.00234963
I0407 22:22:15.697032 23658 solver.cpp:218] Iteration 2892 (2.39934 iter/s, 5.00138s/12 iters), loss = 1.6191
I0407 22:22:15.697075 23658 solver.cpp:237]     Train net output #0: loss = 1.6191 (* 1 = 1.6191 loss)
I0407 22:22:15.697084 23658 sgd_solver.cpp:105] Iteration 2892, lr = 0.00233549
I0407 22:22:20.751025 23658 solver.cpp:218] Iteration 2904 (2.37446 iter/s, 5.05379s/12 iters), loss = 1.37775
I0407 22:22:20.751075 23658 solver.cpp:237]     Train net output #0: loss = 1.37775 (* 1 = 1.37775 loss)
I0407 22:22:20.751086 23658 sgd_solver.cpp:105] Iteration 2904, lr = 0.00232144
I0407 22:22:25.955000 23658 solver.cpp:218] Iteration 2916 (2.30602 iter/s, 5.20378s/12 iters), loss = 1.65431
I0407 22:22:25.955039 23658 solver.cpp:237]     Train net output #0: loss = 1.65431 (* 1 = 1.65431 loss)
I0407 22:22:25.955047 23658 sgd_solver.cpp:105] Iteration 2916, lr = 0.00230747
I0407 22:22:31.267976 23658 solver.cpp:218] Iteration 2928 (2.25871 iter/s, 5.31277s/12 iters), loss = 1.49149
I0407 22:22:31.268131 23658 solver.cpp:237]     Train net output #0: loss = 1.49149 (* 1 = 1.49149 loss)
I0407 22:22:31.268146 23658 sgd_solver.cpp:105] Iteration 2928, lr = 0.00229359
I0407 22:22:33.155620 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:36.427320 23658 solver.cpp:218] Iteration 2940 (2.32601 iter/s, 5.15904s/12 iters), loss = 1.53297
I0407 22:22:36.427366 23658 solver.cpp:237]     Train net output #0: loss = 1.53297 (* 1 = 1.53297 loss)
I0407 22:22:36.427379 23658 sgd_solver.cpp:105] Iteration 2940, lr = 0.00227979
I0407 22:22:41.502167 23658 solver.cpp:218] Iteration 2952 (2.3647 iter/s, 5.07465s/12 iters), loss = 1.5305
I0407 22:22:41.502219 23658 solver.cpp:237]     Train net output #0: loss = 1.5305 (* 1 = 1.5305 loss)
I0407 22:22:41.502231 23658 sgd_solver.cpp:105] Iteration 2952, lr = 0.00226607
I0407 22:22:43.522541 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 22:22:46.508049 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 22:22:50.865176 23658 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 22:22:50.865204 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:22:54.138290 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:55.321141 23658 solver.cpp:397]     Test net output #0: accuracy = 0.351103
I0407 22:22:55.321174 23658 solver.cpp:397]     Test net output #1: loss = 2.82224 (* 1 = 2.82224 loss)
I0407 22:22:57.229967 23658 solver.cpp:218] Iteration 2964 (0.763005 iter/s, 15.7273s/12 iters), loss = 1.33498
I0407 22:22:57.230022 23658 solver.cpp:237]     Train net output #0: loss = 1.33498 (* 1 = 1.33498 loss)
I0407 22:22:57.230033 23658 sgd_solver.cpp:105] Iteration 2964, lr = 0.00225244
I0407 22:23:02.276973 23658 solver.cpp:218] Iteration 2976 (2.37775 iter/s, 5.0468s/12 iters), loss = 1.4481
I0407 22:23:02.277076 23658 solver.cpp:237]     Train net output #0: loss = 1.4481 (* 1 = 1.4481 loss)
I0407 22:23:02.277087 23658 sgd_solver.cpp:105] Iteration 2976, lr = 0.00223889
I0407 22:23:07.551044 23658 solver.cpp:218] Iteration 2988 (2.27539 iter/s, 5.27381s/12 iters), loss = 1.28397
I0407 22:23:07.551087 23658 solver.cpp:237]     Train net output #0: loss = 1.28397 (* 1 = 1.28397 loss)
I0407 22:23:07.551096 23658 sgd_solver.cpp:105] Iteration 2988, lr = 0.00222542
I0407 22:23:12.858328 23658 solver.cpp:218] Iteration 3000 (2.26113 iter/s, 5.30708s/12 iters), loss = 1.28128
I0407 22:23:12.858371 23658 solver.cpp:237]     Train net output #0: loss = 1.28128 (* 1 = 1.28128 loss)
I0407 22:23:12.858381 23658 sgd_solver.cpp:105] Iteration 3000, lr = 0.00221203
I0407 22:23:17.906955 23658 solver.cpp:218] Iteration 3012 (2.37698 iter/s, 5.04843s/12 iters), loss = 1.37962
I0407 22:23:17.907001 23658 solver.cpp:237]     Train net output #0: loss = 1.37962 (* 1 = 1.37962 loss)
I0407 22:23:17.907009 23658 sgd_solver.cpp:105] Iteration 3012, lr = 0.00219872
I0407 22:23:22.956055 23658 solver.cpp:218] Iteration 3024 (2.37676 iter/s, 5.04889s/12 iters), loss = 1.16523
I0407 22:23:22.956111 23658 solver.cpp:237]     Train net output #0: loss = 1.16523 (* 1 = 1.16523 loss)
I0407 22:23:22.956122 23658 sgd_solver.cpp:105] Iteration 3024, lr = 0.00218549
I0407 22:23:27.057636 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:28.180729 23658 solver.cpp:218] Iteration 3036 (2.29689 iter/s, 5.22446s/12 iters), loss = 1.13257
I0407 22:23:28.180778 23658 solver.cpp:237]     Train net output #0: loss = 1.13257 (* 1 = 1.13257 loss)
I0407 22:23:28.180789 23658 sgd_solver.cpp:105] Iteration 3036, lr = 0.00217234
I0407 22:23:33.236052 23658 solver.cpp:218] Iteration 3048 (2.37383 iter/s, 5.05512s/12 iters), loss = 1.34675
I0407 22:23:33.236141 23658 solver.cpp:237]     Train net output #0: loss = 1.34675 (* 1 = 1.34675 loss)
I0407 22:23:33.236155 23658 sgd_solver.cpp:105] Iteration 3048, lr = 0.00215927
I0407 22:23:37.907096 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 22:23:43.130698 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 22:23:47.175909 23658 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 22:23:47.175941 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:23:50.415601 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:51.631989 23658 solver.cpp:397]     Test net output #0: accuracy = 0.374387
I0407 22:23:51.632042 23658 solver.cpp:397]     Test net output #1: loss = 2.72128 (* 1 = 2.72128 loss)
I0407 22:23:51.723829 23658 solver.cpp:218] Iteration 3060 (0.649099 iter/s, 18.4872s/12 iters), loss = 1.55039
I0407 22:23:51.723884 23658 solver.cpp:237]     Train net output #0: loss = 1.55039 (* 1 = 1.55039 loss)
I0407 22:23:51.723896 23658 sgd_solver.cpp:105] Iteration 3060, lr = 0.00214628
I0407 22:23:56.018882 23658 solver.cpp:218] Iteration 3072 (2.79404 iter/s, 4.29486s/12 iters), loss = 1.31633
I0407 22:23:56.018934 23658 solver.cpp:237]     Train net output #0: loss = 1.31633 (* 1 = 1.31633 loss)
I0407 22:23:56.018946 23658 sgd_solver.cpp:105] Iteration 3072, lr = 0.00213337
I0407 22:24:01.098693 23658 solver.cpp:218] Iteration 3084 (2.36239 iter/s, 5.07961s/12 iters), loss = 1.26818
I0407 22:24:01.098737 23658 solver.cpp:237]     Train net output #0: loss = 1.26818 (* 1 = 1.26818 loss)
I0407 22:24:01.098747 23658 sgd_solver.cpp:105] Iteration 3084, lr = 0.00212053
I0407 22:24:06.072656 23658 solver.cpp:218] Iteration 3096 (2.41266 iter/s, 4.97377s/12 iters), loss = 1.38897
I0407 22:24:06.072765 23658 solver.cpp:237]     Train net output #0: loss = 1.38897 (* 1 = 1.38897 loss)
I0407 22:24:06.072775 23658 sgd_solver.cpp:105] Iteration 3096, lr = 0.00210777
I0407 22:24:11.156318 23658 solver.cpp:218] Iteration 3108 (2.36063 iter/s, 5.0834s/12 iters), loss = 1.4278
I0407 22:24:11.156364 23658 solver.cpp:237]     Train net output #0: loss = 1.4278 (* 1 = 1.4278 loss)
I0407 22:24:11.156373 23658 sgd_solver.cpp:105] Iteration 3108, lr = 0.00209509
I0407 22:24:16.251176 23658 solver.cpp:218] Iteration 3120 (2.35541 iter/s, 5.09466s/12 iters), loss = 0.960389
I0407 22:24:16.251216 23658 solver.cpp:237]     Train net output #0: loss = 0.960389 (* 1 = 0.960389 loss)
I0407 22:24:16.251226 23658 sgd_solver.cpp:105] Iteration 3120, lr = 0.00208249
I0407 22:24:21.291874 23658 solver.cpp:218] Iteration 3132 (2.38071 iter/s, 5.0405s/12 iters), loss = 1.30706
I0407 22:24:21.291922 23658 solver.cpp:237]     Train net output #0: loss = 1.30706 (* 1 = 1.30706 loss)
I0407 22:24:21.291934 23658 sgd_solver.cpp:105] Iteration 3132, lr = 0.00206996
I0407 22:24:22.430096 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:26.373459 23658 solver.cpp:218] Iteration 3144 (2.36156 iter/s, 5.08139s/12 iters), loss = 1.2783
I0407 22:24:26.373507 23658 solver.cpp:237]     Train net output #0: loss = 1.2783 (* 1 = 1.2783 loss)
I0407 22:24:26.373519 23658 sgd_solver.cpp:105] Iteration 3144, lr = 0.0020575
I0407 22:24:31.589241 23658 solver.cpp:218] Iteration 3156 (2.3008 iter/s, 5.21558s/12 iters), loss = 1.22227
I0407 22:24:31.589296 23658 solver.cpp:237]     Train net output #0: loss = 1.22227 (* 1 = 1.22227 loss)
I0407 22:24:31.589308 23658 sgd_solver.cpp:105] Iteration 3156, lr = 0.00204513
I0407 22:24:33.683979 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 22:24:37.330821 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 22:24:41.561900 23658 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 22:24:41.561935 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:24:44.757019 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:46.020704 23658 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0407 22:24:46.020748 23658 solver.cpp:397]     Test net output #1: loss = 2.71405 (* 1 = 2.71405 loss)
I0407 22:24:47.830296 23658 solver.cpp:218] Iteration 3168 (0.738891 iter/s, 16.2405s/12 iters), loss = 1.04579
I0407 22:24:47.830344 23658 solver.cpp:237]     Train net output #0: loss = 1.04579 (* 1 = 1.04579 loss)
I0407 22:24:47.830355 23658 sgd_solver.cpp:105] Iteration 3168, lr = 0.00203282
I0407 22:24:52.735471 23658 solver.cpp:218] Iteration 3180 (2.44649 iter/s, 4.90498s/12 iters), loss = 1.40631
I0407 22:24:52.735522 23658 solver.cpp:237]     Train net output #0: loss = 1.40631 (* 1 = 1.40631 loss)
I0407 22:24:52.735534 23658 sgd_solver.cpp:105] Iteration 3180, lr = 0.00202059
I0407 22:24:57.641902 23658 solver.cpp:218] Iteration 3192 (2.44587 iter/s, 4.90623s/12 iters), loss = 1.1699
I0407 22:24:57.641983 23658 solver.cpp:237]     Train net output #0: loss = 1.1699 (* 1 = 1.1699 loss)
I0407 22:24:57.641996 23658 sgd_solver.cpp:105] Iteration 3192, lr = 0.00200843
I0407 22:25:02.548805 23658 solver.cpp:218] Iteration 3204 (2.44564 iter/s, 4.90669s/12 iters), loss = 1.09262
I0407 22:25:02.548852 23658 solver.cpp:237]     Train net output #0: loss = 1.09262 (* 1 = 1.09262 loss)
I0407 22:25:02.548864 23658 sgd_solver.cpp:105] Iteration 3204, lr = 0.00199635
I0407 22:25:07.644799 23658 solver.cpp:218] Iteration 3216 (2.35488 iter/s, 5.0958s/12 iters), loss = 1.02426
I0407 22:25:07.644919 23658 solver.cpp:237]     Train net output #0: loss = 1.02426 (* 1 = 1.02426 loss)
I0407 22:25:07.644928 23658 sgd_solver.cpp:105] Iteration 3216, lr = 0.00198434
I0407 22:25:12.796139 23658 solver.cpp:218] Iteration 3228 (2.32962 iter/s, 5.15106s/12 iters), loss = 1.12601
I0407 22:25:12.796185 23658 solver.cpp:237]     Train net output #0: loss = 1.12601 (* 1 = 1.12601 loss)
I0407 22:25:12.796195 23658 sgd_solver.cpp:105] Iteration 3228, lr = 0.0019724
I0407 22:25:16.113531 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:17.912806 23658 solver.cpp:218] Iteration 3240 (2.34537 iter/s, 5.11646s/12 iters), loss = 1.13277
I0407 22:25:17.912856 23658 solver.cpp:237]     Train net output #0: loss = 1.13277 (* 1 = 1.13277 loss)
I0407 22:25:17.912868 23658 sgd_solver.cpp:105] Iteration 3240, lr = 0.00196053
I0407 22:25:23.018383 23658 solver.cpp:218] Iteration 3252 (2.35046 iter/s, 5.10538s/12 iters), loss = 1.12714
I0407 22:25:23.018421 23658 solver.cpp:237]     Train net output #0: loss = 1.12714 (* 1 = 1.12714 loss)
I0407 22:25:23.018429 23658 sgd_solver.cpp:105] Iteration 3252, lr = 0.00194874
I0407 22:25:27.519277 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 22:25:30.510411 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 22:25:32.904186 23658 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 22:25:32.904211 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:25:36.053087 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:37.354856 23658 solver.cpp:397]     Test net output #0: accuracy = 0.36152
I0407 22:25:37.354897 23658 solver.cpp:397]     Test net output #1: loss = 2.79964 (* 1 = 2.79964 loss)
I0407 22:25:37.444770 23658 solver.cpp:218] Iteration 3264 (0.831835 iter/s, 14.4259s/12 iters), loss = 1.30755
I0407 22:25:37.444813 23658 solver.cpp:237]     Train net output #0: loss = 1.30755 (* 1 = 1.30755 loss)
I0407 22:25:37.444823 23658 sgd_solver.cpp:105] Iteration 3264, lr = 0.00193701
I0407 22:25:41.942919 23658 solver.cpp:218] Iteration 3276 (2.66787 iter/s, 4.49797s/12 iters), loss = 1.12744
I0407 22:25:41.943020 23658 solver.cpp:237]     Train net output #0: loss = 1.12744 (* 1 = 1.12744 loss)
I0407 22:25:41.943033 23658 sgd_solver.cpp:105] Iteration 3276, lr = 0.00192536
I0407 22:25:47.458626 23658 solver.cpp:218] Iteration 3288 (2.17571 iter/s, 5.51545s/12 iters), loss = 1.10149
I0407 22:25:47.458671 23658 solver.cpp:237]     Train net output #0: loss = 1.10149 (* 1 = 1.10149 loss)
I0407 22:25:47.458683 23658 sgd_solver.cpp:105] Iteration 3288, lr = 0.00191377
I0407 22:25:52.545912 23658 solver.cpp:218] Iteration 3300 (2.35891 iter/s, 5.08709s/12 iters), loss = 1.17736
I0407 22:25:52.545966 23658 solver.cpp:237]     Train net output #0: loss = 1.17736 (* 1 = 1.17736 loss)
I0407 22:25:52.545979 23658 sgd_solver.cpp:105] Iteration 3300, lr = 0.00190226
I0407 22:25:57.590603 23658 solver.cpp:218] Iteration 3312 (2.37883 iter/s, 5.0445s/12 iters), loss = 1.14221
I0407 22:25:57.590646 23658 solver.cpp:237]     Train net output #0: loss = 1.14221 (* 1 = 1.14221 loss)
I0407 22:25:57.590656 23658 sgd_solver.cpp:105] Iteration 3312, lr = 0.00189082
I0407 22:26:02.719067 23658 solver.cpp:218] Iteration 3324 (2.33997 iter/s, 5.12827s/12 iters), loss = 1.06927
I0407 22:26:02.719115 23658 solver.cpp:237]     Train net output #0: loss = 1.06927 (* 1 = 1.06927 loss)
I0407 22:26:02.719127 23658 sgd_solver.cpp:105] Iteration 3324, lr = 0.00187944
I0407 22:26:07.714398 23658 solver.cpp:218] Iteration 3336 (2.40234 iter/s, 4.99513s/12 iters), loss = 0.979076
I0407 22:26:07.714443 23658 solver.cpp:237]     Train net output #0: loss = 0.979076 (* 1 = 0.979076 loss)
I0407 22:26:07.714454 23658 sgd_solver.cpp:105] Iteration 3336, lr = 0.00186813
I0407 22:26:08.189025 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:12.853439 23658 solver.cpp:218] Iteration 3348 (2.33516 iter/s, 5.13884s/12 iters), loss = 1.19921
I0407 22:26:12.853549 23658 solver.cpp:237]     Train net output #0: loss = 1.19921 (* 1 = 1.19921 loss)
I0407 22:26:12.853561 23658 sgd_solver.cpp:105] Iteration 3348, lr = 0.00185689
I0407 22:26:17.932590 23658 solver.cpp:218] Iteration 3360 (2.36272 iter/s, 5.07889s/12 iters), loss = 0.922437
I0407 22:26:17.932644 23658 solver.cpp:237]     Train net output #0: loss = 0.922437 (* 1 = 0.922437 loss)
I0407 22:26:17.932657 23658 sgd_solver.cpp:105] Iteration 3360, lr = 0.00184572
I0407 22:26:19.992660 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 22:26:24.783304 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 22:26:27.106307 23658 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 22:26:27.106333 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:26:30.225020 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:31.560129 23658 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0407 22:26:31.560174 23658 solver.cpp:397]     Test net output #1: loss = 2.89178 (* 1 = 2.89178 loss)
I0407 22:26:33.452411 23658 solver.cpp:218] Iteration 3372 (0.773229 iter/s, 15.5193s/12 iters), loss = 1.07396
I0407 22:26:33.452458 23658 solver.cpp:237]     Train net output #0: loss = 1.07396 (* 1 = 1.07396 loss)
I0407 22:26:33.452469 23658 sgd_solver.cpp:105] Iteration 3372, lr = 0.00183461
I0407 22:26:38.697657 23658 solver.cpp:218] Iteration 3384 (2.28787 iter/s, 5.24504s/12 iters), loss = 1.20514
I0407 22:26:38.697700 23658 solver.cpp:237]     Train net output #0: loss = 1.20514 (* 1 = 1.20514 loss)
I0407 22:26:38.697710 23658 sgd_solver.cpp:105] Iteration 3384, lr = 0.00182358
I0407 22:26:44.223600 23658 solver.cpp:218] Iteration 3396 (2.17166 iter/s, 5.52573s/12 iters), loss = 0.996732
I0407 22:26:44.223709 23658 solver.cpp:237]     Train net output #0: loss = 0.996732 (* 1 = 0.996732 loss)
I0407 22:26:44.223719 23658 sgd_solver.cpp:105] Iteration 3396, lr = 0.00181261
I0407 22:26:49.611057 23658 solver.cpp:218] Iteration 3408 (2.22751 iter/s, 5.38719s/12 iters), loss = 0.932191
I0407 22:26:49.611104 23658 solver.cpp:237]     Train net output #0: loss = 0.932191 (* 1 = 0.932191 loss)
I0407 22:26:49.611119 23658 sgd_solver.cpp:105] Iteration 3408, lr = 0.0018017
I0407 22:26:54.632354 23658 solver.cpp:218] Iteration 3420 (2.38991 iter/s, 5.02111s/12 iters), loss = 0.762816
I0407 22:26:54.632395 23658 solver.cpp:237]     Train net output #0: loss = 0.762816 (* 1 = 0.762816 loss)
I0407 22:26:54.632403 23658 sgd_solver.cpp:105] Iteration 3420, lr = 0.00179086
I0407 22:26:59.718024 23658 solver.cpp:218] Iteration 3432 (2.35966 iter/s, 5.08547s/12 iters), loss = 1.00516
I0407 22:26:59.718083 23658 solver.cpp:237]     Train net output #0: loss = 1.00516 (* 1 = 1.00516 loss)
I0407 22:26:59.718096 23658 sgd_solver.cpp:105] Iteration 3432, lr = 0.00178008
I0407 22:27:02.347563 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:04.859578 23658 solver.cpp:218] Iteration 3444 (2.33402 iter/s, 5.14134s/12 iters), loss = 0.867781
I0407 22:27:04.859616 23658 solver.cpp:237]     Train net output #0: loss = 0.867781 (* 1 = 0.867781 loss)
I0407 22:27:04.859623 23658 sgd_solver.cpp:105] Iteration 3444, lr = 0.00176937
I0407 22:27:10.382817 23658 solver.cpp:218] Iteration 3456 (2.17272 iter/s, 5.52303s/12 iters), loss = 1.12063
I0407 22:27:10.382858 23658 solver.cpp:237]     Train net output #0: loss = 1.12063 (* 1 = 1.12063 loss)
I0407 22:27:10.382867 23658 sgd_solver.cpp:105] Iteration 3456, lr = 0.00175873
I0407 22:27:15.230576 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 22:27:18.922621 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 22:27:21.222724 23658 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 22:27:21.222745 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:27:21.623875 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:27:24.332721 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:25.716195 23658 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0407 22:27:25.716233 23658 solver.cpp:397]     Test net output #1: loss = 2.85833 (* 1 = 2.85833 loss)
I0407 22:27:25.806107 23658 solver.cpp:218] Iteration 3468 (0.778069 iter/s, 15.4228s/12 iters), loss = 0.92381
I0407 22:27:25.806150 23658 solver.cpp:237]     Train net output #0: loss = 0.92381 (* 1 = 0.92381 loss)
I0407 22:27:25.806161 23658 sgd_solver.cpp:105] Iteration 3468, lr = 0.00174815
I0407 22:27:30.362571 23658 solver.cpp:218] Iteration 3480 (2.63373 iter/s, 4.55627s/12 iters), loss = 0.902301
I0407 22:27:30.362634 23658 solver.cpp:237]     Train net output #0: loss = 0.902301 (* 1 = 0.902301 loss)
I0407 22:27:30.362648 23658 sgd_solver.cpp:105] Iteration 3480, lr = 0.00173763
I0407 22:27:35.392189 23658 solver.cpp:218] Iteration 3492 (2.38597 iter/s, 5.0294s/12 iters), loss = 1.15804
I0407 22:27:35.392233 23658 solver.cpp:237]     Train net output #0: loss = 1.15804 (* 1 = 1.15804 loss)
I0407 22:27:35.392243 23658 sgd_solver.cpp:105] Iteration 3492, lr = 0.00172718
I0407 22:27:40.638020 23658 solver.cpp:218] Iteration 3504 (2.28762 iter/s, 5.24563s/12 iters), loss = 0.978999
I0407 22:27:40.638060 23658 solver.cpp:237]     Train net output #0: loss = 0.978999 (* 1 = 0.978999 loss)
I0407 22:27:40.638068 23658 sgd_solver.cpp:105] Iteration 3504, lr = 0.00171678
I0407 22:27:45.783761 23658 solver.cpp:218] Iteration 3516 (2.33211 iter/s, 5.14555s/12 iters), loss = 0.885437
I0407 22:27:45.783859 23658 solver.cpp:237]     Train net output #0: loss = 0.885437 (* 1 = 0.885437 loss)
I0407 22:27:45.783875 23658 sgd_solver.cpp:105] Iteration 3516, lr = 0.00170646
I0407 22:27:50.754099 23658 solver.cpp:218] Iteration 3528 (2.41444 iter/s, 4.9701s/12 iters), loss = 1.20985
I0407 22:27:50.754137 23658 solver.cpp:237]     Train net output #0: loss = 1.20985 (* 1 = 1.20985 loss)
I0407 22:27:50.754145 23658 sgd_solver.cpp:105] Iteration 3528, lr = 0.00169619
I0407 22:27:55.568579 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:55.826701 23658 solver.cpp:218] Iteration 3540 (2.36574 iter/s, 5.07241s/12 iters), loss = 0.944812
I0407 22:27:55.826747 23658 solver.cpp:237]     Train net output #0: loss = 0.944812 (* 1 = 0.944812 loss)
I0407 22:27:55.826758 23658 sgd_solver.cpp:105] Iteration 3540, lr = 0.00168598
I0407 22:28:00.908396 23658 solver.cpp:218] Iteration 3552 (2.36151 iter/s, 5.08149s/12 iters), loss = 0.734945
I0407 22:28:00.908450 23658 solver.cpp:237]     Train net output #0: loss = 0.734945 (* 1 = 0.734945 loss)
I0407 22:28:00.908461 23658 sgd_solver.cpp:105] Iteration 3552, lr = 0.00167584
I0407 22:28:06.047791 23658 solver.cpp:218] Iteration 3564 (2.335 iter/s, 5.13918s/12 iters), loss = 1.02239
I0407 22:28:06.047843 23658 solver.cpp:237]     Train net output #0: loss = 1.02239 (* 1 = 1.02239 loss)
I0407 22:28:06.047854 23658 sgd_solver.cpp:105] Iteration 3564, lr = 0.00166576
I0407 22:28:08.315905 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 22:28:11.288923 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 22:28:13.626044 23658 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 22:28:13.626070 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:28:16.674789 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:18.090198 23658 solver.cpp:397]     Test net output #0: accuracy = 0.375
I0407 22:28:18.090245 23658 solver.cpp:397]     Test net output #1: loss = 2.98615 (* 1 = 2.98615 loss)
I0407 22:28:19.974246 23658 solver.cpp:218] Iteration 3576 (0.861697 iter/s, 13.926s/12 iters), loss = 0.803345
I0407 22:28:19.974308 23658 solver.cpp:237]     Train net output #0: loss = 0.803345 (* 1 = 0.803345 loss)
I0407 22:28:19.974320 23658 sgd_solver.cpp:105] Iteration 3576, lr = 0.00165573
I0407 22:28:25.058709 23658 solver.cpp:218] Iteration 3588 (2.36023 iter/s, 5.08425s/12 iters), loss = 0.873794
I0407 22:28:25.058756 23658 solver.cpp:237]     Train net output #0: loss = 0.873794 (* 1 = 0.873794 loss)
I0407 22:28:25.058765 23658 sgd_solver.cpp:105] Iteration 3588, lr = 0.00164577
I0407 22:28:30.109939 23658 solver.cpp:218] Iteration 3600 (2.37575 iter/s, 5.05103s/12 iters), loss = 0.841895
I0407 22:28:30.109998 23658 solver.cpp:237]     Train net output #0: loss = 0.841895 (* 1 = 0.841895 loss)
I0407 22:28:30.110006 23658 sgd_solver.cpp:105] Iteration 3600, lr = 0.00163587
I0407 22:28:35.198238 23658 solver.cpp:218] Iteration 3612 (2.35845 iter/s, 5.08808s/12 iters), loss = 0.789527
I0407 22:28:35.198292 23658 solver.cpp:237]     Train net output #0: loss = 0.789527 (* 1 = 0.789527 loss)
I0407 22:28:35.198304 23658 sgd_solver.cpp:105] Iteration 3612, lr = 0.00162603
I0407 22:28:40.297262 23658 solver.cpp:218] Iteration 3624 (2.35349 iter/s, 5.09881s/12 iters), loss = 0.988111
I0407 22:28:40.297319 23658 solver.cpp:237]     Train net output #0: loss = 0.988111 (* 1 = 0.988111 loss)
I0407 22:28:40.297333 23658 sgd_solver.cpp:105] Iteration 3624, lr = 0.00161625
I0407 22:28:45.421586 23658 solver.cpp:218] Iteration 3636 (2.34187 iter/s, 5.1241s/12 iters), loss = 0.793983
I0407 22:28:45.421650 23658 solver.cpp:237]     Train net output #0: loss = 0.793983 (* 1 = 0.793983 loss)
I0407 22:28:45.421664 23658 sgd_solver.cpp:105] Iteration 3636, lr = 0.00160652
I0407 22:28:47.367951 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:50.523576 23658 solver.cpp:218] Iteration 3648 (2.35212 iter/s, 5.10178s/12 iters), loss = 0.861682
I0407 22:28:50.523622 23658 solver.cpp:237]     Train net output #0: loss = 0.861682 (* 1 = 0.861682 loss)
I0407 22:28:50.523633 23658 sgd_solver.cpp:105] Iteration 3648, lr = 0.00159686
I0407 22:28:55.707315 23658 solver.cpp:218] Iteration 3660 (2.31502 iter/s, 5.18354s/12 iters), loss = 0.740375
I0407 22:28:55.707368 23658 solver.cpp:237]     Train net output #0: loss = 0.740375 (* 1 = 0.740375 loss)
I0407 22:28:55.707381 23658 sgd_solver.cpp:105] Iteration 3660, lr = 0.00158725
I0407 22:29:00.526810 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 22:29:03.612466 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 22:29:08.160240 23658 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 22:29:08.160267 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:29:11.028280 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:12.489084 23658 solver.cpp:397]     Test net output #0: accuracy = 0.376226
I0407 22:29:12.489131 23658 solver.cpp:397]     Test net output #1: loss = 2.81976 (* 1 = 2.81976 loss)
I0407 22:29:12.579313 23658 solver.cpp:218] Iteration 3672 (0.71126 iter/s, 16.8715s/12 iters), loss = 0.582268
I0407 22:29:12.579365 23658 solver.cpp:237]     Train net output #0: loss = 0.582268 (* 1 = 0.582268 loss)
I0407 22:29:12.579376 23658 sgd_solver.cpp:105] Iteration 3672, lr = 0.0015777
I0407 22:29:17.107937 23658 solver.cpp:218] Iteration 3684 (2.64993 iter/s, 4.52843s/12 iters), loss = 0.60012
I0407 22:29:17.107995 23658 solver.cpp:237]     Train net output #0: loss = 0.60012 (* 1 = 0.60012 loss)
I0407 22:29:17.108009 23658 sgd_solver.cpp:105] Iteration 3684, lr = 0.00156821
I0407 22:29:22.137634 23658 solver.cpp:218] Iteration 3696 (2.38593 iter/s, 5.02949s/12 iters), loss = 0.63189
I0407 22:29:22.137794 23658 solver.cpp:237]     Train net output #0: loss = 0.63189 (* 1 = 0.63189 loss)
I0407 22:29:22.137809 23658 sgd_solver.cpp:105] Iteration 3696, lr = 0.00155877
I0407 22:29:27.238559 23658 solver.cpp:218] Iteration 3708 (2.35266 iter/s, 5.10061s/12 iters), loss = 0.631278
I0407 22:29:27.238615 23658 solver.cpp:237]     Train net output #0: loss = 0.631278 (* 1 = 0.631278 loss)
I0407 22:29:27.238626 23658 sgd_solver.cpp:105] Iteration 3708, lr = 0.00154939
I0407 22:29:32.343158 23658 solver.cpp:218] Iteration 3720 (2.35092 iter/s, 5.10439s/12 iters), loss = 0.735278
I0407 22:29:32.343219 23658 solver.cpp:237]     Train net output #0: loss = 0.735278 (* 1 = 0.735278 loss)
I0407 22:29:32.343230 23658 sgd_solver.cpp:105] Iteration 3720, lr = 0.00154007
I0407 22:29:37.311733 23658 solver.cpp:218] Iteration 3732 (2.41528 iter/s, 4.96837s/12 iters), loss = 0.721195
I0407 22:29:37.311786 23658 solver.cpp:237]     Train net output #0: loss = 0.721195 (* 1 = 0.721195 loss)
I0407 22:29:37.311798 23658 sgd_solver.cpp:105] Iteration 3732, lr = 0.0015308
I0407 22:29:41.391850 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:42.421900 23658 solver.cpp:218] Iteration 3744 (2.34836 iter/s, 5.10996s/12 iters), loss = 0.67952
I0407 22:29:42.421947 23658 solver.cpp:237]     Train net output #0: loss = 0.67952 (* 1 = 0.67952 loss)
I0407 22:29:42.421972 23658 sgd_solver.cpp:105] Iteration 3744, lr = 0.00152159
I0407 22:29:47.670145 23658 solver.cpp:218] Iteration 3756 (2.28657 iter/s, 5.24804s/12 iters), loss = 1.06768
I0407 22:29:47.670202 23658 solver.cpp:237]     Train net output #0: loss = 1.06768 (* 1 = 1.06768 loss)
I0407 22:29:47.670217 23658 sgd_solver.cpp:105] Iteration 3756, lr = 0.00151244
I0407 22:29:52.666932 23658 solver.cpp:218] Iteration 3768 (2.40163 iter/s, 4.9966s/12 iters), loss = 0.799213
I0407 22:29:52.667019 23658 solver.cpp:237]     Train net output #0: loss = 0.799213 (* 1 = 0.799213 loss)
I0407 22:29:52.667029 23658 sgd_solver.cpp:105] Iteration 3768, lr = 0.00150334
I0407 22:29:54.715265 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 22:29:57.849793 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 22:30:00.157434 23658 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 22:30:00.157461 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:30:03.121271 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:04.617857 23658 solver.cpp:397]     Test net output #0: accuracy = 0.384804
I0407 22:30:04.617903 23658 solver.cpp:397]     Test net output #1: loss = 2.9464 (* 1 = 2.9464 loss)
I0407 22:30:06.562811 23658 solver.cpp:218] Iteration 3780 (0.863595 iter/s, 13.8954s/12 iters), loss = 0.592728
I0407 22:30:06.562856 23658 solver.cpp:237]     Train net output #0: loss = 0.592728 (* 1 = 0.592728 loss)
I0407 22:30:06.562865 23658 sgd_solver.cpp:105] Iteration 3780, lr = 0.0014943
I0407 22:30:11.765815 23658 solver.cpp:218] Iteration 3792 (2.30645 iter/s, 5.20281s/12 iters), loss = 0.777628
I0407 22:30:11.765851 23658 solver.cpp:237]     Train net output #0: loss = 0.777628 (* 1 = 0.777628 loss)
I0407 22:30:11.765859 23658 sgd_solver.cpp:105] Iteration 3792, lr = 0.0014853
I0407 22:30:16.831830 23658 solver.cpp:218] Iteration 3804 (2.36882 iter/s, 5.06582s/12 iters), loss = 0.61143
I0407 22:30:16.831881 23658 solver.cpp:237]     Train net output #0: loss = 0.61143 (* 1 = 0.61143 loss)
I0407 22:30:16.831892 23658 sgd_solver.cpp:105] Iteration 3804, lr = 0.00147637
I0407 22:30:22.215030 23658 solver.cpp:218] Iteration 3816 (2.22924 iter/s, 5.38299s/12 iters), loss = 0.505105
I0407 22:30:22.215070 23658 solver.cpp:237]     Train net output #0: loss = 0.505105 (* 1 = 0.505105 loss)
I0407 22:30:22.215080 23658 sgd_solver.cpp:105] Iteration 3816, lr = 0.00146749
I0407 22:30:27.182874 23658 solver.cpp:218] Iteration 3828 (2.41563 iter/s, 4.96765s/12 iters), loss = 0.642768
I0407 22:30:27.183037 23658 solver.cpp:237]     Train net output #0: loss = 0.642768 (* 1 = 0.642768 loss)
I0407 22:30:27.183050 23658 sgd_solver.cpp:105] Iteration 3828, lr = 0.00145866
I0407 22:30:32.140244 23658 solver.cpp:218] Iteration 3840 (2.42079 iter/s, 4.95706s/12 iters), loss = 0.773939
I0407 22:30:32.140287 23658 solver.cpp:237]     Train net output #0: loss = 0.773939 (* 1 = 0.773939 loss)
I0407 22:30:32.140298 23658 sgd_solver.cpp:105] Iteration 3840, lr = 0.00144988
I0407 22:30:33.309535 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:37.226792 23658 solver.cpp:218] Iteration 3852 (2.35926 iter/s, 5.08635s/12 iters), loss = 0.773017
I0407 22:30:37.226840 23658 solver.cpp:237]     Train net output #0: loss = 0.773017 (* 1 = 0.773017 loss)
I0407 22:30:37.226851 23658 sgd_solver.cpp:105] Iteration 3852, lr = 0.00144116
I0407 22:30:42.178675 23658 solver.cpp:218] Iteration 3864 (2.42342 iter/s, 4.95169s/12 iters), loss = 0.623881
I0407 22:30:42.178721 23658 solver.cpp:237]     Train net output #0: loss = 0.623881 (* 1 = 0.623881 loss)
I0407 22:30:42.178731 23658 sgd_solver.cpp:105] Iteration 3864, lr = 0.00143249
I0407 22:30:46.790475 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 22:30:56.061347 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 22:31:01.544062 23658 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 22:31:01.544107 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:31:04.622212 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:06.164816 23658 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0407 22:31:06.164860 23658 solver.cpp:397]     Test net output #1: loss = 2.88263 (* 1 = 2.88263 loss)
I0407 22:31:06.254825 23658 solver.cpp:218] Iteration 3876 (0.498434 iter/s, 24.0754s/12 iters), loss = 0.81046
I0407 22:31:06.254871 23658 solver.cpp:237]     Train net output #0: loss = 0.81046 (* 1 = 0.81046 loss)
I0407 22:31:06.254881 23658 sgd_solver.cpp:105] Iteration 3876, lr = 0.00142387
I0407 22:31:10.402854 23658 solver.cpp:218] Iteration 3888 (2.89307 iter/s, 4.14785s/12 iters), loss = 0.586513
I0407 22:31:10.402904 23658 solver.cpp:237]     Train net output #0: loss = 0.586513 (* 1 = 0.586513 loss)
I0407 22:31:10.402913 23658 sgd_solver.cpp:105] Iteration 3888, lr = 0.0014153
I0407 22:31:15.359091 23658 solver.cpp:218] Iteration 3900 (2.42129 iter/s, 4.95603s/12 iters), loss = 0.687142
I0407 22:31:15.359148 23658 solver.cpp:237]     Train net output #0: loss = 0.687142 (* 1 = 0.687142 loss)
I0407 22:31:15.359160 23658 sgd_solver.cpp:105] Iteration 3900, lr = 0.00140679
I0407 22:31:20.723448 23658 solver.cpp:218] Iteration 3912 (2.23708 iter/s, 5.36414s/12 iters), loss = 0.432994
I0407 22:31:20.723495 23658 solver.cpp:237]     Train net output #0: loss = 0.432994 (* 1 = 0.432994 loss)
I0407 22:31:20.723505 23658 sgd_solver.cpp:105] Iteration 3912, lr = 0.00139832
I0407 22:31:25.966109 23658 solver.cpp:218] Iteration 3924 (2.289 iter/s, 5.24246s/12 iters), loss = 0.729379
I0407 22:31:25.966158 23658 solver.cpp:237]     Train net output #0: loss = 0.729379 (* 1 = 0.729379 loss)
I0407 22:31:25.966171 23658 sgd_solver.cpp:105] Iteration 3924, lr = 0.00138991
I0407 22:31:31.067795 23658 solver.cpp:218] Iteration 3936 (2.35226 iter/s, 5.10148s/12 iters), loss = 0.480929
I0407 22:31:31.067847 23658 solver.cpp:237]     Train net output #0: loss = 0.480929 (* 1 = 0.480929 loss)
I0407 22:31:31.067858 23658 sgd_solver.cpp:105] Iteration 3936, lr = 0.00138155
I0407 22:31:34.443061 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:36.088274 23658 solver.cpp:218] Iteration 3948 (2.39031 iter/s, 5.02028s/12 iters), loss = 0.665896
I0407 22:31:36.088316 23658 solver.cpp:237]     Train net output #0: loss = 0.665896 (* 1 = 0.665896 loss)
I0407 22:31:36.088325 23658 sgd_solver.cpp:105] Iteration 3948, lr = 0.00137323
I0407 22:31:41.500622 23658 solver.cpp:218] Iteration 3960 (2.21724 iter/s, 5.41214s/12 iters), loss = 0.65301
I0407 22:31:41.500674 23658 solver.cpp:237]     Train net output #0: loss = 0.65301 (* 1 = 0.65301 loss)
I0407 22:31:41.500685 23658 sgd_solver.cpp:105] Iteration 3960, lr = 0.00136497
I0407 22:31:46.961918 23658 solver.cpp:218] Iteration 3972 (2.19737 iter/s, 5.46108s/12 iters), loss = 0.45248
I0407 22:31:46.961978 23658 solver.cpp:237]     Train net output #0: loss = 0.45248 (* 1 = 0.45248 loss)
I0407 22:31:46.961989 23658 sgd_solver.cpp:105] Iteration 3972, lr = 0.00135676
I0407 22:31:49.201900 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 22:31:52.753657 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 22:31:55.994015 23658 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 22:31:55.994040 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:31:58.977638 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:00.558877 23658 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0407 22:32:00.558921 23658 solver.cpp:397]     Test net output #1: loss = 2.86347 (* 1 = 2.86347 loss)
I0407 22:32:02.506012 23658 solver.cpp:218] Iteration 3984 (0.772022 iter/s, 15.5436s/12 iters), loss = 0.547557
I0407 22:32:02.506067 23658 solver.cpp:237]     Train net output #0: loss = 0.547557 (* 1 = 0.547557 loss)
I0407 22:32:02.506079 23658 sgd_solver.cpp:105] Iteration 3984, lr = 0.0013486
I0407 22:32:07.695894 23658 solver.cpp:218] Iteration 3996 (2.31229 iter/s, 5.18967s/12 iters), loss = 0.698489
I0407 22:32:07.695988 23658 solver.cpp:237]     Train net output #0: loss = 0.698489 (* 1 = 0.698489 loss)
I0407 22:32:07.696000 23658 sgd_solver.cpp:105] Iteration 3996, lr = 0.00134048
I0407 22:32:12.785970 23658 solver.cpp:218] Iteration 4008 (2.35765 iter/s, 5.08982s/12 iters), loss = 0.561124
I0407 22:32:12.786017 23658 solver.cpp:237]     Train net output #0: loss = 0.561124 (* 1 = 0.561124 loss)
I0407 22:32:12.786028 23658 sgd_solver.cpp:105] Iteration 4008, lr = 0.00133242
I0407 22:32:17.766463 23658 solver.cpp:218] Iteration 4020 (2.4095 iter/s, 4.98029s/12 iters), loss = 0.543541
I0407 22:32:17.766520 23658 solver.cpp:237]     Train net output #0: loss = 0.543541 (* 1 = 0.543541 loss)
I0407 22:32:17.766532 23658 sgd_solver.cpp:105] Iteration 4020, lr = 0.0013244
I0407 22:32:22.787937 23658 solver.cpp:218] Iteration 4032 (2.38983 iter/s, 5.02127s/12 iters), loss = 0.641222
I0407 22:32:22.787977 23658 solver.cpp:237]     Train net output #0: loss = 0.641222 (* 1 = 0.641222 loss)
I0407 22:32:22.787987 23658 sgd_solver.cpp:105] Iteration 4032, lr = 0.00131643
I0407 22:32:27.793534 23658 solver.cpp:218] Iteration 4044 (2.39741 iter/s, 5.0054s/12 iters), loss = 0.776499
I0407 22:32:27.793581 23658 solver.cpp:237]     Train net output #0: loss = 0.776499 (* 1 = 0.776499 loss)
I0407 22:32:27.793592 23658 sgd_solver.cpp:105] Iteration 4044, lr = 0.00130851
I0407 22:32:28.252048 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:32.767149 23658 solver.cpp:218] Iteration 4056 (2.41283 iter/s, 4.97342s/12 iters), loss = 0.621319
I0407 22:32:32.767194 23658 solver.cpp:237]     Train net output #0: loss = 0.621319 (* 1 = 0.621319 loss)
I0407 22:32:32.767206 23658 sgd_solver.cpp:105] Iteration 4056, lr = 0.00130064
I0407 22:32:37.857038 23658 solver.cpp:218] Iteration 4068 (2.35771 iter/s, 5.08969s/12 iters), loss = 0.41954
I0407 22:32:37.857141 23658 solver.cpp:237]     Train net output #0: loss = 0.41954 (* 1 = 0.41954 loss)
I0407 22:32:37.857153 23658 sgd_solver.cpp:105] Iteration 4068, lr = 0.00129281
I0407 22:32:42.438256 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 22:32:50.610743 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 22:32:57.410164 23658 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 22:32:57.410187 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:33:00.263828 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:01.880654 23658 solver.cpp:397]     Test net output #0: accuracy = 0.411765
I0407 22:33:01.880688 23658 solver.cpp:397]     Test net output #1: loss = 2.83054 (* 1 = 2.83054 loss)
I0407 22:33:01.970508 23658 solver.cpp:218] Iteration 4080 (0.497663 iter/s, 24.1127s/12 iters), loss = 0.706302
I0407 22:33:01.970571 23658 solver.cpp:237]     Train net output #0: loss = 0.706302 (* 1 = 0.706302 loss)
I0407 22:33:01.970583 23658 sgd_solver.cpp:105] Iteration 4080, lr = 0.00128504
I0407 22:33:06.267707 23658 solver.cpp:218] Iteration 4092 (2.79264 iter/s, 4.29701s/12 iters), loss = 0.553371
I0407 22:33:06.267755 23658 solver.cpp:237]     Train net output #0: loss = 0.553371 (* 1 = 0.553371 loss)
I0407 22:33:06.267763 23658 sgd_solver.cpp:105] Iteration 4092, lr = 0.00127731
I0407 22:33:11.284090 23658 solver.cpp:218] Iteration 4104 (2.39226 iter/s, 5.01618s/12 iters), loss = 0.490302
I0407 22:33:11.284219 23658 solver.cpp:237]     Train net output #0: loss = 0.490302 (* 1 = 0.490302 loss)
I0407 22:33:11.284232 23658 sgd_solver.cpp:105] Iteration 4104, lr = 0.00126962
I0407 22:33:16.390496 23658 solver.cpp:218] Iteration 4116 (2.35012 iter/s, 5.10612s/12 iters), loss = 0.581225
I0407 22:33:16.390552 23658 solver.cpp:237]     Train net output #0: loss = 0.581225 (* 1 = 0.581225 loss)
I0407 22:33:16.390564 23658 sgd_solver.cpp:105] Iteration 4116, lr = 0.00126198
I0407 22:33:21.467563 23658 solver.cpp:218] Iteration 4128 (2.36367 iter/s, 5.07685s/12 iters), loss = 0.521602
I0407 22:33:21.467614 23658 solver.cpp:237]     Train net output #0: loss = 0.521602 (* 1 = 0.521602 loss)
I0407 22:33:21.467625 23658 sgd_solver.cpp:105] Iteration 4128, lr = 0.00125439
I0407 22:33:26.603859 23658 solver.cpp:218] Iteration 4140 (2.33641 iter/s, 5.13609s/12 iters), loss = 0.55494
I0407 22:33:26.603907 23658 solver.cpp:237]     Train net output #0: loss = 0.55494 (* 1 = 0.55494 loss)
I0407 22:33:26.603917 23658 sgd_solver.cpp:105] Iteration 4140, lr = 0.00124684
I0407 22:33:29.349941 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:31.764324 23658 solver.cpp:218] Iteration 4152 (2.32546 iter/s, 5.16026s/12 iters), loss = 0.46207
I0407 22:33:31.764367 23658 solver.cpp:237]     Train net output #0: loss = 0.46207 (* 1 = 0.46207 loss)
I0407 22:33:31.764377 23658 sgd_solver.cpp:105] Iteration 4152, lr = 0.00123934
I0407 22:33:33.612926 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:33:37.262693 23658 solver.cpp:218] Iteration 4164 (2.18255 iter/s, 5.49816s/12 iters), loss = 0.40637
I0407 22:33:37.262743 23658 solver.cpp:237]     Train net output #0: loss = 0.40637 (* 1 = 0.40637 loss)
I0407 22:33:37.262753 23658 sgd_solver.cpp:105] Iteration 4164, lr = 0.00123188
I0407 22:33:42.346683 23658 solver.cpp:218] Iteration 4176 (2.36045 iter/s, 5.08379s/12 iters), loss = 0.398952
I0407 22:33:42.346788 23658 solver.cpp:237]     Train net output #0: loss = 0.398952 (* 1 = 0.398952 loss)
I0407 22:33:42.346801 23658 sgd_solver.cpp:105] Iteration 4176, lr = 0.00122447
I0407 22:33:44.431506 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 22:33:50.610946 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 22:33:52.931489 23658 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 22:33:52.931515 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:33:55.839237 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:57.542817 23658 solver.cpp:397]     Test net output #0: accuracy = 0.410539
I0407 22:33:57.542865 23658 solver.cpp:397]     Test net output #1: loss = 2.82982 (* 1 = 2.82982 loss)
I0407 22:33:59.548213 23658 solver.cpp:218] Iteration 4188 (0.697636 iter/s, 17.2009s/12 iters), loss = 0.528226
I0407 22:33:59.548271 23658 solver.cpp:237]     Train net output #0: loss = 0.528226 (* 1 = 0.528226 loss)
I0407 22:33:59.548285 23658 sgd_solver.cpp:105] Iteration 4188, lr = 0.0012171
I0407 22:34:04.580412 23658 solver.cpp:218] Iteration 4200 (2.38474 iter/s, 5.03199s/12 iters), loss = 0.536757
I0407 22:34:04.580466 23658 solver.cpp:237]     Train net output #0: loss = 0.536757 (* 1 = 0.536757 loss)
I0407 22:34:04.580478 23658 sgd_solver.cpp:105] Iteration 4200, lr = 0.00120978
I0407 22:34:09.621177 23658 solver.cpp:218] Iteration 4212 (2.38069 iter/s, 5.04056s/12 iters), loss = 0.448351
I0407 22:34:09.621219 23658 solver.cpp:237]     Train net output #0: loss = 0.448351 (* 1 = 0.448351 loss)
I0407 22:34:09.621229 23658 sgd_solver.cpp:105] Iteration 4212, lr = 0.0012025
I0407 22:34:14.607090 23658 solver.cpp:218] Iteration 4224 (2.40688 iter/s, 4.98572s/12 iters), loss = 0.400102
I0407 22:34:14.607239 23658 solver.cpp:237]     Train net output #0: loss = 0.400102 (* 1 = 0.400102 loss)
I0407 22:34:14.607252 23658 sgd_solver.cpp:105] Iteration 4224, lr = 0.00119527
I0407 22:34:19.703588 23658 solver.cpp:218] Iteration 4236 (2.3547 iter/s, 5.0962s/12 iters), loss = 0.439365
I0407 22:34:19.703644 23658 solver.cpp:237]     Train net output #0: loss = 0.439365 (* 1 = 0.439365 loss)
I0407 22:34:19.703655 23658 sgd_solver.cpp:105] Iteration 4236, lr = 0.00118808
I0407 22:34:24.799356 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:25.027779 23658 solver.cpp:218] Iteration 4248 (2.25395 iter/s, 5.32398s/12 iters), loss = 0.44588
I0407 22:34:25.027829 23658 solver.cpp:237]     Train net output #0: loss = 0.44588 (* 1 = 0.44588 loss)
I0407 22:34:25.027842 23658 sgd_solver.cpp:105] Iteration 4248, lr = 0.00118093
I0407 22:34:30.173043 23658 solver.cpp:218] Iteration 4260 (2.33234 iter/s, 5.14506s/12 iters), loss = 0.482049
I0407 22:34:30.173094 23658 solver.cpp:237]     Train net output #0: loss = 0.482049 (* 1 = 0.482049 loss)
I0407 22:34:30.173107 23658 sgd_solver.cpp:105] Iteration 4260, lr = 0.00117382
I0407 22:34:35.213008 23658 solver.cpp:218] Iteration 4272 (2.38107 iter/s, 5.03976s/12 iters), loss = 0.359172
I0407 22:34:35.213061 23658 solver.cpp:237]     Train net output #0: loss = 0.359172 (* 1 = 0.359172 loss)
I0407 22:34:35.213073 23658 sgd_solver.cpp:105] Iteration 4272, lr = 0.00116676
I0407 22:34:39.781561 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 22:34:44.727285 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 22:34:47.048655 23658 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 22:34:47.048681 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:34:49.819789 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:51.515367 23658 solver.cpp:397]     Test net output #0: accuracy = 0.396446
I0407 22:34:51.515413 23658 solver.cpp:397]     Test net output #1: loss = 2.94097 (* 1 = 2.94097 loss)
I0407 22:34:51.604840 23658 solver.cpp:218] Iteration 4284 (0.732095 iter/s, 16.3913s/12 iters), loss = 0.491227
I0407 22:34:51.604895 23658 solver.cpp:237]     Train net output #0: loss = 0.491227 (* 1 = 0.491227 loss)
I0407 22:34:51.604907 23658 sgd_solver.cpp:105] Iteration 4284, lr = 0.00115974
I0407 22:34:56.034611 23658 solver.cpp:218] Iteration 4296 (2.70906 iter/s, 4.42958s/12 iters), loss = 0.555745
I0407 22:34:56.034651 23658 solver.cpp:237]     Train net output #0: loss = 0.555745 (* 1 = 0.555745 loss)
I0407 22:34:56.034660 23658 sgd_solver.cpp:105] Iteration 4296, lr = 0.00115276
I0407 22:35:01.038780 23658 solver.cpp:218] Iteration 4308 (2.3981 iter/s, 5.00396s/12 iters), loss = 0.51115
I0407 22:35:01.038839 23658 solver.cpp:237]     Train net output #0: loss = 0.51115 (* 1 = 0.51115 loss)
I0407 22:35:01.038856 23658 sgd_solver.cpp:105] Iteration 4308, lr = 0.00114583
I0407 22:35:06.099911 23658 solver.cpp:218] Iteration 4320 (2.37111 iter/s, 5.06092s/12 iters), loss = 0.58762
I0407 22:35:06.099961 23658 solver.cpp:237]     Train net output #0: loss = 0.58762 (* 1 = 0.58762 loss)
I0407 22:35:06.099970 23658 sgd_solver.cpp:105] Iteration 4320, lr = 0.00113893
I0407 22:35:11.105022 23658 solver.cpp:218] Iteration 4332 (2.39765 iter/s, 5.00491s/12 iters), loss = 0.47499
I0407 22:35:11.105074 23658 solver.cpp:237]     Train net output #0: loss = 0.47499 (* 1 = 0.47499 loss)
I0407 22:35:11.105085 23658 sgd_solver.cpp:105] Iteration 4332, lr = 0.00113208
I0407 22:35:16.349197 23658 solver.cpp:218] Iteration 4344 (2.28835 iter/s, 5.24396s/12 iters), loss = 0.507954
I0407 22:35:16.349359 23658 solver.cpp:237]     Train net output #0: loss = 0.507954 (* 1 = 0.507954 loss)
I0407 22:35:16.349371 23658 sgd_solver.cpp:105] Iteration 4344, lr = 0.00112527
I0407 22:35:18.257565 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:21.334157 23658 solver.cpp:218] Iteration 4356 (2.40739 iter/s, 4.98466s/12 iters), loss = 0.555196
I0407 22:35:21.334208 23658 solver.cpp:237]     Train net output #0: loss = 0.555196 (* 1 = 0.555196 loss)
I0407 22:35:21.334220 23658 sgd_solver.cpp:105] Iteration 4356, lr = 0.0011185
I0407 22:35:26.765030 23658 solver.cpp:218] Iteration 4368 (2.20968 iter/s, 5.43066s/12 iters), loss = 0.398083
I0407 22:35:26.765081 23658 solver.cpp:237]     Train net output #0: loss = 0.398083 (* 1 = 0.398083 loss)
I0407 22:35:26.765094 23658 sgd_solver.cpp:105] Iteration 4368, lr = 0.00111177
I0407 22:35:32.106539 23658 solver.cpp:218] Iteration 4380 (2.24664 iter/s, 5.3413s/12 iters), loss = 0.38768
I0407 22:35:32.106582 23658 solver.cpp:237]     Train net output #0: loss = 0.38768 (* 1 = 0.38768 loss)
I0407 22:35:32.106591 23658 sgd_solver.cpp:105] Iteration 4380, lr = 0.00110508
I0407 22:35:34.142153 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 22:35:37.712852 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 22:35:42.085156 23658 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 22:35:42.085182 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:35:44.805367 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:46.546698 23658 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0407 22:35:46.546782 23658 solver.cpp:397]     Test net output #1: loss = 2.93972 (* 1 = 2.93972 loss)
I0407 22:35:48.557973 23658 solver.cpp:218] Iteration 4392 (0.729443 iter/s, 16.4509s/12 iters), loss = 0.320887
I0407 22:35:48.558029 23658 solver.cpp:237]     Train net output #0: loss = 0.320887 (* 1 = 0.320887 loss)
I0407 22:35:48.558041 23658 sgd_solver.cpp:105] Iteration 4392, lr = 0.00109843
I0407 22:35:54.000376 23658 solver.cpp:218] Iteration 4404 (2.205 iter/s, 5.44218s/12 iters), loss = 0.709004
I0407 22:35:54.000425 23658 solver.cpp:237]     Train net output #0: loss = 0.709004 (* 1 = 0.709004 loss)
I0407 22:35:54.000435 23658 sgd_solver.cpp:105] Iteration 4404, lr = 0.00109182
I0407 22:35:58.941228 23658 solver.cpp:218] Iteration 4416 (2.42883 iter/s, 4.94065s/12 iters), loss = 0.396492
I0407 22:35:58.941277 23658 solver.cpp:237]     Train net output #0: loss = 0.396492 (* 1 = 0.396492 loss)
I0407 22:35:58.941287 23658 sgd_solver.cpp:105] Iteration 4416, lr = 0.00108526
I0407 22:36:04.243683 23658 solver.cpp:218] Iteration 4428 (2.26319 iter/s, 5.30225s/12 iters), loss = 0.482165
I0407 22:36:04.243734 23658 solver.cpp:237]     Train net output #0: loss = 0.482165 (* 1 = 0.482165 loss)
I0407 22:36:04.243746 23658 sgd_solver.cpp:105] Iteration 4428, lr = 0.00107873
I0407 22:36:09.691860 23658 solver.cpp:218] Iteration 4440 (2.20266 iter/s, 5.44796s/12 iters), loss = 0.422821
I0407 22:36:09.691917 23658 solver.cpp:237]     Train net output #0: loss = 0.422821 (* 1 = 0.422821 loss)
I0407 22:36:09.691931 23658 sgd_solver.cpp:105] Iteration 4440, lr = 0.00107224
I0407 22:36:14.162056 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:15.207532 23658 solver.cpp:218] Iteration 4452 (2.1757 iter/s, 5.51546s/12 iters), loss = 0.4029
I0407 22:36:15.207577 23658 solver.cpp:237]     Train net output #0: loss = 0.4029 (* 1 = 0.4029 loss)
I0407 22:36:15.207588 23658 sgd_solver.cpp:105] Iteration 4452, lr = 0.00106579
I0407 22:36:20.455044 23658 solver.cpp:218] Iteration 4464 (2.28689 iter/s, 5.24731s/12 iters), loss = 0.424219
I0407 22:36:20.455179 23658 solver.cpp:237]     Train net output #0: loss = 0.424219 (* 1 = 0.424219 loss)
I0407 22:36:20.455191 23658 sgd_solver.cpp:105] Iteration 4464, lr = 0.00105937
I0407 22:36:25.512013 23658 solver.cpp:218] Iteration 4476 (2.3731 iter/s, 5.05668s/12 iters), loss = 0.378003
I0407 22:36:25.512066 23658 solver.cpp:237]     Train net output #0: loss = 0.378003 (* 1 = 0.378003 loss)
I0407 22:36:25.512079 23658 sgd_solver.cpp:105] Iteration 4476, lr = 0.001053
I0407 22:36:30.028048 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 22:36:35.392657 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 22:36:39.054323 23658 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 22:36:39.054349 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:36:41.899628 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:43.673177 23658 solver.cpp:397]     Test net output #0: accuracy = 0.427696
I0407 22:36:43.673226 23658 solver.cpp:397]     Test net output #1: loss = 2.85058 (* 1 = 2.85058 loss)
I0407 22:36:43.763094 23658 solver.cpp:218] Iteration 4488 (0.657516 iter/s, 18.2505s/12 iters), loss = 0.428872
I0407 22:36:43.763145 23658 solver.cpp:237]     Train net output #0: loss = 0.428872 (* 1 = 0.428872 loss)
I0407 22:36:43.763156 23658 sgd_solver.cpp:105] Iteration 4488, lr = 0.00104666
I0407 22:36:48.365494 23658 solver.cpp:218] Iteration 4500 (2.60744 iter/s, 4.60221s/12 iters), loss = 0.357409
I0407 22:36:48.365540 23658 solver.cpp:237]     Train net output #0: loss = 0.357409 (* 1 = 0.357409 loss)
I0407 22:36:48.365551 23658 sgd_solver.cpp:105] Iteration 4500, lr = 0.00104037
I0407 22:36:53.727511 23658 solver.cpp:218] Iteration 4512 (2.23805 iter/s, 5.36181s/12 iters), loss = 0.472927
I0407 22:36:53.727593 23658 solver.cpp:237]     Train net output #0: loss = 0.472927 (* 1 = 0.472927 loss)
I0407 22:36:53.727605 23658 sgd_solver.cpp:105] Iteration 4512, lr = 0.00103411
I0407 22:36:58.804021 23658 solver.cpp:218] Iteration 4524 (2.36394 iter/s, 5.07628s/12 iters), loss = 0.331355
I0407 22:36:58.804071 23658 solver.cpp:237]     Train net output #0: loss = 0.331355 (* 1 = 0.331355 loss)
I0407 22:36:58.804081 23658 sgd_solver.cpp:105] Iteration 4524, lr = 0.00102789
I0407 22:37:04.202272 23658 solver.cpp:218] Iteration 4536 (2.22303 iter/s, 5.39803s/12 iters), loss = 0.492545
I0407 22:37:04.202328 23658 solver.cpp:237]     Train net output #0: loss = 0.492545 (* 1 = 0.492545 loss)
I0407 22:37:04.202342 23658 sgd_solver.cpp:105] Iteration 4536, lr = 0.0010217
I0407 22:37:09.535827 23658 solver.cpp:218] Iteration 4548 (2.25 iter/s, 5.33334s/12 iters), loss = 0.373843
I0407 22:37:09.535879 23658 solver.cpp:237]     Train net output #0: loss = 0.373843 (* 1 = 0.373843 loss)
I0407 22:37:09.535890 23658 sgd_solver.cpp:105] Iteration 4548, lr = 0.00101555
I0407 22:37:10.813951 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:14.544878 23658 solver.cpp:218] Iteration 4560 (2.39576 iter/s, 5.00885s/12 iters), loss = 0.263424
I0407 22:37:14.544915 23658 solver.cpp:237]     Train net output #0: loss = 0.263424 (* 1 = 0.263424 loss)
I0407 22:37:14.544924 23658 sgd_solver.cpp:105] Iteration 4560, lr = 0.00100944
I0407 22:37:19.556798 23658 solver.cpp:218] Iteration 4572 (2.39438 iter/s, 5.01173s/12 iters), loss = 0.269
I0407 22:37:19.556847 23658 solver.cpp:237]     Train net output #0: loss = 0.269 (* 1 = 0.269 loss)
I0407 22:37:19.556859 23658 sgd_solver.cpp:105] Iteration 4572, lr = 0.00100337
I0407 22:37:24.660380 23658 solver.cpp:218] Iteration 4584 (2.35138 iter/s, 5.10338s/12 iters), loss = 0.259278
I0407 22:37:24.660512 23658 solver.cpp:237]     Train net output #0: loss = 0.259278 (* 1 = 0.259278 loss)
I0407 22:37:24.660521 23658 sgd_solver.cpp:105] Iteration 4584, lr = 0.000997334
I0407 22:37:26.735352 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 22:37:31.601213 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 22:37:33.892510 23658 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 22:37:33.892534 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:37:36.536810 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:38.374548 23658 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0407 22:37:38.374588 23658 solver.cpp:397]     Test net output #1: loss = 2.79271 (* 1 = 2.79271 loss)
I0407 22:37:40.386569 23658 solver.cpp:218] Iteration 4596 (0.763087 iter/s, 15.7256s/12 iters), loss = 0.454347
I0407 22:37:40.386621 23658 solver.cpp:237]     Train net output #0: loss = 0.454347 (* 1 = 0.454347 loss)
I0407 22:37:40.386632 23658 sgd_solver.cpp:105] Iteration 4596, lr = 0.000991333
I0407 22:37:45.523067 23658 solver.cpp:218] Iteration 4608 (2.33632 iter/s, 5.13629s/12 iters), loss = 0.285565
I0407 22:37:45.523115 23658 solver.cpp:237]     Train net output #0: loss = 0.285565 (* 1 = 0.285565 loss)
I0407 22:37:45.523128 23658 sgd_solver.cpp:105] Iteration 4608, lr = 0.000985369
I0407 22:37:50.978689 23658 solver.cpp:218] Iteration 4620 (2.19965 iter/s, 5.4554s/12 iters), loss = 0.331494
I0407 22:37:50.978741 23658 solver.cpp:237]     Train net output #0: loss = 0.331494 (* 1 = 0.331494 loss)
I0407 22:37:50.978754 23658 sgd_solver.cpp:105] Iteration 4620, lr = 0.00097944
I0407 22:37:56.060544 23658 solver.cpp:218] Iteration 4632 (2.36144 iter/s, 5.08165s/12 iters), loss = 0.346593
I0407 22:37:56.060685 23658 solver.cpp:237]     Train net output #0: loss = 0.346593 (* 1 = 0.346593 loss)
I0407 22:37:56.060695 23658 sgd_solver.cpp:105] Iteration 4632, lr = 0.000973547
I0407 22:38:01.067050 23658 solver.cpp:218] Iteration 4644 (2.39702 iter/s, 5.00621s/12 iters), loss = 0.270603
I0407 22:38:01.067095 23658 solver.cpp:237]     Train net output #0: loss = 0.270603 (* 1 = 0.270603 loss)
I0407 22:38:01.067104 23658 sgd_solver.cpp:105] Iteration 4644, lr = 0.00096769
I0407 22:38:04.543627 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:06.148249 23658 solver.cpp:218] Iteration 4656 (2.36174 iter/s, 5.081s/12 iters), loss = 0.303603
I0407 22:38:06.148293 23658 solver.cpp:237]     Train net output #0: loss = 0.303603 (* 1 = 0.303603 loss)
I0407 22:38:06.148301 23658 sgd_solver.cpp:105] Iteration 4656, lr = 0.000961868
I0407 22:38:11.087777 23658 solver.cpp:218] Iteration 4668 (2.42947 iter/s, 4.93934s/12 iters), loss = 0.321612
I0407 22:38:11.087811 23658 solver.cpp:237]     Train net output #0: loss = 0.321612 (* 1 = 0.321612 loss)
I0407 22:38:11.087819 23658 sgd_solver.cpp:105] Iteration 4668, lr = 0.000956081
I0407 22:38:16.155599 23658 solver.cpp:218] Iteration 4680 (2.36797 iter/s, 5.06763s/12 iters), loss = 0.383198
I0407 22:38:16.155654 23658 solver.cpp:237]     Train net output #0: loss = 0.383198 (* 1 = 0.383198 loss)
I0407 22:38:16.155665 23658 sgd_solver.cpp:105] Iteration 4680, lr = 0.000950328
I0407 22:38:20.794570 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 22:38:24.238317 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 22:38:26.532279 23658 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 22:38:26.532356 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:38:29.086401 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:30.943329 23658 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0407 22:38:30.943370 23658 solver.cpp:397]     Test net output #1: loss = 2.93357 (* 1 = 2.93357 loss)
I0407 22:38:31.031733 23658 solver.cpp:218] Iteration 4692 (0.806687 iter/s, 14.8757s/12 iters), loss = 0.299609
I0407 22:38:31.031790 23658 solver.cpp:237]     Train net output #0: loss = 0.299609 (* 1 = 0.299609 loss)
I0407 22:38:31.031803 23658 sgd_solver.cpp:105] Iteration 4692, lr = 0.000944611
I0407 22:38:35.714795 23658 solver.cpp:218] Iteration 4704 (2.56253 iter/s, 4.68286s/12 iters), loss = 0.452487
I0407 22:38:35.714838 23658 solver.cpp:237]     Train net output #0: loss = 0.452487 (* 1 = 0.452487 loss)
I0407 22:38:35.714848 23658 sgd_solver.cpp:105] Iteration 4704, lr = 0.000938927
I0407 22:38:40.818501 23658 solver.cpp:218] Iteration 4716 (2.35133 iter/s, 5.1035s/12 iters), loss = 0.189177
I0407 22:38:40.818557 23658 solver.cpp:237]     Train net output #0: loss = 0.189177 (* 1 = 0.189177 loss)
I0407 22:38:40.818569 23658 sgd_solver.cpp:105] Iteration 4716, lr = 0.000933278
I0407 22:38:45.910877 23658 solver.cpp:218] Iteration 4728 (2.35656 iter/s, 5.09217s/12 iters), loss = 0.242785
I0407 22:38:45.910918 23658 solver.cpp:237]     Train net output #0: loss = 0.242785 (* 1 = 0.242785 loss)
I0407 22:38:45.910928 23658 sgd_solver.cpp:105] Iteration 4728, lr = 0.000927663
I0407 22:38:50.884999 23658 solver.cpp:218] Iteration 4740 (2.41258 iter/s, 4.97393s/12 iters), loss = 0.424864
I0407 22:38:50.885036 23658 solver.cpp:237]     Train net output #0: loss = 0.424864 (* 1 = 0.424864 loss)
I0407 22:38:50.885044 23658 sgd_solver.cpp:105] Iteration 4740, lr = 0.000922082
I0407 22:38:55.902343 23658 solver.cpp:218] Iteration 4752 (2.39179 iter/s, 5.01715s/12 iters), loss = 0.306852
I0407 22:38:55.902386 23658 solver.cpp:237]     Train net output #0: loss = 0.306852 (* 1 = 0.306852 loss)
I0407 22:38:55.902395 23658 sgd_solver.cpp:105] Iteration 4752, lr = 0.000916534
I0407 22:38:56.432286 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:00.951529 23658 solver.cpp:218] Iteration 4764 (2.37671 iter/s, 5.04899s/12 iters), loss = 0.166773
I0407 22:39:00.951684 23658 solver.cpp:237]     Train net output #0: loss = 0.166773 (* 1 = 0.166773 loss)
I0407 22:39:00.951699 23658 sgd_solver.cpp:105] Iteration 4764, lr = 0.00091102
I0407 22:39:05.919925 23658 solver.cpp:218] Iteration 4776 (2.41541 iter/s, 4.9681s/12 iters), loss = 0.351015
I0407 22:39:05.919961 23658 solver.cpp:237]     Train net output #0: loss = 0.351015 (* 1 = 0.351015 loss)
I0407 22:39:05.919970 23658 sgd_solver.cpp:105] Iteration 4776, lr = 0.000905539
I0407 22:39:10.909673 23658 solver.cpp:218] Iteration 4788 (2.40502 iter/s, 4.98956s/12 iters), loss = 0.302413
I0407 22:39:10.909724 23658 solver.cpp:237]     Train net output #0: loss = 0.302413 (* 1 = 0.302413 loss)
I0407 22:39:10.909735 23658 sgd_solver.cpp:105] Iteration 4788, lr = 0.00090009
I0407 22:39:12.908574 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 22:39:17.728950 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 22:39:20.282302 23658 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 22:39:20.282326 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:39:22.896729 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:24.945480 23658 solver.cpp:397]     Test net output #0: accuracy = 0.4375
I0407 22:39:24.945539 23658 solver.cpp:397]     Test net output #1: loss = 2.82992 (* 1 = 2.82992 loss)
I0407 22:39:26.849089 23658 solver.cpp:218] Iteration 4800 (0.752875 iter/s, 15.9389s/12 iters), loss = 0.242088
I0407 22:39:26.849150 23658 solver.cpp:237]     Train net output #0: loss = 0.242088 (* 1 = 0.242088 loss)
I0407 22:39:26.849162 23658 sgd_solver.cpp:105] Iteration 4800, lr = 0.000894675
I0407 22:39:31.928020 23658 solver.cpp:218] Iteration 4812 (2.3628 iter/s, 5.07872s/12 iters), loss = 0.367405
I0407 22:39:31.928109 23658 solver.cpp:237]     Train net output #0: loss = 0.367405 (* 1 = 0.367405 loss)
I0407 22:39:31.928118 23658 sgd_solver.cpp:105] Iteration 4812, lr = 0.000889292
I0407 22:39:36.951612 23658 solver.cpp:218] Iteration 4824 (2.38884 iter/s, 5.02335s/12 iters), loss = 0.446036
I0407 22:39:36.951661 23658 solver.cpp:237]     Train net output #0: loss = 0.446036 (* 1 = 0.446036 loss)
I0407 22:39:36.951673 23658 sgd_solver.cpp:105] Iteration 4824, lr = 0.000883942
I0407 22:39:42.028242 23658 solver.cpp:218] Iteration 4836 (2.36387 iter/s, 5.07643s/12 iters), loss = 0.276827
I0407 22:39:42.028295 23658 solver.cpp:237]     Train net output #0: loss = 0.276827 (* 1 = 0.276827 loss)
I0407 22:39:42.028307 23658 sgd_solver.cpp:105] Iteration 4836, lr = 0.000878624
I0407 22:39:44.087556 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:39:47.131705 23658 solver.cpp:218] Iteration 4848 (2.35144 iter/s, 5.10326s/12 iters), loss = 0.257661
I0407 22:39:47.131758 23658 solver.cpp:237]     Train net output #0: loss = 0.257661 (* 1 = 0.257661 loss)
I0407 22:39:47.131770 23658 sgd_solver.cpp:105] Iteration 4848, lr = 0.000873337
I0407 22:39:49.945928 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:52.504880 23658 solver.cpp:218] Iteration 4860 (2.23341 iter/s, 5.37294s/12 iters), loss = 0.265841
I0407 22:39:52.504935 23658 solver.cpp:237]     Train net output #0: loss = 0.265841 (* 1 = 0.265841 loss)
I0407 22:39:52.504948 23658 sgd_solver.cpp:105] Iteration 4860, lr = 0.000868083
I0407 22:39:57.914659 23658 solver.cpp:218] Iteration 4872 (2.21829 iter/s, 5.40957s/12 iters), loss = 0.305404
I0407 22:39:57.914705 23658 solver.cpp:237]     Train net output #0: loss = 0.305404 (* 1 = 0.305404 loss)
I0407 22:39:57.914717 23658 sgd_solver.cpp:105] Iteration 4872, lr = 0.00086286
I0407 22:40:02.905511 23658 solver.cpp:218] Iteration 4884 (2.40449 iter/s, 4.99066s/12 iters), loss = 0.365549
I0407 22:40:02.905645 23658 solver.cpp:237]     Train net output #0: loss = 0.365549 (* 1 = 0.365549 loss)
I0407 22:40:02.905658 23658 sgd_solver.cpp:105] Iteration 4884, lr = 0.000857669
I0407 22:40:07.380452 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 22:40:10.374425 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 22:40:12.694830 23658 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 22:40:12.694857 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:40:15.106031 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:17.078802 23658 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0407 22:40:17.078846 23658 solver.cpp:397]     Test net output #1: loss = 2.79971 (* 1 = 2.79971 loss)
I0407 22:40:17.169173 23658 solver.cpp:218] Iteration 4896 (0.841331 iter/s, 14.2631s/12 iters), loss = 0.380151
I0407 22:40:17.169229 23658 solver.cpp:237]     Train net output #0: loss = 0.380151 (* 1 = 0.380151 loss)
I0407 22:40:17.169241 23658 sgd_solver.cpp:105] Iteration 4896, lr = 0.000852508
I0407 22:40:21.407991 23658 solver.cpp:218] Iteration 4908 (2.8311 iter/s, 4.23863s/12 iters), loss = 0.21897
I0407 22:40:21.408041 23658 solver.cpp:237]     Train net output #0: loss = 0.21897 (* 1 = 0.21897 loss)
I0407 22:40:21.408052 23658 sgd_solver.cpp:105] Iteration 4908, lr = 0.000847379
I0407 22:40:26.466325 23658 solver.cpp:218] Iteration 4920 (2.37242 iter/s, 5.05814s/12 iters), loss = 0.411734
I0407 22:40:26.466364 23658 solver.cpp:237]     Train net output #0: loss = 0.411734 (* 1 = 0.411734 loss)
I0407 22:40:26.466373 23658 sgd_solver.cpp:105] Iteration 4920, lr = 0.000842281
I0407 22:40:31.781360 23658 solver.cpp:218] Iteration 4932 (2.25783 iter/s, 5.31483s/12 iters), loss = 0.187095
I0407 22:40:31.781409 23658 solver.cpp:237]     Train net output #0: loss = 0.187095 (* 1 = 0.187095 loss)
I0407 22:40:31.781419 23658 sgd_solver.cpp:105] Iteration 4932, lr = 0.000837213
I0407 22:40:37.296279 23658 solver.cpp:218] Iteration 4944 (2.176 iter/s, 5.5147s/12 iters), loss = 0.182339
I0407 22:40:37.296396 23658 solver.cpp:237]     Train net output #0: loss = 0.182339 (* 1 = 0.182339 loss)
I0407 22:40:37.296406 23658 sgd_solver.cpp:105] Iteration 4944, lr = 0.000832176
I0407 22:40:42.559383 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:42.773602 23658 solver.cpp:218] Iteration 4956 (2.19096 iter/s, 5.47704s/12 iters), loss = 0.293428
I0407 22:40:42.773646 23658 solver.cpp:237]     Train net output #0: loss = 0.293428 (* 1 = 0.293428 loss)
I0407 22:40:42.773656 23658 sgd_solver.cpp:105] Iteration 4956, lr = 0.000827169
I0407 22:40:48.276533 23658 solver.cpp:218] Iteration 4968 (2.18074 iter/s, 5.50272s/12 iters), loss = 0.292365
I0407 22:40:48.276580 23658 solver.cpp:237]     Train net output #0: loss = 0.292365 (* 1 = 0.292365 loss)
I0407 22:40:48.276588 23658 sgd_solver.cpp:105] Iteration 4968, lr = 0.000822193
I0407 22:40:53.247265 23658 solver.cpp:218] Iteration 4980 (2.41421 iter/s, 4.97058s/12 iters), loss = 0.301567
I0407 22:40:53.247301 23658 solver.cpp:237]     Train net output #0: loss = 0.301567 (* 1 = 0.301567 loss)
I0407 22:40:53.247310 23658 sgd_solver.cpp:105] Iteration 4980, lr = 0.000817246
I0407 22:40:58.255373 23658 solver.cpp:218] Iteration 4992 (2.39619 iter/s, 5.00796s/12 iters), loss = 0.28762
I0407 22:40:58.255420 23658 solver.cpp:237]     Train net output #0: loss = 0.28762 (* 1 = 0.28762 loss)
I0407 22:40:58.255432 23658 sgd_solver.cpp:105] Iteration 4992, lr = 0.000812329
I0407 22:41:00.333714 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 22:41:06.421864 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 22:41:08.803550 23658 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 22:41:08.803647 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:41:11.313899 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:13.321270 23658 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0407 22:41:13.321328 23658 solver.cpp:397]     Test net output #1: loss = 2.85055 (* 1 = 2.85055 loss)
I0407 22:41:15.305155 23658 solver.cpp:218] Iteration 5004 (0.703837 iter/s, 17.0494s/12 iters), loss = 0.192464
I0407 22:41:15.305193 23658 solver.cpp:237]     Train net output #0: loss = 0.192464 (* 1 = 0.192464 loss)
I0407 22:41:15.305202 23658 sgd_solver.cpp:105] Iteration 5004, lr = 0.000807442
I0407 22:41:20.793009 23658 solver.cpp:218] Iteration 5016 (2.18671 iter/s, 5.4877s/12 iters), loss = 0.320128
I0407 22:41:20.793053 23658 solver.cpp:237]     Train net output #0: loss = 0.320128 (* 1 = 0.320128 loss)
I0407 22:41:20.793063 23658 sgd_solver.cpp:105] Iteration 5016, lr = 0.000802584
I0407 22:41:26.018764 23658 solver.cpp:218] Iteration 5028 (2.29639 iter/s, 5.2256s/12 iters), loss = 0.240269
I0407 22:41:26.018800 23658 solver.cpp:237]     Train net output #0: loss = 0.240269 (* 1 = 0.240269 loss)
I0407 22:41:26.018808 23658 sgd_solver.cpp:105] Iteration 5028, lr = 0.000797755
I0407 22:41:31.166968 23658 solver.cpp:218] Iteration 5040 (2.33097 iter/s, 5.14806s/12 iters), loss = 0.253748
I0407 22:41:31.167006 23658 solver.cpp:237]     Train net output #0: loss = 0.253748 (* 1 = 0.253748 loss)
I0407 22:41:31.167013 23658 sgd_solver.cpp:105] Iteration 5040, lr = 0.000792955
I0407 22:41:36.484494 23658 solver.cpp:218] Iteration 5052 (2.25675 iter/s, 5.31737s/12 iters), loss = 0.44678
I0407 22:41:36.484546 23658 solver.cpp:237]     Train net output #0: loss = 0.44678 (* 1 = 0.44678 loss)
I0407 22:41:36.484557 23658 sgd_solver.cpp:105] Iteration 5052, lr = 0.000788184
I0407 22:41:38.448698 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:41.580106 23658 solver.cpp:218] Iteration 5064 (2.35505 iter/s, 5.09544s/12 iters), loss = 0.298712
I0407 22:41:41.580229 23658 solver.cpp:237]     Train net output #0: loss = 0.298712 (* 1 = 0.298712 loss)
I0407 22:41:41.580241 23658 sgd_solver.cpp:105] Iteration 5064, lr = 0.000783442
I0407 22:41:46.678710 23658 solver.cpp:218] Iteration 5076 (2.35369 iter/s, 5.09837s/12 iters), loss = 0.275158
I0407 22:41:46.678761 23658 solver.cpp:237]     Train net output #0: loss = 0.275158 (* 1 = 0.275158 loss)
I0407 22:41:46.678769 23658 sgd_solver.cpp:105] Iteration 5076, lr = 0.000778729
I0407 22:41:51.690019 23658 solver.cpp:218] Iteration 5088 (2.39466 iter/s, 5.01114s/12 iters), loss = 0.238343
I0407 22:41:51.690078 23658 solver.cpp:237]     Train net output #0: loss = 0.238343 (* 1 = 0.238343 loss)
I0407 22:41:51.690089 23658 sgd_solver.cpp:105] Iteration 5088, lr = 0.000774043
I0407 22:41:56.309063 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 22:41:59.332015 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 22:42:02.260311 23658 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 22:42:02.260336 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:42:04.727643 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:06.780551 23658 solver.cpp:397]     Test net output #0: accuracy = 0.45527
I0407 22:42:06.780589 23658 solver.cpp:397]     Test net output #1: loss = 2.80846 (* 1 = 2.80846 loss)
I0407 22:42:06.871902 23658 solver.cpp:218] Iteration 5100 (0.790435 iter/s, 15.1815s/12 iters), loss = 0.233456
I0407 22:42:06.871945 23658 solver.cpp:237]     Train net output #0: loss = 0.233456 (* 1 = 0.233456 loss)
I0407 22:42:06.871954 23658 sgd_solver.cpp:105] Iteration 5100, lr = 0.000769386
I0407 22:42:11.559782 23658 solver.cpp:218] Iteration 5112 (2.55988 iter/s, 4.68773s/12 iters), loss = 0.241637
I0407 22:42:11.559829 23658 solver.cpp:237]     Train net output #0: loss = 0.241637 (* 1 = 0.241637 loss)
I0407 22:42:11.559840 23658 sgd_solver.cpp:105] Iteration 5112, lr = 0.000764757
I0407 22:42:16.524116 23658 solver.cpp:218] Iteration 5124 (2.41732 iter/s, 4.96418s/12 iters), loss = 0.290617
I0407 22:42:16.524247 23658 solver.cpp:237]     Train net output #0: loss = 0.290617 (* 1 = 0.290617 loss)
I0407 22:42:16.524257 23658 sgd_solver.cpp:105] Iteration 5124, lr = 0.000760156
I0407 22:42:21.577255 23658 solver.cpp:218] Iteration 5136 (2.37488 iter/s, 5.05289s/12 iters), loss = 0.302569
I0407 22:42:21.577327 23658 solver.cpp:237]     Train net output #0: loss = 0.302569 (* 1 = 0.302569 loss)
I0407 22:42:21.577344 23658 sgd_solver.cpp:105] Iteration 5136, lr = 0.000755583
I0407 22:42:26.686076 23658 solver.cpp:218] Iteration 5148 (2.34896 iter/s, 5.10864s/12 iters), loss = 0.274944
I0407 22:42:26.686125 23658 solver.cpp:237]     Train net output #0: loss = 0.274944 (* 1 = 0.274944 loss)
I0407 22:42:26.686134 23658 sgd_solver.cpp:105] Iteration 5148, lr = 0.000751037
I0407 22:42:30.809767 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:31.745239 23658 solver.cpp:218] Iteration 5160 (2.37201 iter/s, 5.059s/12 iters), loss = 0.347141
I0407 22:42:31.745290 23658 solver.cpp:237]     Train net output #0: loss = 0.347141 (* 1 = 0.347141 loss)
I0407 22:42:31.745301 23658 sgd_solver.cpp:105] Iteration 5160, lr = 0.000746518
I0407 22:42:36.773475 23658 solver.cpp:218] Iteration 5172 (2.3866 iter/s, 5.02807s/12 iters), loss = 0.276413
I0407 22:42:36.773526 23658 solver.cpp:237]     Train net output #0: loss = 0.276413 (* 1 = 0.276413 loss)
I0407 22:42:36.773537 23658 sgd_solver.cpp:105] Iteration 5172, lr = 0.000742026
I0407 22:42:41.827001 23658 solver.cpp:218] Iteration 5184 (2.37466 iter/s, 5.05337s/12 iters), loss = 0.207325
I0407 22:42:41.827037 23658 solver.cpp:237]     Train net output #0: loss = 0.207325 (* 1 = 0.207325 loss)
I0407 22:42:41.827045 23658 sgd_solver.cpp:105] Iteration 5184, lr = 0.000737562
I0407 22:42:47.071915 23658 solver.cpp:218] Iteration 5196 (2.288 iter/s, 5.24476s/12 iters), loss = 0.327488
I0407 22:42:47.071980 23658 solver.cpp:237]     Train net output #0: loss = 0.327488 (* 1 = 0.327488 loss)
I0407 22:42:47.071988 23658 sgd_solver.cpp:105] Iteration 5196, lr = 0.000733124
I0407 22:42:49.239662 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 22:42:53.224855 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 22:42:57.604454 23658 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 22:42:57.604482 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:42:59.926640 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:02.030046 23658 solver.cpp:397]     Test net output #0: accuracy = 0.457108
I0407 22:43:02.030107 23658 solver.cpp:397]     Test net output #1: loss = 2.82088 (* 1 = 2.82088 loss)
I0407 22:43:04.026002 23658 solver.cpp:218] Iteration 5208 (0.707811 iter/s, 16.9537s/12 iters), loss = 0.236805
I0407 22:43:04.026058 23658 solver.cpp:237]     Train net output #0: loss = 0.236805 (* 1 = 0.236805 loss)
I0407 22:43:04.026072 23658 sgd_solver.cpp:105] Iteration 5208, lr = 0.000728714
I0407 22:43:09.262470 23658 solver.cpp:218] Iteration 5220 (2.2917 iter/s, 5.23629s/12 iters), loss = 0.137685
I0407 22:43:09.262518 23658 solver.cpp:237]     Train net output #0: loss = 0.137685 (* 1 = 0.137685 loss)
I0407 22:43:09.262528 23658 sgd_solver.cpp:105] Iteration 5220, lr = 0.000724329
I0407 22:43:14.300503 23658 solver.cpp:218] Iteration 5232 (2.38196 iter/s, 5.03786s/12 iters), loss = 0.287232
I0407 22:43:14.300557 23658 solver.cpp:237]     Train net output #0: loss = 0.287232 (* 1 = 0.287232 loss)
I0407 22:43:14.300570 23658 sgd_solver.cpp:105] Iteration 5232, lr = 0.000719971
I0407 22:43:19.321316 23658 solver.cpp:218] Iteration 5244 (2.39013 iter/s, 5.02064s/12 iters), loss = 0.26275
I0407 22:43:19.326078 23658 solver.cpp:237]     Train net output #0: loss = 0.26275 (* 1 = 0.26275 loss)
I0407 22:43:19.326094 23658 sgd_solver.cpp:105] Iteration 5244, lr = 0.00071564
I0407 22:43:24.385284 23658 solver.cpp:218] Iteration 5256 (2.37196 iter/s, 5.0591s/12 iters), loss = 0.149091
I0407 22:43:24.385326 23658 solver.cpp:237]     Train net output #0: loss = 0.149091 (* 1 = 0.149091 loss)
I0407 22:43:24.385337 23658 sgd_solver.cpp:105] Iteration 5256, lr = 0.000711334
I0407 22:43:25.691946 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:29.371491 23658 solver.cpp:218] Iteration 5268 (2.40672 iter/s, 4.98605s/12 iters), loss = 0.245643
I0407 22:43:29.371541 23658 solver.cpp:237]     Train net output #0: loss = 0.245643 (* 1 = 0.245643 loss)
I0407 22:43:29.371551 23658 sgd_solver.cpp:105] Iteration 5268, lr = 0.000707054
I0407 22:43:34.456437 23658 solver.cpp:218] Iteration 5280 (2.35998 iter/s, 5.08478s/12 iters), loss = 0.141819
I0407 22:43:34.456486 23658 solver.cpp:237]     Train net output #0: loss = 0.141819 (* 1 = 0.141819 loss)
I0407 22:43:34.456498 23658 sgd_solver.cpp:105] Iteration 5280, lr = 0.0007028
I0407 22:43:39.647720 23658 solver.cpp:218] Iteration 5292 (2.31164 iter/s, 5.19111s/12 iters), loss = 0.282649
I0407 22:43:39.647780 23658 solver.cpp:237]     Train net output #0: loss = 0.282649 (* 1 = 0.282649 loss)
I0407 22:43:39.647794 23658 sgd_solver.cpp:105] Iteration 5292, lr = 0.000698572
I0407 22:43:44.640650 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 22:43:47.630509 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 22:43:51.710681 23658 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 22:43:51.710764 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:43:54.027323 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:56.127189 23658 solver.cpp:397]     Test net output #0: accuracy = 0.462623
I0407 22:43:56.127224 23658 solver.cpp:397]     Test net output #1: loss = 2.8589 (* 1 = 2.8589 loss)
I0407 22:43:56.216949 23658 solver.cpp:218] Iteration 5304 (0.724252 iter/s, 16.5688s/12 iters), loss = 0.269012
I0407 22:43:56.217010 23658 solver.cpp:237]     Train net output #0: loss = 0.269012 (* 1 = 0.269012 loss)
I0407 22:43:56.217021 23658 sgd_solver.cpp:105] Iteration 5304, lr = 0.000694369
I0407 22:44:00.597326 23658 solver.cpp:218] Iteration 5316 (2.73959 iter/s, 4.38021s/12 iters), loss = 0.247563
I0407 22:44:00.597378 23658 solver.cpp:237]     Train net output #0: loss = 0.247563 (* 1 = 0.247563 loss)
I0407 22:44:00.597390 23658 sgd_solver.cpp:105] Iteration 5316, lr = 0.000690191
I0407 22:44:05.562294 23658 solver.cpp:218] Iteration 5328 (2.41702 iter/s, 4.9648s/12 iters), loss = 0.258182
I0407 22:44:05.562347 23658 solver.cpp:237]     Train net output #0: loss = 0.258182 (* 1 = 0.258182 loss)
I0407 22:44:05.562359 23658 sgd_solver.cpp:105] Iteration 5328, lr = 0.000686039
I0407 22:44:10.527662 23658 solver.cpp:218] Iteration 5340 (2.41682 iter/s, 4.9652s/12 iters), loss = 0.258953
I0407 22:44:10.527706 23658 solver.cpp:237]     Train net output #0: loss = 0.258953 (* 1 = 0.258953 loss)
I0407 22:44:10.527716 23658 sgd_solver.cpp:105] Iteration 5340, lr = 0.000681911
I0407 22:44:15.595139 23658 solver.cpp:218] Iteration 5352 (2.36812 iter/s, 5.06732s/12 iters), loss = 0.102175
I0407 22:44:15.595180 23658 solver.cpp:237]     Train net output #0: loss = 0.102175 (* 1 = 0.102175 loss)
I0407 22:44:15.595188 23658 sgd_solver.cpp:105] Iteration 5352, lr = 0.000677808
I0407 22:44:19.011919 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:20.593376 23658 solver.cpp:218] Iteration 5364 (2.40092 iter/s, 4.99808s/12 iters), loss = 0.184955
I0407 22:44:20.593425 23658 solver.cpp:237]     Train net output #0: loss = 0.184955 (* 1 = 0.184955 loss)
I0407 22:44:20.593436 23658 sgd_solver.cpp:105] Iteration 5364, lr = 0.00067373
I0407 22:44:25.614413 23658 solver.cpp:218] Iteration 5376 (2.39002 iter/s, 5.02087s/12 iters), loss = 0.241906
I0407 22:44:25.614560 23658 solver.cpp:237]     Train net output #0: loss = 0.241906 (* 1 = 0.241906 loss)
I0407 22:44:25.614574 23658 sgd_solver.cpp:105] Iteration 5376, lr = 0.000669677
I0407 22:44:30.558761 23658 solver.cpp:218] Iteration 5388 (2.42714 iter/s, 4.94409s/12 iters), loss = 0.22331
I0407 22:44:30.558809 23658 solver.cpp:237]     Train net output #0: loss = 0.22331 (* 1 = 0.22331 loss)
I0407 22:44:30.558820 23658 sgd_solver.cpp:105] Iteration 5388, lr = 0.000665648
I0407 22:44:35.447010 23658 solver.cpp:218] Iteration 5400 (2.45495 iter/s, 4.88807s/12 iters), loss = 0.274406
I0407 22:44:35.447073 23658 solver.cpp:237]     Train net output #0: loss = 0.274406 (* 1 = 0.274406 loss)
I0407 22:44:35.447088 23658 sgd_solver.cpp:105] Iteration 5400, lr = 0.000661643
I0407 22:44:37.460047 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 22:44:42.600806 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 22:44:46.240329 23658 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 22:44:46.240355 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:44:48.612088 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:50.755702 23658 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 22:44:50.755744 23658 solver.cpp:397]     Test net output #1: loss = 2.83763 (* 1 = 2.83763 loss)
I0407 22:44:52.588553 23658 solver.cpp:218] Iteration 5412 (0.700072 iter/s, 17.1411s/12 iters), loss = 0.239368
I0407 22:44:52.588600 23658 solver.cpp:237]     Train net output #0: loss = 0.239368 (* 1 = 0.239368 loss)
I0407 22:44:52.588608 23658 sgd_solver.cpp:105] Iteration 5412, lr = 0.000657662
I0407 22:44:57.985549 23658 solver.cpp:218] Iteration 5424 (2.22353 iter/s, 5.39682s/12 iters), loss = 0.28074
I0407 22:44:57.985689 23658 solver.cpp:237]     Train net output #0: loss = 0.28074 (* 1 = 0.28074 loss)
I0407 22:44:57.985702 23658 sgd_solver.cpp:105] Iteration 5424, lr = 0.000653705
I0407 22:45:03.473733 23658 solver.cpp:218] Iteration 5436 (2.18662 iter/s, 5.48792s/12 iters), loss = 0.243391
I0407 22:45:03.473788 23658 solver.cpp:237]     Train net output #0: loss = 0.243391 (* 1 = 0.243391 loss)
I0407 22:45:03.473800 23658 sgd_solver.cpp:105] Iteration 5436, lr = 0.000649772
I0407 22:45:08.839787 23658 solver.cpp:218] Iteration 5448 (2.23636 iter/s, 5.36587s/12 iters), loss = 0.168493
I0407 22:45:08.839830 23658 solver.cpp:237]     Train net output #0: loss = 0.168493 (* 1 = 0.168493 loss)
I0407 22:45:08.839841 23658 sgd_solver.cpp:105] Iteration 5448, lr = 0.000645863
I0407 22:45:13.899132 23658 solver.cpp:218] Iteration 5460 (2.37193 iter/s, 5.05918s/12 iters), loss = 0.331645
I0407 22:45:13.899180 23658 solver.cpp:237]     Train net output #0: loss = 0.331645 (* 1 = 0.331645 loss)
I0407 22:45:13.899191 23658 sgd_solver.cpp:105] Iteration 5460, lr = 0.000641977
I0407 22:45:14.482702 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:18.914413 23658 solver.cpp:218] Iteration 5472 (2.39277 iter/s, 5.01511s/12 iters), loss = 0.131346
I0407 22:45:18.914467 23658 solver.cpp:237]     Train net output #0: loss = 0.131346 (* 1 = 0.131346 loss)
I0407 22:45:18.914480 23658 sgd_solver.cpp:105] Iteration 5472, lr = 0.000638114
I0407 22:45:23.999537 23658 solver.cpp:218] Iteration 5484 (2.35991 iter/s, 5.08495s/12 iters), loss = 0.233256
I0407 22:45:23.999596 23658 solver.cpp:237]     Train net output #0: loss = 0.233256 (* 1 = 0.233256 loss)
I0407 22:45:23.999608 23658 sgd_solver.cpp:105] Iteration 5484, lr = 0.000634275
I0407 22:45:28.990649 23658 solver.cpp:218] Iteration 5496 (2.40436 iter/s, 4.99093s/12 iters), loss = 0.278678
I0407 22:45:28.991412 23658 solver.cpp:237]     Train net output #0: loss = 0.278678 (* 1 = 0.278678 loss)
I0407 22:45:28.991427 23658 sgd_solver.cpp:105] Iteration 5496, lr = 0.000630459
I0407 22:45:33.624861 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 22:45:39.996842 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 22:45:48.455921 23658 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 22:45:48.455947 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:45:50.680488 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:52.896551 23658 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0407 22:45:52.896598 23658 solver.cpp:397]     Test net output #1: loss = 2.85654 (* 1 = 2.85654 loss)
I0407 22:45:52.984787 23658 solver.cpp:218] Iteration 5508 (0.500149 iter/s, 23.9928s/12 iters), loss = 0.229737
I0407 22:45:52.984841 23658 solver.cpp:237]     Train net output #0: loss = 0.229737 (* 1 = 0.229737 loss)
I0407 22:45:52.984853 23658 sgd_solver.cpp:105] Iteration 5508, lr = 0.000626666
I0407 22:45:57.548825 23658 solver.cpp:218] Iteration 5520 (2.62935 iter/s, 4.56386s/12 iters), loss = 0.124731
I0407 22:45:57.548877 23658 solver.cpp:237]     Train net output #0: loss = 0.124731 (* 1 = 0.124731 loss)
I0407 22:45:57.548887 23658 sgd_solver.cpp:105] Iteration 5520, lr = 0.000622895
I0407 22:46:00.175102 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:46:02.754909 23658 solver.cpp:218] Iteration 5532 (2.30508 iter/s, 5.2059s/12 iters), loss = 0.227452
I0407 22:46:02.754962 23658 solver.cpp:237]     Train net output #0: loss = 0.227452 (* 1 = 0.227452 loss)
I0407 22:46:02.754974 23658 sgd_solver.cpp:105] Iteration 5532, lr = 0.000619148
I0407 22:46:07.780815 23658 solver.cpp:218] Iteration 5544 (2.38771 iter/s, 5.02573s/12 iters), loss = 0.135937
I0407 22:46:07.780858 23658 solver.cpp:237]     Train net output #0: loss = 0.135937 (* 1 = 0.135937 loss)
I0407 22:46:07.780869 23658 sgd_solver.cpp:105] Iteration 5544, lr = 0.000615423
I0407 22:46:12.861415 23658 solver.cpp:218] Iteration 5556 (2.362 iter/s, 5.08043s/12 iters), loss = 0.196917
I0407 22:46:12.861459 23658 solver.cpp:237]     Train net output #0: loss = 0.196917 (* 1 = 0.196917 loss)
I0407 22:46:12.861466 23658 sgd_solver.cpp:105] Iteration 5556, lr = 0.00061172
I0407 22:46:15.566936 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:17.841627 23658 solver.cpp:218] Iteration 5568 (2.40962 iter/s, 4.98004s/12 iters), loss = 0.12634
I0407 22:46:17.841676 23658 solver.cpp:237]     Train net output #0: loss = 0.12634 (* 1 = 0.12634 loss)
I0407 22:46:17.841686 23658 sgd_solver.cpp:105] Iteration 5568, lr = 0.000608039
I0407 22:46:22.786825 23658 solver.cpp:218] Iteration 5580 (2.42668 iter/s, 4.94503s/12 iters), loss = 0.272837
I0407 22:46:22.786864 23658 solver.cpp:237]     Train net output #0: loss = 0.272837 (* 1 = 0.272837 loss)
I0407 22:46:22.786872 23658 sgd_solver.cpp:105] Iteration 5580, lr = 0.000604381
I0407 22:46:27.984082 23658 solver.cpp:218] Iteration 5592 (2.30899 iter/s, 5.19709s/12 iters), loss = 0.227827
I0407 22:46:27.984127 23658 solver.cpp:237]     Train net output #0: loss = 0.227827 (* 1 = 0.227827 loss)
I0407 22:46:27.984136 23658 sgd_solver.cpp:105] Iteration 5592, lr = 0.000600745
I0407 22:46:33.203874 23658 solver.cpp:218] Iteration 5604 (2.29902 iter/s, 5.21962s/12 iters), loss = 0.260015
I0407 22:46:33.205432 23658 solver.cpp:237]     Train net output #0: loss = 0.260015 (* 1 = 0.260015 loss)
I0407 22:46:33.205441 23658 sgd_solver.cpp:105] Iteration 5604, lr = 0.000597131
I0407 22:46:35.409943 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 22:46:43.852805 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 22:46:50.428211 23658 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 22:46:50.428238 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:46:52.679517 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:54.889487 23658 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 22:46:54.889525 23658 solver.cpp:397]     Test net output #1: loss = 2.82352 (* 1 = 2.82352 loss)
I0407 22:46:56.831243 23658 solver.cpp:218] Iteration 5616 (0.507931 iter/s, 23.6253s/12 iters), loss = 0.236887
I0407 22:46:56.831284 23658 solver.cpp:237]     Train net output #0: loss = 0.236887 (* 1 = 0.236887 loss)
I0407 22:46:56.831295 23658 sgd_solver.cpp:105] Iteration 5616, lr = 0.000593538
I0407 22:47:01.938655 23658 solver.cpp:218] Iteration 5628 (2.3496 iter/s, 5.10724s/12 iters), loss = 0.10937
I0407 22:47:01.938709 23658 solver.cpp:237]     Train net output #0: loss = 0.10937 (* 1 = 0.10937 loss)
I0407 22:47:01.938721 23658 sgd_solver.cpp:105] Iteration 5628, lr = 0.000589967
I0407 22:47:07.030802 23658 solver.cpp:218] Iteration 5640 (2.35665 iter/s, 5.09197s/12 iters), loss = 0.183273
I0407 22:47:07.033156 23658 solver.cpp:237]     Train net output #0: loss = 0.183273 (* 1 = 0.183273 loss)
I0407 22:47:07.033169 23658 sgd_solver.cpp:105] Iteration 5640, lr = 0.000586417
I0407 22:47:12.369890 23658 solver.cpp:218] Iteration 5652 (2.24862 iter/s, 5.33661s/12 iters), loss = 0.230319
I0407 22:47:12.369936 23658 solver.cpp:237]     Train net output #0: loss = 0.230319 (* 1 = 0.230319 loss)
I0407 22:47:12.369947 23658 sgd_solver.cpp:105] Iteration 5652, lr = 0.000582889
I0407 22:47:17.283643 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:17.458901 23658 solver.cpp:218] Iteration 5664 (2.3581 iter/s, 5.08884s/12 iters), loss = 0.125466
I0407 22:47:17.458946 23658 solver.cpp:237]     Train net output #0: loss = 0.125466 (* 1 = 0.125466 loss)
I0407 22:47:17.458958 23658 sgd_solver.cpp:105] Iteration 5664, lr = 0.000579382
I0407 22:47:22.611167 23658 solver.cpp:218] Iteration 5676 (2.32915 iter/s, 5.15209s/12 iters), loss = 0.0776306
I0407 22:47:22.611222 23658 solver.cpp:237]     Train net output #0: loss = 0.0776306 (* 1 = 0.0776306 loss)
I0407 22:47:22.611234 23658 sgd_solver.cpp:105] Iteration 5676, lr = 0.000575896
I0407 22:47:27.651955 23658 solver.cpp:218] Iteration 5688 (2.38067 iter/s, 5.04061s/12 iters), loss = 0.149579
I0407 22:47:27.652005 23658 solver.cpp:237]     Train net output #0: loss = 0.149579 (* 1 = 0.149579 loss)
I0407 22:47:27.652017 23658 sgd_solver.cpp:105] Iteration 5688, lr = 0.000572431
I0407 22:47:32.609920 23658 solver.cpp:218] Iteration 5700 (2.42043 iter/s, 4.95779s/12 iters), loss = 0.216524
I0407 22:47:32.609975 23658 solver.cpp:237]     Train net output #0: loss = 0.216524 (* 1 = 0.216524 loss)
I0407 22:47:32.609987 23658 sgd_solver.cpp:105] Iteration 5700, lr = 0.000568987
I0407 22:47:37.379639 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 22:47:42.406903 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 22:47:46.473488 23658 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 22:47:46.473515 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:47:48.683017 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:50.928994 23658 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0407 22:47:50.929042 23658 solver.cpp:397]     Test net output #1: loss = 2.82519 (* 1 = 2.82519 loss)
I0407 22:47:51.017302 23658 solver.cpp:218] Iteration 5712 (0.65193 iter/s, 18.4069s/12 iters), loss = 0.239691
I0407 22:47:51.017359 23658 solver.cpp:237]     Train net output #0: loss = 0.239691 (* 1 = 0.239691 loss)
I0407 22:47:51.017371 23658 sgd_solver.cpp:105] Iteration 5712, lr = 0.000565564
I0407 22:47:55.423511 23658 solver.cpp:218] Iteration 5724 (2.72354 iter/s, 4.40604s/12 iters), loss = 0.115356
I0407 22:47:55.423549 23658 solver.cpp:237]     Train net output #0: loss = 0.115356 (* 1 = 0.115356 loss)
I0407 22:47:55.423558 23658 sgd_solver.cpp:105] Iteration 5724, lr = 0.000562161
I0407 22:48:00.547721 23658 solver.cpp:218] Iteration 5736 (2.3419 iter/s, 5.12404s/12 iters), loss = 0.252541
I0407 22:48:00.547765 23658 solver.cpp:237]     Train net output #0: loss = 0.252541 (* 1 = 0.252541 loss)
I0407 22:48:00.547775 23658 sgd_solver.cpp:105] Iteration 5736, lr = 0.000558779
I0407 22:48:05.752431 23658 solver.cpp:218] Iteration 5748 (2.30568 iter/s, 5.20453s/12 iters), loss = 0.173083
I0407 22:48:05.752482 23658 solver.cpp:237]     Train net output #0: loss = 0.173083 (* 1 = 0.173083 loss)
I0407 22:48:05.752492 23658 sgd_solver.cpp:105] Iteration 5748, lr = 0.000555417
I0407 22:48:10.773434 23658 solver.cpp:218] Iteration 5760 (2.39004 iter/s, 5.02083s/12 iters), loss = 0.163008
I0407 22:48:10.773528 23658 solver.cpp:237]     Train net output #0: loss = 0.163008 (* 1 = 0.163008 loss)
I0407 22:48:10.773538 23658 sgd_solver.cpp:105] Iteration 5760, lr = 0.000552075
I0407 22:48:12.788720 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:15.811466 23658 solver.cpp:218] Iteration 5772 (2.38199 iter/s, 5.03781s/12 iters), loss = 0.144025
I0407 22:48:15.811518 23658 solver.cpp:237]     Train net output #0: loss = 0.144025 (* 1 = 0.144025 loss)
I0407 22:48:15.811532 23658 sgd_solver.cpp:105] Iteration 5772, lr = 0.000548754
I0407 22:48:20.903669 23658 solver.cpp:218] Iteration 5784 (2.35663 iter/s, 5.09202s/12 iters), loss = 0.109654
I0407 22:48:20.903724 23658 solver.cpp:237]     Train net output #0: loss = 0.109654 (* 1 = 0.109654 loss)
I0407 22:48:20.903736 23658 sgd_solver.cpp:105] Iteration 5784, lr = 0.000545452
I0407 22:48:25.940420 23658 solver.cpp:218] Iteration 5796 (2.38257 iter/s, 5.03657s/12 iters), loss = 0.135694
I0407 22:48:25.940466 23658 solver.cpp:237]     Train net output #0: loss = 0.135694 (* 1 = 0.135694 loss)
I0407 22:48:25.940475 23658 sgd_solver.cpp:105] Iteration 5796, lr = 0.000542171
I0407 22:48:31.011667 23658 solver.cpp:218] Iteration 5808 (2.36636 iter/s, 5.07107s/12 iters), loss = 0.260098
I0407 22:48:31.011704 23658 solver.cpp:237]     Train net output #0: loss = 0.260098 (* 1 = 0.260098 loss)
I0407 22:48:31.011713 23658 sgd_solver.cpp:105] Iteration 5808, lr = 0.000538909
I0407 22:48:33.066530 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 22:48:37.476292 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 22:48:43.252740 23658 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 22:48:43.252799 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:48:45.426223 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:47.818207 23658 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 22:48:47.818255 23658 solver.cpp:397]     Test net output #1: loss = 2.82589 (* 1 = 2.82589 loss)
I0407 22:48:49.800307 23658 solver.cpp:218] Iteration 5820 (0.6387 iter/s, 18.7882s/12 iters), loss = 0.180145
I0407 22:48:49.800359 23658 solver.cpp:237]     Train net output #0: loss = 0.180145 (* 1 = 0.180145 loss)
I0407 22:48:49.800369 23658 sgd_solver.cpp:105] Iteration 5820, lr = 0.000535666
I0407 22:48:55.186270 23658 solver.cpp:218] Iteration 5832 (2.22809 iter/s, 5.38577s/12 iters), loss = 0.139782
I0407 22:48:55.186316 23658 solver.cpp:237]     Train net output #0: loss = 0.139782 (* 1 = 0.139782 loss)
I0407 22:48:55.186324 23658 sgd_solver.cpp:105] Iteration 5832, lr = 0.000532443
I0407 22:49:00.238931 23658 solver.cpp:218] Iteration 5844 (2.37507 iter/s, 5.05248s/12 iters), loss = 0.148065
I0407 22:49:00.238981 23658 solver.cpp:237]     Train net output #0: loss = 0.148065 (* 1 = 0.148065 loss)
I0407 22:49:00.238991 23658 sgd_solver.cpp:105] Iteration 5844, lr = 0.00052924
I0407 22:49:05.197379 23658 solver.cpp:218] Iteration 5856 (2.4202 iter/s, 4.95827s/12 iters), loss = 0.13525
I0407 22:49:05.197428 23658 solver.cpp:237]     Train net output #0: loss = 0.13525 (* 1 = 0.13525 loss)
I0407 22:49:05.197440 23658 sgd_solver.cpp:105] Iteration 5856, lr = 0.000526056
I0407 22:49:09.358745 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:10.179901 23658 solver.cpp:218] Iteration 5868 (2.4085 iter/s, 4.98235s/12 iters), loss = 0.202327
I0407 22:49:10.179939 23658 solver.cpp:237]     Train net output #0: loss = 0.202327 (* 1 = 0.202327 loss)
I0407 22:49:10.179946 23658 sgd_solver.cpp:105] Iteration 5868, lr = 0.000522891
I0407 22:49:15.382401 23658 solver.cpp:218] Iteration 5880 (2.30666 iter/s, 5.20232s/12 iters), loss = 0.161201
I0407 22:49:15.382555 23658 solver.cpp:237]     Train net output #0: loss = 0.161201 (* 1 = 0.161201 loss)
I0407 22:49:15.382568 23658 sgd_solver.cpp:105] Iteration 5880, lr = 0.000519745
I0407 22:49:20.465617 23658 solver.cpp:218] Iteration 5892 (2.36084 iter/s, 5.08293s/12 iters), loss = 0.0944153
I0407 22:49:20.465664 23658 solver.cpp:237]     Train net output #0: loss = 0.0944153 (* 1 = 0.0944153 loss)
I0407 22:49:20.465673 23658 sgd_solver.cpp:105] Iteration 5892, lr = 0.000516618
I0407 22:49:25.458277 23658 solver.cpp:218] Iteration 5904 (2.40361 iter/s, 4.99248s/12 iters), loss = 0.193957
I0407 22:49:25.458336 23658 solver.cpp:237]     Train net output #0: loss = 0.193957 (* 1 = 0.193957 loss)
I0407 22:49:25.458348 23658 sgd_solver.cpp:105] Iteration 5904, lr = 0.000513509
I0407 22:49:29.998762 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 22:49:43.204836 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 22:49:45.654322 23658 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 22:49:45.654379 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:49:47.776165 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:50.103958 23658 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0407 22:49:50.104007 23658 solver.cpp:397]     Test net output #1: loss = 2.92417 (* 1 = 2.92417 loss)
I0407 22:49:50.194134 23658 solver.cpp:218] Iteration 5916 (0.485139 iter/s, 24.7352s/12 iters), loss = 0.148163
I0407 22:49:50.194198 23658 solver.cpp:237]     Train net output #0: loss = 0.148163 (* 1 = 0.148163 loss)
I0407 22:49:50.194211 23658 sgd_solver.cpp:105] Iteration 5916, lr = 0.00051042
I0407 22:49:54.391207 23658 solver.cpp:218] Iteration 5928 (2.85926 iter/s, 4.1969s/12 iters), loss = 0.181704
I0407 22:49:54.391258 23658 solver.cpp:237]     Train net output #0: loss = 0.181704 (* 1 = 0.181704 loss)
I0407 22:49:54.391273 23658 sgd_solver.cpp:105] Iteration 5928, lr = 0.000507349
I0407 22:49:59.331038 23658 solver.cpp:218] Iteration 5940 (2.42932 iter/s, 4.93965s/12 iters), loss = 0.16875
I0407 22:49:59.331089 23658 solver.cpp:237]     Train net output #0: loss = 0.16875 (* 1 = 0.16875 loss)
I0407 22:49:59.331101 23658 sgd_solver.cpp:105] Iteration 5940, lr = 0.000504296
I0407 22:50:04.565052 23658 solver.cpp:218] Iteration 5952 (2.29278 iter/s, 5.23383s/12 iters), loss = 0.117238
I0407 22:50:04.565100 23658 solver.cpp:237]     Train net output #0: loss = 0.117238 (* 1 = 0.117238 loss)
I0407 22:50:04.565112 23658 sgd_solver.cpp:105] Iteration 5952, lr = 0.000501262
I0407 22:50:09.963673 23658 solver.cpp:218] Iteration 5964 (2.22287 iter/s, 5.39843s/12 iters), loss = 0.103714
I0407 22:50:09.963739 23658 solver.cpp:237]     Train net output #0: loss = 0.103714 (* 1 = 0.103714 loss)
I0407 22:50:09.963755 23658 sgd_solver.cpp:105] Iteration 5964, lr = 0.000498246
I0407 22:50:11.280565 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:15.036267 23658 solver.cpp:218] Iteration 5976 (2.36574 iter/s, 5.0724s/12 iters), loss = 0.118248
I0407 22:50:15.036321 23658 solver.cpp:237]     Train net output #0: loss = 0.118248 (* 1 = 0.118248 loss)
I0407 22:50:15.036334 23658 sgd_solver.cpp:105] Iteration 5976, lr = 0.000495249
I0407 22:50:20.068478 23658 solver.cpp:218] Iteration 5988 (2.38472 iter/s, 5.03203s/12 iters), loss = 0.231606
I0407 22:50:20.068598 23658 solver.cpp:237]     Train net output #0: loss = 0.231606 (* 1 = 0.231606 loss)
I0407 22:50:20.068612 23658 sgd_solver.cpp:105] Iteration 5988, lr = 0.000492269
I0407 22:50:25.324697 23658 solver.cpp:218] Iteration 6000 (2.28312 iter/s, 5.25596s/12 iters), loss = 0.190104
I0407 22:50:25.324739 23658 solver.cpp:237]     Train net output #0: loss = 0.190104 (* 1 = 0.190104 loss)
I0407 22:50:25.324750 23658 sgd_solver.cpp:105] Iteration 6000, lr = 0.000489307
I0407 22:50:30.809828 23658 solver.cpp:218] Iteration 6012 (2.18781 iter/s, 5.48494s/12 iters), loss = 0.202393
I0407 22:50:30.809885 23658 solver.cpp:237]     Train net output #0: loss = 0.202393 (* 1 = 0.202393 loss)
I0407 22:50:30.809897 23658 sgd_solver.cpp:105] Iteration 6012, lr = 0.000486363
I0407 22:50:33.025585 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 22:50:37.349707 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 22:50:39.864594 23658 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 22:50:39.864622 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:50:41.946364 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:44.318621 23658 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0407 22:50:44.318670 23658 solver.cpp:397]     Test net output #1: loss = 2.91645 (* 1 = 2.91645 loss)
I0407 22:50:46.233080 23658 solver.cpp:218] Iteration 6024 (0.778068 iter/s, 15.4228s/12 iters), loss = 0.232355
I0407 22:50:46.233117 23658 solver.cpp:237]     Train net output #0: loss = 0.232355 (* 1 = 0.232355 loss)
I0407 22:50:46.233126 23658 sgd_solver.cpp:105] Iteration 6024, lr = 0.000483437
I0407 22:50:51.241343 23658 solver.cpp:218] Iteration 6036 (2.39612 iter/s, 5.00809s/12 iters), loss = 0.118573
I0407 22:50:51.241428 23658 solver.cpp:237]     Train net output #0: loss = 0.118573 (* 1 = 0.118573 loss)
I0407 22:50:51.241442 23658 sgd_solver.cpp:105] Iteration 6036, lr = 0.000480529
I0407 22:50:56.259533 23658 solver.cpp:218] Iteration 6048 (2.3914 iter/s, 5.01797s/12 iters), loss = 0.305105
I0407 22:50:56.259585 23658 solver.cpp:237]     Train net output #0: loss = 0.305105 (* 1 = 0.305105 loss)
I0407 22:50:56.259598 23658 sgd_solver.cpp:105] Iteration 6048, lr = 0.000477637
I0407 22:51:01.342455 23658 solver.cpp:218] Iteration 6060 (2.36093 iter/s, 5.08274s/12 iters), loss = 0.260453
I0407 22:51:01.342502 23658 solver.cpp:237]     Train net output #0: loss = 0.260453 (* 1 = 0.260453 loss)
I0407 22:51:01.342512 23658 sgd_solver.cpp:105] Iteration 6060, lr = 0.000474764
I0407 22:51:04.913633 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:06.608927 23658 solver.cpp:218] Iteration 6072 (2.27864 iter/s, 5.26629s/12 iters), loss = 0.168777
I0407 22:51:06.608973 23658 solver.cpp:237]     Train net output #0: loss = 0.168777 (* 1 = 0.168777 loss)
I0407 22:51:06.608983 23658 sgd_solver.cpp:105] Iteration 6072, lr = 0.000471907
I0407 22:51:12.117947 23658 solver.cpp:218] Iteration 6084 (2.17832 iter/s, 5.50883s/12 iters), loss = 0.150543
I0407 22:51:12.118013 23658 solver.cpp:237]     Train net output #0: loss = 0.150543 (* 1 = 0.150543 loss)
I0407 22:51:12.118023 23658 sgd_solver.cpp:105] Iteration 6084, lr = 0.000469068
I0407 22:51:17.413862 23658 solver.cpp:218] Iteration 6096 (2.26599 iter/s, 5.29571s/12 iters), loss = 0.140573
I0407 22:51:17.413903 23658 solver.cpp:237]     Train net output #0: loss = 0.140573 (* 1 = 0.140573 loss)
I0407 22:51:17.413913 23658 sgd_solver.cpp:105] Iteration 6096, lr = 0.000466246
I0407 22:51:22.803470 23658 solver.cpp:218] Iteration 6108 (2.22658 iter/s, 5.38943s/12 iters), loss = 0.147364
I0407 22:51:22.803598 23658 solver.cpp:237]     Train net output #0: loss = 0.147364 (* 1 = 0.147364 loss)
I0407 22:51:22.803608 23658 sgd_solver.cpp:105] Iteration 6108, lr = 0.000463441
I0407 22:51:27.726651 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 22:51:32.073107 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 22:51:34.390409 23658 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 22:51:34.390432 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:51:36.435153 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:38.838932 23658 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0407 22:51:38.838974 23658 solver.cpp:397]     Test net output #1: loss = 2.90857 (* 1 = 2.90857 loss)
I0407 22:51:38.928575 23658 solver.cpp:218] Iteration 6120 (0.744205 iter/s, 16.1246s/12 iters), loss = 0.116583
I0407 22:51:38.928622 23658 solver.cpp:237]     Train net output #0: loss = 0.116583 (* 1 = 0.116583 loss)
I0407 22:51:38.928633 23658 sgd_solver.cpp:105] Iteration 6120, lr = 0.000460652
I0407 22:51:43.425611 23658 solver.cpp:218] Iteration 6132 (2.66852 iter/s, 4.49687s/12 iters), loss = 0.164433
I0407 22:51:43.425654 23658 solver.cpp:237]     Train net output #0: loss = 0.164433 (* 1 = 0.164433 loss)
I0407 22:51:43.425666 23658 sgd_solver.cpp:105] Iteration 6132, lr = 0.000457881
I0407 22:51:48.766088 23658 solver.cpp:218] Iteration 6144 (2.24707 iter/s, 5.34029s/12 iters), loss = 0.164568
I0407 22:51:48.766134 23658 solver.cpp:237]     Train net output #0: loss = 0.164568 (* 1 = 0.164568 loss)
I0407 22:51:48.766146 23658 sgd_solver.cpp:105] Iteration 6144, lr = 0.000455126
I0407 22:51:54.271432 23658 solver.cpp:218] Iteration 6156 (2.17978 iter/s, 5.50515s/12 iters), loss = 0.136408
I0407 22:51:54.271534 23658 solver.cpp:237]     Train net output #0: loss = 0.136408 (* 1 = 0.136408 loss)
I0407 22:51:54.271545 23658 sgd_solver.cpp:105] Iteration 6156, lr = 0.000452388
I0407 22:51:59.485309 23658 solver.cpp:218] Iteration 6168 (2.30166 iter/s, 5.21363s/12 iters), loss = 0.151472
I0407 22:51:59.485365 23658 solver.cpp:237]     Train net output #0: loss = 0.151472 (* 1 = 0.151472 loss)
I0407 22:51:59.485378 23658 sgd_solver.cpp:105] Iteration 6168, lr = 0.000449666
I0407 22:52:00.066196 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:04.544394 23658 solver.cpp:218] Iteration 6180 (2.37206 iter/s, 5.05889s/12 iters), loss = 0.0772352
I0407 22:52:04.544452 23658 solver.cpp:237]     Train net output #0: loss = 0.0772352 (* 1 = 0.0772352 loss)
I0407 22:52:04.544464 23658 sgd_solver.cpp:105] Iteration 6180, lr = 0.000446961
I0407 22:52:09.759500 23658 solver.cpp:218] Iteration 6192 (2.3011 iter/s, 5.21491s/12 iters), loss = 0.127883
I0407 22:52:09.759552 23658 solver.cpp:237]     Train net output #0: loss = 0.127883 (* 1 = 0.127883 loss)
I0407 22:52:09.759562 23658 sgd_solver.cpp:105] Iteration 6192, lr = 0.000444271
I0407 22:52:14.834566 23658 solver.cpp:218] Iteration 6204 (2.36459 iter/s, 5.07488s/12 iters), loss = 0.156161
I0407 22:52:14.834612 23658 solver.cpp:237]     Train net output #0: loss = 0.156161 (* 1 = 0.156161 loss)
I0407 22:52:14.834622 23658 sgd_solver.cpp:105] Iteration 6204, lr = 0.000441598
I0407 22:52:19.891002 23658 solver.cpp:218] Iteration 6216 (2.3733 iter/s, 5.05625s/12 iters), loss = 0.0709236
I0407 22:52:19.891057 23658 solver.cpp:237]     Train net output #0: loss = 0.0709236 (* 1 = 0.0709236 loss)
I0407 22:52:19.891068 23658 sgd_solver.cpp:105] Iteration 6216, lr = 0.000438941
I0407 22:52:21.973939 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 22:52:25.108553 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 22:52:27.433667 23658 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 22:52:27.433693 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:52:29.461642 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:30.737794 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:52:31.912230 23658 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0407 22:52:31.912276 23658 solver.cpp:397]     Test net output #1: loss = 2.93547 (* 1 = 2.93547 loss)
I0407 22:52:33.896035 23658 solver.cpp:218] Iteration 6228 (0.85686 iter/s, 14.0046s/12 iters), loss = 0.105902
I0407 22:52:33.896082 23658 solver.cpp:237]     Train net output #0: loss = 0.105902 (* 1 = 0.105902 loss)
I0407 22:52:33.896095 23658 sgd_solver.cpp:105] Iteration 6228, lr = 0.000436301
I0407 22:52:38.832087 23658 solver.cpp:218] Iteration 6240 (2.43118 iter/s, 4.93587s/12 iters), loss = 0.168541
I0407 22:52:38.832134 23658 solver.cpp:237]     Train net output #0: loss = 0.168541 (* 1 = 0.168541 loss)
I0407 22:52:38.832145 23658 sgd_solver.cpp:105] Iteration 6240, lr = 0.000433676
I0407 22:52:43.874765 23658 solver.cpp:218] Iteration 6252 (2.37977 iter/s, 5.0425s/12 iters), loss = 0.143938
I0407 22:52:43.874811 23658 solver.cpp:237]     Train net output #0: loss = 0.143938 (* 1 = 0.143938 loss)
I0407 22:52:43.874822 23658 sgd_solver.cpp:105] Iteration 6252, lr = 0.000431066
I0407 22:52:48.901515 23658 solver.cpp:218] Iteration 6264 (2.38731 iter/s, 5.02657s/12 iters), loss = 0.0733281
I0407 22:52:48.901561 23658 solver.cpp:237]     Train net output #0: loss = 0.0733281 (* 1 = 0.0733281 loss)
I0407 22:52:48.901572 23658 sgd_solver.cpp:105] Iteration 6264, lr = 0.000428473
I0407 22:52:51.680802 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:53.947953 23658 solver.cpp:218] Iteration 6276 (2.378 iter/s, 5.04625s/12 iters), loss = 0.229044
I0407 22:52:53.948004 23658 solver.cpp:237]     Train net output #0: loss = 0.229044 (* 1 = 0.229044 loss)
I0407 22:52:53.948015 23658 sgd_solver.cpp:105] Iteration 6276, lr = 0.000425895
I0407 22:52:58.974638 23658 solver.cpp:218] Iteration 6288 (2.38735 iter/s, 5.0265s/12 iters), loss = 0.139464
I0407 22:52:58.974754 23658 solver.cpp:237]     Train net output #0: loss = 0.139464 (* 1 = 0.139464 loss)
I0407 22:52:58.974766 23658 sgd_solver.cpp:105] Iteration 6288, lr = 0.000423333
I0407 22:53:04.068778 23658 solver.cpp:218] Iteration 6300 (2.35576 iter/s, 5.09389s/12 iters), loss = 0.0997628
I0407 22:53:04.068837 23658 solver.cpp:237]     Train net output #0: loss = 0.0997628 (* 1 = 0.0997628 loss)
I0407 22:53:04.068850 23658 sgd_solver.cpp:105] Iteration 6300, lr = 0.000420786
I0407 22:53:09.365583 23658 solver.cpp:218] Iteration 6312 (2.2656 iter/s, 5.2966s/12 iters), loss = 0.222729
I0407 22:53:09.365643 23658 solver.cpp:237]     Train net output #0: loss = 0.222729 (* 1 = 0.222729 loss)
I0407 22:53:09.365655 23658 sgd_solver.cpp:105] Iteration 6312, lr = 0.000418254
I0407 22:53:14.006820 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 22:53:17.008460 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 22:53:19.335592 23658 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 22:53:19.335620 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:53:21.183696 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:23.666383 23658 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 22:53:23.666427 23658 solver.cpp:397]     Test net output #1: loss = 2.91687 (* 1 = 2.91687 loss)
I0407 22:53:23.756353 23658 solver.cpp:218] Iteration 6324 (0.833892 iter/s, 14.3904s/12 iters), loss = 0.0756657
I0407 22:53:23.756402 23658 solver.cpp:237]     Train net output #0: loss = 0.0756657 (* 1 = 0.0756657 loss)
I0407 22:53:23.756412 23658 sgd_solver.cpp:105] Iteration 6324, lr = 0.000415737
I0407 22:53:28.251780 23658 solver.cpp:218] Iteration 6336 (2.66948 iter/s, 4.49525s/12 iters), loss = 0.187569
I0407 22:53:28.251828 23658 solver.cpp:237]     Train net output #0: loss = 0.187569 (* 1 = 0.187569 loss)
I0407 22:53:28.251840 23658 sgd_solver.cpp:105] Iteration 6336, lr = 0.000413236
I0407 22:53:33.340049 23658 solver.cpp:218] Iteration 6348 (2.35845 iter/s, 5.08808s/12 iters), loss = 0.201658
I0407 22:53:33.340696 23658 solver.cpp:237]     Train net output #0: loss = 0.201658 (* 1 = 0.201658 loss)
I0407 22:53:33.340709 23658 sgd_solver.cpp:105] Iteration 6348, lr = 0.00041075
I0407 22:53:38.454289 23658 solver.cpp:218] Iteration 6360 (2.34675 iter/s, 5.11346s/12 iters), loss = 0.102891
I0407 22:53:38.454340 23658 solver.cpp:237]     Train net output #0: loss = 0.102891 (* 1 = 0.102891 loss)
I0407 22:53:38.454352 23658 sgd_solver.cpp:105] Iteration 6360, lr = 0.000408279
I0407 22:53:43.403048 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:43.541630 23658 solver.cpp:218] Iteration 6372 (2.35888 iter/s, 5.08715s/12 iters), loss = 0.0900903
I0407 22:53:43.541674 23658 solver.cpp:237]     Train net output #0: loss = 0.0900903 (* 1 = 0.0900903 loss)
I0407 22:53:43.541685 23658 sgd_solver.cpp:105] Iteration 6372, lr = 0.000405822
I0407 22:53:48.532577 23658 solver.cpp:218] Iteration 6384 (2.40444 iter/s, 4.99077s/12 iters), loss = 0.167363
I0407 22:53:48.532627 23658 solver.cpp:237]     Train net output #0: loss = 0.167363 (* 1 = 0.167363 loss)
I0407 22:53:48.532639 23658 sgd_solver.cpp:105] Iteration 6384, lr = 0.000403381
I0407 22:53:53.818768 23658 solver.cpp:218] Iteration 6396 (2.27015 iter/s, 5.286s/12 iters), loss = 0.154022
I0407 22:53:53.818825 23658 solver.cpp:237]     Train net output #0: loss = 0.154022 (* 1 = 0.154022 loss)
I0407 22:53:53.818836 23658 sgd_solver.cpp:105] Iteration 6396, lr = 0.000400954
I0407 22:53:58.888779 23658 solver.cpp:218] Iteration 6408 (2.36695 iter/s, 5.06982s/12 iters), loss = 0.175277
I0407 22:53:58.888839 23658 solver.cpp:237]     Train net output #0: loss = 0.175277 (* 1 = 0.175277 loss)
I0407 22:53:58.888852 23658 sgd_solver.cpp:105] Iteration 6408, lr = 0.000398541
I0407 22:54:03.961639 23658 solver.cpp:218] Iteration 6420 (2.36562 iter/s, 5.07266s/12 iters), loss = 0.18549
I0407 22:54:03.961763 23658 solver.cpp:237]     Train net output #0: loss = 0.18549 (* 1 = 0.18549 loss)
I0407 22:54:03.961776 23658 sgd_solver.cpp:105] Iteration 6420, lr = 0.000396143
I0407 22:54:05.966331 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 22:54:08.975930 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 22:54:11.370385 23658 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 22:54:11.370412 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:54:13.318766 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:15.849411 23658 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0407 22:54:15.849453 23658 solver.cpp:397]     Test net output #1: loss = 2.94986 (* 1 = 2.94986 loss)
I0407 22:54:17.732954 23658 solver.cpp:218] Iteration 6432 (0.871406 iter/s, 13.7708s/12 iters), loss = 0.277526
I0407 22:54:17.732995 23658 solver.cpp:237]     Train net output #0: loss = 0.277526 (* 1 = 0.277526 loss)
I0407 22:54:17.733004 23658 sgd_solver.cpp:105] Iteration 6432, lr = 0.00039376
I0407 22:54:22.724603 23658 solver.cpp:218] Iteration 6444 (2.4041 iter/s, 4.99147s/12 iters), loss = 0.134337
I0407 22:54:22.724653 23658 solver.cpp:237]     Train net output #0: loss = 0.134337 (* 1 = 0.134337 loss)
I0407 22:54:22.724663 23658 sgd_solver.cpp:105] Iteration 6444, lr = 0.000391391
I0407 22:54:27.830104 23658 solver.cpp:218] Iteration 6456 (2.35049 iter/s, 5.10531s/12 iters), loss = 0.168408
I0407 22:54:27.830160 23658 solver.cpp:237]     Train net output #0: loss = 0.168408 (* 1 = 0.168408 loss)
I0407 22:54:27.830170 23658 sgd_solver.cpp:105] Iteration 6456, lr = 0.000389036
I0407 22:54:32.889971 23658 solver.cpp:218] Iteration 6468 (2.3717 iter/s, 5.05966s/12 iters), loss = 0.19071
I0407 22:54:32.890019 23658 solver.cpp:237]     Train net output #0: loss = 0.19071 (* 1 = 0.19071 loss)
I0407 22:54:32.890044 23658 sgd_solver.cpp:105] Iteration 6468, lr = 0.000386695
I0407 22:54:34.822772 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:37.799440 23658 solver.cpp:218] Iteration 6480 (2.44435 iter/s, 4.90929s/12 iters), loss = 0.179855
I0407 22:54:37.799494 23658 solver.cpp:237]     Train net output #0: loss = 0.179855 (* 1 = 0.179855 loss)
I0407 22:54:37.799505 23658 sgd_solver.cpp:105] Iteration 6480, lr = 0.000384369
I0407 22:54:42.870666 23658 solver.cpp:218] Iteration 6492 (2.36638 iter/s, 5.07103s/12 iters), loss = 0.144678
I0407 22:54:42.870718 23658 solver.cpp:237]     Train net output #0: loss = 0.144678 (* 1 = 0.144678 loss)
I0407 22:54:42.870731 23658 sgd_solver.cpp:105] Iteration 6492, lr = 0.000382056
I0407 22:54:48.251566 23658 solver.cpp:218] Iteration 6504 (2.23019 iter/s, 5.3807s/12 iters), loss = 0.0971562
I0407 22:54:48.251616 23658 solver.cpp:237]     Train net output #0: loss = 0.0971562 (* 1 = 0.0971562 loss)
I0407 22:54:48.251626 23658 sgd_solver.cpp:105] Iteration 6504, lr = 0.000379758
I0407 22:54:53.304571 23658 solver.cpp:218] Iteration 6516 (2.37491 iter/s, 5.05282s/12 iters), loss = 0.181617
I0407 22:54:53.304620 23658 solver.cpp:237]     Train net output #0: loss = 0.181617 (* 1 = 0.181617 loss)
I0407 22:54:53.304630 23658 sgd_solver.cpp:105] Iteration 6516, lr = 0.000377473
I0407 22:54:57.796066 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 22:55:00.829362 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 22:55:03.152021 23658 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 22:55:03.152050 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:55:05.039978 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:07.604879 23658 solver.cpp:397]     Test net output #0: accuracy = 0.479167
I0407 22:55:07.604921 23658 solver.cpp:397]     Test net output #1: loss = 2.94633 (* 1 = 2.94633 loss)
I0407 22:55:07.694816 23658 solver.cpp:218] Iteration 6528 (0.833922 iter/s, 14.3898s/12 iters), loss = 0.1272
I0407 22:55:07.694865 23658 solver.cpp:237]     Train net output #0: loss = 0.1272 (* 1 = 0.1272 loss)
I0407 22:55:07.694876 23658 sgd_solver.cpp:105] Iteration 6528, lr = 0.000375202
I0407 22:55:12.104833 23658 solver.cpp:218] Iteration 6540 (2.72119 iter/s, 4.40984s/12 iters), loss = 0.190708
I0407 22:55:12.104890 23658 solver.cpp:237]     Train net output #0: loss = 0.190708 (* 1 = 0.190708 loss)
I0407 22:55:12.104902 23658 sgd_solver.cpp:105] Iteration 6540, lr = 0.000372944
I0407 22:55:17.217516 23658 solver.cpp:218] Iteration 6552 (2.34719 iter/s, 5.11249s/12 iters), loss = 0.105185
I0407 22:55:17.217563 23658 solver.cpp:237]     Train net output #0: loss = 0.105185 (* 1 = 0.105185 loss)
I0407 22:55:17.217576 23658 sgd_solver.cpp:105] Iteration 6552, lr = 0.000370701
I0407 22:55:22.183614 23658 solver.cpp:218] Iteration 6564 (2.41647 iter/s, 4.96591s/12 iters), loss = 0.141539
I0407 22:55:22.183670 23658 solver.cpp:237]     Train net output #0: loss = 0.141539 (* 1 = 0.141539 loss)
I0407 22:55:22.183682 23658 sgd_solver.cpp:105] Iteration 6564, lr = 0.00036847
I0407 22:55:26.621564 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:27.461864 23658 solver.cpp:218] Iteration 6576 (2.27357 iter/s, 5.27805s/12 iters), loss = 0.130467
I0407 22:55:27.461923 23658 solver.cpp:237]     Train net output #0: loss = 0.130467 (* 1 = 0.130467 loss)
I0407 22:55:27.461936 23658 sgd_solver.cpp:105] Iteration 6576, lr = 0.000366253
I0407 22:55:32.890795 23658 solver.cpp:218] Iteration 6588 (2.21046 iter/s, 5.42872s/12 iters), loss = 0.193296
I0407 22:55:32.890847 23658 solver.cpp:237]     Train net output #0: loss = 0.193296 (* 1 = 0.193296 loss)
I0407 22:55:32.890858 23658 sgd_solver.cpp:105] Iteration 6588, lr = 0.00036405
I0407 22:55:37.896981 23658 solver.cpp:218] Iteration 6600 (2.39713 iter/s, 5.00599s/12 iters), loss = 0.146028
I0407 22:55:37.897150 23658 solver.cpp:237]     Train net output #0: loss = 0.146028 (* 1 = 0.146028 loss)
I0407 22:55:37.897163 23658 sgd_solver.cpp:105] Iteration 6600, lr = 0.000361859
I0407 22:55:42.957118 23658 solver.cpp:218] Iteration 6612 (2.37162 iter/s, 5.05983s/12 iters), loss = 0.111545
I0407 22:55:42.957173 23658 solver.cpp:237]     Train net output #0: loss = 0.111545 (* 1 = 0.111545 loss)
I0407 22:55:42.957185 23658 sgd_solver.cpp:105] Iteration 6612, lr = 0.000359682
I0407 22:55:48.021888 23658 solver.cpp:218] Iteration 6624 (2.3694 iter/s, 5.06458s/12 iters), loss = 0.166012
I0407 22:55:48.021930 23658 solver.cpp:237]     Train net output #0: loss = 0.166012 (* 1 = 0.166012 loss)
I0407 22:55:48.021939 23658 sgd_solver.cpp:105] Iteration 6624, lr = 0.000357518
I0407 22:55:50.123558 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 22:55:53.140337 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 22:55:55.446597 23658 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 22:55:55.446619 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:55:57.294013 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:59.944679 23658 solver.cpp:397]     Test net output #0: accuracy = 0.47549
I0407 22:55:59.944727 23658 solver.cpp:397]     Test net output #1: loss = 2.93767 (* 1 = 2.93767 loss)
I0407 22:56:01.758285 23658 solver.cpp:218] Iteration 6636 (0.873617 iter/s, 13.736s/12 iters), loss = 0.0892733
I0407 22:56:01.758345 23658 solver.cpp:237]     Train net output #0: loss = 0.0892733 (* 1 = 0.0892733 loss)
I0407 22:56:01.758358 23658 sgd_solver.cpp:105] Iteration 6636, lr = 0.000355367
I0407 22:56:06.707355 23658 solver.cpp:218] Iteration 6648 (2.42479 iter/s, 4.94888s/12 iters), loss = 0.0918662
I0407 22:56:06.707401 23658 solver.cpp:237]     Train net output #0: loss = 0.0918662 (* 1 = 0.0918662 loss)
I0407 22:56:06.707412 23658 sgd_solver.cpp:105] Iteration 6648, lr = 0.000353229
I0407 22:56:11.648226 23658 solver.cpp:218] Iteration 6660 (2.42881 iter/s, 4.94069s/12 iters), loss = 0.135083
I0407 22:56:11.648342 23658 solver.cpp:237]     Train net output #0: loss = 0.135083 (* 1 = 0.135083 loss)
I0407 22:56:11.648355 23658 sgd_solver.cpp:105] Iteration 6660, lr = 0.000351104
I0407 22:56:16.636440 23658 solver.cpp:218] Iteration 6672 (2.40579 iter/s, 4.98796s/12 iters), loss = 0.146716
I0407 22:56:16.636503 23658 solver.cpp:237]     Train net output #0: loss = 0.146716 (* 1 = 0.146716 loss)
I0407 22:56:16.636515 23658 sgd_solver.cpp:105] Iteration 6672, lr = 0.000348991
I0407 22:56:17.997088 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:21.629021 23658 solver.cpp:218] Iteration 6684 (2.40367 iter/s, 4.99237s/12 iters), loss = 0.164745
I0407 22:56:21.629070 23658 solver.cpp:237]     Train net output #0: loss = 0.164745 (* 1 = 0.164745 loss)
I0407 22:56:21.629081 23658 sgd_solver.cpp:105] Iteration 6684, lr = 0.000346892
I0407 22:56:26.550963 23658 solver.cpp:218] Iteration 6696 (2.43815 iter/s, 4.92176s/12 iters), loss = 0.110977
I0407 22:56:26.551019 23658 solver.cpp:237]     Train net output #0: loss = 0.110977 (* 1 = 0.110977 loss)
I0407 22:56:26.551030 23658 sgd_solver.cpp:105] Iteration 6696, lr = 0.000344805
I0407 22:56:31.474812 23658 solver.cpp:218] Iteration 6708 (2.43721 iter/s, 4.92366s/12 iters), loss = 0.120452
I0407 22:56:31.474874 23658 solver.cpp:237]     Train net output #0: loss = 0.120452 (* 1 = 0.120452 loss)
I0407 22:56:31.474885 23658 sgd_solver.cpp:105] Iteration 6708, lr = 0.00034273
I0407 22:56:36.412052 23658 solver.cpp:218] Iteration 6720 (2.4306 iter/s, 4.93705s/12 iters), loss = 0.0926198
I0407 22:56:36.412102 23658 solver.cpp:237]     Train net output #0: loss = 0.0926198 (* 1 = 0.0926198 loss)
I0407 22:56:36.412111 23658 sgd_solver.cpp:105] Iteration 6720, lr = 0.000340668
I0407 22:56:41.187801 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 22:56:45.034296 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 22:56:47.409235 23658 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 22:56:47.409261 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:56:49.236665 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:51.876185 23658 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 22:56:51.876233 23658 solver.cpp:397]     Test net output #1: loss = 2.95746 (* 1 = 2.95746 loss)
I0407 22:56:51.966320 23658 solver.cpp:218] Iteration 6732 (0.771515 iter/s, 15.5538s/12 iters), loss = 0.0977402
I0407 22:56:51.966377 23658 solver.cpp:237]     Train net output #0: loss = 0.0977402 (* 1 = 0.0977402 loss)
I0407 22:56:51.966388 23658 sgd_solver.cpp:105] Iteration 6732, lr = 0.000338618
I0407 22:56:56.236688 23658 solver.cpp:218] Iteration 6744 (2.81018 iter/s, 4.27019s/12 iters), loss = 0.134969
I0407 22:56:56.236743 23658 solver.cpp:237]     Train net output #0: loss = 0.134969 (* 1 = 0.134969 loss)
I0407 22:56:56.236755 23658 sgd_solver.cpp:105] Iteration 6744, lr = 0.000336581
I0407 22:57:01.399124 23658 solver.cpp:218] Iteration 6756 (2.32457 iter/s, 5.16224s/12 iters), loss = 0.0867761
I0407 22:57:01.399168 23658 solver.cpp:237]     Train net output #0: loss = 0.0867762 (* 1 = 0.0867762 loss)
I0407 22:57:01.399178 23658 sgd_solver.cpp:105] Iteration 6756, lr = 0.000334556
I0407 22:57:06.730008 23658 solver.cpp:218] Iteration 6768 (2.25111 iter/s, 5.3307s/12 iters), loss = 0.100448
I0407 22:57:06.730046 23658 solver.cpp:237]     Train net output #0: loss = 0.100448 (* 1 = 0.100448 loss)
I0407 22:57:06.730054 23658 sgd_solver.cpp:105] Iteration 6768, lr = 0.000332543
I0407 22:57:10.472992 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:11.997654 23658 solver.cpp:218] Iteration 6780 (2.27814 iter/s, 5.26746s/12 iters), loss = 0.0777793
I0407 22:57:11.997712 23658 solver.cpp:237]     Train net output #0: loss = 0.0777793 (* 1 = 0.0777793 loss)
I0407 22:57:11.997725 23658 sgd_solver.cpp:105] Iteration 6780, lr = 0.000330543
I0407 22:57:17.140367 23658 solver.cpp:218] Iteration 6792 (2.33349 iter/s, 5.14251s/12 iters), loss = 0.074442
I0407 22:57:17.140486 23658 solver.cpp:237]     Train net output #0: loss = 0.074442 (* 1 = 0.074442 loss)
I0407 22:57:17.140498 23658 sgd_solver.cpp:105] Iteration 6792, lr = 0.000328554
I0407 22:57:22.420397 23658 solver.cpp:218] Iteration 6804 (2.27283 iter/s, 5.27977s/12 iters), loss = 0.195858
I0407 22:57:22.420451 23658 solver.cpp:237]     Train net output #0: loss = 0.195858 (* 1 = 0.195858 loss)
I0407 22:57:22.420464 23658 sgd_solver.cpp:105] Iteration 6804, lr = 0.000326577
I0407 22:57:27.395496 23658 solver.cpp:218] Iteration 6816 (2.4121 iter/s, 4.97491s/12 iters), loss = 0.0917436
I0407 22:57:27.395541 23658 solver.cpp:237]     Train net output #0: loss = 0.0917436 (* 1 = 0.0917436 loss)
I0407 22:57:27.395552 23658 sgd_solver.cpp:105] Iteration 6816, lr = 0.000324612
I0407 22:57:32.728801 23658 solver.cpp:218] Iteration 6828 (2.25009 iter/s, 5.33312s/12 iters), loss = 0.125472
I0407 22:57:32.728852 23658 solver.cpp:237]     Train net output #0: loss = 0.125472 (* 1 = 0.125472 loss)
I0407 22:57:32.728864 23658 sgd_solver.cpp:105] Iteration 6828, lr = 0.000322659
I0407 22:57:34.793535 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 22:57:37.812307 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 22:57:40.115597 23658 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 22:57:40.115622 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:57:41.910128 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:44.588627 23658 solver.cpp:397]     Test net output #0: accuracy = 0.474877
I0407 22:57:44.588685 23658 solver.cpp:397]     Test net output #1: loss = 2.97056 (* 1 = 2.97056 loss)
I0407 22:57:46.427314 23658 solver.cpp:218] Iteration 6840 (0.876034 iter/s, 13.6981s/12 iters), loss = 0.0641453
I0407 22:57:46.427373 23658 solver.cpp:237]     Train net output #0: loss = 0.0641453 (* 1 = 0.0641453 loss)
I0407 22:57:46.427386 23658 sgd_solver.cpp:105] Iteration 6840, lr = 0.000320718
I0407 22:57:51.571274 23658 solver.cpp:218] Iteration 6852 (2.33293 iter/s, 5.14376s/12 iters), loss = 0.168877
I0407 22:57:51.571422 23658 solver.cpp:237]     Train net output #0: loss = 0.168877 (* 1 = 0.168877 loss)
I0407 22:57:51.571436 23658 sgd_solver.cpp:105] Iteration 6852, lr = 0.000318788
I0407 22:57:56.960984 23658 solver.cpp:218] Iteration 6864 (2.22659 iter/s, 5.38942s/12 iters), loss = 0.237774
I0407 22:57:56.961037 23658 solver.cpp:237]     Train net output #0: loss = 0.237775 (* 1 = 0.237775 loss)
I0407 22:57:56.961050 23658 sgd_solver.cpp:105] Iteration 6864, lr = 0.00031687
I0407 22:58:02.049196 23658 solver.cpp:218] Iteration 6876 (2.35848 iter/s, 5.08802s/12 iters), loss = 0.201018
I0407 22:58:02.049233 23658 solver.cpp:237]     Train net output #0: loss = 0.201018 (* 1 = 0.201018 loss)
I0407 22:58:02.049242 23658 sgd_solver.cpp:105] Iteration 6876, lr = 0.000314964
I0407 22:58:02.674787 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:07.227226 23658 solver.cpp:218] Iteration 6888 (2.31757 iter/s, 5.17785s/12 iters), loss = 0.105328
I0407 22:58:07.227273 23658 solver.cpp:237]     Train net output #0: loss = 0.105328 (* 1 = 0.105328 loss)
I0407 22:58:07.227285 23658 sgd_solver.cpp:105] Iteration 6888, lr = 0.000313069
I0407 22:58:12.403596 23658 solver.cpp:218] Iteration 6900 (2.31831 iter/s, 5.17618s/12 iters), loss = 0.102642
I0407 22:58:12.403640 23658 solver.cpp:237]     Train net output #0: loss = 0.102642 (* 1 = 0.102642 loss)
I0407 22:58:12.403650 23658 sgd_solver.cpp:105] Iteration 6900, lr = 0.000311185
I0407 22:58:17.434913 23658 solver.cpp:218] Iteration 6912 (2.38515 iter/s, 5.03113s/12 iters), loss = 0.195884
I0407 22:58:17.434960 23658 solver.cpp:237]     Train net output #0: loss = 0.195884 (* 1 = 0.195884 loss)
I0407 22:58:17.434970 23658 sgd_solver.cpp:105] Iteration 6912, lr = 0.000309313
I0407 22:58:23.508448 23658 solver.cpp:218] Iteration 6924 (1.97585 iter/s, 6.07332s/12 iters), loss = 0.116529
I0407 22:58:23.508538 23658 solver.cpp:237]     Train net output #0: loss = 0.116529 (* 1 = 0.116529 loss)
I0407 22:58:23.508550 23658 sgd_solver.cpp:105] Iteration 6924, lr = 0.000307452
I0407 22:58:28.314054 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 22:58:31.361588 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 22:58:33.663158 23658 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 22:58:33.663179 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:58:34.325130 23658 blocking_queue.cpp:49] Waiting for data
I0407 22:58:35.413128 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:38.132195 23658 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 22:58:38.132242 23658 solver.cpp:397]     Test net output #1: loss = 2.96581 (* 1 = 2.96581 loss)
I0407 22:58:38.221520 23658 solver.cpp:218] Iteration 6936 (0.815628 iter/s, 14.7126s/12 iters), loss = 0.110489
I0407 22:58:38.221577 23658 solver.cpp:237]     Train net output #0: loss = 0.110489 (* 1 = 0.110489 loss)
I0407 22:58:38.221591 23658 sgd_solver.cpp:105] Iteration 6936, lr = 0.000305602
I0407 22:58:42.795821 23658 solver.cpp:218] Iteration 6948 (2.62346 iter/s, 4.57411s/12 iters), loss = 0.0503204
I0407 22:58:42.795876 23658 solver.cpp:237]     Train net output #0: loss = 0.0503204 (* 1 = 0.0503204 loss)
I0407 22:58:42.795888 23658 sgd_solver.cpp:105] Iteration 6948, lr = 0.000303764
I0407 22:58:48.290052 23658 solver.cpp:218] Iteration 6960 (2.18419 iter/s, 5.49403s/12 iters), loss = 0.0653407
I0407 22:58:48.290099 23658 solver.cpp:237]     Train net output #0: loss = 0.0653407 (* 1 = 0.0653407 loss)
I0407 22:58:48.290110 23658 sgd_solver.cpp:105] Iteration 6960, lr = 0.000301936
I0407 22:58:53.689285 23658 solver.cpp:218] Iteration 6972 (2.22262 iter/s, 5.39904s/12 iters), loss = 0.0830429
I0407 22:58:53.689393 23658 solver.cpp:237]     Train net output #0: loss = 0.0830429 (* 1 = 0.0830429 loss)
I0407 22:58:53.689405 23658 sgd_solver.cpp:105] Iteration 6972, lr = 0.000300119
I0407 22:58:56.501032 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:58.923115 23658 solver.cpp:218] Iteration 6984 (2.29289 iter/s, 5.23357s/12 iters), loss = 0.100422
I0407 22:58:58.923173 23658 solver.cpp:237]     Train net output #0: loss = 0.100422 (* 1 = 0.100422 loss)
I0407 22:58:58.923185 23658 sgd_solver.cpp:105] Iteration 6984, lr = 0.000298314
I0407 22:59:04.249872 23658 solver.cpp:218] Iteration 6996 (2.25286 iter/s, 5.32655s/12 iters), loss = 0.0733542
I0407 22:59:04.249922 23658 solver.cpp:237]     Train net output #0: loss = 0.0733542 (* 1 = 0.0733542 loss)
I0407 22:59:04.249934 23658 sgd_solver.cpp:105] Iteration 6996, lr = 0.000296519
I0407 22:59:09.491739 23658 solver.cpp:218] Iteration 7008 (2.28935 iter/s, 5.24167s/12 iters), loss = 0.108713
I0407 22:59:09.491788 23658 solver.cpp:237]     Train net output #0: loss = 0.108713 (* 1 = 0.108713 loss)
I0407 22:59:09.491798 23658 sgd_solver.cpp:105] Iteration 7008, lr = 0.000294735
I0407 22:59:14.711629 23658 solver.cpp:218] Iteration 7020 (2.29898 iter/s, 5.2197s/12 iters), loss = 0.111664
I0407 22:59:14.711664 23658 solver.cpp:237]     Train net output #0: loss = 0.111664 (* 1 = 0.111664 loss)
I0407 22:59:14.711673 23658 sgd_solver.cpp:105] Iteration 7020, lr = 0.000292962
I0407 22:59:20.054555 23658 solver.cpp:218] Iteration 7032 (2.24604 iter/s, 5.34274s/12 iters), loss = 0.156644
I0407 22:59:20.054605 23658 solver.cpp:237]     Train net output #0: loss = 0.156644 (* 1 = 0.156644 loss)
I0407 22:59:20.054617 23658 sgd_solver.cpp:105] Iteration 7032, lr = 0.000291199
I0407 22:59:22.085160 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 22:59:25.121834 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 22:59:29.755162 23658 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 22:59:29.755187 23658 net.cpp:676] Ignoring source layer train-data
I0407 22:59:31.469687 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:34.280216 23658 solver.cpp:397]     Test net output #0: accuracy = 0.480392
I0407 22:59:34.280261 23658 solver.cpp:397]     Test net output #1: loss = 2.95632 (* 1 = 2.95632 loss)
I0407 22:59:36.154707 23658 solver.cpp:218] Iteration 7044 (0.745356 iter/s, 16.0997s/12 iters), loss = 0.106163
I0407 22:59:36.154765 23658 solver.cpp:237]     Train net output #0: loss = 0.106163 (* 1 = 0.106163 loss)
I0407 22:59:36.154778 23658 sgd_solver.cpp:105] Iteration 7044, lr = 0.000289447
I0407 22:59:41.649610 23658 solver.cpp:218] Iteration 7056 (2.18393 iter/s, 5.49469s/12 iters), loss = 0.0897238
I0407 22:59:41.649665 23658 solver.cpp:237]     Train net output #0: loss = 0.0897238 (* 1 = 0.0897238 loss)
I0407 22:59:41.649678 23658 sgd_solver.cpp:105] Iteration 7056, lr = 0.000287705
I0407 22:59:47.163839 23658 solver.cpp:218] Iteration 7068 (2.17627 iter/s, 5.51403s/12 iters), loss = 0.0998114
I0407 22:59:47.163884 23658 solver.cpp:237]     Train net output #0: loss = 0.0998115 (* 1 = 0.0998115 loss)
I0407 22:59:47.163893 23658 sgd_solver.cpp:105] Iteration 7068, lr = 0.000285974
I0407 22:59:52.176900 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:52.293093 23658 solver.cpp:218] Iteration 7080 (2.33961 iter/s, 5.12906s/12 iters), loss = 0.17194
I0407 22:59:52.293141 23658 solver.cpp:237]     Train net output #0: loss = 0.17194 (* 1 = 0.17194 loss)
I0407 22:59:52.293150 23658 sgd_solver.cpp:105] Iteration 7080, lr = 0.000284254
I0407 22:59:57.284062 23658 solver.cpp:218] Iteration 7092 (2.40443 iter/s, 4.99078s/12 iters), loss = 0.0709675
I0407 22:59:57.284162 23658 solver.cpp:237]     Train net output #0: loss = 0.0709675 (* 1 = 0.0709675 loss)
I0407 22:59:57.284173 23658 sgd_solver.cpp:105] Iteration 7092, lr = 0.000282544
I0407 23:00:02.406162 23658 solver.cpp:218] Iteration 7104 (2.3429 iter/s, 5.12186s/12 iters), loss = 0.243561
I0407 23:00:02.406217 23658 solver.cpp:237]     Train net output #0: loss = 0.243561 (* 1 = 0.243561 loss)
I0407 23:00:02.406234 23658 sgd_solver.cpp:105] Iteration 7104, lr = 0.000280844
I0407 23:00:07.497326 23658 solver.cpp:218] Iteration 7116 (2.35711 iter/s, 5.09097s/12 iters), loss = 0.0887128
I0407 23:00:07.497371 23658 solver.cpp:237]     Train net output #0: loss = 0.0887128 (* 1 = 0.0887128 loss)
I0407 23:00:07.497382 23658 sgd_solver.cpp:105] Iteration 7116, lr = 0.000279154
I0407 23:00:12.905799 23658 solver.cpp:218] Iteration 7128 (2.21882 iter/s, 5.40828s/12 iters), loss = 0.156025
I0407 23:00:12.905848 23658 solver.cpp:237]     Train net output #0: loss = 0.156025 (* 1 = 0.156025 loss)
I0407 23:00:12.905858 23658 sgd_solver.cpp:105] Iteration 7128, lr = 0.000277474
I0407 23:00:17.404613 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 23:00:22.499866 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 23:00:27.230654 23658 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 23:00:27.230681 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:00:28.838858 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:31.642607 23658 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0407 23:00:31.642648 23658 solver.cpp:397]     Test net output #1: loss = 2.97864 (* 1 = 2.97864 loss)
I0407 23:00:31.732426 23658 solver.cpp:218] Iteration 7140 (0.637413 iter/s, 18.8261s/12 iters), loss = 0.167394
I0407 23:00:31.732477 23658 solver.cpp:237]     Train net output #0: loss = 0.167394 (* 1 = 0.167394 loss)
I0407 23:00:31.732489 23658 sgd_solver.cpp:105] Iteration 7140, lr = 0.000275805
I0407 23:00:36.019650 23658 solver.cpp:218] Iteration 7152 (2.79913 iter/s, 4.28705s/12 iters), loss = 0.100882
I0407 23:00:36.019695 23658 solver.cpp:237]     Train net output #0: loss = 0.100882 (* 1 = 0.100882 loss)
I0407 23:00:36.019704 23658 sgd_solver.cpp:105] Iteration 7152, lr = 0.000274146
I0407 23:00:41.058126 23658 solver.cpp:218] Iteration 7164 (2.38176 iter/s, 5.03829s/12 iters), loss = 0.139174
I0407 23:00:41.058171 23658 solver.cpp:237]     Train net output #0: loss = 0.139174 (* 1 = 0.139174 loss)
I0407 23:00:41.058179 23658 sgd_solver.cpp:105] Iteration 7164, lr = 0.000272496
I0407 23:00:46.100646 23658 solver.cpp:218] Iteration 7176 (2.37985 iter/s, 5.04234s/12 iters), loss = 0.106097
I0407 23:00:46.100688 23658 solver.cpp:237]     Train net output #0: loss = 0.106097 (* 1 = 0.106097 loss)
I0407 23:00:46.100697 23658 sgd_solver.cpp:105] Iteration 7176, lr = 0.000270857
I0407 23:00:48.326004 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:51.350713 23658 solver.cpp:218] Iteration 7188 (2.28577 iter/s, 5.24988s/12 iters), loss = 0.0883517
I0407 23:00:51.350755 23658 solver.cpp:237]     Train net output #0: loss = 0.0883518 (* 1 = 0.0883518 loss)
I0407 23:00:51.350765 23658 sgd_solver.cpp:105] Iteration 7188, lr = 0.000269227
I0407 23:00:56.495860 23658 solver.cpp:218] Iteration 7200 (2.33239 iter/s, 5.14493s/12 iters), loss = 0.144174
I0407 23:00:56.495923 23658 solver.cpp:237]     Train net output #0: loss = 0.144174 (* 1 = 0.144174 loss)
I0407 23:00:56.495935 23658 sgd_solver.cpp:105] Iteration 7200, lr = 0.000267607
I0407 23:01:01.565259 23658 solver.cpp:218] Iteration 7212 (2.36724 iter/s, 5.0692s/12 iters), loss = 0.160434
I0407 23:01:01.565346 23658 solver.cpp:237]     Train net output #0: loss = 0.160434 (* 1 = 0.160434 loss)
I0407 23:01:01.565356 23658 sgd_solver.cpp:105] Iteration 7212, lr = 0.000265997
I0407 23:01:06.495339 23658 solver.cpp:218] Iteration 7224 (2.43415 iter/s, 4.92986s/12 iters), loss = 0.0447144
I0407 23:01:06.495380 23658 solver.cpp:237]     Train net output #0: loss = 0.0447144 (* 1 = 0.0447144 loss)
I0407 23:01:06.495388 23658 sgd_solver.cpp:105] Iteration 7224, lr = 0.000264397
I0407 23:01:11.560415 23658 solver.cpp:218] Iteration 7236 (2.36925 iter/s, 5.06489s/12 iters), loss = 0.0833848
I0407 23:01:11.560472 23658 solver.cpp:237]     Train net output #0: loss = 0.0833849 (* 1 = 0.0833849 loss)
I0407 23:01:11.560484 23658 sgd_solver.cpp:105] Iteration 7236, lr = 0.000262806
I0407 23:01:13.582358 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 23:01:17.480618 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 23:01:27.973316 23658 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 23:01:27.973342 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:01:29.598968 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:32.434489 23658 solver.cpp:397]     Test net output #0: accuracy = 0.479167
I0407 23:01:32.434655 23658 solver.cpp:397]     Test net output #1: loss = 2.91277 (* 1 = 2.91277 loss)
I0407 23:01:34.370110 23658 solver.cpp:218] Iteration 7248 (0.526107 iter/s, 22.809s/12 iters), loss = 0.182471
I0407 23:01:34.370167 23658 solver.cpp:237]     Train net output #0: loss = 0.182471 (* 1 = 0.182471 loss)
I0407 23:01:34.370179 23658 sgd_solver.cpp:105] Iteration 7248, lr = 0.000261225
I0407 23:01:39.579602 23658 solver.cpp:218] Iteration 7260 (2.30358 iter/s, 5.20929s/12 iters), loss = 0.0917557
I0407 23:01:39.579650 23658 solver.cpp:237]     Train net output #0: loss = 0.0917557 (* 1 = 0.0917557 loss)
I0407 23:01:39.579663 23658 sgd_solver.cpp:105] Iteration 7260, lr = 0.000259653
I0407 23:01:44.649989 23658 solver.cpp:218] Iteration 7272 (2.36678 iter/s, 5.07019s/12 iters), loss = 0.174988
I0407 23:01:44.650036 23658 solver.cpp:237]     Train net output #0: loss = 0.174988 (* 1 = 0.174988 loss)
I0407 23:01:44.650050 23658 sgd_solver.cpp:105] Iteration 7272, lr = 0.000258091
I0407 23:01:49.091666 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:49.846108 23658 solver.cpp:218] Iteration 7284 (2.3095 iter/s, 5.19593s/12 iters), loss = 0.05976
I0407 23:01:49.846159 23658 solver.cpp:237]     Train net output #0: loss = 0.05976 (* 1 = 0.05976 loss)
I0407 23:01:49.846169 23658 sgd_solver.cpp:105] Iteration 7284, lr = 0.000256538
I0407 23:01:54.872402 23658 solver.cpp:218] Iteration 7296 (2.38754 iter/s, 5.0261s/12 iters), loss = 0.0562884
I0407 23:01:54.872452 23658 solver.cpp:237]     Train net output #0: loss = 0.0562884 (* 1 = 0.0562884 loss)
I0407 23:01:54.872463 23658 sgd_solver.cpp:105] Iteration 7296, lr = 0.000254995
I0407 23:01:59.940162 23658 solver.cpp:218] Iteration 7308 (2.368 iter/s, 5.06757s/12 iters), loss = 0.0864673
I0407 23:01:59.940208 23658 solver.cpp:237]     Train net output #0: loss = 0.0864673 (* 1 = 0.0864673 loss)
I0407 23:01:59.940218 23658 sgd_solver.cpp:105] Iteration 7308, lr = 0.000253461
I0407 23:02:04.916954 23658 solver.cpp:218] Iteration 7320 (2.41128 iter/s, 4.9766s/12 iters), loss = 0.118437
I0407 23:02:04.917080 23658 solver.cpp:237]     Train net output #0: loss = 0.118437 (* 1 = 0.118437 loss)
I0407 23:02:04.917093 23658 sgd_solver.cpp:105] Iteration 7320, lr = 0.000251936
I0407 23:02:10.256116 23658 solver.cpp:218] Iteration 7332 (2.24766 iter/s, 5.33888s/12 iters), loss = 0.0638622
I0407 23:02:10.256172 23658 solver.cpp:237]     Train net output #0: loss = 0.0638623 (* 1 = 0.0638623 loss)
I0407 23:02:10.256186 23658 sgd_solver.cpp:105] Iteration 7332, lr = 0.00025042
I0407 23:02:15.183997 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 23:02:20.066071 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 23:02:22.388823 23658 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 23:02:22.388851 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:02:23.945222 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:26.878917 23658 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 23:02:26.878966 23658 solver.cpp:397]     Test net output #1: loss = 2.93096 (* 1 = 2.93096 loss)
I0407 23:02:26.967674 23658 solver.cpp:218] Iteration 7344 (0.718087 iter/s, 16.7111s/12 iters), loss = 0.17402
I0407 23:02:26.967725 23658 solver.cpp:237]     Train net output #0: loss = 0.17402 (* 1 = 0.17402 loss)
I0407 23:02:26.967736 23658 sgd_solver.cpp:105] Iteration 7344, lr = 0.000248913
I0407 23:02:32.372045 23658 solver.cpp:218] Iteration 7356 (2.22051 iter/s, 5.40417s/12 iters), loss = 0.0623237
I0407 23:02:32.372093 23658 solver.cpp:237]     Train net output #0: loss = 0.0623237 (* 1 = 0.0623237 loss)
I0407 23:02:32.372105 23658 sgd_solver.cpp:105] Iteration 7356, lr = 0.000247416
I0407 23:02:37.441933 23658 solver.cpp:218] Iteration 7368 (2.36701 iter/s, 5.0697s/12 iters), loss = 0.057331
I0407 23:02:37.442116 23658 solver.cpp:237]     Train net output #0: loss = 0.0573311 (* 1 = 0.0573311 loss)
I0407 23:02:37.442129 23658 sgd_solver.cpp:105] Iteration 7368, lr = 0.000245927
I0407 23:02:42.316103 23658 solver.cpp:218] Iteration 7380 (2.46212 iter/s, 4.87385s/12 iters), loss = 0.0728637
I0407 23:02:42.316165 23658 solver.cpp:237]     Train net output #0: loss = 0.0728637 (* 1 = 0.0728637 loss)
I0407 23:02:42.316177 23658 sgd_solver.cpp:105] Iteration 7380, lr = 0.000244447
I0407 23:02:43.704057 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:47.341558 23658 solver.cpp:218] Iteration 7392 (2.38794 iter/s, 5.02525s/12 iters), loss = 0.0720368
I0407 23:02:47.341609 23658 solver.cpp:237]     Train net output #0: loss = 0.0720368 (* 1 = 0.0720368 loss)
I0407 23:02:47.341619 23658 sgd_solver.cpp:105] Iteration 7392, lr = 0.000242977
I0407 23:02:53.117383 23658 solver.cpp:218] Iteration 7404 (2.0777 iter/s, 5.77561s/12 iters), loss = 0.0752433
I0407 23:02:53.117439 23658 solver.cpp:237]     Train net output #0: loss = 0.0752433 (* 1 = 0.0752433 loss)
I0407 23:02:53.117451 23658 sgd_solver.cpp:105] Iteration 7404, lr = 0.000241515
I0407 23:02:58.238808 23658 solver.cpp:218] Iteration 7416 (2.34319 iter/s, 5.12122s/12 iters), loss = 0.0921694
I0407 23:02:58.238867 23658 solver.cpp:237]     Train net output #0: loss = 0.0921695 (* 1 = 0.0921695 loss)
I0407 23:02:58.238881 23658 sgd_solver.cpp:105] Iteration 7416, lr = 0.000240062
I0407 23:03:03.302428 23658 solver.cpp:218] Iteration 7428 (2.36994 iter/s, 5.06342s/12 iters), loss = 0.107353
I0407 23:03:03.302484 23658 solver.cpp:237]     Train net output #0: loss = 0.107353 (* 1 = 0.107353 loss)
I0407 23:03:03.302496 23658 sgd_solver.cpp:105] Iteration 7428, lr = 0.000238617
I0407 23:03:08.239969 23658 solver.cpp:218] Iteration 7440 (2.43045 iter/s, 4.93735s/12 iters), loss = 0.0486377
I0407 23:03:08.240094 23658 solver.cpp:237]     Train net output #0: loss = 0.0486377 (* 1 = 0.0486377 loss)
I0407 23:03:08.240108 23658 sgd_solver.cpp:105] Iteration 7440, lr = 0.000237182
I0407 23:03:10.297883 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 23:03:17.030905 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 23:03:20.709458 23658 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 23:03:20.709478 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:03:22.220444 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:25.186322 23658 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0407 23:03:25.186357 23658 solver.cpp:397]     Test net output #1: loss = 2.98223 (* 1 = 2.98223 loss)
I0407 23:03:26.942320 23658 solver.cpp:218] Iteration 7452 (0.641652 iter/s, 18.7017s/12 iters), loss = 0.0332496
I0407 23:03:26.942375 23658 solver.cpp:237]     Train net output #0: loss = 0.0332496 (* 1 = 0.0332496 loss)
I0407 23:03:26.942389 23658 sgd_solver.cpp:105] Iteration 7452, lr = 0.000235755
I0407 23:03:32.070647 23658 solver.cpp:218] Iteration 7464 (2.34003 iter/s, 5.12813s/12 iters), loss = 0.143431
I0407 23:03:32.070694 23658 solver.cpp:237]     Train net output #0: loss = 0.143431 (* 1 = 0.143431 loss)
I0407 23:03:32.070706 23658 sgd_solver.cpp:105] Iteration 7464, lr = 0.000234336
I0407 23:03:37.127053 23658 solver.cpp:218] Iteration 7476 (2.37331 iter/s, 5.05622s/12 iters), loss = 0.0983412
I0407 23:03:37.127086 23658 solver.cpp:237]     Train net output #0: loss = 0.0983412 (* 1 = 0.0983412 loss)
I0407 23:03:37.127094 23658 sgd_solver.cpp:105] Iteration 7476, lr = 0.000232926
I0407 23:03:40.734661 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:42.237146 23658 solver.cpp:218] Iteration 7488 (2.34838 iter/s, 5.10992s/12 iters), loss = 0.0854501
I0407 23:03:42.237192 23658 solver.cpp:237]     Train net output #0: loss = 0.0854501 (* 1 = 0.0854501 loss)
I0407 23:03:42.237203 23658 sgd_solver.cpp:105] Iteration 7488, lr = 0.000231525
I0407 23:03:47.904810 23658 solver.cpp:218] Iteration 7500 (2.11735 iter/s, 5.66746s/12 iters), loss = 0.142692
I0407 23:03:47.904858 23658 solver.cpp:237]     Train net output #0: loss = 0.142693 (* 1 = 0.142693 loss)
I0407 23:03:47.904870 23658 sgd_solver.cpp:105] Iteration 7500, lr = 0.000230132
I0407 23:03:54.066709 23658 solver.cpp:218] Iteration 7512 (1.94752 iter/s, 6.16168s/12 iters), loss = 0.0691698
I0407 23:03:54.066758 23658 solver.cpp:237]     Train net output #0: loss = 0.0691699 (* 1 = 0.0691699 loss)
I0407 23:03:54.066771 23658 sgd_solver.cpp:105] Iteration 7512, lr = 0.000228747
I0407 23:03:59.138702 23658 solver.cpp:218] Iteration 7524 (2.36602 iter/s, 5.07181s/12 iters), loss = 0.12232
I0407 23:03:59.138741 23658 solver.cpp:237]     Train net output #0: loss = 0.12232 (* 1 = 0.12232 loss)
I0407 23:03:59.138748 23658 sgd_solver.cpp:105] Iteration 7524, lr = 0.000227371
I0407 23:04:04.142521 23658 solver.cpp:218] Iteration 7536 (2.39825 iter/s, 5.00364s/12 iters), loss = 0.0922359
I0407 23:04:04.142561 23658 solver.cpp:237]     Train net output #0: loss = 0.092236 (* 1 = 0.092236 loss)
I0407 23:04:04.142570 23658 sgd_solver.cpp:105] Iteration 7536, lr = 0.000226003
I0407 23:04:08.949262 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 23:04:14.641355 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 23:04:20.256991 23658 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 23:04:20.257019 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:04:21.764540 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:24.778334 23658 solver.cpp:397]     Test net output #0: accuracy = 0.481005
I0407 23:04:24.778383 23658 solver.cpp:397]     Test net output #1: loss = 2.96675 (* 1 = 2.96675 loss)
I0407 23:04:24.868115 23658 solver.cpp:218] Iteration 7548 (0.579011 iter/s, 20.725s/12 iters), loss = 0.0887877
I0407 23:04:24.868165 23658 solver.cpp:237]     Train net output #0: loss = 0.0887877 (* 1 = 0.0887877 loss)
I0407 23:04:24.868178 23658 sgd_solver.cpp:105] Iteration 7548, lr = 0.000224643
I0407 23:04:29.151932 23658 solver.cpp:218] Iteration 7560 (2.80136 iter/s, 4.28364s/12 iters), loss = 0.0996464
I0407 23:04:29.151989 23658 solver.cpp:237]     Train net output #0: loss = 0.0996464 (* 1 = 0.0996464 loss)
I0407 23:04:29.152002 23658 sgd_solver.cpp:105] Iteration 7560, lr = 0.000223292
I0407 23:04:34.189029 23658 solver.cpp:218] Iteration 7572 (2.38242 iter/s, 5.0369s/12 iters), loss = 0.10105
I0407 23:04:34.189072 23658 solver.cpp:237]     Train net output #0: loss = 0.10105 (* 1 = 0.10105 loss)
I0407 23:04:34.189082 23658 sgd_solver.cpp:105] Iteration 7572, lr = 0.000221948
I0407 23:04:39.132908 23658 solver.cpp:218] Iteration 7584 (2.42734 iter/s, 4.94369s/12 iters), loss = 0.0949946
I0407 23:04:39.132958 23658 solver.cpp:237]     Train net output #0: loss = 0.0949946 (* 1 = 0.0949946 loss)
I0407 23:04:39.132970 23658 sgd_solver.cpp:105] Iteration 7584, lr = 0.000220613
I0407 23:04:39.772029 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:44.076503 23658 solver.cpp:218] Iteration 7596 (2.42748 iter/s, 4.9434s/12 iters), loss = 0.0972363
I0407 23:04:44.076556 23658 solver.cpp:237]     Train net output #0: loss = 0.0972364 (* 1 = 0.0972364 loss)
I0407 23:04:44.076570 23658 sgd_solver.cpp:105] Iteration 7596, lr = 0.000219286
I0407 23:04:49.170413 23658 solver.cpp:218] Iteration 7608 (2.35585 iter/s, 5.09371s/12 iters), loss = 0.111801
I0407 23:04:49.170545 23658 solver.cpp:237]     Train net output #0: loss = 0.111801 (* 1 = 0.111801 loss)
I0407 23:04:49.170557 23658 sgd_solver.cpp:105] Iteration 7608, lr = 0.000217966
I0407 23:04:54.370066 23658 solver.cpp:218] Iteration 7620 (2.30797 iter/s, 5.19938s/12 iters), loss = 0.236859
I0407 23:04:54.370105 23658 solver.cpp:237]     Train net output #0: loss = 0.236859 (* 1 = 0.236859 loss)
I0407 23:04:54.370115 23658 sgd_solver.cpp:105] Iteration 7620, lr = 0.000216655
I0407 23:04:56.850453 23658 blocking_queue.cpp:49] Waiting for data
I0407 23:04:59.444947 23658 solver.cpp:218] Iteration 7632 (2.36467 iter/s, 5.07469s/12 iters), loss = 0.20092
I0407 23:04:59.445006 23658 solver.cpp:237]     Train net output #0: loss = 0.20092 (* 1 = 0.20092 loss)
I0407 23:04:59.445019 23658 sgd_solver.cpp:105] Iteration 7632, lr = 0.000215352
I0407 23:05:04.478513 23658 solver.cpp:218] Iteration 7644 (2.38409 iter/s, 5.03337s/12 iters), loss = 0.0865669
I0407 23:05:04.478554 23658 solver.cpp:237]     Train net output #0: loss = 0.0865669 (* 1 = 0.0865669 loss)
I0407 23:05:04.478561 23658 sgd_solver.cpp:105] Iteration 7644, lr = 0.000214056
I0407 23:05:06.495913 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 23:05:10.131467 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 23:05:12.453495 23658 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 23:05:12.453519 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:05:13.881911 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:16.895069 23658 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 23:05:16.895118 23658 solver.cpp:397]     Test net output #1: loss = 2.98842 (* 1 = 2.98842 loss)
I0407 23:05:19.301681 23658 solver.cpp:218] Iteration 7656 (0.809567 iter/s, 14.8227s/12 iters), loss = 0.18578
I0407 23:05:19.301795 23658 solver.cpp:237]     Train net output #0: loss = 0.18578 (* 1 = 0.18578 loss)
I0407 23:05:19.301808 23658 sgd_solver.cpp:105] Iteration 7656, lr = 0.000212768
I0407 23:05:24.504083 23658 solver.cpp:218] Iteration 7668 (2.30674 iter/s, 5.20214s/12 iters), loss = 0.14691
I0407 23:05:24.504145 23658 solver.cpp:237]     Train net output #0: loss = 0.146911 (* 1 = 0.146911 loss)
I0407 23:05:24.504158 23658 sgd_solver.cpp:105] Iteration 7668, lr = 0.000211488
I0407 23:05:29.703516 23658 solver.cpp:218] Iteration 7680 (2.30804 iter/s, 5.19923s/12 iters), loss = 0.171029
I0407 23:05:29.703567 23658 solver.cpp:237]     Train net output #0: loss = 0.171029 (* 1 = 0.171029 loss)
I0407 23:05:29.703579 23658 sgd_solver.cpp:105] Iteration 7680, lr = 0.000210215
I0407 23:05:32.665733 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:35.071952 23658 solver.cpp:218] Iteration 7692 (2.23537 iter/s, 5.36824s/12 iters), loss = 0.139277
I0407 23:05:35.071990 23658 solver.cpp:237]     Train net output #0: loss = 0.139277 (* 1 = 0.139277 loss)
I0407 23:05:35.071998 23658 sgd_solver.cpp:105] Iteration 7692, lr = 0.000208951
I0407 23:05:40.162941 23658 solver.cpp:218] Iteration 7704 (2.35719 iter/s, 5.0908s/12 iters), loss = 0.180461
I0407 23:05:40.162989 23658 solver.cpp:237]     Train net output #0: loss = 0.180461 (* 1 = 0.180461 loss)
I0407 23:05:40.163002 23658 sgd_solver.cpp:105] Iteration 7704, lr = 0.000207693
I0407 23:05:45.336839 23658 solver.cpp:218] Iteration 7716 (2.31942 iter/s, 5.17371s/12 iters), loss = 0.0722346
I0407 23:05:45.336885 23658 solver.cpp:237]     Train net output #0: loss = 0.0722346 (* 1 = 0.0722346 loss)
I0407 23:05:45.336896 23658 sgd_solver.cpp:105] Iteration 7716, lr = 0.000206444
I0407 23:05:50.525393 23658 solver.cpp:218] Iteration 7728 (2.31287 iter/s, 5.18836s/12 iters), loss = 0.122753
I0407 23:05:50.525506 23658 solver.cpp:237]     Train net output #0: loss = 0.122753 (* 1 = 0.122753 loss)
I0407 23:05:50.525521 23658 sgd_solver.cpp:105] Iteration 7728, lr = 0.000205202
I0407 23:05:55.548486 23658 solver.cpp:218] Iteration 7740 (2.38909 iter/s, 5.02284s/12 iters), loss = 0.107859
I0407 23:05:55.548537 23658 solver.cpp:237]     Train net output #0: loss = 0.107859 (* 1 = 0.107859 loss)
I0407 23:05:55.548549 23658 sgd_solver.cpp:105] Iteration 7740, lr = 0.000203967
I0407 23:06:00.237042 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 23:06:03.264693 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 23:06:05.567394 23658 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 23:06:05.567417 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:06:07.003819 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:10.088554 23658 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 23:06:10.088604 23658 solver.cpp:397]     Test net output #1: loss = 2.99023 (* 1 = 2.99023 loss)
I0407 23:06:10.176849 23658 solver.cpp:218] Iteration 7752 (0.820349 iter/s, 14.6279s/12 iters), loss = 0.101801
I0407 23:06:10.176905 23658 solver.cpp:237]     Train net output #0: loss = 0.101801 (* 1 = 0.101801 loss)
I0407 23:06:10.176918 23658 sgd_solver.cpp:105] Iteration 7752, lr = 0.00020274
I0407 23:06:14.520227 23658 solver.cpp:218] Iteration 7764 (2.76294 iter/s, 4.3432s/12 iters), loss = 0.0961305
I0407 23:06:14.520277 23658 solver.cpp:237]     Train net output #0: loss = 0.0961306 (* 1 = 0.0961306 loss)
I0407 23:06:14.520288 23658 sgd_solver.cpp:105] Iteration 7764, lr = 0.00020152
I0407 23:06:19.530659 23658 solver.cpp:218] Iteration 7776 (2.3951 iter/s, 5.01024s/12 iters), loss = 0.0941074
I0407 23:06:19.530717 23658 solver.cpp:237]     Train net output #0: loss = 0.0941074 (* 1 = 0.0941074 loss)
I0407 23:06:19.530731 23658 sgd_solver.cpp:105] Iteration 7776, lr = 0.000200308
I0407 23:06:24.654036 23658 solver.cpp:218] Iteration 7788 (2.3423 iter/s, 5.12317s/12 iters), loss = 0.0491404
I0407 23:06:24.654160 23658 solver.cpp:237]     Train net output #0: loss = 0.0491404 (* 1 = 0.0491404 loss)
I0407 23:06:24.654173 23658 sgd_solver.cpp:105] Iteration 7788, lr = 0.000199103
I0407 23:06:24.665452 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:29.899627 23658 solver.cpp:218] Iteration 7800 (2.28775 iter/s, 5.24532s/12 iters), loss = 0.0766859
I0407 23:06:29.899674 23658 solver.cpp:237]     Train net output #0: loss = 0.076686 (* 1 = 0.076686 loss)
I0407 23:06:29.899683 23658 sgd_solver.cpp:105] Iteration 7800, lr = 0.000197905
I0407 23:06:34.993803 23658 solver.cpp:218] Iteration 7812 (2.35572 iter/s, 5.09399s/12 iters), loss = 0.136923
I0407 23:06:34.993845 23658 solver.cpp:237]     Train net output #0: loss = 0.136923 (* 1 = 0.136923 loss)
I0407 23:06:34.993855 23658 sgd_solver.cpp:105] Iteration 7812, lr = 0.000196714
I0407 23:06:39.990420 23658 solver.cpp:218] Iteration 7824 (2.40171 iter/s, 4.99643s/12 iters), loss = 0.0439479
I0407 23:06:39.990471 23658 solver.cpp:237]     Train net output #0: loss = 0.043948 (* 1 = 0.043948 loss)
I0407 23:06:39.990483 23658 sgd_solver.cpp:105] Iteration 7824, lr = 0.000195531
I0407 23:06:45.088812 23658 solver.cpp:218] Iteration 7836 (2.35377 iter/s, 5.0982s/12 iters), loss = 0.159331
I0407 23:06:45.088865 23658 solver.cpp:237]     Train net output #0: loss = 0.159331 (* 1 = 0.159331 loss)
I0407 23:06:45.088876 23658 sgd_solver.cpp:105] Iteration 7836, lr = 0.000194354
I0407 23:06:50.175081 23658 solver.cpp:218] Iteration 7848 (2.35938 iter/s, 5.08608s/12 iters), loss = 0.074174
I0407 23:06:50.175122 23658 solver.cpp:237]     Train net output #0: loss = 0.074174 (* 1 = 0.074174 loss)
I0407 23:06:50.175132 23658 sgd_solver.cpp:105] Iteration 7848, lr = 0.000193185
I0407 23:06:52.211426 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 23:06:55.360672 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 23:06:57.686070 23658 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 23:06:57.686096 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:06:59.091428 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:02.174026 23658 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0407 23:07:02.174078 23658 solver.cpp:397]     Test net output #1: loss = 3.00697 (* 1 = 3.00697 loss)
I0407 23:07:04.179872 23658 solver.cpp:218] Iteration 7860 (0.856875 iter/s, 14.0044s/12 iters), loss = 0.147833
I0407 23:07:04.179937 23658 solver.cpp:237]     Train net output #0: loss = 0.147833 (* 1 = 0.147833 loss)
I0407 23:07:04.179950 23658 sgd_solver.cpp:105] Iteration 7860, lr = 0.000192022
I0407 23:07:09.391360 23658 solver.cpp:218] Iteration 7872 (2.3027 iter/s, 5.21128s/12 iters), loss = 0.142282
I0407 23:07:09.391400 23658 solver.cpp:237]     Train net output #0: loss = 0.142282 (* 1 = 0.142282 loss)
I0407 23:07:09.391410 23658 sgd_solver.cpp:105] Iteration 7872, lr = 0.000190867
I0407 23:07:14.843119 23658 solver.cpp:218] Iteration 7884 (2.2012 iter/s, 5.45157s/12 iters), loss = 0.0910908
I0407 23:07:14.843161 23658 solver.cpp:237]     Train net output #0: loss = 0.0910909 (* 1 = 0.0910909 loss)
I0407 23:07:14.843170 23658 sgd_solver.cpp:105] Iteration 7884, lr = 0.000189719
I0407 23:07:17.068084 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:19.927327 23658 solver.cpp:218] Iteration 7896 (2.36034 iter/s, 5.08401s/12 iters), loss = 0.10941
I0407 23:07:19.927382 23658 solver.cpp:237]     Train net output #0: loss = 0.10941 (* 1 = 0.10941 loss)
I0407 23:07:19.927394 23658 sgd_solver.cpp:105] Iteration 7896, lr = 0.000188577
I0407 23:07:25.038230 23658 solver.cpp:218] Iteration 7908 (2.34801 iter/s, 5.1107s/12 iters), loss = 0.0564959
I0407 23:07:25.038282 23658 solver.cpp:237]     Train net output #0: loss = 0.0564959 (* 1 = 0.0564959 loss)
I0407 23:07:25.038295 23658 sgd_solver.cpp:105] Iteration 7908, lr = 0.000187443
I0407 23:07:30.337399 23658 solver.cpp:218] Iteration 7920 (2.26459 iter/s, 5.29897s/12 iters), loss = 0.144215
I0407 23:07:30.337478 23658 solver.cpp:237]     Train net output #0: loss = 0.144215 (* 1 = 0.144215 loss)
I0407 23:07:30.337489 23658 sgd_solver.cpp:105] Iteration 7920, lr = 0.000186315
I0407 23:07:35.335846 23658 solver.cpp:218] Iteration 7932 (2.40085 iter/s, 4.99822s/12 iters), loss = 0.0690946
I0407 23:07:35.335893 23658 solver.cpp:237]     Train net output #0: loss = 0.0690946 (* 1 = 0.0690946 loss)
I0407 23:07:35.335902 23658 sgd_solver.cpp:105] Iteration 7932, lr = 0.000185194
I0407 23:07:40.624472 23658 solver.cpp:218] Iteration 7944 (2.2691 iter/s, 5.28843s/12 iters), loss = 0.0778103
I0407 23:07:40.624521 23658 solver.cpp:237]     Train net output #0: loss = 0.0778103 (* 1 = 0.0778103 loss)
I0407 23:07:40.624531 23658 sgd_solver.cpp:105] Iteration 7944, lr = 0.00018408
I0407 23:07:45.583061 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 23:07:48.571655 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 23:07:50.880486 23658 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 23:07:50.880511 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:07:52.238337 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:55.352056 23658 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0407 23:07:55.352104 23658 solver.cpp:397]     Test net output #1: loss = 2.98466 (* 1 = 2.98466 loss)
I0407 23:07:55.442127 23658 solver.cpp:218] Iteration 7956 (0.809869 iter/s, 14.8172s/12 iters), loss = 0.119609
I0407 23:07:55.442183 23658 solver.cpp:237]     Train net output #0: loss = 0.119609 (* 1 = 0.119609 loss)
I0407 23:07:55.442193 23658 sgd_solver.cpp:105] Iteration 7956, lr = 0.000182972
I0407 23:07:59.882524 23658 solver.cpp:218] Iteration 7968 (2.70257 iter/s, 4.44022s/12 iters), loss = 0.0809751
I0407 23:07:59.882570 23658 solver.cpp:237]     Train net output #0: loss = 0.0809751 (* 1 = 0.0809751 loss)
I0407 23:07:59.882582 23658 sgd_solver.cpp:105] Iteration 7968, lr = 0.000181871
I0407 23:08:05.192750 23658 solver.cpp:218] Iteration 7980 (2.25987 iter/s, 5.31003s/12 iters), loss = 0.124834
I0407 23:08:05.192898 23658 solver.cpp:237]     Train net output #0: loss = 0.124834 (* 1 = 0.124834 loss)
I0407 23:08:05.192911 23658 sgd_solver.cpp:105] Iteration 7980, lr = 0.000180777
I0407 23:08:09.555169 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:10.282719 23658 solver.cpp:218] Iteration 7992 (2.35771 iter/s, 5.08968s/12 iters), loss = 0.156162
I0407 23:08:10.282771 23658 solver.cpp:237]     Train net output #0: loss = 0.156162 (* 1 = 0.156162 loss)
I0407 23:08:10.282783 23658 sgd_solver.cpp:105] Iteration 7992, lr = 0.00017969
I0407 23:08:15.240320 23658 solver.cpp:218] Iteration 8004 (2.42062 iter/s, 4.9574s/12 iters), loss = 0.102888
I0407 23:08:15.240375 23658 solver.cpp:237]     Train net output #0: loss = 0.102888 (* 1 = 0.102888 loss)
I0407 23:08:15.240387 23658 sgd_solver.cpp:105] Iteration 8004, lr = 0.000178608
I0407 23:08:20.303894 23658 solver.cpp:218] Iteration 8016 (2.36996 iter/s, 5.06338s/12 iters), loss = 0.205399
I0407 23:08:20.303937 23658 solver.cpp:237]     Train net output #0: loss = 0.205399 (* 1 = 0.205399 loss)
I0407 23:08:20.303947 23658 sgd_solver.cpp:105] Iteration 8016, lr = 0.000177534
I0407 23:08:25.402464 23658 solver.cpp:218] Iteration 8028 (2.35369 iter/s, 5.09838s/12 iters), loss = 0.144024
I0407 23:08:25.402518 23658 solver.cpp:237]     Train net output #0: loss = 0.144024 (* 1 = 0.144024 loss)
I0407 23:08:25.402530 23658 sgd_solver.cpp:105] Iteration 8028, lr = 0.000176466
I0407 23:08:30.925287 23658 solver.cpp:218] Iteration 8040 (2.17288 iter/s, 5.52262s/12 iters), loss = 0.0518308
I0407 23:08:30.925328 23658 solver.cpp:237]     Train net output #0: loss = 0.0518308 (* 1 = 0.0518308 loss)
I0407 23:08:30.925338 23658 sgd_solver.cpp:105] Iteration 8040, lr = 0.000175404
I0407 23:08:36.014071 23658 solver.cpp:218] Iteration 8052 (2.35821 iter/s, 5.0886s/12 iters), loss = 0.0476075
I0407 23:08:36.014175 23658 solver.cpp:237]     Train net output #0: loss = 0.0476075 (* 1 = 0.0476075 loss)
I0407 23:08:36.014186 23658 sgd_solver.cpp:105] Iteration 8052, lr = 0.000174349
I0407 23:08:38.083344 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 23:08:41.122290 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 23:08:43.422046 23658 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 23:08:43.422063 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:08:44.826764 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:47.985778 23658 solver.cpp:397]     Test net output #0: accuracy = 0.487745
I0407 23:08:47.985827 23658 solver.cpp:397]     Test net output #1: loss = 2.99357 (* 1 = 2.99357 loss)
I0407 23:08:49.997900 23658 solver.cpp:218] Iteration 8064 (0.858163 iter/s, 13.9834s/12 iters), loss = 0.0862874
I0407 23:08:49.997951 23658 solver.cpp:237]     Train net output #0: loss = 0.0862874 (* 1 = 0.0862874 loss)
I0407 23:08:49.997973 23658 sgd_solver.cpp:105] Iteration 8064, lr = 0.0001733
I0407 23:08:55.309353 23658 solver.cpp:218] Iteration 8076 (2.25935 iter/s, 5.31126s/12 iters), loss = 0.0857287
I0407 23:08:55.309402 23658 solver.cpp:237]     Train net output #0: loss = 0.0857288 (* 1 = 0.0857288 loss)
I0407 23:08:55.309412 23658 sgd_solver.cpp:105] Iteration 8076, lr = 0.000172257
I0407 23:09:00.376777 23658 solver.cpp:218] Iteration 8088 (2.36816 iter/s, 5.06723s/12 iters), loss = 0.0803614
I0407 23:09:00.376832 23658 solver.cpp:237]     Train net output #0: loss = 0.0803614 (* 1 = 0.0803614 loss)
I0407 23:09:00.376844 23658 sgd_solver.cpp:105] Iteration 8088, lr = 0.000171221
I0407 23:09:01.892781 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:05.845358 23658 solver.cpp:218] Iteration 8100 (2.19444 iter/s, 5.46837s/12 iters), loss = 0.0800746
I0407 23:09:05.845412 23658 solver.cpp:237]     Train net output #0: loss = 0.0800747 (* 1 = 0.0800747 loss)
I0407 23:09:05.845423 23658 sgd_solver.cpp:105] Iteration 8100, lr = 0.00017019
I0407 23:09:10.909065 23658 solver.cpp:218] Iteration 8112 (2.3699 iter/s, 5.0635s/12 iters), loss = 0.0645936
I0407 23:09:10.909269 23658 solver.cpp:237]     Train net output #0: loss = 0.0645936 (* 1 = 0.0645936 loss)
I0407 23:09:10.909282 23658 sgd_solver.cpp:105] Iteration 8112, lr = 0.000169167
I0407 23:09:15.873139 23658 solver.cpp:218] Iteration 8124 (2.41753 iter/s, 4.96374s/12 iters), loss = 0.0581823
I0407 23:09:15.873178 23658 solver.cpp:237]     Train net output #0: loss = 0.0581823 (* 1 = 0.0581823 loss)
I0407 23:09:15.873188 23658 sgd_solver.cpp:105] Iteration 8124, lr = 0.000168149
I0407 23:09:21.329893 23658 solver.cpp:218] Iteration 8136 (2.19919 iter/s, 5.45656s/12 iters), loss = 0.152789
I0407 23:09:21.329938 23658 solver.cpp:237]     Train net output #0: loss = 0.152789 (* 1 = 0.152789 loss)
I0407 23:09:21.329949 23658 sgd_solver.cpp:105] Iteration 8136, lr = 0.000167137
I0407 23:09:26.507745 23658 solver.cpp:218] Iteration 8148 (2.31765 iter/s, 5.17766s/12 iters), loss = 0.0649766
I0407 23:09:26.507798 23658 solver.cpp:237]     Train net output #0: loss = 0.0649767 (* 1 = 0.0649767 loss)
I0407 23:09:26.507810 23658 sgd_solver.cpp:105] Iteration 8148, lr = 0.000166131
I0407 23:09:31.093259 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 23:09:34.136351 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 23:09:36.485550 23658 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 23:09:36.485575 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:09:37.738803 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:40.929994 23658 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0407 23:09:40.930119 23658 solver.cpp:397]     Test net output #1: loss = 2.96994 (* 1 = 2.96994 loss)
I0407 23:09:41.020310 23658 solver.cpp:218] Iteration 8160 (0.826895 iter/s, 14.5121s/12 iters), loss = 0.122802
I0407 23:09:41.020362 23658 solver.cpp:237]     Train net output #0: loss = 0.122802 (* 1 = 0.122802 loss)
I0407 23:09:41.020375 23658 sgd_solver.cpp:105] Iteration 8160, lr = 0.000165132
I0407 23:09:45.359293 23658 solver.cpp:218] Iteration 8172 (2.76574 iter/s, 4.33881s/12 iters), loss = 0.120194
I0407 23:09:45.359344 23658 solver.cpp:237]     Train net output #0: loss = 0.120194 (* 1 = 0.120194 loss)
I0407 23:09:45.359356 23658 sgd_solver.cpp:105] Iteration 8172, lr = 0.000164138
I0407 23:09:50.743710 23658 solver.cpp:218] Iteration 8184 (2.22874 iter/s, 5.38422s/12 iters), loss = 0.154351
I0407 23:09:50.743762 23658 solver.cpp:237]     Train net output #0: loss = 0.154351 (* 1 = 0.154351 loss)
I0407 23:09:50.743777 23658 sgd_solver.cpp:105] Iteration 8184, lr = 0.000163151
I0407 23:09:55.037801 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:56.492063 23658 solver.cpp:218] Iteration 8196 (2.08763 iter/s, 5.74814s/12 iters), loss = 0.0702209
I0407 23:09:56.492111 23658 solver.cpp:237]     Train net output #0: loss = 0.0702209 (* 1 = 0.0702209 loss)
I0407 23:09:56.492125 23658 sgd_solver.cpp:105] Iteration 8196, lr = 0.000162169
I0407 23:10:01.716084 23658 solver.cpp:218] Iteration 8208 (2.29717 iter/s, 5.22382s/12 iters), loss = 0.0740044
I0407 23:10:01.716140 23658 solver.cpp:237]     Train net output #0: loss = 0.0740045 (* 1 = 0.0740045 loss)
I0407 23:10:01.716152 23658 sgd_solver.cpp:105] Iteration 8208, lr = 0.000161194
I0407 23:10:07.157681 23658 solver.cpp:218] Iteration 8220 (2.20532 iter/s, 5.44139s/12 iters), loss = 0.0970673
I0407 23:10:07.157734 23658 solver.cpp:237]     Train net output #0: loss = 0.0970674 (* 1 = 0.0970674 loss)
I0407 23:10:07.157747 23658 sgd_solver.cpp:105] Iteration 8220, lr = 0.000160224
I0407 23:10:12.185969 23658 solver.cpp:218] Iteration 8232 (2.3866 iter/s, 5.02808s/12 iters), loss = 0.0951295
I0407 23:10:12.186080 23658 solver.cpp:237]     Train net output #0: loss = 0.0951295 (* 1 = 0.0951295 loss)
I0407 23:10:12.186094 23658 sgd_solver.cpp:105] Iteration 8232, lr = 0.00015926
I0407 23:10:17.242383 23658 solver.cpp:218] Iteration 8244 (2.37334 iter/s, 5.05617s/12 iters), loss = 0.0456155
I0407 23:10:17.242434 23658 solver.cpp:237]     Train net output #0: loss = 0.0456155 (* 1 = 0.0456155 loss)
I0407 23:10:17.242447 23658 sgd_solver.cpp:105] Iteration 8244, lr = 0.000158302
I0407 23:10:22.195281 23658 solver.cpp:218] Iteration 8256 (2.42292 iter/s, 4.9527s/12 iters), loss = 0.0615929
I0407 23:10:22.195335 23658 solver.cpp:237]     Train net output #0: loss = 0.0615929 (* 1 = 0.0615929 loss)
I0407 23:10:22.195346 23658 sgd_solver.cpp:105] Iteration 8256, lr = 0.000157349
I0407 23:10:24.195086 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 23:10:27.274753 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 23:10:29.580018 23658 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 23:10:29.580041 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:10:30.880961 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:34.112869 23658 solver.cpp:397]     Test net output #0: accuracy = 0.484069
I0407 23:10:34.112905 23658 solver.cpp:397]     Test net output #1: loss = 2.97997 (* 1 = 2.97997 loss)
I0407 23:10:35.926798 23658 solver.cpp:218] Iteration 8268 (0.873929 iter/s, 13.7311s/12 iters), loss = 0.0881153
I0407 23:10:35.926847 23658 solver.cpp:237]     Train net output #0: loss = 0.0881153 (* 1 = 0.0881153 loss)
I0407 23:10:35.926859 23658 sgd_solver.cpp:105] Iteration 8268, lr = 0.000156402
I0407 23:10:40.976547 23658 solver.cpp:218] Iteration 8280 (2.37645 iter/s, 5.04955s/12 iters), loss = 0.16291
I0407 23:10:40.976598 23658 solver.cpp:237]     Train net output #0: loss = 0.16291 (* 1 = 0.16291 loss)
I0407 23:10:40.976610 23658 sgd_solver.cpp:105] Iteration 8280, lr = 0.000155461
I0407 23:10:46.090864 23658 solver.cpp:218] Iteration 8292 (2.34645 iter/s, 5.11412s/12 iters), loss = 0.0607589
I0407 23:10:46.090988 23658 solver.cpp:237]     Train net output #0: loss = 0.0607589 (* 1 = 0.0607589 loss)
I0407 23:10:46.091002 23658 sgd_solver.cpp:105] Iteration 8292, lr = 0.000154526
I0407 23:10:46.724232 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:51.111474 23658 solver.cpp:218] Iteration 8304 (2.39028 iter/s, 5.02034s/12 iters), loss = 0.0886923
I0407 23:10:51.111527 23658 solver.cpp:237]     Train net output #0: loss = 0.0886924 (* 1 = 0.0886924 loss)
I0407 23:10:51.111539 23658 sgd_solver.cpp:105] Iteration 8304, lr = 0.000153596
I0407 23:10:54.076601 23658 blocking_queue.cpp:49] Waiting for data
I0407 23:10:56.239717 23658 solver.cpp:218] Iteration 8316 (2.34007 iter/s, 5.12805s/12 iters), loss = 0.0827017
I0407 23:10:56.239764 23658 solver.cpp:237]     Train net output #0: loss = 0.0827018 (* 1 = 0.0827018 loss)
I0407 23:10:56.239776 23658 sgd_solver.cpp:105] Iteration 8316, lr = 0.000152672
I0407 23:11:01.417102 23658 solver.cpp:218] Iteration 8328 (2.31786 iter/s, 5.17719s/12 iters), loss = 0.0540971
I0407 23:11:01.417153 23658 solver.cpp:237]     Train net output #0: loss = 0.0540971 (* 1 = 0.0540971 loss)
I0407 23:11:01.417165 23658 sgd_solver.cpp:105] Iteration 8328, lr = 0.000151754
I0407 23:11:06.535833 23658 solver.cpp:218] Iteration 8340 (2.34442 iter/s, 5.11853s/12 iters), loss = 0.076166
I0407 23:11:06.535883 23658 solver.cpp:237]     Train net output #0: loss = 0.0761661 (* 1 = 0.0761661 loss)
I0407 23:11:06.535894 23658 sgd_solver.cpp:105] Iteration 8340, lr = 0.000150841
I0407 23:11:11.573184 23658 solver.cpp:218] Iteration 8352 (2.38229 iter/s, 5.03716s/12 iters), loss = 0.0970472
I0407 23:11:11.573223 23658 solver.cpp:237]     Train net output #0: loss = 0.0970473 (* 1 = 0.0970473 loss)
I0407 23:11:11.573233 23658 sgd_solver.cpp:105] Iteration 8352, lr = 0.000149933
I0407 23:11:16.104439 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 23:11:19.160089 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 23:11:22.003818 23658 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 23:11:22.003844 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:11:23.175403 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:26.636945 23658 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 23:11:26.636986 23658 solver.cpp:397]     Test net output #1: loss = 2.97895 (* 1 = 2.97895 loss)
I0407 23:11:26.726868 23658 solver.cpp:218] Iteration 8364 (0.79191 iter/s, 15.1532s/12 iters), loss = 0.139137
I0407 23:11:26.726907 23658 solver.cpp:237]     Train net output #0: loss = 0.139137 (* 1 = 0.139137 loss)
I0407 23:11:26.726917 23658 sgd_solver.cpp:105] Iteration 8364, lr = 0.000149031
I0407 23:11:31.041757 23658 solver.cpp:218] Iteration 8376 (2.78118 iter/s, 4.31472s/12 iters), loss = 0.130051
I0407 23:11:31.041815 23658 solver.cpp:237]     Train net output #0: loss = 0.130051 (* 1 = 0.130051 loss)
I0407 23:11:31.041829 23658 sgd_solver.cpp:105] Iteration 8376, lr = 0.000148134
I0407 23:11:36.287863 23658 solver.cpp:218] Iteration 8388 (2.2875 iter/s, 5.24589s/12 iters), loss = 0.0902788
I0407 23:11:36.287914 23658 solver.cpp:237]     Train net output #0: loss = 0.0902789 (* 1 = 0.0902789 loss)
I0407 23:11:36.287925 23658 sgd_solver.cpp:105] Iteration 8388, lr = 0.000147243
I0407 23:11:39.150211 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:41.418339 23658 solver.cpp:218] Iteration 8400 (2.33906 iter/s, 5.13028s/12 iters), loss = 0.0979695
I0407 23:11:41.418395 23658 solver.cpp:237]     Train net output #0: loss = 0.0979696 (* 1 = 0.0979696 loss)
I0407 23:11:41.418409 23658 sgd_solver.cpp:105] Iteration 8400, lr = 0.000146357
I0407 23:11:46.397714 23658 solver.cpp:218] Iteration 8412 (2.41004 iter/s, 4.97918s/12 iters), loss = 0.0510425
I0407 23:11:46.397886 23658 solver.cpp:237]     Train net output #0: loss = 0.0510425 (* 1 = 0.0510425 loss)
I0407 23:11:46.397899 23658 sgd_solver.cpp:105] Iteration 8412, lr = 0.000145477
I0407 23:11:51.436177 23658 solver.cpp:218] Iteration 8424 (2.38183 iter/s, 5.03815s/12 iters), loss = 0.0345978
I0407 23:11:51.436233 23658 solver.cpp:237]     Train net output #0: loss = 0.0345979 (* 1 = 0.0345979 loss)
I0407 23:11:51.436246 23658 sgd_solver.cpp:105] Iteration 8424, lr = 0.000144601
I0407 23:11:56.408666 23658 solver.cpp:218] Iteration 8436 (2.41338 iter/s, 4.97228s/12 iters), loss = 0.0716307
I0407 23:11:56.408716 23658 solver.cpp:237]     Train net output #0: loss = 0.0716307 (* 1 = 0.0716307 loss)
I0407 23:11:56.408726 23658 sgd_solver.cpp:105] Iteration 8436, lr = 0.000143731
I0407 23:12:01.414889 23658 solver.cpp:218] Iteration 8448 (2.39711 iter/s, 5.00602s/12 iters), loss = 0.134223
I0407 23:12:01.414947 23658 solver.cpp:237]     Train net output #0: loss = 0.134223 (* 1 = 0.134223 loss)
I0407 23:12:01.414959 23658 sgd_solver.cpp:105] Iteration 8448, lr = 0.000142867
I0407 23:12:06.438376 23658 solver.cpp:218] Iteration 8460 (2.38888 iter/s, 5.02328s/12 iters), loss = 0.118504
I0407 23:12:06.438426 23658 solver.cpp:237]     Train net output #0: loss = 0.118504 (* 1 = 0.118504 loss)
I0407 23:12:06.438437 23658 sgd_solver.cpp:105] Iteration 8460, lr = 0.000142007
I0407 23:12:08.483472 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 23:12:11.547940 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 23:12:13.856323 23658 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 23:12:13.856348 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:12:15.010732 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:18.405818 23658 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0407 23:12:18.405943 23658 solver.cpp:397]     Test net output #1: loss = 2.97696 (* 1 = 2.97696 loss)
I0407 23:12:20.331063 23658 solver.cpp:218] Iteration 8472 (0.86379 iter/s, 13.8923s/12 iters), loss = 0.119788
I0407 23:12:20.331117 23658 solver.cpp:237]     Train net output #0: loss = 0.119788 (* 1 = 0.119788 loss)
I0407 23:12:20.331130 23658 sgd_solver.cpp:105] Iteration 8472, lr = 0.000141153
I0407 23:12:25.699828 23658 solver.cpp:218] Iteration 8484 (2.23524 iter/s, 5.36856s/12 iters), loss = 0.0678663
I0407 23:12:25.699888 23658 solver.cpp:237]     Train net output #0: loss = 0.0678664 (* 1 = 0.0678664 loss)
I0407 23:12:25.699900 23658 sgd_solver.cpp:105] Iteration 8484, lr = 0.000140303
I0407 23:12:30.783330 23658 solver.cpp:218] Iteration 8496 (2.36067 iter/s, 5.0833s/12 iters), loss = 0.0732774
I0407 23:12:30.783380 23658 solver.cpp:237]     Train net output #0: loss = 0.0732775 (* 1 = 0.0732775 loss)
I0407 23:12:30.783392 23658 sgd_solver.cpp:105] Iteration 8496, lr = 0.000139459
I0407 23:12:30.839684 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:36.298722 23658 solver.cpp:218] Iteration 8508 (2.17581 iter/s, 5.51518s/12 iters), loss = 0.179829
I0407 23:12:36.298787 23658 solver.cpp:237]     Train net output #0: loss = 0.179829 (* 1 = 0.179829 loss)
I0407 23:12:36.298801 23658 sgd_solver.cpp:105] Iteration 8508, lr = 0.00013862
I0407 23:12:41.784648 23658 solver.cpp:218] Iteration 8520 (2.1875 iter/s, 5.48571s/12 iters), loss = 0.0982298
I0407 23:12:41.784693 23658 solver.cpp:237]     Train net output #0: loss = 0.0982299 (* 1 = 0.0982299 loss)
I0407 23:12:41.784703 23658 sgd_solver.cpp:105] Iteration 8520, lr = 0.000137786
I0407 23:12:47.277981 23658 solver.cpp:218] Iteration 8532 (2.18455 iter/s, 5.49311s/12 iters), loss = 0.110036
I0407 23:12:47.278023 23658 solver.cpp:237]     Train net output #0: loss = 0.110036 (* 1 = 0.110036 loss)
I0407 23:12:47.278031 23658 sgd_solver.cpp:105] Iteration 8532, lr = 0.000136957
I0407 23:12:52.782968 23658 solver.cpp:218] Iteration 8544 (2.17992 iter/s, 5.50479s/12 iters), loss = 0.138173
I0407 23:12:52.783120 23658 solver.cpp:237]     Train net output #0: loss = 0.138173 (* 1 = 0.138173 loss)
I0407 23:12:52.783135 23658 sgd_solver.cpp:105] Iteration 8544, lr = 0.000136133
I0407 23:12:58.313342 23658 solver.cpp:218] Iteration 8556 (2.16996 iter/s, 5.53006s/12 iters), loss = 0.109757
I0407 23:12:58.313395 23658 solver.cpp:237]     Train net output #0: loss = 0.109757 (* 1 = 0.109757 loss)
I0407 23:12:58.313405 23658 sgd_solver.cpp:105] Iteration 8556, lr = 0.000135314
I0407 23:13:03.364833 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 23:13:06.456645 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 23:13:09.070894 23658 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 23:13:09.070922 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:13:10.181951 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:13.599331 23658 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 23:13:13.599381 23658 solver.cpp:397]     Test net output #1: loss = 2.97028 (* 1 = 2.97028 loss)
I0407 23:13:13.689271 23658 solver.cpp:218] Iteration 8568 (0.780464 iter/s, 15.3755s/12 iters), loss = 0.172414
I0407 23:13:13.689325 23658 solver.cpp:237]     Train net output #0: loss = 0.172414 (* 1 = 0.172414 loss)
I0407 23:13:13.689337 23658 sgd_solver.cpp:105] Iteration 8568, lr = 0.0001345
I0407 23:13:18.237753 23658 solver.cpp:218] Iteration 8580 (2.63835 iter/s, 4.5483s/12 iters), loss = 0.106188
I0407 23:13:18.237795 23658 solver.cpp:237]     Train net output #0: loss = 0.106188 (* 1 = 0.106188 loss)
I0407 23:13:18.237807 23658 sgd_solver.cpp:105] Iteration 8580, lr = 0.000133691
I0407 23:13:23.545989 23658 solver.cpp:218] Iteration 8592 (2.26072 iter/s, 5.30804s/12 iters), loss = 0.0717305
I0407 23:13:23.546100 23658 solver.cpp:237]     Train net output #0: loss = 0.0717306 (* 1 = 0.0717306 loss)
I0407 23:13:23.546115 23658 sgd_solver.cpp:105] Iteration 8592, lr = 0.000132887
I0407 23:13:25.733004 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:28.546495 23658 solver.cpp:218] Iteration 8604 (2.39988 iter/s, 5.00026s/12 iters), loss = 0.129351
I0407 23:13:28.546542 23658 solver.cpp:237]     Train net output #0: loss = 0.129352 (* 1 = 0.129352 loss)
I0407 23:13:28.546555 23658 sgd_solver.cpp:105] Iteration 8604, lr = 0.000132087
I0407 23:13:33.500239 23658 solver.cpp:218] Iteration 8616 (2.4225 iter/s, 4.95355s/12 iters), loss = 0.141187
I0407 23:13:33.500286 23658 solver.cpp:237]     Train net output #0: loss = 0.141187 (* 1 = 0.141187 loss)
I0407 23:13:33.500296 23658 sgd_solver.cpp:105] Iteration 8616, lr = 0.000131292
I0407 23:13:38.438139 23658 solver.cpp:218] Iteration 8628 (2.43028 iter/s, 4.9377s/12 iters), loss = 0.113983
I0407 23:13:38.438195 23658 solver.cpp:237]     Train net output #0: loss = 0.113983 (* 1 = 0.113983 loss)
I0407 23:13:38.438207 23658 sgd_solver.cpp:105] Iteration 8628, lr = 0.000130502
I0407 23:13:43.510450 23658 solver.cpp:218] Iteration 8640 (2.36588 iter/s, 5.07211s/12 iters), loss = 0.0945002
I0407 23:13:43.510506 23658 solver.cpp:237]     Train net output #0: loss = 0.0945003 (* 1 = 0.0945003 loss)
I0407 23:13:43.510520 23658 sgd_solver.cpp:105] Iteration 8640, lr = 0.000129717
I0407 23:13:48.657310 23658 solver.cpp:218] Iteration 8652 (2.33161 iter/s, 5.14665s/12 iters), loss = 0.037269
I0407 23:13:48.657361 23658 solver.cpp:237]     Train net output #0: loss = 0.0372691 (* 1 = 0.0372691 loss)
I0407 23:13:48.657373 23658 sgd_solver.cpp:105] Iteration 8652, lr = 0.000128937
I0407 23:13:53.977741 23658 solver.cpp:218] Iteration 8664 (2.25554 iter/s, 5.32024s/12 iters), loss = 0.12678
I0407 23:13:53.977836 23658 solver.cpp:237]     Train net output #0: loss = 0.12678 (* 1 = 0.12678 loss)
I0407 23:13:53.977846 23658 sgd_solver.cpp:105] Iteration 8664, lr = 0.000128161
I0407 23:13:56.010040 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 23:13:59.004115 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 23:14:01.283382 23658 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 23:14:01.283402 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:14:02.357695 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:05.760881 23658 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 23:14:05.760931 23658 solver.cpp:397]     Test net output #1: loss = 2.96648 (* 1 = 2.96648 loss)
I0407 23:14:07.764302 23658 solver.cpp:218] Iteration 8676 (0.870442 iter/s, 13.7861s/12 iters), loss = 0.065185
I0407 23:14:07.764348 23658 solver.cpp:237]     Train net output #0: loss = 0.0651851 (* 1 = 0.0651851 loss)
I0407 23:14:07.764358 23658 sgd_solver.cpp:105] Iteration 8676, lr = 0.00012739
I0407 23:14:12.947108 23658 solver.cpp:218] Iteration 8688 (2.31544 iter/s, 5.18261s/12 iters), loss = 0.126905
I0407 23:14:12.947154 23658 solver.cpp:237]     Train net output #0: loss = 0.126906 (* 1 = 0.126906 loss)
I0407 23:14:12.947165 23658 sgd_solver.cpp:105] Iteration 8688, lr = 0.000126623
I0407 23:14:17.349833 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:18.056866 23658 solver.cpp:218] Iteration 8700 (2.34853 iter/s, 5.10957s/12 iters), loss = 0.212586
I0407 23:14:18.056913 23658 solver.cpp:237]     Train net output #0: loss = 0.212586 (* 1 = 0.212586 loss)
I0407 23:14:18.056926 23658 sgd_solver.cpp:105] Iteration 8700, lr = 0.000125862
I0407 23:14:23.212977 23658 solver.cpp:218] Iteration 8712 (2.32742 iter/s, 5.15592s/12 iters), loss = 0.102977
I0407 23:14:23.213021 23658 solver.cpp:237]     Train net output #0: loss = 0.102977 (* 1 = 0.102977 loss)
I0407 23:14:23.213032 23658 sgd_solver.cpp:105] Iteration 8712, lr = 0.000125104
I0407 23:14:28.299252 23658 solver.cpp:218] Iteration 8724 (2.35938 iter/s, 5.08608s/12 iters), loss = 0.0601144
I0407 23:14:28.299357 23658 solver.cpp:237]     Train net output #0: loss = 0.0601145 (* 1 = 0.0601145 loss)
I0407 23:14:28.299374 23658 sgd_solver.cpp:105] Iteration 8724, lr = 0.000124352
I0407 23:14:33.223244 23658 solver.cpp:218] Iteration 8736 (2.43717 iter/s, 4.92375s/12 iters), loss = 0.100859
I0407 23:14:33.223292 23658 solver.cpp:237]     Train net output #0: loss = 0.100859 (* 1 = 0.100859 loss)
I0407 23:14:33.223304 23658 sgd_solver.cpp:105] Iteration 8736, lr = 0.000123604
I0407 23:14:38.420050 23658 solver.cpp:218] Iteration 8748 (2.3092 iter/s, 5.19661s/12 iters), loss = 0.0412937
I0407 23:14:38.420099 23658 solver.cpp:237]     Train net output #0: loss = 0.0412938 (* 1 = 0.0412938 loss)
I0407 23:14:38.420109 23658 sgd_solver.cpp:105] Iteration 8748, lr = 0.00012286
I0407 23:14:43.715389 23658 solver.cpp:218] Iteration 8760 (2.26623 iter/s, 5.29515s/12 iters), loss = 0.0391437
I0407 23:14:43.715421 23658 solver.cpp:237]     Train net output #0: loss = 0.0391437 (* 1 = 0.0391437 loss)
I0407 23:14:43.715430 23658 sgd_solver.cpp:105] Iteration 8760, lr = 0.000122121
I0407 23:14:48.262804 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 23:14:51.282788 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 23:14:53.625941 23658 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 23:14:53.625982 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:14:54.662228 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:58.109817 23658 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0407 23:14:58.109845 23658 solver.cpp:397]     Test net output #1: loss = 2.98252 (* 1 = 2.98252 loss)
I0407 23:14:58.199580 23658 solver.cpp:218] Iteration 8772 (0.828514 iter/s, 14.4838s/12 iters), loss = 0.0250738
I0407 23:14:58.199640 23658 solver.cpp:237]     Train net output #0: loss = 0.0250739 (* 1 = 0.0250739 loss)
I0407 23:14:58.199651 23658 sgd_solver.cpp:105] Iteration 8772, lr = 0.000121386
I0407 23:15:02.765581 23658 solver.cpp:218] Iteration 8784 (2.62822 iter/s, 4.56583s/12 iters), loss = 0.0569003
I0407 23:15:02.765727 23658 solver.cpp:237]     Train net output #0: loss = 0.0569004 (* 1 = 0.0569004 loss)
I0407 23:15:02.765738 23658 sgd_solver.cpp:105] Iteration 8784, lr = 0.000120656
I0407 23:15:07.865921 23658 solver.cpp:218] Iteration 8796 (2.35291 iter/s, 5.10007s/12 iters), loss = 0.0860678
I0407 23:15:07.865978 23658 solver.cpp:237]     Train net output #0: loss = 0.0860678 (* 1 = 0.0860678 loss)
I0407 23:15:07.865990 23658 sgd_solver.cpp:105] Iteration 8796, lr = 0.00011993
I0407 23:15:09.313858 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:12.920220 23658 solver.cpp:218] Iteration 8808 (2.3743 iter/s, 5.05412s/12 iters), loss = 0.0742808
I0407 23:15:12.920264 23658 solver.cpp:237]     Train net output #0: loss = 0.0742809 (* 1 = 0.0742809 loss)
I0407 23:15:12.920274 23658 sgd_solver.cpp:105] Iteration 8808, lr = 0.000119208
I0407 23:15:17.968926 23658 solver.cpp:218] Iteration 8820 (2.37693 iter/s, 5.04853s/12 iters), loss = 0.0387864
I0407 23:15:17.968979 23658 solver.cpp:237]     Train net output #0: loss = 0.0387864 (* 1 = 0.0387864 loss)
I0407 23:15:17.968991 23658 sgd_solver.cpp:105] Iteration 8820, lr = 0.000118491
I0407 23:15:23.292675 23658 solver.cpp:218] Iteration 8832 (2.25413 iter/s, 5.32356s/12 iters), loss = 0.0408638
I0407 23:15:23.292721 23658 solver.cpp:237]     Train net output #0: loss = 0.0408638 (* 1 = 0.0408638 loss)
I0407 23:15:23.292733 23658 sgd_solver.cpp:105] Iteration 8832, lr = 0.000117778
I0407 23:15:28.394454 23658 solver.cpp:218] Iteration 8844 (2.3522 iter/s, 5.1016s/12 iters), loss = 0.0652438
I0407 23:15:28.394501 23658 solver.cpp:237]     Train net output #0: loss = 0.0652438 (* 1 = 0.0652438 loss)
I0407 23:15:28.394513 23658 sgd_solver.cpp:105] Iteration 8844, lr = 0.000117069
I0407 23:15:33.487416 23658 solver.cpp:218] Iteration 8856 (2.35627 iter/s, 5.09279s/12 iters), loss = 0.0568283
I0407 23:15:33.487831 23658 solver.cpp:237]     Train net output #0: loss = 0.0568283 (* 1 = 0.0568283 loss)
I0407 23:15:33.487846 23658 sgd_solver.cpp:105] Iteration 8856, lr = 0.000116365
I0407 23:15:38.757771 23658 solver.cpp:218] Iteration 8868 (2.27712 iter/s, 5.26982s/12 iters), loss = 0.0252308
I0407 23:15:38.757809 23658 solver.cpp:237]     Train net output #0: loss = 0.0252309 (* 1 = 0.0252309 loss)
I0407 23:15:38.757818 23658 sgd_solver.cpp:105] Iteration 8868, lr = 0.000115665
I0407 23:15:40.937789 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 23:15:43.929462 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 23:15:46.228240 23658 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 23:15:46.228261 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:15:47.223577 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:50.693606 23658 solver.cpp:397]     Test net output #0: accuracy = 0.492034
I0407 23:15:50.693662 23658 solver.cpp:397]     Test net output #1: loss = 2.96265 (* 1 = 2.96265 loss)
I0407 23:15:52.458043 23658 solver.cpp:218] Iteration 8880 (0.875918 iter/s, 13.6999s/12 iters), loss = 0.154737
I0407 23:15:52.458086 23658 solver.cpp:237]     Train net output #0: loss = 0.154737 (* 1 = 0.154737 loss)
I0407 23:15:52.458096 23658 sgd_solver.cpp:105] Iteration 8880, lr = 0.000114969
I0407 23:15:57.502781 23658 solver.cpp:218] Iteration 8892 (2.3788 iter/s, 5.04457s/12 iters), loss = 0.070788
I0407 23:15:57.502825 23658 solver.cpp:237]     Train net output #0: loss = 0.070788 (* 1 = 0.070788 loss)
I0407 23:15:57.502835 23658 sgd_solver.cpp:105] Iteration 8892, lr = 0.000114277
I0407 23:16:01.173982 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:02.611413 23658 solver.cpp:218] Iteration 8904 (2.34905 iter/s, 5.10845s/12 iters), loss = 0.05441
I0407 23:16:02.611460 23658 solver.cpp:237]     Train net output #0: loss = 0.0544101 (* 1 = 0.0544101 loss)
I0407 23:16:02.611470 23658 sgd_solver.cpp:105] Iteration 8904, lr = 0.00011359
I0407 23:16:07.676704 23658 solver.cpp:218] Iteration 8916 (2.36914 iter/s, 5.06512s/12 iters), loss = 0.0739151
I0407 23:16:07.676841 23658 solver.cpp:237]     Train net output #0: loss = 0.0739151 (* 1 = 0.0739151 loss)
I0407 23:16:07.676853 23658 sgd_solver.cpp:105] Iteration 8916, lr = 0.000112906
I0407 23:16:12.682675 23658 solver.cpp:218] Iteration 8928 (2.39726 iter/s, 5.00571s/12 iters), loss = 0.052469
I0407 23:16:12.682724 23658 solver.cpp:237]     Train net output #0: loss = 0.052469 (* 1 = 0.052469 loss)
I0407 23:16:12.682734 23658 sgd_solver.cpp:105] Iteration 8928, lr = 0.000112227
I0407 23:16:17.578809 23658 solver.cpp:218] Iteration 8940 (2.451 iter/s, 4.89595s/12 iters), loss = 0.170249
I0407 23:16:17.578872 23658 solver.cpp:237]     Train net output #0: loss = 0.170249 (* 1 = 0.170249 loss)
I0407 23:16:17.578887 23658 sgd_solver.cpp:105] Iteration 8940, lr = 0.000111552
I0407 23:16:22.595192 23658 solver.cpp:218] Iteration 8952 (2.39225 iter/s, 5.01619s/12 iters), loss = 0.0946961
I0407 23:16:22.595245 23658 solver.cpp:237]     Train net output #0: loss = 0.0946961 (* 1 = 0.0946961 loss)
I0407 23:16:22.595257 23658 sgd_solver.cpp:105] Iteration 8952, lr = 0.000110881
I0407 23:16:27.602393 23658 solver.cpp:218] Iteration 8964 (2.39664 iter/s, 5.00702s/12 iters), loss = 0.0546663
I0407 23:16:27.602468 23658 solver.cpp:237]     Train net output #0: loss = 0.0546664 (* 1 = 0.0546664 loss)
I0407 23:16:27.602494 23658 sgd_solver.cpp:105] Iteration 8964, lr = 0.000110214
I0407 23:16:32.209003 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 23:16:35.223116 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 23:16:37.529839 23658 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 23:16:37.529862 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:16:38.689884 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:42.192572 23658 solver.cpp:397]     Test net output #0: accuracy = 0.48652
I0407 23:16:42.192622 23658 solver.cpp:397]     Test net output #1: loss = 2.98666 (* 1 = 2.98666 loss)
I0407 23:16:42.282737 23658 solver.cpp:218] Iteration 8976 (0.817443 iter/s, 14.6799s/12 iters), loss = 0.102669
I0407 23:16:42.282789 23658 solver.cpp:237]     Train net output #0: loss = 0.102669 (* 1 = 0.102669 loss)
I0407 23:16:42.282801 23658 sgd_solver.cpp:105] Iteration 8976, lr = 0.00010955
I0407 23:16:46.615456 23658 solver.cpp:218] Iteration 8988 (2.76973 iter/s, 4.33255s/12 iters), loss = 0.0577148
I0407 23:16:46.615502 23658 solver.cpp:237]     Train net output #0: loss = 0.0577148 (* 1 = 0.0577148 loss)
I0407 23:16:46.615511 23658 sgd_solver.cpp:105] Iteration 8988, lr = 0.000108891
I0407 23:16:49.923959 23658 blocking_queue.cpp:49] Waiting for data
I0407 23:16:51.660457 23658 solver.cpp:218] Iteration 9000 (2.37868 iter/s, 5.04483s/12 iters), loss = 0.197473
I0407 23:16:51.660499 23658 solver.cpp:237]     Train net output #0: loss = 0.197473 (* 1 = 0.197473 loss)
I0407 23:16:51.660509 23658 sgd_solver.cpp:105] Iteration 9000, lr = 0.000108236
I0407 23:16:52.363377 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:56.693222 23658 solver.cpp:218] Iteration 9012 (2.38446 iter/s, 5.03259s/12 iters), loss = 0.109252
I0407 23:16:56.693267 23658 solver.cpp:237]     Train net output #0: loss = 0.109252 (* 1 = 0.109252 loss)
I0407 23:16:56.693280 23658 sgd_solver.cpp:105] Iteration 9012, lr = 0.000107585
I0407 23:17:01.769585 23658 solver.cpp:218] Iteration 9024 (2.36398 iter/s, 5.07619s/12 iters), loss = 0.0783496
I0407 23:17:01.769631 23658 solver.cpp:237]     Train net output #0: loss = 0.0783497 (* 1 = 0.0783497 loss)
I0407 23:17:01.769642 23658 sgd_solver.cpp:105] Iteration 9024, lr = 0.000106938
I0407 23:17:06.814214 23658 solver.cpp:218] Iteration 9036 (2.37885 iter/s, 5.04445s/12 iters), loss = 0.0696063
I0407 23:17:06.814270 23658 solver.cpp:237]     Train net output #0: loss = 0.0696064 (* 1 = 0.0696064 loss)
I0407 23:17:06.814285 23658 sgd_solver.cpp:105] Iteration 9036, lr = 0.000106294
I0407 23:17:11.786197 23658 solver.cpp:218] Iteration 9048 (2.41361 iter/s, 4.9718s/12 iters), loss = 0.0762541
I0407 23:17:11.786371 23658 solver.cpp:237]     Train net output #0: loss = 0.0762541 (* 1 = 0.0762541 loss)
I0407 23:17:11.786386 23658 sgd_solver.cpp:105] Iteration 9048, lr = 0.000105655
I0407 23:17:16.837764 23658 solver.cpp:218] Iteration 9060 (2.37564 iter/s, 5.05126s/12 iters), loss = 0.0391058
I0407 23:17:16.837821 23658 solver.cpp:237]     Train net output #0: loss = 0.0391059 (* 1 = 0.0391059 loss)
I0407 23:17:16.837834 23658 sgd_solver.cpp:105] Iteration 9060, lr = 0.000105019
I0407 23:17:22.180604 23658 solver.cpp:218] Iteration 9072 (2.24608 iter/s, 5.34265s/12 iters), loss = 0.1517
I0407 23:17:22.180644 23658 solver.cpp:237]     Train net output #0: loss = 0.1517 (* 1 = 0.1517 loss)
I0407 23:17:22.180652 23658 sgd_solver.cpp:105] Iteration 9072, lr = 0.000104387
I0407 23:17:24.253350 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 23:17:27.284204 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 23:17:30.715631 23658 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 23:17:30.715657 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:17:31.620471 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:35.171219 23658 solver.cpp:397]     Test net output #0: accuracy = 0.484069
I0407 23:17:35.171267 23658 solver.cpp:397]     Test net output #1: loss = 2.9953 (* 1 = 2.9953 loss)
I0407 23:17:37.041491 23658 solver.cpp:218] Iteration 9084 (0.807511 iter/s, 14.8605s/12 iters), loss = 0.0411978
I0407 23:17:37.041543 23658 solver.cpp:237]     Train net output #0: loss = 0.0411978 (* 1 = 0.0411978 loss)
I0407 23:17:37.041555 23658 sgd_solver.cpp:105] Iteration 9084, lr = 0.000103759
I0407 23:17:42.059316 23658 solver.cpp:218] Iteration 9096 (2.39156 iter/s, 5.01764s/12 iters), loss = 0.139389
I0407 23:17:42.059460 23658 solver.cpp:237]     Train net output #0: loss = 0.139389 (* 1 = 0.139389 loss)
I0407 23:17:42.059476 23658 sgd_solver.cpp:105] Iteration 9096, lr = 0.000103135
I0407 23:17:45.015262 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:47.095773 23658 solver.cpp:218] Iteration 9108 (2.38276 iter/s, 5.03618s/12 iters), loss = 0.138633
I0407 23:17:47.095820 23658 solver.cpp:237]     Train net output #0: loss = 0.138633 (* 1 = 0.138633 loss)
I0407 23:17:47.095829 23658 sgd_solver.cpp:105] Iteration 9108, lr = 0.000102514
I0407 23:17:52.166580 23658 solver.cpp:218] Iteration 9120 (2.36657 iter/s, 5.07063s/12 iters), loss = 0.0754545
I0407 23:17:52.166626 23658 solver.cpp:237]     Train net output #0: loss = 0.0754545 (* 1 = 0.0754545 loss)
I0407 23:17:52.166636 23658 sgd_solver.cpp:105] Iteration 9120, lr = 0.000101898
I0407 23:17:57.225154 23658 solver.cpp:218] Iteration 9132 (2.37229 iter/s, 5.0584s/12 iters), loss = 0.0756677
I0407 23:17:57.225205 23658 solver.cpp:237]     Train net output #0: loss = 0.0756677 (* 1 = 0.0756677 loss)
I0407 23:17:57.225217 23658 sgd_solver.cpp:105] Iteration 9132, lr = 0.000101285
I0407 23:18:02.139369 23658 solver.cpp:218] Iteration 9144 (2.44198 iter/s, 4.91404s/12 iters), loss = 0.162325
I0407 23:18:02.139417 23658 solver.cpp:237]     Train net output #0: loss = 0.162325 (* 1 = 0.162325 loss)
I0407 23:18:02.139429 23658 sgd_solver.cpp:105] Iteration 9144, lr = 0.000100675
I0407 23:18:07.100436 23658 solver.cpp:218] Iteration 9156 (2.41892 iter/s, 4.96089s/12 iters), loss = 0.0853219
I0407 23:18:07.100493 23658 solver.cpp:237]     Train net output #0: loss = 0.0853219 (* 1 = 0.0853219 loss)
I0407 23:18:07.100505 23658 sgd_solver.cpp:105] Iteration 9156, lr = 0.000100069
I0407 23:18:12.353137 23658 solver.cpp:218] Iteration 9168 (2.28462 iter/s, 5.25251s/12 iters), loss = 0.118906
I0407 23:18:12.353272 23658 solver.cpp:237]     Train net output #0: loss = 0.118906 (* 1 = 0.118906 loss)
I0407 23:18:12.353286 23658 sgd_solver.cpp:105] Iteration 9168, lr = 9.94674e-05
I0407 23:18:16.919407 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 23:18:20.120551 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 23:18:22.449116 23658 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 23:18:22.449142 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:18:23.314800 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:26.929522 23658 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0407 23:18:26.929564 23658 solver.cpp:397]     Test net output #1: loss = 2.97748 (* 1 = 2.97748 loss)
I0407 23:18:27.019423 23658 solver.cpp:218] Iteration 9180 (0.818231 iter/s, 14.6658s/12 iters), loss = 0.136178
I0407 23:18:27.019479 23658 solver.cpp:237]     Train net output #0: loss = 0.136178 (* 1 = 0.136178 loss)
I0407 23:18:27.019492 23658 sgd_solver.cpp:105] Iteration 9180, lr = 9.8869e-05
I0407 23:18:31.545289 23658 solver.cpp:218] Iteration 9192 (2.65153 iter/s, 4.52569s/12 iters), loss = 0.100535
I0407 23:18:31.545341 23658 solver.cpp:237]     Train net output #0: loss = 0.100535 (* 1 = 0.100535 loss)
I0407 23:18:31.545353 23658 sgd_solver.cpp:105] Iteration 9192, lr = 9.82741e-05
I0407 23:18:36.754489 23658 solver.cpp:218] Iteration 9204 (2.3037 iter/s, 5.20901s/12 iters), loss = 0.108197
I0407 23:18:36.754544 23658 solver.cpp:237]     Train net output #0: loss = 0.108197 (* 1 = 0.108197 loss)
I0407 23:18:36.754556 23658 sgd_solver.cpp:105] Iteration 9204, lr = 9.76829e-05
I0407 23:18:36.835402 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:41.638859 23658 solver.cpp:218] Iteration 9216 (2.45691 iter/s, 4.88418s/12 iters), loss = 0.108776
I0407 23:18:41.638919 23658 solver.cpp:237]     Train net output #0: loss = 0.108776 (* 1 = 0.108776 loss)
I0407 23:18:41.638931 23658 sgd_solver.cpp:105] Iteration 9216, lr = 9.70951e-05
I0407 23:18:46.712083 23658 solver.cpp:218] Iteration 9228 (2.36545 iter/s, 5.07303s/12 iters), loss = 0.125991
I0407 23:18:46.712210 23658 solver.cpp:237]     Train net output #0: loss = 0.125991 (* 1 = 0.125991 loss)
I0407 23:18:46.712224 23658 sgd_solver.cpp:105] Iteration 9228, lr = 9.6511e-05
I0407 23:18:51.726989 23658 solver.cpp:218] Iteration 9240 (2.39299 iter/s, 5.01465s/12 iters), loss = 0.0762056
I0407 23:18:51.727038 23658 solver.cpp:237]     Train net output #0: loss = 0.0762056 (* 1 = 0.0762056 loss)
I0407 23:18:51.727049 23658 sgd_solver.cpp:105] Iteration 9240, lr = 9.59303e-05
I0407 23:18:56.794301 23658 solver.cpp:218] Iteration 9252 (2.36821 iter/s, 5.06713s/12 iters), loss = 0.176687
I0407 23:18:56.794358 23658 solver.cpp:237]     Train net output #0: loss = 0.176687 (* 1 = 0.176687 loss)
I0407 23:18:56.794368 23658 sgd_solver.cpp:105] Iteration 9252, lr = 9.53531e-05
I0407 23:19:01.999450 23658 solver.cpp:218] Iteration 9264 (2.30549 iter/s, 5.20496s/12 iters), loss = 0.179639
I0407 23:19:01.999500 23658 solver.cpp:237]     Train net output #0: loss = 0.179639 (* 1 = 0.179639 loss)
I0407 23:19:01.999511 23658 sgd_solver.cpp:105] Iteration 9264, lr = 9.47794e-05
I0407 23:19:07.443614 23658 solver.cpp:218] Iteration 9276 (2.20427 iter/s, 5.44397s/12 iters), loss = 0.0591326
I0407 23:19:07.443675 23658 solver.cpp:237]     Train net output #0: loss = 0.0591326 (* 1 = 0.0591326 loss)
I0407 23:19:07.443686 23658 sgd_solver.cpp:105] Iteration 9276, lr = 9.42092e-05
I0407 23:19:09.513382 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 23:19:12.563014 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 23:19:14.840690 23658 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 23:19:14.840711 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:19:15.638623 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:19.279659 23658 solver.cpp:397]     Test net output #0: accuracy = 0.48223
I0407 23:19:19.279834 23658 solver.cpp:397]     Test net output #1: loss = 3.00258 (* 1 = 3.00258 loss)
I0407 23:19:21.175913 23658 solver.cpp:218] Iteration 9288 (0.873877 iter/s, 13.7319s/12 iters), loss = 0.0422532
I0407 23:19:21.175957 23658 solver.cpp:237]     Train net output #0: loss = 0.0422532 (* 1 = 0.0422532 loss)
I0407 23:19:21.175966 23658 sgd_solver.cpp:105] Iteration 9288, lr = 9.36424e-05
I0407 23:19:26.186883 23658 solver.cpp:218] Iteration 9300 (2.39483 iter/s, 5.01079s/12 iters), loss = 0.0443386
I0407 23:19:26.186929 23658 solver.cpp:237]     Train net output #0: loss = 0.0443387 (* 1 = 0.0443387 loss)
I0407 23:19:26.186939 23658 sgd_solver.cpp:105] Iteration 9300, lr = 9.3079e-05
I0407 23:19:28.438971 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:31.277834 23658 solver.cpp:218] Iteration 9312 (2.35721 iter/s, 5.09076s/12 iters), loss = 0.0411394
I0407 23:19:31.277889 23658 solver.cpp:237]     Train net output #0: loss = 0.0411394 (* 1 = 0.0411394 loss)
I0407 23:19:31.277900 23658 sgd_solver.cpp:105] Iteration 9312, lr = 9.2519e-05
I0407 23:19:36.394057 23658 solver.cpp:218] Iteration 9324 (2.34557 iter/s, 5.11603s/12 iters), loss = 0.0833225
I0407 23:19:36.394106 23658 solver.cpp:237]     Train net output #0: loss = 0.0833225 (* 1 = 0.0833225 loss)
I0407 23:19:36.394119 23658 sgd_solver.cpp:105] Iteration 9324, lr = 9.19623e-05
I0407 23:19:41.453544 23658 solver.cpp:218] Iteration 9336 (2.37187 iter/s, 5.0593s/12 iters), loss = 0.102274
I0407 23:19:41.453598 23658 solver.cpp:237]     Train net output #0: loss = 0.102274 (* 1 = 0.102274 loss)
I0407 23:19:41.453609 23658 sgd_solver.cpp:105] Iteration 9336, lr = 9.1409e-05
I0407 23:19:46.477358 23658 solver.cpp:218] Iteration 9348 (2.38871 iter/s, 5.02363s/12 iters), loss = 0.0943032
I0407 23:19:46.477413 23658 solver.cpp:237]     Train net output #0: loss = 0.0943032 (* 1 = 0.0943032 loss)
I0407 23:19:46.477425 23658 sgd_solver.cpp:105] Iteration 9348, lr = 9.08591e-05
I0407 23:19:51.567665 23658 solver.cpp:218] Iteration 9360 (2.35751 iter/s, 5.09012s/12 iters), loss = 0.0941433
I0407 23:19:51.568298 23658 solver.cpp:237]     Train net output #0: loss = 0.0941433 (* 1 = 0.0941433 loss)
I0407 23:19:51.568307 23658 sgd_solver.cpp:105] Iteration 9360, lr = 9.03124e-05
I0407 23:19:56.477094 23658 solver.cpp:218] Iteration 9372 (2.44466 iter/s, 4.90866s/12 iters), loss = 0.100873
I0407 23:19:56.477151 23658 solver.cpp:237]     Train net output #0: loss = 0.100873 (* 1 = 0.100873 loss)
I0407 23:19:56.477164 23658 sgd_solver.cpp:105] Iteration 9372, lr = 8.9769e-05
I0407 23:20:01.293978 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 23:20:04.759145 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 23:20:07.085791 23658 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 23:20:07.085819 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:20:07.869733 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:11.548672 23658 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 23:20:11.548717 23658 solver.cpp:397]     Test net output #1: loss = 2.97417 (* 1 = 2.97417 loss)
I0407 23:20:11.638638 23658 solver.cpp:218] Iteration 9384 (0.791499 iter/s, 15.1611s/12 iters), loss = 0.0345529
I0407 23:20:11.638687 23658 solver.cpp:237]     Train net output #0: loss = 0.0345529 (* 1 = 0.0345529 loss)
I0407 23:20:11.638696 23658 sgd_solver.cpp:105] Iteration 9384, lr = 8.92289e-05
I0407 23:20:16.070377 23658 solver.cpp:218] Iteration 9396 (2.70784 iter/s, 4.43157s/12 iters), loss = 0.0501502
I0407 23:20:16.070426 23658 solver.cpp:237]     Train net output #0: loss = 0.0501502 (* 1 = 0.0501502 loss)
I0407 23:20:16.070437 23658 sgd_solver.cpp:105] Iteration 9396, lr = 8.86921e-05
I0407 23:20:20.426419 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:21.086344 23658 solver.cpp:218] Iteration 9408 (2.39245 iter/s, 5.01578s/12 iters), loss = 0.120144
I0407 23:20:21.086400 23658 solver.cpp:237]     Train net output #0: loss = 0.120144 (* 1 = 0.120144 loss)
I0407 23:20:21.086412 23658 sgd_solver.cpp:105] Iteration 9408, lr = 8.81585e-05
I0407 23:20:26.675194 23658 solver.cpp:218] Iteration 9420 (2.14721 iter/s, 5.58865s/12 iters), loss = 0.120644
I0407 23:20:26.675343 23658 solver.cpp:237]     Train net output #0: loss = 0.120644 (* 1 = 0.120644 loss)
I0407 23:20:26.675355 23658 sgd_solver.cpp:105] Iteration 9420, lr = 8.76281e-05
I0407 23:20:31.906070 23658 solver.cpp:218] Iteration 9432 (2.2942 iter/s, 5.23059s/12 iters), loss = 0.101484
I0407 23:20:31.906126 23658 solver.cpp:237]     Train net output #0: loss = 0.101484 (* 1 = 0.101484 loss)
I0407 23:20:31.906138 23658 sgd_solver.cpp:105] Iteration 9432, lr = 8.71009e-05
I0407 23:20:36.790120 23658 solver.cpp:218] Iteration 9444 (2.45707 iter/s, 4.88387s/12 iters), loss = 0.0590823
I0407 23:20:36.790161 23658 solver.cpp:237]     Train net output #0: loss = 0.0590823 (* 1 = 0.0590823 loss)
I0407 23:20:36.790172 23658 sgd_solver.cpp:105] Iteration 9444, lr = 8.65768e-05
I0407 23:20:41.753046 23658 solver.cpp:218] Iteration 9456 (2.41801 iter/s, 4.96275s/12 iters), loss = 0.0700587
I0407 23:20:41.753096 23658 solver.cpp:237]     Train net output #0: loss = 0.0700587 (* 1 = 0.0700587 loss)
I0407 23:20:41.753108 23658 sgd_solver.cpp:105] Iteration 9456, lr = 8.60559e-05
I0407 23:20:46.855223 23658 solver.cpp:218] Iteration 9468 (2.35202 iter/s, 5.10199s/12 iters), loss = 0.0676
I0407 23:20:46.855281 23658 solver.cpp:237]     Train net output #0: loss = 0.0676001 (* 1 = 0.0676001 loss)
I0407 23:20:46.855295 23658 sgd_solver.cpp:105] Iteration 9468, lr = 8.55382e-05
I0407 23:20:51.886482 23658 solver.cpp:218] Iteration 9480 (2.38518 iter/s, 5.03107s/12 iters), loss = 0.0663443
I0407 23:20:51.886533 23658 solver.cpp:237]     Train net output #0: loss = 0.0663443 (* 1 = 0.0663443 loss)
I0407 23:20:51.886545 23658 sgd_solver.cpp:105] Iteration 9480, lr = 8.50235e-05
I0407 23:20:53.969728 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 23:20:57.963230 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 23:21:00.279475 23658 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 23:21:00.279498 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:21:01.011775 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:04.740023 23658 solver.cpp:397]     Test net output #0: accuracy = 0.490196
I0407 23:21:04.740065 23658 solver.cpp:397]     Test net output #1: loss = 2.98626 (* 1 = 2.98626 loss)
I0407 23:21:06.742406 23658 solver.cpp:218] Iteration 9492 (0.807782 iter/s, 14.8555s/12 iters), loss = 0.0613918
I0407 23:21:06.742453 23658 solver.cpp:237]     Train net output #0: loss = 0.0613919 (* 1 = 0.0613919 loss)
I0407 23:21:06.742461 23658 sgd_solver.cpp:105] Iteration 9492, lr = 8.4512e-05
I0407 23:21:12.009310 23658 solver.cpp:218] Iteration 9504 (2.27846 iter/s, 5.26671s/12 iters), loss = 0.0688908
I0407 23:21:12.009373 23658 solver.cpp:237]     Train net output #0: loss = 0.0688908 (* 1 = 0.0688908 loss)
I0407 23:21:12.009387 23658 sgd_solver.cpp:105] Iteration 9504, lr = 8.40035e-05
I0407 23:21:13.494917 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:17.143267 23658 solver.cpp:218] Iteration 9516 (2.33747 iter/s, 5.13375s/12 iters), loss = 0.0593819
I0407 23:21:17.143314 23658 solver.cpp:237]     Train net output #0: loss = 0.0593819 (* 1 = 0.0593819 loss)
I0407 23:21:17.143322 23658 sgd_solver.cpp:105] Iteration 9516, lr = 8.34981e-05
I0407 23:21:22.227447 23658 solver.cpp:218] Iteration 9528 (2.36035 iter/s, 5.084s/12 iters), loss = 0.179031
I0407 23:21:22.227484 23658 solver.cpp:237]     Train net output #0: loss = 0.179031 (* 1 = 0.179031 loss)
I0407 23:21:22.227494 23658 sgd_solver.cpp:105] Iteration 9528, lr = 8.29957e-05
I0407 23:21:27.101469 23658 solver.cpp:218] Iteration 9540 (2.46212 iter/s, 4.87385s/12 iters), loss = 0.065546
I0407 23:21:27.101518 23658 solver.cpp:237]     Train net output #0: loss = 0.0655461 (* 1 = 0.0655461 loss)
I0407 23:21:27.101528 23658 sgd_solver.cpp:105] Iteration 9540, lr = 8.24964e-05
I0407 23:21:32.161073 23658 solver.cpp:218] Iteration 9552 (2.37181 iter/s, 5.05942s/12 iters), loss = 0.0933294
I0407 23:21:32.161201 23658 solver.cpp:237]     Train net output #0: loss = 0.0933294 (* 1 = 0.0933294 loss)
I0407 23:21:32.161209 23658 sgd_solver.cpp:105] Iteration 9552, lr = 8.2e-05
I0407 23:21:37.187176 23658 solver.cpp:218] Iteration 9564 (2.38766 iter/s, 5.02585s/12 iters), loss = 0.0853141
I0407 23:21:37.187213 23658 solver.cpp:237]     Train net output #0: loss = 0.0853141 (* 1 = 0.0853141 loss)
I0407 23:21:37.187224 23658 sgd_solver.cpp:105] Iteration 9564, lr = 8.15067e-05
I0407 23:21:42.116263 23658 solver.cpp:218] Iteration 9576 (2.43461 iter/s, 4.92892s/12 iters), loss = 0.0550664
I0407 23:21:42.116309 23658 solver.cpp:237]     Train net output #0: loss = 0.0550664 (* 1 = 0.0550664 loss)
I0407 23:21:42.116318 23658 sgd_solver.cpp:105] Iteration 9576, lr = 8.10163e-05
I0407 23:21:46.673946 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 23:21:51.781594 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 23:21:56.157230 23658 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 23:21:56.157258 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:21:56.831820 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:00.590629 23658 solver.cpp:397]     Test net output #0: accuracy = 0.488358
I0407 23:22:00.590662 23658 solver.cpp:397]     Test net output #1: loss = 2.97741 (* 1 = 2.97741 loss)
I0407 23:22:00.680474 23658 solver.cpp:218] Iteration 9588 (0.646423 iter/s, 18.5637s/12 iters), loss = 0.0479756
I0407 23:22:00.680516 23658 solver.cpp:237]     Train net output #0: loss = 0.0479756 (* 1 = 0.0479756 loss)
I0407 23:22:00.680526 23658 sgd_solver.cpp:105] Iteration 9588, lr = 8.05289e-05
I0407 23:22:05.270686 23658 solver.cpp:218] Iteration 9600 (2.61436 iter/s, 4.59004s/12 iters), loss = 0.169117
I0407 23:22:05.270821 23658 solver.cpp:237]     Train net output #0: loss = 0.169117 (* 1 = 0.169117 loss)
I0407 23:22:05.270838 23658 sgd_solver.cpp:105] Iteration 9600, lr = 8.00444e-05
I0407 23:22:09.086169 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:10.512769 23658 solver.cpp:218] Iteration 9612 (2.28928 iter/s, 5.24181s/12 iters), loss = 0.17104
I0407 23:22:10.512818 23658 solver.cpp:237]     Train net output #0: loss = 0.17104 (* 1 = 0.17104 loss)
I0407 23:22:10.512830 23658 sgd_solver.cpp:105] Iteration 9612, lr = 7.95628e-05
I0407 23:22:15.597138 23658 solver.cpp:218] Iteration 9624 (2.36026 iter/s, 5.08418s/12 iters), loss = 0.0628442
I0407 23:22:15.597196 23658 solver.cpp:237]     Train net output #0: loss = 0.0628442 (* 1 = 0.0628442 loss)
I0407 23:22:15.597208 23658 sgd_solver.cpp:105] Iteration 9624, lr = 7.90841e-05
I0407 23:22:20.594271 23658 solver.cpp:218] Iteration 9636 (2.40147 iter/s, 4.99694s/12 iters), loss = 0.0852135
I0407 23:22:20.594311 23658 solver.cpp:237]     Train net output #0: loss = 0.0852135 (* 1 = 0.0852135 loss)
I0407 23:22:20.594321 23658 sgd_solver.cpp:105] Iteration 9636, lr = 7.86083e-05
I0407 23:22:25.596952 23658 solver.cpp:218] Iteration 9648 (2.3988 iter/s, 5.0025s/12 iters), loss = 0.107699
I0407 23:22:25.597000 23658 solver.cpp:237]     Train net output #0: loss = 0.107699 (* 1 = 0.107699 loss)
I0407 23:22:25.597012 23658 sgd_solver.cpp:105] Iteration 9648, lr = 7.81353e-05
I0407 23:22:30.970685 23658 solver.cpp:218] Iteration 9660 (2.23317 iter/s, 5.37353s/12 iters), loss = 0.0396048
I0407 23:22:30.970746 23658 solver.cpp:237]     Train net output #0: loss = 0.0396048 (* 1 = 0.0396048 loss)
I0407 23:22:30.970760 23658 sgd_solver.cpp:105] Iteration 9660, lr = 7.76652e-05
I0407 23:22:36.477967 23658 solver.cpp:218] Iteration 9672 (2.17902 iter/s, 5.50706s/12 iters), loss = 0.124343
I0407 23:22:36.478108 23658 solver.cpp:237]     Train net output #0: loss = 0.124343 (* 1 = 0.124343 loss)
I0407 23:22:36.478121 23658 sgd_solver.cpp:105] Iteration 9672, lr = 7.71979e-05
I0407 23:22:41.586356 23658 solver.cpp:218] Iteration 9684 (2.3492 iter/s, 5.10811s/12 iters), loss = 0.071571
I0407 23:22:41.586398 23658 solver.cpp:237]     Train net output #0: loss = 0.071571 (* 1 = 0.071571 loss)
I0407 23:22:41.586408 23658 sgd_solver.cpp:105] Iteration 9684, lr = 7.67335e-05
I0407 23:22:43.576489 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 23:22:50.781416 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 23:22:53.170661 23658 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 23:22:53.170686 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:22:53.731813 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:56.554117 23658 blocking_queue.cpp:49] Waiting for data
I0407 23:22:57.539556 23658 solver.cpp:397]     Test net output #0: accuracy = 0.485907
I0407 23:22:57.539603 23658 solver.cpp:397]     Test net output #1: loss = 2.99631 (* 1 = 2.99631 loss)
I0407 23:22:59.568857 23658 solver.cpp:218] Iteration 9696 (0.667334 iter/s, 17.982s/12 iters), loss = 0.0563227
I0407 23:22:59.568914 23658 solver.cpp:237]     Train net output #0: loss = 0.0563228 (* 1 = 0.0563228 loss)
I0407 23:22:59.568926 23658 sgd_solver.cpp:105] Iteration 9696, lr = 7.62718e-05
I0407 23:23:04.673573 23658 solver.cpp:218] Iteration 9708 (2.35086 iter/s, 5.10453s/12 iters), loss = 0.0849874
I0407 23:23:04.673620 23658 solver.cpp:237]     Train net output #0: loss = 0.0849874 (* 1 = 0.0849874 loss)
I0407 23:23:04.673632 23658 sgd_solver.cpp:105] Iteration 9708, lr = 7.58129e-05
I0407 23:23:05.401655 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:09.660599 23658 solver.cpp:218] Iteration 9720 (2.40633 iter/s, 4.98685s/12 iters), loss = 0.0360097
I0407 23:23:09.660698 23658 solver.cpp:237]     Train net output #0: loss = 0.0360097 (* 1 = 0.0360097 loss)
I0407 23:23:09.660707 23658 sgd_solver.cpp:105] Iteration 9720, lr = 7.53568e-05
I0407 23:23:14.627135 23658 solver.cpp:218] Iteration 9732 (2.41629 iter/s, 4.9663s/12 iters), loss = 0.0663851
I0407 23:23:14.627188 23658 solver.cpp:237]     Train net output #0: loss = 0.0663851 (* 1 = 0.0663851 loss)
I0407 23:23:14.627202 23658 sgd_solver.cpp:105] Iteration 9732, lr = 7.49034e-05
I0407 23:23:19.551302 23658 solver.cpp:218] Iteration 9744 (2.43705 iter/s, 4.92398s/12 iters), loss = 0.114896
I0407 23:23:19.551349 23658 solver.cpp:237]     Train net output #0: loss = 0.114896 (* 1 = 0.114896 loss)
I0407 23:23:19.551359 23658 sgd_solver.cpp:105] Iteration 9744, lr = 7.44527e-05
I0407 23:23:24.912461 23658 solver.cpp:218] Iteration 9756 (2.2384 iter/s, 5.36096s/12 iters), loss = 0.0598078
I0407 23:23:24.912514 23658 solver.cpp:237]     Train net output #0: loss = 0.0598078 (* 1 = 0.0598078 loss)
I0407 23:23:24.912528 23658 sgd_solver.cpp:105] Iteration 9756, lr = 7.40048e-05
I0407 23:23:30.035187 23658 solver.cpp:218] Iteration 9768 (2.34259 iter/s, 5.12253s/12 iters), loss = 0.0675789
I0407 23:23:30.035243 23658 solver.cpp:237]     Train net output #0: loss = 0.067579 (* 1 = 0.067579 loss)
I0407 23:23:30.035255 23658 sgd_solver.cpp:105] Iteration 9768, lr = 7.35595e-05
I0407 23:23:35.170714 23658 solver.cpp:218] Iteration 9780 (2.33675 iter/s, 5.13533s/12 iters), loss = 0.164363
I0407 23:23:35.170769 23658 solver.cpp:237]     Train net output #0: loss = 0.164363 (* 1 = 0.164363 loss)
I0407 23:23:35.170783 23658 sgd_solver.cpp:105] Iteration 9780, lr = 7.3117e-05
I0407 23:23:39.661855 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 23:23:46.685636 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 23:23:49.017634 23658 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 23:23:49.017663 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:23:49.596513 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:53.474704 23658 solver.cpp:397]     Test net output #0: accuracy = 0.492034
I0407 23:23:53.474743 23658 solver.cpp:397]     Test net output #1: loss = 2.97823 (* 1 = 2.97823 loss)
I0407 23:23:53.564714 23658 solver.cpp:218] Iteration 9792 (0.652405 iter/s, 18.3935s/12 iters), loss = 0.0472157
I0407 23:23:53.564759 23658 solver.cpp:237]     Train net output #0: loss = 0.0472157 (* 1 = 0.0472157 loss)
I0407 23:23:53.564770 23658 sgd_solver.cpp:105] Iteration 9792, lr = 7.26771e-05
I0407 23:23:57.725642 23658 solver.cpp:218] Iteration 9804 (2.88408 iter/s, 4.16077s/12 iters), loss = 0.121566
I0407 23:23:57.725690 23658 solver.cpp:237]     Train net output #0: loss = 0.121566 (* 1 = 0.121566 loss)
I0407 23:23:57.725703 23658 sgd_solver.cpp:105] Iteration 9804, lr = 7.22398e-05
I0407 23:24:00.663808 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:02.744961 23658 solver.cpp:218] Iteration 9816 (2.39085 iter/s, 5.01913s/12 iters), loss = 0.162177
I0407 23:24:02.745009 23658 solver.cpp:237]     Train net output #0: loss = 0.162177 (* 1 = 0.162177 loss)
I0407 23:24:02.745020 23658 sgd_solver.cpp:105] Iteration 9816, lr = 7.18052e-05
I0407 23:24:07.759477 23658 solver.cpp:218] Iteration 9828 (2.39314 iter/s, 5.01433s/12 iters), loss = 0.0779073
I0407 23:24:07.759526 23658 solver.cpp:237]     Train net output #0: loss = 0.0779073 (* 1 = 0.0779073 loss)
I0407 23:24:07.759538 23658 sgd_solver.cpp:105] Iteration 9828, lr = 7.13731e-05
I0407 23:24:12.769650 23658 solver.cpp:218] Iteration 9840 (2.39521 iter/s, 5.00999s/12 iters), loss = 0.0437443
I0407 23:24:12.769752 23658 solver.cpp:237]     Train net output #0: loss = 0.0437443 (* 1 = 0.0437443 loss)
I0407 23:24:12.769765 23658 sgd_solver.cpp:105] Iteration 9840, lr = 7.09437e-05
I0407 23:24:18.145030 23658 solver.cpp:218] Iteration 9852 (2.2325 iter/s, 5.37514s/12 iters), loss = 0.062932
I0407 23:24:18.145076 23658 solver.cpp:237]     Train net output #0: loss = 0.0629321 (* 1 = 0.0629321 loss)
I0407 23:24:18.145087 23658 sgd_solver.cpp:105] Iteration 9852, lr = 7.05169e-05
I0407 23:24:23.315790 23658 solver.cpp:218] Iteration 9864 (2.32082 iter/s, 5.17058s/12 iters), loss = 0.0940818
I0407 23:24:23.315834 23658 solver.cpp:237]     Train net output #0: loss = 0.0940819 (* 1 = 0.0940819 loss)
I0407 23:24:23.315843 23658 sgd_solver.cpp:105] Iteration 9864, lr = 7.00926e-05
I0407 23:24:28.542821 23658 solver.cpp:218] Iteration 9876 (2.29584 iter/s, 5.22684s/12 iters), loss = 0.059732
I0407 23:24:28.542876 23658 solver.cpp:237]     Train net output #0: loss = 0.059732 (* 1 = 0.059732 loss)
I0407 23:24:28.542888 23658 sgd_solver.cpp:105] Iteration 9876, lr = 6.96709e-05
I0407 23:24:33.894675 23658 solver.cpp:218] Iteration 9888 (2.2423 iter/s, 5.35166s/12 iters), loss = 0.127023
I0407 23:24:33.894721 23658 solver.cpp:237]     Train net output #0: loss = 0.127023 (* 1 = 0.127023 loss)
I0407 23:24:33.894731 23658 sgd_solver.cpp:105] Iteration 9888, lr = 6.92517e-05
I0407 23:24:35.949992 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 23:24:40.367731 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 23:24:43.419723 23658 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 23:24:43.419836 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:24:43.990350 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:47.886138 23658 solver.cpp:397]     Test net output #0: accuracy = 0.492647
I0407 23:24:47.886188 23658 solver.cpp:397]     Test net output #1: loss = 2.98411 (* 1 = 2.98411 loss)
I0407 23:24:49.844064 23658 solver.cpp:218] Iteration 9900 (0.752401 iter/s, 15.9489s/12 iters), loss = 0.0604213
I0407 23:24:49.844108 23658 solver.cpp:237]     Train net output #0: loss = 0.0604214 (* 1 = 0.0604214 loss)
I0407 23:24:49.844118 23658 sgd_solver.cpp:105] Iteration 9900, lr = 6.88351e-05
I0407 23:24:54.882699 23658 solver.cpp:218] Iteration 9912 (2.38169 iter/s, 5.03845s/12 iters), loss = 0.107191
I0407 23:24:54.882750 23658 solver.cpp:237]     Train net output #0: loss = 0.107191 (* 1 = 0.107191 loss)
I0407 23:24:54.882761 23658 sgd_solver.cpp:105] Iteration 9912, lr = 6.84209e-05
I0407 23:24:54.981345 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:59.919576 23658 solver.cpp:218] Iteration 9924 (2.38252 iter/s, 5.03668s/12 iters), loss = 0.151432
I0407 23:24:59.919623 23658 solver.cpp:237]     Train net output #0: loss = 0.151432 (* 1 = 0.151432 loss)
I0407 23:24:59.919632 23658 sgd_solver.cpp:105] Iteration 9924, lr = 6.80093e-05
I0407 23:25:05.204583 23658 solver.cpp:218] Iteration 9936 (2.27066 iter/s, 5.28481s/12 iters), loss = 0.10367
I0407 23:25:05.204633 23658 solver.cpp:237]     Train net output #0: loss = 0.10367 (* 1 = 0.10367 loss)
I0407 23:25:05.204644 23658 sgd_solver.cpp:105] Iteration 9936, lr = 6.76001e-05
I0407 23:25:10.837251 23658 solver.cpp:218] Iteration 9948 (2.13051 iter/s, 5.63247s/12 iters), loss = 0.0252709
I0407 23:25:10.837303 23658 solver.cpp:237]     Train net output #0: loss = 0.025271 (* 1 = 0.025271 loss)
I0407 23:25:10.837316 23658 sgd_solver.cpp:105] Iteration 9948, lr = 6.71934e-05
I0407 23:25:15.918323 23658 solver.cpp:218] Iteration 9960 (2.36179 iter/s, 5.08088s/12 iters), loss = 0.0586257
I0407 23:25:15.918449 23658 solver.cpp:237]     Train net output #0: loss = 0.0586257 (* 1 = 0.0586257 loss)
I0407 23:25:15.918462 23658 sgd_solver.cpp:105] Iteration 9960, lr = 6.67891e-05
I0407 23:25:20.965523 23658 solver.cpp:218] Iteration 9972 (2.37768 iter/s, 5.04694s/12 iters), loss = 0.0755563
I0407 23:25:20.965575 23658 solver.cpp:237]     Train net output #0: loss = 0.0755563 (* 1 = 0.0755563 loss)
I0407 23:25:20.965587 23658 sgd_solver.cpp:105] Iteration 9972, lr = 6.63873e-05
I0407 23:25:26.004890 23658 solver.cpp:218] Iteration 9984 (2.38134 iter/s, 5.03917s/12 iters), loss = 0.0532398
I0407 23:25:26.004947 23658 solver.cpp:237]     Train net output #0: loss = 0.0532398 (* 1 = 0.0532398 loss)
I0407 23:25:26.004959 23658 sgd_solver.cpp:105] Iteration 9984, lr = 6.59878e-05
I0407 23:25:30.578784 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 23:25:35.828521 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 23:25:39.485366 23658 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 23:25:39.485388 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:25:40.036351 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:44.021855 23658 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0407 23:25:44.021904 23658 solver.cpp:397]     Test net output #1: loss = 2.96517 (* 1 = 2.96517 loss)
I0407 23:25:44.111928 23658 solver.cpp:218] Iteration 9996 (0.662745 iter/s, 18.1065s/12 iters), loss = 0.0379793
I0407 23:25:44.111981 23658 solver.cpp:237]     Train net output #0: loss = 0.0379793 (* 1 = 0.0379793 loss)
I0407 23:25:44.111994 23658 sgd_solver.cpp:105] Iteration 9996, lr = 6.55908e-05
I0407 23:25:48.727607 23658 solver.cpp:218] Iteration 10008 (2.59994 iter/s, 4.61549s/12 iters), loss = 0.0965735
I0407 23:25:48.727758 23658 solver.cpp:237]     Train net output #0: loss = 0.0965736 (* 1 = 0.0965736 loss)
I0407 23:25:48.727771 23658 sgd_solver.cpp:105] Iteration 10008, lr = 6.51962e-05
I0407 23:25:51.172360 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:53.997164 23658 solver.cpp:218] Iteration 10020 (2.27736 iter/s, 5.26926s/12 iters), loss = 0.103447
I0407 23:25:53.997220 23658 solver.cpp:237]     Train net output #0: loss = 0.103447 (* 1 = 0.103447 loss)
I0407 23:25:53.997231 23658 sgd_solver.cpp:105] Iteration 10020, lr = 6.48039e-05
I0407 23:25:59.037148 23658 solver.cpp:218] Iteration 10032 (2.38105 iter/s, 5.03979s/12 iters), loss = 0.0525122
I0407 23:25:59.037199 23658 solver.cpp:237]     Train net output #0: loss = 0.0525123 (* 1 = 0.0525123 loss)
I0407 23:25:59.037211 23658 sgd_solver.cpp:105] Iteration 10032, lr = 6.4414e-05
I0407 23:26:04.106729 23658 solver.cpp:218] Iteration 10044 (2.36715 iter/s, 5.06939s/12 iters), loss = 0.069367
I0407 23:26:04.106781 23658 solver.cpp:237]     Train net output #0: loss = 0.0693671 (* 1 = 0.0693671 loss)
I0407 23:26:04.106793 23658 sgd_solver.cpp:105] Iteration 10044, lr = 6.40265e-05
I0407 23:26:09.348652 23658 solver.cpp:218] Iteration 10056 (2.28932 iter/s, 5.24173s/12 iters), loss = 0.159502
I0407 23:26:09.348697 23658 solver.cpp:237]     Train net output #0: loss = 0.159502 (* 1 = 0.159502 loss)
I0407 23:26:09.348706 23658 sgd_solver.cpp:105] Iteration 10056, lr = 6.36413e-05
I0407 23:26:14.715824 23658 solver.cpp:218] Iteration 10068 (2.23589 iter/s, 5.36698s/12 iters), loss = 0.0595826
I0407 23:26:14.715860 23658 solver.cpp:237]     Train net output #0: loss = 0.0595826 (* 1 = 0.0595826 loss)
I0407 23:26:14.715868 23658 sgd_solver.cpp:105] Iteration 10068, lr = 6.32584e-05
I0407 23:26:19.699077 23658 solver.cpp:218] Iteration 10080 (2.40815 iter/s, 4.98308s/12 iters), loss = 0.164047
I0407 23:26:19.700314 23658 solver.cpp:237]     Train net output #0: loss = 0.164047 (* 1 = 0.164047 loss)
I0407 23:26:19.700323 23658 sgd_solver.cpp:105] Iteration 10080, lr = 6.28778e-05
I0407 23:26:24.859580 23658 solver.cpp:218] Iteration 10092 (2.32598 iter/s, 5.15913s/12 iters), loss = 0.0601272
I0407 23:26:24.859623 23658 solver.cpp:237]     Train net output #0: loss = 0.0601273 (* 1 = 0.0601273 loss)
I0407 23:26:24.859632 23658 sgd_solver.cpp:105] Iteration 10092, lr = 6.24995e-05
I0407 23:26:27.046738 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 23:26:31.428833 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 23:26:36.810941 23658 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 23:26:36.810968 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:26:37.296437 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:41.342926 23658 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 23:26:41.342974 23658 solver.cpp:397]     Test net output #1: loss = 2.9879 (* 1 = 2.9879 loss)
I0407 23:26:43.343636 23658 solver.cpp:218] Iteration 10104 (0.649226 iter/s, 18.4835s/12 iters), loss = 0.111633
I0407 23:26:43.343681 23658 solver.cpp:237]     Train net output #0: loss = 0.111633 (* 1 = 0.111633 loss)
I0407 23:26:43.343690 23658 sgd_solver.cpp:105] Iteration 10104, lr = 6.21235e-05
I0407 23:26:48.117869 23662 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:48.778158 23658 solver.cpp:218] Iteration 10116 (2.20819 iter/s, 5.43433s/12 iters), loss = 0.0462122
I0407 23:26:48.778205 23658 solver.cpp:237]     Train net output #0: loss = 0.0462122 (* 1 = 0.0462122 loss)
I0407 23:26:48.778218 23658 sgd_solver.cpp:105] Iteration 10116, lr = 6.17497e-05
I0407 23:26:53.860597 23658 solver.cpp:218] Iteration 10128 (2.36116 iter/s, 5.08225s/12 iters), loss = 0.0882787
I0407 23:26:53.860730 23658 solver.cpp:237]     Train net output #0: loss = 0.0882788 (* 1 = 0.0882788 loss)
I0407 23:26:53.860739 23658 sgd_solver.cpp:105] Iteration 10128, lr = 6.13782e-05
I0407 23:26:58.957541 23658 solver.cpp:218] Iteration 10140 (2.35448 iter/s, 5.09667s/12 iters), loss = 0.124868
I0407 23:26:58.957588 23658 solver.cpp:237]     Train net output #0: loss = 0.124868 (* 1 = 0.124868 loss)
I0407 23:26:58.957599 23658 sgd_solver.cpp:105] Iteration 10140, lr = 6.10089e-05
I0407 23:27:03.900596 23658 solver.cpp:218] Iteration 10152 (2.42774 iter/s, 4.94287s/12 iters), loss = 0.0364071
I0407 23:27:03.900646 23658 solver.cpp:237]     Train net output #0: loss = 0.0364072 (* 1 = 0.0364072 loss)
I0407 23:27:03.900658 23658 sgd_solver.cpp:105] Iteration 10152, lr = 6.06418e-05
I0407 23:27:08.988173 23658 solver.cpp:218] Iteration 10164 (2.35877 iter/s, 5.08739s/12 iters), loss = 0.0601882
I0407 23:27:08.988224 23658 solver.cpp:237]     Train net output #0: loss = 0.0601882 (* 1 = 0.0601882 loss)
I0407 23:27:08.988235 23658 sgd_solver.cpp:105] Iteration 10164, lr = 6.0277e-05
I0407 23:27:14.053594 23658 solver.cpp:218] Iteration 10176 (2.36909 iter/s, 5.06523s/12 iters), loss = 0.0416632
I0407 23:27:14.053643 23658 solver.cpp:237]     Train net output #0: loss = 0.0416632 (* 1 = 0.0416632 loss)
I0407 23:27:14.053654 23658 sgd_solver.cpp:105] Iteration 10176, lr = 5.99143e-05
I0407 23:27:19.147051 23658 solver.cpp:218] Iteration 10188 (2.35605 iter/s, 5.09327s/12 iters), loss = 0.0777601
I0407 23:27:19.147096 23658 solver.cpp:237]     Train net output #0: loss = 0.0777602 (* 1 = 0.0777602 loss)
I0407 23:27:19.147107 23658 sgd_solver.cpp:105] Iteration 10188, lr = 5.95538e-05
I0407 23:27:24.171682 23658 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 23:27:39.183360 23658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 23:27:43.705667 23658 solver.cpp:310] Iteration 10200, loss = 0.0584653
I0407 23:27:43.705690 23658 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 23:27:43.705695 23658 net.cpp:676] Ignoring source layer train-data
I0407 23:27:44.141790 23663 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:48.127856 23658 solver.cpp:397]     Test net output #0: accuracy = 0.488358
I0407 23:27:48.127903 23658 solver.cpp:397]     Test net output #1: loss = 2.98682 (* 1 = 2.98682 loss)
I0407 23:27:48.127915 23658 solver.cpp:315] Optimization Done.
I0407 23:27:48.127923 23658 caffe.cpp:259] Optimization Done.