I0410 13:30:23.774256 18606 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210410-133021-4b8a/solver.prototxt
I0410 13:30:23.774418 18606 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 13:30:23.774425 18606 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 13:30:23.774482 18606 caffe.cpp:218] Using GPUs 3
I0410 13:30:23.797574 18606 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
I0410 13:30:24.089777 18606 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 13:30:24.090523 18606 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 13:30:24.091147 18606 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 13:30:24.091167 18606 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 13:30:24.091339 18606 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: 256
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: 256
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 13:30:24.091445 18606 layer_factory.hpp:77] Creating layer train-data
I0410 13:30:24.093165 18606 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 13:30:24.093561 18606 net.cpp:84] Creating Layer train-data
I0410 13:30:24.093585 18606 net.cpp:380] train-data -> data
I0410 13:30:24.093623 18606 net.cpp:380] train-data -> label
I0410 13:30:24.093647 18606 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:30:24.104099 18606 data_layer.cpp:45] output data size: 128,3,227,227
I0410 13:30:24.269528 18606 net.cpp:122] Setting up train-data
I0410 13:30:24.269552 18606 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 13:30:24.269558 18606 net.cpp:129] Top shape: 128 (128)
I0410 13:30:24.269562 18606 net.cpp:137] Memory required for data: 79149056
I0410 13:30:24.269593 18606 layer_factory.hpp:77] Creating layer conv1
I0410 13:30:24.269614 18606 net.cpp:84] Creating Layer conv1
I0410 13:30:24.269621 18606 net.cpp:406] conv1 <- data
I0410 13:30:24.269634 18606 net.cpp:380] conv1 -> conv1
I0410 13:30:24.876385 18606 net.cpp:122] Setting up conv1
I0410 13:30:24.876408 18606 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:30:24.876412 18606 net.cpp:137] Memory required for data: 227833856
I0410 13:30:24.876432 18606 layer_factory.hpp:77] Creating layer relu1
I0410 13:30:24.876444 18606 net.cpp:84] Creating Layer relu1
I0410 13:30:24.876448 18606 net.cpp:406] relu1 <- conv1
I0410 13:30:24.876456 18606 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:30:24.876765 18606 net.cpp:122] Setting up relu1
I0410 13:30:24.876773 18606 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:30:24.876777 18606 net.cpp:137] Memory required for data: 376518656
I0410 13:30:24.876781 18606 layer_factory.hpp:77] Creating layer norm1
I0410 13:30:24.876791 18606 net.cpp:84] Creating Layer norm1
I0410 13:30:24.876794 18606 net.cpp:406] norm1 <- conv1
I0410 13:30:24.876799 18606 net.cpp:380] norm1 -> norm1
I0410 13:30:24.877271 18606 net.cpp:122] Setting up norm1
I0410 13:30:24.877282 18606 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:30:24.877285 18606 net.cpp:137] Memory required for data: 525203456
I0410 13:30:24.877290 18606 layer_factory.hpp:77] Creating layer pool1
I0410 13:30:24.877297 18606 net.cpp:84] Creating Layer pool1
I0410 13:30:24.877301 18606 net.cpp:406] pool1 <- norm1
I0410 13:30:24.877306 18606 net.cpp:380] pool1 -> pool1
I0410 13:30:24.877346 18606 net.cpp:122] Setting up pool1
I0410 13:30:24.877351 18606 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 13:30:24.877355 18606 net.cpp:137] Memory required for data: 561035264
I0410 13:30:24.877358 18606 layer_factory.hpp:77] Creating layer conv2
I0410 13:30:24.877368 18606 net.cpp:84] Creating Layer conv2
I0410 13:30:24.877372 18606 net.cpp:406] conv2 <- pool1
I0410 13:30:24.877377 18606 net.cpp:380] conv2 -> conv2
I0410 13:30:24.884753 18606 net.cpp:122] Setting up conv2
I0410 13:30:24.884770 18606 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:30:24.884774 18606 net.cpp:137] Memory required for data: 656586752
I0410 13:30:24.884784 18606 layer_factory.hpp:77] Creating layer relu2
I0410 13:30:24.884793 18606 net.cpp:84] Creating Layer relu2
I0410 13:30:24.884796 18606 net.cpp:406] relu2 <- conv2
I0410 13:30:24.884801 18606 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:30:24.885254 18606 net.cpp:122] Setting up relu2
I0410 13:30:24.885264 18606 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:30:24.885268 18606 net.cpp:137] Memory required for data: 752138240
I0410 13:30:24.885272 18606 layer_factory.hpp:77] Creating layer norm2
I0410 13:30:24.885280 18606 net.cpp:84] Creating Layer norm2
I0410 13:30:24.885284 18606 net.cpp:406] norm2 <- conv2
I0410 13:30:24.885289 18606 net.cpp:380] norm2 -> norm2
I0410 13:30:24.885599 18606 net.cpp:122] Setting up norm2
I0410 13:30:24.885607 18606 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:30:24.885612 18606 net.cpp:137] Memory required for data: 847689728
I0410 13:30:24.885614 18606 layer_factory.hpp:77] Creating layer pool2
I0410 13:30:24.885622 18606 net.cpp:84] Creating Layer pool2
I0410 13:30:24.885627 18606 net.cpp:406] pool2 <- norm2
I0410 13:30:24.885632 18606 net.cpp:380] pool2 -> pool2
I0410 13:30:24.885660 18606 net.cpp:122] Setting up pool2
I0410 13:30:24.885665 18606 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:30:24.885668 18606 net.cpp:137] Memory required for data: 869840896
I0410 13:30:24.885671 18606 layer_factory.hpp:77] Creating layer conv3
I0410 13:30:24.885681 18606 net.cpp:84] Creating Layer conv3
I0410 13:30:24.885684 18606 net.cpp:406] conv3 <- pool2
I0410 13:30:24.885689 18606 net.cpp:380] conv3 -> conv3
I0410 13:30:24.906244 18606 net.cpp:122] Setting up conv3
I0410 13:30:24.906261 18606 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:30:24.906265 18606 net.cpp:137] Memory required for data: 903067648
I0410 13:30:24.906294 18606 layer_factory.hpp:77] Creating layer relu3
I0410 13:30:24.906306 18606 net.cpp:84] Creating Layer relu3
I0410 13:30:24.906311 18606 net.cpp:406] relu3 <- conv3
I0410 13:30:24.906318 18606 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:30:24.906838 18606 net.cpp:122] Setting up relu3
I0410 13:30:24.906848 18606 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:30:24.906852 18606 net.cpp:137] Memory required for data: 936294400
I0410 13:30:24.906857 18606 layer_factory.hpp:77] Creating layer conv4
I0410 13:30:24.906870 18606 net.cpp:84] Creating Layer conv4
I0410 13:30:24.906874 18606 net.cpp:406] conv4 <- conv3
I0410 13:30:24.906880 18606 net.cpp:380] conv4 -> conv4
I0410 13:30:24.922952 18606 net.cpp:122] Setting up conv4
I0410 13:30:24.922971 18606 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:30:24.922976 18606 net.cpp:137] Memory required for data: 969521152
I0410 13:30:24.922984 18606 layer_factory.hpp:77] Creating layer relu4
I0410 13:30:24.922996 18606 net.cpp:84] Creating Layer relu4
I0410 13:30:24.922999 18606 net.cpp:406] relu4 <- conv4
I0410 13:30:24.923007 18606 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:30:24.923377 18606 net.cpp:122] Setting up relu4
I0410 13:30:24.923385 18606 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:30:24.923389 18606 net.cpp:137] Memory required for data: 1002747904
I0410 13:30:24.923394 18606 layer_factory.hpp:77] Creating layer conv5
I0410 13:30:24.923406 18606 net.cpp:84] Creating Layer conv5
I0410 13:30:24.923410 18606 net.cpp:406] conv5 <- conv4
I0410 13:30:24.923418 18606 net.cpp:380] conv5 -> conv5
I0410 13:30:24.947127 18606 net.cpp:122] Setting up conv5
I0410 13:30:24.947149 18606 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:30:24.947152 18606 net.cpp:137] Memory required for data: 1024899072
I0410 13:30:24.947167 18606 layer_factory.hpp:77] Creating layer relu5
I0410 13:30:24.947177 18606 net.cpp:84] Creating Layer relu5
I0410 13:30:24.947182 18606 net.cpp:406] relu5 <- conv5
I0410 13:30:24.947190 18606 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:30:24.947758 18606 net.cpp:122] Setting up relu5
I0410 13:30:24.947770 18606 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:30:24.947774 18606 net.cpp:137] Memory required for data: 1047050240
I0410 13:30:24.947778 18606 layer_factory.hpp:77] Creating layer pool5
I0410 13:30:24.947788 18606 net.cpp:84] Creating Layer pool5
I0410 13:30:24.947793 18606 net.cpp:406] pool5 <- conv5
I0410 13:30:24.947799 18606 net.cpp:380] pool5 -> pool5
I0410 13:30:24.947841 18606 net.cpp:122] Setting up pool5
I0410 13:30:24.947849 18606 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 13:30:24.947851 18606 net.cpp:137] Memory required for data: 1051768832
I0410 13:30:24.947854 18606 layer_factory.hpp:77] Creating layer fc6
I0410 13:30:24.947865 18606 net.cpp:84] Creating Layer fc6
I0410 13:30:24.947870 18606 net.cpp:406] fc6 <- pool5
I0410 13:30:24.947875 18606 net.cpp:380] fc6 -> fc6
I0410 13:30:24.972143 18606 net.cpp:122] Setting up fc6
I0410 13:30:24.972163 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.972167 18606 net.cpp:137] Memory required for data: 1051899904
I0410 13:30:24.972177 18606 layer_factory.hpp:77] Creating layer relu6
I0410 13:30:24.972185 18606 net.cpp:84] Creating Layer relu6
I0410 13:30:24.972190 18606 net.cpp:406] relu6 <- fc6
I0410 13:30:24.972196 18606 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:30:24.972859 18606 net.cpp:122] Setting up relu6
I0410 13:30:24.972868 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.972872 18606 net.cpp:137] Memory required for data: 1052030976
I0410 13:30:24.972875 18606 layer_factory.hpp:77] Creating layer drop6
I0410 13:30:24.972883 18606 net.cpp:84] Creating Layer drop6
I0410 13:30:24.972887 18606 net.cpp:406] drop6 <- fc6
I0410 13:30:24.972893 18606 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:30:24.972923 18606 net.cpp:122] Setting up drop6
I0410 13:30:24.972927 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.972950 18606 net.cpp:137] Memory required for data: 1052162048
I0410 13:30:24.972954 18606 layer_factory.hpp:77] Creating layer fc7
I0410 13:30:24.972963 18606 net.cpp:84] Creating Layer fc7
I0410 13:30:24.972967 18606 net.cpp:406] fc7 <- fc6
I0410 13:30:24.972972 18606 net.cpp:380] fc7 -> fc7
I0410 13:30:24.973672 18606 net.cpp:122] Setting up fc7
I0410 13:30:24.973680 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.973682 18606 net.cpp:137] Memory required for data: 1052293120
I0410 13:30:24.973688 18606 layer_factory.hpp:77] Creating layer relu7
I0410 13:30:24.973695 18606 net.cpp:84] Creating Layer relu7
I0410 13:30:24.973699 18606 net.cpp:406] relu7 <- fc7
I0410 13:30:24.973703 18606 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:30:24.974231 18606 net.cpp:122] Setting up relu7
I0410 13:30:24.974241 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.974243 18606 net.cpp:137] Memory required for data: 1052424192
I0410 13:30:24.974247 18606 layer_factory.hpp:77] Creating layer drop7
I0410 13:30:24.974253 18606 net.cpp:84] Creating Layer drop7
I0410 13:30:24.974257 18606 net.cpp:406] drop7 <- fc7
I0410 13:30:24.974263 18606 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:30:24.974289 18606 net.cpp:122] Setting up drop7
I0410 13:30:24.974294 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.974298 18606 net.cpp:137] Memory required for data: 1052555264
I0410 13:30:24.974300 18606 layer_factory.hpp:77] Creating layer fc7.5
I0410 13:30:24.974306 18606 net.cpp:84] Creating Layer fc7.5
I0410 13:30:24.974310 18606 net.cpp:406] fc7.5 <- fc7
I0410 13:30:24.974316 18606 net.cpp:380] fc7.5 -> fc7.5
I0410 13:30:24.975018 18606 net.cpp:122] Setting up fc7.5
I0410 13:30:24.975025 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.975028 18606 net.cpp:137] Memory required for data: 1052686336
I0410 13:30:24.975034 18606 layer_factory.hpp:77] Creating layer relu7.5
I0410 13:30:24.975042 18606 net.cpp:84] Creating Layer relu7.5
I0410 13:30:24.975046 18606 net.cpp:406] relu7.5 <- fc7.5
I0410 13:30:24.975050 18606 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 13:30:24.975571 18606 net.cpp:122] Setting up relu7.5
I0410 13:30:24.975580 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.975584 18606 net.cpp:137] Memory required for data: 1052817408
I0410 13:30:24.975587 18606 layer_factory.hpp:77] Creating layer drop7.5
I0410 13:30:24.975594 18606 net.cpp:84] Creating Layer drop7.5
I0410 13:30:24.975597 18606 net.cpp:406] drop7.5 <- fc7.5
I0410 13:30:24.975605 18606 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 13:30:24.975630 18606 net.cpp:122] Setting up drop7.5
I0410 13:30:24.975636 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.975638 18606 net.cpp:137] Memory required for data: 1052948480
I0410 13:30:24.975641 18606 layer_factory.hpp:77] Creating layer fc7.6
I0410 13:30:24.975647 18606 net.cpp:84] Creating Layer fc7.6
I0410 13:30:24.975651 18606 net.cpp:406] fc7.6 <- fc7.5
I0410 13:30:24.975657 18606 net.cpp:380] fc7.6 -> fc7.6
I0410 13:30:24.976347 18606 net.cpp:122] Setting up fc7.6
I0410 13:30:24.976353 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.976357 18606 net.cpp:137] Memory required for data: 1053079552
I0410 13:30:24.976369 18606 layer_factory.hpp:77] Creating layer relu7.6
I0410 13:30:24.976374 18606 net.cpp:84] Creating Layer relu7.6
I0410 13:30:24.976378 18606 net.cpp:406] relu7.6 <- fc7.6
I0410 13:30:24.976383 18606 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 13:30:24.982831 18606 net.cpp:122] Setting up relu7.6
I0410 13:30:24.982841 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.982844 18606 net.cpp:137] Memory required for data: 1053210624
I0410 13:30:24.982848 18606 layer_factory.hpp:77] Creating layer drop7.6
I0410 13:30:24.982856 18606 net.cpp:84] Creating Layer drop7.6
I0410 13:30:24.982859 18606 net.cpp:406] drop7.6 <- fc7.6
I0410 13:30:24.982867 18606 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 13:30:24.982889 18606 net.cpp:122] Setting up drop7.6
I0410 13:30:24.982895 18606 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:30:24.982908 18606 net.cpp:137] Memory required for data: 1053341696
I0410 13:30:24.982913 18606 layer_factory.hpp:77] Creating layer fc8
I0410 13:30:24.982920 18606 net.cpp:84] Creating Layer fc8
I0410 13:30:24.982923 18606 net.cpp:406] fc8 <- fc7.6
I0410 13:30:24.982930 18606 net.cpp:380] fc8 -> fc8
I0410 13:30:24.983494 18606 net.cpp:122] Setting up fc8
I0410 13:30:24.983501 18606 net.cpp:129] Top shape: 128 196 (25088)
I0410 13:30:24.983505 18606 net.cpp:137] Memory required for data: 1053442048
I0410 13:30:24.983511 18606 layer_factory.hpp:77] Creating layer loss
I0410 13:30:24.983517 18606 net.cpp:84] Creating Layer loss
I0410 13:30:24.983521 18606 net.cpp:406] loss <- fc8
I0410 13:30:24.983525 18606 net.cpp:406] loss <- label
I0410 13:30:24.983531 18606 net.cpp:380] loss -> loss
I0410 13:30:24.983539 18606 layer_factory.hpp:77] Creating layer loss
I0410 13:30:24.984172 18606 net.cpp:122] Setting up loss
I0410 13:30:24.984182 18606 net.cpp:129] Top shape: (1)
I0410 13:30:24.984186 18606 net.cpp:132]     with loss weight 1
I0410 13:30:24.984205 18606 net.cpp:137] Memory required for data: 1053442052
I0410 13:30:24.984210 18606 net.cpp:198] loss needs backward computation.
I0410 13:30:24.984216 18606 net.cpp:198] fc8 needs backward computation.
I0410 13:30:24.984220 18606 net.cpp:198] drop7.6 needs backward computation.
I0410 13:30:24.984225 18606 net.cpp:198] relu7.6 needs backward computation.
I0410 13:30:24.984227 18606 net.cpp:198] fc7.6 needs backward computation.
I0410 13:30:24.984231 18606 net.cpp:198] drop7.5 needs backward computation.
I0410 13:30:24.984234 18606 net.cpp:198] relu7.5 needs backward computation.
I0410 13:30:24.984237 18606 net.cpp:198] fc7.5 needs backward computation.
I0410 13:30:24.984241 18606 net.cpp:198] drop7 needs backward computation.
I0410 13:30:24.984246 18606 net.cpp:198] relu7 needs backward computation.
I0410 13:30:24.984249 18606 net.cpp:198] fc7 needs backward computation.
I0410 13:30:24.984252 18606 net.cpp:198] drop6 needs backward computation.
I0410 13:30:24.984256 18606 net.cpp:198] relu6 needs backward computation.
I0410 13:30:24.984261 18606 net.cpp:198] fc6 needs backward computation.
I0410 13:30:24.984264 18606 net.cpp:198] pool5 needs backward computation.
I0410 13:30:24.984268 18606 net.cpp:198] relu5 needs backward computation.
I0410 13:30:24.984272 18606 net.cpp:198] conv5 needs backward computation.
I0410 13:30:24.984277 18606 net.cpp:198] relu4 needs backward computation.
I0410 13:30:24.984280 18606 net.cpp:198] conv4 needs backward computation.
I0410 13:30:24.984285 18606 net.cpp:198] relu3 needs backward computation.
I0410 13:30:24.984289 18606 net.cpp:198] conv3 needs backward computation.
I0410 13:30:24.984293 18606 net.cpp:198] pool2 needs backward computation.
I0410 13:30:24.984297 18606 net.cpp:198] norm2 needs backward computation.
I0410 13:30:24.984302 18606 net.cpp:198] relu2 needs backward computation.
I0410 13:30:24.984305 18606 net.cpp:198] conv2 needs backward computation.
I0410 13:30:24.984309 18606 net.cpp:198] pool1 needs backward computation.
I0410 13:30:24.984313 18606 net.cpp:198] norm1 needs backward computation.
I0410 13:30:24.984318 18606 net.cpp:198] relu1 needs backward computation.
I0410 13:30:24.984321 18606 net.cpp:198] conv1 needs backward computation.
I0410 13:30:24.984325 18606 net.cpp:200] train-data does not need backward computation.
I0410 13:30:24.984328 18606 net.cpp:242] This network produces output loss
I0410 13:30:24.984346 18606 net.cpp:255] Network initialization done.
I0410 13:30:24.984916 18606 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 13:30:24.984951 18606 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 13:30:24.985126 18606 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: 256
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: 256
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 13:30:24.985237 18606 layer_factory.hpp:77] Creating layer val-data
I0410 13:30:24.986884 18606 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 13:30:24.987090 18606 net.cpp:84] Creating Layer val-data
I0410 13:30:24.987099 18606 net.cpp:380] val-data -> data
I0410 13:30:24.987109 18606 net.cpp:380] val-data -> label
I0410 13:30:24.987116 18606 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:30:24.991339 18606 data_layer.cpp:45] output data size: 32,3,227,227
I0410 13:30:25.024708 18606 net.cpp:122] Setting up val-data
I0410 13:30:25.024727 18606 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 13:30:25.024732 18606 net.cpp:129] Top shape: 32 (32)
I0410 13:30:25.024735 18606 net.cpp:137] Memory required for data: 19787264
I0410 13:30:25.024741 18606 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 13:30:25.024753 18606 net.cpp:84] Creating Layer label_val-data_1_split
I0410 13:30:25.024758 18606 net.cpp:406] label_val-data_1_split <- label
I0410 13:30:25.024765 18606 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 13:30:25.024775 18606 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 13:30:25.024829 18606 net.cpp:122] Setting up label_val-data_1_split
I0410 13:30:25.024835 18606 net.cpp:129] Top shape: 32 (32)
I0410 13:30:25.024839 18606 net.cpp:129] Top shape: 32 (32)
I0410 13:30:25.024842 18606 net.cpp:137] Memory required for data: 19787520
I0410 13:30:25.024847 18606 layer_factory.hpp:77] Creating layer conv1
I0410 13:30:25.024857 18606 net.cpp:84] Creating Layer conv1
I0410 13:30:25.024861 18606 net.cpp:406] conv1 <- data
I0410 13:30:25.024868 18606 net.cpp:380] conv1 -> conv1
I0410 13:30:25.028849 18606 net.cpp:122] Setting up conv1
I0410 13:30:25.028861 18606 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:30:25.028863 18606 net.cpp:137] Memory required for data: 56958720
I0410 13:30:25.028874 18606 layer_factory.hpp:77] Creating layer relu1
I0410 13:30:25.028882 18606 net.cpp:84] Creating Layer relu1
I0410 13:30:25.028887 18606 net.cpp:406] relu1 <- conv1
I0410 13:30:25.028892 18606 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:30:25.033108 18606 net.cpp:122] Setting up relu1
I0410 13:30:25.033121 18606 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:30:25.033125 18606 net.cpp:137] Memory required for data: 94129920
I0410 13:30:25.033129 18606 layer_factory.hpp:77] Creating layer norm1
I0410 13:30:25.033138 18606 net.cpp:84] Creating Layer norm1
I0410 13:30:25.033143 18606 net.cpp:406] norm1 <- conv1
I0410 13:30:25.033149 18606 net.cpp:380] norm1 -> norm1
I0410 13:30:25.033640 18606 net.cpp:122] Setting up norm1
I0410 13:30:25.033650 18606 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:30:25.033654 18606 net.cpp:137] Memory required for data: 131301120
I0410 13:30:25.033658 18606 layer_factory.hpp:77] Creating layer pool1
I0410 13:30:25.033665 18606 net.cpp:84] Creating Layer pool1
I0410 13:30:25.033669 18606 net.cpp:406] pool1 <- norm1
I0410 13:30:25.033674 18606 net.cpp:380] pool1 -> pool1
I0410 13:30:25.033705 18606 net.cpp:122] Setting up pool1
I0410 13:30:25.033711 18606 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 13:30:25.033715 18606 net.cpp:137] Memory required for data: 140259072
I0410 13:30:25.033717 18606 layer_factory.hpp:77] Creating layer conv2
I0410 13:30:25.033726 18606 net.cpp:84] Creating Layer conv2
I0410 13:30:25.033730 18606 net.cpp:406] conv2 <- pool1
I0410 13:30:25.033735 18606 net.cpp:380] conv2 -> conv2
I0410 13:30:25.040444 18606 net.cpp:122] Setting up conv2
I0410 13:30:25.040459 18606 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:30:25.040463 18606 net.cpp:137] Memory required for data: 164146944
I0410 13:30:25.040474 18606 layer_factory.hpp:77] Creating layer relu2
I0410 13:30:25.040482 18606 net.cpp:84] Creating Layer relu2
I0410 13:30:25.040485 18606 net.cpp:406] relu2 <- conv2
I0410 13:30:25.040493 18606 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:30:25.040866 18606 net.cpp:122] Setting up relu2
I0410 13:30:25.040875 18606 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:30:25.040879 18606 net.cpp:137] Memory required for data: 188034816
I0410 13:30:25.040882 18606 layer_factory.hpp:77] Creating layer norm2
I0410 13:30:25.040892 18606 net.cpp:84] Creating Layer norm2
I0410 13:30:25.040896 18606 net.cpp:406] norm2 <- conv2
I0410 13:30:25.040901 18606 net.cpp:380] norm2 -> norm2
I0410 13:30:25.041487 18606 net.cpp:122] Setting up norm2
I0410 13:30:25.041497 18606 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:30:25.041501 18606 net.cpp:137] Memory required for data: 211922688
I0410 13:30:25.041505 18606 layer_factory.hpp:77] Creating layer pool2
I0410 13:30:25.041513 18606 net.cpp:84] Creating Layer pool2
I0410 13:30:25.041517 18606 net.cpp:406] pool2 <- norm2
I0410 13:30:25.041523 18606 net.cpp:380] pool2 -> pool2
I0410 13:30:25.041555 18606 net.cpp:122] Setting up pool2
I0410 13:30:25.041560 18606 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:30:25.041563 18606 net.cpp:137] Memory required for data: 217460480
I0410 13:30:25.041568 18606 layer_factory.hpp:77] Creating layer conv3
I0410 13:30:25.041576 18606 net.cpp:84] Creating Layer conv3
I0410 13:30:25.041580 18606 net.cpp:406] conv3 <- pool2
I0410 13:30:25.041586 18606 net.cpp:380] conv3 -> conv3
I0410 13:30:25.054193 18606 net.cpp:122] Setting up conv3
I0410 13:30:25.054211 18606 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:30:25.054215 18606 net.cpp:137] Memory required for data: 225767168
I0410 13:30:25.054227 18606 layer_factory.hpp:77] Creating layer relu3
I0410 13:30:25.054239 18606 net.cpp:84] Creating Layer relu3
I0410 13:30:25.054242 18606 net.cpp:406] relu3 <- conv3
I0410 13:30:25.054250 18606 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:30:25.054797 18606 net.cpp:122] Setting up relu3
I0410 13:30:25.054807 18606 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:30:25.054811 18606 net.cpp:137] Memory required for data: 234073856
I0410 13:30:25.054816 18606 layer_factory.hpp:77] Creating layer conv4
I0410 13:30:25.054826 18606 net.cpp:84] Creating Layer conv4
I0410 13:30:25.054831 18606 net.cpp:406] conv4 <- conv3
I0410 13:30:25.054857 18606 net.cpp:380] conv4 -> conv4
I0410 13:30:25.065182 18606 net.cpp:122] Setting up conv4
I0410 13:30:25.065201 18606 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:30:25.065204 18606 net.cpp:137] Memory required for data: 242380544
I0410 13:30:25.065213 18606 layer_factory.hpp:77] Creating layer relu4
I0410 13:30:25.065224 18606 net.cpp:84] Creating Layer relu4
I0410 13:30:25.065228 18606 net.cpp:406] relu4 <- conv4
I0410 13:30:25.065237 18606 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:30:25.069399 18606 net.cpp:122] Setting up relu4
I0410 13:30:25.069412 18606 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:30:25.069416 18606 net.cpp:137] Memory required for data: 250687232
I0410 13:30:25.069420 18606 layer_factory.hpp:77] Creating layer conv5
I0410 13:30:25.069434 18606 net.cpp:84] Creating Layer conv5
I0410 13:30:25.069438 18606 net.cpp:406] conv5 <- conv4
I0410 13:30:25.069444 18606 net.cpp:380] conv5 -> conv5
I0410 13:30:25.083218 18606 net.cpp:122] Setting up conv5
I0410 13:30:25.083237 18606 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:30:25.083241 18606 net.cpp:137] Memory required for data: 256225024
I0410 13:30:25.083253 18606 layer_factory.hpp:77] Creating layer relu5
I0410 13:30:25.083263 18606 net.cpp:84] Creating Layer relu5
I0410 13:30:25.083267 18606 net.cpp:406] relu5 <- conv5
I0410 13:30:25.083276 18606 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:30:25.083660 18606 net.cpp:122] Setting up relu5
I0410 13:30:25.083669 18606 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:30:25.083673 18606 net.cpp:137] Memory required for data: 261762816
I0410 13:30:25.083676 18606 layer_factory.hpp:77] Creating layer pool5
I0410 13:30:25.083688 18606 net.cpp:84] Creating Layer pool5
I0410 13:30:25.083691 18606 net.cpp:406] pool5 <- conv5
I0410 13:30:25.083698 18606 net.cpp:380] pool5 -> pool5
I0410 13:30:25.083737 18606 net.cpp:122] Setting up pool5
I0410 13:30:25.083743 18606 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 13:30:25.083746 18606 net.cpp:137] Memory required for data: 262942464
I0410 13:30:25.083750 18606 layer_factory.hpp:77] Creating layer fc6
I0410 13:30:25.083757 18606 net.cpp:84] Creating Layer fc6
I0410 13:30:25.083760 18606 net.cpp:406] fc6 <- pool5
I0410 13:30:25.083767 18606 net.cpp:380] fc6 -> fc6
I0410 13:30:25.110872 18606 net.cpp:122] Setting up fc6
I0410 13:30:25.110894 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.110898 18606 net.cpp:137] Memory required for data: 262975232
I0410 13:30:25.110908 18606 layer_factory.hpp:77] Creating layer relu6
I0410 13:30:25.110919 18606 net.cpp:84] Creating Layer relu6
I0410 13:30:25.110924 18606 net.cpp:406] relu6 <- fc6
I0410 13:30:25.110930 18606 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:30:25.111964 18606 net.cpp:122] Setting up relu6
I0410 13:30:25.111974 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.111977 18606 net.cpp:137] Memory required for data: 263008000
I0410 13:30:25.111981 18606 layer_factory.hpp:77] Creating layer drop6
I0410 13:30:25.111989 18606 net.cpp:84] Creating Layer drop6
I0410 13:30:25.111992 18606 net.cpp:406] drop6 <- fc6
I0410 13:30:25.111999 18606 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:30:25.112025 18606 net.cpp:122] Setting up drop6
I0410 13:30:25.112030 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.112032 18606 net.cpp:137] Memory required for data: 263040768
I0410 13:30:25.112035 18606 layer_factory.hpp:77] Creating layer fc7
I0410 13:30:25.112044 18606 net.cpp:84] Creating Layer fc7
I0410 13:30:25.112048 18606 net.cpp:406] fc7 <- fc6
I0410 13:30:25.112054 18606 net.cpp:380] fc7 -> fc7
I0410 13:30:25.112751 18606 net.cpp:122] Setting up fc7
I0410 13:30:25.112758 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.112761 18606 net.cpp:137] Memory required for data: 263073536
I0410 13:30:25.112767 18606 layer_factory.hpp:77] Creating layer relu7
I0410 13:30:25.112776 18606 net.cpp:84] Creating Layer relu7
I0410 13:30:25.112780 18606 net.cpp:406] relu7 <- fc7
I0410 13:30:25.112784 18606 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:30:25.113333 18606 net.cpp:122] Setting up relu7
I0410 13:30:25.113343 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.113345 18606 net.cpp:137] Memory required for data: 263106304
I0410 13:30:25.113349 18606 layer_factory.hpp:77] Creating layer drop7
I0410 13:30:25.113355 18606 net.cpp:84] Creating Layer drop7
I0410 13:30:25.113359 18606 net.cpp:406] drop7 <- fc7
I0410 13:30:25.113365 18606 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:30:25.113390 18606 net.cpp:122] Setting up drop7
I0410 13:30:25.113395 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.113399 18606 net.cpp:137] Memory required for data: 263139072
I0410 13:30:25.113401 18606 layer_factory.hpp:77] Creating layer fc7.5
I0410 13:30:25.113410 18606 net.cpp:84] Creating Layer fc7.5
I0410 13:30:25.113415 18606 net.cpp:406] fc7.5 <- fc7
I0410 13:30:25.113421 18606 net.cpp:380] fc7.5 -> fc7.5
I0410 13:30:25.114135 18606 net.cpp:122] Setting up fc7.5
I0410 13:30:25.114143 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.114146 18606 net.cpp:137] Memory required for data: 263171840
I0410 13:30:25.114152 18606 layer_factory.hpp:77] Creating layer relu7.5
I0410 13:30:25.114158 18606 net.cpp:84] Creating Layer relu7.5
I0410 13:30:25.114161 18606 net.cpp:406] relu7.5 <- fc7.5
I0410 13:30:25.114166 18606 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 13:30:25.115809 18606 net.cpp:122] Setting up relu7.5
I0410 13:30:25.115819 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.115823 18606 net.cpp:137] Memory required for data: 263204608
I0410 13:30:25.115828 18606 layer_factory.hpp:77] Creating layer drop7.5
I0410 13:30:25.115833 18606 net.cpp:84] Creating Layer drop7.5
I0410 13:30:25.115837 18606 net.cpp:406] drop7.5 <- fc7.5
I0410 13:30:25.115844 18606 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 13:30:25.115869 18606 net.cpp:122] Setting up drop7.5
I0410 13:30:25.115875 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.115877 18606 net.cpp:137] Memory required for data: 263237376
I0410 13:30:25.115880 18606 layer_factory.hpp:77] Creating layer fc7.6
I0410 13:30:25.115888 18606 net.cpp:84] Creating Layer fc7.6
I0410 13:30:25.115891 18606 net.cpp:406] fc7.6 <- fc7.5
I0410 13:30:25.115897 18606 net.cpp:380] fc7.6 -> fc7.6
I0410 13:30:25.116597 18606 net.cpp:122] Setting up fc7.6
I0410 13:30:25.116603 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.116607 18606 net.cpp:137] Memory required for data: 263270144
I0410 13:30:25.116618 18606 layer_factory.hpp:77] Creating layer relu7.6
I0410 13:30:25.116623 18606 net.cpp:84] Creating Layer relu7.6
I0410 13:30:25.116627 18606 net.cpp:406] relu7.6 <- fc7.6
I0410 13:30:25.116631 18606 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 13:30:25.116999 18606 net.cpp:122] Setting up relu7.6
I0410 13:30:25.117007 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.117012 18606 net.cpp:137] Memory required for data: 263302912
I0410 13:30:25.117014 18606 layer_factory.hpp:77] Creating layer drop7.6
I0410 13:30:25.117022 18606 net.cpp:84] Creating Layer drop7.6
I0410 13:30:25.117025 18606 net.cpp:406] drop7.6 <- fc7.6
I0410 13:30:25.117030 18606 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 13:30:25.117054 18606 net.cpp:122] Setting up drop7.6
I0410 13:30:25.117059 18606 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:30:25.117063 18606 net.cpp:137] Memory required for data: 263335680
I0410 13:30:25.117065 18606 layer_factory.hpp:77] Creating layer fc8
I0410 13:30:25.117071 18606 net.cpp:84] Creating Layer fc8
I0410 13:30:25.117074 18606 net.cpp:406] fc8 <- fc7.6
I0410 13:30:25.117081 18606 net.cpp:380] fc8 -> fc8
I0410 13:30:25.117643 18606 net.cpp:122] Setting up fc8
I0410 13:30:25.117650 18606 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:30:25.117652 18606 net.cpp:137] Memory required for data: 263360768
I0410 13:30:25.117658 18606 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 13:30:25.117664 18606 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 13:30:25.117667 18606 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 13:30:25.117673 18606 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 13:30:25.117695 18606 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 13:30:25.117727 18606 net.cpp:122] Setting up fc8_fc8_0_split
I0410 13:30:25.117733 18606 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:30:25.117736 18606 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:30:25.117740 18606 net.cpp:137] Memory required for data: 263410944
I0410 13:30:25.117743 18606 layer_factory.hpp:77] Creating layer accuracy
I0410 13:30:25.117750 18606 net.cpp:84] Creating Layer accuracy
I0410 13:30:25.117754 18606 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 13:30:25.117758 18606 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 13:30:25.117763 18606 net.cpp:380] accuracy -> accuracy
I0410 13:30:25.117770 18606 net.cpp:122] Setting up accuracy
I0410 13:30:25.117774 18606 net.cpp:129] Top shape: (1)
I0410 13:30:25.117777 18606 net.cpp:137] Memory required for data: 263410948
I0410 13:30:25.117781 18606 layer_factory.hpp:77] Creating layer loss
I0410 13:30:25.117791 18606 net.cpp:84] Creating Layer loss
I0410 13:30:25.117795 18606 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 13:30:25.117799 18606 net.cpp:406] loss <- label_val-data_1_split_1
I0410 13:30:25.117805 18606 net.cpp:380] loss -> loss
I0410 13:30:25.117811 18606 layer_factory.hpp:77] Creating layer loss
I0410 13:30:25.118439 18606 net.cpp:122] Setting up loss
I0410 13:30:25.118449 18606 net.cpp:129] Top shape: (1)
I0410 13:30:25.118453 18606 net.cpp:132]     with loss weight 1
I0410 13:30:25.118463 18606 net.cpp:137] Memory required for data: 263410952
I0410 13:30:25.118467 18606 net.cpp:198] loss needs backward computation.
I0410 13:30:25.118472 18606 net.cpp:200] accuracy does not need backward computation.
I0410 13:30:25.118476 18606 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 13:30:25.118479 18606 net.cpp:198] fc8 needs backward computation.
I0410 13:30:25.118484 18606 net.cpp:198] drop7.6 needs backward computation.
I0410 13:30:25.118486 18606 net.cpp:198] relu7.6 needs backward computation.
I0410 13:30:25.118489 18606 net.cpp:198] fc7.6 needs backward computation.
I0410 13:30:25.118494 18606 net.cpp:198] drop7.5 needs backward computation.
I0410 13:30:25.118496 18606 net.cpp:198] relu7.5 needs backward computation.
I0410 13:30:25.118499 18606 net.cpp:198] fc7.5 needs backward computation.
I0410 13:30:25.118503 18606 net.cpp:198] drop7 needs backward computation.
I0410 13:30:25.118506 18606 net.cpp:198] relu7 needs backward computation.
I0410 13:30:25.118510 18606 net.cpp:198] fc7 needs backward computation.
I0410 13:30:25.118513 18606 net.cpp:198] drop6 needs backward computation.
I0410 13:30:25.118516 18606 net.cpp:198] relu6 needs backward computation.
I0410 13:30:25.118520 18606 net.cpp:198] fc6 needs backward computation.
I0410 13:30:25.118522 18606 net.cpp:198] pool5 needs backward computation.
I0410 13:30:25.118526 18606 net.cpp:198] relu5 needs backward computation.
I0410 13:30:25.118530 18606 net.cpp:198] conv5 needs backward computation.
I0410 13:30:25.118533 18606 net.cpp:198] relu4 needs backward computation.
I0410 13:30:25.118536 18606 net.cpp:198] conv4 needs backward computation.
I0410 13:30:25.118541 18606 net.cpp:198] relu3 needs backward computation.
I0410 13:30:25.118543 18606 net.cpp:198] conv3 needs backward computation.
I0410 13:30:25.118547 18606 net.cpp:198] pool2 needs backward computation.
I0410 13:30:25.118551 18606 net.cpp:198] norm2 needs backward computation.
I0410 13:30:25.118554 18606 net.cpp:198] relu2 needs backward computation.
I0410 13:30:25.118559 18606 net.cpp:198] conv2 needs backward computation.
I0410 13:30:25.118563 18606 net.cpp:198] pool1 needs backward computation.
I0410 13:30:25.118566 18606 net.cpp:198] norm1 needs backward computation.
I0410 13:30:25.118571 18606 net.cpp:198] relu1 needs backward computation.
I0410 13:30:25.118573 18606 net.cpp:198] conv1 needs backward computation.
I0410 13:30:25.118577 18606 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 13:30:25.118582 18606 net.cpp:200] val-data does not need backward computation.
I0410 13:30:25.118594 18606 net.cpp:242] This network produces output accuracy
I0410 13:30:25.118599 18606 net.cpp:242] This network produces output loss
I0410 13:30:25.118620 18606 net.cpp:255] Network initialization done.
I0410 13:30:25.118702 18606 solver.cpp:56] Solver scaffolding done.
I0410 13:30:25.119271 18606 caffe.cpp:248] Starting Optimization
I0410 13:30:25.119282 18606 solver.cpp:272] Solving
I0410 13:30:25.119284 18606 solver.cpp:273] Learning Rate Policy: exp
I0410 13:30:25.120290 18606 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 13:30:25.120301 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:30:25.122989 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:30:29.729367 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:29.773607 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0410 13:30:29.773656 18606 solver.cpp:397]     Test net output #1: loss = 5.27809 (* 1 = 5.27809 loss)
I0410 13:30:29.859964 18606 solver.cpp:218] Iteration 0 (-6.69562e-37 iter/s, 4.74045s/12 iters), loss = 5.27697
I0410 13:30:29.860013 18606 solver.cpp:237]     Train net output #0: loss = 5.27697 (* 1 = 5.27697 loss)
I0410 13:30:29.860033 18606 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 13:30:33.700996 18606 solver.cpp:218] Iteration 12 (3.12434 iter/s, 3.84081s/12 iters), loss = 5.27919
I0410 13:30:33.701058 18606 solver.cpp:237]     Train net output #0: loss = 5.27919 (* 1 = 5.27919 loss)
I0410 13:30:33.701071 18606 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 13:30:38.521875 18606 solver.cpp:218] Iteration 24 (2.48931 iter/s, 4.82061s/12 iters), loss = 5.27749
I0410 13:30:38.521942 18606 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0410 13:30:38.521976 18606 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 13:30:43.297777 18606 solver.cpp:218] Iteration 36 (2.51276 iter/s, 4.77563s/12 iters), loss = 5.27866
I0410 13:30:43.297834 18606 solver.cpp:237]     Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
I0410 13:30:43.297847 18606 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 13:30:48.232411 18606 solver.cpp:218] Iteration 48 (2.43192 iter/s, 4.93437s/12 iters), loss = 5.27938
I0410 13:30:48.232457 18606 solver.cpp:237]     Train net output #0: loss = 5.27938 (* 1 = 5.27938 loss)
I0410 13:30:48.232468 18606 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 13:30:53.063068 18606 solver.cpp:218] Iteration 60 (2.48427 iter/s, 4.8304s/12 iters), loss = 5.27438
I0410 13:30:53.063128 18606 solver.cpp:237]     Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
I0410 13:30:53.063141 18606 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 13:30:57.835345 18606 solver.cpp:218] Iteration 72 (2.51466 iter/s, 4.77201s/12 iters), loss = 5.27799
I0410 13:30:57.835471 18606 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0410 13:30:57.835484 18606 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 13:31:02.605298 18606 solver.cpp:218] Iteration 84 (2.51592 iter/s, 4.76962s/12 iters), loss = 5.27982
I0410 13:31:02.605356 18606 solver.cpp:237]     Train net output #0: loss = 5.27982 (* 1 = 5.27982 loss)
I0410 13:31:02.605367 18606 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 13:31:07.385391 18606 solver.cpp:218] Iteration 96 (2.51055 iter/s, 4.77982s/12 iters), loss = 5.28648
I0410 13:31:07.385452 18606 solver.cpp:237]     Train net output #0: loss = 5.28648 (* 1 = 5.28648 loss)
I0410 13:31:07.385462 18606 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 13:31:09.029522 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:09.334677 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 13:31:09.647948 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 13:31:09.847574 18606 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 13:31:09.847597 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:31:14.315831 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:14.392798 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:31:14.392848 18606 solver.cpp:397]     Test net output #1: loss = 5.27873 (* 1 = 5.27873 loss)
I0410 13:31:16.106721 18606 solver.cpp:218] Iteration 108 (1.376 iter/s, 8.7209s/12 iters), loss = 5.27826
I0410 13:31:16.106767 18606 solver.cpp:237]     Train net output #0: loss = 5.27826 (* 1 = 5.27826 loss)
I0410 13:31:16.106777 18606 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 13:31:20.905746 18606 solver.cpp:218] Iteration 120 (2.50064 iter/s, 4.79877s/12 iters), loss = 5.2789
I0410 13:31:20.905799 18606 solver.cpp:237]     Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
I0410 13:31:20.905812 18606 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 13:31:25.677898 18606 solver.cpp:218] Iteration 132 (2.51473 iter/s, 4.77189s/12 iters), loss = 5.25895
I0410 13:31:25.677947 18606 solver.cpp:237]     Train net output #0: loss = 5.25895 (* 1 = 5.25895 loss)
I0410 13:31:25.677971 18606 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 13:31:30.505519 18606 solver.cpp:218] Iteration 144 (2.48583 iter/s, 4.82736s/12 iters), loss = 5.28369
I0410 13:31:30.505667 18606 solver.cpp:237]     Train net output #0: loss = 5.28369 (* 1 = 5.28369 loss)
I0410 13:31:30.505681 18606 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 13:31:35.334419 18606 solver.cpp:218] Iteration 156 (2.48522 iter/s, 4.82854s/12 iters), loss = 5.26772
I0410 13:31:35.334468 18606 solver.cpp:237]     Train net output #0: loss = 5.26772 (* 1 = 5.26772 loss)
I0410 13:31:35.334479 18606 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 13:31:40.164674 18606 solver.cpp:218] Iteration 168 (2.48448 iter/s, 4.82999s/12 iters), loss = 5.27274
I0410 13:31:40.164726 18606 solver.cpp:237]     Train net output #0: loss = 5.27274 (* 1 = 5.27274 loss)
I0410 13:31:40.164739 18606 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 13:31:45.022190 18606 solver.cpp:218] Iteration 180 (2.47053 iter/s, 4.85725s/12 iters), loss = 5.27071
I0410 13:31:45.022240 18606 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0410 13:31:45.022253 18606 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 13:31:49.827512 18606 solver.cpp:218] Iteration 192 (2.49737 iter/s, 4.80506s/12 iters), loss = 5.27732
I0410 13:31:49.827574 18606 solver.cpp:237]     Train net output #0: loss = 5.27732 (* 1 = 5.27732 loss)
I0410 13:31:49.827586 18606 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 13:31:53.503304 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:54.158974 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 13:31:54.721592 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 13:31:54.920779 18606 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 13:31:54.920799 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:31:59.203292 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:59.325606 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:31:59.325644 18606 solver.cpp:397]     Test net output #1: loss = 5.27988 (* 1 = 5.27988 loss)
I0410 13:31:59.407341 18606 solver.cpp:218] Iteration 204 (1.25269 iter/s, 9.57935s/12 iters), loss = 5.27384
I0410 13:31:59.407384 18606 solver.cpp:237]     Train net output #0: loss = 5.27384 (* 1 = 5.27384 loss)
I0410 13:31:59.407393 18606 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 13:32:03.532560 18606 solver.cpp:218] Iteration 216 (2.9091 iter/s, 4.12499s/12 iters), loss = 5.27642
I0410 13:32:03.532666 18606 solver.cpp:237]     Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
I0410 13:32:03.532675 18606 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 13:32:08.314553 18606 solver.cpp:218] Iteration 228 (2.50958 iter/s, 4.78167s/12 iters), loss = 5.26395
I0410 13:32:08.314608 18606 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0410 13:32:08.314620 18606 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 13:32:13.113016 18606 solver.cpp:218] Iteration 240 (2.50094 iter/s, 4.79819s/12 iters), loss = 5.28384
I0410 13:32:13.113057 18606 solver.cpp:237]     Train net output #0: loss = 5.28384 (* 1 = 5.28384 loss)
I0410 13:32:13.113067 18606 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 13:32:17.933226 18606 solver.cpp:218] Iteration 252 (2.48965 iter/s, 4.81995s/12 iters), loss = 5.26896
I0410 13:32:17.933270 18606 solver.cpp:237]     Train net output #0: loss = 5.26896 (* 1 = 5.26896 loss)
I0410 13:32:17.933280 18606 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 13:32:22.749768 18606 solver.cpp:218] Iteration 264 (2.49155 iter/s, 4.81628s/12 iters), loss = 5.27551
I0410 13:32:22.749817 18606 solver.cpp:237]     Train net output #0: loss = 5.27551 (* 1 = 5.27551 loss)
I0410 13:32:22.749827 18606 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 13:32:27.555341 18606 solver.cpp:218] Iteration 276 (2.49724 iter/s, 4.80531s/12 iters), loss = 5.28554
I0410 13:32:27.555384 18606 solver.cpp:237]     Train net output #0: loss = 5.28554 (* 1 = 5.28554 loss)
I0410 13:32:27.555394 18606 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 13:32:32.375418 18606 solver.cpp:218] Iteration 288 (2.48972 iter/s, 4.81981s/12 iters), loss = 5.27832
I0410 13:32:32.375461 18606 solver.cpp:237]     Train net output #0: loss = 5.27832 (* 1 = 5.27832 loss)
I0410 13:32:32.375470 18606 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 13:32:37.217624 18606 solver.cpp:218] Iteration 300 (2.47834 iter/s, 4.84194s/12 iters), loss = 5.27918
I0410 13:32:37.217736 18606 solver.cpp:237]     Train net output #0: loss = 5.27918 (* 1 = 5.27918 loss)
I0410 13:32:37.217748 18606 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 13:32:38.159901 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:39.157706 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 13:32:39.478621 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 13:32:39.704670 18606 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 13:32:39.704695 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:32:43.933794 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:44.090646 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:32:44.090690 18606 solver.cpp:397]     Test net output #1: loss = 5.28102 (* 1 = 5.28102 loss)
I0410 13:32:45.902230 18606 solver.cpp:218] Iteration 312 (1.38183 iter/s, 8.68411s/12 iters), loss = 5.28083
I0410 13:32:45.902281 18606 solver.cpp:237]     Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
I0410 13:32:45.902292 18606 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 13:32:50.941581 18606 solver.cpp:218] Iteration 324 (2.38139 iter/s, 5.03907s/12 iters), loss = 5.25802
I0410 13:32:50.941628 18606 solver.cpp:237]     Train net output #0: loss = 5.25802 (* 1 = 5.25802 loss)
I0410 13:32:50.941638 18606 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 13:32:55.747627 18606 solver.cpp:218] Iteration 336 (2.497 iter/s, 4.80577s/12 iters), loss = 5.26499
I0410 13:32:55.747689 18606 solver.cpp:237]     Train net output #0: loss = 5.26499 (* 1 = 5.26499 loss)
I0410 13:32:55.747702 18606 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 13:33:00.629025 18606 solver.cpp:218] Iteration 348 (2.45845 iter/s, 4.88111s/12 iters), loss = 5.26816
I0410 13:33:00.629078 18606 solver.cpp:237]     Train net output #0: loss = 5.26816 (* 1 = 5.26816 loss)
I0410 13:33:00.629091 18606 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 13:33:05.543591 18606 solver.cpp:218] Iteration 360 (2.44186 iter/s, 4.91428s/12 iters), loss = 5.28717
I0410 13:33:05.543637 18606 solver.cpp:237]     Train net output #0: loss = 5.28717 (* 1 = 5.28717 loss)
I0410 13:33:05.543645 18606 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 13:33:10.360301 18606 solver.cpp:218] Iteration 372 (2.49147 iter/s, 4.81644s/12 iters), loss = 5.26809
I0410 13:33:10.360426 18606 solver.cpp:237]     Train net output #0: loss = 5.26809 (* 1 = 5.26809 loss)
I0410 13:33:10.360440 18606 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 13:33:15.174480 18606 solver.cpp:218] Iteration 384 (2.49281 iter/s, 4.81384s/12 iters), loss = 5.27693
I0410 13:33:15.174525 18606 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0410 13:33:15.174537 18606 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 13:33:19.996640 18606 solver.cpp:218] Iteration 396 (2.48865 iter/s, 4.8219s/12 iters), loss = 5.27079
I0410 13:33:19.996685 18606 solver.cpp:237]     Train net output #0: loss = 5.27079 (* 1 = 5.27079 loss)
I0410 13:33:19.996695 18606 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 13:33:23.004659 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:24.366647 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 13:33:24.720675 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 13:33:26.654757 18606 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 13:33:26.654793 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:33:30.947834 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:31.156095 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:33:31.156144 18606 solver.cpp:397]     Test net output #1: loss = 5.28271 (* 1 = 5.28271 loss)
I0410 13:33:31.237102 18606 solver.cpp:218] Iteration 408 (1.06762 iter/s, 11.2399s/12 iters), loss = 5.27569
I0410 13:33:31.237157 18606 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0410 13:33:31.237169 18606 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 13:33:35.358408 18606 solver.cpp:218] Iteration 420 (2.91187 iter/s, 4.12106s/12 iters), loss = 5.27596
I0410 13:33:35.358454 18606 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0410 13:33:35.358464 18606 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 13:33:40.189118 18606 solver.cpp:218] Iteration 432 (2.48425 iter/s, 4.83043s/12 iters), loss = 5.2703
I0410 13:33:40.189178 18606 solver.cpp:237]     Train net output #0: loss = 5.2703 (* 1 = 5.2703 loss)
I0410 13:33:40.189191 18606 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 13:33:45.174001 18606 solver.cpp:218] Iteration 444 (2.40742 iter/s, 4.98459s/12 iters), loss = 5.28451
I0410 13:33:45.174116 18606 solver.cpp:237]     Train net output #0: loss = 5.28451 (* 1 = 5.28451 loss)
I0410 13:33:45.174129 18606 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 13:33:49.985504 18606 solver.cpp:218] Iteration 456 (2.4942 iter/s, 4.81117s/12 iters), loss = 5.28084
I0410 13:33:49.985543 18606 solver.cpp:237]     Train net output #0: loss = 5.28084 (* 1 = 5.28084 loss)
I0410 13:33:49.985553 18606 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 13:33:54.778981 18606 solver.cpp:218] Iteration 468 (2.50354 iter/s, 4.79322s/12 iters), loss = 5.28138
I0410 13:33:54.779036 18606 solver.cpp:237]     Train net output #0: loss = 5.28138 (* 1 = 5.28138 loss)
I0410 13:33:54.779048 18606 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 13:33:59.801807 18606 solver.cpp:218] Iteration 480 (2.38923 iter/s, 5.02254s/12 iters), loss = 5.26828
I0410 13:33:59.801864 18606 solver.cpp:237]     Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
I0410 13:33:59.801877 18606 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 13:34:04.621285 18606 solver.cpp:218] Iteration 492 (2.49004 iter/s, 4.8192s/12 iters), loss = 5.28477
I0410 13:34:04.621337 18606 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0410 13:34:04.621349 18606 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 13:34:09.410110 18606 solver.cpp:218] Iteration 504 (2.50598 iter/s, 4.78855s/12 iters), loss = 5.26714
I0410 13:34:09.410169 18606 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0410 13:34:09.410181 18606 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 13:34:09.657531 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:11.351917 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 13:34:11.658424 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 13:34:11.876214 18606 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 13:34:11.876243 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:34:16.029981 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:16.271086 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:34:16.271136 18606 solver.cpp:397]     Test net output #1: loss = 5.28302 (* 1 = 5.28302 loss)
I0410 13:34:18.041715 18606 solver.cpp:218] Iteration 516 (1.39031 iter/s, 8.63116s/12 iters), loss = 5.27643
I0410 13:34:18.041770 18606 solver.cpp:237]     Train net output #0: loss = 5.27643 (* 1 = 5.27643 loss)
I0410 13:34:18.041782 18606 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 13:34:22.923270 18606 solver.cpp:218] Iteration 528 (2.45837 iter/s, 4.88128s/12 iters), loss = 5.27142
I0410 13:34:22.923319 18606 solver.cpp:237]     Train net output #0: loss = 5.27142 (* 1 = 5.27142 loss)
I0410 13:34:22.923331 18606 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 13:34:27.784938 18606 solver.cpp:218] Iteration 540 (2.46843 iter/s, 4.86139s/12 iters), loss = 5.27439
I0410 13:34:27.784991 18606 solver.cpp:237]     Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
I0410 13:34:27.785002 18606 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 13:34:32.582572 18606 solver.cpp:218] Iteration 552 (2.50138 iter/s, 4.79736s/12 iters), loss = 5.27134
I0410 13:34:32.582623 18606 solver.cpp:237]     Train net output #0: loss = 5.27134 (* 1 = 5.27134 loss)
I0410 13:34:32.582633 18606 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 13:34:37.359896 18606 solver.cpp:218] Iteration 564 (2.51201 iter/s, 4.77705s/12 iters), loss = 5.25471
I0410 13:34:37.359956 18606 solver.cpp:237]     Train net output #0: loss = 5.25471 (* 1 = 5.25471 loss)
I0410 13:34:37.359969 18606 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 13:34:42.247980 18606 solver.cpp:218] Iteration 576 (2.45509 iter/s, 4.8878s/12 iters), loss = 5.27889
I0410 13:34:42.248026 18606 solver.cpp:237]     Train net output #0: loss = 5.27889 (* 1 = 5.27889 loss)
I0410 13:34:42.248035 18606 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 13:34:47.069393 18606 solver.cpp:218] Iteration 588 (2.48904 iter/s, 4.82114s/12 iters), loss = 5.26492
I0410 13:34:47.069486 18606 solver.cpp:237]     Train net output #0: loss = 5.26492 (* 1 = 5.26492 loss)
I0410 13:34:47.069499 18606 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 13:34:52.068065 18606 solver.cpp:218] Iteration 600 (2.40079 iter/s, 4.99835s/12 iters), loss = 5.26396
I0410 13:34:52.068111 18606 solver.cpp:237]     Train net output #0: loss = 5.26396 (* 1 = 5.26396 loss)
I0410 13:34:52.068120 18606 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 13:34:54.426692 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:56.506198 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 13:34:56.837849 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 13:34:57.064486 18606 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 13:34:57.064518 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:35:01.369001 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:01.659498 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:35:01.659539 18606 solver.cpp:397]     Test net output #1: loss = 5.28372 (* 1 = 5.28372 loss)
I0410 13:35:01.742198 18606 solver.cpp:218] Iteration 612 (1.24048 iter/s, 9.67365s/12 iters), loss = 5.27231
I0410 13:35:01.742246 18606 solver.cpp:237]     Train net output #0: loss = 5.27231 (* 1 = 5.27231 loss)
I0410 13:35:01.742256 18606 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 13:35:05.895210 18606 solver.cpp:218] Iteration 624 (2.88964 iter/s, 4.15276s/12 iters), loss = 5.28595
I0410 13:35:05.895270 18606 solver.cpp:237]     Train net output #0: loss = 5.28595 (* 1 = 5.28595 loss)
I0410 13:35:05.895287 18606 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 13:35:10.739652 18606 solver.cpp:218] Iteration 636 (2.47721 iter/s, 4.84415s/12 iters), loss = 5.28347
I0410 13:35:10.739708 18606 solver.cpp:237]     Train net output #0: loss = 5.28347 (* 1 = 5.28347 loss)
I0410 13:35:10.739722 18606 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 13:35:15.672580 18606 solver.cpp:218] Iteration 648 (2.43277 iter/s, 4.93264s/12 iters), loss = 5.27208
I0410 13:35:15.672629 18606 solver.cpp:237]     Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
I0410 13:35:15.672641 18606 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 13:35:20.521415 18606 solver.cpp:218] Iteration 660 (2.47496 iter/s, 4.84856s/12 iters), loss = 5.27148
I0410 13:35:20.522852 18606 solver.cpp:237]     Train net output #0: loss = 5.27148 (* 1 = 5.27148 loss)
I0410 13:35:20.522867 18606 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 13:35:25.345736 18606 solver.cpp:218] Iteration 672 (2.48825 iter/s, 4.82266s/12 iters), loss = 5.27449
I0410 13:35:25.345796 18606 solver.cpp:237]     Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
I0410 13:35:25.345808 18606 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 13:35:29.327458 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:35:30.183768 18606 solver.cpp:218] Iteration 684 (2.4805 iter/s, 4.83774s/12 iters), loss = 5.2756
I0410 13:35:30.183823 18606 solver.cpp:237]     Train net output #0: loss = 5.2756 (* 1 = 5.2756 loss)
I0410 13:35:30.183835 18606 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 13:35:35.303030 18606 solver.cpp:218] Iteration 696 (2.34422 iter/s, 5.11897s/12 iters), loss = 5.26553
I0410 13:35:35.303074 18606 solver.cpp:237]     Train net output #0: loss = 5.26553 (* 1 = 5.26553 loss)
I0410 13:35:35.303083 18606 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 13:35:39.753947 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:40.122745 18606 solver.cpp:218] Iteration 708 (2.48992 iter/s, 4.81944s/12 iters), loss = 5.25746
I0410 13:35:40.122803 18606 solver.cpp:237]     Train net output #0: loss = 5.25746 (* 1 = 5.25746 loss)
I0410 13:35:40.122815 18606 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 13:35:42.088063 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 13:35:43.066727 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 13:35:43.342692 18606 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 13:35:43.342721 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:35:47.422540 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:47.748832 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:35:47.748883 18606 solver.cpp:397]     Test net output #1: loss = 5.28506 (* 1 = 5.28506 loss)
I0410 13:35:49.538138 18606 solver.cpp:218] Iteration 720 (1.27457 iter/s, 9.41491s/12 iters), loss = 5.27692
I0410 13:35:49.538183 18606 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0410 13:35:49.538192 18606 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 13:35:54.396360 18606 solver.cpp:218] Iteration 732 (2.47018 iter/s, 4.85795s/12 iters), loss = 5.28045
I0410 13:35:54.406049 18606 solver.cpp:237]     Train net output #0: loss = 5.28045 (* 1 = 5.28045 loss)
I0410 13:35:54.406060 18606 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 13:35:59.230288 18606 solver.cpp:218] Iteration 744 (2.48755 iter/s, 4.82402s/12 iters), loss = 5.27435
I0410 13:35:59.230340 18606 solver.cpp:237]     Train net output #0: loss = 5.27435 (* 1 = 5.27435 loss)
I0410 13:35:59.230353 18606 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 13:36:04.125831 18606 solver.cpp:218] Iteration 756 (2.45135 iter/s, 4.89526s/12 iters), loss = 5.27336
I0410 13:36:04.125885 18606 solver.cpp:237]     Train net output #0: loss = 5.27336 (* 1 = 5.27336 loss)
I0410 13:36:04.125897 18606 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 13:36:08.981253 18606 solver.cpp:218] Iteration 768 (2.47161 iter/s, 4.85514s/12 iters), loss = 5.27874
I0410 13:36:08.981312 18606 solver.cpp:237]     Train net output #0: loss = 5.27874 (* 1 = 5.27874 loss)
I0410 13:36:08.981323 18606 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 13:36:13.815325 18606 solver.cpp:218] Iteration 780 (2.48253 iter/s, 4.83379s/12 iters), loss = 5.2663
I0410 13:36:13.815380 18606 solver.cpp:237]     Train net output #0: loss = 5.2663 (* 1 = 5.2663 loss)
I0410 13:36:13.815392 18606 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 13:36:18.613546 18606 solver.cpp:218] Iteration 792 (2.50108 iter/s, 4.79793s/12 iters), loss = 5.26582
I0410 13:36:18.613600 18606 solver.cpp:237]     Train net output #0: loss = 5.26582 (* 1 = 5.26582 loss)
I0410 13:36:18.613611 18606 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 13:36:23.440753 18606 solver.cpp:218] Iteration 804 (2.48605 iter/s, 4.82693s/12 iters), loss = 5.28368
I0410 13:36:23.440805 18606 solver.cpp:237]     Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
I0410 13:36:23.440817 18606 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 13:36:25.111796 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:27.782084 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 13:36:28.092840 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 13:36:28.308207 18606 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 13:36:28.308239 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:36:32.441428 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:32.802034 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:36:32.802083 18606 solver.cpp:397]     Test net output #1: loss = 5.2853 (* 1 = 5.2853 loss)
I0410 13:36:32.885155 18606 solver.cpp:218] Iteration 816 (1.27066 iter/s, 9.44392s/12 iters), loss = 5.27441
I0410 13:36:32.885231 18606 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0410 13:36:32.885246 18606 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 13:36:36.958703 18606 solver.cpp:218] Iteration 828 (2.94603 iter/s, 4.07328s/12 iters), loss = 5.28004
I0410 13:36:36.958765 18606 solver.cpp:237]     Train net output #0: loss = 5.28004 (* 1 = 5.28004 loss)
I0410 13:36:36.958779 18606 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 13:36:41.735807 18606 solver.cpp:218] Iteration 840 (2.51213 iter/s, 4.77682s/12 iters), loss = 5.22996
I0410 13:36:41.735870 18606 solver.cpp:237]     Train net output #0: loss = 5.22996 (* 1 = 5.22996 loss)
I0410 13:36:41.735882 18606 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 13:36:46.748353 18606 solver.cpp:218] Iteration 852 (2.39414 iter/s, 5.01225s/12 iters), loss = 5.2974
I0410 13:36:46.748410 18606 solver.cpp:237]     Train net output #0: loss = 5.2974 (* 1 = 5.2974 loss)
I0410 13:36:46.748421 18606 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 13:36:51.632467 18606 solver.cpp:218] Iteration 864 (2.45709 iter/s, 4.88383s/12 iters), loss = 5.2622
I0410 13:36:51.632515 18606 solver.cpp:237]     Train net output #0: loss = 5.2622 (* 1 = 5.2622 loss)
I0410 13:36:51.632526 18606 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 13:36:56.472038 18606 solver.cpp:218] Iteration 876 (2.4797 iter/s, 4.8393s/12 iters), loss = 5.26941
I0410 13:36:56.472766 18606 solver.cpp:237]     Train net output #0: loss = 5.26941 (* 1 = 5.26941 loss)
I0410 13:36:56.472779 18606 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 13:37:01.281888 18606 solver.cpp:218] Iteration 888 (2.49537 iter/s, 4.8089s/12 iters), loss = 5.26553
I0410 13:37:01.281944 18606 solver.cpp:237]     Train net output #0: loss = 5.26553 (* 1 = 5.26553 loss)
I0410 13:37:01.281975 18606 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 13:37:06.172340 18606 solver.cpp:218] Iteration 900 (2.4539 iter/s, 4.89017s/12 iters), loss = 5.27382
I0410 13:37:06.172394 18606 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0410 13:37:06.172406 18606 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 13:37:09.931720 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:11.008677 18606 solver.cpp:218] Iteration 912 (2.48136 iter/s, 4.83606s/12 iters), loss = 5.26068
I0410 13:37:11.008731 18606 solver.cpp:237]     Train net output #0: loss = 5.26068 (* 1 = 5.26068 loss)
I0410 13:37:11.008744 18606 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 13:37:12.967162 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 13:37:13.295271 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 13:37:13.512044 18606 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 13:37:13.512068 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:37:17.746731 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:18.147575 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:37:18.147625 18606 solver.cpp:397]     Test net output #1: loss = 5.28553 (* 1 = 5.28553 loss)
I0410 13:37:19.983428 18606 solver.cpp:218] Iteration 924 (1.33715 iter/s, 8.97429s/12 iters), loss = 5.28262
I0410 13:37:19.983474 18606 solver.cpp:237]     Train net output #0: loss = 5.28262 (* 1 = 5.28262 loss)
I0410 13:37:19.983482 18606 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 13:37:24.769140 18606 solver.cpp:218] Iteration 936 (2.50761 iter/s, 4.78544s/12 iters), loss = 5.26154
I0410 13:37:24.769186 18606 solver.cpp:237]     Train net output #0: loss = 5.26154 (* 1 = 5.26154 loss)
I0410 13:37:24.769196 18606 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 13:37:29.597201 18606 solver.cpp:218] Iteration 948 (2.48561 iter/s, 4.82779s/12 iters), loss = 5.28423
I0410 13:37:29.597316 18606 solver.cpp:237]     Train net output #0: loss = 5.28423 (* 1 = 5.28423 loss)
I0410 13:37:29.597328 18606 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 13:37:34.683863 18606 solver.cpp:218] Iteration 960 (2.35927 iter/s, 5.08631s/12 iters), loss = 5.25904
I0410 13:37:34.683907 18606 solver.cpp:237]     Train net output #0: loss = 5.25904 (* 1 = 5.25904 loss)
I0410 13:37:34.683917 18606 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 13:37:39.518450 18606 solver.cpp:218] Iteration 972 (2.48225 iter/s, 4.83432s/12 iters), loss = 5.2728
I0410 13:37:39.518494 18606 solver.cpp:237]     Train net output #0: loss = 5.2728 (* 1 = 5.2728 loss)
I0410 13:37:39.518503 18606 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 13:37:44.370689 18606 solver.cpp:218] Iteration 984 (2.47322 iter/s, 4.85197s/12 iters), loss = 5.29006
I0410 13:37:44.370743 18606 solver.cpp:237]     Train net output #0: loss = 5.29006 (* 1 = 5.29006 loss)
I0410 13:37:44.370755 18606 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 13:37:49.149150 18606 solver.cpp:218] Iteration 996 (2.51141 iter/s, 4.77819s/12 iters), loss = 5.27708
I0410 13:37:49.149188 18606 solver.cpp:237]     Train net output #0: loss = 5.27708 (* 1 = 5.27708 loss)
I0410 13:37:49.149196 18606 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 13:37:54.059607 18606 solver.cpp:218] Iteration 1008 (2.4439 iter/s, 4.91018s/12 iters), loss = 5.28658
I0410 13:37:54.059672 18606 solver.cpp:237]     Train net output #0: loss = 5.28658 (* 1 = 5.28658 loss)
I0410 13:37:54.059689 18606 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 13:37:55.054857 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:58.413657 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 13:37:58.724054 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 13:37:58.944399 18606 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 13:37:58.944420 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:38:03.070755 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:03.507347 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:38:03.507395 18606 solver.cpp:397]     Test net output #1: loss = 5.28557 (* 1 = 5.28557 loss)
I0410 13:38:03.590344 18606 solver.cpp:218] Iteration 1020 (1.25915 iter/s, 9.53025s/12 iters), loss = 5.28942
I0410 13:38:03.590389 18606 solver.cpp:237]     Train net output #0: loss = 5.28942 (* 1 = 5.28942 loss)
I0410 13:38:03.590400 18606 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 13:38:07.735267 18606 solver.cpp:218] Iteration 1032 (2.89528 iter/s, 4.14468s/12 iters), loss = 5.25158
I0410 13:38:07.735322 18606 solver.cpp:237]     Train net output #0: loss = 5.25158 (* 1 = 5.25158 loss)
I0410 13:38:07.735335 18606 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 13:38:12.599265 18606 solver.cpp:218] Iteration 1044 (2.46725 iter/s, 4.86372s/12 iters), loss = 5.26016
I0410 13:38:12.599309 18606 solver.cpp:237]     Train net output #0: loss = 5.26016 (* 1 = 5.26016 loss)
I0410 13:38:12.599318 18606 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 13:38:17.470558 18606 solver.cpp:218] Iteration 1056 (2.46355 iter/s, 4.87102s/12 iters), loss = 5.26212
I0410 13:38:17.470610 18606 solver.cpp:237]     Train net output #0: loss = 5.26212 (* 1 = 5.26212 loss)
I0410 13:38:17.470621 18606 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 13:38:22.354007 18606 solver.cpp:218] Iteration 1068 (2.45742 iter/s, 4.88317s/12 iters), loss = 5.28613
I0410 13:38:22.354061 18606 solver.cpp:237]     Train net output #0: loss = 5.28613 (* 1 = 5.28613 loss)
I0410 13:38:22.354074 18606 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 13:38:27.228814 18606 solver.cpp:218] Iteration 1080 (2.46178 iter/s, 4.87452s/12 iters), loss = 5.27022
I0410 13:38:27.228864 18606 solver.cpp:237]     Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
I0410 13:38:27.228875 18606 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 13:38:32.125854 18606 solver.cpp:218] Iteration 1092 (2.4506 iter/s, 4.89676s/12 iters), loss = 5.27983
I0410 13:38:32.125906 18606 solver.cpp:237]     Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
I0410 13:38:32.125917 18606 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 13:38:37.179786 18606 solver.cpp:218] Iteration 1104 (2.37453 iter/s, 5.05364s/12 iters), loss = 5.27313
I0410 13:38:37.179905 18606 solver.cpp:237]     Train net output #0: loss = 5.27313 (* 1 = 5.27313 loss)
I0410 13:38:37.179919 18606 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 13:38:40.249761 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:42.047032 18606 solver.cpp:218] Iteration 1116 (2.46563 iter/s, 4.8669s/12 iters), loss = 5.27112
I0410 13:38:42.047086 18606 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0410 13:38:42.047098 18606 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 13:38:44.053246 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 13:38:45.008071 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 13:38:45.237393 18606 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 13:38:45.237412 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:38:49.113687 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:49.589568 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:38:49.589604 18606 solver.cpp:397]     Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
I0410 13:38:51.556970 18606 solver.cpp:218] Iteration 1128 (1.2619 iter/s, 9.50945s/12 iters), loss = 5.27635
I0410 13:38:51.557027 18606 solver.cpp:237]     Train net output #0: loss = 5.27635 (* 1 = 5.27635 loss)
I0410 13:38:51.557039 18606 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 13:38:56.430078 18606 solver.cpp:218] Iteration 1140 (2.46264 iter/s, 4.87282s/12 iters), loss = 5.26577
I0410 13:38:56.430138 18606 solver.cpp:237]     Train net output #0: loss = 5.26577 (* 1 = 5.26577 loss)
I0410 13:38:56.430150 18606 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 13:39:01.293347 18606 solver.cpp:218] Iteration 1152 (2.46762 iter/s, 4.86298s/12 iters), loss = 5.27865
I0410 13:39:01.293404 18606 solver.cpp:237]     Train net output #0: loss = 5.27865 (* 1 = 5.27865 loss)
I0410 13:39:01.293417 18606 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 13:39:06.171049 18606 solver.cpp:218] Iteration 1164 (2.46032 iter/s, 4.87742s/12 iters), loss = 5.27239
I0410 13:39:06.171099 18606 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0410 13:39:06.171108 18606 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 13:39:11.388366 18606 solver.cpp:218] Iteration 1176 (2.30016 iter/s, 5.21703s/12 iters), loss = 5.28569
I0410 13:39:11.390339 18606 solver.cpp:237]     Train net output #0: loss = 5.28569 (* 1 = 5.28569 loss)
I0410 13:39:11.390349 18606 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 13:39:16.228138 18606 solver.cpp:218] Iteration 1188 (2.48058 iter/s, 4.83758s/12 iters), loss = 5.27054
I0410 13:39:16.228197 18606 solver.cpp:237]     Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
I0410 13:39:16.228212 18606 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 13:39:21.142947 18606 solver.cpp:218] Iteration 1200 (2.44175 iter/s, 4.91451s/12 iters), loss = 5.28611
I0410 13:39:21.143007 18606 solver.cpp:237]     Train net output #0: loss = 5.28611 (* 1 = 5.28611 loss)
I0410 13:39:21.143021 18606 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 13:39:26.144096 18606 solver.cpp:218] Iteration 1212 (2.39959 iter/s, 5.00086s/12 iters), loss = 5.2632
I0410 13:39:26.144137 18606 solver.cpp:237]     Train net output #0: loss = 5.2632 (* 1 = 5.2632 loss)
I0410 13:39:26.144145 18606 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 13:39:26.449120 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:30.599543 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 13:39:30.919117 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 13:39:31.127737 18606 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 13:39:31.127755 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:39:35.034444 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:35.543975 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:39:35.544025 18606 solver.cpp:397]     Test net output #1: loss = 5.28595 (* 1 = 5.28595 loss)
I0410 13:39:35.626756 18606 solver.cpp:218] Iteration 1224 (1.26553 iter/s, 9.48218s/12 iters), loss = 5.2825
I0410 13:39:35.626809 18606 solver.cpp:237]     Train net output #0: loss = 5.2825 (* 1 = 5.2825 loss)
I0410 13:39:35.626822 18606 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 13:39:39.739409 18606 solver.cpp:218] Iteration 1236 (2.918 iter/s, 4.1124s/12 iters), loss = 5.27117
I0410 13:39:39.739468 18606 solver.cpp:237]     Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
I0410 13:39:39.739481 18606 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 13:39:44.604250 18606 solver.cpp:218] Iteration 1248 (2.46682 iter/s, 4.86455s/12 iters), loss = 5.27749
I0410 13:39:44.604385 18606 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0410 13:39:44.604400 18606 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 13:39:49.421844 18606 solver.cpp:218] Iteration 1260 (2.49106 iter/s, 4.81723s/12 iters), loss = 5.27181
I0410 13:39:49.421895 18606 solver.cpp:237]     Train net output #0: loss = 5.27181 (* 1 = 5.27181 loss)
I0410 13:39:49.421906 18606 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 13:39:54.250331 18606 solver.cpp:218] Iteration 1272 (2.48539 iter/s, 4.82821s/12 iters), loss = 5.24661
I0410 13:39:54.250383 18606 solver.cpp:237]     Train net output #0: loss = 5.24661 (* 1 = 5.24661 loss)
I0410 13:39:54.250394 18606 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 13:39:59.257668 18606 solver.cpp:218] Iteration 1284 (2.39662 iter/s, 5.00705s/12 iters), loss = 5.28171
I0410 13:39:59.257709 18606 solver.cpp:237]     Train net output #0: loss = 5.28171 (* 1 = 5.28171 loss)
I0410 13:39:59.257719 18606 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 13:40:04.111578 18606 solver.cpp:218] Iteration 1296 (2.47237 iter/s, 4.85364s/12 iters), loss = 5.26894
I0410 13:40:04.111629 18606 solver.cpp:237]     Train net output #0: loss = 5.26894 (* 1 = 5.26894 loss)
I0410 13:40:04.111639 18606 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 13:40:09.049122 18606 solver.cpp:218] Iteration 1308 (2.4305 iter/s, 4.93726s/12 iters), loss = 5.25388
I0410 13:40:09.049170 18606 solver.cpp:237]     Train net output #0: loss = 5.25388 (* 1 = 5.25388 loss)
I0410 13:40:09.049183 18606 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 13:40:11.623423 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:14.234570 18606 solver.cpp:218] Iteration 1320 (2.3143 iter/s, 5.18515s/12 iters), loss = 5.27298
I0410 13:40:14.234629 18606 solver.cpp:237]     Train net output #0: loss = 5.27298 (* 1 = 5.27298 loss)
I0410 13:40:14.234640 18606 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 13:40:16.230947 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 13:40:16.545215 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 13:40:16.762457 18606 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 13:40:16.762482 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:40:20.914196 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:21.468336 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:40:21.468384 18606 solver.cpp:397]     Test net output #1: loss = 5.28667 (* 1 = 5.28667 loss)
I0410 13:40:23.397218 18606 solver.cpp:218] Iteration 1332 (1.30973 iter/s, 9.16217s/12 iters), loss = 5.28648
I0410 13:40:23.397269 18606 solver.cpp:237]     Train net output #0: loss = 5.28648 (* 1 = 5.28648 loss)
I0410 13:40:23.397280 18606 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 13:40:28.228885 18606 solver.cpp:218] Iteration 1344 (2.48376 iter/s, 4.83139s/12 iters), loss = 5.28575
I0410 13:40:28.228940 18606 solver.cpp:237]     Train net output #0: loss = 5.28575 (* 1 = 5.28575 loss)
I0410 13:40:28.228951 18606 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 13:40:33.195530 18606 solver.cpp:218] Iteration 1356 (2.41626 iter/s, 4.96636s/12 iters), loss = 5.27602
I0410 13:40:33.195577 18606 solver.cpp:237]     Train net output #0: loss = 5.27602 (* 1 = 5.27602 loss)
I0410 13:40:33.195588 18606 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 13:40:37.607610 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:40:38.109624 18606 solver.cpp:218] Iteration 1368 (2.4421 iter/s, 4.91381s/12 iters), loss = 5.2649
I0410 13:40:38.109676 18606 solver.cpp:237]     Train net output #0: loss = 5.2649 (* 1 = 5.2649 loss)
I0410 13:40:38.109688 18606 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 13:40:43.060400 18606 solver.cpp:218] Iteration 1380 (2.424 iter/s, 4.95049s/12 iters), loss = 5.27341
I0410 13:40:43.060447 18606 solver.cpp:237]     Train net output #0: loss = 5.27341 (* 1 = 5.27341 loss)
I0410 13:40:43.060457 18606 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 13:40:47.964228 18606 solver.cpp:218] Iteration 1392 (2.44721 iter/s, 4.90355s/12 iters), loss = 5.27374
I0410 13:40:47.964360 18606 solver.cpp:237]     Train net output #0: loss = 5.27374 (* 1 = 5.27374 loss)
I0410 13:40:47.964375 18606 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 13:40:52.890309 18606 solver.cpp:218] Iteration 1404 (2.43619 iter/s, 4.92572s/12 iters), loss = 5.27624
I0410 13:40:52.890365 18606 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0410 13:40:52.890378 18606 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 13:40:57.445298 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:57.797433 18606 solver.cpp:218] Iteration 1416 (2.44557 iter/s, 4.90684s/12 iters), loss = 5.25678
I0410 13:40:57.797477 18606 solver.cpp:237]     Train net output #0: loss = 5.25678 (* 1 = 5.25678 loss)
I0410 13:40:57.797485 18606 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 13:41:02.244244 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 13:41:03.899081 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 13:41:04.784505 18606 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 13:41:04.784531 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:41:08.650705 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:09.239159 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:41:09.239208 18606 solver.cpp:397]     Test net output #1: loss = 5.28627 (* 1 = 5.28627 loss)
I0410 13:41:09.320161 18606 solver.cpp:218] Iteration 1428 (1.04147 iter/s, 11.5222s/12 iters), loss = 5.27562
I0410 13:41:09.320228 18606 solver.cpp:237]     Train net output #0: loss = 5.27562 (* 1 = 5.27562 loss)
I0410 13:41:09.320245 18606 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 13:41:13.461740 18606 solver.cpp:218] Iteration 1440 (2.89763 iter/s, 4.14132s/12 iters), loss = 5.2811
I0410 13:41:13.461784 18606 solver.cpp:237]     Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
I0410 13:41:13.461796 18606 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 13:41:18.319852 18606 solver.cpp:218] Iteration 1452 (2.47023 iter/s, 4.85784s/12 iters), loss = 5.28039
I0410 13:41:18.319949 18606 solver.cpp:237]     Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
I0410 13:41:18.319962 18606 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 13:41:23.280968 18606 solver.cpp:218] Iteration 1464 (2.41897 iter/s, 4.96078s/12 iters), loss = 5.27726
I0410 13:41:23.281025 18606 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0410 13:41:23.281038 18606 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 13:41:28.121016 18606 solver.cpp:218] Iteration 1476 (2.47946 iter/s, 4.83976s/12 iters), loss = 5.277
I0410 13:41:28.121070 18606 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0410 13:41:28.121083 18606 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 13:41:32.920446 18606 solver.cpp:218] Iteration 1488 (2.50044 iter/s, 4.79915s/12 iters), loss = 5.25419
I0410 13:41:32.920500 18606 solver.cpp:237]     Train net output #0: loss = 5.25419 (* 1 = 5.25419 loss)
I0410 13:41:32.920512 18606 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 13:41:37.737365 18606 solver.cpp:218] Iteration 1500 (2.49136 iter/s, 4.81664s/12 iters), loss = 5.26927
I0410 13:41:37.737413 18606 solver.cpp:237]     Train net output #0: loss = 5.26927 (* 1 = 5.26927 loss)
I0410 13:41:37.737421 18606 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 13:41:42.601138 18606 solver.cpp:218] Iteration 1512 (2.46736 iter/s, 4.8635s/12 iters), loss = 5.28461
I0410 13:41:42.601183 18606 solver.cpp:237]     Train net output #0: loss = 5.28461 (* 1 = 5.28461 loss)
I0410 13:41:42.601194 18606 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 13:41:44.346756 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:47.456384 18606 solver.cpp:218] Iteration 1524 (2.47169 iter/s, 4.85498s/12 iters), loss = 5.27729
I0410 13:41:47.456429 18606 solver.cpp:237]     Train net output #0: loss = 5.27729 (* 1 = 5.27729 loss)
I0410 13:41:47.456437 18606 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 13:41:49.440754 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 13:41:49.749084 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 13:41:49.952121 18606 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 13:41:49.952142 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:41:53.689368 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:54.324106 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:41:54.324143 18606 solver.cpp:397]     Test net output #1: loss = 5.28611 (* 1 = 5.28611 loss)
I0410 13:41:56.099144 18606 solver.cpp:218] Iteration 1536 (1.38852 iter/s, 8.64232s/12 iters), loss = 5.27579
I0410 13:41:56.099189 18606 solver.cpp:237]     Train net output #0: loss = 5.27579 (* 1 = 5.27579 loss)
I0410 13:41:56.099198 18606 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 13:42:00.939739 18606 solver.cpp:218] Iteration 1548 (2.47917 iter/s, 4.84032s/12 iters), loss = 5.23159
I0410 13:42:00.939782 18606 solver.cpp:237]     Train net output #0: loss = 5.23159 (* 1 = 5.23159 loss)
I0410 13:42:00.939790 18606 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 13:42:05.740132 18606 solver.cpp:218] Iteration 1560 (2.49994 iter/s, 4.80012s/12 iters), loss = 5.29033
I0410 13:42:05.740182 18606 solver.cpp:237]     Train net output #0: loss = 5.29033 (* 1 = 5.29033 loss)
I0410 13:42:05.740193 18606 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 13:42:10.720075 18606 solver.cpp:218] Iteration 1572 (2.4098 iter/s, 4.97966s/12 iters), loss = 5.2595
I0410 13:42:10.720121 18606 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0410 13:42:10.720130 18606 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 13:42:15.586423 18606 solver.cpp:218] Iteration 1584 (2.46606 iter/s, 4.86606s/12 iters), loss = 5.26689
I0410 13:42:15.586477 18606 solver.cpp:237]     Train net output #0: loss = 5.26689 (* 1 = 5.26689 loss)
I0410 13:42:15.586486 18606 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 13:42:20.381984 18606 solver.cpp:218] Iteration 1596 (2.50247 iter/s, 4.79526s/12 iters), loss = 5.26921
I0410 13:42:20.382091 18606 solver.cpp:237]     Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
I0410 13:42:20.382103 18606 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 13:42:25.193534 18606 solver.cpp:218] Iteration 1608 (2.49417 iter/s, 4.81122s/12 iters), loss = 5.26867
I0410 13:42:25.193575 18606 solver.cpp:237]     Train net output #0: loss = 5.26867 (* 1 = 5.26867 loss)
I0410 13:42:25.193583 18606 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 13:42:28.999239 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:30.050801 18606 solver.cpp:218] Iteration 1620 (2.47066 iter/s, 4.85699s/12 iters), loss = 5.2576
I0410 13:42:30.050841 18606 solver.cpp:237]     Train net output #0: loss = 5.2576 (* 1 = 5.2576 loss)
I0410 13:42:30.050849 18606 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 13:42:34.432169 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 13:42:34.734776 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 13:42:34.946393 18606 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 13:42:34.946424 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:42:38.729387 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:39.402719 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:42:39.402770 18606 solver.cpp:397]     Test net output #1: loss = 5.28628 (* 1 = 5.28628 loss)
I0410 13:42:39.485903 18606 solver.cpp:218] Iteration 1632 (1.27191 iter/s, 9.43463s/12 iters), loss = 5.28629
I0410 13:42:39.485978 18606 solver.cpp:237]     Train net output #0: loss = 5.28629 (* 1 = 5.28629 loss)
I0410 13:42:39.485991 18606 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 13:42:43.600980 18606 solver.cpp:218] Iteration 1644 (2.91628 iter/s, 4.11483s/12 iters), loss = 5.25308
I0410 13:42:43.601030 18606 solver.cpp:237]     Train net output #0: loss = 5.25308 (* 1 = 5.25308 loss)
I0410 13:42:43.601042 18606 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 13:42:48.423717 18606 solver.cpp:218] Iteration 1656 (2.48836 iter/s, 4.82246s/12 iters), loss = 5.28923
I0410 13:42:48.423776 18606 solver.cpp:237]     Train net output #0: loss = 5.28923 (* 1 = 5.28923 loss)
I0410 13:42:48.423789 18606 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 13:42:53.217044 18606 solver.cpp:218] Iteration 1668 (2.50363 iter/s, 4.79304s/12 iters), loss = 5.26303
I0410 13:42:53.217172 18606 solver.cpp:237]     Train net output #0: loss = 5.26303 (* 1 = 5.26303 loss)
I0410 13:42:53.217185 18606 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 13:42:57.989080 18606 solver.cpp:218] Iteration 1680 (2.51483 iter/s, 4.77169s/12 iters), loss = 5.27386
I0410 13:42:57.989138 18606 solver.cpp:237]     Train net output #0: loss = 5.27386 (* 1 = 5.27386 loss)
I0410 13:42:57.989151 18606 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 13:43:02.777607 18606 solver.cpp:218] Iteration 1692 (2.50614 iter/s, 4.78825s/12 iters), loss = 5.28813
I0410 13:43:02.777658 18606 solver.cpp:237]     Train net output #0: loss = 5.28813 (* 1 = 5.28813 loss)
I0410 13:43:02.777669 18606 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 13:43:07.558552 18606 solver.cpp:218] Iteration 1704 (2.51011 iter/s, 4.78067s/12 iters), loss = 5.27046
I0410 13:43:07.558611 18606 solver.cpp:237]     Train net output #0: loss = 5.27046 (* 1 = 5.27046 loss)
I0410 13:43:07.558624 18606 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 13:43:12.419327 18606 solver.cpp:218] Iteration 1716 (2.46889 iter/s, 4.86049s/12 iters), loss = 5.27962
I0410 13:43:12.419370 18606 solver.cpp:237]     Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
I0410 13:43:12.419379 18606 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 13:43:13.453244 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:17.404920 18606 solver.cpp:218] Iteration 1728 (2.40707 iter/s, 4.98531s/12 iters), loss = 5.28357
I0410 13:43:17.404992 18606 solver.cpp:237]     Train net output #0: loss = 5.28357 (* 1 = 5.28357 loss)
I0410 13:43:17.405010 18606 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 13:43:19.382721 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 13:43:19.714442 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 13:43:19.919009 18606 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 13:43:19.919036 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:43:23.722165 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:24.446286 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:43:24.446336 18606 solver.cpp:397]     Test net output #1: loss = 5.28662 (* 1 = 5.28662 loss)
I0410 13:43:26.347728 18606 solver.cpp:218] Iteration 1740 (1.34193 iter/s, 8.94233s/12 iters), loss = 5.25682
I0410 13:43:26.347781 18606 solver.cpp:237]     Train net output #0: loss = 5.25682 (* 1 = 5.25682 loss)
I0410 13:43:26.347792 18606 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 13:43:31.184676 18606 solver.cpp:218] Iteration 1752 (2.48105 iter/s, 4.83667s/12 iters), loss = 5.26605
I0410 13:43:31.184729 18606 solver.cpp:237]     Train net output #0: loss = 5.26605 (* 1 = 5.26605 loss)
I0410 13:43:31.184741 18606 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 13:43:36.069973 18606 solver.cpp:218] Iteration 1764 (2.4565 iter/s, 4.885s/12 iters), loss = 5.26488
I0410 13:43:36.070024 18606 solver.cpp:237]     Train net output #0: loss = 5.26488 (* 1 = 5.26488 loss)
I0410 13:43:36.070035 18606 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 13:43:40.899914 18606 solver.cpp:218] Iteration 1776 (2.48465 iter/s, 4.82966s/12 iters), loss = 5.2812
I0410 13:43:40.899966 18606 solver.cpp:237]     Train net output #0: loss = 5.2812 (* 1 = 5.2812 loss)
I0410 13:43:40.899976 18606 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 13:43:45.726182 18606 solver.cpp:218] Iteration 1788 (2.48654 iter/s, 4.82599s/12 iters), loss = 5.26247
I0410 13:43:45.726239 18606 solver.cpp:237]     Train net output #0: loss = 5.26247 (* 1 = 5.26247 loss)
I0410 13:43:45.726253 18606 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 13:43:50.565418 18606 solver.cpp:218] Iteration 1800 (2.47988 iter/s, 4.83895s/12 iters), loss = 5.27885
I0410 13:43:50.565459 18606 solver.cpp:237]     Train net output #0: loss = 5.27885 (* 1 = 5.27885 loss)
I0410 13:43:50.565469 18606 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 13:43:55.401576 18606 solver.cpp:218] Iteration 1812 (2.48145 iter/s, 4.83589s/12 iters), loss = 5.26749
I0410 13:43:55.401721 18606 solver.cpp:237]     Train net output #0: loss = 5.26749 (* 1 = 5.26749 loss)
I0410 13:43:55.401732 18606 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 13:43:58.539810 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:00.370806 18606 solver.cpp:218] Iteration 1824 (2.41505 iter/s, 4.96885s/12 iters), loss = 5.27528
I0410 13:44:00.370865 18606 solver.cpp:237]     Train net output #0: loss = 5.27528 (* 1 = 5.27528 loss)
I0410 13:44:00.370877 18606 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 13:44:04.794447 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 13:44:05.210223 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 13:44:05.476802 18606 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 13:44:05.476830 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:44:09.332299 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:10.078810 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:44:10.078860 18606 solver.cpp:397]     Test net output #1: loss = 5.28615 (* 1 = 5.28615 loss)
I0410 13:44:10.162375 18606 solver.cpp:218] Iteration 1836 (1.22561 iter/s, 9.79106s/12 iters), loss = 5.27813
I0410 13:44:10.162447 18606 solver.cpp:237]     Train net output #0: loss = 5.27813 (* 1 = 5.27813 loss)
I0410 13:44:10.162463 18606 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 13:44:14.352742 18606 solver.cpp:218] Iteration 1848 (2.86389 iter/s, 4.1901s/12 iters), loss = 5.27174
I0410 13:44:14.352785 18606 solver.cpp:237]     Train net output #0: loss = 5.27174 (* 1 = 5.27174 loss)
I0410 13:44:14.352795 18606 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 13:44:19.229863 18606 solver.cpp:218] Iteration 1860 (2.46061 iter/s, 4.87685s/12 iters), loss = 5.28196
I0410 13:44:19.229912 18606 solver.cpp:237]     Train net output #0: loss = 5.28196 (* 1 = 5.28196 loss)
I0410 13:44:19.229923 18606 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 13:44:24.080493 18606 solver.cpp:218] Iteration 1872 (2.47405 iter/s, 4.85035s/12 iters), loss = 5.2715
I0410 13:44:24.080543 18606 solver.cpp:237]     Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
I0410 13:44:24.080554 18606 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 13:44:29.004307 18606 solver.cpp:218] Iteration 1884 (2.43727 iter/s, 4.92353s/12 iters), loss = 5.28641
I0410 13:44:29.004412 18606 solver.cpp:237]     Train net output #0: loss = 5.28641 (* 1 = 5.28641 loss)
I0410 13:44:29.004426 18606 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 13:44:33.941799 18606 solver.cpp:218] Iteration 1896 (2.43055 iter/s, 4.93716s/12 iters), loss = 5.26526
I0410 13:44:33.941839 18606 solver.cpp:237]     Train net output #0: loss = 5.26526 (* 1 = 5.26526 loss)
I0410 13:44:33.941848 18606 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 13:44:38.831176 18606 solver.cpp:218] Iteration 1908 (2.45444 iter/s, 4.88911s/12 iters), loss = 5.28227
I0410 13:44:38.831218 18606 solver.cpp:237]     Train net output #0: loss = 5.28227 (* 1 = 5.28227 loss)
I0410 13:44:38.831228 18606 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 13:44:43.751217 18606 solver.cpp:218] Iteration 1920 (2.43914 iter/s, 4.91976s/12 iters), loss = 5.27498
I0410 13:44:43.751264 18606 solver.cpp:237]     Train net output #0: loss = 5.27498 (* 1 = 5.27498 loss)
I0410 13:44:43.751274 18606 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 13:44:44.103229 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:48.653102 18606 solver.cpp:218] Iteration 1932 (2.44818 iter/s, 4.90161s/12 iters), loss = 5.28023
I0410 13:44:48.653152 18606 solver.cpp:237]     Train net output #0: loss = 5.28023 (* 1 = 5.28023 loss)
I0410 13:44:48.653163 18606 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 13:44:50.649821 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 13:44:50.979672 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 13:44:51.200075 18606 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 13:44:51.200103 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:44:54.841003 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:55.623576 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:44:55.623639 18606 solver.cpp:397]     Test net output #1: loss = 5.28666 (* 1 = 5.28666 loss)
I0410 13:44:57.524142 18606 solver.cpp:218] Iteration 1944 (1.35279 iter/s, 8.87058s/12 iters), loss = 5.27315
I0410 13:44:57.524188 18606 solver.cpp:237]     Train net output #0: loss = 5.27315 (* 1 = 5.27315 loss)
I0410 13:44:57.524197 18606 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 13:45:02.397099 18606 solver.cpp:218] Iteration 1956 (2.46271 iter/s, 4.87268s/12 iters), loss = 5.28035
I0410 13:45:02.397207 18606 solver.cpp:237]     Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
I0410 13:45:02.397217 18606 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 13:45:07.205689 18606 solver.cpp:218] Iteration 1968 (2.49571 iter/s, 4.80825s/12 iters), loss = 5.27253
I0410 13:45:07.205744 18606 solver.cpp:237]     Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
I0410 13:45:07.205754 18606 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 13:45:12.366852 18606 solver.cpp:218] Iteration 1980 (2.32519 iter/s, 5.16087s/12 iters), loss = 5.25468
I0410 13:45:12.366899 18606 solver.cpp:237]     Train net output #0: loss = 5.25468 (* 1 = 5.25468 loss)
I0410 13:45:12.366910 18606 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 13:45:17.156647 18606 solver.cpp:218] Iteration 1992 (2.50547 iter/s, 4.78951s/12 iters), loss = 5.28296
I0410 13:45:17.156703 18606 solver.cpp:237]     Train net output #0: loss = 5.28296 (* 1 = 5.28296 loss)
I0410 13:45:17.156714 18606 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 13:45:21.993310 18606 solver.cpp:218] Iteration 2004 (2.4812 iter/s, 4.83638s/12 iters), loss = 5.27675
I0410 13:45:21.993364 18606 solver.cpp:237]     Train net output #0: loss = 5.27675 (* 1 = 5.27675 loss)
I0410 13:45:21.993376 18606 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 13:45:26.853660 18606 solver.cpp:218] Iteration 2016 (2.4691 iter/s, 4.86006s/12 iters), loss = 5.25294
I0410 13:45:26.853715 18606 solver.cpp:237]     Train net output #0: loss = 5.25294 (* 1 = 5.25294 loss)
I0410 13:45:26.853726 18606 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 13:45:29.310040 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:31.708813 18606 solver.cpp:218] Iteration 2028 (2.47175 iter/s, 4.85487s/12 iters), loss = 5.27605
I0410 13:45:31.708863 18606 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0410 13:45:31.708873 18606 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 13:45:36.097605 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 13:45:36.424723 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 13:45:36.645347 18606 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 13:45:36.645377 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:45:40.163497 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:40.990979 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:45:40.991029 18606 solver.cpp:397]     Test net output #1: loss = 5.28638 (* 1 = 5.28638 loss)
I0410 13:45:41.072881 18606 solver.cpp:218] Iteration 2040 (1.28156 iter/s, 9.36359s/12 iters), loss = 5.28077
I0410 13:45:41.072934 18606 solver.cpp:237]     Train net output #0: loss = 5.28077 (* 1 = 5.28077 loss)
I0410 13:45:41.072947 18606 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 13:45:45.198143 18606 solver.cpp:218] Iteration 2052 (2.90908 iter/s, 4.12501s/12 iters), loss = 5.28284
I0410 13:45:45.198195 18606 solver.cpp:237]     Train net output #0: loss = 5.28284 (* 1 = 5.28284 loss)
I0410 13:45:45.198207 18606 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 13:45:45.198554 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:45:50.162179 18606 solver.cpp:218] Iteration 2064 (2.41753 iter/s, 4.96375s/12 iters), loss = 5.27134
I0410 13:45:50.162238 18606 solver.cpp:237]     Train net output #0: loss = 5.27134 (* 1 = 5.27134 loss)
I0410 13:45:50.162250 18606 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 13:45:55.036386 18606 solver.cpp:218] Iteration 2076 (2.46208 iter/s, 4.87392s/12 iters), loss = 5.27945
I0410 13:45:55.036434 18606 solver.cpp:237]     Train net output #0: loss = 5.27945 (* 1 = 5.27945 loss)
I0410 13:45:55.036446 18606 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 13:45:59.842056 18606 solver.cpp:218] Iteration 2088 (2.4972 iter/s, 4.80539s/12 iters), loss = 5.2734
I0410 13:45:59.842113 18606 solver.cpp:237]     Train net output #0: loss = 5.2734 (* 1 = 5.2734 loss)
I0410 13:45:59.842124 18606 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 13:46:04.668133 18606 solver.cpp:218] Iteration 2100 (2.48664 iter/s, 4.82579s/12 iters), loss = 5.2707
I0410 13:46:04.668175 18606 solver.cpp:237]     Train net output #0: loss = 5.2707 (* 1 = 5.2707 loss)
I0410 13:46:04.668184 18606 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 13:46:09.464921 18606 solver.cpp:218] Iteration 2112 (2.50181 iter/s, 4.79652s/12 iters), loss = 5.28061
I0410 13:46:09.464994 18606 solver.cpp:237]     Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
I0410 13:46:09.465004 18606 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 13:46:14.043661 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:14.352869 18606 solver.cpp:218] Iteration 2124 (2.45517 iter/s, 4.88764s/12 iters), loss = 5.26034
I0410 13:46:14.352928 18606 solver.cpp:237]     Train net output #0: loss = 5.26034 (* 1 = 5.26034 loss)
I0410 13:46:14.352941 18606 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 13:46:19.241557 18606 solver.cpp:218] Iteration 2136 (2.45479 iter/s, 4.8884s/12 iters), loss = 5.27511
I0410 13:46:19.241600 18606 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0410 13:46:19.241607 18606 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 13:46:21.214627 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 13:46:21.515399 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 13:46:21.726166 18606 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 13:46:21.726184 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:46:25.152887 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:26.012284 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:46:26.012315 18606 solver.cpp:397]     Test net output #1: loss = 5.2866 (* 1 = 5.2866 loss)
I0410 13:46:27.770421 18606 solver.cpp:218] Iteration 2148 (1.40706 iter/s, 8.52841s/12 iters), loss = 5.28094
I0410 13:46:27.770489 18606 solver.cpp:237]     Train net output #0: loss = 5.28094 (* 1 = 5.28094 loss)
I0410 13:46:27.770507 18606 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 13:46:32.599968 18606 solver.cpp:218] Iteration 2160 (2.48486 iter/s, 4.82925s/12 iters), loss = 5.28516
I0410 13:46:32.600024 18606 solver.cpp:237]     Train net output #0: loss = 5.28516 (* 1 = 5.28516 loss)
I0410 13:46:32.600037 18606 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 13:46:37.510458 18606 solver.cpp:218] Iteration 2172 (2.44389 iter/s, 4.9102s/12 iters), loss = 5.27503
I0410 13:46:37.510512 18606 solver.cpp:237]     Train net output #0: loss = 5.27503 (* 1 = 5.27503 loss)
I0410 13:46:37.510524 18606 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 13:46:42.614193 18606 solver.cpp:218] Iteration 2184 (2.35135 iter/s, 5.10344s/12 iters), loss = 5.27399
I0410 13:46:42.614341 18606 solver.cpp:237]     Train net output #0: loss = 5.27399 (* 1 = 5.27399 loss)
I0410 13:46:42.614356 18606 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 13:46:47.504549 18606 solver.cpp:218] Iteration 2196 (2.454 iter/s, 4.88998s/12 iters), loss = 5.25293
I0410 13:46:47.504606 18606 solver.cpp:237]     Train net output #0: loss = 5.25293 (* 1 = 5.25293 loss)
I0410 13:46:47.504617 18606 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 13:46:52.402873 18606 solver.cpp:218] Iteration 2208 (2.44996 iter/s, 4.89804s/12 iters), loss = 5.27068
I0410 13:46:52.402930 18606 solver.cpp:237]     Train net output #0: loss = 5.27068 (* 1 = 5.27068 loss)
I0410 13:46:52.402945 18606 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 13:46:57.324285 18606 solver.cpp:218] Iteration 2220 (2.43847 iter/s, 4.92112s/12 iters), loss = 5.28365
I0410 13:46:57.324329 18606 solver.cpp:237]     Train net output #0: loss = 5.28365 (* 1 = 5.28365 loss)
I0410 13:46:57.324337 18606 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 13:46:59.083675 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:02.389840 18606 solver.cpp:218] Iteration 2232 (2.36907 iter/s, 5.06527s/12 iters), loss = 5.28621
I0410 13:47:02.389887 18606 solver.cpp:237]     Train net output #0: loss = 5.28621 (* 1 = 5.28621 loss)
I0410 13:47:02.389899 18606 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 13:47:06.802521 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 13:47:07.094413 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 13:47:07.294268 18606 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 13:47:07.294287 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:47:10.831626 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:11.736234 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:47:11.736274 18606 solver.cpp:397]     Test net output #1: loss = 5.28653 (* 1 = 5.28653 loss)
I0410 13:47:11.819046 18606 solver.cpp:218] Iteration 2244 (1.27271 iter/s, 9.42873s/12 iters), loss = 5.2795
I0410 13:47:11.819094 18606 solver.cpp:237]     Train net output #0: loss = 5.2795 (* 1 = 5.2795 loss)
I0410 13:47:11.819103 18606 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 13:47:15.882097 18606 solver.cpp:218] Iteration 2256 (2.95362 iter/s, 4.06281s/12 iters), loss = 5.24029
I0410 13:47:15.882210 18606 solver.cpp:237]     Train net output #0: loss = 5.24029 (* 1 = 5.24029 loss)
I0410 13:47:15.882222 18606 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 13:47:20.789948 18606 solver.cpp:218] Iteration 2268 (2.44523 iter/s, 4.90751s/12 iters), loss = 5.28302
I0410 13:47:20.790019 18606 solver.cpp:237]     Train net output #0: loss = 5.28302 (* 1 = 5.28302 loss)
I0410 13:47:20.790031 18606 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 13:47:25.690582 18606 solver.cpp:218] Iteration 2280 (2.44881 iter/s, 4.90033s/12 iters), loss = 5.25588
I0410 13:47:25.690634 18606 solver.cpp:237]     Train net output #0: loss = 5.25588 (* 1 = 5.25588 loss)
I0410 13:47:25.690646 18606 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 13:47:30.524320 18606 solver.cpp:218] Iteration 2292 (2.48269 iter/s, 4.83346s/12 iters), loss = 5.27083
I0410 13:47:30.524374 18606 solver.cpp:237]     Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
I0410 13:47:30.524387 18606 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 13:47:35.407256 18606 solver.cpp:218] Iteration 2304 (2.45768 iter/s, 4.88265s/12 iters), loss = 5.26881
I0410 13:47:35.407307 18606 solver.cpp:237]     Train net output #0: loss = 5.26881 (* 1 = 5.26881 loss)
I0410 13:47:35.407320 18606 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 13:47:40.303778 18606 solver.cpp:218] Iteration 2316 (2.45086 iter/s, 4.89624s/12 iters), loss = 5.26244
I0410 13:47:40.303831 18606 solver.cpp:237]     Train net output #0: loss = 5.26244 (* 1 = 5.26244 loss)
I0410 13:47:40.303843 18606 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 13:47:44.202314 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:45.220502 18606 solver.cpp:218] Iteration 2328 (2.44079 iter/s, 4.91644s/12 iters), loss = 5.26004
I0410 13:47:45.220543 18606 solver.cpp:237]     Train net output #0: loss = 5.26004 (* 1 = 5.26004 loss)
I0410 13:47:45.220552 18606 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 13:47:50.024140 18606 solver.cpp:218] Iteration 2340 (2.49825 iter/s, 4.80337s/12 iters), loss = 5.2908
I0410 13:47:50.024282 18606 solver.cpp:237]     Train net output #0: loss = 5.2908 (* 1 = 5.2908 loss)
I0410 13:47:50.024296 18606 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 13:47:52.001883 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 13:47:52.330806 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 13:47:52.552950 18606 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 13:47:52.552983 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:47:56.182436 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:57.122900 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:47:57.122949 18606 solver.cpp:397]     Test net output #1: loss = 5.28692 (* 1 = 5.28692 loss)
I0410 13:47:58.907047 18606 solver.cpp:218] Iteration 2352 (1.35099 iter/s, 8.88236s/12 iters), loss = 5.25795
I0410 13:47:58.907089 18606 solver.cpp:237]     Train net output #0: loss = 5.25795 (* 1 = 5.25795 loss)
I0410 13:47:58.907099 18606 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 13:48:03.759902 18606 solver.cpp:218] Iteration 2364 (2.47291 iter/s, 4.85258s/12 iters), loss = 5.30565
I0410 13:48:03.759938 18606 solver.cpp:237]     Train net output #0: loss = 5.30565 (* 1 = 5.30565 loss)
I0410 13:48:03.759948 18606 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 13:48:08.636510 18606 solver.cpp:218] Iteration 2376 (2.46086 iter/s, 4.87634s/12 iters), loss = 5.26432
I0410 13:48:08.636566 18606 solver.cpp:237]     Train net output #0: loss = 5.26432 (* 1 = 5.26432 loss)
I0410 13:48:08.636579 18606 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 13:48:13.470144 18606 solver.cpp:218] Iteration 2388 (2.48275 iter/s, 4.83335s/12 iters), loss = 5.27443
I0410 13:48:13.470198 18606 solver.cpp:237]     Train net output #0: loss = 5.27443 (* 1 = 5.27443 loss)
I0410 13:48:13.470211 18606 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 13:48:18.365942 18606 solver.cpp:218] Iteration 2400 (2.45123 iter/s, 4.89551s/12 iters), loss = 5.28244
I0410 13:48:18.366016 18606 solver.cpp:237]     Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
I0410 13:48:18.366030 18606 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 13:48:23.254880 18606 solver.cpp:218] Iteration 2412 (2.45467 iter/s, 4.88863s/12 iters), loss = 5.27092
I0410 13:48:23.255048 18606 solver.cpp:237]     Train net output #0: loss = 5.27092 (* 1 = 5.27092 loss)
I0410 13:48:23.255060 18606 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 13:48:28.163029 18606 solver.cpp:218] Iteration 2424 (2.44511 iter/s, 4.90775s/12 iters), loss = 5.2758
I0410 13:48:28.163074 18606 solver.cpp:237]     Train net output #0: loss = 5.2758 (* 1 = 5.2758 loss)
I0410 13:48:28.163084 18606 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 13:48:29.194039 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:33.037273 18606 solver.cpp:218] Iteration 2436 (2.46206 iter/s, 4.87396s/12 iters), loss = 5.27648
I0410 13:48:33.037330 18606 solver.cpp:237]     Train net output #0: loss = 5.27648 (* 1 = 5.27648 loss)
I0410 13:48:33.037343 18606 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 13:48:37.473227 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 13:48:37.781875 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 13:48:37.984673 18606 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 13:48:37.984706 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:48:41.446245 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:42.419883 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:48:42.419936 18606 solver.cpp:397]     Test net output #1: loss = 5.28664 (* 1 = 5.28664 loss)
I0410 13:48:42.502805 18606 solver.cpp:218] Iteration 2448 (1.26782 iter/s, 9.46504s/12 iters), loss = 5.25444
I0410 13:48:42.502854 18606 solver.cpp:237]     Train net output #0: loss = 5.25444 (* 1 = 5.25444 loss)
I0410 13:48:42.502866 18606 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 13:48:46.680320 18606 solver.cpp:218] Iteration 2460 (2.87269 iter/s, 4.17727s/12 iters), loss = 5.26394
I0410 13:48:46.680359 18606 solver.cpp:237]     Train net output #0: loss = 5.26394 (* 1 = 5.26394 loss)
I0410 13:48:46.680368 18606 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 13:48:51.543390 18606 solver.cpp:218] Iteration 2472 (2.46772 iter/s, 4.8628s/12 iters), loss = 5.26721
I0410 13:48:51.543452 18606 solver.cpp:237]     Train net output #0: loss = 5.26721 (* 1 = 5.26721 loss)
I0410 13:48:51.543464 18606 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 13:48:56.364346 18606 solver.cpp:218] Iteration 2484 (2.48928 iter/s, 4.82066s/12 iters), loss = 5.27463
I0410 13:48:56.364471 18606 solver.cpp:237]     Train net output #0: loss = 5.27463 (* 1 = 5.27463 loss)
I0410 13:48:56.364488 18606 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 13:49:01.148653 18606 solver.cpp:218] Iteration 2496 (2.50838 iter/s, 4.78396s/12 iters), loss = 5.26919
I0410 13:49:01.148703 18606 solver.cpp:237]     Train net output #0: loss = 5.26919 (* 1 = 5.26919 loss)
I0410 13:49:01.148715 18606 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 13:49:06.007611 18606 solver.cpp:218] Iteration 2508 (2.46981 iter/s, 4.85867s/12 iters), loss = 5.28992
I0410 13:49:06.007673 18606 solver.cpp:237]     Train net output #0: loss = 5.28992 (* 1 = 5.28992 loss)
I0410 13:49:06.007686 18606 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 13:49:10.845281 18606 solver.cpp:218] Iteration 2520 (2.48068 iter/s, 4.83738s/12 iters), loss = 5.27734
I0410 13:49:10.845331 18606 solver.cpp:237]     Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
I0410 13:49:10.845341 18606 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 13:49:14.034152 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:15.755359 18606 solver.cpp:218] Iteration 2532 (2.44409 iter/s, 4.90979s/12 iters), loss = 5.28345
I0410 13:49:15.755403 18606 solver.cpp:237]     Train net output #0: loss = 5.28345 (* 1 = 5.28345 loss)
I0410 13:49:15.755414 18606 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 13:49:20.585090 18606 solver.cpp:218] Iteration 2544 (2.48475 iter/s, 4.82946s/12 iters), loss = 5.27455
I0410 13:49:20.585146 18606 solver.cpp:237]     Train net output #0: loss = 5.27455 (* 1 = 5.27455 loss)
I0410 13:49:20.585158 18606 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 13:49:22.555240 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 13:49:23.052280 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 13:49:23.257458 18606 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 13:49:23.257484 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:49:26.574880 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:27.592371 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:49:27.592402 18606 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0410 13:49:29.466490 18606 solver.cpp:218] Iteration 2556 (1.35121 iter/s, 8.88094s/12 iters), loss = 5.27975
I0410 13:49:29.466536 18606 solver.cpp:237]     Train net output #0: loss = 5.27975 (* 1 = 5.27975 loss)
I0410 13:49:29.466544 18606 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 13:49:34.283860 18606 solver.cpp:218] Iteration 2568 (2.49113 iter/s, 4.81709s/12 iters), loss = 5.28757
I0410 13:49:34.283918 18606 solver.cpp:237]     Train net output #0: loss = 5.28757 (* 1 = 5.28757 loss)
I0410 13:49:34.283931 18606 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 13:49:39.153841 18606 solver.cpp:218] Iteration 2580 (2.46423 iter/s, 4.86968s/12 iters), loss = 5.26716
I0410 13:49:39.153901 18606 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0410 13:49:39.153913 18606 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 13:49:43.977442 18606 solver.cpp:218] Iteration 2592 (2.48792 iter/s, 4.82331s/12 iters), loss = 5.28773
I0410 13:49:43.977492 18606 solver.cpp:237]     Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
I0410 13:49:43.977504 18606 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 13:49:48.914371 18606 solver.cpp:218] Iteration 2604 (2.4308 iter/s, 4.93665s/12 iters), loss = 5.2589
I0410 13:49:48.914422 18606 solver.cpp:237]     Train net output #0: loss = 5.2589 (* 1 = 5.2589 loss)
I0410 13:49:48.914434 18606 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 13:49:53.831815 18606 solver.cpp:218] Iteration 2616 (2.44043 iter/s, 4.91716s/12 iters), loss = 5.2784
I0410 13:49:53.831868 18606 solver.cpp:237]     Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
I0410 13:49:53.831879 18606 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 13:49:58.729620 18606 solver.cpp:218] Iteration 2628 (2.45022 iter/s, 4.89752s/12 iters), loss = 5.27931
I0410 13:49:58.729703 18606 solver.cpp:237]     Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
I0410 13:49:58.729714 18606 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 13:49:59.148990 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:03.693575 18606 solver.cpp:218] Iteration 2640 (2.41758 iter/s, 4.96364s/12 iters), loss = 5.27964
I0410 13:50:03.693622 18606 solver.cpp:237]     Train net output #0: loss = 5.27964 (* 1 = 5.27964 loss)
I0410 13:50:03.693632 18606 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 13:50:08.043200 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 13:50:08.372515 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 13:50:08.592008 18606 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 13:50:08.592038 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:50:11.934973 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:13.074043 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:50:13.074092 18606 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0410 13:50:13.157127 18606 solver.cpp:218] Iteration 2652 (1.26809 iter/s, 9.46307s/12 iters), loss = 5.27096
I0410 13:50:13.157181 18606 solver.cpp:237]     Train net output #0: loss = 5.27096 (* 1 = 5.27096 loss)
I0410 13:50:13.157193 18606 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 13:50:17.240898 18606 solver.cpp:218] Iteration 2664 (2.93864 iter/s, 4.08352s/12 iters), loss = 5.28226
I0410 13:50:17.240958 18606 solver.cpp:237]     Train net output #0: loss = 5.28226 (* 1 = 5.28226 loss)
I0410 13:50:17.240973 18606 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 13:50:22.152899 18606 solver.cpp:218] Iteration 2676 (2.44314 iter/s, 4.91171s/12 iters), loss = 5.26839
I0410 13:50:22.152952 18606 solver.cpp:237]     Train net output #0: loss = 5.26839 (* 1 = 5.26839 loss)
I0410 13:50:22.152963 18606 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 13:50:27.045904 18606 solver.cpp:218] Iteration 2688 (2.45262 iter/s, 4.89272s/12 iters), loss = 5.25718
I0410 13:50:27.045975 18606 solver.cpp:237]     Train net output #0: loss = 5.25718 (* 1 = 5.25718 loss)
I0410 13:50:27.045989 18606 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 13:50:31.945003 18606 solver.cpp:218] Iteration 2700 (2.44957 iter/s, 4.89881s/12 iters), loss = 5.28238
I0410 13:50:31.945173 18606 solver.cpp:237]     Train net output #0: loss = 5.28238 (* 1 = 5.28238 loss)
I0410 13:50:31.945188 18606 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 13:50:37.139981 18606 solver.cpp:218] Iteration 2712 (2.31011 iter/s, 5.19456s/12 iters), loss = 5.28324
I0410 13:50:37.140039 18606 solver.cpp:237]     Train net output #0: loss = 5.28324 (* 1 = 5.28324 loss)
I0410 13:50:37.140054 18606 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 13:50:41.956775 18606 solver.cpp:218] Iteration 2724 (2.49141 iter/s, 4.81656s/12 iters), loss = 5.2579
I0410 13:50:41.956823 18606 solver.cpp:237]     Train net output #0: loss = 5.2579 (* 1 = 5.2579 loss)
I0410 13:50:41.956831 18606 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 13:50:44.428074 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:46.771641 18606 solver.cpp:218] Iteration 2736 (2.4924 iter/s, 4.81463s/12 iters), loss = 5.27978
I0410 13:50:46.771703 18606 solver.cpp:237]     Train net output #0: loss = 5.27978 (* 1 = 5.27978 loss)
I0410 13:50:46.771715 18606 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 13:50:51.564422 18606 solver.cpp:218] Iteration 2748 (2.50389 iter/s, 4.79254s/12 iters), loss = 5.2778
I0410 13:50:51.564482 18606 solver.cpp:237]     Train net output #0: loss = 5.2778 (* 1 = 5.2778 loss)
I0410 13:50:51.564496 18606 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 13:50:53.516675 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 13:50:53.843427 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 13:50:54.057921 18606 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 13:50:54.057940 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:50:56.682636 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:50:57.280117 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:58.755046 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:50:58.755095 18606 solver.cpp:397]     Test net output #1: loss = 5.28665 (* 1 = 5.28665 loss)
I0410 13:51:00.556427 18606 solver.cpp:218] Iteration 2760 (1.33457 iter/s, 8.99163s/12 iters), loss = 5.27992
I0410 13:51:00.556471 18606 solver.cpp:237]     Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
I0410 13:51:00.556480 18606 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 13:51:05.375870 18606 solver.cpp:218] Iteration 2772 (2.49003 iter/s, 4.81921s/12 iters), loss = 5.27658
I0410 13:51:05.376001 18606 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0410 13:51:05.376015 18606 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 13:51:10.186556 18606 solver.cpp:218] Iteration 2784 (2.49461 iter/s, 4.81038s/12 iters), loss = 5.27678
I0410 13:51:10.186612 18606 solver.cpp:237]     Train net output #0: loss = 5.27678 (* 1 = 5.27678 loss)
I0410 13:51:10.186625 18606 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 13:51:15.134459 18606 solver.cpp:218] Iteration 2796 (2.42539 iter/s, 4.94766s/12 iters), loss = 5.27292
I0410 13:51:15.134505 18606 solver.cpp:237]     Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
I0410 13:51:15.134512 18606 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 13:51:19.887550 18606 solver.cpp:218] Iteration 2808 (2.52479 iter/s, 4.75286s/12 iters), loss = 5.26401
I0410 13:51:19.887599 18606 solver.cpp:237]     Train net output #0: loss = 5.26401 (* 1 = 5.26401 loss)
I0410 13:51:19.887610 18606 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 13:51:24.707245 18606 solver.cpp:218] Iteration 2820 (2.48991 iter/s, 4.81946s/12 iters), loss = 5.27641
I0410 13:51:24.707302 18606 solver.cpp:237]     Train net output #0: loss = 5.27641 (* 1 = 5.27641 loss)
I0410 13:51:24.707314 18606 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 13:51:29.312268 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:29.611104 18606 solver.cpp:218] Iteration 2832 (2.44717 iter/s, 4.90362s/12 iters), loss = 5.26147
I0410 13:51:29.611148 18606 solver.cpp:237]     Train net output #0: loss = 5.26147 (* 1 = 5.26147 loss)
I0410 13:51:29.611157 18606 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 13:51:34.555950 18606 solver.cpp:218] Iteration 2844 (2.42689 iter/s, 4.94461s/12 iters), loss = 5.26933
I0410 13:51:34.556007 18606 solver.cpp:237]     Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
I0410 13:51:34.556016 18606 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 13:51:38.891683 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 13:51:39.233470 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 13:51:39.445760 18606 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 13:51:39.445786 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:51:42.625252 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:43.761266 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:51:43.761314 18606 solver.cpp:397]     Test net output #1: loss = 5.28693 (* 1 = 5.28693 loss)
I0410 13:51:43.844250 18606 solver.cpp:218] Iteration 2856 (1.292 iter/s, 9.2879s/12 iters), loss = 5.29
I0410 13:51:43.844301 18606 solver.cpp:237]     Train net output #0: loss = 5.29 (* 1 = 5.29 loss)
I0410 13:51:43.844313 18606 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 13:51:47.981176 18606 solver.cpp:218] Iteration 2868 (2.90085 iter/s, 4.13671s/12 iters), loss = 5.27988
I0410 13:51:47.981230 18606 solver.cpp:237]     Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
I0410 13:51:47.981242 18606 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 13:51:52.923797 18606 solver.cpp:218] Iteration 2880 (2.42798 iter/s, 4.94238s/12 iters), loss = 5.28076
I0410 13:51:52.923851 18606 solver.cpp:237]     Train net output #0: loss = 5.28076 (* 1 = 5.28076 loss)
I0410 13:51:52.923859 18606 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 13:51:57.719012 18606 solver.cpp:218] Iteration 2892 (2.50262 iter/s, 4.79498s/12 iters), loss = 5.27383
I0410 13:51:57.719065 18606 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0410 13:51:57.719076 18606 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 13:52:02.673928 18606 solver.cpp:218] Iteration 2904 (2.42196 iter/s, 4.95467s/12 iters), loss = 5.25398
I0410 13:52:02.673995 18606 solver.cpp:237]     Train net output #0: loss = 5.25398 (* 1 = 5.25398 loss)
I0410 13:52:02.674005 18606 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 13:52:07.719029 18606 solver.cpp:218] Iteration 2916 (2.37867 iter/s, 5.04485s/12 iters), loss = 5.27059
I0410 13:52:07.719071 18606 solver.cpp:237]     Train net output #0: loss = 5.27059 (* 1 = 5.27059 loss)
I0410 13:52:07.719081 18606 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 13:52:12.520432 18606 solver.cpp:218] Iteration 2928 (2.49939 iter/s, 4.80117s/12 iters), loss = 5.28078
I0410 13:52:12.523978 18606 solver.cpp:237]     Train net output #0: loss = 5.28078 (* 1 = 5.28078 loss)
I0410 13:52:12.523993 18606 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 13:52:14.277045 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:17.308035 18606 solver.cpp:218] Iteration 2940 (2.50842 iter/s, 4.78389s/12 iters), loss = 5.28398
I0410 13:52:17.308068 18606 solver.cpp:237]     Train net output #0: loss = 5.28398 (* 1 = 5.28398 loss)
I0410 13:52:17.308076 18606 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 13:52:22.157748 18606 solver.cpp:218] Iteration 2952 (2.47449 iter/s, 4.84949s/12 iters), loss = 5.28095
I0410 13:52:22.157793 18606 solver.cpp:237]     Train net output #0: loss = 5.28095 (* 1 = 5.28095 loss)
I0410 13:52:22.157802 18606 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 13:52:24.133145 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 13:52:24.438663 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 13:52:24.745038 18606 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 13:52:24.745069 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:52:27.996328 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:29.176324 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:52:29.176365 18606 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 13:52:30.976379 18606 solver.cpp:218] Iteration 2964 (1.36081 iter/s, 8.81826s/12 iters), loss = 5.24267
I0410 13:52:30.976423 18606 solver.cpp:237]     Train net output #0: loss = 5.24267 (* 1 = 5.24267 loss)
I0410 13:52:30.976430 18606 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 13:52:35.794276 18606 solver.cpp:218] Iteration 2976 (2.49083 iter/s, 4.81767s/12 iters), loss = 5.28312
I0410 13:52:35.794327 18606 solver.cpp:237]     Train net output #0: loss = 5.28312 (* 1 = 5.28312 loss)
I0410 13:52:35.794337 18606 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 13:52:40.709540 18606 solver.cpp:218] Iteration 2988 (2.4415 iter/s, 4.91502s/12 iters), loss = 5.26476
I0410 13:52:40.709599 18606 solver.cpp:237]     Train net output #0: loss = 5.26476 (* 1 = 5.26476 loss)
I0410 13:52:40.709611 18606 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 13:52:45.609686 18606 solver.cpp:218] Iteration 3000 (2.44903 iter/s, 4.8999s/12 iters), loss = 5.26796
I0410 13:52:45.609819 18606 solver.cpp:237]     Train net output #0: loss = 5.26796 (* 1 = 5.26796 loss)
I0410 13:52:45.609833 18606 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 13:52:50.497213 18606 solver.cpp:218] Iteration 3012 (2.45539 iter/s, 4.8872s/12 iters), loss = 5.27279
I0410 13:52:50.497273 18606 solver.cpp:237]     Train net output #0: loss = 5.27279 (* 1 = 5.27279 loss)
I0410 13:52:50.497285 18606 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 13:52:55.396157 18606 solver.cpp:218] Iteration 3024 (2.44963 iter/s, 4.89869s/12 iters), loss = 5.25795
I0410 13:52:55.396211 18606 solver.cpp:237]     Train net output #0: loss = 5.25795 (* 1 = 5.25795 loss)
I0410 13:52:55.396222 18606 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 13:52:59.222633 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:00.226461 18606 solver.cpp:218] Iteration 3036 (2.48444 iter/s, 4.83006s/12 iters), loss = 5.257
I0410 13:53:00.226505 18606 solver.cpp:237]     Train net output #0: loss = 5.257 (* 1 = 5.257 loss)
I0410 13:53:00.226513 18606 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 13:53:05.116634 18606 solver.cpp:218] Iteration 3048 (2.45402 iter/s, 4.88993s/12 iters), loss = 5.29353
I0410 13:53:05.116689 18606 solver.cpp:237]     Train net output #0: loss = 5.29353 (* 1 = 5.29353 loss)
I0410 13:53:05.116700 18606 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 13:53:09.578119 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 13:53:09.892972 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 13:53:10.104571 18606 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 13:53:10.104596 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:53:13.241524 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:14.454118 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:53:14.454166 18606 solver.cpp:397]     Test net output #1: loss = 5.28642 (* 1 = 5.28642 loss)
I0410 13:53:14.537500 18606 solver.cpp:218] Iteration 3060 (1.27382 iter/s, 9.42045s/12 iters), loss = 5.25724
I0410 13:53:14.537550 18606 solver.cpp:237]     Train net output #0: loss = 5.25724 (* 1 = 5.25724 loss)
I0410 13:53:14.537560 18606 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 13:53:18.701100 18606 solver.cpp:218] Iteration 3072 (2.88228 iter/s, 4.16338s/12 iters), loss = 5.30564
I0410 13:53:18.701280 18606 solver.cpp:237]     Train net output #0: loss = 5.30564 (* 1 = 5.30564 loss)
I0410 13:53:18.701293 18606 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 13:53:23.606123 18606 solver.cpp:218] Iteration 3084 (2.44666 iter/s, 4.90465s/12 iters), loss = 5.27656
I0410 13:53:23.606182 18606 solver.cpp:237]     Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
I0410 13:53:23.606194 18606 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 13:53:28.396327 18606 solver.cpp:218] Iteration 3096 (2.50524 iter/s, 4.78996s/12 iters), loss = 5.27349
I0410 13:53:28.396374 18606 solver.cpp:237]     Train net output #0: loss = 5.27349 (* 1 = 5.27349 loss)
I0410 13:53:28.396384 18606 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 13:53:33.209233 18606 solver.cpp:218] Iteration 3108 (2.49342 iter/s, 4.81266s/12 iters), loss = 5.27901
I0410 13:53:33.209275 18606 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0410 13:53:33.209282 18606 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 13:53:38.040050 18606 solver.cpp:218] Iteration 3120 (2.48418 iter/s, 4.83058s/12 iters), loss = 5.26638
I0410 13:53:38.040107 18606 solver.cpp:237]     Train net output #0: loss = 5.26638 (* 1 = 5.26638 loss)
I0410 13:53:38.040118 18606 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 13:53:42.885560 18606 solver.cpp:218] Iteration 3132 (2.47665 iter/s, 4.84526s/12 iters), loss = 5.27493
I0410 13:53:42.885605 18606 solver.cpp:237]     Train net output #0: loss = 5.27493 (* 1 = 5.27493 loss)
I0410 13:53:42.885614 18606 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 13:53:43.964184 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:47.773108 18606 solver.cpp:218] Iteration 3144 (2.45534 iter/s, 4.8873s/12 iters), loss = 5.28097
I0410 13:53:47.773149 18606 solver.cpp:237]     Train net output #0: loss = 5.28097 (* 1 = 5.28097 loss)
I0410 13:53:47.773159 18606 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 13:53:52.532155 18606 solver.cpp:218] Iteration 3156 (2.52164 iter/s, 4.75881s/12 iters), loss = 5.24982
I0410 13:53:52.532258 18606 solver.cpp:237]     Train net output #0: loss = 5.24982 (* 1 = 5.24982 loss)
I0410 13:53:52.532267 18606 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 13:53:54.509057 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 13:53:54.796236 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 13:53:54.995463 18606 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 13:53:54.995481 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:53:58.097455 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:59.356933 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:53:59.356982 18606 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0410 13:54:01.226655 18606 solver.cpp:218] Iteration 3168 (1.38025 iter/s, 8.69406s/12 iters), loss = 5.26476
I0410 13:54:01.226704 18606 solver.cpp:237]     Train net output #0: loss = 5.26476 (* 1 = 5.26476 loss)
I0410 13:54:01.226714 18606 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 13:54:06.033515 18606 solver.cpp:218] Iteration 3180 (2.49656 iter/s, 4.80661s/12 iters), loss = 5.27326
I0410 13:54:06.033569 18606 solver.cpp:237]     Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
I0410 13:54:06.033582 18606 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 13:54:10.811702 18606 solver.cpp:218] Iteration 3192 (2.51154 iter/s, 4.77794s/12 iters), loss = 5.27821
I0410 13:54:10.811750 18606 solver.cpp:237]     Train net output #0: loss = 5.27821 (* 1 = 5.27821 loss)
I0410 13:54:10.811760 18606 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 13:54:15.624073 18606 solver.cpp:218] Iteration 3204 (2.4937 iter/s, 4.81213s/12 iters), loss = 5.26109
I0410 13:54:15.624125 18606 solver.cpp:237]     Train net output #0: loss = 5.26109 (* 1 = 5.26109 loss)
I0410 13:54:15.624136 18606 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 13:54:20.454870 18606 solver.cpp:218] Iteration 3216 (2.48419 iter/s, 4.83055s/12 iters), loss = 5.2867
I0410 13:54:20.454919 18606 solver.cpp:237]     Train net output #0: loss = 5.2867 (* 1 = 5.2867 loss)
I0410 13:54:20.454931 18606 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 13:54:25.306784 18606 solver.cpp:218] Iteration 3228 (2.47338 iter/s, 4.85167s/12 iters), loss = 5.27848
I0410 13:54:25.306912 18606 solver.cpp:237]     Train net output #0: loss = 5.27848 (* 1 = 5.27848 loss)
I0410 13:54:25.306924 18606 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 13:54:28.427166 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:30.106645 18606 solver.cpp:218] Iteration 3240 (2.50024 iter/s, 4.79954s/12 iters), loss = 5.28399
I0410 13:54:30.106693 18606 solver.cpp:237]     Train net output #0: loss = 5.28399 (* 1 = 5.28399 loss)
I0410 13:54:30.106704 18606 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 13:54:34.996392 18606 solver.cpp:218] Iteration 3252 (2.45424 iter/s, 4.8895s/12 iters), loss = 5.26868
I0410 13:54:34.996433 18606 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0410 13:54:34.996443 18606 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 13:54:39.461592 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 13:54:39.911367 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 13:54:40.152967 18606 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 13:54:40.152997 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:54:43.422246 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:44.717797 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:54:44.717847 18606 solver.cpp:397]     Test net output #1: loss = 5.28711 (* 1 = 5.28711 loss)
I0410 13:54:44.800709 18606 solver.cpp:218] Iteration 3264 (1.224 iter/s, 9.80389s/12 iters), loss = 5.27487
I0410 13:54:44.800760 18606 solver.cpp:237]     Train net output #0: loss = 5.27487 (* 1 = 5.27487 loss)
I0410 13:54:44.800770 18606 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 13:54:48.914902 18606 solver.cpp:218] Iteration 3276 (2.91689 iter/s, 4.11398s/12 iters), loss = 5.28584
I0410 13:54:48.914947 18606 solver.cpp:237]     Train net output #0: loss = 5.28584 (* 1 = 5.28584 loss)
I0410 13:54:48.914956 18606 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 13:54:53.811600 18606 solver.cpp:218] Iteration 3288 (2.45076 iter/s, 4.89645s/12 iters), loss = 5.25935
I0410 13:54:53.811652 18606 solver.cpp:237]     Train net output #0: loss = 5.25935 (* 1 = 5.25935 loss)
I0410 13:54:53.811663 18606 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 13:54:58.637887 18606 solver.cpp:218] Iteration 3300 (2.48651 iter/s, 4.82604s/12 iters), loss = 5.2857
I0410 13:54:58.638038 18606 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0410 13:54:58.638052 18606 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 13:55:03.483773 18606 solver.cpp:218] Iteration 3312 (2.4765 iter/s, 4.84554s/12 iters), loss = 5.25628
I0410 13:55:03.483824 18606 solver.cpp:237]     Train net output #0: loss = 5.25628 (* 1 = 5.25628 loss)
I0410 13:55:03.483834 18606 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 13:55:08.346678 18606 solver.cpp:218] Iteration 3324 (2.46779 iter/s, 4.86266s/12 iters), loss = 5.28192
I0410 13:55:08.346733 18606 solver.cpp:237]     Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
I0410 13:55:08.346745 18606 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 13:55:13.239187 18606 solver.cpp:218] Iteration 3336 (2.45286 iter/s, 4.89225s/12 iters), loss = 5.27505
I0410 13:55:13.239239 18606 solver.cpp:237]     Train net output #0: loss = 5.27505 (* 1 = 5.27505 loss)
I0410 13:55:13.239251 18606 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 13:55:13.692978 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:18.145941 18606 solver.cpp:218] Iteration 3348 (2.44573 iter/s, 4.9065s/12 iters), loss = 5.2796
I0410 13:55:18.145996 18606 solver.cpp:237]     Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
I0410 13:55:18.146005 18606 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 13:55:23.025136 18606 solver.cpp:218] Iteration 3360 (2.45955 iter/s, 4.87894s/12 iters), loss = 5.2678
I0410 13:55:23.025192 18606 solver.cpp:237]     Train net output #0: loss = 5.2678 (* 1 = 5.2678 loss)
I0410 13:55:23.025203 18606 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 13:55:25.102628 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 13:55:25.414201 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 13:55:25.644984 18606 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 13:55:25.645005 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:55:28.737296 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:30.070924 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:55:30.070979 18606 solver.cpp:397]     Test net output #1: loss = 5.2868 (* 1 = 5.2868 loss)
I0410 13:55:31.957036 18606 solver.cpp:218] Iteration 3372 (1.34356 iter/s, 8.93149s/12 iters), loss = 5.28743
I0410 13:55:31.957080 18606 solver.cpp:237]     Train net output #0: loss = 5.28743 (* 1 = 5.28743 loss)
I0410 13:55:31.957089 18606 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 13:55:36.814507 18606 solver.cpp:218] Iteration 3384 (2.47055 iter/s, 4.85722s/12 iters), loss = 5.26311
I0410 13:55:36.814568 18606 solver.cpp:237]     Train net output #0: loss = 5.26311 (* 1 = 5.26311 loss)
I0410 13:55:36.814580 18606 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 13:55:41.657147 18606 solver.cpp:218] Iteration 3396 (2.47812 iter/s, 4.84238s/12 iters), loss = 5.26395
I0410 13:55:41.657193 18606 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0410 13:55:41.657204 18606 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 13:55:46.512605 18606 solver.cpp:218] Iteration 3408 (2.47157 iter/s, 4.85521s/12 iters), loss = 5.28573
I0410 13:55:46.512655 18606 solver.cpp:237]     Train net output #0: loss = 5.28573 (* 1 = 5.28573 loss)
I0410 13:55:46.512665 18606 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 13:55:51.373775 18606 solver.cpp:218] Iteration 3420 (2.46867 iter/s, 4.86092s/12 iters), loss = 5.2795
I0410 13:55:51.373831 18606 solver.cpp:237]     Train net output #0: loss = 5.2795 (* 1 = 5.2795 loss)
I0410 13:55:51.373844 18606 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 13:55:56.250799 18606 solver.cpp:218] Iteration 3432 (2.46065 iter/s, 4.87676s/12 iters), loss = 5.26391
I0410 13:55:56.250866 18606 solver.cpp:237]     Train net output #0: loss = 5.26391 (* 1 = 5.26391 loss)
I0410 13:55:56.250882 18606 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 13:55:58.753434 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:01.062130 18606 solver.cpp:218] Iteration 3444 (2.49425 iter/s, 4.81107s/12 iters), loss = 5.273
I0410 13:56:01.062175 18606 solver.cpp:237]     Train net output #0: loss = 5.273 (* 1 = 5.273 loss)
I0410 13:56:01.062186 18606 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 13:56:05.947073 18606 solver.cpp:218] Iteration 3456 (2.45665 iter/s, 4.8847s/12 iters), loss = 5.27223
I0410 13:56:05.947115 18606 solver.cpp:237]     Train net output #0: loss = 5.27223 (* 1 = 5.27223 loss)
I0410 13:56:05.947124 18606 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 13:56:10.304541 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 13:56:10.614746 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 13:56:10.823207 18606 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 13:56:10.823233 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:56:10.844612 18606 blocking_queue.cpp:49] Waiting for data
I0410 13:56:13.752207 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:15.128726 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:56:15.128777 18606 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 13:56:15.212276 18606 solver.cpp:218] Iteration 3468 (1.29523 iter/s, 9.26479s/12 iters), loss = 5.27371
I0410 13:56:15.212322 18606 solver.cpp:237]     Train net output #0: loss = 5.27371 (* 1 = 5.27371 loss)
I0410 13:56:15.212334 18606 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 13:56:19.330545 18606 solver.cpp:218] Iteration 3480 (2.914 iter/s, 4.11805s/12 iters), loss = 5.2793
I0410 13:56:19.330590 18606 solver.cpp:237]     Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
I0410 13:56:19.330600 18606 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 13:56:24.175390 18606 solver.cpp:218] Iteration 3492 (2.47699 iter/s, 4.8446s/12 iters), loss = 5.29008
I0410 13:56:24.175438 18606 solver.cpp:237]     Train net output #0: loss = 5.29008 (* 1 = 5.29008 loss)
I0410 13:56:24.175449 18606 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 13:56:29.011658 18606 solver.cpp:218] Iteration 3504 (2.48138 iter/s, 4.83602s/12 iters), loss = 5.27226
I0410 13:56:29.011751 18606 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0410 13:56:29.011760 18606 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 13:56:33.819505 18606 solver.cpp:218] Iteration 3516 (2.49607 iter/s, 4.80755s/12 iters), loss = 5.26522
I0410 13:56:33.819555 18606 solver.cpp:237]     Train net output #0: loss = 5.26522 (* 1 = 5.26522 loss)
I0410 13:56:33.819563 18606 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 13:56:38.649109 18606 solver.cpp:218] Iteration 3528 (2.48481 iter/s, 4.82935s/12 iters), loss = 5.27093
I0410 13:56:38.649152 18606 solver.cpp:237]     Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
I0410 13:56:38.649160 18606 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 13:56:43.197898 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:43.448704 18606 solver.cpp:218] Iteration 3540 (2.50034 iter/s, 4.79935s/12 iters), loss = 5.25724
I0410 13:56:43.448745 18606 solver.cpp:237]     Train net output #0: loss = 5.25724 (* 1 = 5.25724 loss)
I0410 13:56:43.448752 18606 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 13:56:48.280727 18606 solver.cpp:218] Iteration 3552 (2.48356 iter/s, 4.83178s/12 iters), loss = 5.26698
I0410 13:56:48.280773 18606 solver.cpp:237]     Train net output #0: loss = 5.26698 (* 1 = 5.26698 loss)
I0410 13:56:48.280781 18606 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 13:56:53.090683 18606 solver.cpp:218] Iteration 3564 (2.49495 iter/s, 4.80971s/12 iters), loss = 5.29157
I0410 13:56:53.090730 18606 solver.cpp:237]     Train net output #0: loss = 5.29157 (* 1 = 5.29157 loss)
I0410 13:56:53.090741 18606 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 13:56:55.049157 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 13:56:56.077369 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 13:56:57.078701 18606 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 13:56:57.078728 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:57:00.090854 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:01.498852 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:57:01.498880 18606 solver.cpp:397]     Test net output #1: loss = 5.28689 (* 1 = 5.28689 loss)
I0410 13:57:03.311437 18606 solver.cpp:218] Iteration 3576 (1.17413 iter/s, 10.2203s/12 iters), loss = 5.28109
I0410 13:57:03.311498 18606 solver.cpp:237]     Train net output #0: loss = 5.28109 (* 1 = 5.28109 loss)
I0410 13:57:03.311511 18606 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 13:57:08.143740 18606 solver.cpp:218] Iteration 3588 (2.48342 iter/s, 4.83204s/12 iters), loss = 5.2768
I0410 13:57:08.143785 18606 solver.cpp:237]     Train net output #0: loss = 5.2768 (* 1 = 5.2768 loss)
I0410 13:57:08.143795 18606 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 13:57:13.011135 18606 solver.cpp:218] Iteration 3600 (2.46551 iter/s, 4.86714s/12 iters), loss = 5.26681
I0410 13:57:13.011180 18606 solver.cpp:237]     Train net output #0: loss = 5.26681 (* 1 = 5.26681 loss)
I0410 13:57:13.011189 18606 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 13:57:17.827821 18606 solver.cpp:218] Iteration 3612 (2.49147 iter/s, 4.81643s/12 iters), loss = 5.24323
I0410 13:57:17.827881 18606 solver.cpp:237]     Train net output #0: loss = 5.24323 (* 1 = 5.24323 loss)
I0410 13:57:17.827894 18606 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 13:57:22.598490 18606 solver.cpp:218] Iteration 3624 (2.51551 iter/s, 4.7704s/12 iters), loss = 5.27456
I0410 13:57:22.598548 18606 solver.cpp:237]     Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
I0410 13:57:22.598560 18606 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 13:57:27.416333 18606 solver.cpp:218] Iteration 3636 (2.49087 iter/s, 4.81759s/12 iters), loss = 5.27882
I0410 13:57:27.416368 18606 solver.cpp:237]     Train net output #0: loss = 5.27882 (* 1 = 5.27882 loss)
I0410 13:57:27.416376 18606 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 13:57:29.246310 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:32.237141 18606 solver.cpp:218] Iteration 3648 (2.48933 iter/s, 4.82057s/12 iters), loss = 5.28763
I0410 13:57:32.239532 18606 solver.cpp:237]     Train net output #0: loss = 5.28763 (* 1 = 5.28763 loss)
I0410 13:57:32.239545 18606 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 13:57:37.047710 18606 solver.cpp:218] Iteration 3660 (2.49585 iter/s, 4.80798s/12 iters), loss = 5.28003
I0410 13:57:37.047749 18606 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0410 13:57:37.047758 18606 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 13:57:41.418326 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 13:57:41.740329 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 13:57:41.952410 18606 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 13:57:41.952435 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:57:44.871471 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:46.381644 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:57:46.381675 18606 solver.cpp:397]     Test net output #1: loss = 5.28632 (* 1 = 5.28632 loss)
I0410 13:57:46.454411 18606 solver.cpp:218] Iteration 3672 (1.27574 iter/s, 9.40627s/12 iters), loss = 5.25046
I0410 13:57:46.454457 18606 solver.cpp:237]     Train net output #0: loss = 5.25046 (* 1 = 5.25046 loss)
I0410 13:57:46.454465 18606 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 13:57:50.669720 18606 solver.cpp:218] Iteration 3684 (2.84692 iter/s, 4.21508s/12 iters), loss = 5.2682
I0410 13:57:50.669775 18606 solver.cpp:237]     Train net output #0: loss = 5.2682 (* 1 = 5.2682 loss)
I0410 13:57:50.669788 18606 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 13:57:55.432440 18606 solver.cpp:218] Iteration 3696 (2.5197 iter/s, 4.76246s/12 iters), loss = 5.26141
I0410 13:57:55.432490 18606 solver.cpp:237]     Train net output #0: loss = 5.26141 (* 1 = 5.26141 loss)
I0410 13:57:55.432500 18606 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 13:58:00.273734 18606 solver.cpp:218] Iteration 3708 (2.47881 iter/s, 4.84104s/12 iters), loss = 5.26998
I0410 13:58:00.273789 18606 solver.cpp:237]     Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
I0410 13:58:00.273802 18606 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 13:58:05.102613 18606 solver.cpp:218] Iteration 3720 (2.48518 iter/s, 4.82862s/12 iters), loss = 5.27081
I0410 13:58:05.102784 18606 solver.cpp:237]     Train net output #0: loss = 5.27081 (* 1 = 5.27081 loss)
I0410 13:58:05.102799 18606 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 13:58:09.981763 18606 solver.cpp:218] Iteration 3732 (2.45963 iter/s, 4.87878s/12 iters), loss = 5.25521
I0410 13:58:09.981810 18606 solver.cpp:237]     Train net output #0: loss = 5.25521 (* 1 = 5.25521 loss)
I0410 13:58:09.981820 18606 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 13:58:13.863339 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:14.828564 18606 solver.cpp:218] Iteration 3744 (2.47599 iter/s, 4.84655s/12 iters), loss = 5.25646
I0410 13:58:14.828617 18606 solver.cpp:237]     Train net output #0: loss = 5.25646 (* 1 = 5.25646 loss)
I0410 13:58:14.828630 18606 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 13:58:19.655438 18606 solver.cpp:218] Iteration 3756 (2.48621 iter/s, 4.82662s/12 iters), loss = 5.27972
I0410 13:58:19.655490 18606 solver.cpp:237]     Train net output #0: loss = 5.27972 (* 1 = 5.27972 loss)
I0410 13:58:19.655500 18606 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 13:58:24.458526 18606 solver.cpp:218] Iteration 3768 (2.49853 iter/s, 4.80283s/12 iters), loss = 5.26289
I0410 13:58:24.458576 18606 solver.cpp:237]     Train net output #0: loss = 5.26289 (* 1 = 5.26289 loss)
I0410 13:58:24.458588 18606 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 13:58:26.419847 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 13:58:26.716773 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 13:58:26.922745 18606 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 13:58:26.922768 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:58:29.824255 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:31.495963 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:58:31.496014 18606 solver.cpp:397]     Test net output #1: loss = 5.28717 (* 1 = 5.28717 loss)
I0410 13:58:33.198984 18606 solver.cpp:218] Iteration 3780 (1.37299 iter/s, 8.74004s/12 iters), loss = 5.30865
I0410 13:58:33.199043 18606 solver.cpp:237]     Train net output #0: loss = 5.30865 (* 1 = 5.30865 loss)
I0410 13:58:33.199054 18606 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 13:58:38.130743 18606 solver.cpp:218] Iteration 3792 (2.43334 iter/s, 4.93149s/12 iters), loss = 5.27735
I0410 13:58:38.130867 18606 solver.cpp:237]     Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
I0410 13:58:38.130879 18606 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 13:58:42.975505 18606 solver.cpp:218] Iteration 3804 (2.47707 iter/s, 4.84443s/12 iters), loss = 5.26929
I0410 13:58:42.975548 18606 solver.cpp:237]     Train net output #0: loss = 5.26929 (* 1 = 5.26929 loss)
I0410 13:58:42.975556 18606 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 13:58:47.773753 18606 solver.cpp:218] Iteration 3816 (2.50105 iter/s, 4.79799s/12 iters), loss = 5.27258
I0410 13:58:47.773818 18606 solver.cpp:237]     Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
I0410 13:58:47.773835 18606 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 13:58:52.690101 18606 solver.cpp:218] Iteration 3828 (2.44097 iter/s, 4.91608s/12 iters), loss = 5.26193
I0410 13:58:52.690151 18606 solver.cpp:237]     Train net output #0: loss = 5.26193 (* 1 = 5.26193 loss)
I0410 13:58:52.690162 18606 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 13:58:57.528312 18606 solver.cpp:218] Iteration 3840 (2.48039 iter/s, 4.83795s/12 iters), loss = 5.26923
I0410 13:58:57.528359 18606 solver.cpp:237]     Train net output #0: loss = 5.26923 (* 1 = 5.26923 loss)
I0410 13:58:57.528368 18606 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 13:58:58.637902 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:02.583171 18606 solver.cpp:218] Iteration 3852 (2.37407 iter/s, 5.0546s/12 iters), loss = 5.27472
I0410 13:59:02.583209 18606 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0410 13:59:02.583217 18606 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 13:59:07.385604 18606 solver.cpp:218] Iteration 3864 (2.49887 iter/s, 4.80218s/12 iters), loss = 5.25224
I0410 13:59:07.385663 18606 solver.cpp:237]     Train net output #0: loss = 5.25224 (* 1 = 5.25224 loss)
I0410 13:59:07.385676 18606 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 13:59:11.809609 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 13:59:12.141638 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 13:59:12.360868 18606 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 13:59:12.360899 18606 net.cpp:676] Ignoring source layer train-data
I0410 13:59:15.347499 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:16.885103 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:59:16.885145 18606 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 13:59:16.968065 18606 solver.cpp:218] Iteration 3876 (1.25235 iter/s, 9.582s/12 iters), loss = 5.27317
I0410 13:59:16.968123 18606 solver.cpp:237]     Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
I0410 13:59:16.968135 18606 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 13:59:20.990504 18606 solver.cpp:218] Iteration 3888 (2.98344 iter/s, 4.02221s/12 iters), loss = 5.26991
I0410 13:59:20.990552 18606 solver.cpp:237]     Train net output #0: loss = 5.26991 (* 1 = 5.26991 loss)
I0410 13:59:20.990561 18606 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 13:59:25.877948 18606 solver.cpp:218] Iteration 3900 (2.4554 iter/s, 4.88718s/12 iters), loss = 5.27488
I0410 13:59:25.878021 18606 solver.cpp:237]     Train net output #0: loss = 5.27488 (* 1 = 5.27488 loss)
I0410 13:59:25.878033 18606 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 13:59:30.780555 18606 solver.cpp:218] Iteration 3912 (2.44782 iter/s, 4.90232s/12 iters), loss = 5.2606
I0410 13:59:30.780611 18606 solver.cpp:237]     Train net output #0: loss = 5.2606 (* 1 = 5.2606 loss)
I0410 13:59:30.780623 18606 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 13:59:35.559546 18606 solver.cpp:218] Iteration 3924 (2.51113 iter/s, 4.77873s/12 iters), loss = 5.29255
I0410 13:59:35.559609 18606 solver.cpp:237]     Train net output #0: loss = 5.29255 (* 1 = 5.29255 loss)
I0410 13:59:35.559623 18606 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 13:59:40.466614 18606 solver.cpp:218] Iteration 3936 (2.44559 iter/s, 4.9068s/12 iters), loss = 5.27299
I0410 13:59:40.466662 18606 solver.cpp:237]     Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
I0410 13:59:40.466671 18606 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 13:59:43.719444 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:45.302263 18606 solver.cpp:218] Iteration 3948 (2.4817 iter/s, 4.83539s/12 iters), loss = 5.28378
I0410 13:59:45.302311 18606 solver.cpp:237]     Train net output #0: loss = 5.28378 (* 1 = 5.28378 loss)
I0410 13:59:45.302321 18606 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 13:59:50.114696 18606 solver.cpp:218] Iteration 3960 (2.49367 iter/s, 4.81218s/12 iters), loss = 5.27096
I0410 13:59:50.114740 18606 solver.cpp:237]     Train net output #0: loss = 5.27096 (* 1 = 5.27096 loss)
I0410 13:59:50.114751 18606 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 13:59:55.115056 18606 solver.cpp:218] Iteration 3972 (2.39995 iter/s, 5.0001s/12 iters), loss = 5.28177
I0410 13:59:55.115108 18606 solver.cpp:237]     Train net output #0: loss = 5.28177 (* 1 = 5.28177 loss)
I0410 13:59:55.115119 18606 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 13:59:57.080972 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 13:59:57.407989 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 13:59:57.612540 18606 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 13:59:57.612560 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:00:00.666501 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:02.399937 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:00:02.399971 18606 solver.cpp:397]     Test net output #1: loss = 5.28652 (* 1 = 5.28652 loss)
I0410 14:00:04.249752 18606 solver.cpp:218] Iteration 3984 (1.31373 iter/s, 9.13426s/12 iters), loss = 5.28141
I0410 14:00:04.249794 18606 solver.cpp:237]     Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
I0410 14:00:04.249804 18606 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 14:00:09.109678 18606 solver.cpp:218] Iteration 3996 (2.4693 iter/s, 4.85967s/12 iters), loss = 5.26538
I0410 14:00:09.109724 18606 solver.cpp:237]     Train net output #0: loss = 5.26538 (* 1 = 5.26538 loss)
I0410 14:00:09.109735 18606 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 14:00:13.894639 18606 solver.cpp:218] Iteration 4008 (2.50799 iter/s, 4.78471s/12 iters), loss = 5.28797
I0410 14:00:13.898034 18606 solver.cpp:237]     Train net output #0: loss = 5.28797 (* 1 = 5.28797 loss)
I0410 14:00:13.898046 18606 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 14:00:18.716637 18606 solver.cpp:218] Iteration 4020 (2.49045 iter/s, 4.8184s/12 iters), loss = 5.25721
I0410 14:00:18.716676 18606 solver.cpp:237]     Train net output #0: loss = 5.25721 (* 1 = 5.25721 loss)
I0410 14:00:18.716684 18606 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 14:00:23.595978 18606 solver.cpp:218] Iteration 4032 (2.45947 iter/s, 4.87909s/12 iters), loss = 5.27322
I0410 14:00:23.596024 18606 solver.cpp:237]     Train net output #0: loss = 5.27322 (* 1 = 5.27322 loss)
I0410 14:00:23.596033 18606 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 14:00:28.507310 18606 solver.cpp:218] Iteration 4044 (2.44346 iter/s, 4.91108s/12 iters), loss = 5.27554
I0410 14:00:28.507352 18606 solver.cpp:237]     Train net output #0: loss = 5.27554 (* 1 = 5.27554 loss)
I0410 14:00:28.507361 18606 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 14:00:28.993311 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:33.425415 18606 solver.cpp:218] Iteration 4056 (2.44009 iter/s, 4.91785s/12 iters), loss = 5.27542
I0410 14:00:33.425454 18606 solver.cpp:237]     Train net output #0: loss = 5.27542 (* 1 = 5.27542 loss)
I0410 14:00:33.425465 18606 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 14:00:38.290288 18606 solver.cpp:218] Iteration 4068 (2.46679 iter/s, 4.86462s/12 iters), loss = 5.27231
I0410 14:00:38.290331 18606 solver.cpp:237]     Train net output #0: loss = 5.27231 (* 1 = 5.27231 loss)
I0410 14:00:38.290340 18606 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 14:00:42.751628 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 14:00:43.065873 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 14:00:43.277554 18606 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 14:00:43.277573 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:00:45.997107 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:47.607935 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:00:47.607985 18606 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0410 14:00:47.691107 18606 solver.cpp:218] Iteration 4080 (1.27654 iter/s, 9.40038s/12 iters), loss = 5.28647
I0410 14:00:47.691156 18606 solver.cpp:237]     Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
I0410 14:00:47.691167 18606 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 14:00:51.878629 18606 solver.cpp:218] Iteration 4092 (2.86582 iter/s, 4.18729s/12 iters), loss = 5.26297
I0410 14:00:51.878674 18606 solver.cpp:237]     Train net output #0: loss = 5.26297 (* 1 = 5.26297 loss)
I0410 14:00:51.878684 18606 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 14:00:56.782646 18606 solver.cpp:218] Iteration 4104 (2.4471 iter/s, 4.90376s/12 iters), loss = 5.26124
I0410 14:00:56.782693 18606 solver.cpp:237]     Train net output #0: loss = 5.26124 (* 1 = 5.26124 loss)
I0410 14:00:56.782703 18606 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 14:01:01.693251 18606 solver.cpp:218] Iteration 4116 (2.44382 iter/s, 4.91035s/12 iters), loss = 5.29311
I0410 14:01:01.693298 18606 solver.cpp:237]     Train net output #0: loss = 5.29311 (* 1 = 5.29311 loss)
I0410 14:01:01.693310 18606 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 14:01:06.720490 18606 solver.cpp:218] Iteration 4128 (2.38712 iter/s, 5.02697s/12 iters), loss = 5.26753
I0410 14:01:06.720551 18606 solver.cpp:237]     Train net output #0: loss = 5.26753 (* 1 = 5.26753 loss)
I0410 14:01:06.720566 18606 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 14:01:11.517349 18606 solver.cpp:218] Iteration 4140 (2.50178 iter/s, 4.79659s/12 iters), loss = 5.25823
I0410 14:01:11.517397 18606 solver.cpp:237]     Train net output #0: loss = 5.25823 (* 1 = 5.25823 loss)
I0410 14:01:11.517406 18606 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 14:01:14.067147 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:16.331468 18606 solver.cpp:218] Iteration 4152 (2.4928 iter/s, 4.81386s/12 iters), loss = 5.26856
I0410 14:01:16.331591 18606 solver.cpp:237]     Train net output #0: loss = 5.26856 (* 1 = 5.26856 loss)
I0410 14:01:16.331604 18606 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 14:01:16.331964 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:01:21.162050 18606 solver.cpp:218] Iteration 4164 (2.48434 iter/s, 4.83025s/12 iters), loss = 5.26438
I0410 14:01:21.162103 18606 solver.cpp:237]     Train net output #0: loss = 5.26438 (* 1 = 5.26438 loss)
I0410 14:01:21.162115 18606 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 14:01:25.981971 18606 solver.cpp:218] Iteration 4176 (2.48981 iter/s, 4.81964s/12 iters), loss = 5.26601
I0410 14:01:25.982017 18606 solver.cpp:237]     Train net output #0: loss = 5.26601 (* 1 = 5.26601 loss)
I0410 14:01:25.982026 18606 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 14:01:27.936503 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 14:01:28.244376 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 14:01:28.462625 18606 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 14:01:28.462651 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:01:31.439479 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:33.252626 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:01:33.252655 18606 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 14:01:35.068053 18606 solver.cpp:218] Iteration 4188 (1.32076 iter/s, 9.08565s/12 iters), loss = 5.26609
I0410 14:01:35.068107 18606 solver.cpp:237]     Train net output #0: loss = 5.26609 (* 1 = 5.26609 loss)
I0410 14:01:35.068117 18606 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 14:01:39.990592 18606 solver.cpp:218] Iteration 4200 (2.4379 iter/s, 4.92227s/12 iters), loss = 5.28192
I0410 14:01:39.990648 18606 solver.cpp:237]     Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
I0410 14:01:39.990661 18606 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 14:01:44.794421 18606 solver.cpp:218] Iteration 4212 (2.49814 iter/s, 4.80357s/12 iters), loss = 5.27112
I0410 14:01:44.794476 18606 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0410 14:01:44.794488 18606 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 14:01:49.876324 18606 solver.cpp:218] Iteration 4224 (2.36145 iter/s, 5.08163s/12 iters), loss = 5.26334
I0410 14:01:49.876499 18606 solver.cpp:237]     Train net output #0: loss = 5.26334 (* 1 = 5.26334 loss)
I0410 14:01:49.876514 18606 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 14:01:54.884364 18606 solver.cpp:218] Iteration 4236 (2.39633 iter/s, 5.00765s/12 iters), loss = 5.26616
I0410 14:01:54.884410 18606 solver.cpp:237]     Train net output #0: loss = 5.26616 (* 1 = 5.26616 loss)
I0410 14:01:54.884420 18606 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 14:01:59.900601 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:00.117734 18606 solver.cpp:218] Iteration 4248 (2.2931 iter/s, 5.2331s/12 iters), loss = 5.2445
I0410 14:02:00.117787 18606 solver.cpp:237]     Train net output #0: loss = 5.2445 (* 1 = 5.2445 loss)
I0410 14:02:00.117799 18606 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 14:02:04.940223 18606 solver.cpp:218] Iteration 4260 (2.48848 iter/s, 4.82223s/12 iters), loss = 5.26717
I0410 14:02:04.940271 18606 solver.cpp:237]     Train net output #0: loss = 5.26717 (* 1 = 5.26717 loss)
I0410 14:02:04.940282 18606 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 14:02:09.718241 18606 solver.cpp:218] Iteration 4272 (2.51164 iter/s, 4.77775s/12 iters), loss = 5.29044
I0410 14:02:09.718303 18606 solver.cpp:237]     Train net output #0: loss = 5.29044 (* 1 = 5.29044 loss)
I0410 14:02:09.718317 18606 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 14:02:14.053889 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 14:02:14.521123 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 14:02:14.995435 18606 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 14:02:14.995467 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:02:17.641376 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:19.334110 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:02:19.334146 18606 solver.cpp:397]     Test net output #1: loss = 5.28685 (* 1 = 5.28685 loss)
I0410 14:02:19.416546 18606 solver.cpp:218] Iteration 4284 (1.23739 iter/s, 9.69783s/12 iters), loss = 5.27777
I0410 14:02:19.416602 18606 solver.cpp:237]     Train net output #0: loss = 5.27777 (* 1 = 5.27777 loss)
I0410 14:02:19.416615 18606 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 14:02:23.506827 18606 solver.cpp:218] Iteration 4296 (2.93395 iter/s, 4.09005s/12 iters), loss = 5.2748
I0410 14:02:23.506925 18606 solver.cpp:237]     Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
I0410 14:02:23.506937 18606 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 14:02:28.317414 18606 solver.cpp:218] Iteration 4308 (2.49466 iter/s, 4.81028s/12 iters), loss = 5.26258
I0410 14:02:28.317458 18606 solver.cpp:237]     Train net output #0: loss = 5.26258 (* 1 = 5.26258 loss)
I0410 14:02:28.317468 18606 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 14:02:33.102428 18606 solver.cpp:218] Iteration 4320 (2.50797 iter/s, 4.78476s/12 iters), loss = 5.24858
I0410 14:02:33.102483 18606 solver.cpp:237]     Train net output #0: loss = 5.24858 (* 1 = 5.24858 loss)
I0410 14:02:33.102495 18606 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 14:02:37.894629 18606 solver.cpp:218] Iteration 4332 (2.50421 iter/s, 4.79194s/12 iters), loss = 5.27726
I0410 14:02:37.894677 18606 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0410 14:02:37.894686 18606 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 14:02:42.700366 18606 solver.cpp:218] Iteration 4344 (2.49715 iter/s, 4.80547s/12 iters), loss = 5.27867
I0410 14:02:42.700421 18606 solver.cpp:237]     Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
I0410 14:02:42.700433 18606 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 14:02:44.540557 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:47.499310 18606 solver.cpp:218] Iteration 4356 (2.50069 iter/s, 4.79868s/12 iters), loss = 5.28731
I0410 14:02:47.499372 18606 solver.cpp:237]     Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
I0410 14:02:47.499383 18606 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 14:02:52.329746 18606 solver.cpp:218] Iteration 4368 (2.48439 iter/s, 4.83016s/12 iters), loss = 5.27677
I0410 14:02:52.329802 18606 solver.cpp:237]     Train net output #0: loss = 5.27677 (* 1 = 5.27677 loss)
I0410 14:02:52.329814 18606 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 14:02:57.119155 18606 solver.cpp:218] Iteration 4380 (2.50567 iter/s, 4.78914s/12 iters), loss = 5.25975
I0410 14:02:57.120689 18606 solver.cpp:237]     Train net output #0: loss = 5.25975 (* 1 = 5.25975 loss)
I0410 14:02:57.120702 18606 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 14:02:59.100538 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 14:02:59.638135 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 14:02:59.843204 18606 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 14:02:59.843233 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:03:02.425429 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:04.177682 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:03:04.177712 18606 solver.cpp:397]     Test net output #1: loss = 5.28684 (* 1 = 5.28684 loss)
I0410 14:03:06.004812 18606 solver.cpp:218] Iteration 4392 (1.35078 iter/s, 8.88375s/12 iters), loss = 5.26992
I0410 14:03:06.004868 18606 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0410 14:03:06.004880 18606 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 14:03:10.946521 18606 solver.cpp:218] Iteration 4404 (2.42845 iter/s, 4.94143s/12 iters), loss = 5.26239
I0410 14:03:10.946578 18606 solver.cpp:237]     Train net output #0: loss = 5.26239 (* 1 = 5.26239 loss)
I0410 14:03:10.946589 18606 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 14:03:15.764753 18606 solver.cpp:218] Iteration 4416 (2.49068 iter/s, 4.81796s/12 iters), loss = 5.2668
I0410 14:03:15.764807 18606 solver.cpp:237]     Train net output #0: loss = 5.2668 (* 1 = 5.2668 loss)
I0410 14:03:15.764820 18606 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 14:03:20.602330 18606 solver.cpp:218] Iteration 4428 (2.48072 iter/s, 4.83731s/12 iters), loss = 5.26687
I0410 14:03:20.602376 18606 solver.cpp:237]     Train net output #0: loss = 5.26687 (* 1 = 5.26687 loss)
I0410 14:03:20.602386 18606 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 14:03:25.400290 18606 solver.cpp:218] Iteration 4440 (2.5012 iter/s, 4.7977s/12 iters), loss = 5.26166
I0410 14:03:25.400350 18606 solver.cpp:237]     Train net output #0: loss = 5.26166 (* 1 = 5.26166 loss)
I0410 14:03:25.400363 18606 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 14:03:29.283332 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:30.206548 18606 solver.cpp:218] Iteration 4452 (2.49689 iter/s, 4.80598s/12 iters), loss = 5.258
I0410 14:03:30.206605 18606 solver.cpp:237]     Train net output #0: loss = 5.258 (* 1 = 5.258 loss)
I0410 14:03:30.206617 18606 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 14:03:35.030150 18606 solver.cpp:218] Iteration 4464 (2.48791 iter/s, 4.82334s/12 iters), loss = 5.28124
I0410 14:03:35.030189 18606 solver.cpp:237]     Train net output #0: loss = 5.28124 (* 1 = 5.28124 loss)
I0410 14:03:35.030198 18606 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 14:03:39.810824 18606 solver.cpp:218] Iteration 4476 (2.51024 iter/s, 4.78042s/12 iters), loss = 5.25913
I0410 14:03:39.810880 18606 solver.cpp:237]     Train net output #0: loss = 5.25913 (* 1 = 5.25913 loss)
I0410 14:03:39.810892 18606 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 14:03:44.176017 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 14:03:44.526525 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 14:03:44.741034 18606 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 14:03:44.741061 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:03:47.460391 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:49.226347 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:03:49.226379 18606 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 14:03:49.309085 18606 solver.cpp:218] Iteration 4488 (1.26345 iter/s, 9.4978s/12 iters), loss = 5.30729
I0410 14:03:49.309126 18606 solver.cpp:237]     Train net output #0: loss = 5.30729 (* 1 = 5.30729 loss)
I0410 14:03:49.309135 18606 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 14:03:53.413986 18606 solver.cpp:218] Iteration 4500 (2.92351 iter/s, 4.10466s/12 iters), loss = 5.26838
I0410 14:03:53.414050 18606 solver.cpp:237]     Train net output #0: loss = 5.26838 (* 1 = 5.26838 loss)
I0410 14:03:53.414063 18606 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 14:03:58.184864 18606 solver.cpp:218] Iteration 4512 (2.51541 iter/s, 4.7706s/12 iters), loss = 5.26913
I0410 14:03:58.184921 18606 solver.cpp:237]     Train net output #0: loss = 5.26913 (* 1 = 5.26913 loss)
I0410 14:03:58.184932 18606 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 14:04:02.962983 18606 solver.cpp:218] Iteration 4524 (2.51159 iter/s, 4.77785s/12 iters), loss = 5.27364
I0410 14:04:02.963075 18606 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0410 14:04:02.963088 18606 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 14:04:07.742954 18606 solver.cpp:218] Iteration 4536 (2.51063 iter/s, 4.77967s/12 iters), loss = 5.26768
I0410 14:04:07.743016 18606 solver.cpp:237]     Train net output #0: loss = 5.26768 (* 1 = 5.26768 loss)
I0410 14:04:07.743027 18606 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 14:04:12.527788 18606 solver.cpp:218] Iteration 4548 (2.50807 iter/s, 4.78456s/12 iters), loss = 5.2649
I0410 14:04:12.527851 18606 solver.cpp:237]     Train net output #0: loss = 5.2649 (* 1 = 5.2649 loss)
I0410 14:04:12.527863 18606 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 14:04:13.733393 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:17.318883 18606 solver.cpp:218] Iteration 4560 (2.50479 iter/s, 4.79082s/12 iters), loss = 5.27411
I0410 14:04:17.318946 18606 solver.cpp:237]     Train net output #0: loss = 5.27411 (* 1 = 5.27411 loss)
I0410 14:04:17.318958 18606 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 14:04:22.334690 18606 solver.cpp:218] Iteration 4572 (2.39257 iter/s, 5.01552s/12 iters), loss = 5.26462
I0410 14:04:22.334745 18606 solver.cpp:237]     Train net output #0: loss = 5.26462 (* 1 = 5.26462 loss)
I0410 14:04:22.334758 18606 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 14:04:27.145838 18606 solver.cpp:218] Iteration 4584 (2.49435 iter/s, 4.81087s/12 iters), loss = 5.27433
I0410 14:04:27.145892 18606 solver.cpp:237]     Train net output #0: loss = 5.27433 (* 1 = 5.27433 loss)
I0410 14:04:27.145905 18606 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 14:04:29.112999 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 14:04:29.427423 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 14:04:29.647044 18606 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 14:04:29.647073 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:04:32.423566 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:34.237346 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:04:34.237457 18606 solver.cpp:397]     Test net output #1: loss = 5.28665 (* 1 = 5.28665 loss)
I0410 14:04:35.978222 18606 solver.cpp:218] Iteration 4596 (1.3587 iter/s, 8.83195s/12 iters), loss = 5.27364
I0410 14:04:35.978284 18606 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0410 14:04:35.978296 18606 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 14:04:40.806964 18606 solver.cpp:218] Iteration 4608 (2.48526 iter/s, 4.82846s/12 iters), loss = 5.27578
I0410 14:04:40.807015 18606 solver.cpp:237]     Train net output #0: loss = 5.27578 (* 1 = 5.27578 loss)
I0410 14:04:40.807025 18606 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 14:04:45.607440 18606 solver.cpp:218] Iteration 4620 (2.49989 iter/s, 4.80021s/12 iters), loss = 5.26081
I0410 14:04:45.607497 18606 solver.cpp:237]     Train net output #0: loss = 5.26081 (* 1 = 5.26081 loss)
I0410 14:04:45.607509 18606 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 14:04:50.424844 18606 solver.cpp:218] Iteration 4632 (2.49111 iter/s, 4.81713s/12 iters), loss = 5.29358
I0410 14:04:50.424902 18606 solver.cpp:237]     Train net output #0: loss = 5.29358 (* 1 = 5.29358 loss)
I0410 14:04:50.424914 18606 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 14:04:55.336522 18606 solver.cpp:218] Iteration 4644 (2.44329 iter/s, 4.91141s/12 iters), loss = 5.26962
I0410 14:04:55.336571 18606 solver.cpp:237]     Train net output #0: loss = 5.26962 (* 1 = 5.26962 loss)
I0410 14:04:55.336580 18606 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 14:04:58.881211 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:00.492038 18606 solver.cpp:218] Iteration 4656 (2.32773 iter/s, 5.15523s/12 iters), loss = 5.28075
I0410 14:05:00.492097 18606 solver.cpp:237]     Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
I0410 14:05:00.492110 18606 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 14:05:05.482473 18606 solver.cpp:218] Iteration 4668 (2.40473 iter/s, 4.99016s/12 iters), loss = 5.26575
I0410 14:05:05.482570 18606 solver.cpp:237]     Train net output #0: loss = 5.26575 (* 1 = 5.26575 loss)
I0410 14:05:05.482579 18606 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 14:05:10.328120 18606 solver.cpp:218] Iteration 4680 (2.47661 iter/s, 4.84534s/12 iters), loss = 5.27269
I0410 14:05:10.328163 18606 solver.cpp:237]     Train net output #0: loss = 5.27269 (* 1 = 5.27269 loss)
I0410 14:05:10.328172 18606 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 14:05:14.665280 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 14:05:14.963277 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 14:05:15.165755 18606 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 14:05:15.165782 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:05:17.619431 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:19.471491 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:05:19.471540 18606 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0410 14:05:19.553385 18606 solver.cpp:218] Iteration 4692 (1.30084 iter/s, 9.22482s/12 iters), loss = 5.26712
I0410 14:05:19.553434 18606 solver.cpp:237]     Train net output #0: loss = 5.26712 (* 1 = 5.26712 loss)
I0410 14:05:19.553445 18606 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 14:05:23.744484 18606 solver.cpp:218] Iteration 4704 (2.86337 iter/s, 4.19086s/12 iters), loss = 5.26622
I0410 14:05:23.744530 18606 solver.cpp:237]     Train net output #0: loss = 5.26622 (* 1 = 5.26622 loss)
I0410 14:05:23.744539 18606 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 14:05:28.577896 18606 solver.cpp:218] Iteration 4716 (2.48285 iter/s, 4.83315s/12 iters), loss = 5.28245
I0410 14:05:28.577970 18606 solver.cpp:237]     Train net output #0: loss = 5.28245 (* 1 = 5.28245 loss)
I0410 14:05:28.577982 18606 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 14:05:33.398655 18606 solver.cpp:218] Iteration 4728 (2.48938 iter/s, 4.82049s/12 iters), loss = 5.26216
I0410 14:05:33.398705 18606 solver.cpp:237]     Train net output #0: loss = 5.26216 (* 1 = 5.26216 loss)
I0410 14:05:33.398715 18606 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 14:05:38.242913 18606 solver.cpp:218] Iteration 4740 (2.4773 iter/s, 4.84399s/12 iters), loss = 5.27482
I0410 14:05:38.243075 18606 solver.cpp:237]     Train net output #0: loss = 5.27482 (* 1 = 5.27482 loss)
I0410 14:05:38.243089 18606 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 14:05:43.107430 18606 solver.cpp:218] Iteration 4752 (2.46703 iter/s, 4.86414s/12 iters), loss = 5.28013
I0410 14:05:43.107476 18606 solver.cpp:237]     Train net output #0: loss = 5.28013 (* 1 = 5.28013 loss)
I0410 14:05:43.107486 18606 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 14:05:43.612257 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:47.900902 18606 solver.cpp:218] Iteration 4764 (2.50355 iter/s, 4.79319s/12 iters), loss = 5.28101
I0410 14:05:47.900952 18606 solver.cpp:237]     Train net output #0: loss = 5.28101 (* 1 = 5.28101 loss)
I0410 14:05:47.900964 18606 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 14:05:52.713456 18606 solver.cpp:218] Iteration 4776 (2.49362 iter/s, 4.81228s/12 iters), loss = 5.26553
I0410 14:05:52.713516 18606 solver.cpp:237]     Train net output #0: loss = 5.26553 (* 1 = 5.26553 loss)
I0410 14:05:52.713528 18606 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 14:05:57.540452 18606 solver.cpp:218] Iteration 4788 (2.48616 iter/s, 4.82671s/12 iters), loss = 5.29171
I0410 14:05:57.540509 18606 solver.cpp:237]     Train net output #0: loss = 5.29171 (* 1 = 5.29171 loss)
I0410 14:05:57.540521 18606 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 14:05:59.565768 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 14:06:01.324178 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 14:06:01.543222 18606 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 14:06:01.543242 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:06:04.137971 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:06.035521 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:06:06.035558 18606 solver.cpp:397]     Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
I0410 14:06:07.834303 18606 solver.cpp:218] Iteration 4800 (1.1658 iter/s, 10.2934s/12 iters), loss = 5.27374
I0410 14:06:07.834348 18606 solver.cpp:237]     Train net output #0: loss = 5.27374 (* 1 = 5.27374 loss)
I0410 14:06:07.834357 18606 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 14:06:12.741719 18606 solver.cpp:218] Iteration 4812 (2.44541 iter/s, 4.90715s/12 iters), loss = 5.26412
I0410 14:06:12.741843 18606 solver.cpp:237]     Train net output #0: loss = 5.26412 (* 1 = 5.26412 loss)
I0410 14:06:12.741858 18606 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 14:06:17.687788 18606 solver.cpp:218] Iteration 4824 (2.42634 iter/s, 4.94573s/12 iters), loss = 5.28896
I0410 14:06:17.687837 18606 solver.cpp:237]     Train net output #0: loss = 5.28896 (* 1 = 5.28896 loss)
I0410 14:06:17.687847 18606 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 14:06:22.552706 18606 solver.cpp:218] Iteration 4836 (2.46678 iter/s, 4.86465s/12 iters), loss = 5.26469
I0410 14:06:22.552759 18606 solver.cpp:237]     Train net output #0: loss = 5.26469 (* 1 = 5.26469 loss)
I0410 14:06:22.552772 18606 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 14:06:22.917996 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:06:27.383141 18606 solver.cpp:218] Iteration 4848 (2.48439 iter/s, 4.83016s/12 iters), loss = 5.26971
I0410 14:06:27.383200 18606 solver.cpp:237]     Train net output #0: loss = 5.26971 (* 1 = 5.26971 loss)
I0410 14:06:27.383213 18606 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 14:06:29.953099 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:32.199744 18606 solver.cpp:218] Iteration 4860 (2.49153 iter/s, 4.81632s/12 iters), loss = 5.26957
I0410 14:06:32.199801 18606 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0410 14:06:32.199823 18606 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 14:06:37.043620 18606 solver.cpp:218] Iteration 4872 (2.47749 iter/s, 4.84361s/12 iters), loss = 5.26252
I0410 14:06:37.043661 18606 solver.cpp:237]     Train net output #0: loss = 5.26252 (* 1 = 5.26252 loss)
I0410 14:06:37.043668 18606 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 14:06:41.825327 18606 solver.cpp:218] Iteration 4884 (2.5097 iter/s, 4.78145s/12 iters), loss = 5.26916
I0410 14:06:41.825376 18606 solver.cpp:237]     Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
I0410 14:06:41.825385 18606 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 14:06:46.182646 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 14:06:46.500566 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 14:06:46.717903 18606 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 14:06:46.717926 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:06:49.144224 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:51.071696 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:06:51.071741 18606 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0410 14:06:51.154628 18606 solver.cpp:218] Iteration 4896 (1.28633 iter/s, 9.32885s/12 iters), loss = 5.27131
I0410 14:06:51.154676 18606 solver.cpp:237]     Train net output #0: loss = 5.27131 (* 1 = 5.27131 loss)
I0410 14:06:51.154687 18606 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 14:06:55.235546 18606 solver.cpp:218] Iteration 4908 (2.94068 iter/s, 4.08069s/12 iters), loss = 5.28962
I0410 14:06:55.235594 18606 solver.cpp:237]     Train net output #0: loss = 5.28962 (* 1 = 5.28962 loss)
I0410 14:06:55.235605 18606 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 14:07:00.155536 18606 solver.cpp:218] Iteration 4920 (2.43916 iter/s, 4.91972s/12 iters), loss = 5.27289
I0410 14:07:00.155588 18606 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0410 14:07:00.155601 18606 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 14:07:05.043416 18606 solver.cpp:218] Iteration 4932 (2.45519 iter/s, 4.88762s/12 iters), loss = 5.26298
I0410 14:07:05.043457 18606 solver.cpp:237]     Train net output #0: loss = 5.26298 (* 1 = 5.26298 loss)
I0410 14:07:05.043467 18606 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 14:07:09.928237 18606 solver.cpp:218] Iteration 4944 (2.45672 iter/s, 4.88456s/12 iters), loss = 5.26599
I0410 14:07:09.928285 18606 solver.cpp:237]     Train net output #0: loss = 5.26599 (* 1 = 5.26599 loss)
I0410 14:07:09.928294 18606 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 14:07:14.640698 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:14.831413 18606 solver.cpp:218] Iteration 4956 (2.44753 iter/s, 4.90291s/12 iters), loss = 5.25145
I0410 14:07:14.831461 18606 solver.cpp:237]     Train net output #0: loss = 5.25145 (* 1 = 5.25145 loss)
I0410 14:07:14.831470 18606 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 14:07:19.757208 18606 solver.cpp:218] Iteration 4968 (2.43629 iter/s, 4.92552s/12 iters), loss = 5.26284
I0410 14:07:19.757354 18606 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0410 14:07:19.757364 18606 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 14:07:24.693740 18606 solver.cpp:218] Iteration 4980 (2.43104 iter/s, 4.93616s/12 iters), loss = 5.291
I0410 14:07:24.693794 18606 solver.cpp:237]     Train net output #0: loss = 5.291 (* 1 = 5.291 loss)
I0410 14:07:24.693804 18606 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 14:07:29.492172 18606 solver.cpp:218] Iteration 4992 (2.50096 iter/s, 4.79816s/12 iters), loss = 5.28466
I0410 14:07:29.492216 18606 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0410 14:07:29.492225 18606 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 14:07:31.454432 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 14:07:31.809309 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 14:07:32.175355 18606 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 14:07:32.175377 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:07:34.643828 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:36.674137 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:07:36.674187 18606 solver.cpp:397]     Test net output #1: loss = 5.28684 (* 1 = 5.28684 loss)
I0410 14:07:38.640343 18606 solver.cpp:218] Iteration 5004 (1.3118 iter/s, 9.14773s/12 iters), loss = 5.27988
I0410 14:07:38.640403 18606 solver.cpp:237]     Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
I0410 14:07:38.640414 18606 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 14:07:43.519879 18606 solver.cpp:218] Iteration 5016 (2.45939 iter/s, 4.87926s/12 iters), loss = 5.26573
I0410 14:07:43.519927 18606 solver.cpp:237]     Train net output #0: loss = 5.26573 (* 1 = 5.26573 loss)
I0410 14:07:43.519934 18606 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 14:07:48.368839 18606 solver.cpp:218] Iteration 5028 (2.47489 iter/s, 4.8487s/12 iters), loss = 5.25009
I0410 14:07:48.368880 18606 solver.cpp:237]     Train net output #0: loss = 5.25009 (* 1 = 5.25009 loss)
I0410 14:07:48.368889 18606 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 14:07:53.263875 18606 solver.cpp:218] Iteration 5040 (2.45159 iter/s, 4.89477s/12 iters), loss = 5.28497
I0410 14:07:53.263955 18606 solver.cpp:237]     Train net output #0: loss = 5.28497 (* 1 = 5.28497 loss)
I0410 14:07:53.263967 18606 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 14:07:58.200531 18606 solver.cpp:218] Iteration 5052 (2.43094 iter/s, 4.93636s/12 iters), loss = 5.27203
I0410 14:07:58.200577 18606 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0410 14:07:58.200588 18606 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 14:08:00.108194 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:03.248001 18606 solver.cpp:218] Iteration 5064 (2.37756 iter/s, 5.0472s/12 iters), loss = 5.28936
I0410 14:08:03.248057 18606 solver.cpp:237]     Train net output #0: loss = 5.28936 (* 1 = 5.28936 loss)
I0410 14:08:03.248070 18606 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 14:08:08.234038 18606 solver.cpp:218] Iteration 5076 (2.40685 iter/s, 4.98576s/12 iters), loss = 5.27573
I0410 14:08:08.234094 18606 solver.cpp:237]     Train net output #0: loss = 5.27573 (* 1 = 5.27573 loss)
I0410 14:08:08.234109 18606 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 14:08:13.172560 18606 solver.cpp:218] Iteration 5088 (2.43001 iter/s, 4.93825s/12 iters), loss = 5.2627
I0410 14:08:13.172617 18606 solver.cpp:237]     Train net output #0: loss = 5.2627 (* 1 = 5.2627 loss)
I0410 14:08:13.172631 18606 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 14:08:17.575860 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 14:08:17.864403 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 14:08:18.064077 18606 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 14:08:18.064097 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:08:20.505532 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:22.521235 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:08:22.521278 18606 solver.cpp:397]     Test net output #1: loss = 5.28654 (* 1 = 5.28654 loss)
I0410 14:08:22.604465 18606 solver.cpp:218] Iteration 5100 (1.27234 iter/s, 9.43143s/12 iters), loss = 5.26694
I0410 14:08:22.604535 18606 solver.cpp:237]     Train net output #0: loss = 5.26694 (* 1 = 5.26694 loss)
I0410 14:08:22.604550 18606 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 14:08:26.680573 18606 solver.cpp:218] Iteration 5112 (2.94417 iter/s, 4.07586s/12 iters), loss = 5.26539
I0410 14:08:26.680670 18606 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0410 14:08:26.680681 18606 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 14:08:31.462242 18606 solver.cpp:218] Iteration 5124 (2.50975 iter/s, 4.78135s/12 iters), loss = 5.27378
I0410 14:08:31.462306 18606 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0410 14:08:31.462317 18606 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 14:08:36.451797 18606 solver.cpp:218] Iteration 5136 (2.40516 iter/s, 4.98927s/12 iters), loss = 5.26579
I0410 14:08:36.451853 18606 solver.cpp:237]     Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
I0410 14:08:36.451865 18606 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 14:08:41.351267 18606 solver.cpp:218] Iteration 5148 (2.44938 iter/s, 4.89919s/12 iters), loss = 5.26168
I0410 14:08:41.351326 18606 solver.cpp:237]     Train net output #0: loss = 5.26168 (* 1 = 5.26168 loss)
I0410 14:08:41.351339 18606 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 14:08:45.295231 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:46.201937 18606 solver.cpp:218] Iteration 5160 (2.47403 iter/s, 4.85039s/12 iters), loss = 5.25489
I0410 14:08:46.202013 18606 solver.cpp:237]     Train net output #0: loss = 5.25489 (* 1 = 5.25489 loss)
I0410 14:08:46.202025 18606 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 14:08:51.079649 18606 solver.cpp:218] Iteration 5172 (2.46032 iter/s, 4.87741s/12 iters), loss = 5.273
I0410 14:08:51.079707 18606 solver.cpp:237]     Train net output #0: loss = 5.273 (* 1 = 5.273 loss)
I0410 14:08:51.079720 18606 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 14:08:55.944145 18606 solver.cpp:218] Iteration 5184 (2.46699 iter/s, 4.86422s/12 iters), loss = 5.27071
I0410 14:08:55.944196 18606 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0410 14:08:55.944208 18606 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 14:09:00.865070 18606 solver.cpp:218] Iteration 5196 (2.4387 iter/s, 4.92066s/12 iters), loss = 5.30847
I0410 14:09:00.865154 18606 solver.cpp:237]     Train net output #0: loss = 5.30847 (* 1 = 5.30847 loss)
I0410 14:09:00.865166 18606 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 14:09:02.913489 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 14:09:04.369163 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 14:09:04.628702 18606 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 14:09:04.628733 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:09:06.895867 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:08.955072 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:09:08.955106 18606 solver.cpp:397]     Test net output #1: loss = 5.2867 (* 1 = 5.2867 loss)
I0410 14:09:10.792918 18606 solver.cpp:218] Iteration 5208 (1.20878 iter/s, 9.92733s/12 iters), loss = 5.27451
I0410 14:09:10.792973 18606 solver.cpp:237]     Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
I0410 14:09:10.792984 18606 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 14:09:15.651332 18606 solver.cpp:218] Iteration 5220 (2.47008 iter/s, 4.85814s/12 iters), loss = 5.27472
I0410 14:09:15.651381 18606 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0410 14:09:15.651389 18606 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 14:09:20.520761 18606 solver.cpp:218] Iteration 5232 (2.46449 iter/s, 4.86916s/12 iters), loss = 5.27979
I0410 14:09:20.520812 18606 solver.cpp:237]     Train net output #0: loss = 5.27979 (* 1 = 5.27979 loss)
I0410 14:09:20.520823 18606 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 14:09:25.377719 18606 solver.cpp:218] Iteration 5244 (2.47082 iter/s, 4.85668s/12 iters), loss = 5.27143
I0410 14:09:25.377776 18606 solver.cpp:237]     Train net output #0: loss = 5.27143 (* 1 = 5.27143 loss)
I0410 14:09:25.377789 18606 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 14:09:30.330896 18606 solver.cpp:218] Iteration 5256 (2.42283 iter/s, 4.9529s/12 iters), loss = 5.26013
I0410 14:09:30.330952 18606 solver.cpp:237]     Train net output #0: loss = 5.26013 (* 1 = 5.26013 loss)
I0410 14:09:30.330965 18606 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 14:09:31.596568 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:35.258083 18606 solver.cpp:218] Iteration 5268 (2.43561 iter/s, 4.9269s/12 iters), loss = 5.27785
I0410 14:09:35.258144 18606 solver.cpp:237]     Train net output #0: loss = 5.27785 (* 1 = 5.27785 loss)
I0410 14:09:35.258163 18606 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 14:09:40.162961 18606 solver.cpp:218] Iteration 5280 (2.44669 iter/s, 4.90459s/12 iters), loss = 5.26878
I0410 14:09:40.163013 18606 solver.cpp:237]     Train net output #0: loss = 5.26878 (* 1 = 5.26878 loss)
I0410 14:09:40.163025 18606 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 14:09:45.026932 18606 solver.cpp:218] Iteration 5292 (2.46726 iter/s, 4.8637s/12 iters), loss = 5.28071
I0410 14:09:45.026990 18606 solver.cpp:237]     Train net output #0: loss = 5.28071 (* 1 = 5.28071 loss)
I0410 14:09:45.027002 18606 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 14:09:49.428865 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 14:09:49.746109 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 14:09:49.953225 18606 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 14:09:49.953246 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:09:52.405592 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:54.561673 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:09:54.561715 18606 solver.cpp:397]     Test net output #1: loss = 5.28647 (* 1 = 5.28647 loss)
I0410 14:09:54.644810 18606 solver.cpp:218] Iteration 5304 (1.24774 iter/s, 9.61739s/12 iters), loss = 5.27277
I0410 14:09:54.644881 18606 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0410 14:09:54.644896 18606 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 14:09:59.001164 18606 solver.cpp:218] Iteration 5316 (2.75477 iter/s, 4.35608s/12 iters), loss = 5.27214
I0410 14:09:59.001214 18606 solver.cpp:237]     Train net output #0: loss = 5.27214 (* 1 = 5.27214 loss)
I0410 14:09:59.001224 18606 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 14:10:03.882092 18606 solver.cpp:218] Iteration 5328 (2.45868 iter/s, 4.88066s/12 iters), loss = 5.25837
I0410 14:10:03.882205 18606 solver.cpp:237]     Train net output #0: loss = 5.25837 (* 1 = 5.25837 loss)
I0410 14:10:03.882218 18606 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 14:10:08.826267 18606 solver.cpp:218] Iteration 5340 (2.42726 iter/s, 4.94384s/12 iters), loss = 5.29928
I0410 14:10:08.826319 18606 solver.cpp:237]     Train net output #0: loss = 5.29928 (* 1 = 5.29928 loss)
I0410 14:10:08.826330 18606 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 14:10:13.618104 18606 solver.cpp:218] Iteration 5352 (2.5044 iter/s, 4.79157s/12 iters), loss = 5.2757
I0410 14:10:13.618158 18606 solver.cpp:237]     Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
I0410 14:10:13.618171 18606 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 14:10:16.892119 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:18.417153 18606 solver.cpp:218] Iteration 5364 (2.50064 iter/s, 4.79878s/12 iters), loss = 5.27661
I0410 14:10:18.417207 18606 solver.cpp:237]     Train net output #0: loss = 5.27661 (* 1 = 5.27661 loss)
I0410 14:10:18.417218 18606 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 14:10:23.264961 18606 solver.cpp:218] Iteration 5376 (2.47549 iter/s, 4.84753s/12 iters), loss = 5.26417
I0410 14:10:23.265023 18606 solver.cpp:237]     Train net output #0: loss = 5.26417 (* 1 = 5.26417 loss)
I0410 14:10:23.265035 18606 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 14:10:28.427907 18606 solver.cpp:218] Iteration 5388 (2.32438 iter/s, 5.16266s/12 iters), loss = 5.2681
I0410 14:10:28.427956 18606 solver.cpp:237]     Train net output #0: loss = 5.2681 (* 1 = 5.2681 loss)
I0410 14:10:28.427966 18606 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 14:10:33.199200 18606 solver.cpp:218] Iteration 5400 (2.51518 iter/s, 4.77103s/12 iters), loss = 5.2694
I0410 14:10:33.199247 18606 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0410 14:10:33.199256 18606 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 14:10:35.181666 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 14:10:36.007827 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 14:10:36.443799 18606 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 14:10:36.443826 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:10:38.657094 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:40.780498 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:10:40.780546 18606 solver.cpp:397]     Test net output #1: loss = 5.28658 (* 1 = 5.28658 loss)
I0410 14:10:42.534358 18606 solver.cpp:218] Iteration 5412 (1.28553 iter/s, 9.33471s/12 iters), loss = 5.26319
I0410 14:10:42.534397 18606 solver.cpp:237]     Train net output #0: loss = 5.26319 (* 1 = 5.26319 loss)
I0410 14:10:42.534407 18606 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 14:10:47.328783 18606 solver.cpp:218] Iteration 5424 (2.50304 iter/s, 4.79416s/12 iters), loss = 5.28161
I0410 14:10:47.328843 18606 solver.cpp:237]     Train net output #0: loss = 5.28161 (* 1 = 5.28161 loss)
I0410 14:10:47.328856 18606 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 14:10:52.145973 18606 solver.cpp:218] Iteration 5436 (2.49123 iter/s, 4.8169s/12 iters), loss = 5.26608
I0410 14:10:52.146034 18606 solver.cpp:237]     Train net output #0: loss = 5.26608 (* 1 = 5.26608 loss)
I0410 14:10:52.146045 18606 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 14:10:56.966616 18606 solver.cpp:218] Iteration 5448 (2.48944 iter/s, 4.82036s/12 iters), loss = 5.27647
I0410 14:10:56.966670 18606 solver.cpp:237]     Train net output #0: loss = 5.27647 (* 1 = 5.27647 loss)
I0410 14:10:56.966681 18606 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 14:11:01.804482 18606 solver.cpp:218] Iteration 5460 (2.48057 iter/s, 4.8376s/12 iters), loss = 5.28095
I0410 14:11:01.804531 18606 solver.cpp:237]     Train net output #0: loss = 5.28095 (* 1 = 5.28095 loss)
I0410 14:11:01.804540 18606 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 14:11:02.371476 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:06.659054 18606 solver.cpp:218] Iteration 5472 (2.47204 iter/s, 4.8543s/12 iters), loss = 5.27825
I0410 14:11:06.659188 18606 solver.cpp:237]     Train net output #0: loss = 5.27825 (* 1 = 5.27825 loss)
I0410 14:11:06.659198 18606 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 14:11:11.505152 18606 solver.cpp:218] Iteration 5484 (2.4764 iter/s, 4.84574s/12 iters), loss = 5.27338
I0410 14:11:11.505206 18606 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0410 14:11:11.505218 18606 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 14:11:16.309969 18606 solver.cpp:218] Iteration 5496 (2.49764 iter/s, 4.80453s/12 iters), loss = 5.29275
I0410 14:11:16.310022 18606 solver.cpp:237]     Train net output #0: loss = 5.29275 (* 1 = 5.29275 loss)
I0410 14:11:16.310034 18606 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 14:11:20.728022 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 14:11:21.040446 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 14:11:21.247809 18606 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 14:11:21.247838 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:11:23.416785 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:25.596614 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:11:25.596666 18606 solver.cpp:397]     Test net output #1: loss = 5.28751 (* 1 = 5.28751 loss)
I0410 14:11:25.678936 18606 solver.cpp:218] Iteration 5508 (1.28089 iter/s, 9.3685s/12 iters), loss = 5.27542
I0410 14:11:25.678987 18606 solver.cpp:237]     Train net output #0: loss = 5.27542 (* 1 = 5.27542 loss)
I0410 14:11:25.678999 18606 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 14:11:29.932080 18606 solver.cpp:218] Iteration 5520 (2.8216 iter/s, 4.2529s/12 iters), loss = 5.27176
I0410 14:11:29.932119 18606 solver.cpp:237]     Train net output #0: loss = 5.27176 (* 1 = 5.27176 loss)
I0410 14:11:29.932129 18606 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 14:11:30.676751 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:11:35.303225 18606 solver.cpp:218] Iteration 5532 (2.23428 iter/s, 5.37086s/12 iters), loss = 5.28679
I0410 14:11:35.303280 18606 solver.cpp:237]     Train net output #0: loss = 5.28679 (* 1 = 5.28679 loss)
I0410 14:11:35.303290 18606 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 14:11:40.116153 18606 solver.cpp:218] Iteration 5544 (2.49343 iter/s, 4.81265s/12 iters), loss = 5.2587
I0410 14:11:40.116286 18606 solver.cpp:237]     Train net output #0: loss = 5.2587 (* 1 = 5.2587 loss)
I0410 14:11:40.116302 18606 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 14:11:44.940609 18606 solver.cpp:218] Iteration 5556 (2.48751 iter/s, 4.82411s/12 iters), loss = 5.26942
I0410 14:11:44.940654 18606 solver.cpp:237]     Train net output #0: loss = 5.26942 (* 1 = 5.26942 loss)
I0410 14:11:44.940663 18606 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 14:11:47.541388 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:49.750900 18606 solver.cpp:218] Iteration 5568 (2.49479 iter/s, 4.81003s/12 iters), loss = 5.27499
I0410 14:11:49.750960 18606 solver.cpp:237]     Train net output #0: loss = 5.27499 (* 1 = 5.27499 loss)
I0410 14:11:49.750977 18606 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 14:11:54.562153 18606 solver.cpp:218] Iteration 5580 (2.4943 iter/s, 4.81098s/12 iters), loss = 5.25914
I0410 14:11:54.562207 18606 solver.cpp:237]     Train net output #0: loss = 5.25914 (* 1 = 5.25914 loss)
I0410 14:11:54.562217 18606 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 14:11:59.596202 18606 solver.cpp:218] Iteration 5592 (2.3839 iter/s, 5.03377s/12 iters), loss = 5.27104
I0410 14:11:59.596247 18606 solver.cpp:237]     Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
I0410 14:11:59.596256 18606 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 14:12:04.461194 18606 solver.cpp:218] Iteration 5604 (2.46674 iter/s, 4.86472s/12 iters), loss = 5.26302
I0410 14:12:04.461244 18606 solver.cpp:237]     Train net output #0: loss = 5.26302 (* 1 = 5.26302 loss)
I0410 14:12:04.461256 18606 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 14:12:06.463054 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 14:12:06.767870 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 14:12:06.969218 18606 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 14:12:06.969236 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:12:09.157711 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:11.364478 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:12:11.364655 18606 solver.cpp:397]     Test net output #1: loss = 5.28688 (* 1 = 5.28688 loss)
I0410 14:12:13.235530 18606 solver.cpp:218] Iteration 5616 (1.36769 iter/s, 8.7739s/12 iters), loss = 5.29456
I0410 14:12:13.235587 18606 solver.cpp:237]     Train net output #0: loss = 5.29456 (* 1 = 5.29456 loss)
I0410 14:12:13.235601 18606 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 14:12:18.117832 18606 solver.cpp:218] Iteration 5628 (2.458 iter/s, 4.88202s/12 iters), loss = 5.27476
I0410 14:12:18.117885 18606 solver.cpp:237]     Train net output #0: loss = 5.27476 (* 1 = 5.27476 loss)
I0410 14:12:18.117897 18606 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 14:12:23.531421 18606 solver.cpp:218] Iteration 5640 (2.21677 iter/s, 5.41329s/12 iters), loss = 5.26332
I0410 14:12:23.531479 18606 solver.cpp:237]     Train net output #0: loss = 5.26332 (* 1 = 5.26332 loss)
I0410 14:12:23.531491 18606 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 14:12:28.583969 18606 solver.cpp:218] Iteration 5652 (2.37518 iter/s, 5.05225s/12 iters), loss = 5.26757
I0410 14:12:28.584024 18606 solver.cpp:237]     Train net output #0: loss = 5.26757 (* 1 = 5.26757 loss)
I0410 14:12:28.584036 18606 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 14:12:33.251344 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:33.410856 18606 solver.cpp:218] Iteration 5664 (2.48621 iter/s, 4.82662s/12 iters), loss = 5.25239
I0410 14:12:33.410910 18606 solver.cpp:237]     Train net output #0: loss = 5.25239 (* 1 = 5.25239 loss)
I0410 14:12:33.410921 18606 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 14:12:38.342687 18606 solver.cpp:218] Iteration 5676 (2.43331 iter/s, 4.93155s/12 iters), loss = 5.2636
I0410 14:12:38.342736 18606 solver.cpp:237]     Train net output #0: loss = 5.2636 (* 1 = 5.2636 loss)
I0410 14:12:38.342746 18606 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 14:12:43.208998 18606 solver.cpp:218] Iteration 5688 (2.46607 iter/s, 4.86604s/12 iters), loss = 5.29615
I0410 14:12:43.209105 18606 solver.cpp:237]     Train net output #0: loss = 5.29615 (* 1 = 5.29615 loss)
I0410 14:12:43.209116 18606 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 14:12:48.028218 18606 solver.cpp:218] Iteration 5700 (2.4902 iter/s, 4.81889s/12 iters), loss = 5.28626
I0410 14:12:48.028266 18606 solver.cpp:237]     Train net output #0: loss = 5.28626 (* 1 = 5.28626 loss)
I0410 14:12:48.028276 18606 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 14:12:52.413301 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 14:12:52.840662 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 14:12:53.058212 18606 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 14:12:53.058239 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:12:55.225198 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:57.464390 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:12:57.464428 18606 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 14:12:57.547559 18606 solver.cpp:218] Iteration 5712 (1.26065 iter/s, 9.51887s/12 iters), loss = 5.27863
I0410 14:12:57.547605 18606 solver.cpp:237]     Train net output #0: loss = 5.27863 (* 1 = 5.27863 loss)
I0410 14:12:57.547613 18606 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 14:13:01.855170 18606 solver.cpp:218] Iteration 5724 (2.78592 iter/s, 4.30737s/12 iters), loss = 5.26579
I0410 14:13:01.855211 18606 solver.cpp:237]     Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
I0410 14:13:01.855221 18606 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 14:13:06.824302 18606 solver.cpp:218] Iteration 5736 (2.41504 iter/s, 4.96886s/12 iters), loss = 5.24601
I0410 14:13:06.824365 18606 solver.cpp:237]     Train net output #0: loss = 5.24601 (* 1 = 5.24601 loss)
I0410 14:13:06.824378 18606 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 14:13:11.645021 18606 solver.cpp:218] Iteration 5748 (2.4894 iter/s, 4.82043s/12 iters), loss = 5.28072
I0410 14:13:11.645079 18606 solver.cpp:237]     Train net output #0: loss = 5.28072 (* 1 = 5.28072 loss)
I0410 14:13:11.645092 18606 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 14:13:16.560770 18606 solver.cpp:218] Iteration 5760 (2.44127 iter/s, 4.91547s/12 iters), loss = 5.26418
I0410 14:13:16.560936 18606 solver.cpp:237]     Train net output #0: loss = 5.26418 (* 1 = 5.26418 loss)
I0410 14:13:16.560950 18606 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 14:13:18.484045 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:21.459812 18606 solver.cpp:218] Iteration 5772 (2.44965 iter/s, 4.89866s/12 iters), loss = 5.29394
I0410 14:13:21.459867 18606 solver.cpp:237]     Train net output #0: loss = 5.29394 (* 1 = 5.29394 loss)
I0410 14:13:21.459879 18606 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 14:13:26.386536 18606 solver.cpp:218] Iteration 5784 (2.43583 iter/s, 4.92644s/12 iters), loss = 5.26921
I0410 14:13:26.386582 18606 solver.cpp:237]     Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
I0410 14:13:26.386592 18606 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 14:13:31.304817 18606 solver.cpp:218] Iteration 5796 (2.44001 iter/s, 4.918s/12 iters), loss = 5.26692
I0410 14:13:31.304881 18606 solver.cpp:237]     Train net output #0: loss = 5.26692 (* 1 = 5.26692 loss)
I0410 14:13:31.304894 18606 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 14:13:36.153946 18606 solver.cpp:218] Iteration 5808 (2.47481 iter/s, 4.84885s/12 iters), loss = 5.26497
I0410 14:13:36.154003 18606 solver.cpp:237]     Train net output #0: loss = 5.26497 (* 1 = 5.26497 loss)
I0410 14:13:36.154013 18606 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 14:13:38.207839 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 14:13:38.639030 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 14:13:38.843195 18606 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 14:13:38.843220 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:13:40.936308 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:43.405737 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:13:43.405791 18606 solver.cpp:397]     Test net output #1: loss = 5.28677 (* 1 = 5.28677 loss)
I0410 14:13:45.322602 18606 solver.cpp:218] Iteration 5820 (1.30887 iter/s, 9.16819s/12 iters), loss = 5.27549
I0410 14:13:45.322654 18606 solver.cpp:237]     Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
I0410 14:13:45.322664 18606 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 14:13:50.119510 18606 solver.cpp:218] Iteration 5832 (2.50175 iter/s, 4.79664s/12 iters), loss = 5.27391
I0410 14:13:50.119632 18606 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0410 14:13:50.119644 18606 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 14:13:54.931375 18606 solver.cpp:218] Iteration 5844 (2.49401 iter/s, 4.81153s/12 iters), loss = 5.2646
I0410 14:13:54.931433 18606 solver.cpp:237]     Train net output #0: loss = 5.2646 (* 1 = 5.2646 loss)
I0410 14:13:54.931447 18606 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 14:13:59.807703 18606 solver.cpp:218] Iteration 5856 (2.46101 iter/s, 4.87605s/12 iters), loss = 5.26131
I0410 14:13:59.807754 18606 solver.cpp:237]     Train net output #0: loss = 5.26131 (* 1 = 5.26131 loss)
I0410 14:13:59.807765 18606 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 14:14:03.978732 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:04.758424 18606 solver.cpp:218] Iteration 5868 (2.42402 iter/s, 4.95045s/12 iters), loss = 5.25619
I0410 14:14:04.758466 18606 solver.cpp:237]     Train net output #0: loss = 5.25619 (* 1 = 5.25619 loss)
I0410 14:14:04.758476 18606 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 14:14:09.595238 18606 solver.cpp:218] Iteration 5880 (2.48111 iter/s, 4.83654s/12 iters), loss = 5.27724
I0410 14:14:09.595301 18606 solver.cpp:237]     Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
I0410 14:14:09.595315 18606 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 14:14:14.372790 18606 solver.cpp:218] Iteration 5892 (2.51189 iter/s, 4.77727s/12 iters), loss = 5.2702
I0410 14:14:14.372840 18606 solver.cpp:237]     Train net output #0: loss = 5.2702 (* 1 = 5.2702 loss)
I0410 14:14:14.372851 18606 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 14:14:19.286720 18606 solver.cpp:218] Iteration 5904 (2.44217 iter/s, 4.91366s/12 iters), loss = 5.30341
I0410 14:14:19.286770 18606 solver.cpp:237]     Train net output #0: loss = 5.30341 (* 1 = 5.30341 loss)
I0410 14:14:19.286780 18606 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 14:14:23.699501 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 14:14:24.031630 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 14:14:24.247866 18606 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 14:14:24.247885 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:14:26.341157 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:28.784627 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:14:28.784678 18606 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 14:14:28.868000 18606 solver.cpp:218] Iteration 5916 (1.2525 iter/s, 9.5808s/12 iters), loss = 5.26609
I0410 14:14:28.868053 18606 solver.cpp:237]     Train net output #0: loss = 5.26609 (* 1 = 5.26609 loss)
I0410 14:14:28.868065 18606 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 14:14:32.945164 18606 solver.cpp:218] Iteration 5928 (2.9434 iter/s, 4.07692s/12 iters), loss = 5.27107
I0410 14:14:32.945209 18606 solver.cpp:237]     Train net output #0: loss = 5.27107 (* 1 = 5.27107 loss)
I0410 14:14:32.945219 18606 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 14:14:37.755585 18606 solver.cpp:218] Iteration 5940 (2.49472 iter/s, 4.81016s/12 iters), loss = 5.28097
I0410 14:14:37.755627 18606 solver.cpp:237]     Train net output #0: loss = 5.28097 (* 1 = 5.28097 loss)
I0410 14:14:37.755635 18606 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 14:14:42.542171 18606 solver.cpp:218] Iteration 5952 (2.50714 iter/s, 4.78632s/12 iters), loss = 5.27458
I0410 14:14:42.542223 18606 solver.cpp:237]     Train net output #0: loss = 5.27458 (* 1 = 5.27458 loss)
I0410 14:14:42.542235 18606 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 14:14:47.557369 18606 solver.cpp:218] Iteration 5964 (2.39286 iter/s, 5.01492s/12 iters), loss = 5.25839
I0410 14:14:47.557418 18606 solver.cpp:237]     Train net output #0: loss = 5.25839 (* 1 = 5.25839 loss)
I0410 14:14:47.557428 18606 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 14:14:48.918598 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:52.472872 18606 solver.cpp:218] Iteration 5976 (2.44139 iter/s, 4.91523s/12 iters), loss = 5.27648
I0410 14:14:52.472914 18606 solver.cpp:237]     Train net output #0: loss = 5.27648 (* 1 = 5.27648 loss)
I0410 14:14:52.472924 18606 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 14:14:57.293213 18606 solver.cpp:218] Iteration 5988 (2.48958 iter/s, 4.82008s/12 iters), loss = 5.2638
I0410 14:14:57.293344 18606 solver.cpp:237]     Train net output #0: loss = 5.2638 (* 1 = 5.2638 loss)
I0410 14:14:57.293354 18606 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 14:15:02.118278 18606 solver.cpp:218] Iteration 6000 (2.48719 iter/s, 4.82472s/12 iters), loss = 5.28045
I0410 14:15:02.118320 18606 solver.cpp:237]     Train net output #0: loss = 5.28045 (* 1 = 5.28045 loss)
I0410 14:15:02.118330 18606 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 14:15:06.943420 18606 solver.cpp:218] Iteration 6012 (2.48711 iter/s, 4.82488s/12 iters), loss = 5.26814
I0410 14:15:06.943481 18606 solver.cpp:237]     Train net output #0: loss = 5.26814 (* 1 = 5.26814 loss)
I0410 14:15:06.943495 18606 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 14:15:08.945430 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 14:15:09.258366 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 14:15:09.466022 18606 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 14:15:09.466049 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:15:11.483726 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:13.871227 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:15:13.871275 18606 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 14:15:15.749620 18606 solver.cpp:218] Iteration 6024 (1.36275 iter/s, 8.80575s/12 iters), loss = 5.26749
I0410 14:15:15.749660 18606 solver.cpp:237]     Train net output #0: loss = 5.26749 (* 1 = 5.26749 loss)
I0410 14:15:15.749667 18606 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 14:15:20.608562 18606 solver.cpp:218] Iteration 6036 (2.46981 iter/s, 4.85868s/12 iters), loss = 5.2607
I0410 14:15:20.608608 18606 solver.cpp:237]     Train net output #0: loss = 5.2607 (* 1 = 5.2607 loss)
I0410 14:15:20.608618 18606 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 14:15:25.521006 18606 solver.cpp:218] Iteration 6048 (2.44291 iter/s, 4.91217s/12 iters), loss = 5.30268
I0410 14:15:25.521060 18606 solver.cpp:237]     Train net output #0: loss = 5.30268 (* 1 = 5.30268 loss)
I0410 14:15:25.521073 18606 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 14:15:30.358203 18606 solver.cpp:218] Iteration 6060 (2.48091 iter/s, 4.83693s/12 iters), loss = 5.27756
I0410 14:15:30.358294 18606 solver.cpp:237]     Train net output #0: loss = 5.27756 (* 1 = 5.27756 loss)
I0410 14:15:30.358305 18606 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 14:15:33.662904 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:35.221904 18606 solver.cpp:218] Iteration 6072 (2.46742 iter/s, 4.86338s/12 iters), loss = 5.27396
I0410 14:15:35.221976 18606 solver.cpp:237]     Train net output #0: loss = 5.27396 (* 1 = 5.27396 loss)
I0410 14:15:35.221988 18606 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 14:15:40.073585 18606 solver.cpp:218] Iteration 6084 (2.47351 iter/s, 4.85141s/12 iters), loss = 5.26017
I0410 14:15:40.073637 18606 solver.cpp:237]     Train net output #0: loss = 5.26017 (* 1 = 5.26017 loss)
I0410 14:15:40.073647 18606 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 14:15:45.128301 18606 solver.cpp:218] Iteration 6096 (2.37415 iter/s, 5.05444s/12 iters), loss = 5.26121
I0410 14:15:45.128351 18606 solver.cpp:237]     Train net output #0: loss = 5.26121 (* 1 = 5.26121 loss)
I0410 14:15:45.128360 18606 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 14:15:50.028594 18606 solver.cpp:218] Iteration 6108 (2.44897 iter/s, 4.90002s/12 iters), loss = 5.2744
I0410 14:15:50.028640 18606 solver.cpp:237]     Train net output #0: loss = 5.2744 (* 1 = 5.2744 loss)
I0410 14:15:50.028651 18606 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 14:15:54.430259 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 14:15:54.749049 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 14:15:54.965543 18606 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 14:15:54.965564 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:15:57.026083 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:59.445247 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:15:59.445297 18606 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 14:15:59.526506 18606 solver.cpp:218] Iteration 6120 (1.2635 iter/s, 9.49744s/12 iters), loss = 5.26508
I0410 14:15:59.526585 18606 solver.cpp:237]     Train net output #0: loss = 5.26508 (* 1 = 5.26508 loss)
I0410 14:15:59.526602 18606 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 14:16:03.651469 18606 solver.cpp:218] Iteration 6132 (2.90931 iter/s, 4.12469s/12 iters), loss = 5.27623
I0410 14:16:03.651612 18606 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0410 14:16:03.651624 18606 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 14:16:08.526007 18606 solver.cpp:218] Iteration 6144 (2.46196 iter/s, 4.87417s/12 iters), loss = 5.26829
I0410 14:16:08.526072 18606 solver.cpp:237]     Train net output #0: loss = 5.26829 (* 1 = 5.26829 loss)
I0410 14:16:08.526085 18606 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 14:16:13.310883 18606 solver.cpp:218] Iteration 6156 (2.50805 iter/s, 4.78459s/12 iters), loss = 5.27734
I0410 14:16:13.310935 18606 solver.cpp:237]     Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
I0410 14:16:13.310945 18606 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 14:16:18.131881 18606 solver.cpp:218] Iteration 6168 (2.48925 iter/s, 4.82072s/12 iters), loss = 5.28978
I0410 14:16:18.131934 18606 solver.cpp:237]     Train net output #0: loss = 5.28978 (* 1 = 5.28978 loss)
I0410 14:16:18.131947 18606 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 14:16:18.698813 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:22.928088 18606 solver.cpp:218] Iteration 6180 (2.50212 iter/s, 4.79594s/12 iters), loss = 5.28236
I0410 14:16:22.928139 18606 solver.cpp:237]     Train net output #0: loss = 5.28236 (* 1 = 5.28236 loss)
I0410 14:16:22.928150 18606 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 14:16:27.746431 18606 solver.cpp:218] Iteration 6192 (2.49062 iter/s, 4.81808s/12 iters), loss = 5.27028
I0410 14:16:27.746477 18606 solver.cpp:237]     Train net output #0: loss = 5.27028 (* 1 = 5.27028 loss)
I0410 14:16:27.746487 18606 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 14:16:32.718829 18606 solver.cpp:218] Iteration 6204 (2.41346 iter/s, 4.97212s/12 iters), loss = 5.28807
I0410 14:16:32.718879 18606 solver.cpp:237]     Train net output #0: loss = 5.28807 (* 1 = 5.28807 loss)
I0410 14:16:32.718889 18606 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 14:16:37.663851 18606 solver.cpp:218] Iteration 6216 (2.42682 iter/s, 4.94474s/12 iters), loss = 5.27869
I0410 14:16:37.663971 18606 solver.cpp:237]     Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
I0410 14:16:37.663985 18606 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 14:16:39.627892 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 14:16:40.004349 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 14:16:40.221585 18606 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 14:16:40.221614 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:16:42.150281 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:43.058531 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:16:44.586686 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:16:44.586730 18606 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0410 14:16:46.428620 18606 solver.cpp:218] Iteration 6228 (1.3692 iter/s, 8.76426s/12 iters), loss = 5.27599
I0410 14:16:46.428665 18606 solver.cpp:237]     Train net output #0: loss = 5.27599 (* 1 = 5.27599 loss)
I0410 14:16:46.428674 18606 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 14:16:51.194311 18606 solver.cpp:218] Iteration 6240 (2.51814 iter/s, 4.76542s/12 iters), loss = 5.28051
I0410 14:16:51.194372 18606 solver.cpp:237]     Train net output #0: loss = 5.28051 (* 1 = 5.28051 loss)
I0410 14:16:51.194384 18606 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 14:16:56.060418 18606 solver.cpp:218] Iteration 6252 (2.46618 iter/s, 4.86583s/12 iters), loss = 5.26049
I0410 14:16:56.060472 18606 solver.cpp:237]     Train net output #0: loss = 5.26049 (* 1 = 5.26049 loss)
I0410 14:16:56.060482 18606 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 14:17:00.891330 18606 solver.cpp:218] Iteration 6264 (2.48415 iter/s, 4.83063s/12 iters), loss = 5.26478
I0410 14:17:00.891393 18606 solver.cpp:237]     Train net output #0: loss = 5.26478 (* 1 = 5.26478 loss)
I0410 14:17:00.891407 18606 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 14:17:03.637398 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:05.801021 18606 solver.cpp:218] Iteration 6276 (2.44429 iter/s, 4.9094s/12 iters), loss = 5.27825
I0410 14:17:05.801077 18606 solver.cpp:237]     Train net output #0: loss = 5.27825 (* 1 = 5.27825 loss)
I0410 14:17:05.801090 18606 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 14:17:10.626241 18606 solver.cpp:218] Iteration 6288 (2.48708 iter/s, 4.82494s/12 iters), loss = 5.25672
I0410 14:17:10.628345 18606 solver.cpp:237]     Train net output #0: loss = 5.25672 (* 1 = 5.25672 loss)
I0410 14:17:10.628357 18606 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 14:17:15.422228 18606 solver.cpp:218] Iteration 6300 (2.5033 iter/s, 4.79366s/12 iters), loss = 5.27175
I0410 14:17:15.422287 18606 solver.cpp:237]     Train net output #0: loss = 5.27175 (* 1 = 5.27175 loss)
I0410 14:17:15.422299 18606 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 14:17:20.247684 18606 solver.cpp:218] Iteration 6312 (2.48696 iter/s, 4.82518s/12 iters), loss = 5.2608
I0410 14:17:20.247743 18606 solver.cpp:237]     Train net output #0: loss = 5.2608 (* 1 = 5.2608 loss)
I0410 14:17:20.247756 18606 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 14:17:24.841351 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 14:17:25.175529 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 14:17:25.394007 18606 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 14:17:25.394033 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:17:27.239471 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:29.710734 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:17:29.710783 18606 solver.cpp:397]     Test net output #1: loss = 5.28673 (* 1 = 5.28673 loss)
I0410 14:17:29.793594 18606 solver.cpp:218] Iteration 6324 (1.25715 iter/s, 9.54543s/12 iters), loss = 5.30023
I0410 14:17:29.793644 18606 solver.cpp:237]     Train net output #0: loss = 5.30023 (* 1 = 5.30023 loss)
I0410 14:17:29.793655 18606 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 14:17:33.889364 18606 solver.cpp:218] Iteration 6336 (2.93002 iter/s, 4.09553s/12 iters), loss = 5.27071
I0410 14:17:33.889415 18606 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0410 14:17:33.889426 18606 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 14:17:38.701447 18606 solver.cpp:218] Iteration 6348 (2.49386 iter/s, 4.81182s/12 iters), loss = 5.26082
I0410 14:17:38.701483 18606 solver.cpp:237]     Train net output #0: loss = 5.26082 (* 1 = 5.26082 loss)
I0410 14:17:38.701491 18606 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 14:17:43.490765 18606 solver.cpp:218] Iteration 6360 (2.50571 iter/s, 4.78906s/12 iters), loss = 5.27076
I0410 14:17:43.490909 18606 solver.cpp:237]     Train net output #0: loss = 5.27076 (* 1 = 5.27076 loss)
I0410 14:17:43.490923 18606 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 14:17:48.288197 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:48.418970 18606 solver.cpp:218] Iteration 6372 (2.43515 iter/s, 4.92784s/12 iters), loss = 5.25195
I0410 14:17:48.419013 18606 solver.cpp:237]     Train net output #0: loss = 5.25195 (* 1 = 5.25195 loss)
I0410 14:17:48.419023 18606 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 14:17:53.252856 18606 solver.cpp:218] Iteration 6384 (2.48261 iter/s, 4.83362s/12 iters), loss = 5.26746
I0410 14:17:53.252898 18606 solver.cpp:237]     Train net output #0: loss = 5.26746 (* 1 = 5.26746 loss)
I0410 14:17:53.252908 18606 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 14:17:58.097843 18606 solver.cpp:218] Iteration 6396 (2.47692 iter/s, 4.84473s/12 iters), loss = 5.29437
I0410 14:17:58.097894 18606 solver.cpp:237]     Train net output #0: loss = 5.29437 (* 1 = 5.29437 loss)
I0410 14:17:58.097908 18606 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 14:18:02.919298 18606 solver.cpp:218] Iteration 6408 (2.48902 iter/s, 4.82118s/12 iters), loss = 5.28551
I0410 14:18:02.919354 18606 solver.cpp:237]     Train net output #0: loss = 5.28551 (* 1 = 5.28551 loss)
I0410 14:18:02.919366 18606 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 14:18:07.709403 18606 solver.cpp:218] Iteration 6420 (2.50531 iter/s, 4.78983s/12 iters), loss = 5.27843
I0410 14:18:07.709455 18606 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0410 14:18:07.709467 18606 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 14:18:09.675045 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 14:18:10.329300 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 14:18:10.538285 18606 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 14:18:10.538306 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:18:12.464666 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:14.983409 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:18:14.983500 18606 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 14:18:16.822515 18606 solver.cpp:218] Iteration 6432 (1.31685 iter/s, 9.11266s/12 iters), loss = 5.27258
I0410 14:18:16.822571 18606 solver.cpp:237]     Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
I0410 14:18:16.822583 18606 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 14:18:21.638154 18606 solver.cpp:218] Iteration 6444 (2.49203 iter/s, 4.81536s/12 iters), loss = 5.24543
I0410 14:18:21.638211 18606 solver.cpp:237]     Train net output #0: loss = 5.24543 (* 1 = 5.24543 loss)
I0410 14:18:21.638223 18606 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 14:18:26.445904 18606 solver.cpp:218] Iteration 6456 (2.49611 iter/s, 4.80747s/12 iters), loss = 5.27351
I0410 14:18:26.445951 18606 solver.cpp:237]     Train net output #0: loss = 5.27351 (* 1 = 5.27351 loss)
I0410 14:18:26.445986 18606 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 14:18:31.281411 18606 solver.cpp:218] Iteration 6468 (2.48178 iter/s, 4.83524s/12 iters), loss = 5.26675
I0410 14:18:31.281451 18606 solver.cpp:237]     Train net output #0: loss = 5.26675 (* 1 = 5.26675 loss)
I0410 14:18:31.281459 18606 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 14:18:33.204650 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:36.258296 18606 solver.cpp:218] Iteration 6480 (2.41128 iter/s, 4.97661s/12 iters), loss = 5.29009
I0410 14:18:36.258342 18606 solver.cpp:237]     Train net output #0: loss = 5.29009 (* 1 = 5.29009 loss)
I0410 14:18:36.258352 18606 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 14:18:41.092566 18606 solver.cpp:218] Iteration 6492 (2.48241 iter/s, 4.834s/12 iters), loss = 5.27015
I0410 14:18:41.092608 18606 solver.cpp:237]     Train net output #0: loss = 5.27015 (* 1 = 5.27015 loss)
I0410 14:18:41.092619 18606 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 14:18:45.928966 18606 solver.cpp:218] Iteration 6504 (2.48132 iter/s, 4.83613s/12 iters), loss = 5.27201
I0410 14:18:45.929129 18606 solver.cpp:237]     Train net output #0: loss = 5.27201 (* 1 = 5.27201 loss)
I0410 14:18:45.929144 18606 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 14:18:50.727105 18606 solver.cpp:218] Iteration 6516 (2.50117 iter/s, 4.79776s/12 iters), loss = 5.26436
I0410 14:18:50.727149 18606 solver.cpp:237]     Train net output #0: loss = 5.26436 (* 1 = 5.26436 loss)
I0410 14:18:50.727159 18606 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 14:18:55.154825 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 14:18:55.529613 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 14:18:55.898505 18606 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 14:18:55.898531 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:18:57.689580 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:00.249146 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:19:00.249200 18606 solver.cpp:397]     Test net output #1: loss = 5.28676 (* 1 = 5.28676 loss)
I0410 14:19:00.331533 18606 solver.cpp:218] Iteration 6528 (1.24949 iter/s, 9.60396s/12 iters), loss = 5.2725
I0410 14:19:00.331583 18606 solver.cpp:237]     Train net output #0: loss = 5.2725 (* 1 = 5.2725 loss)
I0410 14:19:00.331593 18606 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 14:19:04.511019 18606 solver.cpp:218] Iteration 6540 (2.87133 iter/s, 4.17924s/12 iters), loss = 5.2734
I0410 14:19:04.511062 18606 solver.cpp:237]     Train net output #0: loss = 5.2734 (* 1 = 5.2734 loss)
I0410 14:19:04.511072 18606 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 14:19:09.390213 18606 solver.cpp:218] Iteration 6552 (2.45956 iter/s, 4.87892s/12 iters), loss = 5.26883
I0410 14:19:09.390264 18606 solver.cpp:237]     Train net output #0: loss = 5.26883 (* 1 = 5.26883 loss)
I0410 14:19:09.390276 18606 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 14:19:14.196934 18606 solver.cpp:218] Iteration 6564 (2.49665 iter/s, 4.80645s/12 iters), loss = 5.26049
I0410 14:19:14.196991 18606 solver.cpp:237]     Train net output #0: loss = 5.26049 (* 1 = 5.26049 loss)
I0410 14:19:14.197003 18606 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 14:19:18.280817 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:19.045423 18606 solver.cpp:218] Iteration 6576 (2.47514 iter/s, 4.84821s/12 iters), loss = 5.25845
I0410 14:19:19.045486 18606 solver.cpp:237]     Train net output #0: loss = 5.25845 (* 1 = 5.25845 loss)
I0410 14:19:19.045500 18606 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 14:19:23.974717 18606 solver.cpp:218] Iteration 6588 (2.43457 iter/s, 4.92901s/12 iters), loss = 5.28243
I0410 14:19:23.974766 18606 solver.cpp:237]     Train net output #0: loss = 5.28243 (* 1 = 5.28243 loss)
I0410 14:19:23.974774 18606 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 14:19:28.793066 18606 solver.cpp:218] Iteration 6600 (2.49062 iter/s, 4.81808s/12 iters), loss = 5.27331
I0410 14:19:28.793115 18606 solver.cpp:237]     Train net output #0: loss = 5.27331 (* 1 = 5.27331 loss)
I0410 14:19:28.793126 18606 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 14:19:33.626616 18606 solver.cpp:218] Iteration 6612 (2.48279 iter/s, 4.83328s/12 iters), loss = 5.30222
I0410 14:19:33.626664 18606 solver.cpp:237]     Train net output #0: loss = 5.30222 (* 1 = 5.30222 loss)
I0410 14:19:33.626673 18606 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 14:19:38.596695 18606 solver.cpp:218] Iteration 6624 (2.41458 iter/s, 4.9698s/12 iters), loss = 5.27022
I0410 14:19:38.596745 18606 solver.cpp:237]     Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
I0410 14:19:38.596755 18606 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 14:19:40.589784 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 14:19:41.038887 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 14:19:41.424118 18606 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 14:19:41.424147 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:19:43.240835 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:45.837289 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:19:45.837321 18606 solver.cpp:397]     Test net output #1: loss = 5.28684 (* 1 = 5.28684 loss)
I0410 14:19:47.670444 18606 solver.cpp:218] Iteration 6636 (1.32256 iter/s, 9.0733s/12 iters), loss = 5.27364
I0410 14:19:47.670495 18606 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0410 14:19:47.670507 18606 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 14:19:52.578330 18606 solver.cpp:218] Iteration 6648 (2.44518 iter/s, 4.90761s/12 iters), loss = 5.27698
I0410 14:19:52.578493 18606 solver.cpp:237]     Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
I0410 14:19:52.578508 18606 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 14:19:57.457202 18606 solver.cpp:218] Iteration 6660 (2.45978 iter/s, 4.87849s/12 iters), loss = 5.2797
I0410 14:19:57.457257 18606 solver.cpp:237]     Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
I0410 14:19:57.457268 18606 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 14:20:02.375727 18606 solver.cpp:218] Iteration 6672 (2.4399 iter/s, 4.91824s/12 iters), loss = 5.26447
I0410 14:20:02.375789 18606 solver.cpp:237]     Train net output #0: loss = 5.26447 (* 1 = 5.26447 loss)
I0410 14:20:02.375802 18606 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 14:20:03.668233 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:07.187707 18606 solver.cpp:218] Iteration 6684 (2.49392 iter/s, 4.8117s/12 iters), loss = 5.27455
I0410 14:20:07.187765 18606 solver.cpp:237]     Train net output #0: loss = 5.27455 (* 1 = 5.27455 loss)
I0410 14:20:07.187777 18606 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 14:20:12.007431 18606 solver.cpp:218] Iteration 6696 (2.48991 iter/s, 4.81944s/12 iters), loss = 5.2685
I0410 14:20:12.007490 18606 solver.cpp:237]     Train net output #0: loss = 5.2685 (* 1 = 5.2685 loss)
I0410 14:20:12.007503 18606 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 14:20:16.876617 18606 solver.cpp:218] Iteration 6708 (2.46462 iter/s, 4.86891s/12 iters), loss = 5.27455
I0410 14:20:16.876672 18606 solver.cpp:237]     Train net output #0: loss = 5.27455 (* 1 = 5.27455 loss)
I0410 14:20:16.876683 18606 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 14:20:21.748523 18606 solver.cpp:218] Iteration 6720 (2.46324 iter/s, 4.87163s/12 iters), loss = 5.27193
I0410 14:20:21.748574 18606 solver.cpp:237]     Train net output #0: loss = 5.27193 (* 1 = 5.27193 loss)
I0410 14:20:21.748586 18606 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 14:20:26.249953 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 14:20:26.654130 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 14:20:27.266858 18606 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 14:20:27.266887 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:20:29.055249 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:31.686641 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:20:31.686692 18606 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0410 14:20:31.769697 18606 solver.cpp:218] Iteration 6732 (1.19752 iter/s, 10.0207s/12 iters), loss = 5.27126
I0410 14:20:31.769757 18606 solver.cpp:237]     Train net output #0: loss = 5.27126 (* 1 = 5.27126 loss)
I0410 14:20:31.769768 18606 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 14:20:35.928725 18606 solver.cpp:218] Iteration 6744 (2.88546 iter/s, 4.15878s/12 iters), loss = 5.26009
I0410 14:20:35.928771 18606 solver.cpp:237]     Train net output #0: loss = 5.26009 (* 1 = 5.26009 loss)
I0410 14:20:35.928781 18606 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 14:20:40.816664 18606 solver.cpp:218] Iteration 6756 (2.45516 iter/s, 4.88766s/12 iters), loss = 5.29122
I0410 14:20:40.816717 18606 solver.cpp:237]     Train net output #0: loss = 5.29122 (* 1 = 5.29122 loss)
I0410 14:20:40.816730 18606 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 14:20:45.666055 18606 solver.cpp:218] Iteration 6768 (2.47468 iter/s, 4.84912s/12 iters), loss = 5.273
I0410 14:20:45.666100 18606 solver.cpp:237]     Train net output #0: loss = 5.273 (* 1 = 5.273 loss)
I0410 14:20:45.666108 18606 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 14:20:49.047870 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:50.493847 18606 solver.cpp:218] Iteration 6780 (2.48575 iter/s, 4.82752s/12 iters), loss = 5.2753
I0410 14:20:50.493898 18606 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0410 14:20:50.493911 18606 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 14:20:55.351359 18606 solver.cpp:218] Iteration 6792 (2.47054 iter/s, 4.85724s/12 iters), loss = 5.26074
I0410 14:20:55.351403 18606 solver.cpp:237]     Train net output #0: loss = 5.26074 (* 1 = 5.26074 loss)
I0410 14:20:55.351414 18606 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 14:21:00.245617 18606 solver.cpp:218] Iteration 6804 (2.45199 iter/s, 4.89398s/12 iters), loss = 5.26654
I0410 14:21:00.245776 18606 solver.cpp:237]     Train net output #0: loss = 5.26654 (* 1 = 5.26654 loss)
I0410 14:21:00.245790 18606 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 14:21:05.129878 18606 solver.cpp:218] Iteration 6816 (2.45706 iter/s, 4.88388s/12 iters), loss = 5.27842
I0410 14:21:05.129940 18606 solver.cpp:237]     Train net output #0: loss = 5.27842 (* 1 = 5.27842 loss)
I0410 14:21:05.129978 18606 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 14:21:10.067108 18606 solver.cpp:218] Iteration 6828 (2.43065 iter/s, 4.93695s/12 iters), loss = 5.26548
I0410 14:21:10.067159 18606 solver.cpp:237]     Train net output #0: loss = 5.26548 (* 1 = 5.26548 loss)
I0410 14:21:10.067170 18606 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 14:21:12.073052 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 14:21:12.382383 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 14:21:12.598975 18606 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 14:21:12.598994 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:21:14.364948 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:17.027607 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:21:17.027645 18606 solver.cpp:397]     Test net output #1: loss = 5.28735 (* 1 = 5.28735 loss)
I0410 14:21:18.784186 18606 solver.cpp:218] Iteration 6840 (1.37668 iter/s, 8.71663s/12 iters), loss = 5.27667
I0410 14:21:18.784242 18606 solver.cpp:237]     Train net output #0: loss = 5.27667 (* 1 = 5.27667 loss)
I0410 14:21:18.784255 18606 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 14:21:23.678932 18606 solver.cpp:218] Iteration 6852 (2.45175 iter/s, 4.89446s/12 iters), loss = 5.27476
I0410 14:21:23.678988 18606 solver.cpp:237]     Train net output #0: loss = 5.27476 (* 1 = 5.27476 loss)
I0410 14:21:23.678999 18606 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 14:21:28.586438 18606 solver.cpp:218] Iteration 6864 (2.44537 iter/s, 4.90723s/12 iters), loss = 5.27614
I0410 14:21:28.586493 18606 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0410 14:21:28.586504 18606 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 14:21:33.555133 18606 solver.cpp:218] Iteration 6876 (2.41526 iter/s, 4.96841s/12 iters), loss = 5.28349
I0410 14:21:33.555254 18606 solver.cpp:237]     Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
I0410 14:21:33.555264 18606 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 14:21:34.152547 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:38.466317 18606 solver.cpp:218] Iteration 6888 (2.44357 iter/s, 4.91084s/12 iters), loss = 5.28228
I0410 14:21:38.466365 18606 solver.cpp:237]     Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
I0410 14:21:38.466377 18606 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 14:21:43.310122 18606 solver.cpp:218] Iteration 6900 (2.47753 iter/s, 4.84353s/12 iters), loss = 5.26852
I0410 14:21:43.310170 18606 solver.cpp:237]     Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
I0410 14:21:43.310180 18606 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 14:21:48.137635 18606 solver.cpp:218] Iteration 6912 (2.48589 iter/s, 4.82724s/12 iters), loss = 5.2873
I0410 14:21:48.137682 18606 solver.cpp:237]     Train net output #0: loss = 5.2873 (* 1 = 5.2873 loss)
I0410 14:21:48.137691 18606 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 14:21:53.015727 18606 solver.cpp:218] Iteration 6924 (2.46012 iter/s, 4.87782s/12 iters), loss = 5.28232
I0410 14:21:53.015787 18606 solver.cpp:237]     Train net output #0: loss = 5.28232 (* 1 = 5.28232 loss)
I0410 14:21:53.015800 18606 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 14:21:57.479758 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 14:21:57.793068 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 14:21:58.007447 18606 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 14:21:58.007473 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:21:58.254441 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:21:59.707283 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:02.414254 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:22:02.414300 18606 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0410 14:22:02.495750 18606 solver.cpp:218] Iteration 6936 (1.26588 iter/s, 9.47954s/12 iters), loss = 5.28442
I0410 14:22:02.495798 18606 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0410 14:22:02.495810 18606 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 14:22:06.554046 18606 solver.cpp:218] Iteration 6948 (2.95708 iter/s, 4.05806s/12 iters), loss = 5.27656
I0410 14:22:06.554157 18606 solver.cpp:237]     Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
I0410 14:22:06.554169 18606 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 14:22:11.341478 18606 solver.cpp:218] Iteration 6960 (2.50673 iter/s, 4.78711s/12 iters), loss = 5.26529
I0410 14:22:11.341531 18606 solver.cpp:237]     Train net output #0: loss = 5.26529 (* 1 = 5.26529 loss)
I0410 14:22:11.341542 18606 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 14:22:16.135701 18606 solver.cpp:218] Iteration 6972 (2.50316 iter/s, 4.79395s/12 iters), loss = 5.26723
I0410 14:22:16.135762 18606 solver.cpp:237]     Train net output #0: loss = 5.26723 (* 1 = 5.26723 loss)
I0410 14:22:16.135774 18606 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 14:22:18.765792 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:20.910118 18606 solver.cpp:218] Iteration 6984 (2.51354 iter/s, 4.77413s/12 iters), loss = 5.27282
I0410 14:22:20.910176 18606 solver.cpp:237]     Train net output #0: loss = 5.27282 (* 1 = 5.27282 loss)
I0410 14:22:20.910189 18606 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 14:22:25.706652 18606 solver.cpp:218] Iteration 6996 (2.50195 iter/s, 4.79626s/12 iters), loss = 5.25826
I0410 14:22:25.706717 18606 solver.cpp:237]     Train net output #0: loss = 5.25826 (* 1 = 5.25826 loss)
I0410 14:22:25.706729 18606 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 14:22:30.470268 18606 solver.cpp:218] Iteration 7008 (2.51924 iter/s, 4.76333s/12 iters), loss = 5.25921
I0410 14:22:30.470325 18606 solver.cpp:237]     Train net output #0: loss = 5.25921 (* 1 = 5.25921 loss)
I0410 14:22:30.470337 18606 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 14:22:35.250716 18606 solver.cpp:218] Iteration 7020 (2.51037 iter/s, 4.78017s/12 iters), loss = 5.26022
I0410 14:22:35.250775 18606 solver.cpp:237]     Train net output #0: loss = 5.26022 (* 1 = 5.26022 loss)
I0410 14:22:35.250787 18606 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 14:22:40.029665 18606 solver.cpp:218] Iteration 7032 (2.51116 iter/s, 4.77867s/12 iters), loss = 5.30342
I0410 14:22:40.032295 18606 solver.cpp:237]     Train net output #0: loss = 5.30342 (* 1 = 5.30342 loss)
I0410 14:22:40.032310 18606 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 14:22:41.975559 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 14:22:42.367782 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 14:22:42.601119 18606 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 14:22:42.601140 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:22:44.267836 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:46.982566 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:22:46.982617 18606 solver.cpp:397]     Test net output #1: loss = 5.28679 (* 1 = 5.28679 loss)
I0410 14:22:48.740762 18606 solver.cpp:218] Iteration 7044 (1.37803 iter/s, 8.70809s/12 iters), loss = 5.27098
I0410 14:22:48.740804 18606 solver.cpp:237]     Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
I0410 14:22:48.740813 18606 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 14:22:53.547389 18606 solver.cpp:218] Iteration 7056 (2.49669 iter/s, 4.80636s/12 iters), loss = 5.26943
I0410 14:22:53.547439 18606 solver.cpp:237]     Train net output #0: loss = 5.26943 (* 1 = 5.26943 loss)
I0410 14:22:53.547453 18606 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 14:22:58.356297 18606 solver.cpp:218] Iteration 7068 (2.49551 iter/s, 4.80864s/12 iters), loss = 5.26873
I0410 14:22:58.356348 18606 solver.cpp:237]     Train net output #0: loss = 5.26873 (* 1 = 5.26873 loss)
I0410 14:22:58.356359 18606 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 14:23:03.138263 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:03.237985 18606 solver.cpp:218] Iteration 7080 (2.45831 iter/s, 4.8814s/12 iters), loss = 5.24135
I0410 14:23:03.238044 18606 solver.cpp:237]     Train net output #0: loss = 5.24135 (* 1 = 5.24135 loss)
I0410 14:23:03.238056 18606 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 14:23:08.279772 18606 solver.cpp:218] Iteration 7092 (2.38024 iter/s, 5.0415s/12 iters), loss = 5.26716
I0410 14:23:08.279815 18606 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0410 14:23:08.279824 18606 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 14:23:13.150310 18606 solver.cpp:218] Iteration 7104 (2.46393 iter/s, 4.87027s/12 iters), loss = 5.29424
I0410 14:23:13.150403 18606 solver.cpp:237]     Train net output #0: loss = 5.29424 (* 1 = 5.29424 loss)
I0410 14:23:13.150414 18606 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 14:23:17.967550 18606 solver.cpp:218] Iteration 7116 (2.49122 iter/s, 4.81692s/12 iters), loss = 5.27772
I0410 14:23:17.967610 18606 solver.cpp:237]     Train net output #0: loss = 5.27772 (* 1 = 5.27772 loss)
I0410 14:23:17.967624 18606 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 14:23:22.792954 18606 solver.cpp:218] Iteration 7128 (2.48698 iter/s, 4.82512s/12 iters), loss = 5.27685
I0410 14:23:22.793010 18606 solver.cpp:237]     Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
I0410 14:23:22.793022 18606 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 14:23:27.189795 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 14:23:27.498929 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 14:23:27.700867 18606 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 14:23:27.700894 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:23:29.396139 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:32.250145 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:23:32.250195 18606 solver.cpp:397]     Test net output #1: loss = 5.28698 (* 1 = 5.28698 loss)
I0410 14:23:32.333492 18606 solver.cpp:218] Iteration 7140 (1.25785 iter/s, 9.54006s/12 iters), loss = 5.26334
I0410 14:23:32.333535 18606 solver.cpp:237]     Train net output #0: loss = 5.26334 (* 1 = 5.26334 loss)
I0410 14:23:32.333545 18606 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 14:23:36.427209 18606 solver.cpp:218] Iteration 7152 (2.93149 iter/s, 4.09348s/12 iters), loss = 5.24802
I0410 14:23:36.427265 18606 solver.cpp:237]     Train net output #0: loss = 5.24802 (* 1 = 5.24802 loss)
I0410 14:23:36.427278 18606 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 14:23:41.328378 18606 solver.cpp:218] Iteration 7164 (2.44853 iter/s, 4.90089s/12 iters), loss = 5.27235
I0410 14:23:41.328424 18606 solver.cpp:237]     Train net output #0: loss = 5.27235 (* 1 = 5.27235 loss)
I0410 14:23:41.328434 18606 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 14:23:46.236187 18606 solver.cpp:218] Iteration 7176 (2.44522 iter/s, 4.90754s/12 iters), loss = 5.2573
I0410 14:23:46.236366 18606 solver.cpp:237]     Train net output #0: loss = 5.2573 (* 1 = 5.2573 loss)
I0410 14:23:46.236379 18606 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 14:23:48.420222 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:51.282069 18606 solver.cpp:218] Iteration 7188 (2.37837 iter/s, 5.04547s/12 iters), loss = 5.27629
I0410 14:23:51.282138 18606 solver.cpp:237]     Train net output #0: loss = 5.27629 (* 1 = 5.27629 loss)
I0410 14:23:51.282156 18606 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 14:23:56.140115 18606 solver.cpp:218] Iteration 7200 (2.47028 iter/s, 4.85776s/12 iters), loss = 5.27149
I0410 14:23:56.140172 18606 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0410 14:23:56.140184 18606 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 14:24:01.067221 18606 solver.cpp:218] Iteration 7212 (2.43564 iter/s, 4.92683s/12 iters), loss = 5.27909
I0410 14:24:01.067268 18606 solver.cpp:237]     Train net output #0: loss = 5.27909 (* 1 = 5.27909 loss)
I0410 14:24:01.067278 18606 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 14:24:05.882616 18606 solver.cpp:218] Iteration 7224 (2.49215 iter/s, 4.81512s/12 iters), loss = 5.26577
I0410 14:24:05.882669 18606 solver.cpp:237]     Train net output #0: loss = 5.26577 (* 1 = 5.26577 loss)
I0410 14:24:05.882683 18606 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 14:24:10.741338 18606 solver.cpp:218] Iteration 7236 (2.46993 iter/s, 4.85844s/12 iters), loss = 5.27255
I0410 14:24:10.741387 18606 solver.cpp:237]     Train net output #0: loss = 5.27255 (* 1 = 5.27255 loss)
I0410 14:24:10.741397 18606 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 14:24:12.727128 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 14:24:13.052892 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 14:24:13.272559 18606 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 14:24:13.272594 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:24:14.863332 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:17.712658 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:24:17.712831 18606 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 14:24:19.500944 18606 solver.cpp:218] Iteration 7248 (1.36999 iter/s, 8.75917s/12 iters), loss = 5.27308
I0410 14:24:19.500985 18606 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0410 14:24:19.500995 18606 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 14:24:24.358803 18606 solver.cpp:218] Iteration 7260 (2.47036 iter/s, 4.85759s/12 iters), loss = 5.2727
I0410 14:24:24.358861 18606 solver.cpp:237]     Train net output #0: loss = 5.2727 (* 1 = 5.2727 loss)
I0410 14:24:24.358873 18606 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 14:24:29.235191 18606 solver.cpp:218] Iteration 7272 (2.46098 iter/s, 4.8761s/12 iters), loss = 5.25423
I0410 14:24:29.235249 18606 solver.cpp:237]     Train net output #0: loss = 5.25423 (* 1 = 5.25423 loss)
I0410 14:24:29.235260 18606 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 14:24:33.406982 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:34.136936 18606 solver.cpp:218] Iteration 7284 (2.44825 iter/s, 4.90146s/12 iters), loss = 5.25606
I0410 14:24:34.136989 18606 solver.cpp:237]     Train net output #0: loss = 5.25606 (* 1 = 5.25606 loss)
I0410 14:24:34.137001 18606 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 14:24:39.187232 18606 solver.cpp:218] Iteration 7296 (2.37623 iter/s, 5.05001s/12 iters), loss = 5.28159
I0410 14:24:39.187283 18606 solver.cpp:237]     Train net output #0: loss = 5.28159 (* 1 = 5.28159 loss)
I0410 14:24:39.187294 18606 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 14:24:44.020396 18606 solver.cpp:218] Iteration 7308 (2.48299 iter/s, 4.83289s/12 iters), loss = 5.28184
I0410 14:24:44.020452 18606 solver.cpp:237]     Train net output #0: loss = 5.28184 (* 1 = 5.28184 loss)
I0410 14:24:44.020464 18606 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 14:24:48.851516 18606 solver.cpp:218] Iteration 7320 (2.48401 iter/s, 4.83089s/12 iters), loss = 5.29499
I0410 14:24:48.851636 18606 solver.cpp:237]     Train net output #0: loss = 5.29499 (* 1 = 5.29499 loss)
I0410 14:24:48.851647 18606 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 14:24:53.654662 18606 solver.cpp:218] Iteration 7332 (2.49851 iter/s, 4.80287s/12 iters), loss = 5.26941
I0410 14:24:53.654709 18606 solver.cpp:237]     Train net output #0: loss = 5.26941 (* 1 = 5.26941 loss)
I0410 14:24:53.654721 18606 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 14:24:58.023933 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 14:24:58.352779 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 14:24:58.571204 18606 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 14:24:58.571233 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:25:00.205051 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:03.068862 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:25:03.068913 18606 solver.cpp:397]     Test net output #1: loss = 5.28748 (* 1 = 5.28748 loss)
I0410 14:25:03.151952 18606 solver.cpp:218] Iteration 7344 (1.26357 iter/s, 9.49693s/12 iters), loss = 5.27596
I0410 14:25:03.152009 18606 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0410 14:25:03.152019 18606 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 14:25:07.210884 18606 solver.cpp:218] Iteration 7356 (2.95659 iter/s, 4.05873s/12 iters), loss = 5.28369
I0410 14:25:07.210938 18606 solver.cpp:237]     Train net output #0: loss = 5.28369 (* 1 = 5.28369 loss)
I0410 14:25:07.210950 18606 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 14:25:12.016130 18606 solver.cpp:218] Iteration 7368 (2.49738 iter/s, 4.80503s/12 iters), loss = 5.2762
I0410 14:25:12.016178 18606 solver.cpp:237]     Train net output #0: loss = 5.2762 (* 1 = 5.2762 loss)
I0410 14:25:12.016187 18606 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 14:25:16.865902 18606 solver.cpp:218] Iteration 7380 (2.47445 iter/s, 4.84956s/12 iters), loss = 5.26404
I0410 14:25:16.865981 18606 solver.cpp:237]     Train net output #0: loss = 5.26404 (* 1 = 5.26404 loss)
I0410 14:25:16.865994 18606 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 14:25:18.212088 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:21.976325 18606 solver.cpp:218] Iteration 7392 (2.34825 iter/s, 5.11019s/12 iters), loss = 5.27187
I0410 14:25:21.976488 18606 solver.cpp:237]     Train net output #0: loss = 5.27187 (* 1 = 5.27187 loss)
I0410 14:25:21.976503 18606 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 14:25:26.871699 18606 solver.cpp:218] Iteration 7404 (2.45146 iter/s, 4.89504s/12 iters), loss = 5.26986
I0410 14:25:26.871752 18606 solver.cpp:237]     Train net output #0: loss = 5.26986 (* 1 = 5.26986 loss)
I0410 14:25:26.871763 18606 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 14:25:31.674108 18606 solver.cpp:218] Iteration 7416 (2.49886 iter/s, 4.80219s/12 iters), loss = 5.26551
I0410 14:25:31.674149 18606 solver.cpp:237]     Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
I0410 14:25:31.674158 18606 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 14:25:36.535491 18606 solver.cpp:218] Iteration 7428 (2.46854 iter/s, 4.86117s/12 iters), loss = 5.27755
I0410 14:25:36.535542 18606 solver.cpp:237]     Train net output #0: loss = 5.27755 (* 1 = 5.27755 loss)
I0410 14:25:36.535554 18606 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 14:25:41.395356 18606 solver.cpp:218] Iteration 7440 (2.46931 iter/s, 4.85965s/12 iters), loss = 5.25554
I0410 14:25:41.395395 18606 solver.cpp:237]     Train net output #0: loss = 5.25554 (* 1 = 5.25554 loss)
I0410 14:25:41.395403 18606 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 14:25:43.338330 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 14:25:43.670588 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 14:25:43.889617 18606 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 14:25:43.889644 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:25:45.426044 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:48.403201 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:25:48.403246 18606 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 14:25:50.286799 18606 solver.cpp:218] Iteration 7452 (1.34966 iter/s, 8.89111s/12 iters), loss = 5.26389
I0410 14:25:50.286849 18606 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0410 14:25:50.286861 18606 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 14:25:55.127038 18606 solver.cpp:218] Iteration 7464 (2.47933 iter/s, 4.84002s/12 iters), loss = 5.2867
I0410 14:25:55.127156 18606 solver.cpp:237]     Train net output #0: loss = 5.2867 (* 1 = 5.2867 loss)
I0410 14:25:55.127167 18606 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 14:26:00.132299 18606 solver.cpp:218] Iteration 7476 (2.39762 iter/s, 5.00497s/12 iters), loss = 5.27609
I0410 14:26:00.132347 18606 solver.cpp:237]     Train net output #0: loss = 5.27609 (* 1 = 5.27609 loss)
I0410 14:26:00.132356 18606 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 14:26:03.535017 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:04.957267 18606 solver.cpp:218] Iteration 7488 (2.48718 iter/s, 4.82475s/12 iters), loss = 5.26834
I0410 14:26:04.957324 18606 solver.cpp:237]     Train net output #0: loss = 5.26834 (* 1 = 5.26834 loss)
I0410 14:26:04.957337 18606 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 14:26:09.771425 18606 solver.cpp:218] Iteration 7500 (2.49277 iter/s, 4.81393s/12 iters), loss = 5.2596
I0410 14:26:09.771478 18606 solver.cpp:237]     Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
I0410 14:26:09.771489 18606 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 14:26:14.904176 18606 solver.cpp:218] Iteration 7512 (2.33804 iter/s, 5.13251s/12 iters), loss = 5.26066
I0410 14:26:14.904234 18606 solver.cpp:237]     Train net output #0: loss = 5.26066 (* 1 = 5.26066 loss)
I0410 14:26:14.904247 18606 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 14:26:19.787199 18606 solver.cpp:218] Iteration 7524 (2.45761 iter/s, 4.88279s/12 iters), loss = 5.27071
I0410 14:26:19.787245 18606 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0410 14:26:19.787254 18606 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 14:26:24.631202 18606 solver.cpp:218] Iteration 7536 (2.4774 iter/s, 4.84378s/12 iters), loss = 5.26135
I0410 14:26:24.631247 18606 solver.cpp:237]     Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
I0410 14:26:24.631255 18606 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 14:26:28.990587 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 14:26:29.305428 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 14:26:29.520444 18606 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 14:26:29.520469 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:26:31.024261 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:34.024233 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:26:34.024264 18606 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0410 14:26:34.109455 18606 solver.cpp:218] Iteration 7548 (1.26611 iter/s, 9.47788s/12 iters), loss = 5.27902
I0410 14:26:34.109500 18606 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 14:26:34.109509 18606 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 14:26:38.240684 18606 solver.cpp:218] Iteration 7560 (2.90484 iter/s, 4.13103s/12 iters), loss = 5.27084
I0410 14:26:38.240731 18606 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0410 14:26:38.240739 18606 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 14:26:43.113593 18606 solver.cpp:218] Iteration 7572 (2.46271 iter/s, 4.87269s/12 iters), loss = 5.28071
I0410 14:26:43.113641 18606 solver.cpp:237]     Train net output #0: loss = 5.28071 (* 1 = 5.28071 loss)
I0410 14:26:43.113649 18606 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 14:26:48.122958 18606 solver.cpp:218] Iteration 7584 (2.39563 iter/s, 5.00913s/12 iters), loss = 5.28915
I0410 14:26:48.123019 18606 solver.cpp:237]     Train net output #0: loss = 5.28915 (* 1 = 5.28915 loss)
I0410 14:26:48.123032 18606 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 14:26:48.762907 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:53.087659 18606 solver.cpp:218] Iteration 7596 (2.41718 iter/s, 4.96446s/12 iters), loss = 5.27819
I0410 14:26:53.087719 18606 solver.cpp:237]     Train net output #0: loss = 5.27819 (* 1 = 5.27819 loss)
I0410 14:26:53.087731 18606 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 14:26:58.110080 18606 solver.cpp:218] Iteration 7608 (2.3894 iter/s, 5.02218s/12 iters), loss = 5.26317
I0410 14:26:58.110131 18606 solver.cpp:237]     Train net output #0: loss = 5.26317 (* 1 = 5.26317 loss)
I0410 14:26:58.110141 18606 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 14:27:02.962160 18606 solver.cpp:218] Iteration 7620 (2.47328 iter/s, 4.85185s/12 iters), loss = 5.27776
I0410 14:27:02.962267 18606 solver.cpp:237]     Train net output #0: loss = 5.27776 (* 1 = 5.27776 loss)
I0410 14:27:02.962280 18606 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 14:27:03.707746 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:27:07.823983 18606 solver.cpp:218] Iteration 7632 (2.46835 iter/s, 4.86155s/12 iters), loss = 5.28032
I0410 14:27:07.824016 18606 solver.cpp:237]     Train net output #0: loss = 5.28032 (* 1 = 5.28032 loss)
I0410 14:27:07.824024 18606 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 14:27:12.753895 18606 solver.cpp:218] Iteration 7644 (2.43423 iter/s, 4.9297s/12 iters), loss = 5.28449
I0410 14:27:12.753940 18606 solver.cpp:237]     Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
I0410 14:27:12.753949 18606 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 14:27:14.724545 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 14:27:15.038059 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 14:27:15.257227 18606 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 14:27:15.257256 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:27:16.806941 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:19.799355 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:27:19.799386 18606 solver.cpp:397]     Test net output #1: loss = 5.2871 (* 1 = 5.2871 loss)
I0410 14:27:21.595916 18606 solver.cpp:218] Iteration 7656 (1.35721 iter/s, 8.84166s/12 iters), loss = 5.27164
I0410 14:27:21.595963 18606 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0410 14:27:21.595971 18606 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 14:27:26.402951 18606 solver.cpp:218] Iteration 7668 (2.49646 iter/s, 4.80681s/12 iters), loss = 5.26806
I0410 14:27:26.403007 18606 solver.cpp:237]     Train net output #0: loss = 5.26806 (* 1 = 5.26806 loss)
I0410 14:27:26.403019 18606 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 14:27:31.221698 18606 solver.cpp:218] Iteration 7680 (2.4904 iter/s, 4.81851s/12 iters), loss = 5.26361
I0410 14:27:31.221753 18606 solver.cpp:237]     Train net output #0: loss = 5.26361 (* 1 = 5.26361 loss)
I0410 14:27:31.221765 18606 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 14:27:33.890236 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:36.000730 18606 solver.cpp:218] Iteration 7692 (2.51109 iter/s, 4.7788s/12 iters), loss = 5.27218
I0410 14:27:36.000775 18606 solver.cpp:237]     Train net output #0: loss = 5.27218 (* 1 = 5.27218 loss)
I0410 14:27:36.000784 18606 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 14:27:41.101537 18606 solver.cpp:218] Iteration 7704 (2.35268 iter/s, 5.10057s/12 iters), loss = 5.25213
I0410 14:27:41.101580 18606 solver.cpp:237]     Train net output #0: loss = 5.25213 (* 1 = 5.25213 loss)
I0410 14:27:41.101591 18606 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 14:27:45.992316 18606 solver.cpp:218] Iteration 7716 (2.45371 iter/s, 4.89055s/12 iters), loss = 5.25366
I0410 14:27:45.992368 18606 solver.cpp:237]     Train net output #0: loss = 5.25366 (* 1 = 5.25366 loss)
I0410 14:27:45.992379 18606 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 14:27:50.864387 18606 solver.cpp:218] Iteration 7728 (2.46313 iter/s, 4.87184s/12 iters), loss = 5.25646
I0410 14:27:50.864424 18606 solver.cpp:237]     Train net output #0: loss = 5.25646 (* 1 = 5.25646 loss)
I0410 14:27:50.864434 18606 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 14:27:55.719327 18606 solver.cpp:218] Iteration 7740 (2.47182 iter/s, 4.85472s/12 iters), loss = 5.29855
I0410 14:27:55.719367 18606 solver.cpp:237]     Train net output #0: loss = 5.29855 (* 1 = 5.29855 loss)
I0410 14:27:55.719377 18606 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 14:28:00.160375 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 14:28:00.474467 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 14:28:00.688951 18606 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 14:28:00.688973 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:28:01.965950 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:04.992955 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:28:04.993081 18606 solver.cpp:397]     Test net output #1: loss = 5.28674 (* 1 = 5.28674 loss)
I0410 14:28:05.076118 18606 solver.cpp:218] Iteration 7752 (1.28254 iter/s, 9.35641s/12 iters), loss = 5.26838
I0410 14:28:05.076169 18606 solver.cpp:237]     Train net output #0: loss = 5.26838 (* 1 = 5.26838 loss)
I0410 14:28:05.076180 18606 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 14:28:09.082618 18606 solver.cpp:218] Iteration 7764 (2.99528 iter/s, 4.0063s/12 iters), loss = 5.274
I0410 14:28:09.082670 18606 solver.cpp:237]     Train net output #0: loss = 5.274 (* 1 = 5.274 loss)
I0410 14:28:09.082684 18606 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 14:28:13.941555 18606 solver.cpp:218] Iteration 7776 (2.46979 iter/s, 4.8587s/12 iters), loss = 5.27012
I0410 14:28:13.941604 18606 solver.cpp:237]     Train net output #0: loss = 5.27012 (* 1 = 5.27012 loss)
I0410 14:28:13.941613 18606 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 14:28:18.817034 18606 solver.cpp:218] Iteration 7788 (2.46142 iter/s, 4.87524s/12 iters), loss = 5.24613
I0410 14:28:18.817081 18606 solver.cpp:237]     Train net output #0: loss = 5.24613 (* 1 = 5.24613 loss)
I0410 14:28:18.817090 18606 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 14:28:18.828275 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:23.702591 18606 solver.cpp:218] Iteration 7800 (2.45634 iter/s, 4.88531s/12 iters), loss = 5.269
I0410 14:28:23.702647 18606 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0410 14:28:23.702659 18606 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 14:28:28.587584 18606 solver.cpp:218] Iteration 7812 (2.45662 iter/s, 4.88475s/12 iters), loss = 5.29587
I0410 14:28:28.587639 18606 solver.cpp:237]     Train net output #0: loss = 5.29587 (* 1 = 5.29587 loss)
I0410 14:28:28.587651 18606 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 14:28:33.474712 18606 solver.cpp:218] Iteration 7824 (2.45555 iter/s, 4.88689s/12 iters), loss = 5.27214
I0410 14:28:33.474768 18606 solver.cpp:237]     Train net output #0: loss = 5.27214 (* 1 = 5.27214 loss)
I0410 14:28:33.474781 18606 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 14:28:38.304019 18606 solver.cpp:218] Iteration 7836 (2.48495 iter/s, 4.82907s/12 iters), loss = 5.2759
I0410 14:28:38.304167 18606 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0410 14:28:38.304179 18606 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 14:28:43.150761 18606 solver.cpp:218] Iteration 7848 (2.47606 iter/s, 4.84642s/12 iters), loss = 5.2578
I0410 14:28:43.150806 18606 solver.cpp:237]     Train net output #0: loss = 5.2578 (* 1 = 5.2578 loss)
I0410 14:28:43.150815 18606 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 14:28:45.109911 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 14:28:45.438299 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 14:28:45.656054 18606 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 14:28:45.656081 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:28:46.954828 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:50.014370 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:28:50.014420 18606 solver.cpp:397]     Test net output #1: loss = 5.28662 (* 1 = 5.28662 loss)
I0410 14:28:51.833346 18606 solver.cpp:218] Iteration 7860 (1.38214 iter/s, 8.68222s/12 iters), loss = 5.24125
I0410 14:28:51.833397 18606 solver.cpp:237]     Train net output #0: loss = 5.24125 (* 1 = 5.24125 loss)
I0410 14:28:51.833406 18606 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 14:28:56.726756 18606 solver.cpp:218] Iteration 7872 (2.4524 iter/s, 4.89317s/12 iters), loss = 5.26486
I0410 14:28:56.726801 18606 solver.cpp:237]     Train net output #0: loss = 5.26486 (* 1 = 5.26486 loss)
I0410 14:28:56.726811 18606 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 14:29:01.660303 18606 solver.cpp:218] Iteration 7884 (2.43245 iter/s, 4.9333s/12 iters), loss = 5.25794
I0410 14:29:01.660377 18606 solver.cpp:237]     Train net output #0: loss = 5.25794 (* 1 = 5.25794 loss)
I0410 14:29:01.660393 18606 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 14:29:03.746592 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:06.498277 18606 solver.cpp:218] Iteration 7896 (2.48051 iter/s, 4.83772s/12 iters), loss = 5.27768
I0410 14:29:06.498332 18606 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0410 14:29:06.498344 18606 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 14:29:11.402606 18606 solver.cpp:218] Iteration 7908 (2.44694 iter/s, 4.90409s/12 iters), loss = 5.26977
I0410 14:29:11.402777 18606 solver.cpp:237]     Train net output #0: loss = 5.26977 (* 1 = 5.26977 loss)
I0410 14:29:11.402791 18606 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 14:29:16.287134 18606 solver.cpp:218] Iteration 7920 (2.45691 iter/s, 4.88418s/12 iters), loss = 5.28327
I0410 14:29:16.287184 18606 solver.cpp:237]     Train net output #0: loss = 5.28327 (* 1 = 5.28327 loss)
I0410 14:29:16.287196 18606 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 14:29:21.135583 18606 solver.cpp:218] Iteration 7932 (2.47514 iter/s, 4.84821s/12 iters), loss = 5.26236
I0410 14:29:21.135639 18606 solver.cpp:237]     Train net output #0: loss = 5.26236 (* 1 = 5.26236 loss)
I0410 14:29:21.135653 18606 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 14:29:26.023356 18606 solver.cpp:218] Iteration 7944 (2.45523 iter/s, 4.88753s/12 iters), loss = 5.26735
I0410 14:29:26.023404 18606 solver.cpp:237]     Train net output #0: loss = 5.26735 (* 1 = 5.26735 loss)
I0410 14:29:26.023414 18606 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 14:29:30.504676 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 14:29:31.798257 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 14:29:32.569855 18606 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 14:29:32.569886 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:29:33.908551 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:37.010736 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:29:37.010772 18606 solver.cpp:397]     Test net output #1: loss = 5.28712 (* 1 = 5.28712 loss)
I0410 14:29:37.093444 18606 solver.cpp:218] Iteration 7956 (1.08405 iter/s, 11.0696s/12 iters), loss = 5.27794
I0410 14:29:37.093492 18606 solver.cpp:237]     Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
I0410 14:29:37.093500 18606 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 14:29:41.278322 18606 solver.cpp:218] Iteration 7968 (2.86761 iter/s, 4.18466s/12 iters), loss = 5.27433
I0410 14:29:41.278373 18606 solver.cpp:237]     Train net output #0: loss = 5.27433 (* 1 = 5.27433 loss)
I0410 14:29:41.278383 18606 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 14:29:46.187767 18606 solver.cpp:218] Iteration 7980 (2.44439 iter/s, 4.9092s/12 iters), loss = 5.2544
I0410 14:29:46.187922 18606 solver.cpp:237]     Train net output #0: loss = 5.2544 (* 1 = 5.2544 loss)
I0410 14:29:46.187938 18606 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 14:29:50.395736 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:51.095053 18606 solver.cpp:218] Iteration 7992 (2.44551 iter/s, 4.90695s/12 iters), loss = 5.2547
I0410 14:29:51.095096 18606 solver.cpp:237]     Train net output #0: loss = 5.2547 (* 1 = 5.2547 loss)
I0410 14:29:51.095105 18606 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 14:29:56.097321 18606 solver.cpp:218] Iteration 8004 (2.39903 iter/s, 5.00203s/12 iters), loss = 5.27962
I0410 14:29:56.097368 18606 solver.cpp:237]     Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
I0410 14:29:56.097378 18606 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 14:30:00.922662 18606 solver.cpp:218] Iteration 8016 (2.48699 iter/s, 4.82511s/12 iters), loss = 5.27729
I0410 14:30:00.922708 18606 solver.cpp:237]     Train net output #0: loss = 5.27729 (* 1 = 5.27729 loss)
I0410 14:30:00.922715 18606 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 14:30:05.739651 18606 solver.cpp:218] Iteration 8028 (2.4913 iter/s, 4.81676s/12 iters), loss = 5.29278
I0410 14:30:05.739706 18606 solver.cpp:237]     Train net output #0: loss = 5.29278 (* 1 = 5.29278 loss)
I0410 14:30:05.739717 18606 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 14:30:10.742463 18606 solver.cpp:218] Iteration 8040 (2.39877 iter/s, 5.00256s/12 iters), loss = 5.26431
I0410 14:30:10.742519 18606 solver.cpp:237]     Train net output #0: loss = 5.26431 (* 1 = 5.26431 loss)
I0410 14:30:10.742533 18606 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 14:30:15.531168 18606 solver.cpp:218] Iteration 8052 (2.50602 iter/s, 4.78847s/12 iters), loss = 5.28092
I0410 14:30:15.531219 18606 solver.cpp:237]     Train net output #0: loss = 5.28092 (* 1 = 5.28092 loss)
I0410 14:30:15.531229 18606 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 14:30:17.498728 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 14:30:17.827330 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 14:30:18.044147 18606 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 14:30:18.044173 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:30:19.450073 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:22.656337 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:30:22.656373 18606 solver.cpp:397]     Test net output #1: loss = 5.28702 (* 1 = 5.28702 loss)
I0410 14:30:24.519150 18606 solver.cpp:218] Iteration 8064 (1.33517 iter/s, 8.98759s/12 iters), loss = 5.27877
I0410 14:30:24.519198 18606 solver.cpp:237]     Train net output #0: loss = 5.27877 (* 1 = 5.27877 loss)
I0410 14:30:24.519208 18606 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 14:30:29.327603 18606 solver.cpp:218] Iteration 8076 (2.49573 iter/s, 4.80821s/12 iters), loss = 5.27796
I0410 14:30:29.327667 18606 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0410 14:30:29.327683 18606 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 14:30:34.182334 18606 solver.cpp:218] Iteration 8088 (2.47194 iter/s, 4.85448s/12 iters), loss = 5.26266
I0410 14:30:34.182379 18606 solver.cpp:237]     Train net output #0: loss = 5.26266 (* 1 = 5.26266 loss)
I0410 14:30:34.182389 18606 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 14:30:35.551996 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:39.003415 18606 solver.cpp:218] Iteration 8100 (2.48919 iter/s, 4.82085s/12 iters), loss = 5.26138
I0410 14:30:39.003460 18606 solver.cpp:237]     Train net output #0: loss = 5.26138 (* 1 = 5.26138 loss)
I0410 14:30:39.003469 18606 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 14:30:43.974117 18606 solver.cpp:218] Iteration 8112 (2.41426 iter/s, 4.97046s/12 iters), loss = 5.26519
I0410 14:30:43.974162 18606 solver.cpp:237]     Train net output #0: loss = 5.26519 (* 1 = 5.26519 loss)
I0410 14:30:43.974174 18606 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 14:30:48.949599 18606 solver.cpp:218] Iteration 8124 (2.41194 iter/s, 4.97524s/12 iters), loss = 5.27073
I0410 14:30:48.949719 18606 solver.cpp:237]     Train net output #0: loss = 5.27073 (* 1 = 5.27073 loss)
I0410 14:30:48.949733 18606 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 14:30:53.832226 18606 solver.cpp:218] Iteration 8136 (2.45785 iter/s, 4.88232s/12 iters), loss = 5.28466
I0410 14:30:53.832281 18606 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0410 14:30:53.832293 18606 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 14:30:58.759809 18606 solver.cpp:218] Iteration 8148 (2.43539 iter/s, 4.92734s/12 iters), loss = 5.24975
I0410 14:30:58.759856 18606 solver.cpp:237]     Train net output #0: loss = 5.24975 (* 1 = 5.24975 loss)
I0410 14:30:58.759865 18606 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 14:31:03.199347 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 14:31:03.532158 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 14:31:03.750360 18606 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 14:31:03.750391 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:31:04.981465 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:08.158324 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:31:08.158368 18606 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 14:31:08.241349 18606 solver.cpp:218] Iteration 8160 (1.26567 iter/s, 9.48113s/12 iters), loss = 5.26429
I0410 14:31:08.241400 18606 solver.cpp:237]     Train net output #0: loss = 5.26429 (* 1 = 5.26429 loss)
I0410 14:31:08.241410 18606 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 14:31:12.341831 18606 solver.cpp:218] Iteration 8172 (2.92664 iter/s, 4.10026s/12 iters), loss = 5.28387
I0410 14:31:12.341889 18606 solver.cpp:237]     Train net output #0: loss = 5.28387 (* 1 = 5.28387 loss)
I0410 14:31:12.341902 18606 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 14:31:17.211539 18606 solver.cpp:218] Iteration 8184 (2.46434 iter/s, 4.86946s/12 iters), loss = 5.27247
I0410 14:31:17.211588 18606 solver.cpp:237]     Train net output #0: loss = 5.27247 (* 1 = 5.27247 loss)
I0410 14:31:17.211599 18606 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 14:31:20.701035 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:22.116516 18606 solver.cpp:218] Iteration 8196 (2.44662 iter/s, 4.90473s/12 iters), loss = 5.27242
I0410 14:31:22.116565 18606 solver.cpp:237]     Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
I0410 14:31:22.116576 18606 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 14:31:27.009755 18606 solver.cpp:218] Iteration 8208 (2.45249 iter/s, 4.89299s/12 iters), loss = 5.25876
I0410 14:31:27.009809 18606 solver.cpp:237]     Train net output #0: loss = 5.25876 (* 1 = 5.25876 loss)
I0410 14:31:27.009821 18606 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 14:31:31.854689 18606 solver.cpp:218] Iteration 8220 (2.47694 iter/s, 4.84469s/12 iters), loss = 5.26345
I0410 14:31:31.854743 18606 solver.cpp:237]     Train net output #0: loss = 5.26345 (* 1 = 5.26345 loss)
I0410 14:31:31.854754 18606 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 14:31:36.690254 18606 solver.cpp:218] Iteration 8232 (2.48174 iter/s, 4.83532s/12 iters), loss = 5.26868
I0410 14:31:36.690310 18606 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0410 14:31:36.690320 18606 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 14:31:41.618144 18606 solver.cpp:218] Iteration 8244 (2.43524 iter/s, 4.92764s/12 iters), loss = 5.25444
I0410 14:31:41.618193 18606 solver.cpp:237]     Train net output #0: loss = 5.25444 (* 1 = 5.25444 loss)
I0410 14:31:41.618203 18606 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 14:31:46.424727 18606 solver.cpp:218] Iteration 8256 (2.4967 iter/s, 4.80634s/12 iters), loss = 5.27287
I0410 14:31:46.424782 18606 solver.cpp:237]     Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
I0410 14:31:46.424794 18606 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 14:31:48.395395 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 14:31:48.975453 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 14:31:49.193768 18606 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 14:31:49.193795 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:31:50.506862 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:53.846462 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:31:53.846592 18606 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 14:31:55.720628 18606 solver.cpp:218] Iteration 8268 (1.29095 iter/s, 9.29548s/12 iters), loss = 5.27975
I0410 14:31:55.720680 18606 solver.cpp:237]     Train net output #0: loss = 5.27975 (* 1 = 5.27975 loss)
I0410 14:31:55.720691 18606 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 14:32:00.536726 18606 solver.cpp:218] Iteration 8280 (2.49177 iter/s, 4.81586s/12 iters), loss = 5.28413
I0410 14:32:00.536765 18606 solver.cpp:237]     Train net output #0: loss = 5.28413 (* 1 = 5.28413 loss)
I0410 14:32:00.536773 18606 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 14:32:05.400930 18606 solver.cpp:218] Iteration 8292 (2.46712 iter/s, 4.86397s/12 iters), loss = 5.29138
I0410 14:32:05.400981 18606 solver.cpp:237]     Train net output #0: loss = 5.29138 (* 1 = 5.29138 loss)
I0410 14:32:05.400992 18606 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 14:32:06.077474 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:10.266034 18606 solver.cpp:218] Iteration 8304 (2.46667 iter/s, 4.86485s/12 iters), loss = 5.27859
I0410 14:32:10.266081 18606 solver.cpp:237]     Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
I0410 14:32:10.266089 18606 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 14:32:11.433857 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:32:15.199115 18606 solver.cpp:218] Iteration 8316 (2.43268 iter/s, 4.93283s/12 iters), loss = 5.27063
I0410 14:32:15.199178 18606 solver.cpp:237]     Train net output #0: loss = 5.27063 (* 1 = 5.27063 loss)
I0410 14:32:15.199191 18606 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 14:32:20.226233 18606 solver.cpp:218] Iteration 8328 (2.38718 iter/s, 5.02686s/12 iters), loss = 5.28075
I0410 14:32:20.226274 18606 solver.cpp:237]     Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
I0410 14:32:20.226284 18606 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 14:32:25.046705 18606 solver.cpp:218] Iteration 8340 (2.48951 iter/s, 4.82023s/12 iters), loss = 5.27351
I0410 14:32:25.058035 18606 solver.cpp:237]     Train net output #0: loss = 5.27351 (* 1 = 5.27351 loss)
I0410 14:32:25.058049 18606 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 14:32:29.867841 18606 solver.cpp:218] Iteration 8352 (2.495 iter/s, 4.80962s/12 iters), loss = 5.28995
I0410 14:32:29.867882 18606 solver.cpp:237]     Train net output #0: loss = 5.28995 (* 1 = 5.28995 loss)
I0410 14:32:29.867892 18606 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 14:32:34.315814 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 14:32:36.744832 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 14:32:37.532042 18606 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 14:32:37.532063 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:32:38.806499 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:42.095176 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:32:42.095227 18606 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 14:32:42.178336 18606 solver.cpp:218] Iteration 8364 (0.97482 iter/s, 12.31s/12 iters), loss = 5.26598
I0410 14:32:42.178393 18606 solver.cpp:237]     Train net output #0: loss = 5.26598 (* 1 = 5.26598 loss)
I0410 14:32:42.178406 18606 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 14:32:46.258193 18606 solver.cpp:218] Iteration 8376 (2.94144 iter/s, 4.07963s/12 iters), loss = 5.26579
I0410 14:32:46.258246 18606 solver.cpp:237]     Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
I0410 14:32:46.258258 18606 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 14:32:51.147536 18606 solver.cpp:218] Iteration 8388 (2.45444 iter/s, 4.88909s/12 iters), loss = 5.25903
I0410 14:32:51.147589 18606 solver.cpp:237]     Train net output #0: loss = 5.25903 (* 1 = 5.25903 loss)
I0410 14:32:51.147603 18606 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 14:32:53.962148 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:56.089864 18606 solver.cpp:218] Iteration 8400 (2.42813 iter/s, 4.94208s/12 iters), loss = 5.26444
I0410 14:32:56.090021 18606 solver.cpp:237]     Train net output #0: loss = 5.26444 (* 1 = 5.26444 loss)
I0410 14:32:56.090030 18606 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 14:33:00.917217 18606 solver.cpp:218] Iteration 8412 (2.48601 iter/s, 4.82701s/12 iters), loss = 5.24889
I0410 14:33:00.917258 18606 solver.cpp:237]     Train net output #0: loss = 5.24889 (* 1 = 5.24889 loss)
I0410 14:33:00.917268 18606 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 14:33:05.823361 18606 solver.cpp:218] Iteration 8424 (2.44603 iter/s, 4.90591s/12 iters), loss = 5.25428
I0410 14:33:05.823396 18606 solver.cpp:237]     Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
I0410 14:33:05.823405 18606 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 14:33:10.667881 18606 solver.cpp:218] Iteration 8436 (2.47715 iter/s, 4.84428s/12 iters), loss = 5.25682
I0410 14:33:10.667929 18606 solver.cpp:237]     Train net output #0: loss = 5.25682 (* 1 = 5.25682 loss)
I0410 14:33:10.667938 18606 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 14:33:15.498474 18606 solver.cpp:218] Iteration 8448 (2.48429 iter/s, 4.83034s/12 iters), loss = 5.29487
I0410 14:33:15.498533 18606 solver.cpp:237]     Train net output #0: loss = 5.29487 (* 1 = 5.29487 loss)
I0410 14:33:15.498545 18606 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 14:33:20.286024 18606 solver.cpp:218] Iteration 8460 (2.50663 iter/s, 4.7873s/12 iters), loss = 5.27424
I0410 14:33:20.286077 18606 solver.cpp:237]     Train net output #0: loss = 5.27424 (* 1 = 5.27424 loss)
I0410 14:33:20.286089 18606 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 14:33:22.264000 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 14:33:22.586820 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 14:33:22.788681 18606 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 14:33:22.788702 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:33:23.795806 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:27.082547 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:33:27.082654 18606 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 14:33:28.975420 18606 solver.cpp:218] Iteration 8472 (1.38106 iter/s, 8.689s/12 iters), loss = 5.27384
I0410 14:33:28.975461 18606 solver.cpp:237]     Train net output #0: loss = 5.27384 (* 1 = 5.27384 loss)
I0410 14:33:28.975469 18606 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 14:33:33.910698 18606 solver.cpp:218] Iteration 8484 (2.4316 iter/s, 4.93503s/12 iters), loss = 5.2718
I0410 14:33:33.910753 18606 solver.cpp:237]     Train net output #0: loss = 5.2718 (* 1 = 5.2718 loss)
I0410 14:33:33.910766 18606 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 14:33:38.800182 18606 solver.cpp:218] Iteration 8496 (2.45437 iter/s, 4.88923s/12 iters), loss = 5.25446
I0410 14:33:38.800230 18606 solver.cpp:237]     Train net output #0: loss = 5.25446 (* 1 = 5.25446 loss)
I0410 14:33:38.800243 18606 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 14:33:38.851267 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:43.687407 18606 solver.cpp:218] Iteration 8508 (2.45551 iter/s, 4.88698s/12 iters), loss = 5.27944
I0410 14:33:43.687469 18606 solver.cpp:237]     Train net output #0: loss = 5.27944 (* 1 = 5.27944 loss)
I0410 14:33:43.687480 18606 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 14:33:48.566468 18606 solver.cpp:218] Iteration 8520 (2.45962 iter/s, 4.8788s/12 iters), loss = 5.29444
I0410 14:33:48.566519 18606 solver.cpp:237]     Train net output #0: loss = 5.29444 (* 1 = 5.29444 loss)
I0410 14:33:48.566531 18606 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 14:33:53.427089 18606 solver.cpp:218] Iteration 8532 (2.46895 iter/s, 4.86037s/12 iters), loss = 5.2693
I0410 14:33:53.427130 18606 solver.cpp:237]     Train net output #0: loss = 5.2693 (* 1 = 5.2693 loss)
I0410 14:33:53.427140 18606 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 14:33:58.257612 18606 solver.cpp:218] Iteration 8544 (2.48433 iter/s, 4.83029s/12 iters), loss = 5.27213
I0410 14:33:58.257751 18606 solver.cpp:237]     Train net output #0: loss = 5.27213 (* 1 = 5.27213 loss)
I0410 14:33:58.257761 18606 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 14:34:03.268057 18606 solver.cpp:218] Iteration 8556 (2.39516 iter/s, 5.0101s/12 iters), loss = 5.25646
I0410 14:34:03.268107 18606 solver.cpp:237]     Train net output #0: loss = 5.25646 (* 1 = 5.25646 loss)
I0410 14:34:03.268118 18606 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 14:34:07.681479 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 14:34:08.023303 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 14:34:08.306356 18606 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 14:34:08.306375 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:34:09.376823 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:12.876699 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:34:12.876750 18606 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 14:34:12.959389 18606 solver.cpp:218] Iteration 8568 (1.23827 iter/s, 9.6909s/12 iters), loss = 5.24604
I0410 14:34:12.959439 18606 solver.cpp:237]     Train net output #0: loss = 5.24604 (* 1 = 5.24604 loss)
I0410 14:34:12.959451 18606 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 14:34:17.090029 18606 solver.cpp:218] Iteration 8580 (2.90528 iter/s, 4.13042s/12 iters), loss = 5.26303
I0410 14:34:17.090075 18606 solver.cpp:237]     Train net output #0: loss = 5.26303 (* 1 = 5.26303 loss)
I0410 14:34:17.090085 18606 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 14:34:21.929991 18606 solver.cpp:218] Iteration 8592 (2.47949 iter/s, 4.83971s/12 iters), loss = 5.25403
I0410 14:34:21.930048 18606 solver.cpp:237]     Train net output #0: loss = 5.25403 (* 1 = 5.25403 loss)
I0410 14:34:21.930061 18606 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 14:34:24.045011 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:26.791963 18606 solver.cpp:218] Iteration 8604 (2.46826 iter/s, 4.86171s/12 iters), loss = 5.27128
I0410 14:34:26.792007 18606 solver.cpp:237]     Train net output #0: loss = 5.27128 (* 1 = 5.27128 loss)
I0410 14:34:26.792017 18606 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 14:34:31.708726 18606 solver.cpp:218] Iteration 8616 (2.44075 iter/s, 4.91652s/12 iters), loss = 5.26632
I0410 14:34:31.708815 18606 solver.cpp:237]     Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
I0410 14:34:31.708824 18606 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 14:34:36.557224 18606 solver.cpp:218] Iteration 8628 (2.47514 iter/s, 4.84821s/12 iters), loss = 5.28366
I0410 14:34:36.557282 18606 solver.cpp:237]     Train net output #0: loss = 5.28366 (* 1 = 5.28366 loss)
I0410 14:34:36.557297 18606 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 14:34:41.310245 18606 solver.cpp:218] Iteration 8640 (2.52485 iter/s, 4.75277s/12 iters), loss = 5.26515
I0410 14:34:41.310297 18606 solver.cpp:237]     Train net output #0: loss = 5.26515 (* 1 = 5.26515 loss)
I0410 14:34:41.310308 18606 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 14:34:46.171831 18606 solver.cpp:218] Iteration 8652 (2.46846 iter/s, 4.86133s/12 iters), loss = 5.26561
I0410 14:34:46.171880 18606 solver.cpp:237]     Train net output #0: loss = 5.26561 (* 1 = 5.26561 loss)
I0410 14:34:46.171890 18606 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 14:34:51.077682 18606 solver.cpp:218] Iteration 8664 (2.44618 iter/s, 4.9056s/12 iters), loss = 5.276
I0410 14:34:51.077740 18606 solver.cpp:237]     Train net output #0: loss = 5.276 (* 1 = 5.276 loss)
I0410 14:34:51.077752 18606 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 14:34:53.107733 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 14:34:53.428043 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 14:34:53.630787 18606 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 14:34:53.630817 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:34:54.656903 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:58.067625 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:34:58.067669 18606 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 14:34:59.956830 18606 solver.cpp:218] Iteration 8676 (1.35154 iter/s, 8.87874s/12 iters), loss = 5.27632
I0410 14:34:59.956861 18606 solver.cpp:237]     Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
I0410 14:34:59.956869 18606 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 14:35:04.851135 18606 solver.cpp:218] Iteration 8688 (2.45195 iter/s, 4.89406s/12 iters), loss = 5.26383
I0410 14:35:04.851285 18606 solver.cpp:237]     Train net output #0: loss = 5.26383 (* 1 = 5.26383 loss)
I0410 14:35:04.851298 18606 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 14:35:09.087119 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:09.755376 18606 solver.cpp:218] Iteration 8700 (2.44704 iter/s, 4.90389s/12 iters), loss = 5.26372
I0410 14:35:09.755434 18606 solver.cpp:237]     Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
I0410 14:35:09.755450 18606 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 14:35:14.676520 18606 solver.cpp:218] Iteration 8712 (2.43859 iter/s, 4.92088s/12 iters), loss = 5.28168
I0410 14:35:14.676578 18606 solver.cpp:237]     Train net output #0: loss = 5.28168 (* 1 = 5.28168 loss)
I0410 14:35:14.676589 18606 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 14:35:19.572129 18606 solver.cpp:218] Iteration 8724 (2.45131 iter/s, 4.89534s/12 iters), loss = 5.27902
I0410 14:35:19.572191 18606 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 14:35:19.572206 18606 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 14:35:24.421258 18606 solver.cpp:218] Iteration 8736 (2.4748 iter/s, 4.84887s/12 iters), loss = 5.29435
I0410 14:35:24.421306 18606 solver.cpp:237]     Train net output #0: loss = 5.29435 (* 1 = 5.29435 loss)
I0410 14:35:24.421316 18606 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 14:35:29.274364 18606 solver.cpp:218] Iteration 8748 (2.47277 iter/s, 4.85286s/12 iters), loss = 5.26882
I0410 14:35:29.274408 18606 solver.cpp:237]     Train net output #0: loss = 5.26882 (* 1 = 5.26882 loss)
I0410 14:35:29.274418 18606 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 14:35:34.135891 18606 solver.cpp:218] Iteration 8760 (2.46849 iter/s, 4.86128s/12 iters), loss = 5.2745
I0410 14:35:34.135941 18606 solver.cpp:237]     Train net output #0: loss = 5.2745 (* 1 = 5.2745 loss)
I0410 14:35:34.135951 18606 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 14:35:38.581552 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 14:35:39.196710 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 14:35:39.409158 18606 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 14:35:39.409188 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:35:40.417248 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:43.918491 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:35:43.918542 18606 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0410 14:35:44.001890 18606 solver.cpp:218] Iteration 8772 (1.21635 iter/s, 9.86556s/12 iters), loss = 5.27853
I0410 14:35:44.001940 18606 solver.cpp:237]     Train net output #0: loss = 5.27853 (* 1 = 5.27853 loss)
I0410 14:35:44.001951 18606 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 14:35:48.100443 18606 solver.cpp:218] Iteration 8784 (2.92802 iter/s, 4.09833s/12 iters), loss = 5.27631
I0410 14:35:48.100484 18606 solver.cpp:237]     Train net output #0: loss = 5.27631 (* 1 = 5.27631 loss)
I0410 14:35:48.100493 18606 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 14:35:52.914391 18606 solver.cpp:218] Iteration 8796 (2.49288 iter/s, 4.8137s/12 iters), loss = 5.25657
I0410 14:35:52.914448 18606 solver.cpp:237]     Train net output #0: loss = 5.25657 (* 1 = 5.25657 loss)
I0410 14:35:52.914463 18606 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 14:35:54.333395 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:57.760829 18606 solver.cpp:218] Iteration 8808 (2.47618 iter/s, 4.84618s/12 iters), loss = 5.26257
I0410 14:35:57.760879 18606 solver.cpp:237]     Train net output #0: loss = 5.26257 (* 1 = 5.26257 loss)
I0410 14:35:57.760887 18606 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 14:36:02.598593 18606 solver.cpp:218] Iteration 8820 (2.48061 iter/s, 4.83752s/12 iters), loss = 5.26773
I0410 14:36:02.598634 18606 solver.cpp:237]     Train net output #0: loss = 5.26773 (* 1 = 5.26773 loss)
I0410 14:36:02.598642 18606 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 14:36:07.388486 18606 solver.cpp:218] Iteration 8832 (2.5054 iter/s, 4.78965s/12 iters), loss = 5.26488
I0410 14:36:07.388543 18606 solver.cpp:237]     Train net output #0: loss = 5.26488 (* 1 = 5.26488 loss)
I0410 14:36:07.388556 18606 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 14:36:12.245183 18606 solver.cpp:218] Iteration 8844 (2.47095 iter/s, 4.85643s/12 iters), loss = 5.29729
I0410 14:36:12.245306 18606 solver.cpp:237]     Train net output #0: loss = 5.29729 (* 1 = 5.29729 loss)
I0410 14:36:12.245317 18606 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 14:36:17.058385 18606 solver.cpp:218] Iteration 8856 (2.49331 iter/s, 4.81288s/12 iters), loss = 5.25523
I0410 14:36:17.058441 18606 solver.cpp:237]     Train net output #0: loss = 5.25523 (* 1 = 5.25523 loss)
I0410 14:36:17.058454 18606 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 14:36:21.900720 18606 solver.cpp:218] Iteration 8868 (2.47828 iter/s, 4.84207s/12 iters), loss = 5.26039
I0410 14:36:21.900772 18606 solver.cpp:237]     Train net output #0: loss = 5.26039 (* 1 = 5.26039 loss)
I0410 14:36:21.900784 18606 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 14:36:23.850616 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 14:36:24.155354 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 14:36:24.371567 18606 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 14:36:24.371593 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:36:25.470470 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:29.033587 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:36:29.033625 18606 solver.cpp:397]     Test net output #1: loss = 5.28687 (* 1 = 5.28687 loss)
I0410 14:36:30.923722 18606 solver.cpp:218] Iteration 8880 (1.33 iter/s, 9.02259s/12 iters), loss = 5.28044
I0410 14:36:30.923765 18606 solver.cpp:237]     Train net output #0: loss = 5.28044 (* 1 = 5.28044 loss)
I0410 14:36:30.923775 18606 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 14:36:35.809298 18606 solver.cpp:218] Iteration 8892 (2.45634 iter/s, 4.88533s/12 iters), loss = 5.27903
I0410 14:36:35.809352 18606 solver.cpp:237]     Train net output #0: loss = 5.27903 (* 1 = 5.27903 loss)
I0410 14:36:35.809363 18606 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 14:36:39.376109 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:40.730206 18606 solver.cpp:218] Iteration 8904 (2.4387 iter/s, 4.92065s/12 iters), loss = 5.27467
I0410 14:36:40.730260 18606 solver.cpp:237]     Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
I0410 14:36:40.730273 18606 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 14:36:45.559901 18606 solver.cpp:218] Iteration 8916 (2.48476 iter/s, 4.82944s/12 iters), loss = 5.26564
I0410 14:36:45.560039 18606 solver.cpp:237]     Train net output #0: loss = 5.26564 (* 1 = 5.26564 loss)
I0410 14:36:45.560050 18606 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 14:36:50.343932 18606 solver.cpp:218] Iteration 8928 (2.50852 iter/s, 4.78369s/12 iters), loss = 5.26252
I0410 14:36:50.343971 18606 solver.cpp:237]     Train net output #0: loss = 5.26252 (* 1 = 5.26252 loss)
I0410 14:36:50.343978 18606 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 14:36:55.102774 18606 solver.cpp:218] Iteration 8940 (2.52175 iter/s, 4.7586s/12 iters), loss = 5.26684
I0410 14:36:55.102823 18606 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0410 14:36:55.102831 18606 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 14:37:00.185729 18606 solver.cpp:218] Iteration 8952 (2.36096 iter/s, 5.08269s/12 iters), loss = 5.25743
I0410 14:37:00.185786 18606 solver.cpp:237]     Train net output #0: loss = 5.25743 (* 1 = 5.25743 loss)
I0410 14:37:00.185796 18606 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 14:37:05.106178 18606 solver.cpp:218] Iteration 8964 (2.43893 iter/s, 4.92019s/12 iters), loss = 5.27857
I0410 14:37:05.106230 18606 solver.cpp:237]     Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
I0410 14:37:05.106242 18606 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 14:37:09.626322 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 14:37:09.923830 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 14:37:10.136871 18606 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 14:37:10.136893 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:37:11.037259 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:14.632324 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:37:14.632369 18606 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0410 14:37:14.715425 18606 solver.cpp:218] Iteration 8976 (1.24885 iter/s, 9.60881s/12 iters), loss = 5.2773
I0410 14:37:14.715473 18606 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0410 14:37:14.715484 18606 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 14:37:18.873461 18606 solver.cpp:218] Iteration 8988 (2.88613 iter/s, 4.15781s/12 iters), loss = 5.28213
I0410 14:37:18.873582 18606 solver.cpp:237]     Train net output #0: loss = 5.28213 (* 1 = 5.28213 loss)
I0410 14:37:18.873594 18606 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 14:37:20.435382 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:37:23.670055 18606 solver.cpp:218] Iteration 9000 (2.50194 iter/s, 4.79627s/12 iters), loss = 5.28719
I0410 14:37:23.670109 18606 solver.cpp:237]     Train net output #0: loss = 5.28719 (* 1 = 5.28719 loss)
I0410 14:37:23.670120 18606 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 14:37:24.382526 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:28.526808 18606 solver.cpp:218] Iteration 9012 (2.47092 iter/s, 4.85649s/12 iters), loss = 5.2857
I0410 14:37:28.526865 18606 solver.cpp:237]     Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
I0410 14:37:28.526877 18606 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 14:37:33.330047 18606 solver.cpp:218] Iteration 9024 (2.49845 iter/s, 4.80298s/12 iters), loss = 5.26474
I0410 14:37:33.330092 18606 solver.cpp:237]     Train net output #0: loss = 5.26474 (* 1 = 5.26474 loss)
I0410 14:37:33.330101 18606 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 14:37:38.188203 18606 solver.cpp:218] Iteration 9036 (2.4702 iter/s, 4.8579s/12 iters), loss = 5.27339
I0410 14:37:38.188266 18606 solver.cpp:237]     Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
I0410 14:37:38.188279 18606 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 14:37:43.084925 18606 solver.cpp:218] Iteration 9048 (2.45075 iter/s, 4.89645s/12 iters), loss = 5.27308
I0410 14:37:43.084976 18606 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0410 14:37:43.084990 18606 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 14:37:47.974720 18606 solver.cpp:218] Iteration 9060 (2.45422 iter/s, 4.88954s/12 iters), loss = 5.2915
I0410 14:37:47.974768 18606 solver.cpp:237]     Train net output #0: loss = 5.2915 (* 1 = 5.2915 loss)
I0410 14:37:47.974777 18606 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 14:37:52.800388 18606 solver.cpp:218] Iteration 9072 (2.48683 iter/s, 4.82541s/12 iters), loss = 5.26737
I0410 14:37:52.800535 18606 solver.cpp:237]     Train net output #0: loss = 5.26737 (* 1 = 5.26737 loss)
I0410 14:37:52.800546 18606 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 14:37:54.790261 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 14:37:55.113376 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 14:37:55.319635 18606 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 14:37:55.319662 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:37:56.192905 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:59.873164 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:37:59.873211 18606 solver.cpp:397]     Test net output #1: loss = 5.28713 (* 1 = 5.28713 loss)
I0410 14:38:01.662763 18606 solver.cpp:218] Iteration 9084 (1.35412 iter/s, 8.86187s/12 iters), loss = 5.25903
I0410 14:38:01.662809 18606 solver.cpp:237]     Train net output #0: loss = 5.25903 (* 1 = 5.25903 loss)
I0410 14:38:01.662820 18606 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 14:38:06.479903 18606 solver.cpp:218] Iteration 9096 (2.49124 iter/s, 4.81688s/12 iters), loss = 5.26481
I0410 14:38:06.479959 18606 solver.cpp:237]     Train net output #0: loss = 5.26481 (* 1 = 5.26481 loss)
I0410 14:38:06.479972 18606 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 14:38:09.521284 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:11.502686 18606 solver.cpp:218] Iteration 9108 (2.38924 iter/s, 5.02252s/12 iters), loss = 5.26098
I0410 14:38:11.502738 18606 solver.cpp:237]     Train net output #0: loss = 5.26098 (* 1 = 5.26098 loss)
I0410 14:38:11.502749 18606 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 14:38:16.346894 18606 solver.cpp:218] Iteration 9120 (2.47732 iter/s, 4.84395s/12 iters), loss = 5.24977
I0410 14:38:16.346949 18606 solver.cpp:237]     Train net output #0: loss = 5.24977 (* 1 = 5.24977 loss)
I0410 14:38:16.346962 18606 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 14:38:21.147418 18606 solver.cpp:218] Iteration 9132 (2.49986 iter/s, 4.80026s/12 iters), loss = 5.24904
I0410 14:38:21.147467 18606 solver.cpp:237]     Train net output #0: loss = 5.24904 (* 1 = 5.24904 loss)
I0410 14:38:21.147478 18606 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 14:38:25.962659 18606 solver.cpp:218] Iteration 9144 (2.49222 iter/s, 4.81499s/12 iters), loss = 5.2588
I0410 14:38:25.962788 18606 solver.cpp:237]     Train net output #0: loss = 5.2588 (* 1 = 5.2588 loss)
I0410 14:38:25.962802 18606 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 14:38:30.810623 18606 solver.cpp:218] Iteration 9156 (2.47544 iter/s, 4.84763s/12 iters), loss = 5.2868
I0410 14:38:30.810678 18606 solver.cpp:237]     Train net output #0: loss = 5.2868 (* 1 = 5.2868 loss)
I0410 14:38:30.810690 18606 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 14:38:35.744762 18606 solver.cpp:218] Iteration 9168 (2.43217 iter/s, 4.93387s/12 iters), loss = 5.2712
I0410 14:38:35.744819 18606 solver.cpp:237]     Train net output #0: loss = 5.2712 (* 1 = 5.2712 loss)
I0410 14:38:35.744832 18606 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 14:38:40.134681 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 14:38:41.316921 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 14:38:41.889742 18606 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 14:38:41.889773 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:38:42.750692 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:46.517418 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:38:46.517468 18606 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0410 14:38:46.600509 18606 solver.cpp:218] Iteration 9180 (1.10546 iter/s, 10.8552s/12 iters), loss = 5.27408
I0410 14:38:46.600565 18606 solver.cpp:237]     Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
I0410 14:38:46.600579 18606 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 14:38:50.776664 18606 solver.cpp:218] Iteration 9192 (2.87362 iter/s, 4.17592s/12 iters), loss = 5.27456
I0410 14:38:50.776722 18606 solver.cpp:237]     Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
I0410 14:38:50.776734 18606 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 14:38:55.684630 18606 solver.cpp:218] Iteration 9204 (2.44514 iter/s, 4.9077s/12 iters), loss = 5.26609
I0410 14:38:55.684689 18606 solver.cpp:237]     Train net output #0: loss = 5.26609 (* 1 = 5.26609 loss)
I0410 14:38:55.684701 18606 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 14:38:55.767992 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:00.594305 18606 solver.cpp:218] Iteration 9216 (2.44429 iter/s, 4.9094s/12 iters), loss = 5.27657
I0410 14:39:00.594441 18606 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0410 14:39:00.594455 18606 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 14:39:05.493597 18606 solver.cpp:218] Iteration 9228 (2.4495 iter/s, 4.89895s/12 iters), loss = 5.28558
I0410 14:39:05.493651 18606 solver.cpp:237]     Train net output #0: loss = 5.28558 (* 1 = 5.28558 loss)
I0410 14:39:05.493664 18606 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 14:39:10.397445 18606 solver.cpp:218] Iteration 9240 (2.44719 iter/s, 4.90359s/12 iters), loss = 5.26124
I0410 14:39:10.397497 18606 solver.cpp:237]     Train net output #0: loss = 5.26124 (* 1 = 5.26124 loss)
I0410 14:39:10.397509 18606 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 14:39:15.195456 18606 solver.cpp:218] Iteration 9252 (2.50117 iter/s, 4.79775s/12 iters), loss = 5.2751
I0410 14:39:15.195513 18606 solver.cpp:237]     Train net output #0: loss = 5.2751 (* 1 = 5.2751 loss)
I0410 14:39:15.195524 18606 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 14:39:20.002452 18606 solver.cpp:218] Iteration 9264 (2.4965 iter/s, 4.80673s/12 iters), loss = 5.26057
I0410 14:39:20.002516 18606 solver.cpp:237]     Train net output #0: loss = 5.26057 (* 1 = 5.26057 loss)
I0410 14:39:20.002528 18606 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 14:39:24.784512 18606 solver.cpp:218] Iteration 9276 (2.50952 iter/s, 4.78179s/12 iters), loss = 5.24909
I0410 14:39:24.784576 18606 solver.cpp:237]     Train net output #0: loss = 5.24909 (* 1 = 5.24909 loss)
I0410 14:39:24.784588 18606 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 14:39:26.722353 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 14:39:27.655640 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 14:39:28.085861 18606 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 14:39:28.085888 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:39:28.902964 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:32.525593 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:39:32.525789 18606 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0410 14:39:34.340989 18606 solver.cpp:218] Iteration 9288 (1.25575 iter/s, 9.55602s/12 iters), loss = 5.26528
I0410 14:39:34.341046 18606 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0410 14:39:34.341058 18606 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 14:39:39.169996 18606 solver.cpp:218] Iteration 9300 (2.48512 iter/s, 4.82873s/12 iters), loss = 5.2519
I0410 14:39:39.170051 18606 solver.cpp:237]     Train net output #0: loss = 5.2519 (* 1 = 5.2519 loss)
I0410 14:39:39.170063 18606 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 14:39:41.272330 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:43.988004 18606 solver.cpp:218] Iteration 9312 (2.49079 iter/s, 4.81774s/12 iters), loss = 5.27378
I0410 14:39:43.988055 18606 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0410 14:39:43.988067 18606 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 14:39:48.803339 18606 solver.cpp:218] Iteration 9324 (2.49217 iter/s, 4.81508s/12 iters), loss = 5.27766
I0410 14:39:48.803393 18606 solver.cpp:237]     Train net output #0: loss = 5.27766 (* 1 = 5.27766 loss)
I0410 14:39:48.803406 18606 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 14:39:53.589232 18606 solver.cpp:218] Iteration 9336 (2.5075 iter/s, 4.78564s/12 iters), loss = 5.28664
I0410 14:39:53.589282 18606 solver.cpp:237]     Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
I0410 14:39:53.589294 18606 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 14:39:58.392323 18606 solver.cpp:218] Iteration 9348 (2.49853 iter/s, 4.80283s/12 iters), loss = 5.27194
I0410 14:39:58.392375 18606 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0410 14:39:58.392386 18606 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 14:40:03.236167 18606 solver.cpp:218] Iteration 9360 (2.4775 iter/s, 4.84359s/12 iters), loss = 5.26898
I0410 14:40:03.236255 18606 solver.cpp:237]     Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
I0410 14:40:03.236268 18606 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 14:40:08.080849 18606 solver.cpp:218] Iteration 9372 (2.4771 iter/s, 4.84438s/12 iters), loss = 5.27257
I0410 14:40:08.080907 18606 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0410 14:40:08.080920 18606 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 14:40:12.457706 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 14:40:12.747756 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 14:40:12.948335 18606 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 14:40:12.948354 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:40:13.698848 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:17.356762 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:40:17.356812 18606 solver.cpp:397]     Test net output #1: loss = 5.28699 (* 1 = 5.28699 loss)
I0410 14:40:17.439621 18606 solver.cpp:218] Iteration 9384 (1.28228 iter/s, 9.35833s/12 iters), loss = 5.27619
I0410 14:40:17.439683 18606 solver.cpp:237]     Train net output #0: loss = 5.27619 (* 1 = 5.27619 loss)
I0410 14:40:17.439697 18606 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 14:40:21.572508 18606 solver.cpp:218] Iteration 9396 (2.90371 iter/s, 4.13264s/12 iters), loss = 5.26735
I0410 14:40:21.572568 18606 solver.cpp:237]     Train net output #0: loss = 5.26735 (* 1 = 5.26735 loss)
I0410 14:40:21.572582 18606 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 14:40:25.788375 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:26.413692 18606 solver.cpp:218] Iteration 9408 (2.47887 iter/s, 4.84091s/12 iters), loss = 5.26999
I0410 14:40:26.413753 18606 solver.cpp:237]     Train net output #0: loss = 5.26999 (* 1 = 5.26999 loss)
I0410 14:40:26.413766 18606 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 14:40:31.259341 18606 solver.cpp:218] Iteration 9420 (2.47659 iter/s, 4.84538s/12 iters), loss = 5.27512
I0410 14:40:31.259402 18606 solver.cpp:237]     Train net output #0: loss = 5.27512 (* 1 = 5.27512 loss)
I0410 14:40:31.259414 18606 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 14:40:36.139367 18606 solver.cpp:218] Iteration 9432 (2.45914 iter/s, 4.87976s/12 iters), loss = 5.28248
I0410 14:40:36.139530 18606 solver.cpp:237]     Train net output #0: loss = 5.28248 (* 1 = 5.28248 loss)
I0410 14:40:36.139545 18606 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 14:40:41.006778 18606 solver.cpp:218] Iteration 9444 (2.46556 iter/s, 4.86705s/12 iters), loss = 5.28434
I0410 14:40:41.006824 18606 solver.cpp:237]     Train net output #0: loss = 5.28434 (* 1 = 5.28434 loss)
I0410 14:40:41.006834 18606 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 14:40:45.854270 18606 solver.cpp:218] Iteration 9456 (2.47564 iter/s, 4.84723s/12 iters), loss = 5.2664
I0410 14:40:45.854327 18606 solver.cpp:237]     Train net output #0: loss = 5.2664 (* 1 = 5.2664 loss)
I0410 14:40:45.854339 18606 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 14:40:50.669803 18606 solver.cpp:218] Iteration 9468 (2.49207 iter/s, 4.81527s/12 iters), loss = 5.28047
I0410 14:40:50.669853 18606 solver.cpp:237]     Train net output #0: loss = 5.28047 (* 1 = 5.28047 loss)
I0410 14:40:50.669864 18606 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 14:40:55.489851 18606 solver.cpp:218] Iteration 9480 (2.48973 iter/s, 4.8198s/12 iters), loss = 5.27523
I0410 14:40:55.489897 18606 solver.cpp:237]     Train net output #0: loss = 5.27523 (* 1 = 5.27523 loss)
I0410 14:40:55.489907 18606 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 14:40:57.466714 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 14:40:57.819658 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 14:40:58.033432 18606 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 14:40:58.033449 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:40:58.726552 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:02.438659 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:41:02.438701 18606 solver.cpp:397]     Test net output #1: loss = 5.28646 (* 1 = 5.28646 loss)
I0410 14:41:04.259604 18606 solver.cpp:218] Iteration 9492 (1.3684 iter/s, 8.76934s/12 iters), loss = 5.26922
I0410 14:41:04.259660 18606 solver.cpp:237]     Train net output #0: loss = 5.26922 (* 1 = 5.26922 loss)
I0410 14:41:04.259671 18606 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 14:41:09.077505 18606 solver.cpp:218] Iteration 9504 (2.49085 iter/s, 4.81763s/12 iters), loss = 5.2608
I0410 14:41:09.077618 18606 solver.cpp:237]     Train net output #0: loss = 5.2608 (* 1 = 5.2608 loss)
I0410 14:41:09.077631 18606 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 14:41:10.485790 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:13.814097 18606 solver.cpp:218] Iteration 9516 (2.53364 iter/s, 4.73627s/12 iters), loss = 5.26172
I0410 14:41:13.814167 18606 solver.cpp:237]     Train net output #0: loss = 5.26172 (* 1 = 5.26172 loss)
I0410 14:41:13.814182 18606 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 14:41:18.590164 18606 solver.cpp:218] Iteration 9528 (2.51267 iter/s, 4.7758s/12 iters), loss = 5.26334
I0410 14:41:18.590212 18606 solver.cpp:237]     Train net output #0: loss = 5.26334 (* 1 = 5.26334 loss)
I0410 14:41:18.590222 18606 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 14:41:23.526196 18606 solver.cpp:218] Iteration 9540 (2.43123 iter/s, 4.93577s/12 iters), loss = 5.24667
I0410 14:41:23.526238 18606 solver.cpp:237]     Train net output #0: loss = 5.24667 (* 1 = 5.24667 loss)
I0410 14:41:23.526247 18606 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 14:41:28.355842 18606 solver.cpp:218] Iteration 9552 (2.48478 iter/s, 4.82939s/12 iters), loss = 5.29952
I0410 14:41:28.355906 18606 solver.cpp:237]     Train net output #0: loss = 5.29952 (* 1 = 5.29952 loss)
I0410 14:41:28.355918 18606 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 14:41:33.866652 18606 solver.cpp:218] Iteration 9564 (2.17765 iter/s, 5.51052s/12 iters), loss = 5.25523
I0410 14:41:33.866690 18606 solver.cpp:237]     Train net output #0: loss = 5.25523 (* 1 = 5.25523 loss)
I0410 14:41:33.866700 18606 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 14:41:38.762981 18606 solver.cpp:218] Iteration 9576 (2.45094 iter/s, 4.89607s/12 iters), loss = 5.2617
I0410 14:41:38.763036 18606 solver.cpp:237]     Train net output #0: loss = 5.2617 (* 1 = 5.2617 loss)
I0410 14:41:38.763046 18606 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 14:41:43.246316 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 14:41:43.691432 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 14:41:44.044092 18606 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 14:41:44.044123 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:41:44.737457 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:48.503779 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:41:48.503829 18606 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 14:41:48.587124 18606 solver.cpp:218] Iteration 9588 (1.22154 iter/s, 9.82367s/12 iters), loss = 5.2754
I0410 14:41:48.587193 18606 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0410 14:41:48.587208 18606 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 14:41:52.824594 18606 solver.cpp:218] Iteration 9600 (2.83204 iter/s, 4.23723s/12 iters), loss = 5.27531
I0410 14:41:52.824636 18606 solver.cpp:237]     Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
I0410 14:41:52.824645 18606 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 14:41:56.366477 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:57.719905 18606 solver.cpp:218] Iteration 9612 (2.45145 iter/s, 4.89506s/12 iters), loss = 5.27151
I0410 14:41:57.719960 18606 solver.cpp:237]     Train net output #0: loss = 5.27151 (* 1 = 5.27151 loss)
I0410 14:41:57.719972 18606 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 14:42:02.632966 18606 solver.cpp:218] Iteration 9624 (2.4426 iter/s, 4.91279s/12 iters), loss = 5.26714
I0410 14:42:02.633023 18606 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0410 14:42:02.633038 18606 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 14:42:07.500032 18606 solver.cpp:218] Iteration 9636 (2.46569 iter/s, 4.8668s/12 iters), loss = 5.25592
I0410 14:42:07.500090 18606 solver.cpp:237]     Train net output #0: loss = 5.25592 (* 1 = 5.25592 loss)
I0410 14:42:07.500103 18606 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 14:42:12.351953 18606 solver.cpp:218] Iteration 9648 (2.47338 iter/s, 4.85165s/12 iters), loss = 5.2666
I0410 14:42:12.351997 18606 solver.cpp:237]     Train net output #0: loss = 5.2666 (* 1 = 5.2666 loss)
I0410 14:42:12.352007 18606 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 14:42:17.257949 18606 solver.cpp:218] Iteration 9660 (2.44612 iter/s, 4.90574s/12 iters), loss = 5.25419
I0410 14:42:17.258266 18606 solver.cpp:237]     Train net output #0: loss = 5.25419 (* 1 = 5.25419 loss)
I0410 14:42:17.258280 18606 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 14:42:22.324357 18606 solver.cpp:218] Iteration 9672 (2.36879 iter/s, 5.06587s/12 iters), loss = 5.27051
I0410 14:42:22.324415 18606 solver.cpp:237]     Train net output #0: loss = 5.27051 (* 1 = 5.27051 loss)
I0410 14:42:22.324429 18606 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 14:42:27.182010 18606 solver.cpp:218] Iteration 9684 (2.47047 iter/s, 4.85738s/12 iters), loss = 5.28905
I0410 14:42:27.182070 18606 solver.cpp:237]     Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
I0410 14:42:27.182083 18606 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 14:42:29.149649 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 14:42:29.498610 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 14:42:29.714547 18606 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 14:42:29.714566 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:42:30.275943 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:32.808842 18606 blocking_queue.cpp:49] Waiting for data
I0410 14:42:34.165793 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:42:34.165841 18606 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0410 14:42:35.908547 18606 solver.cpp:218] Iteration 9696 (1.37518 iter/s, 8.72611s/12 iters), loss = 5.28696
I0410 14:42:35.908604 18606 solver.cpp:237]     Train net output #0: loss = 5.28696 (* 1 = 5.28696 loss)
I0410 14:42:35.908617 18606 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 14:42:40.708297 18606 solver.cpp:218] Iteration 9708 (2.50027 iter/s, 4.79948s/12 iters), loss = 5.28801
I0410 14:42:40.708364 18606 solver.cpp:237]     Train net output #0: loss = 5.28801 (* 1 = 5.28801 loss)
I0410 14:42:40.708376 18606 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 14:42:41.424788 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:45.615420 18606 solver.cpp:218] Iteration 9720 (2.44556 iter/s, 4.90685s/12 iters), loss = 5.28914
I0410 14:42:45.615463 18606 solver.cpp:237]     Train net output #0: loss = 5.28914 (* 1 = 5.28914 loss)
I0410 14:42:45.615471 18606 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 14:42:50.719691 18606 solver.cpp:218] Iteration 9732 (2.3511 iter/s, 5.104s/12 iters), loss = 5.26225
I0410 14:42:50.719859 18606 solver.cpp:237]     Train net output #0: loss = 5.26225 (* 1 = 5.26225 loss)
I0410 14:42:50.719873 18606 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 14:42:55.645648 18606 solver.cpp:218] Iteration 9744 (2.43626 iter/s, 4.92558s/12 iters), loss = 5.2682
I0410 14:42:55.645696 18606 solver.cpp:237]     Train net output #0: loss = 5.2682 (* 1 = 5.2682 loss)
I0410 14:42:55.645705 18606 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 14:43:00.470695 18606 solver.cpp:218] Iteration 9756 (2.48715 iter/s, 4.82479s/12 iters), loss = 5.27418
I0410 14:43:00.470741 18606 solver.cpp:237]     Train net output #0: loss = 5.27418 (* 1 = 5.27418 loss)
I0410 14:43:00.470750 18606 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 14:43:05.306035 18606 solver.cpp:218] Iteration 9768 (2.48186 iter/s, 4.83508s/12 iters), loss = 5.28814
I0410 14:43:05.306089 18606 solver.cpp:237]     Train net output #0: loss = 5.28814 (* 1 = 5.28814 loss)
I0410 14:43:05.306100 18606 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 14:43:10.074985 18606 solver.cpp:218] Iteration 9780 (2.51642 iter/s, 4.76869s/12 iters), loss = 5.2709
I0410 14:43:10.075035 18606 solver.cpp:237]     Train net output #0: loss = 5.2709 (* 1 = 5.2709 loss)
I0410 14:43:10.075047 18606 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 14:43:14.844563 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 14:43:15.154974 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 14:43:15.357342 18606 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 14:43:15.357362 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:43:16.003763 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:19.833856 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:43:19.833901 18606 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 14:43:19.916857 18606 solver.cpp:218] Iteration 9792 (1.21934 iter/s, 9.84141s/12 iters), loss = 5.2519
I0410 14:43:19.916908 18606 solver.cpp:237]     Train net output #0: loss = 5.2519 (* 1 = 5.2519 loss)
I0410 14:43:19.916918 18606 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 14:43:24.163828 18606 solver.cpp:218] Iteration 9804 (2.8257 iter/s, 4.24673s/12 iters), loss = 5.27284
I0410 14:43:24.163959 18606 solver.cpp:237]     Train net output #0: loss = 5.27284 (* 1 = 5.27284 loss)
I0410 14:43:24.163973 18606 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 14:43:27.023586 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:28.983841 18606 solver.cpp:218] Iteration 9816 (2.48979 iter/s, 4.81968s/12 iters), loss = 5.26638
I0410 14:43:28.983887 18606 solver.cpp:237]     Train net output #0: loss = 5.26638 (* 1 = 5.26638 loss)
I0410 14:43:28.983898 18606 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 14:43:33.823899 18606 solver.cpp:218] Iteration 9828 (2.47944 iter/s, 4.8398s/12 iters), loss = 5.25323
I0410 14:43:33.823952 18606 solver.cpp:237]     Train net output #0: loss = 5.25323 (* 1 = 5.25323 loss)
I0410 14:43:33.823966 18606 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 14:43:38.664674 18606 solver.cpp:218] Iteration 9840 (2.47908 iter/s, 4.84051s/12 iters), loss = 5.24908
I0410 14:43:38.664731 18606 solver.cpp:237]     Train net output #0: loss = 5.24908 (* 1 = 5.24908 loss)
I0410 14:43:38.664744 18606 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 14:43:43.445374 18606 solver.cpp:218] Iteration 9852 (2.51023 iter/s, 4.78044s/12 iters), loss = 5.26702
I0410 14:43:43.445410 18606 solver.cpp:237]     Train net output #0: loss = 5.26702 (* 1 = 5.26702 loss)
I0410 14:43:43.445417 18606 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 14:43:48.274632 18606 solver.cpp:218] Iteration 9864 (2.48498 iter/s, 4.82901s/12 iters), loss = 5.28992
I0410 14:43:48.274682 18606 solver.cpp:237]     Train net output #0: loss = 5.28992 (* 1 = 5.28992 loss)
I0410 14:43:48.274691 18606 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 14:43:53.124430 18606 solver.cpp:218] Iteration 9876 (2.47446 iter/s, 4.84953s/12 iters), loss = 5.27175
I0410 14:43:53.124487 18606 solver.cpp:237]     Train net output #0: loss = 5.27175 (* 1 = 5.27175 loss)
I0410 14:43:53.124500 18606 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 14:43:57.922662 18606 solver.cpp:218] Iteration 9888 (2.50106 iter/s, 4.79797s/12 iters), loss = 5.27498
I0410 14:43:57.922772 18606 solver.cpp:237]     Train net output #0: loss = 5.27498 (* 1 = 5.27498 loss)
I0410 14:43:57.922783 18606 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 14:43:59.888773 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 14:44:00.185231 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 14:44:00.403820 18606 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 14:44:00.403851 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:44:00.978271 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:04.873889 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:44:04.873935 18606 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0410 14:44:06.680490 18606 solver.cpp:218] Iteration 9900 (1.37028 iter/s, 8.75735s/12 iters), loss = 5.27323
I0410 14:44:06.680548 18606 solver.cpp:237]     Train net output #0: loss = 5.27323 (* 1 = 5.27323 loss)
I0410 14:44:06.680562 18606 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 14:44:11.586750 18606 solver.cpp:218] Iteration 9912 (2.44599 iter/s, 4.90599s/12 iters), loss = 5.25792
I0410 14:44:11.586817 18606 solver.cpp:237]     Train net output #0: loss = 5.25792 (* 1 = 5.25792 loss)
I0410 14:44:11.586829 18606 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 14:44:11.685933 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:16.433832 18606 solver.cpp:218] Iteration 9924 (2.47586 iter/s, 4.84681s/12 iters), loss = 5.27072
I0410 14:44:16.433887 18606 solver.cpp:237]     Train net output #0: loss = 5.27072 (* 1 = 5.27072 loss)
I0410 14:44:16.433899 18606 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 14:44:21.278825 18606 solver.cpp:218] Iteration 9936 (2.47692 iter/s, 4.84473s/12 iters), loss = 5.28874
I0410 14:44:21.278867 18606 solver.cpp:237]     Train net output #0: loss = 5.28874 (* 1 = 5.28874 loss)
I0410 14:44:21.278874 18606 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 14:44:26.262732 18606 solver.cpp:218] Iteration 9948 (2.40788 iter/s, 4.98365s/12 iters), loss = 5.26094
I0410 14:44:26.262785 18606 solver.cpp:237]     Train net output #0: loss = 5.26094 (* 1 = 5.26094 loss)
I0410 14:44:26.262799 18606 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 14:44:31.317164 18606 solver.cpp:218] Iteration 9960 (2.37428 iter/s, 5.05416s/12 iters), loss = 5.26921
I0410 14:44:31.317312 18606 solver.cpp:237]     Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
I0410 14:44:31.317327 18606 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 14:44:36.239707 18606 solver.cpp:218] Iteration 9972 (2.43794 iter/s, 4.92218s/12 iters), loss = 5.26029
I0410 14:44:36.239764 18606 solver.cpp:237]     Train net output #0: loss = 5.26029 (* 1 = 5.26029 loss)
I0410 14:44:36.239778 18606 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 14:44:41.218606 18606 solver.cpp:218] Iteration 9984 (2.41031 iter/s, 4.97862s/12 iters), loss = 5.24684
I0410 14:44:41.218662 18606 solver.cpp:237]     Train net output #0: loss = 5.24684 (* 1 = 5.24684 loss)
I0410 14:44:41.218673 18606 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 14:44:45.627400 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 14:44:46.502239 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 14:44:47.488729 18606 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 14:44:47.488760 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:44:48.053797 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:51.998631 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:44:51.998669 18606 solver.cpp:397]     Test net output #1: loss = 5.28751 (* 1 = 5.28751 loss)
I0410 14:44:52.079151 18606 solver.cpp:218] Iteration 9996 (1.10497 iter/s, 10.86s/12 iters), loss = 5.27081
I0410 14:44:52.079193 18606 solver.cpp:237]     Train net output #0: loss = 5.27081 (* 1 = 5.27081 loss)
I0410 14:44:52.079203 18606 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 14:44:56.459944 18606 solver.cpp:218] Iteration 10008 (2.73938 iter/s, 4.38055s/12 iters), loss = 5.24463
I0410 14:44:56.459995 18606 solver.cpp:237]     Train net output #0: loss = 5.24463 (* 1 = 5.24463 loss)
I0410 14:44:56.460009 18606 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 14:44:58.633816 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:01.276156 18606 solver.cpp:218] Iteration 10020 (2.49172 iter/s, 4.81596s/12 iters), loss = 5.26792
I0410 14:45:01.276206 18606 solver.cpp:237]     Train net output #0: loss = 5.26792 (* 1 = 5.26792 loss)
I0410 14:45:01.276219 18606 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 14:45:06.193828 18606 solver.cpp:218] Iteration 10032 (2.44031 iter/s, 4.91741s/12 iters), loss = 5.27592
I0410 14:45:06.195443 18606 solver.cpp:237]     Train net output #0: loss = 5.27592 (* 1 = 5.27592 loss)
I0410 14:45:06.195457 18606 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 14:45:11.032318 18606 solver.cpp:218] Iteration 10044 (2.48105 iter/s, 4.83667s/12 iters), loss = 5.28372
I0410 14:45:11.032361 18606 solver.cpp:237]     Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
I0410 14:45:11.032371 18606 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 14:45:15.869455 18606 solver.cpp:218] Iteration 10056 (2.48094 iter/s, 4.83688s/12 iters), loss = 5.27484
I0410 14:45:15.869508 18606 solver.cpp:237]     Train net output #0: loss = 5.27484 (* 1 = 5.27484 loss)
I0410 14:45:15.869519 18606 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 14:45:20.741991 18606 solver.cpp:218] Iteration 10068 (2.46293 iter/s, 4.87225s/12 iters), loss = 5.27273
I0410 14:45:20.742038 18606 solver.cpp:237]     Train net output #0: loss = 5.27273 (* 1 = 5.27273 loss)
I0410 14:45:20.742046 18606 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 14:45:25.585355 18606 solver.cpp:218] Iteration 10080 (2.47775 iter/s, 4.84311s/12 iters), loss = 5.26235
I0410 14:45:25.585398 18606 solver.cpp:237]     Train net output #0: loss = 5.26235 (* 1 = 5.26235 loss)
I0410 14:45:25.585407 18606 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 14:45:30.547355 18606 solver.cpp:218] Iteration 10092 (2.41851 iter/s, 4.96174s/12 iters), loss = 5.27629
I0410 14:45:30.547401 18606 solver.cpp:237]     Train net output #0: loss = 5.27629 (* 1 = 5.27629 loss)
I0410 14:45:30.547410 18606 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 14:45:32.484161 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 14:45:32.806130 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 14:45:33.019099 18606 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 14:45:33.019120 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:45:33.437436 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:37.442986 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:45:37.443125 18606 solver.cpp:397]     Test net output #1: loss = 5.28715 (* 1 = 5.28715 loss)
I0410 14:45:39.259793 18606 solver.cpp:218] Iteration 10104 (1.37741 iter/s, 8.71202s/12 iters), loss = 5.27132
I0410 14:45:39.259850 18606 solver.cpp:237]     Train net output #0: loss = 5.27132 (* 1 = 5.27132 loss)
I0410 14:45:39.259863 18606 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 14:45:43.509893 18610 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:44.106019 18606 solver.cpp:218] Iteration 10116 (2.47629 iter/s, 4.84596s/12 iters), loss = 5.25845
I0410 14:45:44.106072 18606 solver.cpp:237]     Train net output #0: loss = 5.25845 (* 1 = 5.25845 loss)
I0410 14:45:44.106084 18606 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 14:45:48.890290 18606 solver.cpp:218] Iteration 10128 (2.50836 iter/s, 4.78401s/12 iters), loss = 5.27415
I0410 14:45:48.890333 18606 solver.cpp:237]     Train net output #0: loss = 5.27415 (* 1 = 5.27415 loss)
I0410 14:45:48.890342 18606 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 14:45:53.754304 18606 solver.cpp:218] Iteration 10140 (2.46723 iter/s, 4.86376s/12 iters), loss = 5.28228
I0410 14:45:53.754348 18606 solver.cpp:237]     Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
I0410 14:45:53.754359 18606 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 14:45:58.746632 18606 solver.cpp:218] Iteration 10152 (2.40382 iter/s, 4.99206s/12 iters), loss = 5.27595
I0410 14:45:58.746686 18606 solver.cpp:237]     Train net output #0: loss = 5.27595 (* 1 = 5.27595 loss)
I0410 14:45:58.746698 18606 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 14:46:03.625676 18606 solver.cpp:218] Iteration 10164 (2.45963 iter/s, 4.87878s/12 iters), loss = 5.26367
I0410 14:46:03.625733 18606 solver.cpp:237]     Train net output #0: loss = 5.26367 (* 1 = 5.26367 loss)
I0410 14:46:03.625746 18606 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 14:46:08.459496 18606 solver.cpp:218] Iteration 10176 (2.48264 iter/s, 4.83356s/12 iters), loss = 5.27805
I0410 14:46:08.459594 18606 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0410 14:46:08.459604 18606 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 14:46:13.358700 18606 solver.cpp:218] Iteration 10188 (2.44953 iter/s, 4.89889s/12 iters), loss = 5.27595
I0410 14:46:13.358736 18606 solver.cpp:237]     Train net output #0: loss = 5.27595 (* 1 = 5.27595 loss)
I0410 14:46:13.358743 18606 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 14:46:17.751695 18606 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 14:46:18.068476 18606 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 14:46:18.308171 18606 solver.cpp:310] Iteration 10200, loss = 5.2626
I0410 14:46:18.308197 18606 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 14:46:18.308202 18606 net.cpp:676] Ignoring source layer train-data
I0410 14:46:18.648090 18611 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:46:22.595371 18606 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:46:22.595418 18606 solver.cpp:397]     Test net output #1: loss = 5.28649 (* 1 = 5.28649 loss)
I0410 14:46:22.595429 18606 solver.cpp:315] Optimization Done.
I0410 14:46:22.595436 18606 caffe.cpp:259] Optimization Done.