DIGITS-CNN/cars/architecture-investigations/fc/4-layers/2048/caffe_output.log

I0410 01:41:16.280050 25920 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-221024-898c/solver.prototxt
I0410 01:41:16.280222 25920 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 01:41:16.280230 25920 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 01:41:16.280294 25920 caffe.cpp:218] Using GPUs 1
I0410 01:41:16.309846 25920 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0410 01:41:16.617899 25920 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: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 01:41:16.618705 25920 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 01:41:16.619279 25920 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 01:41:16.619297 25920 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 01:41:16.619460 25920 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: 2048
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: 2048
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: 2048
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: 2048
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 01:41:16.619558 25920 layer_factory.hpp:77] Creating layer train-data
I0410 01:41:16.621362 25920 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 01:41:16.621568 25920 net.cpp:84] Creating Layer train-data
I0410 01:41:16.621579 25920 net.cpp:380] train-data -> data
I0410 01:41:16.621599 25920 net.cpp:380] train-data -> label
I0410 01:41:16.621611 25920 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 01:41:16.626471 25920 data_layer.cpp:45] output data size: 128,3,227,227
I0410 01:41:16.762548 25920 net.cpp:122] Setting up train-data
I0410 01:41:16.762574 25920 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 01:41:16.762580 25920 net.cpp:129] Top shape: 128 (128)
I0410 01:41:16.762583 25920 net.cpp:137] Memory required for data: 79149056
I0410 01:41:16.762612 25920 layer_factory.hpp:77] Creating layer conv1
I0410 01:41:16.762636 25920 net.cpp:84] Creating Layer conv1
I0410 01:41:16.762642 25920 net.cpp:406] conv1 <- data
I0410 01:41:16.762655 25920 net.cpp:380] conv1 -> conv1
I0410 01:41:17.429100 25920 net.cpp:122] Setting up conv1
I0410 01:41:17.429122 25920 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:41:17.429127 25920 net.cpp:137] Memory required for data: 227833856
I0410 01:41:17.429150 25920 layer_factory.hpp:77] Creating layer relu1
I0410 01:41:17.429163 25920 net.cpp:84] Creating Layer relu1
I0410 01:41:17.429168 25920 net.cpp:406] relu1 <- conv1
I0410 01:41:17.429177 25920 net.cpp:367] relu1 -> conv1 (in-place)
I0410 01:41:17.429497 25920 net.cpp:122] Setting up relu1
I0410 01:41:17.429508 25920 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:41:17.429513 25920 net.cpp:137] Memory required for data: 376518656
I0410 01:41:17.429518 25920 layer_factory.hpp:77] Creating layer norm1
I0410 01:41:17.429531 25920 net.cpp:84] Creating Layer norm1
I0410 01:41:17.429536 25920 net.cpp:406] norm1 <- conv1
I0410 01:41:17.429543 25920 net.cpp:380] norm1 -> norm1
I0410 01:41:17.430040 25920 net.cpp:122] Setting up norm1
I0410 01:41:17.430052 25920 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:41:17.430056 25920 net.cpp:137] Memory required for data: 525203456
I0410 01:41:17.430061 25920 layer_factory.hpp:77] Creating layer pool1
I0410 01:41:17.430071 25920 net.cpp:84] Creating Layer pool1
I0410 01:41:17.430075 25920 net.cpp:406] pool1 <- norm1
I0410 01:41:17.430081 25920 net.cpp:380] pool1 -> pool1
I0410 01:41:17.430122 25920 net.cpp:122] Setting up pool1
I0410 01:41:17.430130 25920 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 01:41:17.430133 25920 net.cpp:137] Memory required for data: 561035264
I0410 01:41:17.430137 25920 layer_factory.hpp:77] Creating layer conv2
I0410 01:41:17.430150 25920 net.cpp:84] Creating Layer conv2
I0410 01:41:17.430155 25920 net.cpp:406] conv2 <- pool1
I0410 01:41:17.430162 25920 net.cpp:380] conv2 -> conv2
I0410 01:41:17.437330 25920 net.cpp:122] Setting up conv2
I0410 01:41:17.437345 25920 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:41:17.437350 25920 net.cpp:137] Memory required for data: 656586752
I0410 01:41:17.437361 25920 layer_factory.hpp:77] Creating layer relu2
I0410 01:41:17.437368 25920 net.cpp:84] Creating Layer relu2
I0410 01:41:17.437372 25920 net.cpp:406] relu2 <- conv2
I0410 01:41:17.437381 25920 net.cpp:367] relu2 -> conv2 (in-place)
I0410 01:41:17.437901 25920 net.cpp:122] Setting up relu2
I0410 01:41:17.437911 25920 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:41:17.437917 25920 net.cpp:137] Memory required for data: 752138240
I0410 01:41:17.437924 25920 layer_factory.hpp:77] Creating layer norm2
I0410 01:41:17.437932 25920 net.cpp:84] Creating Layer norm2
I0410 01:41:17.437937 25920 net.cpp:406] norm2 <- conv2
I0410 01:41:17.437943 25920 net.cpp:380] norm2 -> norm2
I0410 01:41:17.438347 25920 net.cpp:122] Setting up norm2
I0410 01:41:17.438359 25920 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:41:17.438362 25920 net.cpp:137] Memory required for data: 847689728
I0410 01:41:17.438367 25920 layer_factory.hpp:77] Creating layer pool2
I0410 01:41:17.438376 25920 net.cpp:84] Creating Layer pool2
I0410 01:41:17.438380 25920 net.cpp:406] pool2 <- norm2
I0410 01:41:17.438388 25920 net.cpp:380] pool2 -> pool2
I0410 01:41:17.438419 25920 net.cpp:122] Setting up pool2
I0410 01:41:17.438426 25920 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:41:17.438429 25920 net.cpp:137] Memory required for data: 869840896
I0410 01:41:17.438433 25920 layer_factory.hpp:77] Creating layer conv3
I0410 01:41:17.438446 25920 net.cpp:84] Creating Layer conv3
I0410 01:41:17.438449 25920 net.cpp:406] conv3 <- pool2
I0410 01:41:17.438457 25920 net.cpp:380] conv3 -> conv3
I0410 01:41:17.449296 25920 net.cpp:122] Setting up conv3
I0410 01:41:17.449309 25920 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:41:17.449313 25920 net.cpp:137] Memory required for data: 903067648
I0410 01:41:17.449343 25920 layer_factory.hpp:77] Creating layer relu3
I0410 01:41:17.449352 25920 net.cpp:84] Creating Layer relu3
I0410 01:41:17.449357 25920 net.cpp:406] relu3 <- conv3
I0410 01:41:17.449362 25920 net.cpp:367] relu3 -> conv3 (in-place)
I0410 01:41:17.449887 25920 net.cpp:122] Setting up relu3
I0410 01:41:17.449896 25920 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:41:17.449900 25920 net.cpp:137] Memory required for data: 936294400
I0410 01:41:17.449904 25920 layer_factory.hpp:77] Creating layer conv4
I0410 01:41:17.449915 25920 net.cpp:84] Creating Layer conv4
I0410 01:41:17.449920 25920 net.cpp:406] conv4 <- conv3
I0410 01:41:17.449928 25920 net.cpp:380] conv4 -> conv4
I0410 01:41:17.463598 25920 net.cpp:122] Setting up conv4
I0410 01:41:17.463613 25920 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:41:17.463618 25920 net.cpp:137] Memory required for data: 969521152
I0410 01:41:17.463625 25920 layer_factory.hpp:77] Creating layer relu4
I0410 01:41:17.463632 25920 net.cpp:84] Creating Layer relu4
I0410 01:41:17.463637 25920 net.cpp:406] relu4 <- conv4
I0410 01:41:17.463644 25920 net.cpp:367] relu4 -> conv4 (in-place)
I0410 01:41:17.464012 25920 net.cpp:122] Setting up relu4
I0410 01:41:17.464021 25920 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:41:17.464025 25920 net.cpp:137] Memory required for data: 1002747904
I0410 01:41:17.464030 25920 layer_factory.hpp:77] Creating layer conv5
I0410 01:41:17.464040 25920 net.cpp:84] Creating Layer conv5
I0410 01:41:17.464044 25920 net.cpp:406] conv5 <- conv4
I0410 01:41:17.464053 25920 net.cpp:380] conv5 -> conv5
I0410 01:41:17.475076 25920 net.cpp:122] Setting up conv5
I0410 01:41:17.475092 25920 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:41:17.475096 25920 net.cpp:137] Memory required for data: 1024899072
I0410 01:41:17.475107 25920 layer_factory.hpp:77] Creating layer relu5
I0410 01:41:17.475116 25920 net.cpp:84] Creating Layer relu5
I0410 01:41:17.475121 25920 net.cpp:406] relu5 <- conv5
I0410 01:41:17.475127 25920 net.cpp:367] relu5 -> conv5 (in-place)
I0410 01:41:17.475642 25920 net.cpp:122] Setting up relu5
I0410 01:41:17.475652 25920 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:41:17.475656 25920 net.cpp:137] Memory required for data: 1047050240
I0410 01:41:17.475661 25920 layer_factory.hpp:77] Creating layer pool5
I0410 01:41:17.475670 25920 net.cpp:84] Creating Layer pool5
I0410 01:41:17.475674 25920 net.cpp:406] pool5 <- conv5
I0410 01:41:17.475680 25920 net.cpp:380] pool5 -> pool5
I0410 01:41:17.475721 25920 net.cpp:122] Setting up pool5
I0410 01:41:17.475728 25920 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 01:41:17.475731 25920 net.cpp:137] Memory required for data: 1051768832
I0410 01:41:17.475735 25920 layer_factory.hpp:77] Creating layer fc6
I0410 01:41:17.475745 25920 net.cpp:84] Creating Layer fc6
I0410 01:41:17.475749 25920 net.cpp:406] fc6 <- pool5
I0410 01:41:17.475756 25920 net.cpp:380] fc6 -> fc6
I0410 01:41:17.669117 25920 net.cpp:122] Setting up fc6
I0410 01:41:17.669137 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.669142 25920 net.cpp:137] Memory required for data: 1052817408
I0410 01:41:17.669152 25920 layer_factory.hpp:77] Creating layer relu6
I0410 01:41:17.669160 25920 net.cpp:84] Creating Layer relu6
I0410 01:41:17.669165 25920 net.cpp:406] relu6 <- fc6
I0410 01:41:17.669173 25920 net.cpp:367] relu6 -> fc6 (in-place)
I0410 01:41:17.669844 25920 net.cpp:122] Setting up relu6
I0410 01:41:17.669855 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.669860 25920 net.cpp:137] Memory required for data: 1053865984
I0410 01:41:17.669864 25920 layer_factory.hpp:77] Creating layer drop6
I0410 01:41:17.669872 25920 net.cpp:84] Creating Layer drop6
I0410 01:41:17.669875 25920 net.cpp:406] drop6 <- fc6
I0410 01:41:17.669883 25920 net.cpp:367] drop6 -> fc6 (in-place)
I0410 01:41:17.669910 25920 net.cpp:122] Setting up drop6
I0410 01:41:17.669917 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.669940 25920 net.cpp:137] Memory required for data: 1054914560
I0410 01:41:17.669945 25920 layer_factory.hpp:77] Creating layer fc7
I0410 01:41:17.669973 25920 net.cpp:84] Creating Layer fc7
I0410 01:41:17.669978 25920 net.cpp:406] fc7 <- fc6
I0410 01:41:17.669986 25920 net.cpp:380] fc7 -> fc7
I0410 01:41:17.713100 25920 net.cpp:122] Setting up fc7
I0410 01:41:17.713124 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.713127 25920 net.cpp:137] Memory required for data: 1055963136
I0410 01:41:17.713137 25920 layer_factory.hpp:77] Creating layer relu7
I0410 01:41:17.713146 25920 net.cpp:84] Creating Layer relu7
I0410 01:41:17.713151 25920 net.cpp:406] relu7 <- fc7
I0410 01:41:17.713160 25920 net.cpp:367] relu7 -> fc7 (in-place)
I0410 01:41:17.713797 25920 net.cpp:122] Setting up relu7
I0410 01:41:17.713806 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.713810 25920 net.cpp:137] Memory required for data: 1057011712
I0410 01:41:17.713815 25920 layer_factory.hpp:77] Creating layer drop7
I0410 01:41:17.713824 25920 net.cpp:84] Creating Layer drop7
I0410 01:41:17.713827 25920 net.cpp:406] drop7 <- fc7
I0410 01:41:17.713832 25920 net.cpp:367] drop7 -> fc7 (in-place)
I0410 01:41:17.713862 25920 net.cpp:122] Setting up drop7
I0410 01:41:17.713868 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.713872 25920 net.cpp:137] Memory required for data: 1058060288
I0410 01:41:17.713876 25920 layer_factory.hpp:77] Creating layer fc7.5
I0410 01:41:17.713883 25920 net.cpp:84] Creating Layer fc7.5
I0410 01:41:17.713887 25920 net.cpp:406] fc7.5 <- fc7
I0410 01:41:17.713894 25920 net.cpp:380] fc7.5 -> fc7.5
I0410 01:41:17.756947 25920 net.cpp:122] Setting up fc7.5
I0410 01:41:17.756968 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.756973 25920 net.cpp:137] Memory required for data: 1059108864
I0410 01:41:17.756981 25920 layer_factory.hpp:77] Creating layer relu7.5
I0410 01:41:17.756991 25920 net.cpp:84] Creating Layer relu7.5
I0410 01:41:17.756996 25920 net.cpp:406] relu7.5 <- fc7.5
I0410 01:41:17.757004 25920 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 01:41:17.757700 25920 net.cpp:122] Setting up relu7.5
I0410 01:41:17.757711 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.757715 25920 net.cpp:137] Memory required for data: 1060157440
I0410 01:41:17.757719 25920 layer_factory.hpp:77] Creating layer drop7.5
I0410 01:41:17.757727 25920 net.cpp:84] Creating Layer drop7.5
I0410 01:41:17.757731 25920 net.cpp:406] drop7.5 <- fc7.5
I0410 01:41:17.757737 25920 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 01:41:17.757763 25920 net.cpp:122] Setting up drop7.5
I0410 01:41:17.757769 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.757773 25920 net.cpp:137] Memory required for data: 1061206016
I0410 01:41:17.757777 25920 layer_factory.hpp:77] Creating layer fc7.6
I0410 01:41:17.757786 25920 net.cpp:84] Creating Layer fc7.6
I0410 01:41:17.757789 25920 net.cpp:406] fc7.6 <- fc7.5
I0410 01:41:17.757795 25920 net.cpp:380] fc7.6 -> fc7.6
I0410 01:41:17.800765 25920 net.cpp:122] Setting up fc7.6
I0410 01:41:17.800786 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.800791 25920 net.cpp:137] Memory required for data: 1062254592
I0410 01:41:17.800806 25920 layer_factory.hpp:77] Creating layer relu7.6
I0410 01:41:17.800814 25920 net.cpp:84] Creating Layer relu7.6
I0410 01:41:17.800819 25920 net.cpp:406] relu7.6 <- fc7.6
I0410 01:41:17.800827 25920 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 01:41:17.801494 25920 net.cpp:122] Setting up relu7.6
I0410 01:41:17.801504 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.801508 25920 net.cpp:137] Memory required for data: 1063303168
I0410 01:41:17.801512 25920 layer_factory.hpp:77] Creating layer drop7.6
I0410 01:41:17.801522 25920 net.cpp:84] Creating Layer drop7.6
I0410 01:41:17.801525 25920 net.cpp:406] drop7.6 <- fc7.6
I0410 01:41:17.801532 25920 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 01:41:17.801558 25920 net.cpp:122] Setting up drop7.6
I0410 01:41:17.801563 25920 net.cpp:129] Top shape: 128 2048 (262144)
I0410 01:41:17.801584 25920 net.cpp:137] Memory required for data: 1064351744
I0410 01:41:17.801589 25920 layer_factory.hpp:77] Creating layer fc8
I0410 01:41:17.801595 25920 net.cpp:84] Creating Layer fc8
I0410 01:41:17.801599 25920 net.cpp:406] fc8 <- fc7.6
I0410 01:41:17.801606 25920 net.cpp:380] fc8 -> fc8
I0410 01:41:17.806046 25920 net.cpp:122] Setting up fc8
I0410 01:41:17.806056 25920 net.cpp:129] Top shape: 128 196 (25088)
I0410 01:41:17.806061 25920 net.cpp:137] Memory required for data: 1064452096
I0410 01:41:17.806066 25920 layer_factory.hpp:77] Creating layer loss
I0410 01:41:17.806074 25920 net.cpp:84] Creating Layer loss
I0410 01:41:17.806079 25920 net.cpp:406] loss <- fc8
I0410 01:41:17.806084 25920 net.cpp:406] loss <- label
I0410 01:41:17.806092 25920 net.cpp:380] loss -> loss
I0410 01:41:17.806102 25920 layer_factory.hpp:77] Creating layer loss
I0410 01:41:17.806730 25920 net.cpp:122] Setting up loss
I0410 01:41:17.806740 25920 net.cpp:129] Top shape: (1)
I0410 01:41:17.806743 25920 net.cpp:132]     with loss weight 1
I0410 01:41:17.806761 25920 net.cpp:137] Memory required for data: 1064452100
I0410 01:41:17.806766 25920 net.cpp:198] loss needs backward computation.
I0410 01:41:17.806774 25920 net.cpp:198] fc8 needs backward computation.
I0410 01:41:17.806778 25920 net.cpp:198] drop7.6 needs backward computation.
I0410 01:41:17.806782 25920 net.cpp:198] relu7.6 needs backward computation.
I0410 01:41:17.806785 25920 net.cpp:198] fc7.6 needs backward computation.
I0410 01:41:17.806789 25920 net.cpp:198] drop7.5 needs backward computation.
I0410 01:41:17.806792 25920 net.cpp:198] relu7.5 needs backward computation.
I0410 01:41:17.806797 25920 net.cpp:198] fc7.5 needs backward computation.
I0410 01:41:17.806800 25920 net.cpp:198] drop7 needs backward computation.
I0410 01:41:17.806804 25920 net.cpp:198] relu7 needs backward computation.
I0410 01:41:17.806807 25920 net.cpp:198] fc7 needs backward computation.
I0410 01:41:17.806813 25920 net.cpp:198] drop6 needs backward computation.
I0410 01:41:17.806816 25920 net.cpp:198] relu6 needs backward computation.
I0410 01:41:17.806820 25920 net.cpp:198] fc6 needs backward computation.
I0410 01:41:17.806823 25920 net.cpp:198] pool5 needs backward computation.
I0410 01:41:17.806828 25920 net.cpp:198] relu5 needs backward computation.
I0410 01:41:17.806831 25920 net.cpp:198] conv5 needs backward computation.
I0410 01:41:17.806835 25920 net.cpp:198] relu4 needs backward computation.
I0410 01:41:17.806839 25920 net.cpp:198] conv4 needs backward computation.
I0410 01:41:17.806843 25920 net.cpp:198] relu3 needs backward computation.
I0410 01:41:17.806847 25920 net.cpp:198] conv3 needs backward computation.
I0410 01:41:17.806851 25920 net.cpp:198] pool2 needs backward computation.
I0410 01:41:17.806857 25920 net.cpp:198] norm2 needs backward computation.
I0410 01:41:17.806861 25920 net.cpp:198] relu2 needs backward computation.
I0410 01:41:17.806865 25920 net.cpp:198] conv2 needs backward computation.
I0410 01:41:17.806869 25920 net.cpp:198] pool1 needs backward computation.
I0410 01:41:17.806874 25920 net.cpp:198] norm1 needs backward computation.
I0410 01:41:17.806877 25920 net.cpp:198] relu1 needs backward computation.
I0410 01:41:17.806881 25920 net.cpp:198] conv1 needs backward computation.
I0410 01:41:17.806885 25920 net.cpp:200] train-data does not need backward computation.
I0410 01:41:17.806890 25920 net.cpp:242] This network produces output loss
I0410 01:41:17.806906 25920 net.cpp:255] Network initialization done.
I0410 01:41:17.875249 25920 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 01:41:17.875340 25920 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 01:41:17.875761 25920 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: 2048
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: 2048
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: 2048
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: 2048
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 01:41:17.876035 25920 layer_factory.hpp:77] Creating layer val-data
I0410 01:41:18.103448 25920 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 01:41:18.103982 25920 net.cpp:84] Creating Layer val-data
I0410 01:41:18.104009 25920 net.cpp:380] val-data -> data
I0410 01:41:18.104034 25920 net.cpp:380] val-data -> label
I0410 01:41:18.104051 25920 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 01:41:18.151717 25920 data_layer.cpp:45] output data size: 32,3,227,227
I0410 01:41:18.209585 25920 net.cpp:122] Setting up val-data
I0410 01:41:18.209614 25920 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 01:41:18.209622 25920 net.cpp:129] Top shape: 32 (32)
I0410 01:41:18.209628 25920 net.cpp:137] Memory required for data: 19787264
I0410 01:41:18.209637 25920 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 01:41:18.209654 25920 net.cpp:84] Creating Layer label_val-data_1_split
I0410 01:41:18.209661 25920 net.cpp:406] label_val-data_1_split <- label
I0410 01:41:18.209671 25920 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 01:41:18.209686 25920 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 01:41:18.209826 25920 net.cpp:122] Setting up label_val-data_1_split
I0410 01:41:18.209836 25920 net.cpp:129] Top shape: 32 (32)
I0410 01:41:18.209843 25920 net.cpp:129] Top shape: 32 (32)
I0410 01:41:18.209848 25920 net.cpp:137] Memory required for data: 19787520
I0410 01:41:18.209853 25920 layer_factory.hpp:77] Creating layer conv1
I0410 01:41:18.209873 25920 net.cpp:84] Creating Layer conv1
I0410 01:41:18.209879 25920 net.cpp:406] conv1 <- data
I0410 01:41:18.209889 25920 net.cpp:380] conv1 -> conv1
I0410 01:41:18.212980 25920 net.cpp:122] Setting up conv1
I0410 01:41:18.212999 25920 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:41:18.213006 25920 net.cpp:137] Memory required for data: 56958720
I0410 01:41:18.213021 25920 layer_factory.hpp:77] Creating layer relu1
I0410 01:41:18.213032 25920 net.cpp:84] Creating Layer relu1
I0410 01:41:18.213038 25920 net.cpp:406] relu1 <- conv1
I0410 01:41:18.213073 25920 net.cpp:367] relu1 -> conv1 (in-place)
I0410 01:41:18.215301 25920 net.cpp:122] Setting up relu1
I0410 01:41:18.215319 25920 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:41:18.215325 25920 net.cpp:137] Memory required for data: 94129920
I0410 01:41:18.215332 25920 layer_factory.hpp:77] Creating layer norm1
I0410 01:41:18.215345 25920 net.cpp:84] Creating Layer norm1
I0410 01:41:18.215351 25920 net.cpp:406] norm1 <- conv1
I0410 01:41:18.215361 25920 net.cpp:380] norm1 -> norm1
I0410 01:41:18.216141 25920 net.cpp:122] Setting up norm1
I0410 01:41:18.216158 25920 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:41:18.216164 25920 net.cpp:137] Memory required for data: 131301120
I0410 01:41:18.216171 25920 layer_factory.hpp:77] Creating layer pool1
I0410 01:41:18.216181 25920 net.cpp:84] Creating Layer pool1
I0410 01:41:18.216187 25920 net.cpp:406] pool1 <- norm1
I0410 01:41:18.216197 25920 net.cpp:380] pool1 -> pool1
I0410 01:41:18.216249 25920 net.cpp:122] Setting up pool1
I0410 01:41:18.216259 25920 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 01:41:18.216264 25920 net.cpp:137] Memory required for data: 140259072
I0410 01:41:18.216269 25920 layer_factory.hpp:77] Creating layer conv2
I0410 01:41:18.216284 25920 net.cpp:84] Creating Layer conv2
I0410 01:41:18.216289 25920 net.cpp:406] conv2 <- pool1
I0410 01:41:18.216298 25920 net.cpp:380] conv2 -> conv2
I0410 01:41:18.227139 25920 net.cpp:122] Setting up conv2
I0410 01:41:18.227159 25920 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:41:18.227164 25920 net.cpp:137] Memory required for data: 164146944
I0410 01:41:18.227180 25920 layer_factory.hpp:77] Creating layer relu2
I0410 01:41:18.227193 25920 net.cpp:84] Creating Layer relu2
I0410 01:41:18.227200 25920 net.cpp:406] relu2 <- conv2
I0410 01:41:18.227208 25920 net.cpp:367] relu2 -> conv2 (in-place)
I0410 01:41:18.227807 25920 net.cpp:122] Setting up relu2
I0410 01:41:18.227819 25920 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:41:18.227824 25920 net.cpp:137] Memory required for data: 188034816
I0410 01:41:18.227830 25920 layer_factory.hpp:77] Creating layer norm2
I0410 01:41:18.227845 25920 net.cpp:84] Creating Layer norm2
I0410 01:41:18.227851 25920 net.cpp:406] norm2 <- conv2
I0410 01:41:18.227860 25920 net.cpp:380] norm2 -> norm2
I0410 01:41:18.228727 25920 net.cpp:122] Setting up norm2
I0410 01:41:18.228742 25920 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:41:18.228749 25920 net.cpp:137] Memory required for data: 211922688
I0410 01:41:18.228755 25920 layer_factory.hpp:77] Creating layer pool2
I0410 01:41:18.228765 25920 net.cpp:84] Creating Layer pool2
I0410 01:41:18.228771 25920 net.cpp:406] pool2 <- norm2
I0410 01:41:18.228781 25920 net.cpp:380] pool2 -> pool2
I0410 01:41:18.228832 25920 net.cpp:122] Setting up pool2
I0410 01:41:18.228842 25920 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:41:18.228847 25920 net.cpp:137] Memory required for data: 217460480
I0410 01:41:18.228852 25920 layer_factory.hpp:77] Creating layer conv3
I0410 01:41:18.228869 25920 net.cpp:84] Creating Layer conv3
I0410 01:41:18.228874 25920 net.cpp:406] conv3 <- pool2
I0410 01:41:18.228883 25920 net.cpp:380] conv3 -> conv3
I0410 01:41:18.249855 25920 net.cpp:122] Setting up conv3
I0410 01:41:18.249878 25920 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:41:18.249884 25920 net.cpp:137] Memory required for data: 225767168
I0410 01:41:18.249902 25920 layer_factory.hpp:77] Creating layer relu3
I0410 01:41:18.249915 25920 net.cpp:84] Creating Layer relu3
I0410 01:41:18.249922 25920 net.cpp:406] relu3 <- conv3
I0410 01:41:18.249930 25920 net.cpp:367] relu3 -> conv3 (in-place)
I0410 01:41:18.250715 25920 net.cpp:122] Setting up relu3
I0410 01:41:18.250730 25920 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:41:18.250735 25920 net.cpp:137] Memory required for data: 234073856
I0410 01:41:18.250741 25920 layer_factory.hpp:77] Creating layer conv4
I0410 01:41:18.250757 25920 net.cpp:84] Creating Layer conv4
I0410 01:41:18.250764 25920 net.cpp:406] conv4 <- conv3
I0410 01:41:18.250795 25920 net.cpp:380] conv4 -> conv4
I0410 01:41:18.264240 25920 net.cpp:122] Setting up conv4
I0410 01:41:18.264258 25920 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:41:18.264262 25920 net.cpp:137] Memory required for data: 242380544
I0410 01:41:18.264272 25920 layer_factory.hpp:77] Creating layer relu4
I0410 01:41:18.264283 25920 net.cpp:84] Creating Layer relu4
I0410 01:41:18.264288 25920 net.cpp:406] relu4 <- conv4
I0410 01:41:18.264295 25920 net.cpp:367] relu4 -> conv4 (in-place)
I0410 01:41:18.266108 25920 net.cpp:122] Setting up relu4
I0410 01:41:18.266120 25920 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:41:18.266125 25920 net.cpp:137] Memory required for data: 250687232
I0410 01:41:18.266130 25920 layer_factory.hpp:77] Creating layer conv5
I0410 01:41:18.266144 25920 net.cpp:84] Creating Layer conv5
I0410 01:41:18.266149 25920 net.cpp:406] conv5 <- conv4
I0410 01:41:18.266158 25920 net.cpp:380] conv5 -> conv5
I0410 01:41:18.277709 25920 net.cpp:122] Setting up conv5
I0410 01:41:18.277727 25920 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:41:18.277731 25920 net.cpp:137] Memory required for data: 256225024
I0410 01:41:18.277745 25920 layer_factory.hpp:77] Creating layer relu5
I0410 01:41:18.277755 25920 net.cpp:84] Creating Layer relu5
I0410 01:41:18.277760 25920 net.cpp:406] relu5 <- conv5
I0410 01:41:18.277768 25920 net.cpp:367] relu5 -> conv5 (in-place)
I0410 01:41:18.278223 25920 net.cpp:122] Setting up relu5
I0410 01:41:18.278234 25920 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:41:18.278237 25920 net.cpp:137] Memory required for data: 261762816
I0410 01:41:18.278242 25920 layer_factory.hpp:77] Creating layer pool5
I0410 01:41:18.278254 25920 net.cpp:84] Creating Layer pool5
I0410 01:41:18.278259 25920 net.cpp:406] pool5 <- conv5
I0410 01:41:18.278266 25920 net.cpp:380] pool5 -> pool5
I0410 01:41:18.278312 25920 net.cpp:122] Setting up pool5
I0410 01:41:18.278319 25920 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 01:41:18.278324 25920 net.cpp:137] Memory required for data: 262942464
I0410 01:41:18.278329 25920 layer_factory.hpp:77] Creating layer fc6
I0410 01:41:18.278338 25920 net.cpp:84] Creating Layer fc6
I0410 01:41:18.278343 25920 net.cpp:406] fc6 <- pool5
I0410 01:41:18.278350 25920 net.cpp:380] fc6 -> fc6
I0410 01:41:18.474421 25920 net.cpp:122] Setting up fc6
I0410 01:41:18.474442 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.474447 25920 net.cpp:137] Memory required for data: 263204608
I0410 01:41:18.474455 25920 layer_factory.hpp:77] Creating layer relu6
I0410 01:41:18.474467 25920 net.cpp:84] Creating Layer relu6
I0410 01:41:18.474470 25920 net.cpp:406] relu6 <- fc6
I0410 01:41:18.474478 25920 net.cpp:367] relu6 -> fc6 (in-place)
I0410 01:41:18.475145 25920 net.cpp:122] Setting up relu6
I0410 01:41:18.475154 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.475158 25920 net.cpp:137] Memory required for data: 263466752
I0410 01:41:18.475162 25920 layer_factory.hpp:77] Creating layer drop6
I0410 01:41:18.475172 25920 net.cpp:84] Creating Layer drop6
I0410 01:41:18.475175 25920 net.cpp:406] drop6 <- fc6
I0410 01:41:18.475183 25920 net.cpp:367] drop6 -> fc6 (in-place)
I0410 01:41:18.475208 25920 net.cpp:122] Setting up drop6
I0410 01:41:18.475212 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.475216 25920 net.cpp:137] Memory required for data: 263728896
I0410 01:41:18.475219 25920 layer_factory.hpp:77] Creating layer fc7
I0410 01:41:18.475229 25920 net.cpp:84] Creating Layer fc7
I0410 01:41:18.475232 25920 net.cpp:406] fc7 <- fc6
I0410 01:41:18.475239 25920 net.cpp:380] fc7 -> fc7
I0410 01:41:18.518512 25920 net.cpp:122] Setting up fc7
I0410 01:41:18.518533 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.518537 25920 net.cpp:137] Memory required for data: 263991040
I0410 01:41:18.518546 25920 layer_factory.hpp:77] Creating layer relu7
I0410 01:41:18.518555 25920 net.cpp:84] Creating Layer relu7
I0410 01:41:18.518560 25920 net.cpp:406] relu7 <- fc7
I0410 01:41:18.518585 25920 net.cpp:367] relu7 -> fc7 (in-place)
I0410 01:41:18.522886 25920 net.cpp:122] Setting up relu7
I0410 01:41:18.522898 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.522902 25920 net.cpp:137] Memory required for data: 264253184
I0410 01:41:18.522908 25920 layer_factory.hpp:77] Creating layer drop7
I0410 01:41:18.522915 25920 net.cpp:84] Creating Layer drop7
I0410 01:41:18.522919 25920 net.cpp:406] drop7 <- fc7
I0410 01:41:18.522924 25920 net.cpp:367] drop7 -> fc7 (in-place)
I0410 01:41:18.522953 25920 net.cpp:122] Setting up drop7
I0410 01:41:18.522958 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.522961 25920 net.cpp:137] Memory required for data: 264515328
I0410 01:41:18.522965 25920 layer_factory.hpp:77] Creating layer fc7.5
I0410 01:41:18.522974 25920 net.cpp:84] Creating Layer fc7.5
I0410 01:41:18.522977 25920 net.cpp:406] fc7.5 <- fc7
I0410 01:41:18.522982 25920 net.cpp:380] fc7.5 -> fc7.5
I0410 01:41:18.566023 25920 net.cpp:122] Setting up fc7.5
I0410 01:41:18.566042 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.566046 25920 net.cpp:137] Memory required for data: 264777472
I0410 01:41:18.566056 25920 layer_factory.hpp:77] Creating layer relu7.5
I0410 01:41:18.566066 25920 net.cpp:84] Creating Layer relu7.5
I0410 01:41:18.566071 25920 net.cpp:406] relu7.5 <- fc7.5
I0410 01:41:18.566079 25920 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 01:41:18.567873 25920 net.cpp:122] Setting up relu7.5
I0410 01:41:18.567885 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.567889 25920 net.cpp:137] Memory required for data: 265039616
I0410 01:41:18.567893 25920 layer_factory.hpp:77] Creating layer drop7.5
I0410 01:41:18.567903 25920 net.cpp:84] Creating Layer drop7.5
I0410 01:41:18.567907 25920 net.cpp:406] drop7.5 <- fc7.5
I0410 01:41:18.567914 25920 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 01:41:18.567941 25920 net.cpp:122] Setting up drop7.5
I0410 01:41:18.567947 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.567950 25920 net.cpp:137] Memory required for data: 265301760
I0410 01:41:18.567955 25920 layer_factory.hpp:77] Creating layer fc7.6
I0410 01:41:18.567961 25920 net.cpp:84] Creating Layer fc7.6
I0410 01:41:18.567965 25920 net.cpp:406] fc7.6 <- fc7.5
I0410 01:41:18.567972 25920 net.cpp:380] fc7.6 -> fc7.6
I0410 01:41:18.611063 25920 net.cpp:122] Setting up fc7.6
I0410 01:41:18.611083 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.611086 25920 net.cpp:137] Memory required for data: 265563904
I0410 01:41:18.611100 25920 layer_factory.hpp:77] Creating layer relu7.6
I0410 01:41:18.611111 25920 net.cpp:84] Creating Layer relu7.6
I0410 01:41:18.611115 25920 net.cpp:406] relu7.6 <- fc7.6
I0410 01:41:18.611124 25920 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 01:41:18.611573 25920 net.cpp:122] Setting up relu7.6
I0410 01:41:18.611582 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.611584 25920 net.cpp:137] Memory required for data: 265826048
I0410 01:41:18.611588 25920 layer_factory.hpp:77] Creating layer drop7.6
I0410 01:41:18.611595 25920 net.cpp:84] Creating Layer drop7.6
I0410 01:41:18.611599 25920 net.cpp:406] drop7.6 <- fc7.6
I0410 01:41:18.611606 25920 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 01:41:18.611631 25920 net.cpp:122] Setting up drop7.6
I0410 01:41:18.611636 25920 net.cpp:129] Top shape: 32 2048 (65536)
I0410 01:41:18.611639 25920 net.cpp:137] Memory required for data: 266088192
I0410 01:41:18.611644 25920 layer_factory.hpp:77] Creating layer fc8
I0410 01:41:18.611652 25920 net.cpp:84] Creating Layer fc8
I0410 01:41:18.611655 25920 net.cpp:406] fc8 <- fc7.6
I0410 01:41:18.611663 25920 net.cpp:380] fc8 -> fc8
I0410 01:41:18.616133 25920 net.cpp:122] Setting up fc8
I0410 01:41:18.616143 25920 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:41:18.616147 25920 net.cpp:137] Memory required for data: 266113280
I0410 01:41:18.616154 25920 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 01:41:18.616163 25920 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 01:41:18.616166 25920 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 01:41:18.616191 25920 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 01:41:18.616199 25920 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 01:41:18.616237 25920 net.cpp:122] Setting up fc8_fc8_0_split
I0410 01:41:18.616243 25920 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:41:18.616248 25920 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:41:18.616251 25920 net.cpp:137] Memory required for data: 266163456
I0410 01:41:18.616255 25920 layer_factory.hpp:77] Creating layer accuracy
I0410 01:41:18.616262 25920 net.cpp:84] Creating Layer accuracy
I0410 01:41:18.616266 25920 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 01:41:18.616271 25920 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 01:41:18.616276 25920 net.cpp:380] accuracy -> accuracy
I0410 01:41:18.616284 25920 net.cpp:122] Setting up accuracy
I0410 01:41:18.616288 25920 net.cpp:129] Top shape: (1)
I0410 01:41:18.616292 25920 net.cpp:137] Memory required for data: 266163460
I0410 01:41:18.616295 25920 layer_factory.hpp:77] Creating layer loss
I0410 01:41:18.616308 25920 net.cpp:84] Creating Layer loss
I0410 01:41:18.616312 25920 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 01:41:18.616317 25920 net.cpp:406] loss <- label_val-data_1_split_1
I0410 01:41:18.616322 25920 net.cpp:380] loss -> loss
I0410 01:41:18.616330 25920 layer_factory.hpp:77] Creating layer loss
I0410 01:41:18.617004 25920 net.cpp:122] Setting up loss
I0410 01:41:18.617014 25920 net.cpp:129] Top shape: (1)
I0410 01:41:18.617017 25920 net.cpp:132]     with loss weight 1
I0410 01:41:18.617028 25920 net.cpp:137] Memory required for data: 266163464
I0410 01:41:18.617033 25920 net.cpp:198] loss needs backward computation.
I0410 01:41:18.617038 25920 net.cpp:200] accuracy does not need backward computation.
I0410 01:41:18.617043 25920 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 01:41:18.617046 25920 net.cpp:198] fc8 needs backward computation.
I0410 01:41:18.617050 25920 net.cpp:198] drop7.6 needs backward computation.
I0410 01:41:18.617053 25920 net.cpp:198] relu7.6 needs backward computation.
I0410 01:41:18.617058 25920 net.cpp:198] fc7.6 needs backward computation.
I0410 01:41:18.617061 25920 net.cpp:198] drop7.5 needs backward computation.
I0410 01:41:18.617065 25920 net.cpp:198] relu7.5 needs backward computation.
I0410 01:41:18.617071 25920 net.cpp:198] fc7.5 needs backward computation.
I0410 01:41:18.617075 25920 net.cpp:198] drop7 needs backward computation.
I0410 01:41:18.617079 25920 net.cpp:198] relu7 needs backward computation.
I0410 01:41:18.617082 25920 net.cpp:198] fc7 needs backward computation.
I0410 01:41:18.617086 25920 net.cpp:198] drop6 needs backward computation.
I0410 01:41:18.617090 25920 net.cpp:198] relu6 needs backward computation.
I0410 01:41:18.617094 25920 net.cpp:198] fc6 needs backward computation.
I0410 01:41:18.617098 25920 net.cpp:198] pool5 needs backward computation.
I0410 01:41:18.617102 25920 net.cpp:198] relu5 needs backward computation.
I0410 01:41:18.617106 25920 net.cpp:198] conv5 needs backward computation.
I0410 01:41:18.617110 25920 net.cpp:198] relu4 needs backward computation.
I0410 01:41:18.617115 25920 net.cpp:198] conv4 needs backward computation.
I0410 01:41:18.617117 25920 net.cpp:198] relu3 needs backward computation.
I0410 01:41:18.617121 25920 net.cpp:198] conv3 needs backward computation.
I0410 01:41:18.617125 25920 net.cpp:198] pool2 needs backward computation.
I0410 01:41:18.617130 25920 net.cpp:198] norm2 needs backward computation.
I0410 01:41:18.617133 25920 net.cpp:198] relu2 needs backward computation.
I0410 01:41:18.617136 25920 net.cpp:198] conv2 needs backward computation.
I0410 01:41:18.617141 25920 net.cpp:198] pool1 needs backward computation.
I0410 01:41:18.617144 25920 net.cpp:198] norm1 needs backward computation.
I0410 01:41:18.617148 25920 net.cpp:198] relu1 needs backward computation.
I0410 01:41:18.617152 25920 net.cpp:198] conv1 needs backward computation.
I0410 01:41:18.617157 25920 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 01:41:18.617172 25920 net.cpp:200] val-data does not need backward computation.
I0410 01:41:18.617174 25920 net.cpp:242] This network produces output accuracy
I0410 01:41:18.617179 25920 net.cpp:242] This network produces output loss
I0410 01:41:18.617199 25920 net.cpp:255] Network initialization done.
I0410 01:41:18.617285 25920 solver.cpp:56] Solver scaffolding done.
I0410 01:41:18.617859 25920 caffe.cpp:248] Starting Optimization
I0410 01:41:18.617868 25920 solver.cpp:272] Solving
I0410 01:41:18.617871 25920 solver.cpp:273] Learning Rate Policy: exp
I0410 01:41:18.623039 25920 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 01:41:18.623049 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:41:18.689673 25920 blocking_queue.cpp:49] Waiting for data
I0410 01:41:22.901533 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:22.946089 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00306373
I0410 01:41:22.946132 25920 solver.cpp:397]     Test net output #1: loss = 5.27893 (* 1 = 5.27893 loss)
I0410 01:41:23.041874 25920 solver.cpp:218] Iteration 0 (-2.69048e-38 iter/s, 4.42383s/12 iters), loss = 5.2887
I0410 01:41:23.043388 25920 solver.cpp:237]     Train net output #0: loss = 5.2887 (* 1 = 5.2887 loss)
I0410 01:41:23.043416 25920 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 01:41:27.162381 25920 solver.cpp:218] Iteration 12 (2.91343 iter/s, 4.11886s/12 iters), loss = 5.28211
I0410 01:41:27.162425 25920 solver.cpp:237]     Train net output #0: loss = 5.28211 (* 1 = 5.28211 loss)
I0410 01:41:27.162434 25920 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 01:41:32.194862 25920 solver.cpp:218] Iteration 24 (2.38461 iter/s, 5.03227s/12 iters), loss = 5.28689
I0410 01:41:32.194914 25920 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0410 01:41:32.194927 25920 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 01:41:37.300762 25920 solver.cpp:218] Iteration 36 (2.35032 iter/s, 5.10569s/12 iters), loss = 5.28249
I0410 01:41:37.300806 25920 solver.cpp:237]     Train net output #0: loss = 5.28249 (* 1 = 5.28249 loss)
I0410 01:41:37.300815 25920 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 01:41:42.253314 25920 solver.cpp:218] Iteration 48 (2.42309 iter/s, 4.95235s/12 iters), loss = 5.28639
I0410 01:41:42.253358 25920 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0410 01:41:42.253367 25920 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 01:41:47.201326 25920 solver.cpp:218] Iteration 60 (2.42532 iter/s, 4.94781s/12 iters), loss = 5.28826
I0410 01:41:47.201452 25920 solver.cpp:237]     Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
I0410 01:41:47.201463 25920 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 01:41:52.211798 25920 solver.cpp:218] Iteration 72 (2.39512 iter/s, 5.01019s/12 iters), loss = 5.28101
I0410 01:41:52.211843 25920 solver.cpp:237]     Train net output #0: loss = 5.28101 (* 1 = 5.28101 loss)
I0410 01:41:52.211853 25920 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 01:41:57.201001 25920 solver.cpp:218] Iteration 84 (2.40529 iter/s, 4.989s/12 iters), loss = 5.29095
I0410 01:41:57.201050 25920 solver.cpp:237]     Train net output #0: loss = 5.29095 (* 1 = 5.29095 loss)
I0410 01:41:57.201059 25920 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 01:42:02.254276 25920 solver.cpp:218] Iteration 96 (2.37479 iter/s, 5.05307s/12 iters), loss = 5.29736
I0410 01:42:02.254313 25920 solver.cpp:237]     Train net output #0: loss = 5.29736 (* 1 = 5.29736 loss)
I0410 01:42:02.254323 25920 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 01:42:03.962142 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:04.270999 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 01:42:07.284962 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 01:42:10.024329 25920 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 01:42:10.024350 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:42:14.377101 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:14.453534 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:42:14.453572 25920 solver.cpp:397]     Test net output #1: loss = 5.28409 (* 1 = 5.28409 loss)
I0410 01:42:16.396631 25920 solver.cpp:218] Iteration 108 (0.848543 iter/s, 14.1419s/12 iters), loss = 5.28568
I0410 01:42:16.396689 25920 solver.cpp:237]     Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
I0410 01:42:16.396703 25920 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 01:42:21.396550 25920 solver.cpp:218] Iteration 120 (2.40014 iter/s, 4.99971s/12 iters), loss = 5.27202
I0410 01:42:21.396682 25920 solver.cpp:237]     Train net output #0: loss = 5.27202 (* 1 = 5.27202 loss)
I0410 01:42:21.396692 25920 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 01:42:26.470582 25920 solver.cpp:218] Iteration 132 (2.36512 iter/s, 5.07374s/12 iters), loss = 5.23067
I0410 01:42:26.470635 25920 solver.cpp:237]     Train net output #0: loss = 5.23067 (* 1 = 5.23067 loss)
I0410 01:42:26.470645 25920 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 01:42:31.482751 25920 solver.cpp:218] Iteration 144 (2.39427 iter/s, 5.01196s/12 iters), loss = 5.29989
I0410 01:42:31.482801 25920 solver.cpp:237]     Train net output #0: loss = 5.29989 (* 1 = 5.29989 loss)
I0410 01:42:31.482812 25920 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 01:42:36.520687 25920 solver.cpp:218] Iteration 156 (2.38203 iter/s, 5.03773s/12 iters), loss = 5.25435
I0410 01:42:36.520726 25920 solver.cpp:237]     Train net output #0: loss = 5.25435 (* 1 = 5.25435 loss)
I0410 01:42:36.520735 25920 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 01:42:41.611913 25920 solver.cpp:218] Iteration 168 (2.35709 iter/s, 5.09102s/12 iters), loss = 5.26937
I0410 01:42:41.611963 25920 solver.cpp:237]     Train net output #0: loss = 5.26937 (* 1 = 5.26937 loss)
I0410 01:42:41.611970 25920 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 01:42:46.607583 25920 solver.cpp:218] Iteration 180 (2.40218 iter/s, 4.99546s/12 iters), loss = 5.26827
I0410 01:42:46.607638 25920 solver.cpp:237]     Train net output #0: loss = 5.26827 (* 1 = 5.26827 loss)
I0410 01:42:46.607651 25920 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 01:42:51.640991 25920 solver.cpp:218] Iteration 192 (2.38417 iter/s, 5.03319s/12 iters), loss = 5.27473
I0410 01:42:51.641074 25920 solver.cpp:237]     Train net output #0: loss = 5.27473 (* 1 = 5.27473 loss)
I0410 01:42:51.641088 25920 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 01:42:55.627313 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:56.367338 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 01:42:59.529875 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 01:43:03.024351 25920 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 01:43:03.024376 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:43:07.306228 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:07.429486 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:43:07.429539 25920 solver.cpp:397]     Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
I0410 01:43:07.516933 25920 solver.cpp:218] Iteration 204 (0.755887 iter/s, 15.8754s/12 iters), loss = 5.27339
I0410 01:43:07.516989 25920 solver.cpp:237]     Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
I0410 01:43:07.517001 25920 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 01:43:11.872196 25920 solver.cpp:218] Iteration 216 (2.75541 iter/s, 4.35507s/12 iters), loss = 5.28149
I0410 01:43:11.872242 25920 solver.cpp:237]     Train net output #0: loss = 5.28149 (* 1 = 5.28149 loss)
I0410 01:43:11.872252 25920 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 01:43:16.842185 25920 solver.cpp:218] Iteration 228 (2.4146 iter/s, 4.96978s/12 iters), loss = 5.25893
I0410 01:43:16.842247 25920 solver.cpp:237]     Train net output #0: loss = 5.25893 (* 1 = 5.25893 loss)
I0410 01:43:16.842260 25920 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 01:43:21.810011 25920 solver.cpp:218] Iteration 240 (2.41565 iter/s, 4.96761s/12 iters), loss = 5.29833
I0410 01:43:21.810128 25920 solver.cpp:237]     Train net output #0: loss = 5.29833 (* 1 = 5.29833 loss)
I0410 01:43:21.810142 25920 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 01:43:26.832327 25920 solver.cpp:218] Iteration 252 (2.38947 iter/s, 5.02204s/12 iters), loss = 5.27074
I0410 01:43:26.832376 25920 solver.cpp:237]     Train net output #0: loss = 5.27074 (* 1 = 5.27074 loss)
I0410 01:43:26.832384 25920 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 01:43:31.821950 25920 solver.cpp:218] Iteration 264 (2.40509 iter/s, 4.98942s/12 iters), loss = 5.27784
I0410 01:43:31.822011 25920 solver.cpp:237]     Train net output #0: loss = 5.27784 (* 1 = 5.27784 loss)
I0410 01:43:31.822021 25920 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 01:43:36.794260 25920 solver.cpp:218] Iteration 276 (2.41347 iter/s, 4.97209s/12 iters), loss = 5.29742
I0410 01:43:36.794312 25920 solver.cpp:237]     Train net output #0: loss = 5.29742 (* 1 = 5.29742 loss)
I0410 01:43:36.794324 25920 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 01:43:42.007957 25920 solver.cpp:218] Iteration 288 (2.30173 iter/s, 5.21348s/12 iters), loss = 5.27559
I0410 01:43:42.008013 25920 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0410 01:43:42.008025 25920 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 01:43:47.084844 25920 solver.cpp:218] Iteration 300 (2.36376 iter/s, 5.07666s/12 iters), loss = 5.28914
I0410 01:43:47.084904 25920 solver.cpp:237]     Train net output #0: loss = 5.28914 (* 1 = 5.28914 loss)
I0410 01:43:47.084916 25920 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 01:43:48.104061 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:49.175088 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 01:43:52.300242 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 01:43:55.056247 25920 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 01:43:55.056269 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:43:59.377564 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:59.535250 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:43:59.535298 25920 solver.cpp:397]     Test net output #1: loss = 5.28638 (* 1 = 5.28638 loss)
I0410 01:44:01.442219 25920 solver.cpp:218] Iteration 312 (0.835836 iter/s, 14.3569s/12 iters), loss = 5.28927
I0410 01:44:01.442271 25920 solver.cpp:237]     Train net output #0: loss = 5.28927 (* 1 = 5.28927 loss)
I0410 01:44:01.442281 25920 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 01:44:06.430956 25920 solver.cpp:218] Iteration 324 (2.40552 iter/s, 4.98852s/12 iters), loss = 5.24985
I0410 01:44:06.431005 25920 solver.cpp:237]     Train net output #0: loss = 5.24985 (* 1 = 5.24985 loss)
I0410 01:44:06.431015 25920 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 01:44:11.477599 25920 solver.cpp:218] Iteration 336 (2.37791 iter/s, 5.04644s/12 iters), loss = 5.2664
I0410 01:44:11.477645 25920 solver.cpp:237]     Train net output #0: loss = 5.2664 (* 1 = 5.2664 loss)
I0410 01:44:11.477658 25920 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 01:44:16.640128 25920 solver.cpp:218] Iteration 348 (2.32454 iter/s, 5.16231s/12 iters), loss = 5.27356
I0410 01:44:16.640187 25920 solver.cpp:237]     Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
I0410 01:44:16.640200 25920 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 01:44:21.675868 25920 solver.cpp:218] Iteration 360 (2.38306 iter/s, 5.03553s/12 iters), loss = 5.29993
I0410 01:44:21.675904 25920 solver.cpp:237]     Train net output #0: loss = 5.29993 (* 1 = 5.29993 loss)
I0410 01:44:21.675913 25920 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 01:44:26.703826 25920 solver.cpp:218] Iteration 372 (2.38675 iter/s, 5.02777s/12 iters), loss = 5.27702
I0410 01:44:26.703963 25920 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0410 01:44:26.703975 25920 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 01:44:31.712733 25920 solver.cpp:218] Iteration 384 (2.39587 iter/s, 5.00862s/12 iters), loss = 5.28511
I0410 01:44:31.712774 25920 solver.cpp:237]     Train net output #0: loss = 5.28511 (* 1 = 5.28511 loss)
I0410 01:44:31.712785 25920 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 01:44:36.753722 25920 solver.cpp:218] Iteration 396 (2.38058 iter/s, 5.04079s/12 iters), loss = 5.27714
I0410 01:44:36.753755 25920 solver.cpp:237]     Train net output #0: loss = 5.27714 (* 1 = 5.27714 loss)
I0410 01:44:36.753764 25920 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 01:44:39.890595 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:41.331753 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 01:44:43.409677 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 01:44:44.785600 25920 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 01:44:44.785622 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:44:49.229346 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:49.435521 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:44:49.435561 25920 solver.cpp:397]     Test net output #1: loss = 5.28739 (* 1 = 5.28739 loss)
I0410 01:44:49.523007 25920 solver.cpp:218] Iteration 408 (0.939786 iter/s, 12.7689s/12 iters), loss = 5.28719
I0410 01:44:49.523067 25920 solver.cpp:237]     Train net output #0: loss = 5.28719 (* 1 = 5.28719 loss)
I0410 01:44:49.523079 25920 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 01:44:53.800504 25920 solver.cpp:218] Iteration 420 (2.80551 iter/s, 4.2773s/12 iters), loss = 5.28079
I0410 01:44:53.800544 25920 solver.cpp:237]     Train net output #0: loss = 5.28079 (* 1 = 5.28079 loss)
I0410 01:44:53.800554 25920 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 01:44:58.795763 25920 solver.cpp:218] Iteration 432 (2.40237 iter/s, 4.99506s/12 iters), loss = 5.27568
I0410 01:44:58.796324 25920 solver.cpp:237]     Train net output #0: loss = 5.27568 (* 1 = 5.27568 loss)
I0410 01:44:58.796336 25920 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 01:45:03.763357 25920 solver.cpp:218] Iteration 444 (2.416 iter/s, 4.96688s/12 iters), loss = 5.29087
I0410 01:45:03.763411 25920 solver.cpp:237]     Train net output #0: loss = 5.29087 (* 1 = 5.29087 loss)
I0410 01:45:03.763424 25920 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 01:45:08.680379 25920 solver.cpp:218] Iteration 456 (2.44061 iter/s, 4.91681s/12 iters), loss = 5.28364
I0410 01:45:08.680429 25920 solver.cpp:237]     Train net output #0: loss = 5.28364 (* 1 = 5.28364 loss)
I0410 01:45:08.680441 25920 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 01:45:13.620095 25920 solver.cpp:218] Iteration 468 (2.42939 iter/s, 4.93951s/12 iters), loss = 5.28639
I0410 01:45:13.620133 25920 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0410 01:45:13.620143 25920 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 01:45:18.616935 25920 solver.cpp:218] Iteration 480 (2.40161 iter/s, 4.99665s/12 iters), loss = 5.26893
I0410 01:45:18.616976 25920 solver.cpp:237]     Train net output #0: loss = 5.26893 (* 1 = 5.26893 loss)
I0410 01:45:18.616986 25920 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 01:45:23.742097 25920 solver.cpp:218] Iteration 492 (2.34148 iter/s, 5.12495s/12 iters), loss = 5.29188
I0410 01:45:23.742146 25920 solver.cpp:237]     Train net output #0: loss = 5.29188 (* 1 = 5.29188 loss)
I0410 01:45:23.742154 25920 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 01:45:29.159842 25920 solver.cpp:218] Iteration 504 (2.21503 iter/s, 5.41752s/12 iters), loss = 5.27078
I0410 01:45:29.160009 25920 solver.cpp:237]     Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
I0410 01:45:29.160022 25920 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 01:45:29.431509 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:45:31.286912 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 01:45:42.538725 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 01:45:47.607254 25920 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 01:45:47.607288 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:45:51.881165 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:45:52.122434 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 01:45:52.122478 25920 solver.cpp:397]     Test net output #1: loss = 5.28638 (* 1 = 5.28638 loss)
I0410 01:45:54.114486 25920 solver.cpp:218] Iteration 516 (0.480889 iter/s, 24.9538s/12 iters), loss = 5.282
I0410 01:45:54.114538 25920 solver.cpp:237]     Train net output #0: loss = 5.282 (* 1 = 5.282 loss)
I0410 01:45:54.114548 25920 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 01:45:59.166661 25920 solver.cpp:218] Iteration 528 (2.37531 iter/s, 5.05197s/12 iters), loss = 5.27703
I0410 01:45:59.166754 25920 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0410 01:45:59.166764 25920 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 01:46:04.176043 25920 solver.cpp:218] Iteration 540 (2.39563 iter/s, 5.00913s/12 iters), loss = 5.28198
I0410 01:46:04.176091 25920 solver.cpp:237]     Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
I0410 01:46:04.176102 25920 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 01:46:09.231864 25920 solver.cpp:218] Iteration 552 (2.3736 iter/s, 5.05561s/12 iters), loss = 5.27233
I0410 01:46:09.231918 25920 solver.cpp:237]     Train net output #0: loss = 5.27233 (* 1 = 5.27233 loss)
I0410 01:46:09.231930 25920 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 01:46:14.211932 25920 solver.cpp:218] Iteration 564 (2.40971 iter/s, 4.97985s/12 iters), loss = 5.25378
I0410 01:46:14.211988 25920 solver.cpp:237]     Train net output #0: loss = 5.25378 (* 1 = 5.25378 loss)
I0410 01:46:14.211999 25920 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 01:46:19.393900 25920 solver.cpp:218] Iteration 576 (2.31582 iter/s, 5.18175s/12 iters), loss = 5.28257
I0410 01:46:19.393940 25920 solver.cpp:237]     Train net output #0: loss = 5.28257 (* 1 = 5.28257 loss)
I0410 01:46:19.393949 25920 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 01:46:24.403329 25920 solver.cpp:218] Iteration 588 (2.39558 iter/s, 5.00923s/12 iters), loss = 5.26804
I0410 01:46:24.403373 25920 solver.cpp:237]     Train net output #0: loss = 5.26804 (* 1 = 5.26804 loss)
I0410 01:46:24.403383 25920 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 01:46:29.447780 25920 solver.cpp:218] Iteration 600 (2.37895 iter/s, 5.04424s/12 iters), loss = 5.26643
I0410 01:46:29.447887 25920 solver.cpp:237]     Train net output #0: loss = 5.26643 (* 1 = 5.26643 loss)
I0410 01:46:29.447899 25920 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 01:46:31.895098 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:34.185988 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 01:46:43.457873 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 01:46:45.395238 25920 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 01:46:45.395268 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:46:49.588879 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:49.874464 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:46:49.874506 25920 solver.cpp:397]     Test net output #1: loss = 5.286 (* 1 = 5.286 loss)
I0410 01:46:49.962028 25920 solver.cpp:218] Iteration 612 (0.58498 iter/s, 20.5135s/12 iters), loss = 5.28402
I0410 01:46:49.962095 25920 solver.cpp:237]     Train net output #0: loss = 5.28402 (* 1 = 5.28402 loss)
I0410 01:46:49.962111 25920 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 01:46:54.154541 25920 solver.cpp:218] Iteration 624 (2.86238 iter/s, 4.19231s/12 iters), loss = 5.28703
I0410 01:46:54.154601 25920 solver.cpp:237]     Train net output #0: loss = 5.28703 (* 1 = 5.28703 loss)
I0410 01:46:54.154614 25920 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 01:46:59.121264 25920 solver.cpp:218] Iteration 636 (2.41619 iter/s, 4.9665s/12 iters), loss = 5.28442
I0410 01:46:59.121317 25920 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0410 01:46:59.121330 25920 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 01:47:04.143749 25920 solver.cpp:218] Iteration 648 (2.38935 iter/s, 5.02228s/12 iters), loss = 5.27768
I0410 01:47:04.143868 25920 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0410 01:47:04.143882 25920 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 01:47:09.155196 25920 solver.cpp:218] Iteration 660 (2.39465 iter/s, 5.01117s/12 iters), loss = 5.26873
I0410 01:47:09.155239 25920 solver.cpp:237]     Train net output #0: loss = 5.26873 (* 1 = 5.26873 loss)
I0410 01:47:09.155247 25920 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 01:47:14.280966 25920 solver.cpp:218] Iteration 672 (2.34121 iter/s, 5.12556s/12 iters), loss = 5.27767
I0410 01:47:14.281019 25920 solver.cpp:237]     Train net output #0: loss = 5.27767 (* 1 = 5.27767 loss)
I0410 01:47:14.281030 25920 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 01:47:19.314442 25920 solver.cpp:218] Iteration 684 (2.38414 iter/s, 5.03327s/12 iters), loss = 5.28055
I0410 01:47:19.314486 25920 solver.cpp:237]     Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
I0410 01:47:19.314496 25920 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 01:47:20.191699 25920 blocking_queue.cpp:49] Waiting for data
I0410 01:47:24.420296 25920 solver.cpp:218] Iteration 696 (2.35034 iter/s, 5.10565s/12 iters), loss = 5.27408
I0410 01:47:24.420342 25920 solver.cpp:237]     Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
I0410 01:47:24.420351 25920 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 01:47:29.016695 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:29.397207 25920 solver.cpp:218] Iteration 708 (2.41123 iter/s, 4.9767s/12 iters), loss = 5.26395
I0410 01:47:29.397262 25920 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0410 01:47:29.397275 25920 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 01:47:31.400560 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 01:47:33.199131 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 01:47:34.638164 25920 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 01:47:34.642022 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:47:38.811827 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:39.131331 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:47:39.131379 25920 solver.cpp:397]     Test net output #1: loss = 5.28663 (* 1 = 5.28663 loss)
I0410 01:47:40.854704 25920 solver.cpp:218] Iteration 720 (1.04739 iter/s, 11.4571s/12 iters), loss = 5.27618
I0410 01:47:40.854769 25920 solver.cpp:237]     Train net output #0: loss = 5.27618 (* 1 = 5.27618 loss)
I0410 01:47:40.854785 25920 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 01:47:45.871264 25920 solver.cpp:218] Iteration 732 (2.39218 iter/s, 5.01634s/12 iters), loss = 5.27875
I0410 01:47:45.871312 25920 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0410 01:47:45.871325 25920 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 01:47:50.854943 25920 solver.cpp:218] Iteration 744 (2.40796 iter/s, 4.98348s/12 iters), loss = 5.28348
I0410 01:47:50.854982 25920 solver.cpp:237]     Train net output #0: loss = 5.28348 (* 1 = 5.28348 loss)
I0410 01:47:50.854991 25920 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 01:47:55.834612 25920 solver.cpp:218] Iteration 756 (2.4099 iter/s, 4.97947s/12 iters), loss = 5.2771
I0410 01:47:55.834674 25920 solver.cpp:237]     Train net output #0: loss = 5.2771 (* 1 = 5.2771 loss)
I0410 01:47:55.834689 25920 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 01:48:00.863443 25920 solver.cpp:218] Iteration 768 (2.38634 iter/s, 5.02861s/12 iters), loss = 5.28412
I0410 01:48:00.863492 25920 solver.cpp:237]     Train net output #0: loss = 5.28412 (* 1 = 5.28412 loss)
I0410 01:48:00.863503 25920 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 01:48:05.947507 25920 solver.cpp:218] Iteration 780 (2.36041 iter/s, 5.08385s/12 iters), loss = 5.26986
I0410 01:48:05.947659 25920 solver.cpp:237]     Train net output #0: loss = 5.26986 (* 1 = 5.26986 loss)
I0410 01:48:05.947674 25920 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 01:48:11.059381 25920 solver.cpp:218] Iteration 792 (2.34762 iter/s, 5.11157s/12 iters), loss = 5.26513
I0410 01:48:11.059422 25920 solver.cpp:237]     Train net output #0: loss = 5.26513 (* 1 = 5.26513 loss)
I0410 01:48:11.059432 25920 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 01:48:16.162747 25920 solver.cpp:218] Iteration 804 (2.35148 iter/s, 5.10316s/12 iters), loss = 5.29336
I0410 01:48:16.162803 25920 solver.cpp:237]     Train net output #0: loss = 5.29336 (* 1 = 5.29336 loss)
I0410 01:48:16.162815 25920 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 01:48:17.947850 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:20.720253 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 01:48:23.824688 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 01:48:26.560073 25920 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 01:48:26.560097 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:48:30.658944 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:31.013188 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:48:31.013226 25920 solver.cpp:397]     Test net output #1: loss = 5.28635 (* 1 = 5.28635 loss)
I0410 01:48:31.100625 25920 solver.cpp:218] Iteration 816 (0.803354 iter/s, 14.9374s/12 iters), loss = 5.2823
I0410 01:48:31.100682 25920 solver.cpp:237]     Train net output #0: loss = 5.2823 (* 1 = 5.2823 loss)
I0410 01:48:31.100695 25920 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 01:48:35.548419 25920 solver.cpp:218] Iteration 828 (2.69809 iter/s, 4.44759s/12 iters), loss = 5.28552
I0410 01:48:35.548475 25920 solver.cpp:237]     Train net output #0: loss = 5.28552 (* 1 = 5.28552 loss)
I0410 01:48:35.548487 25920 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 01:48:40.503320 25920 solver.cpp:218] Iteration 840 (2.42195 iter/s, 4.95469s/12 iters), loss = 5.23267
I0410 01:48:40.503398 25920 solver.cpp:237]     Train net output #0: loss = 5.23267 (* 1 = 5.23267 loss)
I0410 01:48:40.503412 25920 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 01:48:45.509276 25920 solver.cpp:218] Iteration 852 (2.39726 iter/s, 5.00572s/12 iters), loss = 5.29153
I0410 01:48:45.509320 25920 solver.cpp:237]     Train net output #0: loss = 5.29153 (* 1 = 5.29153 loss)
I0410 01:48:45.509331 25920 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 01:48:50.478044 25920 solver.cpp:218] Iteration 864 (2.41518 iter/s, 4.96857s/12 iters), loss = 5.26139
I0410 01:48:50.478089 25920 solver.cpp:237]     Train net output #0: loss = 5.26139 (* 1 = 5.26139 loss)
I0410 01:48:50.478103 25920 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 01:48:55.432117 25920 solver.cpp:218] Iteration 876 (2.42235 iter/s, 4.95387s/12 iters), loss = 5.27637
I0410 01:48:55.432163 25920 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0410 01:48:55.432173 25920 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 01:49:00.460880 25920 solver.cpp:218] Iteration 888 (2.38637 iter/s, 5.02856s/12 iters), loss = 5.26469
I0410 01:49:00.460925 25920 solver.cpp:237]     Train net output #0: loss = 5.26469 (* 1 = 5.26469 loss)
I0410 01:49:00.460937 25920 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 01:49:05.468391 25920 solver.cpp:218] Iteration 900 (2.3965 iter/s, 5.00731s/12 iters), loss = 5.27199
I0410 01:49:05.468444 25920 solver.cpp:237]     Train net output #0: loss = 5.27199 (* 1 = 5.27199 loss)
I0410 01:49:05.468458 25920 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 01:49:09.270663 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:10.371990 25920 solver.cpp:218] Iteration 912 (2.44728 iter/s, 4.9034s/12 iters), loss = 5.26
I0410 01:49:10.372040 25920 solver.cpp:237]     Train net output #0: loss = 5.26 (* 1 = 5.26 loss)
I0410 01:49:10.372051 25920 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 01:49:12.370923 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 01:49:14.095213 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 01:49:16.507292 25920 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 01:49:16.507313 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:49:20.559651 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:20.960389 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:49:20.960438 25920 solver.cpp:397]     Test net output #1: loss = 5.28613 (* 1 = 5.28613 loss)
I0410 01:49:22.939874 25920 solver.cpp:218] Iteration 924 (0.954847 iter/s, 12.5675s/12 iters), loss = 5.28554
I0410 01:49:22.939929 25920 solver.cpp:237]     Train net output #0: loss = 5.28554 (* 1 = 5.28554 loss)
I0410 01:49:22.939942 25920 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 01:49:27.963786 25920 solver.cpp:218] Iteration 936 (2.38868 iter/s, 5.0237s/12 iters), loss = 5.26456
I0410 01:49:27.963843 25920 solver.cpp:237]     Train net output #0: loss = 5.26456 (* 1 = 5.26456 loss)
I0410 01:49:27.963855 25920 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 01:49:33.050925 25920 solver.cpp:218] Iteration 948 (2.35899 iter/s, 5.08692s/12 iters), loss = 5.29136
I0410 01:49:33.050977 25920 solver.cpp:237]     Train net output #0: loss = 5.29136 (* 1 = 5.29136 loss)
I0410 01:49:33.050990 25920 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 01:49:38.031265 25920 solver.cpp:218] Iteration 960 (2.40957 iter/s, 4.98013s/12 iters), loss = 5.2641
I0410 01:49:38.031311 25920 solver.cpp:237]     Train net output #0: loss = 5.2641 (* 1 = 5.2641 loss)
I0410 01:49:38.031322 25920 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 01:49:42.983285 25920 solver.cpp:218] Iteration 972 (2.42335 iter/s, 4.95182s/12 iters), loss = 5.27587
I0410 01:49:42.983388 25920 solver.cpp:237]     Train net output #0: loss = 5.27587 (* 1 = 5.27587 loss)
I0410 01:49:42.983398 25920 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 01:49:48.030853 25920 solver.cpp:218] Iteration 984 (2.37751 iter/s, 5.0473s/12 iters), loss = 5.29132
I0410 01:49:48.030911 25920 solver.cpp:237]     Train net output #0: loss = 5.29132 (* 1 = 5.29132 loss)
I0410 01:49:48.030923 25920 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 01:49:53.005859 25920 solver.cpp:218] Iteration 996 (2.41216 iter/s, 4.97479s/12 iters), loss = 5.27792
I0410 01:49:53.005909 25920 solver.cpp:237]     Train net output #0: loss = 5.27792 (* 1 = 5.27792 loss)
I0410 01:49:53.005921 25920 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 01:49:58.210373 25920 solver.cpp:218] Iteration 1008 (2.30578 iter/s, 5.20431s/12 iters), loss = 5.28553
I0410 01:49:58.210417 25920 solver.cpp:237]     Train net output #0: loss = 5.28553 (* 1 = 5.28553 loss)
I0410 01:49:58.210429 25920 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 01:49:59.246834 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:02.773520 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 01:50:08.287750 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 01:50:12.209360 25920 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 01:50:12.209383 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:50:16.145048 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:16.575747 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:50:16.575793 25920 solver.cpp:397]     Test net output #1: loss = 5.28599 (* 1 = 5.28599 loss)
I0410 01:50:16.663288 25920 solver.cpp:218] Iteration 1020 (0.650324 iter/s, 18.4523s/12 iters), loss = 5.28618
I0410 01:50:16.663337 25920 solver.cpp:237]     Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
I0410 01:50:16.663349 25920 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 01:50:20.833223 25920 solver.cpp:218] Iteration 1032 (2.87787 iter/s, 4.16975s/12 iters), loss = 5.25387
I0410 01:50:20.833271 25920 solver.cpp:237]     Train net output #0: loss = 5.25387 (* 1 = 5.25387 loss)
I0410 01:50:20.833282 25920 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 01:50:25.878338 25920 solver.cpp:218] Iteration 1044 (2.37864 iter/s, 5.04491s/12 iters), loss = 5.26077
I0410 01:50:25.878391 25920 solver.cpp:237]     Train net output #0: loss = 5.26077 (* 1 = 5.26077 loss)
I0410 01:50:25.878403 25920 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 01:50:31.055445 25920 solver.cpp:218] Iteration 1056 (2.318 iter/s, 5.17689s/12 iters), loss = 5.26364
I0410 01:50:31.055503 25920 solver.cpp:237]     Train net output #0: loss = 5.26364 (* 1 = 5.26364 loss)
I0410 01:50:31.055516 25920 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 01:50:35.978380 25920 solver.cpp:218] Iteration 1068 (2.43768 iter/s, 4.92272s/12 iters), loss = 5.28801
I0410 01:50:35.978422 25920 solver.cpp:237]     Train net output #0: loss = 5.28801 (* 1 = 5.28801 loss)
I0410 01:50:35.978431 25920 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 01:50:41.046538 25920 solver.cpp:218] Iteration 1080 (2.36782 iter/s, 5.06796s/12 iters), loss = 5.2755
I0410 01:50:41.046592 25920 solver.cpp:237]     Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
I0410 01:50:41.046605 25920 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 01:50:46.273906 25920 solver.cpp:218] Iteration 1092 (2.29571 iter/s, 5.22715s/12 iters), loss = 5.28317
I0410 01:50:46.274052 25920 solver.cpp:237]     Train net output #0: loss = 5.28317 (* 1 = 5.28317 loss)
I0410 01:50:46.274066 25920 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 01:50:51.363495 25920 solver.cpp:218] Iteration 1104 (2.35789 iter/s, 5.08929s/12 iters), loss = 5.27371
I0410 01:50:51.363540 25920 solver.cpp:237]     Train net output #0: loss = 5.27371 (* 1 = 5.27371 loss)
I0410 01:50:51.363550 25920 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 01:50:54.455910 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:56.362610 25920 solver.cpp:218] Iteration 1116 (2.40053 iter/s, 4.99891s/12 iters), loss = 5.2758
I0410 01:50:56.362664 25920 solver.cpp:237]     Train net output #0: loss = 5.2758 (* 1 = 5.2758 loss)
I0410 01:50:56.362676 25920 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 01:50:58.716297 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 01:51:01.928445 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 01:51:04.418977 25920 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 01:51:04.418996 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:51:08.593176 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:09.069186 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:51:09.069222 25920 solver.cpp:397]     Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
I0410 01:51:10.986251 25920 solver.cpp:218] Iteration 1128 (0.820616 iter/s, 14.6232s/12 iters), loss = 5.27363
I0410 01:51:10.986310 25920 solver.cpp:237]     Train net output #0: loss = 5.27363 (* 1 = 5.27363 loss)
I0410 01:51:10.986320 25920 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 01:51:15.997609 25920 solver.cpp:218] Iteration 1140 (2.39466 iter/s, 5.01114s/12 iters), loss = 5.26527
I0410 01:51:15.997665 25920 solver.cpp:237]     Train net output #0: loss = 5.26527 (* 1 = 5.26527 loss)
I0410 01:51:15.997678 25920 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 01:51:20.997120 25920 solver.cpp:218] Iteration 1152 (2.40034 iter/s, 4.99929s/12 iters), loss = 5.27708
I0410 01:51:20.997274 25920 solver.cpp:237]     Train net output #0: loss = 5.27708 (* 1 = 5.27708 loss)
I0410 01:51:20.997288 25920 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 01:51:25.954895 25920 solver.cpp:218] Iteration 1164 (2.42059 iter/s, 4.95746s/12 iters), loss = 5.27525
I0410 01:51:25.954955 25920 solver.cpp:237]     Train net output #0: loss = 5.27525 (* 1 = 5.27525 loss)
I0410 01:51:25.954968 25920 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 01:51:30.929147 25920 solver.cpp:218] Iteration 1176 (2.41253 iter/s, 4.97403s/12 iters), loss = 5.29031
I0410 01:51:30.929193 25920 solver.cpp:237]     Train net output #0: loss = 5.29031 (* 1 = 5.29031 loss)
I0410 01:51:30.929204 25920 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 01:51:35.979949 25920 solver.cpp:218] Iteration 1188 (2.37595 iter/s, 5.0506s/12 iters), loss = 5.27609
I0410 01:51:35.979997 25920 solver.cpp:237]     Train net output #0: loss = 5.27609 (* 1 = 5.27609 loss)
I0410 01:51:35.980010 25920 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 01:51:41.029951 25920 solver.cpp:218] Iteration 1200 (2.37634 iter/s, 5.04979s/12 iters), loss = 5.2952
I0410 01:51:41.030036 25920 solver.cpp:237]     Train net output #0: loss = 5.2952 (* 1 = 5.2952 loss)
I0410 01:51:41.030050 25920 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 01:51:45.999819 25920 solver.cpp:218] Iteration 1212 (2.41467 iter/s, 4.96963s/12 iters), loss = 5.26982
I0410 01:51:45.999874 25920 solver.cpp:237]     Train net output #0: loss = 5.26982 (* 1 = 5.26982 loss)
I0410 01:51:45.999886 25920 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 01:51:46.291536 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:50.531692 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 01:51:56.946442 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 01:52:02.746587 25920 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 01:52:02.746618 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:52:07.122823 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:07.657251 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 01:52:07.657287 25920 solver.cpp:397]     Test net output #1: loss = 5.28618 (* 1 = 5.28618 loss)
I0410 01:52:07.744493 25920 solver.cpp:218] Iteration 1224 (0.551877 iter/s, 21.744s/12 iters), loss = 5.27978
I0410 01:52:07.744550 25920 solver.cpp:237]     Train net output #0: loss = 5.27978 (* 1 = 5.27978 loss)
I0410 01:52:07.744561 25920 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 01:52:11.948591 25920 solver.cpp:218] Iteration 1236 (2.85449 iter/s, 4.20391s/12 iters), loss = 5.27711
I0410 01:52:11.948643 25920 solver.cpp:237]     Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
I0410 01:52:11.948653 25920 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 01:52:16.932067 25920 solver.cpp:218] Iteration 1248 (2.40806 iter/s, 4.98326s/12 iters), loss = 5.28039
I0410 01:52:16.932122 25920 solver.cpp:237]     Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
I0410 01:52:16.932132 25920 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 01:52:21.951807 25920 solver.cpp:218] Iteration 1260 (2.39066 iter/s, 5.01953s/12 iters), loss = 5.26996
I0410 01:52:21.951862 25920 solver.cpp:237]     Train net output #0: loss = 5.26996 (* 1 = 5.26996 loss)
I0410 01:52:21.951874 25920 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 01:52:27.007094 25920 solver.cpp:218] Iteration 1272 (2.37385 iter/s, 5.05508s/12 iters), loss = 5.249
I0410 01:52:27.007205 25920 solver.cpp:237]     Train net output #0: loss = 5.249 (* 1 = 5.249 loss)
I0410 01:52:27.007217 25920 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 01:52:31.916610 25920 solver.cpp:218] Iteration 1284 (2.44436 iter/s, 4.90926s/12 iters), loss = 5.28047
I0410 01:52:31.916649 25920 solver.cpp:237]     Train net output #0: loss = 5.28047 (* 1 = 5.28047 loss)
I0410 01:52:31.916658 25920 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 01:52:37.066493 25920 solver.cpp:218] Iteration 1296 (2.33024 iter/s, 5.14968s/12 iters), loss = 5.27249
I0410 01:52:37.066534 25920 solver.cpp:237]     Train net output #0: loss = 5.27249 (* 1 = 5.27249 loss)
I0410 01:52:37.066542 25920 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 01:52:42.109448 25920 solver.cpp:218] Iteration 1308 (2.37966 iter/s, 5.04275s/12 iters), loss = 5.25927
I0410 01:52:42.109503 25920 solver.cpp:237]     Train net output #0: loss = 5.25927 (* 1 = 5.25927 loss)
I0410 01:52:42.109513 25920 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 01:52:44.596884 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:47.054935 25920 solver.cpp:218] Iteration 1320 (2.42656 iter/s, 4.94528s/12 iters), loss = 5.28058
I0410 01:52:47.054976 25920 solver.cpp:237]     Train net output #0: loss = 5.28058 (* 1 = 5.28058 loss)
I0410 01:52:47.054986 25920 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 01:52:49.072191 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 01:52:50.806146 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 01:52:54.287055 25920 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 01:52:54.287078 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:52:58.307998 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:58.880864 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:52:58.880903 25920 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 01:53:00.880220 25920 solver.cpp:218] Iteration 1332 (0.868003 iter/s, 13.8248s/12 iters), loss = 5.28633
I0410 01:53:00.880282 25920 solver.cpp:237]     Train net output #0: loss = 5.28633 (* 1 = 5.28633 loss)
I0410 01:53:00.880295 25920 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 01:53:06.044541 25920 solver.cpp:218] Iteration 1344 (2.32373 iter/s, 5.1641s/12 iters), loss = 5.2861
I0410 01:53:06.044579 25920 solver.cpp:237]     Train net output #0: loss = 5.2861 (* 1 = 5.2861 loss)
I0410 01:53:06.044589 25920 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 01:53:11.029418 25920 solver.cpp:218] Iteration 1356 (2.40738 iter/s, 4.98468s/12 iters), loss = 5.27627
I0410 01:53:11.029464 25920 solver.cpp:237]     Train net output #0: loss = 5.27627 (* 1 = 5.27627 loss)
I0410 01:53:11.029475 25920 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 01:53:16.272444 25920 solver.cpp:218] Iteration 1368 (2.28885 iter/s, 5.24281s/12 iters), loss = 5.27044
I0410 01:53:16.272493 25920 solver.cpp:237]     Train net output #0: loss = 5.27044 (* 1 = 5.27044 loss)
I0410 01:53:16.272503 25920 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 01:53:17.469148 25920 blocking_queue.cpp:49] Waiting for data
I0410 01:53:21.268101 25920 solver.cpp:218] Iteration 1380 (2.40219 iter/s, 4.99545s/12 iters), loss = 5.27511
I0410 01:53:21.268151 25920 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0410 01:53:21.268167 25920 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 01:53:26.186432 25920 solver.cpp:218] Iteration 1392 (2.43995 iter/s, 4.91813s/12 iters), loss = 5.2707
I0410 01:53:26.186473 25920 solver.cpp:237]     Train net output #0: loss = 5.2707 (* 1 = 5.2707 loss)
I0410 01:53:26.186482 25920 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 01:53:31.184545 25920 solver.cpp:218] Iteration 1404 (2.401 iter/s, 4.99791s/12 iters), loss = 5.27686
I0410 01:53:31.184664 25920 solver.cpp:237]     Train net output #0: loss = 5.27686 (* 1 = 5.27686 loss)
I0410 01:53:31.184677 25920 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 01:53:35.823488 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:36.170207 25920 solver.cpp:218] Iteration 1416 (2.40703 iter/s, 4.98539s/12 iters), loss = 5.26395
I0410 01:53:36.170251 25920 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0410 01:53:36.170261 25920 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 01:53:40.717095 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 01:53:44.391731 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 01:53:46.858297 25920 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 01:53:46.858325 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:53:50.835016 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:51.426048 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:53:51.426091 25920 solver.cpp:397]     Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
I0410 01:53:51.513792 25920 solver.cpp:218] Iteration 1428 (0.782111 iter/s, 15.3431s/12 iters), loss = 5.27425
I0410 01:53:51.513850 25920 solver.cpp:237]     Train net output #0: loss = 5.27425 (* 1 = 5.27425 loss)
I0410 01:53:51.513861 25920 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 01:53:55.658344 25920 solver.cpp:218] Iteration 1440 (2.8955 iter/s, 4.14436s/12 iters), loss = 5.28198
I0410 01:53:55.658392 25920 solver.cpp:237]     Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
I0410 01:53:55.658404 25920 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 01:54:00.608708 25920 solver.cpp:218] Iteration 1452 (2.42417 iter/s, 4.95016s/12 iters), loss = 5.28751
I0410 01:54:00.608764 25920 solver.cpp:237]     Train net output #0: loss = 5.28751 (* 1 = 5.28751 loss)
I0410 01:54:00.608777 25920 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 01:54:05.658838 25920 solver.cpp:218] Iteration 1464 (2.37628 iter/s, 5.04991s/12 iters), loss = 5.2803
I0410 01:54:05.658959 25920 solver.cpp:237]     Train net output #0: loss = 5.2803 (* 1 = 5.2803 loss)
I0410 01:54:05.658974 25920 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 01:54:10.753088 25920 solver.cpp:218] Iteration 1476 (2.35573 iter/s, 5.09397s/12 iters), loss = 5.27697
I0410 01:54:10.753145 25920 solver.cpp:237]     Train net output #0: loss = 5.27697 (* 1 = 5.27697 loss)
I0410 01:54:10.753158 25920 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 01:54:15.820861 25920 solver.cpp:218] Iteration 1488 (2.36801 iter/s, 5.06756s/12 iters), loss = 5.25297
I0410 01:54:15.820905 25920 solver.cpp:237]     Train net output #0: loss = 5.25297 (* 1 = 5.25297 loss)
I0410 01:54:15.820915 25920 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 01:54:20.862095 25920 solver.cpp:218] Iteration 1500 (2.38047 iter/s, 5.04103s/12 iters), loss = 5.27508
I0410 01:54:20.862145 25920 solver.cpp:237]     Train net output #0: loss = 5.27508 (* 1 = 5.27508 loss)
I0410 01:54:20.862154 25920 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 01:54:25.938802 25920 solver.cpp:218] Iteration 1512 (2.36384 iter/s, 5.0765s/12 iters), loss = 5.29163
I0410 01:54:25.938851 25920 solver.cpp:237]     Train net output #0: loss = 5.29163 (* 1 = 5.29163 loss)
I0410 01:54:25.938864 25920 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 01:54:27.747422 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:31.050531 25920 solver.cpp:218] Iteration 1524 (2.34764 iter/s, 5.11152s/12 iters), loss = 5.27658
I0410 01:54:31.050585 25920 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0410 01:54:31.050596 25920 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 01:54:33.173800 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 01:54:35.162942 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 01:54:42.832531 25920 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 01:54:42.832670 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:54:46.647490 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:47.281371 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:54:47.281417 25920 solver.cpp:397]     Test net output #1: loss = 5.28594 (* 1 = 5.28594 loss)
I0410 01:54:49.193792 25920 solver.cpp:218] Iteration 1536 (0.661424 iter/s, 18.1427s/12 iters), loss = 5.27637
I0410 01:54:49.193843 25920 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0410 01:54:49.193856 25920 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 01:54:54.373580 25920 solver.cpp:218] Iteration 1548 (2.31679 iter/s, 5.17958s/12 iters), loss = 5.2378
I0410 01:54:54.373628 25920 solver.cpp:237]     Train net output #0: loss = 5.2378 (* 1 = 5.2378 loss)
I0410 01:54:54.373641 25920 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 01:54:59.364431 25920 solver.cpp:218] Iteration 1560 (2.4045 iter/s, 4.99065s/12 iters), loss = 5.29119
I0410 01:54:59.364488 25920 solver.cpp:237]     Train net output #0: loss = 5.29119 (* 1 = 5.29119 loss)
I0410 01:54:59.364501 25920 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 01:55:04.378145 25920 solver.cpp:218] Iteration 1572 (2.39354 iter/s, 5.0135s/12 iters), loss = 5.25743
I0410 01:55:04.378191 25920 solver.cpp:237]     Train net output #0: loss = 5.25743 (* 1 = 5.25743 loss)
I0410 01:55:04.378203 25920 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 01:55:09.334022 25920 solver.cpp:218] Iteration 1584 (2.42146 iter/s, 4.95568s/12 iters), loss = 5.26908
I0410 01:55:09.334072 25920 solver.cpp:237]     Train net output #0: loss = 5.26908 (* 1 = 5.26908 loss)
I0410 01:55:09.334086 25920 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 01:55:14.464457 25920 solver.cpp:218] Iteration 1596 (2.33908 iter/s, 5.13022s/12 iters), loss = 5.27021
I0410 01:55:14.464541 25920 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0410 01:55:14.464551 25920 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 01:55:19.438676 25920 solver.cpp:218] Iteration 1608 (2.41256 iter/s, 4.97398s/12 iters), loss = 5.26957
I0410 01:55:19.438732 25920 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0410 01:55:19.438745 25920 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 01:55:23.354064 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:24.452621 25920 solver.cpp:218] Iteration 1620 (2.39343 iter/s, 5.01373s/12 iters), loss = 5.25922
I0410 01:55:24.452674 25920 solver.cpp:237]     Train net output #0: loss = 5.25922 (* 1 = 5.25922 loss)
I0410 01:55:24.452687 25920 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 01:55:28.976991 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 01:55:30.755543 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 01:55:32.141402 25920 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 01:55:32.141427 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:55:35.948400 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:36.636723 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:55:36.636771 25920 solver.cpp:397]     Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
I0410 01:55:36.724252 25920 solver.cpp:218] Iteration 1632 (0.977899 iter/s, 12.2712s/12 iters), loss = 5.28761
I0410 01:55:36.724304 25920 solver.cpp:237]     Train net output #0: loss = 5.28761 (* 1 = 5.28761 loss)
I0410 01:55:36.724315 25920 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 01:55:41.018105 25920 solver.cpp:218] Iteration 1644 (2.79482 iter/s, 4.29365s/12 iters), loss = 5.25958
I0410 01:55:41.018157 25920 solver.cpp:237]     Train net output #0: loss = 5.25958 (* 1 = 5.25958 loss)
I0410 01:55:41.018169 25920 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 01:55:46.017379 25920 solver.cpp:218] Iteration 1656 (2.40045 iter/s, 4.99907s/12 iters), loss = 5.28926
I0410 01:55:46.017504 25920 solver.cpp:237]     Train net output #0: loss = 5.28926 (* 1 = 5.28926 loss)
I0410 01:55:46.017514 25920 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 01:55:50.950301 25920 solver.cpp:218] Iteration 1668 (2.43277 iter/s, 4.93264s/12 iters), loss = 5.26248
I0410 01:55:50.950346 25920 solver.cpp:237]     Train net output #0: loss = 5.26248 (* 1 = 5.26248 loss)
I0410 01:55:50.950356 25920 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 01:55:56.243094 25920 solver.cpp:218] Iteration 1680 (2.26733 iter/s, 5.29257s/12 iters), loss = 5.27618
I0410 01:55:56.243149 25920 solver.cpp:237]     Train net output #0: loss = 5.27618 (* 1 = 5.27618 loss)
I0410 01:55:56.243160 25920 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 01:56:01.228466 25920 solver.cpp:218] Iteration 1692 (2.40714 iter/s, 4.98516s/12 iters), loss = 5.28901
I0410 01:56:01.228513 25920 solver.cpp:237]     Train net output #0: loss = 5.28901 (* 1 = 5.28901 loss)
I0410 01:56:01.228526 25920 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 01:56:06.268070 25920 solver.cpp:218] Iteration 1704 (2.38123 iter/s, 5.0394s/12 iters), loss = 5.27352
I0410 01:56:06.268117 25920 solver.cpp:237]     Train net output #0: loss = 5.27352 (* 1 = 5.27352 loss)
I0410 01:56:06.268128 25920 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 01:56:11.257735 25920 solver.cpp:218] Iteration 1716 (2.40507 iter/s, 4.98946s/12 iters), loss = 5.28219
I0410 01:56:11.257781 25920 solver.cpp:237]     Train net output #0: loss = 5.28219 (* 1 = 5.28219 loss)
I0410 01:56:11.257794 25920 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 01:56:12.292585 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:16.255056 25920 solver.cpp:218] Iteration 1728 (2.40139 iter/s, 4.99711s/12 iters), loss = 5.29166
I0410 01:56:16.255180 25920 solver.cpp:237]     Train net output #0: loss = 5.29166 (* 1 = 5.29166 loss)
I0410 01:56:16.255196 25920 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 01:56:18.207273 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 01:56:24.449313 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 01:56:28.375680 25920 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 01:56:28.375710 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:56:32.173250 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:32.898252 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:56:32.898301 25920 solver.cpp:397]     Test net output #1: loss = 5.28613 (* 1 = 5.28613 loss)
I0410 01:56:34.860162 25920 solver.cpp:218] Iteration 1740 (0.645007 iter/s, 18.6044s/12 iters), loss = 5.25815
I0410 01:56:34.860204 25920 solver.cpp:237]     Train net output #0: loss = 5.25815 (* 1 = 5.25815 loss)
I0410 01:56:34.860214 25920 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 01:56:39.839910 25920 solver.cpp:218] Iteration 1752 (2.40986 iter/s, 4.97955s/12 iters), loss = 5.27092
I0410 01:56:39.839954 25920 solver.cpp:237]     Train net output #0: loss = 5.27092 (* 1 = 5.27092 loss)
I0410 01:56:39.839964 25920 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 01:56:44.803999 25920 solver.cpp:218] Iteration 1764 (2.41746 iter/s, 4.96388s/12 iters), loss = 5.26713
I0410 01:56:44.804050 25920 solver.cpp:237]     Train net output #0: loss = 5.26713 (* 1 = 5.26713 loss)
I0410 01:56:44.804061 25920 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 01:56:49.839969 25920 solver.cpp:218] Iteration 1776 (2.38296 iter/s, 5.03576s/12 iters), loss = 5.27465
I0410 01:56:49.840091 25920 solver.cpp:237]     Train net output #0: loss = 5.27465 (* 1 = 5.27465 loss)
I0410 01:56:49.840101 25920 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 01:56:55.015760 25920 solver.cpp:218] Iteration 1788 (2.31862 iter/s, 5.1755s/12 iters), loss = 5.26579
I0410 01:56:55.015817 25920 solver.cpp:237]     Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
I0410 01:56:55.015828 25920 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 01:57:00.091902 25920 solver.cpp:218] Iteration 1800 (2.3641 iter/s, 5.07593s/12 iters), loss = 5.27505
I0410 01:57:00.091948 25920 solver.cpp:237]     Train net output #0: loss = 5.27505 (* 1 = 5.27505 loss)
I0410 01:57:00.091956 25920 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 01:57:05.171895 25920 solver.cpp:218] Iteration 1812 (2.36231 iter/s, 5.07978s/12 iters), loss = 5.27064
I0410 01:57:05.171950 25920 solver.cpp:237]     Train net output #0: loss = 5.27064 (* 1 = 5.27064 loss)
I0410 01:57:05.171963 25920 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 01:57:08.356269 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:10.223697 25920 solver.cpp:218] Iteration 1824 (2.37549 iter/s, 5.05159s/12 iters), loss = 5.27498
I0410 01:57:10.223752 25920 solver.cpp:237]     Train net output #0: loss = 5.27498 (* 1 = 5.27498 loss)
I0410 01:57:10.223765 25920 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 01:57:14.807585 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 01:57:21.427983 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 01:57:25.504209 25920 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 01:57:25.504232 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:57:29.257766 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:30.015802 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:57:30.015856 25920 solver.cpp:397]     Test net output #1: loss = 5.28503 (* 1 = 5.28503 loss)
I0410 01:57:30.103394 25920 solver.cpp:218] Iteration 1836 (0.603651 iter/s, 19.8791s/12 iters), loss = 5.27319
I0410 01:57:30.103447 25920 solver.cpp:237]     Train net output #0: loss = 5.27319 (* 1 = 5.27319 loss)
I0410 01:57:30.103458 25920 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 01:57:34.278440 25920 solver.cpp:218] Iteration 1848 (2.87435 iter/s, 4.17486s/12 iters), loss = 5.27157
I0410 01:57:34.278487 25920 solver.cpp:237]     Train net output #0: loss = 5.27157 (* 1 = 5.27157 loss)
I0410 01:57:34.278498 25920 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 01:57:39.452112 25920 solver.cpp:218] Iteration 1860 (2.31953 iter/s, 5.17346s/12 iters), loss = 5.28598
I0410 01:57:39.452160 25920 solver.cpp:237]     Train net output #0: loss = 5.28598 (* 1 = 5.28598 loss)
I0410 01:57:39.452170 25920 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 01:57:44.442988 25920 solver.cpp:218] Iteration 1872 (2.40448 iter/s, 4.99067s/12 iters), loss = 5.27359
I0410 01:57:44.443027 25920 solver.cpp:237]     Train net output #0: loss = 5.27359 (* 1 = 5.27359 loss)
I0410 01:57:44.443037 25920 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 01:57:49.506711 25920 solver.cpp:218] Iteration 1884 (2.36989 iter/s, 5.06352s/12 iters), loss = 5.28224
I0410 01:57:49.506749 25920 solver.cpp:237]     Train net output #0: loss = 5.28224 (* 1 = 5.28224 loss)
I0410 01:57:49.506758 25920 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 01:57:54.470031 25920 solver.cpp:218] Iteration 1896 (2.41783 iter/s, 4.96312s/12 iters), loss = 5.26592
I0410 01:57:54.470158 25920 solver.cpp:237]     Train net output #0: loss = 5.26592 (* 1 = 5.26592 loss)
I0410 01:57:54.470168 25920 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 01:57:59.430518 25920 solver.cpp:218] Iteration 1908 (2.41925 iter/s, 4.96021s/12 iters), loss = 5.28466
I0410 01:57:59.430563 25920 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0410 01:57:59.430573 25920 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 01:58:04.469419 25920 solver.cpp:218] Iteration 1920 (2.38157 iter/s, 5.0387s/12 iters), loss = 5.27346
I0410 01:58:04.469461 25920 solver.cpp:237]     Train net output #0: loss = 5.27346 (* 1 = 5.27346 loss)
I0410 01:58:04.469472 25920 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 01:58:04.780772 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:09.506291 25920 solver.cpp:218] Iteration 1932 (2.38252 iter/s, 5.03667s/12 iters), loss = 5.27666
I0410 01:58:09.506337 25920 solver.cpp:237]     Train net output #0: loss = 5.27666 (* 1 = 5.27666 loss)
I0410 01:58:09.506346 25920 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 01:58:11.587661 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 01:58:13.407765 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 01:58:14.785799 25920 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 01:58:14.785825 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:58:18.436926 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:19.222612 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:58:19.222643 25920 solver.cpp:397]     Test net output #1: loss = 5.28479 (* 1 = 5.28479 loss)
I0410 01:58:21.231256 25920 solver.cpp:218] Iteration 1944 (1.02349 iter/s, 11.7246s/12 iters), loss = 5.27353
I0410 01:58:21.231315 25920 solver.cpp:237]     Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
I0410 01:58:21.231328 25920 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 01:58:26.273551 25920 solver.cpp:218] Iteration 1956 (2.37997 iter/s, 5.04207s/12 iters), loss = 5.27848
I0410 01:58:26.273666 25920 solver.cpp:237]     Train net output #0: loss = 5.27848 (* 1 = 5.27848 loss)
I0410 01:58:26.273679 25920 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 01:58:31.507180 25920 solver.cpp:218] Iteration 1968 (2.29298 iter/s, 5.23336s/12 iters), loss = 5.27847
I0410 01:58:31.507220 25920 solver.cpp:237]     Train net output #0: loss = 5.27847 (* 1 = 5.27847 loss)
I0410 01:58:31.507230 25920 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 01:58:36.527375 25920 solver.cpp:218] Iteration 1980 (2.39044 iter/s, 5.01999s/12 iters), loss = 5.2558
I0410 01:58:36.527437 25920 solver.cpp:237]     Train net output #0: loss = 5.2558 (* 1 = 5.2558 loss)
I0410 01:58:36.527449 25920 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 01:58:41.629284 25920 solver.cpp:218] Iteration 1992 (2.35216 iter/s, 5.10169s/12 iters), loss = 5.27853
I0410 01:58:41.629338 25920 solver.cpp:237]     Train net output #0: loss = 5.27853 (* 1 = 5.27853 loss)
I0410 01:58:41.629349 25920 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 01:58:46.724442 25920 solver.cpp:218] Iteration 2004 (2.35528 iter/s, 5.09494s/12 iters), loss = 5.27947
I0410 01:58:46.724496 25920 solver.cpp:237]     Train net output #0: loss = 5.27947 (* 1 = 5.27947 loss)
I0410 01:58:46.724507 25920 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 01:58:51.757766 25920 solver.cpp:218] Iteration 2016 (2.38421 iter/s, 5.03311s/12 iters), loss = 5.25173
I0410 01:58:51.757818 25920 solver.cpp:237]     Train net output #0: loss = 5.25173 (* 1 = 5.25173 loss)
I0410 01:58:51.757831 25920 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 01:58:54.295837 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:56.717703 25920 solver.cpp:218] Iteration 2028 (2.41949 iter/s, 4.95972s/12 iters), loss = 5.27623
I0410 01:58:56.717823 25920 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0410 01:58:56.717835 25920 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 01:59:01.312408 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 01:59:05.156756 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 01:59:09.101702 25920 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 01:59:09.101732 25920 net.cpp:676] Ignoring source layer train-data
I0410 01:59:12.881135 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:59:13.709066 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 01:59:13.709111 25920 solver.cpp:397]     Test net output #1: loss = 5.28263 (* 1 = 5.28263 loss)
I0410 01:59:13.796726 25920 solver.cpp:218] Iteration 2040 (0.702642 iter/s, 17.0784s/12 iters), loss = 5.28146
I0410 01:59:13.796782 25920 solver.cpp:237]     Train net output #0: loss = 5.28146 (* 1 = 5.28146 loss)
I0410 01:59:13.796793 25920 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 01:59:18.295131 25920 solver.cpp:218] Iteration 2052 (2.66773 iter/s, 4.49821s/12 iters), loss = 5.27656
I0410 01:59:18.295173 25920 solver.cpp:237]     Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
I0410 01:59:18.295181 25920 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 01:59:19.992461 25920 blocking_queue.cpp:49] Waiting for data
I0410 01:59:23.422832 25920 solver.cpp:218] Iteration 2064 (2.34032 iter/s, 5.1275s/12 iters), loss = 5.27987
I0410 01:59:23.422878 25920 solver.cpp:237]     Train net output #0: loss = 5.27987 (* 1 = 5.27987 loss)
I0410 01:59:23.422891 25920 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 01:59:28.461654 25920 solver.cpp:218] Iteration 2076 (2.3816 iter/s, 5.03862s/12 iters), loss = 5.27416
I0410 01:59:28.461766 25920 solver.cpp:237]     Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
I0410 01:59:28.461781 25920 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 01:59:33.841588 25920 solver.cpp:218] Iteration 2088 (2.23062 iter/s, 5.37966s/12 iters), loss = 5.26828
I0410 01:59:33.841634 25920 solver.cpp:237]     Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
I0410 01:59:33.841645 25920 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 01:59:38.998605 25920 solver.cpp:218] Iteration 2100 (2.32702 iter/s, 5.15681s/12 iters), loss = 5.26435
I0410 01:59:38.998661 25920 solver.cpp:237]     Train net output #0: loss = 5.26435 (* 1 = 5.26435 loss)
I0410 01:59:38.998673 25920 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 01:59:44.089825 25920 solver.cpp:218] Iteration 2112 (2.3571 iter/s, 5.091s/12 iters), loss = 5.27885
I0410 01:59:44.089884 25920 solver.cpp:237]     Train net output #0: loss = 5.27885 (* 1 = 5.27885 loss)
I0410 01:59:44.089895 25920 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 01:59:48.726723 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:59:49.050261 25920 solver.cpp:218] Iteration 2124 (2.41925 iter/s, 4.96022s/12 iters), loss = 5.26339
I0410 01:59:49.050310 25920 solver.cpp:237]     Train net output #0: loss = 5.26339 (* 1 = 5.26339 loss)
I0410 01:59:49.050321 25920 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 01:59:54.000994 25920 solver.cpp:218] Iteration 2136 (2.42398 iter/s, 4.95053s/12 iters), loss = 5.26838
I0410 01:59:54.001045 25920 solver.cpp:237]     Train net output #0: loss = 5.26838 (* 1 = 5.26838 loss)
I0410 01:59:54.001057 25920 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 01:59:56.069933 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 01:59:58.402154 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 02:00:01.116376 25920 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 02:00:01.116473 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:00:04.691792 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:05.554077 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:00:05.554117 25920 solver.cpp:397]     Test net output #1: loss = 5.27582 (* 1 = 5.27582 loss)
I0410 02:00:07.446923 25920 solver.cpp:218] Iteration 2148 (0.892493 iter/s, 13.4455s/12 iters), loss = 5.26738
I0410 02:00:07.446964 25920 solver.cpp:237]     Train net output #0: loss = 5.26738 (* 1 = 5.26738 loss)
I0410 02:00:07.446972 25920 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 02:00:12.374542 25920 solver.cpp:218] Iteration 2160 (2.43535 iter/s, 4.92742s/12 iters), loss = 5.27825
I0410 02:00:12.374598 25920 solver.cpp:237]     Train net output #0: loss = 5.27825 (* 1 = 5.27825 loss)
I0410 02:00:12.374609 25920 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 02:00:17.353992 25920 solver.cpp:218] Iteration 2172 (2.41002 iter/s, 4.97922s/12 iters), loss = 5.26537
I0410 02:00:17.354048 25920 solver.cpp:237]     Train net output #0: loss = 5.26537 (* 1 = 5.26537 loss)
I0410 02:00:17.354058 25920 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 02:00:22.339629 25920 solver.cpp:218] Iteration 2184 (2.40702 iter/s, 4.98542s/12 iters), loss = 5.259
I0410 02:00:22.339689 25920 solver.cpp:237]     Train net output #0: loss = 5.259 (* 1 = 5.259 loss)
I0410 02:00:22.339702 25920 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 02:00:27.463256 25920 solver.cpp:218] Iteration 2196 (2.34219 iter/s, 5.1234s/12 iters), loss = 5.23643
I0410 02:00:27.463304 25920 solver.cpp:237]     Train net output #0: loss = 5.23643 (* 1 = 5.23643 loss)
I0410 02:00:27.463313 25920 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 02:00:32.535465 25920 solver.cpp:218] Iteration 2208 (2.36593 iter/s, 5.072s/12 iters), loss = 5.2352
I0410 02:00:32.535581 25920 solver.cpp:237]     Train net output #0: loss = 5.2352 (* 1 = 5.2352 loss)
I0410 02:00:32.535595 25920 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 02:00:37.518800 25920 solver.cpp:218] Iteration 2220 (2.40815 iter/s, 4.98307s/12 iters), loss = 5.24769
I0410 02:00:37.518851 25920 solver.cpp:237]     Train net output #0: loss = 5.24769 (* 1 = 5.24769 loss)
I0410 02:00:37.518863 25920 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 02:00:39.357635 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:42.557061 25920 solver.cpp:218] Iteration 2232 (2.38188 iter/s, 5.03805s/12 iters), loss = 5.26478
I0410 02:00:42.557107 25920 solver.cpp:237]     Train net output #0: loss = 5.26478 (* 1 = 5.26478 loss)
I0410 02:00:42.557116 25920 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 02:00:47.039132 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 02:00:49.056568 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 02:00:51.821753 25920 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 02:00:51.821784 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:00:55.464602 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:56.370983 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0410 02:00:56.371033 25920 solver.cpp:397]     Test net output #1: loss = 5.22843 (* 1 = 5.22843 loss)
I0410 02:00:56.458458 25920 solver.cpp:218] Iteration 2244 (0.863251 iter/s, 13.9009s/12 iters), loss = 5.25839
I0410 02:00:56.458504 25920 solver.cpp:237]     Train net output #0: loss = 5.25839 (* 1 = 5.25839 loss)
I0410 02:00:56.458515 25920 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 02:01:00.891484 25920 solver.cpp:218] Iteration 2256 (2.70707 iter/s, 4.43284s/12 iters), loss = 5.18415
I0410 02:01:00.891527 25920 solver.cpp:237]     Train net output #0: loss = 5.18415 (* 1 = 5.18415 loss)
I0410 02:01:00.891541 25920 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 02:01:05.733366 25920 solver.cpp:218] Iteration 2268 (2.47847 iter/s, 4.84169s/12 iters), loss = 5.1628
I0410 02:01:05.733512 25920 solver.cpp:237]     Train net output #0: loss = 5.1628 (* 1 = 5.1628 loss)
I0410 02:01:05.733526 25920 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 02:01:10.805517 25920 solver.cpp:218] Iteration 2280 (2.366 iter/s, 5.07185s/12 iters), loss = 5.14708
I0410 02:01:10.805560 25920 solver.cpp:237]     Train net output #0: loss = 5.14708 (* 1 = 5.14708 loss)
I0410 02:01:10.805569 25920 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 02:01:15.893651 25920 solver.cpp:218] Iteration 2292 (2.35853 iter/s, 5.08793s/12 iters), loss = 5.18827
I0410 02:01:15.893708 25920 solver.cpp:237]     Train net output #0: loss = 5.18827 (* 1 = 5.18827 loss)
I0410 02:01:15.893721 25920 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 02:01:20.909678 25920 solver.cpp:218] Iteration 2304 (2.39243 iter/s, 5.01581s/12 iters), loss = 5.17371
I0410 02:01:20.909736 25920 solver.cpp:237]     Train net output #0: loss = 5.17371 (* 1 = 5.17371 loss)
I0410 02:01:20.909749 25920 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 02:01:25.878222 25920 solver.cpp:218] Iteration 2316 (2.4153 iter/s, 4.96833s/12 iters), loss = 5.257
I0410 02:01:25.878278 25920 solver.cpp:237]     Train net output #0: loss = 5.257 (* 1 = 5.257 loss)
I0410 02:01:25.878289 25920 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 02:01:29.833197 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:30.910053 25920 solver.cpp:218] Iteration 2328 (2.38492 iter/s, 5.03162s/12 iters), loss = 5.10387
I0410 02:01:30.910102 25920 solver.cpp:237]     Train net output #0: loss = 5.10387 (* 1 = 5.10387 loss)
I0410 02:01:30.910115 25920 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 02:01:35.798570 25920 solver.cpp:218] Iteration 2340 (2.45483 iter/s, 4.88832s/12 iters), loss = 5.19244
I0410 02:01:35.798671 25920 solver.cpp:237]     Train net output #0: loss = 5.19244 (* 1 = 5.19244 loss)
I0410 02:01:35.798681 25920 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 02:01:37.840095 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 02:01:41.084033 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 02:01:44.349066 25920 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 02:01:44.349092 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:01:47.928874 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:48.884851 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0410 02:01:48.884884 25920 solver.cpp:397]     Test net output #1: loss = 5.15949 (* 1 = 5.15949 loss)
I0410 02:01:50.844499 25920 solver.cpp:218] Iteration 2352 (0.797587 iter/s, 15.0454s/12 iters), loss = 5.20443
I0410 02:01:50.844558 25920 solver.cpp:237]     Train net output #0: loss = 5.20443 (* 1 = 5.20443 loss)
I0410 02:01:50.844571 25920 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 02:01:55.838727 25920 solver.cpp:218] Iteration 2364 (2.40288 iter/s, 4.99401s/12 iters), loss = 5.17369
I0410 02:01:55.838780 25920 solver.cpp:237]     Train net output #0: loss = 5.17369 (* 1 = 5.17369 loss)
I0410 02:01:55.838793 25920 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 02:02:00.826304 25920 solver.cpp:218] Iteration 2376 (2.40608 iter/s, 4.98737s/12 iters), loss = 5.11769
I0410 02:02:00.826360 25920 solver.cpp:237]     Train net output #0: loss = 5.11769 (* 1 = 5.11769 loss)
I0410 02:02:00.826372 25920 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 02:02:05.732496 25920 solver.cpp:218] Iteration 2388 (2.44599 iter/s, 4.90598s/12 iters), loss = 5.19471
I0410 02:02:05.732550 25920 solver.cpp:237]     Train net output #0: loss = 5.19471 (* 1 = 5.19471 loss)
I0410 02:02:05.732563 25920 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 02:02:10.701557 25920 solver.cpp:218] Iteration 2400 (2.41505 iter/s, 4.96885s/12 iters), loss = 5.17641
I0410 02:02:10.701683 25920 solver.cpp:237]     Train net output #0: loss = 5.17641 (* 1 = 5.17641 loss)
I0410 02:02:10.701694 25920 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 02:02:15.750092 25920 solver.cpp:218] Iteration 2412 (2.37706 iter/s, 5.04825s/12 iters), loss = 5.12002
I0410 02:02:15.750136 25920 solver.cpp:237]     Train net output #0: loss = 5.12002 (* 1 = 5.12002 loss)
I0410 02:02:15.750147 25920 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 02:02:20.784193 25920 solver.cpp:218] Iteration 2424 (2.38384 iter/s, 5.0339s/12 iters), loss = 5.12036
I0410 02:02:20.784235 25920 solver.cpp:237]     Train net output #0: loss = 5.12036 (* 1 = 5.12036 loss)
I0410 02:02:20.784246 25920 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 02:02:21.870990 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:25.818928 25920 solver.cpp:218] Iteration 2436 (2.38354 iter/s, 5.03453s/12 iters), loss = 5.12961
I0410 02:02:25.818984 25920 solver.cpp:237]     Train net output #0: loss = 5.12961 (* 1 = 5.12961 loss)
I0410 02:02:25.818998 25920 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 02:02:30.367949 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 02:02:32.231822 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 02:02:33.593575 25920 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 02:02:33.593600 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:02:37.092479 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:38.200071 25920 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0410 02:02:38.200109 25920 solver.cpp:397]     Test net output #1: loss = 5.1333 (* 1 = 5.1333 loss)
I0410 02:02:38.287428 25920 solver.cpp:218] Iteration 2448 (0.962458 iter/s, 12.4681s/12 iters), loss = 5.12037
I0410 02:02:38.287487 25920 solver.cpp:237]     Train net output #0: loss = 5.12037 (* 1 = 5.12037 loss)
I0410 02:02:38.287497 25920 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 02:02:42.620972 25920 solver.cpp:218] Iteration 2460 (2.76922 iter/s, 4.33335s/12 iters), loss = 5.14747
I0410 02:02:42.621062 25920 solver.cpp:237]     Train net output #0: loss = 5.14747 (* 1 = 5.14747 loss)
I0410 02:02:42.621075 25920 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 02:02:47.638114 25920 solver.cpp:218] Iteration 2472 (2.39192 iter/s, 5.01689s/12 iters), loss = 5.14502
I0410 02:02:47.638171 25920 solver.cpp:237]     Train net output #0: loss = 5.14502 (* 1 = 5.14502 loss)
I0410 02:02:47.638185 25920 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 02:02:52.958165 25920 solver.cpp:218] Iteration 2484 (2.25571 iter/s, 5.31983s/12 iters), loss = 5.22867
I0410 02:02:52.958220 25920 solver.cpp:237]     Train net output #0: loss = 5.22867 (* 1 = 5.22867 loss)
I0410 02:02:52.958235 25920 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 02:02:58.068928 25920 solver.cpp:218] Iteration 2496 (2.34808 iter/s, 5.11055s/12 iters), loss = 5.18094
I0410 02:02:58.068982 25920 solver.cpp:237]     Train net output #0: loss = 5.18094 (* 1 = 5.18094 loss)
I0410 02:02:58.068995 25920 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 02:03:03.094022 25920 solver.cpp:218] Iteration 2508 (2.38812 iter/s, 5.02488s/12 iters), loss = 5.15153
I0410 02:03:03.094075 25920 solver.cpp:237]     Train net output #0: loss = 5.15153 (* 1 = 5.15153 loss)
I0410 02:03:03.094089 25920 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 02:03:08.138377 25920 solver.cpp:218] Iteration 2520 (2.379 iter/s, 5.04414s/12 iters), loss = 5.11705
I0410 02:03:08.138433 25920 solver.cpp:237]     Train net output #0: loss = 5.11705 (* 1 = 5.11705 loss)
I0410 02:03:08.138446 25920 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 02:03:11.487695 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:13.362789 25920 solver.cpp:218] Iteration 2532 (2.297 iter/s, 5.2242s/12 iters), loss = 5.19418
I0410 02:03:13.362893 25920 solver.cpp:237]     Train net output #0: loss = 5.19418 (* 1 = 5.19418 loss)
I0410 02:03:13.362903 25920 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 02:03:18.378762 25920 solver.cpp:218] Iteration 2544 (2.39248 iter/s, 5.01571s/12 iters), loss = 5.17739
I0410 02:03:18.378813 25920 solver.cpp:237]     Train net output #0: loss = 5.17739 (* 1 = 5.17739 loss)
I0410 02:03:18.378825 25920 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 02:03:20.448499 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 02:03:22.315604 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 02:03:23.882640 25920 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 02:03:23.882665 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:03:27.319250 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:28.366456 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0410 02:03:28.366504 25920 solver.cpp:397]     Test net output #1: loss = 5.107 (* 1 = 5.107 loss)
I0410 02:03:30.117866 25920 solver.cpp:218] Iteration 2556 (1.02226 iter/s, 11.7387s/12 iters), loss = 5.15606
I0410 02:03:30.117924 25920 solver.cpp:237]     Train net output #0: loss = 5.15606 (* 1 = 5.15606 loss)
I0410 02:03:30.117938 25920 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 02:03:35.132519 25920 solver.cpp:218] Iteration 2568 (2.39309 iter/s, 5.01444s/12 iters), loss = 5.07811
I0410 02:03:35.132573 25920 solver.cpp:237]     Train net output #0: loss = 5.07811 (* 1 = 5.07811 loss)
I0410 02:03:35.132586 25920 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 02:03:40.155855 25920 solver.cpp:218] Iteration 2580 (2.38895 iter/s, 5.02313s/12 iters), loss = 5.11742
I0410 02:03:40.155900 25920 solver.cpp:237]     Train net output #0: loss = 5.11742 (* 1 = 5.11742 loss)
I0410 02:03:40.155910 25920 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 02:03:45.148082 25920 solver.cpp:218] Iteration 2592 (2.40383 iter/s, 4.99203s/12 iters), loss = 5.14705
I0410 02:03:45.148159 25920 solver.cpp:237]     Train net output #0: loss = 5.14705 (* 1 = 5.14705 loss)
I0410 02:03:45.148173 25920 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 02:03:50.431401 25920 solver.cpp:218] Iteration 2604 (2.2714 iter/s, 5.28308s/12 iters), loss = 5.12429
I0410 02:03:50.431448 25920 solver.cpp:237]     Train net output #0: loss = 5.12429 (* 1 = 5.12429 loss)
I0410 02:03:50.431463 25920 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 02:03:55.684677 25920 solver.cpp:218] Iteration 2616 (2.28438 iter/s, 5.25306s/12 iters), loss = 5.17713
I0410 02:03:55.684725 25920 solver.cpp:237]     Train net output #0: loss = 5.17713 (* 1 = 5.17713 loss)
I0410 02:03:55.684737 25920 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 02:04:00.749645 25920 solver.cpp:218] Iteration 2628 (2.36931 iter/s, 5.06476s/12 iters), loss = 5.14538
I0410 02:04:00.749694 25920 solver.cpp:237]     Train net output #0: loss = 5.14538 (* 1 = 5.14538 loss)
I0410 02:04:00.749707 25920 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 02:04:01.198493 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:04:05.766144 25920 solver.cpp:218] Iteration 2640 (2.3922 iter/s, 5.01629s/12 iters), loss = 5.05892
I0410 02:04:05.766201 25920 solver.cpp:237]     Train net output #0: loss = 5.05892 (* 1 = 5.05892 loss)
I0410 02:04:05.766214 25920 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 02:04:10.289305 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 02:04:12.131850 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 02:04:13.498373 25920 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 02:04:13.498396 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:04:16.904644 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:04:17.962764 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0410 02:04:17.962801 25920 solver.cpp:397]     Test net output #1: loss = 5.09476 (* 1 = 5.09476 loss)
I0410 02:04:18.050231 25920 solver.cpp:218] Iteration 2652 (0.976907 iter/s, 12.2837s/12 iters), loss = 5.13304
I0410 02:04:18.050289 25920 solver.cpp:237]     Train net output #0: loss = 5.13304 (* 1 = 5.13304 loss)
I0410 02:04:18.050300 25920 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 02:04:22.548738 25920 solver.cpp:218] Iteration 2664 (2.66767 iter/s, 4.49831s/12 iters), loss = 5.07261
I0410 02:04:22.548794 25920 solver.cpp:237]     Train net output #0: loss = 5.07261 (* 1 = 5.07261 loss)
I0410 02:04:22.548807 25920 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 02:04:27.638566 25920 solver.cpp:218] Iteration 2676 (2.35774 iter/s, 5.08962s/12 iters), loss = 5.03823
I0410 02:04:27.638612 25920 solver.cpp:237]     Train net output #0: loss = 5.03823 (* 1 = 5.03823 loss)
I0410 02:04:27.638624 25920 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 02:04:33.076700 25920 solver.cpp:218] Iteration 2688 (2.20672 iter/s, 5.43792s/12 iters), loss = 5.11147
I0410 02:04:33.076748 25920 solver.cpp:237]     Train net output #0: loss = 5.11147 (* 1 = 5.11147 loss)
I0410 02:04:33.076758 25920 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 02:04:38.116901 25920 solver.cpp:218] Iteration 2700 (2.38096 iter/s, 5.03999s/12 iters), loss = 5.05951
I0410 02:04:38.116941 25920 solver.cpp:237]     Train net output #0: loss = 5.05951 (* 1 = 5.05951 loss)
I0410 02:04:38.116950 25920 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 02:04:43.101215 25920 solver.cpp:218] Iteration 2712 (2.40765 iter/s, 4.98412s/12 iters), loss = 5.03901
I0410 02:04:43.101266 25920 solver.cpp:237]     Train net output #0: loss = 5.03901 (* 1 = 5.03901 loss)
I0410 02:04:43.101277 25920 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 02:04:48.071096 25920 solver.cpp:218] Iteration 2724 (2.41464 iter/s, 4.96968s/12 iters), loss = 5.03798
I0410 02:04:48.071182 25920 solver.cpp:237]     Train net output #0: loss = 5.03798 (* 1 = 5.03798 loss)
I0410 02:04:48.071190 25920 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 02:04:50.670756 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:04:53.236743 25920 solver.cpp:218] Iteration 2736 (2.32315 iter/s, 5.1654s/12 iters), loss = 5.04831
I0410 02:04:53.236791 25920 solver.cpp:237]     Train net output #0: loss = 5.04831 (* 1 = 5.04831 loss)
I0410 02:04:53.236804 25920 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 02:04:58.160706 25920 solver.cpp:218] Iteration 2748 (2.43716 iter/s, 4.92376s/12 iters), loss = 5.10878
I0410 02:04:58.160756 25920 solver.cpp:237]     Train net output #0: loss = 5.10878 (* 1 = 5.10878 loss)
I0410 02:04:58.160769 25920 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 02:05:00.159637 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 02:05:03.789690 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 02:05:06.647030 25920 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 02:05:06.647059 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:05:09.740909 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:05:09.976666 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:11.077587 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0410 02:05:11.077625 25920 solver.cpp:397]     Test net output #1: loss = 5.08194 (* 1 = 5.08194 loss)
I0410 02:05:13.086511 25920 solver.cpp:218] Iteration 2760 (0.804003 iter/s, 14.9253s/12 iters), loss = 5.02228
I0410 02:05:13.086560 25920 solver.cpp:237]     Train net output #0: loss = 5.02228 (* 1 = 5.02228 loss)
I0410 02:05:13.086570 25920 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 02:05:18.215485 25920 solver.cpp:218] Iteration 2772 (2.33974 iter/s, 5.12877s/12 iters), loss = 5.07772
I0410 02:05:18.215628 25920 solver.cpp:237]     Train net output #0: loss = 5.07772 (* 1 = 5.07772 loss)
I0410 02:05:18.215638 25920 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 02:05:23.167418 25920 solver.cpp:218] Iteration 2784 (2.42344 iter/s, 4.95163s/12 iters), loss = 5.11345
I0410 02:05:23.167465 25920 solver.cpp:237]     Train net output #0: loss = 5.11345 (* 1 = 5.11345 loss)
I0410 02:05:23.167474 25920 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 02:05:28.105477 25920 solver.cpp:218] Iteration 2796 (2.4302 iter/s, 4.93786s/12 iters), loss = 5.01832
I0410 02:05:28.105525 25920 solver.cpp:237]     Train net output #0: loss = 5.01832 (* 1 = 5.01832 loss)
I0410 02:05:28.105535 25920 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 02:05:33.075867 25920 solver.cpp:218] Iteration 2808 (2.4144 iter/s, 4.97018s/12 iters), loss = 4.88697
I0410 02:05:33.075911 25920 solver.cpp:237]     Train net output #0: loss = 4.88697 (* 1 = 4.88697 loss)
I0410 02:05:33.075919 25920 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 02:05:38.144541 25920 solver.cpp:218] Iteration 2820 (2.36758 iter/s, 5.06847s/12 iters), loss = 5.0292
I0410 02:05:38.144583 25920 solver.cpp:237]     Train net output #0: loss = 5.0292 (* 1 = 5.0292 loss)
I0410 02:05:38.144593 25920 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 02:05:42.915401 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:43.211942 25920 solver.cpp:218] Iteration 2832 (2.36817 iter/s, 5.0672s/12 iters), loss = 5.13399
I0410 02:05:43.211993 25920 solver.cpp:237]     Train net output #0: loss = 5.13399 (* 1 = 5.13399 loss)
I0410 02:05:43.212004 25920 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 02:05:48.292109 25920 solver.cpp:218] Iteration 2844 (2.36222 iter/s, 5.07996s/12 iters), loss = 5.09961
I0410 02:05:48.292215 25920 solver.cpp:237]     Train net output #0: loss = 5.09961 (* 1 = 5.09961 loss)
I0410 02:05:48.292227 25920 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 02:05:52.861536 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 02:05:55.820329 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 02:06:04.014667 25920 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 02:06:04.014695 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:06:07.334578 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:06:08.474560 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0410 02:06:08.474611 25920 solver.cpp:397]     Test net output #1: loss = 5.05897 (* 1 = 5.05897 loss)
I0410 02:06:08.561998 25920 solver.cpp:218] Iteration 2856 (0.592031 iter/s, 20.2692s/12 iters), loss = 5.00602
I0410 02:06:08.562047 25920 solver.cpp:237]     Train net output #0: loss = 5.00602 (* 1 = 5.00602 loss)
I0410 02:06:08.562059 25920 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 02:06:12.855932 25920 solver.cpp:218] Iteration 2868 (2.79476 iter/s, 4.29374s/12 iters), loss = 5.06054
I0410 02:06:12.855993 25920 solver.cpp:237]     Train net output #0: loss = 5.06054 (* 1 = 5.06054 loss)
I0410 02:06:12.856005 25920 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 02:06:17.911032 25920 solver.cpp:218] Iteration 2880 (2.37394 iter/s, 5.05488s/12 iters), loss = 5.11923
I0410 02:06:17.911087 25920 solver.cpp:237]     Train net output #0: loss = 5.11923 (* 1 = 5.11923 loss)
I0410 02:06:17.911098 25920 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 02:06:23.022615 25920 solver.cpp:218] Iteration 2892 (2.34771 iter/s, 5.11137s/12 iters), loss = 5.01892
I0410 02:06:23.022716 25920 solver.cpp:237]     Train net output #0: loss = 5.01892 (* 1 = 5.01892 loss)
I0410 02:06:23.022728 25920 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 02:06:28.143323 25920 solver.cpp:218] Iteration 2904 (2.34355 iter/s, 5.12045s/12 iters), loss = 5.09425
I0410 02:06:28.143378 25920 solver.cpp:237]     Train net output #0: loss = 5.09425 (* 1 = 5.09425 loss)
I0410 02:06:28.143391 25920 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 02:06:33.443959 25920 solver.cpp:218] Iteration 2916 (2.26398 iter/s, 5.30041s/12 iters), loss = 5.0286
I0410 02:06:33.444025 25920 solver.cpp:237]     Train net output #0: loss = 5.0286 (* 1 = 5.0286 loss)
I0410 02:06:33.444038 25920 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 02:06:38.652590 25920 solver.cpp:218] Iteration 2928 (2.30397 iter/s, 5.2084s/12 iters), loss = 5.11778
I0410 02:06:38.652653 25920 solver.cpp:237]     Train net output #0: loss = 5.11778 (* 1 = 5.11778 loss)
I0410 02:06:38.652667 25920 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 02:06:40.482729 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:06:43.746461 25920 solver.cpp:218] Iteration 2940 (2.35588 iter/s, 5.09364s/12 iters), loss = 5.13297
I0410 02:06:43.746511 25920 solver.cpp:237]     Train net output #0: loss = 5.13297 (* 1 = 5.13297 loss)
I0410 02:06:43.746521 25920 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 02:06:48.796233 25920 solver.cpp:218] Iteration 2952 (2.37645 iter/s, 5.04955s/12 iters), loss = 5.08189
I0410 02:06:48.796293 25920 solver.cpp:237]     Train net output #0: loss = 5.08189 (* 1 = 5.08189 loss)
I0410 02:06:48.796305 25920 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 02:06:50.808046 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 02:06:53.934175 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 02:06:56.397780 25920 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 02:06:56.397801 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:06:59.721269 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:00.934998 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0410 02:07:00.935047 25920 solver.cpp:397]     Test net output #1: loss = 5.02881 (* 1 = 5.02881 loss)
I0410 02:07:02.858963 25920 solver.cpp:218] Iteration 2964 (0.853348 iter/s, 14.0623s/12 iters), loss = 5.05743
I0410 02:07:02.859026 25920 solver.cpp:237]     Train net output #0: loss = 5.05743 (* 1 = 5.05743 loss)
I0410 02:07:02.859043 25920 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 02:07:07.848661 25920 solver.cpp:218] Iteration 2976 (2.40506 iter/s, 4.98948s/12 iters), loss = 5.00561
I0410 02:07:07.848709 25920 solver.cpp:237]     Train net output #0: loss = 5.00561 (* 1 = 5.00561 loss)
I0410 02:07:07.848721 25920 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 02:07:12.890347 25920 solver.cpp:218] Iteration 2988 (2.38026 iter/s, 5.04147s/12 iters), loss = 5.00907
I0410 02:07:12.890405 25920 solver.cpp:237]     Train net output #0: loss = 5.00907 (* 1 = 5.00907 loss)
I0410 02:07:12.890417 25920 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 02:07:18.097303 25920 solver.cpp:218] Iteration 3000 (2.30471 iter/s, 5.20674s/12 iters), loss = 5.03457
I0410 02:07:18.097353 25920 solver.cpp:237]     Train net output #0: loss = 5.03457 (* 1 = 5.03457 loss)
I0410 02:07:18.097365 25920 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 02:07:23.192966 25920 solver.cpp:218] Iteration 3012 (2.35504 iter/s, 5.09545s/12 iters), loss = 5.04815
I0410 02:07:23.193020 25920 solver.cpp:237]     Train net output #0: loss = 5.04815 (* 1 = 5.04815 loss)
I0410 02:07:23.193032 25920 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 02:07:28.324641 25920 solver.cpp:218] Iteration 3024 (2.33852 iter/s, 5.13146s/12 iters), loss = 5.04332
I0410 02:07:28.324726 25920 solver.cpp:237]     Train net output #0: loss = 5.04332 (* 1 = 5.04332 loss)
I0410 02:07:28.324739 25920 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 02:07:32.266165 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:33.271960 25920 solver.cpp:218] Iteration 3036 (2.42567 iter/s, 4.94708s/12 iters), loss = 4.9346
I0410 02:07:33.272008 25920 solver.cpp:237]     Train net output #0: loss = 4.9346 (* 1 = 4.9346 loss)
I0410 02:07:33.272020 25920 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 02:07:38.310056 25920 solver.cpp:218] Iteration 3048 (2.38195 iter/s, 5.03789s/12 iters), loss = 5.08232
I0410 02:07:38.310103 25920 solver.cpp:237]     Train net output #0: loss = 5.08232 (* 1 = 5.08232 loss)
I0410 02:07:38.310115 25920 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 02:07:42.951261 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 02:07:44.822993 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 02:07:47.843643 25920 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 02:07:47.843670 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:07:51.222072 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:52.615197 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0410 02:07:52.615244 25920 solver.cpp:397]     Test net output #1: loss = 5.0141 (* 1 = 5.0141 loss)
I0410 02:07:52.702864 25920 solver.cpp:218] Iteration 3060 (0.833777 iter/s, 14.3923s/12 iters), loss = 5.06293
I0410 02:07:52.702913 25920 solver.cpp:237]     Train net output #0: loss = 5.06293 (* 1 = 5.06293 loss)
I0410 02:07:52.702924 25920 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 02:07:56.984885 25920 solver.cpp:218] Iteration 3072 (2.80254 iter/s, 4.28183s/12 iters), loss = 4.99027
I0410 02:07:56.984935 25920 solver.cpp:237]     Train net output #0: loss = 4.99027 (* 1 = 4.99027 loss)
I0410 02:07:56.984947 25920 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 02:08:01.978133 25920 solver.cpp:218] Iteration 3084 (2.40335 iter/s, 4.99304s/12 iters), loss = 4.97024
I0410 02:08:01.978253 25920 solver.cpp:237]     Train net output #0: loss = 4.97024 (* 1 = 4.97024 loss)
I0410 02:08:01.978266 25920 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 02:08:06.964123 25920 solver.cpp:218] Iteration 3096 (2.40688 iter/s, 4.98571s/12 iters), loss = 5.09436
I0410 02:08:06.964179 25920 solver.cpp:237]     Train net output #0: loss = 5.09436 (* 1 = 5.09436 loss)
I0410 02:08:06.964190 25920 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 02:08:11.953891 25920 solver.cpp:218] Iteration 3108 (2.40503 iter/s, 4.98955s/12 iters), loss = 5.11431
I0410 02:08:11.953951 25920 solver.cpp:237]     Train net output #0: loss = 5.11431 (* 1 = 5.11431 loss)
I0410 02:08:11.953984 25920 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 02:08:16.912103 25920 solver.cpp:218] Iteration 3120 (2.42033 iter/s, 4.95799s/12 iters), loss = 4.92799
I0410 02:08:16.912168 25920 solver.cpp:237]     Train net output #0: loss = 4.92799 (* 1 = 4.92799 loss)
I0410 02:08:16.912182 25920 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 02:08:21.943480 25920 solver.cpp:218] Iteration 3132 (2.38514 iter/s, 5.03115s/12 iters), loss = 5.02638
I0410 02:08:21.943526 25920 solver.cpp:237]     Train net output #0: loss = 5.02638 (* 1 = 5.02638 loss)
I0410 02:08:21.943536 25920 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 02:08:23.026538 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:08:26.971410 25920 solver.cpp:218] Iteration 3144 (2.38677 iter/s, 5.02771s/12 iters), loss = 4.91578
I0410 02:08:26.971463 25920 solver.cpp:237]     Train net output #0: loss = 4.91578 (* 1 = 4.91578 loss)
I0410 02:08:26.971477 25920 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 02:08:32.043233 25920 solver.cpp:218] Iteration 3156 (2.36611 iter/s, 5.07161s/12 iters), loss = 4.93887
I0410 02:08:32.043335 25920 solver.cpp:237]     Train net output #0: loss = 4.93887 (* 1 = 4.93887 loss)
I0410 02:08:32.043347 25920 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 02:08:34.129590 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 02:08:39.311616 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 02:08:42.112876 25920 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 02:08:42.112905 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:08:45.282255 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:08:46.545586 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0410 02:08:46.545632 25920 solver.cpp:397]     Test net output #1: loss = 4.97943 (* 1 = 4.97943 loss)
I0410 02:08:48.576685 25920 solver.cpp:218] Iteration 3168 (0.725827 iter/s, 16.5329s/12 iters), loss = 5.09726
I0410 02:08:48.576735 25920 solver.cpp:237]     Train net output #0: loss = 5.09726 (* 1 = 5.09726 loss)
I0410 02:08:48.576745 25920 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 02:08:53.490202 25920 solver.cpp:218] Iteration 3180 (2.44234 iter/s, 4.91332s/12 iters), loss = 5.03213
I0410 02:08:53.490250 25920 solver.cpp:237]     Train net output #0: loss = 5.03213 (* 1 = 5.03213 loss)
I0410 02:08:53.490262 25920 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 02:08:58.421773 25920 solver.cpp:218] Iteration 3192 (2.4334 iter/s, 4.93137s/12 iters), loss = 4.97516
I0410 02:08:58.421823 25920 solver.cpp:237]     Train net output #0: loss = 4.97516 (* 1 = 4.97516 loss)
I0410 02:08:58.421836 25920 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 02:09:03.348121 25920 solver.cpp:218] Iteration 3204 (2.43598 iter/s, 4.92614s/12 iters), loss = 5.1243
I0410 02:09:03.348268 25920 solver.cpp:237]     Train net output #0: loss = 5.1243 (* 1 = 5.1243 loss)
I0410 02:09:03.348281 25920 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 02:09:08.291808 25920 solver.cpp:218] Iteration 3216 (2.42748 iter/s, 4.94339s/12 iters), loss = 5.0409
I0410 02:09:08.291854 25920 solver.cpp:237]     Train net output #0: loss = 5.0409 (* 1 = 5.0409 loss)
I0410 02:09:08.291865 25920 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 02:09:13.315950 25920 solver.cpp:218] Iteration 3228 (2.38856 iter/s, 5.02394s/12 iters), loss = 5.03315
I0410 02:09:13.316004 25920 solver.cpp:237]     Train net output #0: loss = 5.03315 (* 1 = 5.03315 loss)
I0410 02:09:13.316015 25920 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 02:09:16.554613 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:18.312604 25920 solver.cpp:218] Iteration 3240 (2.40171 iter/s, 4.99645s/12 iters), loss = 5.04823
I0410 02:09:18.312649 25920 solver.cpp:237]     Train net output #0: loss = 5.04823 (* 1 = 5.04823 loss)
I0410 02:09:18.312657 25920 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 02:09:23.268246 25920 solver.cpp:218] Iteration 3252 (2.42158 iter/s, 4.95544s/12 iters), loss = 5.04156
I0410 02:09:23.268311 25920 solver.cpp:237]     Train net output #0: loss = 5.04156 (* 1 = 5.04156 loss)
I0410 02:09:23.268326 25920 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 02:09:27.740121 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 02:09:29.486212 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 02:09:30.912652 25920 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 02:09:30.912683 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:09:34.082237 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:35.386397 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0410 02:09:35.386446 25920 solver.cpp:397]     Test net output #1: loss = 4.96093 (* 1 = 4.96093 loss)
I0410 02:09:35.474205 25920 solver.cpp:218] Iteration 3264 (0.983161 iter/s, 12.2055s/12 iters), loss = 5.12782
I0410 02:09:35.474252 25920 solver.cpp:237]     Train net output #0: loss = 5.12782 (* 1 = 5.12782 loss)
I0410 02:09:35.474263 25920 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 02:09:39.788262 25920 solver.cpp:218] Iteration 3276 (2.78172 iter/s, 4.31387s/12 iters), loss = 4.89949
I0410 02:09:39.788312 25920 solver.cpp:237]     Train net output #0: loss = 4.89949 (* 1 = 4.89949 loss)
I0410 02:09:39.788323 25920 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 02:09:44.790975 25920 solver.cpp:218] Iteration 3288 (2.3988 iter/s, 5.0025s/12 iters), loss = 4.86939
I0410 02:09:44.791030 25920 solver.cpp:237]     Train net output #0: loss = 4.86939 (* 1 = 4.86939 loss)
I0410 02:09:44.791044 25920 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 02:09:50.035554 25920 solver.cpp:218] Iteration 3300 (2.28817 iter/s, 5.24437s/12 iters), loss = 4.88484
I0410 02:09:50.035594 25920 solver.cpp:237]     Train net output #0: loss = 4.88484 (* 1 = 4.88484 loss)
I0410 02:09:50.035602 25920 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 02:09:54.960168 25920 solver.cpp:218] Iteration 3312 (2.43684 iter/s, 4.92441s/12 iters), loss = 4.9132
I0410 02:09:54.960238 25920 solver.cpp:237]     Train net output #0: loss = 4.9132 (* 1 = 4.9132 loss)
I0410 02:09:54.960254 25920 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 02:09:59.985915 25920 solver.cpp:218] Iteration 3324 (2.38781 iter/s, 5.02552s/12 iters), loss = 5.0154
I0410 02:09:59.985981 25920 solver.cpp:237]     Train net output #0: loss = 5.0154 (* 1 = 5.0154 loss)
I0410 02:09:59.985991 25920 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 02:10:05.301545 25920 solver.cpp:218] Iteration 3336 (2.25759 iter/s, 5.3154s/12 iters), loss = 4.96311
I0410 02:10:05.301669 25920 solver.cpp:237]     Train net output #0: loss = 4.96311 (* 1 = 4.96311 loss)
I0410 02:10:05.301683 25920 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 02:10:05.803143 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:10.626789 25920 solver.cpp:218] Iteration 3348 (2.25354 iter/s, 5.32495s/12 iters), loss = 4.91461
I0410 02:10:10.626845 25920 solver.cpp:237]     Train net output #0: loss = 4.91461 (* 1 = 4.91461 loss)
I0410 02:10:10.626859 25920 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 02:10:15.764984 25920 solver.cpp:218] Iteration 3360 (2.33555 iter/s, 5.13798s/12 iters), loss = 4.96704
I0410 02:10:15.765040 25920 solver.cpp:237]     Train net output #0: loss = 4.96704 (* 1 = 4.96704 loss)
I0410 02:10:15.765053 25920 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 02:10:17.828472 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 02:10:20.787153 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 02:10:23.555282 25920 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 02:10:23.555306 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:10:26.678165 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:28.012306 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0410 02:10:28.012348 25920 solver.cpp:397]     Test net output #1: loss = 4.90072 (* 1 = 4.90072 loss)
I0410 02:10:30.017611 25920 solver.cpp:218] Iteration 3372 (0.841978 iter/s, 14.2522s/12 iters), loss = 4.83598
I0410 02:10:30.017661 25920 solver.cpp:237]     Train net output #0: loss = 4.83598 (* 1 = 4.83598 loss)
I0410 02:10:30.017673 25920 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 02:10:35.087661 25920 solver.cpp:218] Iteration 3384 (2.36694 iter/s, 5.06984s/12 iters), loss = 4.97795
I0410 02:10:35.087707 25920 solver.cpp:237]     Train net output #0: loss = 4.97795 (* 1 = 4.97795 loss)
I0410 02:10:35.087718 25920 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 02:10:40.185048 25920 solver.cpp:218] Iteration 3396 (2.35424 iter/s, 5.09718s/12 iters), loss = 4.85609
I0410 02:10:40.185164 25920 solver.cpp:237]     Train net output #0: loss = 4.85609 (* 1 = 4.85609 loss)
I0410 02:10:40.185174 25920 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 02:10:45.164657 25920 solver.cpp:218] Iteration 3408 (2.40996 iter/s, 4.97934s/12 iters), loss = 4.72962
I0410 02:10:45.164710 25920 solver.cpp:237]     Train net output #0: loss = 4.72962 (* 1 = 4.72962 loss)
I0410 02:10:45.164722 25920 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 02:10:50.204730 25920 solver.cpp:218] Iteration 3420 (2.38101 iter/s, 5.03987s/12 iters), loss = 4.9066
I0410 02:10:50.204771 25920 solver.cpp:237]     Train net output #0: loss = 4.9066 (* 1 = 4.9066 loss)
I0410 02:10:50.204779 25920 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 02:10:55.490211 25920 solver.cpp:218] Iteration 3432 (2.27046 iter/s, 5.28528s/12 iters), loss = 4.8121
I0410 02:10:55.490264 25920 solver.cpp:237]     Train net output #0: loss = 4.8121 (* 1 = 4.8121 loss)
I0410 02:10:55.490276 25920 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 02:10:58.069906 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:00.578727 25920 solver.cpp:218] Iteration 3444 (2.35835 iter/s, 5.0883s/12 iters), loss = 4.83022
I0410 02:11:00.578778 25920 solver.cpp:237]     Train net output #0: loss = 4.83022 (* 1 = 4.83022 loss)
I0410 02:11:00.578790 25920 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 02:11:05.593291 25920 solver.cpp:218] Iteration 3456 (2.39313 iter/s, 5.01435s/12 iters), loss = 4.87445
I0410 02:11:05.593333 25920 solver.cpp:237]     Train net output #0: loss = 4.87445 (* 1 = 4.87445 loss)
I0410 02:11:05.593343 25920 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 02:11:10.160336 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 02:11:12.053704 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 02:11:13.450028 25920 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 02:11:13.450057 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:11:13.920332 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:11:16.540691 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:17.925091 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0410 02:11:17.925134 25920 solver.cpp:397]     Test net output #1: loss = 4.85893 (* 1 = 4.85893 loss)
I0410 02:11:18.012519 25920 solver.cpp:218] Iteration 3468 (0.966276 iter/s, 12.4188s/12 iters), loss = 4.71472
I0410 02:11:18.012564 25920 solver.cpp:237]     Train net output #0: loss = 4.71472 (* 1 = 4.71472 loss)
I0410 02:11:18.012574 25920 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 02:11:22.133800 25920 solver.cpp:218] Iteration 3480 (2.91184 iter/s, 4.1211s/12 iters), loss = 4.82185
I0410 02:11:22.133854 25920 solver.cpp:237]     Train net output #0: loss = 4.82185 (* 1 = 4.82185 loss)
I0410 02:11:22.133867 25920 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 02:11:27.144608 25920 solver.cpp:218] Iteration 3492 (2.39493 iter/s, 5.01059s/12 iters), loss = 4.84819
I0410 02:11:27.144665 25920 solver.cpp:237]     Train net output #0: loss = 4.84819 (* 1 = 4.84819 loss)
I0410 02:11:27.144678 25920 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 02:11:32.163738 25920 solver.cpp:218] Iteration 3504 (2.39095 iter/s, 5.01892s/12 iters), loss = 4.84359
I0410 02:11:32.163791 25920 solver.cpp:237]     Train net output #0: loss = 4.84359 (* 1 = 4.84359 loss)
I0410 02:11:32.163805 25920 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 02:11:37.168264 25920 solver.cpp:218] Iteration 3516 (2.39793 iter/s, 5.00431s/12 iters), loss = 4.63667
I0410 02:11:37.168318 25920 solver.cpp:237]     Train net output #0: loss = 4.63667 (* 1 = 4.63667 loss)
I0410 02:11:37.168331 25920 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 02:11:42.230901 25920 solver.cpp:218] Iteration 3528 (2.3704 iter/s, 5.06243s/12 iters), loss = 4.74254
I0410 02:11:42.231180 25920 solver.cpp:237]     Train net output #0: loss = 4.74254 (* 1 = 4.74254 loss)
I0410 02:11:42.231194 25920 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 02:11:47.116449 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:47.380900 25920 solver.cpp:218] Iteration 3540 (2.33029 iter/s, 5.14957s/12 iters), loss = 4.67328
I0410 02:11:47.380939 25920 solver.cpp:237]     Train net output #0: loss = 4.67328 (* 1 = 4.67328 loss)
I0410 02:11:47.380949 25920 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 02:11:52.576182 25920 solver.cpp:218] Iteration 3552 (2.30988 iter/s, 5.19508s/12 iters), loss = 4.71248
I0410 02:11:52.576238 25920 solver.cpp:237]     Train net output #0: loss = 4.71248 (* 1 = 4.71248 loss)
I0410 02:11:52.576253 25920 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 02:11:57.690562 25920 solver.cpp:218] Iteration 3564 (2.34642 iter/s, 5.11417s/12 iters), loss = 4.76337
I0410 02:11:57.690611 25920 solver.cpp:237]     Train net output #0: loss = 4.76337 (* 1 = 4.76337 loss)
I0410 02:11:57.690623 25920 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 02:11:59.786355 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 02:12:01.540150 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 02:12:02.905267 25920 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 02:12:02.905293 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:12:05.923209 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:12:07.371726 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0410 02:12:07.371765 25920 solver.cpp:397]     Test net output #1: loss = 4.79017 (* 1 = 4.79017 loss)
I0410 02:12:09.328373 25920 solver.cpp:218] Iteration 3576 (1.03116 iter/s, 11.6374s/12 iters), loss = 4.85567
I0410 02:12:09.328428 25920 solver.cpp:237]     Train net output #0: loss = 4.85567 (* 1 = 4.85567 loss)
I0410 02:12:09.328441 25920 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 02:12:14.417814 25920 solver.cpp:218] Iteration 3588 (2.35792 iter/s, 5.08923s/12 iters), loss = 4.84636
I0410 02:12:14.417929 25920 solver.cpp:237]     Train net output #0: loss = 4.84636 (* 1 = 4.84636 loss)
I0410 02:12:14.417939 25920 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 02:12:19.336905 25920 solver.cpp:218] Iteration 3600 (2.43961 iter/s, 4.91882s/12 iters), loss = 4.84195
I0410 02:12:19.336956 25920 solver.cpp:237]     Train net output #0: loss = 4.84195 (* 1 = 4.84195 loss)
I0410 02:12:19.336966 25920 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 02:12:24.249099 25920 solver.cpp:218] Iteration 3612 (2.443 iter/s, 4.91199s/12 iters), loss = 4.72994
I0410 02:12:24.249142 25920 solver.cpp:237]     Train net output #0: loss = 4.72994 (* 1 = 4.72994 loss)
I0410 02:12:24.249153 25920 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 02:12:29.277189 25920 solver.cpp:218] Iteration 3624 (2.38669 iter/s, 5.02788s/12 iters), loss = 4.74408
I0410 02:12:29.277242 25920 solver.cpp:237]     Train net output #0: loss = 4.74408 (* 1 = 4.74408 loss)
I0410 02:12:29.277256 25920 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 02:12:34.560906 25920 solver.cpp:218] Iteration 3636 (2.27122 iter/s, 5.2835s/12 iters), loss = 4.95772
I0410 02:12:34.560956 25920 solver.cpp:237]     Train net output #0: loss = 4.95772 (* 1 = 4.95772 loss)
I0410 02:12:34.560966 25920 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 02:12:36.455163 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:12:39.737666 25920 solver.cpp:218] Iteration 3648 (2.31814 iter/s, 5.17655s/12 iters), loss = 4.79283
I0410 02:12:39.737709 25920 solver.cpp:237]     Train net output #0: loss = 4.79283 (* 1 = 4.79283 loss)
I0410 02:12:39.737717 25920 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 02:12:44.782442 25920 solver.cpp:218] Iteration 3660 (2.37879 iter/s, 5.04458s/12 iters), loss = 4.88341
I0410 02:12:44.782559 25920 solver.cpp:237]     Train net output #0: loss = 4.88341 (* 1 = 4.88341 loss)
I0410 02:12:44.782572 25920 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 02:12:49.320420 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 02:12:53.628816 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 02:12:57.570133 25920 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 02:12:57.570158 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:13:00.552662 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:02.011600 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0410 02:13:02.011646 25920 solver.cpp:397]     Test net output #1: loss = 4.71255 (* 1 = 4.71255 loss)
I0410 02:13:02.098985 25920 solver.cpp:218] Iteration 3672 (0.693004 iter/s, 17.3159s/12 iters), loss = 4.55918
I0410 02:13:02.099030 25920 solver.cpp:237]     Train net output #0: loss = 4.55918 (* 1 = 4.55918 loss)
I0410 02:13:02.099042 25920 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 02:13:06.417591 25920 solver.cpp:218] Iteration 3684 (2.77879 iter/s, 4.31842s/12 iters), loss = 4.76932
I0410 02:13:06.417647 25920 solver.cpp:237]     Train net output #0: loss = 4.76932 (* 1 = 4.76932 loss)
I0410 02:13:06.417661 25920 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 02:13:11.676050 25920 solver.cpp:218] Iteration 3696 (2.28213 iter/s, 5.25824s/12 iters), loss = 4.66571
I0410 02:13:11.676107 25920 solver.cpp:237]     Train net output #0: loss = 4.66571 (* 1 = 4.66571 loss)
I0410 02:13:11.676120 25920 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 02:13:16.621028 25920 solver.cpp:218] Iteration 3708 (2.42681 iter/s, 4.94477s/12 iters), loss = 4.75078
I0410 02:13:16.621134 25920 solver.cpp:237]     Train net output #0: loss = 4.75078 (* 1 = 4.75078 loss)
I0410 02:13:16.621143 25920 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 02:13:21.619158 25920 solver.cpp:218] Iteration 3720 (2.40103 iter/s, 4.99787s/12 iters), loss = 4.76115
I0410 02:13:21.619202 25920 solver.cpp:237]     Train net output #0: loss = 4.76115 (* 1 = 4.76115 loss)
I0410 02:13:21.619212 25920 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 02:13:26.779791 25920 solver.cpp:218] Iteration 3732 (2.32539 iter/s, 5.16043s/12 iters), loss = 4.70273
I0410 02:13:26.779834 25920 solver.cpp:237]     Train net output #0: loss = 4.70273 (* 1 = 4.70273 loss)
I0410 02:13:26.779842 25920 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 02:13:31.118572 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:32.111440 25920 solver.cpp:218] Iteration 3744 (2.2508 iter/s, 5.33144s/12 iters), loss = 4.66679
I0410 02:13:32.111497 25920 solver.cpp:237]     Train net output #0: loss = 4.66679 (* 1 = 4.66679 loss)
I0410 02:13:32.111510 25920 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 02:13:37.140947 25920 solver.cpp:218] Iteration 3756 (2.38602 iter/s, 5.02929s/12 iters), loss = 4.80766
I0410 02:13:37.141003 25920 solver.cpp:237]     Train net output #0: loss = 4.80766 (* 1 = 4.80766 loss)
I0410 02:13:37.141016 25920 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 02:13:42.082336 25920 solver.cpp:218] Iteration 3768 (2.42857 iter/s, 4.94118s/12 iters), loss = 4.74065
I0410 02:13:42.082379 25920 solver.cpp:237]     Train net output #0: loss = 4.74065 (* 1 = 4.74065 loss)
I0410 02:13:42.082388 25920 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 02:13:44.093793 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 02:13:48.007246 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 02:13:52.068186 25920 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 02:13:52.068208 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:13:55.110692 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:56.604379 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0410 02:13:56.604415 25920 solver.cpp:397]     Test net output #1: loss = 4.66976 (* 1 = 4.66976 loss)
I0410 02:13:58.608188 25920 solver.cpp:218] Iteration 3780 (0.726158 iter/s, 16.5253s/12 iters), loss = 4.62659
I0410 02:13:58.608242 25920 solver.cpp:237]     Train net output #0: loss = 4.62659 (* 1 = 4.62659 loss)
I0410 02:13:58.608256 25920 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 02:14:03.916193 25920 solver.cpp:218] Iteration 3792 (2.26083 iter/s, 5.30778s/12 iters), loss = 4.69481
I0410 02:14:03.916244 25920 solver.cpp:237]     Train net output #0: loss = 4.69481 (* 1 = 4.69481 loss)
I0410 02:14:03.916255 25920 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 02:14:08.980908 25920 solver.cpp:218] Iteration 3804 (2.36944 iter/s, 5.06449s/12 iters), loss = 4.76857
I0410 02:14:08.980967 25920 solver.cpp:237]     Train net output #0: loss = 4.76857 (* 1 = 4.76857 loss)
I0410 02:14:08.980980 25920 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 02:14:13.986944 25920 solver.cpp:218] Iteration 3816 (2.39721 iter/s, 5.00582s/12 iters), loss = 4.68598
I0410 02:14:13.986999 25920 solver.cpp:237]     Train net output #0: loss = 4.68598 (* 1 = 4.68598 loss)
I0410 02:14:13.987011 25920 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 02:14:18.982259 25920 solver.cpp:218] Iteration 3828 (2.40235 iter/s, 4.9951s/12 iters), loss = 4.53621
I0410 02:14:18.983894 25920 solver.cpp:237]     Train net output #0: loss = 4.53621 (* 1 = 4.53621 loss)
I0410 02:14:18.983907 25920 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 02:14:24.049932 25920 solver.cpp:218] Iteration 3840 (2.36879 iter/s, 5.06588s/12 iters), loss = 4.81396
I0410 02:14:24.050001 25920 solver.cpp:237]     Train net output #0: loss = 4.81396 (* 1 = 4.81396 loss)
I0410 02:14:24.050014 25920 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 02:14:25.199146 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:29.142674 25920 solver.cpp:218] Iteration 3852 (2.3564 iter/s, 5.09251s/12 iters), loss = 4.45942
I0410 02:14:29.142724 25920 solver.cpp:237]     Train net output #0: loss = 4.45942 (* 1 = 4.45942 loss)
I0410 02:14:29.142735 25920 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 02:14:34.162089 25920 solver.cpp:218] Iteration 3864 (2.39082 iter/s, 5.01921s/12 iters), loss = 4.50126
I0410 02:14:34.162140 25920 solver.cpp:237]     Train net output #0: loss = 4.50126 (* 1 = 4.50126 loss)
I0410 02:14:34.162153 25920 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 02:14:38.892020 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 02:14:40.930303 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 02:14:42.996002 25920 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 02:14:42.996028 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:14:45.911512 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:47.454439 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0410 02:14:47.454488 25920 solver.cpp:397]     Test net output #1: loss = 4.61602 (* 1 = 4.61602 loss)
I0410 02:14:47.542665 25920 solver.cpp:218] Iteration 3876 (0.896853 iter/s, 13.3801s/12 iters), loss = 4.72819
I0410 02:14:47.542721 25920 solver.cpp:237]     Train net output #0: loss = 4.72819 (* 1 = 4.72819 loss)
I0410 02:14:47.542732 25920 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 02:14:51.676975 25920 solver.cpp:218] Iteration 3888 (2.90267 iter/s, 4.13413s/12 iters), loss = 4.66054
I0410 02:14:51.677068 25920 solver.cpp:237]     Train net output #0: loss = 4.66054 (* 1 = 4.66054 loss)
I0410 02:14:51.677083 25920 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 02:14:56.638428 25920 solver.cpp:218] Iteration 3900 (2.41877 iter/s, 4.9612s/12 iters), loss = 4.72599
I0410 02:14:56.638479 25920 solver.cpp:237]     Train net output #0: loss = 4.72599 (* 1 = 4.72599 loss)
I0410 02:14:56.638490 25920 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 02:15:01.575204 25920 solver.cpp:218] Iteration 3912 (2.43084 iter/s, 4.93657s/12 iters), loss = 4.67161
I0410 02:15:01.575254 25920 solver.cpp:237]     Train net output #0: loss = 4.67161 (* 1 = 4.67161 loss)
I0410 02:15:01.575265 25920 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 02:15:06.634670 25920 solver.cpp:218] Iteration 3924 (2.37189 iter/s, 5.05925s/12 iters), loss = 4.63037
I0410 02:15:06.634727 25920 solver.cpp:237]     Train net output #0: loss = 4.63037 (* 1 = 4.63037 loss)
I0410 02:15:06.634739 25920 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 02:15:11.831575 25920 solver.cpp:218] Iteration 3936 (2.30917 iter/s, 5.19668s/12 iters), loss = 4.67439
I0410 02:15:11.831631 25920 solver.cpp:237]     Train net output #0: loss = 4.67439 (* 1 = 4.67439 loss)
I0410 02:15:11.831645 25920 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 02:15:15.282563 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:15:16.910087 25920 solver.cpp:218] Iteration 3948 (2.363 iter/s, 5.07829s/12 iters), loss = 4.71941
I0410 02:15:16.910145 25920 solver.cpp:237]     Train net output #0: loss = 4.71941 (* 1 = 4.71941 loss)
I0410 02:15:16.910158 25920 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 02:15:21.834990 25920 solver.cpp:218] Iteration 3960 (2.4367 iter/s, 4.92469s/12 iters), loss = 4.72138
I0410 02:15:21.835139 25920 solver.cpp:237]     Train net output #0: loss = 4.72138 (* 1 = 4.72138 loss)
I0410 02:15:21.835151 25920 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 02:15:26.821914 25920 solver.cpp:218] Iteration 3972 (2.40644 iter/s, 4.98662s/12 iters), loss = 4.78726
I0410 02:15:26.821972 25920 solver.cpp:237]     Train net output #0: loss = 4.78726 (* 1 = 4.78726 loss)
I0410 02:15:26.821982 25920 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 02:15:28.881068 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 02:15:32.101024 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 02:15:34.845580 25920 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 02:15:34.845613 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:15:37.724138 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:15:39.302778 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0410 02:15:39.302812 25920 solver.cpp:397]     Test net output #1: loss = 4.5964 (* 1 = 4.5964 loss)
I0410 02:15:41.213968 25920 solver.cpp:218] Iteration 3984 (0.833821 iter/s, 14.3916s/12 iters), loss = 4.61813
I0410 02:15:41.214005 25920 solver.cpp:237]     Train net output #0: loss = 4.61813 (* 1 = 4.61813 loss)
I0410 02:15:41.214013 25920 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 02:15:46.190834 25920 solver.cpp:218] Iteration 3996 (2.41125 iter/s, 4.97667s/12 iters), loss = 4.70621
I0410 02:15:46.190879 25920 solver.cpp:237]     Train net output #0: loss = 4.70621 (* 1 = 4.70621 loss)
I0410 02:15:46.190891 25920 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 02:15:51.215025 25920 solver.cpp:218] Iteration 4008 (2.38854 iter/s, 5.02398s/12 iters), loss = 4.60963
I0410 02:15:51.215075 25920 solver.cpp:237]     Train net output #0: loss = 4.60963 (* 1 = 4.60963 loss)
I0410 02:15:51.215085 25920 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 02:15:56.188133 25920 solver.cpp:218] Iteration 4020 (2.41308 iter/s, 4.97291s/12 iters), loss = 4.54246
I0410 02:15:56.188216 25920 solver.cpp:237]     Train net output #0: loss = 4.54246 (* 1 = 4.54246 loss)
I0410 02:15:56.188226 25920 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 02:16:01.149143 25920 solver.cpp:218] Iteration 4032 (2.41898 iter/s, 4.96077s/12 iters), loss = 4.64005
I0410 02:16:01.149191 25920 solver.cpp:237]     Train net output #0: loss = 4.64005 (* 1 = 4.64005 loss)
I0410 02:16:01.149202 25920 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 02:16:06.075989 25920 solver.cpp:218] Iteration 4044 (2.43574 iter/s, 4.92664s/12 iters), loss = 4.64827
I0410 02:16:06.076035 25920 solver.cpp:237]     Train net output #0: loss = 4.64827 (* 1 = 4.64827 loss)
I0410 02:16:06.076043 25920 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 02:16:06.565827 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:11.068223 25920 solver.cpp:218] Iteration 4056 (2.40383 iter/s, 4.99203s/12 iters), loss = 4.50843
I0410 02:16:11.068282 25920 solver.cpp:237]     Train net output #0: loss = 4.50843 (* 1 = 4.50843 loss)
I0410 02:16:11.068293 25920 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 02:16:16.032444 25920 solver.cpp:218] Iteration 4068 (2.4174 iter/s, 4.964s/12 iters), loss = 4.48445
I0410 02:16:16.032491 25920 solver.cpp:237]     Train net output #0: loss = 4.48445 (* 1 = 4.48445 loss)
I0410 02:16:16.032501 25920 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 02:16:20.551127 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 02:16:22.350922 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 02:16:23.725052 25920 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 02:16:23.725077 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:16:26.651208 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:28.264196 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0386029
I0410 02:16:28.264243 25920 solver.cpp:397]     Test net output #1: loss = 4.50416 (* 1 = 4.50416 loss)
I0410 02:16:28.351753 25920 solver.cpp:218] Iteration 4080 (0.974113 iter/s, 12.3189s/12 iters), loss = 4.37477
I0410 02:16:28.351809 25920 solver.cpp:237]     Train net output #0: loss = 4.37477 (* 1 = 4.37477 loss)
I0410 02:16:28.351821 25920 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 02:16:32.713809 25920 solver.cpp:218] Iteration 4092 (2.75113 iter/s, 4.36185s/12 iters), loss = 4.32207
I0410 02:16:32.713876 25920 solver.cpp:237]     Train net output #0: loss = 4.32207 (* 1 = 4.32207 loss)
I0410 02:16:32.713889 25920 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 02:16:37.624109 25920 solver.cpp:218] Iteration 4104 (2.44395 iter/s, 4.91008s/12 iters), loss = 4.49187
I0410 02:16:37.624162 25920 solver.cpp:237]     Train net output #0: loss = 4.49187 (* 1 = 4.49187 loss)
I0410 02:16:37.624174 25920 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 02:16:42.611186 25920 solver.cpp:218] Iteration 4116 (2.40632 iter/s, 4.98686s/12 iters), loss = 4.39974
I0410 02:16:42.611240 25920 solver.cpp:237]     Train net output #0: loss = 4.39974 (* 1 = 4.39974 loss)
I0410 02:16:42.611253 25920 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 02:16:47.643348 25920 solver.cpp:218] Iteration 4128 (2.38476 iter/s, 5.03195s/12 iters), loss = 4.56827
I0410 02:16:47.643402 25920 solver.cpp:237]     Train net output #0: loss = 4.56827 (* 1 = 4.56827 loss)
I0410 02:16:47.643414 25920 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 02:16:52.647264 25920 solver.cpp:218] Iteration 4140 (2.39823 iter/s, 5.0037s/12 iters), loss = 4.55972
I0410 02:16:52.647315 25920 solver.cpp:237]     Train net output #0: loss = 4.55972 (* 1 = 4.55972 loss)
I0410 02:16:52.647325 25920 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 02:16:55.330163 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:57.634590 25920 solver.cpp:218] Iteration 4152 (2.4062 iter/s, 4.98712s/12 iters), loss = 4.2876
I0410 02:16:57.634689 25920 solver.cpp:237]     Train net output #0: loss = 4.2876 (* 1 = 4.2876 loss)
I0410 02:16:57.634706 25920 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 02:16:59.279753 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:17:02.625262 25920 solver.cpp:218] Iteration 4164 (2.4046 iter/s, 4.99043s/12 iters), loss = 4.47475
I0410 02:17:02.625301 25920 solver.cpp:237]     Train net output #0: loss = 4.47475 (* 1 = 4.47475 loss)
I0410 02:17:02.625310 25920 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 02:17:07.804533 25920 solver.cpp:218] Iteration 4176 (2.31702 iter/s, 5.17907s/12 iters), loss = 4.39173
I0410 02:17:07.804594 25920 solver.cpp:237]     Train net output #0: loss = 4.39173 (* 1 = 4.39173 loss)
I0410 02:17:07.804611 25920 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 02:17:09.849226 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 02:17:14.514961 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 02:17:19.285809 25920 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 02:17:19.285840 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:17:22.255884 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:24.078372 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0447304
I0410 02:17:24.078418 25920 solver.cpp:397]     Test net output #1: loss = 4.48045 (* 1 = 4.48045 loss)
I0410 02:17:25.867748 25920 solver.cpp:218] Iteration 4188 (0.664355 iter/s, 18.0626s/12 iters), loss = 4.41633
I0410 02:17:25.867799 25920 solver.cpp:237]     Train net output #0: loss = 4.41633 (* 1 = 4.41633 loss)
I0410 02:17:25.867810 25920 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 02:17:30.798493 25920 solver.cpp:218] Iteration 4200 (2.43381 iter/s, 4.93054s/12 iters), loss = 4.46154
I0410 02:17:30.798645 25920 solver.cpp:237]     Train net output #0: loss = 4.46154 (* 1 = 4.46154 loss)
I0410 02:17:30.798657 25920 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 02:17:35.774976 25920 solver.cpp:218] Iteration 4212 (2.41149 iter/s, 4.97618s/12 iters), loss = 4.34529
I0410 02:17:35.775013 25920 solver.cpp:237]     Train net output #0: loss = 4.34529 (* 1 = 4.34529 loss)
I0410 02:17:35.775022 25920 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 02:17:40.799176 25920 solver.cpp:218] Iteration 4224 (2.38853 iter/s, 5.024s/12 iters), loss = 4.44996
I0410 02:17:40.799232 25920 solver.cpp:237]     Train net output #0: loss = 4.44996 (* 1 = 4.44996 loss)
I0410 02:17:40.799243 25920 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 02:17:45.809181 25920 solver.cpp:218] Iteration 4236 (2.39531 iter/s, 5.00979s/12 iters), loss = 4.43201
I0410 02:17:45.809223 25920 solver.cpp:237]     Train net output #0: loss = 4.43201 (* 1 = 4.43201 loss)
I0410 02:17:45.809232 25920 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 02:17:50.557415 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:50.781713 25920 solver.cpp:218] Iteration 4248 (2.41335 iter/s, 4.97233s/12 iters), loss = 4.39301
I0410 02:17:50.781759 25920 solver.cpp:237]     Train net output #0: loss = 4.39301 (* 1 = 4.39301 loss)
I0410 02:17:50.781769 25920 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 02:17:55.825733 25920 solver.cpp:218] Iteration 4260 (2.37915 iter/s, 5.04382s/12 iters), loss = 4.44892
I0410 02:17:55.825779 25920 solver.cpp:237]     Train net output #0: loss = 4.44892 (* 1 = 4.44892 loss)
I0410 02:17:55.825789 25920 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 02:18:00.849709 25920 solver.cpp:218] Iteration 4272 (2.38864 iter/s, 5.02377s/12 iters), loss = 4.4305
I0410 02:18:00.849807 25920 solver.cpp:237]     Train net output #0: loss = 4.4305 (* 1 = 4.4305 loss)
I0410 02:18:00.849817 25920 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 02:18:05.334689 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 02:18:07.068259 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 02:18:09.214450 25920 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 02:18:09.214479 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:18:11.860541 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:18:13.551512 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0410 02:18:13.551553 25920 solver.cpp:397]     Test net output #1: loss = 4.40973 (* 1 = 4.40973 loss)
I0410 02:18:13.638794 25920 solver.cpp:218] Iteration 4284 (0.938335 iter/s, 12.7886s/12 iters), loss = 4.60963
I0410 02:18:13.638854 25920 solver.cpp:237]     Train net output #0: loss = 4.60963 (* 1 = 4.60963 loss)
I0410 02:18:13.638867 25920 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 02:18:17.797271 25920 solver.cpp:218] Iteration 4296 (2.8858 iter/s, 4.15829s/12 iters), loss = 4.43726
I0410 02:18:17.797317 25920 solver.cpp:237]     Train net output #0: loss = 4.43726 (* 1 = 4.43726 loss)
I0410 02:18:17.797325 25920 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 02:18:23.081017 25920 solver.cpp:218] Iteration 4308 (2.27121 iter/s, 5.28353s/12 iters), loss = 4.36905
I0410 02:18:23.081075 25920 solver.cpp:237]     Train net output #0: loss = 4.36905 (* 1 = 4.36905 loss)
I0410 02:18:23.081089 25920 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 02:18:28.101294 25920 solver.cpp:218] Iteration 4320 (2.39041 iter/s, 5.02006s/12 iters), loss = 4.51083
I0410 02:18:28.101346 25920 solver.cpp:237]     Train net output #0: loss = 4.51083 (* 1 = 4.51083 loss)
I0410 02:18:28.101356 25920 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 02:18:33.436216 25920 solver.cpp:218] Iteration 4332 (2.24942 iter/s, 5.3347s/12 iters), loss = 4.41635
I0410 02:18:33.436357 25920 solver.cpp:237]     Train net output #0: loss = 4.41635 (* 1 = 4.41635 loss)
I0410 02:18:33.436368 25920 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 02:18:38.399367 25920 solver.cpp:218] Iteration 4344 (2.41796 iter/s, 4.96286s/12 iters), loss = 4.62311
I0410 02:18:38.399420 25920 solver.cpp:237]     Train net output #0: loss = 4.62311 (* 1 = 4.62311 loss)
I0410 02:18:38.399432 25920 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 02:18:40.283160 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:18:43.386747 25920 solver.cpp:218] Iteration 4356 (2.40618 iter/s, 4.98717s/12 iters), loss = 4.43269
I0410 02:18:43.386799 25920 solver.cpp:237]     Train net output #0: loss = 4.43269 (* 1 = 4.43269 loss)
I0410 02:18:43.386811 25920 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 02:18:48.458719 25920 solver.cpp:218] Iteration 4368 (2.36604 iter/s, 5.07176s/12 iters), loss = 4.44957
I0410 02:18:48.458768 25920 solver.cpp:237]     Train net output #0: loss = 4.44957 (* 1 = 4.44957 loss)
I0410 02:18:48.458778 25920 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 02:18:53.461620 25920 solver.cpp:218] Iteration 4380 (2.39871 iter/s, 5.00269s/12 iters), loss = 4.29831
I0410 02:18:53.461678 25920 solver.cpp:237]     Train net output #0: loss = 4.29831 (* 1 = 4.29831 loss)
I0410 02:18:53.461689 25920 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 02:18:55.505244 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 02:18:57.743902 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 02:18:59.113394 25920 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 02:18:59.113415 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:19:01.841219 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:03.581948 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0508578
I0410 02:19:03.582077 25920 solver.cpp:397]     Test net output #1: loss = 4.37658 (* 1 = 4.37658 loss)
I0410 02:19:05.357345 25920 solver.cpp:218] Iteration 4392 (1.0088 iter/s, 11.8953s/12 iters), loss = 4.51577
I0410 02:19:05.357401 25920 solver.cpp:237]     Train net output #0: loss = 4.51577 (* 1 = 4.51577 loss)
I0410 02:19:05.357414 25920 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 02:19:10.347998 25920 solver.cpp:218] Iteration 4404 (2.4046 iter/s, 4.99044s/12 iters), loss = 4.27383
I0410 02:19:10.348054 25920 solver.cpp:237]     Train net output #0: loss = 4.27383 (* 1 = 4.27383 loss)
I0410 02:19:10.348067 25920 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 02:19:15.277571 25920 solver.cpp:218] Iteration 4416 (2.43439 iter/s, 4.92936s/12 iters), loss = 4.28736
I0410 02:19:15.277617 25920 solver.cpp:237]     Train net output #0: loss = 4.28736 (* 1 = 4.28736 loss)
I0410 02:19:15.277628 25920 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 02:19:20.147305 25920 solver.cpp:218] Iteration 4428 (2.4643 iter/s, 4.86953s/12 iters), loss = 4.33551
I0410 02:19:20.147357 25920 solver.cpp:237]     Train net output #0: loss = 4.33551 (* 1 = 4.33551 loss)
I0410 02:19:20.147368 25920 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 02:19:25.100464 25920 solver.cpp:218] Iteration 4440 (2.4228 iter/s, 4.95295s/12 iters), loss = 4.35777
I0410 02:19:25.100522 25920 solver.cpp:237]     Train net output #0: loss = 4.35777 (* 1 = 4.35777 loss)
I0410 02:19:25.100533 25920 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 02:19:29.176421 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:30.230644 25920 solver.cpp:218] Iteration 4452 (2.3392 iter/s, 5.12996s/12 iters), loss = 4.32752
I0410 02:19:30.230703 25920 solver.cpp:237]     Train net output #0: loss = 4.32752 (* 1 = 4.32752 loss)
I0410 02:19:30.230715 25920 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 02:19:35.355872 25920 solver.cpp:218] Iteration 4464 (2.34146 iter/s, 5.12501s/12 iters), loss = 4.33418
I0410 02:19:35.356017 25920 solver.cpp:237]     Train net output #0: loss = 4.33418 (* 1 = 4.33418 loss)
I0410 02:19:35.356030 25920 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 02:19:40.390681 25920 solver.cpp:218] Iteration 4476 (2.38355 iter/s, 5.03451s/12 iters), loss = 4.37191
I0410 02:19:40.390730 25920 solver.cpp:237]     Train net output #0: loss = 4.37191 (* 1 = 4.37191 loss)
I0410 02:19:40.390741 25920 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 02:19:45.099670 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 02:19:52.379745 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 02:19:53.773870 25920 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 02:19:53.773893 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:19:56.587131 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:58.395357 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0502451
I0410 02:19:58.395416 25920 solver.cpp:397]     Test net output #1: loss = 4.39383 (* 1 = 4.39383 loss)
I0410 02:19:58.483108 25920 solver.cpp:218] Iteration 4488 (0.663282 iter/s, 18.0918s/12 iters), loss = 4.28627
I0410 02:19:58.483179 25920 solver.cpp:237]     Train net output #0: loss = 4.28627 (* 1 = 4.28627 loss)
I0410 02:19:58.483196 25920 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 02:20:02.899742 25920 solver.cpp:218] Iteration 4500 (2.71713 iter/s, 4.41643s/12 iters), loss = 4.24958
I0410 02:20:02.899792 25920 solver.cpp:237]     Train net output #0: loss = 4.24958 (* 1 = 4.24958 loss)
I0410 02:20:02.899807 25920 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 02:20:07.901125 25920 solver.cpp:218] Iteration 4512 (2.39943 iter/s, 5.00118s/12 iters), loss = 4.38623
I0410 02:20:07.901213 25920 solver.cpp:237]     Train net output #0: loss = 4.38623 (* 1 = 4.38623 loss)
I0410 02:20:07.901226 25920 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 02:20:13.197873 25920 solver.cpp:218] Iteration 4524 (2.26565 iter/s, 5.29649s/12 iters), loss = 4.36539
I0410 02:20:13.197927 25920 solver.cpp:237]     Train net output #0: loss = 4.36539 (* 1 = 4.36539 loss)
I0410 02:20:13.197939 25920 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 02:20:18.160466 25920 solver.cpp:218] Iteration 4536 (2.4182 iter/s, 4.96238s/12 iters), loss = 4.23792
I0410 02:20:18.160524 25920 solver.cpp:237]     Train net output #0: loss = 4.23792 (* 1 = 4.23792 loss)
I0410 02:20:18.160537 25920 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 02:20:23.179100 25920 solver.cpp:218] Iteration 4548 (2.39119 iter/s, 5.01842s/12 iters), loss = 4.41958
I0410 02:20:23.179141 25920 solver.cpp:237]     Train net output #0: loss = 4.41958 (* 1 = 4.41958 loss)
I0410 02:20:23.179149 25920 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 02:20:24.417883 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:28.198665 25920 solver.cpp:218] Iteration 4560 (2.39074 iter/s, 5.01936s/12 iters), loss = 4.13006
I0410 02:20:28.198719 25920 solver.cpp:237]     Train net output #0: loss = 4.13006 (* 1 = 4.13006 loss)
I0410 02:20:28.198731 25920 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 02:20:33.164774 25920 solver.cpp:218] Iteration 4572 (2.41648 iter/s, 4.9659s/12 iters), loss = 4.17857
I0410 02:20:33.164824 25920 solver.cpp:237]     Train net output #0: loss = 4.17857 (* 1 = 4.17857 loss)
I0410 02:20:33.164835 25920 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 02:20:38.266106 25920 solver.cpp:218] Iteration 4584 (2.35243 iter/s, 5.10111s/12 iters), loss = 4.39393
I0410 02:20:38.266250 25920 solver.cpp:237]     Train net output #0: loss = 4.39393 (* 1 = 4.39393 loss)
I0410 02:20:38.266263 25920 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 02:20:40.446741 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 02:20:42.250006 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 02:20:43.634758 25920 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 02:20:43.634790 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:20:46.358806 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:48.178488 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0563725
I0410 02:20:48.178535 25920 solver.cpp:397]     Test net output #1: loss = 4.26276 (* 1 = 4.26276 loss)
I0410 02:20:50.099644 25920 solver.cpp:218] Iteration 4596 (1.01411 iter/s, 11.833s/12 iters), loss = 4.26298
I0410 02:20:50.099696 25920 solver.cpp:237]     Train net output #0: loss = 4.26298 (* 1 = 4.26298 loss)
I0410 02:20:50.099706 25920 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 02:20:55.112712 25920 solver.cpp:218] Iteration 4608 (2.39384 iter/s, 5.01286s/12 iters), loss = 4.32345
I0410 02:20:55.112761 25920 solver.cpp:237]     Train net output #0: loss = 4.32345 (* 1 = 4.32345 loss)
I0410 02:20:55.112771 25920 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 02:21:00.112358 25920 solver.cpp:218] Iteration 4620 (2.40027 iter/s, 4.99944s/12 iters), loss = 4.39852
I0410 02:21:00.112394 25920 solver.cpp:237]     Train net output #0: loss = 4.39852 (* 1 = 4.39852 loss)
I0410 02:21:00.112403 25920 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 02:21:05.085485 25920 solver.cpp:218] Iteration 4632 (2.41307 iter/s, 4.97292s/12 iters), loss = 4.12669
I0410 02:21:05.085542 25920 solver.cpp:237]     Train net output #0: loss = 4.12669 (* 1 = 4.12669 loss)
I0410 02:21:05.085554 25920 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 02:21:10.071346 25920 solver.cpp:218] Iteration 4644 (2.40691 iter/s, 4.98565s/12 iters), loss = 4.41522
I0410 02:21:10.071435 25920 solver.cpp:237]     Train net output #0: loss = 4.41522 (* 1 = 4.41522 loss)
I0410 02:21:10.071445 25920 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 02:21:13.496201 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:15.103597 25920 solver.cpp:218] Iteration 4656 (2.38474 iter/s, 5.03201s/12 iters), loss = 4.27935
I0410 02:21:15.103649 25920 solver.cpp:237]     Train net output #0: loss = 4.27935 (* 1 = 4.27935 loss)
I0410 02:21:15.103662 25920 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 02:21:20.209550 25920 solver.cpp:218] Iteration 4668 (2.3503 iter/s, 5.10574s/12 iters), loss = 4.29723
I0410 02:21:20.209591 25920 solver.cpp:237]     Train net output #0: loss = 4.29723 (* 1 = 4.29723 loss)
I0410 02:21:20.209601 25920 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 02:21:25.249188 25920 solver.cpp:218] Iteration 4680 (2.38122 iter/s, 5.03944s/12 iters), loss = 4.64388
I0410 02:21:25.249240 25920 solver.cpp:237]     Train net output #0: loss = 4.64388 (* 1 = 4.64388 loss)
I0410 02:21:25.249253 25920 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 02:21:29.852330 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 02:21:31.606602 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 02:21:33.042598 25920 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 02:21:33.042618 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:21:35.652781 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:37.517168 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0582108
I0410 02:21:37.517215 25920 solver.cpp:397]     Test net output #1: loss = 4.23241 (* 1 = 4.23241 loss)
I0410 02:21:37.604825 25920 solver.cpp:218] Iteration 4692 (0.97125 iter/s, 12.3552s/12 iters), loss = 4.18041
I0410 02:21:37.604880 25920 solver.cpp:237]     Train net output #0: loss = 4.18041 (* 1 = 4.18041 loss)
I0410 02:21:37.604892 25920 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 02:21:41.766896 25920 solver.cpp:218] Iteration 4704 (2.88331 iter/s, 4.16188s/12 iters), loss = 4.2919
I0410 02:21:41.767026 25920 solver.cpp:237]     Train net output #0: loss = 4.2919 (* 1 = 4.2919 loss)
I0410 02:21:41.767037 25920 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 02:21:46.770644 25920 solver.cpp:218] Iteration 4716 (2.39834 iter/s, 5.00346s/12 iters), loss = 4.20378
I0410 02:21:46.770691 25920 solver.cpp:237]     Train net output #0: loss = 4.20378 (* 1 = 4.20378 loss)
I0410 02:21:46.770702 25920 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 02:21:51.791528 25920 solver.cpp:218] Iteration 4728 (2.39012 iter/s, 5.02068s/12 iters), loss = 4.23169
I0410 02:21:51.791585 25920 solver.cpp:237]     Train net output #0: loss = 4.23169 (* 1 = 4.23169 loss)
I0410 02:21:51.791597 25920 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 02:21:56.712982 25920 solver.cpp:218] Iteration 4740 (2.43841 iter/s, 4.92124s/12 iters), loss = 4.31487
I0410 02:21:56.713035 25920 solver.cpp:237]     Train net output #0: loss = 4.31487 (* 1 = 4.31487 loss)
I0410 02:21:56.713045 25920 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 02:22:01.852041 25920 solver.cpp:218] Iteration 4752 (2.33516 iter/s, 5.13884s/12 iters), loss = 4.28748
I0410 02:22:01.852092 25920 solver.cpp:237]     Train net output #0: loss = 4.28748 (* 1 = 4.28748 loss)
I0410 02:22:01.852104 25920 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 02:22:02.384421 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:22:06.892118 25920 solver.cpp:218] Iteration 4764 (2.38102 iter/s, 5.03986s/12 iters), loss = 4.15896
I0410 02:22:06.892176 25920 solver.cpp:237]     Train net output #0: loss = 4.15896 (* 1 = 4.15896 loss)
I0410 02:22:06.892189 25920 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 02:22:12.005865 25920 solver.cpp:218] Iteration 4776 (2.34672 iter/s, 5.11353s/12 iters), loss = 4.22528
I0410 02:22:12.006008 25920 solver.cpp:237]     Train net output #0: loss = 4.22528 (* 1 = 4.22528 loss)
I0410 02:22:12.006026 25920 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 02:22:17.057024 25920 solver.cpp:218] Iteration 4788 (2.37583 iter/s, 5.05086s/12 iters), loss = 4.16091
I0410 02:22:17.057077 25920 solver.cpp:237]     Train net output #0: loss = 4.16091 (* 1 = 4.16091 loss)
I0410 02:22:17.057090 25920 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 02:22:19.101711 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 02:22:26.278470 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 02:22:29.298162 25920 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 02:22:29.298189 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:22:31.835978 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:22:33.740541 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0551471
I0410 02:22:33.740576 25920 solver.cpp:397]     Test net output #1: loss = 4.23398 (* 1 = 4.23398 loss)
I0410 02:22:35.898403 25920 solver.cpp:218] Iteration 4800 (0.636917 iter/s, 18.8408s/12 iters), loss = 4.08951
I0410 02:22:35.898452 25920 solver.cpp:237]     Train net output #0: loss = 4.08951 (* 1 = 4.08951 loss)
I0410 02:22:35.898463 25920 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 02:22:40.813977 25920 solver.cpp:218] Iteration 4812 (2.44133 iter/s, 4.91535s/12 iters), loss = 4.09438
I0410 02:22:40.814034 25920 solver.cpp:237]     Train net output #0: loss = 4.09438 (* 1 = 4.09438 loss)
I0410 02:22:40.814046 25920 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 02:22:45.877529 25920 solver.cpp:218] Iteration 4824 (2.36998 iter/s, 5.06334s/12 iters), loss = 4.09348
I0410 02:22:45.877633 25920 solver.cpp:237]     Train net output #0: loss = 4.09348 (* 1 = 4.09348 loss)
I0410 02:22:45.877643 25920 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 02:22:51.159214 25920 solver.cpp:218] Iteration 4836 (2.27212 iter/s, 5.28142s/12 iters), loss = 4.22761
I0410 02:22:51.159253 25920 solver.cpp:237]     Train net output #0: loss = 4.22761 (* 1 = 4.22761 loss)
I0410 02:22:51.159262 25920 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 02:22:53.136895 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:22:56.075971 25920 solver.cpp:218] Iteration 4848 (2.44073 iter/s, 4.91656s/12 iters), loss = 4.29877
I0410 02:22:56.076020 25920 solver.cpp:237]     Train net output #0: loss = 4.29877 (* 1 = 4.29877 loss)
I0410 02:22:56.076036 25920 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 02:22:58.782423 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:01.310600 25920 solver.cpp:218] Iteration 4860 (2.29252 iter/s, 5.23441s/12 iters), loss = 4.05026
I0410 02:23:01.310653 25920 solver.cpp:237]     Train net output #0: loss = 4.05026 (* 1 = 4.05026 loss)
I0410 02:23:01.310664 25920 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 02:23:06.836315 25920 solver.cpp:218] Iteration 4872 (2.17175 iter/s, 5.52549s/12 iters), loss = 4.02496
I0410 02:23:06.836357 25920 solver.cpp:237]     Train net output #0: loss = 4.02496 (* 1 = 4.02496 loss)
I0410 02:23:06.836369 25920 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 02:23:11.831562 25920 solver.cpp:218] Iteration 4884 (2.40238 iter/s, 4.99505s/12 iters), loss = 3.98825
I0410 02:23:11.831606 25920 solver.cpp:237]     Train net output #0: loss = 3.98825 (* 1 = 3.98825 loss)
I0410 02:23:11.831616 25920 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 02:23:16.404507 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 02:23:18.187398 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 02:23:20.724627 25920 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 02:23:20.724656 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:23:23.247418 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:25.258592 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0410 02:23:25.258622 25920 solver.cpp:397]     Test net output #1: loss = 4.11323 (* 1 = 4.11323 loss)
I0410 02:23:25.345935 25920 solver.cpp:218] Iteration 4896 (0.887973 iter/s, 13.5139s/12 iters), loss = 4.07362
I0410 02:23:25.346004 25920 solver.cpp:237]     Train net output #0: loss = 4.07362 (* 1 = 4.07362 loss)
I0410 02:23:25.346019 25920 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 02:23:29.797866 25920 solver.cpp:218] Iteration 4908 (2.69559 iter/s, 4.45172s/12 iters), loss = 4.00545
I0410 02:23:29.797922 25920 solver.cpp:237]     Train net output #0: loss = 4.00545 (* 1 = 4.00545 loss)
I0410 02:23:29.797935 25920 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 02:23:34.801257 25920 solver.cpp:218] Iteration 4920 (2.39847 iter/s, 5.00318s/12 iters), loss = 4.01802
I0410 02:23:34.801301 25920 solver.cpp:237]     Train net output #0: loss = 4.01802 (* 1 = 4.01802 loss)
I0410 02:23:34.801311 25920 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 02:23:39.875331 25920 solver.cpp:218] Iteration 4932 (2.36506 iter/s, 5.07388s/12 iters), loss = 4.25459
I0410 02:23:39.875367 25920 solver.cpp:237]     Train net output #0: loss = 4.25459 (* 1 = 4.25459 loss)
I0410 02:23:39.875375 25920 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 02:23:44.808898 25920 solver.cpp:218] Iteration 4944 (2.43241 iter/s, 4.93337s/12 iters), loss = 4.10758
I0410 02:23:44.808952 25920 solver.cpp:237]     Train net output #0: loss = 4.10758 (* 1 = 4.10758 loss)
I0410 02:23:44.808964 25920 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 02:23:49.625075 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:49.826270 25920 solver.cpp:218] Iteration 4956 (2.39179 iter/s, 5.01716s/12 iters), loss = 4.14633
I0410 02:23:49.826318 25920 solver.cpp:237]     Train net output #0: loss = 4.14633 (* 1 = 4.14633 loss)
I0410 02:23:49.826330 25920 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 02:23:54.826957 25920 solver.cpp:218] Iteration 4968 (2.39977 iter/s, 5.00048s/12 iters), loss = 3.92526
I0410 02:23:54.827015 25920 solver.cpp:237]     Train net output #0: loss = 3.92526 (* 1 = 3.92526 loss)
I0410 02:23:54.827026 25920 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 02:24:00.078858 25920 solver.cpp:218] Iteration 4980 (2.28498 iter/s, 5.25168s/12 iters), loss = 3.97341
I0410 02:24:00.078912 25920 solver.cpp:237]     Train net output #0: loss = 3.97341 (* 1 = 3.97341 loss)
I0410 02:24:00.078924 25920 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 02:24:05.385213 25920 solver.cpp:218] Iteration 4992 (2.26153 iter/s, 5.30613s/12 iters), loss = 4.27727
I0410 02:24:05.385265 25920 solver.cpp:237]     Train net output #0: loss = 4.27727 (* 1 = 4.27727 loss)
I0410 02:24:05.385278 25920 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 02:24:07.617753 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 02:24:09.409473 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 02:24:10.794459 25920 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 02:24:10.794488 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:24:13.213481 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:15.181933 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0410 02:24:15.181991 25920 solver.cpp:397]     Test net output #1: loss = 4.09287 (* 1 = 4.09287 loss)
I0410 02:24:17.157459 25920 solver.cpp:218] Iteration 5004 (1.01938 iter/s, 11.7718s/12 iters), loss = 4.03556
I0410 02:24:17.157507 25920 solver.cpp:237]     Train net output #0: loss = 4.03556 (* 1 = 4.03556 loss)
I0410 02:24:17.157518 25920 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 02:24:22.242738 25920 solver.cpp:218] Iteration 5016 (2.35985 iter/s, 5.08507s/12 iters), loss = 4.11563
I0410 02:24:22.242832 25920 solver.cpp:237]     Train net output #0: loss = 4.11563 (* 1 = 4.11563 loss)
I0410 02:24:22.242846 25920 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 02:24:27.261464 25920 solver.cpp:218] Iteration 5028 (2.39116 iter/s, 5.01848s/12 iters), loss = 4.15692
I0410 02:24:27.261519 25920 solver.cpp:237]     Train net output #0: loss = 4.15692 (* 1 = 4.15692 loss)
I0410 02:24:27.261533 25920 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 02:24:32.204355 25920 solver.cpp:218] Iteration 5040 (2.42783 iter/s, 4.94268s/12 iters), loss = 4.19054
I0410 02:24:32.204404 25920 solver.cpp:237]     Train net output #0: loss = 4.19054 (* 1 = 4.19054 loss)
I0410 02:24:32.204417 25920 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 02:24:37.239243 25920 solver.cpp:218] Iteration 5052 (2.38347 iter/s, 5.03468s/12 iters), loss = 4.18806
I0410 02:24:37.239290 25920 solver.cpp:237]     Train net output #0: loss = 4.18806 (* 1 = 4.18806 loss)
I0410 02:24:37.239300 25920 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 02:24:39.275693 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:42.253509 25920 solver.cpp:218] Iteration 5064 (2.39327 iter/s, 5.01405s/12 iters), loss = 4.05815
I0410 02:24:42.253559 25920 solver.cpp:237]     Train net output #0: loss = 4.05815 (* 1 = 4.05815 loss)
I0410 02:24:42.253571 25920 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 02:24:47.384989 25920 solver.cpp:218] Iteration 5076 (2.3386 iter/s, 5.13127s/12 iters), loss = 4.12754
I0410 02:24:47.385035 25920 solver.cpp:237]     Train net output #0: loss = 4.12754 (* 1 = 4.12754 loss)
I0410 02:24:47.385046 25920 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 02:24:52.512184 25920 solver.cpp:218] Iteration 5088 (2.34056 iter/s, 5.12698s/12 iters), loss = 4.03336
I0410 02:24:52.512329 25920 solver.cpp:237]     Train net output #0: loss = 4.03336 (* 1 = 4.03336 loss)
I0410 02:24:52.512343 25920 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 02:24:57.020584 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 02:25:01.426836 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 02:25:03.342204 25920 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 02:25:03.342226 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:25:05.930302 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:07.944658 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0410 02:25:07.944707 25920 solver.cpp:397]     Test net output #1: loss = 4.06718 (* 1 = 4.06718 loss)
I0410 02:25:08.032173 25920 solver.cpp:218] Iteration 5100 (0.773226 iter/s, 15.5194s/12 iters), loss = 3.99573
I0410 02:25:08.032229 25920 solver.cpp:237]     Train net output #0: loss = 3.99573 (* 1 = 3.99573 loss)
I0410 02:25:08.032241 25920 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 02:25:12.269436 25920 solver.cpp:218] Iteration 5112 (2.83214 iter/s, 4.23707s/12 iters), loss = 3.8626
I0410 02:25:12.269485 25920 solver.cpp:237]     Train net output #0: loss = 3.8626 (* 1 = 3.8626 loss)
I0410 02:25:12.269497 25920 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 02:25:17.198210 25920 solver.cpp:218] Iteration 5124 (2.43478 iter/s, 4.92857s/12 iters), loss = 3.87411
I0410 02:25:17.198254 25920 solver.cpp:237]     Train net output #0: loss = 3.87411 (* 1 = 3.87411 loss)
I0410 02:25:17.198262 25920 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 02:25:22.358732 25920 solver.cpp:218] Iteration 5136 (2.32544 iter/s, 5.16032s/12 iters), loss = 3.96903
I0410 02:25:22.358785 25920 solver.cpp:237]     Train net output #0: loss = 3.96903 (* 1 = 3.96903 loss)
I0410 02:25:22.358796 25920 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 02:25:27.388607 25920 solver.cpp:218] Iteration 5148 (2.38585 iter/s, 5.02966s/12 iters), loss = 3.9867
I0410 02:25:27.388703 25920 solver.cpp:237]     Train net output #0: loss = 3.9867 (* 1 = 3.9867 loss)
I0410 02:25:27.388715 25920 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 02:25:31.447582 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:32.381094 25920 solver.cpp:218] Iteration 5160 (2.40373 iter/s, 4.99223s/12 iters), loss = 3.84401
I0410 02:25:32.381145 25920 solver.cpp:237]     Train net output #0: loss = 3.84401 (* 1 = 3.84401 loss)
I0410 02:25:32.381158 25920 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 02:25:37.490864 25920 solver.cpp:218] Iteration 5172 (2.34854 iter/s, 5.10955s/12 iters), loss = 3.94225
I0410 02:25:37.490911 25920 solver.cpp:237]     Train net output #0: loss = 3.94225 (* 1 = 3.94225 loss)
I0410 02:25:37.490919 25920 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 02:25:42.468830 25920 solver.cpp:218] Iteration 5184 (2.41072 iter/s, 4.97776s/12 iters), loss = 3.9492
I0410 02:25:42.468876 25920 solver.cpp:237]     Train net output #0: loss = 3.9492 (* 1 = 3.9492 loss)
I0410 02:25:42.468885 25920 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 02:25:47.491057 25920 solver.cpp:218] Iteration 5196 (2.38948 iter/s, 5.02202s/12 iters), loss = 3.89092
I0410 02:25:47.491103 25920 solver.cpp:237]     Train net output #0: loss = 3.89092 (* 1 = 3.89092 loss)
I0410 02:25:47.491113 25920 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 02:25:49.559231 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 02:25:51.343461 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 02:25:52.727872 25920 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 02:25:52.727903 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:25:55.112246 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:57.165091 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0790441
I0410 02:25:57.165140 25920 solver.cpp:397]     Test net output #1: loss = 4.03207 (* 1 = 4.03207 loss)
I0410 02:25:58.984731 25920 solver.cpp:218] Iteration 5208 (1.04409 iter/s, 11.4933s/12 iters), loss = 3.77558
I0410 02:25:58.984863 25920 solver.cpp:237]     Train net output #0: loss = 3.77558 (* 1 = 3.77558 loss)
I0410 02:25:58.984879 25920 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 02:26:04.105731 25920 solver.cpp:218] Iteration 5220 (2.34343 iter/s, 5.12071s/12 iters), loss = 3.9362
I0410 02:26:04.105787 25920 solver.cpp:237]     Train net output #0: loss = 3.9362 (* 1 = 3.9362 loss)
I0410 02:26:04.105798 25920 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 02:26:09.057370 25920 solver.cpp:218] Iteration 5232 (2.42354 iter/s, 4.95143s/12 iters), loss = 3.87892
I0410 02:26:09.057423 25920 solver.cpp:237]     Train net output #0: loss = 3.87892 (* 1 = 3.87892 loss)
I0410 02:26:09.057435 25920 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 02:26:14.115651 25920 solver.cpp:218] Iteration 5244 (2.37245 iter/s, 5.05806s/12 iters), loss = 3.94169
I0410 02:26:14.115705 25920 solver.cpp:237]     Train net output #0: loss = 3.94169 (* 1 = 3.94169 loss)
I0410 02:26:14.115717 25920 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 02:26:19.100474 25920 solver.cpp:218] Iteration 5256 (2.40741 iter/s, 4.98461s/12 iters), loss = 4.00919
I0410 02:26:19.100528 25920 solver.cpp:237]     Train net output #0: loss = 4.00919 (* 1 = 4.00919 loss)
I0410 02:26:19.100539 25920 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 02:26:20.368358 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:26:24.097874 25920 solver.cpp:218] Iteration 5268 (2.40135 iter/s, 4.99719s/12 iters), loss = 3.71422
I0410 02:26:24.097920 25920 solver.cpp:237]     Train net output #0: loss = 3.71422 (* 1 = 3.71422 loss)
I0410 02:26:24.097929 25920 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 02:26:29.103375 25920 solver.cpp:218] Iteration 5280 (2.39746 iter/s, 5.0053s/12 iters), loss = 4.06082
I0410 02:26:29.103466 25920 solver.cpp:237]     Train net output #0: loss = 4.06082 (* 1 = 4.06082 loss)
I0410 02:26:29.103475 25920 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 02:26:34.132831 25920 solver.cpp:218] Iteration 5292 (2.38606 iter/s, 5.02921s/12 iters), loss = 3.97335
I0410 02:26:34.132876 25920 solver.cpp:237]     Train net output #0: loss = 3.97335 (* 1 = 3.97335 loss)
I0410 02:26:34.132887 25920 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 02:26:38.604431 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 02:26:40.837915 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 02:26:43.436551 25920 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 02:26:43.436574 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:26:46.027536 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:26:48.127882 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0790441
I0410 02:26:48.127930 25920 solver.cpp:397]     Test net output #1: loss = 4.02972 (* 1 = 4.02972 loss)
I0410 02:26:48.215471 25920 solver.cpp:218] Iteration 5304 (0.852141 iter/s, 14.0822s/12 iters), loss = 3.91185
I0410 02:26:48.215528 25920 solver.cpp:237]     Train net output #0: loss = 3.91185 (* 1 = 3.91185 loss)
I0410 02:26:48.215539 25920 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 02:26:52.561046 25920 solver.cpp:218] Iteration 5316 (2.76155 iter/s, 4.34538s/12 iters), loss = 3.83613
I0410 02:26:52.561097 25920 solver.cpp:237]     Train net output #0: loss = 3.83613 (* 1 = 3.83613 loss)
I0410 02:26:52.561110 25920 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 02:26:57.647550 25920 solver.cpp:218] Iteration 5328 (2.35928 iter/s, 5.08629s/12 iters), loss = 4.11294
I0410 02:26:57.647609 25920 solver.cpp:237]     Train net output #0: loss = 4.11294 (* 1 = 4.11294 loss)
I0410 02:26:57.647621 25920 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 02:27:02.690896 25920 solver.cpp:218] Iteration 5340 (2.37948 iter/s, 5.04313s/12 iters), loss = 3.86994
I0410 02:27:02.691015 25920 solver.cpp:237]     Train net output #0: loss = 3.86994 (* 1 = 3.86994 loss)
I0410 02:27:02.691027 25920 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 02:27:07.688061 25920 solver.cpp:218] Iteration 5352 (2.40149 iter/s, 4.99689s/12 iters), loss = 4.01787
I0410 02:27:07.688100 25920 solver.cpp:237]     Train net output #0: loss = 4.01787 (* 1 = 4.01787 loss)
I0410 02:27:07.688109 25920 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 02:27:11.129022 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:12.703083 25920 solver.cpp:218] Iteration 5364 (2.39291 iter/s, 5.01482s/12 iters), loss = 4.04696
I0410 02:27:12.703131 25920 solver.cpp:237]     Train net output #0: loss = 4.04696 (* 1 = 4.04696 loss)
I0410 02:27:12.703140 25920 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 02:27:17.655592 25920 solver.cpp:218] Iteration 5376 (2.42312 iter/s, 4.95229s/12 iters), loss = 3.81548
I0410 02:27:17.655647 25920 solver.cpp:237]     Train net output #0: loss = 3.81548 (* 1 = 3.81548 loss)
I0410 02:27:17.655659 25920 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 02:27:22.672889 25920 solver.cpp:218] Iteration 5388 (2.39183 iter/s, 5.01708s/12 iters), loss = 3.98076
I0410 02:27:22.672945 25920 solver.cpp:237]     Train net output #0: loss = 3.98076 (* 1 = 3.98076 loss)
I0410 02:27:22.672956 25920 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 02:27:27.612900 25920 solver.cpp:218] Iteration 5400 (2.42925 iter/s, 4.9398s/12 iters), loss = 3.87909
I0410 02:27:27.612952 25920 solver.cpp:237]     Train net output #0: loss = 3.87909 (* 1 = 3.87909 loss)
I0410 02:27:27.612965 25920 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 02:27:29.668917 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 02:27:35.308343 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 02:27:38.096205 25920 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 02:27:38.096232 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:27:40.499231 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:42.702741 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0808824
I0410 02:27:42.702783 25920 solver.cpp:397]     Test net output #1: loss = 3.88939 (* 1 = 3.88939 loss)
I0410 02:27:44.644063 25920 solver.cpp:218] Iteration 5412 (0.704614 iter/s, 17.0306s/12 iters), loss = 3.86114
I0410 02:27:44.644119 25920 solver.cpp:237]     Train net output #0: loss = 3.86114 (* 1 = 3.86114 loss)
I0410 02:27:44.644132 25920 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 02:27:49.649242 25920 solver.cpp:218] Iteration 5424 (2.39762 iter/s, 5.00496s/12 iters), loss = 3.87311
I0410 02:27:49.649298 25920 solver.cpp:237]     Train net output #0: loss = 3.87311 (* 1 = 3.87311 loss)
I0410 02:27:49.649310 25920 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 02:27:54.701076 25920 solver.cpp:218] Iteration 5436 (2.37548 iter/s, 5.05162s/12 iters), loss = 3.95318
I0410 02:27:54.701118 25920 solver.cpp:237]     Train net output #0: loss = 3.95318 (* 1 = 3.95318 loss)
I0410 02:27:54.701128 25920 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 02:27:59.826915 25920 solver.cpp:218] Iteration 5448 (2.34117 iter/s, 5.12564s/12 iters), loss = 3.67349
I0410 02:27:59.826954 25920 solver.cpp:237]     Train net output #0: loss = 3.67349 (* 1 = 3.67349 loss)
I0410 02:27:59.826962 25920 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 02:28:04.867641 25920 solver.cpp:218] Iteration 5460 (2.3807 iter/s, 5.04052s/12 iters), loss = 4.09386
I0410 02:28:04.867691 25920 solver.cpp:237]     Train net output #0: loss = 4.09386 (* 1 = 4.09386 loss)
I0410 02:28:04.867702 25920 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 02:28:05.446672 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:09.977823 25920 solver.cpp:218] Iteration 5472 (2.34835 iter/s, 5.10997s/12 iters), loss = 3.87446
I0410 02:28:09.977869 25920 solver.cpp:237]     Train net output #0: loss = 3.87446 (* 1 = 3.87446 loss)
I0410 02:28:09.977880 25920 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 02:28:14.990576 25920 solver.cpp:218] Iteration 5484 (2.39399 iter/s, 5.01255s/12 iters), loss = 3.64884
I0410 02:28:14.990628 25920 solver.cpp:237]     Train net output #0: loss = 3.64884 (* 1 = 3.64884 loss)
I0410 02:28:14.990638 25920 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 02:28:20.056176 25920 solver.cpp:218] Iteration 5496 (2.36902 iter/s, 5.06539s/12 iters), loss = 3.7377
I0410 02:28:20.056222 25920 solver.cpp:237]     Train net output #0: loss = 3.7377 (* 1 = 3.7377 loss)
I0410 02:28:20.056232 25920 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 02:28:24.609134 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 02:28:26.385696 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 02:28:27.762796 25920 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 02:28:27.762821 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:28:30.088392 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:32.263994 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0882353
I0410 02:28:32.264040 25920 solver.cpp:397]     Test net output #1: loss = 3.85124 (* 1 = 3.85124 loss)
I0410 02:28:32.351397 25920 solver.cpp:218] Iteration 5508 (0.976022 iter/s, 12.2948s/12 iters), loss = 3.65914
I0410 02:28:32.351452 25920 solver.cpp:237]     Train net output #0: loss = 3.65914 (* 1 = 3.65914 loss)
I0410 02:28:32.351464 25920 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 02:28:36.465898 25920 solver.cpp:218] Iteration 5520 (2.91665 iter/s, 4.11431s/12 iters), loss = 3.72719
I0410 02:28:36.466014 25920 solver.cpp:237]     Train net output #0: loss = 3.72719 (* 1 = 3.72719 loss)
I0410 02:28:36.466025 25920 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 02:28:38.900542 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:28:41.390988 25920 solver.cpp:218] Iteration 5532 (2.43664 iter/s, 4.92482s/12 iters), loss = 3.86285
I0410 02:28:41.391031 25920 solver.cpp:237]     Train net output #0: loss = 3.86285 (* 1 = 3.86285 loss)
I0410 02:28:41.391041 25920 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 02:28:46.424021 25920 solver.cpp:218] Iteration 5544 (2.38434 iter/s, 5.03283s/12 iters), loss = 3.67983
I0410 02:28:46.424077 25920 solver.cpp:237]     Train net output #0: loss = 3.67983 (* 1 = 3.67983 loss)
I0410 02:28:46.424089 25920 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 02:28:51.441992 25920 solver.cpp:218] Iteration 5556 (2.39151 iter/s, 5.01775s/12 iters), loss = 4.01213
I0410 02:28:51.442044 25920 solver.cpp:237]     Train net output #0: loss = 4.01213 (* 1 = 4.01213 loss)
I0410 02:28:51.442057 25920 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 02:28:54.200724 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:56.508170 25920 solver.cpp:218] Iteration 5568 (2.36875 iter/s, 5.06597s/12 iters), loss = 3.69442
I0410 02:28:56.508224 25920 solver.cpp:237]     Train net output #0: loss = 3.69442 (* 1 = 3.69442 loss)
I0410 02:28:56.508237 25920 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 02:29:01.486927 25920 solver.cpp:218] Iteration 5580 (2.41034 iter/s, 4.97855s/12 iters), loss = 3.78424
I0410 02:29:01.486974 25920 solver.cpp:237]     Train net output #0: loss = 3.78424 (* 1 = 3.78424 loss)
I0410 02:29:01.486985 25920 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 02:29:06.363123 25920 solver.cpp:218] Iteration 5592 (2.46104 iter/s, 4.87599s/12 iters), loss = 3.57299
I0410 02:29:06.363175 25920 solver.cpp:237]     Train net output #0: loss = 3.57299 (* 1 = 3.57299 loss)
I0410 02:29:06.363188 25920 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 02:29:11.722477 25920 solver.cpp:218] Iteration 5604 (2.23917 iter/s, 5.35914s/12 iters), loss = 3.70203
I0410 02:29:11.722599 25920 solver.cpp:237]     Train net output #0: loss = 3.70203 (* 1 = 3.70203 loss)
I0410 02:29:11.722610 25920 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 02:29:13.767206 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 02:29:15.570935 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 02:29:16.953836 25920 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 02:29:16.953866 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:29:19.205188 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:21.415575 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0857843
I0410 02:29:21.415623 25920 solver.cpp:397]     Test net output #1: loss = 3.85589 (* 1 = 3.85589 loss)
I0410 02:29:23.172369 25920 solver.cpp:218] Iteration 5616 (1.04809 iter/s, 11.4494s/12 iters), loss = 3.81582
I0410 02:29:23.172441 25920 solver.cpp:237]     Train net output #0: loss = 3.81582 (* 1 = 3.81582 loss)
I0410 02:29:23.172456 25920 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 02:29:28.257786 25920 solver.cpp:218] Iteration 5628 (2.3598 iter/s, 5.08519s/12 iters), loss = 3.8244
I0410 02:29:28.257839 25920 solver.cpp:237]     Train net output #0: loss = 3.8244 (* 1 = 3.8244 loss)
I0410 02:29:28.257853 25920 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 02:29:33.290969 25920 solver.cpp:218] Iteration 5640 (2.38428 iter/s, 5.03297s/12 iters), loss = 3.94531
I0410 02:29:33.291011 25920 solver.cpp:237]     Train net output #0: loss = 3.94531 (* 1 = 3.94531 loss)
I0410 02:29:33.291021 25920 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 02:29:38.362218 25920 solver.cpp:218] Iteration 5652 (2.36638 iter/s, 5.07105s/12 iters), loss = 3.79116
I0410 02:29:38.362262 25920 solver.cpp:237]     Train net output #0: loss = 3.79116 (* 1 = 3.79116 loss)
I0410 02:29:38.362270 25920 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 02:29:43.254561 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:43.419857 25920 solver.cpp:218] Iteration 5664 (2.37275 iter/s, 5.05743s/12 iters), loss = 3.85435
I0410 02:29:43.419909 25920 solver.cpp:237]     Train net output #0: loss = 3.85435 (* 1 = 3.85435 loss)
I0410 02:29:43.419920 25920 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 02:29:48.442852 25920 solver.cpp:218] Iteration 5676 (2.38912 iter/s, 5.02278s/12 iters), loss = 3.65988
I0410 02:29:48.442907 25920 solver.cpp:237]     Train net output #0: loss = 3.65988 (* 1 = 3.65988 loss)
I0410 02:29:48.442919 25920 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 02:29:53.481112 25920 solver.cpp:218] Iteration 5688 (2.38187 iter/s, 5.03805s/12 iters), loss = 3.72777
I0410 02:29:53.481165 25920 solver.cpp:237]     Train net output #0: loss = 3.72777 (* 1 = 3.72777 loss)
I0410 02:29:53.481178 25920 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 02:29:58.428426 25920 solver.cpp:218] Iteration 5700 (2.42566 iter/s, 4.9471s/12 iters), loss = 4.00067
I0410 02:29:58.428484 25920 solver.cpp:237]     Train net output #0: loss = 4.00067 (* 1 = 4.00067 loss)
I0410 02:29:58.428498 25920 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 02:30:02.888557 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 02:30:08.141901 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 02:30:10.973949 25920 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 02:30:10.974004 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:30:13.201339 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:15.453526 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0900735
I0410 02:30:15.453653 25920 solver.cpp:397]     Test net output #1: loss = 3.89237 (* 1 = 3.89237 loss)
I0410 02:30:15.541112 25920 solver.cpp:218] Iteration 5712 (0.701257 iter/s, 17.1121s/12 iters), loss = 3.8119
I0410 02:30:15.541165 25920 solver.cpp:237]     Train net output #0: loss = 3.8119 (* 1 = 3.8119 loss)
I0410 02:30:15.541177 25920 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 02:30:19.879832 25920 solver.cpp:218] Iteration 5724 (2.76591 iter/s, 4.33853s/12 iters), loss = 3.67828
I0410 02:30:19.879871 25920 solver.cpp:237]     Train net output #0: loss = 3.67828 (* 1 = 3.67828 loss)
I0410 02:30:19.879880 25920 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 02:30:24.839859 25920 solver.cpp:218] Iteration 5736 (2.41944 iter/s, 4.95983s/12 iters), loss = 3.97578
I0410 02:30:24.839910 25920 solver.cpp:237]     Train net output #0: loss = 3.97578 (* 1 = 3.97578 loss)
I0410 02:30:24.839921 25920 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 02:30:29.928851 25920 solver.cpp:218] Iteration 5748 (2.35813 iter/s, 5.08878s/12 iters), loss = 3.58528
I0410 02:30:29.928905 25920 solver.cpp:237]     Train net output #0: loss = 3.58528 (* 1 = 3.58528 loss)
I0410 02:30:29.928917 25920 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 02:30:34.935915 25920 solver.cpp:218] Iteration 5760 (2.39672 iter/s, 5.00685s/12 iters), loss = 3.65286
I0410 02:30:34.935968 25920 solver.cpp:237]     Train net output #0: loss = 3.65286 (* 1 = 3.65286 loss)
I0410 02:30:34.935979 25920 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 02:30:36.925196 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:40.170321 25920 solver.cpp:218] Iteration 5772 (2.29262 iter/s, 5.23418s/12 iters), loss = 3.78117
I0410 02:30:40.170379 25920 solver.cpp:237]     Train net output #0: loss = 3.78117 (* 1 = 3.78117 loss)
I0410 02:30:40.170390 25920 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 02:30:45.276477 25920 solver.cpp:218] Iteration 5784 (2.35021 iter/s, 5.10594s/12 iters), loss = 3.75771
I0410 02:30:45.276536 25920 solver.cpp:237]     Train net output #0: loss = 3.75771 (* 1 = 3.75771 loss)
I0410 02:30:45.276547 25920 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 02:30:50.368324 25920 solver.cpp:218] Iteration 5796 (2.35681 iter/s, 5.09162s/12 iters), loss = 3.61661
I0410 02:30:50.368417 25920 solver.cpp:237]     Train net output #0: loss = 3.61661 (* 1 = 3.61661 loss)
I0410 02:30:50.368428 25920 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 02:30:55.417889 25920 solver.cpp:218] Iteration 5808 (2.37656 iter/s, 5.04932s/12 iters), loss = 3.73614
I0410 02:30:55.417934 25920 solver.cpp:237]     Train net output #0: loss = 3.73614 (* 1 = 3.73614 loss)
I0410 02:30:55.417943 25920 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 02:30:57.476645 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 02:30:59.625144 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 02:31:01.485164 25920 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 02:31:01.485184 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:31:03.713380 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:06.023361 25920 solver.cpp:397]     Test net output #0: accuracy = 0.0802696
I0410 02:31:06.023409 25920 solver.cpp:397]     Test net output #1: loss = 3.95457 (* 1 = 3.95457 loss)
I0410 02:31:07.831527 25920 solver.cpp:218] Iteration 5820 (0.966712 iter/s, 12.4132s/12 iters), loss = 3.84181
I0410 02:31:07.831598 25920 solver.cpp:237]     Train net output #0: loss = 3.84181 (* 1 = 3.84181 loss)
I0410 02:31:07.831611 25920 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 02:31:12.713696 25920 solver.cpp:218] Iteration 5832 (2.45804 iter/s, 4.88194s/12 iters), loss = 3.39374
I0410 02:31:12.713750 25920 solver.cpp:237]     Train net output #0: loss = 3.39374 (* 1 = 3.39374 loss)
I0410 02:31:12.713762 25920 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 02:31:17.861158 25920 solver.cpp:218] Iteration 5844 (2.33134 iter/s, 5.14725s/12 iters), loss = 3.70171
I0410 02:31:17.861202 25920 solver.cpp:237]     Train net output #0: loss = 3.70171 (* 1 = 3.70171 loss)
I0410 02:31:17.861212 25920 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 02:31:22.963075 25920 solver.cpp:218] Iteration 5856 (2.35215 iter/s, 5.10171s/12 iters), loss = 3.42455
I0410 02:31:22.963182 25920 solver.cpp:237]     Train net output #0: loss = 3.42455 (* 1 = 3.42455 loss)
I0410 02:31:22.963196 25920 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 02:31:27.198925 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:28.029376 25920 solver.cpp:218] Iteration 5868 (2.36872 iter/s, 5.06603s/12 iters), loss = 3.41023
I0410 02:31:28.029428 25920 solver.cpp:237]     Train net output #0: loss = 3.41023 (* 1 = 3.41023 loss)
I0410 02:31:28.029438 25920 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 02:31:33.100924 25920 solver.cpp:218] Iteration 5880 (2.36624 iter/s, 5.07134s/12 iters), loss = 3.63845
I0410 02:31:33.100963 25920 solver.cpp:237]     Train net output #0: loss = 3.63845 (* 1 = 3.63845 loss)
I0410 02:31:33.100971 25920 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 02:31:38.186887 25920 solver.cpp:218] Iteration 5892 (2.35953 iter/s, 5.08575s/12 iters), loss = 3.69983
I0410 02:31:38.186954 25920 solver.cpp:237]     Train net output #0: loss = 3.69983 (* 1 = 3.69983 loss)
I0410 02:31:38.186970 25920 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 02:31:43.183440 25920 solver.cpp:218] Iteration 5904 (2.40176 iter/s, 4.99633s/12 iters), loss = 3.51618
I0410 02:31:43.183487 25920 solver.cpp:237]     Train net output #0: loss = 3.51618 (* 1 = 3.51618 loss)
I0410 02:31:43.183496 25920 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 02:31:47.748654 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 02:31:50.606940 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 02:31:51.978543 25920 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 02:31:51.978562 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:31:54.095985 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:56.447810 25920 solver.cpp:397]     Test net output #0: accuracy = 0.11152
I0410 02:31:56.447849 25920 solver.cpp:397]     Test net output #1: loss = 3.65352 (* 1 = 3.65352 loss)
I0410 02:31:56.535256 25920 solver.cpp:218] Iteration 5916 (0.898784 iter/s, 13.3514s/12 iters), loss = 3.47825
I0410 02:31:56.535308 25920 solver.cpp:237]     Train net output #0: loss = 3.47825 (* 1 = 3.47825 loss)
I0410 02:31:56.535317 25920 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 02:32:00.797870 25920 solver.cpp:218] Iteration 5928 (2.8153 iter/s, 4.26242s/12 iters), loss = 3.60282
I0410 02:32:00.797921 25920 solver.cpp:237]     Train net output #0: loss = 3.60282 (* 1 = 3.60282 loss)
I0410 02:32:00.797935 25920 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 02:32:05.800865 25920 solver.cpp:218] Iteration 5940 (2.39866 iter/s, 5.00278s/12 iters), loss = 3.59717
I0410 02:32:05.800925 25920 solver.cpp:237]     Train net output #0: loss = 3.59717 (* 1 = 3.59717 loss)
I0410 02:32:05.800937 25920 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 02:32:10.822870 25920 solver.cpp:218] Iteration 5952 (2.38959 iter/s, 5.02179s/12 iters), loss = 3.59525
I0410 02:32:10.822914 25920 solver.cpp:237]     Train net output #0: loss = 3.59525 (* 1 = 3.59525 loss)
I0410 02:32:10.822923 25920 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 02:32:15.824592 25920 solver.cpp:218] Iteration 5964 (2.39927 iter/s, 5.00152s/12 iters), loss = 3.53784
I0410 02:32:15.824641 25920 solver.cpp:237]     Train net output #0: loss = 3.53784 (* 1 = 3.53784 loss)
I0410 02:32:15.824651 25920 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 02:32:17.161542 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:32:20.914592 25920 solver.cpp:218] Iteration 5976 (2.35766 iter/s, 5.08979s/12 iters), loss = 3.33605
I0410 02:32:20.914634 25920 solver.cpp:237]     Train net output #0: loss = 3.33605 (* 1 = 3.33605 loss)
I0410 02:32:20.914644 25920 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 02:32:25.832759 25920 solver.cpp:218] Iteration 5988 (2.44004 iter/s, 4.91796s/12 iters), loss = 3.62576
I0410 02:32:25.832875 25920 solver.cpp:237]     Train net output #0: loss = 3.62576 (* 1 = 3.62576 loss)
I0410 02:32:25.832888 25920 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 02:32:30.867904 25920 solver.cpp:218] Iteration 6000 (2.38338 iter/s, 5.03487s/12 iters), loss = 3.52034
I0410 02:32:30.867956 25920 solver.cpp:237]     Train net output #0: loss = 3.52034 (* 1 = 3.52034 loss)
I0410 02:32:30.867969 25920 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 02:32:35.964977 25920 solver.cpp:218] Iteration 6012 (2.35439 iter/s, 5.09686s/12 iters), loss = 3.50359
I0410 02:32:35.965013 25920 solver.cpp:237]     Train net output #0: loss = 3.50359 (* 1 = 3.50359 loss)
I0410 02:32:35.965021 25920 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 02:32:37.993446 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 02:32:43.590556 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 02:32:48.397074 25920 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 02:32:48.397104 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:32:50.636983 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:32:53.006219 25920 solver.cpp:397]     Test net output #0: accuracy = 0.110907
I0410 02:32:53.006268 25920 solver.cpp:397]     Test net output #1: loss = 3.63229 (* 1 = 3.63229 loss)
I0410 02:32:54.912755 25920 solver.cpp:218] Iteration 6024 (0.63334 iter/s, 18.9472s/12 iters), loss = 3.5204
I0410 02:32:54.912804 25920 solver.cpp:237]     Train net output #0: loss = 3.5204 (* 1 = 3.5204 loss)
I0410 02:32:54.912813 25920 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 02:32:59.881731 25920 solver.cpp:218] Iteration 6036 (2.41508 iter/s, 4.96877s/12 iters), loss = 3.68045
I0410 02:32:59.881801 25920 solver.cpp:237]     Train net output #0: loss = 3.68045 (* 1 = 3.68045 loss)
I0410 02:32:59.881810 25920 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 02:33:05.361989 25920 solver.cpp:218] Iteration 6048 (2.18978 iter/s, 5.47999s/12 iters), loss = 3.44446
I0410 02:33:05.362042 25920 solver.cpp:237]     Train net output #0: loss = 3.44446 (* 1 = 3.44446 loss)
I0410 02:33:05.362056 25920 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 02:33:10.474201 25920 solver.cpp:218] Iteration 6060 (2.34742 iter/s, 5.112s/12 iters), loss = 3.36048
I0410 02:33:10.474248 25920 solver.cpp:237]     Train net output #0: loss = 3.36048 (* 1 = 3.36048 loss)
I0410 02:33:10.474259 25920 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 02:33:14.184406 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:15.724436 25920 solver.cpp:218] Iteration 6072 (2.28571 iter/s, 5.25002s/12 iters), loss = 3.47394
I0410 02:33:15.724479 25920 solver.cpp:237]     Train net output #0: loss = 3.47394 (* 1 = 3.47394 loss)
I0410 02:33:15.724488 25920 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 02:33:20.748903 25920 solver.cpp:218] Iteration 6084 (2.38841 iter/s, 5.02426s/12 iters), loss = 3.54428
I0410 02:33:20.748950 25920 solver.cpp:237]     Train net output #0: loss = 3.54428 (* 1 = 3.54428 loss)
I0410 02:33:20.748960 25920 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 02:33:25.789983 25920 solver.cpp:218] Iteration 6096 (2.38055 iter/s, 5.04085s/12 iters), loss = 3.54171
I0410 02:33:25.790032 25920 solver.cpp:237]     Train net output #0: loss = 3.54171 (* 1 = 3.54171 loss)
I0410 02:33:25.790045 25920 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 02:33:31.190222 25920 solver.cpp:218] Iteration 6108 (2.22222 iter/s, 5.40001s/12 iters), loss = 3.37376
I0410 02:33:31.190333 25920 solver.cpp:237]     Train net output #0: loss = 3.37376 (* 1 = 3.37376 loss)
I0410 02:33:31.190346 25920 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 02:33:35.846091 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 02:33:37.658344 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 02:33:39.054132 25920 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 02:33:39.054162 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:33:41.027554 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:43.503274 25920 solver.cpp:397]     Test net output #0: accuracy = 0.104779
I0410 02:33:43.503316 25920 solver.cpp:397]     Test net output #1: loss = 3.60225 (* 1 = 3.60225 loss)
I0410 02:33:43.590931 25920 solver.cpp:218] Iteration 6120 (0.967724 iter/s, 12.4002s/12 iters), loss = 3.55477
I0410 02:33:43.591001 25920 solver.cpp:237]     Train net output #0: loss = 3.55477 (* 1 = 3.55477 loss)
I0410 02:33:43.591017 25920 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 02:33:47.868438 25920 solver.cpp:218] Iteration 6132 (2.80551 iter/s, 4.2773s/12 iters), loss = 3.4598
I0410 02:33:47.868477 25920 solver.cpp:237]     Train net output #0: loss = 3.4598 (* 1 = 3.4598 loss)
I0410 02:33:47.868486 25920 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 02:33:52.852264 25920 solver.cpp:218] Iteration 6144 (2.40789 iter/s, 4.98363s/12 iters), loss = 3.72605
I0410 02:33:52.852324 25920 solver.cpp:237]     Train net output #0: loss = 3.72605 (* 1 = 3.72605 loss)
I0410 02:33:52.852335 25920 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 02:33:57.800992 25920 solver.cpp:218] Iteration 6156 (2.42497 iter/s, 4.94851s/12 iters), loss = 3.42839
I0410 02:33:57.801055 25920 solver.cpp:237]     Train net output #0: loss = 3.42839 (* 1 = 3.42839 loss)
I0410 02:33:57.801069 25920 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 02:34:02.861483 25920 solver.cpp:218] Iteration 6168 (2.37142 iter/s, 5.06026s/12 iters), loss = 3.50779
I0410 02:34:02.861593 25920 solver.cpp:237]     Train net output #0: loss = 3.50779 (* 1 = 3.50779 loss)
I0410 02:34:02.861605 25920 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 02:34:03.460512 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:07.885071 25920 solver.cpp:218] Iteration 6180 (2.38886 iter/s, 5.02332s/12 iters), loss = 3.5177
I0410 02:34:07.885135 25920 solver.cpp:237]     Train net output #0: loss = 3.5177 (* 1 = 3.5177 loss)
I0410 02:34:07.885150 25920 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 02:34:12.968183 25920 solver.cpp:218] Iteration 6192 (2.36086 iter/s, 5.08289s/12 iters), loss = 3.25601
I0410 02:34:12.968230 25920 solver.cpp:237]     Train net output #0: loss = 3.25601 (* 1 = 3.25601 loss)
I0410 02:34:12.968242 25920 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 02:34:18.065811 25920 solver.cpp:218] Iteration 6204 (2.35413 iter/s, 5.09742s/12 iters), loss = 3.42793
I0410 02:34:18.065869 25920 solver.cpp:237]     Train net output #0: loss = 3.42793 (* 1 = 3.42793 loss)
I0410 02:34:18.065882 25920 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 02:34:23.100819 25920 solver.cpp:218] Iteration 6216 (2.38342 iter/s, 5.03479s/12 iters), loss = 3.16053
I0410 02:34:23.100876 25920 solver.cpp:237]     Train net output #0: loss = 3.16053 (* 1 = 3.16053 loss)
I0410 02:34:23.100889 25920 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 02:34:25.173105 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 02:34:28.348902 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 02:34:31.079550 25920 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 02:34:31.079581 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:34:33.045321 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:34.341704 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:34:35.521631 25920 solver.cpp:397]     Test net output #0: accuracy = 0.105392
I0410 02:34:35.521679 25920 solver.cpp:397]     Test net output #1: loss = 3.67136 (* 1 = 3.67136 loss)
I0410 02:34:37.495024 25920 solver.cpp:218] Iteration 6228 (0.833696 iter/s, 14.3937s/12 iters), loss = 3.28996
I0410 02:34:37.495070 25920 solver.cpp:237]     Train net output #0: loss = 3.28996 (* 1 = 3.28996 loss)
I0410 02:34:37.495079 25920 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 02:34:42.738409 25920 solver.cpp:218] Iteration 6240 (2.28869 iter/s, 5.24318s/12 iters), loss = 3.45686
I0410 02:34:42.738451 25920 solver.cpp:237]     Train net output #0: loss = 3.45686 (* 1 = 3.45686 loss)
I0410 02:34:42.738459 25920 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 02:34:47.759863 25920 solver.cpp:218] Iteration 6252 (2.38984 iter/s, 5.02125s/12 iters), loss = 3.32642
I0410 02:34:47.759915 25920 solver.cpp:237]     Train net output #0: loss = 3.32642 (* 1 = 3.32642 loss)
I0410 02:34:47.759927 25920 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 02:34:52.749606 25920 solver.cpp:218] Iteration 6264 (2.40503 iter/s, 4.98954s/12 iters), loss = 3.61376
I0410 02:34:52.749655 25920 solver.cpp:237]     Train net output #0: loss = 3.61376 (* 1 = 3.61376 loss)
I0410 02:34:52.749663 25920 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 02:34:55.464745 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:57.798907 25920 solver.cpp:218] Iteration 6276 (2.37667 iter/s, 5.04908s/12 iters), loss = 3.33859
I0410 02:34:57.798960 25920 solver.cpp:237]     Train net output #0: loss = 3.33859 (* 1 = 3.33859 loss)
I0410 02:34:57.798974 25920 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 02:35:02.930718 25920 solver.cpp:218] Iteration 6288 (2.33845 iter/s, 5.13159s/12 iters), loss = 3.38911
I0410 02:35:02.930770 25920 solver.cpp:237]     Train net output #0: loss = 3.38911 (* 1 = 3.38911 loss)
I0410 02:35:02.930783 25920 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 02:35:08.190132 25920 solver.cpp:218] Iteration 6300 (2.28172 iter/s, 5.25919s/12 iters), loss = 3.28725
I0410 02:35:08.190227 25920 solver.cpp:237]     Train net output #0: loss = 3.28725 (* 1 = 3.28725 loss)
I0410 02:35:08.190239 25920 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 02:35:13.222869 25920 solver.cpp:218] Iteration 6312 (2.38451 iter/s, 5.03249s/12 iters), loss = 3.43058
I0410 02:35:13.222923 25920 solver.cpp:237]     Train net output #0: loss = 3.43058 (* 1 = 3.43058 loss)
I0410 02:35:13.222934 25920 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 02:35:17.759712 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 02:35:19.505085 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 02:35:21.230132 25920 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 02:35:21.230163 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:35:23.273491 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:25.885165 25920 solver.cpp:397]     Test net output #0: accuracy = 0.109069
I0410 02:35:25.885212 25920 solver.cpp:397]     Test net output #1: loss = 3.55536 (* 1 = 3.55536 loss)
I0410 02:35:25.972759 25920 solver.cpp:218] Iteration 6324 (0.941217 iter/s, 12.7495s/12 iters), loss = 3.33751
I0410 02:35:25.972811 25920 solver.cpp:237]     Train net output #0: loss = 3.33751 (* 1 = 3.33751 loss)
I0410 02:35:25.972822 25920 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 02:35:30.407582 25920 solver.cpp:218] Iteration 6336 (2.70598 iter/s, 4.43463s/12 iters), loss = 3.38812
I0410 02:35:30.407635 25920 solver.cpp:237]     Train net output #0: loss = 3.38812 (* 1 = 3.38812 loss)
I0410 02:35:30.407646 25920 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 02:35:35.406560 25920 solver.cpp:218] Iteration 6348 (2.40059 iter/s, 4.99876s/12 iters), loss = 3.55543
I0410 02:35:35.406605 25920 solver.cpp:237]     Train net output #0: loss = 3.55543 (* 1 = 3.55543 loss)
I0410 02:35:35.406615 25920 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 02:35:40.382789 25920 solver.cpp:218] Iteration 6360 (2.41157 iter/s, 4.97602s/12 iters), loss = 3.43828
I0410 02:35:40.382936 25920 solver.cpp:237]     Train net output #0: loss = 3.43828 (* 1 = 3.43828 loss)
I0410 02:35:40.382948 25920 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 02:35:45.175956 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:45.314679 25920 solver.cpp:218] Iteration 6372 (2.4333 iter/s, 4.93158s/12 iters), loss = 3.52205
I0410 02:35:45.314741 25920 solver.cpp:237]     Train net output #0: loss = 3.52205 (* 1 = 3.52205 loss)
I0410 02:35:45.314754 25920 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 02:35:50.230780 25920 solver.cpp:218] Iteration 6384 (2.44107 iter/s, 4.91589s/12 iters), loss = 3.21456
I0410 02:35:50.230830 25920 solver.cpp:237]     Train net output #0: loss = 3.21456 (* 1 = 3.21456 loss)
I0410 02:35:50.230842 25920 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 02:35:55.149454 25920 solver.cpp:218] Iteration 6396 (2.43978 iter/s, 4.91847s/12 iters), loss = 3.2643
I0410 02:35:55.149513 25920 solver.cpp:237]     Train net output #0: loss = 3.2643 (* 1 = 3.2643 loss)
I0410 02:35:55.149524 25920 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 02:36:00.074687 25920 solver.cpp:218] Iteration 6408 (2.43654 iter/s, 4.92501s/12 iters), loss = 3.57274
I0410 02:36:00.074748 25920 solver.cpp:237]     Train net output #0: loss = 3.57274 (* 1 = 3.57274 loss)
I0410 02:36:00.074759 25920 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 02:36:05.002182 25920 solver.cpp:218] Iteration 6420 (2.43542 iter/s, 4.92728s/12 iters), loss = 3.38088
I0410 02:36:05.002235 25920 solver.cpp:237]     Train net output #0: loss = 3.38088 (* 1 = 3.38088 loss)
I0410 02:36:05.002247 25920 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 02:36:07.010785 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 02:36:10.021010 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 02:36:13.264777 25920 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 02:36:13.264842 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:36:15.242795 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:36:17.879546 25920 solver.cpp:397]     Test net output #0: accuracy = 0.128064
I0410 02:36:17.879596 25920 solver.cpp:397]     Test net output #1: loss = 3.53078 (* 1 = 3.53078 loss)
I0410 02:36:19.864300 25920 solver.cpp:218] Iteration 6432 (0.807449 iter/s, 14.8616s/12 iters), loss = 3.35131
I0410 02:36:19.864351 25920 solver.cpp:237]     Train net output #0: loss = 3.35131 (* 1 = 3.35131 loss)
I0410 02:36:19.864362 25920 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 02:36:24.885927 25920 solver.cpp:218] Iteration 6444 (2.38976 iter/s, 5.02142s/12 iters), loss = 3.42882
I0410 02:36:24.885996 25920 solver.cpp:237]     Train net output #0: loss = 3.42882 (* 1 = 3.42882 loss)
I0410 02:36:24.886009 25920 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 02:36:29.978085 25920 solver.cpp:218] Iteration 6456 (2.35667 iter/s, 5.09192s/12 iters), loss = 3.27002
I0410 02:36:29.978147 25920 solver.cpp:237]     Train net output #0: loss = 3.27002 (* 1 = 3.27002 loss)
I0410 02:36:29.978160 25920 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 02:36:34.936334 25920 solver.cpp:218] Iteration 6468 (2.42031 iter/s, 4.95803s/12 iters), loss = 3.33705
I0410 02:36:34.936380 25920 solver.cpp:237]     Train net output #0: loss = 3.33705 (* 1 = 3.33705 loss)
I0410 02:36:34.936389 25920 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 02:36:36.957505 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:36:39.978914 25920 solver.cpp:218] Iteration 6480 (2.37983 iter/s, 5.04237s/12 iters), loss = 3.31899
I0410 02:36:39.978960 25920 solver.cpp:237]     Train net output #0: loss = 3.31899 (* 1 = 3.31899 loss)
I0410 02:36:39.978968 25920 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 02:36:44.968353 25920 solver.cpp:218] Iteration 6492 (2.40518 iter/s, 4.98924s/12 iters), loss = 3.32891
I0410 02:36:44.968482 25920 solver.cpp:237]     Train net output #0: loss = 3.32891 (* 1 = 3.32891 loss)
I0410 02:36:44.968492 25920 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 02:36:50.208040 25920 solver.cpp:218] Iteration 6504 (2.29034 iter/s, 5.2394s/12 iters), loss = 3.24272
I0410 02:36:50.208087 25920 solver.cpp:237]     Train net output #0: loss = 3.24272 (* 1 = 3.24272 loss)
I0410 02:36:50.208098 25920 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 02:36:55.184878 25920 solver.cpp:218] Iteration 6516 (2.41127 iter/s, 4.97663s/12 iters), loss = 3.40323
I0410 02:36:55.184939 25920 solver.cpp:237]     Train net output #0: loss = 3.40323 (* 1 = 3.40323 loss)
I0410 02:36:55.184953 25920 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 02:36:59.711566 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 02:37:04.529160 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 02:37:06.308413 25920 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 02:37:06.308442 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:37:08.436461 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:11.081066 25920 solver.cpp:397]     Test net output #0: accuracy = 0.122549
I0410 02:37:11.081107 25920 solver.cpp:397]     Test net output #1: loss = 3.51869 (* 1 = 3.51869 loss)
I0410 02:37:11.168331 25920 solver.cpp:218] Iteration 6528 (0.750802 iter/s, 15.9829s/12 iters), loss = 3.48662
I0410 02:37:11.168386 25920 solver.cpp:237]     Train net output #0: loss = 3.48662 (* 1 = 3.48662 loss)
I0410 02:37:11.168401 25920 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 02:37:15.256978 25920 solver.cpp:218] Iteration 6540 (2.93509 iter/s, 4.08846s/12 iters), loss = 3.17463
I0410 02:37:15.257063 25920 solver.cpp:237]     Train net output #0: loss = 3.17463 (* 1 = 3.17463 loss)
I0410 02:37:15.257072 25920 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 02:37:20.181614 25920 solver.cpp:218] Iteration 6552 (2.43685 iter/s, 4.92438s/12 iters), loss = 3.31408
I0410 02:37:20.181669 25920 solver.cpp:237]     Train net output #0: loss = 3.31408 (* 1 = 3.31408 loss)
I0410 02:37:20.181682 25920 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 02:37:25.190085 25920 solver.cpp:218] Iteration 6564 (2.39604 iter/s, 5.00826s/12 iters), loss = 3.2273
I0410 02:37:25.190135 25920 solver.cpp:237]     Train net output #0: loss = 3.2273 (* 1 = 3.2273 loss)
I0410 02:37:25.190145 25920 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 02:37:29.419181 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:30.256001 25920 solver.cpp:218] Iteration 6576 (2.36887 iter/s, 5.0657s/12 iters), loss = 3.21622
I0410 02:37:30.256054 25920 solver.cpp:237]     Train net output #0: loss = 3.21622 (* 1 = 3.21622 loss)
I0410 02:37:30.256067 25920 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 02:37:35.522678 25920 solver.cpp:218] Iteration 6588 (2.27857 iter/s, 5.26645s/12 iters), loss = 3.23409
I0410 02:37:35.522724 25920 solver.cpp:237]     Train net output #0: loss = 3.23409 (* 1 = 3.23409 loss)
I0410 02:37:35.522737 25920 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 02:37:40.516191 25920 solver.cpp:218] Iteration 6600 (2.40322 iter/s, 4.9933s/12 iters), loss = 3.3744
I0410 02:37:40.516242 25920 solver.cpp:237]     Train net output #0: loss = 3.3744 (* 1 = 3.3744 loss)
I0410 02:37:40.516256 25920 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 02:37:45.546262 25920 solver.cpp:218] Iteration 6612 (2.38575 iter/s, 5.02986s/12 iters), loss = 3.20712
I0410 02:37:45.546419 25920 solver.cpp:237]     Train net output #0: loss = 3.20712 (* 1 = 3.20712 loss)
I0410 02:37:45.546433 25920 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 02:37:50.630779 25920 solver.cpp:218] Iteration 6624 (2.36025 iter/s, 5.0842s/12 iters), loss = 3.15265
I0410 02:37:50.630831 25920 solver.cpp:237]     Train net output #0: loss = 3.15265 (* 1 = 3.15265 loss)
I0410 02:37:50.630843 25920 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 02:37:52.705842 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 02:37:56.384896 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 02:37:59.651479 25920 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 02:37:59.651507 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:38:01.474613 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:04.070188 25920 solver.cpp:397]     Test net output #0: accuracy = 0.125
I0410 02:38:04.070233 25920 solver.cpp:397]     Test net output #1: loss = 3.50752 (* 1 = 3.50752 loss)
I0410 02:38:05.926352 25920 solver.cpp:218] Iteration 6636 (0.784567 iter/s, 15.2951s/12 iters), loss = 3.38068
I0410 02:38:05.926415 25920 solver.cpp:237]     Train net output #0: loss = 3.38068 (* 1 = 3.38068 loss)
I0410 02:38:05.926430 25920 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 02:38:10.915596 25920 solver.cpp:218] Iteration 6648 (2.40528 iter/s, 4.98902s/12 iters), loss = 3.23096
I0410 02:38:10.915639 25920 solver.cpp:237]     Train net output #0: loss = 3.23096 (* 1 = 3.23096 loss)
I0410 02:38:10.915648 25920 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 02:38:16.009740 25920 solver.cpp:218] Iteration 6660 (2.35574 iter/s, 5.09393s/12 iters), loss = 3.25817
I0410 02:38:16.009855 25920 solver.cpp:237]     Train net output #0: loss = 3.25817 (* 1 = 3.25817 loss)
I0410 02:38:16.009868 25920 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 02:38:21.267853 25920 solver.cpp:218] Iteration 6672 (2.28231 iter/s, 5.25783s/12 iters), loss = 3.07233
I0410 02:38:21.267900 25920 solver.cpp:237]     Train net output #0: loss = 3.07233 (* 1 = 3.07233 loss)
I0410 02:38:21.267910 25920 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 02:38:22.638633 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:26.246188 25920 solver.cpp:218] Iteration 6684 (2.41055 iter/s, 4.97812s/12 iters), loss = 2.94768
I0410 02:38:26.246237 25920 solver.cpp:237]     Train net output #0: loss = 2.94768 (* 1 = 2.94768 loss)
I0410 02:38:26.246248 25920 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 02:38:31.221761 25920 solver.cpp:218] Iteration 6696 (2.41188 iter/s, 4.97537s/12 iters), loss = 3.27968
I0410 02:38:31.221814 25920 solver.cpp:237]     Train net output #0: loss = 3.27968 (* 1 = 3.27968 loss)
I0410 02:38:31.221827 25920 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 02:38:36.180343 25920 solver.cpp:218] Iteration 6708 (2.42015 iter/s, 4.95837s/12 iters), loss = 3.26051
I0410 02:38:36.180402 25920 solver.cpp:237]     Train net output #0: loss = 3.26051 (* 1 = 3.26051 loss)
I0410 02:38:36.180414 25920 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 02:38:41.082427 25920 solver.cpp:218] Iteration 6720 (2.44805 iter/s, 4.90187s/12 iters), loss = 3.18847
I0410 02:38:41.082489 25920 solver.cpp:237]     Train net output #0: loss = 3.18847 (* 1 = 3.18847 loss)
I0410 02:38:41.082501 25920 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 02:38:45.552639 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 02:38:49.291755 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 02:38:52.417012 25920 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 02:38:52.417034 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:38:54.209136 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:56.849328 25920 solver.cpp:397]     Test net output #0: accuracy = 0.125613
I0410 02:38:56.849371 25920 solver.cpp:397]     Test net output #1: loss = 3.43616 (* 1 = 3.43616 loss)
I0410 02:38:56.936470 25920 solver.cpp:218] Iteration 6732 (0.75693 iter/s, 15.8535s/12 iters), loss = 3.25248
I0410 02:38:56.936519 25920 solver.cpp:237]     Train net output #0: loss = 3.25248 (* 1 = 3.25248 loss)
I0410 02:38:56.936528 25920 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 02:39:01.122473 25920 solver.cpp:218] Iteration 6744 (2.86682 iter/s, 4.18582s/12 iters), loss = 3.41496
I0410 02:39:01.122527 25920 solver.cpp:237]     Train net output #0: loss = 3.41496 (* 1 = 3.41496 loss)
I0410 02:39:01.122539 25920 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 02:39:06.073498 25920 solver.cpp:218] Iteration 6756 (2.42385 iter/s, 4.95081s/12 iters), loss = 3.23257
I0410 02:39:06.073556 25920 solver.cpp:237]     Train net output #0: loss = 3.23257 (* 1 = 3.23257 loss)
I0410 02:39:06.073567 25920 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 02:39:11.138474 25920 solver.cpp:218] Iteration 6768 (2.36931 iter/s, 5.06476s/12 iters), loss = 3.08855
I0410 02:39:11.138527 25920 solver.cpp:237]     Train net output #0: loss = 3.08855 (* 1 = 3.08855 loss)
I0410 02:39:11.138538 25920 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 02:39:14.667374 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:39:16.164708 25920 solver.cpp:218] Iteration 6780 (2.38757 iter/s, 5.02603s/12 iters), loss = 3.12055
I0410 02:39:16.164759 25920 solver.cpp:237]     Train net output #0: loss = 3.12055 (* 1 = 3.12055 loss)
I0410 02:39:16.164772 25920 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 02:39:21.253984 25920 solver.cpp:218] Iteration 6792 (2.358 iter/s, 5.08906s/12 iters), loss = 3.13866
I0410 02:39:21.254093 25920 solver.cpp:237]     Train net output #0: loss = 3.13866 (* 1 = 3.13866 loss)
I0410 02:39:21.254104 25920 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 02:39:26.357100 25920 solver.cpp:218] Iteration 6804 (2.35163 iter/s, 5.10285s/12 iters), loss = 3.14537
I0410 02:39:26.357146 25920 solver.cpp:237]     Train net output #0: loss = 3.14537 (* 1 = 3.14537 loss)
I0410 02:39:26.357156 25920 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 02:39:31.349593 25920 solver.cpp:218] Iteration 6816 (2.40371 iter/s, 4.99229s/12 iters), loss = 3.28447
I0410 02:39:31.349647 25920 solver.cpp:237]     Train net output #0: loss = 3.28447 (* 1 = 3.28447 loss)
I0410 02:39:31.349660 25920 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 02:39:36.347739 25920 solver.cpp:218] Iteration 6828 (2.40099 iter/s, 4.99794s/12 iters), loss = 3.24592
I0410 02:39:36.347786 25920 solver.cpp:237]     Train net output #0: loss = 3.24592 (* 1 = 3.24592 loss)
I0410 02:39:36.347795 25920 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 02:39:38.418889 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 02:39:40.254436 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 02:39:41.626578 25920 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 02:39:41.626607 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:39:43.475562 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:39:46.567039 25920 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0410 02:39:46.567083 25920 solver.cpp:397]     Test net output #1: loss = 3.3833 (* 1 = 3.3833 loss)
I0410 02:39:48.421619 25920 solver.cpp:218] Iteration 6840 (0.993915 iter/s, 12.0735s/12 iters), loss = 3.22988
I0410 02:39:48.421670 25920 solver.cpp:237]     Train net output #0: loss = 3.22988 (* 1 = 3.22988 loss)
I0410 02:39:48.421680 25920 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 02:39:53.529222 25920 solver.cpp:218] Iteration 6852 (2.34954 iter/s, 5.10739s/12 iters), loss = 3.2573
I0410 02:39:53.529321 25920 solver.cpp:237]     Train net output #0: loss = 3.2573 (* 1 = 3.2573 loss)
I0410 02:39:53.529330 25920 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 02:39:58.511953 25920 solver.cpp:218] Iteration 6864 (2.40844 iter/s, 4.98247s/12 iters), loss = 2.91982
I0410 02:39:58.512006 25920 solver.cpp:237]     Train net output #0: loss = 2.91982 (* 1 = 2.91982 loss)
I0410 02:39:58.512017 25920 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 02:40:03.602946 25920 solver.cpp:218] Iteration 6876 (2.3572 iter/s, 5.09078s/12 iters), loss = 3.03705
I0410 02:40:03.602986 25920 solver.cpp:237]     Train net output #0: loss = 3.03705 (* 1 = 3.03705 loss)
I0410 02:40:03.602995 25920 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 02:40:04.210335 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:08.669431 25920 solver.cpp:218] Iteration 6888 (2.3686 iter/s, 5.06628s/12 iters), loss = 3.07636
I0410 02:40:08.669481 25920 solver.cpp:237]     Train net output #0: loss = 3.07636 (* 1 = 3.07636 loss)
I0410 02:40:08.669492 25920 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 02:40:13.669879 25920 solver.cpp:218] Iteration 6900 (2.39989 iter/s, 5.00024s/12 iters), loss = 3.14201
I0410 02:40:13.669939 25920 solver.cpp:237]     Train net output #0: loss = 3.14201 (* 1 = 3.14201 loss)
I0410 02:40:13.669951 25920 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 02:40:18.778393 25920 solver.cpp:218] Iteration 6912 (2.34912 iter/s, 5.10829s/12 iters), loss = 3.01736
I0410 02:40:18.778442 25920 solver.cpp:237]     Train net output #0: loss = 3.01736 (* 1 = 3.01736 loss)
I0410 02:40:18.778453 25920 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 02:40:23.810302 25920 solver.cpp:218] Iteration 6924 (2.38488 iter/s, 5.0317s/12 iters), loss = 2.94187
I0410 02:40:23.811506 25920 solver.cpp:237]     Train net output #0: loss = 2.94187 (* 1 = 2.94187 loss)
I0410 02:40:23.811518 25920 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 02:40:28.681725 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 02:40:31.796249 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 02:40:34.599818 25920 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 02:40:34.599843 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:40:35.236779 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:40:36.307912 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:39.182363 25920 solver.cpp:397]     Test net output #0: accuracy = 0.146446
I0410 02:40:39.182396 25920 solver.cpp:397]     Test net output #1: loss = 3.36297 (* 1 = 3.36297 loss)
I0410 02:40:39.269006 25920 solver.cpp:218] Iteration 6936 (0.776345 iter/s, 15.457s/12 iters), loss = 2.94739
I0410 02:40:39.269060 25920 solver.cpp:237]     Train net output #0: loss = 2.94739 (* 1 = 2.94739 loss)
I0410 02:40:39.269071 25920 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 02:40:43.538525 25920 solver.cpp:218] Iteration 6948 (2.81075 iter/s, 4.26933s/12 iters), loss = 3.17241
I0410 02:40:43.538573 25920 solver.cpp:237]     Train net output #0: loss = 3.17241 (* 1 = 3.17241 loss)
I0410 02:40:43.538584 25920 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 02:40:48.590065 25920 solver.cpp:218] Iteration 6960 (2.37561 iter/s, 5.05133s/12 iters), loss = 3.01402
I0410 02:40:48.590112 25920 solver.cpp:237]     Train net output #0: loss = 3.01402 (* 1 = 3.01402 loss)
I0410 02:40:48.590123 25920 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 02:40:54.019109 25920 solver.cpp:218] Iteration 6972 (2.21042 iter/s, 5.42882s/12 iters), loss = 3.19036
I0410 02:40:54.019215 25920 solver.cpp:237]     Train net output #0: loss = 3.19036 (* 1 = 3.19036 loss)
I0410 02:40:54.019228 25920 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 02:40:56.724856 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:58.996930 25920 solver.cpp:218] Iteration 6984 (2.41082 iter/s, 4.97756s/12 iters), loss = 3.02566
I0410 02:40:58.996978 25920 solver.cpp:237]     Train net output #0: loss = 3.02566 (* 1 = 3.02566 loss)
I0410 02:40:58.996989 25920 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 02:41:04.023766 25920 solver.cpp:218] Iteration 6996 (2.38729 iter/s, 5.02663s/12 iters), loss = 3.17553
I0410 02:41:04.023805 25920 solver.cpp:237]     Train net output #0: loss = 3.17553 (* 1 = 3.17553 loss)
I0410 02:41:04.023813 25920 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 02:41:09.031174 25920 solver.cpp:218] Iteration 7008 (2.39655 iter/s, 5.00721s/12 iters), loss = 2.98952
I0410 02:41:09.031236 25920 solver.cpp:237]     Train net output #0: loss = 2.98952 (* 1 = 2.98952 loss)
I0410 02:41:09.031250 25920 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 02:41:14.013497 25920 solver.cpp:218] Iteration 7020 (2.40862 iter/s, 4.9821s/12 iters), loss = 3.19858
I0410 02:41:14.013561 25920 solver.cpp:237]     Train net output #0: loss = 3.19858 (* 1 = 3.19858 loss)
I0410 02:41:14.013576 25920 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 02:41:18.947183 25920 solver.cpp:218] Iteration 7032 (2.43237 iter/s, 4.93346s/12 iters), loss = 3.01537
I0410 02:41:18.947243 25920 solver.cpp:237]     Train net output #0: loss = 3.01537 (* 1 = 3.01537 loss)
I0410 02:41:18.947257 25920 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 02:41:20.959189 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 02:41:22.758392 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 02:41:24.131315 25920 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 02:41:24.131423 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:41:25.897635 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:28.866883 25920 solver.cpp:397]     Test net output #0: accuracy = 0.145221
I0410 02:41:28.866914 25920 solver.cpp:397]     Test net output #1: loss = 3.39738 (* 1 = 3.39738 loss)
I0410 02:41:31.041107 25920 solver.cpp:218] Iteration 7044 (0.992269 iter/s, 12.0935s/12 iters), loss = 3.13639
I0410 02:41:31.041157 25920 solver.cpp:237]     Train net output #0: loss = 3.13639 (* 1 = 3.13639 loss)
I0410 02:41:31.041167 25920 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 02:41:36.459848 25920 solver.cpp:218] Iteration 7056 (2.21464 iter/s, 5.41849s/12 iters), loss = 3.30096
I0410 02:41:36.459892 25920 solver.cpp:237]     Train net output #0: loss = 3.30096 (* 1 = 3.30096 loss)
I0410 02:41:36.459900 25920 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 02:41:41.467679 25920 solver.cpp:218] Iteration 7068 (2.39635 iter/s, 5.00763s/12 iters), loss = 3.08187
I0410 02:41:41.467732 25920 solver.cpp:237]     Train net output #0: loss = 3.08187 (* 1 = 3.08187 loss)
I0410 02:41:41.467744 25920 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 02:41:46.345208 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:46.449577 25920 solver.cpp:218] Iteration 7080 (2.40885 iter/s, 4.98162s/12 iters), loss = 3.13819
I0410 02:41:46.449628 25920 solver.cpp:237]     Train net output #0: loss = 3.13819 (* 1 = 3.13819 loss)
I0410 02:41:46.449641 25920 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 02:41:51.487383 25920 solver.cpp:218] Iteration 7092 (2.38209 iter/s, 5.03759s/12 iters), loss = 2.9059
I0410 02:41:51.487447 25920 solver.cpp:237]     Train net output #0: loss = 2.9059 (* 1 = 2.9059 loss)
I0410 02:41:51.487459 25920 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 02:41:56.423558 25920 solver.cpp:218] Iteration 7104 (2.43114 iter/s, 4.93596s/12 iters), loss = 3.06472
I0410 02:41:56.423676 25920 solver.cpp:237]     Train net output #0: loss = 3.06472 (* 1 = 3.06472 loss)
I0410 02:41:56.423691 25920 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 02:42:01.381537 25920 solver.cpp:218] Iteration 7116 (2.42048 iter/s, 4.9577s/12 iters), loss = 3.30844
I0410 02:42:01.381590 25920 solver.cpp:237]     Train net output #0: loss = 3.30844 (* 1 = 3.30844 loss)
I0410 02:42:01.381603 25920 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 02:42:06.343448 25920 solver.cpp:218] Iteration 7128 (2.41852 iter/s, 4.9617s/12 iters), loss = 3.09966
I0410 02:42:06.343488 25920 solver.cpp:237]     Train net output #0: loss = 3.09966 (* 1 = 3.09966 loss)
I0410 02:42:06.343497 25920 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 02:42:11.005609 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 02:42:19.103365 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 02:42:21.902233 25920 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 02:42:21.902261 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:42:23.564867 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:26.365705 25920 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0410 02:42:26.365754 25920 solver.cpp:397]     Test net output #1: loss = 3.487 (* 1 = 3.487 loss)
I0410 02:42:26.453342 25920 solver.cpp:218] Iteration 7140 (0.59674 iter/s, 20.1093s/12 iters), loss = 3.0783
I0410 02:42:26.453502 25920 solver.cpp:237]     Train net output #0: loss = 3.0783 (* 1 = 3.0783 loss)
I0410 02:42:26.453521 25920 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 02:42:30.789203 25920 solver.cpp:218] Iteration 7152 (2.7678 iter/s, 4.33557s/12 iters), loss = 3.14335
I0410 02:42:30.789258 25920 solver.cpp:237]     Train net output #0: loss = 3.14335 (* 1 = 3.14335 loss)
I0410 02:42:30.789269 25920 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 02:42:35.872092 25920 solver.cpp:218] Iteration 7164 (2.36096 iter/s, 5.08267s/12 iters), loss = 2.90479
I0410 02:42:35.872140 25920 solver.cpp:237]     Train net output #0: loss = 2.90479 (* 1 = 2.90479 loss)
I0410 02:42:35.872150 25920 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 02:42:40.992259 25920 solver.cpp:218] Iteration 7176 (2.34377 iter/s, 5.11996s/12 iters), loss = 3.06862
I0410 02:42:40.992301 25920 solver.cpp:237]     Train net output #0: loss = 3.06862 (* 1 = 3.06862 loss)
I0410 02:42:40.992309 25920 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 02:42:43.188074 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:46.123203 25920 solver.cpp:218] Iteration 7188 (2.33885 iter/s, 5.13074s/12 iters), loss = 2.9678
I0410 02:42:46.123246 25920 solver.cpp:237]     Train net output #0: loss = 2.9678 (* 1 = 2.9678 loss)
I0410 02:42:46.123256 25920 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 02:42:51.107036 25920 solver.cpp:218] Iteration 7200 (2.40788 iter/s, 4.98363s/12 iters), loss = 2.97324
I0410 02:42:51.107085 25920 solver.cpp:237]     Train net output #0: loss = 2.97324 (* 1 = 2.97324 loss)
I0410 02:42:51.107095 25920 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 02:42:56.068084 25920 solver.cpp:218] Iteration 7212 (2.41895 iter/s, 4.96084s/12 iters), loss = 2.92826
I0410 02:42:56.068148 25920 solver.cpp:237]     Train net output #0: loss = 2.92826 (* 1 = 2.92826 loss)
I0410 02:42:56.068166 25920 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 02:43:01.091837 25920 solver.cpp:218] Iteration 7224 (2.38876 iter/s, 5.02353s/12 iters), loss = 3.01266
I0410 02:43:01.091946 25920 solver.cpp:237]     Train net output #0: loss = 3.01266 (* 1 = 3.01266 loss)
I0410 02:43:01.091958 25920 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 02:43:06.107004 25920 solver.cpp:218] Iteration 7236 (2.39287 iter/s, 5.0149s/12 iters), loss = 3.06078
I0410 02:43:06.107050 25920 solver.cpp:237]     Train net output #0: loss = 3.06078 (* 1 = 3.06078 loss)
I0410 02:43:06.107059 25920 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 02:43:08.126929 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 02:43:10.099632 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 02:43:12.625252 25920 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 02:43:12.625280 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:43:14.235272 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:43:17.091658 25920 solver.cpp:397]     Test net output #0: accuracy = 0.141544
I0410 02:43:17.091701 25920 solver.cpp:397]     Test net output #1: loss = 3.38232 (* 1 = 3.38232 loss)
I0410 02:43:18.890200 25920 solver.cpp:218] Iteration 7248 (0.938764 iter/s, 12.7828s/12 iters), loss = 2.93331
I0410 02:43:18.890252 25920 solver.cpp:237]     Train net output #0: loss = 2.93331 (* 1 = 2.93331 loss)
I0410 02:43:18.890261 25920 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 02:43:23.911126 25920 solver.cpp:218] Iteration 7260 (2.3901 iter/s, 5.02072s/12 iters), loss = 2.92662
I0410 02:43:23.911170 25920 solver.cpp:237]     Train net output #0: loss = 2.92662 (* 1 = 2.92662 loss)
I0410 02:43:23.911180 25920 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 02:43:28.841512 25920 solver.cpp:218] Iteration 7272 (2.43399 iter/s, 4.93018s/12 iters), loss = 2.90177
I0410 02:43:28.841569 25920 solver.cpp:237]     Train net output #0: loss = 2.90177 (* 1 = 2.90177 loss)
I0410 02:43:28.841583 25920 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 02:43:33.148854 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:43:33.908512 25920 solver.cpp:218] Iteration 7284 (2.36837 iter/s, 5.06679s/12 iters), loss = 2.98696
I0410 02:43:33.908565 25920 solver.cpp:237]     Train net output #0: loss = 2.98696 (* 1 = 2.98696 loss)
I0410 02:43:33.908577 25920 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 02:43:38.955152 25920 solver.cpp:218] Iteration 7296 (2.37792 iter/s, 5.04643s/12 iters), loss = 2.92295
I0410 02:43:38.955205 25920 solver.cpp:237]     Train net output #0: loss = 2.92295 (* 1 = 2.92295 loss)
I0410 02:43:38.955219 25920 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 02:43:44.105577 25920 solver.cpp:218] Iteration 7308 (2.33 iter/s, 5.15021s/12 iters), loss = 3.11494
I0410 02:43:44.105623 25920 solver.cpp:237]     Train net output #0: loss = 3.11494 (* 1 = 3.11494 loss)
I0410 02:43:44.105634 25920 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 02:43:49.067504 25920 solver.cpp:218] Iteration 7320 (2.41851 iter/s, 4.96173s/12 iters), loss = 2.81721
I0410 02:43:49.067551 25920 solver.cpp:237]     Train net output #0: loss = 2.81721 (* 1 = 2.81721 loss)
I0410 02:43:49.067560 25920 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 02:43:54.065634 25920 solver.cpp:218] Iteration 7332 (2.401 iter/s, 4.99792s/12 iters), loss = 2.93764
I0410 02:43:54.065683 25920 solver.cpp:237]     Train net output #0: loss = 2.93764 (* 1 = 2.93764 loss)
I0410 02:43:54.065692 25920 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 02:43:58.434684 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 02:44:00.155414 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 02:44:01.514626 25920 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 02:44:01.514644 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:44:03.060405 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:05.956125 25920 solver.cpp:397]     Test net output #0: accuracy = 0.161152
I0410 02:44:05.956259 25920 solver.cpp:397]     Test net output #1: loss = 3.30434 (* 1 = 3.30434 loss)
I0410 02:44:06.043637 25920 solver.cpp:218] Iteration 7344 (1.00187 iter/s, 11.9776s/12 iters), loss = 2.99448
I0410 02:44:06.043689 25920 solver.cpp:237]     Train net output #0: loss = 2.99448 (* 1 = 2.99448 loss)
I0410 02:44:06.043700 25920 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 02:44:10.254182 25920 solver.cpp:218] Iteration 7356 (2.85011 iter/s, 4.21037s/12 iters), loss = 2.82491
I0410 02:44:10.254223 25920 solver.cpp:237]     Train net output #0: loss = 2.82491 (* 1 = 2.82491 loss)
I0410 02:44:10.254233 25920 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 02:44:15.276422 25920 solver.cpp:218] Iteration 7368 (2.38947 iter/s, 5.02203s/12 iters), loss = 2.96845
I0410 02:44:15.276470 25920 solver.cpp:237]     Train net output #0: loss = 2.96845 (* 1 = 2.96845 loss)
I0410 02:44:15.276480 25920 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 02:44:20.335014 25920 solver.cpp:218] Iteration 7380 (2.3723 iter/s, 5.05838s/12 iters), loss = 2.72627
I0410 02:44:20.335074 25920 solver.cpp:237]     Train net output #0: loss = 2.72627 (* 1 = 2.72627 loss)
I0410 02:44:20.335088 25920 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 02:44:21.717840 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:25.347489 25920 solver.cpp:218] Iteration 7392 (2.39413 iter/s, 5.01226s/12 iters), loss = 2.87777
I0410 02:44:25.347535 25920 solver.cpp:237]     Train net output #0: loss = 2.87777 (* 1 = 2.87777 loss)
I0410 02:44:25.347544 25920 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 02:44:30.439013 25920 solver.cpp:218] Iteration 7404 (2.35695 iter/s, 5.09132s/12 iters), loss = 2.69441
I0410 02:44:30.439076 25920 solver.cpp:237]     Train net output #0: loss = 2.69441 (* 1 = 2.69441 loss)
I0410 02:44:30.439090 25920 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 02:44:35.355432 25920 solver.cpp:218] Iteration 7416 (2.44091 iter/s, 4.9162s/12 iters), loss = 2.91561
I0410 02:44:35.355495 25920 solver.cpp:237]     Train net output #0: loss = 2.91561 (* 1 = 2.91561 loss)
I0410 02:44:35.355507 25920 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 02:44:40.276134 25920 solver.cpp:218] Iteration 7428 (2.43879 iter/s, 4.92048s/12 iters), loss = 3.06118
I0410 02:44:40.276289 25920 solver.cpp:237]     Train net output #0: loss = 3.06118 (* 1 = 3.06118 loss)
I0410 02:44:40.276304 25920 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 02:44:45.408706 25920 solver.cpp:218] Iteration 7440 (2.33815 iter/s, 5.13227s/12 iters), loss = 3.07815
I0410 02:44:45.408746 25920 solver.cpp:237]     Train net output #0: loss = 3.07815 (* 1 = 3.07815 loss)
I0410 02:44:45.408753 25920 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 02:44:47.421139 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 02:44:50.543568 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 02:44:53.287735 25920 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 02:44:53.287755 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:44:54.851883 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:57.768038 25920 solver.cpp:397]     Test net output #0: accuracy = 0.158701
I0410 02:44:57.768067 25920 solver.cpp:397]     Test net output #1: loss = 3.44566 (* 1 = 3.44566 loss)
I0410 02:44:59.536666 25920 solver.cpp:218] Iteration 7452 (0.849407 iter/s, 14.1275s/12 iters), loss = 2.91489
I0410 02:44:59.536708 25920 solver.cpp:237]     Train net output #0: loss = 2.91489 (* 1 = 2.91489 loss)
I0410 02:44:59.536717 25920 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 02:45:04.510428 25920 solver.cpp:218] Iteration 7464 (2.41276 iter/s, 4.97357s/12 iters), loss = 2.94016
I0410 02:45:04.510468 25920 solver.cpp:237]     Train net output #0: loss = 2.94016 (* 1 = 2.94016 loss)
I0410 02:45:04.510478 25920 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 02:45:09.518704 25920 solver.cpp:218] Iteration 7476 (2.39613 iter/s, 5.00808s/12 iters), loss = 2.83775
I0410 02:45:09.518754 25920 solver.cpp:237]     Train net output #0: loss = 2.83775 (* 1 = 2.83775 loss)
I0410 02:45:09.518766 25920 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 02:45:13.005518 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:14.489673 25920 solver.cpp:218] Iteration 7488 (2.41412 iter/s, 4.97076s/12 iters), loss = 2.84955
I0410 02:45:14.489720 25920 solver.cpp:237]     Train net output #0: loss = 2.84955 (* 1 = 2.84955 loss)
I0410 02:45:14.489732 25920 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 02:45:19.442553 25920 solver.cpp:218] Iteration 7500 (2.42293 iter/s, 4.95268s/12 iters), loss = 2.83474
I0410 02:45:19.442595 25920 solver.cpp:237]     Train net output #0: loss = 2.83474 (* 1 = 2.83474 loss)
I0410 02:45:19.442602 25920 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 02:45:24.494511 25920 solver.cpp:218] Iteration 7512 (2.37541 iter/s, 5.05176s/12 iters), loss = 2.77332
I0410 02:45:24.494560 25920 solver.cpp:237]     Train net output #0: loss = 2.77332 (* 1 = 2.77332 loss)
I0410 02:45:24.494570 25920 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 02:45:29.786190 25920 solver.cpp:218] Iteration 7524 (2.2678 iter/s, 5.29147s/12 iters), loss = 3.03635
I0410 02:45:29.786237 25920 solver.cpp:237]     Train net output #0: loss = 3.03635 (* 1 = 3.03635 loss)
I0410 02:45:29.786247 25920 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 02:45:34.956514 25920 solver.cpp:218] Iteration 7536 (2.32103 iter/s, 5.17011s/12 iters), loss = 2.7672
I0410 02:45:34.956560 25920 solver.cpp:237]     Train net output #0: loss = 2.7672 (* 1 = 2.7672 loss)
I0410 02:45:34.956570 25920 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 02:45:39.476173 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 02:45:41.554770 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 02:45:45.458513 25920 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 02:45:45.458650 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:45:46.963212 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:49.912250 25920 solver.cpp:397]     Test net output #0: accuracy = 0.169118
I0410 02:45:49.912298 25920 solver.cpp:397]     Test net output #1: loss = 3.2161 (* 1 = 3.2161 loss)
I0410 02:45:49.999609 25920 solver.cpp:218] Iteration 7548 (0.797734 iter/s, 15.0426s/12 iters), loss = 2.82431
I0410 02:45:49.999665 25920 solver.cpp:237]     Train net output #0: loss = 2.82431 (* 1 = 2.82431 loss)
I0410 02:45:49.999676 25920 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 02:45:54.281582 25920 solver.cpp:218] Iteration 7560 (2.80257 iter/s, 4.28178s/12 iters), loss = 3.06896
I0410 02:45:54.281634 25920 solver.cpp:237]     Train net output #0: loss = 3.06896 (* 1 = 3.06896 loss)
I0410 02:45:54.281646 25920 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 02:45:59.517370 25920 solver.cpp:218] Iteration 7572 (2.29202 iter/s, 5.23556s/12 iters), loss = 2.86536
I0410 02:45:59.517431 25920 solver.cpp:237]     Train net output #0: loss = 2.86536 (* 1 = 2.86536 loss)
I0410 02:45:59.517446 25920 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 02:46:04.449344 25920 solver.cpp:218] Iteration 7584 (2.43321 iter/s, 4.93176s/12 iters), loss = 2.88652
I0410 02:46:04.449390 25920 solver.cpp:237]     Train net output #0: loss = 2.88652 (* 1 = 2.88652 loss)
I0410 02:46:04.449400 25920 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 02:46:05.103206 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:09.404336 25920 solver.cpp:218] Iteration 7596 (2.4219 iter/s, 4.95479s/12 iters), loss = 2.91284
I0410 02:46:09.404393 25920 solver.cpp:237]     Train net output #0: loss = 2.91284 (* 1 = 2.91284 loss)
I0410 02:46:09.404407 25920 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 02:46:14.347806 25920 solver.cpp:218] Iteration 7608 (2.42755 iter/s, 4.94326s/12 iters), loss = 2.76554
I0410 02:46:14.347851 25920 solver.cpp:237]     Train net output #0: loss = 2.76554 (* 1 = 2.76554 loss)
I0410 02:46:14.347859 25920 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 02:46:19.254699 25920 solver.cpp:218] Iteration 7620 (2.44564 iter/s, 4.90669s/12 iters), loss = 2.7983
I0410 02:46:19.254813 25920 solver.cpp:237]     Train net output #0: loss = 2.7983 (* 1 = 2.7983 loss)
I0410 02:46:19.254825 25920 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 02:46:21.741214 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:46:24.334724 25920 solver.cpp:218] Iteration 7632 (2.36232 iter/s, 5.07976s/12 iters), loss = 2.75788
I0410 02:46:24.334769 25920 solver.cpp:237]     Train net output #0: loss = 2.75788 (* 1 = 2.75788 loss)
I0410 02:46:24.334780 25920 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 02:46:29.436199 25920 solver.cpp:218] Iteration 7644 (2.35236 iter/s, 5.10126s/12 iters), loss = 2.73365
I0410 02:46:29.436252 25920 solver.cpp:237]     Train net output #0: loss = 2.73365 (* 1 = 2.73365 loss)
I0410 02:46:29.436264 25920 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 02:46:31.497009 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 02:46:33.667361 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 02:46:35.052691 25920 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 02:46:35.052716 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:46:36.408274 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:39.954429 25920 solver.cpp:397]     Test net output #0: accuracy = 0.172794
I0410 02:46:39.954476 25920 solver.cpp:397]     Test net output #1: loss = 3.25785 (* 1 = 3.25785 loss)
I0410 02:46:41.746573 25920 solver.cpp:218] Iteration 7656 (0.97482 iter/s, 12.31s/12 iters), loss = 2.86938
I0410 02:46:41.746627 25920 solver.cpp:237]     Train net output #0: loss = 2.86938 (* 1 = 2.86938 loss)
I0410 02:46:41.746639 25920 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 02:46:46.590693 25920 solver.cpp:218] Iteration 7668 (2.47734 iter/s, 4.84391s/12 iters), loss = 2.7609
I0410 02:46:46.590750 25920 solver.cpp:237]     Train net output #0: loss = 2.7609 (* 1 = 2.7609 loss)
I0410 02:46:46.590761 25920 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 02:46:51.631418 25920 solver.cpp:218] Iteration 7680 (2.38071 iter/s, 5.04051s/12 iters), loss = 2.70375
I0410 02:46:51.631561 25920 solver.cpp:237]     Train net output #0: loss = 2.70375 (* 1 = 2.70375 loss)
I0410 02:46:51.631574 25920 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 02:46:54.439786 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:56.670352 25920 solver.cpp:218] Iteration 7692 (2.3816 iter/s, 5.03863s/12 iters), loss = 2.78221
I0410 02:46:56.670401 25920 solver.cpp:237]     Train net output #0: loss = 2.78221 (* 1 = 2.78221 loss)
I0410 02:46:56.670414 25920 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 02:47:01.719405 25920 solver.cpp:218] Iteration 7704 (2.37678 iter/s, 5.04884s/12 iters), loss = 3.04988
I0410 02:47:01.719460 25920 solver.cpp:237]     Train net output #0: loss = 3.04988 (* 1 = 3.04988 loss)
I0410 02:47:01.719471 25920 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 02:47:06.860059 25920 solver.cpp:218] Iteration 7716 (2.33443 iter/s, 5.14044s/12 iters), loss = 2.71235
I0410 02:47:06.860111 25920 solver.cpp:237]     Train net output #0: loss = 2.71235 (* 1 = 2.71235 loss)
I0410 02:47:06.860124 25920 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 02:47:12.024204 25920 solver.cpp:218] Iteration 7728 (2.32381 iter/s, 5.16393s/12 iters), loss = 2.98425
I0410 02:47:12.024262 25920 solver.cpp:237]     Train net output #0: loss = 2.98425 (* 1 = 2.98425 loss)
I0410 02:47:12.024274 25920 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 02:47:17.027377 25920 solver.cpp:218] Iteration 7740 (2.39858 iter/s, 5.00296s/12 iters), loss = 2.7618
I0410 02:47:17.027424 25920 solver.cpp:237]     Train net output #0: loss = 2.7618 (* 1 = 2.7618 loss)
I0410 02:47:17.027436 25920 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 02:47:21.769758 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 02:47:25.753407 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 02:47:28.667510 25920 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 02:47:28.667531 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:47:30.071581 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:33.103989 25920 solver.cpp:397]     Test net output #0: accuracy = 0.171569
I0410 02:47:33.104027 25920 solver.cpp:397]     Test net output #1: loss = 3.22413 (* 1 = 3.22413 loss)
I0410 02:47:33.190769 25920 solver.cpp:218] Iteration 7752 (0.742443 iter/s, 16.1629s/12 iters), loss = 2.73839
I0410 02:47:33.190821 25920 solver.cpp:237]     Train net output #0: loss = 2.73839 (* 1 = 2.73839 loss)
I0410 02:47:33.190834 25920 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 02:47:37.477526 25920 solver.cpp:218] Iteration 7764 (2.79944 iter/s, 4.28657s/12 iters), loss = 2.91049
I0410 02:47:37.477579 25920 solver.cpp:237]     Train net output #0: loss = 2.91049 (* 1 = 2.91049 loss)
I0410 02:47:37.477591 25920 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 02:47:42.478561 25920 solver.cpp:218] Iteration 7776 (2.3996 iter/s, 5.00083s/12 iters), loss = 2.8162
I0410 02:47:42.478598 25920 solver.cpp:237]     Train net output #0: loss = 2.8162 (* 1 = 2.8162 loss)
I0410 02:47:42.478606 25920 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 02:47:47.881729 25920 solver.cpp:218] Iteration 7788 (2.22101 iter/s, 5.40296s/12 iters), loss = 2.93986
I0410 02:47:47.881775 25920 solver.cpp:237]     Train net output #0: loss = 2.93986 (* 1 = 2.93986 loss)
I0410 02:47:47.881784 25920 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 02:47:47.894096 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:53.294865 25920 solver.cpp:218] Iteration 7800 (2.21692 iter/s, 5.41292s/12 iters), loss = 2.71552
I0410 02:47:53.295014 25920 solver.cpp:237]     Train net output #0: loss = 2.71552 (* 1 = 2.71552 loss)
I0410 02:47:53.295028 25920 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 02:47:58.270454 25920 solver.cpp:218] Iteration 7812 (2.41192 iter/s, 4.97529s/12 iters), loss = 2.56962
I0410 02:47:58.270503 25920 solver.cpp:237]     Train net output #0: loss = 2.56962 (* 1 = 2.56962 loss)
I0410 02:47:58.270514 25920 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 02:48:03.290525 25920 solver.cpp:218] Iteration 7824 (2.3905 iter/s, 5.01986s/12 iters), loss = 2.8205
I0410 02:48:03.290581 25920 solver.cpp:237]     Train net output #0: loss = 2.8205 (* 1 = 2.8205 loss)
I0410 02:48:03.290593 25920 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 02:48:08.297210 25920 solver.cpp:218] Iteration 7836 (2.3969 iter/s, 5.00647s/12 iters), loss = 2.6959
I0410 02:48:08.297264 25920 solver.cpp:237]     Train net output #0: loss = 2.6959 (* 1 = 2.6959 loss)
I0410 02:48:08.297276 25920 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 02:48:13.318550 25920 solver.cpp:218] Iteration 7848 (2.3899 iter/s, 5.02113s/12 iters), loss = 2.79028
I0410 02:48:13.318595 25920 solver.cpp:237]     Train net output #0: loss = 2.79028 (* 1 = 2.79028 loss)
I0410 02:48:13.318604 25920 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 02:48:15.336083 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 02:48:17.646538 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 02:48:19.912175 25920 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 02:48:19.912204 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:48:21.204751 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:24.295642 25920 solver.cpp:397]     Test net output #0: accuracy = 0.163603
I0410 02:48:24.295718 25920 solver.cpp:397]     Test net output #1: loss = 3.37657 (* 1 = 3.37657 loss)
I0410 02:48:26.109930 25920 solver.cpp:218] Iteration 7860 (0.938163 iter/s, 12.791s/12 iters), loss = 2.90565
I0410 02:48:26.110005 25920 solver.cpp:237]     Train net output #0: loss = 2.90565 (* 1 = 2.90565 loss)
I0410 02:48:26.110018 25920 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 02:48:31.337625 25920 solver.cpp:218] Iteration 7872 (2.29557 iter/s, 5.22746s/12 iters), loss = 2.64425
I0410 02:48:31.337666 25920 solver.cpp:237]     Train net output #0: loss = 2.64425 (* 1 = 2.64425 loss)
I0410 02:48:31.337677 25920 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 02:48:36.419629 25920 solver.cpp:218] Iteration 7884 (2.36136 iter/s, 5.08181s/12 iters), loss = 2.72282
I0410 02:48:36.419659 25920 solver.cpp:237]     Train net output #0: loss = 2.72282 (* 1 = 2.72282 loss)
I0410 02:48:36.419667 25920 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 02:48:38.587889 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:41.484028 25920 solver.cpp:218] Iteration 7896 (2.36957 iter/s, 5.06421s/12 iters), loss = 2.71413
I0410 02:48:41.484074 25920 solver.cpp:237]     Train net output #0: loss = 2.71413 (* 1 = 2.71413 loss)
I0410 02:48:41.484083 25920 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 02:48:46.557794 25920 solver.cpp:218] Iteration 7908 (2.3652 iter/s, 5.07356s/12 iters), loss = 2.83557
I0410 02:48:46.557842 25920 solver.cpp:237]     Train net output #0: loss = 2.83557 (* 1 = 2.83557 loss)
I0410 02:48:46.557853 25920 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 02:48:51.610977 25920 solver.cpp:218] Iteration 7920 (2.37484 iter/s, 5.05298s/12 iters), loss = 2.63684
I0410 02:48:51.611032 25920 solver.cpp:237]     Train net output #0: loss = 2.63684 (* 1 = 2.63684 loss)
I0410 02:48:51.611042 25920 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 02:48:56.703305 25920 solver.cpp:218] Iteration 7932 (2.35658 iter/s, 5.09212s/12 iters), loss = 2.61537
I0410 02:48:56.703421 25920 solver.cpp:237]     Train net output #0: loss = 2.61537 (* 1 = 2.61537 loss)
I0410 02:48:56.703430 25920 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 02:49:01.700592 25920 solver.cpp:218] Iteration 7944 (2.40144 iter/s, 4.99701s/12 iters), loss = 2.71061
I0410 02:49:01.700650 25920 solver.cpp:237]     Train net output #0: loss = 2.71061 (* 1 = 2.71061 loss)
I0410 02:49:01.700662 25920 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 02:49:06.362637 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 02:49:08.165421 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 02:49:09.549829 25920 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 02:49:09.549860 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:49:10.780038 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:49:13.894618 25920 solver.cpp:397]     Test net output #0: accuracy = 0.184436
I0410 02:49:13.894659 25920 solver.cpp:397]     Test net output #1: loss = 3.27434 (* 1 = 3.27434 loss)
I0410 02:49:13.982038 25920 solver.cpp:218] Iteration 7956 (0.977117 iter/s, 12.281s/12 iters), loss = 2.93969
I0410 02:49:13.982087 25920 solver.cpp:237]     Train net output #0: loss = 2.93969 (* 1 = 2.93969 loss)
I0410 02:49:13.982098 25920 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 02:49:18.236315 25920 solver.cpp:218] Iteration 7968 (2.82081 iter/s, 4.25409s/12 iters), loss = 2.59338
I0410 02:49:18.236356 25920 solver.cpp:237]     Train net output #0: loss = 2.59338 (* 1 = 2.59338 loss)
I0410 02:49:18.236363 25920 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 02:49:23.305066 25920 solver.cpp:218] Iteration 7980 (2.36754 iter/s, 5.06855s/12 iters), loss = 2.68965
I0410 02:49:23.305121 25920 solver.cpp:237]     Train net output #0: loss = 2.68965 (* 1 = 2.68965 loss)
I0410 02:49:23.305135 25920 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 02:49:27.685016 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:49:28.426143 25920 solver.cpp:218] Iteration 7992 (2.34336 iter/s, 5.12086s/12 iters), loss = 2.80039
I0410 02:49:28.426199 25920 solver.cpp:237]     Train net output #0: loss = 2.80039 (* 1 = 2.80039 loss)
I0410 02:49:28.426213 25920 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 02:49:33.427210 25920 solver.cpp:218] Iteration 8004 (2.39959 iter/s, 5.00086s/12 iters), loss = 2.62644
I0410 02:49:33.427258 25920 solver.cpp:237]     Train net output #0: loss = 2.62644 (* 1 = 2.62644 loss)
I0410 02:49:33.427268 25920 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 02:49:38.536895 25920 solver.cpp:218] Iteration 8016 (2.34858 iter/s, 5.10947s/12 iters), loss = 2.76969
I0410 02:49:38.536947 25920 solver.cpp:237]     Train net output #0: loss = 2.76969 (* 1 = 2.76969 loss)
I0410 02:49:38.536959 25920 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 02:49:43.507302 25920 solver.cpp:218] Iteration 8028 (2.41439 iter/s, 4.9702s/12 iters), loss = 2.56363
I0410 02:49:43.507350 25920 solver.cpp:237]     Train net output #0: loss = 2.56363 (* 1 = 2.56363 loss)
I0410 02:49:43.507361 25920 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 02:49:48.517400 25920 solver.cpp:218] Iteration 8040 (2.39526 iter/s, 5.00989s/12 iters), loss = 2.76303
I0410 02:49:48.517455 25920 solver.cpp:237]     Train net output #0: loss = 2.76303 (* 1 = 2.76303 loss)
I0410 02:49:48.517467 25920 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 02:49:53.552597 25920 solver.cpp:218] Iteration 8052 (2.38333 iter/s, 5.03498s/12 iters), loss = 2.59143
I0410 02:49:53.552654 25920 solver.cpp:237]     Train net output #0: loss = 2.59143 (* 1 = 2.59143 loss)
I0410 02:49:53.552667 25920 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 02:49:55.609069 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 02:49:58.680953 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 02:50:01.452069 25920 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 02:50:01.452095 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:50:02.753945 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:05.911862 25920 solver.cpp:397]     Test net output #0: accuracy = 0.170956
I0410 02:50:05.911900 25920 solver.cpp:397]     Test net output #1: loss = 3.24312 (* 1 = 3.24312 loss)
I0410 02:50:07.788379 25920 solver.cpp:218] Iteration 8064 (0.842974 iter/s, 14.2353s/12 iters), loss = 2.78616
I0410 02:50:07.788424 25920 solver.cpp:237]     Train net output #0: loss = 2.78616 (* 1 = 2.78616 loss)
I0410 02:50:07.788434 25920 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 02:50:12.745738 25920 solver.cpp:218] Iteration 8076 (2.42075 iter/s, 4.95715s/12 iters), loss = 2.74197
I0410 02:50:12.745792 25920 solver.cpp:237]     Train net output #0: loss = 2.74197 (* 1 = 2.74197 loss)
I0410 02:50:12.745805 25920 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 02:50:17.793414 25920 solver.cpp:218] Iteration 8088 (2.37743 iter/s, 5.04746s/12 iters), loss = 2.47386
I0410 02:50:17.793457 25920 solver.cpp:237]     Train net output #0: loss = 2.47386 (* 1 = 2.47386 loss)
I0410 02:50:17.793467 25920 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 02:50:19.183743 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:22.746968 25920 solver.cpp:218] Iteration 8100 (2.4226 iter/s, 4.95335s/12 iters), loss = 2.62892
I0410 02:50:22.747009 25920 solver.cpp:237]     Train net output #0: loss = 2.62892 (* 1 = 2.62892 loss)
I0410 02:50:22.747016 25920 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 02:50:27.696851 25920 solver.cpp:218] Iteration 8112 (2.4244 iter/s, 4.94968s/12 iters), loss = 2.61957
I0410 02:50:27.696900 25920 solver.cpp:237]     Train net output #0: loss = 2.61957 (* 1 = 2.61957 loss)
I0410 02:50:27.696911 25920 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 02:50:32.860822 25920 solver.cpp:218] Iteration 8124 (2.32389 iter/s, 5.16376s/12 iters), loss = 2.77943
I0410 02:50:32.860937 25920 solver.cpp:237]     Train net output #0: loss = 2.77943 (* 1 = 2.77943 loss)
I0410 02:50:32.860952 25920 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 02:50:37.894989 25920 solver.cpp:218] Iteration 8136 (2.38384 iter/s, 5.0339s/12 iters), loss = 2.63413
I0410 02:50:37.895038 25920 solver.cpp:237]     Train net output #0: loss = 2.63413 (* 1 = 2.63413 loss)
I0410 02:50:37.895051 25920 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 02:50:42.955129 25920 solver.cpp:218] Iteration 8148 (2.37157 iter/s, 5.05994s/12 iters), loss = 2.76646
I0410 02:50:42.955171 25920 solver.cpp:237]     Train net output #0: loss = 2.76646 (* 1 = 2.76646 loss)
I0410 02:50:42.955180 25920 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 02:50:47.467494 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 02:50:49.279153 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 02:50:50.651257 25920 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 02:50:50.651278 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:50:51.911518 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:55.285492 25920 solver.cpp:397]     Test net output #0: accuracy = 0.191789
I0410 02:50:55.285537 25920 solver.cpp:397]     Test net output #1: loss = 3.11819 (* 1 = 3.11819 loss)
I0410 02:50:55.375344 25920 solver.cpp:218] Iteration 8160 (0.966199 iter/s, 12.4198s/12 iters), loss = 2.43885
I0410 02:50:55.375397 25920 solver.cpp:237]     Train net output #0: loss = 2.43885 (* 1 = 2.43885 loss)
I0410 02:50:55.375409 25920 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 02:50:59.927260 25920 solver.cpp:218] Iteration 8172 (2.63637 iter/s, 4.55171s/12 iters), loss = 2.49975
I0410 02:50:59.927318 25920 solver.cpp:237]     Train net output #0: loss = 2.49975 (* 1 = 2.49975 loss)
I0410 02:50:59.927330 25920 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 02:51:05.192071 25920 solver.cpp:218] Iteration 8184 (2.27938 iter/s, 5.26459s/12 iters), loss = 2.45766
I0410 02:51:05.192239 25920 solver.cpp:237]     Train net output #0: loss = 2.45766 (* 1 = 2.45766 loss)
I0410 02:51:05.192252 25920 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 02:51:08.710090 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:10.158596 25920 solver.cpp:218] Iteration 8196 (2.41633 iter/s, 4.96621s/12 iters), loss = 2.63276
I0410 02:51:10.158648 25920 solver.cpp:237]     Train net output #0: loss = 2.63276 (* 1 = 2.63276 loss)
I0410 02:51:10.158659 25920 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 02:51:15.121860 25920 solver.cpp:218] Iteration 8208 (2.41786 iter/s, 4.96306s/12 iters), loss = 2.62911
I0410 02:51:15.121904 25920 solver.cpp:237]     Train net output #0: loss = 2.62911 (* 1 = 2.62911 loss)
I0410 02:51:15.121914 25920 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 02:51:20.034447 25920 solver.cpp:218] Iteration 8220 (2.4428 iter/s, 4.91239s/12 iters), loss = 2.49441
I0410 02:51:20.034500 25920 solver.cpp:237]     Train net output #0: loss = 2.49441 (* 1 = 2.49441 loss)
I0410 02:51:20.034512 25920 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 02:51:24.942679 25920 solver.cpp:218] Iteration 8232 (2.44498 iter/s, 4.90802s/12 iters), loss = 2.41197
I0410 02:51:24.942735 25920 solver.cpp:237]     Train net output #0: loss = 2.41197 (* 1 = 2.41197 loss)
I0410 02:51:24.942747 25920 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 02:51:29.891031 25920 solver.cpp:218] Iteration 8244 (2.42516 iter/s, 4.94813s/12 iters), loss = 2.54402
I0410 02:51:29.891090 25920 solver.cpp:237]     Train net output #0: loss = 2.54402 (* 1 = 2.54402 loss)
I0410 02:51:29.891101 25920 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 02:51:34.812755 25920 solver.cpp:218] Iteration 8256 (2.43828 iter/s, 4.92151s/12 iters), loss = 2.66722
I0410 02:51:34.812817 25920 solver.cpp:237]     Train net output #0: loss = 2.66722 (* 1 = 2.66722 loss)
I0410 02:51:34.812829 25920 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 02:51:36.801563 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 02:51:38.605003 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 02:51:39.988759 25920 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 02:51:39.988788 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:51:41.194475 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:44.488111 25920 solver.cpp:397]     Test net output #0: accuracy = 0.186887
I0410 02:51:44.488162 25920 solver.cpp:397]     Test net output #1: loss = 3.13766 (* 1 = 3.13766 loss)
I0410 02:51:46.460023 25920 solver.cpp:218] Iteration 8268 (1.03032 iter/s, 11.6469s/12 iters), loss = 2.7004
I0410 02:51:46.460074 25920 solver.cpp:237]     Train net output #0: loss = 2.7004 (* 1 = 2.7004 loss)
I0410 02:51:46.460085 25920 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 02:51:51.419903 25920 solver.cpp:218] Iteration 8280 (2.41951 iter/s, 4.95967s/12 iters), loss = 2.73157
I0410 02:51:51.419941 25920 solver.cpp:237]     Train net output #0: loss = 2.73157 (* 1 = 2.73157 loss)
I0410 02:51:51.419950 25920 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 02:51:56.429863 25920 solver.cpp:218] Iteration 8292 (2.39532 iter/s, 5.00976s/12 iters), loss = 2.51509
I0410 02:51:56.429919 25920 solver.cpp:237]     Train net output #0: loss = 2.51509 (* 1 = 2.51509 loss)
I0410 02:51:56.429930 25920 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 02:51:57.068045 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:01.398010 25920 solver.cpp:218] Iteration 8304 (2.41549 iter/s, 4.96794s/12 iters), loss = 2.52732
I0410 02:52:01.398064 25920 solver.cpp:237]     Train net output #0: loss = 2.52732 (* 1 = 2.52732 loss)
I0410 02:52:01.398078 25920 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 02:52:04.271947 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:52:06.583781 25920 solver.cpp:218] Iteration 8316 (2.31412 iter/s, 5.18556s/12 iters), loss = 2.81017
I0410 02:52:06.583828 25920 solver.cpp:237]     Train net output #0: loss = 2.81017 (* 1 = 2.81017 loss)
I0410 02:52:06.583838 25920 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 02:52:11.621153 25920 solver.cpp:218] Iteration 8328 (2.3823 iter/s, 5.03716s/12 iters), loss = 2.51986
I0410 02:52:11.621316 25920 solver.cpp:237]     Train net output #0: loss = 2.51986 (* 1 = 2.51986 loss)
I0410 02:52:11.621331 25920 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 02:52:16.520865 25920 solver.cpp:218] Iteration 8340 (2.44928 iter/s, 4.8994s/12 iters), loss = 2.37848
I0410 02:52:16.520913 25920 solver.cpp:237]     Train net output #0: loss = 2.37848 (* 1 = 2.37848 loss)
I0410 02:52:16.520923 25920 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 02:52:21.539497 25920 solver.cpp:218] Iteration 8352 (2.39119 iter/s, 5.01843s/12 iters), loss = 2.5566
I0410 02:52:21.539551 25920 solver.cpp:237]     Train net output #0: loss = 2.5566 (* 1 = 2.5566 loss)
I0410 02:52:21.539561 25920 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 02:52:26.011546 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 02:52:32.026203 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 02:52:36.104461 25920 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 02:52:36.104490 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:52:37.160713 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:40.452965 25920 solver.cpp:397]     Test net output #0: accuracy = 0.193627
I0410 02:52:40.453008 25920 solver.cpp:397]     Test net output #1: loss = 3.13533 (* 1 = 3.13533 loss)
I0410 02:52:40.540254 25920 solver.cpp:218] Iteration 8364 (0.631574 iter/s, 19.0002s/12 iters), loss = 2.68683
I0410 02:52:40.540293 25920 solver.cpp:237]     Train net output #0: loss = 2.68683 (* 1 = 2.68683 loss)
I0410 02:52:40.540303 25920 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 02:52:44.839625 25920 solver.cpp:218] Iteration 8376 (2.79122 iter/s, 4.29919s/12 iters), loss = 2.38184
I0410 02:52:44.841416 25920 solver.cpp:237]     Train net output #0: loss = 2.38184 (* 1 = 2.38184 loss)
I0410 02:52:44.841432 25920 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 02:52:49.900724 25920 solver.cpp:218] Iteration 8388 (2.37194 iter/s, 5.05915s/12 iters), loss = 2.36722
I0410 02:52:49.900781 25920 solver.cpp:237]     Train net output #0: loss = 2.36722 (* 1 = 2.36722 loss)
I0410 02:52:49.900792 25920 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 02:52:52.794152 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:55.039806 25920 solver.cpp:218] Iteration 8400 (2.33515 iter/s, 5.13886s/12 iters), loss = 2.37109
I0410 02:52:55.039847 25920 solver.cpp:237]     Train net output #0: loss = 2.37109 (* 1 = 2.37109 loss)
I0410 02:52:55.039855 25920 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 02:53:00.133328 25920 solver.cpp:218] Iteration 8412 (2.35603 iter/s, 5.09332s/12 iters), loss = 2.37188
I0410 02:53:00.133373 25920 solver.cpp:237]     Train net output #0: loss = 2.37188 (* 1 = 2.37188 loss)
I0410 02:53:00.133383 25920 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 02:53:05.329608 25920 solver.cpp:218] Iteration 8424 (2.30944 iter/s, 5.19607s/12 iters), loss = 2.47387
I0410 02:53:05.329646 25920 solver.cpp:237]     Train net output #0: loss = 2.47387 (* 1 = 2.47387 loss)
I0410 02:53:05.329656 25920 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 02:53:10.285540 25920 solver.cpp:218] Iteration 8436 (2.42143 iter/s, 4.95574s/12 iters), loss = 2.62247
I0410 02:53:10.285581 25920 solver.cpp:237]     Train net output #0: loss = 2.62247 (* 1 = 2.62247 loss)
I0410 02:53:10.285590 25920 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 02:53:15.335639 25920 solver.cpp:218] Iteration 8448 (2.37629 iter/s, 5.0499s/12 iters), loss = 2.43245
I0410 02:53:15.335777 25920 solver.cpp:237]     Train net output #0: loss = 2.43245 (* 1 = 2.43245 loss)
I0410 02:53:15.335786 25920 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 02:53:20.319986 25920 solver.cpp:218] Iteration 8460 (2.40768 iter/s, 4.98405s/12 iters), loss = 2.41212
I0410 02:53:20.320037 25920 solver.cpp:237]     Train net output #0: loss = 2.41212 (* 1 = 2.41212 loss)
I0410 02:53:20.320050 25920 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 02:53:22.325019 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 02:53:25.314672 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 02:53:27.954249 25920 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 02:53:27.954279 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:53:29.101265 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:53:32.407676 25920 solver.cpp:397]     Test net output #0: accuracy = 0.191789
I0410 02:53:32.407723 25920 solver.cpp:397]     Test net output #1: loss = 3.20009 (* 1 = 3.20009 loss)
I0410 02:53:34.641903 25920 solver.cpp:218] Iteration 8472 (0.837905 iter/s, 14.3214s/12 iters), loss = 2.40302
I0410 02:53:34.641990 25920 solver.cpp:237]     Train net output #0: loss = 2.40302 (* 1 = 2.40302 loss)
I0410 02:53:34.642004 25920 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 02:53:39.736750 25920 solver.cpp:218] Iteration 8484 (2.35542 iter/s, 5.09463s/12 iters), loss = 2.34616
I0410 02:53:39.736804 25920 solver.cpp:237]     Train net output #0: loss = 2.34616 (* 1 = 2.34616 loss)
I0410 02:53:39.736816 25920 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 02:53:44.692427 25920 solver.cpp:218] Iteration 8496 (2.42157 iter/s, 4.95547s/12 iters), loss = 2.41557
I0410 02:53:44.692479 25920 solver.cpp:237]     Train net output #0: loss = 2.41557 (* 1 = 2.41557 loss)
I0410 02:53:44.692492 25920 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 02:53:44.740799 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:53:49.683044 25920 solver.cpp:218] Iteration 8508 (2.40461 iter/s, 4.9904s/12 iters), loss = 2.64859
I0410 02:53:49.683146 25920 solver.cpp:237]     Train net output #0: loss = 2.64859 (* 1 = 2.64859 loss)
I0410 02:53:49.683157 25920 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 02:53:54.649552 25920 solver.cpp:218] Iteration 8520 (2.41631 iter/s, 4.96625s/12 iters), loss = 2.47494
I0410 02:53:54.649596 25920 solver.cpp:237]     Train net output #0: loss = 2.47494 (* 1 = 2.47494 loss)
I0410 02:53:54.649606 25920 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 02:53:59.626619 25920 solver.cpp:218] Iteration 8532 (2.41116 iter/s, 4.97686s/12 iters), loss = 2.42514
I0410 02:53:59.626680 25920 solver.cpp:237]     Train net output #0: loss = 2.42514 (* 1 = 2.42514 loss)
I0410 02:53:59.626693 25920 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 02:54:04.548662 25920 solver.cpp:218] Iteration 8544 (2.43812 iter/s, 4.92183s/12 iters), loss = 2.40116
I0410 02:54:04.548713 25920 solver.cpp:237]     Train net output #0: loss = 2.40116 (* 1 = 2.40116 loss)
I0410 02:54:04.548724 25920 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 02:54:09.864742 25920 solver.cpp:218] Iteration 8556 (2.2574 iter/s, 5.31586s/12 iters), loss = 2.60494
I0410 02:54:09.864799 25920 solver.cpp:237]     Train net output #0: loss = 2.60494 (* 1 = 2.60494 loss)
I0410 02:54:09.864814 25920 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 02:54:14.377446 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 02:54:16.561794 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 02:54:18.362164 25920 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 02:54:18.362198 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:54:19.449941 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:22.979594 25920 solver.cpp:397]     Test net output #0: accuracy = 0.210784
I0410 02:54:22.979728 25920 solver.cpp:397]     Test net output #1: loss = 3.08777 (* 1 = 3.08777 loss)
I0410 02:54:23.068226 25920 solver.cpp:218] Iteration 8568 (0.908882 iter/s, 13.203s/12 iters), loss = 2.47636
I0410 02:54:23.068276 25920 solver.cpp:237]     Train net output #0: loss = 2.47636 (* 1 = 2.47636 loss)
I0410 02:54:23.068289 25920 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 02:54:27.700264 25920 solver.cpp:218] Iteration 8580 (2.59076 iter/s, 4.63184s/12 iters), loss = 2.39116
I0410 02:54:27.700316 25920 solver.cpp:237]     Train net output #0: loss = 2.39116 (* 1 = 2.39116 loss)
I0410 02:54:27.700330 25920 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 02:54:32.811133 25920 solver.cpp:218] Iteration 8592 (2.34804 iter/s, 5.11066s/12 iters), loss = 2.34371
I0410 02:54:32.811182 25920 solver.cpp:237]     Train net output #0: loss = 2.34371 (* 1 = 2.34371 loss)
I0410 02:54:32.811192 25920 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 02:54:34.988839 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:37.822057 25920 solver.cpp:218] Iteration 8604 (2.39487 iter/s, 5.01071s/12 iters), loss = 2.33724
I0410 02:54:37.822110 25920 solver.cpp:237]     Train net output #0: loss = 2.33724 (* 1 = 2.33724 loss)
I0410 02:54:37.822124 25920 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 02:54:42.831243 25920 solver.cpp:218] Iteration 8616 (2.3957 iter/s, 5.00898s/12 iters), loss = 2.30775
I0410 02:54:42.831287 25920 solver.cpp:237]     Train net output #0: loss = 2.30775 (* 1 = 2.30775 loss)
I0410 02:54:42.831297 25920 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 02:54:47.758055 25920 solver.cpp:218] Iteration 8628 (2.43575 iter/s, 4.92661s/12 iters), loss = 2.54675
I0410 02:54:47.758112 25920 solver.cpp:237]     Train net output #0: loss = 2.54675 (* 1 = 2.54675 loss)
I0410 02:54:47.758123 25920 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 02:54:52.687516 25920 solver.cpp:218] Iteration 8640 (2.43445 iter/s, 4.92925s/12 iters), loss = 2.4949
I0410 02:54:52.687570 25920 solver.cpp:237]     Train net output #0: loss = 2.4949 (* 1 = 2.4949 loss)
I0410 02:54:52.687583 25920 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 02:54:57.643467 25920 solver.cpp:218] Iteration 8652 (2.42143 iter/s, 4.95574s/12 iters), loss = 2.42983
I0410 02:54:57.643589 25920 solver.cpp:237]     Train net output #0: loss = 2.42983 (* 1 = 2.42983 loss)
I0410 02:54:57.643602 25920 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 02:55:02.648916 25920 solver.cpp:218] Iteration 8664 (2.39752 iter/s, 5.00518s/12 iters), loss = 2.3761
I0410 02:55:02.648958 25920 solver.cpp:237]     Train net output #0: loss = 2.3761 (* 1 = 2.3761 loss)
I0410 02:55:02.648969 25920 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 02:55:04.688153 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 02:55:08.005519 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 02:55:11.188410 25920 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 02:55:11.188429 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:55:12.331727 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:15.875938 25920 solver.cpp:397]     Test net output #0: accuracy = 0.219363
I0410 02:55:15.875985 25920 solver.cpp:397]     Test net output #1: loss = 3.1643 (* 1 = 3.1643 loss)
I0410 02:55:17.855998 25920 solver.cpp:218] Iteration 8676 (0.789132 iter/s, 15.2066s/12 iters), loss = 2.28282
I0410 02:55:17.856058 25920 solver.cpp:237]     Train net output #0: loss = 2.28282 (* 1 = 2.28282 loss)
I0410 02:55:17.856070 25920 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 02:55:22.850736 25920 solver.cpp:218] Iteration 8688 (2.40263 iter/s, 4.99452s/12 iters), loss = 2.40718
I0410 02:55:22.850790 25920 solver.cpp:237]     Train net output #0: loss = 2.40718 (* 1 = 2.40718 loss)
I0410 02:55:22.850802 25920 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 02:55:27.170966 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:27.856659 25920 solver.cpp:218] Iteration 8700 (2.39726 iter/s, 5.00571s/12 iters), loss = 2.41018
I0410 02:55:27.856810 25920 solver.cpp:237]     Train net output #0: loss = 2.41018 (* 1 = 2.41018 loss)
I0410 02:55:27.856824 25920 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 02:55:32.901898 25920 solver.cpp:218] Iteration 8712 (2.37863 iter/s, 5.04493s/12 iters), loss = 2.3291
I0410 02:55:32.901971 25920 solver.cpp:237]     Train net output #0: loss = 2.3291 (* 1 = 2.3291 loss)
I0410 02:55:32.901984 25920 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 02:55:37.915705 25920 solver.cpp:218] Iteration 8724 (2.39349 iter/s, 5.01359s/12 iters), loss = 2.59231
I0410 02:55:37.915758 25920 solver.cpp:237]     Train net output #0: loss = 2.59231 (* 1 = 2.59231 loss)
I0410 02:55:37.915771 25920 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 02:55:42.825932 25920 solver.cpp:218] Iteration 8736 (2.44399 iter/s, 4.91001s/12 iters), loss = 2.31292
I0410 02:55:42.826000 25920 solver.cpp:237]     Train net output #0: loss = 2.31292 (* 1 = 2.31292 loss)
I0410 02:55:42.826012 25920 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 02:55:47.798518 25920 solver.cpp:218] Iteration 8748 (2.41334 iter/s, 4.97236s/12 iters), loss = 2.36193
I0410 02:55:47.798573 25920 solver.cpp:237]     Train net output #0: loss = 2.36193 (* 1 = 2.36193 loss)
I0410 02:55:47.798584 25920 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 02:55:52.669857 25920 solver.cpp:218] Iteration 8760 (2.46349 iter/s, 4.87113s/12 iters), loss = 2.29693
I0410 02:55:52.669909 25920 solver.cpp:237]     Train net output #0: loss = 2.29693 (* 1 = 2.29693 loss)
I0410 02:55:52.669919 25920 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 02:55:57.401330 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 02:55:59.240561 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 02:56:01.268054 25920 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 02:56:01.268077 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:56:02.241806 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:05.678853 25920 solver.cpp:397]     Test net output #0: accuracy = 0.226103
I0410 02:56:05.678889 25920 solver.cpp:397]     Test net output #1: loss = 3.10934 (* 1 = 3.10934 loss)
I0410 02:56:05.766176 25920 solver.cpp:218] Iteration 8772 (0.916319 iter/s, 13.0959s/12 iters), loss = 2.45877
I0410 02:56:05.766235 25920 solver.cpp:237]     Train net output #0: loss = 2.45877 (* 1 = 2.45877 loss)
I0410 02:56:05.766247 25920 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 02:56:09.907330 25920 solver.cpp:218] Iteration 8784 (2.89788 iter/s, 4.14096s/12 iters), loss = 2.22228
I0410 02:56:09.907388 25920 solver.cpp:237]     Train net output #0: loss = 2.22228 (* 1 = 2.22228 loss)
I0410 02:56:09.907399 25920 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 02:56:14.978242 25920 solver.cpp:218] Iteration 8796 (2.36654 iter/s, 5.0707s/12 iters), loss = 2.21506
I0410 02:56:14.978281 25920 solver.cpp:237]     Train net output #0: loss = 2.21506 (* 1 = 2.21506 loss)
I0410 02:56:14.978289 25920 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 02:56:16.409543 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:19.926440 25920 solver.cpp:218] Iteration 8808 (2.42522 iter/s, 4.948s/12 iters), loss = 2.22544
I0410 02:56:19.926492 25920 solver.cpp:237]     Train net output #0: loss = 2.22544 (* 1 = 2.22544 loss)
I0410 02:56:19.926506 25920 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 02:56:25.044159 25920 solver.cpp:218] Iteration 8820 (2.34489 iter/s, 5.11751s/12 iters), loss = 2.28891
I0410 02:56:25.044206 25920 solver.cpp:237]     Train net output #0: loss = 2.28891 (* 1 = 2.28891 loss)
I0410 02:56:25.044215 25920 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 02:56:29.936257 25920 solver.cpp:218] Iteration 8832 (2.45304 iter/s, 4.89189s/12 iters), loss = 2.45794
I0410 02:56:29.936401 25920 solver.cpp:237]     Train net output #0: loss = 2.45794 (* 1 = 2.45794 loss)
I0410 02:56:29.936414 25920 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 02:56:34.891623 25920 solver.cpp:218] Iteration 8844 (2.42176 iter/s, 4.95507s/12 iters), loss = 2.3807
I0410 02:56:34.891670 25920 solver.cpp:237]     Train net output #0: loss = 2.3807 (* 1 = 2.3807 loss)
I0410 02:56:34.891680 25920 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 02:56:39.893649 25920 solver.cpp:218] Iteration 8856 (2.39913 iter/s, 5.00182s/12 iters), loss = 2.22431
I0410 02:56:39.893692 25920 solver.cpp:237]     Train net output #0: loss = 2.22431 (* 1 = 2.22431 loss)
I0410 02:56:39.893700 25920 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 02:56:44.878649 25920 solver.cpp:218] Iteration 8868 (2.40732 iter/s, 4.9848s/12 iters), loss = 2.13446
I0410 02:56:44.878703 25920 solver.cpp:237]     Train net output #0: loss = 2.13446 (* 1 = 2.13446 loss)
I0410 02:56:44.878715 25920 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 02:56:46.933462 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 02:56:48.701126 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 02:56:50.134421 25920 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 02:56:50.134449 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:56:50.998041 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:54.614133 25920 solver.cpp:397]     Test net output #0: accuracy = 0.226716
I0410 02:56:54.614171 25920 solver.cpp:397]     Test net output #1: loss = 3.05941 (* 1 = 3.05941 loss)
I0410 02:56:56.506004 25920 solver.cpp:218] Iteration 8880 (1.03209 iter/s, 11.6269s/12 iters), loss = 2.29721
I0410 02:56:56.506078 25920 solver.cpp:237]     Train net output #0: loss = 2.29721 (* 1 = 2.29721 loss)
I0410 02:56:56.506093 25920 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 02:57:01.484388 25920 solver.cpp:218] Iteration 8892 (2.41053 iter/s, 4.97816s/12 iters), loss = 2.16039
I0410 02:57:01.484499 25920 solver.cpp:237]     Train net output #0: loss = 2.16039 (* 1 = 2.16039 loss)
I0410 02:57:01.484509 25920 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 02:57:05.023188 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:57:06.418092 25920 solver.cpp:218] Iteration 8904 (2.43238 iter/s, 4.93344s/12 iters), loss = 2.24794
I0410 02:57:06.418141 25920 solver.cpp:237]     Train net output #0: loss = 2.24794 (* 1 = 2.24794 loss)
I0410 02:57:06.418151 25920 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 02:57:11.401623 25920 solver.cpp:218] Iteration 8916 (2.40803 iter/s, 4.98333s/12 iters), loss = 2.30267
I0410 02:57:11.401669 25920 solver.cpp:237]     Train net output #0: loss = 2.30267 (* 1 = 2.30267 loss)
I0410 02:57:11.401679 25920 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 02:57:16.401867 25920 solver.cpp:218] Iteration 8928 (2.39998 iter/s, 5.00004s/12 iters), loss = 2.72663
I0410 02:57:16.401909 25920 solver.cpp:237]     Train net output #0: loss = 2.72663 (* 1 = 2.72663 loss)
I0410 02:57:16.401919 25920 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 02:57:21.602399 25920 solver.cpp:218] Iteration 8940 (2.30755 iter/s, 5.20033s/12 iters), loss = 2.38348
I0410 02:57:21.602447 25920 solver.cpp:237]     Train net output #0: loss = 2.38348 (* 1 = 2.38348 loss)
I0410 02:57:21.602456 25920 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 02:57:26.708329 25920 solver.cpp:218] Iteration 8952 (2.35031 iter/s, 5.10572s/12 iters), loss = 2.20188
I0410 02:57:26.708374 25920 solver.cpp:237]     Train net output #0: loss = 2.20188 (* 1 = 2.20188 loss)
I0410 02:57:26.708384 25920 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 02:57:31.785892 25920 solver.cpp:218] Iteration 8964 (2.36343 iter/s, 5.07736s/12 iters), loss = 2.07825
I0410 02:57:31.786015 25920 solver.cpp:237]     Train net output #0: loss = 2.07825 (* 1 = 2.07825 loss)
I0410 02:57:31.786023 25920 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 02:57:36.310967 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 02:57:42.746847 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 02:57:47.499369 25920 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 02:57:47.499397 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:57:48.459842 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:57:51.963585 25920 solver.cpp:397]     Test net output #0: accuracy = 0.255515
I0410 02:57:51.963635 25920 solver.cpp:397]     Test net output #1: loss = 2.9367 (* 1 = 2.9367 loss)
I0410 02:57:52.051236 25920 solver.cpp:218] Iteration 8976 (0.592165 iter/s, 20.2646s/12 iters), loss = 2.31635
I0410 02:57:52.051287 25920 solver.cpp:237]     Train net output #0: loss = 2.31635 (* 1 = 2.31635 loss)
I0410 02:57:52.051301 25920 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 02:57:56.552132 25920 solver.cpp:218] Iteration 8988 (2.66625 iter/s, 4.5007s/12 iters), loss = 1.94718
I0410 02:57:56.552191 25920 solver.cpp:237]     Train net output #0: loss = 1.94718 (* 1 = 1.94718 loss)
I0410 02:57:56.552203 25920 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 02:57:59.783063 25920 blocking_queue.cpp:49] Waiting for data
I0410 02:58:01.459715 25920 solver.cpp:218] Iteration 9000 (2.4453 iter/s, 4.90736s/12 iters), loss = 2.16726
I0410 02:58:01.459779 25920 solver.cpp:237]     Train net output #0: loss = 2.16726 (* 1 = 2.16726 loss)
I0410 02:58:01.459795 25920 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 02:58:02.137756 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:06.468219 25920 solver.cpp:218] Iteration 9012 (2.39603 iter/s, 5.00828s/12 iters), loss = 2.11206
I0410 02:58:06.468268 25920 solver.cpp:237]     Train net output #0: loss = 2.11206 (* 1 = 2.11206 loss)
I0410 02:58:06.468279 25920 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 02:58:11.552759 25920 solver.cpp:218] Iteration 9024 (2.36019 iter/s, 5.08433s/12 iters), loss = 2.2476
I0410 02:58:11.552807 25920 solver.cpp:237]     Train net output #0: loss = 2.2476 (* 1 = 2.2476 loss)
I0410 02:58:11.552816 25920 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 02:58:16.481866 25920 solver.cpp:218] Iteration 9036 (2.43462 iter/s, 4.9289s/12 iters), loss = 2.20616
I0410 02:58:16.481918 25920 solver.cpp:237]     Train net output #0: loss = 2.20616 (* 1 = 2.20616 loss)
I0410 02:58:16.481930 25920 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 02:58:21.479988 25920 solver.cpp:218] Iteration 9048 (2.401 iter/s, 4.99791s/12 iters), loss = 2.24188
I0410 02:58:21.480041 25920 solver.cpp:237]     Train net output #0: loss = 2.24188 (* 1 = 2.24188 loss)
I0410 02:58:21.480051 25920 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 02:58:26.620910 25920 solver.cpp:218] Iteration 9060 (2.33431 iter/s, 5.1407s/12 iters), loss = 2.47169
I0410 02:58:26.620960 25920 solver.cpp:237]     Train net output #0: loss = 2.47169 (* 1 = 2.47169 loss)
I0410 02:58:26.620973 25920 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 02:58:31.580449 25920 solver.cpp:218] Iteration 9072 (2.41968 iter/s, 4.95933s/12 iters), loss = 2.24098
I0410 02:58:31.580502 25920 solver.cpp:237]     Train net output #0: loss = 2.24098 (* 1 = 2.24098 loss)
I0410 02:58:31.580515 25920 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 02:58:33.632351 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 02:58:38.001431 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 02:58:42.721765 25920 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 02:58:42.721791 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:58:43.609534 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:47.158113 25920 solver.cpp:397]     Test net output #0: accuracy = 0.247549
I0410 02:58:47.158160 25920 solver.cpp:397]     Test net output #1: loss = 2.96451 (* 1 = 2.96451 loss)
I0410 02:58:49.133221 25920 solver.cpp:218] Iteration 9084 (0.683675 iter/s, 17.5522s/12 iters), loss = 2.02015
I0410 02:58:49.133270 25920 solver.cpp:237]     Train net output #0: loss = 2.02015 (* 1 = 2.02015 loss)
I0410 02:58:49.133280 25920 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 02:58:54.316864 25920 solver.cpp:218] Iteration 9096 (2.31507 iter/s, 5.18343s/12 iters), loss = 2.08841
I0410 02:58:54.316922 25920 solver.cpp:237]     Train net output #0: loss = 2.08841 (* 1 = 2.08841 loss)
I0410 02:58:54.316933 25920 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 02:58:57.205719 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:59.250679 25920 solver.cpp:218] Iteration 9108 (2.4323 iter/s, 4.9336s/12 iters), loss = 2.02578
I0410 02:58:59.250735 25920 solver.cpp:237]     Train net output #0: loss = 2.02578 (* 1 = 2.02578 loss)
I0410 02:58:59.250747 25920 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 02:59:04.244418 25920 solver.cpp:218] Iteration 9120 (2.40311 iter/s, 4.99353s/12 iters), loss = 2.42932
I0410 02:59:04.244498 25920 solver.cpp:237]     Train net output #0: loss = 2.42932 (* 1 = 2.42932 loss)
I0410 02:59:04.244510 25920 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 02:59:09.236089 25920 solver.cpp:218] Iteration 9132 (2.40412 iter/s, 4.99144s/12 iters), loss = 2.41863
I0410 02:59:09.236142 25920 solver.cpp:237]     Train net output #0: loss = 2.41863 (* 1 = 2.41863 loss)
I0410 02:59:09.236155 25920 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 02:59:14.238976 25920 solver.cpp:218] Iteration 9144 (2.39872 iter/s, 5.00267s/12 iters), loss = 2.25878
I0410 02:59:14.239034 25920 solver.cpp:237]     Train net output #0: loss = 2.25878 (* 1 = 2.25878 loss)
I0410 02:59:14.239049 25920 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 02:59:19.238680 25920 solver.cpp:218] Iteration 9156 (2.40025 iter/s, 4.99948s/12 iters), loss = 2.17075
I0410 02:59:19.238744 25920 solver.cpp:237]     Train net output #0: loss = 2.17075 (* 1 = 2.17075 loss)
I0410 02:59:19.238756 25920 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 02:59:24.234767 25920 solver.cpp:218] Iteration 9168 (2.40199 iter/s, 4.99587s/12 iters), loss = 2.2283
I0410 02:59:24.234817 25920 solver.cpp:237]     Train net output #0: loss = 2.2283 (* 1 = 2.2283 loss)
I0410 02:59:24.234828 25920 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 02:59:29.021857 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 02:59:31.510238 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 02:59:33.990383 25920 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 02:59:33.990403 25920 net.cpp:676] Ignoring source layer train-data
I0410 02:59:34.931957 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:38.648013 25920 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0410 02:59:38.648056 25920 solver.cpp:397]     Test net output #1: loss = 2.96722 (* 1 = 2.96722 loss)
I0410 02:59:38.735363 25920 solver.cpp:218] Iteration 9180 (0.82758 iter/s, 14.5001s/12 iters), loss = 2.20937
I0410 02:59:38.735419 25920 solver.cpp:237]     Train net output #0: loss = 2.20937 (* 1 = 2.20937 loss)
I0410 02:59:38.735432 25920 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 02:59:43.083389 25920 solver.cpp:218] Iteration 9192 (2.76 iter/s, 4.34783s/12 iters), loss = 2.23483
I0410 02:59:43.083438 25920 solver.cpp:237]     Train net output #0: loss = 2.23483 (* 1 = 2.23483 loss)
I0410 02:59:43.083447 25920 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 02:59:48.075639 25920 solver.cpp:218] Iteration 9204 (2.40383 iter/s, 4.99204s/12 iters), loss = 2.27348
I0410 02:59:48.075682 25920 solver.cpp:237]     Train net output #0: loss = 2.27348 (* 1 = 2.27348 loss)
I0410 02:59:48.075691 25920 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 02:59:48.143419 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:53.384733 25920 solver.cpp:218] Iteration 9216 (2.26036 iter/s, 5.30888s/12 iters), loss = 2.40421
I0410 02:59:53.384791 25920 solver.cpp:237]     Train net output #0: loss = 2.40421 (* 1 = 2.40421 loss)
I0410 02:59:53.384804 25920 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 02:59:58.538971 25920 solver.cpp:218] Iteration 9228 (2.32828 iter/s, 5.15402s/12 iters), loss = 2.47844
I0410 02:59:58.539026 25920 solver.cpp:237]     Train net output #0: loss = 2.47844 (* 1 = 2.47844 loss)
I0410 02:59:58.539038 25920 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 03:00:03.539528 25920 solver.cpp:218] Iteration 9240 (2.39983 iter/s, 5.00035s/12 iters), loss = 2.31679
I0410 03:00:03.539583 25920 solver.cpp:237]     Train net output #0: loss = 2.31679 (* 1 = 2.31679 loss)
I0410 03:00:03.539594 25920 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 03:00:08.484104 25920 solver.cpp:218] Iteration 9252 (2.42701 iter/s, 4.94436s/12 iters), loss = 2.08309
I0410 03:00:08.484223 25920 solver.cpp:237]     Train net output #0: loss = 2.08309 (* 1 = 2.08309 loss)
I0410 03:00:08.484236 25920 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 03:00:13.411527 25920 solver.cpp:218] Iteration 9264 (2.43548 iter/s, 4.92715s/12 iters), loss = 2.0709
I0410 03:00:13.411583 25920 solver.cpp:237]     Train net output #0: loss = 2.0709 (* 1 = 2.0709 loss)
I0410 03:00:13.411597 25920 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 03:00:18.386817 25920 solver.cpp:218] Iteration 9276 (2.41202 iter/s, 4.97507s/12 iters), loss = 2.21925
I0410 03:00:18.386875 25920 solver.cpp:237]     Train net output #0: loss = 2.21925 (* 1 = 2.21925 loss)
I0410 03:00:18.386888 25920 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 03:00:20.454993 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 03:00:22.205921 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 03:00:23.583623 25920 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 03:00:23.583657 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:00:24.349192 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:28.115929 25920 solver.cpp:397]     Test net output #0: accuracy = 0.246936
I0410 03:00:28.115974 25920 solver.cpp:397]     Test net output #1: loss = 2.94203 (* 1 = 2.94203 loss)
I0410 03:00:29.930263 25920 solver.cpp:218] Iteration 9288 (1.03959 iter/s, 11.543s/12 iters), loss = 2.16823
I0410 03:00:29.930306 25920 solver.cpp:237]     Train net output #0: loss = 2.16823 (* 1 = 2.16823 loss)
I0410 03:00:29.930316 25920 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 03:00:34.941710 25920 solver.cpp:218] Iteration 9300 (2.39462 iter/s, 5.01124s/12 iters), loss = 2.05997
I0410 03:00:34.941756 25920 solver.cpp:237]     Train net output #0: loss = 2.05997 (* 1 = 2.05997 loss)
I0410 03:00:34.941766 25920 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 03:00:37.141392 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:39.955404 25920 solver.cpp:218] Iteration 9312 (2.39354 iter/s, 5.01349s/12 iters), loss = 2.02353
I0410 03:00:39.955549 25920 solver.cpp:237]     Train net output #0: loss = 2.02353 (* 1 = 2.02353 loss)
I0410 03:00:39.955559 25920 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 03:00:45.071192 25920 solver.cpp:218] Iteration 9324 (2.34582 iter/s, 5.11548s/12 iters), loss = 1.93581
I0410 03:00:45.071257 25920 solver.cpp:237]     Train net output #0: loss = 1.93581 (* 1 = 1.93581 loss)
I0410 03:00:45.071270 25920 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 03:00:50.010777 25920 solver.cpp:218] Iteration 9336 (2.42946 iter/s, 4.93936s/12 iters), loss = 2.05842
I0410 03:00:50.010828 25920 solver.cpp:237]     Train net output #0: loss = 2.05842 (* 1 = 2.05842 loss)
I0410 03:00:50.010838 25920 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 03:00:54.920596 25920 solver.cpp:218] Iteration 9348 (2.44418 iter/s, 4.90962s/12 iters), loss = 2.23195
I0410 03:00:54.920644 25920 solver.cpp:237]     Train net output #0: loss = 2.23195 (* 1 = 2.23195 loss)
I0410 03:00:54.920653 25920 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 03:01:00.053256 25920 solver.cpp:218] Iteration 9360 (2.33806 iter/s, 5.13245s/12 iters), loss = 2.12982
I0410 03:01:00.053305 25920 solver.cpp:237]     Train net output #0: loss = 2.12982 (* 1 = 2.12982 loss)
I0410 03:01:00.053316 25920 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 03:01:05.009469 25920 solver.cpp:218] Iteration 9372 (2.4213 iter/s, 4.95601s/12 iters), loss = 2.03431
I0410 03:01:05.009529 25920 solver.cpp:237]     Train net output #0: loss = 2.03431 (* 1 = 2.03431 loss)
I0410 03:01:05.009542 25920 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 03:01:09.483306 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 03:01:12.648627 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 03:01:15.384595 25920 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 03:01:15.384624 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:01:16.171043 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:20.105808 25920 solver.cpp:397]     Test net output #0: accuracy = 0.245098
I0410 03:01:20.105876 25920 solver.cpp:397]     Test net output #1: loss = 2.8821 (* 1 = 2.8821 loss)
I0410 03:01:20.193351 25920 solver.cpp:218] Iteration 9384 (0.790338 iter/s, 15.1834s/12 iters), loss = 2.02487
I0410 03:01:20.193408 25920 solver.cpp:237]     Train net output #0: loss = 2.02487 (* 1 = 2.02487 loss)
I0410 03:01:20.193419 25920 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 03:01:24.653647 25920 solver.cpp:218] Iteration 9396 (2.69052 iter/s, 4.4601s/12 iters), loss = 1.98829
I0410 03:01:24.653692 25920 solver.cpp:237]     Train net output #0: loss = 1.98829 (* 1 = 1.98829 loss)
I0410 03:01:24.653705 25920 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 03:01:29.098074 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:29.752233 25920 solver.cpp:218] Iteration 9408 (2.35369 iter/s, 5.09838s/12 iters), loss = 2.09932
I0410 03:01:29.752279 25920 solver.cpp:237]     Train net output #0: loss = 2.09932 (* 1 = 2.09932 loss)
I0410 03:01:29.752291 25920 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 03:01:34.664264 25920 solver.cpp:218] Iteration 9420 (2.44308 iter/s, 4.91183s/12 iters), loss = 2.04107
I0410 03:01:34.664314 25920 solver.cpp:237]     Train net output #0: loss = 2.04107 (* 1 = 2.04107 loss)
I0410 03:01:34.664326 25920 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 03:01:39.623400 25920 solver.cpp:218] Iteration 9432 (2.41988 iter/s, 4.95893s/12 iters), loss = 2.17601
I0410 03:01:39.623461 25920 solver.cpp:237]     Train net output #0: loss = 2.17601 (* 1 = 2.17601 loss)
I0410 03:01:39.623472 25920 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 03:01:44.551187 25920 solver.cpp:218] Iteration 9444 (2.43528 iter/s, 4.92757s/12 iters), loss = 2.01834
I0410 03:01:44.551316 25920 solver.cpp:237]     Train net output #0: loss = 2.01834 (* 1 = 2.01834 loss)
I0410 03:01:44.551326 25920 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 03:01:49.624513 25920 solver.cpp:218] Iteration 9456 (2.36545 iter/s, 5.07304s/12 iters), loss = 2.13001
I0410 03:01:49.624555 25920 solver.cpp:237]     Train net output #0: loss = 2.13001 (* 1 = 2.13001 loss)
I0410 03:01:49.624564 25920 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 03:01:54.639851 25920 solver.cpp:218] Iteration 9468 (2.39276 iter/s, 5.01513s/12 iters), loss = 2.15068
I0410 03:01:54.639902 25920 solver.cpp:237]     Train net output #0: loss = 2.15068 (* 1 = 2.15068 loss)
I0410 03:01:54.639912 25920 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 03:01:59.677635 25920 solver.cpp:218] Iteration 9480 (2.3821 iter/s, 5.03757s/12 iters), loss = 1.89626
I0410 03:01:59.677685 25920 solver.cpp:237]     Train net output #0: loss = 1.89626 (* 1 = 1.89626 loss)
I0410 03:01:59.677695 25920 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 03:02:01.712944 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 03:02:03.519733 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 03:02:04.881589 25920 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 03:02:04.881613 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:02:05.626008 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:02:09.486405 25920 solver.cpp:397]     Test net output #0: accuracy = 0.257353
I0410 03:02:09.486455 25920 solver.cpp:397]     Test net output #1: loss = 2.93481 (* 1 = 2.93481 loss)
I0410 03:02:11.446439 25920 solver.cpp:218] Iteration 9492 (1.01968 iter/s, 11.7684s/12 iters), loss = 1.98305
I0410 03:02:11.446491 25920 solver.cpp:237]     Train net output #0: loss = 1.98305 (* 1 = 1.98305 loss)
I0410 03:02:11.446501 25920 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 03:02:16.631099 25920 solver.cpp:218] Iteration 9504 (2.31462 iter/s, 5.18445s/12 iters), loss = 1.83331
I0410 03:02:16.631189 25920 solver.cpp:237]     Train net output #0: loss = 1.83331 (* 1 = 1.83331 loss)
I0410 03:02:16.631199 25920 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 03:02:18.083305 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:02:21.617354 25920 solver.cpp:218] Iteration 9516 (2.40673 iter/s, 4.98602s/12 iters), loss = 1.90356
I0410 03:02:21.617393 25920 solver.cpp:237]     Train net output #0: loss = 1.90356 (* 1 = 1.90356 loss)
I0410 03:02:21.617401 25920 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 03:02:26.620082 25920 solver.cpp:218] Iteration 9528 (2.39879 iter/s, 5.00253s/12 iters), loss = 2.22595
I0410 03:02:26.620133 25920 solver.cpp:237]     Train net output #0: loss = 2.22595 (* 1 = 2.22595 loss)
I0410 03:02:26.620144 25920 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 03:02:31.601537 25920 solver.cpp:218] Iteration 9540 (2.40903 iter/s, 4.98125s/12 iters), loss = 2.11796
I0410 03:02:31.601585 25920 solver.cpp:237]     Train net output #0: loss = 2.11796 (* 1 = 2.11796 loss)
I0410 03:02:31.601594 25920 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 03:02:36.716154 25920 solver.cpp:218] Iteration 9552 (2.34631 iter/s, 5.11441s/12 iters), loss = 1.94065
I0410 03:02:36.716205 25920 solver.cpp:237]     Train net output #0: loss = 1.94065 (* 1 = 1.94065 loss)
I0410 03:02:36.716218 25920 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 03:02:41.642704 25920 solver.cpp:218] Iteration 9564 (2.43588 iter/s, 4.92634s/12 iters), loss = 1.91567
I0410 03:02:41.642755 25920 solver.cpp:237]     Train net output #0: loss = 1.91567 (* 1 = 1.91567 loss)
I0410 03:02:41.642766 25920 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 03:02:46.624876 25920 solver.cpp:218] Iteration 9576 (2.40869 iter/s, 4.98197s/12 iters), loss = 1.97528
I0410 03:02:46.624927 25920 solver.cpp:237]     Train net output #0: loss = 1.97528 (* 1 = 1.97528 loss)
I0410 03:02:46.624938 25920 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 03:02:51.200625 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 03:02:56.379017 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 03:02:59.338685 25920 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 03:02:59.338706 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:03:00.036062 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:03.796540 25920 solver.cpp:397]     Test net output #0: accuracy = 0.253064
I0410 03:03:03.796591 25920 solver.cpp:397]     Test net output #1: loss = 2.861 (* 1 = 2.861 loss)
I0410 03:03:03.884279 25920 solver.cpp:218] Iteration 9588 (0.695295 iter/s, 17.2589s/12 iters), loss = 1.94445
I0410 03:03:03.884331 25920 solver.cpp:237]     Train net output #0: loss = 1.94445 (* 1 = 1.94445 loss)
I0410 03:03:03.884343 25920 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 03:03:08.349164 25920 solver.cpp:218] Iteration 9600 (2.68775 iter/s, 4.46469s/12 iters), loss = 1.85281
I0410 03:03:08.349207 25920 solver.cpp:237]     Train net output #0: loss = 1.85281 (* 1 = 1.85281 loss)
I0410 03:03:08.349216 25920 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 03:03:11.970769 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:13.354616 25920 solver.cpp:218] Iteration 9612 (2.39748 iter/s, 5.00525s/12 iters), loss = 2.14673
I0410 03:03:13.354665 25920 solver.cpp:237]     Train net output #0: loss = 2.14673 (* 1 = 2.14673 loss)
I0410 03:03:13.354674 25920 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 03:03:18.319438 25920 solver.cpp:218] Iteration 9624 (2.4171 iter/s, 4.96462s/12 iters), loss = 1.97901
I0410 03:03:18.319491 25920 solver.cpp:237]     Train net output #0: loss = 1.97901 (* 1 = 1.97901 loss)
I0410 03:03:18.319502 25920 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 03:03:23.150525 25920 solver.cpp:218] Iteration 9636 (2.48402 iter/s, 4.83088s/12 iters), loss = 2.02219
I0410 03:03:23.150631 25920 solver.cpp:237]     Train net output #0: loss = 2.02219 (* 1 = 2.02219 loss)
I0410 03:03:23.150645 25920 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 03:03:28.147709 25920 solver.cpp:218] Iteration 9648 (2.40148 iter/s, 4.99692s/12 iters), loss = 1.99302
I0410 03:03:28.147758 25920 solver.cpp:237]     Train net output #0: loss = 1.99302 (* 1 = 1.99302 loss)
I0410 03:03:28.147768 25920 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 03:03:33.081236 25920 solver.cpp:218] Iteration 9660 (2.43243 iter/s, 4.93333s/12 iters), loss = 1.88911
I0410 03:03:33.081279 25920 solver.cpp:237]     Train net output #0: loss = 1.88911 (* 1 = 1.88911 loss)
I0410 03:03:33.081287 25920 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 03:03:38.109746 25920 solver.cpp:218] Iteration 9672 (2.38649 iter/s, 5.02831s/12 iters), loss = 1.91655
I0410 03:03:38.109789 25920 solver.cpp:237]     Train net output #0: loss = 1.91655 (* 1 = 1.91655 loss)
I0410 03:03:38.109799 25920 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 03:03:43.190846 25920 solver.cpp:218] Iteration 9684 (2.36179 iter/s, 5.08089s/12 iters), loss = 1.89532
I0410 03:03:43.190891 25920 solver.cpp:237]     Train net output #0: loss = 1.89532 (* 1 = 1.89532 loss)
I0410 03:03:43.190899 25920 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 03:03:45.262925 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 03:03:49.381243 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 03:03:51.032913 25920 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 03:03:51.032943 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:03:51.658309 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:54.525743 25920 blocking_queue.cpp:49] Waiting for data
I0410 03:03:55.510238 25920 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0410 03:03:55.510282 25920 solver.cpp:397]     Test net output #1: loss = 2.8575 (* 1 = 2.8575 loss)
I0410 03:03:57.236166 25920 solver.cpp:218] Iteration 9696 (0.854405 iter/s, 14.0449s/12 iters), loss = 1.69589
I0410 03:03:57.236212 25920 solver.cpp:237]     Train net output #0: loss = 1.69589 (* 1 = 1.69589 loss)
I0410 03:03:57.236222 25920 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 03:04:02.105283 25920 solver.cpp:218] Iteration 9708 (2.46461 iter/s, 4.86892s/12 iters), loss = 1.90568
I0410 03:04:02.105340 25920 solver.cpp:237]     Train net output #0: loss = 1.90568 (* 1 = 1.90568 loss)
I0410 03:04:02.105352 25920 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 03:04:02.834748 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:07.069289 25920 solver.cpp:218] Iteration 9720 (2.41751 iter/s, 4.96379s/12 iters), loss = 1.86224
I0410 03:04:07.069347 25920 solver.cpp:237]     Train net output #0: loss = 1.86224 (* 1 = 1.86224 loss)
I0410 03:04:07.069358 25920 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 03:04:12.011622 25920 solver.cpp:218] Iteration 9732 (2.42811 iter/s, 4.94212s/12 iters), loss = 2.12626
I0410 03:04:12.011677 25920 solver.cpp:237]     Train net output #0: loss = 2.12626 (* 1 = 2.12626 loss)
I0410 03:04:12.011687 25920 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 03:04:17.054054 25920 solver.cpp:218] Iteration 9744 (2.3799 iter/s, 5.04222s/12 iters), loss = 1.86419
I0410 03:04:17.054093 25920 solver.cpp:237]     Train net output #0: loss = 1.86419 (* 1 = 1.86419 loss)
I0410 03:04:17.054101 25920 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 03:04:22.038759 25920 solver.cpp:218] Iteration 9756 (2.40746 iter/s, 4.98452s/12 iters), loss = 2.04739
I0410 03:04:22.038792 25920 solver.cpp:237]     Train net output #0: loss = 2.04739 (* 1 = 2.04739 loss)
I0410 03:04:22.038801 25920 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 03:04:27.090464 25920 solver.cpp:218] Iteration 9768 (2.37553 iter/s, 5.05151s/12 iters), loss = 2.05036
I0410 03:04:27.090531 25920 solver.cpp:237]     Train net output #0: loss = 2.05036 (* 1 = 2.05036 loss)
I0410 03:04:27.090540 25920 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 03:04:32.594262 25920 solver.cpp:218] Iteration 9780 (2.18041 iter/s, 5.50356s/12 iters), loss = 1.90337
I0410 03:04:32.594323 25920 solver.cpp:237]     Train net output #0: loss = 1.90337 (* 1 = 1.90337 loss)
I0410 03:04:32.594341 25920 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 03:04:37.216048 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 03:04:41.531808 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 03:04:43.059067 25920 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 03:04:43.059088 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:04:43.605170 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:47.640290 25920 solver.cpp:397]     Test net output #0: accuracy = 0.264093
I0410 03:04:47.640349 25920 solver.cpp:397]     Test net output #1: loss = 2.9333 (* 1 = 2.9333 loss)
I0410 03:04:47.727826 25920 solver.cpp:218] Iteration 9792 (0.792966 iter/s, 15.1331s/12 iters), loss = 1.81485
I0410 03:04:47.727880 25920 solver.cpp:237]     Train net output #0: loss = 1.81485 (* 1 = 1.81485 loss)
I0410 03:04:47.727891 25920 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 03:04:52.025504 25920 solver.cpp:218] Iteration 9804 (2.79233 iter/s, 4.29748s/12 iters), loss = 1.84199
I0410 03:04:52.025547 25920 solver.cpp:237]     Train net output #0: loss = 1.84199 (* 1 = 1.84199 loss)
I0410 03:04:52.025554 25920 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 03:04:55.007912 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:57.029165 25920 solver.cpp:218] Iteration 9816 (2.39834 iter/s, 5.00346s/12 iters), loss = 1.81668
I0410 03:04:57.029213 25920 solver.cpp:237]     Train net output #0: loss = 1.81668 (* 1 = 1.81668 loss)
I0410 03:04:57.029223 25920 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 03:05:02.012454 25920 solver.cpp:218] Iteration 9828 (2.40815 iter/s, 4.98308s/12 iters), loss = 1.85992
I0410 03:05:02.012637 25920 solver.cpp:237]     Train net output #0: loss = 1.85992 (* 1 = 1.85992 loss)
I0410 03:05:02.012655 25920 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 03:05:07.328538 25920 solver.cpp:218] Iteration 9840 (2.25745 iter/s, 5.31574s/12 iters), loss = 1.94333
I0410 03:05:07.328589 25920 solver.cpp:237]     Train net output #0: loss = 1.94333 (* 1 = 1.94333 loss)
I0410 03:05:07.328601 25920 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 03:05:12.371323 25920 solver.cpp:218] Iteration 9852 (2.37974 iter/s, 5.04257s/12 iters), loss = 1.99728
I0410 03:05:12.371378 25920 solver.cpp:237]     Train net output #0: loss = 1.99728 (* 1 = 1.99728 loss)
I0410 03:05:12.371390 25920 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 03:05:17.346419 25920 solver.cpp:218] Iteration 9864 (2.41212 iter/s, 4.97489s/12 iters), loss = 2.11901
I0410 03:05:17.346462 25920 solver.cpp:237]     Train net output #0: loss = 2.11901 (* 1 = 2.11901 loss)
I0410 03:05:17.346472 25920 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 03:05:22.302724 25920 solver.cpp:218] Iteration 9876 (2.42126 iter/s, 4.9561s/12 iters), loss = 1.94212
I0410 03:05:22.302783 25920 solver.cpp:237]     Train net output #0: loss = 1.94212 (* 1 = 1.94212 loss)
I0410 03:05:22.302795 25920 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 03:05:27.270479 25920 solver.cpp:218] Iteration 9888 (2.41568 iter/s, 4.96754s/12 iters), loss = 2.11115
I0410 03:05:27.270539 25920 solver.cpp:237]     Train net output #0: loss = 2.11115 (* 1 = 2.11115 loss)
I0410 03:05:27.270551 25920 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 03:05:29.284126 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 03:05:31.602010 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 03:05:33.349268 25920 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 03:05:33.349336 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:05:33.943648 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:37.899583 25920 solver.cpp:397]     Test net output #0: accuracy = 0.262868
I0410 03:05:37.899632 25920 solver.cpp:397]     Test net output #1: loss = 2.95647 (* 1 = 2.95647 loss)
I0410 03:05:39.817715 25920 solver.cpp:218] Iteration 9900 (0.956419 iter/s, 12.5468s/12 iters), loss = 1.93941
I0410 03:05:39.817766 25920 solver.cpp:237]     Train net output #0: loss = 1.93941 (* 1 = 1.93941 loss)
I0410 03:05:39.817775 25920 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 03:05:44.793442 25920 solver.cpp:218] Iteration 9912 (2.41181 iter/s, 4.97552s/12 iters), loss = 2.05867
I0410 03:05:44.793491 25920 solver.cpp:237]     Train net output #0: loss = 2.05867 (* 1 = 2.05867 loss)
I0410 03:05:44.793502 25920 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 03:05:44.904901 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:49.873603 25920 solver.cpp:218] Iteration 9924 (2.36223 iter/s, 5.07995s/12 iters), loss = 1.94933
I0410 03:05:49.873654 25920 solver.cpp:237]     Train net output #0: loss = 1.94933 (* 1 = 1.94933 loss)
I0410 03:05:49.873665 25920 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 03:05:54.916921 25920 solver.cpp:218] Iteration 9936 (2.37948 iter/s, 5.04311s/12 iters), loss = 1.75816
I0410 03:05:54.916966 25920 solver.cpp:237]     Train net output #0: loss = 1.75816 (* 1 = 1.75816 loss)
I0410 03:05:54.916975 25920 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 03:05:59.959638 25920 solver.cpp:218] Iteration 9948 (2.37976 iter/s, 5.04252s/12 iters), loss = 1.81396
I0410 03:05:59.959688 25920 solver.cpp:237]     Train net output #0: loss = 1.81396 (* 1 = 1.81396 loss)
I0410 03:05:59.959702 25920 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 03:06:05.139024 25920 solver.cpp:218] Iteration 9960 (2.31697 iter/s, 5.17918s/12 iters), loss = 1.92748
I0410 03:06:05.139117 25920 solver.cpp:237]     Train net output #0: loss = 1.92748 (* 1 = 1.92748 loss)
I0410 03:06:05.139127 25920 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 03:06:10.197274 25920 solver.cpp:218] Iteration 9972 (2.37248 iter/s, 5.058s/12 iters), loss = 1.89143
I0410 03:06:10.197319 25920 solver.cpp:237]     Train net output #0: loss = 1.89143 (* 1 = 1.89143 loss)
I0410 03:06:10.197329 25920 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 03:06:15.287572 25920 solver.cpp:218] Iteration 9984 (2.35752 iter/s, 5.0901s/12 iters), loss = 1.88099
I0410 03:06:15.287612 25920 solver.cpp:237]     Train net output #0: loss = 1.88099 (* 1 = 1.88099 loss)
I0410 03:06:15.287621 25920 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 03:06:19.835120 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 03:06:23.723815 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 03:06:26.471769 25920 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 03:06:26.471799 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:06:26.948621 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:31.154983 25920 solver.cpp:397]     Test net output #0: accuracy = 0.265319
I0410 03:06:31.155010 25920 solver.cpp:397]     Test net output #1: loss = 2.91111 (* 1 = 2.91111 loss)
I0410 03:06:31.242122 25920 solver.cpp:218] Iteration 9996 (0.752161 iter/s, 15.954s/12 iters), loss = 1.92531
I0410 03:06:31.242179 25920 solver.cpp:237]     Train net output #0: loss = 1.92531 (* 1 = 1.92531 loss)
I0410 03:06:31.242190 25920 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 03:06:35.852578 25920 solver.cpp:218] Iteration 10008 (2.60289 iter/s, 4.61025s/12 iters), loss = 1.5359
I0410 03:06:35.852722 25920 solver.cpp:237]     Train net output #0: loss = 1.5359 (* 1 = 1.5359 loss)
I0410 03:06:35.852742 25920 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 03:06:38.094020 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:40.830790 25920 solver.cpp:218] Iteration 10020 (2.41064 iter/s, 4.97792s/12 iters), loss = 1.86457
I0410 03:06:40.830842 25920 solver.cpp:237]     Train net output #0: loss = 1.86457 (* 1 = 1.86457 loss)
I0410 03:06:40.830854 25920 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 03:06:45.849467 25920 solver.cpp:218] Iteration 10032 (2.39117 iter/s, 5.01847s/12 iters), loss = 1.67762
I0410 03:06:45.849509 25920 solver.cpp:237]     Train net output #0: loss = 1.67762 (* 1 = 1.67762 loss)
I0410 03:06:45.849517 25920 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 03:06:50.885993 25920 solver.cpp:218] Iteration 10044 (2.3827 iter/s, 5.03631s/12 iters), loss = 1.70211
I0410 03:06:50.886039 25920 solver.cpp:237]     Train net output #0: loss = 1.70211 (* 1 = 1.70211 loss)
I0410 03:06:50.886050 25920 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 03:06:55.901883 25920 solver.cpp:218] Iteration 10056 (2.3925 iter/s, 5.01568s/12 iters), loss = 1.87471
I0410 03:06:55.901937 25920 solver.cpp:237]     Train net output #0: loss = 1.87471 (* 1 = 1.87471 loss)
I0410 03:06:55.901947 25920 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 03:07:00.923406 25920 solver.cpp:218] Iteration 10068 (2.38981 iter/s, 5.02131s/12 iters), loss = 1.89423
I0410 03:07:00.923458 25920 solver.cpp:237]     Train net output #0: loss = 1.89423 (* 1 = 1.89423 loss)
I0410 03:07:00.923470 25920 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 03:07:05.874181 25920 solver.cpp:218] Iteration 10080 (2.42397 iter/s, 4.95056s/12 iters), loss = 1.73891
I0410 03:07:05.874331 25920 solver.cpp:237]     Train net output #0: loss = 1.73891 (* 1 = 1.73891 loss)
I0410 03:07:05.874341 25920 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 03:07:11.218111 25920 solver.cpp:218] Iteration 10092 (2.24567 iter/s, 5.34361s/12 iters), loss = 2.06888
I0410 03:07:11.218165 25920 solver.cpp:237]     Train net output #0: loss = 2.06888 (* 1 = 2.06888 loss)
I0410 03:07:11.218175 25920 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 03:07:13.289170 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 03:07:15.103670 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 03:07:16.479063 25920 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 03:07:16.479086 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:07:16.961362 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:07:21.021416 25920 solver.cpp:397]     Test net output #0: accuracy = 0.28125
I0410 03:07:21.021452 25920 solver.cpp:397]     Test net output #1: loss = 2.9773 (* 1 = 2.9773 loss)
I0410 03:07:22.968751 25920 solver.cpp:218] Iteration 10104 (1.02126 iter/s, 11.7502s/12 iters), loss = 1.85471
I0410 03:07:22.968801 25920 solver.cpp:237]     Train net output #0: loss = 1.85471 (* 1 = 1.85471 loss)
I0410 03:07:22.968811 25920 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 03:07:27.435566 25924 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:07:28.074191 25920 solver.cpp:218] Iteration 10116 (2.35053 iter/s, 5.10522s/12 iters), loss = 1.90938
I0410 03:07:28.074247 25920 solver.cpp:237]     Train net output #0: loss = 1.90938 (* 1 = 1.90938 loss)
I0410 03:07:28.074260 25920 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 03:07:33.126543 25920 solver.cpp:218] Iteration 10128 (2.37523 iter/s, 5.05213s/12 iters), loss = 1.86382
I0410 03:07:33.126600 25920 solver.cpp:237]     Train net output #0: loss = 1.86382 (* 1 = 1.86382 loss)
I0410 03:07:33.126613 25920 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 03:07:38.130167 25920 solver.cpp:218] Iteration 10140 (2.39837 iter/s, 5.00341s/12 iters), loss = 2.01126
I0410 03:07:38.130257 25920 solver.cpp:237]     Train net output #0: loss = 2.01126 (* 1 = 2.01126 loss)
I0410 03:07:38.130266 25920 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 03:07:43.228318 25920 solver.cpp:218] Iteration 10152 (2.35391 iter/s, 5.0979s/12 iters), loss = 1.71844
I0410 03:07:43.228374 25920 solver.cpp:237]     Train net output #0: loss = 1.71844 (* 1 = 1.71844 loss)
I0410 03:07:43.228389 25920 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 03:07:48.234205 25920 solver.cpp:218] Iteration 10164 (2.39728 iter/s, 5.00567s/12 iters), loss = 1.97261
I0410 03:07:48.234270 25920 solver.cpp:237]     Train net output #0: loss = 1.97261 (* 1 = 1.97261 loss)
I0410 03:07:48.234282 25920 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 03:07:53.333765 25920 solver.cpp:218] Iteration 10176 (2.35325 iter/s, 5.09934s/12 iters), loss = 1.83125
I0410 03:07:53.333825 25920 solver.cpp:237]     Train net output #0: loss = 1.83125 (* 1 = 1.83125 loss)
I0410 03:07:53.333838 25920 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 03:07:58.284291 25920 solver.cpp:218] Iteration 10188 (2.42409 iter/s, 4.95031s/12 iters), loss = 1.89187
I0410 03:07:58.284350 25920 solver.cpp:237]     Train net output #0: loss = 1.89187 (* 1 = 1.89187 loss)
I0410 03:07:58.284363 25920 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 03:08:02.725432 25920 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 03:08:05.915977 25920 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 03:08:08.742449 25920 solver.cpp:310] Iteration 10200, loss = 1.94018
I0410 03:08:08.742570 25920 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 03:08:08.742579 25920 net.cpp:676] Ignoring source layer train-data
I0410 03:08:09.171403 25926 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:13.242063 25920 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0410 03:08:13.242096 25920 solver.cpp:397]     Test net output #1: loss = 2.83517 (* 1 = 2.83517 loss)
I0410 03:08:13.242105 25920 solver.cpp:315] Optimization Done.
I0410 03:08:13.242110 25920 caffe.cpp:259] Optimization Done.