DIGITS-CNN/cars/architecture-investigations/fc/4-layers/512/caffe_output.log

I0410 02:17:03.731011 30317 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-221156-99ba/solver.prototxt
I0410 02:17:03.731295 30317 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 02:17:03.731307 30317 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 02:17:03.731426 30317 caffe.cpp:218] Using GPUs 2
I0410 02:17:03.785382 30317 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0410 02:17:04.061882 30317 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: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 02:17:04.062584 30317 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 02:17:04.063230 30317 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 02:17:04.063247 30317 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 02:17:04.063410 30317 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: 512
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: 512
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: 512
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: 512
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 02:17:04.063513 30317 layer_factory.hpp:77] Creating layer train-data
I0410 02:17:04.065107 30317 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 02:17:04.065330 30317 net.cpp:84] Creating Layer train-data
I0410 02:17:04.065341 30317 net.cpp:380] train-data -> data
I0410 02:17:04.065362 30317 net.cpp:380] train-data -> label
I0410 02:17:04.065373 30317 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 02:17:04.070286 30317 data_layer.cpp:45] output data size: 128,3,227,227
I0410 02:17:04.199153 30317 net.cpp:122] Setting up train-data
I0410 02:17:04.199180 30317 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 02:17:04.199185 30317 net.cpp:129] Top shape: 128 (128)
I0410 02:17:04.199189 30317 net.cpp:137] Memory required for data: 79149056
I0410 02:17:04.199219 30317 layer_factory.hpp:77] Creating layer conv1
I0410 02:17:04.199242 30317 net.cpp:84] Creating Layer conv1
I0410 02:17:04.199249 30317 net.cpp:406] conv1 <- data
I0410 02:17:04.199261 30317 net.cpp:380] conv1 -> conv1
I0410 02:17:04.872066 30317 net.cpp:122] Setting up conv1
I0410 02:17:04.872089 30317 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 02:17:04.872093 30317 net.cpp:137] Memory required for data: 227833856
I0410 02:17:04.872115 30317 layer_factory.hpp:77] Creating layer relu1
I0410 02:17:04.872128 30317 net.cpp:84] Creating Layer relu1
I0410 02:17:04.872131 30317 net.cpp:406] relu1 <- conv1
I0410 02:17:04.872138 30317 net.cpp:367] relu1 -> conv1 (in-place)
I0410 02:17:04.872449 30317 net.cpp:122] Setting up relu1
I0410 02:17:04.872459 30317 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 02:17:04.872462 30317 net.cpp:137] Memory required for data: 376518656
I0410 02:17:04.872467 30317 layer_factory.hpp:77] Creating layer norm1
I0410 02:17:04.872476 30317 net.cpp:84] Creating Layer norm1
I0410 02:17:04.872480 30317 net.cpp:406] norm1 <- conv1
I0410 02:17:04.872486 30317 net.cpp:380] norm1 -> norm1
I0410 02:17:04.872957 30317 net.cpp:122] Setting up norm1
I0410 02:17:04.872968 30317 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 02:17:04.872972 30317 net.cpp:137] Memory required for data: 525203456
I0410 02:17:04.872977 30317 layer_factory.hpp:77] Creating layer pool1
I0410 02:17:04.872984 30317 net.cpp:84] Creating Layer pool1
I0410 02:17:04.872987 30317 net.cpp:406] pool1 <- norm1
I0410 02:17:04.872993 30317 net.cpp:380] pool1 -> pool1
I0410 02:17:04.873032 30317 net.cpp:122] Setting up pool1
I0410 02:17:04.873039 30317 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 02:17:04.873042 30317 net.cpp:137] Memory required for data: 561035264
I0410 02:17:04.873045 30317 layer_factory.hpp:77] Creating layer conv2
I0410 02:17:04.873055 30317 net.cpp:84] Creating Layer conv2
I0410 02:17:04.873059 30317 net.cpp:406] conv2 <- pool1
I0410 02:17:04.873064 30317 net.cpp:380] conv2 -> conv2
I0410 02:17:04.880571 30317 net.cpp:122] Setting up conv2
I0410 02:17:04.880592 30317 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 02:17:04.880596 30317 net.cpp:137] Memory required for data: 656586752
I0410 02:17:04.880611 30317 layer_factory.hpp:77] Creating layer relu2
I0410 02:17:04.880620 30317 net.cpp:84] Creating Layer relu2
I0410 02:17:04.880625 30317 net.cpp:406] relu2 <- conv2
I0410 02:17:04.880631 30317 net.cpp:367] relu2 -> conv2 (in-place)
I0410 02:17:04.881160 30317 net.cpp:122] Setting up relu2
I0410 02:17:04.881170 30317 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 02:17:04.881175 30317 net.cpp:137] Memory required for data: 752138240
I0410 02:17:04.881178 30317 layer_factory.hpp:77] Creating layer norm2
I0410 02:17:04.881187 30317 net.cpp:84] Creating Layer norm2
I0410 02:17:04.881191 30317 net.cpp:406] norm2 <- conv2
I0410 02:17:04.881197 30317 net.cpp:380] norm2 -> norm2
I0410 02:17:04.881584 30317 net.cpp:122] Setting up norm2
I0410 02:17:04.881593 30317 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 02:17:04.881597 30317 net.cpp:137] Memory required for data: 847689728
I0410 02:17:04.881600 30317 layer_factory.hpp:77] Creating layer pool2
I0410 02:17:04.881609 30317 net.cpp:84] Creating Layer pool2
I0410 02:17:04.881613 30317 net.cpp:406] pool2 <- norm2
I0410 02:17:04.881618 30317 net.cpp:380] pool2 -> pool2
I0410 02:17:04.881650 30317 net.cpp:122] Setting up pool2
I0410 02:17:04.881656 30317 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 02:17:04.881659 30317 net.cpp:137] Memory required for data: 869840896
I0410 02:17:04.881662 30317 layer_factory.hpp:77] Creating layer conv3
I0410 02:17:04.881673 30317 net.cpp:84] Creating Layer conv3
I0410 02:17:04.881677 30317 net.cpp:406] conv3 <- pool2
I0410 02:17:04.881685 30317 net.cpp:380] conv3 -> conv3
I0410 02:17:04.896158 30317 net.cpp:122] Setting up conv3
I0410 02:17:04.896180 30317 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 02:17:04.896184 30317 net.cpp:137] Memory required for data: 903067648
I0410 02:17:04.896222 30317 layer_factory.hpp:77] Creating layer relu3
I0410 02:17:04.896234 30317 net.cpp:84] Creating Layer relu3
I0410 02:17:04.896239 30317 net.cpp:406] relu3 <- conv3
I0410 02:17:04.896246 30317 net.cpp:367] relu3 -> conv3 (in-place)
I0410 02:17:04.896778 30317 net.cpp:122] Setting up relu3
I0410 02:17:04.896788 30317 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 02:17:04.896792 30317 net.cpp:137] Memory required for data: 936294400
I0410 02:17:04.896797 30317 layer_factory.hpp:77] Creating layer conv4
I0410 02:17:04.896811 30317 net.cpp:84] Creating Layer conv4
I0410 02:17:04.896814 30317 net.cpp:406] conv4 <- conv3
I0410 02:17:04.896823 30317 net.cpp:380] conv4 -> conv4
I0410 02:17:04.908443 30317 net.cpp:122] Setting up conv4
I0410 02:17:04.908464 30317 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 02:17:04.908468 30317 net.cpp:137] Memory required for data: 969521152
I0410 02:17:04.908478 30317 layer_factory.hpp:77] Creating layer relu4
I0410 02:17:04.908488 30317 net.cpp:84] Creating Layer relu4
I0410 02:17:04.908493 30317 net.cpp:406] relu4 <- conv4
I0410 02:17:04.908500 30317 net.cpp:367] relu4 -> conv4 (in-place)
I0410 02:17:04.908874 30317 net.cpp:122] Setting up relu4
I0410 02:17:04.908885 30317 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 02:17:04.908888 30317 net.cpp:137] Memory required for data: 1002747904
I0410 02:17:04.908892 30317 layer_factory.hpp:77] Creating layer conv5
I0410 02:17:04.908905 30317 net.cpp:84] Creating Layer conv5
I0410 02:17:04.908908 30317 net.cpp:406] conv5 <- conv4
I0410 02:17:04.908915 30317 net.cpp:380] conv5 -> conv5
I0410 02:17:04.918295 30317 net.cpp:122] Setting up conv5
I0410 02:17:04.918316 30317 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 02:17:04.918320 30317 net.cpp:137] Memory required for data: 1024899072
I0410 02:17:04.918336 30317 layer_factory.hpp:77] Creating layer relu5
I0410 02:17:04.918346 30317 net.cpp:84] Creating Layer relu5
I0410 02:17:04.918351 30317 net.cpp:406] relu5 <- conv5
I0410 02:17:04.918359 30317 net.cpp:367] relu5 -> conv5 (in-place)
I0410 02:17:04.918901 30317 net.cpp:122] Setting up relu5
I0410 02:17:04.918911 30317 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 02:17:04.918915 30317 net.cpp:137] Memory required for data: 1047050240
I0410 02:17:04.918920 30317 layer_factory.hpp:77] Creating layer pool5
I0410 02:17:04.918926 30317 net.cpp:84] Creating Layer pool5
I0410 02:17:04.918931 30317 net.cpp:406] pool5 <- conv5
I0410 02:17:04.918937 30317 net.cpp:380] pool5 -> pool5
I0410 02:17:04.918977 30317 net.cpp:122] Setting up pool5
I0410 02:17:04.918984 30317 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 02:17:04.918988 30317 net.cpp:137] Memory required for data: 1051768832
I0410 02:17:04.918992 30317 layer_factory.hpp:77] Creating layer fc6
I0410 02:17:04.919003 30317 net.cpp:84] Creating Layer fc6
I0410 02:17:04.919006 30317 net.cpp:406] fc6 <- pool5
I0410 02:17:04.919013 30317 net.cpp:380] fc6 -> fc6
I0410 02:17:04.968045 30317 net.cpp:122] Setting up fc6
I0410 02:17:04.968068 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.968073 30317 net.cpp:137] Memory required for data: 1052030976
I0410 02:17:04.968084 30317 layer_factory.hpp:77] Creating layer relu6
I0410 02:17:04.968094 30317 net.cpp:84] Creating Layer relu6
I0410 02:17:04.968101 30317 net.cpp:406] relu6 <- fc6
I0410 02:17:04.968107 30317 net.cpp:367] relu6 -> fc6 (in-place)
I0410 02:17:04.968827 30317 net.cpp:122] Setting up relu6
I0410 02:17:04.968840 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.968844 30317 net.cpp:137] Memory required for data: 1052293120
I0410 02:17:04.968849 30317 layer_factory.hpp:77] Creating layer drop6
I0410 02:17:04.968858 30317 net.cpp:84] Creating Layer drop6
I0410 02:17:04.968863 30317 net.cpp:406] drop6 <- fc6
I0410 02:17:04.968869 30317 net.cpp:367] drop6 -> fc6 (in-place)
I0410 02:17:04.968904 30317 net.cpp:122] Setting up drop6
I0410 02:17:04.968911 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.968935 30317 net.cpp:137] Memory required for data: 1052555264
I0410 02:17:04.968940 30317 layer_factory.hpp:77] Creating layer fc7
I0410 02:17:04.968950 30317 net.cpp:84] Creating Layer fc7
I0410 02:17:04.968953 30317 net.cpp:406] fc7 <- fc6
I0410 02:17:04.968959 30317 net.cpp:380] fc7 -> fc7
I0410 02:17:04.971516 30317 net.cpp:122] Setting up fc7
I0410 02:17:04.971526 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.971529 30317 net.cpp:137] Memory required for data: 1052817408
I0410 02:17:04.971536 30317 layer_factory.hpp:77] Creating layer relu7
I0410 02:17:04.971544 30317 net.cpp:84] Creating Layer relu7
I0410 02:17:04.971547 30317 net.cpp:406] relu7 <- fc7
I0410 02:17:04.971554 30317 net.cpp:367] relu7 -> fc7 (in-place)
I0410 02:17:04.974507 30317 net.cpp:122] Setting up relu7
I0410 02:17:04.974519 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.974521 30317 net.cpp:137] Memory required for data: 1053079552
I0410 02:17:04.974526 30317 layer_factory.hpp:77] Creating layer drop7
I0410 02:17:04.974534 30317 net.cpp:84] Creating Layer drop7
I0410 02:17:04.974539 30317 net.cpp:406] drop7 <- fc7
I0410 02:17:04.974545 30317 net.cpp:367] drop7 -> fc7 (in-place)
I0410 02:17:04.974571 30317 net.cpp:122] Setting up drop7
I0410 02:17:04.974577 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.974581 30317 net.cpp:137] Memory required for data: 1053341696
I0410 02:17:04.974584 30317 layer_factory.hpp:77] Creating layer fc7.5
I0410 02:17:04.974593 30317 net.cpp:84] Creating Layer fc7.5
I0410 02:17:04.974598 30317 net.cpp:406] fc7.5 <- fc7
I0410 02:17:04.974606 30317 net.cpp:380] fc7.5 -> fc7.5
I0410 02:17:04.977777 30317 net.cpp:122] Setting up fc7.5
I0410 02:17:04.977787 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.977792 30317 net.cpp:137] Memory required for data: 1053603840
I0410 02:17:04.977798 30317 layer_factory.hpp:77] Creating layer relu7.5
I0410 02:17:04.977805 30317 net.cpp:84] Creating Layer relu7.5
I0410 02:17:04.977809 30317 net.cpp:406] relu7.5 <- fc7.5
I0410 02:17:04.977816 30317 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 02:17:04.978397 30317 net.cpp:122] Setting up relu7.5
I0410 02:17:04.978408 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.978411 30317 net.cpp:137] Memory required for data: 1053865984
I0410 02:17:04.978415 30317 layer_factory.hpp:77] Creating layer drop7.5
I0410 02:17:04.978422 30317 net.cpp:84] Creating Layer drop7.5
I0410 02:17:04.978426 30317 net.cpp:406] drop7.5 <- fc7.5
I0410 02:17:04.978433 30317 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 02:17:04.978461 30317 net.cpp:122] Setting up drop7.5
I0410 02:17:04.978467 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.978471 30317 net.cpp:137] Memory required for data: 1054128128
I0410 02:17:04.978474 30317 layer_factory.hpp:77] Creating layer fc7.6
I0410 02:17:04.978482 30317 net.cpp:84] Creating Layer fc7.6
I0410 02:17:04.978487 30317 net.cpp:406] fc7.6 <- fc7.5
I0410 02:17:04.978493 30317 net.cpp:380] fc7.6 -> fc7.6
I0410 02:17:04.980983 30317 net.cpp:122] Setting up fc7.6
I0410 02:17:04.980991 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.980995 30317 net.cpp:137] Memory required for data: 1054390272
I0410 02:17:04.981006 30317 layer_factory.hpp:77] Creating layer relu7.6
I0410 02:17:04.981014 30317 net.cpp:84] Creating Layer relu7.6
I0410 02:17:04.981019 30317 net.cpp:406] relu7.6 <- fc7.6
I0410 02:17:04.981024 30317 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 02:17:04.981559 30317 net.cpp:122] Setting up relu7.6
I0410 02:17:04.981570 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.981573 30317 net.cpp:137] Memory required for data: 1054652416
I0410 02:17:04.981577 30317 layer_factory.hpp:77] Creating layer drop7.6
I0410 02:17:04.981585 30317 net.cpp:84] Creating Layer drop7.6
I0410 02:17:04.981588 30317 net.cpp:406] drop7.6 <- fc7.6
I0410 02:17:04.981595 30317 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 02:17:04.981621 30317 net.cpp:122] Setting up drop7.6
I0410 02:17:04.981627 30317 net.cpp:129] Top shape: 128 512 (65536)
I0410 02:17:04.981647 30317 net.cpp:137] Memory required for data: 1054914560
I0410 02:17:04.981652 30317 layer_factory.hpp:77] Creating layer fc8
I0410 02:17:04.981658 30317 net.cpp:84] Creating Layer fc8
I0410 02:17:04.981662 30317 net.cpp:406] fc8 <- fc7.6
I0410 02:17:04.981671 30317 net.cpp:380] fc8 -> fc8
I0410 02:17:04.982730 30317 net.cpp:122] Setting up fc8
I0410 02:17:04.982739 30317 net.cpp:129] Top shape: 128 196 (25088)
I0410 02:17:04.982743 30317 net.cpp:137] Memory required for data: 1055014912
I0410 02:17:04.982749 30317 layer_factory.hpp:77] Creating layer loss
I0410 02:17:04.982756 30317 net.cpp:84] Creating Layer loss
I0410 02:17:04.982760 30317 net.cpp:406] loss <- fc8
I0410 02:17:04.982766 30317 net.cpp:406] loss <- label
I0410 02:17:04.982774 30317 net.cpp:380] loss -> loss
I0410 02:17:04.982782 30317 layer_factory.hpp:77] Creating layer loss
I0410 02:17:04.983418 30317 net.cpp:122] Setting up loss
I0410 02:17:04.983428 30317 net.cpp:129] Top shape: (1)
I0410 02:17:04.983433 30317 net.cpp:132]     with loss weight 1
I0410 02:17:04.983449 30317 net.cpp:137] Memory required for data: 1055014916
I0410 02:17:04.983454 30317 net.cpp:198] loss needs backward computation.
I0410 02:17:04.983462 30317 net.cpp:198] fc8 needs backward computation.
I0410 02:17:04.983467 30317 net.cpp:198] drop7.6 needs backward computation.
I0410 02:17:04.983471 30317 net.cpp:198] relu7.6 needs backward computation.
I0410 02:17:04.983475 30317 net.cpp:198] fc7.6 needs backward computation.
I0410 02:17:04.983479 30317 net.cpp:198] drop7.5 needs backward computation.
I0410 02:17:04.983484 30317 net.cpp:198] relu7.5 needs backward computation.
I0410 02:17:04.983487 30317 net.cpp:198] fc7.5 needs backward computation.
I0410 02:17:04.983492 30317 net.cpp:198] drop7 needs backward computation.
I0410 02:17:04.983496 30317 net.cpp:198] relu7 needs backward computation.
I0410 02:17:04.983500 30317 net.cpp:198] fc7 needs backward computation.
I0410 02:17:04.983505 30317 net.cpp:198] drop6 needs backward computation.
I0410 02:17:04.983510 30317 net.cpp:198] relu6 needs backward computation.
I0410 02:17:04.983513 30317 net.cpp:198] fc6 needs backward computation.
I0410 02:17:04.983518 30317 net.cpp:198] pool5 needs backward computation.
I0410 02:17:04.983522 30317 net.cpp:198] relu5 needs backward computation.
I0410 02:17:04.983526 30317 net.cpp:198] conv5 needs backward computation.
I0410 02:17:04.983530 30317 net.cpp:198] relu4 needs backward computation.
I0410 02:17:04.983536 30317 net.cpp:198] conv4 needs backward computation.
I0410 02:17:04.983539 30317 net.cpp:198] relu3 needs backward computation.
I0410 02:17:04.983543 30317 net.cpp:198] conv3 needs backward computation.
I0410 02:17:04.983548 30317 net.cpp:198] pool2 needs backward computation.
I0410 02:17:04.983552 30317 net.cpp:198] norm2 needs backward computation.
I0410 02:17:04.983556 30317 net.cpp:198] relu2 needs backward computation.
I0410 02:17:04.983561 30317 net.cpp:198] conv2 needs backward computation.
I0410 02:17:04.983566 30317 net.cpp:198] pool1 needs backward computation.
I0410 02:17:04.983570 30317 net.cpp:198] norm1 needs backward computation.
I0410 02:17:04.983574 30317 net.cpp:198] relu1 needs backward computation.
I0410 02:17:04.983578 30317 net.cpp:198] conv1 needs backward computation.
I0410 02:17:04.983583 30317 net.cpp:200] train-data does not need backward computation.
I0410 02:17:04.983587 30317 net.cpp:242] This network produces output loss
I0410 02:17:04.983608 30317 net.cpp:255] Network initialization done.
I0410 02:17:04.984227 30317 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 02:17:04.984268 30317 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 02:17:04.984457 30317 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: 512
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: 512
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: 512
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: 512
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 02:17:04.984578 30317 layer_factory.hpp:77] Creating layer val-data
I0410 02:17:04.986289 30317 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 02:17:04.986495 30317 net.cpp:84] Creating Layer val-data
I0410 02:17:04.986505 30317 net.cpp:380] val-data -> data
I0410 02:17:04.986513 30317 net.cpp:380] val-data -> label
I0410 02:17:04.986521 30317 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 02:17:04.993643 30317 data_layer.cpp:45] output data size: 32,3,227,227
I0410 02:17:05.028856 30317 net.cpp:122] Setting up val-data
I0410 02:17:05.028877 30317 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 02:17:05.028882 30317 net.cpp:129] Top shape: 32 (32)
I0410 02:17:05.028884 30317 net.cpp:137] Memory required for data: 19787264
I0410 02:17:05.028892 30317 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 02:17:05.028903 30317 net.cpp:84] Creating Layer label_val-data_1_split
I0410 02:17:05.028908 30317 net.cpp:406] label_val-data_1_split <- label
I0410 02:17:05.028914 30317 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 02:17:05.028924 30317 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 02:17:05.029089 30317 net.cpp:122] Setting up label_val-data_1_split
I0410 02:17:05.029096 30317 net.cpp:129] Top shape: 32 (32)
I0410 02:17:05.029100 30317 net.cpp:129] Top shape: 32 (32)
I0410 02:17:05.029103 30317 net.cpp:137] Memory required for data: 19787520
I0410 02:17:05.029106 30317 layer_factory.hpp:77] Creating layer conv1
I0410 02:17:05.029119 30317 net.cpp:84] Creating Layer conv1
I0410 02:17:05.029124 30317 net.cpp:406] conv1 <- data
I0410 02:17:05.029130 30317 net.cpp:380] conv1 -> conv1
I0410 02:17:05.032557 30317 net.cpp:122] Setting up conv1
I0410 02:17:05.032573 30317 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 02:17:05.032577 30317 net.cpp:137] Memory required for data: 56958720
I0410 02:17:05.032588 30317 layer_factory.hpp:77] Creating layer relu1
I0410 02:17:05.032598 30317 net.cpp:84] Creating Layer relu1
I0410 02:17:05.032601 30317 net.cpp:406] relu1 <- conv1
I0410 02:17:05.032608 30317 net.cpp:367] relu1 -> conv1 (in-place)
I0410 02:17:05.034198 30317 net.cpp:122] Setting up relu1
I0410 02:17:05.034211 30317 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 02:17:05.034215 30317 net.cpp:137] Memory required for data: 94129920
I0410 02:17:05.034219 30317 layer_factory.hpp:77] Creating layer norm1
I0410 02:17:05.034231 30317 net.cpp:84] Creating Layer norm1
I0410 02:17:05.034236 30317 net.cpp:406] norm1 <- conv1
I0410 02:17:05.034242 30317 net.cpp:380] norm1 -> norm1
I0410 02:17:05.034740 30317 net.cpp:122] Setting up norm1
I0410 02:17:05.034750 30317 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 02:17:05.034754 30317 net.cpp:137] Memory required for data: 131301120
I0410 02:17:05.034759 30317 layer_factory.hpp:77] Creating layer pool1
I0410 02:17:05.034766 30317 net.cpp:84] Creating Layer pool1
I0410 02:17:05.034770 30317 net.cpp:406] pool1 <- norm1
I0410 02:17:05.034775 30317 net.cpp:380] pool1 -> pool1
I0410 02:17:05.034807 30317 net.cpp:122] Setting up pool1
I0410 02:17:05.034813 30317 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 02:17:05.034816 30317 net.cpp:137] Memory required for data: 140259072
I0410 02:17:05.034821 30317 layer_factory.hpp:77] Creating layer conv2
I0410 02:17:05.034830 30317 net.cpp:84] Creating Layer conv2
I0410 02:17:05.034834 30317 net.cpp:406] conv2 <- pool1
I0410 02:17:05.034839 30317 net.cpp:380] conv2 -> conv2
I0410 02:17:05.041988 30317 net.cpp:122] Setting up conv2
I0410 02:17:05.042011 30317 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 02:17:05.042016 30317 net.cpp:137] Memory required for data: 164146944
I0410 02:17:05.042029 30317 layer_factory.hpp:77] Creating layer relu2
I0410 02:17:05.042040 30317 net.cpp:84] Creating Layer relu2
I0410 02:17:05.042047 30317 net.cpp:406] relu2 <- conv2
I0410 02:17:05.042053 30317 net.cpp:367] relu2 -> conv2 (in-place)
I0410 02:17:05.042440 30317 net.cpp:122] Setting up relu2
I0410 02:17:05.042450 30317 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 02:17:05.042455 30317 net.cpp:137] Memory required for data: 188034816
I0410 02:17:05.042459 30317 layer_factory.hpp:77] Creating layer norm2
I0410 02:17:05.042469 30317 net.cpp:84] Creating Layer norm2
I0410 02:17:05.042474 30317 net.cpp:406] norm2 <- conv2
I0410 02:17:05.042480 30317 net.cpp:380] norm2 -> norm2
I0410 02:17:05.043047 30317 net.cpp:122] Setting up norm2
I0410 02:17:05.043057 30317 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 02:17:05.043062 30317 net.cpp:137] Memory required for data: 211922688
I0410 02:17:05.043066 30317 layer_factory.hpp:77] Creating layer pool2
I0410 02:17:05.043076 30317 net.cpp:84] Creating Layer pool2
I0410 02:17:05.043079 30317 net.cpp:406] pool2 <- norm2
I0410 02:17:05.043087 30317 net.cpp:380] pool2 -> pool2
I0410 02:17:05.043123 30317 net.cpp:122] Setting up pool2
I0410 02:17:05.043128 30317 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 02:17:05.043131 30317 net.cpp:137] Memory required for data: 217460480
I0410 02:17:05.043136 30317 layer_factory.hpp:77] Creating layer conv3
I0410 02:17:05.043148 30317 net.cpp:84] Creating Layer conv3
I0410 02:17:05.043152 30317 net.cpp:406] conv3 <- pool2
I0410 02:17:05.043159 30317 net.cpp:380] conv3 -> conv3
I0410 02:17:05.056205 30317 net.cpp:122] Setting up conv3
I0410 02:17:05.056224 30317 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 02:17:05.056228 30317 net.cpp:137] Memory required for data: 225767168
I0410 02:17:05.056242 30317 layer_factory.hpp:77] Creating layer relu3
I0410 02:17:05.056253 30317 net.cpp:84] Creating Layer relu3
I0410 02:17:05.056258 30317 net.cpp:406] relu3 <- conv3
I0410 02:17:05.056265 30317 net.cpp:367] relu3 -> conv3 (in-place)
I0410 02:17:05.056807 30317 net.cpp:122] Setting up relu3
I0410 02:17:05.056818 30317 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 02:17:05.056823 30317 net.cpp:137] Memory required for data: 234073856
I0410 02:17:05.056826 30317 layer_factory.hpp:77] Creating layer conv4
I0410 02:17:05.056839 30317 net.cpp:84] Creating Layer conv4
I0410 02:17:05.056843 30317 net.cpp:406] conv4 <- conv3
I0410 02:17:05.056869 30317 net.cpp:380] conv4 -> conv4
I0410 02:17:05.072791 30317 net.cpp:122] Setting up conv4
I0410 02:17:05.072811 30317 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 02:17:05.072815 30317 net.cpp:137] Memory required for data: 242380544
I0410 02:17:05.072824 30317 layer_factory.hpp:77] Creating layer relu4
I0410 02:17:05.072836 30317 net.cpp:84] Creating Layer relu4
I0410 02:17:05.072841 30317 net.cpp:406] relu4 <- conv4
I0410 02:17:05.072847 30317 net.cpp:367] relu4 -> conv4 (in-place)
I0410 02:17:05.074839 30317 net.cpp:122] Setting up relu4
I0410 02:17:05.074856 30317 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 02:17:05.074860 30317 net.cpp:137] Memory required for data: 250687232
I0410 02:17:05.074867 30317 layer_factory.hpp:77] Creating layer conv5
I0410 02:17:05.074883 30317 net.cpp:84] Creating Layer conv5
I0410 02:17:05.074888 30317 net.cpp:406] conv5 <- conv4
I0410 02:17:05.074896 30317 net.cpp:380] conv5 -> conv5
I0410 02:17:05.083722 30317 net.cpp:122] Setting up conv5
I0410 02:17:05.083745 30317 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 02:17:05.083748 30317 net.cpp:137] Memory required for data: 256225024
I0410 02:17:05.083762 30317 layer_factory.hpp:77] Creating layer relu5
I0410 02:17:05.083773 30317 net.cpp:84] Creating Layer relu5
I0410 02:17:05.083779 30317 net.cpp:406] relu5 <- conv5
I0410 02:17:05.083786 30317 net.cpp:367] relu5 -> conv5 (in-place)
I0410 02:17:05.084177 30317 net.cpp:122] Setting up relu5
I0410 02:17:05.084185 30317 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 02:17:05.084190 30317 net.cpp:137] Memory required for data: 261762816
I0410 02:17:05.084194 30317 layer_factory.hpp:77] Creating layer pool5
I0410 02:17:05.084205 30317 net.cpp:84] Creating Layer pool5
I0410 02:17:05.084209 30317 net.cpp:406] pool5 <- conv5
I0410 02:17:05.084216 30317 net.cpp:380] pool5 -> pool5
I0410 02:17:05.084259 30317 net.cpp:122] Setting up pool5
I0410 02:17:05.084265 30317 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 02:17:05.084270 30317 net.cpp:137] Memory required for data: 262942464
I0410 02:17:05.084273 30317 layer_factory.hpp:77] Creating layer fc6
I0410 02:17:05.084283 30317 net.cpp:84] Creating Layer fc6
I0410 02:17:05.084287 30317 net.cpp:406] fc6 <- pool5
I0410 02:17:05.084293 30317 net.cpp:380] fc6 -> fc6
I0410 02:17:05.135205 30317 net.cpp:122] Setting up fc6
I0410 02:17:05.135226 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.135231 30317 net.cpp:137] Memory required for data: 263008000
I0410 02:17:05.135241 30317 layer_factory.hpp:77] Creating layer relu6
I0410 02:17:05.135248 30317 net.cpp:84] Creating Layer relu6
I0410 02:17:05.135253 30317 net.cpp:406] relu6 <- fc6
I0410 02:17:05.135262 30317 net.cpp:367] relu6 -> fc6 (in-place)
I0410 02:17:05.135949 30317 net.cpp:122] Setting up relu6
I0410 02:17:05.135958 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.135962 30317 net.cpp:137] Memory required for data: 263073536
I0410 02:17:05.135967 30317 layer_factory.hpp:77] Creating layer drop6
I0410 02:17:05.135972 30317 net.cpp:84] Creating Layer drop6
I0410 02:17:05.135977 30317 net.cpp:406] drop6 <- fc6
I0410 02:17:05.135983 30317 net.cpp:367] drop6 -> fc6 (in-place)
I0410 02:17:05.136011 30317 net.cpp:122] Setting up drop6
I0410 02:17:05.136016 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.136019 30317 net.cpp:137] Memory required for data: 263139072
I0410 02:17:05.136023 30317 layer_factory.hpp:77] Creating layer fc7
I0410 02:17:05.136031 30317 net.cpp:84] Creating Layer fc7
I0410 02:17:05.136034 30317 net.cpp:406] fc7 <- fc6
I0410 02:17:05.136041 30317 net.cpp:380] fc7 -> fc7
I0410 02:17:05.139223 30317 net.cpp:122] Setting up fc7
I0410 02:17:05.139237 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.139240 30317 net.cpp:137] Memory required for data: 263204608
I0410 02:17:05.139248 30317 layer_factory.hpp:77] Creating layer relu7
I0410 02:17:05.139256 30317 net.cpp:84] Creating Layer relu7
I0410 02:17:05.139259 30317 net.cpp:406] relu7 <- fc7
I0410 02:17:05.139266 30317 net.cpp:367] relu7 -> fc7 (in-place)
I0410 02:17:05.139860 30317 net.cpp:122] Setting up relu7
I0410 02:17:05.139869 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.139873 30317 net.cpp:137] Memory required for data: 263270144
I0410 02:17:05.139878 30317 layer_factory.hpp:77] Creating layer drop7
I0410 02:17:05.139885 30317 net.cpp:84] Creating Layer drop7
I0410 02:17:05.139889 30317 net.cpp:406] drop7 <- fc7
I0410 02:17:05.139894 30317 net.cpp:367] drop7 -> fc7 (in-place)
I0410 02:17:05.139922 30317 net.cpp:122] Setting up drop7
I0410 02:17:05.139928 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.139931 30317 net.cpp:137] Memory required for data: 263335680
I0410 02:17:05.139935 30317 layer_factory.hpp:77] Creating layer fc7.5
I0410 02:17:05.139941 30317 net.cpp:84] Creating Layer fc7.5
I0410 02:17:05.139945 30317 net.cpp:406] fc7.5 <- fc7
I0410 02:17:05.139951 30317 net.cpp:380] fc7.5 -> fc7.5
I0410 02:17:05.142489 30317 net.cpp:122] Setting up fc7.5
I0410 02:17:05.142503 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.142505 30317 net.cpp:137] Memory required for data: 263401216
I0410 02:17:05.142513 30317 layer_factory.hpp:77] Creating layer relu7.5
I0410 02:17:05.142519 30317 net.cpp:84] Creating Layer relu7.5
I0410 02:17:05.142524 30317 net.cpp:406] relu7.5 <- fc7.5
I0410 02:17:05.142529 30317 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 02:17:05.144284 30317 net.cpp:122] Setting up relu7.5
I0410 02:17:05.144295 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.144299 30317 net.cpp:137] Memory required for data: 263466752
I0410 02:17:05.144304 30317 layer_factory.hpp:77] Creating layer drop7.5
I0410 02:17:05.144310 30317 net.cpp:84] Creating Layer drop7.5
I0410 02:17:05.144315 30317 net.cpp:406] drop7.5 <- fc7.5
I0410 02:17:05.144321 30317 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 02:17:05.144351 30317 net.cpp:122] Setting up drop7.5
I0410 02:17:05.144356 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.144358 30317 net.cpp:137] Memory required for data: 263532288
I0410 02:17:05.144361 30317 layer_factory.hpp:77] Creating layer fc7.6
I0410 02:17:05.144371 30317 net.cpp:84] Creating Layer fc7.6
I0410 02:17:05.144374 30317 net.cpp:406] fc7.6 <- fc7.5
I0410 02:17:05.144380 30317 net.cpp:380] fc7.6 -> fc7.6
I0410 02:17:05.147575 30317 net.cpp:122] Setting up fc7.6
I0410 02:17:05.147588 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.147593 30317 net.cpp:137] Memory required for data: 263597824
I0410 02:17:05.147605 30317 layer_factory.hpp:77] Creating layer relu7.6
I0410 02:17:05.147612 30317 net.cpp:84] Creating Layer relu7.6
I0410 02:17:05.147617 30317 net.cpp:406] relu7.6 <- fc7.6
I0410 02:17:05.147622 30317 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 02:17:05.148027 30317 net.cpp:122] Setting up relu7.6
I0410 02:17:05.148036 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.148039 30317 net.cpp:137] Memory required for data: 263663360
I0410 02:17:05.148043 30317 layer_factory.hpp:77] Creating layer drop7.6
I0410 02:17:05.148051 30317 net.cpp:84] Creating Layer drop7.6
I0410 02:17:05.148054 30317 net.cpp:406] drop7.6 <- fc7.6
I0410 02:17:05.148059 30317 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 02:17:05.148085 30317 net.cpp:122] Setting up drop7.6
I0410 02:17:05.148090 30317 net.cpp:129] Top shape: 32 512 (16384)
I0410 02:17:05.148093 30317 net.cpp:137] Memory required for data: 263728896
I0410 02:17:05.148097 30317 layer_factory.hpp:77] Creating layer fc8
I0410 02:17:05.148104 30317 net.cpp:84] Creating Layer fc8
I0410 02:17:05.148108 30317 net.cpp:406] fc8 <- fc7.6
I0410 02:17:05.148114 30317 net.cpp:380] fc8 -> fc8
I0410 02:17:05.149152 30317 net.cpp:122] Setting up fc8
I0410 02:17:05.149158 30317 net.cpp:129] Top shape: 32 196 (6272)
I0410 02:17:05.149161 30317 net.cpp:137] Memory required for data: 263753984
I0410 02:17:05.149168 30317 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 02:17:05.149173 30317 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 02:17:05.149178 30317 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 02:17:05.149201 30317 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 02:17:05.149209 30317 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 02:17:05.149245 30317 net.cpp:122] Setting up fc8_fc8_0_split
I0410 02:17:05.149250 30317 net.cpp:129] Top shape: 32 196 (6272)
I0410 02:17:05.149253 30317 net.cpp:129] Top shape: 32 196 (6272)
I0410 02:17:05.149256 30317 net.cpp:137] Memory required for data: 263804160
I0410 02:17:05.149260 30317 layer_factory.hpp:77] Creating layer accuracy
I0410 02:17:05.149267 30317 net.cpp:84] Creating Layer accuracy
I0410 02:17:05.149271 30317 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 02:17:05.149276 30317 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 02:17:05.149282 30317 net.cpp:380] accuracy -> accuracy
I0410 02:17:05.149289 30317 net.cpp:122] Setting up accuracy
I0410 02:17:05.149293 30317 net.cpp:129] Top shape: (1)
I0410 02:17:05.149296 30317 net.cpp:137] Memory required for data: 263804164
I0410 02:17:05.149300 30317 layer_factory.hpp:77] Creating layer loss
I0410 02:17:05.149313 30317 net.cpp:84] Creating Layer loss
I0410 02:17:05.149317 30317 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 02:17:05.149322 30317 net.cpp:406] loss <- label_val-data_1_split_1
I0410 02:17:05.149327 30317 net.cpp:380] loss -> loss
I0410 02:17:05.149334 30317 layer_factory.hpp:77] Creating layer loss
I0410 02:17:05.151127 30317 net.cpp:122] Setting up loss
I0410 02:17:05.151137 30317 net.cpp:129] Top shape: (1)
I0410 02:17:05.151141 30317 net.cpp:132]     with loss weight 1
I0410 02:17:05.151152 30317 net.cpp:137] Memory required for data: 263804168
I0410 02:17:05.151156 30317 net.cpp:198] loss needs backward computation.
I0410 02:17:05.151161 30317 net.cpp:200] accuracy does not need backward computation.
I0410 02:17:05.151165 30317 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 02:17:05.151170 30317 net.cpp:198] fc8 needs backward computation.
I0410 02:17:05.151173 30317 net.cpp:198] drop7.6 needs backward computation.
I0410 02:17:05.151176 30317 net.cpp:198] relu7.6 needs backward computation.
I0410 02:17:05.151180 30317 net.cpp:198] fc7.6 needs backward computation.
I0410 02:17:05.151183 30317 net.cpp:198] drop7.5 needs backward computation.
I0410 02:17:05.151186 30317 net.cpp:198] relu7.5 needs backward computation.
I0410 02:17:05.151190 30317 net.cpp:198] fc7.5 needs backward computation.
I0410 02:17:05.151194 30317 net.cpp:198] drop7 needs backward computation.
I0410 02:17:05.151198 30317 net.cpp:198] relu7 needs backward computation.
I0410 02:17:05.151201 30317 net.cpp:198] fc7 needs backward computation.
I0410 02:17:05.151206 30317 net.cpp:198] drop6 needs backward computation.
I0410 02:17:05.151209 30317 net.cpp:198] relu6 needs backward computation.
I0410 02:17:05.151212 30317 net.cpp:198] fc6 needs backward computation.
I0410 02:17:05.151216 30317 net.cpp:198] pool5 needs backward computation.
I0410 02:17:05.151219 30317 net.cpp:198] relu5 needs backward computation.
I0410 02:17:05.151223 30317 net.cpp:198] conv5 needs backward computation.
I0410 02:17:05.151227 30317 net.cpp:198] relu4 needs backward computation.
I0410 02:17:05.151230 30317 net.cpp:198] conv4 needs backward computation.
I0410 02:17:05.151234 30317 net.cpp:198] relu3 needs backward computation.
I0410 02:17:05.151237 30317 net.cpp:198] conv3 needs backward computation.
I0410 02:17:05.151242 30317 net.cpp:198] pool2 needs backward computation.
I0410 02:17:05.151245 30317 net.cpp:198] norm2 needs backward computation.
I0410 02:17:05.151248 30317 net.cpp:198] relu2 needs backward computation.
I0410 02:17:05.151252 30317 net.cpp:198] conv2 needs backward computation.
I0410 02:17:05.151257 30317 net.cpp:198] pool1 needs backward computation.
I0410 02:17:05.151263 30317 net.cpp:198] norm1 needs backward computation.
I0410 02:17:05.151268 30317 net.cpp:198] relu1 needs backward computation.
I0410 02:17:05.151270 30317 net.cpp:198] conv1 needs backward computation.
I0410 02:17:05.151275 30317 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 02:17:05.151279 30317 net.cpp:200] val-data does not need backward computation.
I0410 02:17:05.151291 30317 net.cpp:242] This network produces output accuracy
I0410 02:17:05.151296 30317 net.cpp:242] This network produces output loss
I0410 02:17:05.151315 30317 net.cpp:255] Network initialization done.
I0410 02:17:05.151443 30317 solver.cpp:56] Solver scaffolding done.
I0410 02:17:05.152045 30317 caffe.cpp:248] Starting Optimization
I0410 02:17:05.152055 30317 solver.cpp:272] Solving
I0410 02:17:05.152060 30317 solver.cpp:273] Learning Rate Policy: exp
I0410 02:17:05.153213 30317 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 02:17:05.153223 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:17:05.171100 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:17:09.696645 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:09.740689 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0410 02:17:09.740733 30317 solver.cpp:397]     Test net output #1: loss = 5.27842 (* 1 = 5.27842 loss)
I0410 02:17:09.827991 30317 solver.cpp:218] Iteration 0 (-2.41388e-37 iter/s, 4.67573s/12 iters), loss = 5.27707
I0410 02:17:09.828035 30317 solver.cpp:237]     Train net output #0: loss = 5.27707 (* 1 = 5.27707 loss)
I0410 02:17:09.828061 30317 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 02:17:13.616003 30317 solver.cpp:218] Iteration 12 (3.16806 iter/s, 3.78781s/12 iters), loss = 5.27628
I0410 02:17:13.616050 30317 solver.cpp:237]     Train net output #0: loss = 5.27628 (* 1 = 5.27628 loss)
I0410 02:17:13.616062 30317 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 02:17:18.459928 30317 solver.cpp:218] Iteration 24 (2.47745 iter/s, 4.8437s/12 iters), loss = 5.27743
I0410 02:17:18.459973 30317 solver.cpp:237]     Train net output #0: loss = 5.27743 (* 1 = 5.27743 loss)
I0410 02:17:18.459985 30317 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 02:17:23.257402 30317 solver.cpp:218] Iteration 36 (2.50143 iter/s, 4.79725s/12 iters), loss = 5.28023
I0410 02:17:23.257455 30317 solver.cpp:237]     Train net output #0: loss = 5.28023 (* 1 = 5.28023 loss)
I0410 02:17:23.257467 30317 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 02:17:28.396353 30317 solver.cpp:218] Iteration 48 (2.33522 iter/s, 5.1387s/12 iters), loss = 5.27787
I0410 02:17:28.396406 30317 solver.cpp:237]     Train net output #0: loss = 5.27787 (* 1 = 5.27787 loss)
I0410 02:17:28.396418 30317 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 02:17:33.270864 30317 solver.cpp:218] Iteration 60 (2.4619 iter/s, 4.87428s/12 iters), loss = 5.27612
I0410 02:17:33.270903 30317 solver.cpp:237]     Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
I0410 02:17:33.270911 30317 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 02:17:38.201341 30317 solver.cpp:218] Iteration 72 (2.43395 iter/s, 4.93025s/12 iters), loss = 5.27719
I0410 02:17:38.201445 30317 solver.cpp:237]     Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
I0410 02:17:38.201458 30317 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 02:17:43.138025 30317 solver.cpp:218] Iteration 84 (2.43093 iter/s, 4.93639s/12 iters), loss = 5.28132
I0410 02:17:43.138085 30317 solver.cpp:237]     Train net output #0: loss = 5.28132 (* 1 = 5.28132 loss)
I0410 02:17:43.138095 30317 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 02:17:48.050213 30317 solver.cpp:218] Iteration 96 (2.44302 iter/s, 4.91194s/12 iters), loss = 5.2833
I0410 02:17:48.050261 30317 solver.cpp:237]     Train net output #0: loss = 5.2833 (* 1 = 5.2833 loss)
I0410 02:17:48.050272 30317 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 02:17:49.731124 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:50.042496 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 02:17:50.545480 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 02:17:50.875005 30317 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 02:17:50.875028 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:17:55.276885 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:55.355803 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:17:55.355860 30317 solver.cpp:397]     Test net output #1: loss = 5.2795 (* 1 = 5.2795 loss)
I0410 02:17:57.204741 30317 solver.cpp:218] Iteration 108 (1.31088 iter/s, 9.15415s/12 iters), loss = 5.27901
I0410 02:17:57.204783 30317 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0410 02:17:57.204792 30317 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 02:18:02.065716 30317 solver.cpp:218] Iteration 120 (2.46876 iter/s, 4.86074s/12 iters), loss = 5.27799
I0410 02:18:02.065771 30317 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0410 02:18:02.065783 30317 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 02:18:06.886549 30317 solver.cpp:218] Iteration 132 (2.48932 iter/s, 4.82059s/12 iters), loss = 5.2549
I0410 02:18:06.886601 30317 solver.cpp:237]     Train net output #0: loss = 5.2549 (* 1 = 5.2549 loss)
I0410 02:18:06.886611 30317 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 02:18:11.842559 30317 solver.cpp:218] Iteration 144 (2.42142 iter/s, 4.95576s/12 iters), loss = 5.28843
I0410 02:18:11.842708 30317 solver.cpp:237]     Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
I0410 02:18:11.842727 30317 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 02:18:16.708947 30317 solver.cpp:218] Iteration 156 (2.46606 iter/s, 4.86606s/12 iters), loss = 5.26381
I0410 02:18:16.708995 30317 solver.cpp:237]     Train net output #0: loss = 5.26381 (* 1 = 5.26381 loss)
I0410 02:18:16.709007 30317 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 02:18:21.633414 30317 solver.cpp:218] Iteration 168 (2.43693 iter/s, 4.92424s/12 iters), loss = 5.27538
I0410 02:18:21.633452 30317 solver.cpp:237]     Train net output #0: loss = 5.27538 (* 1 = 5.27538 loss)
I0410 02:18:21.633460 30317 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 02:18:26.560914 30317 solver.cpp:218] Iteration 180 (2.43542 iter/s, 4.92728s/12 iters), loss = 5.27103
I0410 02:18:26.560961 30317 solver.cpp:237]     Train net output #0: loss = 5.27103 (* 1 = 5.27103 loss)
I0410 02:18:26.560971 30317 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 02:18:31.439357 30317 solver.cpp:218] Iteration 192 (2.45992 iter/s, 4.87821s/12 iters), loss = 5.2748
I0410 02:18:31.439412 30317 solver.cpp:237]     Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
I0410 02:18:31.439424 30317 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 02:18:35.177069 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:18:35.885430 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 02:18:37.502163 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 02:18:39.033269 30317 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 02:18:39.033296 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:18:43.363373 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:18:43.484782 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:18:43.484833 30317 solver.cpp:397]     Test net output #1: loss = 5.28138 (* 1 = 5.28138 loss)
I0410 02:18:43.566599 30317 solver.cpp:218] Iteration 204 (0.989548 iter/s, 12.1268s/12 iters), loss = 5.27025
I0410 02:18:43.566654 30317 solver.cpp:237]     Train net output #0: loss = 5.27025 (* 1 = 5.27025 loss)
I0410 02:18:43.566665 30317 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 02:18:47.784996 30317 solver.cpp:218] Iteration 216 (2.84483 iter/s, 4.21818s/12 iters), loss = 5.28309
I0410 02:18:47.785037 30317 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0410 02:18:47.785046 30317 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 02:18:52.643086 30317 solver.cpp:218] Iteration 228 (2.47022 iter/s, 4.85787s/12 iters), loss = 5.26275
I0410 02:18:52.643127 30317 solver.cpp:237]     Train net output #0: loss = 5.26275 (* 1 = 5.26275 loss)
I0410 02:18:52.643136 30317 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 02:18:57.446296 30317 solver.cpp:218] Iteration 240 (2.49845 iter/s, 4.80299s/12 iters), loss = 5.28425
I0410 02:18:57.446349 30317 solver.cpp:237]     Train net output #0: loss = 5.28425 (* 1 = 5.28425 loss)
I0410 02:18:57.446360 30317 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 02:19:02.329393 30317 solver.cpp:218] Iteration 252 (2.45757 iter/s, 4.88286s/12 iters), loss = 5.27068
I0410 02:19:02.329434 30317 solver.cpp:237]     Train net output #0: loss = 5.27068 (* 1 = 5.27068 loss)
I0410 02:19:02.329443 30317 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 02:19:07.300230 30317 solver.cpp:218] Iteration 264 (2.41419 iter/s, 4.97061s/12 iters), loss = 5.27609
I0410 02:19:07.300269 30317 solver.cpp:237]     Train net output #0: loss = 5.27609 (* 1 = 5.27609 loss)
I0410 02:19:07.300278 30317 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 02:19:12.204972 30317 solver.cpp:218] Iteration 276 (2.44673 iter/s, 4.90451s/12 iters), loss = 5.28561
I0410 02:19:12.205027 30317 solver.cpp:237]     Train net output #0: loss = 5.28561 (* 1 = 5.28561 loss)
I0410 02:19:12.205039 30317 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 02:19:17.125252 30317 solver.cpp:218] Iteration 288 (2.439 iter/s, 4.92004s/12 iters), loss = 5.28046
I0410 02:19:17.125388 30317 solver.cpp:237]     Train net output #0: loss = 5.28046 (* 1 = 5.28046 loss)
I0410 02:19:17.125398 30317 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 02:19:22.046540 30317 solver.cpp:218] Iteration 300 (2.43854 iter/s, 4.92097s/12 iters), loss = 5.27729
I0410 02:19:22.046584 30317 solver.cpp:237]     Train net output #0: loss = 5.27729 (* 1 = 5.27729 loss)
I0410 02:19:22.046594 30317 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 02:19:23.044436 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:24.064927 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 02:19:24.527415 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 02:19:24.861629 30317 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 02:19:24.861649 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:19:29.087965 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:29.244231 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:19:29.244278 30317 solver.cpp:397]     Test net output #1: loss = 5.28278 (* 1 = 5.28278 loss)
I0410 02:19:31.021179 30317 solver.cpp:218] Iteration 312 (1.33716 iter/s, 8.97426s/12 iters), loss = 5.28246
I0410 02:19:31.021227 30317 solver.cpp:237]     Train net output #0: loss = 5.28246 (* 1 = 5.28246 loss)
I0410 02:19:31.021237 30317 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 02:19:35.943989 30317 solver.cpp:218] Iteration 324 (2.43775 iter/s, 4.92258s/12 iters), loss = 5.25555
I0410 02:19:35.944033 30317 solver.cpp:237]     Train net output #0: loss = 5.25555 (* 1 = 5.25555 loss)
I0410 02:19:35.944041 30317 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 02:19:40.872233 30317 solver.cpp:218] Iteration 336 (2.43506 iter/s, 4.92801s/12 iters), loss = 5.26439
I0410 02:19:40.872283 30317 solver.cpp:237]     Train net output #0: loss = 5.26439 (* 1 = 5.26439 loss)
I0410 02:19:40.872295 30317 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 02:19:45.712576 30317 solver.cpp:218] Iteration 348 (2.47928 iter/s, 4.84011s/12 iters), loss = 5.27027
I0410 02:19:45.712630 30317 solver.cpp:237]     Train net output #0: loss = 5.27027 (* 1 = 5.27027 loss)
I0410 02:19:45.712642 30317 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 02:19:50.526006 30317 solver.cpp:218] Iteration 360 (2.49314 iter/s, 4.8132s/12 iters), loss = 5.29298
I0410 02:19:50.526155 30317 solver.cpp:237]     Train net output #0: loss = 5.29298 (* 1 = 5.29298 loss)
I0410 02:19:50.526167 30317 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 02:19:55.403563 30317 solver.cpp:218] Iteration 372 (2.46042 iter/s, 4.87722s/12 iters), loss = 5.26685
I0410 02:19:55.403609 30317 solver.cpp:237]     Train net output #0: loss = 5.26685 (* 1 = 5.26685 loss)
I0410 02:19:55.403618 30317 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 02:20:00.312737 30317 solver.cpp:218] Iteration 384 (2.44452 iter/s, 4.90895s/12 iters), loss = 5.27847
I0410 02:20:00.312786 30317 solver.cpp:237]     Train net output #0: loss = 5.27847 (* 1 = 5.27847 loss)
I0410 02:20:00.312798 30317 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 02:20:05.242027 30317 solver.cpp:218] Iteration 396 (2.43454 iter/s, 4.92905s/12 iters), loss = 5.26976
I0410 02:20:05.242081 30317 solver.cpp:237]     Train net output #0: loss = 5.26976 (* 1 = 5.26976 loss)
I0410 02:20:05.242094 30317 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 02:20:08.277781 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:09.739428 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 02:20:10.201534 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 02:20:10.526280 30317 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 02:20:10.526307 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:20:14.815666 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:15.026494 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:20:15.026527 30317 solver.cpp:397]     Test net output #1: loss = 5.28475 (* 1 = 5.28475 loss)
I0410 02:20:15.108033 30317 solver.cpp:218] Iteration 408 (1.21635 iter/s, 9.8656s/12 iters), loss = 5.27923
I0410 02:20:15.108080 30317 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0410 02:20:15.108088 30317 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 02:20:19.174661 30317 solver.cpp:218] Iteration 420 (2.951 iter/s, 4.06642s/12 iters), loss = 5.27485
I0410 02:20:19.174708 30317 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0410 02:20:19.174718 30317 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 02:20:24.085713 30317 solver.cpp:218] Iteration 432 (2.44358 iter/s, 4.91082s/12 iters), loss = 5.27239
I0410 02:20:24.085851 30317 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0410 02:20:24.085865 30317 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 02:20:28.983741 30317 solver.cpp:218] Iteration 444 (2.45013 iter/s, 4.89771s/12 iters), loss = 5.28434
I0410 02:20:28.983793 30317 solver.cpp:237]     Train net output #0: loss = 5.28434 (* 1 = 5.28434 loss)
I0410 02:20:28.983806 30317 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 02:20:34.028241 30317 solver.cpp:218] Iteration 456 (2.37894 iter/s, 5.04426s/12 iters), loss = 5.28247
I0410 02:20:34.028286 30317 solver.cpp:237]     Train net output #0: loss = 5.28247 (* 1 = 5.28247 loss)
I0410 02:20:34.028296 30317 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 02:20:38.830139 30317 solver.cpp:218] Iteration 468 (2.49913 iter/s, 4.80167s/12 iters), loss = 5.28624
I0410 02:20:38.830191 30317 solver.cpp:237]     Train net output #0: loss = 5.28624 (* 1 = 5.28624 loss)
I0410 02:20:38.830204 30317 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 02:20:43.648628 30317 solver.cpp:218] Iteration 480 (2.49053 iter/s, 4.81825s/12 iters), loss = 5.26813
I0410 02:20:43.648682 30317 solver.cpp:237]     Train net output #0: loss = 5.26813 (* 1 = 5.26813 loss)
I0410 02:20:43.648696 30317 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 02:20:48.562002 30317 solver.cpp:218] Iteration 492 (2.44243 iter/s, 4.91314s/12 iters), loss = 5.28524
I0410 02:20:48.562043 30317 solver.cpp:237]     Train net output #0: loss = 5.28524 (* 1 = 5.28524 loss)
I0410 02:20:48.562052 30317 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 02:20:53.393934 30317 solver.cpp:218] Iteration 504 (2.4836 iter/s, 4.83171s/12 iters), loss = 5.26841
I0410 02:20:53.394004 30317 solver.cpp:237]     Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
I0410 02:20:53.394016 30317 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 02:20:53.641060 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:55.381745 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 02:20:56.287144 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 02:20:57.048837 30317 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 02:20:57.048868 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:21:01.344035 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:01.579738 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:21:01.579784 30317 solver.cpp:397]     Test net output #1: loss = 5.28475 (* 1 = 5.28475 loss)
I0410 02:21:03.394112 30317 solver.cpp:218] Iteration 516 (1.20003 iter/s, 9.99975s/12 iters), loss = 5.27869
I0410 02:21:03.394152 30317 solver.cpp:237]     Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
I0410 02:21:03.394160 30317 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 02:21:08.162051 30317 solver.cpp:218] Iteration 528 (2.51693 iter/s, 4.76771s/12 iters), loss = 5.27319
I0410 02:21:08.162101 30317 solver.cpp:237]     Train net output #0: loss = 5.27319 (* 1 = 5.27319 loss)
I0410 02:21:08.162111 30317 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 02:21:12.983472 30317 solver.cpp:218] Iteration 540 (2.48901 iter/s, 4.82119s/12 iters), loss = 5.27424
I0410 02:21:12.983513 30317 solver.cpp:237]     Train net output #0: loss = 5.27424 (* 1 = 5.27424 loss)
I0410 02:21:12.983522 30317 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 02:21:17.816368 30317 solver.cpp:218] Iteration 552 (2.4831 iter/s, 4.83267s/12 iters), loss = 5.27032
I0410 02:21:17.816426 30317 solver.cpp:237]     Train net output #0: loss = 5.27032 (* 1 = 5.27032 loss)
I0410 02:21:17.816438 30317 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 02:21:22.673431 30317 solver.cpp:218] Iteration 564 (2.47075 iter/s, 4.85682s/12 iters), loss = 5.2566
I0410 02:21:22.673480 30317 solver.cpp:237]     Train net output #0: loss = 5.2566 (* 1 = 5.2566 loss)
I0410 02:21:22.673491 30317 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 02:21:27.571254 30317 solver.cpp:218] Iteration 576 (2.45018 iter/s, 4.89759s/12 iters), loss = 5.27692
I0410 02:21:27.571357 30317 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0410 02:21:27.571368 30317 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 02:21:32.504609 30317 solver.cpp:218] Iteration 588 (2.43256 iter/s, 4.93307s/12 iters), loss = 5.26366
I0410 02:21:32.504650 30317 solver.cpp:237]     Train net output #0: loss = 5.26366 (* 1 = 5.26366 loss)
I0410 02:21:32.504658 30317 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 02:21:37.446698 30317 solver.cpp:218] Iteration 600 (2.42823 iter/s, 4.94186s/12 iters), loss = 5.26361
I0410 02:21:37.446743 30317 solver.cpp:237]     Train net output #0: loss = 5.26361 (* 1 = 5.26361 loss)
I0410 02:21:37.446751 30317 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 02:21:39.805773 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:41.985559 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 02:21:42.439740 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 02:21:42.769733 30317 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 02:21:42.769757 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:21:46.961071 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:47.247268 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:21:47.247316 30317 solver.cpp:397]     Test net output #1: loss = 5.28523 (* 1 = 5.28523 loss)
I0410 02:21:47.328989 30317 solver.cpp:218] Iteration 612 (1.21434 iter/s, 9.88189s/12 iters), loss = 5.27383
I0410 02:21:47.329039 30317 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0410 02:21:47.329051 30317 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 02:21:51.487562 30317 solver.cpp:218] Iteration 624 (2.88575 iter/s, 4.15837s/12 iters), loss = 5.28858
I0410 02:21:51.487609 30317 solver.cpp:237]     Train net output #0: loss = 5.28858 (* 1 = 5.28858 loss)
I0410 02:21:51.487622 30317 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 02:21:56.413633 30317 solver.cpp:218] Iteration 636 (2.43613 iter/s, 4.92584s/12 iters), loss = 5.28195
I0410 02:21:56.413689 30317 solver.cpp:237]     Train net output #0: loss = 5.28195 (* 1 = 5.28195 loss)
I0410 02:21:56.413702 30317 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 02:22:01.242877 30317 solver.cpp:218] Iteration 648 (2.48498 iter/s, 4.82901s/12 iters), loss = 5.27693
I0410 02:22:01.243059 30317 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0410 02:22:01.243077 30317 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 02:22:06.180907 30317 solver.cpp:218] Iteration 660 (2.4303 iter/s, 4.93767s/12 iters), loss = 5.26839
I0410 02:22:06.180961 30317 solver.cpp:237]     Train net output #0: loss = 5.26839 (* 1 = 5.26839 loss)
I0410 02:22:06.180974 30317 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 02:22:11.291455 30317 solver.cpp:218] Iteration 672 (2.34819 iter/s, 5.11031s/12 iters), loss = 5.27796
I0410 02:22:11.291492 30317 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0410 02:22:11.291501 30317 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 02:22:15.652935 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:22:16.104007 30317 solver.cpp:218] Iteration 684 (2.49359 iter/s, 4.81233s/12 iters), loss = 5.27021
I0410 02:22:16.104054 30317 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0410 02:22:16.104064 30317 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 02:22:21.143473 30317 solver.cpp:218] Iteration 696 (2.38131 iter/s, 5.03923s/12 iters), loss = 5.26787
I0410 02:22:21.143522 30317 solver.cpp:237]     Train net output #0: loss = 5.26787 (* 1 = 5.26787 loss)
I0410 02:22:21.143532 30317 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 02:22:25.679422 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:22:26.053460 30317 solver.cpp:218] Iteration 708 (2.44411 iter/s, 4.90976s/12 iters), loss = 5.26082
I0410 02:22:26.053508 30317 solver.cpp:237]     Train net output #0: loss = 5.26082 (* 1 = 5.26082 loss)
I0410 02:22:26.053519 30317 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 02:22:28.031481 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 02:22:29.192550 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 02:22:29.962405 30317 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 02:22:29.962435 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:22:34.080739 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:22:34.398005 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:22:34.398033 30317 solver.cpp:397]     Test net output #1: loss = 5.28634 (* 1 = 5.28634 loss)
I0410 02:22:36.536867 30317 solver.cpp:218] Iteration 720 (1.14471 iter/s, 10.483s/12 iters), loss = 5.27411
I0410 02:22:36.536921 30317 solver.cpp:237]     Train net output #0: loss = 5.27411 (* 1 = 5.27411 loss)
I0410 02:22:36.536931 30317 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 02:22:41.369482 30317 solver.cpp:218] Iteration 732 (2.48325 iter/s, 4.83238s/12 iters), loss = 5.2776
I0410 02:22:41.369539 30317 solver.cpp:237]     Train net output #0: loss = 5.2776 (* 1 = 5.2776 loss)
I0410 02:22:41.369551 30317 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 02:22:46.261567 30317 solver.cpp:218] Iteration 744 (2.45306 iter/s, 4.89185s/12 iters), loss = 5.28049
I0410 02:22:46.261606 30317 solver.cpp:237]     Train net output #0: loss = 5.28049 (* 1 = 5.28049 loss)
I0410 02:22:46.261615 30317 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 02:22:51.186478 30317 solver.cpp:218] Iteration 756 (2.43671 iter/s, 4.92468s/12 iters), loss = 5.27239
I0410 02:22:51.186539 30317 solver.cpp:237]     Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
I0410 02:22:51.186553 30317 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 02:22:56.103981 30317 solver.cpp:218] Iteration 768 (2.44038 iter/s, 4.91726s/12 iters), loss = 5.27997
I0410 02:22:56.104027 30317 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0410 02:22:56.104039 30317 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 02:23:01.030264 30317 solver.cpp:218] Iteration 780 (2.43603 iter/s, 4.92605s/12 iters), loss = 5.26539
I0410 02:23:01.030315 30317 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0410 02:23:01.030326 30317 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 02:23:06.037518 30317 solver.cpp:218] Iteration 792 (2.39664 iter/s, 5.00701s/12 iters), loss = 5.26498
I0410 02:23:06.037678 30317 solver.cpp:237]     Train net output #0: loss = 5.26498 (* 1 = 5.26498 loss)
I0410 02:23:06.037693 30317 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 02:23:11.165992 30317 solver.cpp:218] Iteration 804 (2.34005 iter/s, 5.1281s/12 iters), loss = 5.2851
I0410 02:23:11.166035 30317 solver.cpp:237]     Train net output #0: loss = 5.2851 (* 1 = 5.2851 loss)
I0410 02:23:11.166043 30317 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 02:23:12.860414 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:15.564337 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 02:23:16.032155 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 02:23:16.356467 30317 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 02:23:16.356498 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:23:20.611860 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:20.964020 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:23:20.964067 30317 solver.cpp:397]     Test net output #1: loss = 5.28631 (* 1 = 5.28631 loss)
I0410 02:23:21.045356 30317 solver.cpp:218] Iteration 816 (1.2147 iter/s, 9.87896s/12 iters), loss = 5.27721
I0410 02:23:21.045408 30317 solver.cpp:237]     Train net output #0: loss = 5.27721 (* 1 = 5.27721 loss)
I0410 02:23:21.045420 30317 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 02:23:25.255393 30317 solver.cpp:218] Iteration 828 (2.85048 iter/s, 4.20982s/12 iters), loss = 5.28036
I0410 02:23:25.255431 30317 solver.cpp:237]     Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
I0410 02:23:25.255440 30317 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 02:23:30.099906 30317 solver.cpp:218] Iteration 840 (2.47715 iter/s, 4.84428s/12 iters), loss = 5.23076
I0410 02:23:30.099961 30317 solver.cpp:237]     Train net output #0: loss = 5.23076 (* 1 = 5.23076 loss)
I0410 02:23:30.099973 30317 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 02:23:34.961248 30317 solver.cpp:218] Iteration 852 (2.46858 iter/s, 4.8611s/12 iters), loss = 5.30399
I0410 02:23:34.961308 30317 solver.cpp:237]     Train net output #0: loss = 5.30399 (* 1 = 5.30399 loss)
I0410 02:23:34.961321 30317 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 02:23:39.840718 30317 solver.cpp:218] Iteration 864 (2.45941 iter/s, 4.87923s/12 iters), loss = 5.26425
I0410 02:23:39.840848 30317 solver.cpp:237]     Train net output #0: loss = 5.26425 (* 1 = 5.26425 loss)
I0410 02:23:39.840863 30317 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 02:23:44.691978 30317 solver.cpp:218] Iteration 876 (2.47374 iter/s, 4.85095s/12 iters), loss = 5.27132
I0410 02:23:44.692032 30317 solver.cpp:237]     Train net output #0: loss = 5.27132 (* 1 = 5.27132 loss)
I0410 02:23:44.692044 30317 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 02:23:49.520814 30317 solver.cpp:218] Iteration 888 (2.48519 iter/s, 4.8286s/12 iters), loss = 5.26617
I0410 02:23:49.520859 30317 solver.cpp:237]     Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
I0410 02:23:49.520871 30317 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 02:23:54.497877 30317 solver.cpp:218] Iteration 900 (2.41117 iter/s, 4.97683s/12 iters), loss = 5.27201
I0410 02:23:54.497922 30317 solver.cpp:237]     Train net output #0: loss = 5.27201 (* 1 = 5.27201 loss)
I0410 02:23:54.497931 30317 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 02:23:58.424849 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:59.569375 30317 solver.cpp:218] Iteration 912 (2.36628 iter/s, 5.07126s/12 iters), loss = 5.26076
I0410 02:23:59.569422 30317 solver.cpp:237]     Train net output #0: loss = 5.26076 (* 1 = 5.26076 loss)
I0410 02:23:59.569433 30317 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 02:24:01.552976 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 02:24:02.049310 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 02:24:02.390493 30317 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 02:24:02.390523 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:24:06.333664 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:06.734510 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:24:06.734552 30317 solver.cpp:397]     Test net output #1: loss = 5.28642 (* 1 = 5.28642 loss)
I0410 02:24:08.550458 30317 solver.cpp:218] Iteration 924 (1.3362 iter/s, 8.98071s/12 iters), loss = 5.28618
I0410 02:24:08.550509 30317 solver.cpp:237]     Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
I0410 02:24:08.550519 30317 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 02:24:13.475181 30317 solver.cpp:218] Iteration 936 (2.4368 iter/s, 4.92448s/12 iters), loss = 5.26036
I0410 02:24:13.475277 30317 solver.cpp:237]     Train net output #0: loss = 5.26036 (* 1 = 5.26036 loss)
I0410 02:24:13.475294 30317 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 02:24:18.403544 30317 solver.cpp:218] Iteration 948 (2.43502 iter/s, 4.92809s/12 iters), loss = 5.28501
I0410 02:24:18.403585 30317 solver.cpp:237]     Train net output #0: loss = 5.28501 (* 1 = 5.28501 loss)
I0410 02:24:18.403594 30317 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 02:24:23.308375 30317 solver.cpp:218] Iteration 960 (2.44668 iter/s, 4.9046s/12 iters), loss = 5.25986
I0410 02:24:23.308430 30317 solver.cpp:237]     Train net output #0: loss = 5.25986 (* 1 = 5.25986 loss)
I0410 02:24:23.308442 30317 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 02:24:28.148281 30317 solver.cpp:218] Iteration 972 (2.47951 iter/s, 4.83967s/12 iters), loss = 5.277
I0410 02:24:28.148325 30317 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0410 02:24:28.148334 30317 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 02:24:32.941419 30317 solver.cpp:218] Iteration 984 (2.5037 iter/s, 4.79291s/12 iters), loss = 5.29028
I0410 02:24:32.941463 30317 solver.cpp:237]     Train net output #0: loss = 5.29028 (* 1 = 5.29028 loss)
I0410 02:24:32.941473 30317 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 02:24:37.862540 30317 solver.cpp:218] Iteration 996 (2.43859 iter/s, 4.92088s/12 iters), loss = 5.28038
I0410 02:24:37.862591 30317 solver.cpp:237]     Train net output #0: loss = 5.28038 (* 1 = 5.28038 loss)
I0410 02:24:37.862602 30317 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 02:24:42.874063 30317 solver.cpp:218] Iteration 1008 (2.39459 iter/s, 5.01129s/12 iters), loss = 5.28841
I0410 02:24:42.874106 30317 solver.cpp:237]     Train net output #0: loss = 5.28841 (* 1 = 5.28841 loss)
I0410 02:24:42.874116 30317 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 02:24:43.872921 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:47.236353 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 02:24:49.837890 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 02:24:50.591997 30317 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 02:24:50.592029 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:24:54.674208 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:55.115442 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:24:55.115476 30317 solver.cpp:397]     Test net output #1: loss = 5.28631 (* 1 = 5.28631 loss)
I0410 02:24:55.196939 30317 solver.cpp:218] Iteration 1020 (0.973837 iter/s, 12.3224s/12 iters), loss = 5.28873
I0410 02:24:55.196983 30317 solver.cpp:237]     Train net output #0: loss = 5.28873 (* 1 = 5.28873 loss)
I0410 02:24:55.196992 30317 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 02:24:59.219893 30317 solver.cpp:218] Iteration 1032 (2.98303 iter/s, 4.02276s/12 iters), loss = 5.25004
I0410 02:24:59.219933 30317 solver.cpp:237]     Train net output #0: loss = 5.25004 (* 1 = 5.25004 loss)
I0410 02:24:59.219942 30317 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 02:25:04.062719 30317 solver.cpp:218] Iteration 1044 (2.478 iter/s, 4.84261s/12 iters), loss = 5.25391
I0410 02:25:04.062767 30317 solver.cpp:237]     Train net output #0: loss = 5.25391 (* 1 = 5.25391 loss)
I0410 02:25:04.062778 30317 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 02:25:09.010891 30317 solver.cpp:218] Iteration 1056 (2.42525 iter/s, 4.94794s/12 iters), loss = 5.26528
I0410 02:25:09.010941 30317 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0410 02:25:09.010953 30317 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 02:25:14.199311 30317 solver.cpp:218] Iteration 1068 (2.31295 iter/s, 5.18818s/12 iters), loss = 5.28589
I0410 02:25:14.199420 30317 solver.cpp:237]     Train net output #0: loss = 5.28589 (* 1 = 5.28589 loss)
I0410 02:25:14.199432 30317 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 02:25:19.000181 30317 solver.cpp:218] Iteration 1080 (2.4997 iter/s, 4.80058s/12 iters), loss = 5.26623
I0410 02:25:19.000218 30317 solver.cpp:237]     Train net output #0: loss = 5.26623 (* 1 = 5.26623 loss)
I0410 02:25:19.000226 30317 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 02:25:23.919634 30317 solver.cpp:218] Iteration 1092 (2.43941 iter/s, 4.91922s/12 iters), loss = 5.28125
I0410 02:25:23.919688 30317 solver.cpp:237]     Train net output #0: loss = 5.28125 (* 1 = 5.28125 loss)
I0410 02:25:23.919700 30317 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 02:25:28.802599 30317 solver.cpp:218] Iteration 1104 (2.45764 iter/s, 4.88273s/12 iters), loss = 5.27193
I0410 02:25:28.802641 30317 solver.cpp:237]     Train net output #0: loss = 5.27193 (* 1 = 5.27193 loss)
I0410 02:25:28.802649 30317 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 02:25:31.981822 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:33.935950 30317 solver.cpp:218] Iteration 1116 (2.33776 iter/s, 5.13311s/12 iters), loss = 5.27671
I0410 02:25:33.936003 30317 solver.cpp:237]     Train net output #0: loss = 5.27671 (* 1 = 5.27671 loss)
I0410 02:25:33.936013 30317 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 02:25:35.967509 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 02:25:36.464313 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 02:25:36.794914 30317 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 02:25:36.794934 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:25:40.730046 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:41.204206 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:25:41.204248 30317 solver.cpp:397]     Test net output #1: loss = 5.28641 (* 1 = 5.28641 loss)
I0410 02:25:43.030174 30317 solver.cpp:218] Iteration 1128 (1.31957 iter/s, 9.09384s/12 iters), loss = 5.27504
I0410 02:25:43.030223 30317 solver.cpp:237]     Train net output #0: loss = 5.27504 (* 1 = 5.27504 loss)
I0410 02:25:43.030234 30317 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 02:25:47.865824 30317 solver.cpp:218] Iteration 1140 (2.48169 iter/s, 4.83542s/12 iters), loss = 5.26613
I0410 02:25:47.865939 30317 solver.cpp:237]     Train net output #0: loss = 5.26613 (* 1 = 5.26613 loss)
I0410 02:25:47.865952 30317 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 02:25:53.033551 30317 solver.cpp:218] Iteration 1152 (2.32224 iter/s, 5.16742s/12 iters), loss = 5.2781
I0410 02:25:53.033604 30317 solver.cpp:237]     Train net output #0: loss = 5.2781 (* 1 = 5.2781 loss)
I0410 02:25:53.033615 30317 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 02:25:57.934710 30317 solver.cpp:218] Iteration 1164 (2.44852 iter/s, 4.90092s/12 iters), loss = 5.27192
I0410 02:25:57.934767 30317 solver.cpp:237]     Train net output #0: loss = 5.27192 (* 1 = 5.27192 loss)
I0410 02:25:57.934779 30317 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 02:26:02.880345 30317 solver.cpp:218] Iteration 1176 (2.4265 iter/s, 4.94539s/12 iters), loss = 5.29106
I0410 02:26:02.880395 30317 solver.cpp:237]     Train net output #0: loss = 5.29106 (* 1 = 5.29106 loss)
I0410 02:26:02.880406 30317 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 02:26:07.750823 30317 solver.cpp:218] Iteration 1188 (2.46394 iter/s, 4.87025s/12 iters), loss = 5.27179
I0410 02:26:07.750859 30317 solver.cpp:237]     Train net output #0: loss = 5.27179 (* 1 = 5.27179 loss)
I0410 02:26:07.750869 30317 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 02:26:12.695144 30317 solver.cpp:218] Iteration 1200 (2.42714 iter/s, 4.9441s/12 iters), loss = 5.28978
I0410 02:26:12.695189 30317 solver.cpp:237]     Train net output #0: loss = 5.28978 (* 1 = 5.28978 loss)
I0410 02:26:12.695200 30317 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 02:26:17.596483 30317 solver.cpp:218] Iteration 1212 (2.44843 iter/s, 4.90111s/12 iters), loss = 5.26607
I0410 02:26:17.596539 30317 solver.cpp:237]     Train net output #0: loss = 5.26607 (* 1 = 5.26607 loss)
I0410 02:26:17.596550 30317 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 02:26:17.909103 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:26:22.005770 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 02:26:22.937510 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 02:26:23.656740 30317 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 02:26:23.656760 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:26:27.503481 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:26:28.020076 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:26:28.020120 30317 solver.cpp:397]     Test net output #1: loss = 5.28633 (* 1 = 5.28633 loss)
I0410 02:26:28.101845 30317 solver.cpp:218] Iteration 1224 (1.14232 iter/s, 10.5049s/12 iters), loss = 5.28562
I0410 02:26:28.101899 30317 solver.cpp:237]     Train net output #0: loss = 5.28562 (* 1 = 5.28562 loss)
I0410 02:26:28.101909 30317 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 02:26:32.180328 30317 solver.cpp:218] Iteration 1236 (2.94243 iter/s, 4.07827s/12 iters), loss = 5.27501
I0410 02:26:32.180387 30317 solver.cpp:237]     Train net output #0: loss = 5.27501 (* 1 = 5.27501 loss)
I0410 02:26:32.180399 30317 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 02:26:37.084458 30317 solver.cpp:218] Iteration 1248 (2.44704 iter/s, 4.90389s/12 iters), loss = 5.27941
I0410 02:26:37.084507 30317 solver.cpp:237]     Train net output #0: loss = 5.27941 (* 1 = 5.27941 loss)
I0410 02:26:37.084517 30317 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 02:26:42.042752 30317 solver.cpp:218] Iteration 1260 (2.4203 iter/s, 4.95806s/12 iters), loss = 5.26719
I0410 02:26:42.042804 30317 solver.cpp:237]     Train net output #0: loss = 5.26719 (* 1 = 5.26719 loss)
I0410 02:26:42.042814 30317 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 02:26:46.897804 30317 solver.cpp:218] Iteration 1272 (2.47178 iter/s, 4.85481s/12 iters), loss = 5.24618
I0410 02:26:46.897871 30317 solver.cpp:237]     Train net output #0: loss = 5.24618 (* 1 = 5.24618 loss)
I0410 02:26:46.897886 30317 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 02:26:51.795723 30317 solver.cpp:218] Iteration 1284 (2.45014 iter/s, 4.89767s/12 iters), loss = 5.28162
I0410 02:26:51.795843 30317 solver.cpp:237]     Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
I0410 02:26:51.795855 30317 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 02:26:56.670421 30317 solver.cpp:218] Iteration 1296 (2.46184 iter/s, 4.87439s/12 iters), loss = 5.26861
I0410 02:26:56.670475 30317 solver.cpp:237]     Train net output #0: loss = 5.26861 (* 1 = 5.26861 loss)
I0410 02:26:56.670486 30317 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 02:27:01.709815 30317 solver.cpp:218] Iteration 1308 (2.38135 iter/s, 5.03915s/12 iters), loss = 5.25525
I0410 02:27:01.709870 30317 solver.cpp:237]     Train net output #0: loss = 5.25525 (* 1 = 5.25525 loss)
I0410 02:27:01.709882 30317 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 02:27:04.176431 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:06.713681 30317 solver.cpp:218] Iteration 1320 (2.39826 iter/s, 5.00362s/12 iters), loss = 5.27061
I0410 02:27:06.713735 30317 solver.cpp:237]     Train net output #0: loss = 5.27061 (* 1 = 5.27061 loss)
I0410 02:27:06.713747 30317 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 02:27:08.731140 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 02:27:09.242271 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 02:27:09.584575 30317 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 02:27:09.584606 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:27:14.002208 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:14.589670 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:27:14.589705 30317 solver.cpp:397]     Test net output #1: loss = 5.28689 (* 1 = 5.28689 loss)
I0410 02:27:16.381105 30317 solver.cpp:218] Iteration 1332 (1.24133 iter/s, 9.66703s/12 iters), loss = 5.28823
I0410 02:27:16.381146 30317 solver.cpp:237]     Train net output #0: loss = 5.28823 (* 1 = 5.28823 loss)
I0410 02:27:16.381155 30317 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 02:27:21.267756 30317 solver.cpp:218] Iteration 1344 (2.45579 iter/s, 4.88642s/12 iters), loss = 5.28692
I0410 02:27:21.267807 30317 solver.cpp:237]     Train net output #0: loss = 5.28692 (* 1 = 5.28692 loss)
I0410 02:27:21.267818 30317 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 02:27:26.060603 30317 solver.cpp:218] Iteration 1356 (2.50386 iter/s, 4.79261s/12 iters), loss = 5.27482
I0410 02:27:26.060743 30317 solver.cpp:237]     Train net output #0: loss = 5.27482 (* 1 = 5.27482 loss)
I0410 02:27:26.060757 30317 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 02:27:30.926314 30317 solver.cpp:218] Iteration 1368 (2.4664 iter/s, 4.8654s/12 iters), loss = 5.26992
I0410 02:27:30.926365 30317 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0410 02:27:30.926378 30317 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 02:27:30.926723 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:27:35.805694 30317 solver.cpp:218] Iteration 1380 (2.45944 iter/s, 4.87915s/12 iters), loss = 5.27877
I0410 02:27:35.805740 30317 solver.cpp:237]     Train net output #0: loss = 5.27877 (* 1 = 5.27877 loss)
I0410 02:27:35.805749 30317 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 02:27:40.656186 30317 solver.cpp:218] Iteration 1392 (2.4741 iter/s, 4.85025s/12 iters), loss = 5.2715
I0410 02:27:40.656251 30317 solver.cpp:237]     Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
I0410 02:27:40.656267 30317 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 02:27:45.492431 30317 solver.cpp:218] Iteration 1404 (2.48139 iter/s, 4.83601s/12 iters), loss = 5.27423
I0410 02:27:45.492481 30317 solver.cpp:237]     Train net output #0: loss = 5.27423 (* 1 = 5.27423 loss)
I0410 02:27:45.492493 30317 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 02:27:50.032869 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:50.380244 30317 solver.cpp:218] Iteration 1416 (2.4552 iter/s, 4.88758s/12 iters), loss = 5.25903
I0410 02:27:50.380300 30317 solver.cpp:237]     Train net output #0: loss = 5.25903 (* 1 = 5.25903 loss)
I0410 02:27:50.380311 30317 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 02:27:55.028182 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 02:27:55.507752 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 02:27:56.363287 30317 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 02:27:56.363456 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:28:00.325706 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:00.913097 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:28:00.913134 30317 solver.cpp:397]     Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
I0410 02:28:00.994586 30317 solver.cpp:218] Iteration 1428 (1.13059 iter/s, 10.6139s/12 iters), loss = 5.27451
I0410 02:28:00.994630 30317 solver.cpp:237]     Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
I0410 02:28:00.994640 30317 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 02:28:05.146319 30317 solver.cpp:218] Iteration 1440 (2.89051 iter/s, 4.15152s/12 iters), loss = 5.2841
I0410 02:28:05.146378 30317 solver.cpp:237]     Train net output #0: loss = 5.2841 (* 1 = 5.2841 loss)
I0410 02:28:05.146390 30317 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 02:28:09.976730 30317 solver.cpp:218] Iteration 1452 (2.48438 iter/s, 4.83017s/12 iters), loss = 5.28072
I0410 02:28:09.976774 30317 solver.cpp:237]     Train net output #0: loss = 5.28072 (* 1 = 5.28072 loss)
I0410 02:28:09.976784 30317 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 02:28:14.763459 30317 solver.cpp:218] Iteration 1464 (2.50705 iter/s, 4.7865s/12 iters), loss = 5.2745
I0410 02:28:14.763511 30317 solver.cpp:237]     Train net output #0: loss = 5.2745 (* 1 = 5.2745 loss)
I0410 02:28:14.763523 30317 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 02:28:19.627830 30317 solver.cpp:218] Iteration 1476 (2.46704 iter/s, 4.86413s/12 iters), loss = 5.27668
I0410 02:28:19.627887 30317 solver.cpp:237]     Train net output #0: loss = 5.27668 (* 1 = 5.27668 loss)
I0410 02:28:19.627897 30317 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 02:28:24.545184 30317 solver.cpp:218] Iteration 1488 (2.44046 iter/s, 4.91712s/12 iters), loss = 5.2518
I0410 02:28:24.545236 30317 solver.cpp:237]     Train net output #0: loss = 5.2518 (* 1 = 5.2518 loss)
I0410 02:28:24.545249 30317 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 02:28:29.413437 30317 solver.cpp:218] Iteration 1500 (2.46507 iter/s, 4.86802s/12 iters), loss = 5.26774
I0410 02:28:29.413550 30317 solver.cpp:237]     Train net output #0: loss = 5.26774 (* 1 = 5.26774 loss)
I0410 02:28:29.413560 30317 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 02:28:34.231850 30317 solver.cpp:218] Iteration 1512 (2.4906 iter/s, 4.81811s/12 iters), loss = 5.2887
I0410 02:28:34.231904 30317 solver.cpp:237]     Train net output #0: loss = 5.2887 (* 1 = 5.2887 loss)
I0410 02:28:34.231917 30317 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 02:28:36.052425 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:39.200904 30317 solver.cpp:218] Iteration 1524 (2.41506 iter/s, 4.96881s/12 iters), loss = 5.27692
I0410 02:28:39.200950 30317 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0410 02:28:39.200960 30317 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 02:28:41.156541 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 02:28:43.874145 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 02:28:45.238401 30317 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 02:28:45.238430 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:28:49.037917 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:49.669675 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:28:49.669718 30317 solver.cpp:397]     Test net output #1: loss = 5.28629 (* 1 = 5.28629 loss)
I0410 02:28:51.498418 30317 solver.cpp:218] Iteration 1536 (0.975846 iter/s, 12.297s/12 iters), loss = 5.27651
I0410 02:28:51.498474 30317 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0410 02:28:51.498486 30317 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 02:28:56.292819 30317 solver.cpp:218] Iteration 1548 (2.50304 iter/s, 4.79417s/12 iters), loss = 5.23509
I0410 02:28:56.292867 30317 solver.cpp:237]     Train net output #0: loss = 5.23509 (* 1 = 5.23509 loss)
I0410 02:28:56.292878 30317 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 02:29:01.203658 30317 solver.cpp:218] Iteration 1560 (2.44369 iter/s, 4.91061s/12 iters), loss = 5.29289
I0410 02:29:01.203804 30317 solver.cpp:237]     Train net output #0: loss = 5.29289 (* 1 = 5.29289 loss)
I0410 02:29:01.203817 30317 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 02:29:06.161221 30317 solver.cpp:218] Iteration 1572 (2.4207 iter/s, 4.95723s/12 iters), loss = 5.25884
I0410 02:29:06.161271 30317 solver.cpp:237]     Train net output #0: loss = 5.25884 (* 1 = 5.25884 loss)
I0410 02:29:06.161283 30317 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 02:29:11.082927 30317 solver.cpp:218] Iteration 1584 (2.4383 iter/s, 4.92147s/12 iters), loss = 5.2664
I0410 02:29:11.082983 30317 solver.cpp:237]     Train net output #0: loss = 5.2664 (* 1 = 5.2664 loss)
I0410 02:29:11.082994 30317 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 02:29:16.017238 30317 solver.cpp:218] Iteration 1596 (2.43207 iter/s, 4.93407s/12 iters), loss = 5.26729
I0410 02:29:16.017290 30317 solver.cpp:237]     Train net output #0: loss = 5.26729 (* 1 = 5.26729 loss)
I0410 02:29:16.017302 30317 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 02:29:20.936311 30317 solver.cpp:218] Iteration 1608 (2.4396 iter/s, 4.91884s/12 iters), loss = 5.26517
I0410 02:29:20.936354 30317 solver.cpp:237]     Train net output #0: loss = 5.26517 (* 1 = 5.26517 loss)
I0410 02:29:20.936364 30317 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 02:29:24.786173 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:25.852547 30317 solver.cpp:218] Iteration 1620 (2.44101 iter/s, 4.916s/12 iters), loss = 5.25792
I0410 02:29:25.852596 30317 solver.cpp:237]     Train net output #0: loss = 5.25792 (* 1 = 5.25792 loss)
I0410 02:29:25.852608 30317 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 02:29:30.319589 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 02:29:31.096174 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 02:29:31.532209 30317 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 02:29:31.532299 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:29:35.206357 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:35.874752 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:29:35.874784 30317 solver.cpp:397]     Test net output #1: loss = 5.2864 (* 1 = 5.2864 loss)
I0410 02:29:35.956212 30317 solver.cpp:218] Iteration 1632 (1.18774 iter/s, 10.1032s/12 iters), loss = 5.2869
I0410 02:29:35.956259 30317 solver.cpp:237]     Train net output #0: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 02:29:35.956267 30317 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 02:29:40.138054 30317 solver.cpp:218] Iteration 1644 (2.8697 iter/s, 4.18163s/12 iters), loss = 5.25559
I0410 02:29:40.138121 30317 solver.cpp:237]     Train net output #0: loss = 5.25559 (* 1 = 5.25559 loss)
I0410 02:29:40.138134 30317 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 02:29:45.040863 30317 solver.cpp:218] Iteration 1656 (2.4477 iter/s, 4.90256s/12 iters), loss = 5.29321
I0410 02:29:45.040907 30317 solver.cpp:237]     Train net output #0: loss = 5.29321 (* 1 = 5.29321 loss)
I0410 02:29:45.040916 30317 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 02:29:49.889895 30317 solver.cpp:218] Iteration 1668 (2.47484 iter/s, 4.8488s/12 iters), loss = 5.26043
I0410 02:29:49.889945 30317 solver.cpp:237]     Train net output #0: loss = 5.26043 (* 1 = 5.26043 loss)
I0410 02:29:49.889973 30317 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 02:29:54.719616 30317 solver.cpp:218] Iteration 1680 (2.48474 iter/s, 4.82949s/12 iters), loss = 5.27357
I0410 02:29:54.719676 30317 solver.cpp:237]     Train net output #0: loss = 5.27357 (* 1 = 5.27357 loss)
I0410 02:29:54.719686 30317 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 02:29:59.595202 30317 solver.cpp:218] Iteration 1692 (2.46137 iter/s, 4.87534s/12 iters), loss = 5.28835
I0410 02:29:59.595293 30317 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0410 02:29:59.595330 30317 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 02:30:04.525804 30317 solver.cpp:218] Iteration 1704 (2.4339 iter/s, 4.93037s/12 iters), loss = 5.27187
I0410 02:30:04.525969 30317 solver.cpp:237]     Train net output #0: loss = 5.27187 (* 1 = 5.27187 loss)
I0410 02:30:04.525983 30317 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 02:30:09.408921 30317 solver.cpp:218] Iteration 1716 (2.45761 iter/s, 4.88278s/12 iters), loss = 5.28123
I0410 02:30:09.408972 30317 solver.cpp:237]     Train net output #0: loss = 5.28123 (* 1 = 5.28123 loss)
I0410 02:30:09.408984 30317 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 02:30:10.426717 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:14.293330 30317 solver.cpp:218] Iteration 1728 (2.45691 iter/s, 4.88417s/12 iters), loss = 5.28668
I0410 02:30:14.293378 30317 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 02:30:14.293390 30317 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 02:30:16.316437 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 02:30:16.810746 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 02:30:17.188419 30317 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 02:30:17.188448 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:30:20.924917 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:21.626442 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:30:21.626492 30317 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 02:30:23.496814 30317 solver.cpp:218] Iteration 1740 (1.30391 iter/s, 9.20311s/12 iters), loss = 5.2518
I0410 02:30:23.496851 30317 solver.cpp:237]     Train net output #0: loss = 5.2518 (* 1 = 5.2518 loss)
I0410 02:30:23.496860 30317 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 02:30:28.301280 30317 solver.cpp:218] Iteration 1752 (2.49779 iter/s, 4.80424s/12 iters), loss = 5.26464
I0410 02:30:28.301337 30317 solver.cpp:237]     Train net output #0: loss = 5.26464 (* 1 = 5.26464 loss)
I0410 02:30:28.301349 30317 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 02:30:33.148417 30317 solver.cpp:218] Iteration 1764 (2.47581 iter/s, 4.84689s/12 iters), loss = 5.26344
I0410 02:30:33.148478 30317 solver.cpp:237]     Train net output #0: loss = 5.26344 (* 1 = 5.26344 loss)
I0410 02:30:33.148491 30317 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 02:30:37.968307 30317 solver.cpp:218] Iteration 1776 (2.48981 iter/s, 4.81964s/12 iters), loss = 5.28117
I0410 02:30:37.968473 30317 solver.cpp:237]     Train net output #0: loss = 5.28117 (* 1 = 5.28117 loss)
I0410 02:30:37.968487 30317 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 02:30:42.829527 30317 solver.cpp:218] Iteration 1788 (2.46869 iter/s, 4.86087s/12 iters), loss = 5.26406
I0410 02:30:42.829582 30317 solver.cpp:237]     Train net output #0: loss = 5.26406 (* 1 = 5.26406 loss)
I0410 02:30:42.829596 30317 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 02:30:47.885789 30317 solver.cpp:218] Iteration 1800 (2.37341 iter/s, 5.05602s/12 iters), loss = 5.28059
I0410 02:30:47.885840 30317 solver.cpp:237]     Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
I0410 02:30:47.885852 30317 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 02:30:52.725749 30317 solver.cpp:218] Iteration 1812 (2.47948 iter/s, 4.83972s/12 iters), loss = 5.27201
I0410 02:30:52.725805 30317 solver.cpp:237]     Train net output #0: loss = 5.27201 (* 1 = 5.27201 loss)
I0410 02:30:52.725816 30317 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 02:30:55.793789 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:57.564630 30317 solver.cpp:218] Iteration 1824 (2.48003 iter/s, 4.83865s/12 iters), loss = 5.27464
I0410 02:30:57.564672 30317 solver.cpp:237]     Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
I0410 02:30:57.564682 30317 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 02:31:02.107816 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 02:31:02.616593 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 02:31:03.854629 30317 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 02:31:03.854660 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:31:07.904122 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:08.651192 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:31:08.651301 30317 solver.cpp:397]     Test net output #1: loss = 5.28627 (* 1 = 5.28627 loss)
I0410 02:31:08.732976 30317 solver.cpp:218] Iteration 1836 (1.07451 iter/s, 11.1679s/12 iters), loss = 5.27908
I0410 02:31:08.733022 30317 solver.cpp:237]     Train net output #0: loss = 5.27908 (* 1 = 5.27908 loss)
I0410 02:31:08.733031 30317 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 02:31:12.959401 30317 solver.cpp:218] Iteration 1848 (2.83942 iter/s, 4.22622s/12 iters), loss = 5.26894
I0410 02:31:12.959448 30317 solver.cpp:237]     Train net output #0: loss = 5.26894 (* 1 = 5.26894 loss)
I0410 02:31:12.959458 30317 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 02:31:17.828074 30317 solver.cpp:218] Iteration 1860 (2.46485 iter/s, 4.86845s/12 iters), loss = 5.2836
I0410 02:31:17.828109 30317 solver.cpp:237]     Train net output #0: loss = 5.2836 (* 1 = 5.2836 loss)
I0410 02:31:17.828116 30317 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 02:31:22.725364 30317 solver.cpp:218] Iteration 1872 (2.45045 iter/s, 4.89707s/12 iters), loss = 5.26916
I0410 02:31:22.725414 30317 solver.cpp:237]     Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
I0410 02:31:22.725425 30317 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 02:31:27.668793 30317 solver.cpp:218] Iteration 1884 (2.42759 iter/s, 4.94317s/12 iters), loss = 5.28784
I0410 02:31:27.668853 30317 solver.cpp:237]     Train net output #0: loss = 5.28784 (* 1 = 5.28784 loss)
I0410 02:31:27.668864 30317 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 02:31:32.524268 30317 solver.cpp:218] Iteration 1896 (2.47156 iter/s, 4.85523s/12 iters), loss = 5.26584
I0410 02:31:32.524324 30317 solver.cpp:237]     Train net output #0: loss = 5.26584 (* 1 = 5.26584 loss)
I0410 02:31:32.524336 30317 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 02:31:37.327845 30317 solver.cpp:218] Iteration 1908 (2.49826 iter/s, 4.80333s/12 iters), loss = 5.28256
I0410 02:31:37.327896 30317 solver.cpp:237]     Train net output #0: loss = 5.28256 (* 1 = 5.28256 loss)
I0410 02:31:37.327908 30317 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 02:31:42.184260 30317 solver.cpp:218] Iteration 1920 (2.47108 iter/s, 4.85618s/12 iters), loss = 5.27441
I0410 02:31:42.184427 30317 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0410 02:31:42.184437 30317 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 02:31:42.501981 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:47.040053 30317 solver.cpp:218] Iteration 1932 (2.47146 iter/s, 4.85544s/12 iters), loss = 5.28477
I0410 02:31:47.040109 30317 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0410 02:31:47.040122 30317 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 02:31:49.019138 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 02:31:49.943238 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 02:31:50.509503 30317 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 02:31:50.509534 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:31:54.145395 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:54.924468 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:31:54.924506 30317 solver.cpp:397]     Test net output #1: loss = 5.28674 (* 1 = 5.28674 loss)
I0410 02:31:56.803933 30317 solver.cpp:218] Iteration 1944 (1.22907 iter/s, 9.76347s/12 iters), loss = 5.26934
I0410 02:31:56.803975 30317 solver.cpp:237]     Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
I0410 02:31:56.803984 30317 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 02:32:01.671519 30317 solver.cpp:218] Iteration 1956 (2.4654 iter/s, 4.86735s/12 iters), loss = 5.28245
I0410 02:32:01.671572 30317 solver.cpp:237]     Train net output #0: loss = 5.28245 (* 1 = 5.28245 loss)
I0410 02:32:01.671584 30317 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 02:32:06.583091 30317 solver.cpp:218] Iteration 1968 (2.44333 iter/s, 4.91134s/12 iters), loss = 5.26913
I0410 02:32:06.583132 30317 solver.cpp:237]     Train net output #0: loss = 5.26913 (* 1 = 5.26913 loss)
I0410 02:32:06.583139 30317 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 02:32:11.503129 30317 solver.cpp:218] Iteration 1980 (2.43911 iter/s, 4.91982s/12 iters), loss = 5.25712
I0410 02:32:11.503170 30317 solver.cpp:237]     Train net output #0: loss = 5.25712 (* 1 = 5.25712 loss)
I0410 02:32:11.503178 30317 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 02:32:16.447469 30317 solver.cpp:218] Iteration 1992 (2.42713 iter/s, 4.94411s/12 iters), loss = 5.28222
I0410 02:32:16.447563 30317 solver.cpp:237]     Train net output #0: loss = 5.28222 (* 1 = 5.28222 loss)
I0410 02:32:16.447572 30317 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 02:32:21.349691 30317 solver.cpp:218] Iteration 2004 (2.44801 iter/s, 4.90194s/12 iters), loss = 5.27839
I0410 02:32:21.349735 30317 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0410 02:32:21.349745 30317 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 02:32:26.272867 30317 solver.cpp:218] Iteration 2016 (2.43757 iter/s, 4.92294s/12 iters), loss = 5.25579
I0410 02:32:26.272913 30317 solver.cpp:237]     Train net output #0: loss = 5.25579 (* 1 = 5.25579 loss)
I0410 02:32:26.272922 30317 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 02:32:28.741242 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:32:31.128060 30317 solver.cpp:218] Iteration 2028 (2.4717 iter/s, 4.85496s/12 iters), loss = 5.27806
I0410 02:32:31.128106 30317 solver.cpp:237]     Train net output #0: loss = 5.27806 (* 1 = 5.27806 loss)
I0410 02:32:31.128115 30317 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 02:32:35.606658 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 02:32:36.614998 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 02:32:37.599774 30317 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 02:32:37.599802 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:32:41.303362 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:32:42.126070 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:32:42.126116 30317 solver.cpp:397]     Test net output #1: loss = 5.28643 (* 1 = 5.28643 loss)
I0410 02:32:42.207839 30317 solver.cpp:218] Iteration 2040 (1.0831 iter/s, 11.0793s/12 iters), loss = 5.28025
I0410 02:32:42.207882 30317 solver.cpp:237]     Train net output #0: loss = 5.28025 (* 1 = 5.28025 loss)
I0410 02:32:42.207892 30317 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 02:32:46.284729 30317 solver.cpp:218] Iteration 2052 (2.94356 iter/s, 4.07669s/12 iters), loss = 5.28203
I0410 02:32:46.284773 30317 solver.cpp:237]     Train net output #0: loss = 5.28203 (* 1 = 5.28203 loss)
I0410 02:32:46.284783 30317 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 02:32:46.639986 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:32:51.124095 30317 solver.cpp:218] Iteration 2064 (2.47978 iter/s, 4.83914s/12 iters), loss = 5.27632
I0410 02:32:51.124136 30317 solver.cpp:237]     Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
I0410 02:32:51.124145 30317 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 02:32:56.016716 30317 solver.cpp:218] Iteration 2076 (2.45279 iter/s, 4.89239s/12 iters), loss = 5.27542
I0410 02:32:56.016775 30317 solver.cpp:237]     Train net output #0: loss = 5.27542 (* 1 = 5.27542 loss)
I0410 02:32:56.016788 30317 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 02:33:00.840000 30317 solver.cpp:218] Iteration 2088 (2.48806 iter/s, 4.82304s/12 iters), loss = 5.27607
I0410 02:33:00.840059 30317 solver.cpp:237]     Train net output #0: loss = 5.27607 (* 1 = 5.27607 loss)
I0410 02:33:00.840072 30317 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 02:33:05.752445 30317 solver.cpp:218] Iteration 2100 (2.44289 iter/s, 4.9122s/12 iters), loss = 5.2715
I0410 02:33:05.752491 30317 solver.cpp:237]     Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
I0410 02:33:05.752502 30317 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 02:33:10.624317 30317 solver.cpp:218] Iteration 2112 (2.46323 iter/s, 4.87164s/12 iters), loss = 5.2827
I0410 02:33:10.624361 30317 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0410 02:33:10.624370 30317 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 02:33:15.104388 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:15.416277 30317 solver.cpp:218] Iteration 2124 (2.50431 iter/s, 4.79173s/12 iters), loss = 5.25764
I0410 02:33:15.416324 30317 solver.cpp:237]     Train net output #0: loss = 5.25764 (* 1 = 5.25764 loss)
I0410 02:33:15.416334 30317 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 02:33:20.291894 30317 solver.cpp:218] Iteration 2136 (2.46134 iter/s, 4.87538s/12 iters), loss = 5.27572
I0410 02:33:20.291996 30317 solver.cpp:237]     Train net output #0: loss = 5.27572 (* 1 = 5.27572 loss)
I0410 02:33:20.292006 30317 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 02:33:22.298171 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 02:33:22.766147 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 02:33:23.124379 30317 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 02:33:23.124409 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:33:26.592438 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:27.453481 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:33:27.453512 30317 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 02:33:29.412102 30317 solver.cpp:218] Iteration 2148 (1.31582 iter/s, 9.11978s/12 iters), loss = 5.28334
I0410 02:33:29.412154 30317 solver.cpp:237]     Train net output #0: loss = 5.28334 (* 1 = 5.28334 loss)
I0410 02:33:29.412166 30317 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 02:33:34.142940 30317 solver.cpp:218] Iteration 2160 (2.53667 iter/s, 4.73061s/12 iters), loss = 5.28515
I0410 02:33:34.142979 30317 solver.cpp:237]     Train net output #0: loss = 5.28515 (* 1 = 5.28515 loss)
I0410 02:33:34.142990 30317 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 02:33:38.928831 30317 solver.cpp:218] Iteration 2172 (2.50748 iter/s, 4.78567s/12 iters), loss = 5.27622
I0410 02:33:38.928874 30317 solver.cpp:237]     Train net output #0: loss = 5.27622 (* 1 = 5.27622 loss)
I0410 02:33:38.928882 30317 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 02:33:44.055579 30317 solver.cpp:218] Iteration 2184 (2.34077 iter/s, 5.12652s/12 iters), loss = 5.27588
I0410 02:33:44.055617 30317 solver.cpp:237]     Train net output #0: loss = 5.27588 (* 1 = 5.27588 loss)
I0410 02:33:44.055626 30317 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 02:33:48.936694 30317 solver.cpp:218] Iteration 2196 (2.45857 iter/s, 4.88088s/12 iters), loss = 5.25086
I0410 02:33:48.936749 30317 solver.cpp:237]     Train net output #0: loss = 5.25086 (* 1 = 5.25086 loss)
I0410 02:33:48.936759 30317 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 02:33:53.900326 30317 solver.cpp:218] Iteration 2208 (2.4177 iter/s, 4.96339s/12 iters), loss = 5.27078
I0410 02:33:53.900486 30317 solver.cpp:237]     Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
I0410 02:33:53.900499 30317 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 02:33:58.745698 30317 solver.cpp:218] Iteration 2220 (2.47676 iter/s, 4.84503s/12 iters), loss = 5.28478
I0410 02:33:58.745743 30317 solver.cpp:237]     Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
I0410 02:33:58.745751 30317 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 02:34:00.497485 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:03.596246 30317 solver.cpp:218] Iteration 2232 (2.47407 iter/s, 4.85031s/12 iters), loss = 5.28313
I0410 02:34:03.596298 30317 solver.cpp:237]     Train net output #0: loss = 5.28313 (* 1 = 5.28313 loss)
I0410 02:34:03.596310 30317 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 02:34:07.991443 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 02:34:09.032392 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 02:34:10.878237 30317 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 02:34:10.878270 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:34:14.406113 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:15.308873 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:34:15.308909 30317 solver.cpp:397]     Test net output #1: loss = 5.28663 (* 1 = 5.28663 loss)
I0410 02:34:15.390388 30317 solver.cpp:218] Iteration 2244 (1.0175 iter/s, 11.7937s/12 iters), loss = 5.279
I0410 02:34:15.390444 30317 solver.cpp:237]     Train net output #0: loss = 5.279 (* 1 = 5.279 loss)
I0410 02:34:15.390455 30317 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 02:34:19.601673 30317 solver.cpp:218] Iteration 2256 (2.84964 iter/s, 4.21107s/12 iters), loss = 5.24358
I0410 02:34:19.601724 30317 solver.cpp:237]     Train net output #0: loss = 5.24358 (* 1 = 5.24358 loss)
I0410 02:34:19.601735 30317 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 02:34:24.420534 30317 solver.cpp:218] Iteration 2268 (2.49034 iter/s, 4.81863s/12 iters), loss = 5.2837
I0410 02:34:24.420647 30317 solver.cpp:237]     Train net output #0: loss = 5.2837 (* 1 = 5.2837 loss)
I0410 02:34:24.420657 30317 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 02:34:29.280608 30317 solver.cpp:218] Iteration 2280 (2.46925 iter/s, 4.85978s/12 iters), loss = 5.25361
I0410 02:34:29.280659 30317 solver.cpp:237]     Train net output #0: loss = 5.25361 (* 1 = 5.25361 loss)
I0410 02:34:29.280670 30317 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 02:34:34.402236 30317 solver.cpp:218] Iteration 2292 (2.34312 iter/s, 5.12138s/12 iters), loss = 5.27299
I0410 02:34:34.402279 30317 solver.cpp:237]     Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
I0410 02:34:34.402288 30317 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 02:34:39.281363 30317 solver.cpp:218] Iteration 2304 (2.45957 iter/s, 4.8789s/12 iters), loss = 5.26905
I0410 02:34:39.281407 30317 solver.cpp:237]     Train net output #0: loss = 5.26905 (* 1 = 5.26905 loss)
I0410 02:34:39.281417 30317 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 02:34:44.095444 30317 solver.cpp:218] Iteration 2316 (2.4928 iter/s, 4.81386s/12 iters), loss = 5.26491
I0410 02:34:44.095485 30317 solver.cpp:237]     Train net output #0: loss = 5.26491 (* 1 = 5.26491 loss)
I0410 02:34:44.095494 30317 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 02:34:47.991598 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:49.031090 30317 solver.cpp:218] Iteration 2328 (2.4314 iter/s, 4.93542s/12 iters), loss = 5.25982
I0410 02:34:49.031131 30317 solver.cpp:237]     Train net output #0: loss = 5.25982 (* 1 = 5.25982 loss)
I0410 02:34:49.031139 30317 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 02:34:53.923596 30317 solver.cpp:218] Iteration 2340 (2.45285 iter/s, 4.89227s/12 iters), loss = 5.29206
I0410 02:34:53.923661 30317 solver.cpp:237]     Train net output #0: loss = 5.29206 (* 1 = 5.29206 loss)
I0410 02:34:53.923676 30317 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 02:34:55.912899 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 02:34:56.439009 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 02:34:56.860551 30317 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 02:34:56.860574 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:35:00.398993 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:01.346451 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:35:01.346503 30317 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0410 02:35:03.180212 30317 solver.cpp:218] Iteration 2352 (1.29643 iter/s, 9.25621s/12 iters), loss = 5.26024
I0410 02:35:03.180256 30317 solver.cpp:237]     Train net output #0: loss = 5.26024 (* 1 = 5.26024 loss)
I0410 02:35:03.180264 30317 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 02:35:08.007920 30317 solver.cpp:218] Iteration 2364 (2.48577 iter/s, 4.82748s/12 iters), loss = 5.30188
I0410 02:35:08.007973 30317 solver.cpp:237]     Train net output #0: loss = 5.30188 (* 1 = 5.30188 loss)
I0410 02:35:08.007987 30317 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 02:35:12.896924 30317 solver.cpp:218] Iteration 2376 (2.45461 iter/s, 4.88876s/12 iters), loss = 5.26095
I0410 02:35:12.896981 30317 solver.cpp:237]     Train net output #0: loss = 5.26095 (* 1 = 5.26095 loss)
I0410 02:35:12.896994 30317 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 02:35:17.886453 30317 solver.cpp:218] Iteration 2388 (2.40516 iter/s, 4.98928s/12 iters), loss = 5.27743
I0410 02:35:17.886507 30317 solver.cpp:237]     Train net output #0: loss = 5.27743 (* 1 = 5.27743 loss)
I0410 02:35:17.886519 30317 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 02:35:22.775182 30317 solver.cpp:218] Iteration 2400 (2.45475 iter/s, 4.88848s/12 iters), loss = 5.28636
I0410 02:35:22.775243 30317 solver.cpp:237]     Train net output #0: loss = 5.28636 (* 1 = 5.28636 loss)
I0410 02:35:22.775254 30317 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 02:35:27.574582 30317 solver.cpp:218] Iteration 2412 (2.50044 iter/s, 4.79916s/12 iters), loss = 5.27052
I0410 02:35:27.574720 30317 solver.cpp:237]     Train net output #0: loss = 5.27052 (* 1 = 5.27052 loss)
I0410 02:35:27.574733 30317 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 02:35:32.387814 30317 solver.cpp:218] Iteration 2424 (2.49329 iter/s, 4.81291s/12 iters), loss = 5.28019
I0410 02:35:32.387873 30317 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0410 02:35:32.387885 30317 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 02:35:33.421768 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:37.190254 30317 solver.cpp:218] Iteration 2436 (2.49885 iter/s, 4.8022s/12 iters), loss = 5.2824
I0410 02:35:37.190310 30317 solver.cpp:237]     Train net output #0: loss = 5.2824 (* 1 = 5.2824 loss)
I0410 02:35:37.190323 30317 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 02:35:41.544951 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 02:35:42.518831 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 02:35:43.255988 30317 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 02:35:43.256017 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:35:47.002069 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:47.978039 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:35:47.978076 30317 solver.cpp:397]     Test net output #1: loss = 5.28665 (* 1 = 5.28665 loss)
I0410 02:35:48.059697 30317 solver.cpp:218] Iteration 2448 (1.10406 iter/s, 10.869s/12 iters), loss = 5.25817
I0410 02:35:48.059742 30317 solver.cpp:237]     Train net output #0: loss = 5.25817 (* 1 = 5.25817 loss)
I0410 02:35:48.059751 30317 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 02:35:52.283022 30317 solver.cpp:218] Iteration 2460 (2.8415 iter/s, 4.22312s/12 iters), loss = 5.26214
I0410 02:35:52.283064 30317 solver.cpp:237]     Train net output #0: loss = 5.26214 (* 1 = 5.26214 loss)
I0410 02:35:52.283073 30317 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 02:35:57.115393 30317 solver.cpp:218] Iteration 2472 (2.48337 iter/s, 4.83214s/12 iters), loss = 5.27219
I0410 02:35:57.115442 30317 solver.cpp:237]     Train net output #0: loss = 5.27219 (* 1 = 5.27219 loss)
I0410 02:35:57.115453 30317 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 02:36:01.926121 30317 solver.cpp:218] Iteration 2484 (2.49455 iter/s, 4.81049s/12 iters), loss = 5.27535
I0410 02:36:01.926679 30317 solver.cpp:237]     Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
I0410 02:36:01.926690 30317 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 02:36:06.777281 30317 solver.cpp:218] Iteration 2496 (2.47401 iter/s, 4.85042s/12 iters), loss = 5.27084
I0410 02:36:06.777320 30317 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0410 02:36:06.777328 30317 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 02:36:11.818936 30317 solver.cpp:218] Iteration 2508 (2.38028 iter/s, 5.04142s/12 iters), loss = 5.28936
I0410 02:36:11.818989 30317 solver.cpp:237]     Train net output #0: loss = 5.28936 (* 1 = 5.28936 loss)
I0410 02:36:11.819001 30317 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 02:36:16.702754 30317 solver.cpp:218] Iteration 2520 (2.45722 iter/s, 4.88358s/12 iters), loss = 5.27426
I0410 02:36:16.702796 30317 solver.cpp:237]     Train net output #0: loss = 5.27426 (* 1 = 5.27426 loss)
I0410 02:36:16.702805 30317 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 02:36:19.826534 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:36:21.566773 30317 solver.cpp:218] Iteration 2532 (2.46721 iter/s, 4.86379s/12 iters), loss = 5.28221
I0410 02:36:21.566818 30317 solver.cpp:237]     Train net output #0: loss = 5.28221 (* 1 = 5.28221 loss)
I0410 02:36:21.566826 30317 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 02:36:26.496996 30317 solver.cpp:218] Iteration 2544 (2.43408 iter/s, 4.92999s/12 iters), loss = 5.2752
I0410 02:36:26.497036 30317 solver.cpp:237]     Train net output #0: loss = 5.2752 (* 1 = 5.2752 loss)
I0410 02:36:26.497045 30317 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 02:36:28.526298 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 02:36:28.971666 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 02:36:29.297276 30317 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 02:36:29.297305 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:36:32.590737 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:36:33.606716 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:36:33.606767 30317 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 02:36:35.459710 30317 solver.cpp:218] Iteration 2556 (1.33893 iter/s, 8.96235s/12 iters), loss = 5.27674
I0410 02:36:35.459748 30317 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0410 02:36:35.459758 30317 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 02:36:40.301357 30317 solver.cpp:218] Iteration 2568 (2.47861 iter/s, 4.84142s/12 iters), loss = 5.28831
I0410 02:36:40.301398 30317 solver.cpp:237]     Train net output #0: loss = 5.28831 (* 1 = 5.28831 loss)
I0410 02:36:40.301407 30317 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 02:36:45.183606 30317 solver.cpp:218] Iteration 2580 (2.458 iter/s, 4.88202s/12 iters), loss = 5.27011
I0410 02:36:45.183651 30317 solver.cpp:237]     Train net output #0: loss = 5.27011 (* 1 = 5.27011 loss)
I0410 02:36:45.183661 30317 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 02:36:50.023519 30317 solver.cpp:218] Iteration 2592 (2.4795 iter/s, 4.83968s/12 iters), loss = 5.28748
I0410 02:36:50.023573 30317 solver.cpp:237]     Train net output #0: loss = 5.28748 (* 1 = 5.28748 loss)
I0410 02:36:50.023584 30317 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 02:36:54.893798 30317 solver.cpp:218] Iteration 2604 (2.46404 iter/s, 4.87004s/12 iters), loss = 5.26187
I0410 02:36:54.893843 30317 solver.cpp:237]     Train net output #0: loss = 5.26187 (* 1 = 5.26187 loss)
I0410 02:36:54.893852 30317 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 02:36:59.762894 30317 solver.cpp:218] Iteration 2616 (2.46464 iter/s, 4.86887s/12 iters), loss = 5.28192
I0410 02:36:59.762938 30317 solver.cpp:237]     Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
I0410 02:36:59.762948 30317 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 02:37:04.569736 30317 solver.cpp:218] Iteration 2628 (2.49656 iter/s, 4.80662s/12 iters), loss = 5.27848
I0410 02:37:04.569840 30317 solver.cpp:237]     Train net output #0: loss = 5.27848 (* 1 = 5.27848 loss)
I0410 02:37:04.569852 30317 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 02:37:04.998212 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:09.474339 30317 solver.cpp:218] Iteration 2640 (2.44683 iter/s, 4.90431s/12 iters), loss = 5.28141
I0410 02:37:09.474395 30317 solver.cpp:237]     Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
I0410 02:37:09.474406 30317 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 02:37:13.883679 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 02:37:14.378254 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 02:37:14.741093 30317 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 02:37:14.741128 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:37:18.069365 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:19.135941 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:37:19.135983 30317 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0410 02:37:19.217640 30317 solver.cpp:218] Iteration 2652 (1.23167 iter/s, 9.74289s/12 iters), loss = 5.2672
I0410 02:37:19.217694 30317 solver.cpp:237]     Train net output #0: loss = 5.2672 (* 1 = 5.2672 loss)
I0410 02:37:19.217703 30317 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 02:37:23.370347 30317 solver.cpp:218] Iteration 2664 (2.88983 iter/s, 4.15249s/12 iters), loss = 5.28304
I0410 02:37:23.370401 30317 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0410 02:37:23.370416 30317 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 02:37:28.187887 30317 solver.cpp:218] Iteration 2676 (2.49102 iter/s, 4.8173s/12 iters), loss = 5.26814
I0410 02:37:28.187945 30317 solver.cpp:237]     Train net output #0: loss = 5.26814 (* 1 = 5.26814 loss)
I0410 02:37:28.187956 30317 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 02:37:33.043500 30317 solver.cpp:218] Iteration 2688 (2.47149 iter/s, 4.85537s/12 iters), loss = 5.25562
I0410 02:37:33.043548 30317 solver.cpp:237]     Train net output #0: loss = 5.25562 (* 1 = 5.25562 loss)
I0410 02:37:33.043557 30317 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 02:37:37.894979 30317 solver.cpp:218] Iteration 2700 (2.47359 iter/s, 4.85125s/12 iters), loss = 5.28354
I0410 02:37:37.895151 30317 solver.cpp:237]     Train net output #0: loss = 5.28354 (* 1 = 5.28354 loss)
I0410 02:37:37.895165 30317 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 02:37:42.735636 30317 solver.cpp:218] Iteration 2712 (2.47918 iter/s, 4.8403s/12 iters), loss = 5.27978
I0410 02:37:42.735694 30317 solver.cpp:237]     Train net output #0: loss = 5.27978 (* 1 = 5.27978 loss)
I0410 02:37:42.735707 30317 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 02:37:47.605422 30317 solver.cpp:218] Iteration 2724 (2.46429 iter/s, 4.86955s/12 iters), loss = 5.25955
I0410 02:37:47.605461 30317 solver.cpp:237]     Train net output #0: loss = 5.25955 (* 1 = 5.25955 loss)
I0410 02:37:47.605471 30317 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 02:37:50.122700 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:52.525842 30317 solver.cpp:218] Iteration 2736 (2.43893 iter/s, 4.92019s/12 iters), loss = 5.28104
I0410 02:37:52.525893 30317 solver.cpp:237]     Train net output #0: loss = 5.28104 (* 1 = 5.28104 loss)
I0410 02:37:52.525907 30317 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 02:37:57.325150 30317 solver.cpp:218] Iteration 2748 (2.50048 iter/s, 4.79908s/12 iters), loss = 5.27926
I0410 02:37:57.325198 30317 solver.cpp:237]     Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
I0410 02:37:57.325210 30317 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 02:37:59.276753 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 02:38:01.163975 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 02:38:02.454365 30317 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 02:38:02.454396 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:38:05.472005 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:38:06.010462 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:07.171164 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:38:07.171208 30317 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 02:38:09.092294 30317 solver.cpp:218] Iteration 2760 (1.01983 iter/s, 11.7667s/12 iters), loss = 5.27692
I0410 02:38:09.092378 30317 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0410 02:38:09.092387 30317 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 02:38:13.951599 30317 solver.cpp:218] Iteration 2772 (2.46963 iter/s, 4.85904s/12 iters), loss = 5.27717
I0410 02:38:13.951656 30317 solver.cpp:237]     Train net output #0: loss = 5.27717 (* 1 = 5.27717 loss)
I0410 02:38:13.951668 30317 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 02:38:19.124567 30317 solver.cpp:218] Iteration 2784 (2.31986 iter/s, 5.17272s/12 iters), loss = 5.27717
I0410 02:38:19.124612 30317 solver.cpp:237]     Train net output #0: loss = 5.27717 (* 1 = 5.27717 loss)
I0410 02:38:19.124624 30317 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 02:38:24.006335 30317 solver.cpp:218] Iteration 2796 (2.45824 iter/s, 4.88153s/12 iters), loss = 5.27036
I0410 02:38:24.006386 30317 solver.cpp:237]     Train net output #0: loss = 5.27036 (* 1 = 5.27036 loss)
I0410 02:38:24.006397 30317 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 02:38:28.858860 30317 solver.cpp:218] Iteration 2808 (2.47306 iter/s, 4.85229s/12 iters), loss = 5.26274
I0410 02:38:28.858917 30317 solver.cpp:237]     Train net output #0: loss = 5.26274 (* 1 = 5.26274 loss)
I0410 02:38:28.858930 30317 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 02:38:33.694422 30317 solver.cpp:218] Iteration 2820 (2.48174 iter/s, 4.83532s/12 iters), loss = 5.27791
I0410 02:38:33.694473 30317 solver.cpp:237]     Train net output #0: loss = 5.27791 (* 1 = 5.27791 loss)
I0410 02:38:33.694484 30317 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 02:38:38.284619 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:38.563169 30317 solver.cpp:218] Iteration 2832 (2.46482 iter/s, 4.86851s/12 iters), loss = 5.26336
I0410 02:38:38.563230 30317 solver.cpp:237]     Train net output #0: loss = 5.26336 (* 1 = 5.26336 loss)
I0410 02:38:38.563244 30317 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 02:38:43.419289 30317 solver.cpp:218] Iteration 2844 (2.47123 iter/s, 4.85588s/12 iters), loss = 5.26794
I0410 02:38:43.420012 30317 solver.cpp:237]     Train net output #0: loss = 5.26794 (* 1 = 5.26794 loss)
I0410 02:38:43.420023 30317 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 02:38:47.807484 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 02:38:49.331465 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 02:38:50.212647 30317 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 02:38:50.212678 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:38:53.574456 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:54.708647 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:38:54.708685 30317 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 02:38:54.790472 30317 solver.cpp:218] Iteration 2856 (1.0554 iter/s, 11.3701s/12 iters), loss = 5.29099
I0410 02:38:54.790526 30317 solver.cpp:237]     Train net output #0: loss = 5.29099 (* 1 = 5.29099 loss)
I0410 02:38:54.790537 30317 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 02:38:58.812521 30317 solver.cpp:218] Iteration 2868 (2.98371 iter/s, 4.02184s/12 iters), loss = 5.27946
I0410 02:38:58.812574 30317 solver.cpp:237]     Train net output #0: loss = 5.27946 (* 1 = 5.27946 loss)
I0410 02:38:58.812585 30317 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 02:39:03.736804 30317 solver.cpp:218] Iteration 2880 (2.43702 iter/s, 4.92405s/12 iters), loss = 5.28019
I0410 02:39:03.736845 30317 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0410 02:39:03.736853 30317 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 02:39:08.585541 30317 solver.cpp:218] Iteration 2892 (2.47499 iter/s, 4.84851s/12 iters), loss = 5.27497
I0410 02:39:08.585582 30317 solver.cpp:237]     Train net output #0: loss = 5.27497 (* 1 = 5.27497 loss)
I0410 02:39:08.585592 30317 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 02:39:13.401908 30317 solver.cpp:218] Iteration 2904 (2.49162 iter/s, 4.81614s/12 iters), loss = 5.2572
I0410 02:39:13.401983 30317 solver.cpp:237]     Train net output #0: loss = 5.2572 (* 1 = 5.2572 loss)
I0410 02:39:13.401995 30317 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 02:39:18.421108 30317 solver.cpp:218] Iteration 2916 (2.39094 iter/s, 5.01896s/12 iters), loss = 5.27557
I0410 02:39:18.421263 30317 solver.cpp:237]     Train net output #0: loss = 5.27557 (* 1 = 5.27557 loss)
I0410 02:39:18.421276 30317 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 02:39:23.428956 30317 solver.cpp:218] Iteration 2928 (2.3964 iter/s, 5.00751s/12 iters), loss = 5.28104
I0410 02:39:23.429004 30317 solver.cpp:237]     Train net output #0: loss = 5.28104 (* 1 = 5.28104 loss)
I0410 02:39:23.429016 30317 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 02:39:25.220737 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:39:28.364734 30317 solver.cpp:218] Iteration 2940 (2.43134 iter/s, 4.93555s/12 iters), loss = 5.28542
I0410 02:39:28.364776 30317 solver.cpp:237]     Train net output #0: loss = 5.28542 (* 1 = 5.28542 loss)
I0410 02:39:28.364786 30317 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 02:39:33.489498 30317 solver.cpp:218] Iteration 2952 (2.34168 iter/s, 5.12453s/12 iters), loss = 5.2795
I0410 02:39:33.489545 30317 solver.cpp:237]     Train net output #0: loss = 5.2795 (* 1 = 5.2795 loss)
I0410 02:39:33.489555 30317 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 02:39:35.500103 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 02:39:36.365407 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 02:39:37.070139 30317 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 02:39:37.070169 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:39:40.580051 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:39:41.847060 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:39:41.847091 30317 solver.cpp:397]     Test net output #1: loss = 5.28649 (* 1 = 5.28649 loss)
I0410 02:39:43.684327 30317 solver.cpp:218] Iteration 2964 (1.17712 iter/s, 10.1944s/12 iters), loss = 5.24376
I0410 02:39:43.684384 30317 solver.cpp:237]     Train net output #0: loss = 5.24376 (* 1 = 5.24376 loss)
I0410 02:39:43.684396 30317 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 02:39:48.518324 30317 solver.cpp:218] Iteration 2976 (2.48254 iter/s, 4.83375s/12 iters), loss = 5.28294
I0410 02:39:48.518438 30317 solver.cpp:237]     Train net output #0: loss = 5.28294 (* 1 = 5.28294 loss)
I0410 02:39:48.518450 30317 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 02:39:53.399768 30317 solver.cpp:218] Iteration 2988 (2.45844 iter/s, 4.88115s/12 iters), loss = 5.26439
I0410 02:39:53.399808 30317 solver.cpp:237]     Train net output #0: loss = 5.26439 (* 1 = 5.26439 loss)
I0410 02:39:53.399816 30317 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 02:39:58.281728 30317 solver.cpp:218] Iteration 3000 (2.45814 iter/s, 4.88173s/12 iters), loss = 5.2689
I0410 02:39:58.281783 30317 solver.cpp:237]     Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
I0410 02:39:58.281795 30317 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 02:40:03.247310 30317 solver.cpp:218] Iteration 3012 (2.41675 iter/s, 4.96534s/12 iters), loss = 5.27451
I0410 02:40:03.247359 30317 solver.cpp:237]     Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
I0410 02:40:03.247370 30317 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 02:40:08.161609 30317 solver.cpp:218] Iteration 3024 (2.44197 iter/s, 4.91406s/12 iters), loss = 5.25751
I0410 02:40:08.161662 30317 solver.cpp:237]     Train net output #0: loss = 5.25751 (* 1 = 5.25751 loss)
I0410 02:40:08.161674 30317 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 02:40:12.112757 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:13.093739 30317 solver.cpp:218] Iteration 3036 (2.43314 iter/s, 4.93189s/12 iters), loss = 5.26191
I0410 02:40:13.093791 30317 solver.cpp:237]     Train net output #0: loss = 5.26191 (* 1 = 5.26191 loss)
I0410 02:40:13.093801 30317 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 02:40:17.964030 30317 solver.cpp:218] Iteration 3048 (2.46404 iter/s, 4.87005s/12 iters), loss = 5.28756
I0410 02:40:17.964083 30317 solver.cpp:237]     Train net output #0: loss = 5.28756 (* 1 = 5.28756 loss)
I0410 02:40:17.964097 30317 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 02:40:22.404922 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 02:40:22.890518 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 02:40:23.226385 30317 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 02:40:23.226404 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:40:26.569583 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:27.854921 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:40:27.854950 30317 solver.cpp:397]     Test net output #1: loss = 5.28632 (* 1 = 5.28632 loss)
I0410 02:40:27.936493 30317 solver.cpp:218] Iteration 3060 (1.20336 iter/s, 9.97205s/12 iters), loss = 5.25816
I0410 02:40:27.936533 30317 solver.cpp:237]     Train net output #0: loss = 5.25816 (* 1 = 5.25816 loss)
I0410 02:40:27.936542 30317 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 02:40:32.143235 30317 solver.cpp:218] Iteration 3072 (2.8527 iter/s, 4.20654s/12 iters), loss = 5.30722
I0410 02:40:32.143290 30317 solver.cpp:237]     Train net output #0: loss = 5.30722 (* 1 = 5.30722 loss)
I0410 02:40:32.143302 30317 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 02:40:37.022400 30317 solver.cpp:218] Iteration 3084 (2.45956 iter/s, 4.87892s/12 iters), loss = 5.2753
I0410 02:40:37.022452 30317 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0410 02:40:37.022505 30317 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 02:40:41.854776 30317 solver.cpp:218] Iteration 3096 (2.48337 iter/s, 4.83214s/12 iters), loss = 5.27386
I0410 02:40:41.854823 30317 solver.cpp:237]     Train net output #0: loss = 5.27386 (* 1 = 5.27386 loss)
I0410 02:40:41.854831 30317 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 02:40:46.814220 30317 solver.cpp:218] Iteration 3108 (2.41974 iter/s, 4.95921s/12 iters), loss = 5.27794
I0410 02:40:46.814268 30317 solver.cpp:237]     Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
I0410 02:40:46.814280 30317 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 02:40:51.717255 30317 solver.cpp:218] Iteration 3120 (2.44758 iter/s, 4.9028s/12 iters), loss = 5.26731
I0410 02:40:51.717312 30317 solver.cpp:237]     Train net output #0: loss = 5.26731 (* 1 = 5.26731 loss)
I0410 02:40:51.717325 30317 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 02:40:56.598598 30317 solver.cpp:218] Iteration 3132 (2.45846 iter/s, 4.8811s/12 iters), loss = 5.27536
I0410 02:40:56.598698 30317 solver.cpp:237]     Train net output #0: loss = 5.27536 (* 1 = 5.27536 loss)
I0410 02:40:56.598711 30317 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 02:40:57.725126 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:01.641693 30317 solver.cpp:218] Iteration 3144 (2.37963 iter/s, 5.04281s/12 iters), loss = 5.28298
I0410 02:41:01.641737 30317 solver.cpp:237]     Train net output #0: loss = 5.28298 (* 1 = 5.28298 loss)
I0410 02:41:01.641746 30317 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 02:41:06.596724 30317 solver.cpp:218] Iteration 3156 (2.4219 iter/s, 4.9548s/12 iters), loss = 5.24891
I0410 02:41:06.596784 30317 solver.cpp:237]     Train net output #0: loss = 5.24891 (* 1 = 5.24891 loss)
I0410 02:41:06.596801 30317 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 02:41:08.568627 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 02:41:09.056165 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 02:41:09.399345 30317 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 02:41:09.399384 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:41:12.560839 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:13.927016 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:41:13.927053 30317 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0410 02:41:15.698673 30317 solver.cpp:218] Iteration 3168 (1.31846 iter/s, 9.10156s/12 iters), loss = 5.26739
I0410 02:41:15.698719 30317 solver.cpp:237]     Train net output #0: loss = 5.26739 (* 1 = 5.26739 loss)
I0410 02:41:15.698731 30317 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 02:41:20.514755 30317 solver.cpp:218] Iteration 3180 (2.49177 iter/s, 4.81585s/12 iters), loss = 5.2745
I0410 02:41:20.514801 30317 solver.cpp:237]     Train net output #0: loss = 5.2745 (* 1 = 5.2745 loss)
I0410 02:41:20.514812 30317 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 02:41:25.431291 30317 solver.cpp:218] Iteration 3192 (2.44086 iter/s, 4.91631s/12 iters), loss = 5.27646
I0410 02:41:25.431339 30317 solver.cpp:237]     Train net output #0: loss = 5.27646 (* 1 = 5.27646 loss)
I0410 02:41:25.431351 30317 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 02:41:30.291164 30317 solver.cpp:218] Iteration 3204 (2.46932 iter/s, 4.85964s/12 iters), loss = 5.26555
I0410 02:41:30.291244 30317 solver.cpp:237]     Train net output #0: loss = 5.26555 (* 1 = 5.26555 loss)
I0410 02:41:30.291254 30317 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 02:41:35.143848 30317 solver.cpp:218] Iteration 3216 (2.473 iter/s, 4.85241s/12 iters), loss = 5.28858
I0410 02:41:35.143895 30317 solver.cpp:237]     Train net output #0: loss = 5.28858 (* 1 = 5.28858 loss)
I0410 02:41:35.143904 30317 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 02:41:40.107179 30317 solver.cpp:218] Iteration 3228 (2.41784 iter/s, 4.9631s/12 iters), loss = 5.27714
I0410 02:41:40.107220 30317 solver.cpp:237]     Train net output #0: loss = 5.27714 (* 1 = 5.27714 loss)
I0410 02:41:40.107229 30317 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 02:41:43.228782 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:44.933568 30317 solver.cpp:218] Iteration 3240 (2.48645 iter/s, 4.82617s/12 iters), loss = 5.27993
I0410 02:41:44.933611 30317 solver.cpp:237]     Train net output #0: loss = 5.27993 (* 1 = 5.27993 loss)
I0410 02:41:44.933620 30317 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 02:41:49.816040 30317 solver.cpp:218] Iteration 3252 (2.45789 iter/s, 4.88225s/12 iters), loss = 5.27068
I0410 02:41:49.816092 30317 solver.cpp:237]     Train net output #0: loss = 5.27068 (* 1 = 5.27068 loss)
I0410 02:41:49.816103 30317 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 02:41:54.208459 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 02:41:55.077080 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 02:41:55.855439 30317 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 02:41:55.855470 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:41:58.991549 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:00.288008 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:42:00.288041 30317 solver.cpp:397]     Test net output #1: loss = 5.28702 (* 1 = 5.28702 loss)
I0410 02:42:00.369763 30317 solver.cpp:218] Iteration 3264 (1.13709 iter/s, 10.5533s/12 iters), loss = 5.27491
I0410 02:42:00.381533 30317 solver.cpp:237]     Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
I0410 02:42:00.381551 30317 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 02:42:04.390339 30317 solver.cpp:218] Iteration 3276 (2.99351 iter/s, 4.00867s/12 iters), loss = 5.28921
I0410 02:42:04.390383 30317 solver.cpp:237]     Train net output #0: loss = 5.28921 (* 1 = 5.28921 loss)
I0410 02:42:04.390391 30317 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 02:42:09.314579 30317 solver.cpp:218] Iteration 3288 (2.43704 iter/s, 4.92401s/12 iters), loss = 5.26237
I0410 02:42:09.314617 30317 solver.cpp:237]     Train net output #0: loss = 5.26237 (* 1 = 5.26237 loss)
I0410 02:42:09.314625 30317 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 02:42:14.254379 30317 solver.cpp:218] Iteration 3300 (2.42936 iter/s, 4.93958s/12 iters), loss = 5.28245
I0410 02:42:14.254423 30317 solver.cpp:237]     Train net output #0: loss = 5.28245 (* 1 = 5.28245 loss)
I0410 02:42:14.254434 30317 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 02:42:19.187054 30317 solver.cpp:218] Iteration 3312 (2.43287 iter/s, 4.93245s/12 iters), loss = 5.2563
I0410 02:42:19.187105 30317 solver.cpp:237]     Train net output #0: loss = 5.2563 (* 1 = 5.2563 loss)
I0410 02:42:19.187116 30317 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 02:42:24.076287 30317 solver.cpp:218] Iteration 3324 (2.45449 iter/s, 4.889s/12 iters), loss = 5.2808
I0410 02:42:24.076325 30317 solver.cpp:237]     Train net output #0: loss = 5.2808 (* 1 = 5.2808 loss)
I0410 02:42:24.076333 30317 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 02:42:28.991250 30317 solver.cpp:218] Iteration 3336 (2.44164 iter/s, 4.91474s/12 iters), loss = 5.27537
I0410 02:42:28.991302 30317 solver.cpp:237]     Train net output #0: loss = 5.27537 (* 1 = 5.27537 loss)
I0410 02:42:28.991310 30317 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 02:42:29.459091 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:33.929796 30317 solver.cpp:218] Iteration 3348 (2.42998 iter/s, 4.93831s/12 iters), loss = 5.27671
I0410 02:42:33.929914 30317 solver.cpp:237]     Train net output #0: loss = 5.27671 (* 1 = 5.27671 loss)
I0410 02:42:33.929929 30317 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 02:42:38.779919 30317 solver.cpp:218] Iteration 3360 (2.47432 iter/s, 4.84982s/12 iters), loss = 5.26814
I0410 02:42:38.779976 30317 solver.cpp:237]     Train net output #0: loss = 5.26814 (* 1 = 5.26814 loss)
I0410 02:42:38.779989 30317 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 02:42:40.780236 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 02:42:41.243325 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 02:42:41.563238 30317 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 02:42:41.563257 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:42:44.623427 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:45.988014 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:42:45.988067 30317 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 02:42:47.834157 30317 solver.cpp:218] Iteration 3372 (1.3254 iter/s, 9.05385s/12 iters), loss = 5.28476
I0410 02:42:47.834209 30317 solver.cpp:237]     Train net output #0: loss = 5.28476 (* 1 = 5.28476 loss)
I0410 02:42:47.834219 30317 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 02:42:52.717669 30317 solver.cpp:218] Iteration 3384 (2.45737 iter/s, 4.88328s/12 iters), loss = 5.26632
I0410 02:42:52.717717 30317 solver.cpp:237]     Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
I0410 02:42:52.717728 30317 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 02:42:57.611578 30317 solver.cpp:218] Iteration 3396 (2.45215 iter/s, 4.89367s/12 iters), loss = 5.26107
I0410 02:42:57.611632 30317 solver.cpp:237]     Train net output #0: loss = 5.26107 (* 1 = 5.26107 loss)
I0410 02:42:57.611642 30317 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 02:43:02.468866 30317 solver.cpp:218] Iteration 3408 (2.47064 iter/s, 4.85705s/12 iters), loss = 5.28932
I0410 02:43:02.468919 30317 solver.cpp:237]     Train net output #0: loss = 5.28932 (* 1 = 5.28932 loss)
I0410 02:43:02.468930 30317 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 02:43:07.483428 30317 solver.cpp:218] Iteration 3420 (2.39314 iter/s, 5.01432s/12 iters), loss = 5.27593
I0410 02:43:07.483536 30317 solver.cpp:237]     Train net output #0: loss = 5.27593 (* 1 = 5.27593 loss)
I0410 02:43:07.483548 30317 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 02:43:12.387545 30317 solver.cpp:218] Iteration 3432 (2.44707 iter/s, 4.90382s/12 iters), loss = 5.26209
I0410 02:43:12.387599 30317 solver.cpp:237]     Train net output #0: loss = 5.26209 (* 1 = 5.26209 loss)
I0410 02:43:12.387609 30317 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 02:43:14.896943 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:43:17.221573 30317 solver.cpp:218] Iteration 3444 (2.48252 iter/s, 4.83379s/12 iters), loss = 5.27289
I0410 02:43:17.221621 30317 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0410 02:43:17.221630 30317 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 02:43:22.032757 30317 solver.cpp:218] Iteration 3456 (2.49431 iter/s, 4.81095s/12 iters), loss = 5.27226
I0410 02:43:22.032809 30317 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0410 02:43:22.032820 30317 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 02:43:26.430371 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 02:43:27.093302 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 02:43:27.414670 30317 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 02:43:27.414690 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:43:27.557718 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:43:30.438819 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:43:31.807556 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:43:31.807605 30317 solver.cpp:397]     Test net output #1: loss = 5.28693 (* 1 = 5.28693 loss)
I0410 02:43:31.885579 30317 solver.cpp:218] Iteration 3468 (1.21798 iter/s, 9.85241s/12 iters), loss = 5.27303
I0410 02:43:31.885629 30317 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0410 02:43:31.885641 30317 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 02:43:36.072325 30317 solver.cpp:218] Iteration 3480 (2.86633 iter/s, 4.18654s/12 iters), loss = 5.27606
I0410 02:43:36.072376 30317 solver.cpp:237]     Train net output #0: loss = 5.27606 (* 1 = 5.27606 loss)
I0410 02:43:36.072388 30317 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 02:43:40.895329 30317 solver.cpp:218] Iteration 3492 (2.4882 iter/s, 4.82277s/12 iters), loss = 5.28646
I0410 02:43:40.895488 30317 solver.cpp:237]     Train net output #0: loss = 5.28646 (* 1 = 5.28646 loss)
I0410 02:43:40.895504 30317 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 02:43:45.776878 30317 solver.cpp:218] Iteration 3504 (2.45841 iter/s, 4.88121s/12 iters), loss = 5.27164
I0410 02:43:45.776932 30317 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0410 02:43:45.776942 30317 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 02:43:50.683224 30317 solver.cpp:218] Iteration 3516 (2.44593 iter/s, 4.90611s/12 iters), loss = 5.26689
I0410 02:43:50.683277 30317 solver.cpp:237]     Train net output #0: loss = 5.26689 (* 1 = 5.26689 loss)
I0410 02:43:50.683290 30317 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 02:43:55.604715 30317 solver.cpp:218] Iteration 3528 (2.4384 iter/s, 4.92126s/12 iters), loss = 5.27319
I0410 02:43:55.604758 30317 solver.cpp:237]     Train net output #0: loss = 5.27319 (* 1 = 5.27319 loss)
I0410 02:43:55.604766 30317 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 02:44:00.205651 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:00.454919 30317 solver.cpp:218] Iteration 3540 (2.47424 iter/s, 4.84998s/12 iters), loss = 5.2563
I0410 02:44:00.454967 30317 solver.cpp:237]     Train net output #0: loss = 5.2563 (* 1 = 5.2563 loss)
I0410 02:44:00.454979 30317 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 02:44:05.292325 30317 solver.cpp:218] Iteration 3552 (2.48079 iter/s, 4.83717s/12 iters), loss = 5.27052
I0410 02:44:05.292376 30317 solver.cpp:237]     Train net output #0: loss = 5.27052 (* 1 = 5.27052 loss)
I0410 02:44:05.292387 30317 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 02:44:10.149895 30317 solver.cpp:218] Iteration 3564 (2.47049 iter/s, 4.85734s/12 iters), loss = 5.29209
I0410 02:44:10.149938 30317 solver.cpp:237]     Train net output #0: loss = 5.29209 (* 1 = 5.29209 loss)
I0410 02:44:10.149947 30317 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 02:44:12.153501 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 02:44:12.627307 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 02:44:12.963454 30317 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 02:44:12.963471 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:44:16.011678 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:17.431334 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:44:17.431385 30317 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0410 02:44:19.277649 30317 solver.cpp:218] Iteration 3576 (1.31472 iter/s, 9.12738s/12 iters), loss = 5.28212
I0410 02:44:19.277688 30317 solver.cpp:237]     Train net output #0: loss = 5.28212 (* 1 = 5.28212 loss)
I0410 02:44:19.277695 30317 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 02:44:24.307735 30317 solver.cpp:218] Iteration 3588 (2.38575 iter/s, 5.02986s/12 iters), loss = 5.27735
I0410 02:44:24.307777 30317 solver.cpp:237]     Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
I0410 02:44:24.307786 30317 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 02:44:29.197403 30317 solver.cpp:218] Iteration 3600 (2.45427 iter/s, 4.88944s/12 iters), loss = 5.26649
I0410 02:44:29.197454 30317 solver.cpp:237]     Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
I0410 02:44:29.197465 30317 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 02:44:34.076655 30317 solver.cpp:218] Iteration 3612 (2.45951 iter/s, 4.87902s/12 iters), loss = 5.23758
I0410 02:44:34.076705 30317 solver.cpp:237]     Train net output #0: loss = 5.23758 (* 1 = 5.23758 loss)
I0410 02:44:34.076717 30317 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 02:44:38.908004 30317 solver.cpp:218] Iteration 3624 (2.4839 iter/s, 4.83112s/12 iters), loss = 5.27837
I0410 02:44:38.908063 30317 solver.cpp:237]     Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
I0410 02:44:38.908077 30317 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 02:44:43.707273 30317 solver.cpp:218] Iteration 3636 (2.5005 iter/s, 4.79903s/12 iters), loss = 5.2816
I0410 02:44:43.707362 30317 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0410 02:44:43.707371 30317 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 02:44:45.617645 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:48.660957 30317 solver.cpp:218] Iteration 3648 (2.42257 iter/s, 4.95341s/12 iters), loss = 5.28459
I0410 02:44:48.661006 30317 solver.cpp:237]     Train net output #0: loss = 5.28459 (* 1 = 5.28459 loss)
I0410 02:44:48.661020 30317 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 02:44:53.510680 30317 solver.cpp:218] Iteration 3660 (2.47448 iter/s, 4.84949s/12 iters), loss = 5.27972
I0410 02:44:53.510723 30317 solver.cpp:237]     Train net output #0: loss = 5.27972 (* 1 = 5.27972 loss)
I0410 02:44:53.510733 30317 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 02:44:57.877677 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 02:44:58.343731 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 02:44:58.664767 30317 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 02:44:58.664788 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:45:01.627338 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:03.121971 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:45:03.122002 30317 solver.cpp:397]     Test net output #1: loss = 5.28627 (* 1 = 5.28627 loss)
I0410 02:45:03.203832 30317 solver.cpp:218] Iteration 3672 (1.23804 iter/s, 9.69276s/12 iters), loss = 5.25035
I0410 02:45:03.203872 30317 solver.cpp:237]     Train net output #0: loss = 5.25035 (* 1 = 5.25035 loss)
I0410 02:45:03.203881 30317 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 02:45:07.403432 30317 solver.cpp:218] Iteration 3684 (2.85755 iter/s, 4.1994s/12 iters), loss = 5.27124
I0410 02:45:07.403482 30317 solver.cpp:237]     Train net output #0: loss = 5.27124 (* 1 = 5.27124 loss)
I0410 02:45:07.403493 30317 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 02:45:12.358937 30317 solver.cpp:218] Iteration 3696 (2.42166 iter/s, 4.95527s/12 iters), loss = 5.25626
I0410 02:45:12.358989 30317 solver.cpp:237]     Train net output #0: loss = 5.25626 (* 1 = 5.25626 loss)
I0410 02:45:12.359000 30317 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 02:45:17.179554 30317 solver.cpp:218] Iteration 3708 (2.48943 iter/s, 4.82039s/12 iters), loss = 5.26961
I0410 02:45:17.179656 30317 solver.cpp:237]     Train net output #0: loss = 5.26961 (* 1 = 5.26961 loss)
I0410 02:45:17.179666 30317 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 02:45:22.061419 30317 solver.cpp:218] Iteration 3720 (2.45822 iter/s, 4.88158s/12 iters), loss = 5.26903
I0410 02:45:22.061470 30317 solver.cpp:237]     Train net output #0: loss = 5.26903 (* 1 = 5.26903 loss)
I0410 02:45:22.061480 30317 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 02:45:27.063376 30317 solver.cpp:218] Iteration 3732 (2.39917 iter/s, 5.00172s/12 iters), loss = 5.2584
I0410 02:45:27.063423 30317 solver.cpp:237]     Train net output #0: loss = 5.2584 (* 1 = 5.2584 loss)
I0410 02:45:27.063434 30317 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 02:45:30.961701 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:31.908031 30317 solver.cpp:218] Iteration 3744 (2.47707 iter/s, 4.84443s/12 iters), loss = 5.25697
I0410 02:45:31.908074 30317 solver.cpp:237]     Train net output #0: loss = 5.25697 (* 1 = 5.25697 loss)
I0410 02:45:31.908083 30317 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 02:45:36.807057 30317 solver.cpp:218] Iteration 3756 (2.44958 iter/s, 4.8988s/12 iters), loss = 5.28091
I0410 02:45:36.807111 30317 solver.cpp:237]     Train net output #0: loss = 5.28091 (* 1 = 5.28091 loss)
I0410 02:45:36.807124 30317 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 02:45:41.695940 30317 solver.cpp:218] Iteration 3768 (2.45467 iter/s, 4.88864s/12 iters), loss = 5.26077
I0410 02:45:41.695994 30317 solver.cpp:237]     Train net output #0: loss = 5.26077 (* 1 = 5.26077 loss)
I0410 02:45:41.696007 30317 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 02:45:43.703442 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 02:45:45.188464 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 02:45:47.491227 30317 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 02:45:47.491313 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:45:50.323873 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:51.824554 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:45:51.824597 30317 solver.cpp:397]     Test net output #1: loss = 5.28719 (* 1 = 5.28719 loss)
I0410 02:45:53.715413 30317 solver.cpp:218] Iteration 3780 (0.99842 iter/s, 12.019s/12 iters), loss = 5.31199
I0410 02:45:53.715468 30317 solver.cpp:237]     Train net output #0: loss = 5.31199 (* 1 = 5.31199 loss)
I0410 02:45:53.715479 30317 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 02:45:58.506844 30317 solver.cpp:218] Iteration 3792 (2.50459 iter/s, 4.7912s/12 iters), loss = 5.27658
I0410 02:45:58.506883 30317 solver.cpp:237]     Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
I0410 02:45:58.506891 30317 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 02:46:03.364341 30317 solver.cpp:218] Iteration 3804 (2.47052 iter/s, 4.85727s/12 iters), loss = 5.26811
I0410 02:46:03.364398 30317 solver.cpp:237]     Train net output #0: loss = 5.26811 (* 1 = 5.26811 loss)
I0410 02:46:03.364409 30317 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 02:46:08.172662 30317 solver.cpp:218] Iteration 3816 (2.4958 iter/s, 4.80809s/12 iters), loss = 5.27194
I0410 02:46:08.172698 30317 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0410 02:46:08.172706 30317 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 02:46:13.047215 30317 solver.cpp:218] Iteration 3828 (2.46188 iter/s, 4.87433s/12 iters), loss = 5.26483
I0410 02:46:13.047257 30317 solver.cpp:237]     Train net output #0: loss = 5.26483 (* 1 = 5.26483 loss)
I0410 02:46:13.047266 30317 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 02:46:17.944411 30317 solver.cpp:218] Iteration 3840 (2.4505 iter/s, 4.89696s/12 iters), loss = 5.27364
I0410 02:46:17.946357 30317 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0410 02:46:17.946369 30317 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 02:46:19.064327 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:22.906994 30317 solver.cpp:218] Iteration 3852 (2.41913 iter/s, 4.96046s/12 iters), loss = 5.27859
I0410 02:46:22.907039 30317 solver.cpp:237]     Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
I0410 02:46:22.907047 30317 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 02:46:27.812013 30317 solver.cpp:218] Iteration 3864 (2.44659 iter/s, 4.90479s/12 iters), loss = 5.25249
I0410 02:46:27.812062 30317 solver.cpp:237]     Train net output #0: loss = 5.25249 (* 1 = 5.25249 loss)
I0410 02:46:27.812073 30317 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 02:46:32.295832 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 02:46:33.141439 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 02:46:33.625561 30317 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 02:46:33.625592 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:46:36.545424 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:38.080766 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:46:38.080797 30317 solver.cpp:397]     Test net output #1: loss = 5.28666 (* 1 = 5.28666 loss)
I0410 02:46:38.162366 30317 solver.cpp:218] Iteration 3876 (1.15943 iter/s, 10.3499s/12 iters), loss = 5.27063
I0410 02:46:38.162408 30317 solver.cpp:237]     Train net output #0: loss = 5.27063 (* 1 = 5.27063 loss)
I0410 02:46:38.162417 30317 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 02:46:42.788436 30317 solver.cpp:218] Iteration 3888 (2.59412 iter/s, 4.62585s/12 iters), loss = 5.26804
I0410 02:46:42.788484 30317 solver.cpp:237]     Train net output #0: loss = 5.26804 (* 1 = 5.26804 loss)
I0410 02:46:42.788494 30317 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 02:46:47.792779 30317 solver.cpp:218] Iteration 3900 (2.39803 iter/s, 5.00411s/12 iters), loss = 5.27485
I0410 02:46:47.792827 30317 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0410 02:46:47.792838 30317 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 02:46:52.653137 30317 solver.cpp:218] Iteration 3912 (2.46907 iter/s, 4.86013s/12 iters), loss = 5.26152
I0410 02:46:52.653239 30317 solver.cpp:237]     Train net output #0: loss = 5.26152 (* 1 = 5.26152 loss)
I0410 02:46:52.653250 30317 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 02:46:57.550858 30317 solver.cpp:218] Iteration 3924 (2.45026 iter/s, 4.89743s/12 iters), loss = 5.29391
I0410 02:46:57.550904 30317 solver.cpp:237]     Train net output #0: loss = 5.29391 (* 1 = 5.29391 loss)
I0410 02:46:57.550915 30317 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 02:47:02.503156 30317 solver.cpp:218] Iteration 3936 (2.42323 iter/s, 4.95207s/12 iters), loss = 5.27671
I0410 02:47:02.503187 30317 solver.cpp:237]     Train net output #0: loss = 5.27671 (* 1 = 5.27671 loss)
I0410 02:47:02.503196 30317 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 02:47:05.718955 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:07.388783 30317 solver.cpp:218] Iteration 3948 (2.45629 iter/s, 4.88541s/12 iters), loss = 5.28647
I0410 02:47:07.388833 30317 solver.cpp:237]     Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
I0410 02:47:07.388842 30317 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 02:47:12.442364 30317 solver.cpp:218] Iteration 3960 (2.37467 iter/s, 5.05334s/12 iters), loss = 5.27014
I0410 02:47:12.442417 30317 solver.cpp:237]     Train net output #0: loss = 5.27014 (* 1 = 5.27014 loss)
I0410 02:47:12.442430 30317 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 02:47:17.417827 30317 solver.cpp:218] Iteration 3972 (2.41195 iter/s, 4.97522s/12 iters), loss = 5.28182
I0410 02:47:17.417881 30317 solver.cpp:237]     Train net output #0: loss = 5.28182 (* 1 = 5.28182 loss)
I0410 02:47:17.417892 30317 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 02:47:19.440696 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 02:47:20.397848 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 02:47:22.275151 30317 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 02:47:22.275174 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:47:25.145179 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:26.715520 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:47:26.715567 30317 solver.cpp:397]     Test net output #1: loss = 5.28649 (* 1 = 5.28649 loss)
I0410 02:47:28.548869 30317 solver.cpp:218] Iteration 3984 (1.07811 iter/s, 11.1306s/12 iters), loss = 5.28035
I0410 02:47:28.548918 30317 solver.cpp:237]     Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
I0410 02:47:28.548929 30317 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 02:47:33.522775 30317 solver.cpp:218] Iteration 3996 (2.41271 iter/s, 4.97367s/12 iters), loss = 5.26491
I0410 02:47:33.522819 30317 solver.cpp:237]     Train net output #0: loss = 5.26491 (* 1 = 5.26491 loss)
I0410 02:47:33.522827 30317 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 02:47:38.533360 30317 solver.cpp:218] Iteration 4008 (2.39504 iter/s, 5.01035s/12 iters), loss = 5.28983
I0410 02:47:38.533407 30317 solver.cpp:237]     Train net output #0: loss = 5.28983 (* 1 = 5.28983 loss)
I0410 02:47:38.533416 30317 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 02:47:43.410120 30317 solver.cpp:218] Iteration 4020 (2.46077 iter/s, 4.87652s/12 iters), loss = 5.25547
I0410 02:47:43.410161 30317 solver.cpp:237]     Train net output #0: loss = 5.25547 (* 1 = 5.25547 loss)
I0410 02:47:43.410171 30317 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 02:47:48.292712 30317 solver.cpp:218] Iteration 4032 (2.45783 iter/s, 4.88236s/12 iters), loss = 5.27852
I0410 02:47:48.292762 30317 solver.cpp:237]     Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
I0410 02:47:48.292773 30317 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 02:47:53.152822 30317 solver.cpp:218] Iteration 4044 (2.4692 iter/s, 4.85987s/12 iters), loss = 5.27225
I0410 02:47:53.152873 30317 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0410 02:47:53.152881 30317 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 02:47:53.656595 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:58.057802 30317 solver.cpp:218] Iteration 4056 (2.44661 iter/s, 4.90475s/12 iters), loss = 5.27592
I0410 02:47:58.057919 30317 solver.cpp:237]     Train net output #0: loss = 5.27592 (* 1 = 5.27592 loss)
I0410 02:47:58.057929 30317 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 02:48:03.056313 30317 solver.cpp:218] Iteration 4068 (2.40086 iter/s, 4.9982s/12 iters), loss = 5.27439
I0410 02:48:03.056360 30317 solver.cpp:237]     Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
I0410 02:48:03.056368 30317 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 02:48:07.515982 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 02:48:08.022454 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 02:48:08.361801 30317 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 02:48:08.361820 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:48:11.243355 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:12.847770 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:48:12.847810 30317 solver.cpp:397]     Test net output #1: loss = 5.287 (* 1 = 5.287 loss)
I0410 02:48:12.929234 30317 solver.cpp:218] Iteration 4080 (1.2155 iter/s, 9.87252s/12 iters), loss = 5.28547
I0410 02:48:12.929282 30317 solver.cpp:237]     Train net output #0: loss = 5.28547 (* 1 = 5.28547 loss)
I0410 02:48:12.929291 30317 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 02:48:16.933508 30317 solver.cpp:218] Iteration 4092 (2.99695 iter/s, 4.00407s/12 iters), loss = 5.26523
I0410 02:48:16.933558 30317 solver.cpp:237]     Train net output #0: loss = 5.26523 (* 1 = 5.26523 loss)
I0410 02:48:16.933570 30317 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 02:48:21.844506 30317 solver.cpp:218] Iteration 4104 (2.44361 iter/s, 4.91076s/12 iters), loss = 5.26253
I0410 02:48:21.844560 30317 solver.cpp:237]     Train net output #0: loss = 5.26253 (* 1 = 5.26253 loss)
I0410 02:48:21.844571 30317 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 02:48:26.678248 30317 solver.cpp:218] Iteration 4116 (2.48267 iter/s, 4.83351s/12 iters), loss = 5.29447
I0410 02:48:26.678308 30317 solver.cpp:237]     Train net output #0: loss = 5.29447 (* 1 = 5.29447 loss)
I0410 02:48:26.678318 30317 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 02:48:31.509783 30317 solver.cpp:218] Iteration 4128 (2.48381 iter/s, 4.83128s/12 iters), loss = 5.26708
I0410 02:48:31.509927 30317 solver.cpp:237]     Train net output #0: loss = 5.26708 (* 1 = 5.26708 loss)
I0410 02:48:31.509943 30317 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 02:48:36.409229 30317 solver.cpp:218] Iteration 4140 (2.44942 iter/s, 4.89912s/12 iters), loss = 5.26163
I0410 02:48:36.409279 30317 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0410 02:48:36.409291 30317 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 02:48:39.005524 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:41.299938 30317 solver.cpp:218] Iteration 4152 (2.45375 iter/s, 4.89048s/12 iters), loss = 5.27125
I0410 02:48:41.299983 30317 solver.cpp:237]     Train net output #0: loss = 5.27125 (* 1 = 5.27125 loss)
I0410 02:48:41.299993 30317 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 02:48:41.651746 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:48:46.115350 30317 solver.cpp:218] Iteration 4164 (2.49212 iter/s, 4.81519s/12 iters), loss = 5.26518
I0410 02:48:46.115396 30317 solver.cpp:237]     Train net output #0: loss = 5.26518 (* 1 = 5.26518 loss)
I0410 02:48:46.115406 30317 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 02:48:50.945441 30317 solver.cpp:218] Iteration 4176 (2.48454 iter/s, 4.82986s/12 iters), loss = 5.26686
I0410 02:48:50.945498 30317 solver.cpp:237]     Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
I0410 02:48:50.945510 30317 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 02:48:52.950901 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 02:48:53.459280 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 02:48:53.794471 30317 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 02:48:53.794492 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:48:56.632294 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:58.309692 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:48:58.309741 30317 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 02:49:00.170768 30317 solver.cpp:218] Iteration 4188 (1.30082 iter/s, 9.22493s/12 iters), loss = 5.26961
I0410 02:49:00.170827 30317 solver.cpp:237]     Train net output #0: loss = 5.26961 (* 1 = 5.26961 loss)
I0410 02:49:00.170840 30317 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 02:49:05.108268 30317 solver.cpp:218] Iteration 4200 (2.4305 iter/s, 4.93726s/12 iters), loss = 5.28431
I0410 02:49:05.108425 30317 solver.cpp:237]     Train net output #0: loss = 5.28431 (* 1 = 5.28431 loss)
I0410 02:49:05.108438 30317 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 02:49:10.130565 30317 solver.cpp:218] Iteration 4212 (2.38951 iter/s, 5.02196s/12 iters), loss = 5.27169
I0410 02:49:10.130614 30317 solver.cpp:237]     Train net output #0: loss = 5.27169 (* 1 = 5.27169 loss)
I0410 02:49:10.130625 30317 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 02:49:15.063943 30317 solver.cpp:218] Iteration 4224 (2.43252 iter/s, 4.93315s/12 iters), loss = 5.26335
I0410 02:49:15.063984 30317 solver.cpp:237]     Train net output #0: loss = 5.26335 (* 1 = 5.26335 loss)
I0410 02:49:15.063994 30317 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 02:49:19.919696 30317 solver.cpp:218] Iteration 4236 (2.47141 iter/s, 4.85553s/12 iters), loss = 5.26894
I0410 02:49:19.919745 30317 solver.cpp:237]     Train net output #0: loss = 5.26894 (* 1 = 5.26894 loss)
I0410 02:49:19.919756 30317 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 02:49:24.578377 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:49:24.798743 30317 solver.cpp:218] Iteration 4248 (2.45961 iter/s, 4.87882s/12 iters), loss = 5.24361
I0410 02:49:24.798782 30317 solver.cpp:237]     Train net output #0: loss = 5.24361 (* 1 = 5.24361 loss)
I0410 02:49:24.798791 30317 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 02:49:29.725685 30317 solver.cpp:218] Iteration 4260 (2.4357 iter/s, 4.92671s/12 iters), loss = 5.26854
I0410 02:49:29.725734 30317 solver.cpp:237]     Train net output #0: loss = 5.26854 (* 1 = 5.26854 loss)
I0410 02:49:29.725745 30317 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 02:49:34.637987 30317 solver.cpp:218] Iteration 4272 (2.44296 iter/s, 4.91207s/12 iters), loss = 5.29203
I0410 02:49:34.638033 30317 solver.cpp:237]     Train net output #0: loss = 5.29203 (* 1 = 5.29203 loss)
I0410 02:49:34.638042 30317 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 02:49:39.093358 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 02:49:41.016278 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 02:49:42.103920 30317 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 02:49:42.103951 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:49:44.853089 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:49:46.538542 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:49:46.538578 30317 solver.cpp:397]     Test net output #1: loss = 5.28673 (* 1 = 5.28673 loss)
I0410 02:49:46.620076 30317 solver.cpp:218] Iteration 4284 (1.00153 iter/s, 11.9816s/12 iters), loss = 5.27799
I0410 02:49:46.620131 30317 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0410 02:49:46.620143 30317 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 02:49:51.145660 30317 solver.cpp:218] Iteration 4296 (2.65173 iter/s, 4.52535s/12 iters), loss = 5.27493
I0410 02:49:51.145720 30317 solver.cpp:237]     Train net output #0: loss = 5.27493 (* 1 = 5.27493 loss)
I0410 02:49:51.145733 30317 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 02:49:56.075872 30317 solver.cpp:218] Iteration 4308 (2.4341 iter/s, 4.92996s/12 iters), loss = 5.26307
I0410 02:49:56.075930 30317 solver.cpp:237]     Train net output #0: loss = 5.26307 (* 1 = 5.26307 loss)
I0410 02:49:56.075943 30317 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 02:50:01.030625 30317 solver.cpp:218] Iteration 4320 (2.42203 iter/s, 4.95451s/12 iters), loss = 5.24841
I0410 02:50:01.030670 30317 solver.cpp:237]     Train net output #0: loss = 5.24841 (* 1 = 5.24841 loss)
I0410 02:50:01.030681 30317 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 02:50:05.904023 30317 solver.cpp:218] Iteration 4332 (2.46247 iter/s, 4.87316s/12 iters), loss = 5.2763
I0410 02:50:05.904075 30317 solver.cpp:237]     Train net output #0: loss = 5.2763 (* 1 = 5.2763 loss)
I0410 02:50:05.904088 30317 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 02:50:10.859263 30317 solver.cpp:218] Iteration 4344 (2.4218 iter/s, 4.955s/12 iters), loss = 5.27926
I0410 02:50:10.859423 30317 solver.cpp:237]     Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
I0410 02:50:10.859438 30317 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 02:50:12.775760 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:15.822861 30317 solver.cpp:218] Iteration 4356 (2.41777 iter/s, 4.96325s/12 iters), loss = 5.2867
I0410 02:50:15.822918 30317 solver.cpp:237]     Train net output #0: loss = 5.2867 (* 1 = 5.2867 loss)
I0410 02:50:15.822930 30317 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 02:50:20.730072 30317 solver.cpp:218] Iteration 4368 (2.4455 iter/s, 4.90697s/12 iters), loss = 5.27417
I0410 02:50:20.730116 30317 solver.cpp:237]     Train net output #0: loss = 5.27417 (* 1 = 5.27417 loss)
I0410 02:50:20.730126 30317 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 02:50:25.643323 30317 solver.cpp:218] Iteration 4380 (2.44249 iter/s, 4.91302s/12 iters), loss = 5.25941
I0410 02:50:25.643368 30317 solver.cpp:237]     Train net output #0: loss = 5.25941 (* 1 = 5.25941 loss)
I0410 02:50:25.643379 30317 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 02:50:27.622247 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 02:50:28.104547 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 02:50:28.444227 30317 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 02:50:28.444258 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:50:31.181644 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:32.955669 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:50:32.955718 30317 solver.cpp:397]     Test net output #1: loss = 5.28665 (* 1 = 5.28665 loss)
I0410 02:50:34.744005 30317 solver.cpp:218] Iteration 4392 (1.31864 iter/s, 9.10031s/12 iters), loss = 5.2692
I0410 02:50:34.744053 30317 solver.cpp:237]     Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
I0410 02:50:34.744064 30317 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 02:50:39.762639 30317 solver.cpp:218] Iteration 4404 (2.39121 iter/s, 5.01838s/12 iters), loss = 5.26168
I0410 02:50:39.762706 30317 solver.cpp:237]     Train net output #0: loss = 5.26168 (* 1 = 5.26168 loss)
I0410 02:50:39.762723 30317 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 02:50:44.634546 30317 solver.cpp:218] Iteration 4416 (2.46323 iter/s, 4.87166s/12 iters), loss = 5.26563
I0410 02:50:44.634657 30317 solver.cpp:237]     Train net output #0: loss = 5.26563 (* 1 = 5.26563 loss)
I0410 02:50:44.634667 30317 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 02:50:49.528182 30317 solver.cpp:218] Iteration 4428 (2.45231 iter/s, 4.89334s/12 iters), loss = 5.26862
I0410 02:50:49.528234 30317 solver.cpp:237]     Train net output #0: loss = 5.26862 (* 1 = 5.26862 loss)
I0410 02:50:49.528246 30317 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 02:50:54.436480 30317 solver.cpp:218] Iteration 4440 (2.44495 iter/s, 4.90807s/12 iters), loss = 5.26377
I0410 02:50:54.436517 30317 solver.cpp:237]     Train net output #0: loss = 5.26377 (* 1 = 5.26377 loss)
I0410 02:50:54.436524 30317 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 02:50:58.355132 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:59.304531 30317 solver.cpp:218] Iteration 4452 (2.46516 iter/s, 4.86783s/12 iters), loss = 5.25564
I0410 02:50:59.304576 30317 solver.cpp:237]     Train net output #0: loss = 5.25564 (* 1 = 5.25564 loss)
I0410 02:50:59.304585 30317 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 02:51:04.147639 30317 solver.cpp:218] Iteration 4464 (2.47787 iter/s, 4.84288s/12 iters), loss = 5.27902
I0410 02:51:04.147686 30317 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 02:51:04.147696 30317 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 02:51:08.973775 30317 solver.cpp:218] Iteration 4476 (2.48658 iter/s, 4.82591s/12 iters), loss = 5.26013
I0410 02:51:08.973819 30317 solver.cpp:237]     Train net output #0: loss = 5.26013 (* 1 = 5.26013 loss)
I0410 02:51:08.973827 30317 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 02:51:13.419231 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 02:51:16.100136 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 02:51:17.288412 30317 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 02:51:17.288441 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:51:19.956614 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:21.717984 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:51:21.718035 30317 solver.cpp:397]     Test net output #1: loss = 5.2865 (* 1 = 5.2865 loss)
I0410 02:51:21.799628 30317 solver.cpp:218] Iteration 4488 (0.935647 iter/s, 12.8253s/12 iters), loss = 5.31205
I0410 02:51:21.799681 30317 solver.cpp:237]     Train net output #0: loss = 5.31205 (* 1 = 5.31205 loss)
I0410 02:51:21.799692 30317 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 02:51:26.035629 30317 solver.cpp:218] Iteration 4500 (2.833 iter/s, 4.23579s/12 iters), loss = 5.26743
I0410 02:51:26.035674 30317 solver.cpp:237]     Train net output #0: loss = 5.26743 (* 1 = 5.26743 loss)
I0410 02:51:26.035683 30317 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 02:51:30.923700 30317 solver.cpp:218] Iteration 4512 (2.45508 iter/s, 4.88783s/12 iters), loss = 5.27108
I0410 02:51:30.923761 30317 solver.cpp:237]     Train net output #0: loss = 5.27108 (* 1 = 5.27108 loss)
I0410 02:51:30.923772 30317 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 02:51:35.893241 30317 solver.cpp:218] Iteration 4524 (2.41483 iter/s, 4.9693s/12 iters), loss = 5.27418
I0410 02:51:35.893285 30317 solver.cpp:237]     Train net output #0: loss = 5.27418 (* 1 = 5.27418 loss)
I0410 02:51:35.893292 30317 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 02:51:40.741322 30317 solver.cpp:218] Iteration 4536 (2.47532 iter/s, 4.84785s/12 iters), loss = 5.26499
I0410 02:51:40.741371 30317 solver.cpp:237]     Train net output #0: loss = 5.26499 (* 1 = 5.26499 loss)
I0410 02:51:40.741381 30317 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 02:51:45.674275 30317 solver.cpp:218] Iteration 4548 (2.43273 iter/s, 4.93272s/12 iters), loss = 5.26658
I0410 02:51:45.674314 30317 solver.cpp:237]     Train net output #0: loss = 5.26658 (* 1 = 5.26658 loss)
I0410 02:51:45.674322 30317 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 02:51:46.912505 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:50.616147 30317 solver.cpp:218] Iteration 4560 (2.42834 iter/s, 4.94164s/12 iters), loss = 5.27326
I0410 02:51:50.616204 30317 solver.cpp:237]     Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
I0410 02:51:50.616217 30317 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 02:51:55.523613 30317 solver.cpp:218] Iteration 4572 (2.44537 iter/s, 4.90723s/12 iters), loss = 5.26135
I0410 02:51:55.523664 30317 solver.cpp:237]     Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
I0410 02:51:55.523676 30317 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 02:52:00.437001 30317 solver.cpp:218] Iteration 4584 (2.44242 iter/s, 4.91315s/12 iters), loss = 5.27303
I0410 02:52:00.437047 30317 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0410 02:52:00.437055 30317 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 02:52:02.412595 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 02:52:02.918962 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 02:52:03.261675 30317 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 02:52:03.261703 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:52:05.876457 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:07.706214 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:52:07.706264 30317 solver.cpp:397]     Test net output #1: loss = 5.28645 (* 1 = 5.28645 loss)
I0410 02:52:09.515576 30317 solver.cpp:218] Iteration 4596 (1.32185 iter/s, 9.0782s/12 iters), loss = 5.27243
I0410 02:52:09.515626 30317 solver.cpp:237]     Train net output #0: loss = 5.27243 (* 1 = 5.27243 loss)
I0410 02:52:09.515638 30317 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 02:52:14.468041 30317 solver.cpp:218] Iteration 4608 (2.42315 iter/s, 4.95224s/12 iters), loss = 5.27163
I0410 02:52:14.468080 30317 solver.cpp:237]     Train net output #0: loss = 5.27163 (* 1 = 5.27163 loss)
I0410 02:52:14.468087 30317 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 02:52:19.364149 30317 solver.cpp:218] Iteration 4620 (2.45104 iter/s, 4.89588s/12 iters), loss = 5.26243
I0410 02:52:19.364295 30317 solver.cpp:237]     Train net output #0: loss = 5.26243 (* 1 = 5.26243 loss)
I0410 02:52:19.364308 30317 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 02:52:24.308495 30317 solver.cpp:218] Iteration 4632 (2.42718 iter/s, 4.94402s/12 iters), loss = 5.29347
I0410 02:52:24.308545 30317 solver.cpp:237]     Train net output #0: loss = 5.29347 (* 1 = 5.29347 loss)
I0410 02:52:24.308557 30317 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 02:52:29.247110 30317 solver.cpp:218] Iteration 4644 (2.42995 iter/s, 4.93838s/12 iters), loss = 5.26511
I0410 02:52:29.247162 30317 solver.cpp:237]     Train net output #0: loss = 5.26511 (* 1 = 5.26511 loss)
I0410 02:52:29.247174 30317 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 02:52:32.587663 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:34.170737 30317 solver.cpp:218] Iteration 4656 (2.43735 iter/s, 4.92339s/12 iters), loss = 5.28152
I0410 02:52:34.170795 30317 solver.cpp:237]     Train net output #0: loss = 5.28152 (* 1 = 5.28152 loss)
I0410 02:52:34.170807 30317 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 02:52:39.168679 30317 solver.cpp:218] Iteration 4668 (2.4011 iter/s, 4.9977s/12 iters), loss = 5.26593
I0410 02:52:39.168722 30317 solver.cpp:237]     Train net output #0: loss = 5.26593 (* 1 = 5.26593 loss)
I0410 02:52:39.168732 30317 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 02:52:43.981473 30317 solver.cpp:218] Iteration 4680 (2.49347 iter/s, 4.81257s/12 iters), loss = 5.27546
I0410 02:52:43.981526 30317 solver.cpp:237]     Train net output #0: loss = 5.27546 (* 1 = 5.27546 loss)
I0410 02:52:43.981536 30317 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 02:52:48.318743 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 02:52:48.877493 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 02:52:49.220434 30317 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 02:52:49.220464 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:52:51.809041 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:53.697798 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:52:53.697861 30317 solver.cpp:397]     Test net output #1: loss = 5.28677 (* 1 = 5.28677 loss)
I0410 02:52:53.779848 30317 solver.cpp:218] Iteration 4692 (1.22474 iter/s, 9.79797s/12 iters), loss = 5.27228
I0410 02:52:53.779896 30317 solver.cpp:237]     Train net output #0: loss = 5.27228 (* 1 = 5.27228 loss)
I0410 02:52:53.779907 30317 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 02:52:57.971518 30317 solver.cpp:218] Iteration 4704 (2.86296 iter/s, 4.19146s/12 iters), loss = 5.26732
I0410 02:52:57.971557 30317 solver.cpp:237]     Train net output #0: loss = 5.26732 (* 1 = 5.26732 loss)
I0410 02:52:57.971566 30317 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 02:53:02.878818 30317 solver.cpp:218] Iteration 4716 (2.44545 iter/s, 4.90707s/12 iters), loss = 5.28004
I0410 02:53:02.878880 30317 solver.cpp:237]     Train net output #0: loss = 5.28004 (* 1 = 5.28004 loss)
I0410 02:53:02.878890 30317 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 02:53:07.703095 30317 solver.cpp:218] Iteration 4728 (2.48755 iter/s, 4.82403s/12 iters), loss = 5.26298
I0410 02:53:07.703157 30317 solver.cpp:237]     Train net output #0: loss = 5.26298 (* 1 = 5.26298 loss)
I0410 02:53:07.703169 30317 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 02:53:12.506641 30317 solver.cpp:218] Iteration 4740 (2.49828 iter/s, 4.8033s/12 iters), loss = 5.27578
I0410 02:53:12.506696 30317 solver.cpp:237]     Train net output #0: loss = 5.27578 (* 1 = 5.27578 loss)
I0410 02:53:12.506707 30317 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 02:53:17.336570 30317 solver.cpp:218] Iteration 4752 (2.48463 iter/s, 4.82968s/12 iters), loss = 5.28008
I0410 02:53:17.336619 30317 solver.cpp:237]     Train net output #0: loss = 5.28008 (* 1 = 5.28008 loss)
I0410 02:53:17.336632 30317 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 02:53:17.843856 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:53:22.142943 30317 solver.cpp:218] Iteration 4764 (2.4968 iter/s, 4.80614s/12 iters), loss = 5.27894
I0410 02:53:22.143054 30317 solver.cpp:237]     Train net output #0: loss = 5.27894 (* 1 = 5.27894 loss)
I0410 02:53:22.143062 30317 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 02:53:27.186040 30317 solver.cpp:218] Iteration 4776 (2.37963 iter/s, 5.0428s/12 iters), loss = 5.26383
I0410 02:53:27.186087 30317 solver.cpp:237]     Train net output #0: loss = 5.26383 (* 1 = 5.26383 loss)
I0410 02:53:27.186097 30317 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 02:53:32.153396 30317 solver.cpp:218] Iteration 4788 (2.41589 iter/s, 4.96711s/12 iters), loss = 5.29088
I0410 02:53:32.153463 30317 solver.cpp:237]     Train net output #0: loss = 5.29088 (* 1 = 5.29088 loss)
I0410 02:53:32.153481 30317 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 02:53:34.110224 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 02:53:35.113724 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 02:53:36.551641 30317 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 02:53:36.551661 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:53:39.110204 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:53:40.999595 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 02:53:40.999624 30317 solver.cpp:397]     Test net output #1: loss = 5.28605 (* 1 = 5.28605 loss)
I0410 02:53:42.805567 30317 solver.cpp:218] Iteration 4800 (1.12658 iter/s, 10.6517s/12 iters), loss = 5.27263
I0410 02:53:42.805611 30317 solver.cpp:237]     Train net output #0: loss = 5.27263 (* 1 = 5.27263 loss)
I0410 02:53:42.805621 30317 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 02:53:47.981003 30317 solver.cpp:218] Iteration 4812 (2.31875 iter/s, 5.17519s/12 iters), loss = 5.26456
I0410 02:53:47.981060 30317 solver.cpp:237]     Train net output #0: loss = 5.26456 (* 1 = 5.26456 loss)
I0410 02:53:47.981072 30317 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 02:53:53.046247 30317 solver.cpp:218] Iteration 4824 (2.3692 iter/s, 5.065s/12 iters), loss = 5.29048
I0410 02:53:53.046409 30317 solver.cpp:237]     Train net output #0: loss = 5.29048 (* 1 = 5.29048 loss)
I0410 02:53:53.046424 30317 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 02:53:57.944540 30317 solver.cpp:218] Iteration 4836 (2.45 iter/s, 4.89795s/12 iters), loss = 5.2623
I0410 02:53:57.944587 30317 solver.cpp:237]     Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
I0410 02:53:57.944599 30317 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 02:53:58.727387 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:54:02.924386 30317 solver.cpp:218] Iteration 4848 (2.40983 iter/s, 4.97961s/12 iters), loss = 5.26449
I0410 02:54:02.924434 30317 solver.cpp:237]     Train net output #0: loss = 5.26449 (* 1 = 5.26449 loss)
I0410 02:54:02.924445 30317 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 02:54:05.593533 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:07.833598 30317 solver.cpp:218] Iteration 4860 (2.4445 iter/s, 4.90898s/12 iters), loss = 5.26831
I0410 02:54:07.833652 30317 solver.cpp:237]     Train net output #0: loss = 5.26831 (* 1 = 5.26831 loss)
I0410 02:54:07.833664 30317 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 02:54:12.735570 30317 solver.cpp:218] Iteration 4872 (2.44811 iter/s, 4.90174s/12 iters), loss = 5.26234
I0410 02:54:12.735608 30317 solver.cpp:237]     Train net output #0: loss = 5.26234 (* 1 = 5.26234 loss)
I0410 02:54:12.735616 30317 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 02:54:17.641216 30317 solver.cpp:218] Iteration 4884 (2.44627 iter/s, 4.90542s/12 iters), loss = 5.26826
I0410 02:54:17.641261 30317 solver.cpp:237]     Train net output #0: loss = 5.26826 (* 1 = 5.26826 loss)
I0410 02:54:17.641273 30317 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 02:54:22.014328 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 02:54:23.181448 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 02:54:24.693711 30317 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 02:54:24.693753 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:54:27.264753 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:29.188581 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:54:29.188627 30317 solver.cpp:397]     Test net output #1: loss = 5.28691 (* 1 = 5.28691 loss)
I0410 02:54:29.270537 30317 solver.cpp:218] Iteration 4896 (1.03192 iter/s, 11.6289s/12 iters), loss = 5.26894
I0410 02:54:29.270587 30317 solver.cpp:237]     Train net output #0: loss = 5.26894 (* 1 = 5.26894 loss)
I0410 02:54:29.270597 30317 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 02:54:33.392333 30317 solver.cpp:218] Iteration 4908 (2.9115 iter/s, 4.12159s/12 iters), loss = 5.28784
I0410 02:54:33.392385 30317 solver.cpp:237]     Train net output #0: loss = 5.28784 (* 1 = 5.28784 loss)
I0410 02:54:33.392395 30317 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 02:54:38.240334 30317 solver.cpp:218] Iteration 4920 (2.47537 iter/s, 4.84777s/12 iters), loss = 5.26984
I0410 02:54:38.240381 30317 solver.cpp:237]     Train net output #0: loss = 5.26984 (* 1 = 5.26984 loss)
I0410 02:54:38.240389 30317 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 02:54:43.074276 30317 solver.cpp:218] Iteration 4932 (2.48256 iter/s, 4.83371s/12 iters), loss = 5.26449
I0410 02:54:43.074323 30317 solver.cpp:237]     Train net output #0: loss = 5.26449 (* 1 = 5.26449 loss)
I0410 02:54:43.074333 30317 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 02:54:47.961978 30317 solver.cpp:218] Iteration 4944 (2.45526 iter/s, 4.88746s/12 iters), loss = 5.26628
I0410 02:54:47.962030 30317 solver.cpp:237]     Train net output #0: loss = 5.26628 (* 1 = 5.26628 loss)
I0410 02:54:47.962041 30317 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 02:54:52.614805 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:52.807370 30317 solver.cpp:218] Iteration 4956 (2.4767 iter/s, 4.84516s/12 iters), loss = 5.25058
I0410 02:54:52.807420 30317 solver.cpp:237]     Train net output #0: loss = 5.25058 (* 1 = 5.25058 loss)
I0410 02:54:52.807430 30317 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 02:54:57.656955 30317 solver.cpp:218] Iteration 4968 (2.47456 iter/s, 4.84935s/12 iters), loss = 5.26491
I0410 02:54:57.657073 30317 solver.cpp:237]     Train net output #0: loss = 5.26491 (* 1 = 5.26491 loss)
I0410 02:54:57.657083 30317 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 02:55:02.618736 30317 solver.cpp:218] Iteration 4980 (2.41863 iter/s, 4.96148s/12 iters), loss = 5.29099
I0410 02:55:02.618777 30317 solver.cpp:237]     Train net output #0: loss = 5.29099 (* 1 = 5.29099 loss)
I0410 02:55:02.618785 30317 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 02:55:07.473943 30317 solver.cpp:218] Iteration 4992 (2.47169 iter/s, 4.85498s/12 iters), loss = 5.28526
I0410 02:55:07.474004 30317 solver.cpp:237]     Train net output #0: loss = 5.28526 (* 1 = 5.28526 loss)
I0410 02:55:07.474016 30317 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 02:55:09.427546 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 02:55:10.621572 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 02:55:11.508882 30317 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 02:55:11.508913 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:55:14.117738 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:16.076774 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:55:16.076822 30317 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 02:55:17.995594 30317 solver.cpp:218] Iteration 5004 (1.14055 iter/s, 10.5212s/12 iters), loss = 5.2812
I0410 02:55:17.995644 30317 solver.cpp:237]     Train net output #0: loss = 5.2812 (* 1 = 5.2812 loss)
I0410 02:55:17.995656 30317 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 02:55:22.902215 30317 solver.cpp:218] Iteration 5016 (2.44579 iter/s, 4.90639s/12 iters), loss = 5.26368
I0410 02:55:22.902266 30317 solver.cpp:237]     Train net output #0: loss = 5.26368 (* 1 = 5.26368 loss)
I0410 02:55:22.902277 30317 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 02:55:27.761317 30317 solver.cpp:218] Iteration 5028 (2.46971 iter/s, 4.85887s/12 iters), loss = 5.24638
I0410 02:55:27.764350 30317 solver.cpp:237]     Train net output #0: loss = 5.24638 (* 1 = 5.24638 loss)
I0410 02:55:27.764364 30317 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 02:55:32.592617 30317 solver.cpp:218] Iteration 5040 (2.48546 iter/s, 4.82808s/12 iters), loss = 5.28737
I0410 02:55:32.592680 30317 solver.cpp:237]     Train net output #0: loss = 5.28737 (* 1 = 5.28737 loss)
I0410 02:55:32.592692 30317 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 02:55:37.647446 30317 solver.cpp:218] Iteration 5052 (2.37408 iter/s, 5.05458s/12 iters), loss = 5.26966
I0410 02:55:37.647495 30317 solver.cpp:237]     Train net output #0: loss = 5.26966 (* 1 = 5.26966 loss)
I0410 02:55:37.647505 30317 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 02:55:39.626037 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:42.669939 30317 solver.cpp:218] Iteration 5064 (2.38937 iter/s, 5.02224s/12 iters), loss = 5.28596
I0410 02:55:42.670012 30317 solver.cpp:237]     Train net output #0: loss = 5.28596 (* 1 = 5.28596 loss)
I0410 02:55:42.670025 30317 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 02:55:47.527227 30317 solver.cpp:218] Iteration 5076 (2.47064 iter/s, 4.85703s/12 iters), loss = 5.27241
I0410 02:55:47.527279 30317 solver.cpp:237]     Train net output #0: loss = 5.27241 (* 1 = 5.27241 loss)
I0410 02:55:47.527289 30317 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 02:55:52.385378 30317 solver.cpp:218] Iteration 5088 (2.47019 iter/s, 4.85792s/12 iters), loss = 5.26522
I0410 02:55:52.385426 30317 solver.cpp:237]     Train net output #0: loss = 5.26522 (* 1 = 5.26522 loss)
I0410 02:55:52.385437 30317 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 02:55:56.869112 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 02:55:57.354210 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 02:55:57.685470 30317 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 02:55:57.685497 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:56:00.188717 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:02.194681 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:56:02.194725 30317 solver.cpp:397]     Test net output #1: loss = 5.28641 (* 1 = 5.28641 loss)
I0410 02:56:02.274967 30317 solver.cpp:218] Iteration 5100 (1.21345 iter/s, 9.88918s/12 iters), loss = 5.26525
I0410 02:56:02.275012 30317 solver.cpp:237]     Train net output #0: loss = 5.26525 (* 1 = 5.26525 loss)
I0410 02:56:02.275022 30317 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 02:56:06.484427 30317 solver.cpp:218] Iteration 5112 (2.85086 iter/s, 4.20925s/12 iters), loss = 5.26394
I0410 02:56:06.484472 30317 solver.cpp:237]     Train net output #0: loss = 5.26394 (* 1 = 5.26394 loss)
I0410 02:56:06.484480 30317 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 02:56:11.313571 30317 solver.cpp:218] Iteration 5124 (2.48503 iter/s, 4.82891s/12 iters), loss = 5.27187
I0410 02:56:11.313632 30317 solver.cpp:237]     Train net output #0: loss = 5.27187 (* 1 = 5.27187 loss)
I0410 02:56:11.313644 30317 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 02:56:16.215764 30317 solver.cpp:218] Iteration 5136 (2.44801 iter/s, 4.90195s/12 iters), loss = 5.27104
I0410 02:56:16.215809 30317 solver.cpp:237]     Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
I0410 02:56:16.215821 30317 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 02:56:21.171094 30317 solver.cpp:218] Iteration 5148 (2.42175 iter/s, 4.9551s/12 iters), loss = 5.26352
I0410 02:56:21.171135 30317 solver.cpp:237]     Train net output #0: loss = 5.26352 (* 1 = 5.26352 loss)
I0410 02:56:21.171144 30317 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 02:56:25.178387 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:26.090160 30317 solver.cpp:218] Iteration 5160 (2.4396 iter/s, 4.91884s/12 iters), loss = 5.25433
I0410 02:56:26.090215 30317 solver.cpp:237]     Train net output #0: loss = 5.25433 (* 1 = 5.25433 loss)
I0410 02:56:26.090229 30317 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 02:56:31.203253 30317 solver.cpp:218] Iteration 5172 (2.34703 iter/s, 5.11284s/12 iters), loss = 5.27605
I0410 02:56:31.203361 30317 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0410 02:56:31.203374 30317 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 02:56:36.064311 30317 solver.cpp:218] Iteration 5184 (2.46875 iter/s, 4.86077s/12 iters), loss = 5.27018
I0410 02:56:36.064365 30317 solver.cpp:237]     Train net output #0: loss = 5.27018 (* 1 = 5.27018 loss)
I0410 02:56:36.064375 30317 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 02:56:41.072187 30317 solver.cpp:218] Iteration 5196 (2.39634 iter/s, 5.00764s/12 iters), loss = 5.30699
I0410 02:56:41.072238 30317 solver.cpp:237]     Train net output #0: loss = 5.30699 (* 1 = 5.30699 loss)
I0410 02:56:41.072250 30317 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 02:56:43.055075 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 02:56:43.527216 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 02:56:43.872030 30317 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 02:56:43.872061 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:56:46.289188 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:48.374567 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:56:48.374599 30317 solver.cpp:397]     Test net output #1: loss = 5.2866 (* 1 = 5.2866 loss)
I0410 02:56:50.266244 30317 solver.cpp:218] Iteration 5208 (1.30525 iter/s, 9.19366s/12 iters), loss = 5.27341
I0410 02:56:50.266296 30317 solver.cpp:237]     Train net output #0: loss = 5.27341 (* 1 = 5.27341 loss)
I0410 02:56:50.266307 30317 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 02:56:55.609275 30317 solver.cpp:218] Iteration 5220 (2.24602 iter/s, 5.34278s/12 iters), loss = 5.27472
I0410 02:56:55.609315 30317 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0410 02:56:55.609324 30317 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 02:57:00.477445 30317 solver.cpp:218] Iteration 5232 (2.46511 iter/s, 4.86794s/12 iters), loss = 5.275
I0410 02:57:00.477499 30317 solver.cpp:237]     Train net output #0: loss = 5.275 (* 1 = 5.275 loss)
I0410 02:57:00.477510 30317 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 02:57:05.358016 30317 solver.cpp:218] Iteration 5244 (2.45885 iter/s, 4.88033s/12 iters), loss = 5.27703
I0410 02:57:05.358147 30317 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0410 02:57:05.358157 30317 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 02:57:10.298591 30317 solver.cpp:218] Iteration 5256 (2.42902 iter/s, 4.94026s/12 iters), loss = 5.25807
I0410 02:57:10.298636 30317 solver.cpp:237]     Train net output #0: loss = 5.25807 (* 1 = 5.25807 loss)
I0410 02:57:10.298645 30317 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 02:57:11.583061 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:57:15.229312 30317 solver.cpp:218] Iteration 5268 (2.43384 iter/s, 4.93049s/12 iters), loss = 5.27706
I0410 02:57:15.229363 30317 solver.cpp:237]     Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
I0410 02:57:15.229375 30317 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 02:57:20.160861 30317 solver.cpp:218] Iteration 5280 (2.43343 iter/s, 4.93131s/12 iters), loss = 5.26523
I0410 02:57:20.160913 30317 solver.cpp:237]     Train net output #0: loss = 5.26523 (* 1 = 5.26523 loss)
I0410 02:57:20.160926 30317 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 02:57:25.039578 30317 solver.cpp:218] Iteration 5292 (2.45978 iter/s, 4.87848s/12 iters), loss = 5.28016
I0410 02:57:25.039628 30317 solver.cpp:237]     Train net output #0: loss = 5.28016 (* 1 = 5.28016 loss)
I0410 02:57:25.039639 30317 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 02:57:29.463618 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 02:57:33.365648 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 02:57:34.454967 30317 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 02:57:34.454998 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:57:36.744733 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:57:38.828336 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:57:38.828377 30317 solver.cpp:397]     Test net output #1: loss = 5.28638 (* 1 = 5.28638 loss)
I0410 02:57:38.909943 30317 solver.cpp:218] Iteration 5304 (0.865188 iter/s, 13.8698s/12 iters), loss = 5.27044
I0410 02:57:38.910024 30317 solver.cpp:237]     Train net output #0: loss = 5.27044 (* 1 = 5.27044 loss)
I0410 02:57:38.910037 30317 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 02:57:43.045418 30317 solver.cpp:218] Iteration 5316 (2.90189 iter/s, 4.13523s/12 iters), loss = 5.27153
I0410 02:57:43.045477 30317 solver.cpp:237]     Train net output #0: loss = 5.27153 (* 1 = 5.27153 loss)
I0410 02:57:43.045490 30317 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 02:57:47.867871 30317 solver.cpp:218] Iteration 5328 (2.48848 iter/s, 4.82221s/12 iters), loss = 5.25853
I0410 02:57:47.867924 30317 solver.cpp:237]     Train net output #0: loss = 5.25853 (* 1 = 5.25853 loss)
I0410 02:57:47.867936 30317 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 02:57:52.656446 30317 solver.cpp:218] Iteration 5340 (2.50609 iter/s, 4.78834s/12 iters), loss = 5.29862
I0410 02:57:52.656497 30317 solver.cpp:237]     Train net output #0: loss = 5.29862 (* 1 = 5.29862 loss)
I0410 02:57:52.656507 30317 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 02:57:57.607152 30317 solver.cpp:218] Iteration 5352 (2.42401 iter/s, 4.95047s/12 iters), loss = 5.27383
I0410 02:57:57.607199 30317 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0410 02:57:57.607209 30317 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 02:58:00.987555 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:02.495713 30317 solver.cpp:218] Iteration 5364 (2.45483 iter/s, 4.88833s/12 iters), loss = 5.27553
I0410 02:58:02.495759 30317 solver.cpp:237]     Train net output #0: loss = 5.27553 (* 1 = 5.27553 loss)
I0410 02:58:02.495769 30317 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 02:58:07.423111 30317 solver.cpp:218] Iteration 5376 (2.43548 iter/s, 4.92716s/12 iters), loss = 5.26684
I0410 02:58:07.423270 30317 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0410 02:58:07.423282 30317 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 02:58:12.260558 30317 solver.cpp:218] Iteration 5388 (2.48083 iter/s, 4.8371s/12 iters), loss = 5.26856
I0410 02:58:12.260614 30317 solver.cpp:237]     Train net output #0: loss = 5.26856 (* 1 = 5.26856 loss)
I0410 02:58:12.260625 30317 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 02:58:17.095844 30317 solver.cpp:218] Iteration 5400 (2.48188 iter/s, 4.83505s/12 iters), loss = 5.26928
I0410 02:58:17.095888 30317 solver.cpp:237]     Train net output #0: loss = 5.26928 (* 1 = 5.26928 loss)
I0410 02:58:17.095896 30317 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 02:58:19.081452 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 02:58:19.867870 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 02:58:20.334857 30317 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 02:58:20.334885 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:58:22.610899 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:24.715544 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:58:24.715595 30317 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0410 02:58:26.658263 30317 solver.cpp:218] Iteration 5412 (1.25497 iter/s, 9.56201s/12 iters), loss = 5.26711
I0410 02:58:26.658305 30317 solver.cpp:237]     Train net output #0: loss = 5.26711 (* 1 = 5.26711 loss)
I0410 02:58:26.658314 30317 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 02:58:31.666976 30317 solver.cpp:218] Iteration 5424 (2.39594 iter/s, 5.00848s/12 iters), loss = 5.27921
I0410 02:58:31.667034 30317 solver.cpp:237]     Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
I0410 02:58:31.667047 30317 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 02:58:36.568850 30317 solver.cpp:218] Iteration 5436 (2.44816 iter/s, 4.90163s/12 iters), loss = 5.26539
I0410 02:58:36.568892 30317 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0410 02:58:36.568902 30317 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 02:58:41.755059 30317 solver.cpp:218] Iteration 5448 (2.31393 iter/s, 5.18598s/12 iters), loss = 5.2813
I0410 02:58:41.766031 30317 solver.cpp:237]     Train net output #0: loss = 5.2813 (* 1 = 5.2813 loss)
I0410 02:58:41.766043 30317 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 02:58:46.703956 30317 solver.cpp:218] Iteration 5460 (2.43026 iter/s, 4.93775s/12 iters), loss = 5.28002
I0410 02:58:46.703994 30317 solver.cpp:237]     Train net output #0: loss = 5.28002 (* 1 = 5.28002 loss)
I0410 02:58:46.704003 30317 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 02:58:47.264263 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:51.644579 30317 solver.cpp:218] Iteration 5472 (2.42895 iter/s, 4.9404s/12 iters), loss = 5.28067
I0410 02:58:51.644634 30317 solver.cpp:237]     Train net output #0: loss = 5.28067 (* 1 = 5.28067 loss)
I0410 02:58:51.644646 30317 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 02:58:56.602427 30317 solver.cpp:218] Iteration 5484 (2.42052 iter/s, 4.9576s/12 iters), loss = 5.27664
I0410 02:58:56.602484 30317 solver.cpp:237]     Train net output #0: loss = 5.27664 (* 1 = 5.27664 loss)
I0410 02:58:56.602496 30317 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 02:59:01.543000 30317 solver.cpp:218] Iteration 5496 (2.42899 iter/s, 4.94033s/12 iters), loss = 5.29232
I0410 02:59:01.543058 30317 solver.cpp:237]     Train net output #0: loss = 5.29232 (* 1 = 5.29232 loss)
I0410 02:59:01.543071 30317 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 02:59:05.993943 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 02:59:07.044608 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 02:59:08.050930 30317 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 02:59:08.050961 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:59:10.283674 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:12.459271 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:59:12.459408 30317 solver.cpp:397]     Test net output #1: loss = 5.28742 (* 1 = 5.28742 loss)
I0410 02:59:12.541512 30317 solver.cpp:218] Iteration 5508 (1.0911 iter/s, 10.9981s/12 iters), loss = 5.27629
I0410 02:59:12.541570 30317 solver.cpp:237]     Train net output #0: loss = 5.27629 (* 1 = 5.27629 loss)
I0410 02:59:12.541581 30317 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 02:59:16.733039 30317 solver.cpp:218] Iteration 5520 (2.86307 iter/s, 4.19131s/12 iters), loss = 5.27524
I0410 02:59:16.733094 30317 solver.cpp:237]     Train net output #0: loss = 5.27524 (* 1 = 5.27524 loss)
I0410 02:59:16.733108 30317 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 02:59:17.879248 30317 blocking_queue.cpp:49] Waiting for data
I0410 02:59:21.629189 30317 solver.cpp:218] Iteration 5532 (2.45103 iter/s, 4.89591s/12 iters), loss = 5.28631
I0410 02:59:21.629245 30317 solver.cpp:237]     Train net output #0: loss = 5.28631 (* 1 = 5.28631 loss)
I0410 02:59:21.629257 30317 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 02:59:26.502436 30317 solver.cpp:218] Iteration 5544 (2.46254 iter/s, 4.87301s/12 iters), loss = 5.25895
I0410 02:59:26.502482 30317 solver.cpp:237]     Train net output #0: loss = 5.25895 (* 1 = 5.25895 loss)
I0410 02:59:26.502492 30317 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 02:59:31.557243 30317 solver.cpp:218] Iteration 5556 (2.37409 iter/s, 5.05457s/12 iters), loss = 5.26949
I0410 02:59:31.557283 30317 solver.cpp:237]     Train net output #0: loss = 5.26949 (* 1 = 5.26949 loss)
I0410 02:59:31.557291 30317 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 02:59:34.272336 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:36.536381 30317 solver.cpp:218] Iteration 5568 (2.41017 iter/s, 4.97891s/12 iters), loss = 5.27839
I0410 02:59:36.536425 30317 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0410 02:59:36.536434 30317 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 02:59:41.434857 30317 solver.cpp:218] Iteration 5580 (2.44986 iter/s, 4.89824s/12 iters), loss = 5.26347
I0410 02:59:41.434908 30317 solver.cpp:237]     Train net output #0: loss = 5.26347 (* 1 = 5.26347 loss)
I0410 02:59:41.434917 30317 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 02:59:46.377717 30317 solver.cpp:218] Iteration 5592 (2.42786 iter/s, 4.94262s/12 iters), loss = 5.27217
I0410 02:59:46.377822 30317 solver.cpp:237]     Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
I0410 02:59:46.377833 30317 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 02:59:51.348587 30317 solver.cpp:218] Iteration 5604 (2.41421 iter/s, 4.97058s/12 iters), loss = 5.26684
I0410 02:59:51.348639 30317 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0410 02:59:51.348649 30317 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 02:59:53.300278 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 02:59:53.800642 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 02:59:54.142452 30317 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 02:59:54.142482 30317 net.cpp:676] Ignoring source layer train-data
I0410 02:59:56.523790 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:58.957513 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 02:59:58.957549 30317 solver.cpp:397]     Test net output #1: loss = 5.28677 (* 1 = 5.28677 loss)
I0410 03:00:00.713762 30317 solver.cpp:218] Iteration 5616 (1.2814 iter/s, 9.36479s/12 iters), loss = 5.29579
I0410 03:00:00.713801 30317 solver.cpp:237]     Train net output #0: loss = 5.29579 (* 1 = 5.29579 loss)
I0410 03:00:00.713810 30317 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 03:00:05.542429 30317 solver.cpp:218] Iteration 5628 (2.48528 iter/s, 4.82844s/12 iters), loss = 5.27235
I0410 03:00:05.542491 30317 solver.cpp:237]     Train net output #0: loss = 5.27235 (* 1 = 5.27235 loss)
I0410 03:00:05.542510 30317 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 03:00:10.488729 30317 solver.cpp:218] Iteration 5640 (2.42617 iter/s, 4.94606s/12 iters), loss = 5.26546
I0410 03:00:10.488780 30317 solver.cpp:237]     Train net output #0: loss = 5.26546 (* 1 = 5.26546 loss)
I0410 03:00:10.488791 30317 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 03:00:15.454538 30317 solver.cpp:218] Iteration 5652 (2.41664 iter/s, 4.96558s/12 iters), loss = 5.26699
I0410 03:00:15.454584 30317 solver.cpp:237]     Train net output #0: loss = 5.26699 (* 1 = 5.26699 loss)
I0410 03:00:15.454597 30317 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 03:00:20.255209 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:20.412879 30317 solver.cpp:218] Iteration 5664 (2.42028 iter/s, 4.95811s/12 iters), loss = 5.25268
I0410 03:00:20.412933 30317 solver.cpp:237]     Train net output #0: loss = 5.25268 (* 1 = 5.25268 loss)
I0410 03:00:20.412945 30317 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 03:00:25.276142 30317 solver.cpp:218] Iteration 5676 (2.4676 iter/s, 4.86303s/12 iters), loss = 5.26227
I0410 03:00:25.276190 30317 solver.cpp:237]     Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
I0410 03:00:25.276199 30317 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 03:00:30.171289 30317 solver.cpp:218] Iteration 5688 (2.45152 iter/s, 4.89492s/12 iters), loss = 5.29628
I0410 03:00:30.171334 30317 solver.cpp:237]     Train net output #0: loss = 5.29628 (* 1 = 5.29628 loss)
I0410 03:00:30.171341 30317 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 03:00:35.083832 30317 solver.cpp:218] Iteration 5700 (2.44284 iter/s, 4.91232s/12 iters), loss = 5.28648
I0410 03:00:35.083878 30317 solver.cpp:237]     Train net output #0: loss = 5.28648 (* 1 = 5.28648 loss)
I0410 03:00:35.083885 30317 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 03:00:39.561755 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 03:00:40.043553 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 03:00:40.379758 30317 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 03:00:40.379779 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:00:42.718950 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:45.183740 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:00:45.183789 30317 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 03:00:45.263504 30317 solver.cpp:218] Iteration 5712 (1.17887 iter/s, 10.1793s/12 iters), loss = 5.27656
I0410 03:00:45.263561 30317 solver.cpp:237]     Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
I0410 03:00:45.263573 30317 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 03:00:49.425220 30317 solver.cpp:218] Iteration 5724 (2.88358 iter/s, 4.1615s/12 iters), loss = 5.26958
I0410 03:00:49.425274 30317 solver.cpp:237]     Train net output #0: loss = 5.26958 (* 1 = 5.26958 loss)
I0410 03:00:49.425287 30317 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 03:00:54.339654 30317 solver.cpp:218] Iteration 5736 (2.44191 iter/s, 4.91419s/12 iters), loss = 5.24272
I0410 03:00:54.339799 30317 solver.cpp:237]     Train net output #0: loss = 5.24272 (* 1 = 5.24272 loss)
I0410 03:00:54.339812 30317 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 03:00:59.288026 30317 solver.cpp:218] Iteration 5748 (2.4252 iter/s, 4.94804s/12 iters), loss = 5.2772
I0410 03:00:59.288074 30317 solver.cpp:237]     Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
I0410 03:00:59.288085 30317 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 03:01:04.204320 30317 solver.cpp:218] Iteration 5760 (2.44098 iter/s, 4.91606s/12 iters), loss = 5.26717
I0410 03:01:04.204371 30317 solver.cpp:237]     Train net output #0: loss = 5.26717 (* 1 = 5.26717 loss)
I0410 03:01:04.204382 30317 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 03:01:06.150980 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:09.220193 30317 solver.cpp:218] Iteration 5772 (2.39252 iter/s, 5.01563s/12 iters), loss = 5.29066
I0410 03:01:09.220244 30317 solver.cpp:237]     Train net output #0: loss = 5.29066 (* 1 = 5.29066 loss)
I0410 03:01:09.220257 30317 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 03:01:14.094395 30317 solver.cpp:218] Iteration 5784 (2.46206 iter/s, 4.87397s/12 iters), loss = 5.27104
I0410 03:01:14.094431 30317 solver.cpp:237]     Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
I0410 03:01:14.094439 30317 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 03:01:18.948088 30317 solver.cpp:218] Iteration 5796 (2.47246 iter/s, 4.85347s/12 iters), loss = 5.26946
I0410 03:01:18.948141 30317 solver.cpp:237]     Train net output #0: loss = 5.26946 (* 1 = 5.26946 loss)
I0410 03:01:18.948153 30317 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 03:01:23.854537 30317 solver.cpp:218] Iteration 5808 (2.44588 iter/s, 4.90621s/12 iters), loss = 5.2662
I0410 03:01:23.854580 30317 solver.cpp:237]     Train net output #0: loss = 5.2662 (* 1 = 5.2662 loss)
I0410 03:01:23.854589 30317 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 03:01:25.862795 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 03:01:26.717885 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 03:01:27.436262 30317 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 03:01:27.436285 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:01:29.780933 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:32.221237 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:01:32.221283 30317 solver.cpp:397]     Test net output #1: loss = 5.28667 (* 1 = 5.28667 loss)
I0410 03:01:34.208889 30317 solver.cpp:218] Iteration 5820 (1.15898 iter/s, 10.3539s/12 iters), loss = 5.27485
I0410 03:01:34.208935 30317 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0410 03:01:34.208945 30317 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 03:01:39.049669 30317 solver.cpp:218] Iteration 5832 (2.47906 iter/s, 4.84055s/12 iters), loss = 5.27493
I0410 03:01:39.049721 30317 solver.cpp:237]     Train net output #0: loss = 5.27493 (* 1 = 5.27493 loss)
I0410 03:01:39.049732 30317 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 03:01:43.964237 30317 solver.cpp:218] Iteration 5844 (2.44184 iter/s, 4.91433s/12 iters), loss = 5.26288
I0410 03:01:43.964291 30317 solver.cpp:237]     Train net output #0: loss = 5.26288 (* 1 = 5.26288 loss)
I0410 03:01:43.964303 30317 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 03:01:48.833557 30317 solver.cpp:218] Iteration 5856 (2.46453 iter/s, 4.86908s/12 iters), loss = 5.26165
I0410 03:01:48.833608 30317 solver.cpp:237]     Train net output #0: loss = 5.26165 (* 1 = 5.26165 loss)
I0410 03:01:48.833621 30317 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 03:01:52.880643 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:53.685739 30317 solver.cpp:218] Iteration 5868 (2.47324 iter/s, 4.85194s/12 iters), loss = 5.25534
I0410 03:01:53.685792 30317 solver.cpp:237]     Train net output #0: loss = 5.25534 (* 1 = 5.25534 loss)
I0410 03:01:53.685803 30317 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 03:01:58.552781 30317 solver.cpp:218] Iteration 5880 (2.46568 iter/s, 4.8668s/12 iters), loss = 5.2747
I0410 03:01:58.552938 30317 solver.cpp:237]     Train net output #0: loss = 5.2747 (* 1 = 5.2747 loss)
I0410 03:01:58.552953 30317 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 03:02:03.362098 30317 solver.cpp:218] Iteration 5892 (2.49534 iter/s, 4.80897s/12 iters), loss = 5.269
I0410 03:02:03.362182 30317 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0410 03:02:03.362198 30317 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 03:02:08.199311 30317 solver.cpp:218] Iteration 5904 (2.4809 iter/s, 4.83696s/12 iters), loss = 5.30392
I0410 03:02:08.199352 30317 solver.cpp:237]     Train net output #0: loss = 5.30392 (* 1 = 5.30392 loss)
I0410 03:02:08.199359 30317 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 03:02:12.628417 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 03:02:13.123262 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 03:02:13.467675 30317 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 03:02:13.467694 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:02:15.473757 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:02:17.824450 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:02:17.824481 30317 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 03:02:17.905830 30317 solver.cpp:218] Iteration 5916 (1.23633 iter/s, 9.70613s/12 iters), loss = 5.26901
I0410 03:02:17.905869 30317 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0410 03:02:17.905879 30317 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 03:02:22.061465 30317 solver.cpp:218] Iteration 5928 (2.88778 iter/s, 4.15544s/12 iters), loss = 5.27306
I0410 03:02:22.061511 30317 solver.cpp:237]     Train net output #0: loss = 5.27306 (* 1 = 5.27306 loss)
I0410 03:02:22.061519 30317 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 03:02:26.883337 30317 solver.cpp:218] Iteration 5940 (2.48878 iter/s, 4.82164s/12 iters), loss = 5.28087
I0410 03:02:26.883380 30317 solver.cpp:237]     Train net output #0: loss = 5.28087 (* 1 = 5.28087 loss)
I0410 03:02:26.883389 30317 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 03:02:31.747650 30317 solver.cpp:218] Iteration 5952 (2.46706 iter/s, 4.86409s/12 iters), loss = 5.27799
I0410 03:02:31.747716 30317 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0410 03:02:31.747725 30317 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 03:02:36.788188 30317 solver.cpp:218] Iteration 5964 (2.38082 iter/s, 5.04029s/12 iters), loss = 5.25653
I0410 03:02:36.788234 30317 solver.cpp:237]     Train net output #0: loss = 5.25653 (* 1 = 5.25653 loss)
I0410 03:02:36.788245 30317 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 03:02:38.035378 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:02:41.794842 30317 solver.cpp:218] Iteration 5976 (2.39692 iter/s, 5.00643s/12 iters), loss = 5.27512
I0410 03:02:41.794883 30317 solver.cpp:237]     Train net output #0: loss = 5.27512 (* 1 = 5.27512 loss)
I0410 03:02:41.794893 30317 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 03:02:46.784243 30317 solver.cpp:218] Iteration 5988 (2.40521 iter/s, 4.98917s/12 iters), loss = 5.26328
I0410 03:02:46.784286 30317 solver.cpp:237]     Train net output #0: loss = 5.26328 (* 1 = 5.26328 loss)
I0410 03:02:46.784296 30317 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 03:02:51.673928 30317 solver.cpp:218] Iteration 6000 (2.45426 iter/s, 4.88946s/12 iters), loss = 5.28176
I0410 03:02:51.673995 30317 solver.cpp:237]     Train net output #0: loss = 5.28176 (* 1 = 5.28176 loss)
I0410 03:02:51.674006 30317 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 03:02:56.506494 30317 solver.cpp:218] Iteration 6012 (2.48328 iter/s, 4.83232s/12 iters), loss = 5.26904
I0410 03:02:56.506548 30317 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0410 03:02:56.506561 30317 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 03:02:58.460641 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 03:02:59.530776 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 03:03:00.287616 30317 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 03:03:00.287645 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:03:02.506965 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:04.913702 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:03:04.913753 30317 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 03:03:06.716594 30317 solver.cpp:218] Iteration 6024 (1.17536 iter/s, 10.2097s/12 iters), loss = 5.26872
I0410 03:03:06.716641 30317 solver.cpp:237]     Train net output #0: loss = 5.26872 (* 1 = 5.26872 loss)
I0410 03:03:06.716650 30317 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 03:03:11.519910 30317 solver.cpp:218] Iteration 6036 (2.49839 iter/s, 4.80309s/12 iters), loss = 5.2616
I0410 03:03:11.519958 30317 solver.cpp:237]     Train net output #0: loss = 5.2616 (* 1 = 5.2616 loss)
I0410 03:03:11.519968 30317 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 03:03:16.423482 30317 solver.cpp:218] Iteration 6048 (2.44731 iter/s, 4.90334s/12 iters), loss = 5.29974
I0410 03:03:16.423535 30317 solver.cpp:237]     Train net output #0: loss = 5.29974 (* 1 = 5.29974 loss)
I0410 03:03:16.423547 30317 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 03:03:21.295198 30317 solver.cpp:218] Iteration 6060 (2.46332 iter/s, 4.87148s/12 iters), loss = 5.27626
I0410 03:03:21.295256 30317 solver.cpp:237]     Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
I0410 03:03:21.295269 30317 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 03:03:24.613116 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:26.096858 30317 solver.cpp:218] Iteration 6072 (2.49926 iter/s, 4.80142s/12 iters), loss = 5.27281
I0410 03:03:26.096912 30317 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0410 03:03:26.096923 30317 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 03:03:31.047583 30317 solver.cpp:218] Iteration 6084 (2.424 iter/s, 4.95049s/12 iters), loss = 5.26047
I0410 03:03:31.047624 30317 solver.cpp:237]     Train net output #0: loss = 5.26047 (* 1 = 5.26047 loss)
I0410 03:03:31.047636 30317 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 03:03:35.955577 30317 solver.cpp:218] Iteration 6096 (2.4451 iter/s, 4.90777s/12 iters), loss = 5.26252
I0410 03:03:35.955698 30317 solver.cpp:237]     Train net output #0: loss = 5.26252 (* 1 = 5.26252 loss)
I0410 03:03:35.955708 30317 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 03:03:40.858242 30317 solver.cpp:218] Iteration 6108 (2.4478 iter/s, 4.90236s/12 iters), loss = 5.27377
I0410 03:03:40.858294 30317 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0410 03:03:40.858305 30317 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 03:03:45.392590 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 03:03:47.765460 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 03:03:48.725286 30317 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 03:03:48.725319 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:03:50.776762 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:53.164521 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:03:53.164557 30317 solver.cpp:397]     Test net output #1: loss = 5.28666 (* 1 = 5.28666 loss)
I0410 03:03:53.245995 30317 solver.cpp:218] Iteration 6120 (0.968737 iter/s, 12.3873s/12 iters), loss = 5.26539
I0410 03:03:53.246044 30317 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0410 03:03:53.246054 30317 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 03:03:57.495332 30317 solver.cpp:218] Iteration 6132 (2.82411 iter/s, 4.24913s/12 iters), loss = 5.27467
I0410 03:03:57.495373 30317 solver.cpp:237]     Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
I0410 03:03:57.495381 30317 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 03:04:02.366377 30317 solver.cpp:218] Iteration 6144 (2.46365 iter/s, 4.87082s/12 iters), loss = 5.26868
I0410 03:04:02.366433 30317 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0410 03:04:02.366446 30317 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 03:04:07.309903 30317 solver.cpp:218] Iteration 6156 (2.42754 iter/s, 4.94328s/12 iters), loss = 5.27749
I0410 03:04:07.310071 30317 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0410 03:04:07.310084 30317 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 03:04:12.196823 30317 solver.cpp:218] Iteration 6168 (2.45571 iter/s, 4.88657s/12 iters), loss = 5.28998
I0410 03:04:12.196866 30317 solver.cpp:237]     Train net output #0: loss = 5.28998 (* 1 = 5.28998 loss)
I0410 03:04:12.196875 30317 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 03:04:12.775779 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:17.123193 30317 solver.cpp:218] Iteration 6180 (2.43599 iter/s, 4.92614s/12 iters), loss = 5.28416
I0410 03:04:17.123250 30317 solver.cpp:237]     Train net output #0: loss = 5.28416 (* 1 = 5.28416 loss)
I0410 03:04:17.123262 30317 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 03:04:22.004773 30317 solver.cpp:218] Iteration 6192 (2.45834 iter/s, 4.88134s/12 iters), loss = 5.26827
I0410 03:04:22.004827 30317 solver.cpp:237]     Train net output #0: loss = 5.26827 (* 1 = 5.26827 loss)
I0410 03:04:22.004839 30317 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 03:04:26.861902 30317 solver.cpp:218] Iteration 6204 (2.47072 iter/s, 4.85689s/12 iters), loss = 5.2841
I0410 03:04:26.861946 30317 solver.cpp:237]     Train net output #0: loss = 5.2841 (* 1 = 5.2841 loss)
I0410 03:04:26.861971 30317 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 03:04:31.737738 30317 solver.cpp:218] Iteration 6216 (2.46123 iter/s, 4.87561s/12 iters), loss = 5.27789
I0410 03:04:31.737789 30317 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0410 03:04:31.737798 30317 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 03:04:33.706881 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 03:04:34.174263 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 03:04:34.507341 30317 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 03:04:34.507361 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:04:36.486737 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:37.486346 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:04:38.926873 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:04:38.926903 30317 solver.cpp:397]     Test net output #1: loss = 5.28647 (* 1 = 5.28647 loss)
I0410 03:04:40.659812 30317 solver.cpp:218] Iteration 6228 (1.34503 iter/s, 8.92171s/12 iters), loss = 5.27614
I0410 03:04:40.659854 30317 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0410 03:04:40.659865 30317 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 03:04:45.500082 30317 solver.cpp:218] Iteration 6240 (2.47931 iter/s, 4.84005s/12 iters), loss = 5.28141
I0410 03:04:45.500131 30317 solver.cpp:237]     Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
I0410 03:04:45.500144 30317 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 03:04:50.501724 30317 solver.cpp:218] Iteration 6252 (2.39933 iter/s, 5.00141s/12 iters), loss = 5.25919
I0410 03:04:50.501776 30317 solver.cpp:237]     Train net output #0: loss = 5.25919 (* 1 = 5.25919 loss)
I0410 03:04:50.501789 30317 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 03:04:55.414687 30317 solver.cpp:218] Iteration 6264 (2.44264 iter/s, 4.91273s/12 iters), loss = 5.26784
I0410 03:04:55.414737 30317 solver.cpp:237]     Train net output #0: loss = 5.26784 (* 1 = 5.26784 loss)
I0410 03:04:55.414747 30317 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 03:04:58.100965 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:00.327412 30317 solver.cpp:218] Iteration 6276 (2.44275 iter/s, 4.91249s/12 iters), loss = 5.27768
I0410 03:05:00.327471 30317 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0410 03:05:00.327483 30317 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 03:05:05.249153 30317 solver.cpp:218] Iteration 6288 (2.43828 iter/s, 4.9215s/12 iters), loss = 5.25766
I0410 03:05:05.249205 30317 solver.cpp:237]     Train net output #0: loss = 5.25766 (* 1 = 5.25766 loss)
I0410 03:05:05.249217 30317 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 03:05:10.163084 30317 solver.cpp:218] Iteration 6300 (2.44215 iter/s, 4.91369s/12 iters), loss = 5.27009
I0410 03:05:10.163179 30317 solver.cpp:237]     Train net output #0: loss = 5.27009 (* 1 = 5.27009 loss)
I0410 03:05:10.163189 30317 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 03:05:15.050566 30317 solver.cpp:218] Iteration 6312 (2.45539 iter/s, 4.88721s/12 iters), loss = 5.26339
I0410 03:05:15.050614 30317 solver.cpp:237]     Train net output #0: loss = 5.26339 (* 1 = 5.26339 loss)
I0410 03:05:15.050623 30317 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 03:05:19.492257 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 03:05:19.973281 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 03:05:20.525918 30317 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 03:05:20.525940 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:05:22.387148 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:24.900391 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:05:24.900441 30317 solver.cpp:397]     Test net output #1: loss = 5.2866 (* 1 = 5.2866 loss)
I0410 03:05:24.982597 30317 solver.cpp:218] Iteration 6324 (1.20826 iter/s, 9.93162s/12 iters), loss = 5.29749
I0410 03:05:24.982671 30317 solver.cpp:237]     Train net output #0: loss = 5.29749 (* 1 = 5.29749 loss)
I0410 03:05:24.982688 30317 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 03:05:29.073046 30317 solver.cpp:218] Iteration 6336 (2.93382 iter/s, 4.09023s/12 iters), loss = 5.26743
I0410 03:05:29.073098 30317 solver.cpp:237]     Train net output #0: loss = 5.26743 (* 1 = 5.26743 loss)
I0410 03:05:29.073109 30317 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 03:05:33.871162 30317 solver.cpp:218] Iteration 6348 (2.5011 iter/s, 4.79788s/12 iters), loss = 5.26575
I0410 03:05:33.871217 30317 solver.cpp:237]     Train net output #0: loss = 5.26575 (* 1 = 5.26575 loss)
I0410 03:05:33.871229 30317 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 03:05:38.825738 30317 solver.cpp:218] Iteration 6360 (2.42212 iter/s, 4.95434s/12 iters), loss = 5.27098
I0410 03:05:38.825783 30317 solver.cpp:237]     Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
I0410 03:05:38.825790 30317 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 03:05:43.623306 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:43.760612 30317 solver.cpp:218] Iteration 6372 (2.43179 iter/s, 4.93465s/12 iters), loss = 5.25153
I0410 03:05:43.760656 30317 solver.cpp:237]     Train net output #0: loss = 5.25153 (* 1 = 5.25153 loss)
I0410 03:05:43.760666 30317 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 03:05:48.642093 30317 solver.cpp:218] Iteration 6384 (2.45838 iter/s, 4.88125s/12 iters), loss = 5.26876
I0410 03:05:48.642143 30317 solver.cpp:237]     Train net output #0: loss = 5.26876 (* 1 = 5.26876 loss)
I0410 03:05:48.642154 30317 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 03:05:53.550841 30317 solver.cpp:218] Iteration 6396 (2.44473 iter/s, 4.90852s/12 iters), loss = 5.29519
I0410 03:05:53.550887 30317 solver.cpp:237]     Train net output #0: loss = 5.29519 (* 1 = 5.29519 loss)
I0410 03:05:53.550897 30317 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 03:05:58.445300 30317 solver.cpp:218] Iteration 6408 (2.45187 iter/s, 4.89423s/12 iters), loss = 5.28279
I0410 03:05:58.445359 30317 solver.cpp:237]     Train net output #0: loss = 5.28279 (* 1 = 5.28279 loss)
I0410 03:05:58.445372 30317 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 03:06:03.360165 30317 solver.cpp:218] Iteration 6420 (2.44169 iter/s, 4.91463s/12 iters), loss = 5.28035
I0410 03:06:03.360211 30317 solver.cpp:237]     Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
I0410 03:06:03.360220 30317 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 03:06:05.361068 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 03:06:05.833380 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 03:06:06.173267 30317 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 03:06:06.173297 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:06:08.091578 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:10.613350 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:06:10.613399 30317 solver.cpp:397]     Test net output #1: loss = 5.28655 (* 1 = 5.28655 loss)
I0410 03:06:12.409775 30317 solver.cpp:218] Iteration 6432 (1.32608 iter/s, 9.04924s/12 iters), loss = 5.27194
I0410 03:06:12.409821 30317 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0410 03:06:12.409830 30317 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 03:06:17.340427 30317 solver.cpp:218] Iteration 6444 (2.43387 iter/s, 4.93042s/12 iters), loss = 5.24739
I0410 03:06:17.340553 30317 solver.cpp:237]     Train net output #0: loss = 5.24739 (* 1 = 5.24739 loss)
I0410 03:06:17.340566 30317 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 03:06:22.234432 30317 solver.cpp:218] Iteration 6456 (2.45213 iter/s, 4.89371s/12 iters), loss = 5.27289
I0410 03:06:22.234477 30317 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0410 03:06:22.234488 30317 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 03:06:27.138058 30317 solver.cpp:218] Iteration 6468 (2.44728 iter/s, 4.90339s/12 iters), loss = 5.26642
I0410 03:06:27.138113 30317 solver.cpp:237]     Train net output #0: loss = 5.26642 (* 1 = 5.26642 loss)
I0410 03:06:27.138124 30317 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 03:06:29.099022 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:32.055904 30317 solver.cpp:218] Iteration 6480 (2.44021 iter/s, 4.91761s/12 iters), loss = 5.28874
I0410 03:06:32.055958 30317 solver.cpp:237]     Train net output #0: loss = 5.28874 (* 1 = 5.28874 loss)
I0410 03:06:32.055969 30317 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 03:06:36.977288 30317 solver.cpp:218] Iteration 6492 (2.43846 iter/s, 4.92115s/12 iters), loss = 5.27014
I0410 03:06:36.977339 30317 solver.cpp:237]     Train net output #0: loss = 5.27014 (* 1 = 5.27014 loss)
I0410 03:06:36.977349 30317 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 03:06:41.838891 30317 solver.cpp:218] Iteration 6504 (2.46844 iter/s, 4.86137s/12 iters), loss = 5.2712
I0410 03:06:41.838938 30317 solver.cpp:237]     Train net output #0: loss = 5.2712 (* 1 = 5.2712 loss)
I0410 03:06:41.838949 30317 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 03:06:46.649864 30317 solver.cpp:218] Iteration 6516 (2.49442 iter/s, 4.81075s/12 iters), loss = 5.26715
I0410 03:06:46.649919 30317 solver.cpp:237]     Train net output #0: loss = 5.26715 (* 1 = 5.26715 loss)
I0410 03:06:46.649932 30317 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 03:06:51.119289 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 03:06:51.772177 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 03:06:52.228901 30317 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 03:06:52.228929 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:06:53.969503 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:56.552386 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:06:56.552423 30317 solver.cpp:397]     Test net output #1: loss = 5.28664 (* 1 = 5.28664 loss)
I0410 03:06:56.633975 30317 solver.cpp:218] Iteration 6528 (1.20196 iter/s, 9.98368s/12 iters), loss = 5.27166
I0410 03:06:56.634024 30317 solver.cpp:237]     Train net output #0: loss = 5.27166 (* 1 = 5.27166 loss)
I0410 03:06:56.634034 30317 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 03:07:00.801733 30317 solver.cpp:218] Iteration 6540 (2.87939 iter/s, 4.16755s/12 iters), loss = 5.27211
I0410 03:07:00.801786 30317 solver.cpp:237]     Train net output #0: loss = 5.27211 (* 1 = 5.27211 loss)
I0410 03:07:00.801798 30317 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 03:07:05.669968 30317 solver.cpp:218] Iteration 6552 (2.46509 iter/s, 4.86799s/12 iters), loss = 5.26906
I0410 03:07:05.670018 30317 solver.cpp:237]     Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
I0410 03:07:05.670029 30317 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 03:07:10.594249 30317 solver.cpp:218] Iteration 6564 (2.43702 iter/s, 4.92405s/12 iters), loss = 5.25839
I0410 03:07:10.594292 30317 solver.cpp:237]     Train net output #0: loss = 5.25839 (* 1 = 5.25839 loss)
I0410 03:07:10.594300 30317 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 03:07:14.709024 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:07:15.482574 30317 solver.cpp:218] Iteration 6576 (2.45494 iter/s, 4.88809s/12 iters), loss = 5.25773
I0410 03:07:15.482631 30317 solver.cpp:237]     Train net output #0: loss = 5.25773 (* 1 = 5.25773 loss)
I0410 03:07:15.482646 30317 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 03:07:20.417569 30317 solver.cpp:218] Iteration 6588 (2.43173 iter/s, 4.93476s/12 iters), loss = 5.28143
I0410 03:07:20.417606 30317 solver.cpp:237]     Train net output #0: loss = 5.28143 (* 1 = 5.28143 loss)
I0410 03:07:20.417616 30317 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 03:07:25.289019 30317 solver.cpp:218] Iteration 6600 (2.46344 iter/s, 4.87123s/12 iters), loss = 5.27178
I0410 03:07:25.289113 30317 solver.cpp:237]     Train net output #0: loss = 5.27178 (* 1 = 5.27178 loss)
I0410 03:07:25.289126 30317 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 03:07:30.195760 30317 solver.cpp:218] Iteration 6612 (2.44575 iter/s, 4.90647s/12 iters), loss = 5.30521
I0410 03:07:30.195811 30317 solver.cpp:237]     Train net output #0: loss = 5.30521 (* 1 = 5.30521 loss)
I0410 03:07:30.195822 30317 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 03:07:35.089648 30317 solver.cpp:218] Iteration 6624 (2.45215 iter/s, 4.89366s/12 iters), loss = 5.26992
I0410 03:07:35.089694 30317 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0410 03:07:35.089702 30317 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 03:07:37.125560 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 03:07:37.615605 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 03:07:38.219280 30317 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 03:07:38.219297 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:07:40.111666 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:07:42.677012 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:07:42.677049 30317 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 03:07:44.597091 30317 solver.cpp:218] Iteration 6636 (1.26222 iter/s, 9.50705s/12 iters), loss = 5.27404
I0410 03:07:44.597136 30317 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0410 03:07:44.597146 30317 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 03:07:49.563026 30317 solver.cpp:218] Iteration 6648 (2.41657 iter/s, 4.96571s/12 iters), loss = 5.27438
I0410 03:07:49.563067 30317 solver.cpp:237]     Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
I0410 03:07:49.563076 30317 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 03:07:54.393514 30317 solver.cpp:218] Iteration 6660 (2.48434 iter/s, 4.83026s/12 iters), loss = 5.28305
I0410 03:07:54.393569 30317 solver.cpp:237]     Train net output #0: loss = 5.28305 (* 1 = 5.28305 loss)
I0410 03:07:54.393581 30317 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 03:07:59.270123 30317 solver.cpp:218] Iteration 6672 (2.46084 iter/s, 4.87637s/12 iters), loss = 5.26299
I0410 03:07:59.270264 30317 solver.cpp:237]     Train net output #0: loss = 5.26299 (* 1 = 5.26299 loss)
I0410 03:07:59.270278 30317 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 03:08:00.603443 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:04.123854 30317 solver.cpp:218] Iteration 6684 (2.47249 iter/s, 4.85341s/12 iters), loss = 5.27311
I0410 03:08:04.123904 30317 solver.cpp:237]     Train net output #0: loss = 5.27311 (* 1 = 5.27311 loss)
I0410 03:08:04.123916 30317 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 03:08:08.971556 30317 solver.cpp:218] Iteration 6696 (2.47552 iter/s, 4.84747s/12 iters), loss = 5.26844
I0410 03:08:08.971611 30317 solver.cpp:237]     Train net output #0: loss = 5.26844 (* 1 = 5.26844 loss)
I0410 03:08:08.971623 30317 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 03:08:14.020933 30317 solver.cpp:218] Iteration 6708 (2.37664 iter/s, 5.04914s/12 iters), loss = 5.27665
I0410 03:08:14.020988 30317 solver.cpp:237]     Train net output #0: loss = 5.27665 (* 1 = 5.27665 loss)
I0410 03:08:14.021000 30317 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 03:08:18.836084 30317 solver.cpp:218] Iteration 6720 (2.49226 iter/s, 4.81492s/12 iters), loss = 5.27159
I0410 03:08:18.836136 30317 solver.cpp:237]     Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
I0410 03:08:18.836148 30317 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 03:08:23.234264 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 03:08:23.708743 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 03:08:24.032228 30317 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 03:08:24.032248 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:08:25.840469 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:28.466840 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:08:28.466874 30317 solver.cpp:397]     Test net output #1: loss = 5.28644 (* 1 = 5.28644 loss)
I0410 03:08:28.548409 30317 solver.cpp:218] Iteration 6732 (1.23559 iter/s, 9.71192s/12 iters), loss = 5.26703
I0410 03:08:28.548455 30317 solver.cpp:237]     Train net output #0: loss = 5.26703 (* 1 = 5.26703 loss)
I0410 03:08:28.548466 30317 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 03:08:32.887328 30317 solver.cpp:218] Iteration 6744 (2.7658 iter/s, 4.33871s/12 iters), loss = 5.26196
I0410 03:08:32.887451 30317 solver.cpp:237]     Train net output #0: loss = 5.26196 (* 1 = 5.26196 loss)
I0410 03:08:32.887460 30317 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 03:08:37.746323 30317 solver.cpp:218] Iteration 6756 (2.4698 iter/s, 4.85869s/12 iters), loss = 5.28968
I0410 03:08:37.746381 30317 solver.cpp:237]     Train net output #0: loss = 5.28968 (* 1 = 5.28968 loss)
I0410 03:08:37.746392 30317 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 03:08:42.586266 30317 solver.cpp:218] Iteration 6768 (2.47949 iter/s, 4.8397s/12 iters), loss = 5.2733
I0410 03:08:42.586320 30317 solver.cpp:237]     Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
I0410 03:08:42.586333 30317 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 03:08:45.946097 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:47.407624 30317 solver.cpp:218] Iteration 6780 (2.48905 iter/s, 4.82113s/12 iters), loss = 5.27522
I0410 03:08:47.407673 30317 solver.cpp:237]     Train net output #0: loss = 5.27522 (* 1 = 5.27522 loss)
I0410 03:08:47.407685 30317 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 03:08:52.292203 30317 solver.cpp:218] Iteration 6792 (2.45683 iter/s, 4.88435s/12 iters), loss = 5.26022
I0410 03:08:52.292244 30317 solver.cpp:237]     Train net output #0: loss = 5.26022 (* 1 = 5.26022 loss)
I0410 03:08:52.292253 30317 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 03:08:57.124640 30317 solver.cpp:218] Iteration 6804 (2.48333 iter/s, 4.83221s/12 iters), loss = 5.26722
I0410 03:08:57.124683 30317 solver.cpp:237]     Train net output #0: loss = 5.26722 (* 1 = 5.26722 loss)
I0410 03:08:57.124692 30317 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 03:09:01.957485 30317 solver.cpp:218] Iteration 6816 (2.48312 iter/s, 4.83262s/12 iters), loss = 5.27902
I0410 03:09:01.957522 30317 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 03:09:01.957531 30317 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 03:09:06.850891 30317 solver.cpp:218] Iteration 6828 (2.45239 iter/s, 4.89319s/12 iters), loss = 5.26857
I0410 03:09:06.850970 30317 solver.cpp:237]     Train net output #0: loss = 5.26857 (* 1 = 5.26857 loss)
I0410 03:09:06.850982 30317 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 03:09:08.818780 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 03:09:09.778160 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 03:09:10.307416 30317 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 03:09:10.307448 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:09:12.075846 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:09:14.738966 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:09:14.739008 30317 solver.cpp:397]     Test net output #1: loss = 5.28721 (* 1 = 5.28721 loss)
I0410 03:09:16.459197 30317 solver.cpp:218] Iteration 6840 (1.24897 iter/s, 9.60788s/12 iters), loss = 5.27526
I0410 03:09:16.459242 30317 solver.cpp:237]     Train net output #0: loss = 5.27526 (* 1 = 5.27526 loss)
I0410 03:09:16.459254 30317 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 03:09:21.265702 30317 solver.cpp:218] Iteration 6852 (2.49674 iter/s, 4.80627s/12 iters), loss = 5.2756
I0410 03:09:21.265750 30317 solver.cpp:237]     Train net output #0: loss = 5.2756 (* 1 = 5.2756 loss)
I0410 03:09:21.265763 30317 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 03:09:26.139521 30317 solver.cpp:218] Iteration 6864 (2.46225 iter/s, 4.87359s/12 iters), loss = 5.27481
I0410 03:09:26.139560 30317 solver.cpp:237]     Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
I0410 03:09:26.139570 30317 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 03:09:31.000771 30317 solver.cpp:218] Iteration 6876 (2.46862 iter/s, 4.86102s/12 iters), loss = 5.2815
I0410 03:09:31.000820 30317 solver.cpp:237]     Train net output #0: loss = 5.2815 (* 1 = 5.2815 loss)
I0410 03:09:31.000833 30317 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 03:09:31.683557 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:09:35.929843 30317 solver.cpp:218] Iteration 6888 (2.43465 iter/s, 4.92884s/12 iters), loss = 5.28397
I0410 03:09:35.929895 30317 solver.cpp:237]     Train net output #0: loss = 5.28397 (* 1 = 5.28397 loss)
I0410 03:09:35.929908 30317 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 03:09:40.751492 30317 solver.cpp:218] Iteration 6900 (2.48889 iter/s, 4.82142s/12 iters), loss = 5.26566
I0410 03:09:40.751626 30317 solver.cpp:237]     Train net output #0: loss = 5.26566 (* 1 = 5.26566 loss)
I0410 03:09:40.751636 30317 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 03:09:45.595041 30317 solver.cpp:218] Iteration 6912 (2.47768 iter/s, 4.84323s/12 iters), loss = 5.28522
I0410 03:09:45.595086 30317 solver.cpp:237]     Train net output #0: loss = 5.28522 (* 1 = 5.28522 loss)
I0410 03:09:45.595096 30317 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 03:09:50.456282 30317 solver.cpp:218] Iteration 6924 (2.46862 iter/s, 4.86102s/12 iters), loss = 5.2838
I0410 03:09:50.456324 30317 solver.cpp:237]     Train net output #0: loss = 5.2838 (* 1 = 5.2838 loss)
I0410 03:09:50.456333 30317 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 03:09:54.903523 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 03:09:55.361862 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 03:09:55.684909 30317 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 03:09:55.684932 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:09:56.015471 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:09:57.415431 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:10:00.149047 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:10:00.149096 30317 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 03:10:00.230705 30317 solver.cpp:218] Iteration 6936 (1.22774 iter/s, 9.77402s/12 iters), loss = 5.27937
I0410 03:10:00.230751 30317 solver.cpp:237]     Train net output #0: loss = 5.27937 (* 1 = 5.27937 loss)
I0410 03:10:00.230762 30317 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 03:10:04.300698 30317 solver.cpp:218] Iteration 6948 (2.94855 iter/s, 4.06979s/12 iters), loss = 5.27794
I0410 03:10:04.300745 30317 solver.cpp:237]     Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
I0410 03:10:04.300755 30317 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 03:10:09.148592 30317 solver.cpp:218] Iteration 6960 (2.47542 iter/s, 4.84767s/12 iters), loss = 5.26771
I0410 03:10:09.148638 30317 solver.cpp:237]     Train net output #0: loss = 5.26771 (* 1 = 5.26771 loss)
I0410 03:10:09.148649 30317 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 03:10:13.959647 30317 solver.cpp:218] Iteration 6972 (2.49437 iter/s, 4.81083s/12 iters), loss = 5.27007
I0410 03:10:13.959759 30317 solver.cpp:237]     Train net output #0: loss = 5.27007 (* 1 = 5.27007 loss)
I0410 03:10:13.959772 30317 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 03:10:16.636968 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:10:18.807032 30317 solver.cpp:218] Iteration 6984 (2.47571 iter/s, 4.8471s/12 iters), loss = 5.28152
I0410 03:10:18.807081 30317 solver.cpp:237]     Train net output #0: loss = 5.28152 (* 1 = 5.28152 loss)
I0410 03:10:18.807093 30317 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 03:10:23.715510 30317 solver.cpp:218] Iteration 6996 (2.44487 iter/s, 4.90824s/12 iters), loss = 5.25652
I0410 03:10:23.715567 30317 solver.cpp:237]     Train net output #0: loss = 5.25652 (* 1 = 5.25652 loss)
I0410 03:10:23.715579 30317 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 03:10:28.577952 30317 solver.cpp:218] Iteration 7008 (2.46802 iter/s, 4.8622s/12 iters), loss = 5.26091
I0410 03:10:28.578013 30317 solver.cpp:237]     Train net output #0: loss = 5.26091 (* 1 = 5.26091 loss)
I0410 03:10:28.578025 30317 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 03:10:33.456897 30317 solver.cpp:218] Iteration 7020 (2.45967 iter/s, 4.8787s/12 iters), loss = 5.26067
I0410 03:10:33.456943 30317 solver.cpp:237]     Train net output #0: loss = 5.26067 (* 1 = 5.26067 loss)
I0410 03:10:33.456954 30317 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 03:10:38.264232 30317 solver.cpp:218] Iteration 7032 (2.4963 iter/s, 4.80711s/12 iters), loss = 5.30196
I0410 03:10:38.264277 30317 solver.cpp:237]     Train net output #0: loss = 5.30196 (* 1 = 5.30196 loss)
I0410 03:10:38.264287 30317 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 03:10:40.221761 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 03:10:40.673789 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 03:10:41.001564 30317 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 03:10:41.001593 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:10:42.631413 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:10:45.369858 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:10:45.370010 30317 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 03:10:47.318504 30317 solver.cpp:218] Iteration 7044 (1.3254 iter/s, 9.05389s/12 iters), loss = 5.27145
I0410 03:10:47.318552 30317 solver.cpp:237]     Train net output #0: loss = 5.27145 (* 1 = 5.27145 loss)
I0410 03:10:47.318562 30317 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 03:10:52.153122 30317 solver.cpp:218] Iteration 7056 (2.48222 iter/s, 4.83439s/12 iters), loss = 5.27227
I0410 03:10:52.153167 30317 solver.cpp:237]     Train net output #0: loss = 5.27227 (* 1 = 5.27227 loss)
I0410 03:10:52.153177 30317 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 03:10:57.012820 30317 solver.cpp:218] Iteration 7068 (2.4694 iter/s, 4.85947s/12 iters), loss = 5.26658
I0410 03:10:57.012866 30317 solver.cpp:237]     Train net output #0: loss = 5.26658 (* 1 = 5.26658 loss)
I0410 03:10:57.012874 30317 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 03:11:01.709380 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:11:01.809849 30317 solver.cpp:218] Iteration 7080 (2.50167 iter/s, 4.7968s/12 iters), loss = 5.24604
I0410 03:11:01.809900 30317 solver.cpp:237]     Train net output #0: loss = 5.24604 (* 1 = 5.24604 loss)
I0410 03:11:01.809911 30317 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 03:11:06.676735 30317 solver.cpp:218] Iteration 7092 (2.46576 iter/s, 4.86665s/12 iters), loss = 5.26818
I0410 03:11:06.676786 30317 solver.cpp:237]     Train net output #0: loss = 5.26818 (* 1 = 5.26818 loss)
I0410 03:11:06.676800 30317 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 03:11:11.630461 30317 solver.cpp:218] Iteration 7104 (2.42253 iter/s, 4.95349s/12 iters), loss = 5.2939
I0410 03:11:11.630507 30317 solver.cpp:237]     Train net output #0: loss = 5.2939 (* 1 = 5.2939 loss)
I0410 03:11:11.630515 30317 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 03:11:16.545883 30317 solver.cpp:218] Iteration 7116 (2.44141 iter/s, 4.91519s/12 iters), loss = 5.27392
I0410 03:11:16.546015 30317 solver.cpp:237]     Train net output #0: loss = 5.27392 (* 1 = 5.27392 loss)
I0410 03:11:16.546027 30317 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 03:11:21.431308 30317 solver.cpp:218] Iteration 7128 (2.45644 iter/s, 4.88512s/12 iters), loss = 5.27563
I0410 03:11:21.431352 30317 solver.cpp:237]     Train net output #0: loss = 5.27563 (* 1 = 5.27563 loss)
I0410 03:11:21.431361 30317 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 03:11:25.857512 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 03:11:26.357479 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 03:11:27.897879 30317 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 03:11:27.897912 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:11:29.528259 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:11:32.367902 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:11:32.367936 30317 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 03:11:32.449486 30317 solver.cpp:218] Iteration 7140 (1.08915 iter/s, 11.0177s/12 iters), loss = 5.2652
I0410 03:11:32.449530 30317 solver.cpp:237]     Train net output #0: loss = 5.2652 (* 1 = 5.2652 loss)
I0410 03:11:32.449538 30317 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 03:11:36.581811 30317 solver.cpp:218] Iteration 7152 (2.90408 iter/s, 4.13212s/12 iters), loss = 5.25047
I0410 03:11:36.581856 30317 solver.cpp:237]     Train net output #0: loss = 5.25047 (* 1 = 5.25047 loss)
I0410 03:11:36.581867 30317 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 03:11:41.417809 30317 solver.cpp:218] Iteration 7164 (2.48151 iter/s, 4.83576s/12 iters), loss = 5.27716
I0410 03:11:41.417860 30317 solver.cpp:237]     Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
I0410 03:11:41.417870 30317 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 03:11:46.212291 30317 solver.cpp:218] Iteration 7176 (2.503 iter/s, 4.79425s/12 iters), loss = 5.2587
I0410 03:11:46.212338 30317 solver.cpp:237]     Train net output #0: loss = 5.2587 (* 1 = 5.2587 loss)
I0410 03:11:46.212349 30317 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 03:11:48.275656 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:11:51.060742 30317 solver.cpp:218] Iteration 7188 (2.47513 iter/s, 4.84822s/12 iters), loss = 5.27513
I0410 03:11:51.060789 30317 solver.cpp:237]     Train net output #0: loss = 5.27513 (* 1 = 5.27513 loss)
I0410 03:11:51.060801 30317 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 03:11:56.197046 30317 solver.cpp:218] Iteration 7200 (2.33642 iter/s, 5.13606s/12 iters), loss = 5.2722
I0410 03:11:56.197085 30317 solver.cpp:237]     Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
I0410 03:11:56.197094 30317 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 03:12:01.000193 30317 solver.cpp:218] Iteration 7212 (2.49848 iter/s, 4.80292s/12 iters), loss = 5.27937
I0410 03:12:01.000252 30317 solver.cpp:237]     Train net output #0: loss = 5.27937 (* 1 = 5.27937 loss)
I0410 03:12:01.000272 30317 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 03:12:05.856525 30317 solver.cpp:218] Iteration 7224 (2.47113 iter/s, 4.85609s/12 iters), loss = 5.26421
I0410 03:12:05.856582 30317 solver.cpp:237]     Train net output #0: loss = 5.26421 (* 1 = 5.26421 loss)
I0410 03:12:05.856595 30317 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 03:12:10.677533 30317 solver.cpp:218] Iteration 7236 (2.48923 iter/s, 4.82076s/12 iters), loss = 5.27476
I0410 03:12:10.677587 30317 solver.cpp:237]     Train net output #0: loss = 5.27476 (* 1 = 5.27476 loss)
I0410 03:12:10.677599 30317 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 03:12:12.634249 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 03:12:13.109891 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 03:12:13.435678 30317 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 03:12:13.435701 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:12:15.145459 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:12:17.973995 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:12:17.974026 30317 solver.cpp:397]     Test net output #1: loss = 5.28649 (* 1 = 5.28649 loss)
I0410 03:12:19.831059 30317 solver.cpp:218] Iteration 7248 (1.31103 iter/s, 9.15314s/12 iters), loss = 5.27321
I0410 03:12:19.831225 30317 solver.cpp:237]     Train net output #0: loss = 5.27321 (* 1 = 5.27321 loss)
I0410 03:12:19.831236 30317 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 03:12:24.708616 30317 solver.cpp:218] Iteration 7260 (2.46042 iter/s, 4.87721s/12 iters), loss = 5.2705
I0410 03:12:24.708655 30317 solver.cpp:237]     Train net output #0: loss = 5.2705 (* 1 = 5.2705 loss)
I0410 03:12:24.708663 30317 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 03:12:29.531170 30317 solver.cpp:218] Iteration 7272 (2.48842 iter/s, 4.82234s/12 iters), loss = 5.25272
I0410 03:12:29.531206 30317 solver.cpp:237]     Train net output #0: loss = 5.25272 (* 1 = 5.25272 loss)
I0410 03:12:29.531214 30317 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 03:12:33.631009 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:12:34.372215 30317 solver.cpp:218] Iteration 7284 (2.47892 iter/s, 4.84082s/12 iters), loss = 5.25639
I0410 03:12:34.372265 30317 solver.cpp:237]     Train net output #0: loss = 5.25639 (* 1 = 5.25639 loss)
I0410 03:12:34.372277 30317 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 03:12:39.258015 30317 solver.cpp:218] Iteration 7296 (2.45622 iter/s, 4.88556s/12 iters), loss = 5.28331
I0410 03:12:39.258097 30317 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0410 03:12:39.258116 30317 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 03:12:44.091912 30317 solver.cpp:218] Iteration 7308 (2.48261 iter/s, 4.83363s/12 iters), loss = 5.2828
I0410 03:12:44.091958 30317 solver.cpp:237]     Train net output #0: loss = 5.2828 (* 1 = 5.2828 loss)
I0410 03:12:44.091967 30317 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 03:12:48.877585 30317 solver.cpp:218] Iteration 7320 (2.5076 iter/s, 4.78545s/12 iters), loss = 5.29061
I0410 03:12:48.877621 30317 solver.cpp:237]     Train net output #0: loss = 5.29061 (* 1 = 5.29061 loss)
I0410 03:12:48.877630 30317 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 03:12:53.718434 30317 solver.cpp:218] Iteration 7332 (2.47902 iter/s, 4.84063s/12 iters), loss = 5.26652
I0410 03:12:53.718523 30317 solver.cpp:237]     Train net output #0: loss = 5.26652 (* 1 = 5.26652 loss)
I0410 03:12:53.718535 30317 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 03:12:58.141010 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 03:12:59.948966 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 03:13:01.283268 30317 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 03:13:01.283298 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:13:02.860316 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:13:05.727999 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:13:05.728049 30317 solver.cpp:397]     Test net output #1: loss = 5.28736 (* 1 = 5.28736 loss)
I0410 03:13:05.809571 30317 solver.cpp:218] Iteration 7344 (0.992505 iter/s, 12.0906s/12 iters), loss = 5.27693
I0410 03:13:05.809615 30317 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0410 03:13:05.809626 30317 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 03:13:09.916326 30317 solver.cpp:218] Iteration 7356 (2.92216 iter/s, 4.10655s/12 iters), loss = 5.28241
I0410 03:13:09.916373 30317 solver.cpp:237]     Train net output #0: loss = 5.28241 (* 1 = 5.28241 loss)
I0410 03:13:09.916383 30317 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 03:13:14.729565 30317 solver.cpp:218] Iteration 7368 (2.49324 iter/s, 4.81301s/12 iters), loss = 5.27657
I0410 03:13:14.729614 30317 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0410 03:13:14.729624 30317 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 03:13:19.652222 30317 solver.cpp:218] Iteration 7380 (2.43782 iter/s, 4.92242s/12 iters), loss = 5.26147
I0410 03:13:19.652278 30317 solver.cpp:237]     Train net output #0: loss = 5.26147 (* 1 = 5.26147 loss)
I0410 03:13:19.652289 30317 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 03:13:21.012554 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:13:24.552915 30317 solver.cpp:218] Iteration 7392 (2.44875 iter/s, 4.90045s/12 iters), loss = 5.27512
I0410 03:13:24.553056 30317 solver.cpp:237]     Train net output #0: loss = 5.27512 (* 1 = 5.27512 loss)
I0410 03:13:24.553067 30317 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 03:13:29.453419 30317 solver.cpp:218] Iteration 7404 (2.44889 iter/s, 4.90018s/12 iters), loss = 5.27071
I0410 03:13:29.453465 30317 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0410 03:13:29.453475 30317 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 03:13:34.532368 30317 solver.cpp:218] Iteration 7416 (2.3628 iter/s, 5.07871s/12 iters), loss = 5.26857
I0410 03:13:34.532413 30317 solver.cpp:237]     Train net output #0: loss = 5.26857 (* 1 = 5.26857 loss)
I0410 03:13:34.532424 30317 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 03:13:39.374269 30317 solver.cpp:218] Iteration 7428 (2.47848 iter/s, 4.84167s/12 iters), loss = 5.28081
I0410 03:13:39.374321 30317 solver.cpp:237]     Train net output #0: loss = 5.28081 (* 1 = 5.28081 loss)
I0410 03:13:39.374334 30317 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 03:13:44.228510 30317 solver.cpp:218] Iteration 7440 (2.47219 iter/s, 4.854s/12 iters), loss = 5.25883
I0410 03:13:44.228564 30317 solver.cpp:237]     Train net output #0: loss = 5.25883 (* 1 = 5.25883 loss)
I0410 03:13:44.228575 30317 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 03:13:46.168069 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 03:13:46.666065 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 03:13:47.009099 30317 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 03:13:47.009126 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:13:48.545907 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:13:51.433508 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:13:51.433557 30317 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 03:13:53.230429 30317 solver.cpp:218] Iteration 7452 (1.33311 iter/s, 9.00154s/12 iters), loss = 5.26787
I0410 03:13:53.230466 30317 solver.cpp:237]     Train net output #0: loss = 5.26787 (* 1 = 5.26787 loss)
I0410 03:13:53.230474 30317 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 03:13:58.104090 30317 solver.cpp:218] Iteration 7464 (2.46233 iter/s, 4.87344s/12 iters), loss = 5.287
I0410 03:13:58.104195 30317 solver.cpp:237]     Train net output #0: loss = 5.287 (* 1 = 5.287 loss)
I0410 03:13:58.104205 30317 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 03:14:02.961048 30317 solver.cpp:218] Iteration 7476 (2.47083 iter/s, 4.85667s/12 iters), loss = 5.27278
I0410 03:14:02.961093 30317 solver.cpp:237]     Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
I0410 03:14:02.961105 30317 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 03:14:06.356133 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:14:07.774034 30317 solver.cpp:218] Iteration 7488 (2.49337 iter/s, 4.81276s/12 iters), loss = 5.26824
I0410 03:14:07.774087 30317 solver.cpp:237]     Train net output #0: loss = 5.26824 (* 1 = 5.26824 loss)
I0410 03:14:07.774101 30317 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 03:14:12.629066 30317 solver.cpp:218] Iteration 7500 (2.47178 iter/s, 4.8548s/12 iters), loss = 5.26138
I0410 03:14:12.629117 30317 solver.cpp:237]     Train net output #0: loss = 5.26138 (* 1 = 5.26138 loss)
I0410 03:14:12.629129 30317 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 03:14:17.492331 30317 solver.cpp:218] Iteration 7512 (2.4676 iter/s, 4.86303s/12 iters), loss = 5.26441
I0410 03:14:17.492383 30317 solver.cpp:237]     Train net output #0: loss = 5.26441 (* 1 = 5.26441 loss)
I0410 03:14:17.492394 30317 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 03:14:22.467733 30317 solver.cpp:218] Iteration 7524 (2.41198 iter/s, 4.97516s/12 iters), loss = 5.27135
I0410 03:14:22.467780 30317 solver.cpp:237]     Train net output #0: loss = 5.27135 (* 1 = 5.27135 loss)
I0410 03:14:22.467792 30317 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 03:14:27.350592 30317 solver.cpp:218] Iteration 7536 (2.4577 iter/s, 4.88262s/12 iters), loss = 5.26275
I0410 03:14:27.350639 30317 solver.cpp:237]     Train net output #0: loss = 5.26275 (* 1 = 5.26275 loss)
I0410 03:14:27.350651 30317 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 03:14:31.954288 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 03:14:32.456964 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 03:14:32.814445 30317 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 03:14:32.814474 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:14:34.407146 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:14:37.348955 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:14:37.348986 30317 solver.cpp:397]     Test net output #1: loss = 5.28688 (* 1 = 5.28688 loss)
I0410 03:14:37.430474 30317 solver.cpp:218] Iteration 7548 (1.19054 iter/s, 10.0795s/12 iters), loss = 5.28134
I0410 03:14:37.430516 30317 solver.cpp:237]     Train net output #0: loss = 5.28134 (* 1 = 5.28134 loss)
I0410 03:14:37.430523 30317 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 03:14:41.614137 30317 solver.cpp:218] Iteration 7560 (2.86844 iter/s, 4.18346s/12 iters), loss = 5.2701
I0410 03:14:41.614188 30317 solver.cpp:237]     Train net output #0: loss = 5.2701 (* 1 = 5.2701 loss)
I0410 03:14:41.614197 30317 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 03:14:46.462460 30317 solver.cpp:218] Iteration 7572 (2.4752 iter/s, 4.8481s/12 iters), loss = 5.28373
I0410 03:14:46.462512 30317 solver.cpp:237]     Train net output #0: loss = 5.28373 (* 1 = 5.28373 loss)
I0410 03:14:46.462523 30317 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 03:14:51.243122 30317 solver.cpp:218] Iteration 7584 (2.51024 iter/s, 4.78043s/12 iters), loss = 5.29009
I0410 03:14:51.243175 30317 solver.cpp:237]     Train net output #0: loss = 5.29009 (* 1 = 5.29009 loss)
I0410 03:14:51.243187 30317 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 03:14:51.870394 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:14:56.088904 30317 solver.cpp:218] Iteration 7596 (2.4765 iter/s, 4.84554s/12 iters), loss = 5.2792
I0410 03:14:56.088960 30317 solver.cpp:237]     Train net output #0: loss = 5.2792 (* 1 = 5.2792 loss)
I0410 03:14:56.088971 30317 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 03:15:00.883592 30317 solver.cpp:218] Iteration 7608 (2.5029 iter/s, 4.79445s/12 iters), loss = 5.26473
I0410 03:15:00.883652 30317 solver.cpp:237]     Train net output #0: loss = 5.26473 (* 1 = 5.26473 loss)
I0410 03:15:00.883666 30317 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 03:15:05.697414 30317 solver.cpp:218] Iteration 7620 (2.49295 iter/s, 4.81358s/12 iters), loss = 5.27923
I0410 03:15:05.697530 30317 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0410 03:15:05.697543 30317 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 03:15:06.860364 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:15:10.547175 30317 solver.cpp:218] Iteration 7632 (2.4745 iter/s, 4.84946s/12 iters), loss = 5.28235
I0410 03:15:10.547230 30317 solver.cpp:237]     Train net output #0: loss = 5.28235 (* 1 = 5.28235 loss)
I0410 03:15:10.547241 30317 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 03:15:15.338765 30317 solver.cpp:218] Iteration 7644 (2.50451 iter/s, 4.79136s/12 iters), loss = 5.28455
I0410 03:15:15.338810 30317 solver.cpp:237]     Train net output #0: loss = 5.28455 (* 1 = 5.28455 loss)
I0410 03:15:15.338821 30317 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 03:15:17.311349 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 03:15:19.535056 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 03:15:20.841038 30317 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 03:15:20.841070 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:15:22.369493 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:15:25.349969 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:15:25.350013 30317 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0410 03:15:27.249987 30317 solver.cpp:218] Iteration 7656 (1.00749 iter/s, 11.9107s/12 iters), loss = 5.27382
I0410 03:15:27.250026 30317 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0410 03:15:27.250036 30317 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 03:15:32.103258 30317 solver.cpp:218] Iteration 7668 (2.47268 iter/s, 4.85304s/12 iters), loss = 5.26909
I0410 03:15:32.103307 30317 solver.cpp:237]     Train net output #0: loss = 5.26909 (* 1 = 5.26909 loss)
I0410 03:15:32.103315 30317 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 03:15:36.920527 30317 solver.cpp:218] Iteration 7680 (2.49116 iter/s, 4.81703s/12 iters), loss = 5.26263
I0410 03:15:36.920644 30317 solver.cpp:237]     Train net output #0: loss = 5.26263 (* 1 = 5.26263 loss)
I0410 03:15:36.920657 30317 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 03:15:39.594033 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:15:41.741255 30317 solver.cpp:218] Iteration 7692 (2.4894 iter/s, 4.82043s/12 iters), loss = 5.27063
I0410 03:15:41.741298 30317 solver.cpp:237]     Train net output #0: loss = 5.27063 (* 1 = 5.27063 loss)
I0410 03:15:41.741307 30317 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 03:15:46.567538 30317 solver.cpp:218] Iteration 7704 (2.48651 iter/s, 4.82605s/12 iters), loss = 5.25447
I0410 03:15:46.567593 30317 solver.cpp:237]     Train net output #0: loss = 5.25447 (* 1 = 5.25447 loss)
I0410 03:15:46.567606 30317 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 03:15:51.380643 30317 solver.cpp:218] Iteration 7716 (2.49332 iter/s, 4.81287s/12 iters), loss = 5.25503
I0410 03:15:51.380692 30317 solver.cpp:237]     Train net output #0: loss = 5.25503 (* 1 = 5.25503 loss)
I0410 03:15:51.380703 30317 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 03:15:56.222558 30317 solver.cpp:218] Iteration 7728 (2.47848 iter/s, 4.84169s/12 iters), loss = 5.25575
I0410 03:15:56.222609 30317 solver.cpp:237]     Train net output #0: loss = 5.25575 (* 1 = 5.25575 loss)
I0410 03:15:56.222621 30317 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 03:16:01.044250 30317 solver.cpp:218] Iteration 7740 (2.48887 iter/s, 4.82146s/12 iters), loss = 5.2995
I0410 03:16:01.044297 30317 solver.cpp:237]     Train net output #0: loss = 5.2995 (* 1 = 5.2995 loss)
I0410 03:16:01.044308 30317 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 03:16:05.448565 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 03:16:06.176442 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 03:16:07.440287 30317 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 03:16:07.440372 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:16:08.859964 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:16:11.879804 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:16:11.879864 30317 solver.cpp:397]     Test net output #1: loss = 5.28667 (* 1 = 5.28667 loss)
I0410 03:16:11.961988 30317 solver.cpp:218] Iteration 7752 (1.09918 iter/s, 10.9173s/12 iters), loss = 5.267
I0410 03:16:11.962045 30317 solver.cpp:237]     Train net output #0: loss = 5.267 (* 1 = 5.267 loss)
I0410 03:16:11.962059 30317 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 03:16:16.033869 30317 solver.cpp:218] Iteration 7764 (2.94719 iter/s, 4.07167s/12 iters), loss = 5.27653
I0410 03:16:16.033906 30317 solver.cpp:237]     Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
I0410 03:16:16.033915 30317 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 03:16:20.889727 30317 solver.cpp:218] Iteration 7776 (2.47136 iter/s, 4.85563s/12 iters), loss = 5.26911
I0410 03:16:20.889772 30317 solver.cpp:237]     Train net output #0: loss = 5.26911 (* 1 = 5.26911 loss)
I0410 03:16:20.889784 30317 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 03:16:25.730443 30317 solver.cpp:218] Iteration 7788 (2.47909 iter/s, 4.84049s/12 iters), loss = 5.24444
I0410 03:16:25.730496 30317 solver.cpp:237]     Train net output #0: loss = 5.24444 (* 1 = 5.24444 loss)
I0410 03:16:25.730507 30317 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 03:16:25.738507 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:16:30.575090 30317 solver.cpp:218] Iteration 7800 (2.47708 iter/s, 4.84441s/12 iters), loss = 5.27077
I0410 03:16:30.575130 30317 solver.cpp:237]     Train net output #0: loss = 5.27077 (* 1 = 5.27077 loss)
I0410 03:16:30.575139 30317 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 03:16:35.380084 30317 solver.cpp:218] Iteration 7812 (2.49752 iter/s, 4.80477s/12 iters), loss = 5.29479
I0410 03:16:35.380131 30317 solver.cpp:237]     Train net output #0: loss = 5.29479 (* 1 = 5.29479 loss)
I0410 03:16:35.380142 30317 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 03:16:40.249295 30317 solver.cpp:218] Iteration 7824 (2.46458 iter/s, 4.86898s/12 iters), loss = 5.27394
I0410 03:16:40.249442 30317 solver.cpp:237]     Train net output #0: loss = 5.27394 (* 1 = 5.27394 loss)
I0410 03:16:40.249457 30317 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 03:16:45.082770 30317 solver.cpp:218] Iteration 7836 (2.48285 iter/s, 4.83315s/12 iters), loss = 5.27428
I0410 03:16:45.082808 30317 solver.cpp:237]     Train net output #0: loss = 5.27428 (* 1 = 5.27428 loss)
I0410 03:16:45.082818 30317 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 03:16:49.941246 30317 solver.cpp:218] Iteration 7848 (2.47003 iter/s, 4.85825s/12 iters), loss = 5.2574
I0410 03:16:49.941293 30317 solver.cpp:237]     Train net output #0: loss = 5.2574 (* 1 = 5.2574 loss)
I0410 03:16:49.941305 30317 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 03:16:51.914368 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 03:16:52.802403 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 03:16:53.553784 30317 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 03:16:53.553815 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:16:54.824901 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:16:57.951609 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:16:57.951659 30317 solver.cpp:397]     Test net output #1: loss = 5.28654 (* 1 = 5.28654 loss)
I0410 03:16:59.887586 30317 solver.cpp:218] Iteration 7860 (1.20652 iter/s, 9.94593s/12 iters), loss = 5.2448
I0410 03:16:59.887641 30317 solver.cpp:237]     Train net output #0: loss = 5.2448 (* 1 = 5.2448 loss)
I0410 03:16:59.887655 30317 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 03:17:04.709313 30317 solver.cpp:218] Iteration 7872 (2.48886 iter/s, 4.82149s/12 iters), loss = 5.265
I0410 03:17:04.709360 30317 solver.cpp:237]     Train net output #0: loss = 5.265 (* 1 = 5.265 loss)
I0410 03:17:04.709372 30317 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 03:17:09.583194 30317 solver.cpp:218] Iteration 7884 (2.46222 iter/s, 4.87365s/12 iters), loss = 5.25987
I0410 03:17:09.583242 30317 solver.cpp:237]     Train net output #0: loss = 5.25987 (* 1 = 5.25987 loss)
I0410 03:17:09.583253 30317 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 03:17:11.686484 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:17:14.436215 30317 solver.cpp:218] Iteration 7896 (2.4728 iter/s, 4.85279s/12 iters), loss = 5.2757
I0410 03:17:14.436257 30317 solver.cpp:237]     Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
I0410 03:17:14.436269 30317 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 03:17:19.263677 30317 solver.cpp:218] Iteration 7908 (2.4859 iter/s, 4.82723s/12 iters), loss = 5.26925
I0410 03:17:19.263729 30317 solver.cpp:237]     Train net output #0: loss = 5.26925 (* 1 = 5.26925 loss)
I0410 03:17:19.263741 30317 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 03:17:24.153303 30317 solver.cpp:218] Iteration 7920 (2.45429 iter/s, 4.88939s/12 iters), loss = 5.28447
I0410 03:17:24.153358 30317 solver.cpp:237]     Train net output #0: loss = 5.28447 (* 1 = 5.28447 loss)
I0410 03:17:24.153370 30317 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 03:17:28.980625 30317 solver.cpp:218] Iteration 7932 (2.48597 iter/s, 4.82709s/12 iters), loss = 5.26307
I0410 03:17:28.980664 30317 solver.cpp:237]     Train net output #0: loss = 5.26307 (* 1 = 5.26307 loss)
I0410 03:17:28.980674 30317 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 03:17:33.858048 30317 solver.cpp:218] Iteration 7944 (2.46043 iter/s, 4.8772s/12 iters), loss = 5.27017
I0410 03:17:33.858093 30317 solver.cpp:237]     Train net output #0: loss = 5.27017 (* 1 = 5.27017 loss)
I0410 03:17:33.858103 30317 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 03:17:38.297920 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 03:17:38.794783 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 03:17:39.132983 30317 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 03:17:39.133003 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:17:40.461901 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:17:43.544708 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:17:43.544790 30317 solver.cpp:397]     Test net output #1: loss = 5.28702 (* 1 = 5.28702 loss)
I0410 03:17:43.618641 30317 solver.cpp:218] Iteration 7956 (1.22948 iter/s, 9.76019s/12 iters), loss = 5.27406
I0410 03:17:43.618685 30317 solver.cpp:237]     Train net output #0: loss = 5.27406 (* 1 = 5.27406 loss)
I0410 03:17:43.618695 30317 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 03:17:47.769840 30317 solver.cpp:218] Iteration 7968 (2.89087 iter/s, 4.151s/12 iters), loss = 5.27725
I0410 03:17:47.769876 30317 solver.cpp:237]     Train net output #0: loss = 5.27725 (* 1 = 5.27725 loss)
I0410 03:17:47.769884 30317 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 03:17:52.614403 30317 solver.cpp:218] Iteration 7980 (2.47711 iter/s, 4.84435s/12 iters), loss = 5.25603
I0410 03:17:52.614437 30317 solver.cpp:237]     Train net output #0: loss = 5.25603 (* 1 = 5.25603 loss)
I0410 03:17:52.614445 30317 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 03:17:56.752662 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:17:57.446089 30317 solver.cpp:218] Iteration 7992 (2.48372 iter/s, 4.83147s/12 iters), loss = 5.25457
I0410 03:17:57.446130 30317 solver.cpp:237]     Train net output #0: loss = 5.25457 (* 1 = 5.25457 loss)
I0410 03:17:57.446138 30317 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 03:18:02.265259 30317 solver.cpp:218] Iteration 8004 (2.49017 iter/s, 4.81895s/12 iters), loss = 5.27762
I0410 03:18:02.265297 30317 solver.cpp:237]     Train net output #0: loss = 5.27762 (* 1 = 5.27762 loss)
I0410 03:18:02.265305 30317 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 03:18:07.143508 30317 solver.cpp:218] Iteration 8016 (2.46001 iter/s, 4.87802s/12 iters), loss = 5.27839
I0410 03:18:07.143556 30317 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0410 03:18:07.143568 30317 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 03:18:11.993010 30317 solver.cpp:218] Iteration 8028 (2.4746 iter/s, 4.84927s/12 iters), loss = 5.29269
I0410 03:18:11.993057 30317 solver.cpp:237]     Train net output #0: loss = 5.29269 (* 1 = 5.29269 loss)
I0410 03:18:11.993069 30317 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 03:18:16.865577 30317 solver.cpp:218] Iteration 8040 (2.46288 iter/s, 4.87234s/12 iters), loss = 5.26401
I0410 03:18:16.865729 30317 solver.cpp:237]     Train net output #0: loss = 5.26401 (* 1 = 5.26401 loss)
I0410 03:18:16.865742 30317 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 03:18:21.715312 30317 solver.cpp:218] Iteration 8052 (2.47453 iter/s, 4.84941s/12 iters), loss = 5.27923
I0410 03:18:21.715359 30317 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0410 03:18:21.715370 30317 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 03:18:23.736232 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 03:18:24.208911 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 03:18:24.536577 30317 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 03:18:24.536605 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:18:25.826927 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:18:28.964885 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:18:28.964920 30317 solver.cpp:397]     Test net output #1: loss = 5.28693 (* 1 = 5.28693 loss)
I0410 03:18:30.881871 30317 solver.cpp:218] Iteration 8064 (1.30916 iter/s, 9.16617s/12 iters), loss = 5.28056
I0410 03:18:30.881917 30317 solver.cpp:237]     Train net output #0: loss = 5.28056 (* 1 = 5.28056 loss)
I0410 03:18:30.881927 30317 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 03:18:35.797029 30317 solver.cpp:218] Iteration 8076 (2.44154 iter/s, 4.91492s/12 iters), loss = 5.27776
I0410 03:18:35.797075 30317 solver.cpp:237]     Train net output #0: loss = 5.27776 (* 1 = 5.27776 loss)
I0410 03:18:35.797086 30317 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 03:18:40.771421 30317 solver.cpp:218] Iteration 8088 (2.41247 iter/s, 4.97416s/12 iters), loss = 5.26364
I0410 03:18:40.771453 30317 solver.cpp:237]     Train net output #0: loss = 5.26364 (* 1 = 5.26364 loss)
I0410 03:18:40.771461 30317 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 03:18:42.136361 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:18:45.613287 30317 solver.cpp:218] Iteration 8100 (2.47849 iter/s, 4.84165s/12 iters), loss = 5.26059
I0410 03:18:45.613333 30317 solver.cpp:237]     Train net output #0: loss = 5.26059 (* 1 = 5.26059 loss)
I0410 03:18:45.613344 30317 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 03:18:50.435423 30317 solver.cpp:218] Iteration 8112 (2.48864 iter/s, 4.82191s/12 iters), loss = 5.26835
I0410 03:18:50.436719 30317 solver.cpp:237]     Train net output #0: loss = 5.26835 (* 1 = 5.26835 loss)
I0410 03:18:50.436731 30317 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 03:18:55.319934 30317 solver.cpp:218] Iteration 8124 (2.45749 iter/s, 4.88303s/12 iters), loss = 5.26978
I0410 03:18:55.319983 30317 solver.cpp:237]     Train net output #0: loss = 5.26978 (* 1 = 5.26978 loss)
I0410 03:18:55.319994 30317 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 03:19:00.132702 30317 solver.cpp:218] Iteration 8136 (2.49348 iter/s, 4.81254s/12 iters), loss = 5.28241
I0410 03:19:00.132748 30317 solver.cpp:237]     Train net output #0: loss = 5.28241 (* 1 = 5.28241 loss)
I0410 03:19:00.132761 30317 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 03:19:04.944540 30317 solver.cpp:218] Iteration 8148 (2.49397 iter/s, 4.81161s/12 iters), loss = 5.2523
I0410 03:19:04.944586 30317 solver.cpp:237]     Train net output #0: loss = 5.2523 (* 1 = 5.2523 loss)
I0410 03:19:04.944597 30317 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 03:19:09.325625 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 03:19:10.205310 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 03:19:10.969766 30317 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 03:19:10.969795 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:19:12.221163 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:19:15.400099 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:19:15.400133 30317 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 03:19:15.481545 30317 solver.cpp:218] Iteration 8160 (1.13889 iter/s, 10.5366s/12 iters), loss = 5.26552
I0410 03:19:15.481585 30317 solver.cpp:237]     Train net output #0: loss = 5.26552 (* 1 = 5.26552 loss)
I0410 03:19:15.481595 30317 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 03:19:19.557862 30317 solver.cpp:218] Iteration 8172 (2.94398 iter/s, 4.07612s/12 iters), loss = 5.28602
I0410 03:19:19.557907 30317 solver.cpp:237]     Train net output #0: loss = 5.28602 (* 1 = 5.28602 loss)
I0410 03:19:19.557919 30317 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 03:19:24.348130 30317 solver.cpp:218] Iteration 8184 (2.5052 iter/s, 4.79005s/12 iters), loss = 5.27235
I0410 03:19:24.348282 30317 solver.cpp:237]     Train net output #0: loss = 5.27235 (* 1 = 5.27235 loss)
I0410 03:19:24.348294 30317 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 03:19:27.764288 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:19:29.145783 30317 solver.cpp:218] Iteration 8196 (2.50139 iter/s, 4.79733s/12 iters), loss = 5.27442
I0410 03:19:29.145829 30317 solver.cpp:237]     Train net output #0: loss = 5.27442 (* 1 = 5.27442 loss)
I0410 03:19:29.145841 30317 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 03:19:33.960458 30317 solver.cpp:218] Iteration 8208 (2.4925 iter/s, 4.81445s/12 iters), loss = 5.25678
I0410 03:19:33.960490 30317 solver.cpp:237]     Train net output #0: loss = 5.25678 (* 1 = 5.25678 loss)
I0410 03:19:33.960498 30317 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 03:19:38.757282 30317 solver.cpp:218] Iteration 8220 (2.50177 iter/s, 4.79661s/12 iters), loss = 5.26299
I0410 03:19:38.757316 30317 solver.cpp:237]     Train net output #0: loss = 5.26299 (* 1 = 5.26299 loss)
I0410 03:19:38.757323 30317 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 03:19:43.576632 30317 solver.cpp:218] Iteration 8232 (2.49007 iter/s, 4.81914s/12 iters), loss = 5.27008
I0410 03:19:43.576668 30317 solver.cpp:237]     Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
I0410 03:19:43.576675 30317 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 03:19:48.396319 30317 solver.cpp:218] Iteration 8244 (2.4899 iter/s, 4.81947s/12 iters), loss = 5.2539
I0410 03:19:48.396373 30317 solver.cpp:237]     Train net output #0: loss = 5.2539 (* 1 = 5.2539 loss)
I0410 03:19:48.396384 30317 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 03:19:53.183773 30317 solver.cpp:218] Iteration 8256 (2.50667 iter/s, 4.78722s/12 iters), loss = 5.27164
I0410 03:19:53.183817 30317 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0410 03:19:53.183830 30317 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 03:19:55.143035 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 03:19:55.591645 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 03:19:55.914562 30317 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 03:19:55.914580 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:19:57.106889 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:20:00.435279 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 03:20:00.435326 30317 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 03:20:02.279237 30317 solver.cpp:218] Iteration 8268 (1.31939 iter/s, 9.09509s/12 iters), loss = 5.27995
I0410 03:20:02.279282 30317 solver.cpp:237]     Train net output #0: loss = 5.27995 (* 1 = 5.27995 loss)
I0410 03:20:02.279294 30317 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 03:20:07.115012 30317 solver.cpp:218] Iteration 8280 (2.48162 iter/s, 4.83555s/12 iters), loss = 5.28556
I0410 03:20:07.115058 30317 solver.cpp:237]     Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
I0410 03:20:07.115069 30317 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 03:20:11.947115 30317 solver.cpp:218] Iteration 8292 (2.48351 iter/s, 4.83188s/12 iters), loss = 5.29044
I0410 03:20:11.947160 30317 solver.cpp:237]     Train net output #0: loss = 5.29044 (* 1 = 5.29044 loss)
I0410 03:20:11.947171 30317 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 03:20:12.633950 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:20:16.794903 30317 solver.cpp:218] Iteration 8304 (2.47547 iter/s, 4.84756s/12 iters), loss = 5.27896
I0410 03:20:16.794950 30317 solver.cpp:237]     Train net output #0: loss = 5.27896 (* 1 = 5.27896 loss)
I0410 03:20:16.794960 30317 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 03:20:18.348359 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:20:21.576748 30317 solver.cpp:218] Iteration 8316 (2.50961 iter/s, 4.78162s/12 iters), loss = 5.26801
I0410 03:20:21.576790 30317 solver.cpp:237]     Train net output #0: loss = 5.26801 (* 1 = 5.26801 loss)
I0410 03:20:21.576799 30317 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 03:20:26.401540 30317 solver.cpp:218] Iteration 8328 (2.48727 iter/s, 4.82457s/12 iters), loss = 5.27953
I0410 03:20:26.401721 30317 solver.cpp:237]     Train net output #0: loss = 5.27953 (* 1 = 5.27953 loss)
I0410 03:20:26.401736 30317 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 03:20:31.206985 30317 solver.cpp:218] Iteration 8340 (2.49735 iter/s, 4.80509s/12 iters), loss = 5.27247
I0410 03:20:31.207027 30317 solver.cpp:237]     Train net output #0: loss = 5.27247 (* 1 = 5.27247 loss)
I0410 03:20:31.207038 30317 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 03:20:36.012974 30317 solver.cpp:218] Iteration 8352 (2.497 iter/s, 4.80576s/12 iters), loss = 5.28944
I0410 03:20:36.013022 30317 solver.cpp:237]     Train net output #0: loss = 5.28944 (* 1 = 5.28944 loss)
I0410 03:20:36.013033 30317 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 03:20:40.362460 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 03:20:40.815392 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 03:20:41.142494 30317 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 03:20:41.142524 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:20:42.294276 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:20:45.731895 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:20:45.731945 30317 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0410 03:20:45.807153 30317 solver.cpp:218] Iteration 8364 (1.22527 iter/s, 9.79378s/12 iters), loss = 5.26726
I0410 03:20:45.807193 30317 solver.cpp:237]     Train net output #0: loss = 5.26726 (* 1 = 5.26726 loss)
I0410 03:20:45.807204 30317 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 03:20:49.917853 30317 solver.cpp:218] Iteration 8376 (2.91935 iter/s, 4.11051s/12 iters), loss = 5.26415
I0410 03:20:49.917896 30317 solver.cpp:237]     Train net output #0: loss = 5.26415 (* 1 = 5.26415 loss)
I0410 03:20:49.917908 30317 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 03:20:54.712116 30317 solver.cpp:218] Iteration 8388 (2.50311 iter/s, 4.79404s/12 iters), loss = 5.25901
I0410 03:20:54.712146 30317 solver.cpp:237]     Train net output #0: loss = 5.25901 (* 1 = 5.25901 loss)
I0410 03:20:54.712154 30317 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 03:20:57.428634 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:20:59.525832 30317 solver.cpp:218] Iteration 8400 (2.49299 iter/s, 4.8135s/12 iters), loss = 5.2623
I0410 03:20:59.525879 30317 solver.cpp:237]     Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
I0410 03:20:59.525892 30317 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 03:21:04.327093 30317 solver.cpp:218] Iteration 8412 (2.49946 iter/s, 4.80104s/12 iters), loss = 5.25005
I0410 03:21:04.327142 30317 solver.cpp:237]     Train net output #0: loss = 5.25005 (* 1 = 5.25005 loss)
I0410 03:21:04.327155 30317 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 03:21:09.180228 30317 solver.cpp:218] Iteration 8424 (2.47275 iter/s, 4.8529s/12 iters), loss = 5.25578
I0410 03:21:09.180274 30317 solver.cpp:237]     Train net output #0: loss = 5.25578 (* 1 = 5.25578 loss)
I0410 03:21:09.180285 30317 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 03:21:14.020537 30317 solver.cpp:218] Iteration 8436 (2.4793 iter/s, 4.84009s/12 iters), loss = 5.2547
I0410 03:21:14.020577 30317 solver.cpp:237]     Train net output #0: loss = 5.2547 (* 1 = 5.2547 loss)
I0410 03:21:14.020586 30317 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 03:21:18.859397 30317 solver.cpp:218] Iteration 8448 (2.48004 iter/s, 4.83863s/12 iters), loss = 5.2931
I0410 03:21:18.859447 30317 solver.cpp:237]     Train net output #0: loss = 5.2931 (* 1 = 5.2931 loss)
I0410 03:21:18.859459 30317 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 03:21:23.739972 30317 solver.cpp:218] Iteration 8460 (2.45884 iter/s, 4.88034s/12 iters), loss = 5.27349
I0410 03:21:23.740020 30317 solver.cpp:237]     Train net output #0: loss = 5.27349 (* 1 = 5.27349 loss)
I0410 03:21:23.740031 30317 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 03:21:25.699657 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 03:21:26.195439 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 03:21:26.540094 30317 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 03:21:26.540119 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:21:27.653606 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:21:30.942575 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:21:30.942618 30317 solver.cpp:397]     Test net output #1: loss = 5.28652 (* 1 = 5.28652 loss)
I0410 03:21:32.615314 30317 solver.cpp:218] Iteration 8472 (1.35212 iter/s, 8.87498s/12 iters), loss = 5.27368
I0410 03:21:32.615350 30317 solver.cpp:237]     Train net output #0: loss = 5.27368 (* 1 = 5.27368 loss)
I0410 03:21:32.615358 30317 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 03:21:37.493376 30317 solver.cpp:218] Iteration 8484 (2.46011 iter/s, 4.87783s/12 iters), loss = 5.27046
I0410 03:21:37.493422 30317 solver.cpp:237]     Train net output #0: loss = 5.27046 (* 1 = 5.27046 loss)
I0410 03:21:37.493432 30317 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 03:21:42.315037 30317 solver.cpp:218] Iteration 8496 (2.48888 iter/s, 4.82144s/12 iters), loss = 5.25529
I0410 03:21:42.315083 30317 solver.cpp:237]     Train net output #0: loss = 5.25529 (* 1 = 5.25529 loss)
I0410 03:21:42.315093 30317 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 03:21:42.353648 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:21:47.132735 30317 solver.cpp:218] Iteration 8508 (2.49093 iter/s, 4.81747s/12 iters), loss = 5.27548
I0410 03:21:47.132781 30317 solver.cpp:237]     Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
I0410 03:21:47.132792 30317 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 03:21:51.959391 30317 solver.cpp:218] Iteration 8520 (2.48631 iter/s, 4.82642s/12 iters), loss = 5.29535
I0410 03:21:51.959444 30317 solver.cpp:237]     Train net output #0: loss = 5.29535 (* 1 = 5.29535 loss)
I0410 03:21:51.959455 30317 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 03:21:56.750459 30317 solver.cpp:218] Iteration 8532 (2.50478 iter/s, 4.79084s/12 iters), loss = 5.27007
I0410 03:21:56.750494 30317 solver.cpp:237]     Train net output #0: loss = 5.27007 (* 1 = 5.27007 loss)
I0410 03:21:56.750500 30317 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 03:22:01.579468 30317 solver.cpp:218] Iteration 8544 (2.48509 iter/s, 4.82879s/12 iters), loss = 5.27264
I0410 03:22:01.579602 30317 solver.cpp:237]     Train net output #0: loss = 5.27264 (* 1 = 5.27264 loss)
I0410 03:22:01.579617 30317 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 03:22:06.375924 30317 solver.cpp:218] Iteration 8556 (2.50201 iter/s, 4.79615s/12 iters), loss = 5.25739
I0410 03:22:06.375974 30317 solver.cpp:237]     Train net output #0: loss = 5.25739 (* 1 = 5.25739 loss)
I0410 03:22:06.375986 30317 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 03:22:10.825790 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 03:22:11.308868 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 03:22:11.653658 30317 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 03:22:11.653690 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:22:12.731304 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:22:16.063608 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:22:16.063658 30317 solver.cpp:397]     Test net output #1: loss = 5.28656 (* 1 = 5.28656 loss)
I0410 03:22:16.145370 30317 solver.cpp:218] Iteration 8568 (1.22837 iter/s, 9.76904s/12 iters), loss = 5.24878
I0410 03:22:16.145427 30317 solver.cpp:237]     Train net output #0: loss = 5.24878 (* 1 = 5.24878 loss)
I0410 03:22:16.145439 30317 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 03:22:20.357707 30317 solver.cpp:218] Iteration 8580 (2.84892 iter/s, 4.21212s/12 iters), loss = 5.26198
I0410 03:22:20.357753 30317 solver.cpp:237]     Train net output #0: loss = 5.26198 (* 1 = 5.26198 loss)
I0410 03:22:20.357764 30317 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 03:22:25.224856 30317 solver.cpp:218] Iteration 8592 (2.46562 iter/s, 4.86692s/12 iters), loss = 5.25034
I0410 03:22:25.224898 30317 solver.cpp:237]     Train net output #0: loss = 5.25034 (* 1 = 5.25034 loss)
I0410 03:22:25.224907 30317 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 03:22:27.303385 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:22:30.009979 30317 solver.cpp:218] Iteration 8604 (2.50789 iter/s, 4.78489s/12 iters), loss = 5.26975
I0410 03:22:30.010020 30317 solver.cpp:237]     Train net output #0: loss = 5.26975 (* 1 = 5.26975 loss)
I0410 03:22:30.010030 30317 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 03:22:34.858095 30317 solver.cpp:218] Iteration 8616 (2.4753 iter/s, 4.84789s/12 iters), loss = 5.26757
I0410 03:22:34.858183 30317 solver.cpp:237]     Train net output #0: loss = 5.26757 (* 1 = 5.26757 loss)
I0410 03:22:34.858193 30317 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 03:22:39.712260 30317 solver.cpp:218] Iteration 8628 (2.47224 iter/s, 4.8539s/12 iters), loss = 5.28412
I0410 03:22:39.712296 30317 solver.cpp:237]     Train net output #0: loss = 5.28412 (* 1 = 5.28412 loss)
I0410 03:22:39.712303 30317 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 03:22:44.522900 30317 solver.cpp:218] Iteration 8640 (2.49459 iter/s, 4.81042s/12 iters), loss = 5.26682
I0410 03:22:44.522945 30317 solver.cpp:237]     Train net output #0: loss = 5.26682 (* 1 = 5.26682 loss)
I0410 03:22:44.522953 30317 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 03:22:49.366946 30317 solver.cpp:218] Iteration 8652 (2.47738 iter/s, 4.84382s/12 iters), loss = 5.26559
I0410 03:22:49.366991 30317 solver.cpp:237]     Train net output #0: loss = 5.26559 (* 1 = 5.26559 loss)
I0410 03:22:49.366998 30317 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 03:22:54.201068 30317 solver.cpp:218] Iteration 8664 (2.48247 iter/s, 4.8339s/12 iters), loss = 5.27711
I0410 03:22:54.201107 30317 solver.cpp:237]     Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
I0410 03:22:54.201117 30317 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 03:22:56.157008 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 03:22:57.019202 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 03:22:57.767649 30317 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 03:22:57.767670 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:22:58.857157 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:23:02.263685 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:23:02.263734 30317 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 03:23:04.110782 30317 solver.cpp:218] Iteration 8676 (1.21098 iter/s, 9.90932s/12 iters), loss = 5.27569
I0410 03:23:04.110828 30317 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0410 03:23:04.110839 30317 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 03:23:08.994972 30317 solver.cpp:218] Iteration 8688 (2.45702 iter/s, 4.88397s/12 iters), loss = 5.26185
I0410 03:23:08.995110 30317 solver.cpp:237]     Train net output #0: loss = 5.26185 (* 1 = 5.26185 loss)
I0410 03:23:08.995123 30317 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 03:23:13.122550 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:23:13.775969 30317 solver.cpp:218] Iteration 8700 (2.5101 iter/s, 4.78069s/12 iters), loss = 5.26212
I0410 03:23:13.776010 30317 solver.cpp:237]     Train net output #0: loss = 5.26212 (* 1 = 5.26212 loss)
I0410 03:23:13.776021 30317 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 03:23:18.685050 30317 solver.cpp:218] Iteration 8712 (2.44456 iter/s, 4.90885s/12 iters), loss = 5.28144
I0410 03:23:18.685098 30317 solver.cpp:237]     Train net output #0: loss = 5.28144 (* 1 = 5.28144 loss)
I0410 03:23:18.685109 30317 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 03:23:23.541064 30317 solver.cpp:218] Iteration 8724 (2.47128 iter/s, 4.85579s/12 iters), loss = 5.27979
I0410 03:23:23.541108 30317 solver.cpp:237]     Train net output #0: loss = 5.27979 (* 1 = 5.27979 loss)
I0410 03:23:23.541118 30317 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 03:23:28.404192 30317 solver.cpp:218] Iteration 8736 (2.46766 iter/s, 4.8629s/12 iters), loss = 5.29593
I0410 03:23:28.404237 30317 solver.cpp:237]     Train net output #0: loss = 5.29593 (* 1 = 5.29593 loss)
I0410 03:23:28.404247 30317 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 03:23:33.215970 30317 solver.cpp:218] Iteration 8748 (2.49399 iter/s, 4.81156s/12 iters), loss = 5.27037
I0410 03:23:33.216018 30317 solver.cpp:237]     Train net output #0: loss = 5.27037 (* 1 = 5.27037 loss)
I0410 03:23:33.216029 30317 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 03:23:38.068351 30317 solver.cpp:218] Iteration 8760 (2.47313 iter/s, 4.85215s/12 iters), loss = 5.27789
I0410 03:23:38.068390 30317 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0410 03:23:38.068399 30317 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 03:23:42.491573 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 03:23:42.989584 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 03:23:43.338181 30317 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 03:23:43.338215 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:23:44.267810 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:23:47.650276 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:23:47.650326 30317 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0410 03:23:47.722532 30317 solver.cpp:218] Iteration 8772 (1.24303 iter/s, 9.65379s/12 iters), loss = 5.28105
I0410 03:23:47.722579 30317 solver.cpp:237]     Train net output #0: loss = 5.28105 (* 1 = 5.28105 loss)
I0410 03:23:47.722591 30317 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 03:23:51.826136 30317 solver.cpp:218] Iteration 8784 (2.9244 iter/s, 4.1034s/12 iters), loss = 5.27559
I0410 03:23:51.826184 30317 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0410 03:23:51.826195 30317 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 03:23:56.682129 30317 solver.cpp:218] Iteration 8796 (2.47129 iter/s, 4.85577s/12 iters), loss = 5.25698
I0410 03:23:56.682174 30317 solver.cpp:237]     Train net output #0: loss = 5.25698 (* 1 = 5.25698 loss)
I0410 03:23:56.682185 30317 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 03:23:58.073657 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:24:01.511281 30317 solver.cpp:218] Iteration 8808 (2.48502 iter/s, 4.82893s/12 iters), loss = 5.26163
I0410 03:24:01.511323 30317 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0410 03:24:01.511333 30317 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 03:24:06.391530 30317 solver.cpp:218] Iteration 8820 (2.459 iter/s, 4.88003s/12 iters), loss = 5.26742
I0410 03:24:06.391577 30317 solver.cpp:237]     Train net output #0: loss = 5.26742 (* 1 = 5.26742 loss)
I0410 03:24:06.391588 30317 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 03:24:11.198760 30317 solver.cpp:218] Iteration 8832 (2.49636 iter/s, 4.80701s/12 iters), loss = 5.26309
I0410 03:24:11.198794 30317 solver.cpp:237]     Train net output #0: loss = 5.26309 (* 1 = 5.26309 loss)
I0410 03:24:11.198802 30317 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 03:24:16.031805 30317 solver.cpp:218] Iteration 8844 (2.48302 iter/s, 4.83283s/12 iters), loss = 5.29609
I0410 03:24:16.031952 30317 solver.cpp:237]     Train net output #0: loss = 5.29609 (* 1 = 5.29609 loss)
I0410 03:24:16.031965 30317 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 03:24:20.804503 30317 solver.cpp:218] Iteration 8856 (2.51447 iter/s, 4.77238s/12 iters), loss = 5.25778
I0410 03:24:20.804546 30317 solver.cpp:237]     Train net output #0: loss = 5.25778 (* 1 = 5.25778 loss)
I0410 03:24:20.804558 30317 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 03:24:25.650003 30317 solver.cpp:218] Iteration 8868 (2.47664 iter/s, 4.84528s/12 iters), loss = 5.26207
I0410 03:24:25.650055 30317 solver.cpp:237]     Train net output #0: loss = 5.26207 (* 1 = 5.26207 loss)
I0410 03:24:25.650066 30317 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 03:24:27.659778 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 03:24:29.213027 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 03:24:30.572432 30317 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 03:24:30.572468 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:24:31.566494 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:24:35.023650 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:24:35.023699 30317 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 03:24:36.875255 30317 solver.cpp:218] Iteration 8880 (1.06906 iter/s, 11.2248s/12 iters), loss = 5.27943
I0410 03:24:36.875299 30317 solver.cpp:237]     Train net output #0: loss = 5.27943 (* 1 = 5.27943 loss)
I0410 03:24:36.875310 30317 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 03:24:41.676036 30317 solver.cpp:218] Iteration 8892 (2.49971 iter/s, 4.80056s/12 iters), loss = 5.27841
I0410 03:24:41.676080 30317 solver.cpp:237]     Train net output #0: loss = 5.27841 (* 1 = 5.27841 loss)
I0410 03:24:41.676092 30317 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 03:24:45.133116 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:24:46.499389 30317 solver.cpp:218] Iteration 8904 (2.48801 iter/s, 4.82313s/12 iters), loss = 5.27946
I0410 03:24:46.499552 30317 solver.cpp:237]     Train net output #0: loss = 5.27946 (* 1 = 5.27946 loss)
I0410 03:24:46.499565 30317 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 03:24:51.295747 30317 solver.cpp:218] Iteration 8916 (2.50207 iter/s, 4.79602s/12 iters), loss = 5.26289
I0410 03:24:51.295790 30317 solver.cpp:237]     Train net output #0: loss = 5.26289 (* 1 = 5.26289 loss)
I0410 03:24:51.295801 30317 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 03:24:56.138329 30317 solver.cpp:218] Iteration 8928 (2.47813 iter/s, 4.84236s/12 iters), loss = 5.26276
I0410 03:24:56.138377 30317 solver.cpp:237]     Train net output #0: loss = 5.26276 (* 1 = 5.26276 loss)
I0410 03:24:56.138388 30317 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 03:25:00.979176 30317 solver.cpp:218] Iteration 8940 (2.47902 iter/s, 4.84062s/12 iters), loss = 5.26683
I0410 03:25:00.979225 30317 solver.cpp:237]     Train net output #0: loss = 5.26683 (* 1 = 5.26683 loss)
I0410 03:25:00.979238 30317 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 03:25:05.802991 30317 solver.cpp:218] Iteration 8952 (2.48778 iter/s, 4.82359s/12 iters), loss = 5.25599
I0410 03:25:05.803038 30317 solver.cpp:237]     Train net output #0: loss = 5.25599 (* 1 = 5.25599 loss)
I0410 03:25:05.803050 30317 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 03:25:10.635265 30317 solver.cpp:218] Iteration 8964 (2.48342 iter/s, 4.83205s/12 iters), loss = 5.27585
I0410 03:25:10.635313 30317 solver.cpp:237]     Train net output #0: loss = 5.27585 (* 1 = 5.27585 loss)
I0410 03:25:10.635324 30317 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 03:25:15.101008 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 03:25:15.805825 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 03:25:16.292284 30317 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 03:25:16.292311 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:25:17.106992 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:25:20.578893 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:25:20.578941 30317 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0410 03:25:20.660555 30317 solver.cpp:218] Iteration 8976 (1.19702 iter/s, 10.0249s/12 iters), loss = 5.27799
I0410 03:25:20.660607 30317 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0410 03:25:20.660619 30317 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 03:25:24.755884 30317 solver.cpp:218] Iteration 8988 (2.93031 iter/s, 4.09513s/12 iters), loss = 5.28518
I0410 03:25:24.755929 30317 solver.cpp:237]     Train net output #0: loss = 5.28518 (* 1 = 5.28518 loss)
I0410 03:25:24.755940 30317 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 03:25:26.705453 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:25:29.542661 30317 solver.cpp:218] Iteration 9000 (2.50702 iter/s, 4.78656s/12 iters), loss = 5.28755
I0410 03:25:29.542703 30317 solver.cpp:237]     Train net output #0: loss = 5.28755 (* 1 = 5.28755 loss)
I0410 03:25:29.542713 30317 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 03:25:30.227066 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:25:34.340291 30317 solver.cpp:218] Iteration 9012 (2.50135 iter/s, 4.79741s/12 iters), loss = 5.28452
I0410 03:25:34.340334 30317 solver.cpp:237]     Train net output #0: loss = 5.28452 (* 1 = 5.28452 loss)
I0410 03:25:34.340345 30317 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 03:25:39.130568 30317 solver.cpp:218] Iteration 9024 (2.50519 iter/s, 4.79005s/12 iters), loss = 5.26367
I0410 03:25:39.130612 30317 solver.cpp:237]     Train net output #0: loss = 5.26367 (* 1 = 5.26367 loss)
I0410 03:25:39.130625 30317 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 03:25:43.934373 30317 solver.cpp:218] Iteration 9036 (2.49814 iter/s, 4.80358s/12 iters), loss = 5.2739
I0410 03:25:43.934427 30317 solver.cpp:237]     Train net output #0: loss = 5.2739 (* 1 = 5.2739 loss)
I0410 03:25:43.934439 30317 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 03:25:48.760397 30317 solver.cpp:218] Iteration 9048 (2.48664 iter/s, 4.8258s/12 iters), loss = 5.2764
I0410 03:25:48.760545 30317 solver.cpp:237]     Train net output #0: loss = 5.2764 (* 1 = 5.2764 loss)
I0410 03:25:48.760558 30317 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 03:25:53.566717 30317 solver.cpp:218] Iteration 9060 (2.49688 iter/s, 4.806s/12 iters), loss = 5.29096
I0410 03:25:53.566761 30317 solver.cpp:237]     Train net output #0: loss = 5.29096 (* 1 = 5.29096 loss)
I0410 03:25:53.566773 30317 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 03:25:58.439294 30317 solver.cpp:218] Iteration 9072 (2.46288 iter/s, 4.87235s/12 iters), loss = 5.26662
I0410 03:25:58.439339 30317 solver.cpp:237]     Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
I0410 03:25:58.439352 30317 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 03:26:00.439437 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 03:26:00.929870 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 03:26:01.261688 30317 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 03:26:01.261719 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:26:02.139431 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:26:05.662307 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:26:05.662351 30317 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0410 03:26:07.378077 30317 solver.cpp:218] Iteration 9084 (1.34252 iter/s, 8.93841s/12 iters), loss = 5.25891
I0410 03:26:07.378129 30317 solver.cpp:237]     Train net output #0: loss = 5.25891 (* 1 = 5.25891 loss)
I0410 03:26:07.378140 30317 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 03:26:12.213183 30317 solver.cpp:218] Iteration 9096 (2.48197 iter/s, 4.83488s/12 iters), loss = 5.26558
I0410 03:26:12.213229 30317 solver.cpp:237]     Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
I0410 03:26:12.213241 30317 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 03:26:15.062399 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:26:17.057126 30317 solver.cpp:218] Iteration 9108 (2.47744 iter/s, 4.84372s/12 iters), loss = 5.26144
I0410 03:26:17.057169 30317 solver.cpp:237]     Train net output #0: loss = 5.26144 (* 1 = 5.26144 loss)
I0410 03:26:17.057180 30317 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 03:26:21.914115 30317 solver.cpp:218] Iteration 9120 (2.47078 iter/s, 4.85677s/12 iters), loss = 5.25137
I0410 03:26:21.914232 30317 solver.cpp:237]     Train net output #0: loss = 5.25137 (* 1 = 5.25137 loss)
I0410 03:26:21.914247 30317 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 03:26:26.743258 30317 solver.cpp:218] Iteration 9132 (2.48506 iter/s, 4.82885s/12 iters), loss = 5.25362
I0410 03:26:26.743306 30317 solver.cpp:237]     Train net output #0: loss = 5.25362 (* 1 = 5.25362 loss)
I0410 03:26:26.743317 30317 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 03:26:31.582959 30317 solver.cpp:218] Iteration 9144 (2.47961 iter/s, 4.83948s/12 iters), loss = 5.25891
I0410 03:26:31.582998 30317 solver.cpp:237]     Train net output #0: loss = 5.25891 (* 1 = 5.25891 loss)
I0410 03:26:31.583006 30317 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 03:26:36.484236 30317 solver.cpp:218] Iteration 9156 (2.44845 iter/s, 4.90106s/12 iters), loss = 5.29067
I0410 03:26:36.484280 30317 solver.cpp:237]     Train net output #0: loss = 5.29067 (* 1 = 5.29067 loss)
I0410 03:26:36.484292 30317 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 03:26:41.288400 30317 solver.cpp:218] Iteration 9168 (2.49795 iter/s, 4.80394s/12 iters), loss = 5.27388
I0410 03:26:41.288446 30317 solver.cpp:237]     Train net output #0: loss = 5.27388 (* 1 = 5.27388 loss)
I0410 03:26:41.288458 30317 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 03:26:45.656983 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 03:26:46.531523 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 03:26:47.269282 30317 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 03:26:47.269313 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:26:48.105584 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:26:51.659132 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:26:51.659178 30317 solver.cpp:397]     Test net output #1: loss = 5.28728 (* 1 = 5.28728 loss)
I0410 03:26:51.740777 30317 solver.cpp:218] Iteration 9180 (1.14811 iter/s, 10.452s/12 iters), loss = 5.27193
I0410 03:26:51.740823 30317 solver.cpp:237]     Train net output #0: loss = 5.27193 (* 1 = 5.27193 loss)
I0410 03:26:51.740836 30317 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 03:26:55.995039 30317 solver.cpp:218] Iteration 9192 (2.82084 iter/s, 4.25406s/12 iters), loss = 5.27547
I0410 03:26:55.995170 30317 solver.cpp:237]     Train net output #0: loss = 5.27547 (* 1 = 5.27547 loss)
I0410 03:26:55.995180 30317 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 03:27:00.781210 30317 solver.cpp:218] Iteration 9204 (2.50738 iter/s, 4.78587s/12 iters), loss = 5.26412
I0410 03:27:00.781246 30317 solver.cpp:237]     Train net output #0: loss = 5.26412 (* 1 = 5.26412 loss)
I0410 03:27:00.781255 30317 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 03:27:00.848768 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:27:05.620574 30317 solver.cpp:218] Iteration 9216 (2.47978 iter/s, 4.83915s/12 iters), loss = 5.27924
I0410 03:27:05.620621 30317 solver.cpp:237]     Train net output #0: loss = 5.27924 (* 1 = 5.27924 loss)
I0410 03:27:05.620632 30317 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 03:27:10.491061 30317 solver.cpp:218] Iteration 9228 (2.46394 iter/s, 4.87026s/12 iters), loss = 5.28641
I0410 03:27:10.491108 30317 solver.cpp:237]     Train net output #0: loss = 5.28641 (* 1 = 5.28641 loss)
I0410 03:27:10.491119 30317 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 03:27:15.368645 30317 solver.cpp:218] Iteration 9240 (2.46035 iter/s, 4.87736s/12 iters), loss = 5.26037
I0410 03:27:15.368693 30317 solver.cpp:237]     Train net output #0: loss = 5.26037 (* 1 = 5.26037 loss)
I0410 03:27:15.368705 30317 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 03:27:20.145944 30317 solver.cpp:218] Iteration 9252 (2.512 iter/s, 4.77707s/12 iters), loss = 5.27491
I0410 03:27:20.146005 30317 solver.cpp:237]     Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
I0410 03:27:20.146016 30317 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 03:27:24.987779 30317 solver.cpp:218] Iteration 9264 (2.47852 iter/s, 4.8416s/12 iters), loss = 5.25999
I0410 03:27:24.987826 30317 solver.cpp:237]     Train net output #0: loss = 5.25999 (* 1 = 5.25999 loss)
I0410 03:27:24.987838 30317 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 03:27:29.801182 30317 solver.cpp:218] Iteration 9276 (2.49316 iter/s, 4.81318s/12 iters), loss = 5.25364
I0410 03:27:29.801296 30317 solver.cpp:237]     Train net output #0: loss = 5.25364 (* 1 = 5.25364 loss)
I0410 03:27:29.801308 30317 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 03:27:31.771622 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 03:27:32.269246 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 03:27:32.708421 30317 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 03:27:32.708451 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:27:33.525249 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:27:37.138109 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:27:37.138155 30317 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0410 03:27:39.031543 30317 solver.cpp:218] Iteration 9288 (1.30012 iter/s, 9.22993s/12 iters), loss = 5.26663
I0410 03:27:39.031582 30317 solver.cpp:237]     Train net output #0: loss = 5.26663 (* 1 = 5.26663 loss)
I0410 03:27:39.031594 30317 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 03:27:43.874982 30317 solver.cpp:218] Iteration 9300 (2.47769 iter/s, 4.84322s/12 iters), loss = 5.25072
I0410 03:27:43.875022 30317 solver.cpp:237]     Train net output #0: loss = 5.25072 (* 1 = 5.25072 loss)
I0410 03:27:43.875031 30317 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 03:27:46.013085 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:27:48.699967 30317 solver.cpp:218] Iteration 9312 (2.48717 iter/s, 4.82477s/12 iters), loss = 5.27817
I0410 03:27:48.700012 30317 solver.cpp:237]     Train net output #0: loss = 5.27817 (* 1 = 5.27817 loss)
I0410 03:27:48.700022 30317 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 03:27:53.503774 30317 solver.cpp:218] Iteration 9324 (2.49813 iter/s, 4.80359s/12 iters), loss = 5.27823
I0410 03:27:53.503816 30317 solver.cpp:237]     Train net output #0: loss = 5.27823 (* 1 = 5.27823 loss)
I0410 03:27:53.503827 30317 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 03:27:58.275736 30317 solver.cpp:218] Iteration 9336 (2.5148 iter/s, 4.77174s/12 iters), loss = 5.28584
I0410 03:27:58.275782 30317 solver.cpp:237]     Train net output #0: loss = 5.28584 (* 1 = 5.28584 loss)
I0410 03:27:58.275794 30317 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 03:28:03.111740 30317 solver.cpp:218] Iteration 9348 (2.4815 iter/s, 4.83578s/12 iters), loss = 5.2714
I0410 03:28:03.111884 30317 solver.cpp:237]     Train net output #0: loss = 5.2714 (* 1 = 5.2714 loss)
I0410 03:28:03.111898 30317 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 03:28:07.964473 30317 solver.cpp:218] Iteration 9360 (2.473 iter/s, 4.85241s/12 iters), loss = 5.27446
I0410 03:28:07.964531 30317 solver.cpp:237]     Train net output #0: loss = 5.27446 (* 1 = 5.27446 loss)
I0410 03:28:07.964545 30317 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 03:28:12.782721 30317 solver.cpp:218] Iteration 9372 (2.49065 iter/s, 4.81802s/12 iters), loss = 5.27705
I0410 03:28:12.782763 30317 solver.cpp:237]     Train net output #0: loss = 5.27705 (* 1 = 5.27705 loss)
I0410 03:28:12.782774 30317 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 03:28:17.247527 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 03:28:18.533051 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 03:28:20.786630 30317 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 03:28:20.786665 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:28:21.571475 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:28:25.222275 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:28:25.222322 30317 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 03:28:25.303989 30317 solver.cpp:218] Iteration 9384 (0.958407 iter/s, 12.5208s/12 iters), loss = 5.27871
I0410 03:28:25.304042 30317 solver.cpp:237]     Train net output #0: loss = 5.27871 (* 1 = 5.27871 loss)
I0410 03:28:25.304054 30317 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 03:28:29.521354 30317 solver.cpp:218] Iteration 9396 (2.84552 iter/s, 4.21716s/12 iters), loss = 5.26676
I0410 03:28:29.521390 30317 solver.cpp:237]     Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
I0410 03:28:29.521399 30317 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 03:28:33.728514 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:28:34.356971 30317 solver.cpp:218] Iteration 9408 (2.4817 iter/s, 4.8354s/12 iters), loss = 5.2689
I0410 03:28:34.357017 30317 solver.cpp:237]     Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
I0410 03:28:34.357028 30317 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 03:28:39.197124 30317 solver.cpp:218] Iteration 9420 (2.47937 iter/s, 4.83993s/12 iters), loss = 5.27369
I0410 03:28:39.197160 30317 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0410 03:28:39.197168 30317 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 03:28:44.072146 30317 solver.cpp:218] Iteration 9432 (2.46164 iter/s, 4.8748s/12 iters), loss = 5.28168
I0410 03:28:44.072192 30317 solver.cpp:237]     Train net output #0: loss = 5.28168 (* 1 = 5.28168 loss)
I0410 03:28:44.072203 30317 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 03:28:48.943125 30317 solver.cpp:218] Iteration 9444 (2.46368 iter/s, 4.87076s/12 iters), loss = 5.28527
I0410 03:28:48.943166 30317 solver.cpp:237]     Train net output #0: loss = 5.28527 (* 1 = 5.28527 loss)
I0410 03:28:48.943192 30317 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 03:28:53.805974 30317 solver.cpp:218] Iteration 9456 (2.4678 iter/s, 4.86263s/12 iters), loss = 5.26612
I0410 03:28:53.806013 30317 solver.cpp:237]     Train net output #0: loss = 5.26612 (* 1 = 5.26612 loss)
I0410 03:28:53.806022 30317 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 03:28:58.641366 30317 solver.cpp:218] Iteration 9468 (2.48181 iter/s, 4.83518s/12 iters), loss = 5.27805
I0410 03:28:58.641402 30317 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0410 03:28:58.641410 30317 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 03:29:03.538265 30317 solver.cpp:218] Iteration 9480 (2.45064 iter/s, 4.89668s/12 iters), loss = 5.27668
I0410 03:29:03.538302 30317 solver.cpp:237]     Train net output #0: loss = 5.27668 (* 1 = 5.27668 loss)
I0410 03:29:03.538309 30317 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 03:29:05.542959 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 03:29:06.039659 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 03:29:06.381887 30317 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 03:29:06.381915 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:29:07.030740 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:29:10.727583 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:29:10.727632 30317 solver.cpp:397]     Test net output #1: loss = 5.28632 (* 1 = 5.28632 loss)
I0410 03:29:12.652117 30317 solver.cpp:218] Iteration 9492 (1.31673 iter/s, 9.11349s/12 iters), loss = 5.2695
I0410 03:29:12.652150 30317 solver.cpp:237]     Train net output #0: loss = 5.2695 (* 1 = 5.2695 loss)
I0410 03:29:12.652158 30317 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 03:29:17.511920 30317 solver.cpp:218] Iteration 9504 (2.46934 iter/s, 4.85959s/12 iters), loss = 5.25896
I0410 03:29:17.511963 30317 solver.cpp:237]     Train net output #0: loss = 5.25896 (* 1 = 5.25896 loss)
I0410 03:29:17.511974 30317 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 03:29:18.955857 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:29:22.387756 30317 solver.cpp:218] Iteration 9516 (2.46123 iter/s, 4.87561s/12 iters), loss = 5.26272
I0410 03:29:22.387811 30317 solver.cpp:237]     Train net output #0: loss = 5.26272 (* 1 = 5.26272 loss)
I0410 03:29:22.387823 30317 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 03:29:27.245869 30317 solver.cpp:218] Iteration 9528 (2.47021 iter/s, 4.85788s/12 iters), loss = 5.2628
I0410 03:29:27.245909 30317 solver.cpp:237]     Train net output #0: loss = 5.2628 (* 1 = 5.2628 loss)
I0410 03:29:27.245920 30317 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 03:29:32.051599 30317 solver.cpp:218] Iteration 9540 (2.49713 iter/s, 4.80551s/12 iters), loss = 5.2466
I0410 03:29:32.051643 30317 solver.cpp:237]     Train net output #0: loss = 5.2466 (* 1 = 5.2466 loss)
I0410 03:29:32.051656 30317 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 03:29:36.882577 30317 solver.cpp:218] Iteration 9552 (2.48408 iter/s, 4.83076s/12 iters), loss = 5.29974
I0410 03:29:36.882702 30317 solver.cpp:237]     Train net output #0: loss = 5.29974 (* 1 = 5.29974 loss)
I0410 03:29:36.882714 30317 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 03:29:41.717058 30317 solver.cpp:218] Iteration 9564 (2.48233 iter/s, 4.83418s/12 iters), loss = 5.25674
I0410 03:29:41.717113 30317 solver.cpp:237]     Train net output #0: loss = 5.25674 (* 1 = 5.25674 loss)
I0410 03:29:41.717124 30317 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 03:29:46.599172 30317 solver.cpp:218] Iteration 9576 (2.45807 iter/s, 4.88188s/12 iters), loss = 5.26047
I0410 03:29:46.599212 30317 solver.cpp:237]     Train net output #0: loss = 5.26047 (* 1 = 5.26047 loss)
I0410 03:29:46.599221 30317 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 03:29:51.058413 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 03:29:51.542037 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 03:29:51.879901 30317 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 03:29:51.879922 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:29:52.570643 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:29:56.311802 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:29:56.311851 30317 solver.cpp:397]     Test net output #1: loss = 5.2866 (* 1 = 5.2866 loss)
I0410 03:29:56.393414 30317 solver.cpp:218] Iteration 9588 (1.22526 iter/s, 9.79385s/12 iters), loss = 5.27749
I0410 03:29:56.393457 30317 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0410 03:29:56.393468 30317 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 03:30:00.529994 30317 solver.cpp:218] Iteration 9600 (2.90109 iter/s, 4.13638s/12 iters), loss = 5.27318
I0410 03:30:00.530037 30317 solver.cpp:237]     Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
I0410 03:30:00.530050 30317 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 03:30:04.038837 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:30:05.366020 30317 solver.cpp:218] Iteration 9612 (2.48149 iter/s, 4.83581s/12 iters), loss = 5.27174
I0410 03:30:05.366065 30317 solver.cpp:237]     Train net output #0: loss = 5.27174 (* 1 = 5.27174 loss)
I0410 03:30:05.366077 30317 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 03:30:10.174640 30317 solver.cpp:218] Iteration 9624 (2.49563 iter/s, 4.8084s/12 iters), loss = 5.26665
I0410 03:30:10.174726 30317 solver.cpp:237]     Train net output #0: loss = 5.26665 (* 1 = 5.26665 loss)
I0410 03:30:10.174737 30317 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 03:30:15.056165 30317 solver.cpp:218] Iteration 9636 (2.45838 iter/s, 4.88127s/12 iters), loss = 5.25578
I0410 03:30:15.056200 30317 solver.cpp:237]     Train net output #0: loss = 5.25578 (* 1 = 5.25578 loss)
I0410 03:30:15.056210 30317 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 03:30:19.881089 30317 solver.cpp:218] Iteration 9648 (2.4872 iter/s, 4.8247s/12 iters), loss = 5.26733
I0410 03:30:19.881140 30317 solver.cpp:237]     Train net output #0: loss = 5.26733 (* 1 = 5.26733 loss)
I0410 03:30:19.881152 30317 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 03:30:24.708549 30317 solver.cpp:218] Iteration 9660 (2.4859 iter/s, 4.82723s/12 iters), loss = 5.25472
I0410 03:30:24.708600 30317 solver.cpp:237]     Train net output #0: loss = 5.25472 (* 1 = 5.25472 loss)
I0410 03:30:24.708611 30317 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 03:30:29.638572 30317 solver.cpp:218] Iteration 9672 (2.43418 iter/s, 4.92979s/12 iters), loss = 5.2748
I0410 03:30:29.638617 30317 solver.cpp:237]     Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
I0410 03:30:29.638628 30317 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 03:30:34.548564 30317 solver.cpp:218] Iteration 9684 (2.44411 iter/s, 4.90977s/12 iters), loss = 5.29172
I0410 03:30:34.548609 30317 solver.cpp:237]     Train net output #0: loss = 5.29172 (* 1 = 5.29172 loss)
I0410 03:30:34.548621 30317 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 03:30:36.565862 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 03:30:38.519158 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 03:30:39.351428 30317 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 03:30:39.351460 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:30:39.974776 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:30:42.513703 30317 blocking_queue.cpp:49] Waiting for data
I0410 03:30:43.760875 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:30:43.760922 30317 solver.cpp:397]     Test net output #1: loss = 5.28681 (* 1 = 5.28681 loss)
I0410 03:30:45.622048 30317 solver.cpp:218] Iteration 9696 (1.08371 iter/s, 11.073s/12 iters), loss = 5.28899
I0410 03:30:45.622100 30317 solver.cpp:237]     Train net output #0: loss = 5.28899 (* 1 = 5.28899 loss)
I0410 03:30:45.622113 30317 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 03:30:50.517935 30317 solver.cpp:218] Iteration 9708 (2.45115 iter/s, 4.89566s/12 iters), loss = 5.28794
I0410 03:30:50.517990 30317 solver.cpp:237]     Train net output #0: loss = 5.28794 (* 1 = 5.28794 loss)
I0410 03:30:50.518002 30317 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 03:30:51.255579 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:30:55.414908 30317 solver.cpp:218] Iteration 9720 (2.45061 iter/s, 4.89674s/12 iters), loss = 5.28815
I0410 03:30:55.414952 30317 solver.cpp:237]     Train net output #0: loss = 5.28815 (* 1 = 5.28815 loss)
I0410 03:30:55.414963 30317 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 03:31:00.368279 30317 solver.cpp:218] Iteration 9732 (2.4227 iter/s, 4.95314s/12 iters), loss = 5.26177
I0410 03:31:00.368314 30317 solver.cpp:237]     Train net output #0: loss = 5.26177 (* 1 = 5.26177 loss)
I0410 03:31:00.368322 30317 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 03:31:05.146409 30317 solver.cpp:218] Iteration 9744 (2.51155 iter/s, 4.77792s/12 iters), loss = 5.26681
I0410 03:31:05.146446 30317 solver.cpp:237]     Train net output #0: loss = 5.26681 (* 1 = 5.26681 loss)
I0410 03:31:05.146456 30317 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 03:31:09.979014 30317 solver.cpp:218] Iteration 9756 (2.48325 iter/s, 4.83238s/12 iters), loss = 5.27579
I0410 03:31:09.979069 30317 solver.cpp:237]     Train net output #0: loss = 5.27579 (* 1 = 5.27579 loss)
I0410 03:31:09.979082 30317 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 03:31:14.880645 30317 solver.cpp:218] Iteration 9768 (2.44828 iter/s, 4.9014s/12 iters), loss = 5.28608
I0410 03:31:14.880707 30317 solver.cpp:237]     Train net output #0: loss = 5.28608 (* 1 = 5.28608 loss)
I0410 03:31:14.880715 30317 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 03:31:19.716069 30317 solver.cpp:218] Iteration 9780 (2.48181 iter/s, 4.83518s/12 iters), loss = 5.26986
I0410 03:31:19.716114 30317 solver.cpp:237]     Train net output #0: loss = 5.26986 (* 1 = 5.26986 loss)
I0410 03:31:19.716125 30317 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 03:31:24.156147 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 03:31:24.935035 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 03:31:26.076545 30317 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 03:31:26.076572 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:31:26.679601 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:31:30.505324 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:31:30.505365 30317 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0410 03:31:30.586839 30317 solver.cpp:218] Iteration 9792 (1.10392 iter/s, 10.8703s/12 iters), loss = 5.2527
I0410 03:31:30.586890 30317 solver.cpp:237]     Train net output #0: loss = 5.2527 (* 1 = 5.2527 loss)
I0410 03:31:30.586902 30317 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 03:31:34.789866 30317 solver.cpp:218] Iteration 9804 (2.85522 iter/s, 4.20282s/12 iters), loss = 5.27229
I0410 03:31:34.789902 30317 solver.cpp:237]     Train net output #0: loss = 5.27229 (* 1 = 5.27229 loss)
I0410 03:31:34.789911 30317 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 03:31:37.646057 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:31:39.653318 30317 solver.cpp:218] Iteration 9816 (2.46749 iter/s, 4.86324s/12 iters), loss = 5.26337
I0410 03:31:39.653362 30317 solver.cpp:237]     Train net output #0: loss = 5.26337 (* 1 = 5.26337 loss)
I0410 03:31:39.653373 30317 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 03:31:44.559546 30317 solver.cpp:218] Iteration 9828 (2.44598 iter/s, 4.90601s/12 iters), loss = 5.2546
I0410 03:31:44.559589 30317 solver.cpp:237]     Train net output #0: loss = 5.2546 (* 1 = 5.2546 loss)
I0410 03:31:44.559600 30317 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 03:31:49.395395 30317 solver.cpp:218] Iteration 9840 (2.48158 iter/s, 4.83563s/12 iters), loss = 5.24831
I0410 03:31:49.395514 30317 solver.cpp:237]     Train net output #0: loss = 5.24831 (* 1 = 5.24831 loss)
I0410 03:31:49.395521 30317 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 03:31:54.185714 30317 solver.cpp:218] Iteration 9852 (2.50521 iter/s, 4.79001s/12 iters), loss = 5.26625
I0410 03:31:54.185775 30317 solver.cpp:237]     Train net output #0: loss = 5.26625 (* 1 = 5.26625 loss)
I0410 03:31:54.185787 30317 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 03:31:59.062956 30317 solver.cpp:218] Iteration 9864 (2.46052 iter/s, 4.87701s/12 iters), loss = 5.2879
I0410 03:31:59.062999 30317 solver.cpp:237]     Train net output #0: loss = 5.2879 (* 1 = 5.2879 loss)
I0410 03:31:59.063010 30317 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 03:32:03.894879 30317 solver.cpp:218] Iteration 9876 (2.4836 iter/s, 4.8317s/12 iters), loss = 5.27123
I0410 03:32:03.894922 30317 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0410 03:32:03.894933 30317 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 03:32:08.726831 30317 solver.cpp:218] Iteration 9888 (2.48358 iter/s, 4.83173s/12 iters), loss = 5.27502
I0410 03:32:08.726878 30317 solver.cpp:237]     Train net output #0: loss = 5.27502 (* 1 = 5.27502 loss)
I0410 03:32:08.726889 30317 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 03:32:10.723546 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 03:32:11.206799 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 03:32:11.540350 30317 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 03:32:11.540372 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:32:12.009527 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:32:15.842658 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:32:15.842707 30317 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0410 03:32:17.777436 30317 solver.cpp:218] Iteration 9900 (1.32593 iter/s, 9.05023s/12 iters), loss = 5.27464
I0410 03:32:17.777496 30317 solver.cpp:237]     Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
I0410 03:32:17.777508 30317 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 03:32:22.605547 30317 solver.cpp:218] Iteration 9912 (2.48557 iter/s, 4.82788s/12 iters), loss = 5.2556
I0410 03:32:22.605674 30317 solver.cpp:237]     Train net output #0: loss = 5.2556 (* 1 = 5.2556 loss)
I0410 03:32:22.605687 30317 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 03:32:22.703701 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:32:27.417110 30317 solver.cpp:218] Iteration 9924 (2.49415 iter/s, 4.81126s/12 iters), loss = 5.26963
I0410 03:32:27.417153 30317 solver.cpp:237]     Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
I0410 03:32:27.417165 30317 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 03:32:32.239589 30317 solver.cpp:218] Iteration 9936 (2.48846 iter/s, 4.82226s/12 iters), loss = 5.29036
I0410 03:32:32.239636 30317 solver.cpp:237]     Train net output #0: loss = 5.29036 (* 1 = 5.29036 loss)
I0410 03:32:32.239647 30317 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 03:32:37.138259 30317 solver.cpp:218] Iteration 9948 (2.44976 iter/s, 4.89844s/12 iters), loss = 5.2596
I0410 03:32:37.138310 30317 solver.cpp:237]     Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
I0410 03:32:37.138321 30317 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 03:32:41.985980 30317 solver.cpp:218] Iteration 9960 (2.47551 iter/s, 4.84749s/12 iters), loss = 5.2693
I0410 03:32:41.986027 30317 solver.cpp:237]     Train net output #0: loss = 5.2693 (* 1 = 5.2693 loss)
I0410 03:32:41.986037 30317 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 03:32:46.811026 30317 solver.cpp:218] Iteration 9972 (2.48714 iter/s, 4.82482s/12 iters), loss = 5.26088
I0410 03:32:46.811070 30317 solver.cpp:237]     Train net output #0: loss = 5.26088 (* 1 = 5.26088 loss)
I0410 03:32:46.811081 30317 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 03:32:51.742566 30317 solver.cpp:218] Iteration 9984 (2.43343 iter/s, 4.93132s/12 iters), loss = 5.24566
I0410 03:32:51.742612 30317 solver.cpp:237]     Train net output #0: loss = 5.24566 (* 1 = 5.24566 loss)
I0410 03:32:51.742624 30317 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 03:32:56.180135 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 03:32:57.060850 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 03:32:57.793890 30317 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 03:32:57.793920 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:32:58.307479 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:02.215194 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:33:02.215229 30317 solver.cpp:397]     Test net output #1: loss = 5.28738 (* 1 = 5.28738 loss)
I0410 03:33:02.296802 30317 solver.cpp:218] Iteration 9996 (1.13703 iter/s, 10.5538s/12 iters), loss = 5.27123
I0410 03:33:02.296844 30317 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0410 03:33:02.296854 30317 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 03:33:06.463587 30317 solver.cpp:218] Iteration 10008 (2.88006 iter/s, 4.16658s/12 iters), loss = 5.2456
I0410 03:33:06.463634 30317 solver.cpp:237]     Train net output #0: loss = 5.2456 (* 1 = 5.2456 loss)
I0410 03:33:06.463644 30317 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 03:33:08.656471 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:11.382416 30317 solver.cpp:218] Iteration 10020 (2.43972 iter/s, 4.9186s/12 iters), loss = 5.26832
I0410 03:33:11.382454 30317 solver.cpp:237]     Train net output #0: loss = 5.26832 (* 1 = 5.26832 loss)
I0410 03:33:11.382464 30317 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 03:33:16.207403 30317 solver.cpp:218] Iteration 10032 (2.48716 iter/s, 4.82477s/12 iters), loss = 5.27703
I0410 03:33:16.207446 30317 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0410 03:33:16.207458 30317 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 03:33:21.020143 30317 solver.cpp:218] Iteration 10044 (2.49349 iter/s, 4.81252s/12 iters), loss = 5.28271
I0410 03:33:21.020190 30317 solver.cpp:237]     Train net output #0: loss = 5.28271 (* 1 = 5.28271 loss)
I0410 03:33:21.020200 30317 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 03:33:25.945116 30317 solver.cpp:218] Iteration 10056 (2.43668 iter/s, 4.92474s/12 iters), loss = 5.2765
I0410 03:33:25.945165 30317 solver.cpp:237]     Train net output #0: loss = 5.2765 (* 1 = 5.2765 loss)
I0410 03:33:25.945178 30317 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 03:33:30.866273 30317 solver.cpp:218] Iteration 10068 (2.43856 iter/s, 4.92093s/12 iters), loss = 5.27462
I0410 03:33:30.866415 30317 solver.cpp:237]     Train net output #0: loss = 5.27462 (* 1 = 5.27462 loss)
I0410 03:33:30.866428 30317 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 03:33:35.859562 30317 solver.cpp:218] Iteration 10080 (2.40338 iter/s, 4.99297s/12 iters), loss = 5.26289
I0410 03:33:35.859603 30317 solver.cpp:237]     Train net output #0: loss = 5.26289 (* 1 = 5.26289 loss)
I0410 03:33:35.859613 30317 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 03:33:40.733978 30317 solver.cpp:218] Iteration 10092 (2.46195 iter/s, 4.87419s/12 iters), loss = 5.27678
I0410 03:33:40.734011 30317 solver.cpp:237]     Train net output #0: loss = 5.27678 (* 1 = 5.27678 loss)
I0410 03:33:40.734020 30317 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 03:33:42.682755 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 03:33:43.124815 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 03:33:43.546515 30317 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 03:33:43.546542 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:33:43.983180 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:47.937695 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:33:47.937744 30317 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 03:33:49.759014 30317 solver.cpp:218] Iteration 10104 (1.32969 iter/s, 9.02468s/12 iters), loss = 5.27133
I0410 03:33:49.759064 30317 solver.cpp:237]     Train net output #0: loss = 5.27133 (* 1 = 5.27133 loss)
I0410 03:33:49.759076 30317 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 03:33:53.989688 30321 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:54.613590 30317 solver.cpp:218] Iteration 10116 (2.47201 iter/s, 4.85435s/12 iters), loss = 5.25869
I0410 03:33:54.613626 30317 solver.cpp:237]     Train net output #0: loss = 5.25869 (* 1 = 5.25869 loss)
I0410 03:33:54.613632 30317 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 03:33:59.483093 30317 solver.cpp:218] Iteration 10128 (2.46443 iter/s, 4.86929s/12 iters), loss = 5.27331
I0410 03:33:59.483129 30317 solver.cpp:237]     Train net output #0: loss = 5.27331 (* 1 = 5.27331 loss)
I0410 03:33:59.483137 30317 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 03:34:04.301652 30317 solver.cpp:218] Iteration 10140 (2.49048 iter/s, 4.81834s/12 iters), loss = 5.28366
I0410 03:34:04.301754 30317 solver.cpp:237]     Train net output #0: loss = 5.28366 (* 1 = 5.28366 loss)
I0410 03:34:04.301766 30317 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 03:34:09.236268 30317 solver.cpp:218] Iteration 10152 (2.43194 iter/s, 4.93434s/12 iters), loss = 5.27548
I0410 03:34:09.236306 30317 solver.cpp:237]     Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
I0410 03:34:09.236315 30317 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 03:34:14.335661 30317 solver.cpp:218] Iteration 10164 (2.35333 iter/s, 5.09917s/12 iters), loss = 5.26229
I0410 03:34:14.335708 30317 solver.cpp:237]     Train net output #0: loss = 5.26229 (* 1 = 5.26229 loss)
I0410 03:34:14.335719 30317 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 03:34:19.162942 30317 solver.cpp:218] Iteration 10176 (2.48599 iter/s, 4.82705s/12 iters), loss = 5.27667
I0410 03:34:19.163002 30317 solver.cpp:237]     Train net output #0: loss = 5.27667 (* 1 = 5.27667 loss)
I0410 03:34:19.163013 30317 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 03:34:24.045979 30317 solver.cpp:218] Iteration 10188 (2.45761 iter/s, 4.8828s/12 iters), loss = 5.2778
I0410 03:34:24.046022 30317 solver.cpp:237]     Train net output #0: loss = 5.2778 (* 1 = 5.2778 loss)
I0410 03:34:24.046034 30317 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 03:34:28.439805 30317 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 03:34:29.441942 30317 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 03:34:31.600786 30317 solver.cpp:310] Iteration 10200, loss = 5.26458
I0410 03:34:31.600821 30317 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 03:34:31.600831 30317 net.cpp:676] Ignoring source layer train-data
I0410 03:34:32.024827 30322 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:34:36.028393 30317 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 03:34:36.028523 30317 solver.cpp:397]     Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
I0410 03:34:36.028537 30317 solver.cpp:315] Optimization Done.
I0410 03:34:36.028544 30317 caffe.cpp:259] Optimization Done.