DIGITS-CNN/cars/lr-investigations/exponential/1e-2/0.9/caffe_output.log

I0408 14:45:56.274281 20259 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-144554-950e/solver.prototxt
I0408 14:45:56.274528 20259 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 14:45:56.274538 20259 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 14:45:56.274643 20259 caffe.cpp:218] Using GPUs 0
I0408 14:45:56.302160 20259 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0408 14:45:56.604955 20259 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: 0.99896759
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 14:45:56.605783 20259 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 14:45:56.606354 20259 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 14:45:56.606371 20259 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 14:45:56.606513 20259 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 14:45:56.606606 20259 layer_factory.hpp:77] Creating layer train-data
I0408 14:45:56.680151 20259 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 14:45:56.680335 20259 net.cpp:84] Creating Layer train-data
I0408 14:45:56.680349 20259 net.cpp:380] train-data -> data
I0408 14:45:56.680371 20259 net.cpp:380] train-data -> label
I0408 14:45:56.680384 20259 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 14:45:56.685596 20259 data_layer.cpp:45] output data size: 128,3,227,227
I0408 14:45:56.826476 20259 net.cpp:122] Setting up train-data
I0408 14:45:56.826503 20259 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 14:45:56.826508 20259 net.cpp:129] Top shape: 128 (128)
I0408 14:45:56.826512 20259 net.cpp:137] Memory required for data: 79149056
I0408 14:45:56.826524 20259 layer_factory.hpp:77] Creating layer conv1
I0408 14:45:56.826546 20259 net.cpp:84] Creating Layer conv1
I0408 14:45:56.826553 20259 net.cpp:406] conv1 <- data
I0408 14:45:56.826566 20259 net.cpp:380] conv1 -> conv1
I0408 14:45:57.340337 20259 net.cpp:122] Setting up conv1
I0408 14:45:57.340358 20259 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 14:45:57.340363 20259 net.cpp:137] Memory required for data: 227833856
I0408 14:45:57.340380 20259 layer_factory.hpp:77] Creating layer relu1
I0408 14:45:57.340395 20259 net.cpp:84] Creating Layer relu1
I0408 14:45:57.340400 20259 net.cpp:406] relu1 <- conv1
I0408 14:45:57.340406 20259 net.cpp:367] relu1 -> conv1 (in-place)
I0408 14:45:57.340747 20259 net.cpp:122] Setting up relu1
I0408 14:45:57.340757 20259 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 14:45:57.340760 20259 net.cpp:137] Memory required for data: 376518656
I0408 14:45:57.340764 20259 layer_factory.hpp:77] Creating layer norm1
I0408 14:45:57.340773 20259 net.cpp:84] Creating Layer norm1
I0408 14:45:57.340777 20259 net.cpp:406] norm1 <- conv1
I0408 14:45:57.340802 20259 net.cpp:380] norm1 -> norm1
I0408 14:45:57.341315 20259 net.cpp:122] Setting up norm1
I0408 14:45:57.341326 20259 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 14:45:57.341329 20259 net.cpp:137] Memory required for data: 525203456
I0408 14:45:57.341333 20259 layer_factory.hpp:77] Creating layer pool1
I0408 14:45:57.341341 20259 net.cpp:84] Creating Layer pool1
I0408 14:45:57.341346 20259 net.cpp:406] pool1 <- norm1
I0408 14:45:57.341351 20259 net.cpp:380] pool1 -> pool1
I0408 14:45:57.341389 20259 net.cpp:122] Setting up pool1
I0408 14:45:57.341395 20259 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 14:45:57.341398 20259 net.cpp:137] Memory required for data: 561035264
I0408 14:45:57.341403 20259 layer_factory.hpp:77] Creating layer conv2
I0408 14:45:57.341413 20259 net.cpp:84] Creating Layer conv2
I0408 14:45:57.341416 20259 net.cpp:406] conv2 <- pool1
I0408 14:45:57.341423 20259 net.cpp:380] conv2 -> conv2
I0408 14:45:57.348498 20259 net.cpp:122] Setting up conv2
I0408 14:45:57.348510 20259 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 14:45:57.348513 20259 net.cpp:137] Memory required for data: 656586752
I0408 14:45:57.348521 20259 layer_factory.hpp:77] Creating layer relu2
I0408 14:45:57.348529 20259 net.cpp:84] Creating Layer relu2
I0408 14:45:57.348533 20259 net.cpp:406] relu2 <- conv2
I0408 14:45:57.348538 20259 net.cpp:367] relu2 -> conv2 (in-place)
I0408 14:45:57.349035 20259 net.cpp:122] Setting up relu2
I0408 14:45:57.349045 20259 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 14:45:57.349048 20259 net.cpp:137] Memory required for data: 752138240
I0408 14:45:57.349051 20259 layer_factory.hpp:77] Creating layer norm2
I0408 14:45:57.349058 20259 net.cpp:84] Creating Layer norm2
I0408 14:45:57.349061 20259 net.cpp:406] norm2 <- conv2
I0408 14:45:57.349068 20259 net.cpp:380] norm2 -> norm2
I0408 14:45:57.349419 20259 net.cpp:122] Setting up norm2
I0408 14:45:57.349427 20259 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 14:45:57.349431 20259 net.cpp:137] Memory required for data: 847689728
I0408 14:45:57.349434 20259 layer_factory.hpp:77] Creating layer pool2
I0408 14:45:57.349443 20259 net.cpp:84] Creating Layer pool2
I0408 14:45:57.349447 20259 net.cpp:406] pool2 <- norm2
I0408 14:45:57.349452 20259 net.cpp:380] pool2 -> pool2
I0408 14:45:57.349480 20259 net.cpp:122] Setting up pool2
I0408 14:45:57.349485 20259 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 14:45:57.349489 20259 net.cpp:137] Memory required for data: 869840896
I0408 14:45:57.349493 20259 layer_factory.hpp:77] Creating layer conv3
I0408 14:45:57.349501 20259 net.cpp:84] Creating Layer conv3
I0408 14:45:57.349504 20259 net.cpp:406] conv3 <- pool2
I0408 14:45:57.349511 20259 net.cpp:380] conv3 -> conv3
I0408 14:45:57.359480 20259 net.cpp:122] Setting up conv3
I0408 14:45:57.359491 20259 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 14:45:57.359495 20259 net.cpp:137] Memory required for data: 903067648
I0408 14:45:57.359505 20259 layer_factory.hpp:77] Creating layer relu3
I0408 14:45:57.359513 20259 net.cpp:84] Creating Layer relu3
I0408 14:45:57.359516 20259 net.cpp:406] relu3 <- conv3
I0408 14:45:57.359521 20259 net.cpp:367] relu3 -> conv3 (in-place)
I0408 14:45:57.360008 20259 net.cpp:122] Setting up relu3
I0408 14:45:57.360018 20259 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 14:45:57.360023 20259 net.cpp:137] Memory required for data: 936294400
I0408 14:45:57.360026 20259 layer_factory.hpp:77] Creating layer conv4
I0408 14:45:57.360035 20259 net.cpp:84] Creating Layer conv4
I0408 14:45:57.360039 20259 net.cpp:406] conv4 <- conv3
I0408 14:45:57.360045 20259 net.cpp:380] conv4 -> conv4
I0408 14:45:57.370448 20259 net.cpp:122] Setting up conv4
I0408 14:45:57.370460 20259 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 14:45:57.370463 20259 net.cpp:137] Memory required for data: 969521152
I0408 14:45:57.370471 20259 layer_factory.hpp:77] Creating layer relu4
I0408 14:45:57.370478 20259 net.cpp:84] Creating Layer relu4
I0408 14:45:57.370498 20259 net.cpp:406] relu4 <- conv4
I0408 14:45:57.370504 20259 net.cpp:367] relu4 -> conv4 (in-place)
I0408 14:45:57.370842 20259 net.cpp:122] Setting up relu4
I0408 14:45:57.370851 20259 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 14:45:57.370854 20259 net.cpp:137] Memory required for data: 1002747904
I0408 14:45:57.370857 20259 layer_factory.hpp:77] Creating layer conv5
I0408 14:45:57.370867 20259 net.cpp:84] Creating Layer conv5
I0408 14:45:57.370872 20259 net.cpp:406] conv5 <- conv4
I0408 14:45:57.370877 20259 net.cpp:380] conv5 -> conv5
I0408 14:45:57.379165 20259 net.cpp:122] Setting up conv5
I0408 14:45:57.379176 20259 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 14:45:57.379179 20259 net.cpp:137] Memory required for data: 1024899072
I0408 14:45:57.379189 20259 layer_factory.hpp:77] Creating layer relu5
I0408 14:45:57.379196 20259 net.cpp:84] Creating Layer relu5
I0408 14:45:57.379200 20259 net.cpp:406] relu5 <- conv5
I0408 14:45:57.379205 20259 net.cpp:367] relu5 -> conv5 (in-place)
I0408 14:45:57.379686 20259 net.cpp:122] Setting up relu5
I0408 14:45:57.379698 20259 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 14:45:57.379700 20259 net.cpp:137] Memory required for data: 1047050240
I0408 14:45:57.379704 20259 layer_factory.hpp:77] Creating layer pool5
I0408 14:45:57.379711 20259 net.cpp:84] Creating Layer pool5
I0408 14:45:57.379715 20259 net.cpp:406] pool5 <- conv5
I0408 14:45:57.379720 20259 net.cpp:380] pool5 -> pool5
I0408 14:45:57.379757 20259 net.cpp:122] Setting up pool5
I0408 14:45:57.379763 20259 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 14:45:57.379766 20259 net.cpp:137] Memory required for data: 1051768832
I0408 14:45:57.379770 20259 layer_factory.hpp:77] Creating layer fc6
I0408 14:45:57.379779 20259 net.cpp:84] Creating Layer fc6
I0408 14:45:57.379782 20259 net.cpp:406] fc6 <- pool5
I0408 14:45:57.379789 20259 net.cpp:380] fc6 -> fc6
I0408 14:45:57.731482 20259 net.cpp:122] Setting up fc6
I0408 14:45:57.731501 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.731505 20259 net.cpp:137] Memory required for data: 1053865984
I0408 14:45:57.731515 20259 layer_factory.hpp:77] Creating layer relu6
I0408 14:45:57.731524 20259 net.cpp:84] Creating Layer relu6
I0408 14:45:57.731529 20259 net.cpp:406] relu6 <- fc6
I0408 14:45:57.731536 20259 net.cpp:367] relu6 -> fc6 (in-place)
I0408 14:45:57.732173 20259 net.cpp:122] Setting up relu6
I0408 14:45:57.732183 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.732187 20259 net.cpp:137] Memory required for data: 1055963136
I0408 14:45:57.732190 20259 layer_factory.hpp:77] Creating layer drop6
I0408 14:45:57.732196 20259 net.cpp:84] Creating Layer drop6
I0408 14:45:57.732199 20259 net.cpp:406] drop6 <- fc6
I0408 14:45:57.732206 20259 net.cpp:367] drop6 -> fc6 (in-place)
I0408 14:45:57.732234 20259 net.cpp:122] Setting up drop6
I0408 14:45:57.732239 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.732242 20259 net.cpp:137] Memory required for data: 1058060288
I0408 14:45:57.732245 20259 layer_factory.hpp:77] Creating layer fc7
I0408 14:45:57.732252 20259 net.cpp:84] Creating Layer fc7
I0408 14:45:57.732255 20259 net.cpp:406] fc7 <- fc6
I0408 14:45:57.732261 20259 net.cpp:380] fc7 -> fc7
I0408 14:45:57.888497 20259 net.cpp:122] Setting up fc7
I0408 14:45:57.888518 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.888522 20259 net.cpp:137] Memory required for data: 1060157440
I0408 14:45:57.888532 20259 layer_factory.hpp:77] Creating layer relu7
I0408 14:45:57.888541 20259 net.cpp:84] Creating Layer relu7
I0408 14:45:57.888545 20259 net.cpp:406] relu7 <- fc7
I0408 14:45:57.888552 20259 net.cpp:367] relu7 -> fc7 (in-place)
I0408 14:45:57.889185 20259 net.cpp:122] Setting up relu7
I0408 14:45:57.889195 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.889199 20259 net.cpp:137] Memory required for data: 1062254592
I0408 14:45:57.889204 20259 layer_factory.hpp:77] Creating layer drop7
I0408 14:45:57.889209 20259 net.cpp:84] Creating Layer drop7
I0408 14:45:57.889230 20259 net.cpp:406] drop7 <- fc7
I0408 14:45:57.889237 20259 net.cpp:367] drop7 -> fc7 (in-place)
I0408 14:45:57.889261 20259 net.cpp:122] Setting up drop7
I0408 14:45:57.889266 20259 net.cpp:129] Top shape: 128 4096 (524288)
I0408 14:45:57.889269 20259 net.cpp:137] Memory required for data: 1064351744
I0408 14:45:57.889272 20259 layer_factory.hpp:77] Creating layer fc8
I0408 14:45:57.889281 20259 net.cpp:84] Creating Layer fc8
I0408 14:45:57.889284 20259 net.cpp:406] fc8 <- fc7
I0408 14:45:57.889290 20259 net.cpp:380] fc8 -> fc8
I0408 14:45:57.896905 20259 net.cpp:122] Setting up fc8
I0408 14:45:57.896914 20259 net.cpp:129] Top shape: 128 196 (25088)
I0408 14:45:57.896919 20259 net.cpp:137] Memory required for data: 1064452096
I0408 14:45:57.896924 20259 layer_factory.hpp:77] Creating layer loss
I0408 14:45:57.896930 20259 net.cpp:84] Creating Layer loss
I0408 14:45:57.896934 20259 net.cpp:406] loss <- fc8
I0408 14:45:57.896939 20259 net.cpp:406] loss <- label
I0408 14:45:57.896946 20259 net.cpp:380] loss -> loss
I0408 14:45:57.896955 20259 layer_factory.hpp:77] Creating layer loss
I0408 14:45:57.897543 20259 net.cpp:122] Setting up loss
I0408 14:45:57.897552 20259 net.cpp:129] Top shape: (1)
I0408 14:45:57.897555 20259 net.cpp:132]     with loss weight 1
I0408 14:45:57.897572 20259 net.cpp:137] Memory required for data: 1064452100
I0408 14:45:57.897578 20259 net.cpp:198] loss needs backward computation.
I0408 14:45:57.897583 20259 net.cpp:198] fc8 needs backward computation.
I0408 14:45:57.897586 20259 net.cpp:198] drop7 needs backward computation.
I0408 14:45:57.897590 20259 net.cpp:198] relu7 needs backward computation.
I0408 14:45:57.897593 20259 net.cpp:198] fc7 needs backward computation.
I0408 14:45:57.897596 20259 net.cpp:198] drop6 needs backward computation.
I0408 14:45:57.897600 20259 net.cpp:198] relu6 needs backward computation.
I0408 14:45:57.897603 20259 net.cpp:198] fc6 needs backward computation.
I0408 14:45:57.897608 20259 net.cpp:198] pool5 needs backward computation.
I0408 14:45:57.897610 20259 net.cpp:198] relu5 needs backward computation.
I0408 14:45:57.897614 20259 net.cpp:198] conv5 needs backward computation.
I0408 14:45:57.897617 20259 net.cpp:198] relu4 needs backward computation.
I0408 14:45:57.897621 20259 net.cpp:198] conv4 needs backward computation.
I0408 14:45:57.897624 20259 net.cpp:198] relu3 needs backward computation.
I0408 14:45:57.897629 20259 net.cpp:198] conv3 needs backward computation.
I0408 14:45:57.897631 20259 net.cpp:198] pool2 needs backward computation.
I0408 14:45:57.897635 20259 net.cpp:198] norm2 needs backward computation.
I0408 14:45:57.897639 20259 net.cpp:198] relu2 needs backward computation.
I0408 14:45:57.897642 20259 net.cpp:198] conv2 needs backward computation.
I0408 14:45:57.897646 20259 net.cpp:198] pool1 needs backward computation.
I0408 14:45:57.897650 20259 net.cpp:198] norm1 needs backward computation.
I0408 14:45:57.897652 20259 net.cpp:198] relu1 needs backward computation.
I0408 14:45:57.897656 20259 net.cpp:198] conv1 needs backward computation.
I0408 14:45:57.897660 20259 net.cpp:200] train-data does not need backward computation.
I0408 14:45:57.897663 20259 net.cpp:242] This network produces output loss
I0408 14:45:57.897677 20259 net.cpp:255] Network initialization done.
I0408 14:45:57.898813 20259 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 14:45:57.898842 20259 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 14:45:57.898979 20259 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 14:45:57.899075 20259 layer_factory.hpp:77] Creating layer val-data
I0408 14:45:57.910606 20259 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 14:45:57.911365 20259 net.cpp:84] Creating Layer val-data
I0408 14:45:57.911373 20259 net.cpp:380] val-data -> data
I0408 14:45:57.911381 20259 net.cpp:380] val-data -> label
I0408 14:45:57.911388 20259 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 14:45:57.915943 20259 data_layer.cpp:45] output data size: 32,3,227,227
I0408 14:45:57.945870 20259 net.cpp:122] Setting up val-data
I0408 14:45:57.945892 20259 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 14:45:57.945897 20259 net.cpp:129] Top shape: 32 (32)
I0408 14:45:57.945900 20259 net.cpp:137] Memory required for data: 19787264
I0408 14:45:57.945906 20259 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 14:45:57.945919 20259 net.cpp:84] Creating Layer label_val-data_1_split
I0408 14:45:57.945922 20259 net.cpp:406] label_val-data_1_split <- label
I0408 14:45:57.945930 20259 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 14:45:57.945940 20259 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 14:45:57.945987 20259 net.cpp:122] Setting up label_val-data_1_split
I0408 14:45:57.945993 20259 net.cpp:129] Top shape: 32 (32)
I0408 14:45:57.945996 20259 net.cpp:129] Top shape: 32 (32)
I0408 14:45:57.945999 20259 net.cpp:137] Memory required for data: 19787520
I0408 14:45:57.946003 20259 layer_factory.hpp:77] Creating layer conv1
I0408 14:45:57.946015 20259 net.cpp:84] Creating Layer conv1
I0408 14:45:57.946018 20259 net.cpp:406] conv1 <- data
I0408 14:45:57.946024 20259 net.cpp:380] conv1 -> conv1
I0408 14:45:57.948081 20259 net.cpp:122] Setting up conv1
I0408 14:45:57.948091 20259 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 14:45:57.948094 20259 net.cpp:137] Memory required for data: 56958720
I0408 14:45:57.948105 20259 layer_factory.hpp:77] Creating layer relu1
I0408 14:45:57.948112 20259 net.cpp:84] Creating Layer relu1
I0408 14:45:57.948115 20259 net.cpp:406] relu1 <- conv1
I0408 14:45:57.948120 20259 net.cpp:367] relu1 -> conv1 (in-place)
I0408 14:45:57.948410 20259 net.cpp:122] Setting up relu1
I0408 14:45:57.948417 20259 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 14:45:57.948421 20259 net.cpp:137] Memory required for data: 94129920
I0408 14:45:57.948424 20259 layer_factory.hpp:77] Creating layer norm1
I0408 14:45:57.948432 20259 net.cpp:84] Creating Layer norm1
I0408 14:45:57.948436 20259 net.cpp:406] norm1 <- conv1
I0408 14:45:57.948441 20259 net.cpp:380] norm1 -> norm1
I0408 14:45:57.948894 20259 net.cpp:122] Setting up norm1
I0408 14:45:57.948904 20259 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 14:45:57.948907 20259 net.cpp:137] Memory required for data: 131301120
I0408 14:45:57.948910 20259 layer_factory.hpp:77] Creating layer pool1
I0408 14:45:57.948918 20259 net.cpp:84] Creating Layer pool1
I0408 14:45:57.948921 20259 net.cpp:406] pool1 <- norm1
I0408 14:45:57.948926 20259 net.cpp:380] pool1 -> pool1
I0408 14:45:57.948956 20259 net.cpp:122] Setting up pool1
I0408 14:45:57.948961 20259 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 14:45:57.948963 20259 net.cpp:137] Memory required for data: 140259072
I0408 14:45:57.948966 20259 layer_factory.hpp:77] Creating layer conv2
I0408 14:45:57.948974 20259 net.cpp:84] Creating Layer conv2
I0408 14:45:57.948978 20259 net.cpp:406] conv2 <- pool1
I0408 14:45:57.949002 20259 net.cpp:380] conv2 -> conv2
I0408 14:45:57.957722 20259 net.cpp:122] Setting up conv2
I0408 14:45:57.957736 20259 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 14:45:57.957741 20259 net.cpp:137] Memory required for data: 164146944
I0408 14:45:57.957749 20259 layer_factory.hpp:77] Creating layer relu2
I0408 14:45:57.957756 20259 net.cpp:84] Creating Layer relu2
I0408 14:45:57.957759 20259 net.cpp:406] relu2 <- conv2
I0408 14:45:57.957765 20259 net.cpp:367] relu2 -> conv2 (in-place)
I0408 14:45:57.958281 20259 net.cpp:122] Setting up relu2
I0408 14:45:57.958290 20259 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 14:45:57.958294 20259 net.cpp:137] Memory required for data: 188034816
I0408 14:45:57.958298 20259 layer_factory.hpp:77] Creating layer norm2
I0408 14:45:57.958307 20259 net.cpp:84] Creating Layer norm2
I0408 14:45:57.958310 20259 net.cpp:406] norm2 <- conv2
I0408 14:45:57.958317 20259 net.cpp:380] norm2 -> norm2
I0408 14:45:57.958835 20259 net.cpp:122] Setting up norm2
I0408 14:45:57.958844 20259 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 14:45:57.958848 20259 net.cpp:137] Memory required for data: 211922688
I0408 14:45:57.958853 20259 layer_factory.hpp:77] Creating layer pool2
I0408 14:45:57.958858 20259 net.cpp:84] Creating Layer pool2
I0408 14:45:57.958863 20259 net.cpp:406] pool2 <- norm2
I0408 14:45:57.958869 20259 net.cpp:380] pool2 -> pool2
I0408 14:45:57.958899 20259 net.cpp:122] Setting up pool2
I0408 14:45:57.958904 20259 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 14:45:57.958909 20259 net.cpp:137] Memory required for data: 217460480
I0408 14:45:57.958911 20259 layer_factory.hpp:77] Creating layer conv3
I0408 14:45:57.958920 20259 net.cpp:84] Creating Layer conv3
I0408 14:45:57.958923 20259 net.cpp:406] conv3 <- pool2
I0408 14:45:57.958930 20259 net.cpp:380] conv3 -> conv3
I0408 14:45:57.969745 20259 net.cpp:122] Setting up conv3
I0408 14:45:57.969758 20259 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 14:45:57.969763 20259 net.cpp:137] Memory required for data: 225767168
I0408 14:45:57.969772 20259 layer_factory.hpp:77] Creating layer relu3
I0408 14:45:57.969779 20259 net.cpp:84] Creating Layer relu3
I0408 14:45:57.969784 20259 net.cpp:406] relu3 <- conv3
I0408 14:45:57.969789 20259 net.cpp:367] relu3 -> conv3 (in-place)
I0408 14:45:57.970307 20259 net.cpp:122] Setting up relu3
I0408 14:45:57.970316 20259 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 14:45:57.970320 20259 net.cpp:137] Memory required for data: 234073856
I0408 14:45:57.970324 20259 layer_factory.hpp:77] Creating layer conv4
I0408 14:45:57.970335 20259 net.cpp:84] Creating Layer conv4
I0408 14:45:57.970338 20259 net.cpp:406] conv4 <- conv3
I0408 14:45:57.970345 20259 net.cpp:380] conv4 -> conv4
I0408 14:45:57.981468 20259 net.cpp:122] Setting up conv4
I0408 14:45:57.981479 20259 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 14:45:57.981483 20259 net.cpp:137] Memory required for data: 242380544
I0408 14:45:57.981492 20259 layer_factory.hpp:77] Creating layer relu4
I0408 14:45:57.981498 20259 net.cpp:84] Creating Layer relu4
I0408 14:45:57.981503 20259 net.cpp:406] relu4 <- conv4
I0408 14:45:57.981509 20259 net.cpp:367] relu4 -> conv4 (in-place)
I0408 14:45:57.981850 20259 net.cpp:122] Setting up relu4
I0408 14:45:57.981859 20259 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 14:45:57.981864 20259 net.cpp:137] Memory required for data: 250687232
I0408 14:45:57.981868 20259 layer_factory.hpp:77] Creating layer conv5
I0408 14:45:57.981878 20259 net.cpp:84] Creating Layer conv5
I0408 14:45:57.981881 20259 net.cpp:406] conv5 <- conv4
I0408 14:45:57.981887 20259 net.cpp:380] conv5 -> conv5
I0408 14:45:57.997766 20259 net.cpp:122] Setting up conv5
I0408 14:45:57.997778 20259 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 14:45:57.997782 20259 net.cpp:137] Memory required for data: 256225024
I0408 14:45:57.997793 20259 layer_factory.hpp:77] Creating layer relu5
I0408 14:45:57.997800 20259 net.cpp:84] Creating Layer relu5
I0408 14:45:57.997803 20259 net.cpp:406] relu5 <- conv5
I0408 14:45:57.997825 20259 net.cpp:367] relu5 -> conv5 (in-place)
I0408 14:45:57.998327 20259 net.cpp:122] Setting up relu5
I0408 14:45:57.998337 20259 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 14:45:57.998339 20259 net.cpp:137] Memory required for data: 261762816
I0408 14:45:57.998343 20259 layer_factory.hpp:77] Creating layer pool5
I0408 14:45:57.998354 20259 net.cpp:84] Creating Layer pool5
I0408 14:45:57.998358 20259 net.cpp:406] pool5 <- conv5
I0408 14:45:57.998363 20259 net.cpp:380] pool5 -> pool5
I0408 14:45:57.998404 20259 net.cpp:122] Setting up pool5
I0408 14:45:57.998410 20259 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 14:45:57.998414 20259 net.cpp:137] Memory required for data: 262942464
I0408 14:45:57.998416 20259 layer_factory.hpp:77] Creating layer fc6
I0408 14:45:57.998425 20259 net.cpp:84] Creating Layer fc6
I0408 14:45:57.998428 20259 net.cpp:406] fc6 <- pool5
I0408 14:45:57.998433 20259 net.cpp:380] fc6 -> fc6
I0408 14:45:58.349989 20259 net.cpp:122] Setting up fc6
I0408 14:45:58.350009 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.350013 20259 net.cpp:137] Memory required for data: 263466752
I0408 14:45:58.350023 20259 layer_factory.hpp:77] Creating layer relu6
I0408 14:45:58.350031 20259 net.cpp:84] Creating Layer relu6
I0408 14:45:58.350035 20259 net.cpp:406] relu6 <- fc6
I0408 14:45:58.350044 20259 net.cpp:367] relu6 -> fc6 (in-place)
I0408 14:45:58.350894 20259 net.cpp:122] Setting up relu6
I0408 14:45:58.350903 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.350908 20259 net.cpp:137] Memory required for data: 263991040
I0408 14:45:58.350910 20259 layer_factory.hpp:77] Creating layer drop6
I0408 14:45:58.350917 20259 net.cpp:84] Creating Layer drop6
I0408 14:45:58.350920 20259 net.cpp:406] drop6 <- fc6
I0408 14:45:58.350927 20259 net.cpp:367] drop6 -> fc6 (in-place)
I0408 14:45:58.350951 20259 net.cpp:122] Setting up drop6
I0408 14:45:58.350956 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.350960 20259 net.cpp:137] Memory required for data: 264515328
I0408 14:45:58.350962 20259 layer_factory.hpp:77] Creating layer fc7
I0408 14:45:58.350972 20259 net.cpp:84] Creating Layer fc7
I0408 14:45:58.350975 20259 net.cpp:406] fc7 <- fc6
I0408 14:45:58.350982 20259 net.cpp:380] fc7 -> fc7
I0408 14:45:58.507237 20259 net.cpp:122] Setting up fc7
I0408 14:45:58.507261 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.507264 20259 net.cpp:137] Memory required for data: 265039616
I0408 14:45:58.507273 20259 layer_factory.hpp:77] Creating layer relu7
I0408 14:45:58.507283 20259 net.cpp:84] Creating Layer relu7
I0408 14:45:58.507287 20259 net.cpp:406] relu7 <- fc7
I0408 14:45:58.507295 20259 net.cpp:367] relu7 -> fc7 (in-place)
I0408 14:45:58.507721 20259 net.cpp:122] Setting up relu7
I0408 14:45:58.507730 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.507735 20259 net.cpp:137] Memory required for data: 265563904
I0408 14:45:58.507737 20259 layer_factory.hpp:77] Creating layer drop7
I0408 14:45:58.507745 20259 net.cpp:84] Creating Layer drop7
I0408 14:45:58.507747 20259 net.cpp:406] drop7 <- fc7
I0408 14:45:58.507753 20259 net.cpp:367] drop7 -> fc7 (in-place)
I0408 14:45:58.507776 20259 net.cpp:122] Setting up drop7
I0408 14:45:58.507781 20259 net.cpp:129] Top shape: 32 4096 (131072)
I0408 14:45:58.507784 20259 net.cpp:137] Memory required for data: 266088192
I0408 14:45:58.507787 20259 layer_factory.hpp:77] Creating layer fc8
I0408 14:45:58.507797 20259 net.cpp:84] Creating Layer fc8
I0408 14:45:58.507799 20259 net.cpp:406] fc8 <- fc7
I0408 14:45:58.507805 20259 net.cpp:380] fc8 -> fc8
I0408 14:45:58.515491 20259 net.cpp:122] Setting up fc8
I0408 14:45:58.515501 20259 net.cpp:129] Top shape: 32 196 (6272)
I0408 14:45:58.515504 20259 net.cpp:137] Memory required for data: 266113280
I0408 14:45:58.515511 20259 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 14:45:58.515518 20259 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 14:45:58.515522 20259 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 14:45:58.515544 20259 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 14:45:58.515552 20259 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 14:45:58.515584 20259 net.cpp:122] Setting up fc8_fc8_0_split
I0408 14:45:58.515589 20259 net.cpp:129] Top shape: 32 196 (6272)
I0408 14:45:58.515592 20259 net.cpp:129] Top shape: 32 196 (6272)
I0408 14:45:58.515595 20259 net.cpp:137] Memory required for data: 266163456
I0408 14:45:58.515599 20259 layer_factory.hpp:77] Creating layer accuracy
I0408 14:45:58.515605 20259 net.cpp:84] Creating Layer accuracy
I0408 14:45:58.515609 20259 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 14:45:58.515614 20259 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 14:45:58.515619 20259 net.cpp:380] accuracy -> accuracy
I0408 14:45:58.515625 20259 net.cpp:122] Setting up accuracy
I0408 14:45:58.515630 20259 net.cpp:129] Top shape: (1)
I0408 14:45:58.515632 20259 net.cpp:137] Memory required for data: 266163460
I0408 14:45:58.515635 20259 layer_factory.hpp:77] Creating layer loss
I0408 14:45:58.515642 20259 net.cpp:84] Creating Layer loss
I0408 14:45:58.515645 20259 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 14:45:58.515650 20259 net.cpp:406] loss <- label_val-data_1_split_1
I0408 14:45:58.515655 20259 net.cpp:380] loss -> loss
I0408 14:45:58.515661 20259 layer_factory.hpp:77] Creating layer loss
I0408 14:45:58.516252 20259 net.cpp:122] Setting up loss
I0408 14:45:58.516261 20259 net.cpp:129] Top shape: (1)
I0408 14:45:58.516264 20259 net.cpp:132]     with loss weight 1
I0408 14:45:58.516274 20259 net.cpp:137] Memory required for data: 266163464
I0408 14:45:58.516278 20259 net.cpp:198] loss needs backward computation.
I0408 14:45:58.516283 20259 net.cpp:200] accuracy does not need backward computation.
I0408 14:45:58.516288 20259 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 14:45:58.516290 20259 net.cpp:198] fc8 needs backward computation.
I0408 14:45:58.516294 20259 net.cpp:198] drop7 needs backward computation.
I0408 14:45:58.516297 20259 net.cpp:198] relu7 needs backward computation.
I0408 14:45:58.516300 20259 net.cpp:198] fc7 needs backward computation.
I0408 14:45:58.516305 20259 net.cpp:198] drop6 needs backward computation.
I0408 14:45:58.516309 20259 net.cpp:198] relu6 needs backward computation.
I0408 14:45:58.516312 20259 net.cpp:198] fc6 needs backward computation.
I0408 14:45:58.516315 20259 net.cpp:198] pool5 needs backward computation.
I0408 14:45:58.516319 20259 net.cpp:198] relu5 needs backward computation.
I0408 14:45:58.516322 20259 net.cpp:198] conv5 needs backward computation.
I0408 14:45:58.516326 20259 net.cpp:198] relu4 needs backward computation.
I0408 14:45:58.516330 20259 net.cpp:198] conv4 needs backward computation.
I0408 14:45:58.516333 20259 net.cpp:198] relu3 needs backward computation.
I0408 14:45:58.516336 20259 net.cpp:198] conv3 needs backward computation.
I0408 14:45:58.516340 20259 net.cpp:198] pool2 needs backward computation.
I0408 14:45:58.516343 20259 net.cpp:198] norm2 needs backward computation.
I0408 14:45:58.516346 20259 net.cpp:198] relu2 needs backward computation.
I0408 14:45:58.516350 20259 net.cpp:198] conv2 needs backward computation.
I0408 14:45:58.516353 20259 net.cpp:198] pool1 needs backward computation.
I0408 14:45:58.516356 20259 net.cpp:198] norm1 needs backward computation.
I0408 14:45:58.516360 20259 net.cpp:198] relu1 needs backward computation.
I0408 14:45:58.516363 20259 net.cpp:198] conv1 needs backward computation.
I0408 14:45:58.516367 20259 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 14:45:58.516371 20259 net.cpp:200] val-data does not need backward computation.
I0408 14:45:58.516376 20259 net.cpp:242] This network produces output accuracy
I0408 14:45:58.516379 20259 net.cpp:242] This network produces output loss
I0408 14:45:58.516394 20259 net.cpp:255] Network initialization done.
I0408 14:45:58.516464 20259 solver.cpp:56] Solver scaffolding done.
I0408 14:45:58.516886 20259 caffe.cpp:248] Starting Optimization
I0408 14:45:58.516894 20259 solver.cpp:272] Solving
I0408 14:45:58.516906 20259 solver.cpp:273] Learning Rate Policy: exp
I0408 14:45:58.518224 20259 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 14:45:58.518232 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:45:58.596318 20259 blocking_queue.cpp:49] Waiting for data
I0408 14:46:02.891099 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:46:02.935731 20259 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0408 14:46:02.935779 20259 solver.cpp:397]     Test net output #1: loss = 5.28105 (* 1 = 5.28105 loss)
I0408 14:46:03.029799 20259 solver.cpp:218] Iteration 0 (1.17844e+37 iter/s, 4.51271s/12 iters), loss = 5.29687
I0408 14:46:03.031318 20259 solver.cpp:237]     Train net output #0: loss = 5.29687 (* 1 = 5.29687 loss)
I0408 14:46:03.031342 20259 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0408 14:46:07.205482 20259 solver.cpp:218] Iteration 12 (2.87493 iter/s, 4.17401s/12 iters), loss = 5.28522
I0408 14:46:07.205524 20259 solver.cpp:237]     Train net output #0: loss = 5.28522 (* 1 = 5.28522 loss)
I0408 14:46:07.205535 20259 sgd_solver.cpp:105] Iteration 12, lr = 0.00987681
I0408 14:46:12.626185 20259 solver.cpp:218] Iteration 24 (2.21383 iter/s, 5.42046s/12 iters), loss = 5.29181
I0408 14:46:12.626232 20259 solver.cpp:237]     Train net output #0: loss = 5.29181 (* 1 = 5.29181 loss)
I0408 14:46:12.626243 20259 sgd_solver.cpp:105] Iteration 24, lr = 0.00975514
I0408 14:46:17.672689 20259 solver.cpp:218] Iteration 36 (2.37799 iter/s, 5.04628s/12 iters), loss = 5.29581
I0408 14:46:17.672734 20259 solver.cpp:237]     Train net output #0: loss = 5.29581 (* 1 = 5.29581 loss)
I0408 14:46:17.672746 20259 sgd_solver.cpp:105] Iteration 36, lr = 0.00963497
I0408 14:46:22.688122 20259 solver.cpp:218] Iteration 48 (2.39272 iter/s, 5.01521s/12 iters), loss = 5.31134
I0408 14:46:22.688165 20259 solver.cpp:237]     Train net output #0: loss = 5.31134 (* 1 = 5.31134 loss)
I0408 14:46:22.688176 20259 sgd_solver.cpp:105] Iteration 48, lr = 0.00951628
I0408 14:46:27.637338 20259 solver.cpp:218] Iteration 60 (2.42473 iter/s, 4.949s/12 iters), loss = 5.30054
I0408 14:46:27.637517 20259 solver.cpp:237]     Train net output #0: loss = 5.30054 (* 1 = 5.30054 loss)
I0408 14:46:27.637531 20259 sgd_solver.cpp:105] Iteration 60, lr = 0.00939905
I0408 14:46:32.609807 20259 solver.cpp:218] Iteration 72 (2.41346 iter/s, 4.97212s/12 iters), loss = 5.30169
I0408 14:46:32.609849 20259 solver.cpp:237]     Train net output #0: loss = 5.30169 (* 1 = 5.30169 loss)
I0408 14:46:32.609859 20259 sgd_solver.cpp:105] Iteration 72, lr = 0.00928326
I0408 14:46:37.563151 20259 solver.cpp:218] Iteration 84 (2.42271 iter/s, 4.95312s/12 iters), loss = 5.30739
I0408 14:46:37.563192 20259 solver.cpp:237]     Train net output #0: loss = 5.30739 (* 1 = 5.30739 loss)
I0408 14:46:37.563203 20259 sgd_solver.cpp:105] Iteration 84, lr = 0.0091689
I0408 14:46:42.479884 20259 solver.cpp:218] Iteration 96 (2.44075 iter/s, 4.91652s/12 iters), loss = 5.31979
I0408 14:46:42.479928 20259 solver.cpp:237]     Train net output #0: loss = 5.31979 (* 1 = 5.31979 loss)
I0408 14:46:42.479940 20259 sgd_solver.cpp:105] Iteration 96, lr = 0.00905595
I0408 14:46:44.214381 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:46:44.525096 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 14:46:48.815670 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 14:46:56.254591 20259 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 14:46:56.254623 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:47:00.621201 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:47:00.697890 20259 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 14:47:00.697933 20259 solver.cpp:397]     Test net output #1: loss = 5.28917 (* 1 = 5.28917 loss)
I0408 14:47:02.669950 20259 solver.cpp:218] Iteration 108 (0.594374 iter/s, 20.1893s/12 iters), loss = 5.31453
I0408 14:47:02.669997 20259 solver.cpp:237]     Train net output #0: loss = 5.31453 (* 1 = 5.31453 loss)
I0408 14:47:02.670008 20259 sgd_solver.cpp:105] Iteration 108, lr = 0.00894439
I0408 14:47:08.102344 20259 solver.cpp:218] Iteration 120 (2.20907 iter/s, 5.43214s/12 iters), loss = 5.27752
I0408 14:47:08.102394 20259 solver.cpp:237]     Train net output #0: loss = 5.27752 (* 1 = 5.27752 loss)
I0408 14:47:08.102406 20259 sgd_solver.cpp:105] Iteration 120, lr = 0.00883421
I0408 14:47:13.384189 20259 solver.cpp:218] Iteration 132 (2.27204 iter/s, 5.28161s/12 iters), loss = 5.24683
I0408 14:47:13.384223 20259 solver.cpp:237]     Train net output #0: loss = 5.24683 (* 1 = 5.24683 loss)
I0408 14:47:13.384232 20259 sgd_solver.cpp:105] Iteration 132, lr = 0.00872538
I0408 14:47:18.408267 20259 solver.cpp:218] Iteration 144 (2.3886 iter/s, 5.02386s/12 iters), loss = 5.30965
I0408 14:47:18.408300 20259 solver.cpp:237]     Train net output #0: loss = 5.30965 (* 1 = 5.30965 loss)
I0408 14:47:18.408308 20259 sgd_solver.cpp:105] Iteration 144, lr = 0.0086179
I0408 14:47:23.412564 20259 solver.cpp:218] Iteration 156 (2.39805 iter/s, 5.00408s/12 iters), loss = 5.25927
I0408 14:47:23.412607 20259 solver.cpp:237]     Train net output #0: loss = 5.25927 (* 1 = 5.25927 loss)
I0408 14:47:23.412618 20259 sgd_solver.cpp:105] Iteration 156, lr = 0.00851173
I0408 14:47:28.376067 20259 solver.cpp:218] Iteration 168 (2.41776 iter/s, 4.96328s/12 iters), loss = 5.25101
I0408 14:47:28.376111 20259 solver.cpp:237]     Train net output #0: loss = 5.25101 (* 1 = 5.25101 loss)
I0408 14:47:28.376122 20259 sgd_solver.cpp:105] Iteration 168, lr = 0.00840688
I0408 14:47:33.380045 20259 solver.cpp:218] Iteration 180 (2.3982 iter/s, 5.00375s/12 iters), loss = 5.23145
I0408 14:47:33.380291 20259 solver.cpp:237]     Train net output #0: loss = 5.23145 (* 1 = 5.23145 loss)
I0408 14:47:33.380306 20259 sgd_solver.cpp:105] Iteration 180, lr = 0.00830332
I0408 14:47:38.365218 20259 solver.cpp:218] Iteration 192 (2.40734 iter/s, 4.98475s/12 iters), loss = 5.24787
I0408 14:47:38.365259 20259 solver.cpp:237]     Train net output #0: loss = 5.24787 (* 1 = 5.24787 loss)
I0408 14:47:38.365272 20259 sgd_solver.cpp:105] Iteration 192, lr = 0.00820103
I0408 14:47:42.147147 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:47:42.815078 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 14:47:46.343703 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 14:47:49.153228 20259 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 14:47:49.153259 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:47:53.475123 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:47:53.597735 20259 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 14:47:53.597780 20259 solver.cpp:397]     Test net output #1: loss = 5.20013 (* 1 = 5.20013 loss)
I0408 14:47:53.686566 20259 solver.cpp:218] Iteration 204 (0.78325 iter/s, 15.3208s/12 iters), loss = 5.12292
I0408 14:47:53.686602 20259 solver.cpp:237]     Train net output #0: loss = 5.12292 (* 1 = 5.12292 loss)
I0408 14:47:53.686612 20259 sgd_solver.cpp:105] Iteration 204, lr = 0.0081
I0408 14:47:57.945513 20259 solver.cpp:218] Iteration 216 (2.81773 iter/s, 4.25875s/12 iters), loss = 5.17357
I0408 14:47:57.945557 20259 solver.cpp:237]     Train net output #0: loss = 5.17357 (* 1 = 5.17357 loss)
I0408 14:47:57.945569 20259 sgd_solver.cpp:105] Iteration 216, lr = 0.00800022
I0408 14:48:02.891819 20259 solver.cpp:218] Iteration 228 (2.42616 iter/s, 4.94608s/12 iters), loss = 5.2046
I0408 14:48:02.891862 20259 solver.cpp:237]     Train net output #0: loss = 5.2046 (* 1 = 5.2046 loss)
I0408 14:48:02.891875 20259 sgd_solver.cpp:105] Iteration 228, lr = 0.00790166
I0408 14:48:07.746657 20259 solver.cpp:218] Iteration 240 (2.47187 iter/s, 4.85462s/12 iters), loss = 5.25696
I0408 14:48:07.746803 20259 solver.cpp:237]     Train net output #0: loss = 5.25696 (* 1 = 5.25696 loss)
I0408 14:48:07.746816 20259 sgd_solver.cpp:105] Iteration 240, lr = 0.00780433
I0408 14:48:12.762655 20259 solver.cpp:218] Iteration 252 (2.3925 iter/s, 5.01567s/12 iters), loss = 5.1629
I0408 14:48:12.762697 20259 solver.cpp:237]     Train net output #0: loss = 5.1629 (* 1 = 5.1629 loss)
I0408 14:48:12.762709 20259 sgd_solver.cpp:105] Iteration 252, lr = 0.00770819
I0408 14:48:17.705267 20259 solver.cpp:218] Iteration 264 (2.42797 iter/s, 4.94239s/12 iters), loss = 5.26716
I0408 14:48:17.705310 20259 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0408 14:48:17.705322 20259 sgd_solver.cpp:105] Iteration 264, lr = 0.00761323
I0408 14:48:22.629918 20259 solver.cpp:218] Iteration 276 (2.43683 iter/s, 4.92443s/12 iters), loss = 5.2183
I0408 14:48:22.629969 20259 solver.cpp:237]     Train net output #0: loss = 5.2183 (* 1 = 5.2183 loss)
I0408 14:48:22.629981 20259 sgd_solver.cpp:105] Iteration 276, lr = 0.00751944
I0408 14:48:27.509900 20259 solver.cpp:218] Iteration 288 (2.45914 iter/s, 4.87976s/12 iters), loss = 5.04446
I0408 14:48:27.509943 20259 solver.cpp:237]     Train net output #0: loss = 5.04446 (* 1 = 5.04446 loss)
I0408 14:48:27.509953 20259 sgd_solver.cpp:105] Iteration 288, lr = 0.00742681
I0408 14:48:32.483464 20259 solver.cpp:218] Iteration 300 (2.41286 iter/s, 4.97334s/12 iters), loss = 5.17342
I0408 14:48:32.483508 20259 solver.cpp:237]     Train net output #0: loss = 5.17342 (* 1 = 5.17342 loss)
I0408 14:48:32.483520 20259 sgd_solver.cpp:105] Iteration 300, lr = 0.00733532
I0408 14:48:33.486953 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:48:34.527897 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 14:48:38.025527 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 14:48:40.610746 20259 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 14:48:40.610777 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:48:44.869441 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:48:45.026620 20259 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0408 14:48:45.026667 20259 solver.cpp:397]     Test net output #1: loss = 5.15192 (* 1 = 5.15192 loss)
I0408 14:48:47.020915 20259 solver.cpp:218] Iteration 312 (0.825485 iter/s, 14.5369s/12 iters), loss = 5.1343
I0408 14:48:47.020961 20259 solver.cpp:237]     Train net output #0: loss = 5.1343 (* 1 = 5.1343 loss)
I0408 14:48:47.020973 20259 sgd_solver.cpp:105] Iteration 312, lr = 0.00724496
I0408 14:48:52.242085 20259 solver.cpp:218] Iteration 324 (2.29844 iter/s, 5.22094s/12 iters), loss = 5.19956
I0408 14:48:52.242131 20259 solver.cpp:237]     Train net output #0: loss = 5.19956 (* 1 = 5.19956 loss)
I0408 14:48:52.242143 20259 sgd_solver.cpp:105] Iteration 324, lr = 0.00715571
I0408 14:48:57.473065 20259 solver.cpp:218] Iteration 336 (2.29413 iter/s, 5.23075s/12 iters), loss = 5.15021
I0408 14:48:57.473109 20259 solver.cpp:237]     Train net output #0: loss = 5.15021 (* 1 = 5.15021 loss)
I0408 14:48:57.473119 20259 sgd_solver.cpp:105] Iteration 336, lr = 0.00706756
I0408 14:49:02.520428 20259 solver.cpp:218] Iteration 348 (2.37759 iter/s, 5.04714s/12 iters), loss = 5.10575
I0408 14:49:02.520474 20259 solver.cpp:237]     Train net output #0: loss = 5.10575 (* 1 = 5.10575 loss)
I0408 14:49:02.520486 20259 sgd_solver.cpp:105] Iteration 348, lr = 0.0069805
I0408 14:49:07.554611 20259 solver.cpp:218] Iteration 360 (2.38381 iter/s, 5.03395s/12 iters), loss = 5.15994
I0408 14:49:07.554659 20259 solver.cpp:237]     Train net output #0: loss = 5.15994 (* 1 = 5.15994 loss)
I0408 14:49:07.554673 20259 sgd_solver.cpp:105] Iteration 360, lr = 0.00689451
I0408 14:49:12.620097 20259 solver.cpp:218] Iteration 372 (2.36908 iter/s, 5.06526s/12 iters), loss = 5.09078
I0408 14:49:12.620258 20259 solver.cpp:237]     Train net output #0: loss = 5.09078 (* 1 = 5.09078 loss)
I0408 14:49:12.620272 20259 sgd_solver.cpp:105] Iteration 372, lr = 0.00680957
I0408 14:49:17.639410 20259 solver.cpp:218] Iteration 384 (2.39092 iter/s, 5.01898s/12 iters), loss = 5.11732
I0408 14:49:17.639453 20259 solver.cpp:237]     Train net output #0: loss = 5.11732 (* 1 = 5.11732 loss)
I0408 14:49:17.639465 20259 sgd_solver.cpp:105] Iteration 384, lr = 0.00672569
I0408 14:49:22.606729 20259 solver.cpp:218] Iteration 396 (2.4159 iter/s, 4.9671s/12 iters), loss = 5.06889
I0408 14:49:22.606773 20259 solver.cpp:237]     Train net output #0: loss = 5.06889 (* 1 = 5.06889 loss)
I0408 14:49:22.606786 20259 sgd_solver.cpp:105] Iteration 396, lr = 0.00664283
I0408 14:49:25.730962 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:49:27.122496 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 14:49:31.189007 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 14:49:36.088632 20259 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 14:49:36.088662 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:49:40.296902 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:49:40.499719 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0408 14:49:40.499766 20259 solver.cpp:397]     Test net output #1: loss = 5.09544 (* 1 = 5.09544 loss)
I0408 14:49:40.588279 20259 solver.cpp:218] Iteration 408 (0.667375 iter/s, 17.9809s/12 iters), loss = 5.15849
I0408 14:49:40.588320 20259 solver.cpp:237]     Train net output #0: loss = 5.15849 (* 1 = 5.15849 loss)
I0408 14:49:40.588331 20259 sgd_solver.cpp:105] Iteration 408, lr = 0.006561
I0408 14:49:44.946784 20259 solver.cpp:218] Iteration 420 (2.75336 iter/s, 4.35831s/12 iters), loss = 5.18052
I0408 14:49:44.946890 20259 solver.cpp:237]     Train net output #0: loss = 5.18052 (* 1 = 5.18052 loss)
I0408 14:49:44.946903 20259 sgd_solver.cpp:105] Iteration 420, lr = 0.00648018
I0408 14:49:49.938585 20259 solver.cpp:218] Iteration 432 (2.40408 iter/s, 4.99152s/12 iters), loss = 5.09117
I0408 14:49:49.938630 20259 solver.cpp:237]     Train net output #0: loss = 5.09117 (* 1 = 5.09117 loss)
I0408 14:49:49.938642 20259 sgd_solver.cpp:105] Iteration 432, lr = 0.00640035
I0408 14:49:54.935501 20259 solver.cpp:218] Iteration 444 (2.40159 iter/s, 4.99669s/12 iters), loss = 5.06978
I0408 14:49:54.935547 20259 solver.cpp:237]     Train net output #0: loss = 5.06978 (* 1 = 5.06978 loss)
I0408 14:49:54.935559 20259 sgd_solver.cpp:105] Iteration 444, lr = 0.00632151
I0408 14:49:59.881218 20259 solver.cpp:218] Iteration 456 (2.42645 iter/s, 4.9455s/12 iters), loss = 5.11675
I0408 14:49:59.881263 20259 solver.cpp:237]     Train net output #0: loss = 5.11675 (* 1 = 5.11675 loss)
I0408 14:49:59.881274 20259 sgd_solver.cpp:105] Iteration 456, lr = 0.00624363
I0408 14:50:04.786541 20259 solver.cpp:218] Iteration 468 (2.44643 iter/s, 4.90511s/12 iters), loss = 5.11573
I0408 14:50:04.786588 20259 solver.cpp:237]     Train net output #0: loss = 5.11573 (* 1 = 5.11573 loss)
I0408 14:50:04.786599 20259 sgd_solver.cpp:105] Iteration 468, lr = 0.00616672
I0408 14:50:09.706053 20259 solver.cpp:218] Iteration 480 (2.43937 iter/s, 4.9193s/12 iters), loss = 5.02561
I0408 14:50:09.706099 20259 solver.cpp:237]     Train net output #0: loss = 5.02561 (* 1 = 5.02561 loss)
I0408 14:50:09.706111 20259 sgd_solver.cpp:105] Iteration 480, lr = 0.00609075
I0408 14:50:14.616655 20259 solver.cpp:218] Iteration 492 (2.4438 iter/s, 4.91038s/12 iters), loss = 5.07615
I0408 14:50:14.616698 20259 solver.cpp:237]     Train net output #0: loss = 5.07615 (* 1 = 5.07615 loss)
I0408 14:50:14.616708 20259 sgd_solver.cpp:105] Iteration 492, lr = 0.00601572
I0408 14:50:19.531373 20259 solver.cpp:218] Iteration 504 (2.44175 iter/s, 4.9145s/12 iters), loss = 5.09617
I0408 14:50:19.531486 20259 solver.cpp:237]     Train net output #0: loss = 5.09617 (* 1 = 5.09617 loss)
I0408 14:50:19.531497 20259 sgd_solver.cpp:105] Iteration 504, lr = 0.00594161
I0408 14:50:19.778280 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:50:21.526355 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 14:50:25.533151 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 14:50:31.876058 20259 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 14:50:31.876091 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:50:36.098526 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:50:36.335665 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0408 14:50:36.335711 20259 solver.cpp:397]     Test net output #1: loss = 5.04479 (* 1 = 5.04479 loss)
I0408 14:50:38.317932 20259 solver.cpp:218] Iteration 516 (0.63878 iter/s, 18.7858s/12 iters), loss = 4.97861
I0408 14:50:38.317994 20259 solver.cpp:237]     Train net output #0: loss = 4.97861 (* 1 = 4.97861 loss)
I0408 14:50:38.318007 20259 sgd_solver.cpp:105] Iteration 516, lr = 0.00586842
I0408 14:50:43.357089 20259 solver.cpp:218] Iteration 528 (2.38146 iter/s, 5.03892s/12 iters), loss = 5.07809
I0408 14:50:43.357126 20259 solver.cpp:237]     Train net output #0: loss = 5.07809 (* 1 = 5.07809 loss)
I0408 14:50:43.357136 20259 sgd_solver.cpp:105] Iteration 528, lr = 0.00579613
I0408 14:50:48.371644 20259 solver.cpp:218] Iteration 540 (2.39314 iter/s, 5.01434s/12 iters), loss = 4.98648
I0408 14:50:48.371687 20259 solver.cpp:237]     Train net output #0: loss = 4.98648 (* 1 = 4.98648 loss)
I0408 14:50:48.371698 20259 sgd_solver.cpp:105] Iteration 540, lr = 0.00572473
I0408 14:50:53.378374 20259 solver.cpp:218] Iteration 552 (2.39688 iter/s, 5.00651s/12 iters), loss = 5.09692
I0408 14:50:53.378520 20259 solver.cpp:237]     Train net output #0: loss = 5.09692 (* 1 = 5.09692 loss)
I0408 14:50:53.378533 20259 sgd_solver.cpp:105] Iteration 552, lr = 0.0056542
I0408 14:50:58.366642 20259 solver.cpp:218] Iteration 564 (2.4058 iter/s, 4.98795s/12 iters), loss = 5.02079
I0408 14:50:58.366688 20259 solver.cpp:237]     Train net output #0: loss = 5.02079 (* 1 = 5.02079 loss)
I0408 14:50:58.366699 20259 sgd_solver.cpp:105] Iteration 564, lr = 0.00558455
I0408 14:51:03.244710 20259 solver.cpp:218] Iteration 576 (2.4601 iter/s, 4.87785s/12 iters), loss = 5.05422
I0408 14:51:03.244765 20259 solver.cpp:237]     Train net output #0: loss = 5.05422 (* 1 = 5.05422 loss)
I0408 14:51:03.244781 20259 sgd_solver.cpp:105] Iteration 576, lr = 0.00551576
I0408 14:51:08.177812 20259 solver.cpp:218] Iteration 588 (2.43266 iter/s, 4.93287s/12 iters), loss = 4.95669
I0408 14:51:08.177856 20259 solver.cpp:237]     Train net output #0: loss = 4.95669 (* 1 = 4.95669 loss)
I0408 14:51:08.177868 20259 sgd_solver.cpp:105] Iteration 588, lr = 0.00544781
I0408 14:51:13.141171 20259 solver.cpp:218] Iteration 600 (2.41782 iter/s, 4.96314s/12 iters), loss = 5.01581
I0408 14:51:13.141216 20259 solver.cpp:237]     Train net output #0: loss = 5.01581 (* 1 = 5.01581 loss)
I0408 14:51:13.141228 20259 sgd_solver.cpp:105] Iteration 600, lr = 0.0053807
I0408 14:51:15.475916 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:51:17.651005 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 14:51:21.627491 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 14:51:23.966048 20259 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 14:51:23.978013 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:51:28.173923 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:51:28.458940 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0408 14:51:28.458988 20259 solver.cpp:397]     Test net output #1: loss = 4.9928 (* 1 = 4.9928 loss)
I0408 14:51:28.548820 20259 solver.cpp:218] Iteration 612 (0.778862 iter/s, 15.4071s/12 iters), loss = 4.98769
I0408 14:51:28.548868 20259 solver.cpp:237]     Train net output #0: loss = 4.98769 (* 1 = 4.98769 loss)
I0408 14:51:28.548879 20259 sgd_solver.cpp:105] Iteration 612, lr = 0.00531441
I0408 14:51:33.083442 20259 solver.cpp:218] Iteration 624 (2.64643 iter/s, 4.53441s/12 iters), loss = 4.97859
I0408 14:51:33.083498 20259 solver.cpp:237]     Train net output #0: loss = 4.97859 (* 1 = 4.97859 loss)
I0408 14:51:33.083511 20259 sgd_solver.cpp:105] Iteration 624, lr = 0.00524895
I0408 14:51:37.916819 20259 solver.cpp:218] Iteration 636 (2.48285 iter/s, 4.83315s/12 iters), loss = 4.86867
I0408 14:51:37.916872 20259 solver.cpp:237]     Train net output #0: loss = 4.86867 (* 1 = 4.86867 loss)
I0408 14:51:37.916884 20259 sgd_solver.cpp:105] Iteration 636, lr = 0.00518428
I0408 14:51:42.754170 20259 solver.cpp:218] Iteration 648 (2.48081 iter/s, 4.83713s/12 iters), loss = 5.01166
I0408 14:51:42.754221 20259 solver.cpp:237]     Train net output #0: loss = 5.01166 (* 1 = 5.01166 loss)
I0408 14:51:42.754235 20259 sgd_solver.cpp:105] Iteration 648, lr = 0.00512042
I0408 14:51:47.698848 20259 solver.cpp:218] Iteration 660 (2.42696 iter/s, 4.94445s/12 iters), loss = 4.97954
I0408 14:51:47.698899 20259 solver.cpp:237]     Train net output #0: loss = 4.97954 (* 1 = 4.97954 loss)
I0408 14:51:47.698911 20259 sgd_solver.cpp:105] Iteration 660, lr = 0.00505734
I0408 14:51:52.695917 20259 solver.cpp:218] Iteration 672 (2.40151 iter/s, 4.99685s/12 iters), loss = 4.9197
I0408 14:51:52.695969 20259 solver.cpp:237]     Train net output #0: loss = 4.9197 (* 1 = 4.9197 loss)
I0408 14:51:52.695982 20259 sgd_solver.cpp:105] Iteration 672, lr = 0.00499504
I0408 14:51:57.716172 20259 solver.cpp:218] Iteration 684 (2.39042 iter/s, 5.02003s/12 iters), loss = 4.76272
I0408 14:51:57.716351 20259 solver.cpp:237]     Train net output #0: loss = 4.76272 (* 1 = 4.76272 loss)
I0408 14:51:57.716363 20259 sgd_solver.cpp:105] Iteration 684, lr = 0.00493351
I0408 14:51:58.506124 20259 blocking_queue.cpp:49] Waiting for data
I0408 14:52:02.752746 20259 solver.cpp:218] Iteration 696 (2.38274 iter/s, 5.03623s/12 iters), loss = 4.87868
I0408 14:52:02.752795 20259 solver.cpp:237]     Train net output #0: loss = 4.87868 (* 1 = 4.87868 loss)
I0408 14:52:02.752807 20259 sgd_solver.cpp:105] Iteration 696, lr = 0.00487273
I0408 14:52:07.624541 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:52:08.004549 20259 solver.cpp:218] Iteration 708 (2.28503 iter/s, 5.25158s/12 iters), loss = 5.01809
I0408 14:52:08.004592 20259 solver.cpp:237]     Train net output #0: loss = 5.01809 (* 1 = 5.01809 loss)
I0408 14:52:08.004604 20259 sgd_solver.cpp:105] Iteration 708, lr = 0.00481271
I0408 14:52:10.027484 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 14:52:13.801863 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 14:52:18.746202 20259 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 14:52:18.746232 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:52:22.881793 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:52:23.201166 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0408 14:52:23.201210 20259 solver.cpp:397]     Test net output #1: loss = 4.93314 (* 1 = 4.93314 loss)
I0408 14:52:25.138638 20259 solver.cpp:218] Iteration 720 (0.700382 iter/s, 17.1335s/12 iters), loss = 5.04021
I0408 14:52:25.138675 20259 solver.cpp:237]     Train net output #0: loss = 5.04021 (* 1 = 5.04021 loss)
I0408 14:52:25.138684 20259 sgd_solver.cpp:105] Iteration 720, lr = 0.00475342
I0408 14:52:30.354228 20259 solver.cpp:218] Iteration 732 (2.30089 iter/s, 5.21537s/12 iters), loss = 4.84507
I0408 14:52:30.354348 20259 solver.cpp:237]     Train net output #0: loss = 4.84507 (* 1 = 4.84507 loss)
I0408 14:52:30.354362 20259 sgd_solver.cpp:105] Iteration 732, lr = 0.00469486
I0408 14:52:35.349754 20259 solver.cpp:218] Iteration 744 (2.40229 iter/s, 4.99524s/12 iters), loss = 4.89179
I0408 14:52:35.349799 20259 solver.cpp:237]     Train net output #0: loss = 4.89179 (* 1 = 4.89179 loss)
I0408 14:52:35.349812 20259 sgd_solver.cpp:105] Iteration 744, lr = 0.00463703
I0408 14:52:40.220284 20259 solver.cpp:218] Iteration 756 (2.4639 iter/s, 4.87032s/12 iters), loss = 4.91901
I0408 14:52:40.220329 20259 solver.cpp:237]     Train net output #0: loss = 4.91901 (* 1 = 4.91901 loss)
I0408 14:52:40.220341 20259 sgd_solver.cpp:105] Iteration 756, lr = 0.00457991
I0408 14:52:45.266108 20259 solver.cpp:218] Iteration 768 (2.3783 iter/s, 5.04561s/12 iters), loss = 4.96434
I0408 14:52:45.266145 20259 solver.cpp:237]     Train net output #0: loss = 4.96434 (* 1 = 4.96434 loss)
I0408 14:52:45.266155 20259 sgd_solver.cpp:105] Iteration 768, lr = 0.00452349
I0408 14:52:50.306257 20259 solver.cpp:218] Iteration 780 (2.38098 iter/s, 5.03994s/12 iters), loss = 4.93236
I0408 14:52:50.306301 20259 solver.cpp:237]     Train net output #0: loss = 4.93236 (* 1 = 4.93236 loss)
I0408 14:52:50.306313 20259 sgd_solver.cpp:105] Iteration 780, lr = 0.00446776
I0408 14:52:55.314046 20259 solver.cpp:218] Iteration 792 (2.39637 iter/s, 5.00758s/12 iters), loss = 4.79754
I0408 14:52:55.314082 20259 solver.cpp:237]     Train net output #0: loss = 4.79754 (* 1 = 4.79754 loss)
I0408 14:52:55.314090 20259 sgd_solver.cpp:105] Iteration 792, lr = 0.00441272
I0408 14:53:00.353242 20259 solver.cpp:218] Iteration 804 (2.38143 iter/s, 5.03899s/12 iters), loss = 4.82594
I0408 14:53:00.353291 20259 solver.cpp:237]     Train net output #0: loss = 4.82594 (* 1 = 4.82594 loss)
I0408 14:53:00.353302 20259 sgd_solver.cpp:105] Iteration 804, lr = 0.00435837
I0408 14:53:02.110496 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:53:04.895567 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 14:53:07.937889 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 14:53:11.993562 20259 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 14:53:11.993594 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:53:16.110302 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:53:16.464704 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0281863
I0408 14:53:16.464749 20259 solver.cpp:397]     Test net output #1: loss = 4.85983 (* 1 = 4.85983 loss)
I0408 14:53:16.554633 20259 solver.cpp:218] Iteration 816 (0.740703 iter/s, 16.2008s/12 iters), loss = 4.96504
I0408 14:53:16.554673 20259 solver.cpp:237]     Train net output #0: loss = 4.96504 (* 1 = 4.96504 loss)
I0408 14:53:16.554684 20259 sgd_solver.cpp:105] Iteration 816, lr = 0.00430467
I0408 14:53:20.906251 20259 solver.cpp:218] Iteration 828 (2.75771 iter/s, 4.35143s/12 iters), loss = 4.98639
I0408 14:53:20.906297 20259 solver.cpp:237]     Train net output #0: loss = 4.98639 (* 1 = 4.98639 loss)
I0408 14:53:20.906309 20259 sgd_solver.cpp:105] Iteration 828, lr = 0.00425165
I0408 14:53:25.955724 20259 solver.cpp:218] Iteration 840 (2.37659 iter/s, 5.04926s/12 iters), loss = 4.71712
I0408 14:53:25.955768 20259 solver.cpp:237]     Train net output #0: loss = 4.71712 (* 1 = 4.71712 loss)
I0408 14:53:25.955781 20259 sgd_solver.cpp:105] Iteration 840, lr = 0.00419927
I0408 14:53:30.948654 20259 solver.cpp:218] Iteration 852 (2.4035 iter/s, 4.99272s/12 iters), loss = 4.81102
I0408 14:53:30.948701 20259 solver.cpp:237]     Train net output #0: loss = 4.81102 (* 1 = 4.81102 loss)
I0408 14:53:30.948712 20259 sgd_solver.cpp:105] Iteration 852, lr = 0.00414754
I0408 14:53:35.893749 20259 solver.cpp:218] Iteration 864 (2.42675 iter/s, 4.94489s/12 iters), loss = 4.78357
I0408 14:53:35.893888 20259 solver.cpp:237]     Train net output #0: loss = 4.78357 (* 1 = 4.78357 loss)
I0408 14:53:35.893903 20259 sgd_solver.cpp:105] Iteration 864, lr = 0.00409645
I0408 14:53:40.867331 20259 solver.cpp:218] Iteration 876 (2.41289 iter/s, 4.97328s/12 iters), loss = 4.80087
I0408 14:53:40.867373 20259 solver.cpp:237]     Train net output #0: loss = 4.80087 (* 1 = 4.80087 loss)
I0408 14:53:40.867385 20259 sgd_solver.cpp:105] Iteration 876, lr = 0.00404598
I0408 14:53:45.900060 20259 solver.cpp:218] Iteration 888 (2.38449 iter/s, 5.03252s/12 iters), loss = 4.679
I0408 14:53:45.900101 20259 solver.cpp:237]     Train net output #0: loss = 4.679 (* 1 = 4.679 loss)
I0408 14:53:45.900112 20259 sgd_solver.cpp:105] Iteration 888, lr = 0.00399614
I0408 14:53:50.852123 20259 solver.cpp:218] Iteration 900 (2.42333 iter/s, 4.95186s/12 iters), loss = 4.77892
I0408 14:53:50.852169 20259 solver.cpp:237]     Train net output #0: loss = 4.77892 (* 1 = 4.77892 loss)
I0408 14:53:50.852180 20259 sgd_solver.cpp:105] Iteration 900, lr = 0.00394691
I0408 14:53:54.679949 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:53:55.808115 20259 solver.cpp:218] Iteration 912 (2.42141 iter/s, 4.95578s/12 iters), loss = 4.58803
I0408 14:53:55.808161 20259 solver.cpp:237]     Train net output #0: loss = 4.58803 (* 1 = 4.58803 loss)
I0408 14:53:55.808173 20259 sgd_solver.cpp:105] Iteration 912, lr = 0.00389829
I0408 14:53:57.812981 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 14:54:00.871989 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 14:54:03.200280 20259 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 14:54:03.200310 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:54:07.264240 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:54:07.664389 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0408 14:54:07.664436 20259 solver.cpp:397]     Test net output #1: loss = 4.81937 (* 1 = 4.81937 loss)
I0408 14:54:09.642400 20259 solver.cpp:218] Iteration 924 (0.86744 iter/s, 13.8338s/12 iters), loss = 4.86334
I0408 14:54:09.642442 20259 solver.cpp:237]     Train net output #0: loss = 4.86334 (* 1 = 4.86334 loss)
I0408 14:54:09.642453 20259 sgd_solver.cpp:105] Iteration 924, lr = 0.00385027
I0408 14:54:14.728230 20259 solver.cpp:218] Iteration 936 (2.35959 iter/s, 5.08562s/12 iters), loss = 4.88821
I0408 14:54:14.728276 20259 solver.cpp:237]     Train net output #0: loss = 4.88821 (* 1 = 4.88821 loss)
I0408 14:54:14.728286 20259 sgd_solver.cpp:105] Iteration 936, lr = 0.00380284
I0408 14:54:19.724958 20259 solver.cpp:218] Iteration 948 (2.40167 iter/s, 4.99652s/12 iters), loss = 4.75039
I0408 14:54:19.725003 20259 solver.cpp:237]     Train net output #0: loss = 4.75039 (* 1 = 4.75039 loss)
I0408 14:54:19.725014 20259 sgd_solver.cpp:105] Iteration 948, lr = 0.00375599
I0408 14:54:24.707556 20259 solver.cpp:218] Iteration 960 (2.40848 iter/s, 4.98239s/12 iters), loss = 4.61873
I0408 14:54:24.707599 20259 solver.cpp:237]     Train net output #0: loss = 4.61873 (* 1 = 4.61873 loss)
I0408 14:54:24.707612 20259 sgd_solver.cpp:105] Iteration 960, lr = 0.00370972
I0408 14:54:29.697299 20259 solver.cpp:218] Iteration 972 (2.40503 iter/s, 4.98954s/12 iters), loss = 4.63679
I0408 14:54:29.697341 20259 solver.cpp:237]     Train net output #0: loss = 4.63679 (* 1 = 4.63679 loss)
I0408 14:54:29.697353 20259 sgd_solver.cpp:105] Iteration 972, lr = 0.00366402
I0408 14:54:34.694308 20259 solver.cpp:218] Iteration 984 (2.40154 iter/s, 4.9968s/12 iters), loss = 4.76533
I0408 14:54:34.694350 20259 solver.cpp:237]     Train net output #0: loss = 4.76533 (* 1 = 4.76533 loss)
I0408 14:54:34.694360 20259 sgd_solver.cpp:105] Iteration 984, lr = 0.00361889
I0408 14:54:39.695447 20259 solver.cpp:218] Iteration 996 (2.39955 iter/s, 5.00093s/12 iters), loss = 4.60976
I0408 14:54:39.695561 20259 solver.cpp:237]     Train net output #0: loss = 4.60976 (* 1 = 4.60976 loss)
I0408 14:54:39.695574 20259 sgd_solver.cpp:105] Iteration 996, lr = 0.00357431
I0408 14:54:44.638973 20259 solver.cpp:218] Iteration 1008 (2.42755 iter/s, 4.94325s/12 iters), loss = 4.68857
I0408 14:54:44.639014 20259 solver.cpp:237]     Train net output #0: loss = 4.68857 (* 1 = 4.68857 loss)
I0408 14:54:44.639025 20259 sgd_solver.cpp:105] Iteration 1008, lr = 0.00353028
I0408 14:54:45.662845 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:54:49.128005 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 14:54:53.645987 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 14:54:55.973137 20259 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 14:54:55.973168 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:55:00.001734 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:55:00.432750 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0408 14:55:00.432796 20259 solver.cpp:397]     Test net output #1: loss = 4.74213 (* 1 = 4.74213 loss)
I0408 14:55:00.523059 20259 solver.cpp:218] Iteration 1020 (0.755499 iter/s, 15.8836s/12 iters), loss = 4.56774
I0408 14:55:00.523103 20259 solver.cpp:237]     Train net output #0: loss = 4.56774 (* 1 = 4.56774 loss)
I0408 14:55:00.523114 20259 sgd_solver.cpp:105] Iteration 1020, lr = 0.00348679
I0408 14:55:04.836122 20259 solver.cpp:218] Iteration 1032 (2.78237 iter/s, 4.31287s/12 iters), loss = 4.67081
I0408 14:55:04.836165 20259 solver.cpp:237]     Train net output #0: loss = 4.67081 (* 1 = 4.67081 loss)
I0408 14:55:04.836179 20259 sgd_solver.cpp:105] Iteration 1032, lr = 0.00344383
I0408 14:55:09.818311 20259 solver.cpp:218] Iteration 1044 (2.40868 iter/s, 4.98199s/12 iters), loss = 4.73981
I0408 14:55:09.818472 20259 solver.cpp:237]     Train net output #0: loss = 4.73981 (* 1 = 4.73981 loss)
I0408 14:55:09.818486 20259 sgd_solver.cpp:105] Iteration 1044, lr = 0.00340141
I0408 14:55:14.778717 20259 solver.cpp:218] Iteration 1056 (2.41931 iter/s, 4.96009s/12 iters), loss = 4.64103
I0408 14:55:14.778750 20259 solver.cpp:237]     Train net output #0: loss = 4.64103 (* 1 = 4.64103 loss)
I0408 14:55:14.778760 20259 sgd_solver.cpp:105] Iteration 1056, lr = 0.00335951
I0408 14:55:19.719900 20259 solver.cpp:218] Iteration 1068 (2.42866 iter/s, 4.94099s/12 iters), loss = 4.5993
I0408 14:55:19.719944 20259 solver.cpp:237]     Train net output #0: loss = 4.5993 (* 1 = 4.5993 loss)
I0408 14:55:19.719954 20259 sgd_solver.cpp:105] Iteration 1068, lr = 0.00331812
I0408 14:55:24.731392 20259 solver.cpp:218] Iteration 1080 (2.3946 iter/s, 5.01128s/12 iters), loss = 4.65952
I0408 14:55:24.731438 20259 solver.cpp:237]     Train net output #0: loss = 4.65952 (* 1 = 4.65952 loss)
I0408 14:55:24.731451 20259 sgd_solver.cpp:105] Iteration 1080, lr = 0.00327725
I0408 14:55:29.984321 20259 solver.cpp:218] Iteration 1092 (2.28453 iter/s, 5.25272s/12 iters), loss = 4.57057
I0408 14:55:29.984361 20259 solver.cpp:237]     Train net output #0: loss = 4.57057 (* 1 = 4.57057 loss)
I0408 14:55:29.984371 20259 sgd_solver.cpp:105] Iteration 1092, lr = 0.00323688
I0408 14:55:35.035138 20259 solver.cpp:218] Iteration 1104 (2.37595 iter/s, 5.05061s/12 iters), loss = 4.61173
I0408 14:55:35.035187 20259 solver.cpp:237]     Train net output #0: loss = 4.61173 (* 1 = 4.61173 loss)
I0408 14:55:35.035216 20259 sgd_solver.cpp:105] Iteration 1104, lr = 0.003197
I0408 14:55:38.128706 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:55:39.910792 20259 solver.cpp:218] Iteration 1116 (2.46131 iter/s, 4.87545s/12 iters), loss = 4.69021
I0408 14:55:39.910900 20259 solver.cpp:237]     Train net output #0: loss = 4.69021 (* 1 = 4.69021 loss)
I0408 14:55:39.910912 20259 sgd_solver.cpp:105] Iteration 1116, lr = 0.00315762
I0408 14:55:41.956830 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 14:55:45.740370 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 14:55:50.424049 20259 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 14:55:50.424080 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:55:54.407809 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:55:54.883621 20259 solver.cpp:397]     Test net output #0: accuracy = 0.060049
I0408 14:55:54.883666 20259 solver.cpp:397]     Test net output #1: loss = 4.59751 (* 1 = 4.59751 loss)
I0408 14:55:56.860944 20259 solver.cpp:218] Iteration 1128 (0.707985 iter/s, 16.9495s/12 iters), loss = 4.58863
I0408 14:55:56.860993 20259 solver.cpp:237]     Train net output #0: loss = 4.58863 (* 1 = 4.58863 loss)
I0408 14:55:56.861006 20259 sgd_solver.cpp:105] Iteration 1128, lr = 0.00311872
I0408 14:56:02.082231 20259 solver.cpp:218] Iteration 1140 (2.29838 iter/s, 5.22107s/12 iters), loss = 4.52637
I0408 14:56:02.082283 20259 solver.cpp:237]     Train net output #0: loss = 4.52637 (* 1 = 4.52637 loss)
I0408 14:56:02.082296 20259 sgd_solver.cpp:105] Iteration 1140, lr = 0.0030803
I0408 14:56:07.367709 20259 solver.cpp:218] Iteration 1152 (2.27047 iter/s, 5.28525s/12 iters), loss = 4.52727
I0408 14:56:07.367758 20259 solver.cpp:237]     Train net output #0: loss = 4.52727 (* 1 = 4.52727 loss)
I0408 14:56:07.367770 20259 sgd_solver.cpp:105] Iteration 1152, lr = 0.00304236
I0408 14:56:12.419061 20259 solver.cpp:218] Iteration 1164 (2.3757 iter/s, 5.05114s/12 iters), loss = 4.53004
I0408 14:56:12.419185 20259 solver.cpp:237]     Train net output #0: loss = 4.53004 (* 1 = 4.53004 loss)
I0408 14:56:12.419198 20259 sgd_solver.cpp:105] Iteration 1164, lr = 0.00300488
I0408 14:56:17.267153 20259 solver.cpp:218] Iteration 1176 (2.47535 iter/s, 4.84781s/12 iters), loss = 4.48682
I0408 14:56:17.267189 20259 solver.cpp:237]     Train net output #0: loss = 4.48682 (* 1 = 4.48682 loss)
I0408 14:56:17.267200 20259 sgd_solver.cpp:105] Iteration 1176, lr = 0.00296786
I0408 14:56:22.233000 20259 solver.cpp:218] Iteration 1188 (2.4166 iter/s, 4.96565s/12 iters), loss = 4.45211
I0408 14:56:22.233048 20259 solver.cpp:237]     Train net output #0: loss = 4.45211 (* 1 = 4.45211 loss)
I0408 14:56:22.233062 20259 sgd_solver.cpp:105] Iteration 1188, lr = 0.0029313
I0408 14:56:27.202828 20259 solver.cpp:218] Iteration 1200 (2.41467 iter/s, 4.96962s/12 iters), loss = 4.45402
I0408 14:56:27.202873 20259 solver.cpp:237]     Train net output #0: loss = 4.45402 (* 1 = 4.45402 loss)
I0408 14:56:27.202885 20259 sgd_solver.cpp:105] Iteration 1200, lr = 0.00289519
I0408 14:56:32.210443 20259 solver.cpp:218] Iteration 1212 (2.39645 iter/s, 5.00741s/12 iters), loss = 4.56788
I0408 14:56:32.210489 20259 solver.cpp:237]     Train net output #0: loss = 4.56788 (* 1 = 4.56788 loss)
I0408 14:56:32.210500 20259 sgd_solver.cpp:105] Iteration 1212, lr = 0.00285952
I0408 14:56:32.487967 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:56:36.687804 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 14:56:40.524717 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 14:56:44.266259 20259 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 14:56:44.266366 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:56:48.216919 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:56:48.727881 20259 solver.cpp:397]     Test net output #0: accuracy = 0.067402
I0408 14:56:48.727923 20259 solver.cpp:397]     Test net output #1: loss = 4.57227 (* 1 = 4.57227 loss)
I0408 14:56:48.815940 20259 solver.cpp:218] Iteration 1224 (0.722676 iter/s, 16.6049s/12 iters), loss = 4.44153
I0408 14:56:48.815977 20259 solver.cpp:237]     Train net output #0: loss = 4.44153 (* 1 = 4.44153 loss)
I0408 14:56:48.815987 20259 sgd_solver.cpp:105] Iteration 1224, lr = 0.0028243
I0408 14:56:53.364292 20259 solver.cpp:218] Iteration 1236 (2.63842 iter/s, 4.54817s/12 iters), loss = 4.61885
I0408 14:56:53.364331 20259 solver.cpp:237]     Train net output #0: loss = 4.61885 (* 1 = 4.61885 loss)
I0408 14:56:53.364341 20259 sgd_solver.cpp:105] Iteration 1236, lr = 0.00278951
I0408 14:56:58.359423 20259 solver.cpp:218] Iteration 1248 (2.40244 iter/s, 4.99493s/12 iters), loss = 4.42973
I0408 14:56:58.359458 20259 solver.cpp:237]     Train net output #0: loss = 4.42973 (* 1 = 4.42973 loss)
I0408 14:56:58.359467 20259 sgd_solver.cpp:105] Iteration 1248, lr = 0.00275514
I0408 14:57:03.348683 20259 solver.cpp:218] Iteration 1260 (2.40526 iter/s, 4.98906s/12 iters), loss = 4.57651
I0408 14:57:03.348726 20259 solver.cpp:237]     Train net output #0: loss = 4.57651 (* 1 = 4.57651 loss)
I0408 14:57:03.348737 20259 sgd_solver.cpp:105] Iteration 1260, lr = 0.0027212
I0408 14:57:08.353936 20259 solver.cpp:218] Iteration 1272 (2.39758 iter/s, 5.00505s/12 iters), loss = 4.38664
I0408 14:57:08.353982 20259 solver.cpp:237]     Train net output #0: loss = 4.38664 (* 1 = 4.38664 loss)
I0408 14:57:08.353992 20259 sgd_solver.cpp:105] Iteration 1272, lr = 0.00268768
I0408 14:57:13.408617 20259 solver.cpp:218] Iteration 1284 (2.37414 iter/s, 5.05447s/12 iters), loss = 4.49111
I0408 14:57:13.408663 20259 solver.cpp:237]     Train net output #0: loss = 4.49111 (* 1 = 4.49111 loss)
I0408 14:57:13.408674 20259 sgd_solver.cpp:105] Iteration 1284, lr = 0.00265457
I0408 14:57:18.378923 20259 solver.cpp:218] Iteration 1296 (2.41444 iter/s, 4.9701s/12 iters), loss = 4.21013
I0408 14:57:18.379076 20259 solver.cpp:237]     Train net output #0: loss = 4.21013 (* 1 = 4.21013 loss)
I0408 14:57:18.379091 20259 sgd_solver.cpp:105] Iteration 1296, lr = 0.00262187
I0408 14:57:23.447566 20259 solver.cpp:218] Iteration 1308 (2.36764 iter/s, 5.06833s/12 iters), loss = 4.313
I0408 14:57:23.447608 20259 solver.cpp:237]     Train net output #0: loss = 4.313 (* 1 = 4.313 loss)
I0408 14:57:23.447620 20259 sgd_solver.cpp:105] Iteration 1308, lr = 0.00258957
I0408 14:57:25.935195 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:57:28.433252 20259 solver.cpp:218] Iteration 1320 (2.40699 iter/s, 4.98548s/12 iters), loss = 4.3768
I0408 14:57:28.433297 20259 solver.cpp:237]     Train net output #0: loss = 4.3768 (* 1 = 4.3768 loss)
I0408 14:57:28.433307 20259 sgd_solver.cpp:105] Iteration 1320, lr = 0.00255767
I0408 14:57:30.481164 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 14:57:35.526753 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 14:57:38.655412 20259 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 14:57:38.655444 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:57:42.554298 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:57:43.110558 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 14:57:43.110605 20259 solver.cpp:397]     Test net output #1: loss = 4.4676 (* 1 = 4.4676 loss)
I0408 14:57:45.116750 20259 solver.cpp:218] Iteration 1332 (0.719298 iter/s, 16.6829s/12 iters), loss = 4.22474
I0408 14:57:45.116799 20259 solver.cpp:237]     Train net output #0: loss = 4.22474 (* 1 = 4.22474 loss)
I0408 14:57:45.116812 20259 sgd_solver.cpp:105] Iteration 1332, lr = 0.00252616
I0408 14:57:50.132125 20259 solver.cpp:218] Iteration 1344 (2.39274 iter/s, 5.01516s/12 iters), loss = 4.36031
I0408 14:57:50.132418 20259 solver.cpp:237]     Train net output #0: loss = 4.36031 (* 1 = 4.36031 loss)
I0408 14:57:50.132432 20259 sgd_solver.cpp:105] Iteration 1344, lr = 0.00249504
I0408 14:57:55.162997 20259 solver.cpp:218] Iteration 1356 (2.38549 iter/s, 5.03042s/12 iters), loss = 4.47911
I0408 14:57:55.163040 20259 solver.cpp:237]     Train net output #0: loss = 4.47911 (* 1 = 4.47911 loss)
I0408 14:57:55.163051 20259 sgd_solver.cpp:105] Iteration 1356, lr = 0.00246431
I0408 14:58:00.160660 20259 solver.cpp:218] Iteration 1368 (2.40122 iter/s, 4.99746s/12 iters), loss = 4.29936
I0408 14:58:00.160694 20259 solver.cpp:237]     Train net output #0: loss = 4.29936 (* 1 = 4.29936 loss)
I0408 14:58:00.160702 20259 sgd_solver.cpp:105] Iteration 1368, lr = 0.00243395
I0408 14:58:01.370779 20259 blocking_queue.cpp:49] Waiting for data
I0408 14:58:05.182054 20259 solver.cpp:218] Iteration 1380 (2.38987 iter/s, 5.0212s/12 iters), loss = 4.04368
I0408 14:58:05.182087 20259 solver.cpp:237]     Train net output #0: loss = 4.04368 (* 1 = 4.04368 loss)
I0408 14:58:05.182096 20259 sgd_solver.cpp:105] Iteration 1380, lr = 0.00240397
I0408 14:58:10.146550 20259 solver.cpp:218] Iteration 1392 (2.41726 iter/s, 4.9643s/12 iters), loss = 4.21301
I0408 14:58:10.146595 20259 solver.cpp:237]     Train net output #0: loss = 4.21301 (* 1 = 4.21301 loss)
I0408 14:58:10.146605 20259 sgd_solver.cpp:105] Iteration 1392, lr = 0.00237435
I0408 14:58:15.131083 20259 solver.cpp:218] Iteration 1404 (2.40755 iter/s, 4.98433s/12 iters), loss = 4.25075
I0408 14:58:15.131126 20259 solver.cpp:237]     Train net output #0: loss = 4.25075 (* 1 = 4.25075 loss)
I0408 14:58:15.131139 20259 sgd_solver.cpp:105] Iteration 1404, lr = 0.0023451
I0408 14:58:19.800173 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:58:20.147349 20259 solver.cpp:218] Iteration 1416 (2.39232 iter/s, 5.01606s/12 iters), loss = 4.11788
I0408 14:58:20.147521 20259 solver.cpp:237]     Train net output #0: loss = 4.11788 (* 1 = 4.11788 loss)
I0408 14:58:20.147534 20259 sgd_solver.cpp:105] Iteration 1416, lr = 0.00231622
I0408 14:58:24.702978 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 14:58:27.973378 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 14:58:32.019125 20259 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 14:58:32.019150 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:58:35.860916 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:58:36.450994 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0870098
I0408 14:58:36.451038 20259 solver.cpp:397]     Test net output #1: loss = 4.34982 (* 1 = 4.34982 loss)
I0408 14:58:36.538753 20259 solver.cpp:218] Iteration 1428 (0.732121 iter/s, 16.3907s/12 iters), loss = 4.34149
I0408 14:58:36.538791 20259 solver.cpp:237]     Train net output #0: loss = 4.34149 (* 1 = 4.34149 loss)
I0408 14:58:36.538802 20259 sgd_solver.cpp:105] Iteration 1428, lr = 0.00228768
I0408 14:58:40.692587 20259 solver.cpp:218] Iteration 1440 (2.88902 iter/s, 4.15366s/12 iters), loss = 4.18426
I0408 14:58:40.692628 20259 solver.cpp:237]     Train net output #0: loss = 4.18426 (* 1 = 4.18426 loss)
I0408 14:58:40.692639 20259 sgd_solver.cpp:105] Iteration 1440, lr = 0.0022595
I0408 14:58:45.720813 20259 solver.cpp:218] Iteration 1452 (2.38662 iter/s, 5.02802s/12 iters), loss = 4.11828
I0408 14:58:45.720854 20259 solver.cpp:237]     Train net output #0: loss = 4.11828 (* 1 = 4.11828 loss)
I0408 14:58:45.720865 20259 sgd_solver.cpp:105] Iteration 1452, lr = 0.00223167
I0408 14:58:50.698709 20259 solver.cpp:218] Iteration 1464 (2.41075 iter/s, 4.97769s/12 iters), loss = 4.14569
I0408 14:58:50.698820 20259 solver.cpp:237]     Train net output #0: loss = 4.14569 (* 1 = 4.14569 loss)
I0408 14:58:50.698832 20259 sgd_solver.cpp:105] Iteration 1464, lr = 0.00220417
I0408 14:58:55.660012 20259 solver.cpp:218] Iteration 1476 (2.41885 iter/s, 4.96103s/12 iters), loss = 4.3568
I0408 14:58:55.660044 20259 solver.cpp:237]     Train net output #0: loss = 4.3568 (* 1 = 4.3568 loss)
I0408 14:58:55.660053 20259 sgd_solver.cpp:105] Iteration 1476, lr = 0.00217702
I0408 14:59:00.622750 20259 solver.cpp:218] Iteration 1488 (2.41811 iter/s, 4.96255s/12 iters), loss = 4.21057
I0408 14:59:00.622794 20259 solver.cpp:237]     Train net output #0: loss = 4.21057 (* 1 = 4.21057 loss)
I0408 14:59:00.622805 20259 sgd_solver.cpp:105] Iteration 1488, lr = 0.0021502
I0408 14:59:05.561825 20259 solver.cpp:218] Iteration 1500 (2.4297 iter/s, 4.93887s/12 iters), loss = 3.83172
I0408 14:59:05.561867 20259 solver.cpp:237]     Train net output #0: loss = 3.83172 (* 1 = 3.83172 loss)
I0408 14:59:05.561880 20259 sgd_solver.cpp:105] Iteration 1500, lr = 0.00212372
I0408 14:59:10.567283 20259 solver.cpp:218] Iteration 1512 (2.39748 iter/s, 5.00525s/12 iters), loss = 4.04631
I0408 14:59:10.567327 20259 solver.cpp:237]     Train net output #0: loss = 4.04631 (* 1 = 4.04631 loss)
I0408 14:59:10.567338 20259 sgd_solver.cpp:105] Iteration 1512, lr = 0.00209755
I0408 14:59:12.362442 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:59:15.551677 20259 solver.cpp:218] Iteration 1524 (2.40761 iter/s, 4.98419s/12 iters), loss = 4.07797
I0408 14:59:15.551719 20259 solver.cpp:237]     Train net output #0: loss = 4.07797 (* 1 = 4.07797 loss)
I0408 14:59:15.551731 20259 sgd_solver.cpp:105] Iteration 1524, lr = 0.00207171
I0408 14:59:17.596103 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 14:59:21.009210 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 14:59:25.129786 20259 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 14:59:25.129813 20259 net.cpp:676] Ignoring source layer train-data
I0408 14:59:28.953207 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 14:59:29.591075 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0863971
I0408 14:59:29.591104 20259 solver.cpp:397]     Test net output #1: loss = 4.35043 (* 1 = 4.35043 loss)
I0408 14:59:31.579583 20259 solver.cpp:218] Iteration 1536 (0.748719 iter/s, 16.0274s/12 iters), loss = 4.18598
I0408 14:59:31.579629 20259 solver.cpp:237]     Train net output #0: loss = 4.18598 (* 1 = 4.18598 loss)
I0408 14:59:31.579641 20259 sgd_solver.cpp:105] Iteration 1536, lr = 0.00204619
I0408 14:59:36.724593 20259 solver.cpp:218] Iteration 1548 (2.33245 iter/s, 5.1448s/12 iters), loss = 3.65728
I0408 14:59:36.724635 20259 solver.cpp:237]     Train net output #0: loss = 3.65728 (* 1 = 3.65728 loss)
I0408 14:59:36.724647 20259 sgd_solver.cpp:105] Iteration 1548, lr = 0.00202099
I0408 14:59:41.722734 20259 solver.cpp:218] Iteration 1560 (2.40099 iter/s, 4.99794s/12 iters), loss = 4.03168
I0408 14:59:41.722780 20259 solver.cpp:237]     Train net output #0: loss = 4.03168 (* 1 = 4.03168 loss)
I0408 14:59:41.722792 20259 sgd_solver.cpp:105] Iteration 1560, lr = 0.00199609
I0408 14:59:46.697034 20259 solver.cpp:218] Iteration 1572 (2.4125 iter/s, 4.9741s/12 iters), loss = 4.02911
I0408 14:59:46.697079 20259 solver.cpp:237]     Train net output #0: loss = 4.02911 (* 1 = 4.02911 loss)
I0408 14:59:46.697091 20259 sgd_solver.cpp:105] Iteration 1572, lr = 0.0019715
I0408 14:59:51.729408 20259 solver.cpp:218] Iteration 1584 (2.38466 iter/s, 5.03217s/12 iters), loss = 4.17355
I0408 14:59:51.729532 20259 solver.cpp:237]     Train net output #0: loss = 4.17355 (* 1 = 4.17355 loss)
I0408 14:59:51.729545 20259 sgd_solver.cpp:105] Iteration 1584, lr = 0.00194721
I0408 14:59:56.775234 20259 solver.cpp:218] Iteration 1596 (2.37834 iter/s, 5.04555s/12 iters), loss = 3.98342
I0408 14:59:56.775277 20259 solver.cpp:237]     Train net output #0: loss = 3.98342 (* 1 = 3.98342 loss)
I0408 14:59:56.775288 20259 sgd_solver.cpp:105] Iteration 1596, lr = 0.00192323
I0408 15:00:01.764425 20259 solver.cpp:218] Iteration 1608 (2.4053 iter/s, 4.98899s/12 iters), loss = 3.92634
I0408 15:00:01.764472 20259 solver.cpp:237]     Train net output #0: loss = 3.92634 (* 1 = 3.92634 loss)
I0408 15:00:01.764483 20259 sgd_solver.cpp:105] Iteration 1608, lr = 0.00189953
I0408 15:00:05.661789 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:00:06.742993 20259 solver.cpp:218] Iteration 1620 (2.41043 iter/s, 4.97836s/12 iters), loss = 4.01644
I0408 15:00:06.743037 20259 solver.cpp:237]     Train net output #0: loss = 4.01644 (* 1 = 4.01644 loss)
I0408 15:00:06.743049 20259 sgd_solver.cpp:105] Iteration 1620, lr = 0.00187613
I0408 15:00:11.178774 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 15:00:15.134936 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 15:00:18.390198 20259 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 15:00:18.390229 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:00:22.180678 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:00:22.850366 20259 solver.cpp:397]     Test net output #0: accuracy = 0.0980392
I0408 15:00:22.850414 20259 solver.cpp:397]     Test net output #1: loss = 4.22898 (* 1 = 4.22898 loss)
I0408 15:00:22.940922 20259 solver.cpp:218] Iteration 1632 (0.74086 iter/s, 16.1974s/12 iters), loss = 3.89883
I0408 15:00:22.940964 20259 solver.cpp:237]     Train net output #0: loss = 3.89883 (* 1 = 3.89883 loss)
I0408 15:00:22.940976 20259 sgd_solver.cpp:105] Iteration 1632, lr = 0.00185302
I0408 15:00:27.223273 20259 solver.cpp:218] Iteration 1644 (2.80232 iter/s, 4.28217s/12 iters), loss = 4.02713
I0408 15:00:27.223307 20259 solver.cpp:237]     Train net output #0: loss = 4.02713 (* 1 = 4.02713 loss)
I0408 15:00:27.223316 20259 sgd_solver.cpp:105] Iteration 1644, lr = 0.0018302
I0408 15:00:32.169977 20259 solver.cpp:218] Iteration 1656 (2.42595 iter/s, 4.94651s/12 iters), loss = 3.94628
I0408 15:00:32.170022 20259 solver.cpp:237]     Train net output #0: loss = 3.94628 (* 1 = 3.94628 loss)
I0408 15:00:32.170033 20259 sgd_solver.cpp:105] Iteration 1656, lr = 0.00180765
I0408 15:00:37.193637 20259 solver.cpp:218] Iteration 1668 (2.3888 iter/s, 5.02345s/12 iters), loss = 3.60912
I0408 15:00:37.193681 20259 solver.cpp:237]     Train net output #0: loss = 3.60912 (* 1 = 3.60912 loss)
I0408 15:00:37.193693 20259 sgd_solver.cpp:105] Iteration 1668, lr = 0.00178538
I0408 15:00:42.206877 20259 solver.cpp:218] Iteration 1680 (2.39376 iter/s, 5.01304s/12 iters), loss = 3.75345
I0408 15:00:42.206918 20259 solver.cpp:237]     Train net output #0: loss = 3.75345 (* 1 = 3.75345 loss)
I0408 15:00:42.206929 20259 sgd_solver.cpp:105] Iteration 1680, lr = 0.00176339
I0408 15:00:47.191211 20259 solver.cpp:218] Iteration 1692 (2.40764 iter/s, 4.98413s/12 iters), loss = 3.91443
I0408 15:00:47.191255 20259 solver.cpp:237]     Train net output #0: loss = 3.91443 (* 1 = 3.91443 loss)
I0408 15:00:47.191268 20259 sgd_solver.cpp:105] Iteration 1692, lr = 0.00174166
I0408 15:00:52.179962 20259 solver.cpp:218] Iteration 1704 (2.40551 iter/s, 4.98855s/12 iters), loss = 3.45979
I0408 15:00:52.180006 20259 solver.cpp:237]     Train net output #0: loss = 3.45979 (* 1 = 3.45979 loss)
I0408 15:00:52.180016 20259 sgd_solver.cpp:105] Iteration 1704, lr = 0.00172021
I0408 15:00:57.187173 20259 solver.cpp:218] Iteration 1716 (2.39664 iter/s, 5.00701s/12 iters), loss = 3.93349
I0408 15:00:57.187315 20259 solver.cpp:237]     Train net output #0: loss = 3.93349 (* 1 = 3.93349 loss)
I0408 15:00:57.187330 20259 sgd_solver.cpp:105] Iteration 1716, lr = 0.00169902
I0408 15:00:58.211707 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:01:02.163875 20259 solver.cpp:218] Iteration 1728 (2.41138 iter/s, 4.9764s/12 iters), loss = 3.8868
I0408 15:01:02.163918 20259 solver.cpp:237]     Train net output #0: loss = 3.8868 (* 1 = 3.8868 loss)
I0408 15:01:02.163929 20259 sgd_solver.cpp:105] Iteration 1728, lr = 0.00167809
I0408 15:01:04.155122 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 15:01:07.218343 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 15:01:10.119849 20259 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 15:01:10.119875 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:01:13.874931 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:01:14.579568 20259 solver.cpp:397]     Test net output #0: accuracy = 0.11826
I0408 15:01:14.579614 20259 solver.cpp:397]     Test net output #1: loss = 4.06468 (* 1 = 4.06468 loss)
I0408 15:01:16.537494 20259 solver.cpp:218] Iteration 1740 (0.834891 iter/s, 14.3731s/12 iters), loss = 3.73357
I0408 15:01:16.537541 20259 solver.cpp:237]     Train net output #0: loss = 3.73357 (* 1 = 3.73357 loss)
I0408 15:01:16.537554 20259 sgd_solver.cpp:105] Iteration 1740, lr = 0.00165742
I0408 15:01:21.491060 20259 solver.cpp:218] Iteration 1752 (2.4226 iter/s, 4.95336s/12 iters), loss = 3.8574
I0408 15:01:21.491102 20259 solver.cpp:237]     Train net output #0: loss = 3.8574 (* 1 = 3.8574 loss)
I0408 15:01:21.491113 20259 sgd_solver.cpp:105] Iteration 1752, lr = 0.001637
I0408 15:01:26.518065 20259 solver.cpp:218] Iteration 1764 (2.3872 iter/s, 5.0268s/12 iters), loss = 3.91581
I0408 15:01:26.518108 20259 solver.cpp:237]     Train net output #0: loss = 3.91581 (* 1 = 3.91581 loss)
I0408 15:01:26.518121 20259 sgd_solver.cpp:105] Iteration 1764, lr = 0.00161683
I0408 15:01:31.509603 20259 solver.cpp:218] Iteration 1776 (2.40417 iter/s, 4.99133s/12 iters), loss = 3.87993
I0408 15:01:31.509758 20259 solver.cpp:237]     Train net output #0: loss = 3.87993 (* 1 = 3.87993 loss)
I0408 15:01:31.509773 20259 sgd_solver.cpp:105] Iteration 1776, lr = 0.00159692
I0408 15:01:36.430992 20259 solver.cpp:218] Iteration 1788 (2.43849 iter/s, 4.92108s/12 iters), loss = 3.99639
I0408 15:01:36.431035 20259 solver.cpp:237]     Train net output #0: loss = 3.99639 (* 1 = 3.99639 loss)
I0408 15:01:36.431046 20259 sgd_solver.cpp:105] Iteration 1788, lr = 0.00157724
I0408 15:01:41.517416 20259 solver.cpp:218] Iteration 1800 (2.35932 iter/s, 5.08622s/12 iters), loss = 3.89101
I0408 15:01:41.517462 20259 solver.cpp:237]     Train net output #0: loss = 3.89101 (* 1 = 3.89101 loss)
I0408 15:01:41.517473 20259 sgd_solver.cpp:105] Iteration 1800, lr = 0.00155781
I0408 15:01:46.561069 20259 solver.cpp:218] Iteration 1812 (2.37933 iter/s, 5.04345s/12 iters), loss = 3.79552
I0408 15:01:46.561112 20259 solver.cpp:237]     Train net output #0: loss = 3.79552 (* 1 = 3.79552 loss)
I0408 15:01:46.561123 20259 sgd_solver.cpp:105] Iteration 1812, lr = 0.00153862
I0408 15:01:49.636359 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:01:51.384456 20259 solver.cpp:218] Iteration 1824 (2.48798 iter/s, 4.82319s/12 iters), loss = 3.88485
I0408 15:01:51.384506 20259 solver.cpp:237]     Train net output #0: loss = 3.88485 (* 1 = 3.88485 loss)
I0408 15:01:51.384519 20259 sgd_solver.cpp:105] Iteration 1824, lr = 0.00151967
I0408 15:01:55.870790 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 15:02:00.090214 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 15:02:03.708695 20259 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 15:02:03.708799 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:02:07.428861 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:02:08.177227 20259 solver.cpp:397]     Test net output #0: accuracy = 0.121324
I0408 15:02:08.177273 20259 solver.cpp:397]     Test net output #1: loss = 4.09417 (* 1 = 4.09417 loss)
I0408 15:02:08.267189 20259 solver.cpp:218] Iteration 1836 (0.710809 iter/s, 16.8822s/12 iters), loss = 3.92822
I0408 15:02:08.267225 20259 solver.cpp:237]     Train net output #0: loss = 3.92822 (* 1 = 3.92822 loss)
I0408 15:02:08.267235 20259 sgd_solver.cpp:105] Iteration 1836, lr = 0.00150095
I0408 15:02:12.812937 20259 solver.cpp:218] Iteration 1848 (2.63994 iter/s, 4.54556s/12 iters), loss = 3.84958
I0408 15:02:12.812984 20259 solver.cpp:237]     Train net output #0: loss = 3.84958 (* 1 = 3.84958 loss)
I0408 15:02:12.812995 20259 sgd_solver.cpp:105] Iteration 1848, lr = 0.00148246
I0408 15:02:18.237676 20259 solver.cpp:218] Iteration 1860 (2.21218 iter/s, 5.42452s/12 iters), loss = 3.87868
I0408 15:02:18.237722 20259 solver.cpp:237]     Train net output #0: loss = 3.87868 (* 1 = 3.87868 loss)
I0408 15:02:18.237735 20259 sgd_solver.cpp:105] Iteration 1860, lr = 0.0014642
I0408 15:02:23.691640 20259 solver.cpp:218] Iteration 1872 (2.20032 iter/s, 5.45374s/12 iters), loss = 3.81363
I0408 15:02:23.691691 20259 solver.cpp:237]     Train net output #0: loss = 3.81363 (* 1 = 3.81363 loss)
I0408 15:02:23.691705 20259 sgd_solver.cpp:105] Iteration 1872, lr = 0.00144616
I0408 15:02:28.823544 20259 solver.cpp:218] Iteration 1884 (2.33841 iter/s, 5.13169s/12 iters), loss = 3.64066
I0408 15:02:28.823585 20259 solver.cpp:237]     Train net output #0: loss = 3.64066 (* 1 = 3.64066 loss)
I0408 15:02:28.823598 20259 sgd_solver.cpp:105] Iteration 1884, lr = 0.00142834
I0408 15:02:33.820520 20259 solver.cpp:218] Iteration 1896 (2.40155 iter/s, 4.99678s/12 iters), loss = 3.75052
I0408 15:02:33.820634 20259 solver.cpp:237]     Train net output #0: loss = 3.75052 (* 1 = 3.75052 loss)
I0408 15:02:33.820647 20259 sgd_solver.cpp:105] Iteration 1896, lr = 0.00141075
I0408 15:02:38.874197 20259 solver.cpp:218] Iteration 1908 (2.37464 iter/s, 5.0534s/12 iters), loss = 3.81717
I0408 15:02:38.874243 20259 solver.cpp:237]     Train net output #0: loss = 3.81717 (* 1 = 3.81717 loss)
I0408 15:02:38.874254 20259 sgd_solver.cpp:105] Iteration 1908, lr = 0.00139337
I0408 15:02:43.900074 20259 solver.cpp:218] Iteration 1920 (2.38774 iter/s, 5.02567s/12 iters), loss = 3.76455
I0408 15:02:43.900118 20259 solver.cpp:237]     Train net output #0: loss = 3.76455 (* 1 = 3.76455 loss)
I0408 15:02:43.900130 20259 sgd_solver.cpp:105] Iteration 1920, lr = 0.00137621
I0408 15:02:44.221483 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:02:48.889886 20259 solver.cpp:218] Iteration 1932 (2.405 iter/s, 4.98961s/12 iters), loss = 3.61148
I0408 15:02:48.889928 20259 solver.cpp:237]     Train net output #0: loss = 3.61148 (* 1 = 3.61148 loss)
I0408 15:02:48.889940 20259 sgd_solver.cpp:105] Iteration 1932, lr = 0.00135925
I0408 15:02:50.955826 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 15:02:56.972002 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 15:03:03.203152 20259 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 15:03:03.203186 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:03:06.880882 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:03:07.666175 20259 solver.cpp:397]     Test net output #0: accuracy = 0.124387
I0408 15:03:07.666220 20259 solver.cpp:397]     Test net output #1: loss = 4.03324 (* 1 = 4.03324 loss)
I0408 15:03:09.561167 20259 solver.cpp:218] Iteration 1944 (0.580534 iter/s, 20.6706s/12 iters), loss = 4.01062
I0408 15:03:09.561214 20259 solver.cpp:237]     Train net output #0: loss = 4.01062 (* 1 = 4.01062 loss)
I0408 15:03:09.561226 20259 sgd_solver.cpp:105] Iteration 1944, lr = 0.00134251
I0408 15:03:14.570937 20259 solver.cpp:218] Iteration 1956 (2.39542 iter/s, 5.00955s/12 iters), loss = 3.57022
I0408 15:03:14.570973 20259 solver.cpp:237]     Train net output #0: loss = 3.57022 (* 1 = 3.57022 loss)
I0408 15:03:14.570982 20259 sgd_solver.cpp:105] Iteration 1956, lr = 0.00132597
I0408 15:03:19.590667 20259 solver.cpp:218] Iteration 1968 (2.39066 iter/s, 5.01953s/12 iters), loss = 3.34431
I0408 15:03:19.590709 20259 solver.cpp:237]     Train net output #0: loss = 3.34431 (* 1 = 3.34431 loss)
I0408 15:03:19.590721 20259 sgd_solver.cpp:105] Iteration 1968, lr = 0.00130964
I0408 15:03:24.492404 20259 solver.cpp:218] Iteration 1980 (2.44821 iter/s, 4.90154s/12 iters), loss = 3.63317
I0408 15:03:24.492449 20259 solver.cpp:237]     Train net output #0: loss = 3.63317 (* 1 = 3.63317 loss)
I0408 15:03:24.492460 20259 sgd_solver.cpp:105] Iteration 1980, lr = 0.0012935
I0408 15:03:29.473385 20259 solver.cpp:218] Iteration 1992 (2.40926 iter/s, 4.98078s/12 iters), loss = 3.66805
I0408 15:03:29.473420 20259 solver.cpp:237]     Train net output #0: loss = 3.66805 (* 1 = 3.66805 loss)
I0408 15:03:29.473429 20259 sgd_solver.cpp:105] Iteration 1992, lr = 0.00127757
I0408 15:03:34.483935 20259 solver.cpp:218] Iteration 2004 (2.39504 iter/s, 5.01035s/12 iters), loss = 3.26436
I0408 15:03:34.483968 20259 solver.cpp:237]     Train net output #0: loss = 3.26436 (* 1 = 3.26436 loss)
I0408 15:03:34.483975 20259 sgd_solver.cpp:105] Iteration 2004, lr = 0.00126183
I0408 15:03:39.495479 20259 solver.cpp:218] Iteration 2016 (2.39456 iter/s, 5.01135s/12 iters), loss = 3.5474
I0408 15:03:39.495594 20259 solver.cpp:237]     Train net output #0: loss = 3.5474 (* 1 = 3.5474 loss)
I0408 15:03:39.495609 20259 sgd_solver.cpp:105] Iteration 2016, lr = 0.00124629
I0408 15:03:42.035094 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:03:44.485615 20259 solver.cpp:218] Iteration 2028 (2.40488 iter/s, 4.98986s/12 iters), loss = 3.59655
I0408 15:03:44.485658 20259 solver.cpp:237]     Train net output #0: loss = 3.59655 (* 1 = 3.59655 loss)
I0408 15:03:44.485671 20259 sgd_solver.cpp:105] Iteration 2028, lr = 0.00123093
I0408 15:03:49.021239 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 15:03:52.112627 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 15:03:56.453635 20259 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 15:03:56.453666 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:04:00.084406 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:04:00.912760 20259 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 15:04:00.912801 20259 solver.cpp:397]     Test net output #1: loss = 3.95742 (* 1 = 3.95742 loss)
I0408 15:04:01.002737 20259 solver.cpp:218] Iteration 2040 (0.726543 iter/s, 16.5166s/12 iters), loss = 3.71546
I0408 15:04:01.002781 20259 solver.cpp:237]     Train net output #0: loss = 3.71546 (* 1 = 3.71546 loss)
I0408 15:04:01.002791 20259 sgd_solver.cpp:105] Iteration 2040, lr = 0.00121577
I0408 15:04:05.263526 20259 solver.cpp:218] Iteration 2052 (2.8165 iter/s, 4.2606s/12 iters), loss = 3.62387
I0408 15:04:05.263574 20259 solver.cpp:237]     Train net output #0: loss = 3.62387 (* 1 = 3.62387 loss)
I0408 15:04:05.263586 20259 sgd_solver.cpp:105] Iteration 2052, lr = 0.00120079
I0408 15:04:06.891769 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:04:10.311462 20259 solver.cpp:218] Iteration 2064 (2.37731 iter/s, 5.04773s/12 iters), loss = 3.68266
I0408 15:04:10.313899 20259 solver.cpp:237]     Train net output #0: loss = 3.68266 (* 1 = 3.68266 loss)
I0408 15:04:10.313912 20259 sgd_solver.cpp:105] Iteration 2064, lr = 0.001186
I0408 15:04:15.347347 20259 solver.cpp:218] Iteration 2076 (2.38413 iter/s, 5.03328s/12 iters), loss = 3.65474
I0408 15:04:15.347386 20259 solver.cpp:237]     Train net output #0: loss = 3.65474 (* 1 = 3.65474 loss)
I0408 15:04:15.347396 20259 sgd_solver.cpp:105] Iteration 2076, lr = 0.00117139
I0408 15:04:20.322679 20259 solver.cpp:218] Iteration 2088 (2.412 iter/s, 4.97513s/12 iters), loss = 3.53623
I0408 15:04:20.322723 20259 solver.cpp:237]     Train net output #0: loss = 3.53623 (* 1 = 3.53623 loss)
I0408 15:04:20.322736 20259 sgd_solver.cpp:105] Iteration 2088, lr = 0.00115696
I0408 15:04:25.234295 20259 solver.cpp:218] Iteration 2100 (2.44329 iter/s, 4.91141s/12 iters), loss = 3.38764
I0408 15:04:25.234342 20259 solver.cpp:237]     Train net output #0: loss = 3.38764 (* 1 = 3.38764 loss)
I0408 15:04:25.234355 20259 sgd_solver.cpp:105] Iteration 2100, lr = 0.00114271
I0408 15:04:30.237833 20259 solver.cpp:218] Iteration 2112 (2.3984 iter/s, 5.00333s/12 iters), loss = 3.41621
I0408 15:04:30.237879 20259 solver.cpp:237]     Train net output #0: loss = 3.41621 (* 1 = 3.41621 loss)
I0408 15:04:30.237891 20259 sgd_solver.cpp:105] Iteration 2112, lr = 0.00112863
I0408 15:04:34.903178 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:04:35.220469 20259 solver.cpp:218] Iteration 2124 (2.40846 iter/s, 4.98244s/12 iters), loss = 3.4461
I0408 15:04:35.220506 20259 solver.cpp:237]     Train net output #0: loss = 3.4461 (* 1 = 3.4461 loss)
I0408 15:04:35.220515 20259 sgd_solver.cpp:105] Iteration 2124, lr = 0.00111473
I0408 15:04:40.247640 20259 solver.cpp:218] Iteration 2136 (2.38712 iter/s, 5.02697s/12 iters), loss = 3.32062
I0408 15:04:40.247687 20259 solver.cpp:237]     Train net output #0: loss = 3.32062 (* 1 = 3.32062 loss)
I0408 15:04:40.247700 20259 sgd_solver.cpp:105] Iteration 2136, lr = 0.00110099
I0408 15:04:42.287585 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 15:04:45.326403 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 15:04:47.657306 20259 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 15:04:47.657338 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:04:51.251071 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:04:52.113543 20259 solver.cpp:397]     Test net output #0: accuracy = 0.139706
I0408 15:04:52.113590 20259 solver.cpp:397]     Test net output #1: loss = 3.89249 (* 1 = 3.89249 loss)
I0408 15:04:54.081753 20259 solver.cpp:218] Iteration 2148 (0.86745 iter/s, 13.8336s/12 iters), loss = 3.46472
I0408 15:04:54.081804 20259 solver.cpp:237]     Train net output #0: loss = 3.46472 (* 1 = 3.46472 loss)
I0408 15:04:54.081817 20259 sgd_solver.cpp:105] Iteration 2148, lr = 0.00108743
I0408 15:04:59.304219 20259 solver.cpp:218] Iteration 2160 (2.29786 iter/s, 5.22225s/12 iters), loss = 3.64403
I0408 15:04:59.304257 20259 solver.cpp:237]     Train net output #0: loss = 3.64403 (* 1 = 3.64403 loss)
I0408 15:04:59.304266 20259 sgd_solver.cpp:105] Iteration 2160, lr = 0.00107404
I0408 15:05:04.327162 20259 solver.cpp:218] Iteration 2172 (2.38913 iter/s, 5.02274s/12 iters), loss = 3.49851
I0408 15:05:04.327210 20259 solver.cpp:237]     Train net output #0: loss = 3.49851 (* 1 = 3.49851 loss)
I0408 15:05:04.327222 20259 sgd_solver.cpp:105] Iteration 2172, lr = 0.0010608
I0408 15:05:09.362622 20259 solver.cpp:218] Iteration 2184 (2.3832 iter/s, 5.03525s/12 iters), loss = 3.47269
I0408 15:05:09.362668 20259 solver.cpp:237]     Train net output #0: loss = 3.47269 (* 1 = 3.47269 loss)
I0408 15:05:09.362679 20259 sgd_solver.cpp:105] Iteration 2184, lr = 0.00104774
I0408 15:05:14.371472 20259 solver.cpp:218] Iteration 2196 (2.39586 iter/s, 5.00864s/12 iters), loss = 3.27776
I0408 15:05:14.371614 20259 solver.cpp:237]     Train net output #0: loss = 3.27776 (* 1 = 3.27776 loss)
I0408 15:05:14.371627 20259 sgd_solver.cpp:105] Iteration 2196, lr = 0.00103483
I0408 15:05:19.421101 20259 solver.cpp:218] Iteration 2208 (2.37655 iter/s, 5.04933s/12 iters), loss = 3.23975
I0408 15:05:19.421149 20259 solver.cpp:237]     Train net output #0: loss = 3.23975 (* 1 = 3.23975 loss)
I0408 15:05:19.421162 20259 sgd_solver.cpp:105] Iteration 2208, lr = 0.00102208
I0408 15:05:24.422876 20259 solver.cpp:218] Iteration 2220 (2.39925 iter/s, 5.00157s/12 iters), loss = 3.18457
I0408 15:05:24.422924 20259 solver.cpp:237]     Train net output #0: loss = 3.18457 (* 1 = 3.18457 loss)
I0408 15:05:24.422935 20259 sgd_solver.cpp:105] Iteration 2220, lr = 0.00100949
I0408 15:05:26.213872 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:05:29.379809 20259 solver.cpp:218] Iteration 2232 (2.42095 iter/s, 4.95672s/12 iters), loss = 3.57632
I0408 15:05:29.379866 20259 solver.cpp:237]     Train net output #0: loss = 3.57632 (* 1 = 3.57632 loss)
I0408 15:05:29.379881 20259 sgd_solver.cpp:105] Iteration 2232, lr = 0.000997055
I0408 15:05:33.938153 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 15:05:37.829149 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 15:05:40.791296 20259 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 15:05:40.791328 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:05:44.341750 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:05:45.249097 20259 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 15:05:45.249219 20259 solver.cpp:397]     Test net output #1: loss = 3.78959 (* 1 = 3.78959 loss)
I0408 15:05:45.339418 20259 solver.cpp:218] Iteration 2244 (0.751924 iter/s, 15.9591s/12 iters), loss = 3.61703
I0408 15:05:45.339479 20259 solver.cpp:237]     Train net output #0: loss = 3.61703 (* 1 = 3.61703 loss)
I0408 15:05:45.339496 20259 sgd_solver.cpp:105] Iteration 2244, lr = 0.000984773
I0408 15:05:49.900413 20259 solver.cpp:218] Iteration 2256 (2.63112 iter/s, 4.56079s/12 iters), loss = 3.02109
I0408 15:05:49.900460 20259 solver.cpp:237]     Train net output #0: loss = 3.02109 (* 1 = 3.02109 loss)
I0408 15:05:49.900471 20259 sgd_solver.cpp:105] Iteration 2256, lr = 0.000972642
I0408 15:05:54.941917 20259 solver.cpp:218] Iteration 2268 (2.38034 iter/s, 5.0413s/12 iters), loss = 3.19775
I0408 15:05:54.941967 20259 solver.cpp:237]     Train net output #0: loss = 3.19775 (* 1 = 3.19775 loss)
I0408 15:05:54.941977 20259 sgd_solver.cpp:105] Iteration 2268, lr = 0.00096066
I0408 15:05:59.974519 20259 solver.cpp:218] Iteration 2280 (2.38455 iter/s, 5.0324s/12 iters), loss = 3.32155
I0408 15:05:59.974562 20259 solver.cpp:237]     Train net output #0: loss = 3.32155 (* 1 = 3.32155 loss)
I0408 15:05:59.974575 20259 sgd_solver.cpp:105] Iteration 2280, lr = 0.000948826
I0408 15:06:05.020313 20259 solver.cpp:218] Iteration 2292 (2.37832 iter/s, 5.04559s/12 iters), loss = 3.28215
I0408 15:06:05.020360 20259 solver.cpp:237]     Train net output #0: loss = 3.28215 (* 1 = 3.28215 loss)
I0408 15:06:05.020372 20259 sgd_solver.cpp:105] Iteration 2292, lr = 0.000937137
I0408 15:06:10.048588 20259 solver.cpp:218] Iteration 2304 (2.3866 iter/s, 5.02807s/12 iters), loss = 3.42641
I0408 15:06:10.048630 20259 solver.cpp:237]     Train net output #0: loss = 3.42641 (* 1 = 3.42641 loss)
I0408 15:06:10.048641 20259 sgd_solver.cpp:105] Iteration 2304, lr = 0.000925593
I0408 15:06:15.043761 20259 solver.cpp:218] Iteration 2316 (2.40241 iter/s, 4.99497s/12 iters), loss = 3.01448
I0408 15:06:15.043793 20259 solver.cpp:237]     Train net output #0: loss = 3.01448 (* 1 = 3.01448 loss)
I0408 15:06:15.043802 20259 sgd_solver.cpp:105] Iteration 2316, lr = 0.00091419
I0408 15:06:19.012668 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:06:20.061527 20259 solver.cpp:218] Iteration 2328 (2.3916 iter/s, 5.01757s/12 iters), loss = 3.29679
I0408 15:06:20.061568 20259 solver.cpp:237]     Train net output #0: loss = 3.29679 (* 1 = 3.29679 loss)
I0408 15:06:20.061579 20259 sgd_solver.cpp:105] Iteration 2328, lr = 0.000902929
I0408 15:06:25.118849 20259 solver.cpp:218] Iteration 2340 (2.37289 iter/s, 5.05712s/12 iters), loss = 3.28309
I0408 15:06:25.118893 20259 solver.cpp:237]     Train net output #0: loss = 3.28309 (* 1 = 3.28309 loss)
I0408 15:06:25.118904 20259 sgd_solver.cpp:105] Iteration 2340, lr = 0.000891806
I0408 15:06:27.176913 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 15:06:30.558990 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 15:06:33.572320 20259 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 15:06:33.572350 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:06:37.086513 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:06:38.028780 20259 solver.cpp:397]     Test net output #0: accuracy = 0.166054
I0408 15:06:38.028825 20259 solver.cpp:397]     Test net output #1: loss = 3.73196 (* 1 = 3.73196 loss)
I0408 15:06:40.011312 20259 solver.cpp:218] Iteration 2352 (0.805804 iter/s, 14.892s/12 iters), loss = 3.0598
I0408 15:06:40.011356 20259 solver.cpp:237]     Train net output #0: loss = 3.0598 (* 1 = 3.0598 loss)
I0408 15:06:40.011368 20259 sgd_solver.cpp:105] Iteration 2352, lr = 0.00088082
I0408 15:06:45.013599 20259 solver.cpp:218] Iteration 2364 (2.399 iter/s, 5.00208s/12 iters), loss = 3.21826
I0408 15:06:45.013640 20259 solver.cpp:237]     Train net output #0: loss = 3.21826 (* 1 = 3.21826 loss)
I0408 15:06:45.013653 20259 sgd_solver.cpp:105] Iteration 2364, lr = 0.000869969
I0408 15:06:50.047030 20259 solver.cpp:218] Iteration 2376 (2.38416 iter/s, 5.03323s/12 iters), loss = 3.01606
I0408 15:06:50.047142 20259 solver.cpp:237]     Train net output #0: loss = 3.01606 (* 1 = 3.01606 loss)
I0408 15:06:50.047155 20259 sgd_solver.cpp:105] Iteration 2376, lr = 0.000859252
I0408 15:06:55.001895 20259 solver.cpp:218] Iteration 2388 (2.42199 iter/s, 4.9546s/12 iters), loss = 3.13057
I0408 15:06:55.001940 20259 solver.cpp:237]     Train net output #0: loss = 3.13057 (* 1 = 3.13057 loss)
I0408 15:06:55.001951 20259 sgd_solver.cpp:105] Iteration 2388, lr = 0.000848667
I0408 15:07:00.017313 20259 solver.cpp:218] Iteration 2400 (2.39272 iter/s, 5.01521s/12 iters), loss = 3.13841
I0408 15:07:00.017355 20259 solver.cpp:237]     Train net output #0: loss = 3.13841 (* 1 = 3.13841 loss)
I0408 15:07:00.017367 20259 sgd_solver.cpp:105] Iteration 2400, lr = 0.000838212
I0408 15:07:04.953076 20259 solver.cpp:218] Iteration 2412 (2.43133 iter/s, 4.93556s/12 iters), loss = 2.90418
I0408 15:07:04.953121 20259 solver.cpp:237]     Train net output #0: loss = 2.90418 (* 1 = 2.90418 loss)
I0408 15:07:04.953133 20259 sgd_solver.cpp:105] Iteration 2412, lr = 0.000827887
I0408 15:07:09.947500 20259 solver.cpp:218] Iteration 2424 (2.40278 iter/s, 4.99422s/12 iters), loss = 2.9752
I0408 15:07:09.947543 20259 solver.cpp:237]     Train net output #0: loss = 2.9752 (* 1 = 2.9752 loss)
I0408 15:07:09.947556 20259 sgd_solver.cpp:105] Iteration 2424, lr = 0.000817688
I0408 15:07:11.020691 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:07:14.988600 20259 solver.cpp:218] Iteration 2436 (2.38053 iter/s, 5.04089s/12 iters), loss = 3.01001
I0408 15:07:14.988646 20259 solver.cpp:237]     Train net output #0: loss = 3.01001 (* 1 = 3.01001 loss)
I0408 15:07:14.988656 20259 sgd_solver.cpp:105] Iteration 2436, lr = 0.000807615
I0408 15:07:19.550998 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 15:07:23.779616 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 15:07:26.113437 20259 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 15:07:26.113471 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:07:29.593128 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:07:30.568977 20259 solver.cpp:397]     Test net output #0: accuracy = 0.178922
I0408 15:07:30.569025 20259 solver.cpp:397]     Test net output #1: loss = 3.63182 (* 1 = 3.63182 loss)
I0408 15:07:30.658993 20259 solver.cpp:218] Iteration 2448 (0.7658 iter/s, 15.6699s/12 iters), loss = 3.20729
I0408 15:07:30.659030 20259 solver.cpp:237]     Train net output #0: loss = 3.20729 (* 1 = 3.20729 loss)
I0408 15:07:30.659042 20259 sgd_solver.cpp:105] Iteration 2448, lr = 0.000797666
I0408 15:07:34.969417 20259 solver.cpp:218] Iteration 2460 (2.78406 iter/s, 4.31025s/12 iters), loss = 3.18452
I0408 15:07:34.969465 20259 solver.cpp:237]     Train net output #0: loss = 3.18452 (* 1 = 3.18452 loss)
I0408 15:07:34.969477 20259 sgd_solver.cpp:105] Iteration 2460, lr = 0.00078784
I0408 15:07:40.012507 20259 solver.cpp:218] Iteration 2472 (2.37959 iter/s, 5.04288s/12 iters), loss = 3.19344
I0408 15:07:40.012550 20259 solver.cpp:237]     Train net output #0: loss = 3.19344 (* 1 = 3.19344 loss)
I0408 15:07:40.012562 20259 sgd_solver.cpp:105] Iteration 2472, lr = 0.000778135
I0408 15:07:45.019335 20259 solver.cpp:218] Iteration 2484 (2.39682 iter/s, 5.00663s/12 iters), loss = 3.30445
I0408 15:07:45.019379 20259 solver.cpp:237]     Train net output #0: loss = 3.30445 (* 1 = 3.30445 loss)
I0408 15:07:45.019392 20259 sgd_solver.cpp:105] Iteration 2484, lr = 0.000768549
I0408 15:07:49.948812 20259 solver.cpp:218] Iteration 2496 (2.43444 iter/s, 4.92928s/12 iters), loss = 3.30057
I0408 15:07:49.948856 20259 solver.cpp:237]     Train net output #0: loss = 3.30057 (* 1 = 3.30057 loss)
I0408 15:07:49.948868 20259 sgd_solver.cpp:105] Iteration 2496, lr = 0.000759081
I0408 15:07:54.933526 20259 solver.cpp:218] Iteration 2508 (2.40746 iter/s, 4.98451s/12 iters), loss = 3.16229
I0408 15:07:54.933631 20259 solver.cpp:237]     Train net output #0: loss = 3.16229 (* 1 = 3.16229 loss)
I0408 15:07:54.933642 20259 sgd_solver.cpp:105] Iteration 2508, lr = 0.00074973
I0408 15:07:59.932819 20259 solver.cpp:218] Iteration 2520 (2.40047 iter/s, 4.99902s/12 iters), loss = 2.94978
I0408 15:07:59.932868 20259 solver.cpp:237]     Train net output #0: loss = 2.94978 (* 1 = 2.94978 loss)
I0408 15:07:59.932880 20259 sgd_solver.cpp:105] Iteration 2520, lr = 0.000740494
I0408 15:08:03.146049 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:08:04.938446 20259 solver.cpp:218] Iteration 2532 (2.3974 iter/s, 5.00542s/12 iters), loss = 3.06421
I0408 15:08:04.938499 20259 solver.cpp:237]     Train net output #0: loss = 3.06421 (* 1 = 3.06421 loss)
I0408 15:08:04.938513 20259 sgd_solver.cpp:105] Iteration 2532, lr = 0.000731372
I0408 15:08:09.910609 20259 solver.cpp:218] Iteration 2544 (2.41354 iter/s, 4.97195s/12 iters), loss = 3.10415
I0408 15:08:09.910653 20259 solver.cpp:237]     Train net output #0: loss = 3.10415 (* 1 = 3.10415 loss)
I0408 15:08:09.910665 20259 sgd_solver.cpp:105] Iteration 2544, lr = 0.000722363
I0408 15:08:11.917366 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 15:08:18.483606 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 15:08:22.530958 20259 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 15:08:22.530990 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:08:25.951063 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:08:26.973307 20259 solver.cpp:397]     Test net output #0: accuracy = 0.191176
I0408 15:08:26.973354 20259 solver.cpp:397]     Test net output #1: loss = 3.60702 (* 1 = 3.60702 loss)
I0408 15:08:28.952715 20259 solver.cpp:218] Iteration 2556 (0.630203 iter/s, 19.0415s/12 iters), loss = 3.15145
I0408 15:08:28.952764 20259 solver.cpp:237]     Train net output #0: loss = 3.15145 (* 1 = 3.15145 loss)
I0408 15:08:28.952775 20259 sgd_solver.cpp:105] Iteration 2556, lr = 0.000713464
I0408 15:08:33.951498 20259 solver.cpp:218] Iteration 2568 (2.40068 iter/s, 4.99858s/12 iters), loss = 3.10466
I0408 15:08:33.951542 20259 solver.cpp:237]     Train net output #0: loss = 3.10466 (* 1 = 3.10466 loss)
I0408 15:08:33.951555 20259 sgd_solver.cpp:105] Iteration 2568, lr = 0.000704675
I0408 15:08:38.945439 20259 solver.cpp:218] Iteration 2580 (2.40301 iter/s, 4.99373s/12 iters), loss = 2.92836
I0408 15:08:38.945493 20259 solver.cpp:237]     Train net output #0: loss = 2.92836 (* 1 = 2.92836 loss)
I0408 15:08:38.945508 20259 sgd_solver.cpp:105] Iteration 2580, lr = 0.000695994
I0408 15:08:43.950170 20259 solver.cpp:218] Iteration 2592 (2.39783 iter/s, 5.00452s/12 iters), loss = 3.16647
I0408 15:08:43.950224 20259 solver.cpp:237]     Train net output #0: loss = 3.16647 (* 1 = 3.16647 loss)
I0408 15:08:43.950235 20259 sgd_solver.cpp:105] Iteration 2592, lr = 0.00068742
I0408 15:08:48.948268 20259 solver.cpp:218] Iteration 2604 (2.40102 iter/s, 4.99788s/12 iters), loss = 3.00189
I0408 15:08:48.948312 20259 solver.cpp:237]     Train net output #0: loss = 3.00189 (* 1 = 3.00189 loss)
I0408 15:08:48.948323 20259 sgd_solver.cpp:105] Iteration 2604, lr = 0.000678952
I0408 15:08:53.981889 20259 solver.cpp:218] Iteration 2616 (2.38407 iter/s, 5.03342s/12 iters), loss = 2.92384
I0408 15:08:53.981932 20259 solver.cpp:237]     Train net output #0: loss = 2.92384 (* 1 = 2.92384 loss)
I0408 15:08:53.981945 20259 sgd_solver.cpp:105] Iteration 2616, lr = 0.000670588
I0408 15:08:59.008347 20259 solver.cpp:218] Iteration 2628 (2.38747 iter/s, 5.02625s/12 iters), loss = 3.13228
I0408 15:08:59.008452 20259 solver.cpp:237]     Train net output #0: loss = 3.13228 (* 1 = 3.13228 loss)
I0408 15:08:59.008466 20259 sgd_solver.cpp:105] Iteration 2628, lr = 0.000662327
I0408 15:08:59.432703 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:09:03.985122 20259 solver.cpp:218] Iteration 2640 (2.41133 iter/s, 4.97651s/12 iters), loss = 2.92048
I0408 15:09:03.985167 20259 solver.cpp:237]     Train net output #0: loss = 2.92048 (* 1 = 2.92048 loss)
I0408 15:09:03.985179 20259 sgd_solver.cpp:105] Iteration 2640, lr = 0.000654168
I0408 15:09:08.502729 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 15:09:11.435914 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 15:09:17.857976 20259 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 15:09:17.858011 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:09:21.258826 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:09:22.315614 20259 solver.cpp:397]     Test net output #0: accuracy = 0.203431
I0408 15:09:22.315662 20259 solver.cpp:397]     Test net output #1: loss = 3.53559 (* 1 = 3.53559 loss)
I0408 15:09:22.402951 20259 solver.cpp:218] Iteration 2652 (0.651564 iter/s, 18.4172s/12 iters), loss = 2.85117
I0408 15:09:22.402988 20259 solver.cpp:237]     Train net output #0: loss = 2.85117 (* 1 = 2.85117 loss)
I0408 15:09:22.403000 20259 sgd_solver.cpp:105] Iteration 2652, lr = 0.00064611
I0408 15:09:26.778522 20259 solver.cpp:218] Iteration 2664 (2.74261 iter/s, 4.37539s/12 iters), loss = 2.70883
I0408 15:09:26.778564 20259 solver.cpp:237]     Train net output #0: loss = 2.70883 (* 1 = 2.70883 loss)
I0408 15:09:26.778575 20259 sgd_solver.cpp:105] Iteration 2664, lr = 0.00063815
I0408 15:09:31.783215 20259 solver.cpp:218] Iteration 2676 (2.39785 iter/s, 5.00449s/12 iters), loss = 2.79077
I0408 15:09:31.786119 20259 solver.cpp:237]     Train net output #0: loss = 2.79077 (* 1 = 2.79077 loss)
I0408 15:09:31.786130 20259 sgd_solver.cpp:105] Iteration 2676, lr = 0.000630289
I0408 15:09:36.798418 20259 solver.cpp:218] Iteration 2688 (2.39419 iter/s, 5.01214s/12 iters), loss = 2.88317
I0408 15:09:36.798454 20259 solver.cpp:237]     Train net output #0: loss = 2.88317 (* 1 = 2.88317 loss)
I0408 15:09:36.798462 20259 sgd_solver.cpp:105] Iteration 2688, lr = 0.000622525
I0408 15:09:41.805729 20259 solver.cpp:218] Iteration 2700 (2.39659 iter/s, 5.00712s/12 iters), loss = 2.72681
I0408 15:09:41.805774 20259 solver.cpp:237]     Train net output #0: loss = 2.72681 (* 1 = 2.72681 loss)
I0408 15:09:41.805786 20259 sgd_solver.cpp:105] Iteration 2700, lr = 0.000614856
I0408 15:09:46.779611 20259 solver.cpp:218] Iteration 2712 (2.4127 iter/s, 4.97368s/12 iters), loss = 2.84788
I0408 15:09:46.779654 20259 solver.cpp:237]     Train net output #0: loss = 2.84788 (* 1 = 2.84788 loss)
I0408 15:09:46.779664 20259 sgd_solver.cpp:105] Iteration 2712, lr = 0.000607282
I0408 15:09:51.805457 20259 solver.cpp:218] Iteration 2724 (2.38776 iter/s, 5.02564s/12 iters), loss = 2.85771
I0408 15:09:51.805505 20259 solver.cpp:237]     Train net output #0: loss = 2.85771 (* 1 = 2.85771 loss)
I0408 15:09:51.805517 20259 sgd_solver.cpp:105] Iteration 2724, lr = 0.000599801
I0408 15:09:54.400068 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:09:56.795984 20259 solver.cpp:218] Iteration 2736 (2.40466 iter/s, 4.99032s/12 iters), loss = 2.68053
I0408 15:09:56.796027 20259 solver.cpp:237]     Train net output #0: loss = 2.68053 (* 1 = 2.68053 loss)
I0408 15:09:56.796041 20259 sgd_solver.cpp:105] Iteration 2736, lr = 0.000592412
I0408 15:10:01.646788 20259 solver.cpp:218] Iteration 2748 (2.47392 iter/s, 4.8506s/12 iters), loss = 2.85661
I0408 15:10:01.646834 20259 solver.cpp:237]     Train net output #0: loss = 2.85661 (* 1 = 2.85661 loss)
I0408 15:10:01.646847 20259 sgd_solver.cpp:105] Iteration 2748, lr = 0.000585114
I0408 15:10:03.652671 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 15:10:06.596385 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 15:10:08.916200 20259 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 15:10:08.916230 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:10:11.994665 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:10:12.229984 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:10:13.331696 20259 solver.cpp:397]     Test net output #0: accuracy = 0.207721
I0408 15:10:13.331739 20259 solver.cpp:397]     Test net output #1: loss = 3.51276 (* 1 = 3.51276 loss)
I0408 15:10:15.313576 20259 solver.cpp:218] Iteration 2760 (0.87807 iter/s, 13.6663s/12 iters), loss = 2.91869
I0408 15:10:15.313621 20259 solver.cpp:237]     Train net output #0: loss = 2.91869 (* 1 = 2.91869 loss)
I0408 15:10:15.313633 20259 sgd_solver.cpp:105] Iteration 2760, lr = 0.000577906
I0408 15:10:20.522575 20259 solver.cpp:218] Iteration 2772 (2.3038 iter/s, 5.20879s/12 iters), loss = 2.82486
I0408 15:10:20.522620 20259 solver.cpp:237]     Train net output #0: loss = 2.82486 (* 1 = 2.82486 loss)
I0408 15:10:20.522632 20259 sgd_solver.cpp:105] Iteration 2772, lr = 0.000570787
I0408 15:10:25.526260 20259 solver.cpp:218] Iteration 2784 (2.39833 iter/s, 5.00348s/12 iters), loss = 3.02412
I0408 15:10:25.526304 20259 solver.cpp:237]     Train net output #0: loss = 3.02412 (* 1 = 3.02412 loss)
I0408 15:10:25.526316 20259 sgd_solver.cpp:105] Iteration 2784, lr = 0.000563755
I0408 15:10:30.563994 20259 solver.cpp:218] Iteration 2796 (2.38212 iter/s, 5.03753s/12 iters), loss = 3.01716
I0408 15:10:30.564038 20259 solver.cpp:237]     Train net output #0: loss = 3.01716 (* 1 = 3.01716 loss)
I0408 15:10:30.564050 20259 sgd_solver.cpp:105] Iteration 2796, lr = 0.000556811
I0408 15:10:35.542837 20259 solver.cpp:218] Iteration 2808 (2.41029 iter/s, 4.97864s/12 iters), loss = 2.75992
I0408 15:10:35.542953 20259 solver.cpp:237]     Train net output #0: loss = 2.75992 (* 1 = 2.75992 loss)
I0408 15:10:35.542963 20259 sgd_solver.cpp:105] Iteration 2808, lr = 0.000549951
I0408 15:10:40.578519 20259 solver.cpp:218] Iteration 2820 (2.38312 iter/s, 5.03541s/12 iters), loss = 2.7338
I0408 15:10:40.578555 20259 solver.cpp:237]     Train net output #0: loss = 2.7338 (* 1 = 2.7338 loss)
I0408 15:10:40.578565 20259 sgd_solver.cpp:105] Iteration 2820, lr = 0.000543177
I0408 15:10:45.318543 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:10:45.602277 20259 solver.cpp:218] Iteration 2832 (2.38874 iter/s, 5.02356s/12 iters), loss = 2.8311
I0408 15:10:45.602322 20259 solver.cpp:237]     Train net output #0: loss = 2.8311 (* 1 = 2.8311 loss)
I0408 15:10:45.602334 20259 sgd_solver.cpp:105] Iteration 2832, lr = 0.000536485
I0408 15:10:50.544245 20259 solver.cpp:218] Iteration 2844 (2.42828 iter/s, 4.94176s/12 iters), loss = 2.76284
I0408 15:10:50.544291 20259 solver.cpp:237]     Train net output #0: loss = 2.76284 (* 1 = 2.76284 loss)
I0408 15:10:50.544302 20259 sgd_solver.cpp:105] Iteration 2844, lr = 0.000529876
I0408 15:10:55.048542 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 15:10:58.136915 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 15:11:00.678031 20259 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 15:11:00.678063 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:11:03.971307 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:11:05.109498 20259 solver.cpp:397]     Test net output #0: accuracy = 0.208946
I0408 15:11:05.109541 20259 solver.cpp:397]     Test net output #1: loss = 3.49857 (* 1 = 3.49857 loss)
I0408 15:11:05.199476 20259 solver.cpp:218] Iteration 2856 (0.818847 iter/s, 14.6547s/12 iters), loss = 2.7393
I0408 15:11:05.199508 20259 solver.cpp:237]     Train net output #0: loss = 2.7393 (* 1 = 2.7393 loss)
I0408 15:11:05.199519 20259 sgd_solver.cpp:105] Iteration 2856, lr = 0.000523349
I0408 15:11:09.358388 20259 solver.cpp:218] Iteration 2868 (2.88549 iter/s, 4.15875s/12 iters), loss = 2.8615
I0408 15:11:09.358502 20259 solver.cpp:237]     Train net output #0: loss = 2.8615 (* 1 = 2.8615 loss)
I0408 15:11:09.358515 20259 sgd_solver.cpp:105] Iteration 2868, lr = 0.000516902
I0408 15:11:14.355162 20259 solver.cpp:218] Iteration 2880 (2.40168 iter/s, 4.9965s/12 iters), loss = 2.49178
I0408 15:11:14.355199 20259 solver.cpp:237]     Train net output #0: loss = 2.49178 (* 1 = 2.49178 loss)
I0408 15:11:14.355209 20259 sgd_solver.cpp:105] Iteration 2880, lr = 0.000510534
I0408 15:11:19.313696 20259 solver.cpp:218] Iteration 2892 (2.42017 iter/s, 4.95834s/12 iters), loss = 2.69162
I0408 15:11:19.313741 20259 solver.cpp:237]     Train net output #0: loss = 2.69162 (* 1 = 2.69162 loss)
I0408 15:11:19.313753 20259 sgd_solver.cpp:105] Iteration 2892, lr = 0.000504245
I0408 15:11:24.284135 20259 solver.cpp:218] Iteration 2904 (2.41437 iter/s, 4.97024s/12 iters), loss = 2.60449
I0408 15:11:24.284178 20259 solver.cpp:237]     Train net output #0: loss = 2.60449 (* 1 = 2.60449 loss)
I0408 15:11:24.284189 20259 sgd_solver.cpp:105] Iteration 2904, lr = 0.000498033
I0408 15:11:29.264302 20259 solver.cpp:218] Iteration 2916 (2.40965 iter/s, 4.97997s/12 iters), loss = 2.75321
I0408 15:11:29.264344 20259 solver.cpp:237]     Train net output #0: loss = 2.75321 (* 1 = 2.75321 loss)
I0408 15:11:29.264355 20259 sgd_solver.cpp:105] Iteration 2916, lr = 0.000491898
I0408 15:11:34.258313 20259 solver.cpp:218] Iteration 2928 (2.40297 iter/s, 4.99381s/12 iters), loss = 2.41731
I0408 15:11:34.258349 20259 solver.cpp:237]     Train net output #0: loss = 2.41731 (* 1 = 2.41731 loss)
I0408 15:11:34.258358 20259 sgd_solver.cpp:105] Iteration 2928, lr = 0.000485839
I0408 15:11:36.108749 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:11:39.255331 20259 solver.cpp:218] Iteration 2940 (2.40153 iter/s, 4.99682s/12 iters), loss = 2.84523
I0408 15:11:39.255375 20259 solver.cpp:237]     Train net output #0: loss = 2.84523 (* 1 = 2.84523 loss)
I0408 15:11:39.255386 20259 sgd_solver.cpp:105] Iteration 2940, lr = 0.000479854
I0408 15:11:44.220929 20259 solver.cpp:218] Iteration 2952 (2.41672 iter/s, 4.9654s/12 iters), loss = 2.66043
I0408 15:11:44.221079 20259 solver.cpp:237]     Train net output #0: loss = 2.66043 (* 1 = 2.66043 loss)
I0408 15:11:44.221092 20259 sgd_solver.cpp:105] Iteration 2952, lr = 0.000473942
I0408 15:11:46.206670 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 15:11:49.259179 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 15:11:52.073858 20259 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 15:11:52.073886 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:11:55.354790 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:11:56.536296 20259 solver.cpp:397]     Test net output #0: accuracy = 0.219363
I0408 15:11:56.536340 20259 solver.cpp:397]     Test net output #1: loss = 3.46974 (* 1 = 3.46974 loss)
I0408 15:11:58.453583 20259 solver.cpp:218] Iteration 2964 (0.843166 iter/s, 14.2321s/12 iters), loss = 2.57463
I0408 15:11:58.453630 20259 solver.cpp:237]     Train net output #0: loss = 2.57463 (* 1 = 2.57463 loss)
I0408 15:11:58.453642 20259 sgd_solver.cpp:105] Iteration 2964, lr = 0.000468104
I0408 15:12:03.496500 20259 solver.cpp:218] Iteration 2976 (2.37967 iter/s, 5.04271s/12 iters), loss = 2.44253
I0408 15:12:03.496543 20259 solver.cpp:237]     Train net output #0: loss = 2.44253 (* 1 = 2.44253 loss)
I0408 15:12:03.496554 20259 sgd_solver.cpp:105] Iteration 2976, lr = 0.000462337
I0408 15:12:08.468011 20259 solver.cpp:218] Iteration 2988 (2.41385 iter/s, 4.97131s/12 iters), loss = 2.67271
I0408 15:12:08.468058 20259 solver.cpp:237]     Train net output #0: loss = 2.67271 (* 1 = 2.67271 loss)
I0408 15:12:08.468070 20259 sgd_solver.cpp:105] Iteration 2988, lr = 0.000456642
I0408 15:12:13.516521 20259 solver.cpp:218] Iteration 3000 (2.37704 iter/s, 5.0483s/12 iters), loss = 2.56572
I0408 15:12:13.516568 20259 solver.cpp:237]     Train net output #0: loss = 2.56572 (* 1 = 2.56572 loss)
I0408 15:12:13.516580 20259 sgd_solver.cpp:105] Iteration 3000, lr = 0.000451017
I0408 15:12:18.545464 20259 solver.cpp:218] Iteration 3012 (2.38628 iter/s, 5.02874s/12 iters), loss = 2.65478
I0408 15:12:18.545555 20259 solver.cpp:237]     Train net output #0: loss = 2.65478 (* 1 = 2.65478 loss)
I0408 15:12:18.545565 20259 sgd_solver.cpp:105] Iteration 3012, lr = 0.000445461
I0408 15:12:23.548739 20259 solver.cpp:218] Iteration 3024 (2.39855 iter/s, 5.00303s/12 iters), loss = 2.44566
I0408 15:12:23.548784 20259 solver.cpp:237]     Train net output #0: loss = 2.44566 (* 1 = 2.44566 loss)
I0408 15:12:23.548796 20259 sgd_solver.cpp:105] Iteration 3024, lr = 0.000439973
I0408 15:12:27.554788 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:12:28.594398 20259 solver.cpp:218] Iteration 3036 (2.37838 iter/s, 5.04546s/12 iters), loss = 2.62717
I0408 15:12:28.594444 20259 solver.cpp:237]     Train net output #0: loss = 2.62717 (* 1 = 2.62717 loss)
I0408 15:12:28.594455 20259 sgd_solver.cpp:105] Iteration 3036, lr = 0.000434553
I0408 15:12:33.583493 20259 solver.cpp:218] Iteration 3048 (2.40534 iter/s, 4.9889s/12 iters), loss = 2.57846
I0408 15:12:33.583537 20259 solver.cpp:237]     Train net output #0: loss = 2.57846 (* 1 = 2.57846 loss)
I0408 15:12:33.583549 20259 sgd_solver.cpp:105] Iteration 3048, lr = 0.0004292
I0408 15:12:38.050217 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 15:12:42.353797 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 15:12:46.690609 20259 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 15:12:46.690641 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:12:49.924980 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:12:51.140722 20259 solver.cpp:397]     Test net output #0: accuracy = 0.237132
I0408 15:12:51.140769 20259 solver.cpp:397]     Test net output #1: loss = 3.44232 (* 1 = 3.44232 loss)
I0408 15:12:51.230756 20259 solver.cpp:218] Iteration 3060 (0.680014 iter/s, 17.6467s/12 iters), loss = 2.52506
I0408 15:12:51.230792 20259 solver.cpp:237]     Train net output #0: loss = 2.52506 (* 1 = 2.52506 loss)
I0408 15:12:51.230803 20259 sgd_solver.cpp:105] Iteration 3060, lr = 0.000423913
I0408 15:12:55.591177 20259 solver.cpp:218] Iteration 3072 (2.75214 iter/s, 4.36025s/12 iters), loss = 2.62581
I0408 15:12:55.591221 20259 solver.cpp:237]     Train net output #0: loss = 2.62581 (* 1 = 2.62581 loss)
I0408 15:12:55.591233 20259 sgd_solver.cpp:105] Iteration 3072, lr = 0.000418691
I0408 15:13:00.590847 20259 solver.cpp:218] Iteration 3084 (2.40025 iter/s, 4.99947s/12 iters), loss = 2.5736
I0408 15:13:00.590880 20259 solver.cpp:237]     Train net output #0: loss = 2.5736 (* 1 = 2.5736 loss)
I0408 15:13:00.590889 20259 sgd_solver.cpp:105] Iteration 3084, lr = 0.000413533
I0408 15:13:05.534881 20259 solver.cpp:218] Iteration 3096 (2.42726 iter/s, 4.94384s/12 iters), loss = 2.5612
I0408 15:13:05.534915 20259 solver.cpp:237]     Train net output #0: loss = 2.5612 (* 1 = 2.5612 loss)
I0408 15:13:05.534924 20259 sgd_solver.cpp:105] Iteration 3096, lr = 0.000408439
I0408 15:13:10.551599 20259 solver.cpp:218] Iteration 3108 (2.39209 iter/s, 5.01652s/12 iters), loss = 2.50254
I0408 15:13:10.551633 20259 solver.cpp:237]     Train net output #0: loss = 2.50254 (* 1 = 2.50254 loss)
I0408 15:13:10.551641 20259 sgd_solver.cpp:105] Iteration 3108, lr = 0.000403407
I0408 15:13:15.576563 20259 solver.cpp:218] Iteration 3120 (2.38817 iter/s, 5.02477s/12 iters), loss = 2.57725
I0408 15:13:15.576596 20259 solver.cpp:237]     Train net output #0: loss = 2.57725 (* 1 = 2.57725 loss)
I0408 15:13:15.576604 20259 sgd_solver.cpp:105] Iteration 3120, lr = 0.000398438
I0408 15:13:20.564956 20259 solver.cpp:218] Iteration 3132 (2.40568 iter/s, 4.9882s/12 iters), loss = 2.66072
I0408 15:13:20.565064 20259 solver.cpp:237]     Train net output #0: loss = 2.66072 (* 1 = 2.66072 loss)
I0408 15:13:20.565076 20259 sgd_solver.cpp:105] Iteration 3132, lr = 0.000393529
I0408 15:13:21.681764 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:13:25.590152 20259 solver.cpp:218] Iteration 3144 (2.38809 iter/s, 5.02494s/12 iters), loss = 2.31732
I0408 15:13:25.590190 20259 solver.cpp:237]     Train net output #0: loss = 2.31732 (* 1 = 2.31732 loss)
I0408 15:13:25.590200 20259 sgd_solver.cpp:105] Iteration 3144, lr = 0.000388681
I0408 15:13:30.515084 20259 solver.cpp:218] Iteration 3156 (2.43668 iter/s, 4.92474s/12 iters), loss = 2.47986
I0408 15:13:30.515137 20259 solver.cpp:237]     Train net output #0: loss = 2.47986 (* 1 = 2.47986 loss)
I0408 15:13:30.515153 20259 sgd_solver.cpp:105] Iteration 3156, lr = 0.000383893
I0408 15:13:32.582094 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 15:13:35.611198 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 15:13:39.661931 20259 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 15:13:39.661974 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:13:42.855540 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:13:44.118464 20259 solver.cpp:397]     Test net output #0: accuracy = 0.231005
I0408 15:13:44.118510 20259 solver.cpp:397]     Test net output #1: loss = 3.42285 (* 1 = 3.42285 loss)
I0408 15:13:46.103154 20259 solver.cpp:218] Iteration 3168 (0.769845 iter/s, 15.5876s/12 iters), loss = 2.43166
I0408 15:13:46.103204 20259 solver.cpp:237]     Train net output #0: loss = 2.43166 (* 1 = 2.43166 loss)
I0408 15:13:46.103216 20259 sgd_solver.cpp:105] Iteration 3168, lr = 0.000379164
I0408 15:13:51.186843 20259 solver.cpp:218] Iteration 3180 (2.36059 iter/s, 5.08348s/12 iters), loss = 2.73011
I0408 15:13:51.187003 20259 solver.cpp:237]     Train net output #0: loss = 2.73011 (* 1 = 2.73011 loss)
I0408 15:13:51.187016 20259 sgd_solver.cpp:105] Iteration 3180, lr = 0.000374493
I0408 15:13:56.154392 20259 solver.cpp:218] Iteration 3192 (2.41583 iter/s, 4.96724s/12 iters), loss = 2.39718
I0408 15:13:56.154431 20259 solver.cpp:237]     Train net output #0: loss = 2.39718 (* 1 = 2.39718 loss)
I0408 15:13:56.154440 20259 sgd_solver.cpp:105] Iteration 3192, lr = 0.00036988
I0408 15:14:01.188258 20259 solver.cpp:218] Iteration 3204 (2.38395 iter/s, 5.03367s/12 iters), loss = 2.54828
I0408 15:14:01.188303 20259 solver.cpp:237]     Train net output #0: loss = 2.54828 (* 1 = 2.54828 loss)
I0408 15:14:01.188314 20259 sgd_solver.cpp:105] Iteration 3204, lr = 0.000365324
I0408 15:14:06.272471 20259 solver.cpp:218] Iteration 3216 (2.36034 iter/s, 5.08401s/12 iters), loss = 2.53494
I0408 15:14:06.272514 20259 solver.cpp:237]     Train net output #0: loss = 2.53494 (* 1 = 2.53494 loss)
I0408 15:14:06.272526 20259 sgd_solver.cpp:105] Iteration 3216, lr = 0.000360823
I0408 15:14:11.301151 20259 solver.cpp:218] Iteration 3228 (2.38641 iter/s, 5.02848s/12 iters), loss = 2.46476
I0408 15:14:11.301184 20259 solver.cpp:237]     Train net output #0: loss = 2.46476 (* 1 = 2.46476 loss)
I0408 15:14:11.301193 20259 sgd_solver.cpp:105] Iteration 3228, lr = 0.000356378
I0408 15:14:14.570276 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:14:16.315690 20259 solver.cpp:218] Iteration 3240 (2.39313 iter/s, 5.01434s/12 iters), loss = 2.59339
I0408 15:14:16.315734 20259 solver.cpp:237]     Train net output #0: loss = 2.59339 (* 1 = 2.59339 loss)
I0408 15:14:16.315747 20259 sgd_solver.cpp:105] Iteration 3240, lr = 0.000351988
I0408 15:14:21.263993 20259 solver.cpp:218] Iteration 3252 (2.42517 iter/s, 4.9481s/12 iters), loss = 2.20047
I0408 15:14:21.264108 20259 solver.cpp:237]     Train net output #0: loss = 2.20047 (* 1 = 2.20047 loss)
I0408 15:14:21.264122 20259 sgd_solver.cpp:105] Iteration 3252, lr = 0.000347652
I0408 15:14:25.776235 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 15:14:28.819118 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 15:14:31.097450 20259 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 15:14:31.097476 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:14:34.252048 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:14:35.553196 20259 solver.cpp:397]     Test net output #0: accuracy = 0.238358
I0408 15:14:35.553228 20259 solver.cpp:397]     Test net output #1: loss = 3.44045 (* 1 = 3.44045 loss)
I0408 15:14:35.642001 20259 solver.cpp:218] Iteration 3264 (0.834639 iter/s, 14.3775s/12 iters), loss = 2.3911
I0408 15:14:35.642032 20259 solver.cpp:237]     Train net output #0: loss = 2.3911 (* 1 = 2.3911 loss)
I0408 15:14:35.642040 20259 sgd_solver.cpp:105] Iteration 3264, lr = 0.000343369
I0408 15:14:39.810698 20259 solver.cpp:218] Iteration 3276 (2.87871 iter/s, 4.16853s/12 iters), loss = 2.29846
I0408 15:14:39.810737 20259 solver.cpp:237]     Train net output #0: loss = 2.29846 (* 1 = 2.29846 loss)
I0408 15:14:39.810747 20259 sgd_solver.cpp:105] Iteration 3276, lr = 0.000339139
I0408 15:14:44.854532 20259 solver.cpp:218] Iteration 3288 (2.37924 iter/s, 5.04364s/12 iters), loss = 2.45008
I0408 15:14:44.854569 20259 solver.cpp:237]     Train net output #0: loss = 2.45008 (* 1 = 2.45008 loss)
I0408 15:14:44.854578 20259 sgd_solver.cpp:105] Iteration 3288, lr = 0.000334962
I0408 15:14:49.834599 20259 solver.cpp:218] Iteration 3300 (2.4097 iter/s, 4.97987s/12 iters), loss = 2.35783
I0408 15:14:49.834640 20259 solver.cpp:237]     Train net output #0: loss = 2.35783 (* 1 = 2.35783 loss)
I0408 15:14:49.834651 20259 sgd_solver.cpp:105] Iteration 3300, lr = 0.000330835
I0408 15:14:54.865465 20259 solver.cpp:218] Iteration 3312 (2.38537 iter/s, 5.03066s/12 iters), loss = 2.37617
I0408 15:14:54.865630 20259 solver.cpp:237]     Train net output #0: loss = 2.37617 (* 1 = 2.37617 loss)
I0408 15:14:54.865644 20259 sgd_solver.cpp:105] Iteration 3312, lr = 0.00032676
I0408 15:14:59.809264 20259 solver.cpp:218] Iteration 3324 (2.42744 iter/s, 4.94348s/12 iters), loss = 2.26822
I0408 15:14:59.809311 20259 solver.cpp:237]     Train net output #0: loss = 2.26822 (* 1 = 2.26822 loss)
I0408 15:14:59.809322 20259 sgd_solver.cpp:105] Iteration 3324, lr = 0.000322735
I0408 15:15:04.800894 20259 solver.cpp:218] Iteration 3336 (2.40413 iter/s, 4.99142s/12 iters), loss = 2.40552
I0408 15:15:04.800941 20259 solver.cpp:237]     Train net output #0: loss = 2.40552 (* 1 = 2.40552 loss)
I0408 15:15:04.800953 20259 sgd_solver.cpp:105] Iteration 3336, lr = 0.000318759
I0408 15:15:05.295838 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:15:10.056711 20259 solver.cpp:218] Iteration 3348 (2.28328 iter/s, 5.2556s/12 iters), loss = 2.25873
I0408 15:15:10.056754 20259 solver.cpp:237]     Train net output #0: loss = 2.25873 (* 1 = 2.25873 loss)
I0408 15:15:10.056766 20259 sgd_solver.cpp:105] Iteration 3348, lr = 0.000314832
I0408 15:15:15.038326 20259 solver.cpp:218] Iteration 3360 (2.40895 iter/s, 4.98142s/12 iters), loss = 2.41431
I0408 15:15:15.038360 20259 solver.cpp:237]     Train net output #0: loss = 2.41431 (* 1 = 2.41431 loss)
I0408 15:15:15.038368 20259 sgd_solver.cpp:105] Iteration 3360, lr = 0.000310954
I0408 15:15:17.040078 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 15:15:20.086212 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 15:15:22.410194 20259 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 15:15:22.410224 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:15:25.529645 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:15:26.863613 20259 solver.cpp:397]     Test net output #0: accuracy = 0.251226
I0408 15:15:26.863662 20259 solver.cpp:397]     Test net output #1: loss = 3.4102 (* 1 = 3.4102 loss)
I0408 15:15:28.828243 20259 solver.cpp:218] Iteration 3372 (0.870229 iter/s, 13.7895s/12 iters), loss = 2.30877
I0408 15:15:28.828289 20259 solver.cpp:237]     Train net output #0: loss = 2.30877 (* 1 = 2.30877 loss)
I0408 15:15:28.828301 20259 sgd_solver.cpp:105] Iteration 3372, lr = 0.000307123
I0408 15:15:33.745179 20259 solver.cpp:218] Iteration 3384 (2.44065 iter/s, 4.91673s/12 iters), loss = 2.35871
I0408 15:15:33.745223 20259 solver.cpp:237]     Train net output #0: loss = 2.35871 (* 1 = 2.35871 loss)
I0408 15:15:33.745235 20259 sgd_solver.cpp:105] Iteration 3384, lr = 0.00030334
I0408 15:15:38.660660 20259 solver.cpp:218] Iteration 3396 (2.44137 iter/s, 4.91528s/12 iters), loss = 2.52522
I0408 15:15:38.660706 20259 solver.cpp:237]     Train net output #0: loss = 2.52522 (* 1 = 2.52522 loss)
I0408 15:15:38.660718 20259 sgd_solver.cpp:105] Iteration 3396, lr = 0.000299603
I0408 15:15:43.494419 20259 solver.cpp:218] Iteration 3408 (2.48264 iter/s, 4.83356s/12 iters), loss = 2.44715
I0408 15:15:43.494475 20259 solver.cpp:237]     Train net output #0: loss = 2.44715 (* 1 = 2.44715 loss)
I0408 15:15:43.494491 20259 sgd_solver.cpp:105] Iteration 3408, lr = 0.000295912
I0408 15:15:48.393359 20259 solver.cpp:218] Iteration 3420 (2.44962 iter/s, 4.89873s/12 iters), loss = 2.00975
I0408 15:15:48.393405 20259 solver.cpp:237]     Train net output #0: loss = 2.00975 (* 1 = 2.00975 loss)
I0408 15:15:48.393419 20259 sgd_solver.cpp:105] Iteration 3420, lr = 0.000292267
I0408 15:15:53.376818 20259 solver.cpp:218] Iteration 3432 (2.40806 iter/s, 4.98325s/12 iters), loss = 2.23902
I0408 15:15:53.376863 20259 solver.cpp:237]     Train net output #0: loss = 2.23902 (* 1 = 2.23902 loss)
I0408 15:15:53.376874 20259 sgd_solver.cpp:105] Iteration 3432, lr = 0.000288666
I0408 15:15:55.978663 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:15:58.385565 20259 solver.cpp:218] Iteration 3444 (2.39591 iter/s, 5.00854s/12 iters), loss = 2.0213
I0408 15:15:58.385607 20259 solver.cpp:237]     Train net output #0: loss = 2.0213 (* 1 = 2.0213 loss)
I0408 15:15:58.385619 20259 sgd_solver.cpp:105] Iteration 3444, lr = 0.00028511
I0408 15:16:03.335829 20259 solver.cpp:218] Iteration 3456 (2.42421 iter/s, 4.95007s/12 iters), loss = 2.2016
I0408 15:16:03.335863 20259 solver.cpp:237]     Train net output #0: loss = 2.2016 (* 1 = 2.2016 loss)
I0408 15:16:03.335871 20259 sgd_solver.cpp:105] Iteration 3456, lr = 0.000281598
I0408 15:16:07.750785 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 15:16:12.283390 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 15:16:14.617708 20259 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 15:16:14.617740 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:16:15.076900 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:16:17.684154 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:16:19.063997 20259 solver.cpp:397]     Test net output #0: accuracy = 0.25
I0408 15:16:19.064041 20259 solver.cpp:397]     Test net output #1: loss = 3.38171 (* 1 = 3.38171 loss)
I0408 15:16:19.153920 20259 solver.cpp:218] Iteration 3468 (0.758649 iter/s, 15.8176s/12 iters), loss = 2.18149
I0408 15:16:19.153975 20259 solver.cpp:237]     Train net output #0: loss = 2.18149 (* 1 = 2.18149 loss)
I0408 15:16:19.153987 20259 sgd_solver.cpp:105] Iteration 3468, lr = 0.000278129
I0408 15:16:23.354646 20259 solver.cpp:218] Iteration 3480 (2.85676 iter/s, 4.20056s/12 iters), loss = 2.49893
I0408 15:16:23.354687 20259 solver.cpp:237]     Train net output #0: loss = 2.49893 (* 1 = 2.49893 loss)
I0408 15:16:23.354699 20259 sgd_solver.cpp:105] Iteration 3480, lr = 0.000274703
I0408 15:16:28.397028 20259 solver.cpp:218] Iteration 3492 (2.37992 iter/s, 5.04218s/12 iters), loss = 2.30262
I0408 15:16:28.397120 20259 solver.cpp:237]     Train net output #0: loss = 2.30262 (* 1 = 2.30262 loss)
I0408 15:16:28.397132 20259 sgd_solver.cpp:105] Iteration 3492, lr = 0.000271319
I0408 15:16:33.346959 20259 solver.cpp:218] Iteration 3504 (2.4244 iter/s, 4.94969s/12 iters), loss = 2.52281
I0408 15:16:33.346997 20259 solver.cpp:237]     Train net output #0: loss = 2.52281 (* 1 = 2.52281 loss)
I0408 15:16:33.347005 20259 sgd_solver.cpp:105] Iteration 3504, lr = 0.000267977
I0408 15:16:38.319382 20259 solver.cpp:218] Iteration 3516 (2.41341 iter/s, 4.97223s/12 iters), loss = 2.02852
I0408 15:16:38.319425 20259 solver.cpp:237]     Train net output #0: loss = 2.02852 (* 1 = 2.02852 loss)
I0408 15:16:38.319437 20259 sgd_solver.cpp:105] Iteration 3516, lr = 0.000264675
I0408 15:16:43.611330 20259 solver.cpp:218] Iteration 3528 (2.26769 iter/s, 5.29173s/12 iters), loss = 1.95305
I0408 15:16:43.611377 20259 solver.cpp:237]     Train net output #0: loss = 1.95305 (* 1 = 1.95305 loss)
I0408 15:16:43.611389 20259 sgd_solver.cpp:105] Iteration 3528, lr = 0.000261415
I0408 15:16:48.389279 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:16:48.649283 20259 solver.cpp:218] Iteration 3540 (2.38202 iter/s, 5.03775s/12 iters), loss = 2.1208
I0408 15:16:48.649325 20259 solver.cpp:237]     Train net output #0: loss = 2.1208 (* 1 = 2.1208 loss)
I0408 15:16:48.649338 20259 sgd_solver.cpp:105] Iteration 3540, lr = 0.000258195
I0408 15:16:53.656181 20259 solver.cpp:218] Iteration 3552 (2.39679 iter/s, 5.0067s/12 iters), loss = 2.22157
I0408 15:16:53.656211 20259 solver.cpp:237]     Train net output #0: loss = 2.22157 (* 1 = 2.22157 loss)
I0408 15:16:53.656219 20259 sgd_solver.cpp:105] Iteration 3552, lr = 0.000255014
I0408 15:16:58.696828 20259 solver.cpp:218] Iteration 3564 (2.38074 iter/s, 5.04046s/12 iters), loss = 2.05925
I0408 15:16:58.696949 20259 solver.cpp:237]     Train net output #0: loss = 2.05925 (* 1 = 2.05925 loss)
I0408 15:16:58.696961 20259 sgd_solver.cpp:105] Iteration 3564, lr = 0.000251873
I0408 15:17:00.734360 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 15:17:06.249675 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 15:17:11.057160 20259 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 15:17:11.057191 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:17:14.095331 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:17:15.508710 20259 solver.cpp:397]     Test net output #0: accuracy = 0.251838
I0408 15:17:15.508755 20259 solver.cpp:397]     Test net output #1: loss = 3.40355 (* 1 = 3.40355 loss)
I0408 15:17:17.473536 20259 solver.cpp:218] Iteration 3576 (0.639113 iter/s, 18.776s/12 iters), loss = 2.61419
I0408 15:17:17.473582 20259 solver.cpp:237]     Train net output #0: loss = 2.61419 (* 1 = 2.61419 loss)
I0408 15:17:17.473594 20259 sgd_solver.cpp:105] Iteration 3576, lr = 0.00024877
I0408 15:17:22.493481 20259 solver.cpp:218] Iteration 3588 (2.39056 iter/s, 5.01974s/12 iters), loss = 1.96285
I0408 15:17:22.493523 20259 solver.cpp:237]     Train net output #0: loss = 1.96285 (* 1 = 1.96285 loss)
I0408 15:17:22.493536 20259 sgd_solver.cpp:105] Iteration 3588, lr = 0.000245705
I0408 15:17:27.532737 20259 solver.cpp:218] Iteration 3600 (2.3814 iter/s, 5.03906s/12 iters), loss = 2.25013
I0408 15:17:27.532780 20259 solver.cpp:237]     Train net output #0: loss = 2.25013 (* 1 = 2.25013 loss)
I0408 15:17:27.532791 20259 sgd_solver.cpp:105] Iteration 3600, lr = 0.000242678
I0408 15:17:32.491396 20259 solver.cpp:218] Iteration 3612 (2.42011 iter/s, 4.95846s/12 iters), loss = 2.07283
I0408 15:17:32.491521 20259 solver.cpp:237]     Train net output #0: loss = 2.07283 (* 1 = 2.07283 loss)
I0408 15:17:32.491534 20259 sgd_solver.cpp:105] Iteration 3612, lr = 0.000239689
I0408 15:17:37.381690 20259 solver.cpp:218] Iteration 3624 (2.45398 iter/s, 4.89002s/12 iters), loss = 2.1454
I0408 15:17:37.381726 20259 solver.cpp:237]     Train net output #0: loss = 2.1454 (* 1 = 2.1454 loss)
I0408 15:17:37.381736 20259 sgd_solver.cpp:105] Iteration 3624, lr = 0.000236736
I0408 15:17:42.259433 20259 solver.cpp:218] Iteration 3636 (2.46025 iter/s, 4.87755s/12 iters), loss = 2.14376
I0408 15:17:42.259477 20259 solver.cpp:237]     Train net output #0: loss = 2.14376 (* 1 = 2.14376 loss)
I0408 15:17:42.259490 20259 sgd_solver.cpp:105] Iteration 3636, lr = 0.00023382
I0408 15:17:44.098199 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:17:47.172498 20259 solver.cpp:218] Iteration 3648 (2.44257 iter/s, 4.91287s/12 iters), loss = 2.19016
I0408 15:17:47.172544 20259 solver.cpp:237]     Train net output #0: loss = 2.19016 (* 1 = 2.19016 loss)
I0408 15:17:47.172556 20259 sgd_solver.cpp:105] Iteration 3648, lr = 0.000230939
I0408 15:17:52.155081 20259 solver.cpp:218] Iteration 3660 (2.40849 iter/s, 4.98238s/12 iters), loss = 2.25753
I0408 15:17:52.155125 20259 solver.cpp:237]     Train net output #0: loss = 2.25753 (* 1 = 2.25753 loss)
I0408 15:17:52.155138 20259 sgd_solver.cpp:105] Iteration 3660, lr = 0.000228095
I0408 15:17:56.674427 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 15:18:00.191483 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 15:18:02.671655 20259 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 15:18:02.671725 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:18:05.660228 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:18:07.120371 20259 solver.cpp:397]     Test net output #0: accuracy = 0.257353
I0408 15:18:07.120416 20259 solver.cpp:397]     Test net output #1: loss = 3.36658 (* 1 = 3.36658 loss)
I0408 15:18:07.210685 20259 solver.cpp:218] Iteration 3672 (0.797072 iter/s, 15.0551s/12 iters), loss = 1.94612
I0408 15:18:07.210726 20259 solver.cpp:237]     Train net output #0: loss = 1.94612 (* 1 = 1.94612 loss)
I0408 15:18:07.210736 20259 sgd_solver.cpp:105] Iteration 3672, lr = 0.000225285
I0408 15:18:11.624944 20259 solver.cpp:218] Iteration 3684 (2.71858 iter/s, 4.41408s/12 iters), loss = 2.05083
I0408 15:18:11.624995 20259 solver.cpp:237]     Train net output #0: loss = 2.05083 (* 1 = 2.05083 loss)
I0408 15:18:11.625007 20259 sgd_solver.cpp:105] Iteration 3684, lr = 0.000222509
I0408 15:18:16.521661 20259 solver.cpp:218] Iteration 3696 (2.45072 iter/s, 4.89651s/12 iters), loss = 2.30366
I0408 15:18:16.521697 20259 solver.cpp:237]     Train net output #0: loss = 2.30366 (* 1 = 2.30366 loss)
I0408 15:18:16.521708 20259 sgd_solver.cpp:105] Iteration 3696, lr = 0.000219768
I0408 15:18:21.610183 20259 solver.cpp:218] Iteration 3708 (2.35834 iter/s, 5.08832s/12 iters), loss = 2.11528
I0408 15:18:21.610229 20259 solver.cpp:237]     Train net output #0: loss = 2.11528 (* 1 = 2.11528 loss)
I0408 15:18:21.610239 20259 sgd_solver.cpp:105] Iteration 3708, lr = 0.000217061
I0408 15:18:26.650837 20259 solver.cpp:218] Iteration 3720 (2.38074 iter/s, 5.04045s/12 iters), loss = 2.14861
I0408 15:18:26.650880 20259 solver.cpp:237]     Train net output #0: loss = 2.14861 (* 1 = 2.14861 loss)
I0408 15:18:26.650892 20259 sgd_solver.cpp:105] Iteration 3720, lr = 0.000214387
I0408 15:18:31.513193 20259 solver.cpp:218] Iteration 3732 (2.46804 iter/s, 4.86216s/12 iters), loss = 1.92334
I0408 15:18:31.513236 20259 solver.cpp:237]     Train net output #0: loss = 1.92334 (* 1 = 1.92334 loss)
I0408 15:18:31.513247 20259 sgd_solver.cpp:105] Iteration 3732, lr = 0.000211746
I0408 15:18:35.444584 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:18:36.377730 20259 solver.cpp:218] Iteration 3744 (2.46693 iter/s, 4.86434s/12 iters), loss = 2.15775
I0408 15:18:36.377774 20259 solver.cpp:237]     Train net output #0: loss = 2.15775 (* 1 = 2.15775 loss)
I0408 15:18:36.377786 20259 sgd_solver.cpp:105] Iteration 3744, lr = 0.000209138
I0408 15:18:41.491072 20259 solver.cpp:218] Iteration 3756 (2.3469 iter/s, 5.11313s/12 iters), loss = 2.43323
I0408 15:18:41.491122 20259 solver.cpp:237]     Train net output #0: loss = 2.43323 (* 1 = 2.43323 loss)
I0408 15:18:41.491134 20259 sgd_solver.cpp:105] Iteration 3756, lr = 0.000206561
I0408 15:18:46.415561 20259 solver.cpp:218] Iteration 3768 (2.4369 iter/s, 4.92428s/12 iters), loss = 2.09022
I0408 15:18:46.415608 20259 solver.cpp:237]     Train net output #0: loss = 2.09022 (* 1 = 2.09022 loss)
I0408 15:18:46.415621 20259 sgd_solver.cpp:105] Iteration 3768, lr = 0.000204017
I0408 15:18:48.433030 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 15:18:54.965466 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 15:18:57.305182 20259 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 15:18:57.305212 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:19:00.270792 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:19:01.764086 20259 solver.cpp:397]     Test net output #0: accuracy = 0.25674
I0408 15:19:01.764132 20259 solver.cpp:397]     Test net output #1: loss = 3.3339 (* 1 = 3.3339 loss)
I0408 15:19:03.683331 20259 solver.cpp:218] Iteration 3780 (0.694959 iter/s, 17.2672s/12 iters), loss = 2.20626
I0408 15:19:03.683384 20259 solver.cpp:237]     Train net output #0: loss = 2.20626 (* 1 = 2.20626 loss)
I0408 15:19:03.683399 20259 sgd_solver.cpp:105] Iteration 3780, lr = 0.000201504
I0408 15:19:08.686540 20259 solver.cpp:218] Iteration 3792 (2.39856 iter/s, 5.003s/12 iters), loss = 2.13485
I0408 15:19:08.686641 20259 solver.cpp:237]     Train net output #0: loss = 2.13485 (* 1 = 2.13485 loss)
I0408 15:19:08.686650 20259 sgd_solver.cpp:105] Iteration 3792, lr = 0.000199021
I0408 15:19:13.715085 20259 solver.cpp:218] Iteration 3804 (2.3865 iter/s, 5.02828s/12 iters), loss = 2.02356
I0408 15:19:13.715128 20259 solver.cpp:237]     Train net output #0: loss = 2.02356 (* 1 = 2.02356 loss)
I0408 15:19:13.715140 20259 sgd_solver.cpp:105] Iteration 3804, lr = 0.00019657
I0408 15:19:18.717937 20259 solver.cpp:218] Iteration 3816 (2.39873 iter/s, 5.00265s/12 iters), loss = 2.06042
I0408 15:19:18.717993 20259 solver.cpp:237]     Train net output #0: loss = 2.06042 (* 1 = 2.06042 loss)
I0408 15:19:18.718005 20259 sgd_solver.cpp:105] Iteration 3816, lr = 0.000194148
I0408 15:19:23.882249 20259 solver.cpp:218] Iteration 3828 (2.32374 iter/s, 5.16409s/12 iters), loss = 1.9872
I0408 15:19:23.882297 20259 solver.cpp:237]     Train net output #0: loss = 1.9872 (* 1 = 1.9872 loss)
I0408 15:19:23.882309 20259 sgd_solver.cpp:105] Iteration 3828, lr = 0.000191756
I0408 15:19:28.875814 20259 solver.cpp:218] Iteration 3840 (2.40319 iter/s, 4.99335s/12 iters), loss = 2.12378
I0408 15:19:28.875860 20259 solver.cpp:237]     Train net output #0: loss = 2.12378 (* 1 = 2.12378 loss)
I0408 15:19:28.875871 20259 sgd_solver.cpp:105] Iteration 3840, lr = 0.000189394
I0408 15:19:29.997422 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:19:33.895490 20259 solver.cpp:218] Iteration 3852 (2.39069 iter/s, 5.01947s/12 iters), loss = 1.86912
I0408 15:19:33.895536 20259 solver.cpp:237]     Train net output #0: loss = 1.86912 (* 1 = 1.86912 loss)
I0408 15:19:33.895547 20259 sgd_solver.cpp:105] Iteration 3852, lr = 0.000187061
I0408 15:19:38.747684 20259 solver.cpp:218] Iteration 3864 (2.47321 iter/s, 4.85199s/12 iters), loss = 2.23307
I0408 15:19:38.747831 20259 solver.cpp:237]     Train net output #0: loss = 2.23307 (* 1 = 2.23307 loss)
I0408 15:19:38.747844 20259 sgd_solver.cpp:105] Iteration 3864, lr = 0.000184757
I0408 15:19:43.191990 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 15:19:47.994042 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 15:19:52.372921 20259 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 15:19:52.372952 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:19:55.299605 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:19:56.841346 20259 solver.cpp:397]     Test net output #0: accuracy = 0.259191
I0408 15:19:56.841390 20259 solver.cpp:397]     Test net output #1: loss = 3.35031 (* 1 = 3.35031 loss)
I0408 15:19:56.931504 20259 solver.cpp:218] Iteration 3876 (0.659952 iter/s, 18.1831s/12 iters), loss = 2.13698
I0408 15:19:56.931540 20259 solver.cpp:237]     Train net output #0: loss = 2.13698 (* 1 = 2.13698 loss)
I0408 15:19:56.931550 20259 sgd_solver.cpp:105] Iteration 3876, lr = 0.000182481
I0408 15:20:01.248744 20259 solver.cpp:218] Iteration 3888 (2.77967 iter/s, 4.31706s/12 iters), loss = 2.17747
I0408 15:20:01.248801 20259 solver.cpp:237]     Train net output #0: loss = 2.17747 (* 1 = 2.17747 loss)
I0408 15:20:01.248817 20259 sgd_solver.cpp:105] Iteration 3888, lr = 0.000180233
I0408 15:20:06.307523 20259 solver.cpp:218] Iteration 3900 (2.37221 iter/s, 5.05857s/12 iters), loss = 2.19284
I0408 15:20:06.307581 20259 solver.cpp:237]     Train net output #0: loss = 2.19284 (* 1 = 2.19284 loss)
I0408 15:20:06.307593 20259 sgd_solver.cpp:105] Iteration 3900, lr = 0.000178012
I0408 15:20:11.271230 20259 solver.cpp:218] Iteration 3912 (2.41765 iter/s, 4.9635s/12 iters), loss = 1.969
I0408 15:20:11.271332 20259 solver.cpp:237]     Train net output #0: loss = 1.969 (* 1 = 1.969 loss)
I0408 15:20:11.271340 20259 sgd_solver.cpp:105] Iteration 3912, lr = 0.00017582
I0408 15:20:16.256322 20259 solver.cpp:218] Iteration 3924 (2.4073 iter/s, 4.98483s/12 iters), loss = 1.93301
I0408 15:20:16.256368 20259 solver.cpp:237]     Train net output #0: loss = 1.93301 (* 1 = 1.93301 loss)
I0408 15:20:16.256379 20259 sgd_solver.cpp:105] Iteration 3924, lr = 0.000173654
I0408 15:20:21.201033 20259 solver.cpp:218] Iteration 3936 (2.42694 iter/s, 4.9445s/12 iters), loss = 1.87884
I0408 15:20:21.201081 20259 solver.cpp:237]     Train net output #0: loss = 1.87884 (* 1 = 1.87884 loss)
I0408 15:20:21.201092 20259 sgd_solver.cpp:105] Iteration 3936, lr = 0.000171514
I0408 15:20:24.597363 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:20:26.231016 20259 solver.cpp:218] Iteration 3948 (2.38579 iter/s, 5.02978s/12 iters), loss = 2.22511
I0408 15:20:26.231060 20259 solver.cpp:237]     Train net output #0: loss = 2.22511 (* 1 = 2.22511 loss)
I0408 15:20:26.231070 20259 sgd_solver.cpp:105] Iteration 3948, lr = 0.000169402
I0408 15:20:31.281457 20259 solver.cpp:218] Iteration 3960 (2.37613 iter/s, 5.05024s/12 iters), loss = 1.94723
I0408 15:20:31.281497 20259 solver.cpp:237]     Train net output #0: loss = 1.94723 (* 1 = 1.94723 loss)
I0408 15:20:31.281505 20259 sgd_solver.cpp:105] Iteration 3960, lr = 0.000167315
I0408 15:20:36.282706 20259 solver.cpp:218] Iteration 3972 (2.3995 iter/s, 5.00105s/12 iters), loss = 1.83351
I0408 15:20:36.282765 20259 solver.cpp:237]     Train net output #0: loss = 1.83351 (* 1 = 1.83351 loss)
I0408 15:20:36.282780 20259 sgd_solver.cpp:105] Iteration 3972, lr = 0.000165254
I0408 15:20:38.331022 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 15:20:41.660303 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 15:20:44.157557 20259 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 15:20:44.157588 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:20:47.044946 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:20:48.621830 20259 solver.cpp:397]     Test net output #0: accuracy = 0.270833
I0408 15:20:48.621876 20259 solver.cpp:397]     Test net output #1: loss = 3.3392 (* 1 = 3.3392 loss)
I0408 15:20:50.609930 20259 solver.cpp:218] Iteration 3984 (0.837595 iter/s, 14.3267s/12 iters), loss = 2.01294
I0408 15:20:50.609984 20259 solver.cpp:237]     Train net output #0: loss = 2.01294 (* 1 = 2.01294 loss)
I0408 15:20:50.609997 20259 sgd_solver.cpp:105] Iteration 3984, lr = 0.000163218
I0408 15:20:55.908186 20259 solver.cpp:218] Iteration 3996 (2.26499 iter/s, 5.29804s/12 iters), loss = 2.01741
I0408 15:20:55.908231 20259 solver.cpp:237]     Train net output #0: loss = 2.01741 (* 1 = 2.01741 loss)
I0408 15:20:55.908242 20259 sgd_solver.cpp:105] Iteration 3996, lr = 0.000161207
I0408 15:21:00.995841 20259 solver.cpp:218] Iteration 4008 (2.35875 iter/s, 5.08744s/12 iters), loss = 1.97462
I0408 15:21:00.995889 20259 solver.cpp:237]     Train net output #0: loss = 1.97462 (* 1 = 1.97462 loss)
I0408 15:21:00.995901 20259 sgd_solver.cpp:105] Iteration 4008, lr = 0.000159221
I0408 15:21:05.999851 20259 solver.cpp:218] Iteration 4020 (2.39818 iter/s, 5.0038s/12 iters), loss = 2.26349
I0408 15:21:05.999898 20259 solver.cpp:237]     Train net output #0: loss = 2.26349 (* 1 = 2.26349 loss)
I0408 15:21:05.999912 20259 sgd_solver.cpp:105] Iteration 4020, lr = 0.00015726
I0408 15:21:10.974900 20259 solver.cpp:218] Iteration 4032 (2.41214 iter/s, 4.97484s/12 iters), loss = 2.05152
I0408 15:21:10.974949 20259 solver.cpp:237]     Train net output #0: loss = 2.05152 (* 1 = 2.05152 loss)
I0408 15:21:10.974962 20259 sgd_solver.cpp:105] Iteration 4032, lr = 0.000155323
I0408 15:21:16.035255 20259 solver.cpp:218] Iteration 4044 (2.37147 iter/s, 5.06015s/12 iters), loss = 2.13141
I0408 15:21:16.035360 20259 solver.cpp:237]     Train net output #0: loss = 2.13141 (* 1 = 2.13141 loss)
I0408 15:21:16.035372 20259 sgd_solver.cpp:105] Iteration 4044, lr = 0.000153409
I0408 15:21:16.524933 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:21:21.239302 20259 solver.cpp:218] Iteration 4056 (2.30602 iter/s, 5.20378s/12 iters), loss = 1.94269
I0408 15:21:21.239349 20259 solver.cpp:237]     Train net output #0: loss = 1.94269 (* 1 = 1.94269 loss)
I0408 15:21:21.239362 20259 sgd_solver.cpp:105] Iteration 4056, lr = 0.000151519
I0408 15:21:26.633163 20259 solver.cpp:218] Iteration 4068 (2.22484 iter/s, 5.39364s/12 iters), loss = 1.89917
I0408 15:21:26.633208 20259 solver.cpp:237]     Train net output #0: loss = 1.89917 (* 1 = 1.89917 loss)
I0408 15:21:26.633219 20259 sgd_solver.cpp:105] Iteration 4068, lr = 0.000149653
I0408 15:21:31.207808 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 15:21:35.745290 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 15:21:39.246181 20259 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 15:21:39.246212 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:21:42.094470 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:21:43.706470 20259 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0408 15:21:43.706516 20259 solver.cpp:397]     Test net output #1: loss = 3.32707 (* 1 = 3.32707 loss)
I0408 15:21:43.796960 20259 solver.cpp:218] Iteration 4080 (0.699169 iter/s, 17.1632s/12 iters), loss = 2.03832
I0408 15:21:43.797019 20259 solver.cpp:237]     Train net output #0: loss = 2.03832 (* 1 = 2.03832 loss)
I0408 15:21:43.797035 20259 sgd_solver.cpp:105] Iteration 4080, lr = 0.000147809
I0408 15:21:48.009002 20259 solver.cpp:218] Iteration 4092 (2.8491 iter/s, 4.21186s/12 iters), loss = 1.78383
I0408 15:21:48.009135 20259 solver.cpp:237]     Train net output #0: loss = 1.78383 (* 1 = 1.78383 loss)
I0408 15:21:48.009146 20259 sgd_solver.cpp:105] Iteration 4092, lr = 0.000145989
I0408 15:21:52.931219 20259 solver.cpp:218] Iteration 4104 (2.43807 iter/s, 4.92193s/12 iters), loss = 1.90191
I0408 15:21:52.931264 20259 solver.cpp:237]     Train net output #0: loss = 1.90191 (* 1 = 1.90191 loss)
I0408 15:21:52.931277 20259 sgd_solver.cpp:105] Iteration 4104, lr = 0.00014419
I0408 15:21:57.845100 20259 solver.cpp:218] Iteration 4116 (2.44216 iter/s, 4.91368s/12 iters), loss = 2.18834
I0408 15:21:57.845146 20259 solver.cpp:237]     Train net output #0: loss = 2.18834 (* 1 = 2.18834 loss)
I0408 15:21:57.845156 20259 sgd_solver.cpp:105] Iteration 4116, lr = 0.000142414
I0408 15:22:02.759826 20259 solver.cpp:218] Iteration 4128 (2.44174 iter/s, 4.91452s/12 iters), loss = 1.59497
I0408 15:22:02.759869 20259 solver.cpp:237]     Train net output #0: loss = 1.59497 (* 1 = 1.59497 loss)
I0408 15:22:02.759881 20259 sgd_solver.cpp:105] Iteration 4128, lr = 0.000140659
I0408 15:22:07.930110 20259 solver.cpp:218] Iteration 4140 (2.32105 iter/s, 5.17007s/12 iters), loss = 1.95271
I0408 15:22:07.930158 20259 solver.cpp:237]     Train net output #0: loss = 1.95271 (* 1 = 1.95271 loss)
I0408 15:22:07.930171 20259 sgd_solver.cpp:105] Iteration 4140, lr = 0.000138927
I0408 15:22:10.559337 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:22:12.944034 20259 solver.cpp:218] Iteration 4152 (2.39343 iter/s, 5.01372s/12 iters), loss = 1.67475
I0408 15:22:12.944080 20259 solver.cpp:237]     Train net output #0: loss = 1.67475 (* 1 = 1.67475 loss)
I0408 15:22:12.944092 20259 sgd_solver.cpp:105] Iteration 4152, lr = 0.000137215
I0408 15:22:14.551028 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:22:17.965426 20259 solver.cpp:218] Iteration 4164 (2.38987 iter/s, 5.02119s/12 iters), loss = 1.84187
I0408 15:22:17.965472 20259 solver.cpp:237]     Train net output #0: loss = 1.84187 (* 1 = 1.84187 loss)
I0408 15:22:17.965484 20259 sgd_solver.cpp:105] Iteration 4164, lr = 0.000135525
I0408 15:22:22.963990 20259 solver.cpp:218] Iteration 4176 (2.40079 iter/s, 4.99836s/12 iters), loss = 1.93514
I0408 15:22:22.964102 20259 solver.cpp:237]     Train net output #0: loss = 1.93514 (* 1 = 1.93514 loss)
I0408 15:22:22.964115 20259 sgd_solver.cpp:105] Iteration 4176, lr = 0.000133855
I0408 15:22:25.010104 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 15:22:28.757258 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 15:22:31.609259 20259 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 15:22:31.609290 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:22:34.406277 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:22:36.063241 20259 solver.cpp:397]     Test net output #0: accuracy = 0.273897
I0408 15:22:36.063277 20259 solver.cpp:397]     Test net output #1: loss = 3.30413 (* 1 = 3.30413 loss)
I0408 15:22:38.048882 20259 solver.cpp:218] Iteration 4188 (0.795528 iter/s, 15.0843s/12 iters), loss = 1.92269
I0408 15:22:38.048929 20259 solver.cpp:237]     Train net output #0: loss = 1.92269 (* 1 = 1.92269 loss)
I0408 15:22:38.048941 20259 sgd_solver.cpp:105] Iteration 4188, lr = 0.000132207
I0408 15:22:43.050629 20259 solver.cpp:218] Iteration 4200 (2.39926 iter/s, 5.00154s/12 iters), loss = 1.92814
I0408 15:22:43.050673 20259 solver.cpp:237]     Train net output #0: loss = 1.92814 (* 1 = 1.92814 loss)
I0408 15:22:43.050685 20259 sgd_solver.cpp:105] Iteration 4200, lr = 0.000130578
I0408 15:22:48.063274 20259 solver.cpp:218] Iteration 4212 (2.39404 iter/s, 5.01244s/12 iters), loss = 2.03343
I0408 15:22:48.063328 20259 solver.cpp:237]     Train net output #0: loss = 2.03343 (* 1 = 2.03343 loss)
I0408 15:22:48.063344 20259 sgd_solver.cpp:105] Iteration 4212, lr = 0.000128969
I0408 15:22:53.049055 20259 solver.cpp:218] Iteration 4224 (2.40695 iter/s, 4.98557s/12 iters), loss = 1.69351
I0408 15:22:53.049204 20259 solver.cpp:237]     Train net output #0: loss = 1.69351 (* 1 = 1.69351 loss)
I0408 15:22:53.049217 20259 sgd_solver.cpp:105] Iteration 4224, lr = 0.000127381
I0408 15:22:58.070740 20259 solver.cpp:218] Iteration 4236 (2.38978 iter/s, 5.02138s/12 iters), loss = 1.87916
I0408 15:22:58.070782 20259 solver.cpp:237]     Train net output #0: loss = 1.87916 (* 1 = 1.87916 loss)
I0408 15:22:58.070794 20259 sgd_solver.cpp:105] Iteration 4236, lr = 0.000125811
I0408 15:23:02.862196 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:23:03.091171 20259 solver.cpp:218] Iteration 4248 (2.39033 iter/s, 5.02023s/12 iters), loss = 1.87967
I0408 15:23:03.091212 20259 solver.cpp:237]     Train net output #0: loss = 1.87967 (* 1 = 1.87967 loss)
I0408 15:23:03.091223 20259 sgd_solver.cpp:105] Iteration 4248, lr = 0.000124262
I0408 15:23:08.103276 20259 solver.cpp:218] Iteration 4260 (2.3943 iter/s, 5.0119s/12 iters), loss = 1.93028
I0408 15:23:08.103322 20259 solver.cpp:237]     Train net output #0: loss = 1.93028 (* 1 = 1.93028 loss)
I0408 15:23:08.103333 20259 sgd_solver.cpp:105] Iteration 4260, lr = 0.000122731
I0408 15:23:13.089500 20259 solver.cpp:218] Iteration 4272 (2.40673 iter/s, 4.98602s/12 iters), loss = 1.7452
I0408 15:23:13.089555 20259 solver.cpp:237]     Train net output #0: loss = 1.7452 (* 1 = 1.7452 loss)
I0408 15:23:13.089571 20259 sgd_solver.cpp:105] Iteration 4272, lr = 0.000121219
I0408 15:23:17.598070 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 15:23:20.829849 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 15:23:23.541268 20259 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 15:23:23.541375 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:23:26.303488 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:23:27.995059 20259 solver.cpp:397]     Test net output #0: accuracy = 0.272672
I0408 15:23:27.995105 20259 solver.cpp:397]     Test net output #1: loss = 3.30975 (* 1 = 3.30975 loss)
I0408 15:23:28.085516 20259 solver.cpp:218] Iteration 4284 (0.800239 iter/s, 14.9955s/12 iters), loss = 2.18403
I0408 15:23:28.085577 20259 solver.cpp:237]     Train net output #0: loss = 2.18403 (* 1 = 2.18403 loss)
I0408 15:23:28.085592 20259 sgd_solver.cpp:105] Iteration 4284, lr = 0.000119726
I0408 15:23:32.299211 20259 solver.cpp:218] Iteration 4296 (2.84799 iter/s, 4.2135s/12 iters), loss = 1.73786
I0408 15:23:32.299257 20259 solver.cpp:237]     Train net output #0: loss = 1.73786 (* 1 = 1.73786 loss)
I0408 15:23:32.299269 20259 sgd_solver.cpp:105] Iteration 4296, lr = 0.000118251
I0408 15:23:37.489699 20259 solver.cpp:218] Iteration 4308 (2.31201 iter/s, 5.19028s/12 iters), loss = 2.04976
I0408 15:23:37.489737 20259 solver.cpp:237]     Train net output #0: loss = 2.04976 (* 1 = 2.04976 loss)
I0408 15:23:37.489751 20259 sgd_solver.cpp:105] Iteration 4308, lr = 0.000116794
I0408 15:23:42.935312 20259 solver.cpp:218] Iteration 4320 (2.2037 iter/s, 5.4454s/12 iters), loss = 2.09239
I0408 15:23:42.935360 20259 solver.cpp:237]     Train net output #0: loss = 2.09239 (* 1 = 2.09239 loss)
I0408 15:23:42.935374 20259 sgd_solver.cpp:105] Iteration 4320, lr = 0.000115355
I0408 15:23:47.957197 20259 solver.cpp:218] Iteration 4332 (2.38964 iter/s, 5.02168s/12 iters), loss = 1.85445
I0408 15:23:47.957243 20259 solver.cpp:237]     Train net output #0: loss = 1.85445 (* 1 = 1.85445 loss)
I0408 15:23:47.957255 20259 sgd_solver.cpp:105] Iteration 4332, lr = 0.000113934
I0408 15:23:52.892969 20259 solver.cpp:218] Iteration 4344 (2.43133 iter/s, 4.93557s/12 iters), loss = 1.63942
I0408 15:23:52.893018 20259 solver.cpp:237]     Train net output #0: loss = 1.63942 (* 1 = 1.63942 loss)
I0408 15:23:52.893029 20259 sgd_solver.cpp:105] Iteration 4344, lr = 0.000112531
I0408 15:23:54.814090 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:23:57.894850 20259 solver.cpp:218] Iteration 4356 (2.39919 iter/s, 5.00168s/12 iters), loss = 1.98091
I0408 15:23:57.894894 20259 solver.cpp:237]     Train net output #0: loss = 1.98091 (* 1 = 1.98091 loss)
I0408 15:23:57.894906 20259 sgd_solver.cpp:105] Iteration 4356, lr = 0.000111144
I0408 15:24:02.924823 20259 solver.cpp:218] Iteration 4368 (2.38579 iter/s, 5.02977s/12 iters), loss = 1.92794
I0408 15:24:02.924865 20259 solver.cpp:237]     Train net output #0: loss = 1.92794 (* 1 = 1.92794 loss)
I0408 15:24:02.924875 20259 sgd_solver.cpp:105] Iteration 4368, lr = 0.000109775
I0408 15:24:07.805138 20259 solver.cpp:218] Iteration 4380 (2.45896 iter/s, 4.88012s/12 iters), loss = 1.70349
I0408 15:24:07.805181 20259 solver.cpp:237]     Train net output #0: loss = 1.70349 (* 1 = 1.70349 loss)
I0408 15:24:07.805191 20259 sgd_solver.cpp:105] Iteration 4380, lr = 0.000108423
I0408 15:24:09.776223 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 15:24:12.824788 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 15:24:15.154793 20259 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 15:24:15.154824 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:24:17.883872 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:24:19.621820 20259 solver.cpp:397]     Test net output #0: accuracy = 0.276348
I0408 15:24:19.621866 20259 solver.cpp:397]     Test net output #1: loss = 3.31631 (* 1 = 3.31631 loss)
I0408 15:24:21.527909 20259 solver.cpp:218] Iteration 4392 (0.874488 iter/s, 13.7223s/12 iters), loss = 1.87839
I0408 15:24:21.527957 20259 solver.cpp:237]     Train net output #0: loss = 1.87839 (* 1 = 1.87839 loss)
I0408 15:24:21.527969 20259 sgd_solver.cpp:105] Iteration 4392, lr = 0.000107087
I0408 15:24:26.450794 20259 solver.cpp:218] Iteration 4404 (2.4377 iter/s, 4.92268s/12 iters), loss = 1.90253
I0408 15:24:26.450899 20259 solver.cpp:237]     Train net output #0: loss = 1.90253 (* 1 = 1.90253 loss)
I0408 15:24:26.450912 20259 sgd_solver.cpp:105] Iteration 4404, lr = 0.000105768
I0408 15:24:31.237017 20259 solver.cpp:218] Iteration 4416 (2.50733 iter/s, 4.78597s/12 iters), loss = 1.75034
I0408 15:24:31.237063 20259 solver.cpp:237]     Train net output #0: loss = 1.75034 (* 1 = 1.75034 loss)
I0408 15:24:31.237076 20259 sgd_solver.cpp:105] Iteration 4416, lr = 0.000104465
I0408 15:24:36.274399 20259 solver.cpp:218] Iteration 4428 (2.38229 iter/s, 5.03717s/12 iters), loss = 1.8674
I0408 15:24:36.274443 20259 solver.cpp:237]     Train net output #0: loss = 1.8674 (* 1 = 1.8674 loss)
I0408 15:24:36.274454 20259 sgd_solver.cpp:105] Iteration 4428, lr = 0.000103178
I0408 15:24:41.271884 20259 solver.cpp:218] Iteration 4440 (2.4013 iter/s, 4.99728s/12 iters), loss = 1.69025
I0408 15:24:41.271931 20259 solver.cpp:237]     Train net output #0: loss = 1.69025 (* 1 = 1.69025 loss)
I0408 15:24:41.271943 20259 sgd_solver.cpp:105] Iteration 4440, lr = 0.000101907
I0408 15:24:45.334162 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:24:46.292431 20259 solver.cpp:218] Iteration 4452 (2.39028 iter/s, 5.02034s/12 iters), loss = 1.86382
I0408 15:24:46.292475 20259 solver.cpp:237]     Train net output #0: loss = 1.86382 (* 1 = 1.86382 loss)
I0408 15:24:46.292487 20259 sgd_solver.cpp:105] Iteration 4452, lr = 0.000100652
I0408 15:24:51.291518 20259 solver.cpp:218] Iteration 4464 (2.40054 iter/s, 4.99888s/12 iters), loss = 1.98677
I0408 15:24:51.291564 20259 solver.cpp:237]     Train net output #0: loss = 1.98677 (* 1 = 1.98677 loss)
I0408 15:24:51.291576 20259 sgd_solver.cpp:105] Iteration 4464, lr = 9.94119e-05
I0408 15:24:56.231876 20259 solver.cpp:218] Iteration 4476 (2.42907 iter/s, 4.94015s/12 iters), loss = 1.73496
I0408 15:24:56.231921 20259 solver.cpp:237]     Train net output #0: loss = 1.73496 (* 1 = 1.73496 loss)
I0408 15:24:56.231933 20259 sgd_solver.cpp:105] Iteration 4476, lr = 9.81873e-05
I0408 15:25:00.803669 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 15:25:04.662390 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 15:25:08.506767 20259 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 15:25:08.506799 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:25:11.193257 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:25:12.963153 20259 solver.cpp:397]     Test net output #0: accuracy = 0.277574
I0408 15:25:12.963198 20259 solver.cpp:397]     Test net output #1: loss = 3.29556 (* 1 = 3.29556 loss)
I0408 15:25:13.053325 20259 solver.cpp:218] Iteration 4488 (0.713398 iter/s, 16.8209s/12 iters), loss = 1.95452
I0408 15:25:13.053370 20259 solver.cpp:237]     Train net output #0: loss = 1.95452 (* 1 = 1.95452 loss)
I0408 15:25:13.053381 20259 sgd_solver.cpp:105] Iteration 4488, lr = 9.69778e-05
I0408 15:25:17.331054 20259 solver.cpp:218] Iteration 4500 (2.80535 iter/s, 4.27754s/12 iters), loss = 1.63342
I0408 15:25:17.331100 20259 solver.cpp:237]     Train net output #0: loss = 1.63342 (* 1 = 1.63342 loss)
I0408 15:25:17.331112 20259 sgd_solver.cpp:105] Iteration 4500, lr = 9.57831e-05
I0408 15:25:22.338099 20259 solver.cpp:218] Iteration 4512 (2.39672 iter/s, 5.00684s/12 iters), loss = 1.62031
I0408 15:25:22.338147 20259 solver.cpp:237]     Train net output #0: loss = 1.62031 (* 1 = 1.62031 loss)
I0408 15:25:22.338160 20259 sgd_solver.cpp:105] Iteration 4512, lr = 9.46032e-05
I0408 15:25:27.352943 20259 solver.cpp:218] Iteration 4524 (2.39299 iter/s, 5.01464s/12 iters), loss = 1.84866
I0408 15:25:27.352989 20259 solver.cpp:237]     Train net output #0: loss = 1.84866 (* 1 = 1.84866 loss)
I0408 15:25:27.353001 20259 sgd_solver.cpp:105] Iteration 4524, lr = 9.34378e-05
I0408 15:25:32.459995 20259 solver.cpp:218] Iteration 4536 (2.34979 iter/s, 5.10684s/12 iters), loss = 1.84034
I0408 15:25:32.460120 20259 solver.cpp:237]     Train net output #0: loss = 1.84034 (* 1 = 1.84034 loss)
I0408 15:25:32.460134 20259 sgd_solver.cpp:105] Iteration 4536, lr = 9.22867e-05
I0408 15:25:37.474575 20259 solver.cpp:218] Iteration 4548 (2.39316 iter/s, 5.01429s/12 iters), loss = 1.81618
I0408 15:25:37.474623 20259 solver.cpp:237]     Train net output #0: loss = 1.81618 (* 1 = 1.81618 loss)
I0408 15:25:37.474635 20259 sgd_solver.cpp:105] Iteration 4548, lr = 9.11499e-05
I0408 15:25:38.787411 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:25:42.552481 20259 solver.cpp:218] Iteration 4560 (2.36328 iter/s, 5.0777s/12 iters), loss = 1.54352
I0408 15:25:42.552529 20259 solver.cpp:237]     Train net output #0: loss = 1.54352 (* 1 = 1.54352 loss)
I0408 15:25:42.552541 20259 sgd_solver.cpp:105] Iteration 4560, lr = 9.0027e-05
I0408 15:25:47.563750 20259 solver.cpp:218] Iteration 4572 (2.3947 iter/s, 5.01106s/12 iters), loss = 1.86399
I0408 15:25:47.563791 20259 solver.cpp:237]     Train net output #0: loss = 1.86399 (* 1 = 1.86399 loss)
I0408 15:25:47.563800 20259 sgd_solver.cpp:105] Iteration 4572, lr = 8.8918e-05
I0408 15:25:52.600095 20259 solver.cpp:218] Iteration 4584 (2.38278 iter/s, 5.03614s/12 iters), loss = 1.81463
I0408 15:25:52.600139 20259 solver.cpp:237]     Train net output #0: loss = 1.81463 (* 1 = 1.81463 loss)
I0408 15:25:52.600152 20259 sgd_solver.cpp:105] Iteration 4584, lr = 8.78226e-05
I0408 15:25:54.652923 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 15:25:58.473513 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 15:26:00.802372 20259 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 15:26:00.802402 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:26:03.319869 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:26:05.135412 20259 solver.cpp:397]     Test net output #0: accuracy = 0.281863
I0408 15:26:05.135457 20259 solver.cpp:397]     Test net output #1: loss = 3.29593 (* 1 = 3.29593 loss)
I0408 15:26:07.042255 20259 solver.cpp:218] Iteration 4596 (0.830928 iter/s, 14.4417s/12 iters), loss = 1.88265
I0408 15:26:07.042304 20259 solver.cpp:237]     Train net output #0: loss = 1.88265 (* 1 = 1.88265 loss)
I0408 15:26:07.042315 20259 sgd_solver.cpp:105] Iteration 4596, lr = 8.67407e-05
I0408 15:26:12.045559 20259 solver.cpp:218] Iteration 4608 (2.39851 iter/s, 5.0031s/12 iters), loss = 1.89598
I0408 15:26:12.045604 20259 solver.cpp:237]     Train net output #0: loss = 1.89598 (* 1 = 1.89598 loss)
I0408 15:26:12.045615 20259 sgd_solver.cpp:105] Iteration 4608, lr = 8.56722e-05
I0408 15:26:17.057658 20259 solver.cpp:218] Iteration 4620 (2.3943 iter/s, 5.0119s/12 iters), loss = 1.74582
I0408 15:26:17.057703 20259 solver.cpp:237]     Train net output #0: loss = 1.74582 (* 1 = 1.74582 loss)
I0408 15:26:17.057713 20259 sgd_solver.cpp:105] Iteration 4620, lr = 8.46168e-05
I0408 15:26:22.076397 20259 solver.cpp:218] Iteration 4632 (2.39114 iter/s, 5.01853s/12 iters), loss = 1.62466
I0408 15:26:22.076443 20259 solver.cpp:237]     Train net output #0: loss = 1.62466 (* 1 = 1.62466 loss)
I0408 15:26:22.076455 20259 sgd_solver.cpp:105] Iteration 4632, lr = 8.35744e-05
I0408 15:26:27.027050 20259 solver.cpp:218] Iteration 4644 (2.42402 iter/s, 4.95045s/12 iters), loss = 1.65986
I0408 15:26:27.027096 20259 solver.cpp:237]     Train net output #0: loss = 1.65986 (* 1 = 1.65986 loss)
I0408 15:26:27.027107 20259 sgd_solver.cpp:105] Iteration 4644, lr = 8.25449e-05
I0408 15:26:30.448608 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:26:32.069109 20259 solver.cpp:218] Iteration 4656 (2.38008 iter/s, 5.04186s/12 iters), loss = 1.8164
I0408 15:26:32.069154 20259 solver.cpp:237]     Train net output #0: loss = 1.8164 (* 1 = 1.8164 loss)
I0408 15:26:32.069164 20259 sgd_solver.cpp:105] Iteration 4656, lr = 8.1528e-05
I0408 15:26:37.114532 20259 solver.cpp:218] Iteration 4668 (2.37849 iter/s, 5.04522s/12 iters), loss = 1.60117
I0408 15:26:37.114960 20259 solver.cpp:237]     Train net output #0: loss = 1.60117 (* 1 = 1.60117 loss)
I0408 15:26:37.114974 20259 sgd_solver.cpp:105] Iteration 4668, lr = 8.05237e-05
I0408 15:26:42.127030 20259 solver.cpp:218] Iteration 4680 (2.39429 iter/s, 5.01191s/12 iters), loss = 1.60341
I0408 15:26:42.127076 20259 solver.cpp:237]     Train net output #0: loss = 1.60341 (* 1 = 1.60341 loss)
I0408 15:26:42.127089 20259 sgd_solver.cpp:105] Iteration 4680, lr = 7.95317e-05
I0408 15:26:46.641268 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 15:26:49.702215 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 15:26:52.028946 20259 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 15:26:52.028977 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:26:54.631623 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:26:56.483546 20259 solver.cpp:397]     Test net output #0: accuracy = 0.278186
I0408 15:26:56.483592 20259 solver.cpp:397]     Test net output #1: loss = 3.30943 (* 1 = 3.30943 loss)
I0408 15:26:56.572659 20259 solver.cpp:218] Iteration 4692 (0.830729 iter/s, 14.4452s/12 iters), loss = 1.8415
I0408 15:26:56.572696 20259 solver.cpp:237]     Train net output #0: loss = 1.8415 (* 1 = 1.8415 loss)
I0408 15:26:56.572707 20259 sgd_solver.cpp:105] Iteration 4692, lr = 7.8552e-05
I0408 15:27:01.025223 20259 solver.cpp:218] Iteration 4704 (2.69518 iter/s, 4.45239s/12 iters), loss = 1.70275
I0408 15:27:01.025259 20259 solver.cpp:237]     Train net output #0: loss = 1.70275 (* 1 = 1.70275 loss)
I0408 15:27:01.025269 20259 sgd_solver.cpp:105] Iteration 4704, lr = 7.75843e-05
I0408 15:27:06.135251 20259 solver.cpp:218] Iteration 4716 (2.34842 iter/s, 5.10983s/12 iters), loss = 1.72133
I0408 15:27:06.135294 20259 solver.cpp:237]     Train net output #0: loss = 1.72133 (* 1 = 1.72133 loss)
I0408 15:27:06.135308 20259 sgd_solver.cpp:105] Iteration 4716, lr = 7.66286e-05
I0408 15:27:11.129715 20259 solver.cpp:218] Iteration 4728 (2.40276 iter/s, 4.99426s/12 iters), loss = 1.5708
I0408 15:27:11.129855 20259 solver.cpp:237]     Train net output #0: loss = 1.5708 (* 1 = 1.5708 loss)
I0408 15:27:11.129869 20259 sgd_solver.cpp:105] Iteration 4728, lr = 7.56846e-05
I0408 15:27:16.062575 20259 solver.cpp:218] Iteration 4740 (2.43281 iter/s, 4.93257s/12 iters), loss = 1.82024
I0408 15:27:16.062608 20259 solver.cpp:237]     Train net output #0: loss = 1.82024 (* 1 = 1.82024 loss)
I0408 15:27:16.062616 20259 sgd_solver.cpp:105] Iteration 4740, lr = 7.47522e-05
I0408 15:27:21.027052 20259 solver.cpp:218] Iteration 4752 (2.41727 iter/s, 4.96429s/12 iters), loss = 1.93884
I0408 15:27:21.027092 20259 solver.cpp:237]     Train net output #0: loss = 1.93884 (* 1 = 1.93884 loss)
I0408 15:27:21.027103 20259 sgd_solver.cpp:105] Iteration 4752, lr = 7.38314e-05
I0408 15:27:21.543506 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:27:26.003196 20259 solver.cpp:218] Iteration 4764 (2.4116 iter/s, 4.97594s/12 iters), loss = 1.78478
I0408 15:27:26.003234 20259 solver.cpp:237]     Train net output #0: loss = 1.78478 (* 1 = 1.78478 loss)
I0408 15:27:26.003245 20259 sgd_solver.cpp:105] Iteration 4764, lr = 7.29219e-05
I0408 15:27:31.026716 20259 solver.cpp:218] Iteration 4776 (2.38886 iter/s, 5.02332s/12 iters), loss = 1.44013
I0408 15:27:31.026759 20259 solver.cpp:237]     Train net output #0: loss = 1.44013 (* 1 = 1.44013 loss)
I0408 15:27:31.026772 20259 sgd_solver.cpp:105] Iteration 4776, lr = 7.20236e-05
I0408 15:27:36.058799 20259 solver.cpp:218] Iteration 4788 (2.38479 iter/s, 5.03189s/12 iters), loss = 1.7553
I0408 15:27:36.058830 20259 solver.cpp:237]     Train net output #0: loss = 1.7553 (* 1 = 1.7553 loss)
I0408 15:27:36.058838 20259 sgd_solver.cpp:105] Iteration 4788, lr = 7.11363e-05
I0408 15:27:38.067230 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 15:27:41.081166 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 15:27:43.371891 20259 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 15:27:43.371968 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:27:45.927181 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:27:47.825788 20259 solver.cpp:397]     Test net output #0: accuracy = 0.286152
I0408 15:27:47.825834 20259 solver.cpp:397]     Test net output #1: loss = 3.29271 (* 1 = 3.29271 loss)
I0408 15:27:49.823768 20259 solver.cpp:218] Iteration 4800 (0.871807 iter/s, 13.7645s/12 iters), loss = 1.79577
I0408 15:27:49.823819 20259 solver.cpp:237]     Train net output #0: loss = 1.79577 (* 1 = 1.79577 loss)
I0408 15:27:49.823832 20259 sgd_solver.cpp:105] Iteration 4800, lr = 7.026e-05
I0408 15:27:55.022509 20259 solver.cpp:218] Iteration 4812 (2.30835 iter/s, 5.19853s/12 iters), loss = 1.77165
I0408 15:27:55.022552 20259 solver.cpp:237]     Train net output #0: loss = 1.77165 (* 1 = 1.77165 loss)
I0408 15:27:55.022564 20259 sgd_solver.cpp:105] Iteration 4812, lr = 6.93945e-05
I0408 15:28:00.026742 20259 solver.cpp:218] Iteration 4824 (2.39807 iter/s, 5.00403s/12 iters), loss = 2.01948
I0408 15:28:00.026789 20259 solver.cpp:237]     Train net output #0: loss = 2.01948 (* 1 = 2.01948 loss)
I0408 15:28:00.026803 20259 sgd_solver.cpp:105] Iteration 4824, lr = 6.85396e-05
I0408 15:28:05.001433 20259 solver.cpp:218] Iteration 4836 (2.41231 iter/s, 4.97448s/12 iters), loss = 1.53828
I0408 15:28:05.001477 20259 solver.cpp:237]     Train net output #0: loss = 1.53828 (* 1 = 1.53828 loss)
I0408 15:28:05.001489 20259 sgd_solver.cpp:105] Iteration 4836, lr = 6.76953e-05
I0408 15:28:06.962085 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:28:09.894043 20259 solver.cpp:218] Iteration 4848 (2.45278 iter/s, 4.89241s/12 iters), loss = 2.05153
I0408 15:28:09.894078 20259 solver.cpp:237]     Train net output #0: loss = 2.05153 (* 1 = 2.05153 loss)
I0408 15:28:09.894084 20259 sgd_solver.cpp:105] Iteration 4848, lr = 6.68614e-05
I0408 15:28:12.515727 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:28:14.843075 20259 solver.cpp:218] Iteration 4860 (2.42481 iter/s, 4.94884s/12 iters), loss = 1.66108
I0408 15:28:14.843255 20259 solver.cpp:237]     Train net output #0: loss = 1.66108 (* 1 = 1.66108 loss)
I0408 15:28:14.843268 20259 sgd_solver.cpp:105] Iteration 4860, lr = 6.60377e-05
I0408 15:28:19.879696 20259 solver.cpp:218] Iteration 4872 (2.38271 iter/s, 5.03629s/12 iters), loss = 1.70663
I0408 15:28:19.879751 20259 solver.cpp:237]     Train net output #0: loss = 1.70663 (* 1 = 1.70663 loss)
I0408 15:28:19.879760 20259 sgd_solver.cpp:105] Iteration 4872, lr = 6.52242e-05
I0408 15:28:24.910845 20259 solver.cpp:218] Iteration 4884 (2.38524 iter/s, 5.03095s/12 iters), loss = 1.67498
I0408 15:28:24.910890 20259 solver.cpp:237]     Train net output #0: loss = 1.67498 (* 1 = 1.67498 loss)
I0408 15:28:24.910902 20259 sgd_solver.cpp:105] Iteration 4884, lr = 6.44207e-05
I0408 15:28:29.366780 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 15:28:32.419041 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 15:28:34.719633 20259 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 15:28:34.719657 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:28:37.235211 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:28:39.167191 20259 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0408 15:28:39.167235 20259 solver.cpp:397]     Test net output #1: loss = 3.26755 (* 1 = 3.26755 loss)
I0408 15:28:39.257578 20259 solver.cpp:218] Iteration 4896 (0.836455 iter/s, 14.3463s/12 iters), loss = 2.10644
I0408 15:28:39.257619 20259 solver.cpp:237]     Train net output #0: loss = 2.10644 (* 1 = 2.10644 loss)
I0408 15:28:39.257630 20259 sgd_solver.cpp:105] Iteration 4896, lr = 6.36271e-05
I0408 15:28:43.616276 20259 solver.cpp:218] Iteration 4908 (2.75323 iter/s, 4.35852s/12 iters), loss = 1.64009
I0408 15:28:43.616322 20259 solver.cpp:237]     Train net output #0: loss = 1.64009 (* 1 = 1.64009 loss)
I0408 15:28:43.616333 20259 sgd_solver.cpp:105] Iteration 4908, lr = 6.28433e-05
I0408 15:28:48.637775 20259 solver.cpp:218] Iteration 4920 (2.38982 iter/s, 5.0213s/12 iters), loss = 1.74197
I0408 15:28:48.637882 20259 solver.cpp:237]     Train net output #0: loss = 1.74197 (* 1 = 1.74197 loss)
I0408 15:28:48.637895 20259 sgd_solver.cpp:105] Iteration 4920, lr = 6.20692e-05
I0408 15:28:53.686709 20259 solver.cpp:218] Iteration 4932 (2.37686 iter/s, 5.04867s/12 iters), loss = 1.51928
I0408 15:28:53.686748 20259 solver.cpp:237]     Train net output #0: loss = 1.51928 (* 1 = 1.51928 loss)
I0408 15:28:53.686758 20259 sgd_solver.cpp:105] Iteration 4932, lr = 6.13045e-05
I0408 15:28:58.623168 20259 solver.cpp:218] Iteration 4944 (2.43099 iter/s, 4.93626s/12 iters), loss = 1.51662
I0408 15:28:58.623214 20259 solver.cpp:237]     Train net output #0: loss = 1.51662 (* 1 = 1.51662 loss)
I0408 15:28:58.623226 20259 sgd_solver.cpp:105] Iteration 4944, lr = 6.05493e-05
I0408 15:29:03.504830 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:29:03.710212 20259 solver.cpp:218] Iteration 4956 (2.35903 iter/s, 5.08684s/12 iters), loss = 1.90032
I0408 15:29:03.710260 20259 solver.cpp:237]     Train net output #0: loss = 1.90032 (* 1 = 1.90032 loss)
I0408 15:29:03.710273 20259 sgd_solver.cpp:105] Iteration 4956, lr = 5.98034e-05
I0408 15:29:08.916260 20259 solver.cpp:218] Iteration 4968 (2.30511 iter/s, 5.20584s/12 iters), loss = 1.75764
I0408 15:29:08.916303 20259 solver.cpp:237]     Train net output #0: loss = 1.75764 (* 1 = 1.75764 loss)
I0408 15:29:08.916316 20259 sgd_solver.cpp:105] Iteration 4968, lr = 5.90667e-05
I0408 15:29:13.875583 20259 solver.cpp:218] Iteration 4980 (2.41978 iter/s, 4.95913s/12 iters), loss = 1.73588
I0408 15:29:13.875630 20259 solver.cpp:237]     Train net output #0: loss = 1.73588 (* 1 = 1.73588 loss)
I0408 15:29:13.875643 20259 sgd_solver.cpp:105] Iteration 4980, lr = 5.83391e-05
I0408 15:29:18.829838 20259 solver.cpp:218] Iteration 4992 (2.42226 iter/s, 4.95405s/12 iters), loss = 1.83718
I0408 15:29:18.830010 20259 solver.cpp:237]     Train net output #0: loss = 1.83718 (* 1 = 1.83718 loss)
I0408 15:29:18.830024 20259 sgd_solver.cpp:105] Iteration 4992, lr = 5.76204e-05
I0408 15:29:20.859459 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 15:29:24.705224 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 15:29:27.016826 20259 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 15:29:27.016849 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:29:29.501209 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:29:31.467984 20259 solver.cpp:397]     Test net output #0: accuracy = 0.284926
I0408 15:29:31.468029 20259 solver.cpp:397]     Test net output #1: loss = 3.28046 (* 1 = 3.28046 loss)
I0408 15:29:33.328789 20259 solver.cpp:218] Iteration 5004 (0.827681 iter/s, 14.4983s/12 iters), loss = 1.54916
I0408 15:29:33.328835 20259 solver.cpp:237]     Train net output #0: loss = 1.54916 (* 1 = 1.54916 loss)
I0408 15:29:33.328847 20259 sgd_solver.cpp:105] Iteration 5004, lr = 5.69106e-05
I0408 15:29:38.323901 20259 solver.cpp:218] Iteration 5016 (2.40245 iter/s, 4.99491s/12 iters), loss = 1.91824
I0408 15:29:38.323945 20259 solver.cpp:237]     Train net output #0: loss = 1.91824 (* 1 = 1.91824 loss)
I0408 15:29:38.323956 20259 sgd_solver.cpp:105] Iteration 5016, lr = 5.62095e-05
I0408 15:29:43.341681 20259 solver.cpp:218] Iteration 5028 (2.39159 iter/s, 5.01758s/12 iters), loss = 1.91442
I0408 15:29:43.341719 20259 solver.cpp:237]     Train net output #0: loss = 1.91442 (* 1 = 1.91442 loss)
I0408 15:29:43.341729 20259 sgd_solver.cpp:105] Iteration 5028, lr = 5.55171e-05
I0408 15:29:48.344476 20259 solver.cpp:218] Iteration 5040 (2.39875 iter/s, 5.0026s/12 iters), loss = 1.83658
I0408 15:29:48.344518 20259 solver.cpp:237]     Train net output #0: loss = 1.83658 (* 1 = 1.83658 loss)
I0408 15:29:48.344529 20259 sgd_solver.cpp:105] Iteration 5040, lr = 5.48332e-05
I0408 15:29:53.378688 20259 solver.cpp:218] Iteration 5052 (2.38379 iter/s, 5.03401s/12 iters), loss = 1.57613
I0408 15:29:53.378806 20259 solver.cpp:237]     Train net output #0: loss = 1.57613 (* 1 = 1.57613 loss)
I0408 15:29:53.378819 20259 sgd_solver.cpp:105] Iteration 5052, lr = 5.41577e-05
I0408 15:29:55.269383 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:29:58.329227 20259 solver.cpp:218] Iteration 5064 (2.42411 iter/s, 4.95026s/12 iters), loss = 1.93773
I0408 15:29:58.329274 20259 solver.cpp:237]     Train net output #0: loss = 1.93773 (* 1 = 1.93773 loss)
I0408 15:29:58.329286 20259 sgd_solver.cpp:105] Iteration 5064, lr = 5.34906e-05
I0408 15:30:03.541832 20259 solver.cpp:218] Iteration 5076 (2.30221 iter/s, 5.21239s/12 iters), loss = 1.73845
I0408 15:30:03.541877 20259 solver.cpp:237]     Train net output #0: loss = 1.73845 (* 1 = 1.73845 loss)
I0408 15:30:03.541888 20259 sgd_solver.cpp:105] Iteration 5076, lr = 5.28316e-05
I0408 15:30:08.578619 20259 solver.cpp:218] Iteration 5088 (2.38257 iter/s, 5.03658s/12 iters), loss = 1.4774
I0408 15:30:08.578662 20259 solver.cpp:237]     Train net output #0: loss = 1.4774 (* 1 = 1.4774 loss)
I0408 15:30:08.578675 20259 sgd_solver.cpp:105] Iteration 5088, lr = 5.21808e-05
I0408 15:30:13.152658 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 15:30:16.174319 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 15:30:19.895788 20259 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 15:30:19.895821 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:30:22.291364 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:30:24.306123 20259 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0408 15:30:24.306284 20259 solver.cpp:397]     Test net output #1: loss = 3.26028 (* 1 = 3.26028 loss)
I0408 15:30:24.396438 20259 solver.cpp:218] Iteration 5100 (0.758663 iter/s, 15.8173s/12 iters), loss = 1.81392
I0408 15:30:24.396472 20259 solver.cpp:237]     Train net output #0: loss = 1.81392 (* 1 = 1.81392 loss)
I0408 15:30:24.396482 20259 sgd_solver.cpp:105] Iteration 5100, lr = 5.1538e-05
I0408 15:30:28.566385 20259 solver.cpp:218] Iteration 5112 (2.87785 iter/s, 4.16978s/12 iters), loss = 1.80548
I0408 15:30:28.566429 20259 solver.cpp:237]     Train net output #0: loss = 1.80548 (* 1 = 1.80548 loss)
I0408 15:30:28.566440 20259 sgd_solver.cpp:105] Iteration 5112, lr = 5.09031e-05
I0408 15:30:33.544314 20259 solver.cpp:218] Iteration 5124 (2.41074 iter/s, 4.97773s/12 iters), loss = 1.6099
I0408 15:30:33.544358 20259 solver.cpp:237]     Train net output #0: loss = 1.6099 (* 1 = 1.6099 loss)
I0408 15:30:33.544370 20259 sgd_solver.cpp:105] Iteration 5124, lr = 5.0276e-05
I0408 15:30:38.607092 20259 solver.cpp:218] Iteration 5136 (2.37034 iter/s, 5.06257s/12 iters), loss = 1.67252
I0408 15:30:38.607136 20259 solver.cpp:237]     Train net output #0: loss = 1.67252 (* 1 = 1.67252 loss)
I0408 15:30:38.607147 20259 sgd_solver.cpp:105] Iteration 5136, lr = 4.96567e-05
I0408 15:30:43.530026 20259 solver.cpp:218] Iteration 5148 (2.43767 iter/s, 4.92274s/12 iters), loss = 1.48614
I0408 15:30:43.530076 20259 solver.cpp:237]     Train net output #0: loss = 1.48614 (* 1 = 1.48614 loss)
I0408 15:30:43.530087 20259 sgd_solver.cpp:105] Iteration 5148, lr = 4.9045e-05
I0408 15:30:47.590853 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:30:48.540832 20259 solver.cpp:218] Iteration 5160 (2.39492 iter/s, 5.0106s/12 iters), loss = 1.84384
I0408 15:30:48.540875 20259 solver.cpp:237]     Train net output #0: loss = 1.84384 (* 1 = 1.84384 loss)
I0408 15:30:48.540887 20259 sgd_solver.cpp:105] Iteration 5160, lr = 4.84408e-05
I0408 15:30:53.539772 20259 solver.cpp:218] Iteration 5172 (2.40061 iter/s, 4.99874s/12 iters), loss = 2.03096
I0408 15:30:53.539824 20259 solver.cpp:237]     Train net output #0: loss = 2.03096 (* 1 = 2.03096 loss)
I0408 15:30:53.539839 20259 sgd_solver.cpp:105] Iteration 5172, lr = 4.78441e-05
I0408 15:30:58.494871 20259 solver.cpp:218] Iteration 5184 (2.42185 iter/s, 4.95489s/12 iters), loss = 1.72827
I0408 15:30:58.494976 20259 solver.cpp:237]     Train net output #0: loss = 1.72827 (* 1 = 1.72827 loss)
I0408 15:30:58.494988 20259 sgd_solver.cpp:105] Iteration 5184, lr = 4.72547e-05
I0408 15:31:03.348259 20259 solver.cpp:218] Iteration 5196 (2.47263 iter/s, 4.85313s/12 iters), loss = 1.67229
I0408 15:31:03.348302 20259 solver.cpp:237]     Train net output #0: loss = 1.67229 (* 1 = 1.67229 loss)
I0408 15:31:03.348313 20259 sgd_solver.cpp:105] Iteration 5196, lr = 4.66726e-05
I0408 15:31:05.358661 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 15:31:11.696732 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 15:31:14.025517 20259 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 15:31:14.025542 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:31:16.410634 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:31:18.459992 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0408 15:31:18.460038 20259 solver.cpp:397]     Test net output #1: loss = 3.2747 (* 1 = 3.2747 loss)
I0408 15:31:20.368921 20259 solver.cpp:218] Iteration 5208 (0.705049 iter/s, 17.0201s/12 iters), loss = 1.5833
I0408 15:31:20.368968 20259 solver.cpp:237]     Train net output #0: loss = 1.5833 (* 1 = 1.5833 loss)
I0408 15:31:20.368980 20259 sgd_solver.cpp:105] Iteration 5208, lr = 4.60976e-05
I0408 15:31:25.391688 20259 solver.cpp:218] Iteration 5220 (2.38922 iter/s, 5.02256s/12 iters), loss = 1.69733
I0408 15:31:25.391737 20259 solver.cpp:237]     Train net output #0: loss = 1.69733 (* 1 = 1.69733 loss)
I0408 15:31:25.391749 20259 sgd_solver.cpp:105] Iteration 5220, lr = 4.55297e-05
I0408 15:31:30.441722 20259 solver.cpp:218] Iteration 5232 (2.37632 iter/s, 5.04982s/12 iters), loss = 1.66107
I0408 15:31:30.441869 20259 solver.cpp:237]     Train net output #0: loss = 1.66107 (* 1 = 1.66107 loss)
I0408 15:31:30.441881 20259 sgd_solver.cpp:105] Iteration 5232, lr = 4.49689e-05
I0408 15:31:35.420858 20259 solver.cpp:218] Iteration 5244 (2.4102 iter/s, 4.97883s/12 iters), loss = 1.64895
I0408 15:31:35.420900 20259 solver.cpp:237]     Train net output #0: loss = 1.64895 (* 1 = 1.64895 loss)
I0408 15:31:35.420910 20259 sgd_solver.cpp:105] Iteration 5244, lr = 4.44149e-05
I0408 15:31:40.464797 20259 solver.cpp:218] Iteration 5256 (2.37919 iter/s, 5.04374s/12 iters), loss = 1.736
I0408 15:31:40.464843 20259 solver.cpp:237]     Train net output #0: loss = 1.736 (* 1 = 1.736 loss)
I0408 15:31:40.464854 20259 sgd_solver.cpp:105] Iteration 5256, lr = 4.38678e-05
I0408 15:31:41.776548 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:31:45.515383 20259 solver.cpp:218] Iteration 5268 (2.37606 iter/s, 5.05038s/12 iters), loss = 1.54788
I0408 15:31:45.515427 20259 solver.cpp:237]     Train net output #0: loss = 1.54788 (* 1 = 1.54788 loss)
I0408 15:31:45.515439 20259 sgd_solver.cpp:105] Iteration 5268, lr = 4.33274e-05
I0408 15:31:50.519773 20259 solver.cpp:218] Iteration 5280 (2.39799 iter/s, 5.00418s/12 iters), loss = 1.67737
I0408 15:31:50.519816 20259 solver.cpp:237]     Train net output #0: loss = 1.67737 (* 1 = 1.67737 loss)
I0408 15:31:50.519829 20259 sgd_solver.cpp:105] Iteration 5280, lr = 4.27936e-05
I0408 15:31:55.543748 20259 solver.cpp:218] Iteration 5292 (2.38864 iter/s, 5.02377s/12 iters), loss = 1.5465
I0408 15:31:55.543795 20259 solver.cpp:237]     Train net output #0: loss = 1.5465 (* 1 = 1.5465 loss)
I0408 15:31:55.543807 20259 sgd_solver.cpp:105] Iteration 5292, lr = 4.22664e-05
I0408 15:32:00.074949 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 15:32:03.156175 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 15:32:10.140015 20259 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 15:32:10.140044 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:32:12.503530 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:32:14.598479 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 15:32:14.598526 20259 solver.cpp:397]     Test net output #1: loss = 3.27312 (* 1 = 3.27312 loss)
I0408 15:32:14.688648 20259 solver.cpp:218] Iteration 5304 (0.626819 iter/s, 19.1443s/12 iters), loss = 1.72119
I0408 15:32:14.688683 20259 solver.cpp:237]     Train net output #0: loss = 1.72119 (* 1 = 1.72119 loss)
I0408 15:32:14.688694 20259 sgd_solver.cpp:105] Iteration 5304, lr = 4.17458e-05
I0408 15:32:18.990983 20259 solver.cpp:218] Iteration 5316 (2.7893 iter/s, 4.30216s/12 iters), loss = 1.75551
I0408 15:32:18.991031 20259 solver.cpp:237]     Train net output #0: loss = 1.75551 (* 1 = 1.75551 loss)
I0408 15:32:18.991044 20259 sgd_solver.cpp:105] Iteration 5316, lr = 4.12315e-05
I0408 15:32:23.930263 20259 solver.cpp:218] Iteration 5328 (2.4296 iter/s, 4.93908s/12 iters), loss = 1.55439
I0408 15:32:23.930308 20259 solver.cpp:237]     Train net output #0: loss = 1.55439 (* 1 = 1.55439 loss)
I0408 15:32:23.930320 20259 sgd_solver.cpp:105] Iteration 5328, lr = 4.07236e-05
I0408 15:32:28.857858 20259 solver.cpp:218] Iteration 5340 (2.43537 iter/s, 4.92739s/12 iters), loss = 1.41277
I0408 15:32:28.857903 20259 solver.cpp:237]     Train net output #0: loss = 1.41277 (* 1 = 1.41277 loss)
I0408 15:32:28.857914 20259 sgd_solver.cpp:105] Iteration 5340, lr = 4.02219e-05
I0408 15:32:33.797817 20259 solver.cpp:218] Iteration 5352 (2.42927 iter/s, 4.93976s/12 iters), loss = 1.56193
I0408 15:32:33.797971 20259 solver.cpp:237]     Train net output #0: loss = 1.56193 (* 1 = 1.56193 loss)
I0408 15:32:33.797983 20259 sgd_solver.cpp:105] Iteration 5352, lr = 3.97264e-05
I0408 15:32:37.166381 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:32:38.716421 20259 solver.cpp:218] Iteration 5364 (2.43987 iter/s, 4.9183s/12 iters), loss = 1.61966
I0408 15:32:38.716464 20259 solver.cpp:237]     Train net output #0: loss = 1.61966 (* 1 = 1.61966 loss)
I0408 15:32:38.716475 20259 sgd_solver.cpp:105] Iteration 5364, lr = 3.9237e-05
I0408 15:32:43.908639 20259 solver.cpp:218] Iteration 5376 (2.31124 iter/s, 5.19201s/12 iters), loss = 1.3889
I0408 15:32:43.908682 20259 solver.cpp:237]     Train net output #0: loss = 1.3889 (* 1 = 1.3889 loss)
I0408 15:32:43.908694 20259 sgd_solver.cpp:105] Iteration 5376, lr = 3.87537e-05
I0408 15:32:48.938122 20259 solver.cpp:218] Iteration 5388 (2.38603 iter/s, 5.02928s/12 iters), loss = 1.42687
I0408 15:32:48.938155 20259 solver.cpp:237]     Train net output #0: loss = 1.42687 (* 1 = 1.42687 loss)
I0408 15:32:48.938163 20259 sgd_solver.cpp:105] Iteration 5388, lr = 3.82763e-05
I0408 15:32:53.903779 20259 solver.cpp:218] Iteration 5400 (2.41669 iter/s, 4.96547s/12 iters), loss = 1.6895
I0408 15:32:53.903822 20259 solver.cpp:237]     Train net output #0: loss = 1.6895 (* 1 = 1.6895 loss)
I0408 15:32:53.903834 20259 sgd_solver.cpp:105] Iteration 5400, lr = 3.78048e-05
I0408 15:32:55.935175 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 15:32:58.909512 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 15:33:01.361589 20259 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 15:33:01.361622 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:33:03.672029 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:33:05.801720 20259 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0408 15:33:05.801828 20259 solver.cpp:397]     Test net output #1: loss = 3.27516 (* 1 = 3.27516 loss)
I0408 15:33:07.687161 20259 solver.cpp:218] Iteration 5412 (0.870643 iter/s, 13.7829s/12 iters), loss = 1.58082
I0408 15:33:07.687206 20259 solver.cpp:237]     Train net output #0: loss = 1.58082 (* 1 = 1.58082 loss)
I0408 15:33:07.687217 20259 sgd_solver.cpp:105] Iteration 5412, lr = 3.73391e-05
I0408 15:33:12.856941 20259 solver.cpp:218] Iteration 5424 (2.32127 iter/s, 5.16957s/12 iters), loss = 1.61936
I0408 15:33:12.856973 20259 solver.cpp:237]     Train net output #0: loss = 1.61936 (* 1 = 1.61936 loss)
I0408 15:33:12.856982 20259 sgd_solver.cpp:105] Iteration 5424, lr = 3.68791e-05
I0408 15:33:17.913581 20259 solver.cpp:218] Iteration 5436 (2.37321 iter/s, 5.05644s/12 iters), loss = 1.57584
I0408 15:33:17.913628 20259 solver.cpp:237]     Train net output #0: loss = 1.57584 (* 1 = 1.57584 loss)
I0408 15:33:17.913641 20259 sgd_solver.cpp:105] Iteration 5436, lr = 3.64248e-05
I0408 15:33:22.879644 20259 solver.cpp:218] Iteration 5448 (2.4165 iter/s, 4.96586s/12 iters), loss = 1.34376
I0408 15:33:22.879689 20259 solver.cpp:237]     Train net output #0: loss = 1.34376 (* 1 = 1.34376 loss)
I0408 15:33:22.879701 20259 sgd_solver.cpp:105] Iteration 5448, lr = 3.59761e-05
I0408 15:33:27.905567 20259 solver.cpp:218] Iteration 5460 (2.38772 iter/s, 5.02572s/12 iters), loss = 1.71241
I0408 15:33:27.905608 20259 solver.cpp:237]     Train net output #0: loss = 1.71241 (* 1 = 1.71241 loss)
I0408 15:33:27.905620 20259 sgd_solver.cpp:105] Iteration 5460, lr = 3.55329e-05
I0408 15:33:28.455534 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:33:32.869886 20259 solver.cpp:218] Iteration 5472 (2.41735 iter/s, 4.96412s/12 iters), loss = 1.58092
I0408 15:33:32.869927 20259 solver.cpp:237]     Train net output #0: loss = 1.58092 (* 1 = 1.58092 loss)
I0408 15:33:32.869937 20259 sgd_solver.cpp:105] Iteration 5472, lr = 3.50952e-05
I0408 15:33:37.886520 20259 solver.cpp:218] Iteration 5484 (2.39214 iter/s, 5.01644s/12 iters), loss = 1.6868
I0408 15:33:37.886675 20259 solver.cpp:237]     Train net output #0: loss = 1.6868 (* 1 = 1.6868 loss)
I0408 15:33:37.886689 20259 sgd_solver.cpp:105] Iteration 5484, lr = 3.46628e-05
I0408 15:33:42.794589 20259 solver.cpp:218] Iteration 5496 (2.44511 iter/s, 4.90776s/12 iters), loss = 1.54592
I0408 15:33:42.794637 20259 solver.cpp:237]     Train net output #0: loss = 1.54592 (* 1 = 1.54592 loss)
I0408 15:33:42.794651 20259 sgd_solver.cpp:105] Iteration 5496, lr = 3.42358e-05
I0408 15:33:47.258219 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 15:33:50.348737 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 15:33:52.680765 20259 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 15:33:52.680797 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:33:54.973433 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:33:57.147589 20259 solver.cpp:397]     Test net output #0: accuracy = 0.295343
I0408 15:33:57.147634 20259 solver.cpp:397]     Test net output #1: loss = 3.28195 (* 1 = 3.28195 loss)
I0408 15:33:57.237936 20259 solver.cpp:218] Iteration 5508 (0.83086 iter/s, 14.4429s/12 iters), loss = 1.47573
I0408 15:33:57.238001 20259 solver.cpp:237]     Train net output #0: loss = 1.47573 (* 1 = 1.47573 loss)
I0408 15:33:57.238018 20259 sgd_solver.cpp:105] Iteration 5508, lr = 3.38141e-05
I0408 15:34:01.740950 20259 solver.cpp:218] Iteration 5520 (2.66501 iter/s, 4.5028s/12 iters), loss = 1.56171
I0408 15:34:01.740998 20259 solver.cpp:237]     Train net output #0: loss = 1.56171 (* 1 = 1.56171 loss)
I0408 15:34:01.741010 20259 sgd_solver.cpp:105] Iteration 5520, lr = 3.33975e-05
I0408 15:34:04.199939 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:34:06.766906 20259 solver.cpp:218] Iteration 5532 (2.3877 iter/s, 5.02575s/12 iters), loss = 1.691
I0408 15:34:06.766942 20259 solver.cpp:237]     Train net output #0: loss = 1.691 (* 1 = 1.691 loss)
I0408 15:34:06.766952 20259 sgd_solver.cpp:105] Iteration 5532, lr = 3.29861e-05
I0408 15:34:11.824633 20259 solver.cpp:218] Iteration 5544 (2.3727 iter/s, 5.05753s/12 iters), loss = 1.47621
I0408 15:34:11.824736 20259 solver.cpp:237]     Train net output #0: loss = 1.47621 (* 1 = 1.47621 loss)
I0408 15:34:11.824749 20259 sgd_solver.cpp:105] Iteration 5544, lr = 3.25798e-05
I0408 15:34:16.853211 20259 solver.cpp:218] Iteration 5556 (2.38648 iter/s, 5.02832s/12 iters), loss = 1.7106
I0408 15:34:16.853255 20259 solver.cpp:237]     Train net output #0: loss = 1.7106 (* 1 = 1.7106 loss)
I0408 15:34:16.853267 20259 sgd_solver.cpp:105] Iteration 5556, lr = 3.21784e-05
I0408 15:34:19.543337 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:34:21.874043 20259 solver.cpp:218] Iteration 5568 (2.39014 iter/s, 5.02063s/12 iters), loss = 1.42972
I0408 15:34:21.874086 20259 solver.cpp:237]     Train net output #0: loss = 1.42972 (* 1 = 1.42972 loss)
I0408 15:34:21.874099 20259 sgd_solver.cpp:105] Iteration 5568, lr = 3.1782e-05
I0408 15:34:26.850517 20259 solver.cpp:218] Iteration 5580 (2.41144 iter/s, 4.97628s/12 iters), loss = 1.76952
I0408 15:34:26.850560 20259 solver.cpp:237]     Train net output #0: loss = 1.76952 (* 1 = 1.76952 loss)
I0408 15:34:26.850571 20259 sgd_solver.cpp:105] Iteration 5580, lr = 3.13905e-05
I0408 15:34:31.861371 20259 solver.cpp:218] Iteration 5592 (2.3949 iter/s, 5.01065s/12 iters), loss = 1.62653
I0408 15:34:31.861414 20259 solver.cpp:237]     Train net output #0: loss = 1.62653 (* 1 = 1.62653 loss)
I0408 15:34:31.861425 20259 sgd_solver.cpp:105] Iteration 5592, lr = 3.10038e-05
I0408 15:34:36.893761 20259 solver.cpp:218] Iteration 5604 (2.38465 iter/s, 5.03219s/12 iters), loss = 1.76298
I0408 15:34:36.893798 20259 solver.cpp:237]     Train net output #0: loss = 1.76298 (* 1 = 1.76298 loss)
I0408 15:34:36.893810 20259 sgd_solver.cpp:105] Iteration 5604, lr = 3.06219e-05
I0408 15:34:38.916224 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 15:34:42.748253 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 15:34:45.066460 20259 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 15:34:45.066483 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:34:47.322757 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:34:49.651703 20259 solver.cpp:397]     Test net output #0: accuracy = 0.28799
I0408 15:34:49.651751 20259 solver.cpp:397]     Test net output #1: loss = 3.27366 (* 1 = 3.27366 loss)
I0408 15:34:51.539359 20259 solver.cpp:218] Iteration 5616 (0.819386 iter/s, 14.6451s/12 iters), loss = 1.30594
I0408 15:34:51.539422 20259 solver.cpp:237]     Train net output #0: loss = 1.30594 (* 1 = 1.30594 loss)
I0408 15:34:51.539438 20259 sgd_solver.cpp:105] Iteration 5616, lr = 3.02446e-05
I0408 15:34:56.587483 20259 solver.cpp:218] Iteration 5628 (2.37722 iter/s, 5.0479s/12 iters), loss = 1.48412
I0408 15:34:56.587529 20259 solver.cpp:237]     Train net output #0: loss = 1.48412 (* 1 = 1.48412 loss)
I0408 15:34:56.587541 20259 sgd_solver.cpp:105] Iteration 5628, lr = 2.98721e-05
I0408 15:35:01.622522 20259 solver.cpp:218] Iteration 5640 (2.3834 iter/s, 5.03483s/12 iters), loss = 1.53232
I0408 15:35:01.622568 20259 solver.cpp:237]     Train net output #0: loss = 1.53232 (* 1 = 1.53232 loss)
I0408 15:35:01.622581 20259 sgd_solver.cpp:105] Iteration 5640, lr = 2.95041e-05
I0408 15:35:06.653211 20259 solver.cpp:218] Iteration 5652 (2.38546 iter/s, 5.03048s/12 iters), loss = 1.66102
I0408 15:35:06.653254 20259 solver.cpp:237]     Train net output #0: loss = 1.66102 (* 1 = 1.66102 loss)
I0408 15:35:06.653264 20259 sgd_solver.cpp:105] Iteration 5652, lr = 2.91406e-05
I0408 15:35:11.512302 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:11.680653 20259 solver.cpp:218] Iteration 5664 (2.38699 iter/s, 5.02724s/12 iters), loss = 1.75695
I0408 15:35:11.680691 20259 solver.cpp:237]     Train net output #0: loss = 1.75695 (* 1 = 1.75695 loss)
I0408 15:35:11.680701 20259 sgd_solver.cpp:105] Iteration 5664, lr = 2.87816e-05
I0408 15:35:16.704561 20259 solver.cpp:218] Iteration 5676 (2.38867 iter/s, 5.02371s/12 iters), loss = 1.75517
I0408 15:35:16.704640 20259 solver.cpp:237]     Train net output #0: loss = 1.75517 (* 1 = 1.75517 loss)
I0408 15:35:16.704648 20259 sgd_solver.cpp:105] Iteration 5676, lr = 2.84271e-05
I0408 15:35:21.699957 20259 solver.cpp:218] Iteration 5688 (2.40233 iter/s, 4.99516s/12 iters), loss = 1.49891
I0408 15:35:21.700001 20259 solver.cpp:237]     Train net output #0: loss = 1.49891 (* 1 = 1.49891 loss)
I0408 15:35:21.700011 20259 sgd_solver.cpp:105] Iteration 5688, lr = 2.80769e-05
I0408 15:35:26.724489 20259 solver.cpp:218] Iteration 5700 (2.38838 iter/s, 5.02433s/12 iters), loss = 1.57628
I0408 15:35:26.724529 20259 solver.cpp:237]     Train net output #0: loss = 1.57628 (* 1 = 1.57628 loss)
I0408 15:35:26.724537 20259 sgd_solver.cpp:105] Iteration 5700, lr = 2.7731e-05
I0408 15:35:31.511972 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 15:35:35.763010 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 15:35:40.223670 20259 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 15:35:40.223697 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:35:42.471786 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:44.726387 20259 solver.cpp:397]     Test net output #0: accuracy = 0.288603
I0408 15:35:44.726433 20259 solver.cpp:397]     Test net output #1: loss = 3.27965 (* 1 = 3.27965 loss)
I0408 15:35:44.816967 20259 solver.cpp:218] Iteration 5712 (0.663281 iter/s, 18.0919s/12 iters), loss = 1.4255
I0408 15:35:44.817015 20259 solver.cpp:237]     Train net output #0: loss = 1.4255 (* 1 = 1.4255 loss)
I0408 15:35:44.817028 20259 sgd_solver.cpp:105] Iteration 5712, lr = 2.73894e-05
I0408 15:35:49.149641 20259 solver.cpp:218] Iteration 5724 (2.76977 iter/s, 4.33249s/12 iters), loss = 1.83635
I0408 15:35:49.149794 20259 solver.cpp:237]     Train net output #0: loss = 1.83635 (* 1 = 1.83635 loss)
I0408 15:35:49.149809 20259 sgd_solver.cpp:105] Iteration 5724, lr = 2.7052e-05
I0408 15:35:54.126416 20259 solver.cpp:218] Iteration 5736 (2.41135 iter/s, 4.97647s/12 iters), loss = 1.83658
I0408 15:35:54.126459 20259 solver.cpp:237]     Train net output #0: loss = 1.83658 (* 1 = 1.83658 loss)
I0408 15:35:54.126471 20259 sgd_solver.cpp:105] Iteration 5736, lr = 2.67188e-05
I0408 15:35:59.063977 20259 solver.cpp:218] Iteration 5748 (2.43045 iter/s, 4.93735s/12 iters), loss = 1.50384
I0408 15:35:59.064031 20259 solver.cpp:237]     Train net output #0: loss = 1.50384 (* 1 = 1.50384 loss)
I0408 15:35:59.064044 20259 sgd_solver.cpp:105] Iteration 5748, lr = 2.63896e-05
I0408 15:36:04.063288 20259 solver.cpp:218] Iteration 5760 (2.40043 iter/s, 4.9991s/12 iters), loss = 1.56041
I0408 15:36:04.063338 20259 solver.cpp:237]     Train net output #0: loss = 1.56041 (* 1 = 1.56041 loss)
I0408 15:36:04.063349 20259 sgd_solver.cpp:105] Iteration 5760, lr = 2.60645e-05
I0408 15:36:06.016575 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:08.965713 20259 solver.cpp:218] Iteration 5772 (2.44788 iter/s, 4.9022s/12 iters), loss = 1.59929
I0408 15:36:08.965766 20259 solver.cpp:237]     Train net output #0: loss = 1.59929 (* 1 = 1.59929 loss)
I0408 15:36:08.965780 20259 sgd_solver.cpp:105] Iteration 5772, lr = 2.57434e-05
I0408 15:36:14.002707 20259 solver.cpp:218] Iteration 5784 (2.38247 iter/s, 5.03678s/12 iters), loss = 1.58486
I0408 15:36:14.002753 20259 solver.cpp:237]     Train net output #0: loss = 1.58486 (* 1 = 1.58486 loss)
I0408 15:36:14.002761 20259 sgd_solver.cpp:105] Iteration 5784, lr = 2.54263e-05
I0408 15:36:19.034549 20259 solver.cpp:218] Iteration 5796 (2.38491 iter/s, 5.03164s/12 iters), loss = 1.39388
I0408 15:36:19.034588 20259 solver.cpp:237]     Train net output #0: loss = 1.39388 (* 1 = 1.39388 loss)
I0408 15:36:19.034598 20259 sgd_solver.cpp:105] Iteration 5796, lr = 2.51131e-05
I0408 15:36:24.049795 20259 solver.cpp:218] Iteration 5808 (2.3928 iter/s, 5.01504s/12 iters), loss = 1.62252
I0408 15:36:24.049885 20259 solver.cpp:237]     Train net output #0: loss = 1.62252 (* 1 = 1.62252 loss)
I0408 15:36:24.049899 20259 sgd_solver.cpp:105] Iteration 5808, lr = 2.48037e-05
I0408 15:36:26.081849 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 15:36:30.503564 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 15:36:39.062338 20259 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 15:36:39.062376 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:36:41.234472 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:43.529673 20259 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0408 15:36:43.529719 20259 solver.cpp:397]     Test net output #1: loss = 3.26252 (* 1 = 3.26252 loss)
I0408 15:36:45.378437 20259 solver.cpp:218] Iteration 5820 (0.562643 iter/s, 21.3279s/12 iters), loss = 1.53763
I0408 15:36:45.378490 20259 solver.cpp:237]     Train net output #0: loss = 1.53763 (* 1 = 1.53763 loss)
I0408 15:36:45.378504 20259 sgd_solver.cpp:105] Iteration 5820, lr = 2.44982e-05
I0408 15:36:50.375028 20259 solver.cpp:218] Iteration 5832 (2.40174 iter/s, 4.99638s/12 iters), loss = 1.44905
I0408 15:36:50.375082 20259 solver.cpp:237]     Train net output #0: loss = 1.44905 (* 1 = 1.44905 loss)
I0408 15:36:50.375093 20259 sgd_solver.cpp:105] Iteration 5832, lr = 2.41964e-05
I0408 15:36:55.398449 20259 solver.cpp:218] Iteration 5844 (2.38891 iter/s, 5.02321s/12 iters), loss = 1.53043
I0408 15:36:55.398550 20259 solver.cpp:237]     Train net output #0: loss = 1.53043 (* 1 = 1.53043 loss)
I0408 15:36:55.398561 20259 sgd_solver.cpp:105] Iteration 5844, lr = 2.38983e-05
I0408 15:37:00.347007 20259 solver.cpp:218] Iteration 5856 (2.42507 iter/s, 4.9483s/12 iters), loss = 1.45556
I0408 15:37:00.347050 20259 solver.cpp:237]     Train net output #0: loss = 1.45556 (* 1 = 1.45556 loss)
I0408 15:37:00.347061 20259 sgd_solver.cpp:105] Iteration 5856, lr = 2.36039e-05
I0408 15:37:04.514622 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:05.323607 20259 solver.cpp:218] Iteration 5868 (2.41138 iter/s, 4.9764s/12 iters), loss = 1.443
I0408 15:37:05.323650 20259 solver.cpp:237]     Train net output #0: loss = 1.443 (* 1 = 1.443 loss)
I0408 15:37:05.323662 20259 sgd_solver.cpp:105] Iteration 5868, lr = 2.33131e-05
I0408 15:37:10.249943 20259 solver.cpp:218] Iteration 5880 (2.43599 iter/s, 4.92613s/12 iters), loss = 1.83186
I0408 15:37:10.250005 20259 solver.cpp:237]     Train net output #0: loss = 1.83186 (* 1 = 1.83186 loss)
I0408 15:37:10.250017 20259 sgd_solver.cpp:105] Iteration 5880, lr = 2.30259e-05
I0408 15:37:15.178608 20259 solver.cpp:218] Iteration 5892 (2.43484 iter/s, 4.92845s/12 iters), loss = 1.75068
I0408 15:37:15.178665 20259 solver.cpp:237]     Train net output #0: loss = 1.75068 (* 1 = 1.75068 loss)
I0408 15:37:15.178678 20259 sgd_solver.cpp:105] Iteration 5892, lr = 2.27423e-05
I0408 15:37:20.109350 20259 solver.cpp:218] Iteration 5904 (2.43382 iter/s, 4.93053s/12 iters), loss = 1.45542
I0408 15:37:20.109402 20259 solver.cpp:237]     Train net output #0: loss = 1.45542 (* 1 = 1.45542 loss)
I0408 15:37:20.109413 20259 sgd_solver.cpp:105] Iteration 5904, lr = 2.24621e-05
I0408 15:37:24.596175 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 15:37:29.035305 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 15:37:33.275049 20259 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 15:37:33.275075 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:37:35.566403 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:37.897112 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 15:37:37.897167 20259 solver.cpp:397]     Test net output #1: loss = 3.2717 (* 1 = 3.2717 loss)
I0408 15:37:37.987543 20259 solver.cpp:218] Iteration 5916 (0.671231 iter/s, 17.8776s/12 iters), loss = 1.63126
I0408 15:37:37.987591 20259 solver.cpp:237]     Train net output #0: loss = 1.63126 (* 1 = 1.63126 loss)
I0408 15:37:37.987601 20259 sgd_solver.cpp:105] Iteration 5916, lr = 2.21854e-05
I0408 15:37:42.284811 20259 solver.cpp:218] Iteration 5928 (2.7926 iter/s, 4.29708s/12 iters), loss = 1.59823
I0408 15:37:42.284864 20259 solver.cpp:237]     Train net output #0: loss = 1.59823 (* 1 = 1.59823 loss)
I0408 15:37:42.284878 20259 sgd_solver.cpp:105] Iteration 5928, lr = 2.19121e-05
I0408 15:37:47.288873 20259 solver.cpp:218] Iteration 5940 (2.39815 iter/s, 5.00385s/12 iters), loss = 1.58027
I0408 15:37:47.288915 20259 solver.cpp:237]     Train net output #0: loss = 1.58027 (* 1 = 1.58027 loss)
I0408 15:37:47.288925 20259 sgd_solver.cpp:105] Iteration 5940, lr = 2.16422e-05
I0408 15:37:52.414866 20259 solver.cpp:218] Iteration 5952 (2.34111 iter/s, 5.12578s/12 iters), loss = 1.7276
I0408 15:37:52.414921 20259 solver.cpp:237]     Train net output #0: loss = 1.7276 (* 1 = 1.7276 loss)
I0408 15:37:52.414934 20259 sgd_solver.cpp:105] Iteration 5952, lr = 2.13756e-05
I0408 15:37:57.506542 20259 solver.cpp:218] Iteration 5964 (2.35689 iter/s, 5.09146s/12 iters), loss = 1.67218
I0408 15:37:57.506604 20259 solver.cpp:237]     Train net output #0: loss = 1.67218 (* 1 = 1.67218 loss)
I0408 15:37:57.506620 20259 sgd_solver.cpp:105] Iteration 5964, lr = 2.11123e-05
I0408 15:37:58.796963 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:02.598300 20259 solver.cpp:218] Iteration 5976 (2.35685 iter/s, 5.09153s/12 iters), loss = 1.57926
I0408 15:38:02.598385 20259 solver.cpp:237]     Train net output #0: loss = 1.57926 (* 1 = 1.57926 loss)
I0408 15:38:02.598398 20259 sgd_solver.cpp:105] Iteration 5976, lr = 2.08522e-05
I0408 15:38:07.803581 20259 solver.cpp:218] Iteration 5988 (2.30546 iter/s, 5.20503s/12 iters), loss = 1.64414
I0408 15:38:07.803627 20259 solver.cpp:237]     Train net output #0: loss = 1.64414 (* 1 = 1.64414 loss)
I0408 15:38:07.803638 20259 sgd_solver.cpp:105] Iteration 5988, lr = 2.05953e-05
I0408 15:38:12.799757 20259 solver.cpp:218] Iteration 6000 (2.40194 iter/s, 4.99597s/12 iters), loss = 1.71173
I0408 15:38:12.799794 20259 solver.cpp:237]     Train net output #0: loss = 1.71173 (* 1 = 1.71173 loss)
I0408 15:38:12.799804 20259 sgd_solver.cpp:105] Iteration 6000, lr = 2.03416e-05
I0408 15:38:17.749853 20259 solver.cpp:218] Iteration 6012 (2.42429 iter/s, 4.94989s/12 iters), loss = 1.58772
I0408 15:38:17.749908 20259 solver.cpp:237]     Train net output #0: loss = 1.58772 (* 1 = 1.58772 loss)
I0408 15:38:17.749922 20259 sgd_solver.cpp:105] Iteration 6012, lr = 2.0091e-05
I0408 15:38:19.734037 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 15:38:23.352651 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 15:38:27.998204 20259 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 15:38:27.998234 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:38:30.258515 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:32.640089 20259 solver.cpp:397]     Test net output #0: accuracy = 0.286765
I0408 15:38:32.640252 20259 solver.cpp:397]     Test net output #1: loss = 3.28034 (* 1 = 3.28034 loss)
I0408 15:38:34.580926 20259 solver.cpp:218] Iteration 6024 (0.712991 iter/s, 16.8305s/12 iters), loss = 1.61337
I0408 15:38:34.580981 20259 solver.cpp:237]     Train net output #0: loss = 1.61337 (* 1 = 1.61337 loss)
I0408 15:38:34.580994 20259 sgd_solver.cpp:105] Iteration 6024, lr = 1.98435e-05
I0408 15:38:39.711002 20259 solver.cpp:218] Iteration 6036 (2.33924 iter/s, 5.12986s/12 iters), loss = 1.42086
I0408 15:38:39.711050 20259 solver.cpp:237]     Train net output #0: loss = 1.42086 (* 1 = 1.42086 loss)
I0408 15:38:39.711059 20259 sgd_solver.cpp:105] Iteration 6036, lr = 1.95991e-05
I0408 15:38:44.718673 20259 solver.cpp:218] Iteration 6048 (2.39642 iter/s, 5.00747s/12 iters), loss = 1.75275
I0408 15:38:44.718716 20259 solver.cpp:237]     Train net output #0: loss = 1.75275 (* 1 = 1.75275 loss)
I0408 15:38:44.718725 20259 sgd_solver.cpp:105] Iteration 6048, lr = 1.93576e-05
I0408 15:38:49.728032 20259 solver.cpp:218] Iteration 6060 (2.39561 iter/s, 5.00916s/12 iters), loss = 1.61937
I0408 15:38:49.728075 20259 solver.cpp:237]     Train net output #0: loss = 1.61937 (* 1 = 1.61937 loss)
I0408 15:38:49.728085 20259 sgd_solver.cpp:105] Iteration 6060, lr = 1.91192e-05
I0408 15:38:53.214401 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:54.773075 20259 solver.cpp:218] Iteration 6072 (2.37867 iter/s, 5.04484s/12 iters), loss = 1.56757
I0408 15:38:54.773121 20259 solver.cpp:237]     Train net output #0: loss = 1.56757 (* 1 = 1.56757 loss)
I0408 15:38:54.773130 20259 sgd_solver.cpp:105] Iteration 6072, lr = 1.88836e-05
I0408 15:38:59.786103 20259 solver.cpp:218] Iteration 6084 (2.39386 iter/s, 5.01283s/12 iters), loss = 1.56575
I0408 15:38:59.786136 20259 solver.cpp:237]     Train net output #0: loss = 1.56575 (* 1 = 1.56575 loss)
I0408 15:38:59.786144 20259 sgd_solver.cpp:105] Iteration 6084, lr = 1.8651e-05
I0408 15:39:04.740792 20259 solver.cpp:218] Iteration 6096 (2.42204 iter/s, 4.95449s/12 iters), loss = 1.77866
I0408 15:39:04.740919 20259 solver.cpp:237]     Train net output #0: loss = 1.77866 (* 1 = 1.77866 loss)
I0408 15:39:04.740933 20259 sgd_solver.cpp:105] Iteration 6096, lr = 1.84213e-05
I0408 15:39:09.676396 20259 solver.cpp:218] Iteration 6108 (2.43145 iter/s, 4.93532s/12 iters), loss = 1.58839
I0408 15:39:09.676452 20259 solver.cpp:237]     Train net output #0: loss = 1.58839 (* 1 = 1.58839 loss)
I0408 15:39:09.676466 20259 sgd_solver.cpp:105] Iteration 6108, lr = 1.81943e-05
I0408 15:39:14.257416 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 15:39:17.515305 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 15:39:22.570930 20259 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 15:39:22.570952 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:39:24.718660 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:27.222136 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 15:39:27.222185 20259 solver.cpp:397]     Test net output #1: loss = 3.27028 (* 1 = 3.27028 loss)
I0408 15:39:27.312577 20259 solver.cpp:218] Iteration 6120 (0.680442 iter/s, 17.6356s/12 iters), loss = 1.47568
I0408 15:39:27.312631 20259 solver.cpp:237]     Train net output #0: loss = 1.47568 (* 1 = 1.47568 loss)
I0408 15:39:27.312644 20259 sgd_solver.cpp:105] Iteration 6120, lr = 1.79702e-05
I0408 15:39:31.453552 20259 solver.cpp:218] Iteration 6132 (2.898 iter/s, 4.14079s/12 iters), loss = 1.56405
I0408 15:39:31.453601 20259 solver.cpp:237]     Train net output #0: loss = 1.56405 (* 1 = 1.56405 loss)
I0408 15:39:31.453613 20259 sgd_solver.cpp:105] Iteration 6132, lr = 1.77488e-05
I0408 15:39:36.400072 20259 solver.cpp:218] Iteration 6144 (2.42605 iter/s, 4.94631s/12 iters), loss = 1.61531
I0408 15:39:36.400231 20259 solver.cpp:237]     Train net output #0: loss = 1.61531 (* 1 = 1.61531 loss)
I0408 15:39:36.400247 20259 sgd_solver.cpp:105] Iteration 6144, lr = 1.75302e-05
I0408 15:39:41.426631 20259 solver.cpp:218] Iteration 6156 (2.38747 iter/s, 5.02625s/12 iters), loss = 1.36791
I0408 15:39:41.426672 20259 solver.cpp:237]     Train net output #0: loss = 1.36791 (* 1 = 1.36791 loss)
I0408 15:39:41.426683 20259 sgd_solver.cpp:105] Iteration 6156, lr = 1.73142e-05
I0408 15:39:46.431334 20259 solver.cpp:218] Iteration 6168 (2.39784 iter/s, 5.0045s/12 iters), loss = 1.63792
I0408 15:39:46.431381 20259 solver.cpp:237]     Train net output #0: loss = 1.63792 (* 1 = 1.63792 loss)
I0408 15:39:46.431393 20259 sgd_solver.cpp:105] Iteration 6168, lr = 1.71009e-05
I0408 15:39:47.024242 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:51.404853 20259 solver.cpp:218] Iteration 6180 (2.41287 iter/s, 4.97332s/12 iters), loss = 1.52011
I0408 15:39:51.404896 20259 solver.cpp:237]     Train net output #0: loss = 1.52011 (* 1 = 1.52011 loss)
I0408 15:39:51.404906 20259 sgd_solver.cpp:105] Iteration 6180, lr = 1.68903e-05
I0408 15:39:56.292157 20259 solver.cpp:218] Iteration 6192 (2.45544 iter/s, 4.88711s/12 iters), loss = 1.48984
I0408 15:39:56.292203 20259 solver.cpp:237]     Train net output #0: loss = 1.48984 (* 1 = 1.48984 loss)
I0408 15:39:56.292215 20259 sgd_solver.cpp:105] Iteration 6192, lr = 1.66822e-05
I0408 15:40:01.317489 20259 solver.cpp:218] Iteration 6204 (2.388 iter/s, 5.02513s/12 iters), loss = 1.47257
I0408 15:40:01.317544 20259 solver.cpp:237]     Train net output #0: loss = 1.47257 (* 1 = 1.47257 loss)
I0408 15:40:01.317556 20259 sgd_solver.cpp:105] Iteration 6204, lr = 1.64767e-05
I0408 15:40:06.343101 20259 solver.cpp:218] Iteration 6216 (2.38787 iter/s, 5.0254s/12 iters), loss = 1.71377
I0408 15:40:06.343144 20259 solver.cpp:237]     Train net output #0: loss = 1.71377 (* 1 = 1.71377 loss)
I0408 15:40:06.343156 20259 sgd_solver.cpp:105] Iteration 6216, lr = 1.62737e-05
I0408 15:40:08.436094 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 15:40:12.008253 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 15:40:17.804880 20259 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 15:40:17.804899 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:40:19.806064 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:21.083194 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:40:22.256162 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 15:40:22.256206 20259 solver.cpp:397]     Test net output #1: loss = 3.27983 (* 1 = 3.27983 loss)
I0408 15:40:24.234210 20259 solver.cpp:218] Iteration 6228 (0.670746 iter/s, 17.8905s/12 iters), loss = 1.40494
I0408 15:40:24.234266 20259 solver.cpp:237]     Train net output #0: loss = 1.40494 (* 1 = 1.40494 loss)
I0408 15:40:24.234279 20259 sgd_solver.cpp:105] Iteration 6228, lr = 1.60733e-05
I0408 15:40:29.430827 20259 solver.cpp:218] Iteration 6240 (2.30929 iter/s, 5.1964s/12 iters), loss = 1.58856
I0408 15:40:29.430871 20259 solver.cpp:237]     Train net output #0: loss = 1.58856 (* 1 = 1.58856 loss)
I0408 15:40:29.430881 20259 sgd_solver.cpp:105] Iteration 6240, lr = 1.58753e-05
I0408 15:40:34.635108 20259 solver.cpp:218] Iteration 6252 (2.30588 iter/s, 5.20408s/12 iters), loss = 1.27047
I0408 15:40:34.635155 20259 solver.cpp:237]     Train net output #0: loss = 1.27047 (* 1 = 1.27047 loss)
I0408 15:40:34.635166 20259 sgd_solver.cpp:105] Iteration 6252, lr = 1.56797e-05
I0408 15:40:39.704881 20259 solver.cpp:218] Iteration 6264 (2.36706 iter/s, 5.06957s/12 iters), loss = 1.5809
I0408 15:40:39.704999 20259 solver.cpp:237]     Train net output #0: loss = 1.5809 (* 1 = 1.5809 loss)
I0408 15:40:39.705009 20259 sgd_solver.cpp:105] Iteration 6264, lr = 1.54865e-05
I0408 15:40:42.418893 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:44.714846 20259 solver.cpp:218] Iteration 6276 (2.39536 iter/s, 5.0097s/12 iters), loss = 1.49838
I0408 15:40:44.714887 20259 solver.cpp:237]     Train net output #0: loss = 1.49838 (* 1 = 1.49838 loss)
I0408 15:40:44.714900 20259 sgd_solver.cpp:105] Iteration 6276, lr = 1.52958e-05
I0408 15:40:49.741852 20259 solver.cpp:218] Iteration 6288 (2.3872 iter/s, 5.0268s/12 iters), loss = 1.66914
I0408 15:40:49.741905 20259 solver.cpp:237]     Train net output #0: loss = 1.66914 (* 1 = 1.66914 loss)
I0408 15:40:49.741917 20259 sgd_solver.cpp:105] Iteration 6288, lr = 1.51073e-05
I0408 15:40:54.703117 20259 solver.cpp:218] Iteration 6300 (2.41884 iter/s, 4.96106s/12 iters), loss = 1.60042
I0408 15:40:54.703166 20259 solver.cpp:237]     Train net output #0: loss = 1.60042 (* 1 = 1.60042 loss)
I0408 15:40:54.703176 20259 sgd_solver.cpp:105] Iteration 6300, lr = 1.49212e-05
I0408 15:40:59.746345 20259 solver.cpp:218] Iteration 6312 (2.37952 iter/s, 5.04303s/12 iters), loss = 1.58483
I0408 15:40:59.746381 20259 solver.cpp:237]     Train net output #0: loss = 1.58483 (* 1 = 1.58483 loss)
I0408 15:40:59.746388 20259 sgd_solver.cpp:105] Iteration 6312, lr = 1.47374e-05
I0408 15:41:04.465461 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 15:41:08.074236 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 15:41:11.117818 20259 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 15:41:11.117897 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:41:13.096935 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:15.774803 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 15:41:15.774839 20259 solver.cpp:397]     Test net output #1: loss = 3.27468 (* 1 = 3.27468 loss)
I0408 15:41:15.864925 20259 solver.cpp:218] Iteration 6324 (0.744506 iter/s, 16.1181s/12 iters), loss = 1.63211
I0408 15:41:15.864984 20259 solver.cpp:237]     Train net output #0: loss = 1.63211 (* 1 = 1.63211 loss)
I0408 15:41:15.864996 20259 sgd_solver.cpp:105] Iteration 6324, lr = 1.45559e-05
I0408 15:41:20.144616 20259 solver.cpp:218] Iteration 6336 (2.80407 iter/s, 4.2795s/12 iters), loss = 1.46225
I0408 15:41:20.144665 20259 solver.cpp:237]     Train net output #0: loss = 1.46225 (* 1 = 1.46225 loss)
I0408 15:41:20.144677 20259 sgd_solver.cpp:105] Iteration 6336, lr = 1.43766e-05
I0408 15:41:25.140563 20259 solver.cpp:218] Iteration 6348 (2.40204 iter/s, 4.99575s/12 iters), loss = 1.4564
I0408 15:41:25.140609 20259 solver.cpp:237]     Train net output #0: loss = 1.4564 (* 1 = 1.4564 loss)
I0408 15:41:25.140620 20259 sgd_solver.cpp:105] Iteration 6348, lr = 1.41994e-05
I0408 15:41:30.190364 20259 solver.cpp:218] Iteration 6360 (2.37643 iter/s, 5.0496s/12 iters), loss = 1.60436
I0408 15:41:30.190419 20259 solver.cpp:237]     Train net output #0: loss = 1.60436 (* 1 = 1.60436 loss)
I0408 15:41:30.190433 20259 sgd_solver.cpp:105] Iteration 6360, lr = 1.40245e-05
I0408 15:41:35.001929 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:35.143944 20259 solver.cpp:218] Iteration 6372 (2.42259 iter/s, 4.95337s/12 iters), loss = 1.68528
I0408 15:41:35.143991 20259 solver.cpp:237]     Train net output #0: loss = 1.68528 (* 1 = 1.68528 loss)
I0408 15:41:35.144003 20259 sgd_solver.cpp:105] Iteration 6372, lr = 1.38518e-05
I0408 15:41:40.177362 20259 solver.cpp:218] Iteration 6384 (2.38417 iter/s, 5.03321s/12 iters), loss = 1.5294
I0408 15:41:40.177428 20259 solver.cpp:237]     Train net output #0: loss = 1.5294 (* 1 = 1.5294 loss)
I0408 15:41:40.177444 20259 sgd_solver.cpp:105] Iteration 6384, lr = 1.36811e-05
I0408 15:41:45.172235 20259 solver.cpp:218] Iteration 6396 (2.40257 iter/s, 4.99466s/12 iters), loss = 1.40956
I0408 15:41:45.172379 20259 solver.cpp:237]     Train net output #0: loss = 1.40956 (* 1 = 1.40956 loss)
I0408 15:41:45.172391 20259 sgd_solver.cpp:105] Iteration 6396, lr = 1.35126e-05
I0408 15:41:50.170750 20259 solver.cpp:218] Iteration 6408 (2.40085 iter/s, 4.99822s/12 iters), loss = 1.5128
I0408 15:41:50.170800 20259 solver.cpp:237]     Train net output #0: loss = 1.5128 (* 1 = 1.5128 loss)
I0408 15:41:50.170811 20259 sgd_solver.cpp:105] Iteration 6408, lr = 1.33461e-05
I0408 15:41:55.158921 20259 solver.cpp:218] Iteration 6420 (2.40579 iter/s, 4.98797s/12 iters), loss = 1.6945
I0408 15:41:55.158969 20259 solver.cpp:237]     Train net output #0: loss = 1.6945 (* 1 = 1.6945 loss)
I0408 15:41:55.158982 20259 sgd_solver.cpp:105] Iteration 6420, lr = 1.31817e-05
I0408 15:41:57.248147 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 15:42:02.637097 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 15:42:08.316396 20259 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 15:42:08.316421 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:42:10.258898 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:12.798269 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 15:42:12.798317 20259 solver.cpp:397]     Test net output #1: loss = 3.28171 (* 1 = 3.28171 loss)
I0408 15:42:14.896349 20259 solver.cpp:218] Iteration 6432 (0.608001 iter/s, 19.7368s/12 iters), loss = 1.64883
I0408 15:42:14.896399 20259 solver.cpp:237]     Train net output #0: loss = 1.64883 (* 1 = 1.64883 loss)
I0408 15:42:14.896410 20259 sgd_solver.cpp:105] Iteration 6432, lr = 1.30193e-05
I0408 15:42:20.053220 20259 solver.cpp:218] Iteration 6444 (2.32709 iter/s, 5.15666s/12 iters), loss = 1.77173
I0408 15:42:20.053314 20259 solver.cpp:237]     Train net output #0: loss = 1.77173 (* 1 = 1.77173 loss)
I0408 15:42:20.053331 20259 sgd_solver.cpp:105] Iteration 6444, lr = 1.2859e-05
I0408 15:42:25.064617 20259 solver.cpp:218] Iteration 6456 (2.39466 iter/s, 5.01116s/12 iters), loss = 1.54028
I0408 15:42:25.064663 20259 solver.cpp:237]     Train net output #0: loss = 1.54028 (* 1 = 1.54028 loss)
I0408 15:42:25.064675 20259 sgd_solver.cpp:105] Iteration 6456, lr = 1.27005e-05
I0408 15:42:30.099334 20259 solver.cpp:218] Iteration 6468 (2.38355 iter/s, 5.03451s/12 iters), loss = 1.56797
I0408 15:42:30.099380 20259 solver.cpp:237]     Train net output #0: loss = 1.56797 (* 1 = 1.56797 loss)
I0408 15:42:30.099393 20259 sgd_solver.cpp:105] Iteration 6468, lr = 1.25441e-05
I0408 15:42:32.080283 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:35.005607 20259 solver.cpp:218] Iteration 6480 (2.44595 iter/s, 4.90608s/12 iters), loss = 1.82776
I0408 15:42:35.005659 20259 solver.cpp:237]     Train net output #0: loss = 1.82776 (* 1 = 1.82776 loss)
I0408 15:42:35.005671 20259 sgd_solver.cpp:105] Iteration 6480, lr = 1.23896e-05
I0408 15:42:39.995954 20259 solver.cpp:218] Iteration 6492 (2.40474 iter/s, 4.99014s/12 iters), loss = 1.56711
I0408 15:42:39.996009 20259 solver.cpp:237]     Train net output #0: loss = 1.56711 (* 1 = 1.56711 loss)
I0408 15:42:39.996022 20259 sgd_solver.cpp:105] Iteration 6492, lr = 1.22369e-05
I0408 15:42:45.125661 20259 solver.cpp:218] Iteration 6504 (2.33941 iter/s, 5.1295s/12 iters), loss = 1.44305
I0408 15:42:45.125715 20259 solver.cpp:237]     Train net output #0: loss = 1.44305 (* 1 = 1.44305 loss)
I0408 15:42:45.125727 20259 sgd_solver.cpp:105] Iteration 6504, lr = 1.20862e-05
I0408 15:42:50.209467 20259 solver.cpp:218] Iteration 6516 (2.36053 iter/s, 5.08359s/12 iters), loss = 1.41553
I0408 15:42:50.209625 20259 solver.cpp:237]     Train net output #0: loss = 1.41553 (* 1 = 1.41553 loss)
I0408 15:42:50.209640 20259 sgd_solver.cpp:105] Iteration 6516, lr = 1.19373e-05
I0408 15:42:54.754736 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 15:43:02.203675 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 15:43:06.734977 20259 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 15:43:06.735002 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:43:08.635785 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:11.211024 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 15:43:11.211071 20259 solver.cpp:397]     Test net output #1: loss = 3.28605 (* 1 = 3.28605 loss)
I0408 15:43:11.301393 20259 solver.cpp:218] Iteration 6528 (0.568959 iter/s, 21.0912s/12 iters), loss = 1.48997
I0408 15:43:11.301461 20259 solver.cpp:237]     Train net output #0: loss = 1.48997 (* 1 = 1.48997 loss)
I0408 15:43:11.301477 20259 sgd_solver.cpp:105] Iteration 6528, lr = 1.17903e-05
I0408 15:43:15.636314 20259 solver.cpp:218] Iteration 6540 (2.76834 iter/s, 4.33472s/12 iters), loss = 1.40413
I0408 15:43:15.636361 20259 solver.cpp:237]     Train net output #0: loss = 1.40413 (* 1 = 1.40413 loss)
I0408 15:43:15.636373 20259 sgd_solver.cpp:105] Iteration 6540, lr = 1.1645e-05
I0408 15:43:20.671002 20259 solver.cpp:218] Iteration 6552 (2.38356 iter/s, 5.03449s/12 iters), loss = 1.44642
I0408 15:43:20.671103 20259 solver.cpp:237]     Train net output #0: loss = 1.44642 (* 1 = 1.44642 loss)
I0408 15:43:20.671113 20259 sgd_solver.cpp:105] Iteration 6552, lr = 1.15016e-05
I0408 15:43:25.738019 20259 solver.cpp:218] Iteration 6564 (2.36838 iter/s, 5.06676s/12 iters), loss = 1.20085
I0408 15:43:25.738060 20259 solver.cpp:237]     Train net output #0: loss = 1.20085 (* 1 = 1.20085 loss)
I0408 15:43:25.738070 20259 sgd_solver.cpp:105] Iteration 6564, lr = 1.13599e-05
I0408 15:43:29.984270 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:30.760300 20259 solver.cpp:218] Iteration 6576 (2.38944 iter/s, 5.02209s/12 iters), loss = 1.57337
I0408 15:43:30.760337 20259 solver.cpp:237]     Train net output #0: loss = 1.57337 (* 1 = 1.57337 loss)
I0408 15:43:30.760345 20259 sgd_solver.cpp:105] Iteration 6576, lr = 1.12199e-05
I0408 15:43:35.724445 20259 solver.cpp:218] Iteration 6588 (2.41743 iter/s, 4.96395s/12 iters), loss = 1.5141
I0408 15:43:35.724493 20259 solver.cpp:237]     Train net output #0: loss = 1.5141 (* 1 = 1.5141 loss)
I0408 15:43:35.724505 20259 sgd_solver.cpp:105] Iteration 6588, lr = 1.10817e-05
I0408 15:43:40.792313 20259 solver.cpp:218] Iteration 6600 (2.36795 iter/s, 5.06767s/12 iters), loss = 1.7273
I0408 15:43:40.792352 20259 solver.cpp:237]     Train net output #0: loss = 1.7273 (* 1 = 1.7273 loss)
I0408 15:43:40.792361 20259 sgd_solver.cpp:105] Iteration 6600, lr = 1.09452e-05
I0408 15:43:45.809675 20259 solver.cpp:218] Iteration 6612 (2.39179 iter/s, 5.01716s/12 iters), loss = 1.2112
I0408 15:43:45.809736 20259 solver.cpp:237]     Train net output #0: loss = 1.2112 (* 1 = 1.2112 loss)
I0408 15:43:45.809752 20259 sgd_solver.cpp:105] Iteration 6612, lr = 1.08104e-05
I0408 15:43:50.864586 20259 solver.cpp:218] Iteration 6624 (2.37403 iter/s, 5.0547s/12 iters), loss = 1.70659
I0408 15:43:50.864743 20259 solver.cpp:237]     Train net output #0: loss = 1.70659 (* 1 = 1.70659 loss)
I0408 15:43:50.864758 20259 sgd_solver.cpp:105] Iteration 6624, lr = 1.06772e-05
I0408 15:43:52.923074 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 15:44:02.231133 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 15:44:05.920125 20259 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 15:44:05.920152 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:44:07.789881 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:10.395543 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 15:44:10.395577 20259 solver.cpp:397]     Test net output #1: loss = 3.27026 (* 1 = 3.27026 loss)
I0408 15:44:12.368757 20259 solver.cpp:218] Iteration 6636 (0.558051 iter/s, 21.5034s/12 iters), loss = 1.65219
I0408 15:44:12.368808 20259 solver.cpp:237]     Train net output #0: loss = 1.65219 (* 1 = 1.65219 loss)
I0408 15:44:12.368821 20259 sgd_solver.cpp:105] Iteration 6636, lr = 1.05457e-05
I0408 15:44:17.818624 20259 solver.cpp:218] Iteration 6648 (2.20198 iter/s, 5.44965s/12 iters), loss = 1.68717
I0408 15:44:17.818675 20259 solver.cpp:237]     Train net output #0: loss = 1.68717 (* 1 = 1.68717 loss)
I0408 15:44:17.818686 20259 sgd_solver.cpp:105] Iteration 6648, lr = 1.04158e-05
I0408 15:44:23.001596 20259 solver.cpp:218] Iteration 6660 (2.31537 iter/s, 5.18277s/12 iters), loss = 1.47257
I0408 15:44:23.001699 20259 solver.cpp:237]     Train net output #0: loss = 1.47257 (* 1 = 1.47257 loss)
I0408 15:44:23.001709 20259 sgd_solver.cpp:105] Iteration 6660, lr = 1.02874e-05
I0408 15:44:28.165769 20259 solver.cpp:218] Iteration 6672 (2.32382 iter/s, 5.16391s/12 iters), loss = 1.79392
I0408 15:44:28.165810 20259 solver.cpp:237]     Train net output #0: loss = 1.79392 (* 1 = 1.79392 loss)
I0408 15:44:28.165819 20259 sgd_solver.cpp:105] Iteration 6672, lr = 1.01607e-05
I0408 15:44:29.657486 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:33.644484 20259 solver.cpp:218] Iteration 6684 (2.19038 iter/s, 5.4785s/12 iters), loss = 1.57648
I0408 15:44:33.644532 20259 solver.cpp:237]     Train net output #0: loss = 1.57648 (* 1 = 1.57648 loss)
I0408 15:44:33.644543 20259 sgd_solver.cpp:105] Iteration 6684, lr = 1.00355e-05
I0408 15:44:38.812731 20259 solver.cpp:218] Iteration 6696 (2.32196 iter/s, 5.16804s/12 iters), loss = 1.73369
I0408 15:44:38.812773 20259 solver.cpp:237]     Train net output #0: loss = 1.73369 (* 1 = 1.73369 loss)
I0408 15:44:38.812784 20259 sgd_solver.cpp:105] Iteration 6696, lr = 9.91192e-06
I0408 15:44:43.922205 20259 solver.cpp:218] Iteration 6708 (2.34867 iter/s, 5.10928s/12 iters), loss = 1.72639
I0408 15:44:43.922256 20259 solver.cpp:237]     Train net output #0: loss = 1.72639 (* 1 = 1.72639 loss)
I0408 15:44:43.922269 20259 sgd_solver.cpp:105] Iteration 6708, lr = 9.78982e-06
I0408 15:44:49.395648 20259 solver.cpp:218] Iteration 6720 (2.19249 iter/s, 5.47322s/12 iters), loss = 1.5398
I0408 15:44:49.395687 20259 solver.cpp:237]     Train net output #0: loss = 1.5398 (* 1 = 1.5398 loss)
I0408 15:44:49.395696 20259 sgd_solver.cpp:105] Iteration 6720, lr = 9.66922e-06
I0408 15:44:54.059129 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 15:44:59.706882 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 15:45:03.370250 20259 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 15:45:03.370276 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:45:05.512733 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:08.152416 20259 solver.cpp:397]     Test net output #0: accuracy = 0.286765
I0408 15:45:08.152463 20259 solver.cpp:397]     Test net output #1: loss = 3.27717 (* 1 = 3.27717 loss)
I0408 15:45:08.242249 20259 solver.cpp:218] Iteration 6732 (0.636739 iter/s, 18.846s/12 iters), loss = 1.51063
I0408 15:45:08.242303 20259 solver.cpp:237]     Train net output #0: loss = 1.51063 (* 1 = 1.51063 loss)
I0408 15:45:08.242314 20259 sgd_solver.cpp:105] Iteration 6732, lr = 9.55011e-06
I0408 15:45:12.557061 20259 solver.cpp:218] Iteration 6744 (2.78124 iter/s, 4.31463s/12 iters), loss = 1.55143
I0408 15:45:12.557103 20259 solver.cpp:237]     Train net output #0: loss = 1.55143 (* 1 = 1.55143 loss)
I0408 15:45:12.557113 20259 sgd_solver.cpp:105] Iteration 6744, lr = 9.43246e-06
I0408 15:45:17.444718 20259 solver.cpp:218] Iteration 6756 (2.45526 iter/s, 4.88746s/12 iters), loss = 1.55439
I0408 15:45:17.444778 20259 solver.cpp:237]     Train net output #0: loss = 1.55439 (* 1 = 1.55439 loss)
I0408 15:45:17.444790 20259 sgd_solver.cpp:105] Iteration 6756, lr = 9.31626e-06
I0408 15:45:22.536154 20259 solver.cpp:218] Iteration 6768 (2.357 iter/s, 5.09122s/12 iters), loss = 1.57766
I0408 15:45:22.536195 20259 solver.cpp:237]     Train net output #0: loss = 1.57766 (* 1 = 1.57766 loss)
I0408 15:45:22.536206 20259 sgd_solver.cpp:105] Iteration 6768, lr = 9.2015e-06
I0408 15:45:26.147696 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:27.675196 20259 solver.cpp:218] Iteration 6780 (2.33516 iter/s, 5.13884s/12 iters), loss = 1.7106
I0408 15:45:27.675252 20259 solver.cpp:237]     Train net output #0: loss = 1.7106 (* 1 = 1.7106 loss)
I0408 15:45:27.675264 20259 sgd_solver.cpp:105] Iteration 6780, lr = 9.08814e-06
I0408 15:45:32.746213 20259 solver.cpp:218] Iteration 6792 (2.36649 iter/s, 5.07081s/12 iters), loss = 1.29652
I0408 15:45:32.746264 20259 solver.cpp:237]     Train net output #0: loss = 1.29652 (* 1 = 1.29652 loss)
I0408 15:45:32.746277 20259 sgd_solver.cpp:105] Iteration 6792, lr = 8.97619e-06
I0408 15:45:37.743489 20259 solver.cpp:218] Iteration 6804 (2.40141 iter/s, 4.99707s/12 iters), loss = 1.79593
I0408 15:45:37.743544 20259 solver.cpp:237]     Train net output #0: loss = 1.79593 (* 1 = 1.79593 loss)
I0408 15:45:37.743556 20259 sgd_solver.cpp:105] Iteration 6804, lr = 8.86561e-06
I0408 15:45:42.790828 20259 solver.cpp:218] Iteration 6816 (2.37759 iter/s, 5.04713s/12 iters), loss = 1.41587
I0408 15:45:42.790870 20259 solver.cpp:237]     Train net output #0: loss = 1.41587 (* 1 = 1.41587 loss)
I0408 15:45:42.790880 20259 sgd_solver.cpp:105] Iteration 6816, lr = 8.7564e-06
I0408 15:45:48.279721 20259 solver.cpp:218] Iteration 6828 (2.18632 iter/s, 5.48868s/12 iters), loss = 1.4329
I0408 15:45:48.279770 20259 solver.cpp:237]     Train net output #0: loss = 1.4329 (* 1 = 1.4329 loss)
I0408 15:45:48.279781 20259 sgd_solver.cpp:105] Iteration 6828, lr = 8.64853e-06
I0408 15:45:50.427691 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 15:45:57.009527 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 15:46:02.284997 20259 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 15:46:02.285022 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:46:04.096210 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:06.965860 20259 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0408 15:46:06.965909 20259 solver.cpp:397]     Test net output #1: loss = 3.28472 (* 1 = 3.28472 loss)
I0408 15:46:08.950237 20259 solver.cpp:218] Iteration 6840 (0.580555 iter/s, 20.6699s/12 iters), loss = 1.71475
I0408 15:46:08.950291 20259 solver.cpp:237]     Train net output #0: loss = 1.71475 (* 1 = 1.71475 loss)
I0408 15:46:08.950304 20259 sgd_solver.cpp:105] Iteration 6840, lr = 8.54199e-06
I0408 15:46:13.954866 20259 solver.cpp:218] Iteration 6852 (2.39788 iter/s, 5.00443s/12 iters), loss = 1.72485
I0408 15:46:13.954905 20259 solver.cpp:237]     Train net output #0: loss = 1.72485 (* 1 = 1.72485 loss)
I0408 15:46:13.954916 20259 sgd_solver.cpp:105] Iteration 6852, lr = 8.43676e-06
I0408 15:46:18.974269 20259 solver.cpp:218] Iteration 6864 (2.39082 iter/s, 5.0192s/12 iters), loss = 1.41762
I0408 15:46:18.974328 20259 solver.cpp:237]     Train net output #0: loss = 1.41762 (* 1 = 1.41762 loss)
I0408 15:46:18.974340 20259 sgd_solver.cpp:105] Iteration 6864, lr = 8.33283e-06
I0408 15:46:24.016194 20259 solver.cpp:218] Iteration 6876 (2.38014 iter/s, 5.04172s/12 iters), loss = 1.70398
I0408 15:46:24.016244 20259 solver.cpp:237]     Train net output #0: loss = 1.70398 (* 1 = 1.70398 loss)
I0408 15:46:24.016254 20259 sgd_solver.cpp:105] Iteration 6876, lr = 8.23018e-06
I0408 15:46:24.660421 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:29.442899 20259 solver.cpp:218] Iteration 6888 (2.21137 iter/s, 5.42649s/12 iters), loss = 1.58446
I0408 15:46:29.443051 20259 solver.cpp:237]     Train net output #0: loss = 1.58446 (* 1 = 1.58446 loss)
I0408 15:46:29.443065 20259 sgd_solver.cpp:105] Iteration 6888, lr = 8.1288e-06
I0408 15:46:34.925067 20259 solver.cpp:218] Iteration 6900 (2.18904 iter/s, 5.48186s/12 iters), loss = 1.55887
I0408 15:46:34.925112 20259 solver.cpp:237]     Train net output #0: loss = 1.55887 (* 1 = 1.55887 loss)
I0408 15:46:34.925122 20259 sgd_solver.cpp:105] Iteration 6900, lr = 8.02866e-06
I0408 15:46:40.087630 20259 solver.cpp:218] Iteration 6912 (2.32452 iter/s, 5.16236s/12 iters), loss = 1.46836
I0408 15:46:40.087677 20259 solver.cpp:237]     Train net output #0: loss = 1.46836 (* 1 = 1.46836 loss)
I0408 15:46:40.087688 20259 sgd_solver.cpp:105] Iteration 6912, lr = 7.92975e-06
I0408 15:46:45.330150 20259 solver.cpp:218] Iteration 6924 (2.28907 iter/s, 5.24231s/12 iters), loss = 1.47733
I0408 15:46:45.330205 20259 solver.cpp:237]     Train net output #0: loss = 1.47733 (* 1 = 1.47733 loss)
I0408 15:46:45.330217 20259 sgd_solver.cpp:105] Iteration 6924, lr = 7.83207e-06
I0408 15:46:50.375672 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 15:46:57.754724 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 15:47:02.251755 20259 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 15:47:02.251839 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:47:02.824120 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:47:03.888033 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:06.651346 20259 solver.cpp:397]     Test net output #0: accuracy = 0.288603
I0408 15:47:06.651389 20259 solver.cpp:397]     Test net output #1: loss = 3.27493 (* 1 = 3.27493 loss)
I0408 15:47:06.741407 20259 solver.cpp:218] Iteration 6936 (0.56047 iter/s, 21.4106s/12 iters), loss = 1.54381
I0408 15:47:06.741458 20259 solver.cpp:237]     Train net output #0: loss = 1.54381 (* 1 = 1.54381 loss)
I0408 15:47:06.741472 20259 sgd_solver.cpp:105] Iteration 6936, lr = 7.73559e-06
I0408 15:47:10.909771 20259 solver.cpp:218] Iteration 6948 (2.87896 iter/s, 4.16818s/12 iters), loss = 1.60222
I0408 15:47:10.909826 20259 solver.cpp:237]     Train net output #0: loss = 1.60222 (* 1 = 1.60222 loss)
I0408 15:47:10.909838 20259 sgd_solver.cpp:105] Iteration 6948, lr = 7.64029e-06
I0408 15:47:15.922595 20259 solver.cpp:218] Iteration 6960 (2.39396 iter/s, 5.01262s/12 iters), loss = 1.44704
I0408 15:47:15.922634 20259 solver.cpp:237]     Train net output #0: loss = 1.44704 (* 1 = 1.44704 loss)
I0408 15:47:15.922644 20259 sgd_solver.cpp:105] Iteration 6960, lr = 7.54617e-06
I0408 15:47:20.976404 20259 solver.cpp:218] Iteration 6972 (2.37454 iter/s, 5.05361s/12 iters), loss = 1.45317
I0408 15:47:20.976444 20259 solver.cpp:237]     Train net output #0: loss = 1.45317 (* 1 = 1.45317 loss)
I0408 15:47:20.976454 20259 sgd_solver.cpp:105] Iteration 6972, lr = 7.45321e-06
I0408 15:47:23.734763 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:25.992358 20259 solver.cpp:218] Iteration 6984 (2.39246 iter/s, 5.01576s/12 iters), loss = 1.48278
I0408 15:47:25.992394 20259 solver.cpp:237]     Train net output #0: loss = 1.48278 (* 1 = 1.48278 loss)
I0408 15:47:25.992403 20259 sgd_solver.cpp:105] Iteration 6984, lr = 7.3614e-06
I0408 15:47:31.061866 20259 solver.cpp:218] Iteration 6996 (2.36718 iter/s, 5.06932s/12 iters), loss = 1.51608
I0408 15:47:31.061909 20259 solver.cpp:237]     Train net output #0: loss = 1.51608 (* 1 = 1.51608 loss)
I0408 15:47:31.061920 20259 sgd_solver.cpp:105] Iteration 6996, lr = 7.27071e-06
I0408 15:47:36.114159 20259 solver.cpp:218] Iteration 7008 (2.37525 iter/s, 5.05209s/12 iters), loss = 1.60294
I0408 15:47:36.114322 20259 solver.cpp:237]     Train net output #0: loss = 1.60294 (* 1 = 1.60294 loss)
I0408 15:47:36.114337 20259 sgd_solver.cpp:105] Iteration 7008, lr = 7.18115e-06
I0408 15:47:41.085947 20259 solver.cpp:218] Iteration 7020 (2.41377 iter/s, 4.97147s/12 iters), loss = 1.87901
I0408 15:47:41.086017 20259 solver.cpp:237]     Train net output #0: loss = 1.87901 (* 1 = 1.87901 loss)
I0408 15:47:41.086030 20259 sgd_solver.cpp:105] Iteration 7020, lr = 7.09268e-06
I0408 15:47:46.220916 20259 solver.cpp:218] Iteration 7032 (2.33702 iter/s, 5.13474s/12 iters), loss = 1.79627
I0408 15:47:46.220965 20259 solver.cpp:237]     Train net output #0: loss = 1.79627 (* 1 = 1.79627 loss)
I0408 15:47:46.220978 20259 sgd_solver.cpp:105] Iteration 7032, lr = 7.00531e-06
I0408 15:47:48.298111 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 15:47:57.502977 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 15:48:01.349452 20259 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 15:48:01.349479 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:48:03.045029 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:05.814348 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0408 15:48:05.814388 20259 solver.cpp:397]     Test net output #1: loss = 3.26943 (* 1 = 3.26943 loss)
I0408 15:48:07.705834 20259 solver.cpp:218] Iteration 7044 (0.558548 iter/s, 21.4843s/12 iters), loss = 1.50007
I0408 15:48:07.705927 20259 solver.cpp:237]     Train net output #0: loss = 1.50007 (* 1 = 1.50007 loss)
I0408 15:48:07.705938 20259 sgd_solver.cpp:105] Iteration 7044, lr = 6.91901e-06
I0408 15:48:12.748883 20259 solver.cpp:218] Iteration 7056 (2.37963 iter/s, 5.0428s/12 iters), loss = 1.50448
I0408 15:48:12.748934 20259 solver.cpp:237]     Train net output #0: loss = 1.50448 (* 1 = 1.50448 loss)
I0408 15:48:12.748947 20259 sgd_solver.cpp:105] Iteration 7056, lr = 6.83378e-06
I0408 15:48:17.724133 20259 solver.cpp:218] Iteration 7068 (2.41204 iter/s, 4.97504s/12 iters), loss = 1.56803
I0408 15:48:17.724187 20259 solver.cpp:237]     Train net output #0: loss = 1.56803 (* 1 = 1.56803 loss)
I0408 15:48:17.724200 20259 sgd_solver.cpp:105] Iteration 7068, lr = 6.7496e-06
I0408 15:48:22.676563 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:22.789250 20259 solver.cpp:218] Iteration 7080 (2.36924 iter/s, 5.06491s/12 iters), loss = 1.47757
I0408 15:48:22.789300 20259 solver.cpp:237]     Train net output #0: loss = 1.47757 (* 1 = 1.47757 loss)
I0408 15:48:22.789314 20259 sgd_solver.cpp:105] Iteration 7080, lr = 6.66645e-06
I0408 15:48:27.853885 20259 solver.cpp:218] Iteration 7092 (2.36947 iter/s, 5.06443s/12 iters), loss = 1.69741
I0408 15:48:27.853936 20259 solver.cpp:237]     Train net output #0: loss = 1.69741 (* 1 = 1.69741 loss)
I0408 15:48:27.853950 20259 sgd_solver.cpp:105] Iteration 7092, lr = 6.58433e-06
I0408 15:48:32.845713 20259 solver.cpp:218] Iteration 7104 (2.40403 iter/s, 4.99163s/12 iters), loss = 1.30425
I0408 15:48:32.845762 20259 solver.cpp:237]     Train net output #0: loss = 1.30425 (* 1 = 1.30425 loss)
I0408 15:48:32.845774 20259 sgd_solver.cpp:105] Iteration 7104, lr = 6.50321e-06
I0408 15:48:37.820039 20259 solver.cpp:218] Iteration 7116 (2.41248 iter/s, 4.97413s/12 iters), loss = 1.50494
I0408 15:48:37.820149 20259 solver.cpp:237]     Train net output #0: loss = 1.50494 (* 1 = 1.50494 loss)
I0408 15:48:37.820163 20259 sgd_solver.cpp:105] Iteration 7116, lr = 6.4231e-06
I0408 15:48:42.673861 20259 solver.cpp:218] Iteration 7128 (2.47241 iter/s, 4.85357s/12 iters), loss = 1.41337
I0408 15:48:42.673910 20259 solver.cpp:237]     Train net output #0: loss = 1.41337 (* 1 = 1.41337 loss)
I0408 15:48:42.673924 20259 sgd_solver.cpp:105] Iteration 7128, lr = 6.34398e-06
I0408 15:48:47.131186 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 15:48:56.344157 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 15:49:00.079398 20259 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 15:49:00.079424 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:49:01.681859 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:04.481055 20259 solver.cpp:397]     Test net output #0: accuracy = 0.288603
I0408 15:49:04.481099 20259 solver.cpp:397]     Test net output #1: loss = 3.28285 (* 1 = 3.28285 loss)
I0408 15:49:04.571072 20259 solver.cpp:218] Iteration 7140 (0.548032 iter/s, 21.8965s/12 iters), loss = 1.60615
I0408 15:49:04.571122 20259 solver.cpp:237]     Train net output #0: loss = 1.60615 (* 1 = 1.60615 loss)
I0408 15:49:04.571135 20259 sgd_solver.cpp:105] Iteration 7140, lr = 6.26583e-06
I0408 15:49:08.952844 20259 solver.cpp:218] Iteration 7152 (2.73874 iter/s, 4.38158s/12 iters), loss = 1.76658
I0408 15:49:08.952992 20259 solver.cpp:237]     Train net output #0: loss = 1.76658 (* 1 = 1.76658 loss)
I0408 15:49:08.953006 20259 sgd_solver.cpp:105] Iteration 7152, lr = 6.18864e-06
I0408 15:49:13.985071 20259 solver.cpp:218] Iteration 7164 (2.38477 iter/s, 5.03193s/12 iters), loss = 1.55315
I0408 15:49:13.985121 20259 solver.cpp:237]     Train net output #0: loss = 1.55315 (* 1 = 1.55315 loss)
I0408 15:49:13.985131 20259 sgd_solver.cpp:105] Iteration 7164, lr = 6.1124e-06
I0408 15:49:18.998927 20259 solver.cpp:218] Iteration 7176 (2.39346 iter/s, 5.01366s/12 iters), loss = 1.59992
I0408 15:49:18.998970 20259 solver.cpp:237]     Train net output #0: loss = 1.59992 (* 1 = 1.59992 loss)
I0408 15:49:18.998980 20259 sgd_solver.cpp:105] Iteration 7176, lr = 6.0371e-06
I0408 15:49:21.156553 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:24.104902 20259 solver.cpp:218] Iteration 7188 (2.35028 iter/s, 5.10577s/12 iters), loss = 1.68745
I0408 15:49:24.104955 20259 solver.cpp:237]     Train net output #0: loss = 1.68745 (* 1 = 1.68745 loss)
I0408 15:49:24.104969 20259 sgd_solver.cpp:105] Iteration 7188, lr = 5.96273e-06
I0408 15:49:29.307515 20259 solver.cpp:218] Iteration 7200 (2.30663 iter/s, 5.2024s/12 iters), loss = 1.20809
I0408 15:49:29.307565 20259 solver.cpp:237]     Train net output #0: loss = 1.20809 (* 1 = 1.20809 loss)
I0408 15:49:29.307577 20259 sgd_solver.cpp:105] Iteration 7200, lr = 5.88928e-06
I0408 15:49:34.507109 20259 solver.cpp:218] Iteration 7212 (2.30796 iter/s, 5.19939s/12 iters), loss = 1.523
I0408 15:49:34.507150 20259 solver.cpp:237]     Train net output #0: loss = 1.523 (* 1 = 1.523 loss)
I0408 15:49:34.507160 20259 sgd_solver.cpp:105] Iteration 7212, lr = 5.81673e-06
I0408 15:49:39.660898 20259 solver.cpp:218] Iteration 7224 (2.32847 iter/s, 5.15359s/12 iters), loss = 1.65284
I0408 15:49:39.660957 20259 solver.cpp:237]     Train net output #0: loss = 1.65284 (* 1 = 1.65284 loss)
I0408 15:49:39.660967 20259 sgd_solver.cpp:105] Iteration 7224, lr = 5.74508e-06
I0408 15:49:44.835340 20259 solver.cpp:218] Iteration 7236 (2.31919 iter/s, 5.17423s/12 iters), loss = 1.55433
I0408 15:49:44.835379 20259 solver.cpp:237]     Train net output #0: loss = 1.55433 (* 1 = 1.55433 loss)
I0408 15:49:44.835388 20259 sgd_solver.cpp:105] Iteration 7236, lr = 5.6743e-06
I0408 15:49:47.000068 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 15:49:55.345727 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 15:50:01.123723 20259 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 15:50:01.123754 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:50:02.750167 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:05.593828 20259 solver.cpp:397]     Test net output #0: accuracy = 0.28799
I0408 15:50:05.593876 20259 solver.cpp:397]     Test net output #1: loss = 3.27705 (* 1 = 3.27705 loss)
I0408 15:50:07.469552 20259 solver.cpp:218] Iteration 7248 (0.530187 iter/s, 22.6335s/12 iters), loss = 1.23881
I0408 15:50:07.469590 20259 solver.cpp:237]     Train net output #0: loss = 1.23881 (* 1 = 1.23881 loss)
I0408 15:50:07.469599 20259 sgd_solver.cpp:105] Iteration 7248, lr = 5.6044e-06
I0408 15:50:12.451169 20259 solver.cpp:218] Iteration 7260 (2.40895 iter/s, 4.98143s/12 iters), loss = 1.69007
I0408 15:50:12.451265 20259 solver.cpp:237]     Train net output #0: loss = 1.69007 (* 1 = 1.69007 loss)
I0408 15:50:12.451277 20259 sgd_solver.cpp:105] Iteration 7260, lr = 5.53536e-06
I0408 15:50:17.484493 20259 solver.cpp:218] Iteration 7272 (2.38423 iter/s, 5.03307s/12 iters), loss = 1.67976
I0408 15:50:17.484545 20259 solver.cpp:237]     Train net output #0: loss = 1.67976 (* 1 = 1.67976 loss)
I0408 15:50:17.484555 20259 sgd_solver.cpp:105] Iteration 7272, lr = 5.46717e-06
I0408 15:50:21.811843 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:22.572369 20259 solver.cpp:218] Iteration 7284 (2.35865 iter/s, 5.08766s/12 iters), loss = 1.59332
I0408 15:50:22.572417 20259 solver.cpp:237]     Train net output #0: loss = 1.59332 (* 1 = 1.59332 loss)
I0408 15:50:22.572427 20259 sgd_solver.cpp:105] Iteration 7284, lr = 5.39982e-06
I0408 15:50:27.606168 20259 solver.cpp:218] Iteration 7296 (2.38398 iter/s, 5.03359s/12 iters), loss = 1.71394
I0408 15:50:27.606216 20259 solver.cpp:237]     Train net output #0: loss = 1.71394 (* 1 = 1.71394 loss)
I0408 15:50:27.606225 20259 sgd_solver.cpp:105] Iteration 7296, lr = 5.3333e-06
I0408 15:50:32.635277 20259 solver.cpp:218] Iteration 7308 (2.38621 iter/s, 5.0289s/12 iters), loss = 1.62063
I0408 15:50:32.635335 20259 solver.cpp:237]     Train net output #0: loss = 1.62063 (* 1 = 1.62063 loss)
I0408 15:50:32.635352 20259 sgd_solver.cpp:105] Iteration 7308, lr = 5.2676e-06
I0408 15:50:37.694847 20259 solver.cpp:218] Iteration 7320 (2.37184 iter/s, 5.05936s/12 iters), loss = 1.387
I0408 15:50:37.694901 20259 solver.cpp:237]     Train net output #0: loss = 1.387 (* 1 = 1.387 loss)
I0408 15:50:37.694914 20259 sgd_solver.cpp:105] Iteration 7320, lr = 5.20271e-06
I0408 15:50:42.739939 20259 solver.cpp:218] Iteration 7332 (2.37865 iter/s, 5.04489s/12 iters), loss = 1.63477
I0408 15:50:42.740061 20259 solver.cpp:237]     Train net output #0: loss = 1.63477 (* 1 = 1.63477 loss)
I0408 15:50:42.740075 20259 sgd_solver.cpp:105] Iteration 7332, lr = 5.13862e-06
I0408 15:50:47.387560 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 15:50:56.811072 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 15:51:01.782075 20259 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 15:51:01.782100 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:51:03.349786 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:06.267657 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 15:51:06.267702 20259 solver.cpp:397]     Test net output #1: loss = 3.27719 (* 1 = 3.27719 loss)
I0408 15:51:06.357720 20259 solver.cpp:218] Iteration 7344 (0.508109 iter/s, 23.617s/12 iters), loss = 1.51303
I0408 15:51:06.357775 20259 solver.cpp:237]     Train net output #0: loss = 1.51303 (* 1 = 1.51303 loss)
I0408 15:51:06.357789 20259 sgd_solver.cpp:105] Iteration 7344, lr = 5.07532e-06
I0408 15:51:10.581049 20259 solver.cpp:218] Iteration 7356 (2.84149 iter/s, 4.22314s/12 iters), loss = 1.6623
I0408 15:51:10.581097 20259 solver.cpp:237]     Train net output #0: loss = 1.6623 (* 1 = 1.6623 loss)
I0408 15:51:10.581108 20259 sgd_solver.cpp:105] Iteration 7356, lr = 5.0128e-06
I0408 15:51:15.595728 20259 solver.cpp:218] Iteration 7368 (2.39307 iter/s, 5.01448s/12 iters), loss = 1.74052
I0408 15:51:15.595842 20259 solver.cpp:237]     Train net output #0: loss = 1.74052 (* 1 = 1.74052 loss)
I0408 15:51:15.595854 20259 sgd_solver.cpp:105] Iteration 7368, lr = 4.95105e-06
I0408 15:51:20.581460 20259 solver.cpp:218] Iteration 7380 (2.407 iter/s, 4.98547s/12 iters), loss = 1.33494
I0408 15:51:20.581506 20259 solver.cpp:237]     Train net output #0: loss = 1.33494 (* 1 = 1.33494 loss)
I0408 15:51:20.581516 20259 sgd_solver.cpp:105] Iteration 7380, lr = 4.89006e-06
I0408 15:51:22.068190 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:25.732602 20259 solver.cpp:218] Iteration 7392 (2.32967 iter/s, 5.15093s/12 iters), loss = 1.52247
I0408 15:51:25.732661 20259 solver.cpp:237]     Train net output #0: loss = 1.52247 (* 1 = 1.52247 loss)
I0408 15:51:25.732677 20259 sgd_solver.cpp:105] Iteration 7392, lr = 4.82982e-06
I0408 15:51:30.771139 20259 solver.cpp:218] Iteration 7404 (2.38174 iter/s, 5.03833s/12 iters), loss = 1.59351
I0408 15:51:30.771181 20259 solver.cpp:237]     Train net output #0: loss = 1.59351 (* 1 = 1.59351 loss)
I0408 15:51:30.771190 20259 sgd_solver.cpp:105] Iteration 7404, lr = 4.77032e-06
I0408 15:51:35.792557 20259 solver.cpp:218] Iteration 7416 (2.38986 iter/s, 5.02122s/12 iters), loss = 1.51135
I0408 15:51:35.792613 20259 solver.cpp:237]     Train net output #0: loss = 1.51135 (* 1 = 1.51135 loss)
I0408 15:51:35.792623 20259 sgd_solver.cpp:105] Iteration 7416, lr = 4.71155e-06
I0408 15:51:40.817404 20259 solver.cpp:218] Iteration 7428 (2.38823 iter/s, 5.02464s/12 iters), loss = 1.39949
I0408 15:51:40.817461 20259 solver.cpp:237]     Train net output #0: loss = 1.39949 (* 1 = 1.39949 loss)
I0408 15:51:40.817474 20259 sgd_solver.cpp:105] Iteration 7428, lr = 4.65351e-06
I0408 15:51:45.808220 20259 solver.cpp:218] Iteration 7440 (2.40452 iter/s, 4.99061s/12 iters), loss = 1.67648
I0408 15:51:45.808341 20259 solver.cpp:237]     Train net output #0: loss = 1.67648 (* 1 = 1.67648 loss)
I0408 15:51:45.808354 20259 sgd_solver.cpp:105] Iteration 7440, lr = 4.59619e-06
I0408 15:51:47.833078 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 15:51:56.239742 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 15:52:01.206291 20259 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 15:52:01.206318 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:52:02.749297 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:05.667289 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 15:52:05.667337 20259 solver.cpp:397]     Test net output #1: loss = 3.27637 (* 1 = 3.27637 loss)
I0408 15:52:07.567656 20259 solver.cpp:218] Iteration 7452 (0.551503 iter/s, 21.7587s/12 iters), loss = 1.37871
I0408 15:52:07.567701 20259 solver.cpp:237]     Train net output #0: loss = 1.37871 (* 1 = 1.37871 loss)
I0408 15:52:07.567713 20259 sgd_solver.cpp:105] Iteration 7452, lr = 4.53957e-06
I0408 15:52:12.568991 20259 solver.cpp:218] Iteration 7464 (2.39945 iter/s, 5.00114s/12 iters), loss = 1.66179
I0408 15:52:12.569032 20259 solver.cpp:237]     Train net output #0: loss = 1.66179 (* 1 = 1.66179 loss)
I0408 15:52:12.569042 20259 sgd_solver.cpp:105] Iteration 7464, lr = 4.48364e-06
I0408 15:52:17.601907 20259 solver.cpp:218] Iteration 7476 (2.3844 iter/s, 5.03272s/12 iters), loss = 1.51344
I0408 15:52:17.602030 20259 solver.cpp:237]     Train net output #0: loss = 1.51344 (* 1 = 1.51344 loss)
I0408 15:52:17.602046 20259 sgd_solver.cpp:105] Iteration 7476, lr = 4.42841e-06
I0408 15:52:21.142974 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:22.657052 20259 solver.cpp:218] Iteration 7488 (2.37395 iter/s, 5.05487s/12 iters), loss = 1.55202
I0408 15:52:22.657096 20259 solver.cpp:237]     Train net output #0: loss = 1.55202 (* 1 = 1.55202 loss)
I0408 15:52:22.657107 20259 sgd_solver.cpp:105] Iteration 7488, lr = 4.37386e-06
I0408 15:52:27.694043 20259 solver.cpp:218] Iteration 7500 (2.38247 iter/s, 5.03679s/12 iters), loss = 1.32973
I0408 15:52:27.694108 20259 solver.cpp:237]     Train net output #0: loss = 1.32973 (* 1 = 1.32973 loss)
I0408 15:52:27.694120 20259 sgd_solver.cpp:105] Iteration 7500, lr = 4.31998e-06
I0408 15:52:32.744510 20259 solver.cpp:218] Iteration 7512 (2.37612 iter/s, 5.05025s/12 iters), loss = 1.48946
I0408 15:52:32.744568 20259 solver.cpp:237]     Train net output #0: loss = 1.48946 (* 1 = 1.48946 loss)
I0408 15:52:32.744583 20259 sgd_solver.cpp:105] Iteration 7512, lr = 4.26676e-06
I0408 15:52:37.828166 20259 solver.cpp:218] Iteration 7524 (2.3606 iter/s, 5.08345s/12 iters), loss = 1.47831
I0408 15:52:37.828212 20259 solver.cpp:237]     Train net output #0: loss = 1.47831 (* 1 = 1.47831 loss)
I0408 15:52:37.828223 20259 sgd_solver.cpp:105] Iteration 7524, lr = 4.2142e-06
I0408 15:52:42.830642 20259 solver.cpp:218] Iteration 7536 (2.39891 iter/s, 5.00227s/12 iters), loss = 1.38881
I0408 15:52:42.830696 20259 solver.cpp:237]     Train net output #0: loss = 1.38881 (* 1 = 1.38881 loss)
I0408 15:52:42.830708 20259 sgd_solver.cpp:105] Iteration 7536, lr = 4.16228e-06
I0408 15:52:47.414443 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 15:52:52.268508 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 15:52:58.525235 20259 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 15:52:58.525264 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:53:00.025040 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:02.986964 20259 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0408 15:53:02.987012 20259 solver.cpp:397]     Test net output #1: loss = 3.27126 (* 1 = 3.27126 loss)
I0408 15:53:03.077098 20259 solver.cpp:218] Iteration 7548 (0.592715 iter/s, 20.2458s/12 iters), loss = 1.35288
I0408 15:53:03.077147 20259 solver.cpp:237]     Train net output #0: loss = 1.35288 (* 1 = 1.35288 loss)
I0408 15:53:03.077158 20259 sgd_solver.cpp:105] Iteration 7548, lr = 4.11101e-06
I0408 15:53:07.646926 20259 solver.cpp:218] Iteration 7560 (2.62603 iter/s, 4.56964s/12 iters), loss = 1.56351
I0408 15:53:07.646971 20259 solver.cpp:237]     Train net output #0: loss = 1.56351 (* 1 = 1.56351 loss)
I0408 15:53:07.646982 20259 sgd_solver.cpp:105] Iteration 7560, lr = 4.06037e-06
I0408 15:53:13.148587 20259 solver.cpp:218] Iteration 7572 (2.18124 iter/s, 5.50145s/12 iters), loss = 1.55665
I0408 15:53:13.148636 20259 solver.cpp:237]     Train net output #0: loss = 1.55665 (* 1 = 1.55665 loss)
I0408 15:53:13.148649 20259 sgd_solver.cpp:105] Iteration 7572, lr = 4.01035e-06
I0408 15:53:18.319911 20259 solver.cpp:218] Iteration 7584 (2.32058 iter/s, 5.17112s/12 iters), loss = 1.67325
I0408 15:53:18.319958 20259 solver.cpp:237]     Train net output #0: loss = 1.67325 (* 1 = 1.67325 loss)
I0408 15:53:18.319970 20259 sgd_solver.cpp:105] Iteration 7584, lr = 3.96095e-06
I0408 15:53:18.968515 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:23.261286 20259 solver.cpp:218] Iteration 7596 (2.42857 iter/s, 4.94117s/12 iters), loss = 1.63624
I0408 15:53:23.261416 20259 solver.cpp:237]     Train net output #0: loss = 1.63624 (* 1 = 1.63624 loss)
I0408 15:53:23.261431 20259 sgd_solver.cpp:105] Iteration 7596, lr = 3.91215e-06
I0408 15:53:28.246706 20259 solver.cpp:218] Iteration 7608 (2.40715 iter/s, 4.98514s/12 iters), loss = 1.76036
I0408 15:53:28.246752 20259 solver.cpp:237]     Train net output #0: loss = 1.76036 (* 1 = 1.76036 loss)
I0408 15:53:28.246762 20259 sgd_solver.cpp:105] Iteration 7608, lr = 3.86396e-06
I0408 15:53:33.348091 20259 solver.cpp:218] Iteration 7620 (2.3524 iter/s, 5.10118s/12 iters), loss = 1.36318
I0408 15:53:33.348143 20259 solver.cpp:237]     Train net output #0: loss = 1.36318 (* 1 = 1.36318 loss)
I0408 15:53:33.348156 20259 sgd_solver.cpp:105] Iteration 7620, lr = 3.81636e-06
I0408 15:53:35.816028 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:53:38.419757 20259 solver.cpp:218] Iteration 7632 (2.36618 iter/s, 5.07146s/12 iters), loss = 1.5959
I0408 15:53:38.419806 20259 solver.cpp:237]     Train net output #0: loss = 1.5959 (* 1 = 1.5959 loss)
I0408 15:53:38.419817 20259 sgd_solver.cpp:105] Iteration 7632, lr = 3.76935e-06
I0408 15:53:43.467442 20259 solver.cpp:218] Iteration 7644 (2.37742 iter/s, 5.04749s/12 iters), loss = 1.44024
I0408 15:53:43.467491 20259 solver.cpp:237]     Train net output #0: loss = 1.44024 (* 1 = 1.44024 loss)
I0408 15:53:43.467504 20259 sgd_solver.cpp:105] Iteration 7644, lr = 3.72291e-06
I0408 15:53:45.559674 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 15:53:48.617008 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 15:53:54.805377 20259 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 15:53:54.805495 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:53:56.273708 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:59.281534 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 15:53:59.281580 20259 solver.cpp:397]     Test net output #1: loss = 3.27189 (* 1 = 3.27189 loss)
I0408 15:54:01.254276 20259 solver.cpp:218] Iteration 7656 (0.674677 iter/s, 17.7863s/12 iters), loss = 1.68376
I0408 15:54:01.254324 20259 solver.cpp:237]     Train net output #0: loss = 1.68376 (* 1 = 1.68376 loss)
I0408 15:54:01.254335 20259 sgd_solver.cpp:105] Iteration 7656, lr = 3.67705e-06
I0408 15:54:06.615023 20259 solver.cpp:218] Iteration 7668 (2.23858 iter/s, 5.36053s/12 iters), loss = 1.28185
I0408 15:54:06.615079 20259 solver.cpp:237]     Train net output #0: loss = 1.28185 (* 1 = 1.28185 loss)
I0408 15:54:06.615092 20259 sgd_solver.cpp:105] Iteration 7668, lr = 3.63175e-06
I0408 15:54:11.882525 20259 solver.cpp:218] Iteration 7680 (2.27821 iter/s, 5.26729s/12 iters), loss = 1.35575
I0408 15:54:11.882572 20259 solver.cpp:237]     Train net output #0: loss = 1.35575 (* 1 = 1.35575 loss)
I0408 15:54:11.882584 20259 sgd_solver.cpp:105] Iteration 7680, lr = 3.58701e-06
I0408 15:54:14.676180 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:16.926385 20259 solver.cpp:218] Iteration 7692 (2.37922 iter/s, 5.04366s/12 iters), loss = 1.54482
I0408 15:54:16.926430 20259 solver.cpp:237]     Train net output #0: loss = 1.54482 (* 1 = 1.54482 loss)
I0408 15:54:16.926440 20259 sgd_solver.cpp:105] Iteration 7692, lr = 3.54283e-06
I0408 15:54:21.970126 20259 solver.cpp:218] Iteration 7704 (2.37928 iter/s, 5.04354s/12 iters), loss = 1.54673
I0408 15:54:21.970182 20259 solver.cpp:237]     Train net output #0: loss = 1.54673 (* 1 = 1.54673 loss)
I0408 15:54:21.970194 20259 sgd_solver.cpp:105] Iteration 7704, lr = 3.49918e-06
I0408 15:54:27.043066 20259 solver.cpp:218] Iteration 7716 (2.36559 iter/s, 5.07273s/12 iters), loss = 1.2983
I0408 15:54:27.043162 20259 solver.cpp:237]     Train net output #0: loss = 1.2983 (* 1 = 1.2983 loss)
I0408 15:54:27.043174 20259 sgd_solver.cpp:105] Iteration 7716, lr = 3.45608e-06
I0408 15:54:32.364795 20259 solver.cpp:218] Iteration 7728 (2.25501 iter/s, 5.32148s/12 iters), loss = 1.58262
I0408 15:54:32.364836 20259 solver.cpp:237]     Train net output #0: loss = 1.58262 (* 1 = 1.58262 loss)
I0408 15:54:32.364846 20259 sgd_solver.cpp:105] Iteration 7728, lr = 3.4135e-06
I0408 15:54:37.876471 20259 solver.cpp:218] Iteration 7740 (2.17728 iter/s, 5.51147s/12 iters), loss = 1.66972
I0408 15:54:37.876508 20259 solver.cpp:237]     Train net output #0: loss = 1.66972 (* 1 = 1.66972 loss)
I0408 15:54:37.876518 20259 sgd_solver.cpp:105] Iteration 7740, lr = 3.37145e-06
I0408 15:54:42.883329 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 15:54:45.883495 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 15:54:48.208757 20259 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 15:54:48.208786 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:54:49.774673 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:52.909700 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 15:54:52.909747 20259 solver.cpp:397]     Test net output #1: loss = 3.27733 (* 1 = 3.27733 loss)
I0408 15:54:52.999189 20259 solver.cpp:218] Iteration 7752 (0.793533 iter/s, 15.1222s/12 iters), loss = 1.66033
I0408 15:54:52.999236 20259 solver.cpp:237]     Train net output #0: loss = 1.66033 (* 1 = 1.66033 loss)
I0408 15:54:52.999248 20259 sgd_solver.cpp:105] Iteration 7752, lr = 3.32992e-06
I0408 15:54:57.420877 20259 solver.cpp:218] Iteration 7764 (2.71401 iter/s, 4.42151s/12 iters), loss = 1.37196
I0408 15:54:57.421012 20259 solver.cpp:237]     Train net output #0: loss = 1.37196 (* 1 = 1.37196 loss)
I0408 15:54:57.421025 20259 sgd_solver.cpp:105] Iteration 7764, lr = 3.2889e-06
I0408 15:55:02.688267 20259 solver.cpp:218] Iteration 7776 (2.27829 iter/s, 5.2671s/12 iters), loss = 1.4203
I0408 15:55:02.688318 20259 solver.cpp:237]     Train net output #0: loss = 1.4203 (* 1 = 1.4203 loss)
I0408 15:55:02.688330 20259 sgd_solver.cpp:105] Iteration 7776, lr = 3.24838e-06
I0408 15:55:07.942359 20259 solver.cpp:218] Iteration 7788 (2.28402 iter/s, 5.25388s/12 iters), loss = 1.63716
I0408 15:55:07.942407 20259 solver.cpp:237]     Train net output #0: loss = 1.63716 (* 1 = 1.63716 loss)
I0408 15:55:07.942420 20259 sgd_solver.cpp:105] Iteration 7788, lr = 3.20837e-06
I0408 15:55:07.950439 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:13.011909 20259 solver.cpp:218] Iteration 7800 (2.36717 iter/s, 5.06935s/12 iters), loss = 1.53096
I0408 15:55:13.011956 20259 solver.cpp:237]     Train net output #0: loss = 1.53096 (* 1 = 1.53096 loss)
I0408 15:55:13.011967 20259 sgd_solver.cpp:105] Iteration 7800, lr = 3.16884e-06
I0408 15:55:18.187966 20259 solver.cpp:218] Iteration 7812 (2.31846 iter/s, 5.17585s/12 iters), loss = 1.41673
I0408 15:55:18.188015 20259 solver.cpp:237]     Train net output #0: loss = 1.41673 (* 1 = 1.41673 loss)
I0408 15:55:18.188027 20259 sgd_solver.cpp:105] Iteration 7812, lr = 3.12981e-06
I0408 15:55:23.272722 20259 solver.cpp:218] Iteration 7824 (2.36009 iter/s, 5.08455s/12 iters), loss = 1.54846
I0408 15:55:23.272774 20259 solver.cpp:237]     Train net output #0: loss = 1.54846 (* 1 = 1.54846 loss)
I0408 15:55:23.272787 20259 sgd_solver.cpp:105] Iteration 7824, lr = 3.09125e-06
I0408 15:55:28.309135 20259 solver.cpp:218] Iteration 7836 (2.38275 iter/s, 5.03621s/12 iters), loss = 1.50083
I0408 15:55:28.309263 20259 solver.cpp:237]     Train net output #0: loss = 1.50083 (* 1 = 1.50083 loss)
I0408 15:55:28.309278 20259 sgd_solver.cpp:105] Iteration 7836, lr = 3.05317e-06
I0408 15:55:33.764928 20259 solver.cpp:218] Iteration 7848 (2.19962 iter/s, 5.4555s/12 iters), loss = 1.70428
I0408 15:55:33.764981 20259 solver.cpp:237]     Train net output #0: loss = 1.70428 (* 1 = 1.70428 loss)
I0408 15:55:33.764992 20259 sgd_solver.cpp:105] Iteration 7848, lr = 3.01556e-06
I0408 15:55:35.801054 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 15:55:38.812302 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 15:55:41.138300 20259 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 15:55:41.138326 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:55:42.506331 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:45.593159 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0408 15:55:45.593211 20259 solver.cpp:397]     Test net output #1: loss = 3.28351 (* 1 = 3.28351 loss)
I0408 15:55:47.610942 20259 solver.cpp:218] Iteration 7860 (0.866703 iter/s, 13.8456s/12 iters), loss = 1.5625
I0408 15:55:47.610992 20259 solver.cpp:237]     Train net output #0: loss = 1.5625 (* 1 = 1.5625 loss)
I0408 15:55:47.611004 20259 sgd_solver.cpp:105] Iteration 7860, lr = 2.97841e-06
I0408 15:55:52.668678 20259 solver.cpp:218] Iteration 7872 (2.3727 iter/s, 5.05754s/12 iters), loss = 1.64431
I0408 15:55:52.668713 20259 solver.cpp:237]     Train net output #0: loss = 1.64431 (* 1 = 1.64431 loss)
I0408 15:55:52.668721 20259 sgd_solver.cpp:105] Iteration 7872, lr = 2.94172e-06
I0408 15:55:57.849262 20259 solver.cpp:218] Iteration 7884 (2.31643 iter/s, 5.18039s/12 iters), loss = 1.49212
I0408 15:55:57.849313 20259 solver.cpp:237]     Train net output #0: loss = 1.49212 (* 1 = 1.49212 loss)
I0408 15:55:57.849324 20259 sgd_solver.cpp:105] Iteration 7884, lr = 2.90548e-06
I0408 15:56:00.173946 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:03.065991 20259 solver.cpp:218] Iteration 7896 (2.30038 iter/s, 5.21653s/12 iters), loss = 1.53808
I0408 15:56:03.066026 20259 solver.cpp:237]     Train net output #0: loss = 1.53808 (* 1 = 1.53808 loss)
I0408 15:56:03.066037 20259 sgd_solver.cpp:105] Iteration 7896, lr = 2.86969e-06
I0408 15:56:08.146749 20259 solver.cpp:218] Iteration 7908 (2.36194 iter/s, 5.08056s/12 iters), loss = 1.51872
I0408 15:56:08.146795 20259 solver.cpp:237]     Train net output #0: loss = 1.51872 (* 1 = 1.51872 loss)
I0408 15:56:08.146806 20259 sgd_solver.cpp:105] Iteration 7908, lr = 2.83434e-06
I0408 15:56:13.380993 20259 solver.cpp:218] Iteration 7920 (2.29269 iter/s, 5.23403s/12 iters), loss = 1.35266
I0408 15:56:13.381042 20259 solver.cpp:237]     Train net output #0: loss = 1.35266 (* 1 = 1.35266 loss)
I0408 15:56:13.381052 20259 sgd_solver.cpp:105] Iteration 7920, lr = 2.79942e-06
I0408 15:56:18.875598 20259 solver.cpp:218] Iteration 7932 (2.18404 iter/s, 5.4944s/12 iters), loss = 1.54556
I0408 15:56:18.875635 20259 solver.cpp:237]     Train net output #0: loss = 1.54556 (* 1 = 1.54556 loss)
I0408 15:56:18.875643 20259 sgd_solver.cpp:105] Iteration 7932, lr = 2.76494e-06
I0408 15:56:24.108925 20259 solver.cpp:218] Iteration 7944 (2.29308 iter/s, 5.23313s/12 iters), loss = 1.52936
I0408 15:56:24.108975 20259 solver.cpp:237]     Train net output #0: loss = 1.52936 (* 1 = 1.52936 loss)
I0408 15:56:24.108985 20259 sgd_solver.cpp:105] Iteration 7944, lr = 2.73088e-06
I0408 15:56:28.880300 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 15:56:31.894415 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 15:56:34.645328 20259 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 15:56:34.645351 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:56:35.923130 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:39.041749 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 15:56:39.041796 20259 solver.cpp:397]     Test net output #1: loss = 3.27196 (* 1 = 3.27196 loss)
I0408 15:56:39.132097 20259 solver.cpp:218] Iteration 7956 (0.798792 iter/s, 15.0227s/12 iters), loss = 1.45057
I0408 15:56:39.132144 20259 solver.cpp:237]     Train net output #0: loss = 1.45057 (* 1 = 1.45057 loss)
I0408 15:56:39.132156 20259 sgd_solver.cpp:105] Iteration 7956, lr = 2.69723e-06
I0408 15:56:43.693104 20259 solver.cpp:218] Iteration 7968 (2.63111 iter/s, 4.56081s/12 iters), loss = 1.75709
I0408 15:56:43.693162 20259 solver.cpp:237]     Train net output #0: loss = 1.75709 (* 1 = 1.75709 loss)
I0408 15:56:43.693176 20259 sgd_solver.cpp:105] Iteration 7968, lr = 2.66401e-06
I0408 15:56:49.048563 20259 solver.cpp:218] Iteration 7980 (2.2408 iter/s, 5.35523s/12 iters), loss = 1.37372
I0408 15:56:49.048612 20259 solver.cpp:237]     Train net output #0: loss = 1.37372 (* 1 = 1.37372 loss)
I0408 15:56:49.048625 20259 sgd_solver.cpp:105] Iteration 7980, lr = 2.63119e-06
I0408 15:56:53.602522 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:54.339603 20259 solver.cpp:218] Iteration 7992 (2.26808 iter/s, 5.29083s/12 iters), loss = 1.36877
I0408 15:56:54.339649 20259 solver.cpp:237]     Train net output #0: loss = 1.36877 (* 1 = 1.36877 loss)
I0408 15:56:54.339661 20259 sgd_solver.cpp:105] Iteration 7992, lr = 2.59878e-06
I0408 15:56:59.409632 20259 solver.cpp:218] Iteration 8004 (2.36694 iter/s, 5.06983s/12 iters), loss = 1.60321
I0408 15:56:59.409678 20259 solver.cpp:237]     Train net output #0: loss = 1.60321 (* 1 = 1.60321 loss)
I0408 15:56:59.409689 20259 sgd_solver.cpp:105] Iteration 8004, lr = 2.56676e-06
I0408 15:57:04.496012 20259 solver.cpp:218] Iteration 8016 (2.35933 iter/s, 5.08618s/12 iters), loss = 1.54441
I0408 15:57:04.496142 20259 solver.cpp:237]     Train net output #0: loss = 1.54441 (* 1 = 1.54441 loss)
I0408 15:57:04.496155 20259 sgd_solver.cpp:105] Iteration 8016, lr = 2.53514e-06
I0408 15:57:09.541206 20259 solver.cpp:218] Iteration 8028 (2.37863 iter/s, 5.04491s/12 iters), loss = 1.26756
I0408 15:57:09.541256 20259 solver.cpp:237]     Train net output #0: loss = 1.26756 (* 1 = 1.26756 loss)
I0408 15:57:09.541268 20259 sgd_solver.cpp:105] Iteration 8028, lr = 2.50391e-06
I0408 15:57:14.590692 20259 solver.cpp:218] Iteration 8040 (2.37657 iter/s, 5.04928s/12 iters), loss = 1.66213
I0408 15:57:14.590739 20259 solver.cpp:237]     Train net output #0: loss = 1.66213 (* 1 = 1.66213 loss)
I0408 15:57:14.590749 20259 sgd_solver.cpp:105] Iteration 8040, lr = 2.47307e-06
I0408 15:57:19.681356 20259 solver.cpp:218] Iteration 8052 (2.35735 iter/s, 5.09046s/12 iters), loss = 1.41438
I0408 15:57:19.681406 20259 solver.cpp:237]     Train net output #0: loss = 1.41438 (* 1 = 1.41438 loss)
I0408 15:57:19.681418 20259 sgd_solver.cpp:105] Iteration 8052, lr = 2.4426e-06
I0408 15:57:21.760780 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 15:57:24.833215 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 15:57:27.158254 20259 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 15:57:27.158282 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:57:28.465974 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:31.703025 20259 solver.cpp:397]     Test net output #0: accuracy = 0.294118
I0408 15:57:31.703066 20259 solver.cpp:397]     Test net output #1: loss = 3.2805 (* 1 = 3.2805 loss)
I0408 15:57:33.711517 20259 solver.cpp:218] Iteration 8064 (0.855328 iter/s, 14.0297s/12 iters), loss = 1.56343
I0408 15:57:33.711565 20259 solver.cpp:237]     Train net output #0: loss = 1.56343 (* 1 = 1.56343 loss)
I0408 15:57:33.711576 20259 sgd_solver.cpp:105] Iteration 8064, lr = 2.41251e-06
I0408 15:57:39.048552 20259 solver.cpp:218] Iteration 8076 (2.24853 iter/s, 5.33682s/12 iters), loss = 1.84715
I0408 15:57:39.048661 20259 solver.cpp:237]     Train net output #0: loss = 1.84715 (* 1 = 1.84715 loss)
I0408 15:57:39.048674 20259 sgd_solver.cpp:105] Iteration 8076, lr = 2.38279e-06
I0408 15:57:44.127696 20259 solver.cpp:218] Iteration 8088 (2.36272 iter/s, 5.07889s/12 iters), loss = 1.49369
I0408 15:57:44.127737 20259 solver.cpp:237]     Train net output #0: loss = 1.49369 (* 1 = 1.49369 loss)
I0408 15:57:44.127749 20259 sgd_solver.cpp:105] Iteration 8088, lr = 2.35344e-06
I0408 15:57:45.576035 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:49.165758 20259 solver.cpp:218] Iteration 8100 (2.38196 iter/s, 5.03786s/12 iters), loss = 1.49205
I0408 15:57:49.165813 20259 solver.cpp:237]     Train net output #0: loss = 1.49205 (* 1 = 1.49205 loss)
I0408 15:57:49.165827 20259 sgd_solver.cpp:105] Iteration 8100, lr = 2.32445e-06
I0408 15:57:54.270485 20259 solver.cpp:218] Iteration 8112 (2.35086 iter/s, 5.10452s/12 iters), loss = 1.5086
I0408 15:57:54.270534 20259 solver.cpp:237]     Train net output #0: loss = 1.5086 (* 1 = 1.5086 loss)
I0408 15:57:54.270545 20259 sgd_solver.cpp:105] Iteration 8112, lr = 2.29581e-06
I0408 15:57:59.193037 20259 solver.cpp:218] Iteration 8124 (2.43786 iter/s, 4.92236s/12 iters), loss = 1.66941
I0408 15:57:59.193094 20259 solver.cpp:237]     Train net output #0: loss = 1.66941 (* 1 = 1.66941 loss)
I0408 15:57:59.193106 20259 sgd_solver.cpp:105] Iteration 8124, lr = 2.26753e-06
I0408 15:58:04.210618 20259 solver.cpp:218] Iteration 8136 (2.39169 iter/s, 5.01737s/12 iters), loss = 1.5644
I0408 15:58:04.210672 20259 solver.cpp:237]     Train net output #0: loss = 1.5644 (* 1 = 1.5644 loss)
I0408 15:58:04.210685 20259 sgd_solver.cpp:105] Iteration 8136, lr = 2.2396e-06
I0408 15:58:09.298143 20259 solver.cpp:218] Iteration 8148 (2.35881 iter/s, 5.08732s/12 iters), loss = 1.64049
I0408 15:58:09.298229 20259 solver.cpp:237]     Train net output #0: loss = 1.64049 (* 1 = 1.64049 loss)
I0408 15:58:09.298241 20259 sgd_solver.cpp:105] Iteration 8148, lr = 2.21201e-06
I0408 15:58:14.172454 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 15:58:17.197054 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 15:58:19.519954 20259 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 15:58:19.519979 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:58:20.775619 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:23.973451 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 15:58:23.973495 20259 solver.cpp:397]     Test net output #1: loss = 3.26885 (* 1 = 3.26885 loss)
I0408 15:58:24.063256 20259 solver.cpp:218] Iteration 8160 (0.812755 iter/s, 14.7646s/12 iters), loss = 1.46462
I0408 15:58:24.063305 20259 solver.cpp:237]     Train net output #0: loss = 1.46462 (* 1 = 1.46462 loss)
I0408 15:58:24.063315 20259 sgd_solver.cpp:105] Iteration 8160, lr = 2.18476e-06
I0408 15:58:28.664902 20259 solver.cpp:218] Iteration 8172 (2.60787 iter/s, 4.60146s/12 iters), loss = 1.45795
I0408 15:58:28.664947 20259 solver.cpp:237]     Train net output #0: loss = 1.45795 (* 1 = 1.45795 loss)
I0408 15:58:28.664955 20259 sgd_solver.cpp:105] Iteration 8172, lr = 2.15785e-06
I0408 15:58:34.150632 20259 solver.cpp:218] Iteration 8184 (2.18758 iter/s, 5.48551s/12 iters), loss = 1.35441
I0408 15:58:34.150691 20259 solver.cpp:237]     Train net output #0: loss = 1.35441 (* 1 = 1.35441 loss)
I0408 15:58:34.150704 20259 sgd_solver.cpp:105] Iteration 8184, lr = 2.13126e-06
I0408 15:58:37.927211 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:39.425290 20259 solver.cpp:218] Iteration 8196 (2.27513 iter/s, 5.27443s/12 iters), loss = 1.80237
I0408 15:58:39.425443 20259 solver.cpp:237]     Train net output #0: loss = 1.80237 (* 1 = 1.80237 loss)
I0408 15:58:39.425457 20259 sgd_solver.cpp:105] Iteration 8196, lr = 2.10501e-06
I0408 15:58:44.965880 20259 solver.cpp:218] Iteration 8208 (2.16596 iter/s, 5.54027s/12 iters), loss = 1.28946
I0408 15:58:44.965937 20259 solver.cpp:237]     Train net output #0: loss = 1.28946 (* 1 = 1.28946 loss)
I0408 15:58:44.965950 20259 sgd_solver.cpp:105] Iteration 8208, lr = 2.07908e-06
I0408 15:58:50.131405 20259 solver.cpp:218] Iteration 8220 (2.32319 iter/s, 5.16531s/12 iters), loss = 1.57963
I0408 15:58:50.131458 20259 solver.cpp:237]     Train net output #0: loss = 1.57963 (* 1 = 1.57963 loss)
I0408 15:58:50.131470 20259 sgd_solver.cpp:105] Iteration 8220, lr = 2.05347e-06
I0408 15:58:55.225237 20259 solver.cpp:218] Iteration 8232 (2.35589 iter/s, 5.09362s/12 iters), loss = 1.7069
I0408 15:58:55.225283 20259 solver.cpp:237]     Train net output #0: loss = 1.7069 (* 1 = 1.7069 loss)
I0408 15:58:55.225292 20259 sgd_solver.cpp:105] Iteration 8232, lr = 2.02817e-06
I0408 15:59:00.738515 20259 solver.cpp:218] Iteration 8244 (2.17665 iter/s, 5.51306s/12 iters), loss = 1.29983
I0408 15:59:00.738566 20259 solver.cpp:237]     Train net output #0: loss = 1.29983 (* 1 = 1.29983 loss)
I0408 15:59:00.738577 20259 sgd_solver.cpp:105] Iteration 8244, lr = 2.00319e-06
I0408 15:59:06.243484 20259 solver.cpp:218] Iteration 8256 (2.17993 iter/s, 5.50475s/12 iters), loss = 1.51653
I0408 15:59:06.243530 20259 solver.cpp:237]     Train net output #0: loss = 1.51653 (* 1 = 1.51653 loss)
I0408 15:59:06.243541 20259 sgd_solver.cpp:105] Iteration 8256, lr = 1.97851e-06
I0408 15:59:08.464223 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 15:59:11.468151 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 15:59:13.778743 20259 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 15:59:13.778771 20259 net.cpp:676] Ignoring source layer train-data
I0408 15:59:15.080463 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:18.310418 20259 solver.cpp:397]     Test net output #0: accuracy = 0.294118
I0408 15:59:18.310456 20259 solver.cpp:397]     Test net output #1: loss = 3.27537 (* 1 = 3.27537 loss)
I0408 15:59:20.372918 20259 solver.cpp:218] Iteration 8268 (0.849318 iter/s, 14.129s/12 iters), loss = 1.63905
I0408 15:59:20.372968 20259 solver.cpp:237]     Train net output #0: loss = 1.63905 (* 1 = 1.63905 loss)
I0408 15:59:20.372982 20259 sgd_solver.cpp:105] Iteration 8268, lr = 1.95414e-06
I0408 15:59:25.860141 20259 solver.cpp:218] Iteration 8280 (2.18698 iter/s, 5.48701s/12 iters), loss = 1.41469
I0408 15:59:25.860181 20259 solver.cpp:237]     Train net output #0: loss = 1.41469 (* 1 = 1.41469 loss)
I0408 15:59:25.860191 20259 sgd_solver.cpp:105] Iteration 8280, lr = 1.93006e-06
I0408 15:59:31.346740 20259 solver.cpp:218] Iteration 8292 (2.18723 iter/s, 5.48639s/12 iters), loss = 1.66796
I0408 15:59:31.346784 20259 solver.cpp:237]     Train net output #0: loss = 1.66796 (* 1 = 1.66796 loss)
I0408 15:59:31.346796 20259 sgd_solver.cpp:105] Iteration 8292, lr = 1.90629e-06
I0408 15:59:32.097007 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:36.553246 20259 solver.cpp:218] Iteration 8304 (2.3049 iter/s, 5.20631s/12 iters), loss = 1.38548
I0408 15:59:36.553290 20259 solver.cpp:237]     Train net output #0: loss = 1.38548 (* 1 = 1.38548 loss)
I0408 15:59:36.553303 20259 sgd_solver.cpp:105] Iteration 8304, lr = 1.8828e-06
I0408 15:59:39.492836 20259 blocking_queue.cpp:49] Waiting for data
I0408 15:59:41.651613 20259 solver.cpp:218] Iteration 8316 (2.35379 iter/s, 5.09816s/12 iters), loss = 1.60727
I0408 15:59:41.651760 20259 solver.cpp:237]     Train net output #0: loss = 1.60727 (* 1 = 1.60727 loss)
I0408 15:59:41.651774 20259 sgd_solver.cpp:105] Iteration 8316, lr = 1.85961e-06
I0408 15:59:46.839809 20259 solver.cpp:218] Iteration 8328 (2.31308 iter/s, 5.18789s/12 iters), loss = 1.59007
I0408 15:59:46.839852 20259 solver.cpp:237]     Train net output #0: loss = 1.59007 (* 1 = 1.59007 loss)
I0408 15:59:46.839861 20259 sgd_solver.cpp:105] Iteration 8328, lr = 1.8367e-06
I0408 15:59:52.338871 20259 solver.cpp:218] Iteration 8340 (2.18227 iter/s, 5.49885s/12 iters), loss = 1.50293
I0408 15:59:52.338908 20259 solver.cpp:237]     Train net output #0: loss = 1.50293 (* 1 = 1.50293 loss)
I0408 15:59:52.338917 20259 sgd_solver.cpp:105] Iteration 8340, lr = 1.81408e-06
I0408 15:59:57.533100 20259 solver.cpp:218] Iteration 8352 (2.31034 iter/s, 5.19403s/12 iters), loss = 1.42449
I0408 15:59:57.533154 20259 solver.cpp:237]     Train net output #0: loss = 1.42449 (* 1 = 1.42449 loss)
I0408 15:59:57.533167 20259 sgd_solver.cpp:105] Iteration 8352, lr = 1.79173e-06
I0408 16:00:02.115401 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 16:00:05.155017 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 16:00:07.471422 20259 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 16:00:07.471446 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:00:08.665112 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:11.953892 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:00:11.954002 20259 solver.cpp:397]     Test net output #1: loss = 3.27963 (* 1 = 3.27963 loss)
I0408 16:00:12.043985 20259 solver.cpp:218] Iteration 8364 (0.826992 iter/s, 14.5104s/12 iters), loss = 1.5127
I0408 16:00:12.044037 20259 solver.cpp:237]     Train net output #0: loss = 1.5127 (* 1 = 1.5127 loss)
I0408 16:00:12.044049 20259 sgd_solver.cpp:105] Iteration 8364, lr = 1.76966e-06
I0408 16:00:16.382781 20259 solver.cpp:218] Iteration 8376 (2.76586 iter/s, 4.33861s/12 iters), loss = 1.37862
I0408 16:00:16.382827 20259 solver.cpp:237]     Train net output #0: loss = 1.37862 (* 1 = 1.37862 loss)
I0408 16:00:16.382838 20259 sgd_solver.cpp:105] Iteration 8376, lr = 1.74786e-06
I0408 16:00:21.390635 20259 solver.cpp:218] Iteration 8388 (2.39633 iter/s, 5.00765s/12 iters), loss = 1.4545
I0408 16:00:21.390689 20259 solver.cpp:237]     Train net output #0: loss = 1.4545 (* 1 = 1.4545 loss)
I0408 16:00:21.390703 20259 sgd_solver.cpp:105] Iteration 8388, lr = 1.72632e-06
I0408 16:00:24.217152 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:26.430754 20259 solver.cpp:218] Iteration 8400 (2.381 iter/s, 5.03991s/12 iters), loss = 1.81015
I0408 16:00:26.430809 20259 solver.cpp:237]     Train net output #0: loss = 1.81015 (* 1 = 1.81015 loss)
I0408 16:00:26.430821 20259 sgd_solver.cpp:105] Iteration 8400, lr = 1.70506e-06
I0408 16:00:31.507580 20259 solver.cpp:218] Iteration 8412 (2.36378 iter/s, 5.07662s/12 iters), loss = 1.59489
I0408 16:00:31.507624 20259 solver.cpp:237]     Train net output #0: loss = 1.59489 (* 1 = 1.59489 loss)
I0408 16:00:31.507637 20259 sgd_solver.cpp:105] Iteration 8412, lr = 1.68405e-06
I0408 16:00:36.518883 20259 solver.cpp:218] Iteration 8424 (2.39468 iter/s, 5.01111s/12 iters), loss = 1.79883
I0408 16:00:36.518920 20259 solver.cpp:237]     Train net output #0: loss = 1.79883 (* 1 = 1.79883 loss)
I0408 16:00:36.518929 20259 sgd_solver.cpp:105] Iteration 8424, lr = 1.66331e-06
I0408 16:00:41.557535 20259 solver.cpp:218] Iteration 8436 (2.38168 iter/s, 5.03846s/12 iters), loss = 1.34581
I0408 16:00:41.557582 20259 solver.cpp:237]     Train net output #0: loss = 1.34581 (* 1 = 1.34581 loss)
I0408 16:00:41.557595 20259 sgd_solver.cpp:105] Iteration 8436, lr = 1.64282e-06
I0408 16:00:46.626770 20259 solver.cpp:218] Iteration 8448 (2.36732 iter/s, 5.06903s/12 iters), loss = 1.33501
I0408 16:00:46.626915 20259 solver.cpp:237]     Train net output #0: loss = 1.33501 (* 1 = 1.33501 loss)
I0408 16:00:46.626929 20259 sgd_solver.cpp:105] Iteration 8448, lr = 1.62258e-06
I0408 16:00:51.677991 20259 solver.cpp:218] Iteration 8460 (2.3758 iter/s, 5.05093s/12 iters), loss = 1.62333
I0408 16:00:51.678040 20259 solver.cpp:237]     Train net output #0: loss = 1.62333 (* 1 = 1.62333 loss)
I0408 16:00:51.678052 20259 sgd_solver.cpp:105] Iteration 8460, lr = 1.60259e-06
I0408 16:00:53.734988 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 16:00:57.730253 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 16:01:00.034063 20259 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 16:01:00.034085 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:01:01.171743 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:04.541172 20259 solver.cpp:397]     Test net output #0: accuracy = 0.294118
I0408 16:01:04.541221 20259 solver.cpp:397]     Test net output #1: loss = 3.27285 (* 1 = 3.27285 loss)
I0408 16:01:06.364148 20259 solver.cpp:218] Iteration 8472 (0.817122 iter/s, 14.6857s/12 iters), loss = 1.84983
I0408 16:01:06.364207 20259 solver.cpp:237]     Train net output #0: loss = 1.84983 (* 1 = 1.84983 loss)
I0408 16:01:06.364218 20259 sgd_solver.cpp:105] Iteration 8472, lr = 1.58285e-06
I0408 16:01:11.374089 20259 solver.cpp:218] Iteration 8484 (2.39534 iter/s, 5.00973s/12 iters), loss = 1.46639
I0408 16:01:11.374145 20259 solver.cpp:237]     Train net output #0: loss = 1.46639 (* 1 = 1.46639 loss)
I0408 16:01:11.374156 20259 sgd_solver.cpp:105] Iteration 8484, lr = 1.56335e-06
I0408 16:01:16.388453 20259 solver.cpp:218] Iteration 8496 (2.39323 iter/s, 5.01415s/12 iters), loss = 1.40098
I0408 16:01:16.388510 20259 solver.cpp:237]     Train net output #0: loss = 1.40098 (* 1 = 1.40098 loss)
I0408 16:01:16.388523 20259 sgd_solver.cpp:105] Iteration 8496, lr = 1.54409e-06
I0408 16:01:16.436321 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:21.498039 20259 solver.cpp:218] Iteration 8508 (2.34862 iter/s, 5.10938s/12 iters), loss = 1.49852
I0408 16:01:21.498106 20259 solver.cpp:237]     Train net output #0: loss = 1.49852 (* 1 = 1.49852 loss)
I0408 16:01:21.498117 20259 sgd_solver.cpp:105] Iteration 8508, lr = 1.52507e-06
I0408 16:01:26.599406 20259 solver.cpp:218] Iteration 8520 (2.35241 iter/s, 5.10114s/12 iters), loss = 1.62136
I0408 16:01:26.599452 20259 solver.cpp:237]     Train net output #0: loss = 1.62136 (* 1 = 1.62136 loss)
I0408 16:01:26.599462 20259 sgd_solver.cpp:105] Iteration 8520, lr = 1.50628e-06
I0408 16:01:31.675287 20259 solver.cpp:218] Iteration 8532 (2.36421 iter/s, 5.07568s/12 iters), loss = 1.43555
I0408 16:01:31.675329 20259 solver.cpp:237]     Train net output #0: loss = 1.43555 (* 1 = 1.43555 loss)
I0408 16:01:31.675338 20259 sgd_solver.cpp:105] Iteration 8532, lr = 1.48773e-06
I0408 16:01:36.675587 20259 solver.cpp:218] Iteration 8544 (2.39995 iter/s, 5.0001s/12 iters), loss = 1.46221
I0408 16:01:36.675632 20259 solver.cpp:237]     Train net output #0: loss = 1.46221 (* 1 = 1.46221 loss)
I0408 16:01:36.675642 20259 sgd_solver.cpp:105] Iteration 8544, lr = 1.4694e-06
I0408 16:01:41.861938 20259 solver.cpp:218] Iteration 8556 (2.31386 iter/s, 5.18615s/12 iters), loss = 1.64272
I0408 16:01:41.862002 20259 solver.cpp:237]     Train net output #0: loss = 1.64272 (* 1 = 1.64272 loss)
I0408 16:01:41.862015 20259 sgd_solver.cpp:105] Iteration 8556, lr = 1.4513e-06
I0408 16:01:46.491923 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 16:01:49.584295 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 16:01:51.912973 20259 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 16:01:51.913064 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:01:53.135206 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:56.492851 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 16:01:56.492898 20259 solver.cpp:397]     Test net output #1: loss = 3.28522 (* 1 = 3.28522 loss)
I0408 16:01:56.582962 20259 solver.cpp:218] Iteration 8568 (0.815187 iter/s, 14.7205s/12 iters), loss = 1.5824
I0408 16:01:56.583006 20259 solver.cpp:237]     Train net output #0: loss = 1.5824 (* 1 = 1.5824 loss)
I0408 16:01:56.583019 20259 sgd_solver.cpp:105] Iteration 8568, lr = 1.43342e-06
I0408 16:02:00.844506 20259 solver.cpp:218] Iteration 8580 (2.816 iter/s, 4.26136s/12 iters), loss = 1.46915
I0408 16:02:00.844563 20259 solver.cpp:237]     Train net output #0: loss = 1.46915 (* 1 = 1.46915 loss)
I0408 16:02:00.844576 20259 sgd_solver.cpp:105] Iteration 8580, lr = 1.41576e-06
I0408 16:02:05.962811 20259 solver.cpp:218] Iteration 8592 (2.34462 iter/s, 5.1181s/12 iters), loss = 1.43795
I0408 16:02:05.962859 20259 solver.cpp:237]     Train net output #0: loss = 1.43795 (* 1 = 1.43795 loss)
I0408 16:02:05.962872 20259 sgd_solver.cpp:105] Iteration 8592, lr = 1.39832e-06
I0408 16:02:08.145016 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:11.168978 20259 solver.cpp:218] Iteration 8604 (2.30505 iter/s, 5.20596s/12 iters), loss = 1.68549
I0408 16:02:11.169024 20259 solver.cpp:237]     Train net output #0: loss = 1.68549 (* 1 = 1.68549 loss)
I0408 16:02:11.169034 20259 sgd_solver.cpp:105] Iteration 8604, lr = 1.3811e-06
I0408 16:02:16.338146 20259 solver.cpp:218] Iteration 8616 (2.32155 iter/s, 5.16897s/12 iters), loss = 1.39544
I0408 16:02:16.338191 20259 solver.cpp:237]     Train net output #0: loss = 1.39544 (* 1 = 1.39544 loss)
I0408 16:02:16.338203 20259 sgd_solver.cpp:105] Iteration 8616, lr = 1.36408e-06
I0408 16:02:21.332856 20259 solver.cpp:218] Iteration 8628 (2.40264 iter/s, 4.99451s/12 iters), loss = 1.55833
I0408 16:02:21.332907 20259 solver.cpp:237]     Train net output #0: loss = 1.55833 (* 1 = 1.55833 loss)
I0408 16:02:21.332919 20259 sgd_solver.cpp:105] Iteration 8628, lr = 1.34728e-06
I0408 16:02:26.331950 20259 solver.cpp:218] Iteration 8640 (2.40053 iter/s, 4.9989s/12 iters), loss = 1.6072
I0408 16:02:26.332067 20259 solver.cpp:237]     Train net output #0: loss = 1.6072 (* 1 = 1.6072 loss)
I0408 16:02:26.332079 20259 sgd_solver.cpp:105] Iteration 8640, lr = 1.33068e-06
I0408 16:02:31.443226 20259 solver.cpp:218] Iteration 8652 (2.34787 iter/s, 5.11101s/12 iters), loss = 1.45075
I0408 16:02:31.443272 20259 solver.cpp:237]     Train net output #0: loss = 1.45075 (* 1 = 1.45075 loss)
I0408 16:02:31.443284 20259 sgd_solver.cpp:105] Iteration 8652, lr = 1.31429e-06
I0408 16:02:36.556147 20259 solver.cpp:218] Iteration 8664 (2.34709 iter/s, 5.11272s/12 iters), loss = 1.54856
I0408 16:02:36.556200 20259 solver.cpp:237]     Train net output #0: loss = 1.54856 (* 1 = 1.54856 loss)
I0408 16:02:36.556213 20259 sgd_solver.cpp:105] Iteration 8664, lr = 1.2981e-06
I0408 16:02:38.657410 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 16:02:42.497057 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 16:02:45.952839 20259 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 16:02:45.952864 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:02:47.001155 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:50.566177 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:02:50.566210 20259 solver.cpp:397]     Test net output #1: loss = 3.27423 (* 1 = 3.27423 loss)
I0408 16:02:52.567844 20259 solver.cpp:218] Iteration 8676 (0.749476 iter/s, 16.0112s/12 iters), loss = 1.53079
I0408 16:02:52.567890 20259 solver.cpp:237]     Train net output #0: loss = 1.53079 (* 1 = 1.53079 loss)
I0408 16:02:52.567899 20259 sgd_solver.cpp:105] Iteration 8676, lr = 1.28211e-06
I0408 16:02:57.646965 20259 solver.cpp:218] Iteration 8688 (2.36271 iter/s, 5.07892s/12 iters), loss = 1.54358
I0408 16:02:57.647065 20259 solver.cpp:237]     Train net output #0: loss = 1.54358 (* 1 = 1.54358 loss)
I0408 16:02:57.647076 20259 sgd_solver.cpp:105] Iteration 8688, lr = 1.26631e-06
I0408 16:03:02.009352 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:02.699173 20259 solver.cpp:218] Iteration 8700 (2.37532 iter/s, 5.05196s/12 iters), loss = 1.55789
I0408 16:03:02.699216 20259 solver.cpp:237]     Train net output #0: loss = 1.55789 (* 1 = 1.55789 loss)
I0408 16:03:02.699226 20259 sgd_solver.cpp:105] Iteration 8700, lr = 1.25072e-06
I0408 16:03:07.800328 20259 solver.cpp:218] Iteration 8712 (2.3525 iter/s, 5.10096s/12 iters), loss = 1.62188
I0408 16:03:07.800369 20259 solver.cpp:237]     Train net output #0: loss = 1.62188 (* 1 = 1.62188 loss)
I0408 16:03:07.800379 20259 sgd_solver.cpp:105] Iteration 8712, lr = 1.23531e-06
I0408 16:03:13.123198 20259 solver.cpp:218] Iteration 8724 (2.25451 iter/s, 5.32266s/12 iters), loss = 1.53159
I0408 16:03:13.123255 20259 solver.cpp:237]     Train net output #0: loss = 1.53159 (* 1 = 1.53159 loss)
I0408 16:03:13.123267 20259 sgd_solver.cpp:105] Iteration 8724, lr = 1.22009e-06
I0408 16:03:18.668267 20259 solver.cpp:218] Iteration 8736 (2.16417 iter/s, 5.54485s/12 iters), loss = 1.34031
I0408 16:03:18.668313 20259 solver.cpp:237]     Train net output #0: loss = 1.34031 (* 1 = 1.34031 loss)
I0408 16:03:18.668321 20259 sgd_solver.cpp:105] Iteration 8736, lr = 1.20506e-06
I0408 16:03:23.757575 20259 solver.cpp:218] Iteration 8748 (2.35798 iter/s, 5.08911s/12 iters), loss = 1.58361
I0408 16:03:23.757619 20259 solver.cpp:237]     Train net output #0: loss = 1.58361 (* 1 = 1.58361 loss)
I0408 16:03:23.757629 20259 sgd_solver.cpp:105] Iteration 8748, lr = 1.19022e-06
I0408 16:03:28.766618 20259 solver.cpp:218] Iteration 8760 (2.39576 iter/s, 5.00885s/12 iters), loss = 1.41935
I0408 16:03:28.766741 20259 solver.cpp:237]     Train net output #0: loss = 1.41935 (* 1 = 1.41935 loss)
I0408 16:03:28.766750 20259 sgd_solver.cpp:105] Iteration 8760, lr = 1.17555e-06
I0408 16:03:33.523329 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 16:03:37.680403 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 16:03:40.312464 20259 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 16:03:40.312489 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:03:41.292652 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:44.937877 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 16:03:44.937924 20259 solver.cpp:397]     Test net output #1: loss = 3.28059 (* 1 = 3.28059 loss)
I0408 16:03:45.028223 20259 solver.cpp:218] Iteration 8772 (0.737961 iter/s, 16.261s/12 iters), loss = 1.63954
I0408 16:03:45.028271 20259 solver.cpp:237]     Train net output #0: loss = 1.63954 (* 1 = 1.63954 loss)
I0408 16:03:45.028282 20259 sgd_solver.cpp:105] Iteration 8772, lr = 1.16107e-06
I0408 16:03:49.217795 20259 solver.cpp:218] Iteration 8784 (2.86437 iter/s, 4.1894s/12 iters), loss = 1.55229
I0408 16:03:49.217844 20259 solver.cpp:237]     Train net output #0: loss = 1.55229 (* 1 = 1.55229 loss)
I0408 16:03:49.217857 20259 sgd_solver.cpp:105] Iteration 8784, lr = 1.14677e-06
I0408 16:03:54.303505 20259 solver.cpp:218] Iteration 8796 (2.35965 iter/s, 5.0855s/12 iters), loss = 1.32894
I0408 16:03:54.303565 20259 solver.cpp:237]     Train net output #0: loss = 1.32894 (* 1 = 1.32894 loss)
I0408 16:03:54.303577 20259 sgd_solver.cpp:105] Iteration 8796, lr = 1.13264e-06
I0408 16:03:55.758561 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:59.336318 20259 solver.cpp:218] Iteration 8808 (2.38445 iter/s, 5.0326s/12 iters), loss = 1.57443
I0408 16:03:59.336488 20259 solver.cpp:237]     Train net output #0: loss = 1.57443 (* 1 = 1.57443 loss)
I0408 16:03:59.336503 20259 sgd_solver.cpp:105] Iteration 8808, lr = 1.11869e-06
I0408 16:04:04.371170 20259 solver.cpp:218] Iteration 8820 (2.38354 iter/s, 5.03454s/12 iters), loss = 1.54182
I0408 16:04:04.371214 20259 solver.cpp:237]     Train net output #0: loss = 1.54182 (* 1 = 1.54182 loss)
I0408 16:04:04.371225 20259 sgd_solver.cpp:105] Iteration 8820, lr = 1.10491e-06
I0408 16:04:09.425470 20259 solver.cpp:218] Iteration 8832 (2.37431 iter/s, 5.0541s/12 iters), loss = 1.4259
I0408 16:04:09.425518 20259 solver.cpp:237]     Train net output #0: loss = 1.4259 (* 1 = 1.4259 loss)
I0408 16:04:09.425529 20259 sgd_solver.cpp:105] Iteration 8832, lr = 1.0913e-06
I0408 16:04:14.753096 20259 solver.cpp:218] Iteration 8844 (2.2525 iter/s, 5.32742s/12 iters), loss = 1.34566
I0408 16:04:14.753149 20259 solver.cpp:237]     Train net output #0: loss = 1.34566 (* 1 = 1.34566 loss)
I0408 16:04:14.753159 20259 sgd_solver.cpp:105] Iteration 8844, lr = 1.07785e-06
I0408 16:04:19.849367 20259 solver.cpp:218] Iteration 8856 (2.35476 iter/s, 5.09606s/12 iters), loss = 1.62204
I0408 16:04:19.849421 20259 solver.cpp:237]     Train net output #0: loss = 1.62204 (* 1 = 1.62204 loss)
I0408 16:04:19.849434 20259 sgd_solver.cpp:105] Iteration 8856, lr = 1.06458e-06
I0408 16:04:25.185804 20259 solver.cpp:218] Iteration 8868 (2.24878 iter/s, 5.33623s/12 iters), loss = 1.38018
I0408 16:04:25.185851 20259 solver.cpp:237]     Train net output #0: loss = 1.38018 (* 1 = 1.38018 loss)
I0408 16:04:25.185863 20259 sgd_solver.cpp:105] Iteration 8868, lr = 1.05146e-06
I0408 16:04:27.263690 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 16:04:34.346379 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 16:04:36.671352 20259 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 16:04:36.671376 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:04:37.617285 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:41.156415 20259 solver.cpp:397]     Test net output #0: accuracy = 0.295343
I0408 16:04:41.156455 20259 solver.cpp:397]     Test net output #1: loss = 3.27084 (* 1 = 3.27084 loss)
I0408 16:04:43.011126 20259 solver.cpp:218] Iteration 8880 (0.673221 iter/s, 17.8248s/12 iters), loss = 1.66113
I0408 16:04:43.011183 20259 solver.cpp:237]     Train net output #0: loss = 1.66113 (* 1 = 1.66113 loss)
I0408 16:04:43.011196 20259 sgd_solver.cpp:105] Iteration 8880, lr = 1.03851e-06
I0408 16:04:48.084388 20259 solver.cpp:218] Iteration 8892 (2.36544 iter/s, 5.07305s/12 iters), loss = 1.27271
I0408 16:04:48.084443 20259 solver.cpp:237]     Train net output #0: loss = 1.27271 (* 1 = 1.27271 loss)
I0408 16:04:48.084455 20259 sgd_solver.cpp:105] Iteration 8892, lr = 1.02572e-06
I0408 16:04:51.917315 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:53.351692 20259 solver.cpp:218] Iteration 8904 (2.2783 iter/s, 5.26709s/12 iters), loss = 1.51783
I0408 16:04:53.351743 20259 solver.cpp:237]     Train net output #0: loss = 1.51783 (* 1 = 1.51783 loss)
I0408 16:04:53.351755 20259 sgd_solver.cpp:105] Iteration 8904, lr = 1.01308e-06
I0408 16:04:58.816043 20259 solver.cpp:218] Iteration 8916 (2.19614 iter/s, 5.46414s/12 iters), loss = 1.25848
I0408 16:04:58.816090 20259 solver.cpp:237]     Train net output #0: loss = 1.25848 (* 1 = 1.25848 loss)
I0408 16:04:58.816102 20259 sgd_solver.cpp:105] Iteration 8916, lr = 1.0006e-06
I0408 16:05:04.122859 20259 solver.cpp:218] Iteration 8928 (2.26133 iter/s, 5.30661s/12 iters), loss = 1.46473
I0408 16:05:04.122908 20259 solver.cpp:237]     Train net output #0: loss = 1.46473 (* 1 = 1.46473 loss)
I0408 16:05:04.122920 20259 sgd_solver.cpp:105] Iteration 8928, lr = 9.88274e-07
I0408 16:05:09.069769 20259 solver.cpp:218] Iteration 8940 (2.42585 iter/s, 4.94671s/12 iters), loss = 1.52183
I0408 16:05:09.069905 20259 solver.cpp:237]     Train net output #0: loss = 1.52183 (* 1 = 1.52183 loss)
I0408 16:05:09.069914 20259 sgd_solver.cpp:105] Iteration 8940, lr = 9.76099e-07
I0408 16:05:14.246440 20259 solver.cpp:218] Iteration 8952 (2.31822 iter/s, 5.17638s/12 iters), loss = 1.55841
I0408 16:05:14.246491 20259 solver.cpp:237]     Train net output #0: loss = 1.55841 (* 1 = 1.55841 loss)
I0408 16:05:14.246503 20259 sgd_solver.cpp:105] Iteration 8952, lr = 9.64075e-07
I0408 16:05:19.622318 20259 solver.cpp:218] Iteration 8964 (2.23228 iter/s, 5.37567s/12 iters), loss = 1.37778
I0408 16:05:19.622370 20259 solver.cpp:237]     Train net output #0: loss = 1.37778 (* 1 = 1.37778 loss)
I0408 16:05:19.622383 20259 sgd_solver.cpp:105] Iteration 8964, lr = 9.52198e-07
I0408 16:05:24.464756 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 16:05:31.375774 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 16:05:33.714248 20259 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 16:05:33.714277 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:05:34.700675 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:38.255666 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 16:05:38.255707 20259 solver.cpp:397]     Test net output #1: loss = 3.28483 (* 1 = 3.28483 loss)
I0408 16:05:38.342900 20259 solver.cpp:218] Iteration 8976 (0.641026 iter/s, 18.72s/12 iters), loss = 1.63334
I0408 16:05:38.342948 20259 solver.cpp:237]     Train net output #0: loss = 1.63334 (* 1 = 1.63334 loss)
I0408 16:05:38.342959 20259 sgd_solver.cpp:105] Iteration 8976, lr = 9.40468e-07
I0408 16:05:42.690626 20259 solver.cpp:218] Iteration 8988 (2.76018 iter/s, 4.34754s/12 iters), loss = 1.53111
I0408 16:05:42.690716 20259 solver.cpp:237]     Train net output #0: loss = 1.53111 (* 1 = 1.53111 loss)
I0408 16:05:42.690726 20259 sgd_solver.cpp:105] Iteration 8988, lr = 9.28883e-07
I0408 16:05:46.083317 20259 blocking_queue.cpp:49] Waiting for data
I0408 16:05:47.842046 20259 solver.cpp:218] Iteration 9000 (2.32957 iter/s, 5.15117s/12 iters), loss = 1.54896
I0408 16:05:47.842097 20259 solver.cpp:237]     Train net output #0: loss = 1.54896 (* 1 = 1.54896 loss)
I0408 16:05:47.842108 20259 sgd_solver.cpp:105] Iteration 9000, lr = 9.1744e-07
I0408 16:05:48.570582 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:52.886047 20259 solver.cpp:218] Iteration 9012 (2.37916 iter/s, 5.0438s/12 iters), loss = 1.41658
I0408 16:05:52.886097 20259 solver.cpp:237]     Train net output #0: loss = 1.41658 (* 1 = 1.41658 loss)
I0408 16:05:52.886108 20259 sgd_solver.cpp:105] Iteration 9012, lr = 9.06138e-07
I0408 16:05:57.963055 20259 solver.cpp:218] Iteration 9024 (2.36369 iter/s, 5.0768s/12 iters), loss = 1.68597
I0408 16:05:57.963104 20259 solver.cpp:237]     Train net output #0: loss = 1.68597 (* 1 = 1.68597 loss)
I0408 16:05:57.963115 20259 sgd_solver.cpp:105] Iteration 9024, lr = 8.94976e-07
I0408 16:06:03.069141 20259 solver.cpp:218] Iteration 9036 (2.35023 iter/s, 5.10587s/12 iters), loss = 1.5396
I0408 16:06:03.069198 20259 solver.cpp:237]     Train net output #0: loss = 1.5396 (* 1 = 1.5396 loss)
I0408 16:06:03.069212 20259 sgd_solver.cpp:105] Iteration 9036, lr = 8.83951e-07
I0408 16:06:08.191910 20259 solver.cpp:218] Iteration 9048 (2.34258 iter/s, 5.12255s/12 iters), loss = 1.63994
I0408 16:06:08.191972 20259 solver.cpp:237]     Train net output #0: loss = 1.63994 (* 1 = 1.63994 loss)
I0408 16:06:08.191987 20259 sgd_solver.cpp:105] Iteration 9048, lr = 8.73062e-07
I0408 16:06:13.385567 20259 solver.cpp:218] Iteration 9060 (2.31061 iter/s, 5.19344s/12 iters), loss = 1.34219
I0408 16:06:13.385713 20259 solver.cpp:237]     Train net output #0: loss = 1.34219 (* 1 = 1.34219 loss)
I0408 16:06:13.385727 20259 sgd_solver.cpp:105] Iteration 9060, lr = 8.62306e-07
I0408 16:06:18.426993 20259 solver.cpp:218] Iteration 9072 (2.38042 iter/s, 5.04113s/12 iters), loss = 1.40485
I0408 16:06:18.427042 20259 solver.cpp:237]     Train net output #0: loss = 1.40485 (* 1 = 1.40485 loss)
I0408 16:06:18.427053 20259 sgd_solver.cpp:105] Iteration 9072, lr = 8.51684e-07
I0408 16:06:20.485988 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 16:06:23.469067 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 16:06:29.001368 20259 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 16:06:29.001394 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:06:29.936762 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:33.546182 20259 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0408 16:06:33.546227 20259 solver.cpp:397]     Test net output #1: loss = 3.2828 (* 1 = 3.2828 loss)
I0408 16:06:35.582062 20259 solver.cpp:218] Iteration 9084 (0.699524 iter/s, 17.1545s/12 iters), loss = 1.53138
I0408 16:06:35.582120 20259 solver.cpp:237]     Train net output #0: loss = 1.53138 (* 1 = 1.53138 loss)
I0408 16:06:35.582134 20259 sgd_solver.cpp:105] Iteration 9084, lr = 8.41192e-07
I0408 16:06:40.681242 20259 solver.cpp:218] Iteration 9096 (2.35342 iter/s, 5.09897s/12 iters), loss = 1.61097
I0408 16:06:40.681290 20259 solver.cpp:237]     Train net output #0: loss = 1.61097 (* 1 = 1.61097 loss)
I0408 16:06:40.681301 20259 sgd_solver.cpp:105] Iteration 9096, lr = 8.3083e-07
I0408 16:06:43.674623 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:45.757042 20259 solver.cpp:218] Iteration 9108 (2.36425 iter/s, 5.0756s/12 iters), loss = 1.70251
I0408 16:06:45.757091 20259 solver.cpp:237]     Train net output #0: loss = 1.70251 (* 1 = 1.70251 loss)
I0408 16:06:45.757102 20259 sgd_solver.cpp:105] Iteration 9108, lr = 8.20595e-07
I0408 16:06:50.773378 20259 solver.cpp:218] Iteration 9120 (2.39228 iter/s, 5.01614s/12 iters), loss = 1.52616
I0408 16:06:50.773427 20259 solver.cpp:237]     Train net output #0: loss = 1.52616 (* 1 = 1.52616 loss)
I0408 16:06:50.773438 20259 sgd_solver.cpp:105] Iteration 9120, lr = 8.10486e-07
I0408 16:06:55.952812 20259 solver.cpp:218] Iteration 9132 (2.31695 iter/s, 5.17923s/12 iters), loss = 1.45306
I0408 16:06:55.952857 20259 solver.cpp:237]     Train net output #0: loss = 1.45306 (* 1 = 1.45306 loss)
I0408 16:06:55.952867 20259 sgd_solver.cpp:105] Iteration 9132, lr = 8.00502e-07
I0408 16:07:01.146276 20259 solver.cpp:218] Iteration 9144 (2.31069 iter/s, 5.19326s/12 iters), loss = 1.73637
I0408 16:07:01.146320 20259 solver.cpp:237]     Train net output #0: loss = 1.73637 (* 1 = 1.73637 loss)
I0408 16:07:01.146332 20259 sgd_solver.cpp:105] Iteration 9144, lr = 7.9064e-07
I0408 16:07:06.442555 20259 solver.cpp:218] Iteration 9156 (2.26583 iter/s, 5.29608s/12 iters), loss = 1.37674
I0408 16:07:06.442593 20259 solver.cpp:237]     Train net output #0: loss = 1.37674 (* 1 = 1.37674 loss)
I0408 16:07:06.442603 20259 sgd_solver.cpp:105] Iteration 9156, lr = 7.80901e-07
I0408 16:07:11.548780 20259 solver.cpp:218] Iteration 9168 (2.35016 iter/s, 5.10603s/12 iters), loss = 1.54431
I0408 16:07:11.548830 20259 solver.cpp:237]     Train net output #0: loss = 1.54431 (* 1 = 1.54431 loss)
I0408 16:07:11.548841 20259 sgd_solver.cpp:105] Iteration 9168, lr = 7.71281e-07
I0408 16:07:16.152341 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 16:07:19.144953 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 16:07:21.458047 20259 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 16:07:21.458070 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:07:22.352144 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:26.008679 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292279
I0408 16:07:26.008721 20259 solver.cpp:397]     Test net output #1: loss = 3.27471 (* 1 = 3.27471 loss)
I0408 16:07:26.098871 20259 solver.cpp:218] Iteration 9180 (0.824763 iter/s, 14.5496s/12 iters), loss = 1.55813
I0408 16:07:26.098923 20259 solver.cpp:237]     Train net output #0: loss = 1.55813 (* 1 = 1.55813 loss)
I0408 16:07:26.098937 20259 sgd_solver.cpp:105] Iteration 9180, lr = 7.6178e-07
I0408 16:07:30.144093 20259 solver.cpp:218] Iteration 9192 (2.96659 iter/s, 4.04505s/12 iters), loss = 1.54004
I0408 16:07:30.144146 20259 solver.cpp:237]     Train net output #0: loss = 1.54004 (* 1 = 1.54004 loss)
I0408 16:07:30.144160 20259 sgd_solver.cpp:105] Iteration 9192, lr = 7.52395e-07
I0408 16:07:35.086627 20259 solver.cpp:218] Iteration 9204 (2.428 iter/s, 4.94233s/12 iters), loss = 1.43668
I0408 16:07:35.086675 20259 solver.cpp:237]     Train net output #0: loss = 1.43668 (* 1 = 1.43668 loss)
I0408 16:07:35.086688 20259 sgd_solver.cpp:105] Iteration 9204, lr = 7.43127e-07
I0408 16:07:35.171083 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:40.000466 20259 solver.cpp:218] Iteration 9216 (2.44218 iter/s, 4.91364s/12 iters), loss = 1.65809
I0408 16:07:40.000509 20259 solver.cpp:237]     Train net output #0: loss = 1.65809 (* 1 = 1.65809 loss)
I0408 16:07:40.000519 20259 sgd_solver.cpp:105] Iteration 9216, lr = 7.33972e-07
I0408 16:07:45.245474 20259 solver.cpp:218] Iteration 9228 (2.28798 iter/s, 5.24481s/12 iters), loss = 1.29174
I0408 16:07:45.245512 20259 solver.cpp:237]     Train net output #0: loss = 1.29174 (* 1 = 1.29174 loss)
I0408 16:07:45.245519 20259 sgd_solver.cpp:105] Iteration 9228, lr = 7.24931e-07
I0408 16:07:50.721650 20259 solver.cpp:218] Iteration 9240 (2.19139 iter/s, 5.47597s/12 iters), loss = 1.55962
I0408 16:07:50.721742 20259 solver.cpp:237]     Train net output #0: loss = 1.55962 (* 1 = 1.55962 loss)
I0408 16:07:50.721753 20259 sgd_solver.cpp:105] Iteration 9240, lr = 7.16e-07
I0408 16:07:55.873242 20259 solver.cpp:218] Iteration 9252 (2.32949 iter/s, 5.15135s/12 iters), loss = 1.54009
I0408 16:07:55.873289 20259 solver.cpp:237]     Train net output #0: loss = 1.54009 (* 1 = 1.54009 loss)
I0408 16:07:55.873299 20259 sgd_solver.cpp:105] Iteration 9252, lr = 7.0718e-07
I0408 16:08:00.903355 20259 solver.cpp:218] Iteration 9264 (2.38573 iter/s, 5.02991s/12 iters), loss = 1.72844
I0408 16:08:00.903401 20259 solver.cpp:237]     Train net output #0: loss = 1.72844 (* 1 = 1.72844 loss)
I0408 16:08:00.903412 20259 sgd_solver.cpp:105] Iteration 9264, lr = 6.98468e-07
I0408 16:08:06.000308 20259 solver.cpp:218] Iteration 9276 (2.35444 iter/s, 5.09675s/12 iters), loss = 1.71357
I0408 16:08:06.000360 20259 solver.cpp:237]     Train net output #0: loss = 1.71357 (* 1 = 1.71357 loss)
I0408 16:08:06.000370 20259 sgd_solver.cpp:105] Iteration 9276, lr = 6.89864e-07
I0408 16:08:08.154443 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 16:08:11.152710 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 16:08:13.495249 20259 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 16:08:13.495276 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:08:14.324633 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:17.958405 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0408 16:08:17.958456 20259 solver.cpp:397]     Test net output #1: loss = 3.29025 (* 1 = 3.29025 loss)
I0408 16:08:19.949190 20259 solver.cpp:218] Iteration 9288 (0.860312 iter/s, 13.9484s/12 iters), loss = 1.42786
I0408 16:08:19.949250 20259 solver.cpp:237]     Train net output #0: loss = 1.42786 (* 1 = 1.42786 loss)
I0408 16:08:19.949263 20259 sgd_solver.cpp:105] Iteration 9288, lr = 6.81366e-07
I0408 16:08:25.170270 20259 solver.cpp:218] Iteration 9300 (2.29847 iter/s, 5.22087s/12 iters), loss = 1.2944
I0408 16:08:25.170403 20259 solver.cpp:237]     Train net output #0: loss = 1.2944 (* 1 = 1.2944 loss)
I0408 16:08:25.170413 20259 sgd_solver.cpp:105] Iteration 9300, lr = 6.72972e-07
I0408 16:08:27.425173 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:30.245683 20259 solver.cpp:218] Iteration 9312 (2.36447 iter/s, 5.07513s/12 iters), loss = 1.61665
I0408 16:08:30.245731 20259 solver.cpp:237]     Train net output #0: loss = 1.61665 (* 1 = 1.61665 loss)
I0408 16:08:30.245744 20259 sgd_solver.cpp:105] Iteration 9312, lr = 6.64682e-07
I0408 16:08:35.336732 20259 solver.cpp:218] Iteration 9324 (2.35717 iter/s, 5.09085s/12 iters), loss = 1.44168
I0408 16:08:35.336772 20259 solver.cpp:237]     Train net output #0: loss = 1.44168 (* 1 = 1.44168 loss)
I0408 16:08:35.336781 20259 sgd_solver.cpp:105] Iteration 9324, lr = 6.56494e-07
I0408 16:08:40.387236 20259 solver.cpp:218] Iteration 9336 (2.3761 iter/s, 5.0503s/12 iters), loss = 1.45994
I0408 16:08:40.387296 20259 solver.cpp:237]     Train net output #0: loss = 1.45994 (* 1 = 1.45994 loss)
I0408 16:08:40.387310 20259 sgd_solver.cpp:105] Iteration 9336, lr = 6.48406e-07
I0408 16:08:45.792505 20259 solver.cpp:218] Iteration 9348 (2.22015 iter/s, 5.40504s/12 iters), loss = 1.52992
I0408 16:08:45.792567 20259 solver.cpp:237]     Train net output #0: loss = 1.52992 (* 1 = 1.52992 loss)
I0408 16:08:45.792582 20259 sgd_solver.cpp:105] Iteration 9348, lr = 6.40419e-07
I0408 16:08:50.894989 20259 solver.cpp:218] Iteration 9360 (2.3519 iter/s, 5.10226s/12 iters), loss = 1.5335
I0408 16:08:50.895048 20259 solver.cpp:237]     Train net output #0: loss = 1.5335 (* 1 = 1.5335 loss)
I0408 16:08:50.895061 20259 sgd_solver.cpp:105] Iteration 9360, lr = 6.3253e-07
I0408 16:08:55.966091 20259 solver.cpp:218] Iteration 9372 (2.36645 iter/s, 5.0709s/12 iters), loss = 1.42654
I0408 16:08:55.966193 20259 solver.cpp:237]     Train net output #0: loss = 1.42654 (* 1 = 1.42654 loss)
I0408 16:08:55.966203 20259 sgd_solver.cpp:105] Iteration 9372, lr = 6.24738e-07
I0408 16:09:00.541530 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 16:09:03.558348 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 16:09:05.915704 20259 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 16:09:05.915732 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:09:06.668411 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:10.336406 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:09:10.336454 20259 solver.cpp:397]     Test net output #1: loss = 3.27415 (* 1 = 3.27415 loss)
I0408 16:09:10.423491 20259 solver.cpp:218] Iteration 9384 (0.830054 iter/s, 14.4569s/12 iters), loss = 1.60228
I0408 16:09:10.423542 20259 solver.cpp:237]     Train net output #0: loss = 1.60228 (* 1 = 1.60228 loss)
I0408 16:09:10.423553 20259 sgd_solver.cpp:105] Iteration 9384, lr = 6.17042e-07
I0408 16:09:14.648717 20259 solver.cpp:218] Iteration 9396 (2.84021 iter/s, 4.22504s/12 iters), loss = 1.49589
I0408 16:09:14.648769 20259 solver.cpp:237]     Train net output #0: loss = 1.49589 (* 1 = 1.49589 loss)
I0408 16:09:14.648782 20259 sgd_solver.cpp:105] Iteration 9396, lr = 6.0944e-07
I0408 16:09:19.134073 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:19.843071 20259 solver.cpp:218] Iteration 9408 (2.3103 iter/s, 5.19414s/12 iters), loss = 1.61356
I0408 16:09:19.843127 20259 solver.cpp:237]     Train net output #0: loss = 1.61356 (* 1 = 1.61356 loss)
I0408 16:09:19.843140 20259 sgd_solver.cpp:105] Iteration 9408, lr = 6.01933e-07
I0408 16:09:25.220647 20259 solver.cpp:218] Iteration 9420 (2.23158 iter/s, 5.37736s/12 iters), loss = 1.4555
I0408 16:09:25.220688 20259 solver.cpp:237]     Train net output #0: loss = 1.4555 (* 1 = 1.4555 loss)
I0408 16:09:25.220698 20259 sgd_solver.cpp:105] Iteration 9420, lr = 5.94518e-07
I0408 16:09:30.438697 20259 solver.cpp:218] Iteration 9432 (2.2998 iter/s, 5.21785s/12 iters), loss = 1.86224
I0408 16:09:30.438845 20259 solver.cpp:237]     Train net output #0: loss = 1.86224 (* 1 = 1.86224 loss)
I0408 16:09:30.438856 20259 sgd_solver.cpp:105] Iteration 9432, lr = 5.87194e-07
I0408 16:09:35.777252 20259 solver.cpp:218] Iteration 9444 (2.24793 iter/s, 5.33825s/12 iters), loss = 1.47691
I0408 16:09:35.777307 20259 solver.cpp:237]     Train net output #0: loss = 1.47691 (* 1 = 1.47691 loss)
I0408 16:09:35.777320 20259 sgd_solver.cpp:105] Iteration 9444, lr = 5.7996e-07
I0408 16:09:40.850425 20259 solver.cpp:218] Iteration 9456 (2.36548 iter/s, 5.07297s/12 iters), loss = 1.6152
I0408 16:09:40.850478 20259 solver.cpp:237]     Train net output #0: loss = 1.6152 (* 1 = 1.6152 loss)
I0408 16:09:40.850492 20259 sgd_solver.cpp:105] Iteration 9456, lr = 5.72816e-07
I0408 16:09:45.934109 20259 solver.cpp:218] Iteration 9468 (2.36059 iter/s, 5.08348s/12 iters), loss = 1.62533
I0408 16:09:45.934163 20259 solver.cpp:237]     Train net output #0: loss = 1.62533 (* 1 = 1.62533 loss)
I0408 16:09:45.934175 20259 sgd_solver.cpp:105] Iteration 9468, lr = 5.65759e-07
I0408 16:09:51.110944 20259 solver.cpp:218] Iteration 9480 (2.31811 iter/s, 5.17663s/12 iters), loss = 1.58591
I0408 16:09:51.110997 20259 solver.cpp:237]     Train net output #0: loss = 1.58591 (* 1 = 1.58591 loss)
I0408 16:09:51.111011 20259 sgd_solver.cpp:105] Iteration 9480, lr = 5.5879e-07
I0408 16:09:53.350499 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 16:09:57.327425 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 16:09:59.656385 20259 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 16:09:59.656412 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:10:00.391789 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:04.118126 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:10:04.118237 20259 solver.cpp:397]     Test net output #1: loss = 3.27643 (* 1 = 3.27643 loss)
I0408 16:10:06.039902 20259 solver.cpp:218] Iteration 9492 (0.803833 iter/s, 14.9285s/12 iters), loss = 1.64066
I0408 16:10:06.039958 20259 solver.cpp:237]     Train net output #0: loss = 1.64066 (* 1 = 1.64066 loss)
I0408 16:10:06.039973 20259 sgd_solver.cpp:105] Iteration 9492, lr = 5.51906e-07
I0408 16:10:11.054167 20259 solver.cpp:218] Iteration 9504 (2.39327 iter/s, 5.01406s/12 iters), loss = 1.49816
I0408 16:10:11.054214 20259 solver.cpp:237]     Train net output #0: loss = 1.49816 (* 1 = 1.49816 loss)
I0408 16:10:11.054226 20259 sgd_solver.cpp:105] Iteration 9504, lr = 5.45107e-07
I0408 16:10:12.563177 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:16.207428 20259 solver.cpp:218] Iteration 9516 (2.32872 iter/s, 5.15306s/12 iters), loss = 1.66403
I0408 16:10:16.207485 20259 solver.cpp:237]     Train net output #0: loss = 1.66403 (* 1 = 1.66403 loss)
I0408 16:10:16.207497 20259 sgd_solver.cpp:105] Iteration 9516, lr = 5.38392e-07
I0408 16:10:21.240056 20259 solver.cpp:218] Iteration 9528 (2.38454 iter/s, 5.03242s/12 iters), loss = 1.41426
I0408 16:10:21.240103 20259 solver.cpp:237]     Train net output #0: loss = 1.41426 (* 1 = 1.41426 loss)
I0408 16:10:21.240114 20259 sgd_solver.cpp:105] Iteration 9528, lr = 5.3176e-07
I0408 16:10:26.278661 20259 solver.cpp:218] Iteration 9540 (2.38171 iter/s, 5.0384s/12 iters), loss = 1.45804
I0408 16:10:26.278712 20259 solver.cpp:237]     Train net output #0: loss = 1.45804 (* 1 = 1.45804 loss)
I0408 16:10:26.278724 20259 sgd_solver.cpp:105] Iteration 9540, lr = 5.25209e-07
I0408 16:10:31.269182 20259 solver.cpp:218] Iteration 9552 (2.40466 iter/s, 4.99032s/12 iters), loss = 1.46663
I0408 16:10:31.269234 20259 solver.cpp:237]     Train net output #0: loss = 1.46663 (* 1 = 1.46663 loss)
I0408 16:10:31.269248 20259 sgd_solver.cpp:105] Iteration 9552, lr = 5.18739e-07
I0408 16:10:36.697928 20259 solver.cpp:218] Iteration 9564 (2.21054 iter/s, 5.42853s/12 iters), loss = 1.62316
I0408 16:10:36.698091 20259 solver.cpp:237]     Train net output #0: loss = 1.62316 (* 1 = 1.62316 loss)
I0408 16:10:36.698104 20259 sgd_solver.cpp:105] Iteration 9564, lr = 5.12349e-07
I0408 16:10:41.789876 20259 solver.cpp:218] Iteration 9576 (2.35681 iter/s, 5.09164s/12 iters), loss = 1.43697
I0408 16:10:41.789929 20259 solver.cpp:237]     Train net output #0: loss = 1.43697 (* 1 = 1.43697 loss)
I0408 16:10:41.789942 20259 sgd_solver.cpp:105] Iteration 9576, lr = 5.06038e-07
I0408 16:10:46.330101 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 16:10:49.413481 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 16:10:51.722623 20259 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 16:10:51.722645 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:10:52.415767 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:56.178520 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 16:10:56.178565 20259 solver.cpp:397]     Test net output #1: loss = 3.27463 (* 1 = 3.27463 loss)
I0408 16:10:56.268615 20259 solver.cpp:218] Iteration 9588 (0.828829 iter/s, 14.4783s/12 iters), loss = 1.36106
I0408 16:10:56.268666 20259 solver.cpp:237]     Train net output #0: loss = 1.36106 (* 1 = 1.36106 loss)
I0408 16:10:56.268677 20259 sgd_solver.cpp:105] Iteration 9588, lr = 4.99804e-07
I0408 16:11:00.538429 20259 solver.cpp:218] Iteration 9600 (2.81055 iter/s, 4.26963s/12 iters), loss = 1.28628
I0408 16:11:00.538487 20259 solver.cpp:237]     Train net output #0: loss = 1.28628 (* 1 = 1.28628 loss)
I0408 16:11:00.538501 20259 sgd_solver.cpp:105] Iteration 9600, lr = 4.93647e-07
I0408 16:11:04.174705 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:05.605695 20259 solver.cpp:218] Iteration 9612 (2.36824 iter/s, 5.06706s/12 iters), loss = 1.74893
I0408 16:11:05.605742 20259 solver.cpp:237]     Train net output #0: loss = 1.74893 (* 1 = 1.74893 loss)
I0408 16:11:05.605751 20259 sgd_solver.cpp:105] Iteration 9612, lr = 4.87566e-07
I0408 16:11:10.675307 20259 solver.cpp:218] Iteration 9624 (2.36714 iter/s, 5.06942s/12 iters), loss = 1.18274
I0408 16:11:10.675398 20259 solver.cpp:237]     Train net output #0: loss = 1.18274 (* 1 = 1.18274 loss)
I0408 16:11:10.675410 20259 sgd_solver.cpp:105] Iteration 9624, lr = 4.81559e-07
I0408 16:11:15.652788 20259 solver.cpp:218] Iteration 9636 (2.41097 iter/s, 4.97724s/12 iters), loss = 1.70195
I0408 16:11:15.652825 20259 solver.cpp:237]     Train net output #0: loss = 1.70195 (* 1 = 1.70195 loss)
I0408 16:11:15.652834 20259 sgd_solver.cpp:105] Iteration 9636, lr = 4.75627e-07
I0408 16:11:20.651983 20259 solver.cpp:218] Iteration 9648 (2.40048 iter/s, 4.999s/12 iters), loss = 1.65515
I0408 16:11:20.652036 20259 solver.cpp:237]     Train net output #0: loss = 1.65515 (* 1 = 1.65515 loss)
I0408 16:11:20.652048 20259 sgd_solver.cpp:105] Iteration 9648, lr = 4.69768e-07
I0408 16:11:25.615950 20259 solver.cpp:218] Iteration 9660 (2.41752 iter/s, 4.96376s/12 iters), loss = 1.39986
I0408 16:11:25.615998 20259 solver.cpp:237]     Train net output #0: loss = 1.39986 (* 1 = 1.39986 loss)
I0408 16:11:25.616008 20259 sgd_solver.cpp:105] Iteration 9660, lr = 4.63981e-07
I0408 16:11:30.620100 20259 solver.cpp:218] Iteration 9672 (2.39811 iter/s, 5.00394s/12 iters), loss = 1.68673
I0408 16:11:30.620159 20259 solver.cpp:237]     Train net output #0: loss = 1.68673 (* 1 = 1.68673 loss)
I0408 16:11:30.620170 20259 sgd_solver.cpp:105] Iteration 9672, lr = 4.58265e-07
I0408 16:11:35.683465 20259 solver.cpp:218] Iteration 9684 (2.37006 iter/s, 5.06316s/12 iters), loss = 1.55087
I0408 16:11:35.683518 20259 solver.cpp:237]     Train net output #0: loss = 1.55087 (* 1 = 1.55087 loss)
I0408 16:11:35.683531 20259 sgd_solver.cpp:105] Iteration 9684, lr = 4.5262e-07
I0408 16:11:37.756134 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 16:11:40.846837 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 16:11:43.156502 20259 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 16:11:43.156524 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:11:43.763314 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:46.599418 20259 blocking_queue.cpp:49] Waiting for data
I0408 16:11:47.586565 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 16:11:47.586611 20259 solver.cpp:397]     Test net output #1: loss = 3.28889 (* 1 = 3.28889 loss)
I0408 16:11:49.404539 20259 solver.cpp:218] Iteration 9696 (0.874596 iter/s, 13.7206s/12 iters), loss = 1.39203
I0408 16:11:49.404598 20259 solver.cpp:237]     Train net output #0: loss = 1.39203 (* 1 = 1.39203 loss)
I0408 16:11:49.404610 20259 sgd_solver.cpp:105] Iteration 9696, lr = 4.47044e-07
I0408 16:11:54.349273 20259 solver.cpp:218] Iteration 9708 (2.42693 iter/s, 4.94452s/12 iters), loss = 1.62186
I0408 16:11:54.349334 20259 solver.cpp:237]     Train net output #0: loss = 1.62186 (* 1 = 1.62186 loss)
I0408 16:11:54.349346 20259 sgd_solver.cpp:105] Iteration 9708, lr = 4.41537e-07
I0408 16:11:55.073755 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:59.282208 20259 solver.cpp:218] Iteration 9720 (2.43273 iter/s, 4.93272s/12 iters), loss = 1.42922
I0408 16:11:59.282258 20259 solver.cpp:237]     Train net output #0: loss = 1.42922 (* 1 = 1.42922 loss)
I0408 16:11:59.282269 20259 sgd_solver.cpp:105] Iteration 9720, lr = 4.36098e-07
I0408 16:12:04.495468 20259 solver.cpp:218] Iteration 9732 (2.30192 iter/s, 5.21305s/12 iters), loss = 1.65548
I0408 16:12:04.495522 20259 solver.cpp:237]     Train net output #0: loss = 1.65548 (* 1 = 1.65548 loss)
I0408 16:12:04.495535 20259 sgd_solver.cpp:105] Iteration 9732, lr = 4.30726e-07
I0408 16:12:09.503373 20259 solver.cpp:218] Iteration 9744 (2.39631 iter/s, 5.0077s/12 iters), loss = 1.61076
I0408 16:12:09.503427 20259 solver.cpp:237]     Train net output #0: loss = 1.61076 (* 1 = 1.61076 loss)
I0408 16:12:09.503439 20259 sgd_solver.cpp:105] Iteration 9744, lr = 4.2542e-07
I0408 16:12:14.575712 20259 solver.cpp:218] Iteration 9756 (2.36587 iter/s, 5.07213s/12 iters), loss = 1.55969
I0408 16:12:14.575830 20259 solver.cpp:237]     Train net output #0: loss = 1.55969 (* 1 = 1.55969 loss)
I0408 16:12:14.575843 20259 sgd_solver.cpp:105] Iteration 9756, lr = 4.20179e-07
I0408 16:12:19.785558 20259 solver.cpp:218] Iteration 9768 (2.30345 iter/s, 5.20957s/12 iters), loss = 1.51824
I0408 16:12:19.785610 20259 solver.cpp:237]     Train net output #0: loss = 1.51824 (* 1 = 1.51824 loss)
I0408 16:12:19.785622 20259 sgd_solver.cpp:105] Iteration 9768, lr = 4.15003e-07
I0408 16:12:24.987634 20259 solver.cpp:218] Iteration 9780 (2.30686 iter/s, 5.20187s/12 iters), loss = 1.61347
I0408 16:12:24.987673 20259 solver.cpp:237]     Train net output #0: loss = 1.61347 (* 1 = 1.61347 loss)
I0408 16:12:24.987680 20259 sgd_solver.cpp:105] Iteration 9780, lr = 4.09891e-07
I0408 16:12:29.728101 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 16:12:33.653323 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 16:12:35.966068 20259 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 16:12:35.966091 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:12:36.579223 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:40.441742 20259 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0408 16:12:40.441777 20259 solver.cpp:397]     Test net output #1: loss = 3.26977 (* 1 = 3.26977 loss)
I0408 16:12:40.528901 20259 solver.cpp:218] Iteration 9792 (0.772162 iter/s, 15.5408s/12 iters), loss = 1.47726
I0408 16:12:40.528961 20259 solver.cpp:237]     Train net output #0: loss = 1.47726 (* 1 = 1.47726 loss)
I0408 16:12:40.528973 20259 sgd_solver.cpp:105] Iteration 9792, lr = 4.04841e-07
I0408 16:12:45.125795 20259 solver.cpp:218] Iteration 9804 (2.61057 iter/s, 4.59669s/12 iters), loss = 1.53574
I0408 16:12:45.125948 20259 solver.cpp:237]     Train net output #0: loss = 1.53574 (* 1 = 1.53574 loss)
I0408 16:12:45.125988 20259 sgd_solver.cpp:105] Iteration 9804, lr = 3.99854e-07
I0408 16:12:48.220397 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:50.279351 20259 solver.cpp:218] Iteration 9816 (2.32863 iter/s, 5.15325s/12 iters), loss = 1.78226
I0408 16:12:50.279403 20259 solver.cpp:237]     Train net output #0: loss = 1.78226 (* 1 = 1.78226 loss)
I0408 16:12:50.279414 20259 sgd_solver.cpp:105] Iteration 9816, lr = 3.94928e-07
I0408 16:12:55.355563 20259 solver.cpp:218] Iteration 9828 (2.36406 iter/s, 5.07601s/12 iters), loss = 1.51516
I0408 16:12:55.355618 20259 solver.cpp:237]     Train net output #0: loss = 1.51516 (* 1 = 1.51516 loss)
I0408 16:12:55.355629 20259 sgd_solver.cpp:105] Iteration 9828, lr = 3.90063e-07
I0408 16:13:00.708052 20259 solver.cpp:218] Iteration 9840 (2.24204 iter/s, 5.35227s/12 iters), loss = 1.43616
I0408 16:13:00.708106 20259 solver.cpp:237]     Train net output #0: loss = 1.43616 (* 1 = 1.43616 loss)
I0408 16:13:00.708118 20259 sgd_solver.cpp:105] Iteration 9840, lr = 3.85258e-07
I0408 16:13:06.006292 20259 solver.cpp:218] Iteration 9852 (2.26499 iter/s, 5.29803s/12 iters), loss = 1.54564
I0408 16:13:06.006340 20259 solver.cpp:237]     Train net output #0: loss = 1.54564 (* 1 = 1.54564 loss)
I0408 16:13:06.006351 20259 sgd_solver.cpp:105] Iteration 9852, lr = 3.80512e-07
I0408 16:13:11.507820 20259 solver.cpp:218] Iteration 9864 (2.1813 iter/s, 5.50132s/12 iters), loss = 1.59497
I0408 16:13:11.507874 20259 solver.cpp:237]     Train net output #0: loss = 1.59497 (* 1 = 1.59497 loss)
I0408 16:13:11.507885 20259 sgd_solver.cpp:105] Iteration 9864, lr = 3.75825e-07
I0408 16:13:16.582518 20259 solver.cpp:218] Iteration 9876 (2.36477 iter/s, 5.07449s/12 iters), loss = 1.53211
I0408 16:13:16.582633 20259 solver.cpp:237]     Train net output #0: loss = 1.53211 (* 1 = 1.53211 loss)
I0408 16:13:16.582645 20259 sgd_solver.cpp:105] Iteration 9876, lr = 3.71195e-07
I0408 16:13:21.746807 20259 solver.cpp:218] Iteration 9888 (2.32377 iter/s, 5.16402s/12 iters), loss = 1.73489
I0408 16:13:21.746860 20259 solver.cpp:237]     Train net output #0: loss = 1.73489 (* 1 = 1.73489 loss)
I0408 16:13:21.746870 20259 sgd_solver.cpp:105] Iteration 9888, lr = 3.66622e-07
I0408 16:13:23.869879 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 16:13:26.866513 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 16:13:29.213922 20259 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 16:13:29.213946 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:13:29.789970 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:33.722455 20259 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0408 16:13:33.722503 20259 solver.cpp:397]     Test net output #1: loss = 3.28181 (* 1 = 3.28181 loss)
I0408 16:13:35.740301 20259 solver.cpp:218] Iteration 9900 (0.857569 iter/s, 13.993s/12 iters), loss = 1.43052
I0408 16:13:35.740358 20259 solver.cpp:237]     Train net output #0: loss = 1.43052 (* 1 = 1.43052 loss)
I0408 16:13:35.740370 20259 sgd_solver.cpp:105] Iteration 9900, lr = 3.62106e-07
I0408 16:13:40.908004 20259 solver.cpp:218] Iteration 9912 (2.32221 iter/s, 5.16749s/12 iters), loss = 1.47514
I0408 16:13:40.908053 20259 solver.cpp:237]     Train net output #0: loss = 1.47514 (* 1 = 1.47514 loss)
I0408 16:13:40.908063 20259 sgd_solver.cpp:105] Iteration 9912, lr = 3.57645e-07
I0408 16:13:41.022230 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:46.066826 20259 solver.cpp:218] Iteration 9924 (2.32621 iter/s, 5.15861s/12 iters), loss = 1.56249
I0408 16:13:46.066881 20259 solver.cpp:237]     Train net output #0: loss = 1.56249 (* 1 = 1.56249 loss)
I0408 16:13:46.066895 20259 sgd_solver.cpp:105] Iteration 9924, lr = 3.53239e-07
I0408 16:13:51.602458 20259 solver.cpp:218] Iteration 9936 (2.16786 iter/s, 5.53541s/12 iters), loss = 1.43739
I0408 16:13:51.602578 20259 solver.cpp:237]     Train net output #0: loss = 1.43739 (* 1 = 1.43739 loss)
I0408 16:13:51.602589 20259 sgd_solver.cpp:105] Iteration 9936, lr = 3.48888e-07
I0408 16:13:57.007773 20259 solver.cpp:218] Iteration 9948 (2.22015 iter/s, 5.40503s/12 iters), loss = 1.46708
I0408 16:13:57.007822 20259 solver.cpp:237]     Train net output #0: loss = 1.46708 (* 1 = 1.46708 loss)
I0408 16:13:57.007831 20259 sgd_solver.cpp:105] Iteration 9948, lr = 3.4459e-07
I0408 16:14:02.012677 20259 solver.cpp:218] Iteration 9960 (2.39774 iter/s, 5.00471s/12 iters), loss = 1.5802
I0408 16:14:02.012733 20259 solver.cpp:237]     Train net output #0: loss = 1.5802 (* 1 = 1.5802 loss)
I0408 16:14:02.012745 20259 sgd_solver.cpp:105] Iteration 9960, lr = 3.40345e-07
I0408 16:14:07.088116 20259 solver.cpp:218] Iteration 9972 (2.36443 iter/s, 5.07523s/12 iters), loss = 1.48492
I0408 16:14:07.088171 20259 solver.cpp:237]     Train net output #0: loss = 1.48492 (* 1 = 1.48492 loss)
I0408 16:14:07.088184 20259 sgd_solver.cpp:105] Iteration 9972, lr = 3.36152e-07
I0408 16:14:12.165299 20259 solver.cpp:218] Iteration 9984 (2.36361 iter/s, 5.07697s/12 iters), loss = 1.3721
I0408 16:14:12.165347 20259 solver.cpp:237]     Train net output #0: loss = 1.3721 (* 1 = 1.3721 loss)
I0408 16:14:12.165356 20259 sgd_solver.cpp:105] Iteration 9984, lr = 3.32011e-07
I0408 16:14:16.739848 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 16:14:20.613349 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 16:14:23.016172 20259 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 16:14:23.016249 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:14:23.540385 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:27.662297 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:14:27.662324 20259 solver.cpp:397]     Test net output #1: loss = 3.26879 (* 1 = 3.26879 loss)
I0408 16:14:27.750624 20259 solver.cpp:218] Iteration 9996 (0.769979 iter/s, 15.5848s/12 iters), loss = 1.41617
I0408 16:14:27.750663 20259 solver.cpp:237]     Train net output #0: loss = 1.41617 (* 1 = 1.41617 loss)
I0408 16:14:27.750672 20259 sgd_solver.cpp:105] Iteration 9996, lr = 3.27921e-07
I0408 16:14:32.472383 20259 solver.cpp:218] Iteration 10008 (2.54152 iter/s, 4.72158s/12 iters), loss = 1.56023
I0408 16:14:32.472430 20259 solver.cpp:237]     Train net output #0: loss = 1.56023 (* 1 = 1.56023 loss)
I0408 16:14:32.472441 20259 sgd_solver.cpp:105] Iteration 10008, lr = 3.23882e-07
I0408 16:14:34.929520 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:37.933313 20259 solver.cpp:218] Iteration 10020 (2.19751 iter/s, 5.46072s/12 iters), loss = 1.63624
I0408 16:14:37.933362 20259 solver.cpp:237]     Train net output #0: loss = 1.63624 (* 1 = 1.63624 loss)
I0408 16:14:37.933373 20259 sgd_solver.cpp:105] Iteration 10020, lr = 3.19892e-07
I0408 16:14:43.178344 20259 solver.cpp:218] Iteration 10032 (2.28797 iter/s, 5.24482s/12 iters), loss = 1.59165
I0408 16:14:43.178397 20259 solver.cpp:237]     Train net output #0: loss = 1.59165 (* 1 = 1.59165 loss)
I0408 16:14:43.178411 20259 sgd_solver.cpp:105] Iteration 10032, lr = 3.15951e-07
I0408 16:14:48.300165 20259 solver.cpp:218] Iteration 10044 (2.34301 iter/s, 5.12161s/12 iters), loss = 1.25913
I0408 16:14:48.300222 20259 solver.cpp:237]     Train net output #0: loss = 1.25913 (* 1 = 1.25913 loss)
I0408 16:14:48.300236 20259 sgd_solver.cpp:105] Iteration 10044, lr = 3.12059e-07
I0408 16:14:53.750679 20259 solver.cpp:218] Iteration 10056 (2.20171 iter/s, 5.4503s/12 iters), loss = 1.49592
I0408 16:14:53.750787 20259 solver.cpp:237]     Train net output #0: loss = 1.49592 (* 1 = 1.49592 loss)
I0408 16:14:53.750799 20259 sgd_solver.cpp:105] Iteration 10056, lr = 3.08215e-07
I0408 16:14:59.032501 20259 solver.cpp:218] Iteration 10068 (2.27206 iter/s, 5.28155s/12 iters), loss = 1.75287
I0408 16:14:59.032559 20259 solver.cpp:237]     Train net output #0: loss = 1.75287 (* 1 = 1.75287 loss)
I0408 16:14:59.032572 20259 sgd_solver.cpp:105] Iteration 10068, lr = 3.04418e-07
I0408 16:15:04.056104 20259 solver.cpp:218] Iteration 10080 (2.38883 iter/s, 5.02339s/12 iters), loss = 1.39128
I0408 16:15:04.056159 20259 solver.cpp:237]     Train net output #0: loss = 1.39128 (* 1 = 1.39128 loss)
I0408 16:15:04.056171 20259 sgd_solver.cpp:105] Iteration 10080, lr = 3.00668e-07
I0408 16:15:09.446943 20259 solver.cpp:218] Iteration 10092 (2.22609 iter/s, 5.39063s/12 iters), loss = 1.60439
I0408 16:15:09.446981 20259 solver.cpp:237]     Train net output #0: loss = 1.60439 (* 1 = 1.60439 loss)
I0408 16:15:09.446991 20259 sgd_solver.cpp:105] Iteration 10092, lr = 2.96964e-07
I0408 16:15:11.662753 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 16:15:14.687187 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 16:15:17.015518 20259 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 16:15:17.015544 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:15:17.498029 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:21.488771 20259 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0408 16:15:21.488819 20259 solver.cpp:397]     Test net output #1: loss = 3.27969 (* 1 = 3.27969 loss)
I0408 16:15:23.496210 20259 solver.cpp:218] Iteration 10104 (0.854164 iter/s, 14.0488s/12 iters), loss = 1.52612
I0408 16:15:23.496259 20259 solver.cpp:237]     Train net output #0: loss = 1.52612 (* 1 = 1.52612 loss)
I0408 16:15:23.496270 20259 sgd_solver.cpp:105] Iteration 10104, lr = 2.93306e-07
I0408 16:15:28.288316 20263 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:28.921838 20259 solver.cpp:218] Iteration 10116 (2.21181 iter/s, 5.42542s/12 iters), loss = 1.39179
I0408 16:15:28.921880 20259 solver.cpp:237]     Train net output #0: loss = 1.39179 (* 1 = 1.39179 loss)
I0408 16:15:28.921890 20259 sgd_solver.cpp:105] Iteration 10116, lr = 2.89693e-07
I0408 16:15:33.932595 20259 solver.cpp:218] Iteration 10128 (2.39494 iter/s, 5.01056s/12 iters), loss = 1.66037
I0408 16:15:33.932649 20259 solver.cpp:237]     Train net output #0: loss = 1.66037 (* 1 = 1.66037 loss)
I0408 16:15:33.932660 20259 sgd_solver.cpp:105] Iteration 10128, lr = 2.86124e-07
I0408 16:15:39.317703 20259 solver.cpp:218] Iteration 10140 (2.22846 iter/s, 5.38489s/12 iters), loss = 1.68438
I0408 16:15:39.317757 20259 solver.cpp:237]     Train net output #0: loss = 1.68438 (* 1 = 1.68438 loss)
I0408 16:15:39.317770 20259 sgd_solver.cpp:105] Iteration 10140, lr = 2.82599e-07
I0408 16:15:44.808619 20259 solver.cpp:218] Iteration 10152 (2.18551 iter/s, 5.4907s/12 iters), loss = 1.2841
I0408 16:15:44.808656 20259 solver.cpp:237]     Train net output #0: loss = 1.2841 (* 1 = 1.2841 loss)
I0408 16:15:44.808665 20259 sgd_solver.cpp:105] Iteration 10152, lr = 2.79118e-07
I0408 16:15:49.940847 20259 solver.cpp:218] Iteration 10164 (2.33826 iter/s, 5.13203s/12 iters), loss = 1.61247
I0408 16:15:49.940903 20259 solver.cpp:237]     Train net output #0: loss = 1.61247 (* 1 = 1.61247 loss)
I0408 16:15:49.940919 20259 sgd_solver.cpp:105] Iteration 10164, lr = 2.7568e-07
I0408 16:15:55.032377 20259 solver.cpp:218] Iteration 10176 (2.35695 iter/s, 5.09132s/12 iters), loss = 1.71217
I0408 16:15:55.032415 20259 solver.cpp:237]     Train net output #0: loss = 1.71217 (* 1 = 1.71217 loss)
I0408 16:15:55.032423 20259 sgd_solver.cpp:105] Iteration 10176, lr = 2.72283e-07
I0408 16:16:00.148418 20259 solver.cpp:218] Iteration 10188 (2.34565 iter/s, 5.11584s/12 iters), loss = 1.55766
I0408 16:16:00.148571 20259 solver.cpp:237]     Train net output #0: loss = 1.55766 (* 1 = 1.55766 loss)
I0408 16:16:00.148583 20259 sgd_solver.cpp:105] Iteration 10188, lr = 2.68929e-07
I0408 16:16:04.726209 20259 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 16:16:07.767850 20259 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 16:16:10.137701 20259 solver.cpp:310] Iteration 10200, loss = 1.8196
I0408 16:16:10.137730 20259 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 16:16:10.137737 20259 net.cpp:676] Ignoring source layer train-data
I0408 16:16:10.569525 20264 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:14.608161 20259 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0408 16:16:14.608201 20259 solver.cpp:397]     Test net output #1: loss = 3.2751 (* 1 = 3.2751 loss)
I0408 16:16:14.608209 20259 solver.cpp:315] Optimization Done.
I0408 16:16:14.608215 20259 caffe.cpp:259] Optimization Done.