DIGITS-CNN/cars/architecture-investigations/conv/layers/layer1.5/kernel/3/caffe_output.log

I0428 20:36:17.027060 22802 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210428-202643-7020/solver.prototxt
I0428 20:36:17.027266 22802 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0428 20:36:17.027274 22802 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0428 20:36:17.027365 22802 caffe.cpp:218] Using GPUs 1
I0428 20:36:17.058406 22802 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0428 20:36:17.439631 22802 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0428 20:36:17.440351 22802 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0428 20:36:17.440948 22802 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0428 20:36:17.440964 22802 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0428 20:36:17.441105 22802 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-3/digits/jobs/20210421-230320-902c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/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: "conv1.5"
type: "Convolution"
bottom: "pool1"
top: "conv1.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 176
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1.5"
type: "ReLU"
bottom: "conv1.5"
top: "conv1.5"
}
layer {
name: "norm1.5"
type: "LRN"
bottom: "conv1.5"
top: "norm1.5"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1.5"
type: "Pooling"
bottom: "norm1.5"
top: "pool1.5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1.5"
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"
}
I0428 20:36:17.441197 22802 layer_factory.hpp:77] Creating layer train-data
I0428 20:36:17.442821 22802 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/train_db
I0428 20:36:17.442983 22802 net.cpp:84] Creating Layer train-data
I0428 20:36:17.442994 22802 net.cpp:380] train-data -> data
I0428 20:36:17.443012 22802 net.cpp:380] train-data -> label
I0428 20:36:17.443023 22802 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/mean.binaryproto
I0428 20:36:17.448246 22802 data_layer.cpp:45] output data size: 128,3,227,227
I0428 20:36:17.615288 22802 net.cpp:122] Setting up train-data
I0428 20:36:17.615311 22802 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0428 20:36:17.615316 22802 net.cpp:129] Top shape: 128 (128)
I0428 20:36:17.615320 22802 net.cpp:137] Memory required for data: 79149056
I0428 20:36:17.615329 22802 layer_factory.hpp:77] Creating layer conv1
I0428 20:36:17.615348 22802 net.cpp:84] Creating Layer conv1
I0428 20:36:17.615355 22802 net.cpp:406] conv1 <- data
I0428 20:36:17.615367 22802 net.cpp:380] conv1 -> conv1
I0428 20:36:18.407830 22802 net.cpp:122] Setting up conv1
I0428 20:36:18.407871 22802 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 20:36:18.407876 22802 net.cpp:137] Memory required for data: 227833856
I0428 20:36:18.407896 22802 layer_factory.hpp:77] Creating layer relu1
I0428 20:36:18.407907 22802 net.cpp:84] Creating Layer relu1
I0428 20:36:18.407910 22802 net.cpp:406] relu1 <- conv1
I0428 20:36:18.407915 22802 net.cpp:367] relu1 -> conv1 (in-place)
I0428 20:36:18.408214 22802 net.cpp:122] Setting up relu1
I0428 20:36:18.408223 22802 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 20:36:18.408227 22802 net.cpp:137] Memory required for data: 376518656
I0428 20:36:18.408231 22802 layer_factory.hpp:77] Creating layer norm1
I0428 20:36:18.408239 22802 net.cpp:84] Creating Layer norm1
I0428 20:36:18.408243 22802 net.cpp:406] norm1 <- conv1
I0428 20:36:18.408248 22802 net.cpp:380] norm1 -> norm1
I0428 20:36:18.408946 22802 net.cpp:122] Setting up norm1
I0428 20:36:18.408957 22802 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 20:36:18.408962 22802 net.cpp:137] Memory required for data: 525203456
I0428 20:36:18.408965 22802 layer_factory.hpp:77] Creating layer pool1
I0428 20:36:18.408973 22802 net.cpp:84] Creating Layer pool1
I0428 20:36:18.408977 22802 net.cpp:406] pool1 <- norm1
I0428 20:36:18.408983 22802 net.cpp:380] pool1 -> pool1
I0428 20:36:18.409021 22802 net.cpp:122] Setting up pool1
I0428 20:36:18.409027 22802 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0428 20:36:18.409031 22802 net.cpp:137] Memory required for data: 561035264
I0428 20:36:18.409035 22802 layer_factory.hpp:77] Creating layer conv1.5
I0428 20:36:18.409046 22802 net.cpp:84] Creating Layer conv1.5
I0428 20:36:18.409050 22802 net.cpp:406] conv1.5 <- pool1
I0428 20:36:18.409055 22802 net.cpp:380] conv1.5 -> conv1.5
I0428 20:36:18.423207 22802 net.cpp:122] Setting up conv1.5
I0428 20:36:18.423223 22802 net.cpp:129] Top shape: 128 176 25 25 (14080000)
I0428 20:36:18.423228 22802 net.cpp:137] Memory required for data: 617355264
I0428 20:36:18.423238 22802 layer_factory.hpp:77] Creating layer relu1.5
I0428 20:36:18.423246 22802 net.cpp:84] Creating Layer relu1.5
I0428 20:36:18.423251 22802 net.cpp:406] relu1.5 <- conv1.5
I0428 20:36:18.423259 22802 net.cpp:367] relu1.5 -> conv1.5 (in-place)
I0428 20:36:18.423542 22802 net.cpp:122] Setting up relu1.5
I0428 20:36:18.423550 22802 net.cpp:129] Top shape: 128 176 25 25 (14080000)
I0428 20:36:18.423554 22802 net.cpp:137] Memory required for data: 673675264
I0428 20:36:18.423558 22802 layer_factory.hpp:77] Creating layer norm1.5
I0428 20:36:18.423565 22802 net.cpp:84] Creating Layer norm1.5
I0428 20:36:18.423571 22802 net.cpp:406] norm1.5 <- conv1.5
I0428 20:36:18.423576 22802 net.cpp:380] norm1.5 -> norm1.5
I0428 20:36:18.426671 22802 net.cpp:122] Setting up norm1.5
I0428 20:36:18.426681 22802 net.cpp:129] Top shape: 128 176 25 25 (14080000)
I0428 20:36:18.426685 22802 net.cpp:137] Memory required for data: 729995264
I0428 20:36:18.426690 22802 layer_factory.hpp:77] Creating layer pool1.5
I0428 20:36:18.426702 22802 net.cpp:84] Creating Layer pool1.5
I0428 20:36:18.426707 22802 net.cpp:406] pool1.5 <- norm1.5
I0428 20:36:18.426713 22802 net.cpp:380] pool1.5 -> pool1.5
I0428 20:36:18.426744 22802 net.cpp:122] Setting up pool1.5
I0428 20:36:18.426749 22802 net.cpp:129] Top shape: 128 176 12 12 (3244032)
I0428 20:36:18.426753 22802 net.cpp:137] Memory required for data: 742971392
I0428 20:36:18.426757 22802 layer_factory.hpp:77] Creating layer conv2
I0428 20:36:18.426769 22802 net.cpp:84] Creating Layer conv2
I0428 20:36:18.426771 22802 net.cpp:406] conv2 <- pool1.5
I0428 20:36:18.426779 22802 net.cpp:380] conv2 -> conv2
I0428 20:36:18.443066 22802 net.cpp:122] Setting up conv2
I0428 20:36:18.443084 22802 net.cpp:129] Top shape: 128 256 12 12 (4718592)
I0428 20:36:18.443089 22802 net.cpp:137] Memory required for data: 761845760
I0428 20:36:18.443102 22802 layer_factory.hpp:77] Creating layer relu2
I0428 20:36:18.443114 22802 net.cpp:84] Creating Layer relu2
I0428 20:36:18.443118 22802 net.cpp:406] relu2 <- conv2
I0428 20:36:18.443125 22802 net.cpp:367] relu2 -> conv2 (in-place)
I0428 20:36:18.443636 22802 net.cpp:122] Setting up relu2
I0428 20:36:18.443646 22802 net.cpp:129] Top shape: 128 256 12 12 (4718592)
I0428 20:36:18.443651 22802 net.cpp:137] Memory required for data: 780720128
I0428 20:36:18.443655 22802 layer_factory.hpp:77] Creating layer norm2
I0428 20:36:18.443665 22802 net.cpp:84] Creating Layer norm2
I0428 20:36:18.443668 22802 net.cpp:406] norm2 <- conv2
I0428 20:36:18.443675 22802 net.cpp:380] norm2 -> norm2
I0428 20:36:18.444034 22802 net.cpp:122] Setting up norm2
I0428 20:36:18.444042 22802 net.cpp:129] Top shape: 128 256 12 12 (4718592)
I0428 20:36:18.444047 22802 net.cpp:137] Memory required for data: 799594496
I0428 20:36:18.444051 22802 layer_factory.hpp:77] Creating layer pool2
I0428 20:36:18.444056 22802 net.cpp:84] Creating Layer pool2
I0428 20:36:18.444061 22802 net.cpp:406] pool2 <- norm2
I0428 20:36:18.444067 22802 net.cpp:380] pool2 -> pool2
I0428 20:36:18.444097 22802 net.cpp:122] Setting up pool2
I0428 20:36:18.444103 22802 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0428 20:36:18.444105 22802 net.cpp:137] Memory required for data: 804313088
I0428 20:36:18.444108 22802 layer_factory.hpp:77] Creating layer conv3
I0428 20:36:18.444120 22802 net.cpp:84] Creating Layer conv3
I0428 20:36:18.444123 22802 net.cpp:406] conv3 <- pool2
I0428 20:36:18.444128 22802 net.cpp:380] conv3 -> conv3
I0428 20:36:18.456540 22802 net.cpp:122] Setting up conv3
I0428 20:36:18.456559 22802 net.cpp:129] Top shape: 128 384 6 6 (1769472)
I0428 20:36:18.456563 22802 net.cpp:137] Memory required for data: 811390976
I0428 20:36:18.456573 22802 layer_factory.hpp:77] Creating layer relu3
I0428 20:36:18.456581 22802 net.cpp:84] Creating Layer relu3
I0428 20:36:18.456585 22802 net.cpp:406] relu3 <- conv3
I0428 20:36:18.456593 22802 net.cpp:367] relu3 -> conv3 (in-place)
I0428 20:36:18.457093 22802 net.cpp:122] Setting up relu3
I0428 20:36:18.457103 22802 net.cpp:129] Top shape: 128 384 6 6 (1769472)
I0428 20:36:18.457106 22802 net.cpp:137] Memory required for data: 818468864
I0428 20:36:18.457110 22802 layer_factory.hpp:77] Creating layer conv4
I0428 20:36:18.457120 22802 net.cpp:84] Creating Layer conv4
I0428 20:36:18.457124 22802 net.cpp:406] conv4 <- conv3
I0428 20:36:18.457132 22802 net.cpp:380] conv4 -> conv4
I0428 20:36:18.466540 22802 net.cpp:122] Setting up conv4
I0428 20:36:18.466555 22802 net.cpp:129] Top shape: 128 384 6 6 (1769472)
I0428 20:36:18.466560 22802 net.cpp:137] Memory required for data: 825546752
I0428 20:36:18.466573 22802 layer_factory.hpp:77] Creating layer relu4
I0428 20:36:18.466583 22802 net.cpp:84] Creating Layer relu4
I0428 20:36:18.466588 22802 net.cpp:406] relu4 <- conv4
I0428 20:36:18.466595 22802 net.cpp:367] relu4 -> conv4 (in-place)
I0428 20:36:18.467087 22802 net.cpp:122] Setting up relu4
I0428 20:36:18.467095 22802 net.cpp:129] Top shape: 128 384 6 6 (1769472)
I0428 20:36:18.467100 22802 net.cpp:137] Memory required for data: 832624640
I0428 20:36:18.467103 22802 layer_factory.hpp:77] Creating layer conv5
I0428 20:36:18.467114 22802 net.cpp:84] Creating Layer conv5
I0428 20:36:18.467118 22802 net.cpp:406] conv5 <- conv4
I0428 20:36:18.467125 22802 net.cpp:380] conv5 -> conv5
I0428 20:36:18.488747 22802 net.cpp:122] Setting up conv5
I0428 20:36:18.488767 22802 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0428 20:36:18.488771 22802 net.cpp:137] Memory required for data: 837343232
I0428 20:36:18.488780 22802 layer_factory.hpp:77] Creating layer relu5
I0428 20:36:18.488791 22802 net.cpp:84] Creating Layer relu5
I0428 20:36:18.488797 22802 net.cpp:406] relu5 <- conv5
I0428 20:36:18.488803 22802 net.cpp:367] relu5 -> conv5 (in-place)
I0428 20:36:18.489301 22802 net.cpp:122] Setting up relu5
I0428 20:36:18.489311 22802 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0428 20:36:18.489315 22802 net.cpp:137] Memory required for data: 842061824
I0428 20:36:18.489320 22802 layer_factory.hpp:77] Creating layer pool5
I0428 20:36:18.489327 22802 net.cpp:84] Creating Layer pool5
I0428 20:36:18.489332 22802 net.cpp:406] pool5 <- conv5
I0428 20:36:18.489358 22802 net.cpp:380] pool5 -> pool5
I0428 20:36:18.489395 22802 net.cpp:122] Setting up pool5
I0428 20:36:18.489403 22802 net.cpp:129] Top shape: 128 256 3 3 (294912)
I0428 20:36:18.489406 22802 net.cpp:137] Memory required for data: 843241472
I0428 20:36:18.489409 22802 layer_factory.hpp:77] Creating layer fc6
I0428 20:36:18.489418 22802 net.cpp:84] Creating Layer fc6
I0428 20:36:18.489423 22802 net.cpp:406] fc6 <- pool5
I0428 20:36:18.489428 22802 net.cpp:380] fc6 -> fc6
I0428 20:36:18.580911 22802 net.cpp:122] Setting up fc6
I0428 20:36:18.580933 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.580943 22802 net.cpp:137] Memory required for data: 845338624
I0428 20:36:18.580955 22802 layer_factory.hpp:77] Creating layer relu6
I0428 20:36:18.580965 22802 net.cpp:84] Creating Layer relu6
I0428 20:36:18.580969 22802 net.cpp:406] relu6 <- fc6
I0428 20:36:18.580978 22802 net.cpp:367] relu6 -> fc6 (in-place)
I0428 20:36:18.594372 22802 net.cpp:122] Setting up relu6
I0428 20:36:18.594385 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.594389 22802 net.cpp:137] Memory required for data: 847435776
I0428 20:36:18.594394 22802 layer_factory.hpp:77] Creating layer drop6
I0428 20:36:18.594403 22802 net.cpp:84] Creating Layer drop6
I0428 20:36:18.594406 22802 net.cpp:406] drop6 <- fc6
I0428 20:36:18.594415 22802 net.cpp:367] drop6 -> fc6 (in-place)
I0428 20:36:18.594445 22802 net.cpp:122] Setting up drop6
I0428 20:36:18.594452 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.594456 22802 net.cpp:137] Memory required for data: 849532928
I0428 20:36:18.594461 22802 layer_factory.hpp:77] Creating layer fc7
I0428 20:36:18.594470 22802 net.cpp:84] Creating Layer fc7
I0428 20:36:18.594475 22802 net.cpp:406] fc7 <- fc6
I0428 20:36:18.594481 22802 net.cpp:380] fc7 -> fc7
I0428 20:36:18.777006 22802 net.cpp:122] Setting up fc7
I0428 20:36:18.777029 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.777034 22802 net.cpp:137] Memory required for data: 851630080
I0428 20:36:18.777045 22802 layer_factory.hpp:77] Creating layer relu7
I0428 20:36:18.777055 22802 net.cpp:84] Creating Layer relu7
I0428 20:36:18.777060 22802 net.cpp:406] relu7 <- fc7
I0428 20:36:18.777067 22802 net.cpp:367] relu7 -> fc7 (in-place)
I0428 20:36:18.777464 22802 net.cpp:122] Setting up relu7
I0428 20:36:18.777474 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.777478 22802 net.cpp:137] Memory required for data: 853727232
I0428 20:36:18.777482 22802 layer_factory.hpp:77] Creating layer drop7
I0428 20:36:18.777490 22802 net.cpp:84] Creating Layer drop7
I0428 20:36:18.777494 22802 net.cpp:406] drop7 <- fc7
I0428 20:36:18.777499 22802 net.cpp:367] drop7 -> fc7 (in-place)
I0428 20:36:18.777523 22802 net.cpp:122] Setting up drop7
I0428 20:36:18.777529 22802 net.cpp:129] Top shape: 128 4096 (524288)
I0428 20:36:18.777532 22802 net.cpp:137] Memory required for data: 855824384
I0428 20:36:18.777536 22802 layer_factory.hpp:77] Creating layer fc8
I0428 20:36:18.777544 22802 net.cpp:84] Creating Layer fc8
I0428 20:36:18.777547 22802 net.cpp:406] fc8 <- fc7
I0428 20:36:18.777554 22802 net.cpp:380] fc8 -> fc8
I0428 20:36:18.785508 22802 net.cpp:122] Setting up fc8
I0428 20:36:18.785523 22802 net.cpp:129] Top shape: 128 196 (25088)
I0428 20:36:18.785528 22802 net.cpp:137] Memory required for data: 855924736
I0428 20:36:18.785540 22802 layer_factory.hpp:77] Creating layer loss
I0428 20:36:18.785550 22802 net.cpp:84] Creating Layer loss
I0428 20:36:18.785554 22802 net.cpp:406] loss <- fc8
I0428 20:36:18.785559 22802 net.cpp:406] loss <- label
I0428 20:36:18.785567 22802 net.cpp:380] loss -> loss
I0428 20:36:18.785576 22802 layer_factory.hpp:77] Creating layer loss
I0428 20:36:18.792722 22802 net.cpp:122] Setting up loss
I0428 20:36:18.792733 22802 net.cpp:129] Top shape: (1)
I0428 20:36:18.792737 22802 net.cpp:132]     with loss weight 1
I0428 20:36:18.792754 22802 net.cpp:137] Memory required for data: 855924740
I0428 20:36:18.792759 22802 net.cpp:198] loss needs backward computation.
I0428 20:36:18.792766 22802 net.cpp:198] fc8 needs backward computation.
I0428 20:36:18.792790 22802 net.cpp:198] drop7 needs backward computation.
I0428 20:36:18.792795 22802 net.cpp:198] relu7 needs backward computation.
I0428 20:36:18.792800 22802 net.cpp:198] fc7 needs backward computation.
I0428 20:36:18.792804 22802 net.cpp:198] drop6 needs backward computation.
I0428 20:36:18.792809 22802 net.cpp:198] relu6 needs backward computation.
I0428 20:36:18.792814 22802 net.cpp:198] fc6 needs backward computation.
I0428 20:36:18.792817 22802 net.cpp:198] pool5 needs backward computation.
I0428 20:36:18.792821 22802 net.cpp:198] relu5 needs backward computation.
I0428 20:36:18.792826 22802 net.cpp:198] conv5 needs backward computation.
I0428 20:36:18.792829 22802 net.cpp:198] relu4 needs backward computation.
I0428 20:36:18.792834 22802 net.cpp:198] conv4 needs backward computation.
I0428 20:36:18.792837 22802 net.cpp:198] relu3 needs backward computation.
I0428 20:36:18.792842 22802 net.cpp:198] conv3 needs backward computation.
I0428 20:36:18.792847 22802 net.cpp:198] pool2 needs backward computation.
I0428 20:36:18.792851 22802 net.cpp:198] norm2 needs backward computation.
I0428 20:36:18.792855 22802 net.cpp:198] relu2 needs backward computation.
I0428 20:36:18.792860 22802 net.cpp:198] conv2 needs backward computation.
I0428 20:36:18.792863 22802 net.cpp:198] pool1.5 needs backward computation.
I0428 20:36:18.792867 22802 net.cpp:198] norm1.5 needs backward computation.
I0428 20:36:18.792872 22802 net.cpp:198] relu1.5 needs backward computation.
I0428 20:36:18.792877 22802 net.cpp:198] conv1.5 needs backward computation.
I0428 20:36:18.792881 22802 net.cpp:198] pool1 needs backward computation.
I0428 20:36:18.792886 22802 net.cpp:198] norm1 needs backward computation.
I0428 20:36:18.792891 22802 net.cpp:198] relu1 needs backward computation.
I0428 20:36:18.792896 22802 net.cpp:198] conv1 needs backward computation.
I0428 20:36:18.792899 22802 net.cpp:200] train-data does not need backward computation.
I0428 20:36:18.792903 22802 net.cpp:242] This network produces output loss
I0428 20:36:18.792918 22802 net.cpp:255] Network initialization done.
I0428 20:36:18.793428 22802 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0428 20:36:18.793462 22802 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0428 20:36:18.793615 22802 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-3/digits/jobs/20210421-230320-902c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/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: "conv1.5"
type: "Convolution"
bottom: "pool1"
top: "conv1.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 176
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1.5"
type: "ReLU"
bottom: "conv1.5"
top: "conv1.5"
}
layer {
name: "norm1.5"
type: "LRN"
bottom: "conv1.5"
top: "norm1.5"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1.5"
type: "Pooling"
bottom: "norm1.5"
top: "pool1.5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1.5"
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"
}
I0428 20:36:18.793720 22802 layer_factory.hpp:77] Creating layer val-data
I0428 20:36:18.795447 22802 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/val_db
I0428 20:36:18.795634 22802 net.cpp:84] Creating Layer val-data
I0428 20:36:18.795644 22802 net.cpp:380] val-data -> data
I0428 20:36:18.795652 22802 net.cpp:380] val-data -> label
I0428 20:36:18.795660 22802 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/mean.binaryproto
I0428 20:36:18.799757 22802 data_layer.cpp:45] output data size: 32,3,227,227
I0428 20:36:18.876799 22802 net.cpp:122] Setting up val-data
I0428 20:36:18.876817 22802 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0428 20:36:18.876822 22802 net.cpp:129] Top shape: 32 (32)
I0428 20:36:18.876827 22802 net.cpp:137] Memory required for data: 19787264
I0428 20:36:18.876832 22802 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0428 20:36:18.876843 22802 net.cpp:84] Creating Layer label_val-data_1_split
I0428 20:36:18.876848 22802 net.cpp:406] label_val-data_1_split <- label
I0428 20:36:18.876855 22802 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0428 20:36:18.876864 22802 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0428 20:36:18.876963 22802 net.cpp:122] Setting up label_val-data_1_split
I0428 20:36:18.876969 22802 net.cpp:129] Top shape: 32 (32)
I0428 20:36:18.876973 22802 net.cpp:129] Top shape: 32 (32)
I0428 20:36:18.876976 22802 net.cpp:137] Memory required for data: 19787520
I0428 20:36:18.876981 22802 layer_factory.hpp:77] Creating layer conv1
I0428 20:36:18.876993 22802 net.cpp:84] Creating Layer conv1
I0428 20:36:18.876997 22802 net.cpp:406] conv1 <- data
I0428 20:36:18.877004 22802 net.cpp:380] conv1 -> conv1
I0428 20:36:18.894798 22802 net.cpp:122] Setting up conv1
I0428 20:36:18.894817 22802 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 20:36:18.894820 22802 net.cpp:137] Memory required for data: 56958720
I0428 20:36:18.894834 22802 layer_factory.hpp:77] Creating layer relu1
I0428 20:36:18.894842 22802 net.cpp:84] Creating Layer relu1
I0428 20:36:18.894846 22802 net.cpp:406] relu1 <- conv1
I0428 20:36:18.894851 22802 net.cpp:367] relu1 -> conv1 (in-place)
I0428 20:36:18.895144 22802 net.cpp:122] Setting up relu1
I0428 20:36:18.895153 22802 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 20:36:18.895156 22802 net.cpp:137] Memory required for data: 94129920
I0428 20:36:18.895159 22802 layer_factory.hpp:77] Creating layer norm1
I0428 20:36:18.895169 22802 net.cpp:84] Creating Layer norm1
I0428 20:36:18.895171 22802 net.cpp:406] norm1 <- conv1
I0428 20:36:18.895177 22802 net.cpp:380] norm1 -> norm1
I0428 20:36:18.895669 22802 net.cpp:122] Setting up norm1
I0428 20:36:18.895679 22802 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 20:36:18.895682 22802 net.cpp:137] Memory required for data: 131301120
I0428 20:36:18.895686 22802 layer_factory.hpp:77] Creating layer pool1
I0428 20:36:18.895692 22802 net.cpp:84] Creating Layer pool1
I0428 20:36:18.895696 22802 net.cpp:406] pool1 <- norm1
I0428 20:36:18.895701 22802 net.cpp:380] pool1 -> pool1
I0428 20:36:18.895733 22802 net.cpp:122] Setting up pool1
I0428 20:36:18.895740 22802 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0428 20:36:18.895742 22802 net.cpp:137] Memory required for data: 140259072
I0428 20:36:18.895745 22802 layer_factory.hpp:77] Creating layer conv1.5
I0428 20:36:18.895754 22802 net.cpp:84] Creating Layer conv1.5
I0428 20:36:18.895758 22802 net.cpp:406] conv1.5 <- pool1
I0428 20:36:18.895763 22802 net.cpp:380] conv1.5 -> conv1.5
I0428 20:36:18.899282 22802 net.cpp:122] Setting up conv1.5
I0428 20:36:18.899294 22802 net.cpp:129] Top shape: 32 176 25 25 (3520000)
I0428 20:36:18.899298 22802 net.cpp:137] Memory required for data: 154339072
I0428 20:36:18.899308 22802 layer_factory.hpp:77] Creating layer relu1.5
I0428 20:36:18.899315 22802 net.cpp:84] Creating Layer relu1.5
I0428 20:36:18.899341 22802 net.cpp:406] relu1.5 <- conv1.5
I0428 20:36:18.899348 22802 net.cpp:367] relu1.5 -> conv1.5 (in-place)
I0428 20:36:18.899700 22802 net.cpp:122] Setting up relu1.5
I0428 20:36:18.899709 22802 net.cpp:129] Top shape: 32 176 25 25 (3520000)
I0428 20:36:18.899713 22802 net.cpp:137] Memory required for data: 168419072
I0428 20:36:18.899717 22802 layer_factory.hpp:77] Creating layer norm1.5
I0428 20:36:18.899727 22802 net.cpp:84] Creating Layer norm1.5
I0428 20:36:18.899731 22802 net.cpp:406] norm1.5 <- conv1.5
I0428 20:36:18.899739 22802 net.cpp:380] norm1.5 -> norm1.5
I0428 20:36:18.900281 22802 net.cpp:122] Setting up norm1.5
I0428 20:36:18.900292 22802 net.cpp:129] Top shape: 32 176 25 25 (3520000)
I0428 20:36:18.900297 22802 net.cpp:137] Memory required for data: 182499072
I0428 20:36:18.900302 22802 layer_factory.hpp:77] Creating layer pool1.5
I0428 20:36:18.900310 22802 net.cpp:84] Creating Layer pool1.5
I0428 20:36:18.900313 22802 net.cpp:406] pool1.5 <- norm1.5
I0428 20:36:18.900321 22802 net.cpp:380] pool1.5 -> pool1.5
I0428 20:36:18.900352 22802 net.cpp:122] Setting up pool1.5
I0428 20:36:18.900359 22802 net.cpp:129] Top shape: 32 176 12 12 (811008)
I0428 20:36:18.900363 22802 net.cpp:137] Memory required for data: 185743104
I0428 20:36:18.900367 22802 layer_factory.hpp:77] Creating layer conv2
I0428 20:36:18.900377 22802 net.cpp:84] Creating Layer conv2
I0428 20:36:18.900380 22802 net.cpp:406] conv2 <- pool1.5
I0428 20:36:18.900385 22802 net.cpp:380] conv2 -> conv2
I0428 20:36:18.910046 22802 net.cpp:122] Setting up conv2
I0428 20:36:18.910065 22802 net.cpp:129] Top shape: 32 256 12 12 (1179648)
I0428 20:36:18.910069 22802 net.cpp:137] Memory required for data: 190461696
I0428 20:36:18.910080 22802 layer_factory.hpp:77] Creating layer relu2
I0428 20:36:18.910089 22802 net.cpp:84] Creating Layer relu2
I0428 20:36:18.910094 22802 net.cpp:406] relu2 <- conv2
I0428 20:36:18.910101 22802 net.cpp:367] relu2 -> conv2 (in-place)
I0428 20:36:18.910614 22802 net.cpp:122] Setting up relu2
I0428 20:36:18.910626 22802 net.cpp:129] Top shape: 32 256 12 12 (1179648)
I0428 20:36:18.910630 22802 net.cpp:137] Memory required for data: 195180288
I0428 20:36:18.910635 22802 layer_factory.hpp:77] Creating layer norm2
I0428 20:36:18.910643 22802 net.cpp:84] Creating Layer norm2
I0428 20:36:18.910647 22802 net.cpp:406] norm2 <- conv2
I0428 20:36:18.910653 22802 net.cpp:380] norm2 -> norm2
I0428 20:36:18.911020 22802 net.cpp:122] Setting up norm2
I0428 20:36:18.911027 22802 net.cpp:129] Top shape: 32 256 12 12 (1179648)
I0428 20:36:18.911031 22802 net.cpp:137] Memory required for data: 199898880
I0428 20:36:18.911036 22802 layer_factory.hpp:77] Creating layer pool2
I0428 20:36:18.911042 22802 net.cpp:84] Creating Layer pool2
I0428 20:36:18.911046 22802 net.cpp:406] pool2 <- norm2
I0428 20:36:18.911052 22802 net.cpp:380] pool2 -> pool2
I0428 20:36:18.911083 22802 net.cpp:122] Setting up pool2
I0428 20:36:18.911089 22802 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0428 20:36:18.911093 22802 net.cpp:137] Memory required for data: 201078528
I0428 20:36:18.911096 22802 layer_factory.hpp:77] Creating layer conv3
I0428 20:36:18.911108 22802 net.cpp:84] Creating Layer conv3
I0428 20:36:18.911111 22802 net.cpp:406] conv3 <- pool2
I0428 20:36:18.911118 22802 net.cpp:380] conv3 -> conv3
I0428 20:36:18.922485 22802 net.cpp:122] Setting up conv3
I0428 20:36:18.922503 22802 net.cpp:129] Top shape: 32 384 6 6 (442368)
I0428 20:36:18.922508 22802 net.cpp:137] Memory required for data: 202848000
I0428 20:36:18.922518 22802 layer_factory.hpp:77] Creating layer relu3
I0428 20:36:18.922528 22802 net.cpp:84] Creating Layer relu3
I0428 20:36:18.922534 22802 net.cpp:406] relu3 <- conv3
I0428 20:36:18.922540 22802 net.cpp:367] relu3 -> conv3 (in-place)
I0428 20:36:18.923053 22802 net.cpp:122] Setting up relu3
I0428 20:36:18.923063 22802 net.cpp:129] Top shape: 32 384 6 6 (442368)
I0428 20:36:18.923066 22802 net.cpp:137] Memory required for data: 204617472
I0428 20:36:18.923070 22802 layer_factory.hpp:77] Creating layer conv4
I0428 20:36:18.923105 22802 net.cpp:84] Creating Layer conv4
I0428 20:36:18.923108 22802 net.cpp:406] conv4 <- conv3
I0428 20:36:18.923115 22802 net.cpp:380] conv4 -> conv4
I0428 20:36:18.941627 22802 net.cpp:122] Setting up conv4
I0428 20:36:18.941646 22802 net.cpp:129] Top shape: 32 384 6 6 (442368)
I0428 20:36:18.941653 22802 net.cpp:137] Memory required for data: 206386944
I0428 20:36:18.941668 22802 layer_factory.hpp:77] Creating layer relu4
I0428 20:36:18.941679 22802 net.cpp:84] Creating Layer relu4
I0428 20:36:18.941684 22802 net.cpp:406] relu4 <- conv4
I0428 20:36:18.941691 22802 net.cpp:367] relu4 -> conv4 (in-place)
I0428 20:36:18.942195 22802 net.cpp:122] Setting up relu4
I0428 20:36:18.942206 22802 net.cpp:129] Top shape: 32 384 6 6 (442368)
I0428 20:36:18.942211 22802 net.cpp:137] Memory required for data: 208156416
I0428 20:36:18.942217 22802 layer_factory.hpp:77] Creating layer conv5
I0428 20:36:18.942229 22802 net.cpp:84] Creating Layer conv5
I0428 20:36:18.942234 22802 net.cpp:406] conv5 <- conv4
I0428 20:36:18.942242 22802 net.cpp:380] conv5 -> conv5
I0428 20:36:18.951680 22802 net.cpp:122] Setting up conv5
I0428 20:36:18.951699 22802 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0428 20:36:18.951702 22802 net.cpp:137] Memory required for data: 209336064
I0428 20:36:18.951712 22802 layer_factory.hpp:77] Creating layer relu5
I0428 20:36:18.951720 22802 net.cpp:84] Creating Layer relu5
I0428 20:36:18.951726 22802 net.cpp:406] relu5 <- conv5
I0428 20:36:18.951735 22802 net.cpp:367] relu5 -> conv5 (in-place)
I0428 20:36:18.952232 22802 net.cpp:122] Setting up relu5
I0428 20:36:18.952242 22802 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0428 20:36:18.952247 22802 net.cpp:137] Memory required for data: 210515712
I0428 20:36:18.952251 22802 layer_factory.hpp:77] Creating layer pool5
I0428 20:36:18.952260 22802 net.cpp:84] Creating Layer pool5
I0428 20:36:18.952265 22802 net.cpp:406] pool5 <- conv5
I0428 20:36:18.952271 22802 net.cpp:380] pool5 -> pool5
I0428 20:36:18.952312 22802 net.cpp:122] Setting up pool5
I0428 20:36:18.952320 22802 net.cpp:129] Top shape: 32 256 3 3 (73728)
I0428 20:36:18.952324 22802 net.cpp:137] Memory required for data: 210810624
I0428 20:36:18.952328 22802 layer_factory.hpp:77] Creating layer fc6
I0428 20:36:18.952335 22802 net.cpp:84] Creating Layer fc6
I0428 20:36:18.952340 22802 net.cpp:406] fc6 <- pool5
I0428 20:36:18.952347 22802 net.cpp:380] fc6 -> fc6
I0428 20:36:19.042176 22802 net.cpp:122] Setting up fc6
I0428 20:36:19.042196 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.042199 22802 net.cpp:137] Memory required for data: 211334912
I0428 20:36:19.042208 22802 layer_factory.hpp:77] Creating layer relu6
I0428 20:36:19.042219 22802 net.cpp:84] Creating Layer relu6
I0428 20:36:19.042224 22802 net.cpp:406] relu6 <- fc6
I0428 20:36:19.042232 22802 net.cpp:367] relu6 -> fc6 (in-place)
I0428 20:36:19.071784 22802 net.cpp:122] Setting up relu6
I0428 20:36:19.071802 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.071806 22802 net.cpp:137] Memory required for data: 211859200
I0428 20:36:19.071811 22802 layer_factory.hpp:77] Creating layer drop6
I0428 20:36:19.071820 22802 net.cpp:84] Creating Layer drop6
I0428 20:36:19.071825 22802 net.cpp:406] drop6 <- fc6
I0428 20:36:19.071832 22802 net.cpp:367] drop6 -> fc6 (in-place)
I0428 20:36:19.071866 22802 net.cpp:122] Setting up drop6
I0428 20:36:19.071871 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.071873 22802 net.cpp:137] Memory required for data: 212383488
I0428 20:36:19.071877 22802 layer_factory.hpp:77] Creating layer fc7
I0428 20:36:19.071884 22802 net.cpp:84] Creating Layer fc7
I0428 20:36:19.071888 22802 net.cpp:406] fc7 <- fc6
I0428 20:36:19.071892 22802 net.cpp:380] fc7 -> fc7
I0428 20:36:19.243999 22802 net.cpp:122] Setting up fc7
I0428 20:36:19.244019 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.244022 22802 net.cpp:137] Memory required for data: 212907776
I0428 20:36:19.244031 22802 layer_factory.hpp:77] Creating layer relu7
I0428 20:36:19.244043 22802 net.cpp:84] Creating Layer relu7
I0428 20:36:19.244068 22802 net.cpp:406] relu7 <- fc7
I0428 20:36:19.244076 22802 net.cpp:367] relu7 -> fc7 (in-place)
I0428 20:36:19.244518 22802 net.cpp:122] Setting up relu7
I0428 20:36:19.244527 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.244532 22802 net.cpp:137] Memory required for data: 213432064
I0428 20:36:19.244536 22802 layer_factory.hpp:77] Creating layer drop7
I0428 20:36:19.244544 22802 net.cpp:84] Creating Layer drop7
I0428 20:36:19.244555 22802 net.cpp:406] drop7 <- fc7
I0428 20:36:19.244560 22802 net.cpp:367] drop7 -> fc7 (in-place)
I0428 20:36:19.244585 22802 net.cpp:122] Setting up drop7
I0428 20:36:19.244590 22802 net.cpp:129] Top shape: 32 4096 (131072)
I0428 20:36:19.244593 22802 net.cpp:137] Memory required for data: 213956352
I0428 20:36:19.244597 22802 layer_factory.hpp:77] Creating layer fc8
I0428 20:36:19.244606 22802 net.cpp:84] Creating Layer fc8
I0428 20:36:19.244611 22802 net.cpp:406] fc8 <- fc7
I0428 20:36:19.244618 22802 net.cpp:380] fc8 -> fc8
I0428 20:36:19.252360 22802 net.cpp:122] Setting up fc8
I0428 20:36:19.252372 22802 net.cpp:129] Top shape: 32 196 (6272)
I0428 20:36:19.252377 22802 net.cpp:137] Memory required for data: 213981440
I0428 20:36:19.252388 22802 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0428 20:36:19.252396 22802 net.cpp:84] Creating Layer fc8_fc8_0_split
I0428 20:36:19.252399 22802 net.cpp:406] fc8_fc8_0_split <- fc8
I0428 20:36:19.252406 22802 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0428 20:36:19.252414 22802 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0428 20:36:19.252447 22802 net.cpp:122] Setting up fc8_fc8_0_split
I0428 20:36:19.252452 22802 net.cpp:129] Top shape: 32 196 (6272)
I0428 20:36:19.252456 22802 net.cpp:129] Top shape: 32 196 (6272)
I0428 20:36:19.252460 22802 net.cpp:137] Memory required for data: 214031616
I0428 20:36:19.252462 22802 layer_factory.hpp:77] Creating layer accuracy
I0428 20:36:19.252468 22802 net.cpp:84] Creating Layer accuracy
I0428 20:36:19.252472 22802 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0428 20:36:19.252476 22802 net.cpp:406] accuracy <- label_val-data_1_split_0
I0428 20:36:19.252503 22802 net.cpp:380] accuracy -> accuracy
I0428 20:36:19.252512 22802 net.cpp:122] Setting up accuracy
I0428 20:36:19.252517 22802 net.cpp:129] Top shape: (1)
I0428 20:36:19.252521 22802 net.cpp:137] Memory required for data: 214031620
I0428 20:36:19.252523 22802 layer_factory.hpp:77] Creating layer loss
I0428 20:36:19.252529 22802 net.cpp:84] Creating Layer loss
I0428 20:36:19.252533 22802 net.cpp:406] loss <- fc8_fc8_0_split_1
I0428 20:36:19.252537 22802 net.cpp:406] loss <- label_val-data_1_split_1
I0428 20:36:19.252542 22802 net.cpp:380] loss -> loss
I0428 20:36:19.252548 22802 layer_factory.hpp:77] Creating layer loss
I0428 20:36:19.253180 22802 net.cpp:122] Setting up loss
I0428 20:36:19.253188 22802 net.cpp:129] Top shape: (1)
I0428 20:36:19.253192 22802 net.cpp:132]     with loss weight 1
I0428 20:36:19.253202 22802 net.cpp:137] Memory required for data: 214031624
I0428 20:36:19.253206 22802 net.cpp:198] loss needs backward computation.
I0428 20:36:19.253212 22802 net.cpp:200] accuracy does not need backward computation.
I0428 20:36:19.253216 22802 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0428 20:36:19.253221 22802 net.cpp:198] fc8 needs backward computation.
I0428 20:36:19.253224 22802 net.cpp:198] drop7 needs backward computation.
I0428 20:36:19.253227 22802 net.cpp:198] relu7 needs backward computation.
I0428 20:36:19.253230 22802 net.cpp:198] fc7 needs backward computation.
I0428 20:36:19.253234 22802 net.cpp:198] drop6 needs backward computation.
I0428 20:36:19.253238 22802 net.cpp:198] relu6 needs backward computation.
I0428 20:36:19.253242 22802 net.cpp:198] fc6 needs backward computation.
I0428 20:36:19.253245 22802 net.cpp:198] pool5 needs backward computation.
I0428 20:36:19.253248 22802 net.cpp:198] relu5 needs backward computation.
I0428 20:36:19.253252 22802 net.cpp:198] conv5 needs backward computation.
I0428 20:36:19.253269 22802 net.cpp:198] relu4 needs backward computation.
I0428 20:36:19.253273 22802 net.cpp:198] conv4 needs backward computation.
I0428 20:36:19.253276 22802 net.cpp:198] relu3 needs backward computation.
I0428 20:36:19.253280 22802 net.cpp:198] conv3 needs backward computation.
I0428 20:36:19.253284 22802 net.cpp:198] pool2 needs backward computation.
I0428 20:36:19.253288 22802 net.cpp:198] norm2 needs backward computation.
I0428 20:36:19.253291 22802 net.cpp:198] relu2 needs backward computation.
I0428 20:36:19.253294 22802 net.cpp:198] conv2 needs backward computation.
I0428 20:36:19.253298 22802 net.cpp:198] pool1.5 needs backward computation.
I0428 20:36:19.253302 22802 net.cpp:198] norm1.5 needs backward computation.
I0428 20:36:19.253305 22802 net.cpp:198] relu1.5 needs backward computation.
I0428 20:36:19.253309 22802 net.cpp:198] conv1.5 needs backward computation.
I0428 20:36:19.253312 22802 net.cpp:198] pool1 needs backward computation.
I0428 20:36:19.253316 22802 net.cpp:198] norm1 needs backward computation.
I0428 20:36:19.253321 22802 net.cpp:198] relu1 needs backward computation.
I0428 20:36:19.253324 22802 net.cpp:198] conv1 needs backward computation.
I0428 20:36:19.253329 22802 net.cpp:200] label_val-data_1_split does not need backward computation.
I0428 20:36:19.253334 22802 net.cpp:200] val-data does not need backward computation.
I0428 20:36:19.253336 22802 net.cpp:242] This network produces output accuracy
I0428 20:36:19.253340 22802 net.cpp:242] This network produces output loss
I0428 20:36:19.253357 22802 net.cpp:255] Network initialization done.
I0428 20:36:19.253437 22802 solver.cpp:56] Solver scaffolding done.
I0428 20:36:19.253902 22802 caffe.cpp:248] Starting Optimization
I0428 20:36:19.253912 22802 solver.cpp:272] Solving
I0428 20:36:19.253916 22802 solver.cpp:273] Learning Rate Policy: exp
I0428 20:36:19.255439 22802 solver.cpp:330] Iteration 0, Testing net (#0)
I0428 20:36:19.255447 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:36:19.401331 22802 blocking_queue.cpp:49] Waiting for data
I0428 20:36:23.588734 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:36:23.632030 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0428 20:36:23.632055 22802 solver.cpp:397]     Test net output #1: loss = 5.27899 (* 1 = 5.27899 loss)
I0428 20:36:23.715411 22802 solver.cpp:218] Iteration 0 (-7.11452e-37 iter/s, 4.46134s/12 iters), loss = 5.28454
I0428 20:36:23.715447 22802 solver.cpp:237]     Train net output #0: loss = 5.28454 (* 1 = 5.28454 loss)
I0428 20:36:23.715461 22802 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0428 20:36:27.452385 22802 solver.cpp:218] Iteration 12 (3.21129 iter/s, 3.73681s/12 iters), loss = 5.26916
I0428 20:36:27.452419 22802 solver.cpp:237]     Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
I0428 20:36:27.452426 22802 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0428 20:36:32.112531 22802 solver.cpp:218] Iteration 24 (2.57512 iter/s, 4.65997s/12 iters), loss = 5.27115
I0428 20:36:32.112568 22802 solver.cpp:237]     Train net output #0: loss = 5.27115 (* 1 = 5.27115 loss)
I0428 20:36:32.112578 22802 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0428 20:36:36.696542 22802 solver.cpp:218] Iteration 36 (2.6179 iter/s, 4.58383s/12 iters), loss = 5.31894
I0428 20:36:36.696578 22802 solver.cpp:237]     Train net output #0: loss = 5.31894 (* 1 = 5.31894 loss)
I0428 20:36:36.696586 22802 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0428 20:36:41.330250 22802 solver.cpp:218] Iteration 48 (2.58982 iter/s, 4.63353s/12 iters), loss = 5.27225
I0428 20:36:41.330286 22802 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0428 20:36:41.330294 22802 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0428 20:36:46.039937 22802 solver.cpp:218] Iteration 60 (2.54804 iter/s, 4.7095s/12 iters), loss = 5.29397
I0428 20:36:46.039973 22802 solver.cpp:237]     Train net output #0: loss = 5.29397 (* 1 = 5.29397 loss)
I0428 20:36:46.039980 22802 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0428 20:36:50.660439 22802 solver.cpp:218] Iteration 72 (2.59723 iter/s, 4.62032s/12 iters), loss = 5.28269
I0428 20:36:50.660557 22802 solver.cpp:237]     Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
I0428 20:36:50.660567 22802 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0428 20:36:55.266268 22802 solver.cpp:218] Iteration 84 (2.60554 iter/s, 4.60557s/12 iters), loss = 5.30897
I0428 20:36:55.266304 22802 solver.cpp:237]     Train net output #0: loss = 5.30897 (* 1 = 5.30897 loss)
I0428 20:36:55.266311 22802 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0428 20:36:59.944905 22802 solver.cpp:218] Iteration 96 (2.56495 iter/s, 4.67846s/12 iters), loss = 5.29213
I0428 20:36:59.944936 22802 solver.cpp:237]     Train net output #0: loss = 5.29213 (* 1 = 5.29213 loss)
I0428 20:36:59.944945 22802 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0428 20:37:01.629247 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:37:01.931604 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0428 20:37:06.814028 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0428 20:37:08.037510 22802 solver.cpp:330] Iteration 102, Testing net (#0)
I0428 20:37:08.037528 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:37:12.287889 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:37:12.366494 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0428 20:37:12.366523 22802 solver.cpp:397]     Test net output #1: loss = 5.29065 (* 1 = 5.29065 loss)
I0428 20:37:14.206283 22802 solver.cpp:218] Iteration 108 (0.841461 iter/s, 14.2609s/12 iters), loss = 5.26933
I0428 20:37:14.206328 22802 solver.cpp:237]     Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
I0428 20:37:14.206338 22802 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0428 20:37:18.850147 22802 solver.cpp:218] Iteration 120 (2.58416 iter/s, 4.64367s/12 iters), loss = 5.27674
I0428 20:37:18.850193 22802 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0428 20:37:18.850204 22802 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0428 20:37:23.469341 22802 solver.cpp:218] Iteration 132 (2.59797 iter/s, 4.619s/12 iters), loss = 5.27298
I0428 20:37:23.469455 22802 solver.cpp:237]     Train net output #0: loss = 5.27298 (* 1 = 5.27298 loss)
I0428 20:37:23.469467 22802 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0428 20:37:28.082468 22802 solver.cpp:218] Iteration 144 (2.60142 iter/s, 4.61287s/12 iters), loss = 5.2956
I0428 20:37:28.082515 22802 solver.cpp:237]     Train net output #0: loss = 5.2956 (* 1 = 5.2956 loss)
I0428 20:37:28.082528 22802 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0428 20:37:32.708693 22802 solver.cpp:218] Iteration 156 (2.59402 iter/s, 4.62603s/12 iters), loss = 5.29514
I0428 20:37:32.708743 22802 solver.cpp:237]     Train net output #0: loss = 5.29514 (* 1 = 5.29514 loss)
I0428 20:37:32.708755 22802 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0428 20:37:37.305220 22802 solver.cpp:218] Iteration 168 (2.61078 iter/s, 4.59633s/12 iters), loss = 5.29262
I0428 20:37:37.305272 22802 solver.cpp:237]     Train net output #0: loss = 5.29262 (* 1 = 5.29262 loss)
I0428 20:37:37.305284 22802 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0428 20:37:41.916097 22802 solver.cpp:218] Iteration 180 (2.60265 iter/s, 4.61068s/12 iters), loss = 5.30066
I0428 20:37:41.916148 22802 solver.cpp:237]     Train net output #0: loss = 5.30066 (* 1 = 5.30066 loss)
I0428 20:37:41.916159 22802 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0428 20:37:46.537248 22802 solver.cpp:218] Iteration 192 (2.59687 iter/s, 4.62095s/12 iters), loss = 5.27194
I0428 20:37:46.537297 22802 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0428 20:37:46.537308 22802 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0428 20:37:50.095582 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:37:50.728453 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0428 20:37:52.270833 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0428 20:37:53.457036 22802 solver.cpp:330] Iteration 204, Testing net (#0)
I0428 20:37:53.457056 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:37:57.876062 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:37:58.022061 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0428 20:37:58.022089 22802 solver.cpp:397]     Test net output #1: loss = 5.28796 (* 1 = 5.28796 loss)
I0428 20:37:58.080065 22802 solver.cpp:218] Iteration 204 (1.03964 iter/s, 11.5424s/12 iters), loss = 5.27261
I0428 20:37:58.080103 22802 solver.cpp:237]     Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
I0428 20:37:58.080112 22802 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0428 20:38:01.922991 22802 solver.cpp:218] Iteration 216 (3.12276 iter/s, 3.84276s/12 iters), loss = 5.28787
I0428 20:38:01.923033 22802 solver.cpp:237]     Train net output #0: loss = 5.28787 (* 1 = 5.28787 loss)
I0428 20:38:01.923043 22802 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0428 20:38:06.522979 22802 solver.cpp:218] Iteration 228 (2.60881 iter/s, 4.5998s/12 iters), loss = 5.27226
I0428 20:38:06.523023 22802 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0428 20:38:06.523036 22802 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0428 20:38:11.362742 22802 solver.cpp:218] Iteration 240 (2.47956 iter/s, 4.83956s/12 iters), loss = 5.27537
I0428 20:38:11.362792 22802 solver.cpp:237]     Train net output #0: loss = 5.27537 (* 1 = 5.27537 loss)
I0428 20:38:11.362803 22802 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0428 20:38:16.014235 22802 solver.cpp:218] Iteration 252 (2.57993 iter/s, 4.6513s/12 iters), loss = 5.2811
I0428 20:38:16.014272 22802 solver.cpp:237]     Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
I0428 20:38:16.014281 22802 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0428 20:38:20.708222 22802 solver.cpp:218] Iteration 264 (2.55656 iter/s, 4.6938s/12 iters), loss = 5.27899
I0428 20:38:20.708259 22802 solver.cpp:237]     Train net output #0: loss = 5.27899 (* 1 = 5.27899 loss)
I0428 20:38:20.708267 22802 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0428 20:38:25.318084 22802 solver.cpp:218] Iteration 276 (2.60322 iter/s, 4.60968s/12 iters), loss = 5.27911
I0428 20:38:25.318118 22802 solver.cpp:237]     Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
I0428 20:38:25.318128 22802 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0428 20:38:29.975373 22802 solver.cpp:218] Iteration 288 (2.57671 iter/s, 4.6571s/12 iters), loss = 5.30027
I0428 20:38:29.975497 22802 solver.cpp:237]     Train net output #0: loss = 5.30027 (* 1 = 5.30027 loss)
I0428 20:38:29.975512 22802 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0428 20:38:34.631078 22802 solver.cpp:218] Iteration 300 (2.57763 iter/s, 4.65543s/12 iters), loss = 5.29181
I0428 20:38:34.631111 22802 solver.cpp:237]     Train net output #0: loss = 5.29181 (* 1 = 5.29181 loss)
I0428 20:38:34.631119 22802 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0428 20:38:35.580402 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:38:36.562732 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0428 20:38:38.061435 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0428 20:38:39.275722 22802 solver.cpp:330] Iteration 306, Testing net (#0)
I0428 20:38:39.275741 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:38:43.781874 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:38:43.936316 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0428 20:38:43.936345 22802 solver.cpp:397]     Test net output #1: loss = 5.28629 (* 1 = 5.28629 loss)
I0428 20:38:45.565485 22802 solver.cpp:218] Iteration 312 (1.09749 iter/s, 10.9341s/12 iters), loss = 5.28335
I0428 20:38:45.565522 22802 solver.cpp:237]     Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
I0428 20:38:45.565531 22802 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0428 20:38:50.583076 22802 solver.cpp:218] Iteration 324 (2.39166 iter/s, 5.01743s/12 iters), loss = 5.26973
I0428 20:38:50.583109 22802 solver.cpp:237]     Train net output #0: loss = 5.26973 (* 1 = 5.26973 loss)
I0428 20:38:50.583117 22802 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0428 20:38:55.258807 22802 solver.cpp:218] Iteration 336 (2.56653 iter/s, 4.67558s/12 iters), loss = 5.28259
I0428 20:38:55.258843 22802 solver.cpp:237]     Train net output #0: loss = 5.28259 (* 1 = 5.28259 loss)
I0428 20:38:55.258852 22802 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0428 20:38:59.897222 22802 solver.cpp:218] Iteration 348 (2.58717 iter/s, 4.63827s/12 iters), loss = 5.29937
I0428 20:38:59.897258 22802 solver.cpp:237]     Train net output #0: loss = 5.29937 (* 1 = 5.29937 loss)
I0428 20:38:59.897264 22802 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0428 20:39:04.526876 22802 solver.cpp:218] Iteration 360 (2.59207 iter/s, 4.6295s/12 iters), loss = 5.28693
I0428 20:39:04.527010 22802 solver.cpp:237]     Train net output #0: loss = 5.28693 (* 1 = 5.28693 loss)
I0428 20:39:04.527019 22802 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0428 20:39:09.170908 22802 solver.cpp:218] Iteration 372 (2.5841 iter/s, 4.64378s/12 iters), loss = 5.2895
I0428 20:39:09.170948 22802 solver.cpp:237]     Train net output #0: loss = 5.2895 (* 1 = 5.2895 loss)
I0428 20:39:09.170955 22802 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0428 20:39:14.000339 22802 solver.cpp:218] Iteration 384 (2.48485 iter/s, 4.82927s/12 iters), loss = 5.27647
I0428 20:39:14.000377 22802 solver.cpp:237]     Train net output #0: loss = 5.27647 (* 1 = 5.27647 loss)
I0428 20:39:14.000384 22802 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0428 20:39:18.560672 22802 solver.cpp:218] Iteration 396 (2.63148 iter/s, 4.56018s/12 iters), loss = 5.28478
I0428 20:39:18.560710 22802 solver.cpp:237]     Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
I0428 20:39:18.560719 22802 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0428 20:39:21.426200 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:39:22.732834 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0428 20:39:24.408059 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0428 20:39:28.194635 22802 solver.cpp:330] Iteration 408, Testing net (#0)
I0428 20:39:28.194658 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:39:32.322283 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:39:32.521643 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0428 20:39:32.521680 22802 solver.cpp:397]     Test net output #1: loss = 5.27595 (* 1 = 5.27595 loss)
I0428 20:39:32.579447 22802 solver.cpp:218] Iteration 408 (0.856018 iter/s, 14.0184s/12 iters), loss = 5.27082
I0428 20:39:32.579497 22802 solver.cpp:237]     Train net output #0: loss = 5.27082 (* 1 = 5.27082 loss)
I0428 20:39:32.579509 22802 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0428 20:39:36.526170 22802 solver.cpp:218] Iteration 420 (3.04061 iter/s, 3.94657s/12 iters), loss = 5.25747
I0428 20:39:36.526265 22802 solver.cpp:237]     Train net output #0: loss = 5.25747 (* 1 = 5.25747 loss)
I0428 20:39:36.526275 22802 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0428 20:39:41.219331 22802 solver.cpp:218] Iteration 432 (2.55703 iter/s, 4.69294s/12 iters), loss = 5.21136
I0428 20:39:41.219374 22802 solver.cpp:237]     Train net output #0: loss = 5.21136 (* 1 = 5.21136 loss)
I0428 20:39:41.219383 22802 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0428 20:39:45.742152 22802 solver.cpp:218] Iteration 444 (2.6533 iter/s, 4.52266s/12 iters), loss = 5.22043
I0428 20:39:45.742195 22802 solver.cpp:237]     Train net output #0: loss = 5.22043 (* 1 = 5.22043 loss)
I0428 20:39:45.742205 22802 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0428 20:39:50.408017 22802 solver.cpp:218] Iteration 456 (2.57196 iter/s, 4.6657s/12 iters), loss = 5.18773
I0428 20:39:50.408054 22802 solver.cpp:237]     Train net output #0: loss = 5.18773 (* 1 = 5.18773 loss)
I0428 20:39:50.408062 22802 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0428 20:39:55.109742 22802 solver.cpp:218] Iteration 468 (2.55234 iter/s, 4.70157s/12 iters), loss = 5.20174
I0428 20:39:55.109781 22802 solver.cpp:237]     Train net output #0: loss = 5.20174 (* 1 = 5.20174 loss)
I0428 20:39:55.109789 22802 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0428 20:39:59.868302 22802 solver.cpp:218] Iteration 480 (2.52186 iter/s, 4.7584s/12 iters), loss = 5.14754
I0428 20:39:59.868337 22802 solver.cpp:237]     Train net output #0: loss = 5.14754 (* 1 = 5.14754 loss)
I0428 20:39:59.868345 22802 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0428 20:40:04.518079 22802 solver.cpp:218] Iteration 492 (2.58086 iter/s, 4.64962s/12 iters), loss = 5.17465
I0428 20:40:04.518115 22802 solver.cpp:237]     Train net output #0: loss = 5.17465 (* 1 = 5.17465 loss)
I0428 20:40:04.518123 22802 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0428 20:40:09.159974 22802 solver.cpp:218] Iteration 504 (2.58524 iter/s, 4.64174s/12 iters), loss = 5.21482
I0428 20:40:09.160238 22802 solver.cpp:237]     Train net output #0: loss = 5.21482 (* 1 = 5.21482 loss)
I0428 20:40:09.160249 22802 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0428 20:40:09.412263 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:40:11.057513 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0428 20:40:12.545037 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0428 20:40:13.744287 22802 solver.cpp:330] Iteration 510, Testing net (#0)
I0428 20:40:13.744313 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:40:17.765965 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:40:18.001617 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0428 20:40:18.001650 22802 solver.cpp:397]     Test net output #1: loss = 5.1794 (* 1 = 5.1794 loss)
I0428 20:40:19.649559 22802 solver.cpp:218] Iteration 516 (1.14405 iter/s, 10.4891s/12 iters), loss = 5.13191
I0428 20:40:19.649605 22802 solver.cpp:237]     Train net output #0: loss = 5.13191 (* 1 = 5.13191 loss)
I0428 20:40:19.649614 22802 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0428 20:40:24.359176 22802 solver.cpp:218] Iteration 528 (2.54807 iter/s, 4.70945s/12 iters), loss = 5.157
I0428 20:40:24.359216 22802 solver.cpp:237]     Train net output #0: loss = 5.157 (* 1 = 5.157 loss)
I0428 20:40:24.359225 22802 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0428 20:40:29.192986 22802 solver.cpp:218] Iteration 540 (2.4826 iter/s, 4.83364s/12 iters), loss = 5.13854
I0428 20:40:29.193022 22802 solver.cpp:237]     Train net output #0: loss = 5.13854 (* 1 = 5.13854 loss)
I0428 20:40:29.193029 22802 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0428 20:40:33.874806 22802 solver.cpp:218] Iteration 552 (2.56319 iter/s, 4.68166s/12 iters), loss = 5.20822
I0428 20:40:33.874851 22802 solver.cpp:237]     Train net output #0: loss = 5.20822 (* 1 = 5.20822 loss)
I0428 20:40:33.874861 22802 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0428 20:40:38.930488 22802 solver.cpp:218] Iteration 564 (2.37365 iter/s, 5.05551s/12 iters), loss = 5.17107
I0428 20:40:38.930523 22802 solver.cpp:237]     Train net output #0: loss = 5.17107 (* 1 = 5.17107 loss)
I0428 20:40:38.930531 22802 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0428 20:40:43.485038 22802 solver.cpp:218] Iteration 576 (2.63482 iter/s, 4.5544s/12 iters), loss = 5.1789
I0428 20:40:43.485164 22802 solver.cpp:237]     Train net output #0: loss = 5.1789 (* 1 = 5.1789 loss)
I0428 20:40:43.485174 22802 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0428 20:40:48.217322 22802 solver.cpp:218] Iteration 588 (2.53591 iter/s, 4.73203s/12 iters), loss = 5.11071
I0428 20:40:48.217360 22802 solver.cpp:237]     Train net output #0: loss = 5.11071 (* 1 = 5.11071 loss)
I0428 20:40:48.217367 22802 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0428 20:40:52.992938 22802 solver.cpp:218] Iteration 600 (2.51285 iter/s, 4.77545s/12 iters), loss = 5.20028
I0428 20:40:52.992974 22802 solver.cpp:237]     Train net output #0: loss = 5.20028 (* 1 = 5.20028 loss)
I0428 20:40:52.992983 22802 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0428 20:40:55.213127 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:40:57.198776 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0428 20:41:00.434248 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0428 20:41:05.679550 22802 solver.cpp:330] Iteration 612, Testing net (#0)
I0428 20:41:05.679575 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:41:09.892407 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:41:10.282709 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0428 20:41:10.282735 22802 solver.cpp:397]     Test net output #1: loss = 5.15893 (* 1 = 5.15893 loss)
I0428 20:41:10.340742 22802 solver.cpp:218] Iteration 612 (0.691749 iter/s, 17.3473s/12 iters), loss = 5.18947
I0428 20:41:10.340780 22802 solver.cpp:237]     Train net output #0: loss = 5.18947 (* 1 = 5.18947 loss)
I0428 20:41:10.340788 22802 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0428 20:41:14.271756 22802 solver.cpp:218] Iteration 624 (3.05276 iter/s, 3.93087s/12 iters), loss = 5.22927
I0428 20:41:14.271908 22802 solver.cpp:237]     Train net output #0: loss = 5.22927 (* 1 = 5.22927 loss)
I0428 20:41:14.271916 22802 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0428 20:41:18.955802 22802 solver.cpp:218] Iteration 636 (2.56204 iter/s, 4.68377s/12 iters), loss = 5.24755
I0428 20:41:18.955842 22802 solver.cpp:237]     Train net output #0: loss = 5.24755 (* 1 = 5.24755 loss)
I0428 20:41:18.955853 22802 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0428 20:41:23.586711 22802 solver.cpp:218] Iteration 648 (2.59137 iter/s, 4.63075s/12 iters), loss = 5.21374
I0428 20:41:23.586746 22802 solver.cpp:237]     Train net output #0: loss = 5.21374 (* 1 = 5.21374 loss)
I0428 20:41:23.586755 22802 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0428 20:41:28.364313 22802 solver.cpp:218] Iteration 660 (2.51181 iter/s, 4.77744s/12 iters), loss = 5.13706
I0428 20:41:28.364352 22802 solver.cpp:237]     Train net output #0: loss = 5.13706 (* 1 = 5.13706 loss)
I0428 20:41:28.364362 22802 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0428 20:41:33.045065 22802 solver.cpp:218] Iteration 672 (2.56378 iter/s, 4.68059s/12 iters), loss = 5.16583
I0428 20:41:33.045104 22802 solver.cpp:237]     Train net output #0: loss = 5.16583 (* 1 = 5.16583 loss)
I0428 20:41:33.045112 22802 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0428 20:41:37.743611 22802 solver.cpp:218] Iteration 684 (2.55407 iter/s, 4.69838s/12 iters), loss = 5.10857
I0428 20:41:37.743651 22802 solver.cpp:237]     Train net output #0: loss = 5.10857 (* 1 = 5.10857 loss)
I0428 20:41:37.743660 22802 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0428 20:41:38.480106 22802 blocking_queue.cpp:49] Waiting for data
I0428 20:41:42.346634 22802 solver.cpp:218] Iteration 696 (2.60708 iter/s, 4.60285s/12 iters), loss = 5.14886
I0428 20:41:42.346683 22802 solver.cpp:237]     Train net output #0: loss = 5.14886 (* 1 = 5.14886 loss)
I0428 20:41:42.346694 22802 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0428 20:41:46.675319 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:41:47.048049 22802 solver.cpp:218] Iteration 708 (2.55252 iter/s, 4.70124s/12 iters), loss = 5.1206
I0428 20:41:47.048085 22802 solver.cpp:237]     Train net output #0: loss = 5.1206 (* 1 = 5.1206 loss)
I0428 20:41:47.048094 22802 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0428 20:41:49.013955 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0428 20:41:50.514979 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0428 20:41:51.701805 22802 solver.cpp:330] Iteration 714, Testing net (#0)
I0428 20:41:51.701826 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:41:55.733757 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:41:56.045433 22802 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0428 20:41:56.045459 22802 solver.cpp:397]     Test net output #1: loss = 5.13527 (* 1 = 5.13527 loss)
I0428 20:41:57.684013 22802 solver.cpp:218] Iteration 720 (1.12828 iter/s, 10.6357s/12 iters), loss = 5.13582
I0428 20:41:57.684065 22802 solver.cpp:237]     Train net output #0: loss = 5.13582 (* 1 = 5.13582 loss)
I0428 20:41:57.684077 22802 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0428 20:42:02.303903 22802 solver.cpp:218] Iteration 732 (2.59756 iter/s, 4.61971s/12 iters), loss = 5.16028
I0428 20:42:02.303951 22802 solver.cpp:237]     Train net output #0: loss = 5.16028 (* 1 = 5.16028 loss)
I0428 20:42:02.303962 22802 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0428 20:42:06.926192 22802 solver.cpp:218] Iteration 744 (2.59621 iter/s, 4.62212s/12 iters), loss = 5.06642
I0428 20:42:06.926239 22802 solver.cpp:237]     Train net output #0: loss = 5.06642 (* 1 = 5.06642 loss)
I0428 20:42:06.926250 22802 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0428 20:42:11.552155 22802 solver.cpp:218] Iteration 756 (2.59415 iter/s, 4.62579s/12 iters), loss = 5.09808
I0428 20:42:11.552196 22802 solver.cpp:237]     Train net output #0: loss = 5.09808 (* 1 = 5.09808 loss)
I0428 20:42:11.552204 22802 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0428 20:42:16.113040 22802 solver.cpp:218] Iteration 768 (2.63116 iter/s, 4.56072s/12 iters), loss = 5.14996
I0428 20:42:16.113075 22802 solver.cpp:237]     Train net output #0: loss = 5.14996 (* 1 = 5.14996 loss)
I0428 20:42:16.113082 22802 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0428 20:42:20.845911 22802 solver.cpp:218] Iteration 780 (2.53555 iter/s, 4.7327s/12 iters), loss = 5.06966
I0428 20:42:20.846031 22802 solver.cpp:237]     Train net output #0: loss = 5.06966 (* 1 = 5.06966 loss)
I0428 20:42:20.846042 22802 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0428 20:42:25.546401 22802 solver.cpp:218] Iteration 792 (2.55306 iter/s, 4.70024s/12 iters), loss = 5.06829
I0428 20:42:25.546440 22802 solver.cpp:237]     Train net output #0: loss = 5.06829 (* 1 = 5.06829 loss)
I0428 20:42:25.546448 22802 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0428 20:42:30.204197 22802 solver.cpp:218] Iteration 804 (2.57642 iter/s, 4.65763s/12 iters), loss = 5.08326
I0428 20:42:30.204236 22802 solver.cpp:237]     Train net output #0: loss = 5.08326 (* 1 = 5.08326 loss)
I0428 20:42:30.204244 22802 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0428 20:42:31.814065 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:42:34.397440 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0428 20:42:35.935550 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0428 20:42:37.141885 22802 solver.cpp:330] Iteration 816, Testing net (#0)
I0428 20:42:37.141908 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:42:41.251652 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:42:41.598340 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0428 20:42:41.598373 22802 solver.cpp:397]     Test net output #1: loss = 5.0793 (* 1 = 5.0793 loss)
I0428 20:42:41.656165 22802 solver.cpp:218] Iteration 816 (1.04789 iter/s, 11.4516s/12 iters), loss = 5.07393
I0428 20:42:41.656214 22802 solver.cpp:237]     Train net output #0: loss = 5.07393 (* 1 = 5.07393 loss)
I0428 20:42:41.656224 22802 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0428 20:42:45.497027 22802 solver.cpp:218] Iteration 828 (3.12443 iter/s, 3.84071s/12 iters), loss = 5.07867
I0428 20:42:45.497076 22802 solver.cpp:237]     Train net output #0: loss = 5.07867 (* 1 = 5.07867 loss)
I0428 20:42:45.497088 22802 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0428 20:42:50.120268 22802 solver.cpp:218] Iteration 840 (2.59568 iter/s, 4.62306s/12 iters), loss = 5.02486
I0428 20:42:50.120321 22802 solver.cpp:237]     Train net output #0: loss = 5.02486 (* 1 = 5.02486 loss)
I0428 20:42:50.120332 22802 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0428 20:42:54.736361 22802 solver.cpp:218] Iteration 852 (2.5997 iter/s, 4.61591s/12 iters), loss = 5.03588
I0428 20:42:54.736529 22802 solver.cpp:237]     Train net output #0: loss = 5.03588 (* 1 = 5.03588 loss)
I0428 20:42:54.736539 22802 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0428 20:42:59.370299 22802 solver.cpp:218] Iteration 864 (2.58975 iter/s, 4.63365s/12 iters), loss = 5.12344
I0428 20:42:59.370335 22802 solver.cpp:237]     Train net output #0: loss = 5.12344 (* 1 = 5.12344 loss)
I0428 20:42:59.370344 22802 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0428 20:43:03.974858 22802 solver.cpp:218] Iteration 876 (2.60621 iter/s, 4.6044s/12 iters), loss = 5.01206
I0428 20:43:03.974896 22802 solver.cpp:237]     Train net output #0: loss = 5.01206 (* 1 = 5.01206 loss)
I0428 20:43:03.974905 22802 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0428 20:43:08.558216 22802 solver.cpp:218] Iteration 888 (2.61826 iter/s, 4.58319s/12 iters), loss = 5.00133
I0428 20:43:08.558249 22802 solver.cpp:237]     Train net output #0: loss = 5.00133 (* 1 = 5.00133 loss)
I0428 20:43:08.558257 22802 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0428 20:43:13.204008 22802 solver.cpp:218] Iteration 900 (2.58307 iter/s, 4.64563s/12 iters), loss = 5.03012
I0428 20:43:13.204043 22802 solver.cpp:237]     Train net output #0: loss = 5.03012 (* 1 = 5.03012 loss)
I0428 20:43:13.204051 22802 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0428 20:43:16.810981 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:43:17.833097 22802 solver.cpp:218] Iteration 912 (2.5924 iter/s, 4.62892s/12 iters), loss = 4.97936
I0428 20:43:17.833135 22802 solver.cpp:237]     Train net output #0: loss = 4.97936 (* 1 = 4.97936 loss)
I0428 20:43:17.833143 22802 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0428 20:43:19.719563 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0428 20:43:21.272701 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0428 20:43:22.470641 22802 solver.cpp:330] Iteration 918, Testing net (#0)
I0428 20:43:22.470660 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:43:26.310647 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:43:26.699651 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0428 20:43:26.699682 22802 solver.cpp:397]     Test net output #1: loss = 5.0399 (* 1 = 5.0399 loss)
I0428 20:43:28.333176 22802 solver.cpp:218] Iteration 924 (1.14288 iter/s, 10.4998s/12 iters), loss = 5.02528
I0428 20:43:28.333210 22802 solver.cpp:237]     Train net output #0: loss = 5.02528 (* 1 = 5.02528 loss)
I0428 20:43:28.333217 22802 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0428 20:43:33.085227 22802 solver.cpp:218] Iteration 936 (2.52532 iter/s, 4.75188s/12 iters), loss = 4.97787
I0428 20:43:33.085265 22802 solver.cpp:237]     Train net output #0: loss = 4.97787 (* 1 = 4.97787 loss)
I0428 20:43:33.085274 22802 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0428 20:43:37.801215 22802 solver.cpp:218] Iteration 948 (2.54463 iter/s, 4.71582s/12 iters), loss = 5.03575
I0428 20:43:37.801252 22802 solver.cpp:237]     Train net output #0: loss = 5.03575 (* 1 = 5.03575 loss)
I0428 20:43:37.801260 22802 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0428 20:43:42.399093 22802 solver.cpp:218] Iteration 960 (2.60999 iter/s, 4.59771s/12 iters), loss = 5.05093
I0428 20:43:42.399132 22802 solver.cpp:237]     Train net output #0: loss = 5.05093 (* 1 = 5.05093 loss)
I0428 20:43:42.399140 22802 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0428 20:43:47.153555 22802 solver.cpp:218] Iteration 972 (2.52404 iter/s, 4.75428s/12 iters), loss = 4.99623
I0428 20:43:47.153611 22802 solver.cpp:237]     Train net output #0: loss = 4.99623 (* 1 = 4.99623 loss)
I0428 20:43:47.153623 22802 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0428 20:43:53.043303 22802 solver.cpp:218] Iteration 984 (2.03752 iter/s, 5.88952s/12 iters), loss = 5.00964
I0428 20:43:53.046039 22802 solver.cpp:237]     Train net output #0: loss = 5.00964 (* 1 = 5.00964 loss)
I0428 20:43:53.046062 22802 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0428 20:43:59.504779 22802 solver.cpp:218] Iteration 996 (1.858 iter/s, 6.45856s/12 iters), loss = 5.03762
I0428 20:43:59.504981 22802 solver.cpp:237]     Train net output #0: loss = 5.03762 (* 1 = 5.03762 loss)
I0428 20:43:59.504994 22802 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0428 20:44:05.954353 22802 solver.cpp:218] Iteration 1008 (1.8607 iter/s, 6.4492s/12 iters), loss = 5.09434
I0428 20:44:05.964561 22802 solver.cpp:237]     Train net output #0: loss = 5.09434 (* 1 = 5.09434 loss)
I0428 20:44:05.964583 22802 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0428 20:44:07.258819 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:44:11.584185 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0428 20:44:13.204303 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0428 20:44:14.520398 22802 solver.cpp:330] Iteration 1020, Testing net (#0)
I0428 20:44:14.520428 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:44:20.436956 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:44:21.098544 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0428 20:44:21.098580 22802 solver.cpp:397]     Test net output #1: loss = 5.00107 (* 1 = 5.00107 loss)
I0428 20:44:21.166429 22802 solver.cpp:218] Iteration 1020 (0.789397 iter/s, 15.2015s/12 iters), loss = 5.12082
I0428 20:44:21.166479 22802 solver.cpp:237]     Train net output #0: loss = 5.12082 (* 1 = 5.12082 loss)
I0428 20:44:21.166489 22802 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0428 20:44:26.610908 22802 solver.cpp:218] Iteration 1032 (2.20415 iter/s, 5.44427s/12 iters), loss = 5.06739
I0428 20:44:26.610958 22802 solver.cpp:237]     Train net output #0: loss = 5.06739 (* 1 = 5.06739 loss)
I0428 20:44:26.610971 22802 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0428 20:44:33.081463 22802 solver.cpp:218] Iteration 1044 (1.85462 iter/s, 6.47032s/12 iters), loss = 5.00056
I0428 20:44:33.085091 22802 solver.cpp:237]     Train net output #0: loss = 5.00056 (* 1 = 5.00056 loss)
I0428 20:44:33.085103 22802 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0428 20:44:38.969044 22802 solver.cpp:218] Iteration 1056 (2.0395 iter/s, 5.8838s/12 iters), loss = 4.96888
I0428 20:44:38.969079 22802 solver.cpp:237]     Train net output #0: loss = 4.96888 (* 1 = 4.96888 loss)
I0428 20:44:38.969087 22802 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0428 20:44:43.600129 22802 solver.cpp:218] Iteration 1068 (2.59128 iter/s, 4.63091s/12 iters), loss = 4.98921
I0428 20:44:43.600180 22802 solver.cpp:237]     Train net output #0: loss = 4.98921 (* 1 = 4.98921 loss)
I0428 20:44:43.600193 22802 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0428 20:44:48.217840 22802 solver.cpp:218] Iteration 1080 (2.59879 iter/s, 4.61753s/12 iters), loss = 4.99719
I0428 20:44:48.217895 22802 solver.cpp:237]     Train net output #0: loss = 4.99719 (* 1 = 4.99719 loss)
I0428 20:44:48.217906 22802 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0428 20:44:52.876863 22802 solver.cpp:218] Iteration 1092 (2.57575 iter/s, 4.65884s/12 iters), loss = 5.00716
I0428 20:44:52.876900 22802 solver.cpp:237]     Train net output #0: loss = 5.00716 (* 1 = 5.00716 loss)
I0428 20:44:52.876909 22802 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0428 20:44:57.512244 22802 solver.cpp:218] Iteration 1104 (2.58888 iter/s, 4.63521s/12 iters), loss = 4.91142
I0428 20:44:57.512281 22802 solver.cpp:237]     Train net output #0: loss = 4.91142 (* 1 = 4.91142 loss)
I0428 20:44:57.512288 22802 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0428 20:45:00.433876 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:45:02.126782 22802 solver.cpp:218] Iteration 1116 (2.60057 iter/s, 4.61437s/12 iters), loss = 5.06551
I0428 20:45:02.126821 22802 solver.cpp:237]     Train net output #0: loss = 5.06551 (* 1 = 5.06551 loss)
I0428 20:45:02.126829 22802 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0428 20:45:03.983947 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0428 20:45:09.770066 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0428 20:45:12.117789 22802 solver.cpp:330] Iteration 1122, Testing net (#0)
I0428 20:45:12.117810 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:45:16.073422 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:45:16.540870 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0428 20:45:16.540910 22802 solver.cpp:397]     Test net output #1: loss = 4.9346 (* 1 = 4.9346 loss)
I0428 20:45:18.149360 22802 solver.cpp:218] Iteration 1128 (0.748965 iter/s, 16.0221s/12 iters), loss = 4.85472
I0428 20:45:18.149399 22802 solver.cpp:237]     Train net output #0: loss = 4.85472 (* 1 = 4.85472 loss)
I0428 20:45:18.149405 22802 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0428 20:45:22.869072 22802 solver.cpp:218] Iteration 1140 (2.54262 iter/s, 4.71954s/12 iters), loss = 4.75841
I0428 20:45:22.869110 22802 solver.cpp:237]     Train net output #0: loss = 4.75841 (* 1 = 4.75841 loss)
I0428 20:45:22.869118 22802 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0428 20:45:27.441985 22802 solver.cpp:218] Iteration 1152 (2.62425 iter/s, 4.57274s/12 iters), loss = 4.92197
I0428 20:45:27.442023 22802 solver.cpp:237]     Train net output #0: loss = 4.92197 (* 1 = 4.92197 loss)
I0428 20:45:27.442032 22802 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0428 20:45:32.045516 22802 solver.cpp:218] Iteration 1164 (2.60679 iter/s, 4.60336s/12 iters), loss = 4.9035
I0428 20:45:32.045562 22802 solver.cpp:237]     Train net output #0: loss = 4.9035 (* 1 = 4.9035 loss)
I0428 20:45:32.045572 22802 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0428 20:45:36.567590 22802 solver.cpp:218] Iteration 1176 (2.65375 iter/s, 4.5219s/12 iters), loss = 4.92069
I0428 20:45:36.567694 22802 solver.cpp:237]     Train net output #0: loss = 4.92069 (* 1 = 4.92069 loss)
I0428 20:45:36.567703 22802 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0428 20:45:41.159379 22802 solver.cpp:218] Iteration 1188 (2.6135 iter/s, 4.59155s/12 iters), loss = 4.85937
I0428 20:45:41.159428 22802 solver.cpp:237]     Train net output #0: loss = 4.85937 (* 1 = 4.85937 loss)
I0428 20:45:41.159441 22802 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0428 20:45:45.841094 22802 solver.cpp:218] Iteration 1200 (2.56326 iter/s, 4.68153s/12 iters), loss = 4.87943
I0428 20:45:45.841132 22802 solver.cpp:237]     Train net output #0: loss = 4.87943 (* 1 = 4.87943 loss)
I0428 20:45:45.841140 22802 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0428 20:45:50.450763 22802 solver.cpp:218] Iteration 1212 (2.60332 iter/s, 4.60949s/12 iters), loss = 4.95985
I0428 20:45:50.450814 22802 solver.cpp:237]     Train net output #0: loss = 4.95985 (* 1 = 4.95985 loss)
I0428 20:45:50.450824 22802 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0428 20:45:50.734999 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:45:54.734198 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0428 20:45:57.256354 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0428 20:46:00.023278 22802 solver.cpp:330] Iteration 1224, Testing net (#0)
I0428 20:46:00.023298 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:46:03.825208 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:46:04.326877 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0428 20:46:04.326917 22802 solver.cpp:397]     Test net output #1: loss = 4.87201 (* 1 = 4.87201 loss)
I0428 20:46:04.384896 22802 solver.cpp:218] Iteration 1224 (0.861221 iter/s, 13.9337s/12 iters), loss = 4.741
I0428 20:46:04.384939 22802 solver.cpp:237]     Train net output #0: loss = 4.741 (* 1 = 4.741 loss)
I0428 20:46:04.384949 22802 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0428 20:46:08.342775 22802 solver.cpp:218] Iteration 1236 (3.03205 iter/s, 3.95772s/12 iters), loss = 4.74237
I0428 20:46:08.342921 22802 solver.cpp:237]     Train net output #0: loss = 4.74237 (* 1 = 4.74237 loss)
I0428 20:46:08.342929 22802 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0428 20:46:13.044075 22802 solver.cpp:218] Iteration 1248 (2.55264 iter/s, 4.70102s/12 iters), loss = 4.87127
I0428 20:46:13.044111 22802 solver.cpp:237]     Train net output #0: loss = 4.87127 (* 1 = 4.87127 loss)
I0428 20:46:13.044118 22802 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0428 20:46:17.722501 22802 solver.cpp:218] Iteration 1260 (2.56506 iter/s, 4.67825s/12 iters), loss = 4.90996
I0428 20:46:17.722538 22802 solver.cpp:237]     Train net output #0: loss = 4.90996 (* 1 = 4.90996 loss)
I0428 20:46:17.722548 22802 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0428 20:46:22.282164 22802 solver.cpp:218] Iteration 1272 (2.63187 iter/s, 4.55949s/12 iters), loss = 4.83062
I0428 20:46:22.282212 22802 solver.cpp:237]     Train net output #0: loss = 4.83062 (* 1 = 4.83062 loss)
I0428 20:46:22.282222 22802 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0428 20:46:26.938246 22802 solver.cpp:218] Iteration 1284 (2.57737 iter/s, 4.6559s/12 iters), loss = 4.70402
I0428 20:46:26.938280 22802 solver.cpp:237]     Train net output #0: loss = 4.70402 (* 1 = 4.70402 loss)
I0428 20:46:26.938289 22802 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0428 20:46:31.730921 22802 solver.cpp:218] Iteration 1296 (2.50391 iter/s, 4.7925s/12 iters), loss = 4.71214
I0428 20:46:31.730962 22802 solver.cpp:237]     Train net output #0: loss = 4.71214 (* 1 = 4.71214 loss)
I0428 20:46:31.730973 22802 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0428 20:46:36.499104 22802 solver.cpp:218] Iteration 1308 (2.51678 iter/s, 4.768s/12 iters), loss = 4.91891
I0428 20:46:36.499141 22802 solver.cpp:237]     Train net output #0: loss = 4.91891 (* 1 = 4.91891 loss)
I0428 20:46:36.499150 22802 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0428 20:46:38.878851 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:46:41.231765 22802 solver.cpp:218] Iteration 1320 (2.53567 iter/s, 4.73249s/12 iters), loss = 4.83086
I0428 20:46:41.231801 22802 solver.cpp:237]     Train net output #0: loss = 4.83086 (* 1 = 4.83086 loss)
I0428 20:46:41.231809 22802 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0428 20:46:43.208362 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0428 20:46:44.789304 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0428 20:46:46.040514 22802 solver.cpp:330] Iteration 1326, Testing net (#0)
I0428 20:46:46.040534 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:46:49.929980 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:46:50.474781 22802 solver.cpp:397]     Test net output #0: accuracy = 0.036152
I0428 20:46:50.474807 22802 solver.cpp:397]     Test net output #1: loss = 4.7306 (* 1 = 4.7306 loss)
I0428 20:46:52.088093 22802 solver.cpp:218] Iteration 1332 (1.10538 iter/s, 10.856s/12 iters), loss = 4.95675
I0428 20:46:52.088129 22802 solver.cpp:237]     Train net output #0: loss = 4.95675 (* 1 = 4.95675 loss)
I0428 20:46:52.088136 22802 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0428 20:46:56.690508 22802 solver.cpp:218] Iteration 1344 (2.60742 iter/s, 4.60225s/12 iters), loss = 4.90478
I0428 20:46:56.690543 22802 solver.cpp:237]     Train net output #0: loss = 4.90478 (* 1 = 4.90478 loss)
I0428 20:46:56.690552 22802 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0428 20:47:01.188024 22802 solver.cpp:218] Iteration 1356 (2.66824 iter/s, 4.49735s/12 iters), loss = 4.81382
I0428 20:47:01.188071 22802 solver.cpp:237]     Train net output #0: loss = 4.81382 (* 1 = 4.81382 loss)
I0428 20:47:01.188081 22802 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0428 20:47:05.809615 22802 solver.cpp:218] Iteration 1368 (2.59661 iter/s, 4.62141s/12 iters), loss = 4.64185
I0428 20:47:05.809655 22802 solver.cpp:237]     Train net output #0: loss = 4.64185 (* 1 = 4.64185 loss)
I0428 20:47:05.809662 22802 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0428 20:47:06.922550 22802 blocking_queue.cpp:49] Waiting for data
I0428 20:47:10.403959 22802 solver.cpp:218] Iteration 1380 (2.612 iter/s, 4.59417s/12 iters), loss = 4.60771
I0428 20:47:10.404098 22802 solver.cpp:237]     Train net output #0: loss = 4.60771 (* 1 = 4.60771 loss)
I0428 20:47:10.404109 22802 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0428 20:47:15.070439 22802 solver.cpp:218] Iteration 1392 (2.57168 iter/s, 4.66621s/12 iters), loss = 4.66239
I0428 20:47:15.070489 22802 solver.cpp:237]     Train net output #0: loss = 4.66239 (* 1 = 4.66239 loss)
I0428 20:47:15.070502 22802 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0428 20:47:19.683313 22802 solver.cpp:218] Iteration 1404 (2.60152 iter/s, 4.61269s/12 iters), loss = 4.54374
I0428 20:47:19.683360 22802 solver.cpp:237]     Train net output #0: loss = 4.54374 (* 1 = 4.54374 loss)
I0428 20:47:19.683372 22802 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0428 20:47:23.948122 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:47:24.270579 22802 solver.cpp:218] Iteration 1416 (2.61604 iter/s, 4.58709s/12 iters), loss = 4.79609
I0428 20:47:24.270614 22802 solver.cpp:237]     Train net output #0: loss = 4.79609 (* 1 = 4.79609 loss)
I0428 20:47:24.270622 22802 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0428 20:47:28.440220 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0428 20:47:29.978241 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0428 20:47:31.328158 22802 solver.cpp:330] Iteration 1428, Testing net (#0)
I0428 20:47:31.328186 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:47:35.121218 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:47:35.701052 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0520833
I0428 20:47:35.701081 22802 solver.cpp:397]     Test net output #1: loss = 4.68302 (* 1 = 4.68302 loss)
I0428 20:47:35.759060 22802 solver.cpp:218] Iteration 1428 (1.04456 iter/s, 11.4881s/12 iters), loss = 4.74255
I0428 20:47:35.759111 22802 solver.cpp:237]     Train net output #0: loss = 4.74255 (* 1 = 4.74255 loss)
I0428 20:47:35.759122 22802 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0428 20:47:39.556934 22802 solver.cpp:218] Iteration 1440 (3.1598 iter/s, 3.79771s/12 iters), loss = 4.75082
I0428 20:47:39.556970 22802 solver.cpp:237]     Train net output #0: loss = 4.75082 (* 1 = 4.75082 loss)
I0428 20:47:39.556978 22802 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0428 20:47:44.140185 22802 solver.cpp:218] Iteration 1452 (2.61833 iter/s, 4.58308s/12 iters), loss = 4.61764
I0428 20:47:44.140292 22802 solver.cpp:237]     Train net output #0: loss = 4.61764 (* 1 = 4.61764 loss)
I0428 20:47:44.140300 22802 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0428 20:47:48.964694 22802 solver.cpp:218] Iteration 1464 (2.48743 iter/s, 4.82426s/12 iters), loss = 4.77154
I0428 20:47:48.964735 22802 solver.cpp:237]     Train net output #0: loss = 4.77154 (* 1 = 4.77154 loss)
I0428 20:47:48.964745 22802 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0428 20:47:53.792320 22802 solver.cpp:218] Iteration 1476 (2.48579 iter/s, 4.82744s/12 iters), loss = 4.67009
I0428 20:47:53.792356 22802 solver.cpp:237]     Train net output #0: loss = 4.67009 (* 1 = 4.67009 loss)
I0428 20:47:53.792364 22802 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0428 20:47:58.420780 22802 solver.cpp:218] Iteration 1488 (2.59275 iter/s, 4.62829s/12 iters), loss = 4.52374
I0428 20:47:58.420827 22802 solver.cpp:237]     Train net output #0: loss = 4.52374 (* 1 = 4.52374 loss)
I0428 20:47:58.420837 22802 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0428 20:48:03.030500 22802 solver.cpp:218] Iteration 1500 (2.6033 iter/s, 4.60954s/12 iters), loss = 4.45534
I0428 20:48:03.030555 22802 solver.cpp:237]     Train net output #0: loss = 4.45534 (* 1 = 4.45534 loss)
I0428 20:48:03.030566 22802 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0428 20:48:07.651455 22802 solver.cpp:218] Iteration 1512 (2.59697 iter/s, 4.62077s/12 iters), loss = 4.57422
I0428 20:48:07.651508 22802 solver.cpp:237]     Train net output #0: loss = 4.57422 (* 1 = 4.57422 loss)
I0428 20:48:07.651520 22802 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0428 20:48:09.294448 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:48:12.252298 22802 solver.cpp:218] Iteration 1524 (2.60832 iter/s, 4.60065s/12 iters), loss = 4.39511
I0428 20:48:12.252349 22802 solver.cpp:237]     Train net output #0: loss = 4.39511 (* 1 = 4.39511 loss)
I0428 20:48:12.252362 22802 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0428 20:48:14.146945 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0428 20:48:15.698812 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0428 20:48:16.909603 22802 solver.cpp:330] Iteration 1530, Testing net (#0)
I0428 20:48:16.909626 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:48:20.652422 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:48:21.291960 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0477941
I0428 20:48:21.291991 22802 solver.cpp:397]     Test net output #1: loss = 4.52295 (* 1 = 4.52295 loss)
I0428 20:48:22.901300 22802 solver.cpp:218] Iteration 1536 (1.1269 iter/s, 10.6487s/12 iters), loss = 4.52875
I0428 20:48:22.901338 22802 solver.cpp:237]     Train net output #0: loss = 4.52875 (* 1 = 4.52875 loss)
I0428 20:48:22.901346 22802 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0428 20:48:27.475286 22802 solver.cpp:218] Iteration 1548 (2.62364 iter/s, 4.57381s/12 iters), loss = 4.44965
I0428 20:48:27.475338 22802 solver.cpp:237]     Train net output #0: loss = 4.44965 (* 1 = 4.44965 loss)
I0428 20:48:27.475353 22802 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0428 20:48:32.033576 22802 solver.cpp:218] Iteration 1560 (2.63268 iter/s, 4.5581s/12 iters), loss = 4.42193
I0428 20:48:32.033617 22802 solver.cpp:237]     Train net output #0: loss = 4.42193 (* 1 = 4.42193 loss)
I0428 20:48:32.033624 22802 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0428 20:48:36.698017 22802 solver.cpp:218] Iteration 1572 (2.57276 iter/s, 4.66426s/12 iters), loss = 4.61152
I0428 20:48:36.698066 22802 solver.cpp:237]     Train net output #0: loss = 4.61152 (* 1 = 4.61152 loss)
I0428 20:48:36.698076 22802 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0428 20:48:41.308320 22802 solver.cpp:218] Iteration 1584 (2.60297 iter/s, 4.61012s/12 iters), loss = 4.4037
I0428 20:48:41.308363 22802 solver.cpp:237]     Train net output #0: loss = 4.4037 (* 1 = 4.4037 loss)
I0428 20:48:41.308377 22802 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0428 20:48:45.888645 22802 solver.cpp:218] Iteration 1596 (2.62001 iter/s, 4.58014s/12 iters), loss = 4.33743
I0428 20:48:45.888975 22802 solver.cpp:237]     Train net output #0: loss = 4.33743 (* 1 = 4.33743 loss)
I0428 20:48:45.888988 22802 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0428 20:48:50.473242 22802 solver.cpp:218] Iteration 1608 (2.61773 iter/s, 4.58413s/12 iters), loss = 4.45325
I0428 20:48:50.473278 22802 solver.cpp:237]     Train net output #0: loss = 4.45325 (* 1 = 4.45325 loss)
I0428 20:48:50.473286 22802 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0428 20:48:54.160624 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:48:55.240008 22802 solver.cpp:218] Iteration 1620 (2.51752 iter/s, 4.76659s/12 iters), loss = 4.42588
I0428 20:48:55.240044 22802 solver.cpp:237]     Train net output #0: loss = 4.42588 (* 1 = 4.42588 loss)
I0428 20:48:55.240051 22802 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0428 20:48:59.743505 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0428 20:49:03.815121 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0428 20:49:06.655865 22802 solver.cpp:330] Iteration 1632, Testing net (#0)
I0428 20:49:06.655887 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:49:10.267048 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:49:10.922461 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0655637
I0428 20:49:10.922488 22802 solver.cpp:397]     Test net output #1: loss = 4.35822 (* 1 = 4.35822 loss)
I0428 20:49:10.980062 22802 solver.cpp:218] Iteration 1632 (0.762409 iter/s, 15.7396s/12 iters), loss = 4.22043
I0428 20:49:10.980103 22802 solver.cpp:237]     Train net output #0: loss = 4.22043 (* 1 = 4.22043 loss)
I0428 20:49:10.980110 22802 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0428 20:49:14.849148 22802 solver.cpp:218] Iteration 1644 (3.10164 iter/s, 3.86893s/12 iters), loss = 4.10902
I0428 20:49:14.849186 22802 solver.cpp:237]     Train net output #0: loss = 4.10902 (* 1 = 4.10902 loss)
I0428 20:49:14.849195 22802 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0428 20:49:19.459069 22802 solver.cpp:218] Iteration 1656 (2.60318 iter/s, 4.60974s/12 iters), loss = 4.33619
I0428 20:49:19.459189 22802 solver.cpp:237]     Train net output #0: loss = 4.33619 (* 1 = 4.33619 loss)
I0428 20:49:19.459200 22802 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0428 20:49:24.084625 22802 solver.cpp:218] Iteration 1668 (2.59443 iter/s, 4.6253s/12 iters), loss = 4.44623
I0428 20:49:24.084676 22802 solver.cpp:237]     Train net output #0: loss = 4.44623 (* 1 = 4.44623 loss)
I0428 20:49:24.084686 22802 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0428 20:49:28.806635 22802 solver.cpp:218] Iteration 1680 (2.54139 iter/s, 4.72182s/12 iters), loss = 4.27799
I0428 20:49:28.806685 22802 solver.cpp:237]     Train net output #0: loss = 4.27799 (* 1 = 4.27799 loss)
I0428 20:49:28.806696 22802 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0428 20:49:33.489711 22802 solver.cpp:218] Iteration 1692 (2.56252 iter/s, 4.68289s/12 iters), loss = 4.34879
I0428 20:49:33.489745 22802 solver.cpp:237]     Train net output #0: loss = 4.34879 (* 1 = 4.34879 loss)
I0428 20:49:33.489753 22802 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0428 20:49:38.100683 22802 solver.cpp:218] Iteration 1704 (2.60259 iter/s, 4.6108s/12 iters), loss = 4.30113
I0428 20:49:38.100723 22802 solver.cpp:237]     Train net output #0: loss = 4.30113 (* 1 = 4.30113 loss)
I0428 20:49:38.100730 22802 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0428 20:49:42.743762 22802 solver.cpp:218] Iteration 1716 (2.58459 iter/s, 4.6429s/12 iters), loss = 4.24026
I0428 20:49:42.743815 22802 solver.cpp:237]     Train net output #0: loss = 4.24026 (* 1 = 4.24026 loss)
I0428 20:49:42.743829 22802 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0428 20:49:43.731295 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:49:47.378336 22802 solver.cpp:218] Iteration 1728 (2.58934 iter/s, 4.63439s/12 iters), loss = 4.36977
I0428 20:49:47.378386 22802 solver.cpp:237]     Train net output #0: loss = 4.36977 (* 1 = 4.36977 loss)
I0428 20:49:47.378396 22802 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0428 20:49:49.285687 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0428 20:49:52.937857 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0428 20:49:56.254918 22802 solver.cpp:330] Iteration 1734, Testing net (#0)
I0428 20:49:56.254937 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:49:59.944695 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:50:00.636857 22802 solver.cpp:397]     Test net output #0: accuracy = 0.0643382
I0428 20:50:00.636885 22802 solver.cpp:397]     Test net output #1: loss = 4.28826 (* 1 = 4.28826 loss)
I0428 20:50:02.300350 22802 solver.cpp:218] Iteration 1740 (0.804206 iter/s, 14.9215s/12 iters), loss = 4.43384
I0428 20:50:02.300393 22802 solver.cpp:237]     Train net output #0: loss = 4.43384 (* 1 = 4.43384 loss)
I0428 20:50:02.300401 22802 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0428 20:50:07.176388 22802 solver.cpp:218] Iteration 1752 (2.46111 iter/s, 4.87585s/12 iters), loss = 4.29529
I0428 20:50:07.176419 22802 solver.cpp:237]     Train net output #0: loss = 4.29529 (* 1 = 4.29529 loss)
I0428 20:50:07.176426 22802 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0428 20:50:11.850724 22802 solver.cpp:218] Iteration 1764 (2.5673 iter/s, 4.67416s/12 iters), loss = 4.16464
I0428 20:50:11.850761 22802 solver.cpp:237]     Train net output #0: loss = 4.16464 (* 1 = 4.16464 loss)
I0428 20:50:11.850770 22802 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0428 20:50:16.773996 22802 solver.cpp:218] Iteration 1776 (2.4375 iter/s, 4.92308s/12 iters), loss = 4.15064
I0428 20:50:16.774044 22802 solver.cpp:237]     Train net output #0: loss = 4.15064 (* 1 = 4.15064 loss)
I0428 20:50:16.774053 22802 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0428 20:50:21.586450 22802 solver.cpp:218] Iteration 1788 (2.49363 iter/s, 4.81226s/12 iters), loss = 4.16917
I0428 20:50:21.586499 22802 solver.cpp:237]     Train net output #0: loss = 4.16917 (* 1 = 4.16917 loss)
I0428 20:50:21.586510 22802 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0428 20:50:26.282316 22802 solver.cpp:218] Iteration 1800 (2.55554 iter/s, 4.69568s/12 iters), loss = 4.22364
I0428 20:50:26.282475 22802 solver.cpp:237]     Train net output #0: loss = 4.22364 (* 1 = 4.22364 loss)
I0428 20:50:26.282485 22802 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0428 20:50:31.020321 22802 solver.cpp:218] Iteration 1812 (2.53287 iter/s, 4.73771s/12 iters), loss = 4.01293
I0428 20:50:31.020362 22802 solver.cpp:237]     Train net output #0: loss = 4.01293 (* 1 = 4.01293 loss)
I0428 20:50:31.020372 22802 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0428 20:50:34.027010 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:50:35.724825 22802 solver.cpp:218] Iteration 1824 (2.55084 iter/s, 4.70432s/12 iters), loss = 4.2392
I0428 20:50:35.724864 22802 solver.cpp:237]     Train net output #0: loss = 4.2392 (* 1 = 4.2392 loss)
I0428 20:50:35.724871 22802 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0428 20:50:40.321789 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0428 20:50:42.469995 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0428 20:50:43.664846 22802 solver.cpp:330] Iteration 1836, Testing net (#0)
I0428 20:50:43.664871 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:50:47.167122 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:50:47.933667 22802 solver.cpp:397]     Test net output #0: accuracy = 0.091299
I0428 20:50:47.933701 22802 solver.cpp:397]     Test net output #1: loss = 4.06013 (* 1 = 4.06013 loss)
I0428 20:50:47.991359 22802 solver.cpp:218] Iteration 1836 (0.978303 iter/s, 12.2661s/12 iters), loss = 3.94269
I0428 20:50:47.991415 22802 solver.cpp:237]     Train net output #0: loss = 3.94269 (* 1 = 3.94269 loss)
I0428 20:50:47.991425 22802 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0428 20:50:51.836035 22802 solver.cpp:218] Iteration 1848 (3.12134 iter/s, 3.8445s/12 iters), loss = 3.85025
I0428 20:50:51.836076 22802 solver.cpp:237]     Train net output #0: loss = 3.85025 (* 1 = 3.85025 loss)
I0428 20:50:51.836082 22802 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0428 20:50:56.397778 22802 solver.cpp:218] Iteration 1860 (2.63068 iter/s, 4.56157s/12 iters), loss = 4.07581
I0428 20:50:56.397907 22802 solver.cpp:237]     Train net output #0: loss = 4.07581 (* 1 = 4.07581 loss)
I0428 20:50:56.397917 22802 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0428 20:51:01.159438 22802 solver.cpp:218] Iteration 1872 (2.52028 iter/s, 4.76138s/12 iters), loss = 3.81153
I0428 20:51:01.159476 22802 solver.cpp:237]     Train net output #0: loss = 3.81153 (* 1 = 3.81153 loss)
I0428 20:51:01.159484 22802 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0428 20:51:05.850168 22802 solver.cpp:218] Iteration 1884 (2.55834 iter/s, 4.69055s/12 iters), loss = 4.16968
I0428 20:51:05.850216 22802 solver.cpp:237]     Train net output #0: loss = 4.16968 (* 1 = 4.16968 loss)
I0428 20:51:05.850227 22802 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0428 20:51:10.820684 22802 solver.cpp:218] Iteration 1896 (2.41433 iter/s, 4.97032s/12 iters), loss = 3.9111
I0428 20:51:10.820721 22802 solver.cpp:237]     Train net output #0: loss = 3.9111 (* 1 = 3.9111 loss)
I0428 20:51:10.820729 22802 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0428 20:51:15.520874 22802 solver.cpp:218] Iteration 1908 (2.55319 iter/s, 4.7s/12 iters), loss = 4.04966
I0428 20:51:15.520934 22802 solver.cpp:237]     Train net output #0: loss = 4.04966 (* 1 = 4.04966 loss)
I0428 20:51:15.520948 22802 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0428 20:51:20.329387 22802 solver.cpp:218] Iteration 1920 (2.49568 iter/s, 4.80832s/12 iters), loss = 3.97982
I0428 20:51:20.329421 22802 solver.cpp:237]     Train net output #0: loss = 3.97982 (* 1 = 3.97982 loss)
I0428 20:51:20.329428 22802 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0428 20:51:20.643333 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:51:24.936611 22802 solver.cpp:218] Iteration 1932 (2.6047 iter/s, 4.60705s/12 iters), loss = 3.82317
I0428 20:51:24.936648 22802 solver.cpp:237]     Train net output #0: loss = 3.82317 (* 1 = 3.82317 loss)
I0428 20:51:24.936658 22802 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0428 20:51:26.852211 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0428 20:51:30.669534 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0428 20:51:33.368858 22802 solver.cpp:330] Iteration 1938, Testing net (#0)
I0428 20:51:33.368877 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:51:36.908811 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:51:37.674111 22802 solver.cpp:397]     Test net output #0: accuracy = 0.101716
I0428 20:51:37.674141 22802 solver.cpp:397]     Test net output #1: loss = 3.96505 (* 1 = 3.96505 loss)
I0428 20:51:39.370860 22802 solver.cpp:218] Iteration 1944 (0.831382 iter/s, 14.4338s/12 iters), loss = 3.92097
I0428 20:51:39.370895 22802 solver.cpp:237]     Train net output #0: loss = 3.92097 (* 1 = 3.92097 loss)
I0428 20:51:39.370903 22802 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0428 20:51:43.989563 22802 solver.cpp:218] Iteration 1956 (2.59823 iter/s, 4.61853s/12 iters), loss = 4.27826
I0428 20:51:43.989603 22802 solver.cpp:237]     Train net output #0: loss = 4.27826 (* 1 = 4.27826 loss)
I0428 20:51:43.989611 22802 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0428 20:51:48.652520 22802 solver.cpp:218] Iteration 1968 (2.57359 iter/s, 4.66274s/12 iters), loss = 4.08924
I0428 20:51:48.652570 22802 solver.cpp:237]     Train net output #0: loss = 4.08924 (* 1 = 4.08924 loss)
I0428 20:51:48.652582 22802 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0428 20:51:53.364581 22802 solver.cpp:218] Iteration 1980 (2.54676 iter/s, 4.71187s/12 iters), loss = 3.96053
I0428 20:51:53.364615 22802 solver.cpp:237]     Train net output #0: loss = 3.96053 (* 1 = 3.96053 loss)
I0428 20:51:53.364624 22802 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0428 20:51:57.971642 22802 solver.cpp:218] Iteration 1992 (2.6048 iter/s, 4.60688s/12 iters), loss = 3.91462
I0428 20:51:57.971777 22802 solver.cpp:237]     Train net output #0: loss = 3.91462 (* 1 = 3.91462 loss)
I0428 20:51:57.971787 22802 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0428 20:52:02.563344 22802 solver.cpp:218] Iteration 2004 (2.61357 iter/s, 4.59143s/12 iters), loss = 3.76452
I0428 20:52:02.563380 22802 solver.cpp:237]     Train net output #0: loss = 3.76452 (* 1 = 3.76452 loss)
I0428 20:52:02.563390 22802 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0428 20:52:07.417220 22802 solver.cpp:218] Iteration 2016 (2.47235 iter/s, 4.85369s/12 iters), loss = 3.98012
I0428 20:52:07.417270 22802 solver.cpp:237]     Train net output #0: loss = 3.98012 (* 1 = 3.98012 loss)
I0428 20:52:07.417282 22802 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0428 20:52:09.740725 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:52:12.094184 22802 solver.cpp:218] Iteration 2028 (2.56588 iter/s, 4.67677s/12 iters), loss = 3.80209
I0428 20:52:12.094236 22802 solver.cpp:237]     Train net output #0: loss = 3.80209 (* 1 = 3.80209 loss)
I0428 20:52:12.094247 22802 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0428 20:52:16.403069 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0428 20:52:17.939915 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0428 20:52:19.123687 22802 solver.cpp:330] Iteration 2040, Testing net (#0)
I0428 20:52:19.123708 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:52:22.649794 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:52:23.458745 22802 solver.cpp:397]     Test net output #0: accuracy = 0.118873
I0428 20:52:23.458776 22802 solver.cpp:397]     Test net output #1: loss = 3.85121 (* 1 = 3.85121 loss)
I0428 20:52:23.516372 22802 solver.cpp:218] Iteration 2040 (1.05062 iter/s, 11.4218s/12 iters), loss = 3.89452
I0428 20:52:23.516409 22802 solver.cpp:237]     Train net output #0: loss = 3.89452 (* 1 = 3.89452 loss)
I0428 20:52:23.516417 22802 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0428 20:52:27.473201 22802 solver.cpp:218] Iteration 2052 (3.03286 iter/s, 3.95667s/12 iters), loss = 3.76019
I0428 20:52:27.473246 22802 solver.cpp:237]     Train net output #0: loss = 3.76019 (* 1 = 3.76019 loss)
I0428 20:52:27.473254 22802 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0428 20:52:29.009914 22802 blocking_queue.cpp:49] Waiting for data
I0428 20:52:32.082859 22802 solver.cpp:218] Iteration 2064 (2.60334 iter/s, 4.60947s/12 iters), loss = 3.73601
I0428 20:52:32.082906 22802 solver.cpp:237]     Train net output #0: loss = 3.73601 (* 1 = 3.73601 loss)
I0428 20:52:32.082916 22802 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0428 20:52:36.699376 22802 solver.cpp:218] Iteration 2076 (2.59947 iter/s, 4.61633s/12 iters), loss = 3.86792
I0428 20:52:36.699414 22802 solver.cpp:237]     Train net output #0: loss = 3.86792 (* 1 = 3.86792 loss)
I0428 20:52:36.699422 22802 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0428 20:52:41.317436 22802 solver.cpp:218] Iteration 2088 (2.5986 iter/s, 4.61788s/12 iters), loss = 3.59398
I0428 20:52:41.317479 22802 solver.cpp:237]     Train net output #0: loss = 3.59398 (* 1 = 3.59398 loss)
I0428 20:52:41.317489 22802 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0428 20:52:45.929368 22802 solver.cpp:218] Iteration 2100 (2.60206 iter/s, 4.61173s/12 iters), loss = 3.79142
I0428 20:52:45.929412 22802 solver.cpp:237]     Train net output #0: loss = 3.79142 (* 1 = 3.79142 loss)
I0428 20:52:45.929422 22802 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0428 20:52:50.543488 22802 solver.cpp:218] Iteration 2112 (2.60082 iter/s, 4.61394s/12 iters), loss = 3.35633
I0428 20:52:50.543522 22802 solver.cpp:237]     Train net output #0: loss = 3.35633 (* 1 = 3.35633 loss)
I0428 20:52:50.543530 22802 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0428 20:52:54.952622 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:52:55.248664 22802 solver.cpp:218] Iteration 2124 (2.55048 iter/s, 4.70499s/12 iters), loss = 3.53308
I0428 20:52:55.248705 22802 solver.cpp:237]     Train net output #0: loss = 3.53308 (* 1 = 3.53308 loss)
I0428 20:52:55.248713 22802 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0428 20:53:00.035508 22802 solver.cpp:218] Iteration 2136 (2.50697 iter/s, 4.78666s/12 iters), loss = 3.72602
I0428 20:53:00.035650 22802 solver.cpp:237]     Train net output #0: loss = 3.72602 (* 1 = 3.72602 loss)
I0428 20:53:00.035660 22802 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0428 20:53:01.960868 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0428 20:53:08.163682 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0428 20:53:09.692759 22802 solver.cpp:330] Iteration 2142, Testing net (#0)
I0428 20:53:09.692782 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:53:13.175563 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:53:14.050262 22802 solver.cpp:397]     Test net output #0: accuracy = 0.13174
I0428 20:53:14.050292 22802 solver.cpp:397]     Test net output #1: loss = 3.78305 (* 1 = 3.78305 loss)
I0428 20:53:15.738963 22802 solver.cpp:218] Iteration 2148 (0.764192 iter/s, 15.7029s/12 iters), loss = 3.54447
I0428 20:53:15.739002 22802 solver.cpp:237]     Train net output #0: loss = 3.54447 (* 1 = 3.54447 loss)
I0428 20:53:15.739010 22802 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0428 20:53:20.426347 22802 solver.cpp:218] Iteration 2160 (2.56016 iter/s, 4.6872s/12 iters), loss = 3.85457
I0428 20:53:20.426383 22802 solver.cpp:237]     Train net output #0: loss = 3.85457 (* 1 = 3.85457 loss)
I0428 20:53:20.426391 22802 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0428 20:53:25.054034 22802 solver.cpp:218] Iteration 2172 (2.59319 iter/s, 4.62751s/12 iters), loss = 3.9225
I0428 20:53:25.054083 22802 solver.cpp:237]     Train net output #0: loss = 3.9225 (* 1 = 3.9225 loss)
I0428 20:53:25.054093 22802 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0428 20:53:29.659924 22802 solver.cpp:218] Iteration 2184 (2.60547 iter/s, 4.6057s/12 iters), loss = 3.56842
I0428 20:53:29.659979 22802 solver.cpp:237]     Train net output #0: loss = 3.56842 (* 1 = 3.56842 loss)
I0428 20:53:29.659991 22802 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0428 20:53:34.262653 22802 solver.cpp:218] Iteration 2196 (2.60726 iter/s, 4.60254s/12 iters), loss = 3.41572
I0428 20:53:34.262786 22802 solver.cpp:237]     Train net output #0: loss = 3.41572 (* 1 = 3.41572 loss)
I0428 20:53:34.262797 22802 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0428 20:53:38.917644 22802 solver.cpp:218] Iteration 2208 (2.57803 iter/s, 4.65472s/12 iters), loss = 3.43235
I0428 20:53:38.917680 22802 solver.cpp:237]     Train net output #0: loss = 3.43235 (* 1 = 3.43235 loss)
I0428 20:53:38.917688 22802 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0428 20:53:43.512336 22802 solver.cpp:218] Iteration 2220 (2.61181 iter/s, 4.59451s/12 iters), loss = 3.38897
I0428 20:53:43.512382 22802 solver.cpp:237]     Train net output #0: loss = 3.38897 (* 1 = 3.38897 loss)
I0428 20:53:43.512392 22802 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0428 20:53:45.183928 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:53:48.104740 22802 solver.cpp:218] Iteration 2232 (2.61312 iter/s, 4.59222s/12 iters), loss = 3.48141
I0428 20:53:48.104775 22802 solver.cpp:237]     Train net output #0: loss = 3.48141 (* 1 = 3.48141 loss)
I0428 20:53:48.104784 22802 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0428 20:53:52.293248 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0428 20:53:53.815798 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0428 20:53:55.004952 22802 solver.cpp:330] Iteration 2244, Testing net (#0)
I0428 20:53:55.004974 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:53:58.443862 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:53:59.415699 22802 solver.cpp:397]     Test net output #0: accuracy = 0.161765
I0428 20:53:59.415729 22802 solver.cpp:397]     Test net output #1: loss = 3.55351 (* 1 = 3.55351 loss)
I0428 20:53:59.473114 22802 solver.cpp:218] Iteration 2244 (1.05559 iter/s, 11.368s/12 iters), loss = 3.18232
I0428 20:53:59.473153 22802 solver.cpp:237]     Train net output #0: loss = 3.18232 (* 1 = 3.18232 loss)
I0428 20:53:59.473162 22802 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0428 20:54:03.423199 22802 solver.cpp:218] Iteration 2256 (3.03803 iter/s, 3.94992s/12 iters), loss = 3.44376
I0428 20:54:03.423238 22802 solver.cpp:237]     Train net output #0: loss = 3.44376 (* 1 = 3.44376 loss)
I0428 20:54:03.423246 22802 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0428 20:54:08.020068 22802 solver.cpp:218] Iteration 2268 (2.61058 iter/s, 4.59668s/12 iters), loss = 3.3354
I0428 20:54:08.020234 22802 solver.cpp:237]     Train net output #0: loss = 3.3354 (* 1 = 3.3354 loss)
I0428 20:54:08.020244 22802 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0428 20:54:12.669246 22802 solver.cpp:218] Iteration 2280 (2.58127 iter/s, 4.64887s/12 iters), loss = 3.2767
I0428 20:54:12.669292 22802 solver.cpp:237]     Train net output #0: loss = 3.2767 (* 1 = 3.2767 loss)
I0428 20:54:12.669302 22802 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0428 20:54:17.198443 22802 solver.cpp:218] Iteration 2292 (2.64959 iter/s, 4.52901s/12 iters), loss = 3.34232
I0428 20:54:17.198494 22802 solver.cpp:237]     Train net output #0: loss = 3.34232 (* 1 = 3.34232 loss)
I0428 20:54:17.198508 22802 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0428 20:54:21.793061 22802 solver.cpp:218] Iteration 2304 (2.61186 iter/s, 4.59443s/12 iters), loss = 3.33375
I0428 20:54:21.793097 22802 solver.cpp:237]     Train net output #0: loss = 3.33375 (* 1 = 3.33375 loss)
I0428 20:54:21.793107 22802 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0428 20:54:26.452064 22802 solver.cpp:218] Iteration 2316 (2.57576 iter/s, 4.65882s/12 iters), loss = 3.39807
I0428 20:54:26.452101 22802 solver.cpp:237]     Train net output #0: loss = 3.39807 (* 1 = 3.39807 loss)
I0428 20:54:26.452109 22802 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0428 20:54:30.055270 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:54:31.020190 22802 solver.cpp:218] Iteration 2328 (2.627 iter/s, 4.56795s/12 iters), loss = 3.27081
I0428 20:54:31.020226 22802 solver.cpp:237]     Train net output #0: loss = 3.27081 (* 1 = 3.27081 loss)
I0428 20:54:31.020233 22802 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0428 20:54:35.681071 22802 solver.cpp:218] Iteration 2340 (2.57472 iter/s, 4.6607s/12 iters), loss = 3.30587
I0428 20:54:35.681107 22802 solver.cpp:237]     Train net output #0: loss = 3.30587 (* 1 = 3.30587 loss)
I0428 20:54:35.681114 22802 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0428 20:54:37.611969 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0428 20:54:39.095963 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0428 20:54:40.281030 22802 solver.cpp:330] Iteration 2346, Testing net (#0)
I0428 20:54:40.281049 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:54:43.766443 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:54:44.687472 22802 solver.cpp:397]     Test net output #0: accuracy = 0.166667
I0428 20:54:44.687498 22802 solver.cpp:397]     Test net output #1: loss = 3.55835 (* 1 = 3.55835 loss)
I0428 20:54:46.307948 22802 solver.cpp:218] Iteration 2352 (1.12925 iter/s, 10.6265s/12 iters), loss = 3.05831
I0428 20:54:46.307988 22802 solver.cpp:237]     Train net output #0: loss = 3.05831 (* 1 = 3.05831 loss)
I0428 20:54:46.307996 22802 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0428 20:54:50.897799 22802 solver.cpp:218] Iteration 2364 (2.61457 iter/s, 4.58967s/12 iters), loss = 3.37489
I0428 20:54:50.897841 22802 solver.cpp:237]     Train net output #0: loss = 3.37489 (* 1 = 3.37489 loss)
I0428 20:54:50.897850 22802 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0428 20:54:55.567279 22802 solver.cpp:218] Iteration 2376 (2.56998 iter/s, 4.66929s/12 iters), loss = 3.47076
I0428 20:54:55.567325 22802 solver.cpp:237]     Train net output #0: loss = 3.47076 (* 1 = 3.47076 loss)
I0428 20:54:55.567337 22802 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0428 20:55:00.166805 22802 solver.cpp:218] Iteration 2388 (2.60907 iter/s, 4.59934s/12 iters), loss = 3.12317
I0428 20:55:00.166842 22802 solver.cpp:237]     Train net output #0: loss = 3.12317 (* 1 = 3.12317 loss)
I0428 20:55:00.166851 22802 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0428 20:55:04.800976 22802 solver.cpp:218] Iteration 2400 (2.58956 iter/s, 4.63399s/12 iters), loss = 3.14372
I0428 20:55:04.801014 22802 solver.cpp:237]     Train net output #0: loss = 3.14372 (* 1 = 3.14372 loss)
I0428 20:55:04.801024 22802 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0428 20:55:09.416222 22802 solver.cpp:218] Iteration 2412 (2.60018 iter/s, 4.61507s/12 iters), loss = 3.33003
I0428 20:55:09.416383 22802 solver.cpp:237]     Train net output #0: loss = 3.33003 (* 1 = 3.33003 loss)
I0428 20:55:09.416396 22802 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0428 20:55:14.035365 22802 solver.cpp:218] Iteration 2424 (2.59805 iter/s, 4.61885s/12 iters), loss = 3.15554
I0428 20:55:14.035403 22802 solver.cpp:237]     Train net output #0: loss = 3.15554 (* 1 = 3.15554 loss)
I0428 20:55:14.035410 22802 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0428 20:55:15.041903 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:55:18.633999 22802 solver.cpp:218] Iteration 2436 (2.60958 iter/s, 4.59845s/12 iters), loss = 3.14951
I0428 20:55:18.634039 22802 solver.cpp:237]     Train net output #0: loss = 3.14951 (* 1 = 3.14951 loss)
I0428 20:55:18.634047 22802 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0428 20:55:22.831444 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0428 20:55:25.783959 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0428 20:55:27.357468 22802 solver.cpp:330] Iteration 2448, Testing net (#0)
I0428 20:55:27.357493 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:55:30.706939 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:55:31.690059 22802 solver.cpp:397]     Test net output #0: accuracy = 0.1875
I0428 20:55:31.690089 22802 solver.cpp:397]     Test net output #1: loss = 3.30563 (* 1 = 3.30563 loss)
I0428 20:55:31.747751 22802 solver.cpp:218] Iteration 2448 (0.9151 iter/s, 13.1133s/12 iters), loss = 3.14523
I0428 20:55:31.747800 22802 solver.cpp:237]     Train net output #0: loss = 3.14523 (* 1 = 3.14523 loss)
I0428 20:55:31.747812 22802 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0428 20:55:35.620590 22802 solver.cpp:218] Iteration 2460 (3.09864 iter/s, 3.87267s/12 iters), loss = 3.13686
I0428 20:55:35.620630 22802 solver.cpp:237]     Train net output #0: loss = 3.13686 (* 1 = 3.13686 loss)
I0428 20:55:35.620638 22802 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0428 20:55:40.223145 22802 solver.cpp:218] Iteration 2472 (2.60735 iter/s, 4.60237s/12 iters), loss = 3.35965
I0428 20:55:40.223279 22802 solver.cpp:237]     Train net output #0: loss = 3.35965 (* 1 = 3.35965 loss)
I0428 20:55:40.223292 22802 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0428 20:55:44.843436 22802 solver.cpp:218] Iteration 2484 (2.59739 iter/s, 4.62002s/12 iters), loss = 3.23742
I0428 20:55:44.843472 22802 solver.cpp:237]     Train net output #0: loss = 3.23742 (* 1 = 3.23742 loss)
I0428 20:55:44.843479 22802 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0428 20:55:49.497117 22802 solver.cpp:218] Iteration 2496 (2.5787 iter/s, 4.65351s/12 iters), loss = 3.15769
I0428 20:55:49.497154 22802 solver.cpp:237]     Train net output #0: loss = 3.15769 (* 1 = 3.15769 loss)
I0428 20:55:49.497162 22802 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0428 20:55:54.265385 22802 solver.cpp:218] Iteration 2508 (2.51673 iter/s, 4.76809s/12 iters), loss = 2.8971
I0428 20:55:54.265425 22802 solver.cpp:237]     Train net output #0: loss = 2.8971 (* 1 = 2.8971 loss)
I0428 20:55:54.265432 22802 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0428 20:55:58.913249 22802 solver.cpp:218] Iteration 2520 (2.58194 iter/s, 4.64767s/12 iters), loss = 2.95143
I0428 20:55:58.913296 22802 solver.cpp:237]     Train net output #0: loss = 2.95143 (* 1 = 2.95143 loss)
I0428 20:55:58.913306 22802 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0428 20:56:01.903827 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:56:03.544785 22802 solver.cpp:218] Iteration 2532 (2.59104 iter/s, 4.63135s/12 iters), loss = 3.18424
I0428 20:56:03.544819 22802 solver.cpp:237]     Train net output #0: loss = 3.18424 (* 1 = 3.18424 loss)
I0428 20:56:03.544827 22802 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0428 20:56:08.159018 22802 solver.cpp:218] Iteration 2544 (2.60075 iter/s, 4.61405s/12 iters), loss = 2.86271
I0428 20:56:08.159054 22802 solver.cpp:237]     Train net output #0: loss = 2.86271 (* 1 = 2.86271 loss)
I0428 20:56:08.159061 22802 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0428 20:56:10.029340 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0428 20:56:12.359421 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0428 20:56:14.774590 22802 solver.cpp:330] Iteration 2550, Testing net (#0)
I0428 20:56:14.774613 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:56:18.148659 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:56:19.148474 22802 solver.cpp:397]     Test net output #0: accuracy = 0.218137
I0428 20:56:19.148524 22802 solver.cpp:397]     Test net output #1: loss = 3.14529 (* 1 = 3.14529 loss)
I0428 20:56:20.769320 22802 solver.cpp:218] Iteration 2556 (0.951633 iter/s, 12.6099s/12 iters), loss = 2.88087
I0428 20:56:20.769356 22802 solver.cpp:237]     Train net output #0: loss = 2.88087 (* 1 = 2.88087 loss)
I0428 20:56:20.769364 22802 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0428 20:56:25.367020 22802 solver.cpp:218] Iteration 2568 (2.6101 iter/s, 4.59752s/12 iters), loss = 2.91074
I0428 20:56:25.367059 22802 solver.cpp:237]     Train net output #0: loss = 2.91074 (* 1 = 2.91074 loss)
I0428 20:56:25.367069 22802 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0428 20:56:29.933146 22802 solver.cpp:218] Iteration 2580 (2.62815 iter/s, 4.56595s/12 iters), loss = 3.0322
I0428 20:56:29.933185 22802 solver.cpp:237]     Train net output #0: loss = 3.0322 (* 1 = 3.0322 loss)
I0428 20:56:29.933194 22802 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0428 20:56:34.554229 22802 solver.cpp:218] Iteration 2592 (2.5969 iter/s, 4.6209s/12 iters), loss = 3.00821
I0428 20:56:34.554265 22802 solver.cpp:237]     Train net output #0: loss = 3.00821 (* 1 = 3.00821 loss)
I0428 20:56:34.554272 22802 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0428 20:56:39.147330 22802 solver.cpp:218] Iteration 2604 (2.61271 iter/s, 4.59293s/12 iters), loss = 2.88824
I0428 20:56:39.147364 22802 solver.cpp:237]     Train net output #0: loss = 2.88824 (* 1 = 2.88824 loss)
I0428 20:56:39.147373 22802 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0428 20:56:43.759799 22802 solver.cpp:218] Iteration 2616 (2.60175 iter/s, 4.61229s/12 iters), loss = 2.79657
I0428 20:56:43.759902 22802 solver.cpp:237]     Train net output #0: loss = 2.79657 (* 1 = 2.79657 loss)
I0428 20:56:43.759910 22802 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0428 20:56:48.362498 22802 solver.cpp:218] Iteration 2628 (2.60731 iter/s, 4.60245s/12 iters), loss = 2.84663
I0428 20:56:48.362543 22802 solver.cpp:237]     Train net output #0: loss = 2.84663 (* 1 = 2.84663 loss)
I0428 20:56:48.362552 22802 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0428 20:56:48.742187 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:56:52.952481 22802 solver.cpp:218] Iteration 2640 (2.6145 iter/s, 4.58979s/12 iters), loss = 2.81055
I0428 20:56:52.952540 22802 solver.cpp:237]     Train net output #0: loss = 2.81055 (* 1 = 2.81055 loss)
I0428 20:56:52.952549 22802 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0428 20:56:57.095118 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0428 20:56:58.584347 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0428 20:56:59.767813 22802 solver.cpp:330] Iteration 2652, Testing net (#0)
I0428 20:56:59.767833 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:57:03.094735 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:57:04.132220 22802 solver.cpp:397]     Test net output #0: accuracy = 0.219975
I0428 20:57:04.132249 22802 solver.cpp:397]     Test net output #1: loss = 3.1172 (* 1 = 3.1172 loss)
I0428 20:57:04.189771 22802 solver.cpp:218] Iteration 2652 (1.06791 iter/s, 11.2369s/12 iters), loss = 2.95836
I0428 20:57:04.189810 22802 solver.cpp:237]     Train net output #0: loss = 2.95836 (* 1 = 2.95836 loss)
I0428 20:57:04.189818 22802 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0428 20:57:08.104562 22802 solver.cpp:218] Iteration 2664 (3.06543 iter/s, 3.91463s/12 iters), loss = 3.12832
I0428 20:57:08.104599 22802 solver.cpp:237]     Train net output #0: loss = 3.12832 (* 1 = 3.12832 loss)
I0428 20:57:08.104607 22802 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0428 20:57:12.818830 22802 solver.cpp:218] Iteration 2676 (2.54556 iter/s, 4.71409s/12 iters), loss = 2.94653
I0428 20:57:12.818867 22802 solver.cpp:237]     Train net output #0: loss = 2.94653 (* 1 = 2.94653 loss)
I0428 20:57:12.818876 22802 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0428 20:57:17.434103 22802 solver.cpp:218] Iteration 2688 (2.60017 iter/s, 4.61509s/12 iters), loss = 2.7393
I0428 20:57:17.434254 22802 solver.cpp:237]     Train net output #0: loss = 2.7393 (* 1 = 2.7393 loss)
I0428 20:57:17.434267 22802 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0428 20:57:22.102214 22802 solver.cpp:218] Iteration 2700 (2.5708 iter/s, 4.66782s/12 iters), loss = 2.60419
I0428 20:57:22.102269 22802 solver.cpp:237]     Train net output #0: loss = 2.60419 (* 1 = 2.60419 loss)
I0428 20:57:22.102280 22802 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0428 20:57:26.825322 22802 solver.cpp:218] Iteration 2712 (2.54081 iter/s, 4.72291s/12 iters), loss = 2.78624
I0428 20:57:26.825366 22802 solver.cpp:237]     Train net output #0: loss = 2.78624 (* 1 = 2.78624 loss)
I0428 20:57:26.825377 22802 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0428 20:57:31.430729 22802 solver.cpp:218] Iteration 2724 (2.60574 iter/s, 4.60522s/12 iters), loss = 2.77537
I0428 20:57:31.430770 22802 solver.cpp:237]     Train net output #0: loss = 2.77537 (* 1 = 2.77537 loss)
I0428 20:57:31.430779 22802 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0428 20:57:33.784174 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:57:36.023805 22802 solver.cpp:218] Iteration 2736 (2.61273 iter/s, 4.59289s/12 iters), loss = 2.91617
I0428 20:57:36.023845 22802 solver.cpp:237]     Train net output #0: loss = 2.91617 (* 1 = 2.91617 loss)
I0428 20:57:36.023856 22802 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0428 20:57:40.612571 22802 solver.cpp:218] Iteration 2748 (2.61519 iter/s, 4.58858s/12 iters), loss = 2.93608
I0428 20:57:40.612608 22802 solver.cpp:237]     Train net output #0: loss = 2.93608 (* 1 = 2.93608 loss)
I0428 20:57:40.612615 22802 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0428 20:57:42.505415 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0428 20:57:44.014250 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0428 20:57:45.208863 22802 solver.cpp:330] Iteration 2754, Testing net (#0)
I0428 20:57:45.208887 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:57:48.152420 22802 blocking_queue.cpp:49] Waiting for data
I0428 20:57:48.388414 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:57:49.465667 22802 solver.cpp:397]     Test net output #0: accuracy = 0.260417
I0428 20:57:49.465695 22802 solver.cpp:397]     Test net output #1: loss = 2.99962 (* 1 = 2.99962 loss)
I0428 20:57:51.167848 22802 solver.cpp:218] Iteration 2760 (1.13691 iter/s, 10.5549s/12 iters), loss = 2.58187
I0428 20:57:51.167903 22802 solver.cpp:237]     Train net output #0: loss = 2.58187 (* 1 = 2.58187 loss)
I0428 20:57:51.167914 22802 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0428 20:57:55.780782 22802 solver.cpp:218] Iteration 2772 (2.60149 iter/s, 4.61274s/12 iters), loss = 2.64206
I0428 20:57:55.780834 22802 solver.cpp:237]     Train net output #0: loss = 2.64206 (* 1 = 2.64206 loss)
I0428 20:57:55.780845 22802 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0428 20:58:00.392390 22802 solver.cpp:218] Iteration 2784 (2.60224 iter/s, 4.61142s/12 iters), loss = 2.59387
I0428 20:58:00.392427 22802 solver.cpp:237]     Train net output #0: loss = 2.59387 (* 1 = 2.59387 loss)
I0428 20:58:00.392436 22802 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0428 20:58:05.002421 22802 solver.cpp:218] Iteration 2796 (2.60312 iter/s, 4.60985s/12 iters), loss = 2.54674
I0428 20:58:05.002460 22802 solver.cpp:237]     Train net output #0: loss = 2.54674 (* 1 = 2.54674 loss)
I0428 20:58:05.002468 22802 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0428 20:58:09.625735 22802 solver.cpp:218] Iteration 2808 (2.59565 iter/s, 4.62313s/12 iters), loss = 2.68992
I0428 20:58:09.625771 22802 solver.cpp:237]     Train net output #0: loss = 2.68992 (* 1 = 2.68992 loss)
I0428 20:58:09.625779 22802 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0428 20:58:14.215867 22802 solver.cpp:218] Iteration 2820 (2.61441 iter/s, 4.58995s/12 iters), loss = 2.66524
I0428 20:58:14.215903 22802 solver.cpp:237]     Train net output #0: loss = 2.66524 (* 1 = 2.66524 loss)
I0428 20:58:14.215911 22802 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0428 20:58:18.626492 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:58:18.893409 22802 solver.cpp:218] Iteration 2832 (2.56555 iter/s, 4.67736s/12 iters), loss = 2.77549
I0428 20:58:18.893442 22802 solver.cpp:237]     Train net output #0: loss = 2.77549 (* 1 = 2.77549 loss)
I0428 20:58:18.893451 22802 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0428 20:58:23.536898 22802 solver.cpp:218] Iteration 2844 (2.58436 iter/s, 4.64331s/12 iters), loss = 2.72128
I0428 20:58:23.536940 22802 solver.cpp:237]     Train net output #0: loss = 2.72128 (* 1 = 2.72128 loss)
I0428 20:58:23.536948 22802 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0428 20:58:27.712430 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0428 20:58:34.188210 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0428 20:58:35.458443 22802 solver.cpp:330] Iteration 2856, Testing net (#0)
I0428 20:58:35.458469 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:58:38.651798 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:58:39.779978 22802 solver.cpp:397]     Test net output #0: accuracy = 0.230392
I0428 20:58:39.780012 22802 solver.cpp:397]     Test net output #1: loss = 3.12694 (* 1 = 3.12694 loss)
I0428 20:58:39.837894 22802 solver.cpp:218] Iteration 2856 (0.736175 iter/s, 16.3005s/12 iters), loss = 2.65049
I0428 20:58:39.837940 22802 solver.cpp:237]     Train net output #0: loss = 2.65049 (* 1 = 2.65049 loss)
I0428 20:58:39.837951 22802 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0428 20:58:43.710062 22802 solver.cpp:218] Iteration 2868 (3.09917 iter/s, 3.872s/12 iters), loss = 2.60693
I0428 20:58:43.710095 22802 solver.cpp:237]     Train net output #0: loss = 2.60693 (* 1 = 2.60693 loss)
I0428 20:58:43.710103 22802 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0428 20:58:48.303413 22802 solver.cpp:218] Iteration 2880 (2.61257 iter/s, 4.59317s/12 iters), loss = 2.4718
I0428 20:58:48.303454 22802 solver.cpp:237]     Train net output #0: loss = 2.4718 (* 1 = 2.4718 loss)
I0428 20:58:48.303465 22802 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0428 20:58:52.924409 22802 solver.cpp:218] Iteration 2892 (2.59695 iter/s, 4.62081s/12 iters), loss = 2.57897
I0428 20:58:52.924576 22802 solver.cpp:237]     Train net output #0: loss = 2.57897 (* 1 = 2.57897 loss)
I0428 20:58:52.924584 22802 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0428 20:58:57.662926 22802 solver.cpp:218] Iteration 2904 (2.5326 iter/s, 4.7382s/12 iters), loss = 2.48012
I0428 20:58:57.662962 22802 solver.cpp:237]     Train net output #0: loss = 2.48012 (* 1 = 2.48012 loss)
I0428 20:58:57.662971 22802 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0428 20:59:02.319921 22802 solver.cpp:218] Iteration 2916 (2.57687 iter/s, 4.65681s/12 iters), loss = 2.36717
I0428 20:59:02.319958 22802 solver.cpp:237]     Train net output #0: loss = 2.36717 (* 1 = 2.36717 loss)
I0428 20:59:02.319967 22802 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0428 20:59:06.959986 22802 solver.cpp:218] Iteration 2928 (2.58627 iter/s, 4.63988s/12 iters), loss = 2.44635
I0428 20:59:06.960022 22802 solver.cpp:237]     Train net output #0: loss = 2.44635 (* 1 = 2.44635 loss)
I0428 20:59:06.960029 22802 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0428 20:59:08.664726 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:59:11.645260 22802 solver.cpp:218] Iteration 2940 (2.56132 iter/s, 4.68509s/12 iters), loss = 2.55617
I0428 20:59:11.645298 22802 solver.cpp:237]     Train net output #0: loss = 2.55617 (* 1 = 2.55617 loss)
I0428 20:59:11.645305 22802 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0428 20:59:16.287351 22802 solver.cpp:218] Iteration 2952 (2.58514 iter/s, 4.64191s/12 iters), loss = 2.12954
I0428 20:59:16.287390 22802 solver.cpp:237]     Train net output #0: loss = 2.12954 (* 1 = 2.12954 loss)
I0428 20:59:16.287398 22802 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0428 20:59:18.152467 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0428 20:59:19.647513 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0428 20:59:20.840449 22802 solver.cpp:330] Iteration 2958, Testing net (#0)
I0428 20:59:20.840471 22802 net.cpp:676] Ignoring source layer train-data
I0428 20:59:24.102510 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:59:25.328231 22802 solver.cpp:397]     Test net output #0: accuracy = 0.245711
I0428 20:59:25.328269 22802 solver.cpp:397]     Test net output #1: loss = 3.06454 (* 1 = 3.06454 loss)
I0428 20:59:26.889279 22802 solver.cpp:218] Iteration 2964 (1.13191 iter/s, 10.6016s/12 iters), loss = 2.5756
I0428 20:59:26.889331 22802 solver.cpp:237]     Train net output #0: loss = 2.5756 (* 1 = 2.5756 loss)
I0428 20:59:26.889344 22802 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0428 20:59:31.473083 22802 solver.cpp:218] Iteration 2976 (2.61802 iter/s, 4.58362s/12 iters), loss = 2.45059
I0428 20:59:31.473120 22802 solver.cpp:237]     Train net output #0: loss = 2.45059 (* 1 = 2.45059 loss)
I0428 20:59:31.473129 22802 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0428 20:59:36.117101 22802 solver.cpp:218] Iteration 2988 (2.58407 iter/s, 4.64384s/12 iters), loss = 2.31606
I0428 20:59:36.117139 22802 solver.cpp:237]     Train net output #0: loss = 2.31606 (* 1 = 2.31606 loss)
I0428 20:59:36.117147 22802 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0428 20:59:40.633489 22802 solver.cpp:218] Iteration 3000 (2.6571 iter/s, 4.51621s/12 iters), loss = 2.64166
I0428 20:59:40.633527 22802 solver.cpp:237]     Train net output #0: loss = 2.64166 (* 1 = 2.64166 loss)
I0428 20:59:40.633535 22802 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0428 20:59:45.350049 22802 solver.cpp:218] Iteration 3012 (2.54432 iter/s, 4.71638s/12 iters), loss = 2.63259
I0428 20:59:45.350085 22802 solver.cpp:237]     Train net output #0: loss = 2.63259 (* 1 = 2.63259 loss)
I0428 20:59:45.350093 22802 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0428 20:59:50.069099 22802 solver.cpp:218] Iteration 3024 (2.54298 iter/s, 4.71887s/12 iters), loss = 2.22404
I0428 20:59:50.069134 22802 solver.cpp:237]     Train net output #0: loss = 2.22404 (* 1 = 2.22404 loss)
I0428 20:59:50.069141 22802 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0428 20:59:53.768620 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 20:59:54.777304 22802 solver.cpp:218] Iteration 3036 (2.54884 iter/s, 4.70802s/12 iters), loss = 2.4149
I0428 20:59:54.777451 22802 solver.cpp:237]     Train net output #0: loss = 2.4149 (* 1 = 2.4149 loss)
I0428 20:59:54.777460 22802 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0428 20:59:59.385263 22802 solver.cpp:218] Iteration 3048 (2.60435 iter/s, 4.60767s/12 iters), loss = 2.23836
I0428 20:59:59.385316 22802 solver.cpp:237]     Train net output #0: loss = 2.23836 (* 1 = 2.23836 loss)
I0428 20:59:59.385329 22802 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0428 21:00:03.646005 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0428 21:00:07.601292 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0428 21:00:09.272125 22802 solver.cpp:330] Iteration 3060, Testing net (#0)
I0428 21:00:09.272145 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:00:12.799718 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:00:14.155618 22802 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0428 21:00:14.155644 22802 solver.cpp:397]     Test net output #1: loss = 2.88581 (* 1 = 2.88581 loss)
I0428 21:00:14.213150 22802 solver.cpp:218] Iteration 3060 (0.809312 iter/s, 14.8274s/12 iters), loss = 2.26776
I0428 21:00:14.213187 22802 solver.cpp:237]     Train net output #0: loss = 2.26776 (* 1 = 2.26776 loss)
I0428 21:00:14.213196 22802 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0428 21:00:18.415443 22802 solver.cpp:218] Iteration 3072 (2.8557 iter/s, 4.20212s/12 iters), loss = 2.37996
I0428 21:00:18.415478 22802 solver.cpp:237]     Train net output #0: loss = 2.37996 (* 1 = 2.37996 loss)
I0428 21:00:18.415488 22802 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0428 21:00:23.052582 22802 solver.cpp:218] Iteration 3084 (2.58791 iter/s, 4.63695s/12 iters), loss = 2.39004
I0428 21:00:23.052635 22802 solver.cpp:237]     Train net output #0: loss = 2.39004 (* 1 = 2.39004 loss)
I0428 21:00:23.052649 22802 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0428 21:00:27.683120 22802 solver.cpp:218] Iteration 3096 (2.5916 iter/s, 4.63034s/12 iters), loss = 2.26778
I0428 21:00:27.683212 22802 solver.cpp:237]     Train net output #0: loss = 2.26778 (* 1 = 2.26778 loss)
I0428 21:00:27.683221 22802 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0428 21:00:32.319234 22802 solver.cpp:218] Iteration 3108 (2.58851 iter/s, 4.63588s/12 iters), loss = 2.03477
I0428 21:00:32.319272 22802 solver.cpp:237]     Train net output #0: loss = 2.03477 (* 1 = 2.03477 loss)
I0428 21:00:32.319280 22802 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0428 21:00:36.933043 22802 solver.cpp:218] Iteration 3120 (2.60099 iter/s, 4.61363s/12 iters), loss = 2.45433
I0428 21:00:36.933082 22802 solver.cpp:237]     Train net output #0: loss = 2.45433 (* 1 = 2.45433 loss)
I0428 21:00:36.933090 22802 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0428 21:00:41.567904 22802 solver.cpp:218] Iteration 3132 (2.58918 iter/s, 4.63468s/12 iters), loss = 2.05785
I0428 21:00:41.567939 22802 solver.cpp:237]     Train net output #0: loss = 2.05785 (* 1 = 2.05785 loss)
I0428 21:00:41.567948 22802 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0428 21:00:42.599568 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:00:46.205729 22802 solver.cpp:218] Iteration 3144 (2.58752 iter/s, 4.63765s/12 iters), loss = 2.29436
I0428 21:00:46.205765 22802 solver.cpp:237]     Train net output #0: loss = 2.29436 (* 1 = 2.29436 loss)
I0428 21:00:46.205771 22802 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0428 21:00:50.811571 22802 solver.cpp:218] Iteration 3156 (2.60549 iter/s, 4.60566s/12 iters), loss = 2.11276
I0428 21:00:50.811609 22802 solver.cpp:237]     Train net output #0: loss = 2.11276 (* 1 = 2.11276 loss)
I0428 21:00:50.811616 22802 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0428 21:00:52.715112 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0428 21:00:54.252274 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0428 21:00:55.446374 22802 solver.cpp:330] Iteration 3162, Testing net (#0)
I0428 21:00:55.446398 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:00:58.518247 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:00:59.773836 22802 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0428 21:00:59.773867 22802 solver.cpp:397]     Test net output #1: loss = 2.7131 (* 1 = 2.7131 loss)
I0428 21:01:01.482795 22802 solver.cpp:218] Iteration 3168 (1.12456 iter/s, 10.6709s/12 iters), loss = 2.17621
I0428 21:01:01.482832 22802 solver.cpp:237]     Train net output #0: loss = 2.17621 (* 1 = 2.17621 loss)
I0428 21:01:01.482841 22802 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0428 21:01:06.198982 22802 solver.cpp:218] Iteration 3180 (2.54453 iter/s, 4.716s/12 iters), loss = 2.2322
I0428 21:01:06.199015 22802 solver.cpp:237]     Train net output #0: loss = 2.2322 (* 1 = 2.2322 loss)
I0428 21:01:06.199023 22802 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0428 21:01:10.796766 22802 solver.cpp:218] Iteration 3192 (2.61005 iter/s, 4.59761s/12 iters), loss = 2.32335
I0428 21:01:10.796802 22802 solver.cpp:237]     Train net output #0: loss = 2.32335 (* 1 = 2.32335 loss)
I0428 21:01:10.796811 22802 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0428 21:01:15.430258 22802 solver.cpp:218] Iteration 3204 (2.58994 iter/s, 4.63331s/12 iters), loss = 2.15523
I0428 21:01:15.430296 22802 solver.cpp:237]     Train net output #0: loss = 2.15523 (* 1 = 2.15523 loss)
I0428 21:01:15.430305 22802 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0428 21:01:20.039950 22802 solver.cpp:218] Iteration 3216 (2.60331 iter/s, 4.60951s/12 iters), loss = 2.05435
I0428 21:01:20.039989 22802 solver.cpp:237]     Train net output #0: loss = 2.05435 (* 1 = 2.05435 loss)
I0428 21:01:20.039997 22802 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0428 21:01:24.604528 22802 solver.cpp:218] Iteration 3228 (2.62906 iter/s, 4.56436s/12 iters), loss = 2.08717
I0428 21:01:24.604578 22802 solver.cpp:237]     Train net output #0: loss = 2.08717 (* 1 = 2.08717 loss)
I0428 21:01:24.604588 22802 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0428 21:01:27.626255 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:01:29.286218 22802 solver.cpp:218] Iteration 3240 (2.56328 iter/s, 4.6815s/12 iters), loss = 2.39123
I0428 21:01:29.286326 22802 solver.cpp:237]     Train net output #0: loss = 2.39123 (* 1 = 2.39123 loss)
I0428 21:01:29.286334 22802 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0428 21:01:33.899580 22802 solver.cpp:218] Iteration 3252 (2.60128 iter/s, 4.61311s/12 iters), loss = 2.30071
I0428 21:01:33.899616 22802 solver.cpp:237]     Train net output #0: loss = 2.30071 (* 1 = 2.30071 loss)
I0428 21:01:33.899624 22802 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0428 21:01:38.135210 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0428 21:01:39.655616 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0428 21:01:40.908360 22802 solver.cpp:330] Iteration 3264, Testing net (#0)
I0428 21:01:40.908385 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:01:43.872287 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:01:45.144321 22802 solver.cpp:397]     Test net output #0: accuracy = 0.321691
I0428 21:01:45.144349 22802 solver.cpp:397]     Test net output #1: loss = 2.67796 (* 1 = 2.67796 loss)
I0428 21:01:45.202142 22802 solver.cpp:218] Iteration 3264 (1.06174 iter/s, 11.3022s/12 iters), loss = 2.30445
I0428 21:01:45.202180 22802 solver.cpp:237]     Train net output #0: loss = 2.30445 (* 1 = 2.30445 loss)
I0428 21:01:45.202188 22802 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0428 21:01:49.060611 22802 solver.cpp:218] Iteration 3276 (3.11017 iter/s, 3.8583s/12 iters), loss = 1.80453
I0428 21:01:49.060647 22802 solver.cpp:237]     Train net output #0: loss = 1.80453 (* 1 = 1.80453 loss)
I0428 21:01:49.060655 22802 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0428 21:01:53.652701 22802 solver.cpp:218] Iteration 3288 (2.61329 iter/s, 4.59191s/12 iters), loss = 2.46744
I0428 21:01:53.652753 22802 solver.cpp:237]     Train net output #0: loss = 2.46744 (* 1 = 2.46744 loss)
I0428 21:01:53.652765 22802 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0428 21:01:58.256388 22802 solver.cpp:218] Iteration 3300 (2.60672 iter/s, 4.60349s/12 iters), loss = 2.25844
I0428 21:01:58.256441 22802 solver.cpp:237]     Train net output #0: loss = 2.25844 (* 1 = 2.25844 loss)
I0428 21:01:58.256453 22802 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0428 21:02:02.870075 22802 solver.cpp:218] Iteration 3312 (2.60107 iter/s, 4.61348s/12 iters), loss = 2.10857
I0428 21:02:02.870244 22802 solver.cpp:237]     Train net output #0: loss = 2.10857 (* 1 = 2.10857 loss)
I0428 21:02:02.870254 22802 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0428 21:02:07.478392 22802 solver.cpp:218] Iteration 3324 (2.60416 iter/s, 4.60801s/12 iters), loss = 2.31623
I0428 21:02:07.478447 22802 solver.cpp:237]     Train net output #0: loss = 2.31623 (* 1 = 2.31623 loss)
I0428 21:02:07.478458 22802 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0428 21:02:12.088395 22802 solver.cpp:218] Iteration 3336 (2.60315 iter/s, 4.6098s/12 iters), loss = 1.91266
I0428 21:02:12.088449 22802 solver.cpp:237]     Train net output #0: loss = 1.91266 (* 1 = 1.91266 loss)
I0428 21:02:12.088461 22802 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0428 21:02:12.525254 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:02:16.697333 22802 solver.cpp:218] Iteration 3348 (2.60375 iter/s, 4.60874s/12 iters), loss = 2.17007
I0428 21:02:16.697386 22802 solver.cpp:237]     Train net output #0: loss = 2.17007 (* 1 = 2.17007 loss)
I0428 21:02:16.697396 22802 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0428 21:02:21.314731 22802 solver.cpp:218] Iteration 3360 (2.59897 iter/s, 4.61721s/12 iters), loss = 2.00538
I0428 21:02:21.314780 22802 solver.cpp:237]     Train net output #0: loss = 2.00538 (* 1 = 2.00538 loss)
I0428 21:02:21.314791 22802 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0428 21:02:23.204516 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0428 21:02:24.718672 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0428 21:02:26.440126 22802 solver.cpp:330] Iteration 3366, Testing net (#0)
I0428 21:02:26.440146 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:02:29.458696 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:02:30.764727 22802 solver.cpp:397]     Test net output #0: accuracy = 0.352328
I0428 21:02:30.764758 22802 solver.cpp:397]     Test net output #1: loss = 2.49081 (* 1 = 2.49081 loss)
I0428 21:02:32.648643 22802 solver.cpp:218] Iteration 3372 (1.05881 iter/s, 11.3335s/12 iters), loss = 2.00659
I0428 21:02:32.648682 22802 solver.cpp:237]     Train net output #0: loss = 2.00659 (* 1 = 2.00659 loss)
I0428 21:02:32.648690 22802 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0428 21:02:37.295248 22802 solver.cpp:218] Iteration 3384 (2.58263 iter/s, 4.64642s/12 iters), loss = 2.31414
I0428 21:02:37.298820 22802 solver.cpp:237]     Train net output #0: loss = 2.31414 (* 1 = 2.31414 loss)
I0428 21:02:37.298830 22802 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0428 21:02:41.887672 22802 solver.cpp:218] Iteration 3396 (2.61512 iter/s, 4.58871s/12 iters), loss = 2.21549
I0428 21:02:41.887717 22802 solver.cpp:237]     Train net output #0: loss = 2.21549 (* 1 = 2.21549 loss)
I0428 21:02:41.887728 22802 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0428 21:02:46.494205 22802 solver.cpp:218] Iteration 3408 (2.6051 iter/s, 4.60634s/12 iters), loss = 1.74845
I0428 21:02:46.494240 22802 solver.cpp:237]     Train net output #0: loss = 1.74845 (* 1 = 1.74845 loss)
I0428 21:02:46.494251 22802 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0428 21:02:51.148025 22802 solver.cpp:218] Iteration 3420 (2.57863 iter/s, 4.65364s/12 iters), loss = 2.00211
I0428 21:02:51.148064 22802 solver.cpp:237]     Train net output #0: loss = 2.00211 (* 1 = 2.00211 loss)
I0428 21:02:51.148072 22802 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0428 21:02:55.782135 22802 solver.cpp:218] Iteration 3432 (2.5896 iter/s, 4.63392s/12 iters), loss = 1.86334
I0428 21:02:55.782178 22802 solver.cpp:237]     Train net output #0: loss = 1.86334 (* 1 = 1.86334 loss)
I0428 21:02:55.782187 22802 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0428 21:02:58.284014 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:03:00.470598 22802 solver.cpp:218] Iteration 3444 (2.55958 iter/s, 4.68828s/12 iters), loss = 1.74577
I0428 21:03:00.470635 22802 solver.cpp:237]     Train net output #0: loss = 1.74577 (* 1 = 1.74577 loss)
I0428 21:03:00.470643 22802 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0428 21:03:05.069667 22802 solver.cpp:218] Iteration 3456 (2.60933 iter/s, 4.59888s/12 iters), loss = 1.93003
I0428 21:03:05.069712 22802 solver.cpp:237]     Train net output #0: loss = 1.93003 (* 1 = 1.93003 loss)
I0428 21:03:05.069722 22802 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0428 21:03:09.249994 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0428 21:03:11.348549 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0428 21:03:13.774113 22802 solver.cpp:330] Iteration 3468, Testing net (#0)
I0428 21:03:13.774134 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:03:14.160771 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:03:16.704217 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:03:18.088943 22802 solver.cpp:397]     Test net output #0: accuracy = 0.345588
I0428 21:03:18.088977 22802 solver.cpp:397]     Test net output #1: loss = 2.63153 (* 1 = 2.63153 loss)
I0428 21:03:18.146402 22802 solver.cpp:218] Iteration 3468 (0.917691 iter/s, 13.0763s/12 iters), loss = 1.77993
I0428 21:03:18.146440 22802 solver.cpp:237]     Train net output #0: loss = 1.77993 (* 1 = 1.77993 loss)
I0428 21:03:18.146450 22802 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0428 21:03:22.146466 22802 solver.cpp:218] Iteration 3480 (3.00008 iter/s, 3.9999s/12 iters), loss = 1.9038
I0428 21:03:22.146504 22802 solver.cpp:237]     Train net output #0: loss = 1.9038 (* 1 = 1.9038 loss)
I0428 21:03:22.146512 22802 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0428 21:03:26.768103 22802 solver.cpp:218] Iteration 3492 (2.59658 iter/s, 4.62146s/12 iters), loss = 1.89437
I0428 21:03:26.768141 22802 solver.cpp:237]     Train net output #0: loss = 1.89437 (* 1 = 1.89437 loss)
I0428 21:03:26.768149 22802 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0428 21:03:31.478767 22802 solver.cpp:218] Iteration 3504 (2.54751 iter/s, 4.71048s/12 iters), loss = 1.99666
I0428 21:03:31.478803 22802 solver.cpp:237]     Train net output #0: loss = 1.99666 (* 1 = 1.99666 loss)
I0428 21:03:31.478811 22802 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0428 21:03:36.105854 22802 solver.cpp:218] Iteration 3516 (2.59353 iter/s, 4.62691s/12 iters), loss = 1.93334
I0428 21:03:36.105891 22802 solver.cpp:237]     Train net output #0: loss = 1.93334 (* 1 = 1.93334 loss)
I0428 21:03:36.105899 22802 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0428 21:03:40.707971 22802 solver.cpp:218] Iteration 3528 (2.6076 iter/s, 4.60194s/12 iters), loss = 1.84773
I0428 21:03:40.708098 22802 solver.cpp:237]     Train net output #0: loss = 1.84773 (* 1 = 1.84773 loss)
I0428 21:03:40.708107 22802 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0428 21:03:45.108536 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:03:45.345971 22802 solver.cpp:218] Iteration 3540 (2.58747 iter/s, 4.63773s/12 iters), loss = 1.8696
I0428 21:03:45.346009 22802 solver.cpp:237]     Train net output #0: loss = 1.8696 (* 1 = 1.8696 loss)
I0428 21:03:45.346017 22802 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0428 21:03:49.876266 22802 solver.cpp:218] Iteration 3552 (2.64894 iter/s, 4.53011s/12 iters), loss = 1.87035
I0428 21:03:49.876325 22802 solver.cpp:237]     Train net output #0: loss = 1.87035 (* 1 = 1.87035 loss)
I0428 21:03:49.876335 22802 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0428 21:03:54.487396 22802 solver.cpp:218] Iteration 3564 (2.60251 iter/s, 4.61092s/12 iters), loss = 1.75176
I0428 21:03:54.487432 22802 solver.cpp:237]     Train net output #0: loss = 1.75176 (* 1 = 1.75176 loss)
I0428 21:03:54.487439 22802 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0428 21:03:56.393167 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0428 21:03:58.002988 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0428 21:03:59.254606 22802 solver.cpp:330] Iteration 3570, Testing net (#0)
I0428 21:03:59.254631 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:04:02.214005 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:04:04.265635 22802 solver.cpp:397]     Test net output #0: accuracy = 0.373162
I0428 21:04:04.265677 22802 solver.cpp:397]     Test net output #1: loss = 2.58703 (* 1 = 2.58703 loss)
I0428 21:04:06.606278 22802 solver.cpp:218] Iteration 3576 (0.990223 iter/s, 12.1185s/12 iters), loss = 1.64432
I0428 21:04:06.606333 22802 solver.cpp:237]     Train net output #0: loss = 1.64432 (* 1 = 1.64432 loss)
I0428 21:04:06.606343 22802 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0428 21:04:13.051014 22802 solver.cpp:218] Iteration 3588 (1.86206 iter/s, 6.44448s/12 iters), loss = 2.12191
I0428 21:04:13.051139 22802 solver.cpp:237]     Train net output #0: loss = 2.12191 (* 1 = 2.12191 loss)
I0428 21:04:13.051151 22802 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0428 21:04:19.615904 22802 solver.cpp:218] Iteration 3600 (1.828 iter/s, 6.56456s/12 iters), loss = 1.94089
I0428 21:04:19.622066 22802 solver.cpp:237]     Train net output #0: loss = 1.94089 (* 1 = 1.94089 loss)
I0428 21:04:19.622084 22802 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0428 21:04:25.949283 22802 solver.cpp:218] Iteration 3612 (1.89662 iter/s, 6.32703s/12 iters), loss = 1.75141
I0428 21:04:25.949331 22802 solver.cpp:237]     Train net output #0: loss = 1.75141 (* 1 = 1.75141 loss)
I0428 21:04:25.949340 22802 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0428 21:04:32.088110 22802 solver.cpp:218] Iteration 3624 (1.95485 iter/s, 6.13858s/12 iters), loss = 1.72838
I0428 21:04:32.094259 22802 solver.cpp:237]     Train net output #0: loss = 1.72838 (* 1 = 1.72838 loss)
I0428 21:04:32.094285 22802 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0428 21:04:38.642297 22802 solver.cpp:218] Iteration 3636 (1.83266 iter/s, 6.54786s/12 iters), loss = 1.68195
I0428 21:04:38.656563 22802 solver.cpp:237]     Train net output #0: loss = 1.68195 (* 1 = 1.68195 loss)
I0428 21:04:38.656584 22802 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0428 21:04:41.047688 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:04:44.822643 22802 solver.cpp:218] Iteration 3648 (1.94619 iter/s, 6.16591s/12 iters), loss = 1.67874
I0428 21:04:44.822809 22802 solver.cpp:237]     Train net output #0: loss = 1.67874 (* 1 = 1.67874 loss)
I0428 21:04:44.822824 22802 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0428 21:04:51.144541 22802 solver.cpp:218] Iteration 3660 (1.89827 iter/s, 6.32153s/12 iters), loss = 1.72948
I0428 21:04:51.144596 22802 solver.cpp:237]     Train net output #0: loss = 1.72948 (* 1 = 1.72948 loss)
I0428 21:04:51.144611 22802 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0428 21:04:55.713001 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0428 21:05:01.056083 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0428 21:05:04.507079 22802 solver.cpp:330] Iteration 3672, Testing net (#0)
I0428 21:05:04.507107 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:05:07.392271 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:05:08.832648 22802 solver.cpp:397]     Test net output #0: accuracy = 0.376226
I0428 21:05:08.832675 22802 solver.cpp:397]     Test net output #1: loss = 2.56289 (* 1 = 2.56289 loss)
I0428 21:05:08.890579 22802 solver.cpp:218] Iteration 3672 (0.676229 iter/s, 17.7455s/12 iters), loss = 1.95136
I0428 21:05:08.890640 22802 solver.cpp:237]     Train net output #0: loss = 1.95136 (* 1 = 1.95136 loss)
I0428 21:05:08.890651 22802 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0428 21:05:12.856870 22802 solver.cpp:218] Iteration 3684 (3.02564 iter/s, 3.96611s/12 iters), loss = 1.64835
I0428 21:05:12.856904 22802 solver.cpp:237]     Train net output #0: loss = 1.64835 (* 1 = 1.64835 loss)
I0428 21:05:12.856914 22802 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0428 21:05:17.531417 22802 solver.cpp:218] Iteration 3696 (2.5672 iter/s, 4.67436s/12 iters), loss = 1.72956
I0428 21:05:17.531527 22802 solver.cpp:237]     Train net output #0: loss = 1.72956 (* 1 = 1.72956 loss)
I0428 21:05:17.531535 22802 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0428 21:05:22.164047 22802 solver.cpp:218] Iteration 3708 (2.59047 iter/s, 4.63237s/12 iters), loss = 1.76332
I0428 21:05:22.164083 22802 solver.cpp:237]     Train net output #0: loss = 1.76332 (* 1 = 1.76332 loss)
I0428 21:05:22.164093 22802 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0428 21:05:26.801194 22802 solver.cpp:218] Iteration 3720 (2.5879 iter/s, 4.63696s/12 iters), loss = 1.85307
I0428 21:05:26.801234 22802 solver.cpp:237]     Train net output #0: loss = 1.85307 (* 1 = 1.85307 loss)
I0428 21:05:26.801245 22802 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0428 21:05:31.550577 22802 solver.cpp:218] Iteration 3732 (2.52674 iter/s, 4.7492s/12 iters), loss = 1.4993
I0428 21:05:31.550616 22802 solver.cpp:237]     Train net output #0: loss = 1.4993 (* 1 = 1.4993 loss)
I0428 21:05:31.550624 22802 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0428 21:05:35.402529 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:05:36.323199 22802 solver.cpp:218] Iteration 3744 (2.51444 iter/s, 4.77243s/12 iters), loss = 1.63342
I0428 21:05:36.323236 22802 solver.cpp:237]     Train net output #0: loss = 1.63342 (* 1 = 1.63342 loss)
I0428 21:05:36.323246 22802 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0428 21:05:41.013379 22802 solver.cpp:218] Iteration 3756 (2.55864 iter/s, 4.68999s/12 iters), loss = 1.85637
I0428 21:05:41.013427 22802 solver.cpp:237]     Train net output #0: loss = 1.85637 (* 1 = 1.85637 loss)
I0428 21:05:41.013437 22802 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0428 21:05:45.616080 22802 solver.cpp:218] Iteration 3768 (2.60727 iter/s, 4.60251s/12 iters), loss = 1.54144
I0428 21:05:45.616114 22802 solver.cpp:237]     Train net output #0: loss = 1.54144 (* 1 = 1.54144 loss)
I0428 21:05:45.616123 22802 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0428 21:05:47.513588 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0428 21:05:49.013785 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0428 21:05:50.297166 22802 solver.cpp:330] Iteration 3774, Testing net (#0)
I0428 21:05:50.297188 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:05:53.084828 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:05:54.579998 22802 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0428 21:05:54.580034 22802 solver.cpp:397]     Test net output #1: loss = 2.44488 (* 1 = 2.44488 loss)
I0428 21:05:56.374806 22802 solver.cpp:218] Iteration 3780 (1.11541 iter/s, 10.7584s/12 iters), loss = 1.78901
I0428 21:05:56.374842 22802 solver.cpp:237]     Train net output #0: loss = 1.78901 (* 1 = 1.78901 loss)
I0428 21:05:56.374850 22802 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0428 21:06:01.048461 22802 solver.cpp:218] Iteration 3792 (2.56768 iter/s, 4.67347s/12 iters), loss = 1.8186
I0428 21:06:01.048518 22802 solver.cpp:237]     Train net output #0: loss = 1.8186 (* 1 = 1.8186 loss)
I0428 21:06:01.048527 22802 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0428 21:06:05.627104 22802 solver.cpp:218] Iteration 3804 (2.62098 iter/s, 4.57844s/12 iters), loss = 1.6333
I0428 21:06:05.627152 22802 solver.cpp:237]     Train net output #0: loss = 1.6333 (* 1 = 1.6333 loss)
I0428 21:06:05.627164 22802 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0428 21:06:10.297920 22802 solver.cpp:218] Iteration 3816 (2.56925 iter/s, 4.67062s/12 iters), loss = 1.34657
I0428 21:06:10.297956 22802 solver.cpp:237]     Train net output #0: loss = 1.34657 (* 1 = 1.34657 loss)
I0428 21:06:10.297964 22802 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0428 21:06:14.948539 22802 solver.cpp:218] Iteration 3828 (2.58042 iter/s, 4.6504s/12 iters), loss = 1.70906
I0428 21:06:14.948580 22802 solver.cpp:237]     Train net output #0: loss = 1.70906 (* 1 = 1.70906 loss)
I0428 21:06:14.948591 22802 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0428 21:06:19.594981 22802 solver.cpp:218] Iteration 3840 (2.58273 iter/s, 4.64625s/12 iters), loss = 1.3605
I0428 21:06:19.595077 22802 solver.cpp:237]     Train net output #0: loss = 1.3605 (* 1 = 1.3605 loss)
I0428 21:06:19.595085 22802 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0428 21:06:20.665271 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:06:24.250934 22802 solver.cpp:218] Iteration 3852 (2.57748 iter/s, 4.65571s/12 iters), loss = 1.56926
I0428 21:06:24.250974 22802 solver.cpp:237]     Train net output #0: loss = 1.56926 (* 1 = 1.56926 loss)
I0428 21:06:24.250982 22802 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0428 21:06:28.965919 22802 solver.cpp:218] Iteration 3864 (2.54518 iter/s, 4.7148s/12 iters), loss = 1.56628
I0428 21:06:28.965955 22802 solver.cpp:237]     Train net output #0: loss = 1.56628 (* 1 = 1.56628 loss)
I0428 21:06:28.965963 22802 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0428 21:06:33.207764 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0428 21:06:35.617408 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0428 21:06:38.561417 22802 solver.cpp:330] Iteration 3876, Testing net (#0)
I0428 21:06:38.561439 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:06:41.661895 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:06:43.448858 22802 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0428 21:06:43.448889 22802 solver.cpp:397]     Test net output #1: loss = 2.45359 (* 1 = 2.45359 loss)
I0428 21:06:43.506564 22802 solver.cpp:218] Iteration 3876 (0.8253 iter/s, 14.5402s/12 iters), loss = 1.66682
I0428 21:06:43.506601 22802 solver.cpp:237]     Train net output #0: loss = 1.66682 (* 1 = 1.66682 loss)
I0428 21:06:43.506609 22802 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0428 21:06:47.348348 22802 solver.cpp:218] Iteration 3888 (3.12368 iter/s, 3.84162s/12 iters), loss = 1.4922
I0428 21:06:47.348387 22802 solver.cpp:237]     Train net output #0: loss = 1.4922 (* 1 = 1.4922 loss)
I0428 21:06:47.348398 22802 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0428 21:06:52.124348 22802 solver.cpp:218] Iteration 3900 (2.51266 iter/s, 4.77581s/12 iters), loss = 1.47003
I0428 21:06:52.124514 22802 solver.cpp:237]     Train net output #0: loss = 1.47003 (* 1 = 1.47003 loss)
I0428 21:06:52.124527 22802 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0428 21:06:56.725800 22802 solver.cpp:218] Iteration 3912 (2.60803 iter/s, 4.60117s/12 iters), loss = 1.57697
I0428 21:06:56.725837 22802 solver.cpp:237]     Train net output #0: loss = 1.57697 (* 1 = 1.57697 loss)
I0428 21:06:56.725844 22802 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0428 21:07:01.439472 22802 solver.cpp:218] Iteration 3924 (2.54589 iter/s, 4.71349s/12 iters), loss = 1.64728
I0428 21:07:01.439508 22802 solver.cpp:237]     Train net output #0: loss = 1.64728 (* 1 = 1.64728 loss)
I0428 21:07:01.439517 22802 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0428 21:07:06.342188 22802 solver.cpp:218] Iteration 3936 (2.44772 iter/s, 4.90252s/12 iters), loss = 1.49013
I0428 21:07:06.342238 22802 solver.cpp:237]     Train net output #0: loss = 1.49013 (* 1 = 1.49013 loss)
I0428 21:07:06.342252 22802 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0428 21:07:09.521250 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:07:11.032339 22802 solver.cpp:218] Iteration 3948 (2.55866 iter/s, 4.68996s/12 iters), loss = 1.43761
I0428 21:07:11.032377 22802 solver.cpp:237]     Train net output #0: loss = 1.43761 (* 1 = 1.43761 loss)
I0428 21:07:11.032385 22802 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0428 21:07:15.611346 22802 solver.cpp:218] Iteration 3960 (2.62076 iter/s, 4.57882s/12 iters), loss = 1.57325
I0428 21:07:15.611393 22802 solver.cpp:237]     Train net output #0: loss = 1.57325 (* 1 = 1.57325 loss)
I0428 21:07:15.611403 22802 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0428 21:07:20.174989 22802 solver.cpp:218] Iteration 3972 (2.62959 iter/s, 4.56345s/12 iters), loss = 1.76833
I0428 21:07:20.175026 22802 solver.cpp:237]     Train net output #0: loss = 1.76833 (* 1 = 1.76833 loss)
I0428 21:07:20.175035 22802 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0428 21:07:22.122426 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0428 21:07:23.670011 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0428 21:07:24.855373 22802 solver.cpp:330] Iteration 3978, Testing net (#0)
I0428 21:07:24.855393 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:07:27.884374 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:07:29.516156 22802 solver.cpp:397]     Test net output #0: accuracy = 0.424632
I0428 21:07:29.516186 22802 solver.cpp:397]     Test net output #1: loss = 2.34041 (* 1 = 2.34041 loss)
I0428 21:07:31.128352 22802 solver.cpp:218] Iteration 3984 (1.09559 iter/s, 10.953s/12 iters), loss = 1.28756
I0428 21:07:31.128391 22802 solver.cpp:237]     Train net output #0: loss = 1.28756 (* 1 = 1.28756 loss)
I0428 21:07:31.128398 22802 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0428 21:07:35.721783 22802 solver.cpp:218] Iteration 3996 (2.61253 iter/s, 4.59324s/12 iters), loss = 1.27871
I0428 21:07:35.721824 22802 solver.cpp:237]     Train net output #0: loss = 1.27871 (* 1 = 1.27871 loss)
I0428 21:07:35.721834 22802 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0428 21:07:40.347128 22802 solver.cpp:218] Iteration 4008 (2.59451 iter/s, 4.62515s/12 iters), loss = 1.31442
I0428 21:07:40.347179 22802 solver.cpp:237]     Train net output #0: loss = 1.31442 (* 1 = 1.31442 loss)
I0428 21:07:40.347189 22802 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0428 21:07:45.012533 22802 solver.cpp:218] Iteration 4020 (2.57225 iter/s, 4.66518s/12 iters), loss = 1.56205
I0428 21:07:45.012579 22802 solver.cpp:237]     Train net output #0: loss = 1.56205 (* 1 = 1.56205 loss)
I0428 21:07:45.012593 22802 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0428 21:07:49.646615 22802 solver.cpp:218] Iteration 4032 (2.58961 iter/s, 4.63389s/12 iters), loss = 1.417
I0428 21:07:49.646656 22802 solver.cpp:237]     Train net output #0: loss = 1.417 (* 1 = 1.417 loss)
I0428 21:07:49.646663 22802 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0428 21:07:54.223040 22802 solver.cpp:218] Iteration 4044 (2.62224 iter/s, 4.57624s/12 iters), loss = 1.76288
I0428 21:07:54.224289 22802 solver.cpp:237]     Train net output #0: loss = 1.76288 (* 1 = 1.76288 loss)
I0428 21:07:54.224306 22802 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0428 21:07:54.693428 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:07:58.885180 22802 solver.cpp:218] Iteration 4056 (2.57469 iter/s, 4.66075s/12 iters), loss = 1.23962
I0428 21:07:58.885215 22802 solver.cpp:237]     Train net output #0: loss = 1.23962 (* 1 = 1.23962 loss)
I0428 21:07:58.885221 22802 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0428 21:08:03.620827 22802 solver.cpp:218] Iteration 4068 (2.53407 iter/s, 4.73546s/12 iters), loss = 1.59259
I0428 21:08:03.620868 22802 solver.cpp:237]     Train net output #0: loss = 1.59259 (* 1 = 1.59259 loss)
I0428 21:08:03.620878 22802 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0428 21:08:07.821911 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0428 21:08:09.345218 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0428 21:08:10.531821 22802 solver.cpp:330] Iteration 4080, Testing net (#0)
I0428 21:08:10.531838 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:08:13.345027 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:08:14.926266 22802 solver.cpp:397]     Test net output #0: accuracy = 0.422794
I0428 21:08:14.926306 22802 solver.cpp:397]     Test net output #1: loss = 2.32581 (* 1 = 2.32581 loss)
I0428 21:08:14.983916 22802 solver.cpp:218] Iteration 4080 (1.05609 iter/s, 11.3627s/12 iters), loss = 1.4938
I0428 21:08:14.983953 22802 solver.cpp:237]     Train net output #0: loss = 1.4938 (* 1 = 1.4938 loss)
I0428 21:08:14.983963 22802 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0428 21:08:18.830314 22802 solver.cpp:218] Iteration 4092 (3.11994 iter/s, 3.84623s/12 iters), loss = 1.30857
I0428 21:08:18.830361 22802 solver.cpp:237]     Train net output #0: loss = 1.30857 (* 1 = 1.30857 loss)
I0428 21:08:18.830374 22802 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0428 21:08:23.530697 22802 solver.cpp:218] Iteration 4104 (2.55309 iter/s, 4.70018s/12 iters), loss = 1.64707
I0428 21:08:23.530747 22802 solver.cpp:237]     Train net output #0: loss = 1.64707 (* 1 = 1.64707 loss)
I0428 21:08:23.530761 22802 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0428 21:08:28.144325 22802 solver.cpp:218] Iteration 4116 (2.6011 iter/s, 4.61344s/12 iters), loss = 1.32657
I0428 21:08:28.144436 22802 solver.cpp:237]     Train net output #0: loss = 1.32657 (* 1 = 1.32657 loss)
I0428 21:08:28.144446 22802 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0428 21:08:32.743886 22802 solver.cpp:218] Iteration 4128 (2.60909 iter/s, 4.5993s/12 iters), loss = 1.3794
I0428 21:08:32.743929 22802 solver.cpp:237]     Train net output #0: loss = 1.3794 (* 1 = 1.3794 loss)
I0428 21:08:32.743940 22802 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0428 21:08:37.438189 22802 solver.cpp:218] Iteration 4140 (2.55639 iter/s, 4.69411s/12 iters), loss = 1.41868
I0428 21:08:37.438233 22802 solver.cpp:237]     Train net output #0: loss = 1.41868 (* 1 = 1.41868 loss)
I0428 21:08:37.438246 22802 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0428 21:08:39.874047 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:08:42.169965 22802 solver.cpp:218] Iteration 4152 (2.53615 iter/s, 4.73158s/12 iters), loss = 1.42837
I0428 21:08:42.170004 22802 solver.cpp:237]     Train net output #0: loss = 1.42837 (* 1 = 1.42837 loss)
I0428 21:08:42.170012 22802 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0428 21:08:43.658293 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:08:46.802235 22802 solver.cpp:218] Iteration 4164 (2.59063 iter/s, 4.63208s/12 iters), loss = 1.51991
I0428 21:08:46.802271 22802 solver.cpp:237]     Train net output #0: loss = 1.51991 (* 1 = 1.51991 loss)
I0428 21:08:46.802279 22802 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0428 21:08:51.473230 22802 solver.cpp:218] Iteration 4176 (2.56915 iter/s, 4.67081s/12 iters), loss = 1.32883
I0428 21:08:51.473265 22802 solver.cpp:237]     Train net output #0: loss = 1.32883 (* 1 = 1.32883 loss)
I0428 21:08:51.473273 22802 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0428 21:08:53.361387 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0428 21:08:55.460307 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0428 21:08:56.661106 22802 solver.cpp:330] Iteration 4182, Testing net (#0)
I0428 21:08:56.661126 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:08:59.361168 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:09:00.991587 22802 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0428 21:09:00.991616 22802 solver.cpp:397]     Test net output #1: loss = 2.35158 (* 1 = 2.35158 loss)
I0428 21:09:02.616572 22802 solver.cpp:218] Iteration 4188 (1.07691 iter/s, 11.143s/12 iters), loss = 1.58252
I0428 21:09:02.616614 22802 solver.cpp:237]     Train net output #0: loss = 1.58252 (* 1 = 1.58252 loss)
I0428 21:09:02.616622 22802 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0428 21:09:07.320917 22802 solver.cpp:218] Iteration 4200 (2.55094 iter/s, 4.70415s/12 iters), loss = 1.33495
I0428 21:09:07.320953 22802 solver.cpp:237]     Train net output #0: loss = 1.33495 (* 1 = 1.33495 loss)
I0428 21:09:07.320962 22802 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0428 21:09:12.032577 22802 solver.cpp:218] Iteration 4212 (2.54698 iter/s, 4.71147s/12 iters), loss = 1.39599
I0428 21:09:12.032614 22802 solver.cpp:237]     Train net output #0: loss = 1.39599 (* 1 = 1.39599 loss)
I0428 21:09:12.032622 22802 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0428 21:09:16.729465 22802 solver.cpp:218] Iteration 4224 (2.55499 iter/s, 4.6967s/12 iters), loss = 1.32562
I0428 21:09:16.729499 22802 solver.cpp:237]     Train net output #0: loss = 1.32562 (* 1 = 1.32562 loss)
I0428 21:09:16.729507 22802 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0428 21:09:21.449200 22802 solver.cpp:218] Iteration 4236 (2.54262 iter/s, 4.71955s/12 iters), loss = 0.963213
I0428 21:09:21.449246 22802 solver.cpp:237]     Train net output #0: loss = 0.963213 (* 1 = 0.963213 loss)
I0428 21:09:21.449255 22802 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0428 21:09:25.821774 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:09:26.027956 22802 solver.cpp:218] Iteration 4248 (2.62091 iter/s, 4.57856s/12 iters), loss = 1.13267
I0428 21:09:26.027992 22802 solver.cpp:237]     Train net output #0: loss = 1.13267 (* 1 = 1.13267 loss)
I0428 21:09:26.028000 22802 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0428 21:09:30.637904 22802 solver.cpp:218] Iteration 4260 (2.60317 iter/s, 4.60976s/12 iters), loss = 1.27108
I0428 21:09:30.638008 22802 solver.cpp:237]     Train net output #0: loss = 1.27108 (* 1 = 1.27108 loss)
I0428 21:09:30.638016 22802 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0428 21:09:35.338136 22802 solver.cpp:218] Iteration 4272 (2.5532 iter/s, 4.69998s/12 iters), loss = 1.16263
I0428 21:09:35.338169 22802 solver.cpp:237]     Train net output #0: loss = 1.16263 (* 1 = 1.16263 loss)
I0428 21:09:35.338176 22802 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0428 21:09:39.662597 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0428 21:09:41.174880 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0428 21:09:42.372611 22802 solver.cpp:330] Iteration 4284, Testing net (#0)
I0428 21:09:42.372629 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:09:45.016242 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:09:46.805472 22802 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0428 21:09:46.805501 22802 solver.cpp:397]     Test net output #1: loss = 2.22876 (* 1 = 2.22876 loss)
I0428 21:09:46.863019 22802 solver.cpp:218] Iteration 4284 (1.04126 iter/s, 11.5245s/12 iters), loss = 1.18667
I0428 21:09:46.863061 22802 solver.cpp:237]     Train net output #0: loss = 1.18667 (* 1 = 1.18667 loss)
I0428 21:09:46.863068 22802 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0428 21:09:50.688668 22802 solver.cpp:218] Iteration 4296 (3.13686 iter/s, 3.82548s/12 iters), loss = 1.33026
I0428 21:09:50.688702 22802 solver.cpp:237]     Train net output #0: loss = 1.33026 (* 1 = 1.33026 loss)
I0428 21:09:50.688709 22802 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0428 21:09:55.343356 22802 solver.cpp:218] Iteration 4308 (2.57815 iter/s, 4.6545s/12 iters), loss = 1.4687
I0428 21:09:55.343405 22802 solver.cpp:237]     Train net output #0: loss = 1.4687 (* 1 = 1.4687 loss)
I0428 21:09:55.343415 22802 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0428 21:10:00.087442 22802 solver.cpp:218] Iteration 4320 (2.52957 iter/s, 4.74389s/12 iters), loss = 1.11227
I0428 21:10:00.087491 22802 solver.cpp:237]     Train net output #0: loss = 1.11227 (* 1 = 1.11227 loss)
I0428 21:10:00.087502 22802 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0428 21:10:04.692080 22802 solver.cpp:218] Iteration 4332 (2.60618 iter/s, 4.60445s/12 iters), loss = 1.31149
I0428 21:10:04.692215 22802 solver.cpp:237]     Train net output #0: loss = 1.31149 (* 1 = 1.31149 loss)
I0428 21:10:04.692225 22802 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0428 21:10:09.339102 22802 solver.cpp:218] Iteration 4344 (2.58246 iter/s, 4.64674s/12 iters), loss = 1.16229
I0428 21:10:09.339135 22802 solver.cpp:237]     Train net output #0: loss = 1.16229 (* 1 = 1.16229 loss)
I0428 21:10:09.339143 22802 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0428 21:10:11.105521 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:10:13.954114 22802 solver.cpp:218] Iteration 4356 (2.60031 iter/s, 4.61483s/12 iters), loss = 1.08504
I0428 21:10:13.954157 22802 solver.cpp:237]     Train net output #0: loss = 1.08504 (* 1 = 1.08504 loss)
I0428 21:10:13.954166 22802 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0428 21:10:18.644254 22802 solver.cpp:218] Iteration 4368 (2.55866 iter/s, 4.68995s/12 iters), loss = 1.0722
I0428 21:10:18.644294 22802 solver.cpp:237]     Train net output #0: loss = 1.0722 (* 1 = 1.0722 loss)
I0428 21:10:18.644304 22802 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0428 21:10:23.291034 22802 solver.cpp:218] Iteration 4380 (2.58254 iter/s, 4.64659s/12 iters), loss = 1.41986
I0428 21:10:23.291070 22802 solver.cpp:237]     Train net output #0: loss = 1.41986 (* 1 = 1.41986 loss)
I0428 21:10:23.291079 22802 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0428 21:10:25.264845 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0428 21:10:30.010363 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0428 21:10:33.099141 22802 solver.cpp:330] Iteration 4386, Testing net (#0)
I0428 21:10:33.099161 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:10:35.643065 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:10:37.344305 22802 solver.cpp:397]     Test net output #0: accuracy = 0.45527
I0428 21:10:37.344343 22802 solver.cpp:397]     Test net output #1: loss = 2.2751 (* 1 = 2.2751 loss)
I0428 21:10:39.046712 22802 solver.cpp:218] Iteration 4392 (0.761655 iter/s, 15.7552s/12 iters), loss = 1.27406
I0428 21:10:39.046761 22802 solver.cpp:237]     Train net output #0: loss = 1.27406 (* 1 = 1.27406 loss)
I0428 21:10:39.046772 22802 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0428 21:10:43.693006 22802 solver.cpp:218] Iteration 4404 (2.58281 iter/s, 4.64609s/12 iters), loss = 1.38728
I0428 21:10:43.693059 22802 solver.cpp:237]     Train net output #0: loss = 1.38728 (* 1 = 1.38728 loss)
I0428 21:10:43.693071 22802 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0428 21:10:48.274482 22802 solver.cpp:218] Iteration 4416 (2.61936 iter/s, 4.58127s/12 iters), loss = 1.31511
I0428 21:10:48.274518 22802 solver.cpp:237]     Train net output #0: loss = 1.31511 (* 1 = 1.31511 loss)
I0428 21:10:48.274526 22802 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0428 21:10:52.931634 22802 solver.cpp:218] Iteration 4428 (2.57678 iter/s, 4.65697s/12 iters), loss = 0.919312
I0428 21:10:52.931669 22802 solver.cpp:237]     Train net output #0: loss = 0.919312 (* 1 = 0.919312 loss)
I0428 21:10:52.931677 22802 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0428 21:10:57.574355 22802 solver.cpp:218] Iteration 4440 (2.5848 iter/s, 4.64253s/12 iters), loss = 1.14179
I0428 21:10:57.574393 22802 solver.cpp:237]     Train net output #0: loss = 1.14179 (* 1 = 1.14179 loss)
I0428 21:10:57.574401 22802 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0428 21:11:01.316027 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:11:02.191030 22802 solver.cpp:218] Iteration 4452 (2.59938 iter/s, 4.61649s/12 iters), loss = 1.29095
I0428 21:11:02.191066 22802 solver.cpp:237]     Train net output #0: loss = 1.29095 (* 1 = 1.29095 loss)
I0428 21:11:02.191073 22802 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0428 21:11:06.872726 22802 solver.cpp:218] Iteration 4464 (2.56328 iter/s, 4.68151s/12 iters), loss = 1.09985
I0428 21:11:06.872853 22802 solver.cpp:237]     Train net output #0: loss = 1.09985 (* 1 = 1.09985 loss)
I0428 21:11:06.872862 22802 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0428 21:11:11.517360 22802 solver.cpp:218] Iteration 4476 (2.58378 iter/s, 4.64436s/12 iters), loss = 1.08534
I0428 21:11:11.517396 22802 solver.cpp:237]     Train net output #0: loss = 1.08534 (* 1 = 1.08534 loss)
I0428 21:11:11.517405 22802 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0428 21:11:15.693222 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0428 21:11:17.526966 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0428 21:11:18.846808 22802 solver.cpp:330] Iteration 4488, Testing net (#0)
I0428 21:11:18.846827 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:11:21.415033 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:11:23.155020 22802 solver.cpp:397]     Test net output #0: accuracy = 0.457108
I0428 21:11:23.155048 22802 solver.cpp:397]     Test net output #1: loss = 2.19997 (* 1 = 2.19997 loss)
I0428 21:11:23.212834 22802 solver.cpp:218] Iteration 4488 (1.02607 iter/s, 11.6951s/12 iters), loss = 0.835216
I0428 21:11:23.212872 22802 solver.cpp:237]     Train net output #0: loss = 0.835216 (* 1 = 0.835216 loss)
I0428 21:11:23.212882 22802 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0428 21:11:27.208851 22802 solver.cpp:218] Iteration 4500 (3.00312 iter/s, 3.99585s/12 iters), loss = 1.14562
I0428 21:11:27.208897 22802 solver.cpp:237]     Train net output #0: loss = 1.14562 (* 1 = 1.14562 loss)
I0428 21:11:27.208907 22802 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0428 21:11:31.851094 22802 solver.cpp:218] Iteration 4512 (2.58507 iter/s, 4.64205s/12 iters), loss = 1.25081
I0428 21:11:31.851142 22802 solver.cpp:237]     Train net output #0: loss = 1.25081 (* 1 = 1.25081 loss)
I0428 21:11:31.851155 22802 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0428 21:11:36.491645 22802 solver.cpp:218] Iteration 4524 (2.58601 iter/s, 4.64035s/12 iters), loss = 0.999744
I0428 21:11:36.491694 22802 solver.cpp:237]     Train net output #0: loss = 0.999744 (* 1 = 0.999744 loss)
I0428 21:11:36.491706 22802 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0428 21:11:41.110806 22802 solver.cpp:218] Iteration 4536 (2.59798 iter/s, 4.61897s/12 iters), loss = 1.16119
I0428 21:11:41.111111 22802 solver.cpp:237]     Train net output #0: loss = 1.16119 (* 1 = 1.16119 loss)
I0428 21:11:41.111120 22802 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0428 21:11:45.754060 22802 solver.cpp:218] Iteration 4548 (2.58465 iter/s, 4.6428s/12 iters), loss = 1.04947
I0428 21:11:45.754099 22802 solver.cpp:237]     Train net output #0: loss = 1.04947 (* 1 = 1.04947 loss)
I0428 21:11:45.754107 22802 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0428 21:11:46.941967 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:11:50.430127 22802 solver.cpp:218] Iteration 4560 (2.56636 iter/s, 4.67588s/12 iters), loss = 1.02573
I0428 21:11:50.430166 22802 solver.cpp:237]     Train net output #0: loss = 1.02573 (* 1 = 1.02573 loss)
I0428 21:11:50.430173 22802 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0428 21:11:55.217252 22802 solver.cpp:218] Iteration 4572 (2.50683 iter/s, 4.78693s/12 iters), loss = 1.05288
I0428 21:11:55.217303 22802 solver.cpp:237]     Train net output #0: loss = 1.05288 (* 1 = 1.05288 loss)
I0428 21:11:55.217314 22802 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0428 21:11:59.827955 22802 solver.cpp:218] Iteration 4584 (2.60275 iter/s, 4.61051s/12 iters), loss = 1.04492
I0428 21:11:59.827996 22802 solver.cpp:237]     Train net output #0: loss = 1.04492 (* 1 = 1.04492 loss)
I0428 21:11:59.828002 22802 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0428 21:12:01.714157 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0428 21:12:09.152865 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0428 21:12:16.907958 22802 solver.cpp:330] Iteration 4590, Testing net (#0)
I0428 21:12:16.908044 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:12:19.509071 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:12:21.321707 22802 solver.cpp:397]     Test net output #0: accuracy = 0.421569
I0428 21:12:21.321734 22802 solver.cpp:397]     Test net output #1: loss = 2.49999 (* 1 = 2.49999 loss)
I0428 21:12:22.863330 22802 solver.cpp:218] Iteration 4596 (0.520954 iter/s, 23.0347s/12 iters), loss = 1.19704
I0428 21:12:22.863386 22802 solver.cpp:237]     Train net output #0: loss = 1.19704 (* 1 = 1.19704 loss)
I0428 21:12:22.863399 22802 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0428 21:12:27.501283 22802 solver.cpp:218] Iteration 4608 (2.58747 iter/s, 4.63774s/12 iters), loss = 1.18058
I0428 21:12:27.501320 22802 solver.cpp:237]     Train net output #0: loss = 1.18058 (* 1 = 1.18058 loss)
I0428 21:12:27.501329 22802 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0428 21:12:32.120445 22802 solver.cpp:218] Iteration 4620 (2.59798 iter/s, 4.61898s/12 iters), loss = 1.00243
I0428 21:12:32.120483 22802 solver.cpp:237]     Train net output #0: loss = 1.00243 (* 1 = 1.00243 loss)
I0428 21:12:32.120528 22802 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0428 21:12:36.779979 22802 solver.cpp:218] Iteration 4632 (2.57547 iter/s, 4.65935s/12 iters), loss = 1.01014
I0428 21:12:36.780017 22802 solver.cpp:237]     Train net output #0: loss = 1.01014 (* 1 = 1.01014 loss)
I0428 21:12:36.780025 22802 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0428 21:12:41.379916 22802 solver.cpp:218] Iteration 4644 (2.60884 iter/s, 4.59975s/12 iters), loss = 1.16206
I0428 21:12:41.379963 22802 solver.cpp:237]     Train net output #0: loss = 1.16206 (* 1 = 1.16206 loss)
I0428 21:12:41.379976 22802 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0428 21:12:44.545971 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:12:46.126641 22802 solver.cpp:218] Iteration 4656 (2.52816 iter/s, 4.74653s/12 iters), loss = 1.09709
I0428 21:12:46.126677 22802 solver.cpp:237]     Train net output #0: loss = 1.09709 (* 1 = 1.09709 loss)
I0428 21:12:46.126684 22802 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0428 21:12:50.817361 22802 solver.cpp:218] Iteration 4668 (2.55834 iter/s, 4.69053s/12 iters), loss = 1.26636
I0428 21:12:50.817543 22802 solver.cpp:237]     Train net output #0: loss = 1.26636 (* 1 = 1.26636 loss)
I0428 21:12:50.817553 22802 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0428 21:12:55.513797 22802 solver.cpp:218] Iteration 4680 (2.55531 iter/s, 4.6961s/12 iters), loss = 1.14988
I0428 21:12:55.513847 22802 solver.cpp:237]     Train net output #0: loss = 1.14988 (* 1 = 1.14988 loss)
I0428 21:12:55.513859 22802 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0428 21:12:59.894850 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0428 21:13:05.114605 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0428 21:13:09.430805 22802 solver.cpp:330] Iteration 4692, Testing net (#0)
I0428 21:13:09.430830 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:13:11.889173 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:13:13.706578 22802 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0428 21:13:13.706606 22802 solver.cpp:397]     Test net output #1: loss = 2.32237 (* 1 = 2.32237 loss)
I0428 21:13:13.763885 22802 solver.cpp:218] Iteration 4692 (0.657553 iter/s, 18.2495s/12 iters), loss = 0.956172
I0428 21:13:13.763921 22802 solver.cpp:237]     Train net output #0: loss = 0.956172 (* 1 = 0.956172 loss)
I0428 21:13:13.763928 22802 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0428 21:13:17.906549 22802 solver.cpp:218] Iteration 4704 (2.89681 iter/s, 4.14249s/12 iters), loss = 1.4678
I0428 21:13:17.906585 22802 solver.cpp:237]     Train net output #0: loss = 1.4678 (* 1 = 1.4678 loss)
I0428 21:13:17.906595 22802 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0428 21:13:22.762321 22802 solver.cpp:218] Iteration 4716 (2.47138 iter/s, 4.85558s/12 iters), loss = 0.945424
I0428 21:13:22.762430 22802 solver.cpp:237]     Train net output #0: loss = 0.945424 (* 1 = 0.945424 loss)
I0428 21:13:22.762440 22802 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0428 21:13:27.581795 22802 solver.cpp:218] Iteration 4728 (2.49003 iter/s, 4.81921s/12 iters), loss = 1.05996
I0428 21:13:27.581833 22802 solver.cpp:237]     Train net output #0: loss = 1.05996 (* 1 = 1.05996 loss)
I0428 21:13:27.581841 22802 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0428 21:13:32.159657 22802 solver.cpp:218] Iteration 4740 (2.62142 iter/s, 4.57768s/12 iters), loss = 1.07028
I0428 21:13:32.159696 22802 solver.cpp:237]     Train net output #0: loss = 1.07028 (* 1 = 1.07028 loss)
I0428 21:13:32.159703 22802 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0428 21:13:36.837428 22802 solver.cpp:218] Iteration 4752 (2.56543 iter/s, 4.67758s/12 iters), loss = 1.17941
I0428 21:13:36.837468 22802 solver.cpp:237]     Train net output #0: loss = 1.17941 (* 1 = 1.17941 loss)
I0428 21:13:36.837476 22802 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0428 21:13:37.337245 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:13:41.458487 22802 solver.cpp:218] Iteration 4764 (2.59691 iter/s, 4.62087s/12 iters), loss = 0.992692
I0428 21:13:41.458523 22802 solver.cpp:237]     Train net output #0: loss = 0.992692 (* 1 = 0.992692 loss)
I0428 21:13:41.458531 22802 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0428 21:13:46.018730 22802 solver.cpp:218] Iteration 4776 (2.63154 iter/s, 4.56006s/12 iters), loss = 1.06548
I0428 21:13:46.018767 22802 solver.cpp:237]     Train net output #0: loss = 1.06548 (* 1 = 1.06548 loss)
I0428 21:13:46.018774 22802 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0428 21:13:50.633425 22802 solver.cpp:218] Iteration 4788 (2.6005 iter/s, 4.6145s/12 iters), loss = 1.24591
I0428 21:13:50.633463 22802 solver.cpp:237]     Train net output #0: loss = 1.24591 (* 1 = 1.24591 loss)
I0428 21:13:50.633471 22802 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0428 21:13:52.513628 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0428 21:13:54.022047 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0428 21:13:55.215060 22802 solver.cpp:330] Iteration 4794, Testing net (#0)
I0428 21:13:55.215082 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:13:57.968345 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:13:59.820319 22802 solver.cpp:397]     Test net output #0: accuracy = 0.446691
I0428 21:13:59.820348 22802 solver.cpp:397]     Test net output #1: loss = 2.22267 (* 1 = 2.22267 loss)
I0428 21:14:01.444788 22802 solver.cpp:218] Iteration 4800 (1.10998 iter/s, 10.811s/12 iters), loss = 1.24511
I0428 21:14:01.444828 22802 solver.cpp:237]     Train net output #0: loss = 1.24511 (* 1 = 1.24511 loss)
I0428 21:14:01.444835 22802 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0428 21:14:06.069407 22802 solver.cpp:218] Iteration 4812 (2.59492 iter/s, 4.62442s/12 iters), loss = 1.05105
I0428 21:14:06.069449 22802 solver.cpp:237]     Train net output #0: loss = 1.05105 (* 1 = 1.05105 loss)
I0428 21:14:06.069460 22802 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0428 21:14:10.681569 22802 solver.cpp:218] Iteration 4824 (2.60192 iter/s, 4.61197s/12 iters), loss = 1.00256
I0428 21:14:10.681603 22802 solver.cpp:237]     Train net output #0: loss = 1.00256 (* 1 = 1.00256 loss)
I0428 21:14:10.681612 22802 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0428 21:14:15.326424 22802 solver.cpp:218] Iteration 4836 (2.58361 iter/s, 4.64466s/12 iters), loss = 0.990778
I0428 21:14:15.326474 22802 solver.cpp:237]     Train net output #0: loss = 0.990778 (* 1 = 0.990778 loss)
I0428 21:14:15.326486 22802 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0428 21:14:17.225612 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:14:19.958441 22802 solver.cpp:218] Iteration 4848 (2.59077 iter/s, 4.63182s/12 iters), loss = 1.01176
I0428 21:14:19.958487 22802 solver.cpp:237]     Train net output #0: loss = 1.01176 (* 1 = 1.01176 loss)
I0428 21:14:19.958498 22802 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0428 21:14:22.415122 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:14:24.534467 22802 solver.cpp:218] Iteration 4860 (2.62247 iter/s, 4.57584s/12 iters), loss = 1.07046
I0428 21:14:24.534723 22802 solver.cpp:237]     Train net output #0: loss = 1.07046 (* 1 = 1.07046 loss)
I0428 21:14:24.534732 22802 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0428 21:14:29.102182 22802 solver.cpp:218] Iteration 4872 (2.62736 iter/s, 4.56732s/12 iters), loss = 1.07162
I0428 21:14:29.102218 22802 solver.cpp:237]     Train net output #0: loss = 1.07162 (* 1 = 1.07162 loss)
I0428 21:14:29.102227 22802 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0428 21:14:34.042747 22802 solver.cpp:218] Iteration 4884 (2.42897 iter/s, 4.94037s/12 iters), loss = 0.908506
I0428 21:14:34.042804 22802 solver.cpp:237]     Train net output #0: loss = 0.908506 (* 1 = 0.908506 loss)
I0428 21:14:34.042817 22802 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0428 21:14:38.222533 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0428 21:14:39.896761 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0428 21:14:41.851814 22802 solver.cpp:330] Iteration 4896, Testing net (#0)
I0428 21:14:41.851840 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:14:44.194557 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:14:46.167184 22802 solver.cpp:397]     Test net output #0: accuracy = 0.465074
I0428 21:14:46.167213 22802 solver.cpp:397]     Test net output #1: loss = 2.34623 (* 1 = 2.34623 loss)
I0428 21:14:46.224714 22802 solver.cpp:218] Iteration 4896 (0.985097 iter/s, 12.1815s/12 iters), loss = 0.931398
I0428 21:14:46.224761 22802 solver.cpp:237]     Train net output #0: loss = 0.931398 (* 1 = 0.931398 loss)
I0428 21:14:46.224774 22802 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0428 21:14:50.315423 22802 solver.cpp:218] Iteration 4908 (2.93361 iter/s, 4.09053s/12 iters), loss = 1.03571
I0428 21:14:50.315460 22802 solver.cpp:237]     Train net output #0: loss = 1.03571 (* 1 = 1.03571 loss)
I0428 21:14:50.315469 22802 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0428 21:14:55.001816 22802 solver.cpp:218] Iteration 4920 (2.56071 iter/s, 4.68621s/12 iters), loss = 0.832298
I0428 21:14:55.001941 22802 solver.cpp:237]     Train net output #0: loss = 0.832298 (* 1 = 0.832298 loss)
I0428 21:14:55.001951 22802 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0428 21:14:59.826130 22802 solver.cpp:218] Iteration 4932 (2.48754 iter/s, 4.82404s/12 iters), loss = 1.16832
I0428 21:14:59.826169 22802 solver.cpp:237]     Train net output #0: loss = 1.16832 (* 1 = 1.16832 loss)
I0428 21:14:59.826177 22802 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0428 21:15:04.507400 22802 solver.cpp:218] Iteration 4944 (2.56351 iter/s, 4.68108s/12 iters), loss = 0.75096
I0428 21:15:04.507439 22802 solver.cpp:237]     Train net output #0: loss = 0.75096 (* 1 = 0.75096 loss)
I0428 21:15:04.507449 22802 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0428 21:15:08.933197 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:15:09.108156 22802 solver.cpp:218] Iteration 4956 (2.60837 iter/s, 4.60057s/12 iters), loss = 1.02185
I0428 21:15:09.108201 22802 solver.cpp:237]     Train net output #0: loss = 1.02185 (* 1 = 1.02185 loss)
I0428 21:15:09.108211 22802 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0428 21:15:13.707451 22802 solver.cpp:218] Iteration 4968 (2.6092 iter/s, 4.5991s/12 iters), loss = 0.936694
I0428 21:15:13.707486 22802 solver.cpp:237]     Train net output #0: loss = 0.936694 (* 1 = 0.936694 loss)
I0428 21:15:13.707494 22802 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0428 21:15:18.392426 22802 solver.cpp:218] Iteration 4980 (2.56148 iter/s, 4.68478s/12 iters), loss = 0.972663
I0428 21:15:18.392473 22802 solver.cpp:237]     Train net output #0: loss = 0.972663 (* 1 = 0.972663 loss)
I0428 21:15:18.392482 22802 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0428 21:15:23.146422 22802 solver.cpp:218] Iteration 4992 (2.5243 iter/s, 4.7538s/12 iters), loss = 0.767521
I0428 21:15:23.146459 22802 solver.cpp:237]     Train net output #0: loss = 0.767521 (* 1 = 0.767521 loss)
I0428 21:15:23.146468 22802 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0428 21:15:25.089143 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0428 21:15:26.606521 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0428 21:15:27.793913 22802 solver.cpp:330] Iteration 4998, Testing net (#0)
I0428 21:15:27.793932 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:15:30.221961 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:15:32.197743 22802 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0428 21:15:32.197772 22802 solver.cpp:397]     Test net output #1: loss = 2.36506 (* 1 = 2.36506 loss)
I0428 21:15:33.921350 22802 solver.cpp:218] Iteration 5004 (1.11373 iter/s, 10.7746s/12 iters), loss = 0.943789
I0428 21:15:33.921389 22802 solver.cpp:237]     Train net output #0: loss = 0.943789 (* 1 = 0.943789 loss)
I0428 21:15:33.921398 22802 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0428 21:15:38.476527 22802 solver.cpp:218] Iteration 5016 (2.63448 iter/s, 4.55497s/12 iters), loss = 1.18211
I0428 21:15:38.476568 22802 solver.cpp:237]     Train net output #0: loss = 1.18211 (* 1 = 1.18211 loss)
I0428 21:15:38.476588 22802 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0428 21:15:43.125871 22802 solver.cpp:218] Iteration 5028 (2.58111 iter/s, 4.64916s/12 iters), loss = 0.967318
I0428 21:15:43.125910 22802 solver.cpp:237]     Train net output #0: loss = 0.967318 (* 1 = 0.967318 loss)
I0428 21:15:43.125917 22802 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0428 21:15:47.740387 22802 solver.cpp:218] Iteration 5040 (2.6006 iter/s, 4.61432s/12 iters), loss = 0.9155
I0428 21:15:47.740438 22802 solver.cpp:237]     Train net output #0: loss = 0.9155 (* 1 = 0.9155 loss)
I0428 21:15:47.740449 22802 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0428 21:15:52.370410 22802 solver.cpp:218] Iteration 5052 (2.59189 iter/s, 4.62983s/12 iters), loss = 0.762068
I0428 21:15:52.370448 22802 solver.cpp:237]     Train net output #0: loss = 0.762068 (* 1 = 0.762068 loss)
I0428 21:15:52.370455 22802 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0428 21:15:54.224896 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:15:57.158769 22802 solver.cpp:218] Iteration 5064 (2.50618 iter/s, 4.78817s/12 iters), loss = 0.609479
I0428 21:15:57.158890 22802 solver.cpp:237]     Train net output #0: loss = 0.609479 (* 1 = 0.609479 loss)
I0428 21:15:57.158900 22802 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0428 21:16:01.786803 22802 solver.cpp:218] Iteration 5076 (2.59304 iter/s, 4.62777s/12 iters), loss = 0.916023
I0428 21:16:01.786840 22802 solver.cpp:237]     Train net output #0: loss = 0.916023 (* 1 = 0.916023 loss)
I0428 21:16:01.786849 22802 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0428 21:16:06.398181 22802 solver.cpp:218] Iteration 5088 (2.60236 iter/s, 4.61119s/12 iters), loss = 0.740568
I0428 21:16:06.398227 22802 solver.cpp:237]     Train net output #0: loss = 0.740568 (* 1 = 0.740568 loss)
I0428 21:16:06.398239 22802 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0428 21:16:10.594307 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0428 21:16:12.085657 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0428 21:16:13.275466 22802 solver.cpp:330] Iteration 5100, Testing net (#0)
I0428 21:16:13.275486 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:16:15.628823 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:16:17.659462 22802 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0428 21:16:17.659498 22802 solver.cpp:397]     Test net output #1: loss = 2.37785 (* 1 = 2.37785 loss)
I0428 21:16:17.717191 22802 solver.cpp:218] Iteration 5100 (1.0602 iter/s, 11.3186s/12 iters), loss = 0.872815
I0428 21:16:17.717242 22802 solver.cpp:237]     Train net output #0: loss = 0.872815 (* 1 = 0.872815 loss)
I0428 21:16:17.717253 22802 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0428 21:16:21.582243 22802 solver.cpp:218] Iteration 5112 (3.10488 iter/s, 3.86488s/12 iters), loss = 1.05703
I0428 21:16:21.582280 22802 solver.cpp:237]     Train net output #0: loss = 1.05703 (* 1 = 1.05703 loss)
I0428 21:16:21.582288 22802 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0428 21:16:26.140465 22802 solver.cpp:218] Iteration 5124 (2.63271 iter/s, 4.55803s/12 iters), loss = 0.926423
I0428 21:16:26.140520 22802 solver.cpp:237]     Train net output #0: loss = 0.926423 (* 1 = 0.926423 loss)
I0428 21:16:26.140529 22802 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0428 21:16:30.731981 22802 solver.cpp:218] Iteration 5136 (2.61363 iter/s, 4.59131s/12 iters), loss = 1.16282
I0428 21:16:30.732746 22802 solver.cpp:237]     Train net output #0: loss = 1.16282 (* 1 = 1.16282 loss)
I0428 21:16:30.732759 22802 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0428 21:16:35.343703 22802 solver.cpp:218] Iteration 5148 (2.60258 iter/s, 4.61081s/12 iters), loss = 1.16591
I0428 21:16:35.343756 22802 solver.cpp:237]     Train net output #0: loss = 1.16591 (* 1 = 1.16591 loss)
I0428 21:16:35.343767 22802 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0428 21:16:39.110754 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:16:39.954984 22802 solver.cpp:218] Iteration 5160 (2.60243 iter/s, 4.61108s/12 iters), loss = 0.818227
I0428 21:16:39.955041 22802 solver.cpp:237]     Train net output #0: loss = 0.818227 (* 1 = 0.818227 loss)
I0428 21:16:39.955052 22802 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0428 21:16:44.566375 22802 solver.cpp:218] Iteration 5172 (2.60237 iter/s, 4.61119s/12 iters), loss = 0.919034
I0428 21:16:44.566428 22802 solver.cpp:237]     Train net output #0: loss = 0.919034 (* 1 = 0.919034 loss)
I0428 21:16:44.566438 22802 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0428 21:16:49.188004 22802 solver.cpp:218] Iteration 5184 (2.5966 iter/s, 4.62143s/12 iters), loss = 0.81005
I0428 21:16:49.188056 22802 solver.cpp:237]     Train net output #0: loss = 0.81005 (* 1 = 0.81005 loss)
I0428 21:16:49.188067 22802 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0428 21:16:53.814649 22802 solver.cpp:218] Iteration 5196 (2.59378 iter/s, 4.62645s/12 iters), loss = 0.857949
I0428 21:16:53.814697 22802 solver.cpp:237]     Train net output #0: loss = 0.857949 (* 1 = 0.857949 loss)
I0428 21:16:53.814708 22802 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0428 21:16:55.708999 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0428 21:16:57.269079 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0428 21:16:59.380789 22802 solver.cpp:330] Iteration 5202, Testing net (#0)
I0428 21:16:59.380808 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:17:01.658923 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:17:03.663969 22802 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0428 21:17:03.663998 22802 solver.cpp:397]     Test net output #1: loss = 2.30041 (* 1 = 2.30041 loss)
I0428 21:17:05.307973 22802 solver.cpp:218] Iteration 5208 (1.04412 iter/s, 11.4929s/12 iters), loss = 0.63761
I0428 21:17:05.308012 22802 solver.cpp:237]     Train net output #0: loss = 0.63761 (* 1 = 0.63761 loss)
I0428 21:17:05.308019 22802 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0428 21:17:09.910470 22802 solver.cpp:218] Iteration 5220 (2.60739 iter/s, 4.60231s/12 iters), loss = 0.919858
I0428 21:17:09.910508 22802 solver.cpp:237]     Train net output #0: loss = 0.919858 (* 1 = 0.919858 loss)
I0428 21:17:09.910517 22802 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0428 21:17:14.528555 22802 solver.cpp:218] Iteration 5232 (2.59858 iter/s, 4.6179s/12 iters), loss = 0.777826
I0428 21:17:14.528594 22802 solver.cpp:237]     Train net output #0: loss = 0.777826 (* 1 = 0.777826 loss)
I0428 21:17:14.528602 22802 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0428 21:17:19.159610 22802 solver.cpp:218] Iteration 5244 (2.59131 iter/s, 4.63087s/12 iters), loss = 1.05462
I0428 21:17:19.159647 22802 solver.cpp:237]     Train net output #0: loss = 1.05462 (* 1 = 1.05462 loss)
I0428 21:17:19.159654 22802 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0428 21:17:23.887800 22802 solver.cpp:218] Iteration 5256 (2.53807 iter/s, 4.728s/12 iters), loss = 0.700474
I0428 21:17:23.887837 22802 solver.cpp:237]     Train net output #0: loss = 0.700474 (* 1 = 0.700474 loss)
I0428 21:17:23.887846 22802 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0428 21:17:25.088938 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:17:28.497275 22802 solver.cpp:218] Iteration 5268 (2.60344 iter/s, 4.60928s/12 iters), loss = 1.02149
I0428 21:17:28.497321 22802 solver.cpp:237]     Train net output #0: loss = 1.02149 (* 1 = 1.02149 loss)
I0428 21:17:28.497332 22802 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0428 21:17:33.146792 22802 solver.cpp:218] Iteration 5280 (2.58102 iter/s, 4.64932s/12 iters), loss = 0.840103
I0428 21:17:33.157995 22802 solver.cpp:237]     Train net output #0: loss = 0.840103 (* 1 = 0.840103 loss)
I0428 21:17:33.158010 22802 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0428 21:17:37.754585 22802 solver.cpp:218] Iteration 5292 (2.61071 iter/s, 4.59645s/12 iters), loss = 0.535059
I0428 21:17:37.754624 22802 solver.cpp:237]     Train net output #0: loss = 0.535059 (* 1 = 0.535059 loss)
I0428 21:17:37.754633 22802 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0428 21:17:42.196792 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0428 21:17:44.856308 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0428 21:17:46.050599 22802 solver.cpp:330] Iteration 5304, Testing net (#0)
I0428 21:17:46.050621 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:17:48.245218 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:17:50.384840 22802 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0428 21:17:50.384877 22802 solver.cpp:397]     Test net output #1: loss = 2.32123 (* 1 = 2.32123 loss)
I0428 21:17:50.442606 22802 solver.cpp:218] Iteration 5304 (0.945806 iter/s, 12.6876s/12 iters), loss = 0.848982
I0428 21:17:50.442651 22802 solver.cpp:237]     Train net output #0: loss = 0.848982 (* 1 = 0.848982 loss)
I0428 21:17:50.442660 22802 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0428 21:17:54.384529 22802 solver.cpp:218] Iteration 5316 (3.04435 iter/s, 3.94173s/12 iters), loss = 0.864324
I0428 21:17:54.384567 22802 solver.cpp:237]     Train net output #0: loss = 0.864324 (* 1 = 0.864324 loss)
I0428 21:17:54.384574 22802 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0428 21:17:58.953191 22802 solver.cpp:218] Iteration 5328 (2.6267 iter/s, 4.56847s/12 iters), loss = 0.832226
I0428 21:17:58.953238 22802 solver.cpp:237]     Train net output #0: loss = 0.832226 (* 1 = 0.832226 loss)
I0428 21:17:58.953248 22802 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0428 21:18:03.539420 22802 solver.cpp:218] Iteration 5340 (2.61664 iter/s, 4.58604s/12 iters), loss = 0.763639
I0428 21:18:03.539541 22802 solver.cpp:237]     Train net output #0: loss = 0.763639 (* 1 = 0.763639 loss)
I0428 21:18:03.539551 22802 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0428 21:18:08.180830 22802 solver.cpp:218] Iteration 5352 (2.58557 iter/s, 4.64114s/12 iters), loss = 0.807477
I0428 21:18:08.180869 22802 solver.cpp:237]     Train net output #0: loss = 0.807477 (* 1 = 0.807477 loss)
I0428 21:18:08.180878 22802 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0428 21:18:11.353745 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:18:12.811031 22802 solver.cpp:218] Iteration 5364 (2.59179 iter/s, 4.63001s/12 iters), loss = 0.880996
I0428 21:18:12.811089 22802 solver.cpp:237]     Train net output #0: loss = 0.880996 (* 1 = 0.880996 loss)
I0428 21:18:12.811101 22802 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0428 21:18:17.512341 22802 solver.cpp:218] Iteration 5376 (2.55259 iter/s, 4.70111s/12 iters), loss = 0.844708
I0428 21:18:17.512377 22802 solver.cpp:237]     Train net output #0: loss = 0.844708 (* 1 = 0.844708 loss)
I0428 21:18:17.512384 22802 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0428 21:18:22.194065 22802 solver.cpp:218] Iteration 5388 (2.56326 iter/s, 4.68154s/12 iters), loss = 0.742142
I0428 21:18:22.194108 22802 solver.cpp:237]     Train net output #0: loss = 0.742142 (* 1 = 0.742142 loss)
I0428 21:18:22.194116 22802 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0428 21:18:26.813333 22802 solver.cpp:218] Iteration 5400 (2.59792 iter/s, 4.61908s/12 iters), loss = 0.844325
I0428 21:18:26.813370 22802 solver.cpp:237]     Train net output #0: loss = 0.844325 (* 1 = 0.844325 loss)
I0428 21:18:26.813380 22802 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0428 21:18:28.717337 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0428 21:18:33.183794 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0428 21:18:35.442704 22802 solver.cpp:330] Iteration 5406, Testing net (#0)
I0428 21:18:35.442807 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:18:37.674559 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:18:39.766001 22802 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0428 21:18:39.766036 22802 solver.cpp:397]     Test net output #1: loss = 2.37546 (* 1 = 2.37546 loss)
I0428 21:18:41.491973 22802 solver.cpp:218] Iteration 5412 (0.817541 iter/s, 14.6782s/12 iters), loss = 0.811483
I0428 21:18:41.492010 22802 solver.cpp:237]     Train net output #0: loss = 0.811483 (* 1 = 0.811483 loss)
I0428 21:18:41.492018 22802 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0428 21:18:46.104122 22802 solver.cpp:218] Iteration 5424 (2.60193 iter/s, 4.61196s/12 iters), loss = 0.871432
I0428 21:18:46.104162 22802 solver.cpp:237]     Train net output #0: loss = 0.871432 (* 1 = 0.871432 loss)
I0428 21:18:46.104171 22802 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0428 21:18:50.757169 22802 solver.cpp:218] Iteration 5436 (2.57906 iter/s, 4.65286s/12 iters), loss = 0.794611
I0428 21:18:50.757205 22802 solver.cpp:237]     Train net output #0: loss = 0.794611 (* 1 = 0.794611 loss)
I0428 21:18:50.757212 22802 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0428 21:18:55.451390 22802 solver.cpp:218] Iteration 5448 (2.55644 iter/s, 4.69403s/12 iters), loss = 0.718612
I0428 21:18:55.451426 22802 solver.cpp:237]     Train net output #0: loss = 0.718612 (* 1 = 0.718612 loss)
I0428 21:18:55.451434 22802 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0428 21:19:00.088110 22802 solver.cpp:218] Iteration 5460 (2.58814 iter/s, 4.63654s/12 iters), loss = 1.064
I0428 21:19:00.088146 22802 solver.cpp:237]     Train net output #0: loss = 1.064 (* 1 = 1.064 loss)
I0428 21:19:00.088155 22802 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0428 21:19:00.618415 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:19:04.708745 22802 solver.cpp:218] Iteration 5472 (2.59715 iter/s, 4.62045s/12 iters), loss = 0.778216
I0428 21:19:04.708786 22802 solver.cpp:237]     Train net output #0: loss = 0.778216 (* 1 = 0.778216 loss)
I0428 21:19:04.708794 22802 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0428 21:19:09.361821 22802 solver.cpp:218] Iteration 5484 (2.57905 iter/s, 4.65288s/12 iters), loss = 0.815731
I0428 21:19:09.361966 22802 solver.cpp:237]     Train net output #0: loss = 0.815731 (* 1 = 0.815731 loss)
I0428 21:19:09.361975 22802 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0428 21:19:13.947870 22802 solver.cpp:218] Iteration 5496 (2.6168 iter/s, 4.58576s/12 iters), loss = 0.835323
I0428 21:19:13.947916 22802 solver.cpp:237]     Train net output #0: loss = 0.835323 (* 1 = 0.835323 loss)
I0428 21:19:13.947927 22802 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0428 21:19:18.170557 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0428 21:19:20.407166 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0428 21:19:22.601568 22802 solver.cpp:330] Iteration 5508, Testing net (#0)
I0428 21:19:22.601593 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:19:24.709153 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:19:26.868268 22802 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0428 21:19:26.868295 22802 solver.cpp:397]     Test net output #1: loss = 2.31706 (* 1 = 2.31706 loss)
I0428 21:19:26.925885 22802 solver.cpp:218] Iteration 5508 (0.924672 iter/s, 12.9776s/12 iters), loss = 0.738806
I0428 21:19:26.925926 22802 solver.cpp:237]     Train net output #0: loss = 0.738806 (* 1 = 0.738806 loss)
I0428 21:19:26.925937 22802 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0428 21:19:31.172468 22802 solver.cpp:218] Iteration 5520 (2.82592 iter/s, 4.2464s/12 iters), loss = 0.798857
I0428 21:19:31.172547 22802 solver.cpp:237]     Train net output #0: loss = 0.798857 (* 1 = 0.798857 loss)
I0428 21:19:31.172561 22802 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0428 21:19:33.519971 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:19:35.867725 22802 solver.cpp:218] Iteration 5532 (2.5559 iter/s, 4.69503s/12 iters), loss = 0.755917
I0428 21:19:35.867776 22802 solver.cpp:237]     Train net output #0: loss = 0.755917 (* 1 = 0.755917 loss)
I0428 21:19:35.867789 22802 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0428 21:19:40.600386 22802 solver.cpp:218] Iteration 5544 (2.53568 iter/s, 4.73246s/12 iters), loss = 0.594818
I0428 21:19:40.600553 22802 solver.cpp:237]     Train net output #0: loss = 0.594818 (* 1 = 0.594818 loss)
I0428 21:19:40.600562 22802 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0428 21:19:45.315969 22802 solver.cpp:218] Iteration 5556 (2.54492 iter/s, 4.71527s/12 iters), loss = 0.717291
I0428 21:19:45.316009 22802 solver.cpp:237]     Train net output #0: loss = 0.717291 (* 1 = 0.717291 loss)
I0428 21:19:45.316016 22802 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0428 21:19:47.794991 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:19:49.927469 22802 solver.cpp:218] Iteration 5568 (2.6023 iter/s, 4.61131s/12 iters), loss = 0.636527
I0428 21:19:49.927507 22802 solver.cpp:237]     Train net output #0: loss = 0.636527 (* 1 = 0.636527 loss)
I0428 21:19:49.927515 22802 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0428 21:19:54.508220 22802 solver.cpp:218] Iteration 5580 (2.61976 iter/s, 4.58057s/12 iters), loss = 0.737036
I0428 21:19:54.508260 22802 solver.cpp:237]     Train net output #0: loss = 0.737036 (* 1 = 0.737036 loss)
I0428 21:19:54.508267 22802 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0428 21:19:59.328833 22802 solver.cpp:218] Iteration 5592 (2.48941 iter/s, 4.82042s/12 iters), loss = 0.530014
I0428 21:19:59.328881 22802 solver.cpp:237]     Train net output #0: loss = 0.530014 (* 1 = 0.530014 loss)
I0428 21:19:59.328891 22802 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0428 21:20:03.887207 22802 solver.cpp:218] Iteration 5604 (2.6328 iter/s, 4.55789s/12 iters), loss = 0.536041
I0428 21:20:03.887253 22802 solver.cpp:237]     Train net output #0: loss = 0.536041 (* 1 = 0.536041 loss)
I0428 21:20:03.887266 22802 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0428 21:20:05.790927 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0428 21:20:07.334977 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0428 21:20:08.532951 22802 solver.cpp:330] Iteration 5610, Testing net (#0)
I0428 21:20:08.532971 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:20:10.863078 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:20:13.059967 22802 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0428 21:20:13.060009 22802 solver.cpp:397]     Test net output #1: loss = 2.40135 (* 1 = 2.40135 loss)
I0428 21:20:14.986925 22802 solver.cpp:218] Iteration 5616 (1.08115 iter/s, 11.0993s/12 iters), loss = 0.661275
I0428 21:20:14.986964 22802 solver.cpp:237]     Train net output #0: loss = 0.661275 (* 1 = 0.661275 loss)
I0428 21:20:14.986972 22802 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0428 21:20:19.624897 22802 solver.cpp:218] Iteration 5628 (2.58744 iter/s, 4.63779s/12 iters), loss = 0.519488
I0428 21:20:19.624931 22802 solver.cpp:237]     Train net output #0: loss = 0.519488 (* 1 = 0.519488 loss)
I0428 21:20:19.624939 22802 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0428 21:20:24.222041 22802 solver.cpp:218] Iteration 5640 (2.61042 iter/s, 4.59695s/12 iters), loss = 0.726755
I0428 21:20:24.222110 22802 solver.cpp:237]     Train net output #0: loss = 0.726755 (* 1 = 0.726755 loss)
I0428 21:20:24.222124 22802 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0428 21:20:28.909150 22802 solver.cpp:218] Iteration 5652 (2.56034 iter/s, 4.68688s/12 iters), loss = 0.560347
I0428 21:20:28.909205 22802 solver.cpp:237]     Train net output #0: loss = 0.560347 (* 1 = 0.560347 loss)
I0428 21:20:28.909216 22802 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0428 21:20:33.422819 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:20:33.569371 22802 solver.cpp:218] Iteration 5664 (2.5751 iter/s, 4.66001s/12 iters), loss = 0.520757
I0428 21:20:33.569424 22802 solver.cpp:237]     Train net output #0: loss = 0.520757 (* 1 = 0.520757 loss)
I0428 21:20:33.569437 22802 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0428 21:20:38.315366 22802 solver.cpp:218] Iteration 5676 (2.52855 iter/s, 4.74579s/12 iters), loss = 0.604302
I0428 21:20:38.315402 22802 solver.cpp:237]     Train net output #0: loss = 0.604302 (* 1 = 0.604302 loss)
I0428 21:20:38.315409 22802 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0428 21:20:42.982568 22802 solver.cpp:218] Iteration 5688 (2.57124 iter/s, 4.66702s/12 iters), loss = 0.654415
I0428 21:20:42.982692 22802 solver.cpp:237]     Train net output #0: loss = 0.654415 (* 1 = 0.654415 loss)
I0428 21:20:42.982702 22802 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0428 21:20:47.733530 22802 solver.cpp:218] Iteration 5700 (2.52595 iter/s, 4.75069s/12 iters), loss = 0.531018
I0428 21:20:47.733573 22802 solver.cpp:237]     Train net output #0: loss = 0.531018 (* 1 = 0.531018 loss)
I0428 21:20:47.733583 22802 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0428 21:20:51.953382 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0428 21:20:53.497349 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0428 21:20:54.703783 22802 solver.cpp:330] Iteration 5712, Testing net (#0)
I0428 21:20:54.703809 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:20:56.907822 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:20:59.387692 22802 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0428 21:20:59.387722 22802 solver.cpp:397]     Test net output #1: loss = 2.38947 (* 1 = 2.38947 loss)
I0428 21:20:59.445266 22802 solver.cpp:218] Iteration 5712 (1.02465 iter/s, 11.7113s/12 iters), loss = 0.47307
I0428 21:20:59.445320 22802 solver.cpp:237]     Train net output #0: loss = 0.47307 (* 1 = 0.47307 loss)
I0428 21:20:59.445333 22802 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0428 21:21:03.259507 22802 solver.cpp:218] Iteration 5724 (3.14625 iter/s, 3.81406s/12 iters), loss = 0.664561
I0428 21:21:03.259562 22802 solver.cpp:237]     Train net output #0: loss = 0.664561 (* 1 = 0.664561 loss)
I0428 21:21:03.259572 22802 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0428 21:21:07.864634 22802 solver.cpp:218] Iteration 5736 (2.60591 iter/s, 4.60493s/12 iters), loss = 0.646183
I0428 21:21:07.864682 22802 solver.cpp:237]     Train net output #0: loss = 0.646183 (* 1 = 0.646183 loss)
I0428 21:21:07.864692 22802 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0428 21:21:12.660421 22802 solver.cpp:218] Iteration 5748 (2.5023 iter/s, 4.79559s/12 iters), loss = 0.608269
I0428 21:21:12.660456 22802 solver.cpp:237]     Train net output #0: loss = 0.608269 (* 1 = 0.608269 loss)
I0428 21:21:12.660465 22802 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0428 21:21:17.366897 22802 solver.cpp:218] Iteration 5760 (2.54978 iter/s, 4.70629s/12 iters), loss = 0.547454
I0428 21:21:17.366994 22802 solver.cpp:237]     Train net output #0: loss = 0.547454 (* 1 = 0.547454 loss)
I0428 21:21:17.367002 22802 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0428 21:21:19.189663 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:21:21.982324 22802 solver.cpp:218] Iteration 5772 (2.60011 iter/s, 4.61518s/12 iters), loss = 0.534383
I0428 21:21:21.982364 22802 solver.cpp:237]     Train net output #0: loss = 0.534383 (* 1 = 0.534383 loss)
I0428 21:21:21.982373 22802 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0428 21:21:26.584692 22802 solver.cpp:218] Iteration 5784 (2.60746 iter/s, 4.60218s/12 iters), loss = 0.768697
I0428 21:21:26.584731 22802 solver.cpp:237]     Train net output #0: loss = 0.768697 (* 1 = 0.768697 loss)
I0428 21:21:26.584739 22802 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0428 21:21:31.191781 22802 solver.cpp:218] Iteration 5796 (2.60479 iter/s, 4.6069s/12 iters), loss = 0.548333
I0428 21:21:31.191820 22802 solver.cpp:237]     Train net output #0: loss = 0.548333 (* 1 = 0.548333 loss)
I0428 21:21:31.191829 22802 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0428 21:21:35.775826 22802 solver.cpp:218] Iteration 5808 (2.61788 iter/s, 4.58386s/12 iters), loss = 0.533694
I0428 21:21:35.775861 22802 solver.cpp:237]     Train net output #0: loss = 0.533694 (* 1 = 0.533694 loss)
I0428 21:21:35.775868 22802 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0428 21:21:37.685376 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0428 21:21:39.207943 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0428 21:21:41.065420 22802 solver.cpp:330] Iteration 5814, Testing net (#0)
I0428 21:21:41.065441 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:21:43.095460 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:21:45.336102 22802 solver.cpp:397]     Test net output #0: accuracy = 0.496936
I0428 21:21:45.336138 22802 solver.cpp:397]     Test net output #1: loss = 2.35579 (* 1 = 2.35579 loss)
I0428 21:21:47.014308 22802 solver.cpp:218] Iteration 5820 (1.0678 iter/s, 11.2381s/12 iters), loss = 0.55343
I0428 21:21:47.014348 22802 solver.cpp:237]     Train net output #0: loss = 0.55343 (* 1 = 0.55343 loss)
I0428 21:21:47.014355 22802 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0428 21:21:51.650934 22802 solver.cpp:218] Iteration 5832 (2.58819 iter/s, 4.63644s/12 iters), loss = 0.734644
I0428 21:21:51.651077 22802 solver.cpp:237]     Train net output #0: loss = 0.734644 (* 1 = 0.734644 loss)
I0428 21:21:51.651091 22802 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0428 21:21:56.260159 22802 solver.cpp:218] Iteration 5844 (2.60364 iter/s, 4.60894s/12 iters), loss = 0.539798
I0428 21:21:56.260196 22802 solver.cpp:237]     Train net output #0: loss = 0.539798 (* 1 = 0.539798 loss)
I0428 21:21:56.260205 22802 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0428 21:22:00.922590 22802 solver.cpp:218] Iteration 5856 (2.57387 iter/s, 4.66224s/12 iters), loss = 0.638077
I0428 21:22:00.922631 22802 solver.cpp:237]     Train net output #0: loss = 0.638077 (* 1 = 0.638077 loss)
I0428 21:22:00.922638 22802 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0428 21:22:04.802103 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:22:05.552819 22802 solver.cpp:218] Iteration 5868 (2.59177 iter/s, 4.63004s/12 iters), loss = 0.632163
I0428 21:22:05.552856 22802 solver.cpp:237]     Train net output #0: loss = 0.632163 (* 1 = 0.632163 loss)
I0428 21:22:05.552865 22802 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0428 21:22:10.238350 22802 solver.cpp:218] Iteration 5880 (2.56118 iter/s, 4.68535s/12 iters), loss = 0.58009
I0428 21:22:10.238384 22802 solver.cpp:237]     Train net output #0: loss = 0.58009 (* 1 = 0.58009 loss)
I0428 21:22:10.238391 22802 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0428 21:22:14.904182 22802 solver.cpp:218] Iteration 5892 (2.57199 iter/s, 4.66564s/12 iters), loss = 0.644734
I0428 21:22:14.904244 22802 solver.cpp:237]     Train net output #0: loss = 0.644734 (* 1 = 0.644734 loss)
I0428 21:22:14.904258 22802 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0428 21:22:19.601800 22802 solver.cpp:218] Iteration 5904 (2.5546 iter/s, 4.69741s/12 iters), loss = 0.464625
I0428 21:22:19.601836 22802 solver.cpp:237]     Train net output #0: loss = 0.464625 (* 1 = 0.464625 loss)
I0428 21:22:19.601845 22802 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0428 21:22:23.864198 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0428 21:22:27.399461 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0428 21:22:31.834617 22802 solver.cpp:330] Iteration 5916, Testing net (#0)
I0428 21:22:31.834642 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:22:33.789305 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:22:36.120206 22802 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0428 21:22:36.120236 22802 solver.cpp:397]     Test net output #1: loss = 2.44387 (* 1 = 2.44387 loss)
I0428 21:22:36.177680 22802 solver.cpp:218] Iteration 5916 (0.723967 iter/s, 16.5753s/12 iters), loss = 0.554935
I0428 21:22:36.177716 22802 solver.cpp:237]     Train net output #0: loss = 0.554935 (* 1 = 0.554935 loss)
I0428 21:22:36.177724 22802 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0428 21:22:40.049494 22802 solver.cpp:218] Iteration 5928 (3.09945 iter/s, 3.87165s/12 iters), loss = 0.561087
I0428 21:22:40.049532 22802 solver.cpp:237]     Train net output #0: loss = 0.561087 (* 1 = 0.561087 loss)
I0428 21:22:40.049541 22802 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0428 21:22:44.678854 22802 solver.cpp:218] Iteration 5940 (2.59226 iter/s, 4.62917s/12 iters), loss = 0.505088
I0428 21:22:44.678900 22802 solver.cpp:237]     Train net output #0: loss = 0.505088 (* 1 = 0.505088 loss)
I0428 21:22:44.678910 22802 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0428 21:22:49.253167 22802 solver.cpp:218] Iteration 5952 (2.62345 iter/s, 4.57412s/12 iters), loss = 0.603253
I0428 21:22:49.253201 22802 solver.cpp:237]     Train net output #0: loss = 0.603253 (* 1 = 0.603253 loss)
I0428 21:22:49.253211 22802 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0428 21:22:53.895967 22802 solver.cpp:218] Iteration 5964 (2.58475 iter/s, 4.64262s/12 iters), loss = 0.65147
I0428 21:22:53.896095 22802 solver.cpp:237]     Train net output #0: loss = 0.65147 (* 1 = 0.65147 loss)
I0428 21:22:53.896104 22802 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0428 21:22:55.112984 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:22:58.490806 22802 solver.cpp:218] Iteration 5976 (2.61178 iter/s, 4.59457s/12 iters), loss = 0.410212
I0428 21:22:58.490844 22802 solver.cpp:237]     Train net output #0: loss = 0.410212 (* 1 = 0.410212 loss)
I0428 21:22:58.490851 22802 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0428 21:23:03.292191 22802 solver.cpp:218] Iteration 5988 (2.49938 iter/s, 4.80119s/12 iters), loss = 0.633149
I0428 21:23:03.292230 22802 solver.cpp:237]     Train net output #0: loss = 0.633149 (* 1 = 0.633149 loss)
I0428 21:23:03.292239 22802 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0428 21:23:08.101408 22802 solver.cpp:218] Iteration 6000 (2.49531 iter/s, 4.80903s/12 iters), loss = 0.514832
I0428 21:23:08.101440 22802 solver.cpp:237]     Train net output #0: loss = 0.514832 (* 1 = 0.514832 loss)
I0428 21:23:08.101449 22802 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0428 21:23:12.899749 22802 solver.cpp:218] Iteration 6012 (2.50096 iter/s, 4.79815s/12 iters), loss = 0.584726
I0428 21:23:12.899806 22802 solver.cpp:237]     Train net output #0: loss = 0.584726 (* 1 = 0.584726 loss)
I0428 21:23:12.899816 22802 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0428 21:23:14.943501 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0428 21:23:16.440263 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0428 21:23:17.628391 22802 solver.cpp:330] Iteration 6018, Testing net (#0)
I0428 21:23:17.628412 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:23:19.798413 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:23:22.291226 22802 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0428 21:23:22.291252 22802 solver.cpp:397]     Test net output #1: loss = 2.39773 (* 1 = 2.39773 loss)
I0428 21:23:23.954766 22802 solver.cpp:218] Iteration 6024 (1.08552 iter/s, 11.0546s/12 iters), loss = 0.570746
I0428 21:23:23.954871 22802 solver.cpp:237]     Train net output #0: loss = 0.570746 (* 1 = 0.570746 loss)
I0428 21:23:23.954881 22802 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0428 21:23:28.571563 22802 solver.cpp:218] Iteration 6036 (2.59935 iter/s, 4.61654s/12 iters), loss = 0.62073
I0428 21:23:28.571601 22802 solver.cpp:237]     Train net output #0: loss = 0.62073 (* 1 = 0.62073 loss)
I0428 21:23:28.571609 22802 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0428 21:23:33.166061 22802 solver.cpp:218] Iteration 6048 (2.61192 iter/s, 4.59431s/12 iters), loss = 0.474407
I0428 21:23:33.166100 22802 solver.cpp:237]     Train net output #0: loss = 0.474407 (* 1 = 0.474407 loss)
I0428 21:23:33.166106 22802 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0428 21:23:37.829536 22802 solver.cpp:218] Iteration 6060 (2.57329 iter/s, 4.66329s/12 iters), loss = 0.511384
I0428 21:23:37.829574 22802 solver.cpp:237]     Train net output #0: loss = 0.511384 (* 1 = 0.511384 loss)
I0428 21:23:37.829582 22802 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0428 21:23:41.014961 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:23:42.435464 22802 solver.cpp:218] Iteration 6072 (2.60544 iter/s, 4.60574s/12 iters), loss = 0.680307
I0428 21:23:42.435514 22802 solver.cpp:237]     Train net output #0: loss = 0.680307 (* 1 = 0.680307 loss)
I0428 21:23:42.435528 22802 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0428 21:23:47.042999 22802 solver.cpp:218] Iteration 6084 (2.60454 iter/s, 4.60734s/12 iters), loss = 0.631794
I0428 21:23:47.043051 22802 solver.cpp:237]     Train net output #0: loss = 0.631794 (* 1 = 0.631794 loss)
I0428 21:23:47.043062 22802 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0428 21:23:51.693848 22802 solver.cpp:218] Iteration 6096 (2.58028 iter/s, 4.65065s/12 iters), loss = 0.549676
I0428 21:23:51.693886 22802 solver.cpp:237]     Train net output #0: loss = 0.549676 (* 1 = 0.549676 loss)
I0428 21:23:51.693895 22802 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0428 21:23:56.332274 22802 solver.cpp:218] Iteration 6108 (2.58719 iter/s, 4.63824s/12 iters), loss = 0.632517
I0428 21:23:56.332391 22802 solver.cpp:237]     Train net output #0: loss = 0.632517 (* 1 = 0.632517 loss)
I0428 21:23:56.332401 22802 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0428 21:24:00.607880 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0428 21:24:02.651710 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0428 21:24:05.007422 22802 solver.cpp:330] Iteration 6120, Testing net (#0)
I0428 21:24:05.007447 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:24:07.056227 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:24:09.671108 22802 solver.cpp:397]     Test net output #0: accuracy = 0.506127
I0428 21:24:09.671139 22802 solver.cpp:397]     Test net output #1: loss = 2.32732 (* 1 = 2.32732 loss)
I0428 21:24:09.728590 22802 solver.cpp:218] Iteration 6120 (0.895804 iter/s, 13.3958s/12 iters), loss = 0.391763
I0428 21:24:09.728637 22802 solver.cpp:237]     Train net output #0: loss = 0.391763 (* 1 = 0.391763 loss)
I0428 21:24:09.728650 22802 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0428 21:24:13.621094 22802 solver.cpp:218] Iteration 6132 (3.08298 iter/s, 3.89234s/12 iters), loss = 0.452931
I0428 21:24:13.621129 22802 solver.cpp:237]     Train net output #0: loss = 0.452931 (* 1 = 0.452931 loss)
I0428 21:24:13.621136 22802 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0428 21:24:18.237938 22802 solver.cpp:218] Iteration 6144 (2.59928 iter/s, 4.61666s/12 iters), loss = 0.375249
I0428 21:24:18.237989 22802 solver.cpp:237]     Train net output #0: loss = 0.375249 (* 1 = 0.375249 loss)
I0428 21:24:18.238001 22802 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0428 21:24:22.798590 22802 solver.cpp:218] Iteration 6156 (2.63131 iter/s, 4.56046s/12 iters), loss = 0.631589
I0428 21:24:22.798629 22802 solver.cpp:237]     Train net output #0: loss = 0.631589 (* 1 = 0.631589 loss)
I0428 21:24:22.798637 22802 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0428 21:24:27.608346 22802 solver.cpp:218] Iteration 6168 (2.49503 iter/s, 4.80956s/12 iters), loss = 0.499616
I0428 21:24:27.608443 22802 solver.cpp:237]     Train net output #0: loss = 0.499616 (* 1 = 0.499616 loss)
I0428 21:24:27.608453 22802 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0428 21:24:28.182533 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:24:32.304015 22802 solver.cpp:218] Iteration 6180 (2.55568 iter/s, 4.69542s/12 iters), loss = 0.46539
I0428 21:24:32.304050 22802 solver.cpp:237]     Train net output #0: loss = 0.46539 (* 1 = 0.46539 loss)
I0428 21:24:32.304059 22802 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0428 21:24:37.071893 22802 solver.cpp:218] Iteration 6192 (2.51694 iter/s, 4.76769s/12 iters), loss = 0.726389
I0428 21:24:37.071929 22802 solver.cpp:237]     Train net output #0: loss = 0.726389 (* 1 = 0.726389 loss)
I0428 21:24:37.071938 22802 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0428 21:24:41.770242 22802 solver.cpp:218] Iteration 6204 (2.55419 iter/s, 4.69816s/12 iters), loss = 0.496527
I0428 21:24:41.770282 22802 solver.cpp:237]     Train net output #0: loss = 0.496527 (* 1 = 0.496527 loss)
I0428 21:24:41.770290 22802 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0428 21:24:46.462075 22802 solver.cpp:218] Iteration 6216 (2.55774 iter/s, 4.69164s/12 iters), loss = 0.43147
I0428 21:24:46.462112 22802 solver.cpp:237]     Train net output #0: loss = 0.43147 (* 1 = 0.43147 loss)
I0428 21:24:46.462121 22802 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0428 21:24:48.345517 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0428 21:24:50.936398 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0428 21:24:52.345430 22802 solver.cpp:330] Iteration 6222, Testing net (#0)
I0428 21:24:52.345453 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:24:54.338330 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:24:55.614960 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:24:56.802891 22802 solver.cpp:397]     Test net output #0: accuracy = 0.509191
I0428 21:24:56.802919 22802 solver.cpp:397]     Test net output #1: loss = 2.41025 (* 1 = 2.41025 loss)
I0428 21:24:58.427275 22802 solver.cpp:218] Iteration 6228 (1.00294 iter/s, 11.9648s/12 iters), loss = 0.540016
I0428 21:24:58.427443 22802 solver.cpp:237]     Train net output #0: loss = 0.540016 (* 1 = 0.540016 loss)
I0428 21:24:58.427455 22802 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0428 21:25:03.019687 22802 solver.cpp:218] Iteration 6240 (2.61318 iter/s, 4.5921s/12 iters), loss = 0.426623
I0428 21:25:03.019743 22802 solver.cpp:237]     Train net output #0: loss = 0.426623 (* 1 = 0.426623 loss)
I0428 21:25:03.019755 22802 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0428 21:25:07.630214 22802 solver.cpp:218] Iteration 6252 (2.60285 iter/s, 4.61032s/12 iters), loss = 0.395923
I0428 21:25:07.630270 22802 solver.cpp:237]     Train net output #0: loss = 0.395923 (* 1 = 0.395923 loss)
I0428 21:25:07.630280 22802 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0428 21:25:12.250385 22802 solver.cpp:218] Iteration 6264 (2.59742 iter/s, 4.61997s/12 iters), loss = 0.426527
I0428 21:25:12.250439 22802 solver.cpp:237]     Train net output #0: loss = 0.426527 (* 1 = 0.426527 loss)
I0428 21:25:12.250452 22802 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0428 21:25:14.772611 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:25:16.963300 22802 solver.cpp:218] Iteration 6276 (2.5463 iter/s, 4.71272s/12 iters), loss = 0.456327
I0428 21:25:16.963335 22802 solver.cpp:237]     Train net output #0: loss = 0.456327 (* 1 = 0.456327 loss)
I0428 21:25:16.963343 22802 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0428 21:25:21.571132 22802 solver.cpp:218] Iteration 6288 (2.60436 iter/s, 4.60765s/12 iters), loss = 0.451437
I0428 21:25:21.571172 22802 solver.cpp:237]     Train net output #0: loss = 0.451437 (* 1 = 0.451437 loss)
I0428 21:25:21.571180 22802 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0428 21:25:26.214335 22802 solver.cpp:218] Iteration 6300 (2.58453 iter/s, 4.64302s/12 iters), loss = 0.646891
I0428 21:25:26.214373 22802 solver.cpp:237]     Train net output #0: loss = 0.646891 (* 1 = 0.646891 loss)
I0428 21:25:26.214382 22802 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0428 21:25:30.907425 22802 solver.cpp:218] Iteration 6312 (2.55705 iter/s, 4.6929s/12 iters), loss = 0.391485
I0428 21:25:30.907559 22802 solver.cpp:237]     Train net output #0: loss = 0.391485 (* 1 = 0.391485 loss)
I0428 21:25:30.907569 22802 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0428 21:25:35.128060 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0428 21:25:36.695400 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0428 21:25:39.944936 22802 solver.cpp:330] Iteration 6324, Testing net (#0)
I0428 21:25:39.944957 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:25:41.821699 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:25:44.430306 22802 solver.cpp:397]     Test net output #0: accuracy = 0.514093
I0428 21:25:44.430336 22802 solver.cpp:397]     Test net output #1: loss = 2.42804 (* 1 = 2.42804 loss)
I0428 21:25:44.487882 22802 solver.cpp:218] Iteration 6324 (0.883658 iter/s, 13.5799s/12 iters), loss = 0.394714
I0428 21:25:44.487920 22802 solver.cpp:237]     Train net output #0: loss = 0.394714 (* 1 = 0.394714 loss)
I0428 21:25:44.487927 22802 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0428 21:25:48.404392 22802 solver.cpp:218] Iteration 6336 (3.06408 iter/s, 3.91634s/12 iters), loss = 0.284458
I0428 21:25:48.404438 22802 solver.cpp:237]     Train net output #0: loss = 0.284458 (* 1 = 0.284458 loss)
I0428 21:25:48.404449 22802 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0428 21:25:53.095927 22802 solver.cpp:218] Iteration 6348 (2.55791 iter/s, 4.69134s/12 iters), loss = 0.414639
I0428 21:25:53.095975 22802 solver.cpp:237]     Train net output #0: loss = 0.414639 (* 1 = 0.414639 loss)
I0428 21:25:53.095986 22802 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0428 21:25:57.713090 22802 solver.cpp:218] Iteration 6360 (2.59911 iter/s, 4.61697s/12 iters), loss = 0.418624
I0428 21:25:57.713142 22802 solver.cpp:237]     Train net output #0: loss = 0.418624 (* 1 = 0.418624 loss)
I0428 21:25:57.713155 22802 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0428 21:26:02.324542 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:26:02.440186 22802 solver.cpp:218] Iteration 6372 (2.53867 iter/s, 4.72689s/12 iters), loss = 0.351044
I0428 21:26:02.440233 22802 solver.cpp:237]     Train net output #0: loss = 0.351044 (* 1 = 0.351044 loss)
I0428 21:26:02.440244 22802 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0428 21:26:07.038533 22802 solver.cpp:218] Iteration 6384 (2.60975 iter/s, 4.59815s/12 iters), loss = 0.348177
I0428 21:26:07.038580 22802 solver.cpp:237]     Train net output #0: loss = 0.348177 (* 1 = 0.348177 loss)
I0428 21:26:07.038591 22802 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0428 21:26:11.631620 22802 solver.cpp:218] Iteration 6396 (2.61273 iter/s, 4.5929s/12 iters), loss = 0.645702
I0428 21:26:11.631659 22802 solver.cpp:237]     Train net output #0: loss = 0.645702 (* 1 = 0.645702 loss)
I0428 21:26:11.631665 22802 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0428 21:26:16.234148 22802 solver.cpp:218] Iteration 6408 (2.60737 iter/s, 4.60234s/12 iters), loss = 0.715883
I0428 21:26:16.234203 22802 solver.cpp:237]     Train net output #0: loss = 0.715883 (* 1 = 0.715883 loss)
I0428 21:26:16.234215 22802 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0428 21:26:20.856207 22802 solver.cpp:218] Iteration 6420 (2.59636 iter/s, 4.62186s/12 iters), loss = 0.42286
I0428 21:26:20.856261 22802 solver.cpp:237]     Train net output #0: loss = 0.42286 (* 1 = 0.42286 loss)
I0428 21:26:20.856272 22802 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0428 21:26:22.868070 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0428 21:26:24.431372 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0428 21:26:25.616456 22802 solver.cpp:330] Iteration 6426, Testing net (#0)
I0428 21:26:25.616477 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:26:27.410643 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:26:29.980139 22802 solver.cpp:397]     Test net output #0: accuracy = 0.514093
I0428 21:26:29.980176 22802 solver.cpp:397]     Test net output #1: loss = 2.41638 (* 1 = 2.41638 loss)
I0428 21:26:31.548321 22802 solver.cpp:218] Iteration 6432 (1.12236 iter/s, 10.6917s/12 iters), loss = 0.535559
I0428 21:26:31.548372 22802 solver.cpp:237]     Train net output #0: loss = 0.535559 (* 1 = 0.535559 loss)
I0428 21:26:31.548384 22802 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0428 21:26:36.177646 22802 solver.cpp:218] Iteration 6444 (2.59228 iter/s, 4.62913s/12 iters), loss = 0.433568
I0428 21:26:36.177750 22802 solver.cpp:237]     Train net output #0: loss = 0.433568 (* 1 = 0.433568 loss)
I0428 21:26:36.177760 22802 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0428 21:26:40.763461 22802 solver.cpp:218] Iteration 6456 (2.61691 iter/s, 4.58557s/12 iters), loss = 0.53645
I0428 21:26:40.763497 22802 solver.cpp:237]     Train net output #0: loss = 0.53645 (* 1 = 0.53645 loss)
I0428 21:26:40.763504 22802 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0428 21:26:45.541533 22802 solver.cpp:218] Iteration 6468 (2.51157 iter/s, 4.77788s/12 iters), loss = 0.471451
I0428 21:26:45.541584 22802 solver.cpp:237]     Train net output #0: loss = 0.471451 (* 1 = 0.471451 loss)
I0428 21:26:45.541594 22802 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0428 21:26:47.513069 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:26:50.271737 22802 solver.cpp:218] Iteration 6480 (2.537 iter/s, 4.73s/12 iters), loss = 0.431305
I0428 21:26:50.271775 22802 solver.cpp:237]     Train net output #0: loss = 0.431305 (* 1 = 0.431305 loss)
I0428 21:26:50.271785 22802 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0428 21:26:55.205675 22802 solver.cpp:218] Iteration 6492 (2.43223 iter/s, 4.93374s/12 iters), loss = 0.419291
I0428 21:26:55.205713 22802 solver.cpp:237]     Train net output #0: loss = 0.419291 (* 1 = 0.419291 loss)
I0428 21:26:55.205721 22802 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0428 21:26:59.791116 22802 solver.cpp:218] Iteration 6504 (2.61708 iter/s, 4.58526s/12 iters), loss = 0.420489
I0428 21:26:59.791154 22802 solver.cpp:237]     Train net output #0: loss = 0.420489 (* 1 = 0.420489 loss)
I0428 21:26:59.791162 22802 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0428 21:27:04.334286 22802 solver.cpp:218] Iteration 6516 (2.64143 iter/s, 4.54299s/12 iters), loss = 0.453069
I0428 21:27:04.334327 22802 solver.cpp:237]     Train net output #0: loss = 0.453069 (* 1 = 0.453069 loss)
I0428 21:27:04.334336 22802 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0428 21:27:08.533851 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0428 21:27:10.069623 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0428 21:27:11.299903 22802 solver.cpp:330] Iteration 6528, Testing net (#0)
I0428 21:27:11.299926 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:27:13.225041 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:27:15.749792 22802 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0428 21:27:15.749830 22802 solver.cpp:397]     Test net output #1: loss = 2.47355 (* 1 = 2.47355 loss)
I0428 21:27:15.807531 22802 solver.cpp:218] Iteration 6528 (1.04595 iter/s, 11.4729s/12 iters), loss = 0.487968
I0428 21:27:15.807582 22802 solver.cpp:237]     Train net output #0: loss = 0.487968 (* 1 = 0.487968 loss)
I0428 21:27:15.807593 22802 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0428 21:27:19.800765 22802 solver.cpp:218] Iteration 6540 (3.00522 iter/s, 3.99306s/12 iters), loss = 0.509499
I0428 21:27:19.800806 22802 solver.cpp:237]     Train net output #0: loss = 0.509499 (* 1 = 0.509499 loss)
I0428 21:27:19.800814 22802 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0428 21:27:24.420481 22802 solver.cpp:218] Iteration 6552 (2.59767 iter/s, 4.61952s/12 iters), loss = 0.406576
I0428 21:27:24.420549 22802 solver.cpp:237]     Train net output #0: loss = 0.406576 (* 1 = 0.406576 loss)
I0428 21:27:24.420558 22802 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0428 21:27:29.067504 22802 solver.cpp:218] Iteration 6564 (2.58242 iter/s, 4.6468s/12 iters), loss = 0.526885
I0428 21:27:29.067556 22802 solver.cpp:237]     Train net output #0: loss = 0.526885 (* 1 = 0.526885 loss)
I0428 21:27:29.067569 22802 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0428 21:27:33.072916 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:27:33.930667 22802 solver.cpp:218] Iteration 6576 (2.46763 iter/s, 4.86296s/12 iters), loss = 0.505271
I0428 21:27:33.930699 22802 solver.cpp:237]     Train net output #0: loss = 0.505271 (* 1 = 0.505271 loss)
I0428 21:27:33.930707 22802 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0428 21:27:38.708153 22802 solver.cpp:218] Iteration 6588 (2.51188 iter/s, 4.7773s/12 iters), loss = 0.399968
I0428 21:27:38.708667 22802 solver.cpp:237]     Train net output #0: loss = 0.399968 (* 1 = 0.399968 loss)
I0428 21:27:38.708676 22802 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0428 21:27:43.302600 22802 solver.cpp:218] Iteration 6600 (2.61223 iter/s, 4.59378s/12 iters), loss = 0.438378
I0428 21:27:43.302664 22802 solver.cpp:237]     Train net output #0: loss = 0.438378 (* 1 = 0.438378 loss)
I0428 21:27:43.302678 22802 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0428 21:27:47.894934 22802 solver.cpp:218] Iteration 6612 (2.61317 iter/s, 4.59212s/12 iters), loss = 0.485631
I0428 21:27:47.894987 22802 solver.cpp:237]     Train net output #0: loss = 0.485631 (* 1 = 0.485631 loss)
I0428 21:27:47.895000 22802 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0428 21:27:52.609869 22802 solver.cpp:218] Iteration 6624 (2.54521 iter/s, 4.71474s/12 iters), loss = 0.361407
I0428 21:27:52.609902 22802 solver.cpp:237]     Train net output #0: loss = 0.361407 (* 1 = 0.361407 loss)
I0428 21:27:52.609910 22802 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0428 21:27:54.506458 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0428 21:27:57.680289 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0428 21:27:58.864296 22802 solver.cpp:330] Iteration 6630, Testing net (#0)
I0428 21:27:58.864315 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:28:00.710177 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:28:03.285951 22802 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0428 21:28:03.285977 22802 solver.cpp:397]     Test net output #1: loss = 2.44813 (* 1 = 2.44813 loss)
I0428 21:28:04.896534 22802 solver.cpp:218] Iteration 6636 (0.976701 iter/s, 12.2863s/12 iters), loss = 0.351558
I0428 21:28:04.896574 22802 solver.cpp:237]     Train net output #0: loss = 0.351558 (* 1 = 0.351558 loss)
I0428 21:28:04.896582 22802 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0428 21:28:09.578903 22802 solver.cpp:218] Iteration 6648 (2.56291 iter/s, 4.68218s/12 iters), loss = 0.24002
I0428 21:28:09.579195 22802 solver.cpp:237]     Train net output #0: loss = 0.24002 (* 1 = 0.24002 loss)
I0428 21:28:09.579205 22802 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0428 21:28:14.212101 22802 solver.cpp:218] Iteration 6660 (2.59025 iter/s, 4.63276s/12 iters), loss = 0.400034
I0428 21:28:14.212147 22802 solver.cpp:237]     Train net output #0: loss = 0.400034 (* 1 = 0.400034 loss)
I0428 21:28:14.212158 22802 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0428 21:28:18.860396 22802 solver.cpp:218] Iteration 6672 (2.5817 iter/s, 4.6481s/12 iters), loss = 0.380611
I0428 21:28:18.860435 22802 solver.cpp:237]     Train net output #0: loss = 0.380611 (* 1 = 0.380611 loss)
I0428 21:28:18.860445 22802 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0428 21:28:20.106954 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:28:23.468003 22802 solver.cpp:218] Iteration 6684 (2.6045 iter/s, 4.60742s/12 iters), loss = 0.369281
I0428 21:28:23.468055 22802 solver.cpp:237]     Train net output #0: loss = 0.369281 (* 1 = 0.369281 loss)
I0428 21:28:23.468066 22802 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0428 21:28:28.086990 22802 solver.cpp:218] Iteration 6696 (2.59808 iter/s, 4.61879s/12 iters), loss = 0.389623
I0428 21:28:28.087045 22802 solver.cpp:237]     Train net output #0: loss = 0.389623 (* 1 = 0.389623 loss)
I0428 21:28:28.087056 22802 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0428 21:28:32.696815 22802 solver.cpp:218] Iteration 6708 (2.60325 iter/s, 4.60962s/12 iters), loss = 0.428915
I0428 21:28:32.696867 22802 solver.cpp:237]     Train net output #0: loss = 0.428915 (* 1 = 0.428915 loss)
I0428 21:28:32.696878 22802 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0428 21:28:37.321856 22802 solver.cpp:218] Iteration 6720 (2.59469 iter/s, 4.62484s/12 iters), loss = 0.461645
I0428 21:28:37.321910 22802 solver.cpp:237]     Train net output #0: loss = 0.461645 (* 1 = 0.461645 loss)
I0428 21:28:37.321923 22802 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0428 21:28:41.556205 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0428 21:28:43.073259 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0428 21:28:44.264694 22802 solver.cpp:330] Iteration 6732, Testing net (#0)
I0428 21:28:44.264716 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:28:45.990909 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:28:48.653160 22802 solver.cpp:397]     Test net output #0: accuracy = 0.530025
I0428 21:28:48.653198 22802 solver.cpp:397]     Test net output #1: loss = 2.44533 (* 1 = 2.44533 loss)
I0428 21:28:48.711221 22802 solver.cpp:218] Iteration 6732 (1.05365 iter/s, 11.389s/12 iters), loss = 0.407992
I0428 21:28:48.711274 22802 solver.cpp:237]     Train net output #0: loss = 0.407992 (* 1 = 0.407992 loss)
I0428 21:28:48.711287 22802 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0428 21:28:52.707940 22802 solver.cpp:218] Iteration 6744 (3.0026 iter/s, 3.99654s/12 iters), loss = 0.386796
I0428 21:28:52.707976 22802 solver.cpp:237]     Train net output #0: loss = 0.386796 (* 1 = 0.386796 loss)
I0428 21:28:52.707985 22802 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0428 21:28:57.509124 22802 solver.cpp:218] Iteration 6756 (2.49948 iter/s, 4.801s/12 iters), loss = 0.355125
I0428 21:28:57.509163 22802 solver.cpp:237]     Train net output #0: loss = 0.355125 (* 1 = 0.355125 loss)
I0428 21:28:57.509171 22802 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0428 21:29:02.181502 22802 solver.cpp:218] Iteration 6768 (2.56839 iter/s, 4.67219s/12 iters), loss = 0.373768
I0428 21:29:02.181547 22802 solver.cpp:237]     Train net output #0: loss = 0.373768 (* 1 = 0.373768 loss)
I0428 21:29:02.181558 22802 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0428 21:29:05.439535 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:29:06.829428 22802 solver.cpp:218] Iteration 6780 (2.58191 iter/s, 4.64773s/12 iters), loss = 0.43995
I0428 21:29:06.829478 22802 solver.cpp:237]     Train net output #0: loss = 0.43995 (* 1 = 0.43995 loss)
I0428 21:29:06.829488 22802 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0428 21:29:11.500723 22802 solver.cpp:218] Iteration 6792 (2.56899 iter/s, 4.67109s/12 iters), loss = 0.388394
I0428 21:29:11.500775 22802 solver.cpp:237]     Train net output #0: loss = 0.388394 (* 1 = 0.388394 loss)
I0428 21:29:11.500787 22802 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0428 21:29:16.276675 22802 solver.cpp:218] Iteration 6804 (2.51269 iter/s, 4.77575s/12 iters), loss = 0.304912
I0428 21:29:16.276881 22802 solver.cpp:237]     Train net output #0: loss = 0.304912 (* 1 = 0.304912 loss)
I0428 21:29:16.276891 22802 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0428 21:29:20.841316 22802 solver.cpp:218] Iteration 6816 (2.6291 iter/s, 4.56429s/12 iters), loss = 0.401001
I0428 21:29:20.841352 22802 solver.cpp:237]     Train net output #0: loss = 0.401001 (* 1 = 0.401001 loss)
I0428 21:29:20.841361 22802 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0428 21:29:25.543233 22802 solver.cpp:218] Iteration 6828 (2.55225 iter/s, 4.70173s/12 iters), loss = 0.307881
I0428 21:29:25.543267 22802 solver.cpp:237]     Train net output #0: loss = 0.307881 (* 1 = 0.307881 loss)
I0428 21:29:25.543275 22802 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0428 21:29:27.449970 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0428 21:29:28.999353 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0428 21:29:30.204156 22802 solver.cpp:330] Iteration 6834, Testing net (#0)
I0428 21:29:30.204182 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:29:31.862282 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:29:34.513052 22802 solver.cpp:397]     Test net output #0: accuracy = 0.528799
I0428 21:29:34.513082 22802 solver.cpp:397]     Test net output #1: loss = 2.39986 (* 1 = 2.39986 loss)
I0428 21:29:36.132818 22802 solver.cpp:218] Iteration 6840 (1.13323 iter/s, 10.5892s/12 iters), loss = 0.404885
I0428 21:29:36.132858 22802 solver.cpp:237]     Train net output #0: loss = 0.404885 (* 1 = 0.404885 loss)
I0428 21:29:36.132866 22802 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0428 21:29:40.822891 22802 solver.cpp:218] Iteration 6852 (2.5587 iter/s, 4.68988s/12 iters), loss = 0.303104
I0428 21:29:40.822937 22802 solver.cpp:237]     Train net output #0: loss = 0.303104 (* 1 = 0.303104 loss)
I0428 21:29:40.822947 22802 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0428 21:29:45.551398 22802 solver.cpp:218] Iteration 6864 (2.53791 iter/s, 4.72831s/12 iters), loss = 0.226079
I0428 21:29:45.551447 22802 solver.cpp:237]     Train net output #0: loss = 0.226079 (* 1 = 0.226079 loss)
I0428 21:29:45.551460 22802 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0428 21:29:50.261343 22802 solver.cpp:218] Iteration 6876 (2.54791 iter/s, 4.70974s/12 iters), loss = 0.450146
I0428 21:29:50.261461 22802 solver.cpp:237]     Train net output #0: loss = 0.450146 (* 1 = 0.450146 loss)
I0428 21:29:50.261471 22802 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0428 21:29:50.859215 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:29:54.954835 22802 solver.cpp:218] Iteration 6888 (2.55688 iter/s, 4.69322s/12 iters), loss = 0.318374
I0428 21:29:54.954874 22802 solver.cpp:237]     Train net output #0: loss = 0.318374 (* 1 = 0.318374 loss)
I0428 21:29:54.954883 22802 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0428 21:29:59.682498 22802 solver.cpp:218] Iteration 6900 (2.53836 iter/s, 4.72747s/12 iters), loss = 0.385076
I0428 21:29:59.682543 22802 solver.cpp:237]     Train net output #0: loss = 0.385076 (* 1 = 0.385076 loss)
I0428 21:29:59.682554 22802 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0428 21:30:04.237654 22802 solver.cpp:218] Iteration 6912 (2.63448 iter/s, 4.55497s/12 iters), loss = 0.250016
I0428 21:30:04.237690 22802 solver.cpp:237]     Train net output #0: loss = 0.250016 (* 1 = 0.250016 loss)
I0428 21:30:04.237699 22802 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0428 21:30:08.886445 22802 solver.cpp:218] Iteration 6924 (2.58142 iter/s, 4.64861s/12 iters), loss = 0.381221
I0428 21:30:08.886498 22802 solver.cpp:237]     Train net output #0: loss = 0.381221 (* 1 = 0.381221 loss)
I0428 21:30:08.886512 22802 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0428 21:30:13.072270 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0428 21:30:16.552315 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0428 21:30:19.884215 22802 solver.cpp:330] Iteration 6936, Testing net (#0)
I0428 21:30:19.884233 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:30:20.421831 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:30:21.456117 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:30:24.225335 22802 solver.cpp:397]     Test net output #0: accuracy = 0.536152
I0428 21:30:24.225371 22802 solver.cpp:397]     Test net output #1: loss = 2.39442 (* 1 = 2.39442 loss)
I0428 21:30:24.282934 22802 solver.cpp:218] Iteration 6936 (0.779424 iter/s, 15.396s/12 iters), loss = 0.396346
I0428 21:30:24.282979 22802 solver.cpp:237]     Train net output #0: loss = 0.396346 (* 1 = 0.396346 loss)
I0428 21:30:24.282989 22802 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0428 21:30:28.253500 22802 solver.cpp:218] Iteration 6948 (3.02237 iter/s, 3.97039s/12 iters), loss = 0.359247
I0428 21:30:28.253538 22802 solver.cpp:237]     Train net output #0: loss = 0.359247 (* 1 = 0.359247 loss)
I0428 21:30:28.253546 22802 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0428 21:30:32.864886 22802 solver.cpp:218] Iteration 6960 (2.60236 iter/s, 4.6112s/12 iters), loss = 0.149886
I0428 21:30:32.864926 22802 solver.cpp:237]     Train net output #0: loss = 0.149886 (* 1 = 0.149886 loss)
I0428 21:30:32.864935 22802 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0428 21:30:37.520609 22802 solver.cpp:218] Iteration 6972 (2.57758 iter/s, 4.65554s/12 iters), loss = 0.325536
I0428 21:30:37.520648 22802 solver.cpp:237]     Train net output #0: loss = 0.325536 (* 1 = 0.325536 loss)
I0428 21:30:37.520658 22802 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0428 21:30:40.107571 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:30:42.276151 22802 solver.cpp:218] Iteration 6984 (2.52348 iter/s, 4.75534s/12 iters), loss = 0.312062
I0428 21:30:42.276201 22802 solver.cpp:237]     Train net output #0: loss = 0.312062 (* 1 = 0.312062 loss)
I0428 21:30:42.276212 22802 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0428 21:30:47.228037 22802 solver.cpp:218] Iteration 6996 (2.42342 iter/s, 4.95169s/12 iters), loss = 0.34072
I0428 21:30:47.228076 22802 solver.cpp:237]     Train net output #0: loss = 0.340721 (* 1 = 0.340721 loss)
I0428 21:30:47.228085 22802 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0428 21:30:51.986917 22802 solver.cpp:218] Iteration 7008 (2.5217 iter/s, 4.75869s/12 iters), loss = 0.278401
I0428 21:30:51.987043 22802 solver.cpp:237]     Train net output #0: loss = 0.278401 (* 1 = 0.278401 loss)
I0428 21:30:51.987052 22802 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0428 21:30:56.629786 22802 solver.cpp:218] Iteration 7020 (2.58476 iter/s, 4.6426s/12 iters), loss = 0.317144
I0428 21:30:56.629822 22802 solver.cpp:237]     Train net output #0: loss = 0.317144 (* 1 = 0.317144 loss)
I0428 21:30:56.629830 22802 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0428 21:31:01.264890 22802 solver.cpp:218] Iteration 7032 (2.58904 iter/s, 4.63492s/12 iters), loss = 0.246605
I0428 21:31:01.264936 22802 solver.cpp:237]     Train net output #0: loss = 0.246605 (* 1 = 0.246605 loss)
I0428 21:31:01.264947 22802 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0428 21:31:03.186597 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0428 21:31:06.217597 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0428 21:31:08.331717 22802 solver.cpp:330] Iteration 7038, Testing net (#0)
I0428 21:31:08.331734 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:31:10.096601 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:31:12.874809 22802 solver.cpp:397]     Test net output #0: accuracy = 0.531863
I0428 21:31:12.874840 22802 solver.cpp:397]     Test net output #1: loss = 2.36988 (* 1 = 2.36988 loss)
I0428 21:31:14.498174 22802 solver.cpp:218] Iteration 7044 (0.906835 iter/s, 13.2328s/12 iters), loss = 0.228878
I0428 21:31:14.498214 22802 solver.cpp:237]     Train net output #0: loss = 0.228878 (* 1 = 0.228878 loss)
I0428 21:31:14.498224 22802 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0428 21:31:19.195055 22802 solver.cpp:218] Iteration 7056 (2.55499 iter/s, 4.69669s/12 iters), loss = 0.32138
I0428 21:31:19.195102 22802 solver.cpp:237]     Train net output #0: loss = 0.32138 (* 1 = 0.32138 loss)
I0428 21:31:19.195112 22802 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0428 21:31:23.859038 22802 solver.cpp:218] Iteration 7068 (2.57301 iter/s, 4.66379s/12 iters), loss = 0.198325
I0428 21:31:23.859172 22802 solver.cpp:237]     Train net output #0: loss = 0.198325 (* 1 = 0.198325 loss)
I0428 21:31:23.859181 22802 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0428 21:31:28.355943 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:31:28.441371 22802 solver.cpp:218] Iteration 7080 (2.61891 iter/s, 4.58206s/12 iters), loss = 0.246202
I0428 21:31:28.441408 22802 solver.cpp:237]     Train net output #0: loss = 0.246202 (* 1 = 0.246202 loss)
I0428 21:31:28.441417 22802 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0428 21:31:33.075999 22802 solver.cpp:218] Iteration 7092 (2.58931 iter/s, 4.63445s/12 iters), loss = 0.23059
I0428 21:31:33.076036 22802 solver.cpp:237]     Train net output #0: loss = 0.23059 (* 1 = 0.23059 loss)
I0428 21:31:33.076045 22802 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0428 21:31:37.716531 22802 solver.cpp:218] Iteration 7104 (2.58603 iter/s, 4.64031s/12 iters), loss = 0.360758
I0428 21:31:37.716568 22802 solver.cpp:237]     Train net output #0: loss = 0.360758 (* 1 = 0.360758 loss)
I0428 21:31:37.716576 22802 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0428 21:31:42.360543 22802 solver.cpp:218] Iteration 7116 (2.58408 iter/s, 4.64383s/12 iters), loss = 0.279871
I0428 21:31:42.360580 22802 solver.cpp:237]     Train net output #0: loss = 0.279871 (* 1 = 0.279871 loss)
I0428 21:31:42.360590 22802 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0428 21:31:47.013820 22802 solver.cpp:218] Iteration 7128 (2.57894 iter/s, 4.65308s/12 iters), loss = 0.346013
I0428 21:31:47.013868 22802 solver.cpp:237]     Train net output #0: loss = 0.346013 (* 1 = 0.346013 loss)
I0428 21:31:47.013878 22802 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0428 21:31:51.263411 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0428 21:31:54.515025 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0428 21:31:57.020043 22802 solver.cpp:330] Iteration 7140, Testing net (#0)
I0428 21:31:57.020064 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:31:58.626556 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:32:01.447213 22802 solver.cpp:397]     Test net output #0: accuracy = 0.534314
I0428 21:32:01.447240 22802 solver.cpp:397]     Test net output #1: loss = 2.44805 (* 1 = 2.44805 loss)
I0428 21:32:01.504837 22802 solver.cpp:218] Iteration 7140 (0.828127 iter/s, 14.4905s/12 iters), loss = 0.256964
I0428 21:32:01.504887 22802 solver.cpp:237]     Train net output #0: loss = 0.256964 (* 1 = 0.256964 loss)
I0428 21:32:01.504899 22802 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0428 21:32:05.579038 22802 solver.cpp:218] Iteration 7152 (2.94549 iter/s, 4.07402s/12 iters), loss = 0.326207
I0428 21:32:05.579078 22802 solver.cpp:237]     Train net output #0: loss = 0.326207 (* 1 = 0.326207 loss)
I0428 21:32:05.579087 22802 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0428 21:32:10.280081 22802 solver.cpp:218] Iteration 7164 (2.55273 iter/s, 4.70085s/12 iters), loss = 0.249999
I0428 21:32:10.280133 22802 solver.cpp:237]     Train net output #0: loss = 0.249999 (* 1 = 0.249999 loss)
I0428 21:32:10.280145 22802 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0428 21:32:14.954186 22802 solver.cpp:218] Iteration 7176 (2.56744 iter/s, 4.67391s/12 iters), loss = 0.334632
I0428 21:32:14.954226 22802 solver.cpp:237]     Train net output #0: loss = 0.334632 (* 1 = 0.334632 loss)
I0428 21:32:14.954232 22802 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0428 21:32:16.909415 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:32:19.569476 22802 solver.cpp:218] Iteration 7188 (2.60016 iter/s, 4.6151s/12 iters), loss = 0.378358
I0428 21:32:19.569530 22802 solver.cpp:237]     Train net output #0: loss = 0.378358 (* 1 = 0.378358 loss)
I0428 21:32:19.569541 22802 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0428 21:32:24.172942 22802 solver.cpp:218] Iteration 7200 (2.60684 iter/s, 4.60327s/12 iters), loss = 0.259247
I0428 21:32:24.172996 22802 solver.cpp:237]     Train net output #0: loss = 0.259247 (* 1 = 0.259247 loss)
I0428 21:32:24.173007 22802 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0428 21:32:28.785290 22802 solver.cpp:218] Iteration 7212 (2.60182 iter/s, 4.61215s/12 iters), loss = 0.311607
I0428 21:32:28.785447 22802 solver.cpp:237]     Train net output #0: loss = 0.311607 (* 1 = 0.311607 loss)
I0428 21:32:28.785459 22802 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0428 21:32:33.391526 22802 solver.cpp:218] Iteration 7224 (2.60533 iter/s, 4.60594s/12 iters), loss = 0.334492
I0428 21:32:33.391582 22802 solver.cpp:237]     Train net output #0: loss = 0.334492 (* 1 = 0.334492 loss)
I0428 21:32:33.391592 22802 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0428 21:32:37.990917 22802 solver.cpp:218] Iteration 7236 (2.60915 iter/s, 4.5992s/12 iters), loss = 0.226773
I0428 21:32:37.990955 22802 solver.cpp:237]     Train net output #0: loss = 0.226773 (* 1 = 0.226773 loss)
I0428 21:32:37.990963 22802 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0428 21:32:39.811878 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0428 21:32:41.324138 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0428 21:32:42.508709 22802 solver.cpp:330] Iteration 7242, Testing net (#0)
I0428 21:32:42.508729 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:32:44.143354 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:32:46.958954 22802 solver.cpp:397]     Test net output #0: accuracy = 0.541667
I0428 21:32:46.958984 22802 solver.cpp:397]     Test net output #1: loss = 2.37304 (* 1 = 2.37304 loss)
I0428 21:32:48.594733 22802 solver.cpp:218] Iteration 7248 (1.13171 iter/s, 10.6035s/12 iters), loss = 0.194398
I0428 21:32:48.594776 22802 solver.cpp:237]     Train net output #0: loss = 0.194398 (* 1 = 0.194398 loss)
I0428 21:32:48.594784 22802 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0428 21:32:53.177734 22802 solver.cpp:218] Iteration 7260 (2.61848 iter/s, 4.58281s/12 iters), loss = 0.226117
I0428 21:32:53.177772 22802 solver.cpp:237]     Train net output #0: loss = 0.226117 (* 1 = 0.226117 loss)
I0428 21:32:53.177780 22802 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0428 21:32:57.818257 22802 solver.cpp:218] Iteration 7272 (2.58602 iter/s, 4.64034s/12 iters), loss = 0.347736
I0428 21:32:57.818297 22802 solver.cpp:237]     Train net output #0: loss = 0.347736 (* 1 = 0.347736 loss)
I0428 21:32:57.818306 22802 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0428 21:33:01.739213 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:33:02.434208 22802 solver.cpp:218] Iteration 7284 (2.59979 iter/s, 4.61576s/12 iters), loss = 0.227286
I0428 21:33:02.434258 22802 solver.cpp:237]     Train net output #0: loss = 0.227286 (* 1 = 0.227286 loss)
I0428 21:33:02.434271 22802 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0428 21:33:07.053232 22802 solver.cpp:218] Iteration 7296 (2.59806 iter/s, 4.61883s/12 iters), loss = 0.305685
I0428 21:33:07.053267 22802 solver.cpp:237]     Train net output #0: loss = 0.305685 (* 1 = 0.305685 loss)
I0428 21:33:07.053275 22802 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0428 21:33:11.676431 22802 solver.cpp:218] Iteration 7308 (2.59571 iter/s, 4.62301s/12 iters), loss = 0.219839
I0428 21:33:11.676478 22802 solver.cpp:237]     Train net output #0: loss = 0.219839 (* 1 = 0.219839 loss)
I0428 21:33:11.676517 22802 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0428 21:33:16.393852 22802 solver.cpp:218] Iteration 7320 (2.54387 iter/s, 4.71722s/12 iters), loss = 0.314132
I0428 21:33:16.393901 22802 solver.cpp:237]     Train net output #0: loss = 0.314132 (* 1 = 0.314132 loss)
I0428 21:33:16.393913 22802 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0428 21:33:21.032552 22802 solver.cpp:218] Iteration 7332 (2.58706 iter/s, 4.63847s/12 iters), loss = 0.401695
I0428 21:33:21.032598 22802 solver.cpp:237]     Train net output #0: loss = 0.401695 (* 1 = 0.401695 loss)
I0428 21:33:21.032608 22802 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0428 21:33:25.301525 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0428 21:33:28.755543 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0428 21:33:31.753475 22802 solver.cpp:330] Iteration 7344, Testing net (#0)
I0428 21:33:31.753580 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:33:33.202502 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:33:36.425557 22802 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0428 21:33:36.425593 22802 solver.cpp:397]     Test net output #1: loss = 2.5428 (* 1 = 2.5428 loss)
I0428 21:33:36.483356 22802 solver.cpp:218] Iteration 7344 (0.776684 iter/s, 15.4503s/12 iters), loss = 0.221373
I0428 21:33:36.483397 22802 solver.cpp:237]     Train net output #0: loss = 0.221373 (* 1 = 0.221373 loss)
I0428 21:33:36.483404 22802 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0428 21:33:40.536356 22802 solver.cpp:218] Iteration 7356 (2.9609 iter/s, 4.05282s/12 iters), loss = 0.159429
I0428 21:33:40.536412 22802 solver.cpp:237]     Train net output #0: loss = 0.159429 (* 1 = 0.159429 loss)
I0428 21:33:40.536425 22802 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0428 21:33:45.254869 22802 solver.cpp:218] Iteration 7368 (2.54328 iter/s, 4.71831s/12 iters), loss = 0.249542
I0428 21:33:45.254923 22802 solver.cpp:237]     Train net output #0: loss = 0.249542 (* 1 = 0.249542 loss)
I0428 21:33:45.254935 22802 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0428 21:33:49.879663 22802 solver.cpp:218] Iteration 7380 (2.59482 iter/s, 4.62459s/12 iters), loss = 0.21079
I0428 21:33:49.879714 22802 solver.cpp:237]     Train net output #0: loss = 0.21079 (* 1 = 0.21079 loss)
I0428 21:33:49.879726 22802 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0428 21:33:51.157791 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:33:54.484014 22802 solver.cpp:218] Iteration 7392 (2.60634 iter/s, 4.60415s/12 iters), loss = 0.227723
I0428 21:33:54.484069 22802 solver.cpp:237]     Train net output #0: loss = 0.227723 (* 1 = 0.227723 loss)
I0428 21:33:54.484082 22802 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0428 21:33:59.064723 22802 solver.cpp:218] Iteration 7404 (2.6198 iter/s, 4.58051s/12 iters), loss = 0.339467
I0428 21:33:59.064765 22802 solver.cpp:237]     Train net output #0: loss = 0.339467 (* 1 = 0.339467 loss)
I0428 21:33:59.064774 22802 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0428 21:34:03.838200 22802 solver.cpp:218] Iteration 7416 (2.514 iter/s, 4.77327s/12 iters), loss = 0.238531
I0428 21:34:03.838305 22802 solver.cpp:237]     Train net output #0: loss = 0.238531 (* 1 = 0.238531 loss)
I0428 21:34:03.838317 22802 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0428 21:34:08.507334 22802 solver.cpp:218] Iteration 7428 (2.57021 iter/s, 4.66888s/12 iters), loss = 0.162922
I0428 21:34:08.507385 22802 solver.cpp:237]     Train net output #0: loss = 0.162923 (* 1 = 0.162923 loss)
I0428 21:34:08.507396 22802 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0428 21:34:13.176578 22802 solver.cpp:218] Iteration 7440 (2.57012 iter/s, 4.66904s/12 iters), loss = 0.27343
I0428 21:34:13.176616 22802 solver.cpp:237]     Train net output #0: loss = 0.27343 (* 1 = 0.27343 loss)
I0428 21:34:13.176625 22802 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0428 21:34:15.187994 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0428 21:34:16.677575 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0428 21:34:18.569756 22802 solver.cpp:330] Iteration 7446, Testing net (#0)
I0428 21:34:18.569775 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:34:20.029454 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:34:22.891147 22802 solver.cpp:397]     Test net output #0: accuracy = 0.54902
I0428 21:34:22.891188 22802 solver.cpp:397]     Test net output #1: loss = 2.37506 (* 1 = 2.37506 loss)
I0428 21:34:24.551056 22802 solver.cpp:218] Iteration 7452 (1.05503 iter/s, 11.3741s/12 iters), loss = 0.216143
I0428 21:34:24.551095 22802 solver.cpp:237]     Train net output #0: loss = 0.216143 (* 1 = 0.216143 loss)
I0428 21:34:24.551103 22802 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0428 21:34:29.210032 22802 solver.cpp:218] Iteration 7464 (2.57578 iter/s, 4.65879s/12 iters), loss = 0.172255
I0428 21:34:29.210073 22802 solver.cpp:237]     Train net output #0: loss = 0.172255 (* 1 = 0.172255 loss)
I0428 21:34:29.210083 22802 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0428 21:34:33.972010 22802 solver.cpp:218] Iteration 7476 (2.52007 iter/s, 4.76178s/12 iters), loss = 0.207368
I0428 21:34:33.972172 22802 solver.cpp:237]     Train net output #0: loss = 0.207368 (* 1 = 0.207368 loss)
I0428 21:34:33.972185 22802 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0428 21:34:37.315564 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:34:38.698104 22802 solver.cpp:218] Iteration 7488 (2.53926 iter/s, 4.72578s/12 iters), loss = 0.179133
I0428 21:34:38.698158 22802 solver.cpp:237]     Train net output #0: loss = 0.179133 (* 1 = 0.179133 loss)
I0428 21:34:38.698168 22802 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0428 21:34:43.396814 22802 solver.cpp:218] Iteration 7500 (2.554 iter/s, 4.69851s/12 iters), loss = 0.417839
I0428 21:34:43.396865 22802 solver.cpp:237]     Train net output #0: loss = 0.417839 (* 1 = 0.417839 loss)
I0428 21:34:43.396876 22802 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0428 21:34:48.269213 22802 solver.cpp:218] Iteration 7512 (2.46296 iter/s, 4.8722s/12 iters), loss = 0.204106
I0428 21:34:48.269250 22802 solver.cpp:237]     Train net output #0: loss = 0.204106 (* 1 = 0.204106 loss)
I0428 21:34:48.269260 22802 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0428 21:34:52.822227 22802 solver.cpp:218] Iteration 7524 (2.63572 iter/s, 4.55283s/12 iters), loss = 0.215918
I0428 21:34:52.822263 22802 solver.cpp:237]     Train net output #0: loss = 0.215918 (* 1 = 0.215918 loss)
I0428 21:34:52.822273 22802 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0428 21:34:57.554771 22802 solver.cpp:218] Iteration 7536 (2.53574 iter/s, 4.73235s/12 iters), loss = 0.233938
I0428 21:34:57.554811 22802 solver.cpp:237]     Train net output #0: loss = 0.233938 (* 1 = 0.233938 loss)
I0428 21:34:57.554821 22802 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0428 21:35:01.880272 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0428 21:35:03.392181 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0428 21:35:04.591667 22802 solver.cpp:330] Iteration 7548, Testing net (#0)
I0428 21:35:04.592748 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:35:06.021059 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:35:08.935983 22802 solver.cpp:397]     Test net output #0: accuracy = 0.542892
I0428 21:35:08.936020 22802 solver.cpp:397]     Test net output #1: loss = 2.55139 (* 1 = 2.55139 loss)
I0428 21:35:08.993696 22802 solver.cpp:218] Iteration 7548 (1.04909 iter/s, 11.4385s/12 iters), loss = 0.230838
I0428 21:35:08.993746 22802 solver.cpp:237]     Train net output #0: loss = 0.230838 (* 1 = 0.230838 loss)
I0428 21:35:08.993760 22802 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0428 21:35:12.917619 22802 solver.cpp:218] Iteration 7560 (3.0583 iter/s, 3.92375s/12 iters), loss = 0.233115
I0428 21:35:12.917659 22802 solver.cpp:237]     Train net output #0: loss = 0.233115 (* 1 = 0.233115 loss)
I0428 21:35:12.917666 22802 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0428 21:35:17.528762 22802 solver.cpp:218] Iteration 7572 (2.6025 iter/s, 4.61095s/12 iters), loss = 0.219485
I0428 21:35:17.528806 22802 solver.cpp:237]     Train net output #0: loss = 0.219485 (* 1 = 0.219485 loss)
I0428 21:35:17.528815 22802 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0428 21:35:22.159323 22802 solver.cpp:218] Iteration 7584 (2.59159 iter/s, 4.63037s/12 iters), loss = 0.158204
I0428 21:35:22.159363 22802 solver.cpp:237]     Train net output #0: loss = 0.158204 (* 1 = 0.158204 loss)
I0428 21:35:22.159370 22802 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0428 21:35:22.796591 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:35:26.925810 22802 solver.cpp:218] Iteration 7596 (2.51768 iter/s, 4.7663s/12 iters), loss = 0.222077
I0428 21:35:26.925851 22802 solver.cpp:237]     Train net output #0: loss = 0.222077 (* 1 = 0.222077 loss)
I0428 21:35:26.925860 22802 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0428 21:35:31.580029 22802 solver.cpp:218] Iteration 7608 (2.57841 iter/s, 4.65403s/12 iters), loss = 0.304325
I0428 21:35:31.580068 22802 solver.cpp:237]     Train net output #0: loss = 0.304325 (* 1 = 0.304325 loss)
I0428 21:35:31.580076 22802 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0428 21:35:36.192044 22802 solver.cpp:218] Iteration 7620 (2.60201 iter/s, 4.61183s/12 iters), loss = 0.23063
I0428 21:35:36.192216 22802 solver.cpp:237]     Train net output #0: loss = 0.23063 (* 1 = 0.23063 loss)
I0428 21:35:36.192229 22802 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0428 21:35:38.456477 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:35:40.816174 22802 solver.cpp:218] Iteration 7632 (2.59526 iter/s, 4.62381s/12 iters), loss = 0.444256
I0428 21:35:40.816213 22802 solver.cpp:237]     Train net output #0: loss = 0.444256 (* 1 = 0.444256 loss)
I0428 21:35:40.816220 22802 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0428 21:35:45.366621 22802 solver.cpp:218] Iteration 7644 (2.63721 iter/s, 4.55027s/12 iters), loss = 0.192398
I0428 21:35:45.366659 22802 solver.cpp:237]     Train net output #0: loss = 0.192399 (* 1 = 0.192399 loss)
I0428 21:35:45.366667 22802 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0428 21:35:47.213075 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0428 21:35:50.595713 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0428 21:35:51.802373 22802 solver.cpp:330] Iteration 7650, Testing net (#0)
I0428 21:35:51.802392 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:35:53.099628 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:35:56.062896 22802 solver.cpp:397]     Test net output #0: accuracy = 0.54902
I0428 21:35:56.062923 22802 solver.cpp:397]     Test net output #1: loss = 2.48639 (* 1 = 2.48639 loss)
I0428 21:35:57.695313 22802 solver.cpp:218] Iteration 7656 (0.973372 iter/s, 12.3283s/12 iters), loss = 0.192643
I0428 21:35:57.695356 22802 solver.cpp:237]     Train net output #0: loss = 0.192643 (* 1 = 0.192643 loss)
I0428 21:35:57.695366 22802 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0428 21:36:02.220093 22802 solver.cpp:218] Iteration 7668 (2.65217 iter/s, 4.52459s/12 iters), loss = 0.167125
I0428 21:36:02.220142 22802 solver.cpp:237]     Train net output #0: loss = 0.167125 (* 1 = 0.167125 loss)
I0428 21:36:02.220155 22802 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0428 21:36:06.856873 22802 solver.cpp:218] Iteration 7680 (2.58811 iter/s, 4.63659s/12 iters), loss = 0.155762
I0428 21:36:06.856976 22802 solver.cpp:237]     Train net output #0: loss = 0.155762 (* 1 = 0.155762 loss)
I0428 21:36:06.856986 22802 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0428 21:36:09.450402 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:36:11.499907 22802 solver.cpp:218] Iteration 7692 (2.58465 iter/s, 4.64279s/12 iters), loss = 0.208955
I0428 21:36:11.499943 22802 solver.cpp:237]     Train net output #0: loss = 0.208955 (* 1 = 0.208955 loss)
I0428 21:36:11.499951 22802 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0428 21:36:16.113765 22802 solver.cpp:218] Iteration 7704 (2.60097 iter/s, 4.61367s/12 iters), loss = 0.297568
I0428 21:36:16.113806 22802 solver.cpp:237]     Train net output #0: loss = 0.297568 (* 1 = 0.297568 loss)
I0428 21:36:16.113813 22802 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0428 21:36:20.759383 22802 solver.cpp:218] Iteration 7716 (2.58319 iter/s, 4.64543s/12 iters), loss = 0.161659
I0428 21:36:20.759421 22802 solver.cpp:237]     Train net output #0: loss = 0.161659 (* 1 = 0.161659 loss)
I0428 21:36:20.759429 22802 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0428 21:36:25.772776 22802 solver.cpp:218] Iteration 7728 (2.39369 iter/s, 5.01319s/12 iters), loss = 0.144966
I0428 21:36:25.772833 22802 solver.cpp:237]     Train net output #0: loss = 0.144966 (* 1 = 0.144966 loss)
I0428 21:36:25.772847 22802 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0428 21:36:30.444113 22802 solver.cpp:218] Iteration 7740 (2.56897 iter/s, 4.67113s/12 iters), loss = 0.252124
I0428 21:36:30.444151 22802 solver.cpp:237]     Train net output #0: loss = 0.252124 (* 1 = 0.252124 loss)
I0428 21:36:30.444159 22802 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0428 21:36:34.713917 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0428 21:36:36.262022 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0428 21:36:38.088328 22802 solver.cpp:330] Iteration 7752, Testing net (#0)
I0428 21:36:38.088429 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:36:39.352380 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:36:42.322203 22802 solver.cpp:397]     Test net output #0: accuracy = 0.533701
I0428 21:36:42.322230 22802 solver.cpp:397]     Test net output #1: loss = 2.48296 (* 1 = 2.48296 loss)
I0428 21:36:42.380525 22802 solver.cpp:218] Iteration 7752 (1.00536 iter/s, 11.936s/12 iters), loss = 0.206364
I0428 21:36:42.380578 22802 solver.cpp:237]     Train net output #0: loss = 0.206364 (* 1 = 0.206364 loss)
I0428 21:36:42.380589 22802 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0428 21:36:46.211874 22802 solver.cpp:218] Iteration 7764 (3.13219 iter/s, 3.83118s/12 iters), loss = 0.372729
I0428 21:36:46.211911 22802 solver.cpp:237]     Train net output #0: loss = 0.37273 (* 1 = 0.37273 loss)
I0428 21:36:46.211920 22802 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0428 21:36:50.862426 22802 solver.cpp:218] Iteration 7776 (2.58045 iter/s, 4.65036s/12 iters), loss = 0.296557
I0428 21:36:50.862480 22802 solver.cpp:237]     Train net output #0: loss = 0.296557 (* 1 = 0.296557 loss)
I0428 21:36:50.862490 22802 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0428 21:36:55.516839 22802 solver.cpp:218] Iteration 7788 (2.57831 iter/s, 4.65421s/12 iters), loss = 0.204227
I0428 21:36:55.516875 22802 solver.cpp:237]     Train net output #0: loss = 0.204227 (* 1 = 0.204227 loss)
I0428 21:36:55.516883 22802 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0428 21:36:55.523393 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:37:00.214355 22802 solver.cpp:218] Iteration 7800 (2.55465 iter/s, 4.69733s/12 iters), loss = 0.279059
I0428 21:37:00.214393 22802 solver.cpp:237]     Train net output #0: loss = 0.279059 (* 1 = 0.279059 loss)
I0428 21:37:00.214402 22802 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0428 21:37:04.961444 22802 solver.cpp:218] Iteration 7812 (2.52797 iter/s, 4.7469s/12 iters), loss = 0.182028
I0428 21:37:04.961481 22802 solver.cpp:237]     Train net output #0: loss = 0.182028 (* 1 = 0.182028 loss)
I0428 21:37:04.961490 22802 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0428 21:37:09.729671 22802 solver.cpp:218] Iteration 7824 (2.51676 iter/s, 4.76804s/12 iters), loss = 0.225449
I0428 21:37:09.729759 22802 solver.cpp:237]     Train net output #0: loss = 0.225449 (* 1 = 0.225449 loss)
I0428 21:37:09.729768 22802 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0428 21:37:14.457696 22802 solver.cpp:218] Iteration 7836 (2.53819 iter/s, 4.72779s/12 iters), loss = 0.212544
I0428 21:37:14.457743 22802 solver.cpp:237]     Train net output #0: loss = 0.212544 (* 1 = 0.212544 loss)
I0428 21:37:14.457754 22802 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0428 21:37:19.248299 22802 solver.cpp:218] Iteration 7848 (2.50501 iter/s, 4.79041s/12 iters), loss = 0.184077
I0428 21:37:19.248335 22802 solver.cpp:237]     Train net output #0: loss = 0.184077 (* 1 = 0.184077 loss)
I0428 21:37:19.248343 22802 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0428 21:37:21.173151 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0428 21:37:23.356101 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0428 21:37:25.037106 22802 solver.cpp:330] Iteration 7854, Testing net (#0)
I0428 21:37:25.037123 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:37:26.331354 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:37:29.400539 22802 solver.cpp:397]     Test net output #0: accuracy = 0.540441
I0428 21:37:29.400570 22802 solver.cpp:397]     Test net output #1: loss = 2.55523 (* 1 = 2.55523 loss)
I0428 21:37:31.093814 22802 solver.cpp:218] Iteration 7860 (1.01308 iter/s, 11.8451s/12 iters), loss = 0.225583
I0428 21:37:31.093852 22802 solver.cpp:237]     Train net output #0: loss = 0.225583 (* 1 = 0.225583 loss)
I0428 21:37:31.093860 22802 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0428 21:37:35.765573 22802 solver.cpp:218] Iteration 7872 (2.56873 iter/s, 4.67157s/12 iters), loss = 0.299122
I0428 21:37:35.765611 22802 solver.cpp:237]     Train net output #0: loss = 0.299122 (* 1 = 0.299122 loss)
I0428 21:37:35.765619 22802 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0428 21:37:40.437072 22802 solver.cpp:218] Iteration 7884 (2.56887 iter/s, 4.67131s/12 iters), loss = 0.15793
I0428 21:37:40.437216 22802 solver.cpp:237]     Train net output #0: loss = 0.15793 (* 1 = 0.15793 loss)
I0428 21:37:40.437227 22802 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0428 21:37:42.435662 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:37:45.066596 22802 solver.cpp:218] Iteration 7896 (2.59222 iter/s, 4.62923s/12 iters), loss = 0.272957
I0428 21:37:45.066648 22802 solver.cpp:237]     Train net output #0: loss = 0.272957 (* 1 = 0.272957 loss)
I0428 21:37:45.066660 22802 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0428 21:37:49.683111 22802 solver.cpp:218] Iteration 7908 (2.59948 iter/s, 4.61632s/12 iters), loss = 0.183426
I0428 21:37:49.683166 22802 solver.cpp:237]     Train net output #0: loss = 0.183426 (* 1 = 0.183426 loss)
I0428 21:37:49.683177 22802 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0428 21:37:54.297636 22802 solver.cpp:218] Iteration 7920 (2.6006 iter/s, 4.61433s/12 iters), loss = 0.161386
I0428 21:37:54.297691 22802 solver.cpp:237]     Train net output #0: loss = 0.161386 (* 1 = 0.161386 loss)
I0428 21:37:54.297703 22802 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0428 21:37:58.905172 22802 solver.cpp:218] Iteration 7932 (2.60454 iter/s, 4.60733s/12 iters), loss = 0.1349
I0428 21:37:58.905225 22802 solver.cpp:237]     Train net output #0: loss = 0.1349 (* 1 = 0.1349 loss)
I0428 21:37:58.905236 22802 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0428 21:38:03.526302 22802 solver.cpp:218] Iteration 7944 (2.59688 iter/s, 4.62093s/12 iters), loss = 0.174999
I0428 21:38:03.526350 22802 solver.cpp:237]     Train net output #0: loss = 0.174999 (* 1 = 0.174999 loss)
I0428 21:38:03.526361 22802 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0428 21:38:07.704226 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0428 21:38:10.812211 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0428 21:38:12.298231 22802 solver.cpp:330] Iteration 7956, Testing net (#0)
I0428 21:38:12.298251 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:38:13.499017 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:38:16.577630 22802 solver.cpp:397]     Test net output #0: accuracy = 0.563113
I0428 21:38:16.577667 22802 solver.cpp:397]     Test net output #1: loss = 2.39919 (* 1 = 2.39919 loss)
I0428 21:38:16.635512 22802 solver.cpp:218] Iteration 7956 (0.915417 iter/s, 13.1088s/12 iters), loss = 0.107765
I0428 21:38:16.635548 22802 solver.cpp:237]     Train net output #0: loss = 0.107765 (* 1 = 0.107765 loss)
I0428 21:38:16.635557 22802 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0428 21:38:20.719000 22802 solver.cpp:218] Iteration 7968 (2.93879 iter/s, 4.08332s/12 iters), loss = 0.163776
I0428 21:38:20.719035 22802 solver.cpp:237]     Train net output #0: loss = 0.163776 (* 1 = 0.163776 loss)
I0428 21:38:20.719044 22802 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0428 21:38:25.287631 22802 solver.cpp:218] Iteration 7980 (2.62671 iter/s, 4.56845s/12 iters), loss = 0.131946
I0428 21:38:25.287667 22802 solver.cpp:237]     Train net output #0: loss = 0.131946 (* 1 = 0.131946 loss)
I0428 21:38:25.287673 22802 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0428 21:38:29.501768 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:38:30.213865 22802 solver.cpp:218] Iteration 7992 (2.43603 iter/s, 4.92604s/12 iters), loss = 0.199783
I0428 21:38:30.213910 22802 solver.cpp:237]     Train net output #0: loss = 0.199783 (* 1 = 0.199783 loss)
I0428 21:38:30.213920 22802 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0428 21:38:34.940553 22802 solver.cpp:218] Iteration 8004 (2.53888 iter/s, 4.7265s/12 iters), loss = 0.198999
I0428 21:38:34.940590 22802 solver.cpp:237]     Train net output #0: loss = 0.198999 (* 1 = 0.198999 loss)
I0428 21:38:34.940598 22802 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0428 21:38:39.619880 22802 solver.cpp:218] Iteration 8016 (2.56457 iter/s, 4.67914s/12 iters), loss = 0.160909
I0428 21:38:39.619917 22802 solver.cpp:237]     Train net output #0: loss = 0.160909 (* 1 = 0.160909 loss)
I0428 21:38:39.619925 22802 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0428 21:38:44.161938 22802 solver.cpp:218] Iteration 8028 (2.64208 iter/s, 4.54187s/12 iters), loss = 0.253047
I0428 21:38:44.162051 22802 solver.cpp:237]     Train net output #0: loss = 0.253047 (* 1 = 0.253047 loss)
I0428 21:38:44.162060 22802 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0428 21:38:48.766414 22802 solver.cpp:218] Iteration 8040 (2.60631 iter/s, 4.60422s/12 iters), loss = 0.26204
I0428 21:38:48.766461 22802 solver.cpp:237]     Train net output #0: loss = 0.262041 (* 1 = 0.262041 loss)
I0428 21:38:48.766471 22802 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0428 21:38:53.414583 22802 solver.cpp:218] Iteration 8052 (2.58177 iter/s, 4.64797s/12 iters), loss = 0.14816
I0428 21:38:53.414634 22802 solver.cpp:237]     Train net output #0: loss = 0.14816 (* 1 = 0.14816 loss)
I0428 21:38:53.414647 22802 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0428 21:38:55.282358 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0428 21:38:58.451545 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0428 21:39:01.700305 22802 solver.cpp:330] Iteration 8058, Testing net (#0)
I0428 21:39:01.700325 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:39:02.891459 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:39:06.044755 22802 solver.cpp:397]     Test net output #0: accuracy = 0.551471
I0428 21:39:06.044782 22802 solver.cpp:397]     Test net output #1: loss = 2.60152 (* 1 = 2.60152 loss)
I0428 21:39:07.731067 22802 solver.cpp:218] Iteration 8064 (0.838222 iter/s, 14.316s/12 iters), loss = 0.0964695
I0428 21:39:07.731107 22802 solver.cpp:237]     Train net output #0: loss = 0.0964696 (* 1 = 0.0964696 loss)
I0428 21:39:07.731115 22802 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0428 21:39:12.395452 22802 solver.cpp:218] Iteration 8076 (2.57279 iter/s, 4.66419s/12 iters), loss = 0.116942
I0428 21:39:12.395501 22802 solver.cpp:237]     Train net output #0: loss = 0.116942 (* 1 = 0.116942 loss)
I0428 21:39:12.395512 22802 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0428 21:39:17.018790 22802 solver.cpp:218] Iteration 8088 (2.59564 iter/s, 4.62314s/12 iters), loss = 0.134863
I0428 21:39:17.018971 22802 solver.cpp:237]     Train net output #0: loss = 0.134863 (* 1 = 0.134863 loss)
I0428 21:39:17.018983 22802 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0428 21:39:18.326629 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:39:21.617007 22802 solver.cpp:218] Iteration 8100 (2.60989 iter/s, 4.59789s/12 iters), loss = 0.196132
I0428 21:39:21.617058 22802 solver.cpp:237]     Train net output #0: loss = 0.196132 (* 1 = 0.196132 loss)
I0428 21:39:21.617069 22802 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0428 21:39:26.208139 22802 solver.cpp:218] Iteration 8112 (2.61385 iter/s, 4.59094s/12 iters), loss = 0.157986
I0428 21:39:26.208191 22802 solver.cpp:237]     Train net output #0: loss = 0.157986 (* 1 = 0.157986 loss)
I0428 21:39:26.208202 22802 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0428 21:39:30.906659 22802 solver.cpp:218] Iteration 8124 (2.55411 iter/s, 4.69832s/12 iters), loss = 0.0923563
I0428 21:39:30.906708 22802 solver.cpp:237]     Train net output #0: loss = 0.0923564 (* 1 = 0.0923564 loss)
I0428 21:39:30.906721 22802 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0428 21:39:35.564047 22802 solver.cpp:218] Iteration 8136 (2.57666 iter/s, 4.65719s/12 iters), loss = 0.303677
I0428 21:39:35.564097 22802 solver.cpp:237]     Train net output #0: loss = 0.303677 (* 1 = 0.303677 loss)
I0428 21:39:35.564110 22802 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0428 21:39:40.187647 22802 solver.cpp:218] Iteration 8148 (2.59549 iter/s, 4.62341s/12 iters), loss = 0.229096
I0428 21:39:40.187681 22802 solver.cpp:237]     Train net output #0: loss = 0.229096 (* 1 = 0.229096 loss)
I0428 21:39:40.187690 22802 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0428 21:39:44.349918 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0428 21:39:45.967815 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0428 21:39:48.834197 22802 solver.cpp:330] Iteration 8160, Testing net (#0)
I0428 21:39:48.834270 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:39:49.989017 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:39:53.122753 22802 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0428 21:39:53.122793 22802 solver.cpp:397]     Test net output #1: loss = 2.4844 (* 1 = 2.4844 loss)
I0428 21:39:53.180383 22802 solver.cpp:218] Iteration 8160 (0.923623 iter/s, 12.9923s/12 iters), loss = 0.167237
I0428 21:39:53.180429 22802 solver.cpp:237]     Train net output #0: loss = 0.167237 (* 1 = 0.167237 loss)
I0428 21:39:53.180439 22802 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0428 21:39:57.171205 22802 solver.cpp:218] Iteration 8172 (3.00703 iter/s, 3.99065s/12 iters), loss = 0.167775
I0428 21:39:57.171243 22802 solver.cpp:237]     Train net output #0: loss = 0.167775 (* 1 = 0.167775 loss)
I0428 21:39:57.171253 22802 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0428 21:40:01.823212 22802 solver.cpp:218] Iteration 8184 (2.57964 iter/s, 4.65182s/12 iters), loss = 0.292384
I0428 21:40:01.823252 22802 solver.cpp:237]     Train net output #0: loss = 0.292384 (* 1 = 0.292384 loss)
I0428 21:40:01.823259 22802 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0428 21:40:05.121443 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:40:06.448997 22802 solver.cpp:218] Iteration 8196 (2.59426 iter/s, 4.6256s/12 iters), loss = 0.119547
I0428 21:40:06.449033 22802 solver.cpp:237]     Train net output #0: loss = 0.119548 (* 1 = 0.119548 loss)
I0428 21:40:06.449043 22802 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0428 21:40:11.069793 22802 solver.cpp:218] Iteration 8208 (2.59706 iter/s, 4.62061s/12 iters), loss = 0.148547
I0428 21:40:11.069836 22802 solver.cpp:237]     Train net output #0: loss = 0.148547 (* 1 = 0.148547 loss)
I0428 21:40:11.069845 22802 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0428 21:40:15.806214 22802 solver.cpp:218] Iteration 8220 (2.53366 iter/s, 4.73623s/12 iters), loss = 0.164224
I0428 21:40:15.806252 22802 solver.cpp:237]     Train net output #0: loss = 0.164224 (* 1 = 0.164224 loss)
I0428 21:40:15.806258 22802 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0428 21:40:20.924533 22802 solver.cpp:218] Iteration 8232 (2.34462 iter/s, 5.1181s/12 iters), loss = 0.219227
I0428 21:40:20.924654 22802 solver.cpp:237]     Train net output #0: loss = 0.219227 (* 1 = 0.219227 loss)
I0428 21:40:20.924664 22802 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0428 21:40:25.545630 22802 solver.cpp:218] Iteration 8244 (2.59693 iter/s, 4.62083s/12 iters), loss = 0.149744
I0428 21:40:25.545668 22802 solver.cpp:237]     Train net output #0: loss = 0.149744 (* 1 = 0.149744 loss)
I0428 21:40:25.545676 22802 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0428 21:40:30.125095 22802 solver.cpp:218] Iteration 8256 (2.6205 iter/s, 4.57928s/12 iters), loss = 0.17938
I0428 21:40:30.125133 22802 solver.cpp:237]     Train net output #0: loss = 0.17938 (* 1 = 0.17938 loss)
I0428 21:40:30.125142 22802 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0428 21:40:32.073256 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0428 21:40:34.054019 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0428 21:40:35.739660 22802 solver.cpp:330] Iteration 8262, Testing net (#0)
I0428 21:40:35.739681 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:40:36.891675 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:40:40.106884 22802 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0428 21:40:40.106911 22802 solver.cpp:397]     Test net output #1: loss = 2.58262 (* 1 = 2.58262 loss)
I0428 21:40:41.721750 22802 solver.cpp:218] Iteration 8268 (1.03482 iter/s, 11.5963s/12 iters), loss = 0.122194
I0428 21:40:41.721789 22802 solver.cpp:237]     Train net output #0: loss = 0.122194 (* 1 = 0.122194 loss)
I0428 21:40:41.721798 22802 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0428 21:40:46.318440 22802 solver.cpp:218] Iteration 8280 (2.61068 iter/s, 4.5965s/12 iters), loss = 0.236926
I0428 21:40:46.318482 22802 solver.cpp:237]     Train net output #0: loss = 0.236926 (* 1 = 0.236926 loss)
I0428 21:40:46.318490 22802 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0428 21:40:50.909667 22802 solver.cpp:218] Iteration 8292 (2.61379 iter/s, 4.59104s/12 iters), loss = 0.157906
I0428 21:40:50.909705 22802 solver.cpp:237]     Train net output #0: loss = 0.157906 (* 1 = 0.157906 loss)
I0428 21:40:50.909713 22802 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0428 21:40:51.556697 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:40:55.559419 22802 solver.cpp:218] Iteration 8304 (2.58088 iter/s, 4.64957s/12 iters), loss = 0.139772
I0428 21:40:55.559453 22802 solver.cpp:237]     Train net output #0: loss = 0.139772 (* 1 = 0.139772 loss)
I0428 21:40:55.559461 22802 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0428 21:40:58.292049 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:41:00.402168 22802 solver.cpp:218] Iteration 8316 (2.47803 iter/s, 4.84255s/12 iters), loss = 0.230765
I0428 21:41:00.402220 22802 solver.cpp:237]     Train net output #0: loss = 0.230765 (* 1 = 0.230765 loss)
I0428 21:41:00.402231 22802 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0428 21:41:05.709163 22802 solver.cpp:218] Iteration 8328 (2.26126 iter/s, 5.30678s/12 iters), loss = 0.198526
I0428 21:41:05.709200 22802 solver.cpp:237]     Train net output #0: loss = 0.198526 (* 1 = 0.198526 loss)
I0428 21:41:05.709209 22802 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0428 21:41:10.574229 22802 solver.cpp:218] Iteration 8340 (2.46666 iter/s, 4.86487s/12 iters), loss = 0.130942
I0428 21:41:10.574272 22802 solver.cpp:237]     Train net output #0: loss = 0.130942 (* 1 = 0.130942 loss)
I0428 21:41:10.574285 22802 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0428 21:41:15.339864 22802 solver.cpp:218] Iteration 8352 (2.51813 iter/s, 4.76544s/12 iters), loss = 0.149519
I0428 21:41:15.339910 22802 solver.cpp:237]     Train net output #0: loss = 0.149519 (* 1 = 0.149519 loss)
I0428 21:41:15.339918 22802 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0428 21:41:19.561957 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0428 21:41:21.761667 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0428 21:41:24.063352 22802 solver.cpp:330] Iteration 8364, Testing net (#0)
I0428 21:41:24.063377 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:41:25.088937 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:41:28.305502 22802 solver.cpp:397]     Test net output #0: accuracy = 0.547794
I0428 21:41:28.305533 22802 solver.cpp:397]     Test net output #1: loss = 2.64266 (* 1 = 2.64266 loss)
I0428 21:41:28.363159 22802 solver.cpp:218] Iteration 8364 (0.921457 iter/s, 13.0229s/12 iters), loss = 0.0536051
I0428 21:41:28.363199 22802 solver.cpp:237]     Train net output #0: loss = 0.0536052 (* 1 = 0.0536052 loss)
I0428 21:41:28.363207 22802 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0428 21:41:32.250686 22802 solver.cpp:218] Iteration 8376 (3.08693 iter/s, 3.88736s/12 iters), loss = 0.109688
I0428 21:41:32.250725 22802 solver.cpp:237]     Train net output #0: loss = 0.109688 (* 1 = 0.109688 loss)
I0428 21:41:32.250733 22802 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0428 21:41:36.930938 22802 solver.cpp:218] Iteration 8388 (2.56407 iter/s, 4.68006s/12 iters), loss = 0.0606499
I0428 21:41:36.930977 22802 solver.cpp:237]     Train net output #0: loss = 0.06065 (* 1 = 0.06065 loss)
I0428 21:41:36.930986 22802 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0428 21:41:39.543644 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:41:41.524178 22802 solver.cpp:218] Iteration 8400 (2.61264 iter/s, 4.59305s/12 iters), loss = 0.0509424
I0428 21:41:41.524217 22802 solver.cpp:237]     Train net output #0: loss = 0.0509425 (* 1 = 0.0509425 loss)
I0428 21:41:41.524226 22802 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0428 21:41:46.157073 22802 solver.cpp:218] Iteration 8412 (2.59028 iter/s, 4.63271s/12 iters), loss = 0.136877
I0428 21:41:46.157109 22802 solver.cpp:237]     Train net output #0: loss = 0.136877 (* 1 = 0.136877 loss)
I0428 21:41:46.157117 22802 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0428 21:41:50.842286 22802 solver.cpp:218] Iteration 8424 (2.56135 iter/s, 4.68503s/12 iters), loss = 0.0804744
I0428 21:41:50.842325 22802 solver.cpp:237]     Train net output #0: loss = 0.0804745 (* 1 = 0.0804745 loss)
I0428 21:41:50.842334 22802 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0428 21:41:55.422467 22802 solver.cpp:218] Iteration 8436 (2.62009 iter/s, 4.57999s/12 iters), loss = 0.125967
I0428 21:41:55.422572 22802 solver.cpp:237]     Train net output #0: loss = 0.125967 (* 1 = 0.125967 loss)
I0428 21:41:55.422580 22802 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0428 21:42:00.063874 22802 solver.cpp:218] Iteration 8448 (2.58556 iter/s, 4.64115s/12 iters), loss = 0.127688
I0428 21:42:00.063911 22802 solver.cpp:237]     Train net output #0: loss = 0.127688 (* 1 = 0.127688 loss)
I0428 21:42:00.063920 22802 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0428 21:42:04.716197 22802 solver.cpp:218] Iteration 8460 (2.57946 iter/s, 4.65213s/12 iters), loss = 0.0817936
I0428 21:42:04.716235 22802 solver.cpp:237]     Train net output #0: loss = 0.0817937 (* 1 = 0.0817937 loss)
I0428 21:42:04.716243 22802 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0428 21:42:06.629138 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0428 21:42:08.126380 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0428 21:42:09.317720 22802 solver.cpp:330] Iteration 8466, Testing net (#0)
I0428 21:42:09.317741 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:42:10.397255 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:42:13.682488 22802 solver.cpp:397]     Test net output #0: accuracy = 0.542279
I0428 21:42:13.682516 22802 solver.cpp:397]     Test net output #1: loss = 2.65913 (* 1 = 2.65913 loss)
I0428 21:42:15.321445 22802 solver.cpp:218] Iteration 8472 (1.13155 iter/s, 10.6049s/12 iters), loss = 0.097936
I0428 21:42:15.321511 22802 solver.cpp:237]     Train net output #0: loss = 0.097936 (* 1 = 0.097936 loss)
I0428 21:42:15.321524 22802 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0428 21:42:19.968080 22802 solver.cpp:218] Iteration 8484 (2.58263 iter/s, 4.64643s/12 iters), loss = 0.149164
I0428 21:42:19.968119 22802 solver.cpp:237]     Train net output #0: loss = 0.149164 (* 1 = 0.149164 loss)
I0428 21:42:19.968128 22802 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0428 21:42:24.667258 22802 solver.cpp:218] Iteration 8496 (2.55374 iter/s, 4.69899s/12 iters), loss = 0.205842
I0428 21:42:24.667294 22802 solver.cpp:237]     Train net output #0: loss = 0.205843 (* 1 = 0.205843 loss)
I0428 21:42:24.667300 22802 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0428 21:42:24.704918 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:42:29.283072 22802 solver.cpp:218] Iteration 8508 (2.59986 iter/s, 4.61563s/12 iters), loss = 0.138655
I0428 21:42:29.283210 22802 solver.cpp:237]     Train net output #0: loss = 0.138655 (* 1 = 0.138655 loss)
I0428 21:42:29.283219 22802 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0428 21:42:34.125367 22802 solver.cpp:218] Iteration 8520 (2.47831 iter/s, 4.842s/12 iters), loss = 0.106529
I0428 21:42:34.125408 22802 solver.cpp:237]     Train net output #0: loss = 0.106529 (* 1 = 0.106529 loss)
I0428 21:42:34.125416 22802 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0428 21:42:38.739316 22802 solver.cpp:218] Iteration 8532 (2.60091 iter/s, 4.61376s/12 iters), loss = 0.129883
I0428 21:42:38.739362 22802 solver.cpp:237]     Train net output #0: loss = 0.129883 (* 1 = 0.129883 loss)
I0428 21:42:38.739375 22802 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0428 21:42:43.518075 22802 solver.cpp:218] Iteration 8544 (2.51122 iter/s, 4.77856s/12 iters), loss = 0.124593
I0428 21:42:43.518116 22802 solver.cpp:237]     Train net output #0: loss = 0.124593 (* 1 = 0.124593 loss)
I0428 21:42:43.518124 22802 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0428 21:42:48.311491 22802 solver.cpp:218] Iteration 8556 (2.50353 iter/s, 4.79322s/12 iters), loss = 0.120206
I0428 21:42:48.311528 22802 solver.cpp:237]     Train net output #0: loss = 0.120206 (* 1 = 0.120206 loss)
I0428 21:42:48.311537 22802 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0428 21:42:52.607612 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0428 21:42:54.187279 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0428 21:42:58.047293 22802 solver.cpp:330] Iteration 8568, Testing net (#0)
I0428 21:42:58.047315 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:42:59.012531 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:43:02.303431 22802 solver.cpp:397]     Test net output #0: accuracy = 0.550858
I0428 21:43:02.303539 22802 solver.cpp:397]     Test net output #1: loss = 2.70318 (* 1 = 2.70318 loss)
I0428 21:43:02.361850 22802 solver.cpp:218] Iteration 8568 (0.854099 iter/s, 14.0499s/12 iters), loss = 0.144305
I0428 21:43:02.361888 22802 solver.cpp:237]     Train net output #0: loss = 0.144305 (* 1 = 0.144305 loss)
I0428 21:43:02.361896 22802 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0428 21:43:06.262627 22802 solver.cpp:218] Iteration 8580 (3.07644 iter/s, 3.90061s/12 iters), loss = 0.126877
I0428 21:43:06.262670 22802 solver.cpp:237]     Train net output #0: loss = 0.126877 (* 1 = 0.126877 loss)
I0428 21:43:06.262679 22802 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0428 21:43:11.109383 22802 solver.cpp:218] Iteration 8592 (2.47598 iter/s, 4.84656s/12 iters), loss = 0.106299
I0428 21:43:11.109417 22802 solver.cpp:237]     Train net output #0: loss = 0.106299 (* 1 = 0.106299 loss)
I0428 21:43:11.109426 22802 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0428 21:43:13.119750 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:43:15.719671 22802 solver.cpp:218] Iteration 8604 (2.60298 iter/s, 4.61011s/12 iters), loss = 0.155365
I0428 21:43:15.719707 22802 solver.cpp:237]     Train net output #0: loss = 0.155365 (* 1 = 0.155365 loss)
I0428 21:43:15.719714 22802 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0428 21:43:20.334949 22802 solver.cpp:218] Iteration 8616 (2.60017 iter/s, 4.61509s/12 iters), loss = 0.0883316
I0428 21:43:20.334986 22802 solver.cpp:237]     Train net output #0: loss = 0.0883317 (* 1 = 0.0883317 loss)
I0428 21:43:20.334993 22802 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0428 21:43:24.929478 22802 solver.cpp:218] Iteration 8628 (2.61191 iter/s, 4.59435s/12 iters), loss = 0.2545
I0428 21:43:24.929517 22802 solver.cpp:237]     Train net output #0: loss = 0.2545 (* 1 = 0.2545 loss)
I0428 21:43:24.929527 22802 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0428 21:43:29.618742 22802 solver.cpp:218] Iteration 8640 (2.55914 iter/s, 4.68908s/12 iters), loss = 0.161047
I0428 21:43:29.618779 22802 solver.cpp:237]     Train net output #0: loss = 0.161047 (* 1 = 0.161047 loss)
I0428 21:43:29.618788 22802 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0428 21:43:34.222941 22802 solver.cpp:218] Iteration 8652 (2.60642 iter/s, 4.60401s/12 iters), loss = 0.154496
I0428 21:43:34.223067 22802 solver.cpp:237]     Train net output #0: loss = 0.154496 (* 1 = 0.154496 loss)
I0428 21:43:34.223078 22802 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0428 21:43:38.826244 22802 solver.cpp:218] Iteration 8664 (2.60698 iter/s, 4.60303s/12 iters), loss = 0.0811887
I0428 21:43:38.826282 22802 solver.cpp:237]     Train net output #0: loss = 0.0811888 (* 1 = 0.0811888 loss)
I0428 21:43:38.826290 22802 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0428 21:43:40.718187 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0428 21:43:42.222574 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0428 21:43:43.414722 22802 solver.cpp:330] Iteration 8670, Testing net (#0)
I0428 21:43:43.414743 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:43:44.398514 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:43:47.774181 22802 solver.cpp:397]     Test net output #0: accuracy = 0.564338
I0428 21:43:47.774215 22802 solver.cpp:397]     Test net output #1: loss = 2.53392 (* 1 = 2.53392 loss)
I0428 21:43:49.379225 22802 solver.cpp:218] Iteration 8676 (1.13716 iter/s, 10.5526s/12 iters), loss = 0.117022
I0428 21:43:49.379271 22802 solver.cpp:237]     Train net output #0: loss = 0.117022 (* 1 = 0.117022 loss)
I0428 21:43:49.379281 22802 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0428 21:43:53.959170 22802 solver.cpp:218] Iteration 8688 (2.62023 iter/s, 4.57976s/12 iters), loss = 0.167384
I0428 21:43:53.959213 22802 solver.cpp:237]     Train net output #0: loss = 0.167385 (* 1 = 0.167385 loss)
I0428 21:43:53.959223 22802 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0428 21:43:58.005676 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:43:58.631031 22802 solver.cpp:218] Iteration 8700 (2.56868 iter/s, 4.67167s/12 iters), loss = 0.0789768
I0428 21:43:58.631070 22802 solver.cpp:237]     Train net output #0: loss = 0.0789768 (* 1 = 0.0789768 loss)
I0428 21:43:58.631078 22802 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0428 21:44:03.220263 22802 solver.cpp:218] Iteration 8712 (2.61492 iter/s, 4.58905s/12 iters), loss = 0.094197
I0428 21:44:03.220311 22802 solver.cpp:237]     Train net output #0: loss = 0.0941971 (* 1 = 0.0941971 loss)
I0428 21:44:03.220322 22802 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0428 21:44:07.903429 22802 solver.cpp:218] Iteration 8724 (2.56248 iter/s, 4.68297s/12 iters), loss = 0.269752
I0428 21:44:07.903611 22802 solver.cpp:237]     Train net output #0: loss = 0.269752 (* 1 = 0.269752 loss)
I0428 21:44:07.903625 22802 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0428 21:44:12.472107 22802 solver.cpp:218] Iteration 8736 (2.62677 iter/s, 4.56835s/12 iters), loss = 0.194931
I0428 21:44:12.472159 22802 solver.cpp:237]     Train net output #0: loss = 0.194931 (* 1 = 0.194931 loss)
I0428 21:44:12.472172 22802 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0428 21:44:17.107662 22802 solver.cpp:218] Iteration 8748 (2.5888 iter/s, 4.63536s/12 iters), loss = 0.137948
I0428 21:44:17.107704 22802 solver.cpp:237]     Train net output #0: loss = 0.137948 (* 1 = 0.137948 loss)
I0428 21:44:17.107714 22802 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0428 21:44:21.643200 22802 solver.cpp:218] Iteration 8760 (2.64588 iter/s, 4.53535s/12 iters), loss = 0.170668
I0428 21:44:21.643235 22802 solver.cpp:237]     Train net output #0: loss = 0.170668 (* 1 = 0.170668 loss)
I0428 21:44:21.643244 22802 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0428 21:44:25.971822 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0428 21:44:27.475155 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0428 21:44:28.666997 22802 solver.cpp:330] Iteration 8772, Testing net (#0)
I0428 21:44:28.667021 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:44:29.576882 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:44:33.090284 22802 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0428 21:44:33.090324 22802 solver.cpp:397]     Test net output #1: loss = 2.73822 (* 1 = 2.73822 loss)
I0428 21:44:33.148142 22802 solver.cpp:218] Iteration 8772 (1.04307 iter/s, 11.5046s/12 iters), loss = 0.0881788
I0428 21:44:33.148195 22802 solver.cpp:237]     Train net output #0: loss = 0.0881788 (* 1 = 0.0881788 loss)
I0428 21:44:33.148208 22802 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0428 21:44:36.993929 22802 solver.cpp:218] Iteration 8784 (3.12044 iter/s, 3.84561s/12 iters), loss = 0.126645
I0428 21:44:36.993975 22802 solver.cpp:237]     Train net output #0: loss = 0.126645 (* 1 = 0.126645 loss)
I0428 21:44:36.993984 22802 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0428 21:44:41.639597 22802 solver.cpp:218] Iteration 8796 (2.58316 iter/s, 4.64548s/12 iters), loss = 0.0738854
I0428 21:44:41.640906 22802 solver.cpp:237]     Train net output #0: loss = 0.0738855 (* 1 = 0.0738855 loss)
I0428 21:44:41.640915 22802 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0428 21:44:43.005535 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:44:46.270599 22802 solver.cpp:218] Iteration 8808 (2.59204 iter/s, 4.62955s/12 iters), loss = 0.0821336
I0428 21:44:46.270638 22802 solver.cpp:237]     Train net output #0: loss = 0.0821336 (* 1 = 0.0821336 loss)
I0428 21:44:46.270645 22802 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0428 21:44:50.956924 22802 solver.cpp:218] Iteration 8820 (2.56075 iter/s, 4.68613s/12 iters), loss = 0.170375
I0428 21:44:50.956964 22802 solver.cpp:237]     Train net output #0: loss = 0.170375 (* 1 = 0.170375 loss)
I0428 21:44:50.956972 22802 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0428 21:44:55.619796 22802 solver.cpp:218] Iteration 8832 (2.57363 iter/s, 4.66268s/12 iters), loss = 0.135546
I0428 21:44:55.619846 22802 solver.cpp:237]     Train net output #0: loss = 0.135547 (* 1 = 0.135547 loss)
I0428 21:44:55.619856 22802 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0428 21:45:00.250850 22802 solver.cpp:218] Iteration 8844 (2.59131 iter/s, 4.63086s/12 iters), loss = 0.0914359
I0428 21:45:00.250896 22802 solver.cpp:237]     Train net output #0: loss = 0.091436 (* 1 = 0.091436 loss)
I0428 21:45:00.250907 22802 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0428 21:45:04.847271 22802 solver.cpp:218] Iteration 8856 (2.61083 iter/s, 4.59623s/12 iters), loss = 0.174239
I0428 21:45:04.847307 22802 solver.cpp:237]     Train net output #0: loss = 0.174239 (* 1 = 0.174239 loss)
I0428 21:45:04.847316 22802 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0428 21:45:09.479876 22802 solver.cpp:218] Iteration 8868 (2.59044 iter/s, 4.63242s/12 iters), loss = 0.106599
I0428 21:45:09.479924 22802 solver.cpp:237]     Train net output #0: loss = 0.106599 (* 1 = 0.106599 loss)
I0428 21:45:09.479935 22802 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0428 21:45:11.368222 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0428 21:45:13.193150 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0428 21:45:16.094669 22802 solver.cpp:330] Iteration 8874, Testing net (#0)
I0428 21:45:16.094691 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:45:16.993577 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:45:20.533219 22802 solver.cpp:397]     Test net output #0: accuracy = 0.552696
I0428 21:45:20.533267 22802 solver.cpp:397]     Test net output #1: loss = 2.66788 (* 1 = 2.66788 loss)
I0428 21:45:22.257506 22802 solver.cpp:218] Iteration 8880 (0.939173 iter/s, 12.7772s/12 iters), loss = 0.0444824
I0428 21:45:22.257558 22802 solver.cpp:237]     Train net output #0: loss = 0.0444824 (* 1 = 0.0444824 loss)
I0428 21:45:22.257571 22802 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0428 21:45:26.927841 22802 solver.cpp:218] Iteration 8892 (2.56952 iter/s, 4.67014s/12 iters), loss = 0.105423
I0428 21:45:26.927892 22802 solver.cpp:237]     Train net output #0: loss = 0.105423 (* 1 = 0.105423 loss)
I0428 21:45:26.927904 22802 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0428 21:45:30.243664 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:45:31.540521 22802 solver.cpp:218] Iteration 8904 (2.60164 iter/s, 4.61247s/12 iters), loss = 0.118207
I0428 21:45:31.540566 22802 solver.cpp:237]     Train net output #0: loss = 0.118208 (* 1 = 0.118208 loss)
I0428 21:45:31.540576 22802 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0428 21:45:36.156427 22802 solver.cpp:218] Iteration 8916 (2.59981 iter/s, 4.61572s/12 iters), loss = 0.126202
I0428 21:45:36.156466 22802 solver.cpp:237]     Train net output #0: loss = 0.126202 (* 1 = 0.126202 loss)
I0428 21:45:36.156474 22802 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0428 21:45:40.792809 22802 solver.cpp:218] Iteration 8928 (2.58833 iter/s, 4.6362s/12 iters), loss = 0.11799
I0428 21:45:40.792852 22802 solver.cpp:237]     Train net output #0: loss = 0.11799 (* 1 = 0.11799 loss)
I0428 21:45:40.792860 22802 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0428 21:45:45.357592 22802 solver.cpp:218] Iteration 8940 (2.62893 iter/s, 4.56459s/12 iters), loss = 0.139671
I0428 21:45:45.357693 22802 solver.cpp:237]     Train net output #0: loss = 0.139671 (* 1 = 0.139671 loss)
I0428 21:45:45.357702 22802 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0428 21:45:50.004653 22802 solver.cpp:218] Iteration 8952 (2.58241 iter/s, 4.64681s/12 iters), loss = 0.0906741
I0428 21:45:50.004693 22802 solver.cpp:237]     Train net output #0: loss = 0.0906741 (* 1 = 0.0906741 loss)
I0428 21:45:50.004701 22802 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0428 21:45:54.552546 22802 solver.cpp:218] Iteration 8964 (2.6387 iter/s, 4.5477s/12 iters), loss = 0.0916404
I0428 21:45:54.552595 22802 solver.cpp:237]     Train net output #0: loss = 0.0916405 (* 1 = 0.0916405 loss)
I0428 21:45:54.552608 22802 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0428 21:45:58.780591 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0428 21:46:02.093720 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0428 21:46:03.285486 22802 solver.cpp:330] Iteration 8976, Testing net (#0)
I0428 21:46:03.285512 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:46:04.086414 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:46:07.515513 22802 solver.cpp:397]     Test net output #0: accuracy = 0.561887
I0428 21:46:07.515542 22802 solver.cpp:397]     Test net output #1: loss = 2.6378 (* 1 = 2.6378 loss)
I0428 21:46:07.573410 22802 solver.cpp:218] Iteration 8976 (0.921629 iter/s, 13.0204s/12 iters), loss = 0.0622274
I0428 21:46:07.573448 22802 solver.cpp:237]     Train net output #0: loss = 0.0622274 (* 1 = 0.0622274 loss)
I0428 21:46:07.573457 22802 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0428 21:46:11.471935 22802 solver.cpp:218] Iteration 8988 (3.07822 iter/s, 3.89835s/12 iters), loss = 0.134268
I0428 21:46:11.471982 22802 solver.cpp:237]     Train net output #0: loss = 0.134268 (* 1 = 0.134268 loss)
I0428 21:46:11.471992 22802 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0428 21:46:14.530609 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:46:16.094172 22802 solver.cpp:218] Iteration 9000 (2.59625 iter/s, 4.62205s/12 iters), loss = 0.0895531
I0428 21:46:16.094305 22802 solver.cpp:237]     Train net output #0: loss = 0.0895531 (* 1 = 0.0895531 loss)
I0428 21:46:16.094316 22802 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0428 21:46:16.774426 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:46:20.695725 22802 solver.cpp:218] Iteration 9012 (2.60797 iter/s, 4.60128s/12 iters), loss = 0.0803101
I0428 21:46:20.695767 22802 solver.cpp:237]     Train net output #0: loss = 0.0803101 (* 1 = 0.0803101 loss)
I0428 21:46:20.695775 22802 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0428 21:46:25.840167 22802 solver.cpp:218] Iteration 9024 (2.33271 iter/s, 5.14424s/12 iters), loss = 0.098756
I0428 21:46:25.840205 22802 solver.cpp:237]     Train net output #0: loss = 0.098756 (* 1 = 0.098756 loss)
I0428 21:46:25.840214 22802 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0428 21:46:30.762929 22802 solver.cpp:218] Iteration 9036 (2.43775 iter/s, 4.92257s/12 iters), loss = 0.0836608
I0428 21:46:30.762969 22802 solver.cpp:237]     Train net output #0: loss = 0.0836608 (* 1 = 0.0836608 loss)
I0428 21:46:30.762976 22802 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0428 21:46:35.578949 22802 solver.cpp:218] Iteration 9048 (2.49179 iter/s, 4.81582s/12 iters), loss = 0.144326
I0428 21:46:35.579002 22802 solver.cpp:237]     Train net output #0: loss = 0.144326 (* 1 = 0.144326 loss)
I0428 21:46:35.579015 22802 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0428 21:46:40.270913 22802 solver.cpp:218] Iteration 9060 (2.55767 iter/s, 4.69176s/12 iters), loss = 0.0840662
I0428 21:46:40.270956 22802 solver.cpp:237]     Train net output #0: loss = 0.0840662 (* 1 = 0.0840662 loss)
I0428 21:46:40.270963 22802 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0428 21:46:44.943430 22802 solver.cpp:218] Iteration 9072 (2.56831 iter/s, 4.67233s/12 iters), loss = 0.0703278
I0428 21:46:44.943468 22802 solver.cpp:237]     Train net output #0: loss = 0.0703278 (* 1 = 0.0703278 loss)
I0428 21:46:44.943477 22802 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0428 21:46:46.834120 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0428 21:46:48.438643 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0428 21:46:50.369915 22802 solver.cpp:330] Iteration 9078, Testing net (#0)
I0428 21:46:50.369936 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:46:51.223049 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:46:54.730394 22802 solver.cpp:397]     Test net output #0: accuracy = 0.559436
I0428 21:46:54.730428 22802 solver.cpp:397]     Test net output #1: loss = 2.66591 (* 1 = 2.66591 loss)
I0428 21:46:56.493441 22802 solver.cpp:218] Iteration 9084 (1.03899 iter/s, 11.5496s/12 iters), loss = 0.0543468
I0428 21:46:56.493484 22802 solver.cpp:237]     Train net output #0: loss = 0.0543468 (* 1 = 0.0543468 loss)
I0428 21:46:56.493492 22802 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0428 21:47:01.167079 22802 solver.cpp:218] Iteration 9096 (2.5677 iter/s, 4.67345s/12 iters), loss = 0.0978902
I0428 21:47:01.167129 22802 solver.cpp:237]     Train net output #0: loss = 0.0978902 (* 1 = 0.0978902 loss)
I0428 21:47:01.167140 22802 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0428 21:47:03.879993 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:47:05.784670 22802 solver.cpp:218] Iteration 9108 (2.59886 iter/s, 4.61741s/12 iters), loss = 0.119865
I0428 21:47:05.784723 22802 solver.cpp:237]     Train net output #0: loss = 0.119865 (* 1 = 0.119865 loss)
I0428 21:47:05.784734 22802 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0428 21:47:10.419656 22802 solver.cpp:218] Iteration 9120 (2.5891 iter/s, 4.63481s/12 iters), loss = 0.0770805
I0428 21:47:10.419692 22802 solver.cpp:237]     Train net output #0: loss = 0.0770805 (* 1 = 0.0770805 loss)
I0428 21:47:10.419701 22802 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0428 21:47:15.117087 22802 solver.cpp:218] Iteration 9132 (2.55468 iter/s, 4.69726s/12 iters), loss = 0.106602
I0428 21:47:15.117125 22802 solver.cpp:237]     Train net output #0: loss = 0.106602 (* 1 = 0.106602 loss)
I0428 21:47:15.117131 22802 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0428 21:47:19.718227 22802 solver.cpp:218] Iteration 9144 (2.60815 iter/s, 4.60097s/12 iters), loss = 0.0454175
I0428 21:47:19.718430 22802 solver.cpp:237]     Train net output #0: loss = 0.0454175 (* 1 = 0.0454175 loss)
I0428 21:47:19.718438 22802 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0428 21:47:24.586927 22802 solver.cpp:218] Iteration 9156 (2.46489 iter/s, 4.86836s/12 iters), loss = 0.0695059
I0428 21:47:24.586966 22802 solver.cpp:237]     Train net output #0: loss = 0.0695059 (* 1 = 0.0695059 loss)
I0428 21:47:24.586972 22802 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0428 21:47:29.211580 22802 solver.cpp:218] Iteration 9168 (2.59489 iter/s, 4.62448s/12 iters), loss = 0.0834522
I0428 21:47:29.211633 22802 solver.cpp:237]     Train net output #0: loss = 0.0834522 (* 1 = 0.0834522 loss)
I0428 21:47:29.211644 22802 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0428 21:47:33.477196 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0428 21:47:35.007371 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0428 21:47:36.197167 22802 solver.cpp:330] Iteration 9180, Testing net (#0)
I0428 21:47:36.197189 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:47:36.999029 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:47:40.716609 22802 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0428 21:47:40.716637 22802 solver.cpp:397]     Test net output #1: loss = 2.6203 (* 1 = 2.6203 loss)
I0428 21:47:40.774768 22802 solver.cpp:218] Iteration 9180 (1.03781 iter/s, 11.5628s/12 iters), loss = 0.138358
I0428 21:47:40.774827 22802 solver.cpp:237]     Train net output #0: loss = 0.138358 (* 1 = 0.138358 loss)
I0428 21:47:40.774838 22802 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0428 21:47:44.649819 22802 solver.cpp:218] Iteration 9192 (3.09687 iter/s, 3.87488s/12 iters), loss = 0.0589952
I0428 21:47:44.649858 22802 solver.cpp:237]     Train net output #0: loss = 0.0589951 (* 1 = 0.0589951 loss)
I0428 21:47:44.649866 22802 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0428 21:47:49.295794 22802 solver.cpp:218] Iteration 9204 (2.58298 iter/s, 4.6458s/12 iters), loss = 0.0810384
I0428 21:47:49.295841 22802 solver.cpp:237]     Train net output #0: loss = 0.0810384 (* 1 = 0.0810384 loss)
I0428 21:47:49.295853 22802 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0428 21:47:49.366173 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:47:54.048960 22802 solver.cpp:218] Iteration 9216 (2.52473 iter/s, 4.75299s/12 iters), loss = 0.0726675
I0428 21:47:54.049113 22802 solver.cpp:237]     Train net output #0: loss = 0.0726674 (* 1 = 0.0726674 loss)
I0428 21:47:54.049121 22802 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0428 21:47:58.917912 22802 solver.cpp:218] Iteration 9228 (2.46474 iter/s, 4.86866s/12 iters), loss = 0.0798628
I0428 21:47:58.917948 22802 solver.cpp:237]     Train net output #0: loss = 0.0798628 (* 1 = 0.0798628 loss)
I0428 21:47:58.917956 22802 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0428 21:48:03.497140 22802 solver.cpp:218] Iteration 9240 (2.62062 iter/s, 4.57906s/12 iters), loss = 0.0923073
I0428 21:48:03.497177 22802 solver.cpp:237]     Train net output #0: loss = 0.0923073 (* 1 = 0.0923073 loss)
I0428 21:48:03.497185 22802 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0428 21:48:08.220541 22802 solver.cpp:218] Iteration 9252 (2.54065 iter/s, 4.7232s/12 iters), loss = 0.154065
I0428 21:48:08.220582 22802 solver.cpp:237]     Train net output #0: loss = 0.154065 (* 1 = 0.154065 loss)
I0428 21:48:08.220589 22802 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0428 21:48:13.007800 22802 solver.cpp:218] Iteration 9264 (2.50675 iter/s, 4.78708s/12 iters), loss = 0.137088
I0428 21:48:13.007839 22802 solver.cpp:237]     Train net output #0: loss = 0.137088 (* 1 = 0.137088 loss)
I0428 21:48:13.007848 22802 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0428 21:48:17.627024 22802 solver.cpp:218] Iteration 9276 (2.59794 iter/s, 4.61905s/12 iters), loss = 0.0651319
I0428 21:48:17.627077 22802 solver.cpp:237]     Train net output #0: loss = 0.0651318 (* 1 = 0.0651318 loss)
I0428 21:48:17.627089 22802 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0428 21:48:19.562264 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0428 21:48:21.056793 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0428 21:48:22.240813 22802 solver.cpp:330] Iteration 9282, Testing net (#0)
I0428 21:48:22.240833 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:48:23.060081 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:48:26.680480 22802 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0428 21:48:26.680603 22802 solver.cpp:397]     Test net output #1: loss = 2.65175 (* 1 = 2.65175 loss)
I0428 21:48:28.310180 22802 solver.cpp:218] Iteration 9288 (1.1233 iter/s, 10.6828s/12 iters), loss = 0.0923681
I0428 21:48:28.310230 22802 solver.cpp:237]     Train net output #0: loss = 0.092368 (* 1 = 0.092368 loss)
I0428 21:48:28.310240 22802 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0428 21:48:32.940234 22802 solver.cpp:218] Iteration 9300 (2.59187 iter/s, 4.62987s/12 iters), loss = 0.0344862
I0428 21:48:32.940276 22802 solver.cpp:237]     Train net output #0: loss = 0.0344861 (* 1 = 0.0344861 loss)
I0428 21:48:32.940289 22802 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0428 21:48:35.097790 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:48:37.691567 22802 solver.cpp:218] Iteration 9312 (2.5257 iter/s, 4.75116s/12 iters), loss = 0.0699852
I0428 21:48:37.691603 22802 solver.cpp:237]     Train net output #0: loss = 0.0699851 (* 1 = 0.0699851 loss)
I0428 21:48:37.691612 22802 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0428 21:48:42.320952 22802 solver.cpp:218] Iteration 9324 (2.59224 iter/s, 4.62921s/12 iters), loss = 0.146098
I0428 21:48:42.320998 22802 solver.cpp:237]     Train net output #0: loss = 0.146098 (* 1 = 0.146098 loss)
I0428 21:48:42.321007 22802 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0428 21:48:46.939381 22802 solver.cpp:218] Iteration 9336 (2.59839 iter/s, 4.61825s/12 iters), loss = 0.1123
I0428 21:48:46.939420 22802 solver.cpp:237]     Train net output #0: loss = 0.1123 (* 1 = 0.1123 loss)
I0428 21:48:46.939427 22802 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0428 21:48:51.744805 22802 solver.cpp:218] Iteration 9348 (2.49727 iter/s, 4.80525s/12 iters), loss = 0.0530478
I0428 21:48:51.744848 22802 solver.cpp:237]     Train net output #0: loss = 0.0530478 (* 1 = 0.0530478 loss)
I0428 21:48:51.744856 22802 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0428 21:48:56.485514 22802 solver.cpp:218] Iteration 9360 (2.53136 iter/s, 4.74053s/12 iters), loss = 0.0625023
I0428 21:48:56.485553 22802 solver.cpp:237]     Train net output #0: loss = 0.0625022 (* 1 = 0.0625022 loss)
I0428 21:48:56.485563 22802 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0428 21:49:01.082234 22802 solver.cpp:218] Iteration 9372 (2.61066 iter/s, 4.59654s/12 iters), loss = 0.0605932
I0428 21:49:01.082367 22802 solver.cpp:237]     Train net output #0: loss = 0.0605932 (* 1 = 0.0605932 loss)
I0428 21:49:01.082376 22802 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0428 21:49:05.292985 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0428 21:49:07.368284 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0428 21:49:08.563706 22802 solver.cpp:330] Iteration 9384, Testing net (#0)
I0428 21:49:08.563725 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:49:09.214845 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:49:12.817800 22802 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0428 21:49:12.817862 22802 solver.cpp:397]     Test net output #1: loss = 2.73822 (* 1 = 2.73822 loss)
I0428 21:49:12.875479 22802 solver.cpp:218] Iteration 9384 (1.01757 iter/s, 11.7928s/12 iters), loss = 0.0852848
I0428 21:49:12.875531 22802 solver.cpp:237]     Train net output #0: loss = 0.0852848 (* 1 = 0.0852848 loss)
I0428 21:49:12.875543 22802 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0428 21:49:16.994706 22802 solver.cpp:218] Iteration 9396 (2.91329 iter/s, 4.11906s/12 iters), loss = 0.0913261
I0428 21:49:16.994746 22802 solver.cpp:237]     Train net output #0: loss = 0.0913261 (* 1 = 0.0913261 loss)
I0428 21:49:16.994755 22802 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0428 21:49:21.006940 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:49:21.605276 22802 solver.cpp:218] Iteration 9408 (2.60282 iter/s, 4.61039s/12 iters), loss = 0.0892245
I0428 21:49:21.605327 22802 solver.cpp:237]     Train net output #0: loss = 0.0892244 (* 1 = 0.0892244 loss)
I0428 21:49:21.605340 22802 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0428 21:49:26.350952 22802 solver.cpp:218] Iteration 9420 (2.52872 iter/s, 4.74549s/12 iters), loss = 0.0589961
I0428 21:49:26.350991 22802 solver.cpp:237]     Train net output #0: loss = 0.0589961 (* 1 = 0.0589961 loss)
I0428 21:49:26.350999 22802 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0428 21:49:31.036322 22802 solver.cpp:218] Iteration 9432 (2.56126 iter/s, 4.68519s/12 iters), loss = 0.0619084
I0428 21:49:31.036361 22802 solver.cpp:237]     Train net output #0: loss = 0.0619083 (* 1 = 0.0619083 loss)
I0428 21:49:31.036370 22802 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0428 21:49:35.593940 22802 solver.cpp:218] Iteration 9444 (2.63305 iter/s, 4.55745s/12 iters), loss = 0.0671989
I0428 21:49:35.595230 22802 solver.cpp:237]     Train net output #0: loss = 0.0671989 (* 1 = 0.0671989 loss)
I0428 21:49:35.595240 22802 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0428 21:49:40.194270 22802 solver.cpp:218] Iteration 9456 (2.60931 iter/s, 4.59891s/12 iters), loss = 0.10416
I0428 21:49:40.194308 22802 solver.cpp:237]     Train net output #0: loss = 0.10416 (* 1 = 0.10416 loss)
I0428 21:49:40.194316 22802 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0428 21:49:44.808079 22802 solver.cpp:218] Iteration 9468 (2.60099 iter/s, 4.61364s/12 iters), loss = 0.0743709
I0428 21:49:44.808118 22802 solver.cpp:237]     Train net output #0: loss = 0.0743709 (* 1 = 0.0743709 loss)
I0428 21:49:44.808126 22802 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0428 21:49:49.452304 22802 solver.cpp:218] Iteration 9480 (2.58395 iter/s, 4.64404s/12 iters), loss = 0.085659
I0428 21:49:49.452358 22802 solver.cpp:237]     Train net output #0: loss = 0.085659 (* 1 = 0.085659 loss)
I0428 21:49:49.452370 22802 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0428 21:49:51.342609 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0428 21:49:52.838933 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0428 21:49:54.077569 22802 solver.cpp:330] Iteration 9486, Testing net (#0)
I0428 21:49:54.077589 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:49:54.705883 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:49:58.482868 22802 solver.cpp:397]     Test net output #0: accuracy = 0.56924
I0428 21:49:58.482898 22802 solver.cpp:397]     Test net output #1: loss = 2.66774 (* 1 = 2.66774 loss)
I0428 21:50:00.139689 22802 solver.cpp:218] Iteration 9492 (1.12286 iter/s, 10.687s/12 iters), loss = 0.118341
I0428 21:50:00.139730 22802 solver.cpp:237]     Train net output #0: loss = 0.118341 (* 1 = 0.118341 loss)
I0428 21:50:00.139739 22802 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0428 21:50:04.802469 22802 solver.cpp:218] Iteration 9504 (2.57367 iter/s, 4.66259s/12 iters), loss = 0.0869834
I0428 21:50:04.802515 22802 solver.cpp:237]     Train net output #0: loss = 0.0869834 (* 1 = 0.0869834 loss)
I0428 21:50:04.802525 22802 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0428 21:50:06.168555 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:50:09.406100 22802 solver.cpp:218] Iteration 9516 (2.60674 iter/s, 4.60345s/12 iters), loss = 0.0799079
I0428 21:50:09.406153 22802 solver.cpp:237]     Train net output #0: loss = 0.0799079 (* 1 = 0.0799079 loss)
I0428 21:50:09.406165 22802 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0428 21:50:14.051262 22802 solver.cpp:218] Iteration 9528 (2.58344 iter/s, 4.64497s/12 iters), loss = 0.141608
I0428 21:50:14.051304 22802 solver.cpp:237]     Train net output #0: loss = 0.141608 (* 1 = 0.141608 loss)
I0428 21:50:14.051312 22802 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0428 21:50:18.639163 22802 solver.cpp:218] Iteration 9540 (2.61568 iter/s, 4.58772s/12 iters), loss = 0.0457471
I0428 21:50:18.639211 22802 solver.cpp:237]     Train net output #0: loss = 0.0457471 (* 1 = 0.0457471 loss)
I0428 21:50:18.639222 22802 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0428 21:50:23.275490 22802 solver.cpp:218] Iteration 9552 (2.58836 iter/s, 4.63614s/12 iters), loss = 0.073455
I0428 21:50:23.275529 22802 solver.cpp:237]     Train net output #0: loss = 0.073455 (* 1 = 0.073455 loss)
I0428 21:50:23.275538 22802 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0428 21:50:27.991278 22802 solver.cpp:218] Iteration 9564 (2.54474 iter/s, 4.71561s/12 iters), loss = 0.138057
I0428 21:50:27.991315 22802 solver.cpp:237]     Train net output #0: loss = 0.138057 (* 1 = 0.138057 loss)
I0428 21:50:27.991325 22802 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0428 21:50:32.630188 22802 solver.cpp:218] Iteration 9576 (2.58691 iter/s, 4.63873s/12 iters), loss = 0.0483909
I0428 21:50:32.630228 22802 solver.cpp:237]     Train net output #0: loss = 0.0483909 (* 1 = 0.0483909 loss)
I0428 21:50:32.630237 22802 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0428 21:50:36.798650 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0428 21:50:38.310164 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0428 21:50:39.758810 22802 solver.cpp:330] Iteration 9588, Testing net (#0)
I0428 21:50:39.758838 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:50:40.331192 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:50:44.454538 22802 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0428 21:50:44.454576 22802 solver.cpp:397]     Test net output #1: loss = 2.68861 (* 1 = 2.68861 loss)
I0428 21:50:44.512101 22802 solver.cpp:218] Iteration 9588 (1.00997 iter/s, 11.8815s/12 iters), loss = 0.101503
I0428 21:50:44.512142 22802 solver.cpp:237]     Train net output #0: loss = 0.101503 (* 1 = 0.101503 loss)
I0428 21:50:44.512151 22802 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0428 21:50:48.415160 22802 solver.cpp:218] Iteration 9600 (3.07464 iter/s, 3.9029s/12 iters), loss = 0.069766
I0428 21:50:48.415210 22802 solver.cpp:237]     Train net output #0: loss = 0.069766 (* 1 = 0.069766 loss)
I0428 21:50:48.415222 22802 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0428 21:50:51.825299 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:50:53.055888 22802 solver.cpp:218] Iteration 9612 (2.5859 iter/s, 4.64055s/12 iters), loss = 0.0724491
I0428 21:50:53.055922 22802 solver.cpp:237]     Train net output #0: loss = 0.0724491 (* 1 = 0.0724491 loss)
I0428 21:50:53.055930 22802 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0428 21:50:57.695673 22802 solver.cpp:218] Iteration 9624 (2.58642 iter/s, 4.63961s/12 iters), loss = 0.0796993
I0428 21:50:57.695714 22802 solver.cpp:237]     Train net output #0: loss = 0.0796993 (* 1 = 0.0796993 loss)
I0428 21:50:57.695724 22802 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0428 21:51:02.336073 22802 solver.cpp:218] Iteration 9636 (2.58609 iter/s, 4.64021s/12 iters), loss = 0.0789233
I0428 21:51:02.336125 22802 solver.cpp:237]     Train net output #0: loss = 0.0789233 (* 1 = 0.0789233 loss)
I0428 21:51:02.336138 22802 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0428 21:51:06.929862 22802 solver.cpp:218] Iteration 9648 (2.61233 iter/s, 4.5936s/12 iters), loss = 0.0571434
I0428 21:51:06.930011 22802 solver.cpp:237]     Train net output #0: loss = 0.0571434 (* 1 = 0.0571434 loss)
I0428 21:51:06.930023 22802 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0428 21:51:11.553844 22802 solver.cpp:218] Iteration 9660 (2.59533 iter/s, 4.6237s/12 iters), loss = 0.0524652
I0428 21:51:11.553900 22802 solver.cpp:237]     Train net output #0: loss = 0.0524652 (* 1 = 0.0524652 loss)
I0428 21:51:11.553911 22802 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0428 21:51:16.243157 22802 solver.cpp:218] Iteration 9672 (2.55911 iter/s, 4.68912s/12 iters), loss = 0.0582802
I0428 21:51:16.243193 22802 solver.cpp:237]     Train net output #0: loss = 0.0582802 (* 1 = 0.0582802 loss)
I0428 21:51:16.243201 22802 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0428 21:51:21.069792 22802 solver.cpp:218] Iteration 9684 (2.4863 iter/s, 4.82646s/12 iters), loss = 0.0418961
I0428 21:51:21.069830 22802 solver.cpp:237]     Train net output #0: loss = 0.0418961 (* 1 = 0.0418961 loss)
I0428 21:51:21.069839 22802 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0428 21:51:23.002929 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0428 21:51:24.543567 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0428 21:51:25.778404 22802 solver.cpp:330] Iteration 9690, Testing net (#0)
I0428 21:51:25.778427 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:51:26.403281 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:51:29.228031 22802 blocking_queue.cpp:49] Waiting for data
I0428 21:51:30.224817 22802 solver.cpp:397]     Test net output #0: accuracy = 0.582108
I0428 21:51:30.224845 22802 solver.cpp:397]     Test net output #1: loss = 2.66585 (* 1 = 2.66585 loss)
I0428 21:51:31.852075 22802 solver.cpp:218] Iteration 9696 (1.11297 iter/s, 10.7819s/12 iters), loss = 0.014314
I0428 21:51:31.852116 22802 solver.cpp:237]     Train net output #0: loss = 0.014314 (* 1 = 0.014314 loss)
I0428 21:51:31.852124 22802 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0428 21:51:36.760694 22802 solver.cpp:218] Iteration 9708 (2.44477 iter/s, 4.90843s/12 iters), loss = 0.153213
I0428 21:51:36.760730 22802 solver.cpp:237]     Train net output #0: loss = 0.153213 (* 1 = 0.153213 loss)
I0428 21:51:36.760738 22802 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0428 21:51:37.553516 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:51:41.802914 22802 solver.cpp:218] Iteration 9720 (2.37999 iter/s, 5.04203s/12 iters), loss = 0.0855649
I0428 21:51:41.802963 22802 solver.cpp:237]     Train net output #0: loss = 0.0855649 (* 1 = 0.0855649 loss)
I0428 21:51:41.802973 22802 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0428 21:51:46.493486 22802 solver.cpp:218] Iteration 9732 (2.55842 iter/s, 4.69039s/12 iters), loss = 0.0669413
I0428 21:51:46.493522 22802 solver.cpp:237]     Train net output #0: loss = 0.0669414 (* 1 = 0.0669414 loss)
I0428 21:51:46.493530 22802 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0428 21:51:51.119135 22802 solver.cpp:218] Iteration 9744 (2.59433 iter/s, 4.62547s/12 iters), loss = 0.0793548
I0428 21:51:51.119174 22802 solver.cpp:237]     Train net output #0: loss = 0.0793548 (* 1 = 0.0793548 loss)
I0428 21:51:51.119181 22802 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0428 21:51:55.727638 22802 solver.cpp:218] Iteration 9756 (2.60399 iter/s, 4.60832s/12 iters), loss = 0.0444182
I0428 21:51:55.727690 22802 solver.cpp:237]     Train net output #0: loss = 0.0444182 (* 1 = 0.0444182 loss)
I0428 21:51:55.727699 22802 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0428 21:52:00.393095 22802 solver.cpp:218] Iteration 9768 (2.5722 iter/s, 4.66526s/12 iters), loss = 0.0820059
I0428 21:52:00.393132 22802 solver.cpp:237]     Train net output #0: loss = 0.0820059 (* 1 = 0.0820059 loss)
I0428 21:52:00.393141 22802 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0428 21:52:05.031730 22802 solver.cpp:218] Iteration 9780 (2.58707 iter/s, 4.63845s/12 iters), loss = 0.0462825
I0428 21:52:05.031786 22802 solver.cpp:237]     Train net output #0: loss = 0.0462825 (* 1 = 0.0462825 loss)
I0428 21:52:05.031797 22802 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0428 21:52:09.219518 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0428 21:52:11.646270 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0428 21:52:13.670132 22802 solver.cpp:330] Iteration 9792, Testing net (#0)
I0428 21:52:13.670153 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:52:14.180008 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:52:17.939502 22802 solver.cpp:397]     Test net output #0: accuracy = 0.56924
I0428 21:52:17.939535 22802 solver.cpp:397]     Test net output #1: loss = 2.77984 (* 1 = 2.77984 loss)
I0428 21:52:17.997135 22802 solver.cpp:218] Iteration 9792 (0.92557 iter/s, 12.965s/12 iters), loss = 0.0694952
I0428 21:52:17.997189 22802 solver.cpp:237]     Train net output #0: loss = 0.0694952 (* 1 = 0.0694952 loss)
I0428 21:52:17.997203 22802 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0428 21:52:21.895576 22802 solver.cpp:218] Iteration 9804 (3.07829 iter/s, 3.89827s/12 iters), loss = 0.0159734
I0428 21:52:21.895615 22802 solver.cpp:237]     Train net output #0: loss = 0.0159734 (* 1 = 0.0159734 loss)
I0428 21:52:21.895624 22802 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0428 21:52:24.618625 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:52:26.501991 22802 solver.cpp:218] Iteration 9816 (2.60516 iter/s, 4.60624s/12 iters), loss = 0.0628621
I0428 21:52:26.502038 22802 solver.cpp:237]     Train net output #0: loss = 0.0628621 (* 1 = 0.0628621 loss)
I0428 21:52:26.502049 22802 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0428 21:52:31.114576 22802 solver.cpp:218] Iteration 9828 (2.60168 iter/s, 4.6124s/12 iters), loss = 0.10264
I0428 21:52:31.114614 22802 solver.cpp:237]     Train net output #0: loss = 0.10264 (* 1 = 0.10264 loss)
I0428 21:52:31.114624 22802 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0428 21:52:35.647353 22802 solver.cpp:218] Iteration 9840 (2.64749 iter/s, 4.5326s/12 iters), loss = 0.0224287
I0428 21:52:35.647403 22802 solver.cpp:237]     Train net output #0: loss = 0.0224287 (* 1 = 0.0224287 loss)
I0428 21:52:35.647413 22802 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0428 21:52:40.592630 22802 solver.cpp:218] Iteration 9852 (2.42666 iter/s, 4.94508s/12 iters), loss = 0.0589627
I0428 21:52:40.593803 22802 solver.cpp:237]     Train net output #0: loss = 0.0589627 (* 1 = 0.0589627 loss)
I0428 21:52:40.593812 22802 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0428 21:52:45.163961 22802 solver.cpp:218] Iteration 9864 (2.62581 iter/s, 4.57002s/12 iters), loss = 0.0543817
I0428 21:52:45.164003 22802 solver.cpp:237]     Train net output #0: loss = 0.0543817 (* 1 = 0.0543817 loss)
I0428 21:52:45.164011 22802 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0428 21:52:49.761831 22802 solver.cpp:218] Iteration 9876 (2.61001 iter/s, 4.59769s/12 iters), loss = 0.0543833
I0428 21:52:49.761871 22802 solver.cpp:237]     Train net output #0: loss = 0.0543833 (* 1 = 0.0543833 loss)
I0428 21:52:49.761879 22802 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0428 21:52:54.553714 22802 solver.cpp:218] Iteration 9888 (2.50433 iter/s, 4.79169s/12 iters), loss = 0.104445
I0428 21:52:54.553755 22802 solver.cpp:237]     Train net output #0: loss = 0.104445 (* 1 = 0.104445 loss)
I0428 21:52:54.553762 22802 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0428 21:52:56.497270 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0428 21:52:58.005265 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0428 21:52:59.195901 22802 solver.cpp:330] Iteration 9894, Testing net (#0)
I0428 21:52:59.195924 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:52:59.717080 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:53:03.914315 22802 solver.cpp:397]     Test net output #0: accuracy = 0.565564
I0428 21:53:03.914358 22802 solver.cpp:397]     Test net output #1: loss = 2.75547 (* 1 = 2.75547 loss)
I0428 21:53:05.503139 22802 solver.cpp:218] Iteration 9900 (1.09598 iter/s, 10.9491s/12 iters), loss = 0.0756036
I0428 21:53:05.503176 22802 solver.cpp:237]     Train net output #0: loss = 0.0756036 (* 1 = 0.0756036 loss)
I0428 21:53:05.503185 22802 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0428 21:53:10.160347 22802 solver.cpp:218] Iteration 9912 (2.57675 iter/s, 4.65703s/12 iters), loss = 0.0804248
I0428 21:53:10.160387 22802 solver.cpp:237]     Train net output #0: loss = 0.0804248 (* 1 = 0.0804248 loss)
I0428 21:53:10.160396 22802 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0428 21:53:10.261210 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:53:14.746515 22802 solver.cpp:218] Iteration 9924 (2.61666 iter/s, 4.58599s/12 iters), loss = 0.0757501
I0428 21:53:14.746887 22802 solver.cpp:237]     Train net output #0: loss = 0.0757501 (* 1 = 0.0757501 loss)
I0428 21:53:14.746896 22802 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0428 21:53:19.505542 22802 solver.cpp:218] Iteration 9936 (2.5218 iter/s, 4.75851s/12 iters), loss = 0.0427339
I0428 21:53:19.505580 22802 solver.cpp:237]     Train net output #0: loss = 0.0427339 (* 1 = 0.0427339 loss)
I0428 21:53:19.505590 22802 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0428 21:53:24.134266 22802 solver.cpp:218] Iteration 9948 (2.59261 iter/s, 4.62854s/12 iters), loss = 0.078817
I0428 21:53:24.134310 22802 solver.cpp:237]     Train net output #0: loss = 0.078817 (* 1 = 0.078817 loss)
I0428 21:53:24.134320 22802 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0428 21:53:28.849117 22802 solver.cpp:218] Iteration 9960 (2.54525 iter/s, 4.71466s/12 iters), loss = 0.10376
I0428 21:53:28.849156 22802 solver.cpp:237]     Train net output #0: loss = 0.10376 (* 1 = 0.10376 loss)
I0428 21:53:28.849164 22802 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0428 21:53:33.576634 22802 solver.cpp:218] Iteration 9972 (2.53843 iter/s, 4.72734s/12 iters), loss = 0.0998735
I0428 21:53:33.576675 22802 solver.cpp:237]     Train net output #0: loss = 0.0998734 (* 1 = 0.0998734 loss)
I0428 21:53:33.576684 22802 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0428 21:53:38.303968 22802 solver.cpp:218] Iteration 9984 (2.53853 iter/s, 4.72715s/12 iters), loss = 0.0642134
I0428 21:53:38.304005 22802 solver.cpp:237]     Train net output #0: loss = 0.0642134 (* 1 = 0.0642134 loss)
I0428 21:53:38.304013 22802 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0428 21:53:42.538455 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0428 21:53:46.779347 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0428 21:53:49.749635 22802 solver.cpp:330] Iteration 9996, Testing net (#0)
I0428 21:53:49.749655 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:53:50.168515 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:53:54.146862 22802 solver.cpp:397]     Test net output #0: accuracy = 0.579044
I0428 21:53:54.146896 22802 solver.cpp:397]     Test net output #1: loss = 2.69275 (* 1 = 2.69275 loss)
I0428 21:53:54.204542 22802 solver.cpp:218] Iteration 9996 (0.754714 iter/s, 15.9001s/12 iters), loss = 0.115686
I0428 21:53:54.204593 22802 solver.cpp:237]     Train net output #0: loss = 0.115686 (* 1 = 0.115686 loss)
I0428 21:53:54.204603 22802 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0428 21:53:58.194300 22802 solver.cpp:218] Iteration 10008 (3.00783 iter/s, 3.98958s/12 iters), loss = 0.195167
I0428 21:53:58.194345 22802 solver.cpp:237]     Train net output #0: loss = 0.195167 (* 1 = 0.195167 loss)
I0428 21:53:58.194355 22802 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0428 21:54:00.273169 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:54:02.913687 22802 solver.cpp:218] Iteration 10020 (2.5428 iter/s, 4.7192s/12 iters), loss = 0.0421326
I0428 21:54:02.913727 22802 solver.cpp:237]     Train net output #0: loss = 0.0421326 (* 1 = 0.0421326 loss)
I0428 21:54:02.913734 22802 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0428 21:54:07.659384 22802 solver.cpp:218] Iteration 10032 (2.52871 iter/s, 4.74551s/12 iters), loss = 0.0832951
I0428 21:54:07.659425 22802 solver.cpp:237]     Train net output #0: loss = 0.0832951 (* 1 = 0.0832951 loss)
I0428 21:54:07.659433 22802 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0428 21:54:12.310073 22802 solver.cpp:218] Iteration 10044 (2.58036 iter/s, 4.65051s/12 iters), loss = 0.0670171
I0428 21:54:12.310106 22802 solver.cpp:237]     Train net output #0: loss = 0.0670171 (* 1 = 0.0670171 loss)
I0428 21:54:12.310113 22802 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0428 21:54:16.958179 22802 solver.cpp:218] Iteration 10056 (2.58179 iter/s, 4.64793s/12 iters), loss = 0.0763691
I0428 21:54:16.958294 22802 solver.cpp:237]     Train net output #0: loss = 0.0763691 (* 1 = 0.0763691 loss)
I0428 21:54:16.958307 22802 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0428 21:54:21.706104 22802 solver.cpp:218] Iteration 10068 (2.52755 iter/s, 4.74767s/12 iters), loss = 0.0575199
I0428 21:54:21.706143 22802 solver.cpp:237]     Train net output #0: loss = 0.0575199 (* 1 = 0.0575199 loss)
I0428 21:54:21.706152 22802 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0428 21:54:26.247318 22802 solver.cpp:218] Iteration 10080 (2.64257 iter/s, 4.54104s/12 iters), loss = 0.0486399
I0428 21:54:26.247354 22802 solver.cpp:237]     Train net output #0: loss = 0.0486399 (* 1 = 0.0486399 loss)
I0428 21:54:26.247362 22802 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0428 21:54:30.823256 22802 solver.cpp:218] Iteration 10092 (2.62251 iter/s, 4.57577s/12 iters), loss = 0.0572357
I0428 21:54:30.823287 22802 solver.cpp:237]     Train net output #0: loss = 0.0572356 (* 1 = 0.0572356 loss)
I0428 21:54:30.823295 22802 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0428 21:54:32.707065 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0428 21:54:34.246755 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0428 21:54:35.468576 22802 solver.cpp:330] Iteration 10098, Testing net (#0)
I0428 21:54:35.468596 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:54:35.884757 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:54:39.768342 22802 solver.cpp:397]     Test net output #0: accuracy = 0.564338
I0428 21:54:39.768375 22802 solver.cpp:397]     Test net output #1: loss = 2.75415 (* 1 = 2.75415 loss)
I0428 21:54:41.391472 22802 solver.cpp:218] Iteration 10104 (1.13552 iter/s, 10.5679s/12 iters), loss = 0.0411015
I0428 21:54:41.391516 22802 solver.cpp:237]     Train net output #0: loss = 0.0411014 (* 1 = 0.0411014 loss)
I0428 21:54:41.391526 22802 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0428 21:54:45.449952 22865 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:54:46.018553 22802 solver.cpp:218] Iteration 10116 (2.59353 iter/s, 4.6269s/12 iters), loss = 0.156174
I0428 21:54:46.018585 22802 solver.cpp:237]     Train net output #0: loss = 0.156174 (* 1 = 0.156174 loss)
I0428 21:54:46.018594 22802 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0428 21:54:50.605479 22802 solver.cpp:218] Iteration 10128 (2.61623 iter/s, 4.58675s/12 iters), loss = 0.0379506
I0428 21:54:50.605612 22802 solver.cpp:237]     Train net output #0: loss = 0.0379506 (* 1 = 0.0379506 loss)
I0428 21:54:50.605620 22802 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0428 21:54:55.219641 22802 solver.cpp:218] Iteration 10140 (2.60084 iter/s, 4.61389s/12 iters), loss = 0.0536674
I0428 21:54:55.219677 22802 solver.cpp:237]     Train net output #0: loss = 0.0536674 (* 1 = 0.0536674 loss)
I0428 21:54:55.219686 22802 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0428 21:54:59.748551 22802 solver.cpp:218] Iteration 10152 (2.64974 iter/s, 4.52874s/12 iters), loss = 0.0467341
I0428 21:54:59.748586 22802 solver.cpp:237]     Train net output #0: loss = 0.0467341 (* 1 = 0.0467341 loss)
I0428 21:54:59.748594 22802 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0428 21:55:04.347088 22802 solver.cpp:218] Iteration 10164 (2.60963 iter/s, 4.59836s/12 iters), loss = 0.150193
I0428 21:55:04.347127 22802 solver.cpp:237]     Train net output #0: loss = 0.150193 (* 1 = 0.150193 loss)
I0428 21:55:04.347136 22802 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0428 21:55:08.906487 22802 solver.cpp:218] Iteration 10176 (2.63203 iter/s, 4.55922s/12 iters), loss = 0.0431269
I0428 21:55:08.906524 22802 solver.cpp:237]     Train net output #0: loss = 0.0431269 (* 1 = 0.0431269 loss)
I0428 21:55:08.906531 22802 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0428 21:55:13.512511 22802 solver.cpp:218] Iteration 10188 (2.60538 iter/s, 4.60585s/12 iters), loss = 0.0460648
I0428 21:55:13.512552 22802 solver.cpp:237]     Train net output #0: loss = 0.0460648 (* 1 = 0.0460648 loss)
I0428 21:55:13.512562 22802 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0428 21:55:17.708081 22802 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0428 21:55:19.272835 22802 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0428 21:55:20.499614 22802 solver.cpp:310] Iteration 10200, loss = 0.0822989
I0428 21:55:20.499637 22802 solver.cpp:330] Iteration 10200, Testing net (#0)
I0428 21:55:20.499641 22802 net.cpp:676] Ignoring source layer train-data
I0428 21:55:20.878774 22884 data_layer.cpp:73] Restarting data prefetching from start.
I0428 21:55:24.822240 22802 solver.cpp:397]     Test net output #0: accuracy = 0.557598
I0428 21:55:24.822270 22802 solver.cpp:397]     Test net output #1: loss = 2.84157 (* 1 = 2.84157 loss)
I0428 21:55:24.822276 22802 solver.cpp:315] Optimization Done.
I0428 21:55:24.822280 22802 caffe.cpp:259] Optimization Done.