DIGITS-CNN/cars/architecture-investigations/fc/2-layers/8192/caffe_output.log

I0410 00:09:26.357069 14511 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-211225-f552/solver.prototxt
I0410 00:09:26.357208 14511 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 00:09:26.357215 14511 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 00:09:26.357268 14511 caffe.cpp:218] Using GPUs 0
I0410 00:09:26.370074 14511 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0410 00:09:26.624922 14511 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: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 00:09:26.626303 14511 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 00:09:26.626925 14511 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 00:09:26.626940 14511 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 00:09:26.627069 14511 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
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: 8192
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"
}
I0410 00:09:26.627151 14511 layer_factory.hpp:77] Creating layer train-data
I0410 00:09:26.629441 14511 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 00:09:26.629628 14511 net.cpp:84] Creating Layer train-data
I0410 00:09:26.629639 14511 net.cpp:380] train-data -> data
I0410 00:09:26.629658 14511 net.cpp:380] train-data -> label
I0410 00:09:26.629669 14511 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:09:26.634279 14511 data_layer.cpp:45] output data size: 128,3,227,227
I0410 00:09:26.757560 14511 net.cpp:122] Setting up train-data
I0410 00:09:26.757583 14511 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 00:09:26.757589 14511 net.cpp:129] Top shape: 128 (128)
I0410 00:09:26.757592 14511 net.cpp:137] Memory required for data: 79149056
I0410 00:09:26.757601 14511 layer_factory.hpp:77] Creating layer conv1
I0410 00:09:26.757622 14511 net.cpp:84] Creating Layer conv1
I0410 00:09:26.757627 14511 net.cpp:406] conv1 <- data
I0410 00:09:26.757640 14511 net.cpp:380] conv1 -> conv1
I0410 00:09:27.310844 14511 net.cpp:122] Setting up conv1
I0410 00:09:27.310869 14511 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:09:27.310875 14511 net.cpp:137] Memory required for data: 227833856
I0410 00:09:27.310896 14511 layer_factory.hpp:77] Creating layer relu1
I0410 00:09:27.310909 14511 net.cpp:84] Creating Layer relu1
I0410 00:09:27.310914 14511 net.cpp:406] relu1 <- conv1
I0410 00:09:27.310920 14511 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:09:27.311211 14511 net.cpp:122] Setting up relu1
I0410 00:09:27.311223 14511 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:09:27.311226 14511 net.cpp:137] Memory required for data: 376518656
I0410 00:09:27.311231 14511 layer_factory.hpp:77] Creating layer norm1
I0410 00:09:27.311241 14511 net.cpp:84] Creating Layer norm1
I0410 00:09:27.311245 14511 net.cpp:406] norm1 <- conv1
I0410 00:09:27.311275 14511 net.cpp:380] norm1 -> norm1
I0410 00:09:27.311730 14511 net.cpp:122] Setting up norm1
I0410 00:09:27.311743 14511 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:09:27.311746 14511 net.cpp:137] Memory required for data: 525203456
I0410 00:09:27.311750 14511 layer_factory.hpp:77] Creating layer pool1
I0410 00:09:27.311760 14511 net.cpp:84] Creating Layer pool1
I0410 00:09:27.311764 14511 net.cpp:406] pool1 <- norm1
I0410 00:09:27.311769 14511 net.cpp:380] pool1 -> pool1
I0410 00:09:27.311807 14511 net.cpp:122] Setting up pool1
I0410 00:09:27.311815 14511 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 00:09:27.311818 14511 net.cpp:137] Memory required for data: 561035264
I0410 00:09:27.311822 14511 layer_factory.hpp:77] Creating layer conv2
I0410 00:09:27.311833 14511 net.cpp:84] Creating Layer conv2
I0410 00:09:27.311836 14511 net.cpp:406] conv2 <- pool1
I0410 00:09:27.311842 14511 net.cpp:380] conv2 -> conv2
I0410 00:09:27.321084 14511 net.cpp:122] Setting up conv2
I0410 00:09:27.321103 14511 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:09:27.321108 14511 net.cpp:137] Memory required for data: 656586752
I0410 00:09:27.321118 14511 layer_factory.hpp:77] Creating layer relu2
I0410 00:09:27.321126 14511 net.cpp:84] Creating Layer relu2
I0410 00:09:27.321130 14511 net.cpp:406] relu2 <- conv2
I0410 00:09:27.321136 14511 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:09:27.321557 14511 net.cpp:122] Setting up relu2
I0410 00:09:27.321568 14511 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:09:27.321570 14511 net.cpp:137] Memory required for data: 752138240
I0410 00:09:27.321574 14511 layer_factory.hpp:77] Creating layer norm2
I0410 00:09:27.321583 14511 net.cpp:84] Creating Layer norm2
I0410 00:09:27.321586 14511 net.cpp:406] norm2 <- conv2
I0410 00:09:27.321592 14511 net.cpp:380] norm2 -> norm2
I0410 00:09:27.321882 14511 net.cpp:122] Setting up norm2
I0410 00:09:27.321890 14511 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:09:27.321894 14511 net.cpp:137] Memory required for data: 847689728
I0410 00:09:27.321898 14511 layer_factory.hpp:77] Creating layer pool2
I0410 00:09:27.321907 14511 net.cpp:84] Creating Layer pool2
I0410 00:09:27.321910 14511 net.cpp:406] pool2 <- norm2
I0410 00:09:27.321915 14511 net.cpp:380] pool2 -> pool2
I0410 00:09:27.321943 14511 net.cpp:122] Setting up pool2
I0410 00:09:27.321949 14511 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:09:27.321951 14511 net.cpp:137] Memory required for data: 869840896
I0410 00:09:27.321974 14511 layer_factory.hpp:77] Creating layer conv3
I0410 00:09:27.321983 14511 net.cpp:84] Creating Layer conv3
I0410 00:09:27.321987 14511 net.cpp:406] conv3 <- pool2
I0410 00:09:27.321993 14511 net.cpp:380] conv3 -> conv3
I0410 00:09:27.331835 14511 net.cpp:122] Setting up conv3
I0410 00:09:27.331851 14511 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:09:27.331854 14511 net.cpp:137] Memory required for data: 903067648
I0410 00:09:27.331866 14511 layer_factory.hpp:77] Creating layer relu3
I0410 00:09:27.331871 14511 net.cpp:84] Creating Layer relu3
I0410 00:09:27.331876 14511 net.cpp:406] relu3 <- conv3
I0410 00:09:27.331881 14511 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:09:27.332320 14511 net.cpp:122] Setting up relu3
I0410 00:09:27.332331 14511 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:09:27.332335 14511 net.cpp:137] Memory required for data: 936294400
I0410 00:09:27.332340 14511 layer_factory.hpp:77] Creating layer conv4
I0410 00:09:27.332350 14511 net.cpp:84] Creating Layer conv4
I0410 00:09:27.332353 14511 net.cpp:406] conv4 <- conv3
I0410 00:09:27.332360 14511 net.cpp:380] conv4 -> conv4
I0410 00:09:27.343977 14511 net.cpp:122] Setting up conv4
I0410 00:09:27.343994 14511 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:09:27.343998 14511 net.cpp:137] Memory required for data: 969521152
I0410 00:09:27.344007 14511 layer_factory.hpp:77] Creating layer relu4
I0410 00:09:27.344017 14511 net.cpp:84] Creating Layer relu4
I0410 00:09:27.344038 14511 net.cpp:406] relu4 <- conv4
I0410 00:09:27.344044 14511 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:09:27.344347 14511 net.cpp:122] Setting up relu4
I0410 00:09:27.344357 14511 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:09:27.344360 14511 net.cpp:137] Memory required for data: 1002747904
I0410 00:09:27.344364 14511 layer_factory.hpp:77] Creating layer conv5
I0410 00:09:27.344374 14511 net.cpp:84] Creating Layer conv5
I0410 00:09:27.344379 14511 net.cpp:406] conv5 <- conv4
I0410 00:09:27.344385 14511 net.cpp:380] conv5 -> conv5
I0410 00:09:27.352809 14511 net.cpp:122] Setting up conv5
I0410 00:09:27.352826 14511 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:09:27.352830 14511 net.cpp:137] Memory required for data: 1024899072
I0410 00:09:27.352843 14511 layer_factory.hpp:77] Creating layer relu5
I0410 00:09:27.352851 14511 net.cpp:84] Creating Layer relu5
I0410 00:09:27.352855 14511 net.cpp:406] relu5 <- conv5
I0410 00:09:27.352861 14511 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:09:27.353341 14511 net.cpp:122] Setting up relu5
I0410 00:09:27.353351 14511 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:09:27.353355 14511 net.cpp:137] Memory required for data: 1047050240
I0410 00:09:27.353359 14511 layer_factory.hpp:77] Creating layer pool5
I0410 00:09:27.353365 14511 net.cpp:84] Creating Layer pool5
I0410 00:09:27.353369 14511 net.cpp:406] pool5 <- conv5
I0410 00:09:27.353376 14511 net.cpp:380] pool5 -> pool5
I0410 00:09:27.353415 14511 net.cpp:122] Setting up pool5
I0410 00:09:27.353421 14511 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 00:09:27.353425 14511 net.cpp:137] Memory required for data: 1051768832
I0410 00:09:27.353427 14511 layer_factory.hpp:77] Creating layer fc6
I0410 00:09:27.353438 14511 net.cpp:84] Creating Layer fc6
I0410 00:09:27.353442 14511 net.cpp:406] fc6 <- pool5
I0410 00:09:27.353448 14511 net.cpp:380] fc6 -> fc6
I0410 00:09:28.073388 14511 net.cpp:122] Setting up fc6
I0410 00:09:28.073405 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.073408 14511 net.cpp:137] Memory required for data: 1055963136
I0410 00:09:28.073418 14511 layer_factory.hpp:77] Creating layer relu6
I0410 00:09:28.073428 14511 net.cpp:84] Creating Layer relu6
I0410 00:09:28.073433 14511 net.cpp:406] relu6 <- fc6
I0410 00:09:28.073441 14511 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:09:28.077061 14511 net.cpp:122] Setting up relu6
I0410 00:09:28.077073 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.077076 14511 net.cpp:137] Memory required for data: 1060157440
I0410 00:09:28.077080 14511 layer_factory.hpp:77] Creating layer drop6
I0410 00:09:28.077088 14511 net.cpp:84] Creating Layer drop6
I0410 00:09:28.077092 14511 net.cpp:406] drop6 <- fc6
I0410 00:09:28.077098 14511 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:09:28.077128 14511 net.cpp:122] Setting up drop6
I0410 00:09:28.077134 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.077137 14511 net.cpp:137] Memory required for data: 1064351744
I0410 00:09:28.077140 14511 layer_factory.hpp:77] Creating layer fc7
I0410 00:09:28.077148 14511 net.cpp:84] Creating Layer fc7
I0410 00:09:28.077152 14511 net.cpp:406] fc7 <- fc6
I0410 00:09:28.077158 14511 net.cpp:380] fc7 -> fc7
I0410 00:09:28.718230 14511 net.cpp:122] Setting up fc7
I0410 00:09:28.718253 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.718258 14511 net.cpp:137] Memory required for data: 1068546048
I0410 00:09:28.718268 14511 layer_factory.hpp:77] Creating layer relu7
I0410 00:09:28.718277 14511 net.cpp:84] Creating Layer relu7
I0410 00:09:28.718282 14511 net.cpp:406] relu7 <- fc7
I0410 00:09:28.718288 14511 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:09:28.718904 14511 net.cpp:122] Setting up relu7
I0410 00:09:28.718914 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.718919 14511 net.cpp:137] Memory required for data: 1072740352
I0410 00:09:28.718922 14511 layer_factory.hpp:77] Creating layer drop7
I0410 00:09:28.718930 14511 net.cpp:84] Creating Layer drop7
I0410 00:09:28.718959 14511 net.cpp:406] drop7 <- fc7
I0410 00:09:28.718967 14511 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:09:28.718992 14511 net.cpp:122] Setting up drop7
I0410 00:09:28.718999 14511 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 00:09:28.719002 14511 net.cpp:137] Memory required for data: 1076934656
I0410 00:09:28.719007 14511 layer_factory.hpp:77] Creating layer fc8
I0410 00:09:28.719014 14511 net.cpp:84] Creating Layer fc8
I0410 00:09:28.719018 14511 net.cpp:406] fc8 <- fc7
I0410 00:09:28.719024 14511 net.cpp:380] fc8 -> fc8
I0410 00:09:28.734736 14511 net.cpp:122] Setting up fc8
I0410 00:09:28.734753 14511 net.cpp:129] Top shape: 128 196 (25088)
I0410 00:09:28.734757 14511 net.cpp:137] Memory required for data: 1077035008
I0410 00:09:28.734766 14511 layer_factory.hpp:77] Creating layer loss
I0410 00:09:28.734776 14511 net.cpp:84] Creating Layer loss
I0410 00:09:28.734781 14511 net.cpp:406] loss <- fc8
I0410 00:09:28.734786 14511 net.cpp:406] loss <- label
I0410 00:09:28.734794 14511 net.cpp:380] loss -> loss
I0410 00:09:28.734805 14511 layer_factory.hpp:77] Creating layer loss
I0410 00:09:28.735497 14511 net.cpp:122] Setting up loss
I0410 00:09:28.735507 14511 net.cpp:129] Top shape: (1)
I0410 00:09:28.735509 14511 net.cpp:132]     with loss weight 1
I0410 00:09:28.735528 14511 net.cpp:137] Memory required for data: 1077035012
I0410 00:09:28.735532 14511 net.cpp:198] loss needs backward computation.
I0410 00:09:28.735538 14511 net.cpp:198] fc8 needs backward computation.
I0410 00:09:28.735543 14511 net.cpp:198] drop7 needs backward computation.
I0410 00:09:28.735546 14511 net.cpp:198] relu7 needs backward computation.
I0410 00:09:28.735549 14511 net.cpp:198] fc7 needs backward computation.
I0410 00:09:28.735553 14511 net.cpp:198] drop6 needs backward computation.
I0410 00:09:28.735558 14511 net.cpp:198] relu6 needs backward computation.
I0410 00:09:28.735561 14511 net.cpp:198] fc6 needs backward computation.
I0410 00:09:28.735565 14511 net.cpp:198] pool5 needs backward computation.
I0410 00:09:28.735569 14511 net.cpp:198] relu5 needs backward computation.
I0410 00:09:28.735572 14511 net.cpp:198] conv5 needs backward computation.
I0410 00:09:28.735576 14511 net.cpp:198] relu4 needs backward computation.
I0410 00:09:28.735579 14511 net.cpp:198] conv4 needs backward computation.
I0410 00:09:28.735582 14511 net.cpp:198] relu3 needs backward computation.
I0410 00:09:28.735586 14511 net.cpp:198] conv3 needs backward computation.
I0410 00:09:28.735590 14511 net.cpp:198] pool2 needs backward computation.
I0410 00:09:28.735594 14511 net.cpp:198] norm2 needs backward computation.
I0410 00:09:28.735597 14511 net.cpp:198] relu2 needs backward computation.
I0410 00:09:28.735601 14511 net.cpp:198] conv2 needs backward computation.
I0410 00:09:28.735605 14511 net.cpp:198] pool1 needs backward computation.
I0410 00:09:28.735608 14511 net.cpp:198] norm1 needs backward computation.
I0410 00:09:28.735611 14511 net.cpp:198] relu1 needs backward computation.
I0410 00:09:28.735615 14511 net.cpp:198] conv1 needs backward computation.
I0410 00:09:28.735620 14511 net.cpp:200] train-data does not need backward computation.
I0410 00:09:28.735622 14511 net.cpp:242] This network produces output loss
I0410 00:09:28.735636 14511 net.cpp:255] Network initialization done.
I0410 00:09:28.736110 14511 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 00:09:28.736140 14511 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 00:09:28.736279 14511 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
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: 8192
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"
}
I0410 00:09:28.736382 14511 layer_factory.hpp:77] Creating layer val-data
I0410 00:09:28.737849 14511 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 00:09:28.738059 14511 net.cpp:84] Creating Layer val-data
I0410 00:09:28.738070 14511 net.cpp:380] val-data -> data
I0410 00:09:28.738078 14511 net.cpp:380] val-data -> label
I0410 00:09:28.738085 14511 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:09:28.741923 14511 data_layer.cpp:45] output data size: 32,3,227,227
I0410 00:09:28.774201 14511 net.cpp:122] Setting up val-data
I0410 00:09:28.774224 14511 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 00:09:28.774230 14511 net.cpp:129] Top shape: 32 (32)
I0410 00:09:28.774233 14511 net.cpp:137] Memory required for data: 19787264
I0410 00:09:28.774240 14511 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 00:09:28.774252 14511 net.cpp:84] Creating Layer label_val-data_1_split
I0410 00:09:28.774256 14511 net.cpp:406] label_val-data_1_split <- label
I0410 00:09:28.774263 14511 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 00:09:28.774272 14511 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 00:09:28.774322 14511 net.cpp:122] Setting up label_val-data_1_split
I0410 00:09:28.774328 14511 net.cpp:129] Top shape: 32 (32)
I0410 00:09:28.774331 14511 net.cpp:129] Top shape: 32 (32)
I0410 00:09:28.774335 14511 net.cpp:137] Memory required for data: 19787520
I0410 00:09:28.774338 14511 layer_factory.hpp:77] Creating layer conv1
I0410 00:09:28.774350 14511 net.cpp:84] Creating Layer conv1
I0410 00:09:28.774353 14511 net.cpp:406] conv1 <- data
I0410 00:09:28.774359 14511 net.cpp:380] conv1 -> conv1
I0410 00:09:28.776281 14511 net.cpp:122] Setting up conv1
I0410 00:09:28.776293 14511 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:09:28.776295 14511 net.cpp:137] Memory required for data: 56958720
I0410 00:09:28.776305 14511 layer_factory.hpp:77] Creating layer relu1
I0410 00:09:28.776312 14511 net.cpp:84] Creating Layer relu1
I0410 00:09:28.776316 14511 net.cpp:406] relu1 <- conv1
I0410 00:09:28.776321 14511 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:09:28.776610 14511 net.cpp:122] Setting up relu1
I0410 00:09:28.776619 14511 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:09:28.776623 14511 net.cpp:137] Memory required for data: 94129920
I0410 00:09:28.776626 14511 layer_factory.hpp:77] Creating layer norm1
I0410 00:09:28.776634 14511 net.cpp:84] Creating Layer norm1
I0410 00:09:28.776638 14511 net.cpp:406] norm1 <- conv1
I0410 00:09:28.776643 14511 net.cpp:380] norm1 -> norm1
I0410 00:09:28.777243 14511 net.cpp:122] Setting up norm1
I0410 00:09:28.777257 14511 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:09:28.777262 14511 net.cpp:137] Memory required for data: 131301120
I0410 00:09:28.777268 14511 layer_factory.hpp:77] Creating layer pool1
I0410 00:09:28.777277 14511 net.cpp:84] Creating Layer pool1
I0410 00:09:28.777283 14511 net.cpp:406] pool1 <- norm1
I0410 00:09:28.777290 14511 net.cpp:380] pool1 -> pool1
I0410 00:09:28.777340 14511 net.cpp:122] Setting up pool1
I0410 00:09:28.777351 14511 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 00:09:28.777357 14511 net.cpp:137] Memory required for data: 140259072
I0410 00:09:28.777362 14511 layer_factory.hpp:77] Creating layer conv2
I0410 00:09:28.777374 14511 net.cpp:84] Creating Layer conv2
I0410 00:09:28.777380 14511 net.cpp:406] conv2 <- pool1
I0410 00:09:28.777415 14511 net.cpp:380] conv2 -> conv2
I0410 00:09:28.790519 14511 net.cpp:122] Setting up conv2
I0410 00:09:28.790537 14511 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:09:28.790541 14511 net.cpp:137] Memory required for data: 164146944
I0410 00:09:28.790555 14511 layer_factory.hpp:77] Creating layer relu2
I0410 00:09:28.790563 14511 net.cpp:84] Creating Layer relu2
I0410 00:09:28.790567 14511 net.cpp:406] relu2 <- conv2
I0410 00:09:28.790575 14511 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:09:28.792903 14511 net.cpp:122] Setting up relu2
I0410 00:09:28.792914 14511 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:09:28.792917 14511 net.cpp:137] Memory required for data: 188034816
I0410 00:09:28.792922 14511 layer_factory.hpp:77] Creating layer norm2
I0410 00:09:28.792932 14511 net.cpp:84] Creating Layer norm2
I0410 00:09:28.792937 14511 net.cpp:406] norm2 <- conv2
I0410 00:09:28.792941 14511 net.cpp:380] norm2 -> norm2
I0410 00:09:28.797387 14511 net.cpp:122] Setting up norm2
I0410 00:09:28.797399 14511 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:09:28.797401 14511 net.cpp:137] Memory required for data: 211922688
I0410 00:09:28.797405 14511 layer_factory.hpp:77] Creating layer pool2
I0410 00:09:28.797413 14511 net.cpp:84] Creating Layer pool2
I0410 00:09:28.797417 14511 net.cpp:406] pool2 <- norm2
I0410 00:09:28.797423 14511 net.cpp:380] pool2 -> pool2
I0410 00:09:28.797456 14511 net.cpp:122] Setting up pool2
I0410 00:09:28.797461 14511 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:09:28.797463 14511 net.cpp:137] Memory required for data: 217460480
I0410 00:09:28.797467 14511 layer_factory.hpp:77] Creating layer conv3
I0410 00:09:28.797479 14511 net.cpp:84] Creating Layer conv3
I0410 00:09:28.797483 14511 net.cpp:406] conv3 <- pool2
I0410 00:09:28.797488 14511 net.cpp:380] conv3 -> conv3
I0410 00:09:28.808595 14511 net.cpp:122] Setting up conv3
I0410 00:09:28.808615 14511 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:09:28.808619 14511 net.cpp:137] Memory required for data: 225767168
I0410 00:09:28.808634 14511 layer_factory.hpp:77] Creating layer relu3
I0410 00:09:28.808642 14511 net.cpp:84] Creating Layer relu3
I0410 00:09:28.808647 14511 net.cpp:406] relu3 <- conv3
I0410 00:09:28.808655 14511 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:09:28.809170 14511 net.cpp:122] Setting up relu3
I0410 00:09:28.809180 14511 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:09:28.809183 14511 net.cpp:137] Memory required for data: 234073856
I0410 00:09:28.809188 14511 layer_factory.hpp:77] Creating layer conv4
I0410 00:09:28.809201 14511 net.cpp:84] Creating Layer conv4
I0410 00:09:28.809204 14511 net.cpp:406] conv4 <- conv3
I0410 00:09:28.809212 14511 net.cpp:380] conv4 -> conv4
I0410 00:09:28.818682 14511 net.cpp:122] Setting up conv4
I0410 00:09:28.818698 14511 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:09:28.818701 14511 net.cpp:137] Memory required for data: 242380544
I0410 00:09:28.818711 14511 layer_factory.hpp:77] Creating layer relu4
I0410 00:09:28.818717 14511 net.cpp:84] Creating Layer relu4
I0410 00:09:28.818722 14511 net.cpp:406] relu4 <- conv4
I0410 00:09:28.818728 14511 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:09:28.819069 14511 net.cpp:122] Setting up relu4
I0410 00:09:28.819079 14511 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:09:28.819083 14511 net.cpp:137] Memory required for data: 250687232
I0410 00:09:28.819087 14511 layer_factory.hpp:77] Creating layer conv5
I0410 00:09:28.819097 14511 net.cpp:84] Creating Layer conv5
I0410 00:09:28.819101 14511 net.cpp:406] conv5 <- conv4
I0410 00:09:28.819108 14511 net.cpp:380] conv5 -> conv5
I0410 00:09:28.831445 14511 net.cpp:122] Setting up conv5
I0410 00:09:28.831463 14511 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:09:28.831467 14511 net.cpp:137] Memory required for data: 256225024
I0410 00:09:28.831480 14511 layer_factory.hpp:77] Creating layer relu5
I0410 00:09:28.831490 14511 net.cpp:84] Creating Layer relu5
I0410 00:09:28.831514 14511 net.cpp:406] relu5 <- conv5
I0410 00:09:28.831522 14511 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:09:28.832015 14511 net.cpp:122] Setting up relu5
I0410 00:09:28.832026 14511 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:09:28.832028 14511 net.cpp:137] Memory required for data: 261762816
I0410 00:09:28.832032 14511 layer_factory.hpp:77] Creating layer pool5
I0410 00:09:28.832043 14511 net.cpp:84] Creating Layer pool5
I0410 00:09:28.832047 14511 net.cpp:406] pool5 <- conv5
I0410 00:09:28.832052 14511 net.cpp:380] pool5 -> pool5
I0410 00:09:28.832093 14511 net.cpp:122] Setting up pool5
I0410 00:09:28.832098 14511 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 00:09:28.832103 14511 net.cpp:137] Memory required for data: 262942464
I0410 00:09:28.832105 14511 layer_factory.hpp:77] Creating layer fc6
I0410 00:09:28.832113 14511 net.cpp:84] Creating Layer fc6
I0410 00:09:28.832118 14511 net.cpp:406] fc6 <- pool5
I0410 00:09:28.832123 14511 net.cpp:380] fc6 -> fc6
I0410 00:09:29.547739 14511 net.cpp:122] Setting up fc6
I0410 00:09:29.547762 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:29.547766 14511 net.cpp:137] Memory required for data: 263991040
I0410 00:09:29.547777 14511 layer_factory.hpp:77] Creating layer relu6
I0410 00:09:29.547786 14511 net.cpp:84] Creating Layer relu6
I0410 00:09:29.547791 14511 net.cpp:406] relu6 <- fc6
I0410 00:09:29.547797 14511 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:09:29.548619 14511 net.cpp:122] Setting up relu6
I0410 00:09:29.548629 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:29.548633 14511 net.cpp:137] Memory required for data: 265039616
I0410 00:09:29.548637 14511 layer_factory.hpp:77] Creating layer drop6
I0410 00:09:29.548645 14511 net.cpp:84] Creating Layer drop6
I0410 00:09:29.548648 14511 net.cpp:406] drop6 <- fc6
I0410 00:09:29.548653 14511 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:09:29.548682 14511 net.cpp:122] Setting up drop6
I0410 00:09:29.548687 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:29.548691 14511 net.cpp:137] Memory required for data: 266088192
I0410 00:09:29.548694 14511 layer_factory.hpp:77] Creating layer fc7
I0410 00:09:29.548703 14511 net.cpp:84] Creating Layer fc7
I0410 00:09:29.548707 14511 net.cpp:406] fc7 <- fc6
I0410 00:09:29.548712 14511 net.cpp:380] fc7 -> fc7
I0410 00:09:30.179628 14511 net.cpp:122] Setting up fc7
I0410 00:09:30.179648 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:30.179652 14511 net.cpp:137] Memory required for data: 267136768
I0410 00:09:30.179662 14511 layer_factory.hpp:77] Creating layer relu7
I0410 00:09:30.179673 14511 net.cpp:84] Creating Layer relu7
I0410 00:09:30.179679 14511 net.cpp:406] relu7 <- fc7
I0410 00:09:30.179685 14511 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:09:30.180126 14511 net.cpp:122] Setting up relu7
I0410 00:09:30.180135 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:30.180140 14511 net.cpp:137] Memory required for data: 268185344
I0410 00:09:30.180143 14511 layer_factory.hpp:77] Creating layer drop7
I0410 00:09:30.180151 14511 net.cpp:84] Creating Layer drop7
I0410 00:09:30.180155 14511 net.cpp:406] drop7 <- fc7
I0410 00:09:30.180161 14511 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:09:30.180187 14511 net.cpp:122] Setting up drop7
I0410 00:09:30.180192 14511 net.cpp:129] Top shape: 32 8192 (262144)
I0410 00:09:30.180197 14511 net.cpp:137] Memory required for data: 269233920
I0410 00:09:30.180200 14511 layer_factory.hpp:77] Creating layer fc8
I0410 00:09:30.180207 14511 net.cpp:84] Creating Layer fc8
I0410 00:09:30.180212 14511 net.cpp:406] fc8 <- fc7
I0410 00:09:30.180218 14511 net.cpp:380] fc8 -> fc8
I0410 00:09:30.195385 14511 net.cpp:122] Setting up fc8
I0410 00:09:30.195403 14511 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:09:30.195407 14511 net.cpp:137] Memory required for data: 269259008
I0410 00:09:30.195415 14511 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 00:09:30.195425 14511 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 00:09:30.195430 14511 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 00:09:30.195454 14511 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 00:09:30.195463 14511 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 00:09:30.195502 14511 net.cpp:122] Setting up fc8_fc8_0_split
I0410 00:09:30.195508 14511 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:09:30.195511 14511 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:09:30.195514 14511 net.cpp:137] Memory required for data: 269309184
I0410 00:09:30.195518 14511 layer_factory.hpp:77] Creating layer accuracy
I0410 00:09:30.195524 14511 net.cpp:84] Creating Layer accuracy
I0410 00:09:30.195528 14511 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 00:09:30.195533 14511 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 00:09:30.195538 14511 net.cpp:380] accuracy -> accuracy
I0410 00:09:30.195545 14511 net.cpp:122] Setting up accuracy
I0410 00:09:30.195549 14511 net.cpp:129] Top shape: (1)
I0410 00:09:30.195552 14511 net.cpp:137] Memory required for data: 269309188
I0410 00:09:30.195556 14511 layer_factory.hpp:77] Creating layer loss
I0410 00:09:30.195562 14511 net.cpp:84] Creating Layer loss
I0410 00:09:30.195565 14511 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 00:09:30.195569 14511 net.cpp:406] loss <- label_val-data_1_split_1
I0410 00:09:30.195574 14511 net.cpp:380] loss -> loss
I0410 00:09:30.195581 14511 layer_factory.hpp:77] Creating layer loss
I0410 00:09:30.196272 14511 net.cpp:122] Setting up loss
I0410 00:09:30.196280 14511 net.cpp:129] Top shape: (1)
I0410 00:09:30.196285 14511 net.cpp:132]     with loss weight 1
I0410 00:09:30.196295 14511 net.cpp:137] Memory required for data: 269309192
I0410 00:09:30.196298 14511 net.cpp:198] loss needs backward computation.
I0410 00:09:30.196303 14511 net.cpp:200] accuracy does not need backward computation.
I0410 00:09:30.196308 14511 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 00:09:30.196311 14511 net.cpp:198] fc8 needs backward computation.
I0410 00:09:30.196316 14511 net.cpp:198] drop7 needs backward computation.
I0410 00:09:30.196318 14511 net.cpp:198] relu7 needs backward computation.
I0410 00:09:30.196322 14511 net.cpp:198] fc7 needs backward computation.
I0410 00:09:30.196326 14511 net.cpp:198] drop6 needs backward computation.
I0410 00:09:30.196329 14511 net.cpp:198] relu6 needs backward computation.
I0410 00:09:30.196332 14511 net.cpp:198] fc6 needs backward computation.
I0410 00:09:30.196336 14511 net.cpp:198] pool5 needs backward computation.
I0410 00:09:30.196341 14511 net.cpp:198] relu5 needs backward computation.
I0410 00:09:30.196346 14511 net.cpp:198] conv5 needs backward computation.
I0410 00:09:30.196349 14511 net.cpp:198] relu4 needs backward computation.
I0410 00:09:30.196353 14511 net.cpp:198] conv4 needs backward computation.
I0410 00:09:30.196357 14511 net.cpp:198] relu3 needs backward computation.
I0410 00:09:30.196360 14511 net.cpp:198] conv3 needs backward computation.
I0410 00:09:30.196364 14511 net.cpp:198] pool2 needs backward computation.
I0410 00:09:30.196367 14511 net.cpp:198] norm2 needs backward computation.
I0410 00:09:30.196372 14511 net.cpp:198] relu2 needs backward computation.
I0410 00:09:30.196374 14511 net.cpp:198] conv2 needs backward computation.
I0410 00:09:30.196378 14511 net.cpp:198] pool1 needs backward computation.
I0410 00:09:30.196382 14511 net.cpp:198] norm1 needs backward computation.
I0410 00:09:30.196385 14511 net.cpp:198] relu1 needs backward computation.
I0410 00:09:30.196389 14511 net.cpp:198] conv1 needs backward computation.
I0410 00:09:30.196393 14511 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 00:09:30.196398 14511 net.cpp:200] val-data does not need backward computation.
I0410 00:09:30.196400 14511 net.cpp:242] This network produces output accuracy
I0410 00:09:30.196404 14511 net.cpp:242] This network produces output loss
I0410 00:09:30.196421 14511 net.cpp:255] Network initialization done.
I0410 00:09:30.196498 14511 solver.cpp:56] Solver scaffolding done.
I0410 00:09:30.196943 14511 caffe.cpp:248] Starting Optimization
I0410 00:09:30.196952 14511 solver.cpp:272] Solving
I0410 00:09:30.196964 14511 solver.cpp:273] Learning Rate Policy: exp
I0410 00:09:30.198627 14511 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 00:09:30.198642 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:09:30.367471 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:09:34.703045 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:34.750721 14511 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0410 00:09:34.750761 14511 solver.cpp:397]     Test net output #1: loss = 5.28108 (* 1 = 5.28108 loss)
I0410 00:09:34.864949 14511 solver.cpp:218] Iteration 0 (-5.21836e-30 iter/s, 4.66783s/12 iters), loss = 5.30317
I0410 00:09:34.866466 14511 solver.cpp:237]     Train net output #0: loss = 5.30317 (* 1 = 5.30317 loss)
I0410 00:09:34.866492 14511 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 00:09:38.675864 14511 solver.cpp:218] Iteration 12 (3.1502 iter/s, 3.80928s/12 iters), loss = 5.32788
I0410 00:09:38.675917 14511 solver.cpp:237]     Train net output #0: loss = 5.32788 (* 1 = 5.32788 loss)
I0410 00:09:38.675928 14511 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 00:09:43.818076 14511 solver.cpp:218] Iteration 24 (2.33371 iter/s, 5.14202s/12 iters), loss = 5.29336
I0410 00:09:43.818231 14511 solver.cpp:237]     Train net output #0: loss = 5.29336 (* 1 = 5.29336 loss)
I0410 00:09:43.818248 14511 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 00:09:48.975234 14511 solver.cpp:218] Iteration 36 (2.32698 iter/s, 5.15689s/12 iters), loss = 5.30992
I0410 00:09:48.975279 14511 solver.cpp:237]     Train net output #0: loss = 5.30992 (* 1 = 5.30992 loss)
I0410 00:09:48.975288 14511 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 00:09:54.166086 14511 solver.cpp:218] Iteration 48 (2.31184 iter/s, 5.19067s/12 iters), loss = 5.33499
I0410 00:09:54.166141 14511 solver.cpp:237]     Train net output #0: loss = 5.33499 (* 1 = 5.33499 loss)
I0410 00:09:54.166152 14511 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 00:09:59.247921 14511 solver.cpp:218] Iteration 60 (2.36144 iter/s, 5.08164s/12 iters), loss = 5.30973
I0410 00:09:59.248029 14511 solver.cpp:237]     Train net output #0: loss = 5.30973 (* 1 = 5.30973 loss)
I0410 00:09:59.248042 14511 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 00:10:04.125702 14511 solver.cpp:218] Iteration 72 (2.46025 iter/s, 4.87755s/12 iters), loss = 5.34525
I0410 00:10:04.125752 14511 solver.cpp:237]     Train net output #0: loss = 5.34525 (* 1 = 5.34525 loss)
I0410 00:10:04.125764 14511 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 00:10:09.208143 14511 solver.cpp:218] Iteration 84 (2.36116 iter/s, 5.08226s/12 iters), loss = 5.32906
I0410 00:10:09.208187 14511 solver.cpp:237]     Train net output #0: loss = 5.32906 (* 1 = 5.32906 loss)
I0410 00:10:09.208196 14511 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 00:10:13.878206 14511 solver.cpp:218] Iteration 96 (2.56965 iter/s, 4.66989s/12 iters), loss = 5.31637
I0410 00:10:13.878250 14511 solver.cpp:237]     Train net output #0: loss = 5.31637 (* 1 = 5.31637 loss)
I0410 00:10:13.878262 14511 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 00:10:15.402603 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:15.743924 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 00:10:31.197587 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 00:10:40.187091 14511 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 00:10:40.187114 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:10:44.605700 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:44.684685 14511 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:10:44.684736 14511 solver.cpp:397]     Test net output #1: loss = 5.29515 (* 1 = 5.29515 loss)
I0410 00:10:46.402042 14511 solver.cpp:218] Iteration 108 (0.36897 iter/s, 32.523s/12 iters), loss = 5.32579
I0410 00:10:46.402082 14511 solver.cpp:237]     Train net output #0: loss = 5.32579 (* 1 = 5.32579 loss)
I0410 00:10:46.402091 14511 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 00:10:51.048465 14511 solver.cpp:218] Iteration 120 (2.58273 iter/s, 4.64625s/12 iters), loss = 5.24343
I0410 00:10:51.048511 14511 solver.cpp:237]     Train net output #0: loss = 5.24343 (* 1 = 5.24343 loss)
I0410 00:10:51.048519 14511 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 00:10:55.707784 14511 solver.cpp:218] Iteration 132 (2.57558 iter/s, 4.65914s/12 iters), loss = 5.2059
I0410 00:10:55.707828 14511 solver.cpp:237]     Train net output #0: loss = 5.2059 (* 1 = 5.2059 loss)
I0410 00:10:55.707836 14511 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 00:11:00.397220 14511 solver.cpp:218] Iteration 144 (2.55904 iter/s, 4.68926s/12 iters), loss = 5.22529
I0410 00:11:00.397264 14511 solver.cpp:237]     Train net output #0: loss = 5.22529 (* 1 = 5.22529 loss)
I0410 00:11:00.397272 14511 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 00:11:05.358208 14511 solver.cpp:218] Iteration 156 (2.41896 iter/s, 4.9608s/12 iters), loss = 5.21072
I0410 00:11:05.358389 14511 solver.cpp:237]     Train net output #0: loss = 5.21072 (* 1 = 5.21072 loss)
I0410 00:11:05.358407 14511 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 00:11:10.578548 14511 solver.cpp:218] Iteration 168 (2.29884 iter/s, 5.22003s/12 iters), loss = 5.17635
I0410 00:11:10.578593 14511 solver.cpp:237]     Train net output #0: loss = 5.17635 (* 1 = 5.17635 loss)
I0410 00:11:10.578603 14511 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 00:11:15.861634 14511 solver.cpp:218] Iteration 180 (2.27148 iter/s, 5.28289s/12 iters), loss = 5.14874
I0410 00:11:15.861693 14511 solver.cpp:237]     Train net output #0: loss = 5.14874 (* 1 = 5.14874 loss)
I0410 00:11:15.861704 14511 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 00:11:21.110461 14511 solver.cpp:218] Iteration 192 (2.28631 iter/s, 5.24863s/12 iters), loss = 5.2299
I0410 00:11:21.110517 14511 solver.cpp:237]     Train net output #0: loss = 5.2299 (* 1 = 5.2299 loss)
I0410 00:11:21.110529 14511 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 00:11:24.942979 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:25.605868 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 00:11:37.748191 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 00:11:46.961352 14511 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 00:11:46.961376 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:11:51.360360 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:51.486392 14511 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:11:51.486455 14511 solver.cpp:397]     Test net output #1: loss = 5.17997 (* 1 = 5.17997 loss)
I0410 00:11:51.595599 14511 solver.cpp:218] Iteration 204 (0.393645 iter/s, 30.4843s/12 iters), loss = 5.08756
I0410 00:11:51.597121 14511 solver.cpp:237]     Train net output #0: loss = 5.08756 (* 1 = 5.08756 loss)
I0410 00:11:51.597134 14511 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 00:11:55.625726 14511 solver.cpp:218] Iteration 216 (2.97878 iter/s, 4.0285s/12 iters), loss = 5.1809
I0410 00:11:55.625777 14511 solver.cpp:237]     Train net output #0: loss = 5.1809 (* 1 = 5.1809 loss)
I0410 00:11:55.625787 14511 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 00:12:00.262652 14511 solver.cpp:218] Iteration 228 (2.58802 iter/s, 4.63674s/12 iters), loss = 5.19135
I0410 00:12:00.262712 14511 solver.cpp:237]     Train net output #0: loss = 5.19135 (* 1 = 5.19135 loss)
I0410 00:12:00.262723 14511 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 00:12:04.868330 14511 solver.cpp:218] Iteration 240 (2.60558 iter/s, 4.60549s/12 iters), loss = 5.19491
I0410 00:12:04.868381 14511 solver.cpp:237]     Train net output #0: loss = 5.19491 (* 1 = 5.19491 loss)
I0410 00:12:04.868391 14511 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 00:12:09.444624 14511 solver.cpp:218] Iteration 252 (2.62231 iter/s, 4.57612s/12 iters), loss = 5.13466
I0410 00:12:09.447983 14511 solver.cpp:237]     Train net output #0: loss = 5.13466 (* 1 = 5.13466 loss)
I0410 00:12:09.447997 14511 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 00:12:14.127130 14511 solver.cpp:218] Iteration 264 (2.56463 iter/s, 4.67903s/12 iters), loss = 5.23995
I0410 00:12:14.127166 14511 solver.cpp:237]     Train net output #0: loss = 5.23995 (* 1 = 5.23995 loss)
I0410 00:12:14.127174 14511 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 00:12:19.180804 14511 solver.cpp:218] Iteration 276 (2.37459 iter/s, 5.05351s/12 iters), loss = 5.20897
I0410 00:12:19.180846 14511 solver.cpp:237]     Train net output #0: loss = 5.20897 (* 1 = 5.20897 loss)
I0410 00:12:19.180856 14511 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 00:12:24.158504 14511 solver.cpp:218] Iteration 288 (2.41084 iter/s, 4.97753s/12 iters), loss = 5.04028
I0410 00:12:24.158535 14511 solver.cpp:237]     Train net output #0: loss = 5.04028 (* 1 = 5.04028 loss)
I0410 00:12:24.158542 14511 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 00:12:28.883774 14511 solver.cpp:218] Iteration 300 (2.53962 iter/s, 4.72511s/12 iters), loss = 5.18872
I0410 00:12:28.883821 14511 solver.cpp:237]     Train net output #0: loss = 5.18872 (* 1 = 5.18872 loss)
I0410 00:12:28.883833 14511 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 00:12:29.764691 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:30.760749 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 00:12:40.422047 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 00:12:46.742056 14511 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 00:12:46.742079 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:12:51.355896 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:51.548063 14511 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0410 00:12:51.548099 14511 solver.cpp:397]     Test net output #1: loss = 5.14385 (* 1 = 5.14385 loss)
I0410 00:12:53.413628 14511 solver.cpp:218] Iteration 312 (0.489213 iter/s, 24.5292s/12 iters), loss = 5.09423
I0410 00:12:53.413681 14511 solver.cpp:237]     Train net output #0: loss = 5.09423 (* 1 = 5.09423 loss)
I0410 00:12:53.413691 14511 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 00:12:58.672631 14511 solver.cpp:218] Iteration 324 (2.28188 iter/s, 5.25881s/12 iters), loss = 5.16682
I0410 00:12:58.672678 14511 solver.cpp:237]     Train net output #0: loss = 5.16682 (* 1 = 5.16682 loss)
I0410 00:12:58.672690 14511 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 00:13:03.779549 14511 solver.cpp:218] Iteration 336 (2.34984 iter/s, 5.10674s/12 iters), loss = 5.12786
I0410 00:13:03.779593 14511 solver.cpp:237]     Train net output #0: loss = 5.12786 (* 1 = 5.12786 loss)
I0410 00:13:03.779603 14511 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 00:13:08.821739 14511 solver.cpp:218] Iteration 348 (2.38 iter/s, 5.04201s/12 iters), loss = 5.10291
I0410 00:13:08.821781 14511 solver.cpp:237]     Train net output #0: loss = 5.10291 (* 1 = 5.10291 loss)
I0410 00:13:08.821790 14511 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 00:13:13.854246 14511 solver.cpp:218] Iteration 360 (2.38458 iter/s, 5.03233s/12 iters), loss = 5.15073
I0410 00:13:13.857095 14511 solver.cpp:237]     Train net output #0: loss = 5.15073 (* 1 = 5.15073 loss)
I0410 00:13:13.857107 14511 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 00:13:18.643190 14511 solver.cpp:218] Iteration 372 (2.50733 iter/s, 4.78597s/12 iters), loss = 5.09024
I0410 00:13:18.643235 14511 solver.cpp:237]     Train net output #0: loss = 5.09024 (* 1 = 5.09024 loss)
I0410 00:13:18.643244 14511 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 00:13:23.834360 14511 solver.cpp:218] Iteration 384 (2.3117 iter/s, 5.19098s/12 iters), loss = 5.13726
I0410 00:13:23.834414 14511 solver.cpp:237]     Train net output #0: loss = 5.13726 (* 1 = 5.13726 loss)
I0410 00:13:23.834425 14511 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 00:13:28.759002 14511 solver.cpp:218] Iteration 396 (2.43682 iter/s, 4.92446s/12 iters), loss = 5.03328
I0410 00:13:28.759052 14511 solver.cpp:237]     Train net output #0: loss = 5.03328 (* 1 = 5.03328 loss)
I0410 00:13:28.759064 14511 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 00:13:31.842442 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:33.199345 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 00:13:44.115551 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 00:13:51.190986 14511 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 00:13:51.191009 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:13:55.483999 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:55.690696 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0410 00:13:55.690748 14511 solver.cpp:397]     Test net output #1: loss = 5.08019 (* 1 = 5.08019 loss)
I0410 00:13:55.798588 14511 solver.cpp:218] Iteration 408 (0.443805 iter/s, 27.0389s/12 iters), loss = 5.1682
I0410 00:13:55.800115 14511 solver.cpp:237]     Train net output #0: loss = 5.1682 (* 1 = 5.1682 loss)
I0410 00:13:55.800133 14511 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 00:13:59.678072 14511 solver.cpp:218] Iteration 420 (3.09449 iter/s, 3.87786s/12 iters), loss = 5.11867
I0410 00:13:59.678118 14511 solver.cpp:237]     Train net output #0: loss = 5.11867 (* 1 = 5.11867 loss)
I0410 00:13:59.678128 14511 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 00:14:04.565003 14511 solver.cpp:218] Iteration 432 (2.45562 iter/s, 4.88675s/12 iters), loss = 5.07128
I0410 00:14:04.565048 14511 solver.cpp:237]     Train net output #0: loss = 5.07128 (* 1 = 5.07128 loss)
I0410 00:14:04.565059 14511 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 00:14:09.246835 14511 solver.cpp:218] Iteration 444 (2.56319 iter/s, 4.68166s/12 iters), loss = 5.07349
I0410 00:14:09.246896 14511 solver.cpp:237]     Train net output #0: loss = 5.07349 (* 1 = 5.07349 loss)
I0410 00:14:09.246907 14511 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 00:14:14.237331 14511 solver.cpp:218] Iteration 456 (2.40466 iter/s, 4.9903s/12 iters), loss = 5.05303
I0410 00:14:14.237458 14511 solver.cpp:237]     Train net output #0: loss = 5.05303 (* 1 = 5.05303 loss)
I0410 00:14:14.237470 14511 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 00:14:19.486654 14511 solver.cpp:218] Iteration 468 (2.28612 iter/s, 5.24906s/12 iters), loss = 5.03687
I0410 00:14:19.486702 14511 solver.cpp:237]     Train net output #0: loss = 5.03687 (* 1 = 5.03687 loss)
I0410 00:14:19.486713 14511 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 00:14:24.706118 14511 solver.cpp:218] Iteration 480 (2.29917 iter/s, 5.21928s/12 iters), loss = 4.97516
I0410 00:14:24.706167 14511 solver.cpp:237]     Train net output #0: loss = 4.97516 (* 1 = 4.97516 loss)
I0410 00:14:24.706177 14511 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 00:14:29.930949 14511 solver.cpp:218] Iteration 492 (2.29681 iter/s, 5.22465s/12 iters), loss = 5.05264
I0410 00:14:29.930989 14511 solver.cpp:237]     Train net output #0: loss = 5.05264 (* 1 = 5.05264 loss)
I0410 00:14:29.930999 14511 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 00:14:34.704849 14511 solver.cpp:218] Iteration 504 (2.51376 iter/s, 4.77373s/12 iters), loss = 5.07084
I0410 00:14:34.704897 14511 solver.cpp:237]     Train net output #0: loss = 5.07084 (* 1 = 5.07084 loss)
I0410 00:14:34.704910 14511 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 00:14:34.929121 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:36.796604 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 00:14:44.988281 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 00:14:51.311471 14511 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 00:14:51.311496 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:14:55.434211 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:55.674898 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0410 00:14:55.674943 14511 solver.cpp:397]     Test net output #1: loss = 4.98197 (* 1 = 4.98197 loss)
I0410 00:14:57.509212 14511 solver.cpp:218] Iteration 516 (0.526229 iter/s, 22.8038s/12 iters), loss = 4.91555
I0410 00:14:57.509255 14511 solver.cpp:237]     Train net output #0: loss = 4.91555 (* 1 = 4.91555 loss)
I0410 00:14:57.509264 14511 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 00:15:02.572276 14511 solver.cpp:218] Iteration 528 (2.37019 iter/s, 5.06289s/12 iters), loss = 5.08461
I0410 00:15:02.572316 14511 solver.cpp:237]     Train net output #0: loss = 5.08461 (* 1 = 5.08461 loss)
I0410 00:15:02.572325 14511 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 00:15:07.360591 14511 solver.cpp:218] Iteration 540 (2.50619 iter/s, 4.78815s/12 iters), loss = 4.8931
I0410 00:15:07.360639 14511 solver.cpp:237]     Train net output #0: loss = 4.8931 (* 1 = 4.8931 loss)
I0410 00:15:07.360651 14511 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 00:15:12.111133 14511 solver.cpp:218] Iteration 552 (2.52612 iter/s, 4.75037s/12 iters), loss = 5.0427
I0410 00:15:12.111178 14511 solver.cpp:237]     Train net output #0: loss = 5.0427 (* 1 = 5.0427 loss)
I0410 00:15:12.111189 14511 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 00:15:17.168962 14511 solver.cpp:218] Iteration 564 (2.37264 iter/s, 5.05765s/12 iters), loss = 4.92964
I0410 00:15:17.169052 14511 solver.cpp:237]     Train net output #0: loss = 4.92964 (* 1 = 4.92964 loss)
I0410 00:15:17.169064 14511 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 00:15:22.323685 14511 solver.cpp:218] Iteration 576 (2.32807 iter/s, 5.15449s/12 iters), loss = 5.006
I0410 00:15:22.323737 14511 solver.cpp:237]     Train net output #0: loss = 5.006 (* 1 = 5.006 loss)
I0410 00:15:22.323748 14511 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 00:15:26.951911 14511 solver.cpp:218] Iteration 588 (2.59288 iter/s, 4.62805s/12 iters), loss = 4.81234
I0410 00:15:26.951962 14511 solver.cpp:237]     Train net output #0: loss = 4.81234 (* 1 = 4.81234 loss)
I0410 00:15:26.951973 14511 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 00:15:32.167254 14511 solver.cpp:218] Iteration 600 (2.30099 iter/s, 5.21515s/12 iters), loss = 4.88999
I0410 00:15:32.167304 14511 solver.cpp:237]     Train net output #0: loss = 4.88999 (* 1 = 4.88999 loss)
I0410 00:15:32.167315 14511 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 00:15:34.665580 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:36.781127 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 00:15:44.320411 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 00:15:50.174827 14511 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 00:15:50.174894 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:15:54.388744 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:54.677714 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0410 00:15:54.677760 14511 solver.cpp:397]     Test net output #1: loss = 4.89469 (* 1 = 4.89469 loss)
I0410 00:15:54.786504 14511 solver.cpp:218] Iteration 612 (0.530536 iter/s, 22.6187s/12 iters), loss = 4.8589
I0410 00:15:54.786546 14511 solver.cpp:237]     Train net output #0: loss = 4.8589 (* 1 = 4.8589 loss)
I0410 00:15:54.786556 14511 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 00:15:58.557729 14511 solver.cpp:218] Iteration 624 (3.18212 iter/s, 3.77108s/12 iters), loss = 4.806
I0410 00:15:58.557770 14511 solver.cpp:237]     Train net output #0: loss = 4.806 (* 1 = 4.806 loss)
I0410 00:15:58.557780 14511 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 00:16:03.566079 14511 solver.cpp:218] Iteration 636 (2.39608 iter/s, 5.00817s/12 iters), loss = 4.76997
I0410 00:16:03.566124 14511 solver.cpp:237]     Train net output #0: loss = 4.76997 (* 1 = 4.76997 loss)
I0410 00:16:03.566135 14511 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 00:16:08.791560 14511 solver.cpp:218] Iteration 648 (2.29652 iter/s, 5.2253s/12 iters), loss = 4.98752
I0410 00:16:08.791600 14511 solver.cpp:237]     Train net output #0: loss = 4.98752 (* 1 = 4.98752 loss)
I0410 00:16:08.791610 14511 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 00:16:13.482348 14511 solver.cpp:218] Iteration 660 (2.5583 iter/s, 4.69062s/12 iters), loss = 4.92866
I0410 00:16:13.482399 14511 solver.cpp:237]     Train net output #0: loss = 4.92866 (* 1 = 4.92866 loss)
I0410 00:16:13.482410 14511 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 00:16:18.104624 14511 solver.cpp:218] Iteration 672 (2.59622 iter/s, 4.6221s/12 iters), loss = 4.73002
I0410 00:16:18.104676 14511 solver.cpp:237]     Train net output #0: loss = 4.73002 (* 1 = 4.73002 loss)
I0410 00:16:18.104686 14511 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 00:16:23.404783 14511 solver.cpp:218] Iteration 684 (2.26416 iter/s, 5.29997s/12 iters), loss = 4.74155
I0410 00:16:23.404938 14511 solver.cpp:237]     Train net output #0: loss = 4.74155 (* 1 = 4.74155 loss)
I0410 00:16:23.404953 14511 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 00:16:24.365274 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:16:28.351294 14511 solver.cpp:218] Iteration 696 (2.42609 iter/s, 4.94623s/12 iters), loss = 4.80635
I0410 00:16:28.351342 14511 solver.cpp:237]     Train net output #0: loss = 4.80635 (* 1 = 4.80635 loss)
I0410 00:16:28.351354 14511 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 00:16:32.805694 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:33.251994 14511 solver.cpp:218] Iteration 708 (2.44872 iter/s, 4.90052s/12 iters), loss = 4.82862
I0410 00:16:33.252040 14511 solver.cpp:237]     Train net output #0: loss = 4.82862 (* 1 = 4.82862 loss)
I0410 00:16:33.252053 14511 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 00:16:35.378836 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 00:16:42.864394 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 00:16:48.843909 14511 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 00:16:48.843932 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:16:53.022716 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:53.446537 14511 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0410 00:16:53.446645 14511 solver.cpp:397]     Test net output #1: loss = 4.85734 (* 1 = 4.85734 loss)
I0410 00:16:55.349239 14511 solver.cpp:218] Iteration 720 (0.543068 iter/s, 22.0967s/12 iters), loss = 4.86915
I0410 00:16:55.349300 14511 solver.cpp:237]     Train net output #0: loss = 4.86915 (* 1 = 4.86915 loss)
I0410 00:16:55.349313 14511 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 00:17:00.544183 14511 solver.cpp:218] Iteration 732 (2.31003 iter/s, 5.19475s/12 iters), loss = 4.60387
I0410 00:17:00.544230 14511 solver.cpp:237]     Train net output #0: loss = 4.60387 (* 1 = 4.60387 loss)
I0410 00:17:00.544242 14511 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 00:17:05.764984 14511 solver.cpp:218] Iteration 744 (2.29858 iter/s, 5.22062s/12 iters), loss = 4.80867
I0410 00:17:05.765030 14511 solver.cpp:237]     Train net output #0: loss = 4.80867 (* 1 = 4.80867 loss)
I0410 00:17:05.765043 14511 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 00:17:10.998929 14511 solver.cpp:218] Iteration 756 (2.29281 iter/s, 5.23376s/12 iters), loss = 4.81309
I0410 00:17:10.998973 14511 solver.cpp:237]     Train net output #0: loss = 4.81309 (* 1 = 4.81309 loss)
I0410 00:17:10.998983 14511 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 00:17:15.980336 14511 solver.cpp:218] Iteration 768 (2.40905 iter/s, 4.98123s/12 iters), loss = 4.78969
I0410 00:17:15.980388 14511 solver.cpp:237]     Train net output #0: loss = 4.78969 (* 1 = 4.78969 loss)
I0410 00:17:15.980401 14511 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 00:17:20.623039 14511 solver.cpp:218] Iteration 780 (2.5848 iter/s, 4.64253s/12 iters), loss = 4.80403
I0410 00:17:20.623083 14511 solver.cpp:237]     Train net output #0: loss = 4.80403 (* 1 = 4.80403 loss)
I0410 00:17:20.623093 14511 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 00:17:25.273478 14511 solver.cpp:218] Iteration 792 (2.58049 iter/s, 4.65027s/12 iters), loss = 4.55239
I0410 00:17:25.273603 14511 solver.cpp:237]     Train net output #0: loss = 4.55239 (* 1 = 4.55239 loss)
I0410 00:17:25.273613 14511 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 00:17:30.106040 14511 solver.cpp:218] Iteration 804 (2.48329 iter/s, 4.83231s/12 iters), loss = 4.76915
I0410 00:17:30.106091 14511 solver.cpp:237]     Train net output #0: loss = 4.76915 (* 1 = 4.76915 loss)
I0410 00:17:30.106101 14511 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 00:17:31.891371 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:34.667546 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 00:17:42.556520 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 00:17:52.822562 14511 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 00:17:52.822587 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:17:57.101691 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:57.461381 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0392157
I0410 00:17:57.461431 14511 solver.cpp:397]     Test net output #1: loss = 4.75419 (* 1 = 4.75419 loss)
I0410 00:17:57.569890 14511 solver.cpp:218] Iteration 816 (0.436949 iter/s, 27.4631s/12 iters), loss = 4.70203
I0410 00:17:57.569937 14511 solver.cpp:237]     Train net output #0: loss = 4.70203 (* 1 = 4.70203 loss)
I0410 00:17:57.569948 14511 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 00:18:01.665141 14511 solver.cpp:218] Iteration 828 (2.93034 iter/s, 4.09509s/12 iters), loss = 4.87228
I0410 00:18:01.665194 14511 solver.cpp:237]     Train net output #0: loss = 4.87228 (* 1 = 4.87228 loss)
I0410 00:18:01.665205 14511 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 00:18:06.337633 14511 solver.cpp:218] Iteration 840 (2.56832 iter/s, 4.67232s/12 iters), loss = 4.47328
I0410 00:18:06.337672 14511 solver.cpp:237]     Train net output #0: loss = 4.47328 (* 1 = 4.47328 loss)
I0410 00:18:06.337683 14511 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 00:18:11.068580 14511 solver.cpp:218] Iteration 852 (2.53658 iter/s, 4.73078s/12 iters), loss = 4.59469
I0410 00:18:11.068624 14511 solver.cpp:237]     Train net output #0: loss = 4.59469 (* 1 = 4.59469 loss)
I0410 00:18:11.068634 14511 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 00:18:15.744489 14511 solver.cpp:218] Iteration 864 (2.56644 iter/s, 4.67574s/12 iters), loss = 4.64738
I0410 00:18:15.744540 14511 solver.cpp:237]     Train net output #0: loss = 4.64738 (* 1 = 4.64738 loss)
I0410 00:18:15.744552 14511 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 00:18:20.555976 14511 solver.cpp:218] Iteration 876 (2.49412 iter/s, 4.81131s/12 iters), loss = 4.68275
I0410 00:18:20.556021 14511 solver.cpp:237]     Train net output #0: loss = 4.68275 (* 1 = 4.68275 loss)
I0410 00:18:20.556033 14511 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 00:18:25.798221 14511 solver.cpp:218] Iteration 888 (2.28918 iter/s, 5.24205s/12 iters), loss = 4.5978
I0410 00:18:25.798272 14511 solver.cpp:237]     Train net output #0: loss = 4.5978 (* 1 = 4.5978 loss)
I0410 00:18:25.798285 14511 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 00:18:30.828904 14511 solver.cpp:218] Iteration 900 (2.38545 iter/s, 5.0305s/12 iters), loss = 4.57128
I0410 00:18:30.829042 14511 solver.cpp:237]     Train net output #0: loss = 4.57128 (* 1 = 4.57128 loss)
I0410 00:18:30.829056 14511 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 00:18:34.878823 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:36.080806 14511 solver.cpp:218] Iteration 912 (2.28501 iter/s, 5.25163s/12 iters), loss = 4.30019
I0410 00:18:36.080862 14511 solver.cpp:237]     Train net output #0: loss = 4.30019 (* 1 = 4.30019 loss)
I0410 00:18:36.080873 14511 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 00:18:38.231684 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 00:18:53.660018 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 00:18:59.537472 14511 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 00:18:59.537492 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:19:03.954828 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:04.362370 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0484069
I0410 00:19:04.362413 14511 solver.cpp:397]     Test net output #1: loss = 4.66292 (* 1 = 4.66292 loss)
I0410 00:19:06.204903 14511 solver.cpp:218] Iteration 924 (0.398363 iter/s, 30.1233s/12 iters), loss = 4.45751
I0410 00:19:06.204944 14511 solver.cpp:237]     Train net output #0: loss = 4.45751 (* 1 = 4.45751 loss)
I0410 00:19:06.204955 14511 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 00:19:11.258352 14511 solver.cpp:218] Iteration 936 (2.3747 iter/s, 5.05327s/12 iters), loss = 4.64961
I0410 00:19:11.258397 14511 solver.cpp:237]     Train net output #0: loss = 4.64961 (* 1 = 4.64961 loss)
I0410 00:19:11.258406 14511 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 00:19:15.962347 14511 solver.cpp:218] Iteration 948 (2.55112 iter/s, 4.70382s/12 iters), loss = 4.41497
I0410 00:19:15.962395 14511 solver.cpp:237]     Train net output #0: loss = 4.41497 (* 1 = 4.41497 loss)
I0410 00:19:15.962407 14511 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 00:19:20.727313 14511 solver.cpp:218] Iteration 960 (2.51847 iter/s, 4.76479s/12 iters), loss = 4.56111
I0410 00:19:20.727349 14511 solver.cpp:237]     Train net output #0: loss = 4.56111 (* 1 = 4.56111 loss)
I0410 00:19:20.727357 14511 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 00:19:25.363379 14511 solver.cpp:218] Iteration 972 (2.58849 iter/s, 4.63591s/12 iters), loss = 4.38208
I0410 00:19:25.363418 14511 solver.cpp:237]     Train net output #0: loss = 4.38208 (* 1 = 4.38208 loss)
I0410 00:19:25.363427 14511 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 00:19:30.034649 14511 solver.cpp:218] Iteration 984 (2.56899 iter/s, 4.6711s/12 iters), loss = 4.38392
I0410 00:19:30.034700 14511 solver.cpp:237]     Train net output #0: loss = 4.38392 (* 1 = 4.38392 loss)
I0410 00:19:30.034713 14511 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 00:19:34.650426 14511 solver.cpp:218] Iteration 996 (2.59988 iter/s, 4.6156s/12 iters), loss = 4.30993
I0410 00:19:34.650580 14511 solver.cpp:237]     Train net output #0: loss = 4.30993 (* 1 = 4.30993 loss)
I0410 00:19:34.650591 14511 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 00:19:39.431380 14511 solver.cpp:218] Iteration 1008 (2.5101 iter/s, 4.78068s/12 iters), loss = 4.48856
I0410 00:19:39.431417 14511 solver.cpp:237]     Train net output #0: loss = 4.48856 (* 1 = 4.48856 loss)
I0410 00:19:39.431427 14511 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 00:19:40.439540 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:43.992067 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 00:19:52.186844 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 00:19:58.014843 14511 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 00:19:58.014868 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:20:02.080580 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:02.520110 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0661765
I0410 00:20:02.520159 14511 solver.cpp:397]     Test net output #1: loss = 4.48783 (* 1 = 4.48783 loss)
I0410 00:20:02.628898 14511 solver.cpp:218] Iteration 1020 (0.51731 iter/s, 23.1969s/12 iters), loss = 4.29337
I0410 00:20:02.628947 14511 solver.cpp:237]     Train net output #0: loss = 4.29337 (* 1 = 4.29337 loss)
I0410 00:20:02.628958 14511 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 00:20:06.847115 14511 solver.cpp:218] Iteration 1032 (2.84492 iter/s, 4.21805s/12 iters), loss = 4.46554
I0410 00:20:06.847214 14511 solver.cpp:237]     Train net output #0: loss = 4.46554 (* 1 = 4.46554 loss)
I0410 00:20:06.847224 14511 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 00:20:11.384562 14511 solver.cpp:218] Iteration 1044 (2.64479 iter/s, 4.53722s/12 iters), loss = 4.39338
I0410 00:20:11.384619 14511 solver.cpp:237]     Train net output #0: loss = 4.39338 (* 1 = 4.39338 loss)
I0410 00:20:11.384630 14511 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 00:20:16.387369 14511 solver.cpp:218] Iteration 1056 (2.39875 iter/s, 5.00262s/12 iters), loss = 4.39208
I0410 00:20:16.387418 14511 solver.cpp:237]     Train net output #0: loss = 4.39208 (* 1 = 4.39208 loss)
I0410 00:20:16.387429 14511 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 00:20:21.481784 14511 solver.cpp:218] Iteration 1068 (2.35561 iter/s, 5.09422s/12 iters), loss = 4.32488
I0410 00:20:21.481850 14511 solver.cpp:237]     Train net output #0: loss = 4.32488 (* 1 = 4.32488 loss)
I0410 00:20:21.481865 14511 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 00:20:26.285866 14511 solver.cpp:218] Iteration 1080 (2.49798 iter/s, 4.80389s/12 iters), loss = 4.11403
I0410 00:20:26.285907 14511 solver.cpp:237]     Train net output #0: loss = 4.11403 (* 1 = 4.11403 loss)
I0410 00:20:26.285917 14511 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 00:20:31.102761 14511 solver.cpp:218] Iteration 1092 (2.49132 iter/s, 4.81672s/12 iters), loss = 4.32054
I0410 00:20:31.102810 14511 solver.cpp:237]     Train net output #0: loss = 4.32054 (* 1 = 4.32054 loss)
I0410 00:20:31.102823 14511 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 00:20:35.941445 14511 solver.cpp:218] Iteration 1104 (2.48011 iter/s, 4.8385s/12 iters), loss = 4.2746
I0410 00:20:35.941488 14511 solver.cpp:237]     Train net output #0: loss = 4.2746 (* 1 = 4.2746 loss)
I0410 00:20:35.941498 14511 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 00:20:38.938581 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:40.662263 14511 solver.cpp:218] Iteration 1116 (2.54203 iter/s, 4.72063s/12 iters), loss = 4.29436
I0410 00:20:40.662328 14511 solver.cpp:237]     Train net output #0: loss = 4.29436 (* 1 = 4.29436 loss)
I0410 00:20:40.662344 14511 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 00:20:42.618404 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 00:20:50.655661 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 00:20:56.507094 14511 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 00:20:56.507119 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:21:00.496327 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:00.978260 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0753676
I0410 00:21:00.978297 14511 solver.cpp:397]     Test net output #1: loss = 4.37238 (* 1 = 4.37238 loss)
I0410 00:21:02.861475 14511 solver.cpp:218] Iteration 1128 (0.540574 iter/s, 22.1986s/12 iters), loss = 4.26195
I0410 00:21:02.861528 14511 solver.cpp:237]     Train net output #0: loss = 4.26195 (* 1 = 4.26195 loss)
I0410 00:21:02.861538 14511 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 00:21:08.097518 14511 solver.cpp:218] Iteration 1140 (2.29189 iter/s, 5.23586s/12 iters), loss = 4.31795
I0410 00:21:08.097551 14511 solver.cpp:237]     Train net output #0: loss = 4.31795 (* 1 = 4.31795 loss)
I0410 00:21:08.097560 14511 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 00:21:13.186391 14511 solver.cpp:218] Iteration 1152 (2.35816 iter/s, 5.0887s/12 iters), loss = 4.15978
I0410 00:21:13.186515 14511 solver.cpp:237]     Train net output #0: loss = 4.15978 (* 1 = 4.15978 loss)
I0410 00:21:13.186527 14511 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 00:21:18.227350 14511 solver.cpp:218] Iteration 1164 (2.38062 iter/s, 5.0407s/12 iters), loss = 4.10878
I0410 00:21:18.227392 14511 solver.cpp:237]     Train net output #0: loss = 4.10878 (* 1 = 4.10878 loss)
I0410 00:21:18.227401 14511 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 00:21:23.413228 14511 solver.cpp:218] Iteration 1176 (2.31406 iter/s, 5.18569s/12 iters), loss = 4.00491
I0410 00:21:23.413280 14511 solver.cpp:237]     Train net output #0: loss = 4.00491 (* 1 = 4.00491 loss)
I0410 00:21:23.413290 14511 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 00:21:28.602517 14511 solver.cpp:218] Iteration 1188 (2.31254 iter/s, 5.1891s/12 iters), loss = 4.23862
I0410 00:21:28.602562 14511 solver.cpp:237]     Train net output #0: loss = 4.23862 (* 1 = 4.23862 loss)
I0410 00:21:28.602574 14511 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 00:21:33.752527 14511 solver.cpp:218] Iteration 1200 (2.33017 iter/s, 5.14983s/12 iters), loss = 4.11389
I0410 00:21:33.752571 14511 solver.cpp:237]     Train net output #0: loss = 4.11389 (* 1 = 4.11389 loss)
I0410 00:21:33.752580 14511 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 00:21:38.838753 14511 solver.cpp:218] Iteration 1212 (2.3594 iter/s, 5.08605s/12 iters), loss = 4.11847
I0410 00:21:38.838797 14511 solver.cpp:237]     Train net output #0: loss = 4.11847 (* 1 = 4.11847 loss)
I0410 00:21:38.838807 14511 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 00:21:39.064610 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:43.250197 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 00:21:50.632395 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 00:21:58.250824 14511 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 00:21:58.250850 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:22:02.233469 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:02.751721 14511 solver.cpp:397]     Test net output #0: accuracy = 0.0827206
I0410 00:22:02.751770 14511 solver.cpp:397]     Test net output #1: loss = 4.44222 (* 1 = 4.44222 loss)
I0410 00:22:02.860133 14511 solver.cpp:218] Iteration 1224 (0.499568 iter/s, 24.0208s/12 iters), loss = 4.33391
I0410 00:22:02.860185 14511 solver.cpp:237]     Train net output #0: loss = 4.33391 (* 1 = 4.33391 loss)
I0410 00:22:02.860196 14511 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 00:22:07.191651 14511 solver.cpp:218] Iteration 1236 (2.7705 iter/s, 4.33135s/12 iters), loss = 4.15149
I0410 00:22:07.191694 14511 solver.cpp:237]     Train net output #0: loss = 4.15149 (* 1 = 4.15149 loss)
I0410 00:22:07.191704 14511 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 00:22:12.416334 14511 solver.cpp:218] Iteration 1248 (2.29687 iter/s, 5.2245s/12 iters), loss = 3.92253
I0410 00:22:12.416379 14511 solver.cpp:237]     Train net output #0: loss = 3.92253 (* 1 = 3.92253 loss)
I0410 00:22:12.416389 14511 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 00:22:17.624708 14511 solver.cpp:218] Iteration 1260 (2.30407 iter/s, 5.20818s/12 iters), loss = 3.95049
I0410 00:22:17.624855 14511 solver.cpp:237]     Train net output #0: loss = 3.95049 (* 1 = 3.95049 loss)
I0410 00:22:17.624868 14511 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 00:22:22.882206 14511 solver.cpp:218] Iteration 1272 (2.28258 iter/s, 5.25722s/12 iters), loss = 3.94918
I0410 00:22:22.882251 14511 solver.cpp:237]     Train net output #0: loss = 3.94918 (* 1 = 3.94918 loss)
I0410 00:22:22.882259 14511 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 00:22:27.800664 14511 solver.cpp:218] Iteration 1284 (2.43988 iter/s, 4.91828s/12 iters), loss = 3.90582
I0410 00:22:27.800719 14511 solver.cpp:237]     Train net output #0: loss = 3.90582 (* 1 = 3.90582 loss)
I0410 00:22:27.800729 14511 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 00:22:33.002985 14511 solver.cpp:218] Iteration 1296 (2.30675 iter/s, 5.20213s/12 iters), loss = 3.7294
I0410 00:22:33.003031 14511 solver.cpp:237]     Train net output #0: loss = 3.7294 (* 1 = 3.7294 loss)
I0410 00:22:33.003039 14511 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 00:22:38.079396 14511 solver.cpp:218] Iteration 1308 (2.36396 iter/s, 5.07623s/12 iters), loss = 3.91022
I0410 00:22:38.079449 14511 solver.cpp:237]     Train net output #0: loss = 3.91022 (* 1 = 3.91022 loss)
I0410 00:22:38.079463 14511 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 00:22:40.600876 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:43.234899 14511 solver.cpp:218] Iteration 1320 (2.3277 iter/s, 5.15531s/12 iters), loss = 3.87166
I0410 00:22:43.234951 14511 solver.cpp:237]     Train net output #0: loss = 3.87166 (* 1 = 3.87166 loss)
I0410 00:22:43.234962 14511 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 00:22:45.328967 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 00:22:54.716133 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 00:23:04.810672 14511 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 00:23:04.810696 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:23:08.830610 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:09.448482 14511 solver.cpp:397]     Test net output #0: accuracy = 0.091299
I0410 00:23:09.448518 14511 solver.cpp:397]     Test net output #1: loss = 4.29399 (* 1 = 4.29399 loss)
I0410 00:23:11.335603 14511 solver.cpp:218] Iteration 1332 (0.427047 iter/s, 28.1s/12 iters), loss = 4.03026
I0410 00:23:11.335644 14511 solver.cpp:237]     Train net output #0: loss = 4.03026 (* 1 = 4.03026 loss)
I0410 00:23:11.335654 14511 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 00:23:15.948770 14511 solver.cpp:218] Iteration 1344 (2.60135 iter/s, 4.613s/12 iters), loss = 3.8735
I0410 00:23:15.948817 14511 solver.cpp:237]     Train net output #0: loss = 3.8735 (* 1 = 3.8735 loss)
I0410 00:23:15.948827 14511 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 00:23:20.826911 14511 solver.cpp:218] Iteration 1356 (2.46004 iter/s, 4.87796s/12 iters), loss = 3.93002
I0410 00:23:20.826961 14511 solver.cpp:237]     Train net output #0: loss = 3.93002 (* 1 = 3.93002 loss)
I0410 00:23:20.826972 14511 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 00:23:25.624133 14511 solver.cpp:218] Iteration 1368 (2.50154 iter/s, 4.79704s/12 iters), loss = 3.82627
I0410 00:23:25.624219 14511 solver.cpp:237]     Train net output #0: loss = 3.82627 (* 1 = 3.82627 loss)
I0410 00:23:25.624231 14511 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 00:23:26.770546 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:23:30.533866 14511 solver.cpp:218] Iteration 1380 (2.44423 iter/s, 4.90952s/12 iters), loss = 3.51858
I0410 00:23:30.533917 14511 solver.cpp:237]     Train net output #0: loss = 3.51858 (* 1 = 3.51858 loss)
I0410 00:23:30.533928 14511 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 00:23:35.388490 14511 solver.cpp:218] Iteration 1392 (2.47196 iter/s, 4.85444s/12 iters), loss = 3.89949
I0410 00:23:35.388540 14511 solver.cpp:237]     Train net output #0: loss = 3.89949 (* 1 = 3.89949 loss)
I0410 00:23:35.388551 14511 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 00:23:40.012905 14511 solver.cpp:218] Iteration 1404 (2.59502 iter/s, 4.62424s/12 iters), loss = 3.86651
I0410 00:23:40.012951 14511 solver.cpp:237]     Train net output #0: loss = 3.86651 (* 1 = 3.86651 loss)
I0410 00:23:40.012960 14511 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 00:23:44.358399 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:44.740846 14511 solver.cpp:218] Iteration 1416 (2.5382 iter/s, 4.72777s/12 iters), loss = 3.85767
I0410 00:23:44.740900 14511 solver.cpp:237]     Train net output #0: loss = 3.85767 (* 1 = 3.85767 loss)
I0410 00:23:44.740911 14511 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 00:23:49.269781 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 00:24:01.660099 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 00:24:09.566975 14511 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 00:24:09.567003 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:24:14.020447 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:14.625867 14511 solver.cpp:397]     Test net output #0: accuracy = 0.10049
I0410 00:24:14.625897 14511 solver.cpp:397]     Test net output #1: loss = 4.18803 (* 1 = 4.18803 loss)
I0410 00:24:14.734292 14511 solver.cpp:218] Iteration 1428 (0.400098 iter/s, 29.9927s/12 iters), loss = 4.00626
I0410 00:24:14.734334 14511 solver.cpp:237]     Train net output #0: loss = 4.00626 (* 1 = 4.00626 loss)
I0410 00:24:14.734344 14511 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 00:24:18.847885 14511 solver.cpp:218] Iteration 1440 (2.91727 iter/s, 4.11343s/12 iters), loss = 3.64825
I0410 00:24:18.847937 14511 solver.cpp:237]     Train net output #0: loss = 3.64825 (* 1 = 3.64825 loss)
I0410 00:24:18.847949 14511 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 00:24:23.572547 14511 solver.cpp:218] Iteration 1452 (2.53996 iter/s, 4.72449s/12 iters), loss = 3.79083
I0410 00:24:23.572590 14511 solver.cpp:237]     Train net output #0: loss = 3.79083 (* 1 = 3.79083 loss)
I0410 00:24:23.572602 14511 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 00:24:28.190300 14511 solver.cpp:218] Iteration 1464 (2.59876 iter/s, 4.61759s/12 iters), loss = 3.75249
I0410 00:24:28.190342 14511 solver.cpp:237]     Train net output #0: loss = 3.75249 (* 1 = 3.75249 loss)
I0410 00:24:28.190352 14511 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 00:24:33.340973 14511 solver.cpp:218] Iteration 1476 (2.32987 iter/s, 5.1505s/12 iters), loss = 3.59671
I0410 00:24:33.341080 14511 solver.cpp:237]     Train net output #0: loss = 3.59671 (* 1 = 3.59671 loss)
I0410 00:24:33.341094 14511 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 00:24:38.508966 14511 solver.cpp:218] Iteration 1488 (2.32209 iter/s, 5.16775s/12 iters), loss = 3.45534
I0410 00:24:38.509014 14511 solver.cpp:237]     Train net output #0: loss = 3.45534 (* 1 = 3.45534 loss)
I0410 00:24:38.509027 14511 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 00:24:43.478807 14511 solver.cpp:218] Iteration 1500 (2.41465 iter/s, 4.96966s/12 iters), loss = 3.5312
I0410 00:24:43.478847 14511 solver.cpp:237]     Train net output #0: loss = 3.5312 (* 1 = 3.5312 loss)
I0410 00:24:43.478857 14511 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 00:24:48.346525 14511 solver.cpp:218] Iteration 1512 (2.46531 iter/s, 4.86755s/12 iters), loss = 3.64331
I0410 00:24:48.346563 14511 solver.cpp:237]     Train net output #0: loss = 3.64331 (* 1 = 3.64331 loss)
I0410 00:24:48.346572 14511 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 00:24:50.081416 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:53.482918 14511 solver.cpp:218] Iteration 1524 (2.33635 iter/s, 5.13621s/12 iters), loss = 3.66501
I0410 00:24:53.482969 14511 solver.cpp:237]     Train net output #0: loss = 3.66501 (* 1 = 3.66501 loss)
I0410 00:24:53.482980 14511 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 00:24:55.574535 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 00:25:03.053740 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 00:25:13.882997 14511 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 00:25:13.883101 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:25:17.673904 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:18.318006 14511 solver.cpp:397]     Test net output #0: accuracy = 0.140931
I0410 00:25:18.318054 14511 solver.cpp:397]     Test net output #1: loss = 3.90046 (* 1 = 3.90046 loss)
I0410 00:25:19.971253 14511 solver.cpp:218] Iteration 1536 (0.453042 iter/s, 26.4876s/12 iters), loss = 3.7037
I0410 00:25:19.971299 14511 solver.cpp:237]     Train net output #0: loss = 3.7037 (* 1 = 3.7037 loss)
I0410 00:25:19.971308 14511 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 00:25:24.611570 14511 solver.cpp:218] Iteration 1548 (2.58613 iter/s, 4.64014s/12 iters), loss = 3.09241
I0410 00:25:24.611618 14511 solver.cpp:237]     Train net output #0: loss = 3.09241 (* 1 = 3.09241 loss)
I0410 00:25:24.611629 14511 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 00:25:29.487468 14511 solver.cpp:218] Iteration 1560 (2.46117 iter/s, 4.87572s/12 iters), loss = 3.48875
I0410 00:25:29.487511 14511 solver.cpp:237]     Train net output #0: loss = 3.48875 (* 1 = 3.48875 loss)
I0410 00:25:29.487521 14511 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 00:25:34.173496 14511 solver.cpp:218] Iteration 1572 (2.5609 iter/s, 4.68585s/12 iters), loss = 3.46566
I0410 00:25:34.173542 14511 solver.cpp:237]     Train net output #0: loss = 3.46566 (* 1 = 3.46566 loss)
I0410 00:25:34.173552 14511 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 00:25:39.216675 14511 solver.cpp:218] Iteration 1584 (2.37954 iter/s, 5.043s/12 iters), loss = 3.28136
I0410 00:25:39.216720 14511 solver.cpp:237]     Train net output #0: loss = 3.28136 (* 1 = 3.28136 loss)
I0410 00:25:39.216728 14511 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 00:25:44.224460 14511 solver.cpp:218] Iteration 1596 (2.39636 iter/s, 5.0076s/12 iters), loss = 3.59233
I0410 00:25:44.224577 14511 solver.cpp:237]     Train net output #0: loss = 3.59233 (* 1 = 3.59233 loss)
I0410 00:25:44.224587 14511 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 00:25:49.196662 14511 solver.cpp:218] Iteration 1608 (2.41354 iter/s, 4.97195s/12 iters), loss = 3.40847
I0410 00:25:49.196707 14511 solver.cpp:237]     Train net output #0: loss = 3.40847 (* 1 = 3.40847 loss)
I0410 00:25:49.196717 14511 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 00:25:53.065603 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:54.226302 14511 solver.cpp:218] Iteration 1620 (2.38594 iter/s, 5.02946s/12 iters), loss = 3.28963
I0410 00:25:54.226358 14511 solver.cpp:237]     Train net output #0: loss = 3.28963 (* 1 = 3.28963 loss)
I0410 00:25:54.226370 14511 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 00:25:58.909273 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 00:26:13.436741 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 00:26:22.480901 14511 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 00:26:22.480947 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:26:26.480782 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:27.452248 14511 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0410 00:26:27.452287 14511 solver.cpp:397]     Test net output #1: loss = 3.83946 (* 1 = 3.83946 loss)
I0410 00:26:27.563773 14511 solver.cpp:218] Iteration 1632 (0.359965 iter/s, 33.3366s/12 iters), loss = 3.40056
I0410 00:26:27.563817 14511 solver.cpp:237]     Train net output #0: loss = 3.40056 (* 1 = 3.40056 loss)
I0410 00:26:27.563825 14511 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 00:26:31.947242 14511 solver.cpp:218] Iteration 1644 (2.73766 iter/s, 4.3833s/12 iters), loss = 3.46363
I0410 00:26:31.947290 14511 solver.cpp:237]     Train net output #0: loss = 3.46363 (* 1 = 3.46363 loss)
I0410 00:26:31.947300 14511 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 00:26:36.870270 14511 solver.cpp:218] Iteration 1656 (2.43762 iter/s, 4.92284s/12 iters), loss = 3.25553
I0410 00:26:36.870313 14511 solver.cpp:237]     Train net output #0: loss = 3.25553 (* 1 = 3.25553 loss)
I0410 00:26:36.870322 14511 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 00:26:41.966665 14511 solver.cpp:218] Iteration 1668 (2.35469 iter/s, 5.09621s/12 iters), loss = 3.21972
I0410 00:26:41.966706 14511 solver.cpp:237]     Train net output #0: loss = 3.21972 (* 1 = 3.21972 loss)
I0410 00:26:41.966715 14511 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 00:26:47.217267 14511 solver.cpp:218] Iteration 1680 (2.28553 iter/s, 5.25041s/12 iters), loss = 3.28224
I0410 00:26:47.217314 14511 solver.cpp:237]     Train net output #0: loss = 3.28224 (* 1 = 3.28224 loss)
I0410 00:26:47.217325 14511 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 00:26:52.419893 14511 solver.cpp:218] Iteration 1692 (2.30661 iter/s, 5.20244s/12 iters), loss = 3.28963
I0410 00:26:52.419934 14511 solver.cpp:237]     Train net output #0: loss = 3.28963 (* 1 = 3.28963 loss)
I0410 00:26:52.419945 14511 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 00:26:57.222834 14511 solver.cpp:218] Iteration 1704 (2.49856 iter/s, 4.80277s/12 iters), loss = 3.24211
I0410 00:26:57.222952 14511 solver.cpp:237]     Train net output #0: loss = 3.24211 (* 1 = 3.24211 loss)
I0410 00:26:57.222963 14511 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 00:27:02.385406 14511 solver.cpp:218] Iteration 1716 (2.32454 iter/s, 5.16231s/12 iters), loss = 3.14483
I0410 00:27:02.385455 14511 solver.cpp:237]     Train net output #0: loss = 3.14483 (* 1 = 3.14483 loss)
I0410 00:27:02.385465 14511 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 00:27:03.460590 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:07.666548 14511 solver.cpp:218] Iteration 1728 (2.27232 iter/s, 5.28095s/12 iters), loss = 2.98269
I0410 00:27:07.666590 14511 solver.cpp:237]     Train net output #0: loss = 2.98269 (* 1 = 2.98269 loss)
I0410 00:27:07.666600 14511 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 00:27:09.796901 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 00:27:18.710175 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 00:27:28.300339 14511 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 00:27:28.300390 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:27:32.133229 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:32.846565 14511 solver.cpp:397]     Test net output #0: accuracy = 0.161152
I0410 00:27:32.846613 14511 solver.cpp:397]     Test net output #1: loss = 3.77563 (* 1 = 3.77563 loss)
I0410 00:27:34.547262 14511 solver.cpp:218] Iteration 1740 (0.446428 iter/s, 26.88s/12 iters), loss = 3.47182
I0410 00:27:34.547307 14511 solver.cpp:237]     Train net output #0: loss = 3.47182 (* 1 = 3.47182 loss)
I0410 00:27:34.547317 14511 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 00:27:39.643163 14511 solver.cpp:218] Iteration 1752 (2.35492 iter/s, 5.09572s/12 iters), loss = 3.04672
I0410 00:27:39.643214 14511 solver.cpp:237]     Train net output #0: loss = 3.04672 (* 1 = 3.04672 loss)
I0410 00:27:39.643224 14511 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 00:27:44.630196 14511 solver.cpp:218] Iteration 1764 (2.40633 iter/s, 4.98685s/12 iters), loss = 3.25997
I0410 00:27:44.630244 14511 solver.cpp:237]     Train net output #0: loss = 3.25997 (* 1 = 3.25997 loss)
I0410 00:27:44.630255 14511 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 00:27:49.693042 14511 solver.cpp:218] Iteration 1776 (2.37029 iter/s, 5.06266s/12 iters), loss = 3.09318
I0410 00:27:49.693090 14511 solver.cpp:237]     Train net output #0: loss = 3.09318 (* 1 = 3.09318 loss)
I0410 00:27:49.693100 14511 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 00:27:54.892406 14511 solver.cpp:218] Iteration 1788 (2.30806 iter/s, 5.19918s/12 iters), loss = 2.79192
I0410 00:27:54.892447 14511 solver.cpp:237]     Train net output #0: loss = 2.79192 (* 1 = 2.79192 loss)
I0410 00:27:54.892457 14511 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 00:28:00.160521 14511 solver.cpp:218] Iteration 1800 (2.27794 iter/s, 5.26792s/12 iters), loss = 3.19198
I0410 00:28:00.160712 14511 solver.cpp:237]     Train net output #0: loss = 3.19198 (* 1 = 3.19198 loss)
I0410 00:28:00.160732 14511 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 00:28:05.373816 14511 solver.cpp:218] Iteration 1812 (2.30195 iter/s, 5.21297s/12 iters), loss = 3.11702
I0410 00:28:05.373869 14511 solver.cpp:237]     Train net output #0: loss = 3.11702 (* 1 = 3.11702 loss)
I0410 00:28:05.373880 14511 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 00:28:08.686158 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:10.620287 14511 solver.cpp:218] Iteration 1824 (2.28734 iter/s, 5.24628s/12 iters), loss = 3.35278
I0410 00:28:10.620339 14511 solver.cpp:237]     Train net output #0: loss = 3.35278 (* 1 = 3.35278 loss)
I0410 00:28:10.620352 14511 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 00:28:15.167711 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 00:28:26.446626 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 00:28:32.270356 14511 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 00:28:32.270412 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:28:36.020231 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:36.779811 14511 solver.cpp:397]     Test net output #0: accuracy = 0.174632
I0410 00:28:36.779850 14511 solver.cpp:397]     Test net output #1: loss = 3.64853 (* 1 = 3.64853 loss)
I0410 00:28:36.888393 14511 solver.cpp:218] Iteration 1836 (0.45684 iter/s, 26.2674s/12 iters), loss = 3.02181
I0410 00:28:36.888447 14511 solver.cpp:237]     Train net output #0: loss = 3.02181 (* 1 = 3.02181 loss)
I0410 00:28:36.888458 14511 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 00:28:41.084616 14511 solver.cpp:218] Iteration 1848 (2.85983 iter/s, 4.19605s/12 iters), loss = 2.97158
I0410 00:28:41.084654 14511 solver.cpp:237]     Train net output #0: loss = 2.97158 (* 1 = 2.97158 loss)
I0410 00:28:41.084662 14511 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 00:28:46.275104 14511 solver.cpp:218] Iteration 1860 (2.312 iter/s, 5.19031s/12 iters), loss = 2.97939
I0410 00:28:46.275147 14511 solver.cpp:237]     Train net output #0: loss = 2.97939 (* 1 = 2.97939 loss)
I0410 00:28:46.275157 14511 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 00:28:51.544878 14511 solver.cpp:218] Iteration 1872 (2.27722 iter/s, 5.26959s/12 iters), loss = 2.6049
I0410 00:28:51.544929 14511 solver.cpp:237]     Train net output #0: loss = 2.6049 (* 1 = 2.6049 loss)
I0410 00:28:51.544939 14511 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 00:28:56.473232 14511 solver.cpp:218] Iteration 1884 (2.43498 iter/s, 4.92817s/12 iters), loss = 3.06211
I0410 00:28:56.473281 14511 solver.cpp:237]     Train net output #0: loss = 3.06211 (* 1 = 3.06211 loss)
I0410 00:28:56.473292 14511 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 00:29:01.527447 14511 solver.cpp:218] Iteration 1896 (2.37434 iter/s, 5.05403s/12 iters), loss = 3.18665
I0410 00:29:01.527499 14511 solver.cpp:237]     Train net output #0: loss = 3.18665 (* 1 = 3.18665 loss)
I0410 00:29:01.527511 14511 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 00:29:06.659179 14511 solver.cpp:218] Iteration 1908 (2.33848 iter/s, 5.13154s/12 iters), loss = 2.99268
I0410 00:29:06.659312 14511 solver.cpp:237]     Train net output #0: loss = 2.99268 (* 1 = 2.99268 loss)
I0410 00:29:06.659324 14511 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 00:29:11.720924 14511 solver.cpp:218] Iteration 1920 (2.37085 iter/s, 5.06148s/12 iters), loss = 2.7867
I0410 00:29:11.720970 14511 solver.cpp:237]     Train net output #0: loss = 2.7867 (* 1 = 2.7867 loss)
I0410 00:29:11.720983 14511 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 00:29:12.041446 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:16.952733 14511 solver.cpp:218] Iteration 1932 (2.29374 iter/s, 5.23163s/12 iters), loss = 2.96952
I0410 00:29:16.952775 14511 solver.cpp:237]     Train net output #0: loss = 2.96952 (* 1 = 2.96952 loss)
I0410 00:29:16.952783 14511 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 00:29:19.095571 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 00:29:30.715055 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 00:29:38.875211 14511 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 00:29:38.875277 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:29:42.555801 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:43.354562 14511 solver.cpp:397]     Test net output #0: accuracy = 0.193015
I0410 00:29:43.354609 14511 solver.cpp:397]     Test net output #1: loss = 3.59164 (* 1 = 3.59164 loss)
I0410 00:29:45.017750 14511 solver.cpp:218] Iteration 1944 (0.42759 iter/s, 28.0643s/12 iters), loss = 2.73005
I0410 00:29:45.017796 14511 solver.cpp:237]     Train net output #0: loss = 2.73005 (* 1 = 2.73005 loss)
I0410 00:29:45.017807 14511 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 00:29:49.847117 14511 solver.cpp:218] Iteration 1956 (2.48489 iter/s, 4.82919s/12 iters), loss = 2.83072
I0410 00:29:49.847168 14511 solver.cpp:237]     Train net output #0: loss = 2.83072 (* 1 = 2.83072 loss)
I0410 00:29:49.847180 14511 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 00:29:54.758958 14511 solver.cpp:218] Iteration 1968 (2.44316 iter/s, 4.91166s/12 iters), loss = 2.96525
I0410 00:29:54.759001 14511 solver.cpp:237]     Train net output #0: loss = 2.96525 (* 1 = 2.96525 loss)
I0410 00:29:54.759009 14511 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 00:29:59.646154 14511 solver.cpp:218] Iteration 1980 (2.45548 iter/s, 4.88702s/12 iters), loss = 3.11083
I0410 00:29:59.646205 14511 solver.cpp:237]     Train net output #0: loss = 3.11083 (* 1 = 3.11083 loss)
I0410 00:29:59.646217 14511 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 00:30:04.887909 14511 solver.cpp:218] Iteration 1992 (2.28939 iter/s, 5.24157s/12 iters), loss = 2.89238
I0410 00:30:04.887951 14511 solver.cpp:237]     Train net output #0: loss = 2.89238 (* 1 = 2.89238 loss)
I0410 00:30:04.887962 14511 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 00:30:09.964500 14511 solver.cpp:218] Iteration 2004 (2.36387 iter/s, 5.07642s/12 iters), loss = 2.55876
I0410 00:30:09.964596 14511 solver.cpp:237]     Train net output #0: loss = 2.55876 (* 1 = 2.55876 loss)
I0410 00:30:09.964607 14511 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 00:30:15.023581 14511 solver.cpp:218] Iteration 2016 (2.37208 iter/s, 5.05886s/12 iters), loss = 2.81753
I0410 00:30:15.023622 14511 solver.cpp:237]     Train net output #0: loss = 2.81753 (* 1 = 2.81753 loss)
I0410 00:30:15.023633 14511 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 00:30:17.450747 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:20.098134 14511 solver.cpp:218] Iteration 2028 (2.36482 iter/s, 5.07438s/12 iters), loss = 2.51636
I0410 00:30:20.098178 14511 solver.cpp:237]     Train net output #0: loss = 2.51636 (* 1 = 2.51636 loss)
I0410 00:30:20.098191 14511 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 00:30:24.481555 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 00:30:33.312016 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 00:30:43.481442 14511 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 00:30:43.481549 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:30:47.648346 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:48.520622 14511 solver.cpp:397]     Test net output #0: accuracy = 0.178922
I0410 00:30:48.520654 14511 solver.cpp:397]     Test net output #1: loss = 3.6478 (* 1 = 3.6478 loss)
I0410 00:30:48.628877 14511 solver.cpp:218] Iteration 2040 (0.42061 iter/s, 28.53s/12 iters), loss = 2.88066
I0410 00:30:48.628919 14511 solver.cpp:237]     Train net output #0: loss = 2.88066 (* 1 = 2.88066 loss)
I0410 00:30:48.628927 14511 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 00:30:52.883312 14511 solver.cpp:218] Iteration 2052 (2.82069 iter/s, 4.25427s/12 iters), loss = 2.62328
I0410 00:30:52.883360 14511 solver.cpp:237]     Train net output #0: loss = 2.62328 (* 1 = 2.62328 loss)
I0410 00:30:52.883371 14511 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 00:30:54.540691 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:30:57.921741 14511 solver.cpp:218] Iteration 2064 (2.38178 iter/s, 5.03825s/12 iters), loss = 2.54915
I0410 00:30:57.921779 14511 solver.cpp:237]     Train net output #0: loss = 2.54915 (* 1 = 2.54915 loss)
I0410 00:30:57.921787 14511 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 00:31:02.509939 14511 solver.cpp:218] Iteration 2076 (2.6155 iter/s, 4.58803s/12 iters), loss = 2.77827
I0410 00:31:02.509994 14511 solver.cpp:237]     Train net output #0: loss = 2.77827 (* 1 = 2.77827 loss)
I0410 00:31:02.510004 14511 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 00:31:07.175863 14511 solver.cpp:218] Iteration 2088 (2.57194 iter/s, 4.66574s/12 iters), loss = 2.79714
I0410 00:31:07.175911 14511 solver.cpp:237]     Train net output #0: loss = 2.79714 (* 1 = 2.79714 loss)
I0410 00:31:07.175922 14511 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 00:31:11.921583 14511 solver.cpp:218] Iteration 2100 (2.52869 iter/s, 4.74554s/12 iters), loss = 2.64018
I0410 00:31:11.921639 14511 solver.cpp:237]     Train net output #0: loss = 2.64018 (* 1 = 2.64018 loss)
I0410 00:31:11.921650 14511 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 00:31:16.618554 14511 solver.cpp:218] Iteration 2112 (2.55493 iter/s, 4.69679s/12 iters), loss = 2.83705
I0410 00:31:16.618687 14511 solver.cpp:237]     Train net output #0: loss = 2.83705 (* 1 = 2.83705 loss)
I0410 00:31:16.618700 14511 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 00:31:21.057394 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:21.415290 14511 solver.cpp:218] Iteration 2124 (2.50184 iter/s, 4.79648s/12 iters), loss = 2.37966
I0410 00:31:21.415341 14511 solver.cpp:237]     Train net output #0: loss = 2.37966 (* 1 = 2.37966 loss)
I0410 00:31:21.415354 14511 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 00:31:26.399075 14511 solver.cpp:218] Iteration 2136 (2.4079 iter/s, 4.9836s/12 iters), loss = 2.75234
I0410 00:31:26.399127 14511 solver.cpp:237]     Train net output #0: loss = 2.75234 (* 1 = 2.75234 loss)
I0410 00:31:26.399138 14511 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 00:31:28.350091 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 00:31:40.274344 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 00:31:46.093618 14511 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 00:31:46.093641 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:31:50.124123 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:50.995126 14511 solver.cpp:397]     Test net output #0: accuracy = 0.188113
I0410 00:31:50.995177 14511 solver.cpp:397]     Test net output #1: loss = 3.62989 (* 1 = 3.62989 loss)
I0410 00:31:52.857651 14511 solver.cpp:218] Iteration 2148 (0.453551 iter/s, 26.4579s/12 iters), loss = 2.56533
I0410 00:31:52.857689 14511 solver.cpp:237]     Train net output #0: loss = 2.56533 (* 1 = 2.56533 loss)
I0410 00:31:52.857698 14511 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 00:31:57.640026 14511 solver.cpp:218] Iteration 2160 (2.5093 iter/s, 4.7822s/12 iters), loss = 2.83788
I0410 00:31:57.640079 14511 solver.cpp:237]     Train net output #0: loss = 2.83788 (* 1 = 2.83788 loss)
I0410 00:31:57.640091 14511 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 00:32:02.120029 14511 solver.cpp:218] Iteration 2172 (2.67867 iter/s, 4.47983s/12 iters), loss = 2.62709
I0410 00:32:02.120083 14511 solver.cpp:237]     Train net output #0: loss = 2.62709 (* 1 = 2.62709 loss)
I0410 00:32:02.120095 14511 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 00:32:06.615368 14511 solver.cpp:218] Iteration 2184 (2.66954 iter/s, 4.49516s/12 iters), loss = 2.63669
I0410 00:32:06.615424 14511 solver.cpp:237]     Train net output #0: loss = 2.63669 (* 1 = 2.63669 loss)
I0410 00:32:06.615437 14511 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 00:32:11.321494 14511 solver.cpp:218] Iteration 2196 (2.54997 iter/s, 4.70594s/12 iters), loss = 2.40736
I0410 00:32:11.321545 14511 solver.cpp:237]     Train net output #0: loss = 2.40736 (* 1 = 2.40736 loss)
I0410 00:32:11.321557 14511 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 00:32:16.393005 14511 solver.cpp:218] Iteration 2208 (2.36625 iter/s, 5.07132s/12 iters), loss = 2.36214
I0410 00:32:16.393059 14511 solver.cpp:237]     Train net output #0: loss = 2.36214 (* 1 = 2.36214 loss)
I0410 00:32:16.393071 14511 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 00:32:21.547013 14511 solver.cpp:218] Iteration 2220 (2.32837 iter/s, 5.15382s/12 iters), loss = 2.26924
I0410 00:32:21.547143 14511 solver.cpp:237]     Train net output #0: loss = 2.26924 (* 1 = 2.26924 loss)
I0410 00:32:21.547154 14511 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 00:32:23.383816 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:26.741636 14511 solver.cpp:218] Iteration 2232 (2.3102 iter/s, 5.19435s/12 iters), loss = 2.28603
I0410 00:32:26.741689 14511 solver.cpp:237]     Train net output #0: loss = 2.28603 (* 1 = 2.28603 loss)
I0410 00:32:26.741701 14511 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 00:32:31.346985 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 00:32:41.337970 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 00:32:53.473875 14511 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 00:32:53.473930 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:32:57.071566 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:58.011291 14511 solver.cpp:397]     Test net output #0: accuracy = 0.196078
I0410 00:32:58.011332 14511 solver.cpp:397]     Test net output #1: loss = 3.58305 (* 1 = 3.58305 loss)
I0410 00:32:58.119681 14511 solver.cpp:218] Iteration 2244 (0.382443 iter/s, 31.3772s/12 iters), loss = 2.63244
I0410 00:32:58.119727 14511 solver.cpp:237]     Train net output #0: loss = 2.63244 (* 1 = 2.63244 loss)
I0410 00:32:58.119737 14511 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 00:33:02.348291 14511 solver.cpp:218] Iteration 2256 (2.83793 iter/s, 4.22844s/12 iters), loss = 2.25121
I0410 00:33:02.348336 14511 solver.cpp:237]     Train net output #0: loss = 2.25121 (* 1 = 2.25121 loss)
I0410 00:33:02.348346 14511 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 00:33:07.342412 14511 solver.cpp:218] Iteration 2268 (2.40291 iter/s, 4.99394s/12 iters), loss = 2.4051
I0410 00:33:07.342451 14511 solver.cpp:237]     Train net output #0: loss = 2.4051 (* 1 = 2.4051 loss)
I0410 00:33:07.342459 14511 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 00:33:12.424477 14511 solver.cpp:218] Iteration 2280 (2.36133 iter/s, 5.08188s/12 iters), loss = 2.35334
I0410 00:33:12.424521 14511 solver.cpp:237]     Train net output #0: loss = 2.35334 (* 1 = 2.35334 loss)
I0410 00:33:12.424530 14511 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 00:33:17.697729 14511 solver.cpp:218] Iteration 2292 (2.27572 iter/s, 5.27306s/12 iters), loss = 2.3977
I0410 00:33:17.697782 14511 solver.cpp:237]     Train net output #0: loss = 2.3977 (* 1 = 2.3977 loss)
I0410 00:33:17.697793 14511 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 00:33:22.828639 14511 solver.cpp:218] Iteration 2304 (2.33885 iter/s, 5.13072s/12 iters), loss = 2.28605
I0410 00:33:22.828681 14511 solver.cpp:237]     Train net output #0: loss = 2.28605 (* 1 = 2.28605 loss)
I0410 00:33:22.828691 14511 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 00:33:27.456099 14511 solver.cpp:218] Iteration 2316 (2.59331 iter/s, 4.62729s/12 iters), loss = 2.16011
I0410 00:33:27.456250 14511 solver.cpp:237]     Train net output #0: loss = 2.16011 (* 1 = 2.16011 loss)
I0410 00:33:27.456264 14511 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 00:33:31.008404 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:32.050860 14511 solver.cpp:218] Iteration 2328 (2.61182 iter/s, 4.59449s/12 iters), loss = 2.21762
I0410 00:33:32.050911 14511 solver.cpp:237]     Train net output #0: loss = 2.21762 (* 1 = 2.21762 loss)
I0410 00:33:32.050922 14511 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 00:33:36.753759 14511 solver.cpp:218] Iteration 2340 (2.55171 iter/s, 4.70272s/12 iters), loss = 2.30445
I0410 00:33:36.753808 14511 solver.cpp:237]     Train net output #0: loss = 2.30445 (* 1 = 2.30445 loss)
I0410 00:33:36.753820 14511 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 00:33:38.721024 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 00:33:49.471930 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 00:33:59.254256 14511 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 00:33:59.254312 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:34:02.854872 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:03.832803 14511 solver.cpp:397]     Test net output #0: accuracy = 0.204657
I0410 00:34:03.832850 14511 solver.cpp:397]     Test net output #1: loss = 3.56507 (* 1 = 3.56507 loss)
I0410 00:34:05.701467 14511 solver.cpp:218] Iteration 2352 (0.414551 iter/s, 28.9469s/12 iters), loss = 2.37099
I0410 00:34:05.701514 14511 solver.cpp:237]     Train net output #0: loss = 2.37099 (* 1 = 2.37099 loss)
I0410 00:34:05.701522 14511 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 00:34:10.722208 14511 solver.cpp:218] Iteration 2364 (2.39017 iter/s, 5.02056s/12 iters), loss = 2.16107
I0410 00:34:10.722257 14511 solver.cpp:237]     Train net output #0: loss = 2.16107 (* 1 = 2.16107 loss)
I0410 00:34:10.722268 14511 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 00:34:15.990257 14511 solver.cpp:218] Iteration 2376 (2.27797 iter/s, 5.26786s/12 iters), loss = 2.15704
I0410 00:34:15.990306 14511 solver.cpp:237]     Train net output #0: loss = 2.15704 (* 1 = 2.15704 loss)
I0410 00:34:15.990317 14511 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 00:34:21.170838 14511 solver.cpp:218] Iteration 2388 (2.31643 iter/s, 5.18039s/12 iters), loss = 2.41114
I0410 00:34:21.170883 14511 solver.cpp:237]     Train net output #0: loss = 2.41114 (* 1 = 2.41114 loss)
I0410 00:34:21.170893 14511 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 00:34:26.383487 14511 solver.cpp:218] Iteration 2400 (2.30218 iter/s, 5.21246s/12 iters), loss = 2.08963
I0410 00:34:26.383545 14511 solver.cpp:237]     Train net output #0: loss = 2.08963 (* 1 = 2.08963 loss)
I0410 00:34:26.383560 14511 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 00:34:31.557930 14511 solver.cpp:218] Iteration 2412 (2.31918 iter/s, 5.17424s/12 iters), loss = 2.08303
I0410 00:34:31.558019 14511 solver.cpp:237]     Train net output #0: loss = 2.08303 (* 1 = 2.08303 loss)
I0410 00:34:31.558033 14511 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 00:34:36.464360 14511 solver.cpp:218] Iteration 2424 (2.44588 iter/s, 4.90621s/12 iters), loss = 2.54728
I0410 00:34:36.464404 14511 solver.cpp:237]     Train net output #0: loss = 2.54728 (* 1 = 2.54728 loss)
I0410 00:34:36.464413 14511 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 00:34:37.586226 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:41.701109 14511 solver.cpp:218] Iteration 2436 (2.29158 iter/s, 5.23656s/12 iters), loss = 2.42196
I0410 00:34:41.701162 14511 solver.cpp:237]     Train net output #0: loss = 2.42196 (* 1 = 2.42196 loss)
I0410 00:34:41.701174 14511 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 00:34:46.174513 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 00:34:57.297449 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 00:35:03.185209 14511 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 00:35:03.185312 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:35:06.716907 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:07.787744 14511 solver.cpp:397]     Test net output #0: accuracy = 0.219975
I0410 00:35:07.787786 14511 solver.cpp:397]     Test net output #1: loss = 3.4508 (* 1 = 3.4508 loss)
I0410 00:35:07.895857 14511 solver.cpp:218] Iteration 2448 (0.458119 iter/s, 26.1941s/12 iters), loss = 2.65183
I0410 00:35:07.895905 14511 solver.cpp:237]     Train net output #0: loss = 2.65183 (* 1 = 2.65183 loss)
I0410 00:35:07.895915 14511 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 00:35:11.912283 14511 solver.cpp:218] Iteration 2460 (2.98785 iter/s, 4.01627s/12 iters), loss = 1.88264
I0410 00:35:11.912324 14511 solver.cpp:237]     Train net output #0: loss = 1.88264 (* 1 = 1.88264 loss)
I0410 00:35:11.912335 14511 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 00:35:16.644805 14511 solver.cpp:218] Iteration 2472 (2.53574 iter/s, 4.73235s/12 iters), loss = 1.77665
I0410 00:35:16.644851 14511 solver.cpp:237]     Train net output #0: loss = 1.77665 (* 1 = 1.77665 loss)
I0410 00:35:16.644861 14511 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 00:35:21.231197 14511 solver.cpp:218] Iteration 2484 (2.61653 iter/s, 4.58622s/12 iters), loss = 2.12574
I0410 00:35:21.231249 14511 solver.cpp:237]     Train net output #0: loss = 2.12574 (* 1 = 2.12574 loss)
I0410 00:35:21.231261 14511 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 00:35:25.874461 14511 solver.cpp:218] Iteration 2496 (2.58449 iter/s, 4.64308s/12 iters), loss = 2.28908
I0410 00:35:25.874514 14511 solver.cpp:237]     Train net output #0: loss = 2.28908 (* 1 = 2.28908 loss)
I0410 00:35:25.874526 14511 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 00:35:30.752430 14511 solver.cpp:218] Iteration 2508 (2.46013 iter/s, 4.87779s/12 iters), loss = 2.11119
I0410 00:35:30.752471 14511 solver.cpp:237]     Train net output #0: loss = 2.11119 (* 1 = 2.11119 loss)
I0410 00:35:30.752480 14511 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 00:35:35.518419 14511 solver.cpp:218] Iteration 2520 (2.51793 iter/s, 4.76582s/12 iters), loss = 1.77512
I0410 00:35:35.518533 14511 solver.cpp:237]     Train net output #0: loss = 1.77512 (* 1 = 1.77512 loss)
I0410 00:35:35.518546 14511 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 00:35:38.612287 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:40.463903 14511 solver.cpp:218] Iteration 2532 (2.42658 iter/s, 4.94524s/12 iters), loss = 2.1772
I0410 00:35:40.463956 14511 solver.cpp:237]     Train net output #0: loss = 2.1772 (* 1 = 2.1772 loss)
I0410 00:35:40.463969 14511 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 00:35:45.806545 14511 solver.cpp:218] Iteration 2544 (2.24616 iter/s, 5.34245s/12 iters), loss = 2.14314
I0410 00:35:45.806596 14511 solver.cpp:237]     Train net output #0: loss = 2.14314 (* 1 = 2.14314 loss)
I0410 00:35:45.806607 14511 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 00:35:47.862460 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 00:35:55.227798 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 00:36:01.046224 14511 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 00:36:01.046247 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:36:04.590263 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:05.624346 14511 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0410 00:36:05.627043 14511 solver.cpp:397]     Test net output #1: loss = 3.44908 (* 1 = 3.44908 loss)
I0410 00:36:07.498095 14511 solver.cpp:218] Iteration 2556 (0.553226 iter/s, 21.691s/12 iters), loss = 2.20623
I0410 00:36:07.498147 14511 solver.cpp:237]     Train net output #0: loss = 2.20623 (* 1 = 2.20623 loss)
I0410 00:36:07.498157 14511 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 00:36:12.648126 14511 solver.cpp:218] Iteration 2568 (2.33017 iter/s, 5.14984s/12 iters), loss = 1.81627
I0410 00:36:12.648180 14511 solver.cpp:237]     Train net output #0: loss = 1.81627 (* 1 = 1.81627 loss)
I0410 00:36:12.648190 14511 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 00:36:17.503538 14511 solver.cpp:218] Iteration 2580 (2.47156 iter/s, 4.85523s/12 iters), loss = 1.87377
I0410 00:36:17.503584 14511 solver.cpp:237]     Train net output #0: loss = 1.87377 (* 1 = 1.87377 loss)
I0410 00:36:17.503594 14511 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 00:36:22.150074 14511 solver.cpp:218] Iteration 2592 (2.58266 iter/s, 4.64637s/12 iters), loss = 2.30814
I0410 00:36:22.150115 14511 solver.cpp:237]     Train net output #0: loss = 2.30814 (* 1 = 2.30814 loss)
I0410 00:36:22.150125 14511 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 00:36:26.879393 14511 solver.cpp:218] Iteration 2604 (2.53745 iter/s, 4.72915s/12 iters), loss = 2.03813
I0410 00:36:26.879434 14511 solver.cpp:237]     Train net output #0: loss = 2.03813 (* 1 = 2.03813 loss)
I0410 00:36:26.879442 14511 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 00:36:31.919277 14511 solver.cpp:218] Iteration 2616 (2.38109 iter/s, 5.03971s/12 iters), loss = 1.76524
I0410 00:36:31.919324 14511 solver.cpp:237]     Train net output #0: loss = 1.76524 (* 1 = 1.76524 loss)
I0410 00:36:31.919337 14511 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 00:36:37.135329 14511 solver.cpp:218] Iteration 2628 (2.30067 iter/s, 5.21586s/12 iters), loss = 1.92612
I0410 00:36:37.135442 14511 solver.cpp:237]     Train net output #0: loss = 1.92612 (* 1 = 1.92612 loss)
I0410 00:36:37.135453 14511 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 00:36:37.467398 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:41.879689 14511 solver.cpp:218] Iteration 2640 (2.52945 iter/s, 4.74412s/12 iters), loss = 2.27435
I0410 00:36:41.879738 14511 solver.cpp:237]     Train net output #0: loss = 2.27435 (* 1 = 2.27435 loss)
I0410 00:36:41.879750 14511 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 00:36:46.564349 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 00:36:54.429549 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 00:37:03.133389 14511 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 00:37:03.133412 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:37:06.575675 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:07.741902 14511 solver.cpp:397]     Test net output #0: accuracy = 0.240809
I0410 00:37:07.742015 14511 solver.cpp:397]     Test net output #1: loss = 3.59642 (* 1 = 3.59642 loss)
I0410 00:37:07.850533 14511 solver.cpp:218] Iteration 2652 (0.462069 iter/s, 25.9702s/12 iters), loss = 2.2366
I0410 00:37:07.850576 14511 solver.cpp:237]     Train net output #0: loss = 2.2366 (* 1 = 2.2366 loss)
I0410 00:37:07.850586 14511 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 00:37:12.271524 14511 solver.cpp:218] Iteration 2664 (2.71443 iter/s, 4.42082s/12 iters), loss = 1.78639
I0410 00:37:12.271569 14511 solver.cpp:237]     Train net output #0: loss = 1.78639 (* 1 = 1.78639 loss)
I0410 00:37:12.271577 14511 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 00:37:17.123056 14511 solver.cpp:218] Iteration 2676 (2.47353 iter/s, 4.85136s/12 iters), loss = 1.67378
I0410 00:37:17.123095 14511 solver.cpp:237]     Train net output #0: loss = 1.67378 (* 1 = 1.67378 loss)
I0410 00:37:17.123102 14511 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 00:37:22.295806 14511 solver.cpp:218] Iteration 2688 (2.31993 iter/s, 5.17258s/12 iters), loss = 1.73677
I0410 00:37:22.295850 14511 solver.cpp:237]     Train net output #0: loss = 1.73677 (* 1 = 1.73677 loss)
I0410 00:37:22.295859 14511 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 00:37:27.527176 14511 solver.cpp:218] Iteration 2700 (2.29393 iter/s, 5.23119s/12 iters), loss = 1.61768
I0410 00:37:27.527217 14511 solver.cpp:237]     Train net output #0: loss = 1.61768 (* 1 = 1.61768 loss)
I0410 00:37:27.527225 14511 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 00:37:32.236083 14511 solver.cpp:218] Iteration 2712 (2.54845 iter/s, 4.70874s/12 iters), loss = 1.62588
I0410 00:37:32.236131 14511 solver.cpp:237]     Train net output #0: loss = 1.62588 (* 1 = 1.62588 loss)
I0410 00:37:32.236141 14511 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 00:37:37.463639 14511 solver.cpp:218] Iteration 2724 (2.29561 iter/s, 5.22737s/12 iters), loss = 1.56724
I0410 00:37:37.463685 14511 solver.cpp:237]     Train net output #0: loss = 1.56724 (* 1 = 1.56724 loss)
I0410 00:37:37.463696 14511 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 00:37:40.132841 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:42.655033 14511 solver.cpp:218] Iteration 2736 (2.3116 iter/s, 5.19122s/12 iters), loss = 1.59356
I0410 00:37:42.655072 14511 solver.cpp:237]     Train net output #0: loss = 1.59356 (* 1 = 1.59356 loss)
I0410 00:37:42.655081 14511 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 00:37:47.718789 14511 solver.cpp:218] Iteration 2748 (2.36986 iter/s, 5.06358s/12 iters), loss = 1.80964
I0410 00:37:47.718829 14511 solver.cpp:237]     Train net output #0: loss = 1.80964 (* 1 = 1.80964 loss)
I0410 00:37:47.718837 14511 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 00:37:49.703073 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 00:37:57.249022 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 00:38:03.263718 14511 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 00:38:03.263742 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:38:06.434526 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:38:06.671134 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:07.897104 14511 solver.cpp:397]     Test net output #0: accuracy = 0.250613
I0410 00:38:07.897154 14511 solver.cpp:397]     Test net output #1: loss = 3.46522 (* 1 = 3.46522 loss)
I0410 00:38:09.617167 14511 solver.cpp:218] Iteration 2760 (0.548 iter/s, 21.8978s/12 iters), loss = 1.88165
I0410 00:38:09.617218 14511 solver.cpp:237]     Train net output #0: loss = 1.88165 (* 1 = 1.88165 loss)
I0410 00:38:09.617229 14511 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 00:38:14.632303 14511 solver.cpp:218] Iteration 2772 (2.39284 iter/s, 5.01496s/12 iters), loss = 1.53863
I0410 00:38:14.632408 14511 solver.cpp:237]     Train net output #0: loss = 1.53863 (* 1 = 1.53863 loss)
I0410 00:38:14.632421 14511 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 00:38:19.747388 14511 solver.cpp:218] Iteration 2784 (2.34611 iter/s, 5.11484s/12 iters), loss = 1.64034
I0410 00:38:19.747437 14511 solver.cpp:237]     Train net output #0: loss = 1.64034 (* 1 = 1.64034 loss)
I0410 00:38:19.747447 14511 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 00:38:24.937932 14511 solver.cpp:218] Iteration 2796 (2.31198 iter/s, 5.19036s/12 iters), loss = 1.86357
I0410 00:38:24.937997 14511 solver.cpp:237]     Train net output #0: loss = 1.86357 (* 1 = 1.86357 loss)
I0410 00:38:24.938009 14511 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 00:38:30.119163 14511 solver.cpp:218] Iteration 2808 (2.31614 iter/s, 5.18103s/12 iters), loss = 1.77867
I0410 00:38:30.119210 14511 solver.cpp:237]     Train net output #0: loss = 1.77867 (* 1 = 1.77867 loss)
I0410 00:38:30.119220 14511 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 00:38:35.361443 14511 solver.cpp:218] Iteration 2820 (2.28916 iter/s, 5.24209s/12 iters), loss = 1.52648
I0410 00:38:35.361488 14511 solver.cpp:237]     Train net output #0: loss = 1.52648 (* 1 = 1.52648 loss)
I0410 00:38:35.361500 14511 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 00:38:40.149986 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:40.477113 14511 solver.cpp:218] Iteration 2832 (2.34582 iter/s, 5.11549s/12 iters), loss = 1.95779
I0410 00:38:40.477152 14511 solver.cpp:237]     Train net output #0: loss = 1.95779 (* 1 = 1.95779 loss)
I0410 00:38:40.477161 14511 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 00:38:45.652164 14511 solver.cpp:218] Iteration 2844 (2.3189 iter/s, 5.17487s/12 iters), loss = 1.619
I0410 00:38:45.652302 14511 solver.cpp:237]     Train net output #0: loss = 1.619 (* 1 = 1.619 loss)
I0410 00:38:45.652312 14511 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 00:38:49.802299 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 00:38:57.326539 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 00:39:03.299857 14511 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 00:39:03.299880 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:39:06.624990 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:07.874450 14511 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0410 00:39:07.874491 14511 solver.cpp:397]     Test net output #1: loss = 3.39048 (* 1 = 3.39048 loss)
I0410 00:39:07.983004 14511 solver.cpp:218] Iteration 2856 (0.53739 iter/s, 22.3302s/12 iters), loss = 1.54347
I0410 00:39:07.983052 14511 solver.cpp:237]     Train net output #0: loss = 1.54347 (* 1 = 1.54347 loss)
I0410 00:39:07.983063 14511 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 00:39:12.123842 14511 solver.cpp:218] Iteration 2868 (2.89808 iter/s, 4.14068s/12 iters), loss = 1.42936
I0410 00:39:12.123888 14511 solver.cpp:237]     Train net output #0: loss = 1.42936 (* 1 = 1.42936 loss)
I0410 00:39:12.123898 14511 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 00:39:17.381644 14511 solver.cpp:218] Iteration 2880 (2.2824 iter/s, 5.25761s/12 iters), loss = 1.39976
I0410 00:39:17.381739 14511 solver.cpp:237]     Train net output #0: loss = 1.39976 (* 1 = 1.39976 loss)
I0410 00:39:17.381749 14511 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 00:39:22.628909 14511 solver.cpp:218] Iteration 2892 (2.28701 iter/s, 5.24703s/12 iters), loss = 1.5098
I0410 00:39:22.628957 14511 solver.cpp:237]     Train net output #0: loss = 1.5098 (* 1 = 1.5098 loss)
I0410 00:39:22.628965 14511 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 00:39:27.307929 14511 solver.cpp:218] Iteration 2904 (2.56474 iter/s, 4.67884s/12 iters), loss = 1.51839
I0410 00:39:27.307981 14511 solver.cpp:237]     Train net output #0: loss = 1.51839 (* 1 = 1.51839 loss)
I0410 00:39:27.307994 14511 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 00:39:31.768442 14511 solver.cpp:218] Iteration 2916 (2.69038 iter/s, 4.46034s/12 iters), loss = 1.27337
I0410 00:39:31.768484 14511 solver.cpp:237]     Train net output #0: loss = 1.27337 (* 1 = 1.27337 loss)
I0410 00:39:31.768492 14511 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 00:39:36.851056 14511 solver.cpp:218] Iteration 2928 (2.36107 iter/s, 5.08244s/12 iters), loss = 1.33909
I0410 00:39:36.851101 14511 solver.cpp:237]     Train net output #0: loss = 1.33909 (* 1 = 1.33909 loss)
I0410 00:39:36.851110 14511 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 00:39:38.722143 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:42.052049 14511 solver.cpp:218] Iteration 2940 (2.30734 iter/s, 5.2008s/12 iters), loss = 1.73094
I0410 00:39:42.052106 14511 solver.cpp:237]     Train net output #0: loss = 1.73094 (* 1 = 1.73094 loss)
I0410 00:39:42.052119 14511 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 00:39:46.940001 14511 solver.cpp:218] Iteration 2952 (2.45511 iter/s, 4.88776s/12 iters), loss = 1.6855
I0410 00:39:46.940052 14511 solver.cpp:237]     Train net output #0: loss = 1.6855 (* 1 = 1.6855 loss)
I0410 00:39:46.940062 14511 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 00:39:48.935683 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 00:39:56.526109 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 00:40:02.473850 14511 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 00:40:02.473873 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:40:05.762866 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:06.958231 14511 solver.cpp:397]     Test net output #0: accuracy = 0.261642
I0410 00:40:06.958281 14511 solver.cpp:397]     Test net output #1: loss = 3.53024 (* 1 = 3.53024 loss)
I0410 00:40:08.761855 14511 solver.cpp:218] Iteration 2964 (0.549922 iter/s, 21.8213s/12 iters), loss = 1.40367
I0410 00:40:08.761909 14511 solver.cpp:237]     Train net output #0: loss = 1.40367 (* 1 = 1.40367 loss)
I0410 00:40:08.761919 14511 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 00:40:14.010141 14511 solver.cpp:218] Iteration 2976 (2.28654 iter/s, 5.24809s/12 iters), loss = 1.28691
I0410 00:40:14.010188 14511 solver.cpp:237]     Train net output #0: loss = 1.28691 (* 1 = 1.28691 loss)
I0410 00:40:14.010200 14511 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 00:40:19.113338 14511 solver.cpp:218] Iteration 2988 (2.35155 iter/s, 5.10301s/12 iters), loss = 1.00433
I0410 00:40:19.113447 14511 solver.cpp:237]     Train net output #0: loss = 1.00433 (* 1 = 1.00433 loss)
I0410 00:40:19.113461 14511 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 00:40:24.133674 14511 solver.cpp:218] Iteration 3000 (2.39039 iter/s, 5.0201s/12 iters), loss = 1.29623
I0410 00:40:24.133728 14511 solver.cpp:237]     Train net output #0: loss = 1.29623 (* 1 = 1.29623 loss)
I0410 00:40:24.133740 14511 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 00:40:28.950276 14511 solver.cpp:218] Iteration 3012 (2.49148 iter/s, 4.81642s/12 iters), loss = 1.2348
I0410 00:40:28.950322 14511 solver.cpp:237]     Train net output #0: loss = 1.2348 (* 1 = 1.2348 loss)
I0410 00:40:28.950333 14511 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 00:40:33.677443 14511 solver.cpp:218] Iteration 3024 (2.53861 iter/s, 4.72699s/12 iters), loss = 1.21514
I0410 00:40:33.677492 14511 solver.cpp:237]     Train net output #0: loss = 1.21514 (* 1 = 1.21514 loss)
I0410 00:40:33.677505 14511 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 00:40:37.679116 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:38.769212 14511 solver.cpp:218] Iteration 3036 (2.35683 iter/s, 5.09158s/12 iters), loss = 1.29974
I0410 00:40:38.769263 14511 solver.cpp:237]     Train net output #0: loss = 1.29974 (* 1 = 1.29974 loss)
I0410 00:40:38.769275 14511 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 00:40:43.623838 14511 solver.cpp:218] Iteration 3048 (2.47196 iter/s, 4.85445s/12 iters), loss = 1.20989
I0410 00:40:43.623888 14511 solver.cpp:237]     Train net output #0: loss = 1.20989 (* 1 = 1.20989 loss)
I0410 00:40:43.623899 14511 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 00:40:47.933269 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 00:40:55.331086 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 00:41:01.131109 14511 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 00:41:01.131131 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:41:04.397912 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:05.758942 14511 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0410 00:41:05.758991 14511 solver.cpp:397]     Test net output #1: loss = 3.46371 (* 1 = 3.46371 loss)
I0410 00:41:05.867851 14511 solver.cpp:218] Iteration 3060 (0.539486 iter/s, 22.2434s/12 iters), loss = 1.50809
I0410 00:41:05.867915 14511 solver.cpp:237]     Train net output #0: loss = 1.50809 (* 1 = 1.50809 loss)
I0410 00:41:05.867929 14511 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 00:41:10.038970 14511 solver.cpp:218] Iteration 3072 (2.87705 iter/s, 4.17095s/12 iters), loss = 1.19087
I0410 00:41:10.039011 14511 solver.cpp:237]     Train net output #0: loss = 1.19087 (* 1 = 1.19087 loss)
I0410 00:41:10.039019 14511 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 00:41:15.196269 14511 solver.cpp:218] Iteration 3084 (2.32688 iter/s, 5.15711s/12 iters), loss = 1.35049
I0410 00:41:15.196319 14511 solver.cpp:237]     Train net output #0: loss = 1.35049 (* 1 = 1.35049 loss)
I0410 00:41:15.196332 14511 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 00:41:20.254251 14511 solver.cpp:218] Iteration 3096 (2.37258 iter/s, 5.05779s/12 iters), loss = 1.31946
I0410 00:41:20.254308 14511 solver.cpp:237]     Train net output #0: loss = 1.31946 (* 1 = 1.31946 loss)
I0410 00:41:20.254320 14511 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 00:41:25.400579 14511 solver.cpp:218] Iteration 3108 (2.33185 iter/s, 5.14614s/12 iters), loss = 1.0809
I0410 00:41:25.400647 14511 solver.cpp:237]     Train net output #0: loss = 1.0809 (* 1 = 1.0809 loss)
I0410 00:41:25.400657 14511 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 00:41:30.423648 14511 solver.cpp:218] Iteration 3120 (2.38907 iter/s, 5.02287s/12 iters), loss = 1.43504
I0410 00:41:30.423703 14511 solver.cpp:237]     Train net output #0: loss = 1.43504 (* 1 = 1.43504 loss)
I0410 00:41:30.423715 14511 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 00:41:35.429766 14511 solver.cpp:218] Iteration 3132 (2.39716 iter/s, 5.00593s/12 iters), loss = 1.32913
I0410 00:41:35.429811 14511 solver.cpp:237]     Train net output #0: loss = 1.32913 (* 1 = 1.32913 loss)
I0410 00:41:35.429819 14511 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 00:41:36.559051 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:40.660459 14511 solver.cpp:218] Iteration 3144 (2.29423 iter/s, 5.23051s/12 iters), loss = 1.42009
I0410 00:41:40.660504 14511 solver.cpp:237]     Train net output #0: loss = 1.42009 (* 1 = 1.42009 loss)
I0410 00:41:40.660514 14511 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 00:41:45.748694 14511 solver.cpp:218] Iteration 3156 (2.35847 iter/s, 5.08805s/12 iters), loss = 1.6088
I0410 00:41:45.748736 14511 solver.cpp:237]     Train net output #0: loss = 1.6088 (* 1 = 1.6088 loss)
I0410 00:41:45.748744 14511 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 00:41:47.907727 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 00:41:55.360163 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 00:42:03.281234 14511 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 00:42:03.281312 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:42:06.613085 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:07.890374 14511 solver.cpp:397]     Test net output #0: accuracy = 0.284314
I0410 00:42:07.890411 14511 solver.cpp:397]     Test net output #1: loss = 3.54791 (* 1 = 3.54791 loss)
I0410 00:42:09.708561 14511 solver.cpp:218] Iteration 3168 (0.500851 iter/s, 23.9592s/12 iters), loss = 1.17857
I0410 00:42:09.708612 14511 solver.cpp:237]     Train net output #0: loss = 1.17857 (* 1 = 1.17857 loss)
I0410 00:42:09.708622 14511 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 00:42:14.508903 14511 solver.cpp:218] Iteration 3180 (2.49992 iter/s, 4.80016s/12 iters), loss = 1.19066
I0410 00:42:14.508955 14511 solver.cpp:237]     Train net output #0: loss = 1.19066 (* 1 = 1.19066 loss)
I0410 00:42:14.508965 14511 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 00:42:19.297168 14511 solver.cpp:218] Iteration 3192 (2.50622 iter/s, 4.78809s/12 iters), loss = 1.14136
I0410 00:42:19.297206 14511 solver.cpp:237]     Train net output #0: loss = 1.14136 (* 1 = 1.14136 loss)
I0410 00:42:19.297215 14511 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 00:42:24.256127 14511 solver.cpp:218] Iteration 3204 (2.41995 iter/s, 4.95879s/12 iters), loss = 1.06593
I0410 00:42:24.256166 14511 solver.cpp:237]     Train net output #0: loss = 1.06593 (* 1 = 1.06593 loss)
I0410 00:42:24.256176 14511 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 00:42:29.251873 14511 solver.cpp:218] Iteration 3216 (2.40213 iter/s, 4.99557s/12 iters), loss = 1.00577
I0410 00:42:29.251921 14511 solver.cpp:237]     Train net output #0: loss = 1.00577 (* 1 = 1.00577 loss)
I0410 00:42:29.251932 14511 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 00:42:33.984530 14511 solver.cpp:218] Iteration 3228 (2.53567 iter/s, 4.73248s/12 iters), loss = 0.920304
I0410 00:42:33.984668 14511 solver.cpp:237]     Train net output #0: loss = 0.920304 (* 1 = 0.920304 loss)
I0410 00:42:33.984680 14511 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 00:42:37.002563 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:38.858186 14511 solver.cpp:218] Iteration 3240 (2.46235 iter/s, 4.87339s/12 iters), loss = 1.1055
I0410 00:42:38.858238 14511 solver.cpp:237]     Train net output #0: loss = 1.1055 (* 1 = 1.1055 loss)
I0410 00:42:38.858249 14511 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 00:42:44.011296 14511 solver.cpp:218] Iteration 3252 (2.32878 iter/s, 5.15292s/12 iters), loss = 1.17668
I0410 00:42:44.011348 14511 solver.cpp:237]     Train net output #0: loss = 1.17668 (* 1 = 1.17668 loss)
I0410 00:42:44.011360 14511 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 00:42:48.418013 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 00:42:56.232295 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 00:43:02.091336 14511 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 00:43:02.091361 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:43:05.263949 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:06.581871 14511 solver.cpp:397]     Test net output #0: accuracy = 0.272672
I0410 00:43:06.581919 14511 solver.cpp:397]     Test net output #1: loss = 3.45213 (* 1 = 3.45213 loss)
I0410 00:43:06.690268 14511 solver.cpp:218] Iteration 3264 (0.529139 iter/s, 22.6784s/12 iters), loss = 1.44792
I0410 00:43:06.691787 14511 solver.cpp:237]     Train net output #0: loss = 1.44792 (* 1 = 1.44792 loss)
I0410 00:43:06.691802 14511 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 00:43:10.535976 14511 solver.cpp:218] Iteration 3276 (3.12168 iter/s, 3.84409s/12 iters), loss = 0.938424
I0410 00:43:10.536024 14511 solver.cpp:237]     Train net output #0: loss = 0.938424 (* 1 = 0.938424 loss)
I0410 00:43:10.536036 14511 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 00:43:15.325595 14511 solver.cpp:218] Iteration 3288 (2.50551 iter/s, 4.78944s/12 iters), loss = 0.886895
I0410 00:43:15.325650 14511 solver.cpp:237]     Train net output #0: loss = 0.886895 (* 1 = 0.886895 loss)
I0410 00:43:15.325662 14511 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 00:43:20.041510 14511 solver.cpp:218] Iteration 3300 (2.54467 iter/s, 4.71574s/12 iters), loss = 0.923076
I0410 00:43:20.041551 14511 solver.cpp:237]     Train net output #0: loss = 0.923076 (* 1 = 0.923076 loss)
I0410 00:43:20.041560 14511 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 00:43:24.718609 14511 solver.cpp:218] Iteration 3312 (2.56578 iter/s, 4.67693s/12 iters), loss = 1.55954
I0410 00:43:24.718659 14511 solver.cpp:237]     Train net output #0: loss = 1.55954 (* 1 = 1.55954 loss)
I0410 00:43:24.718670 14511 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 00:43:29.428618 14511 solver.cpp:218] Iteration 3324 (2.54786 iter/s, 4.70983s/12 iters), loss = 1.22316
I0410 00:43:29.428668 14511 solver.cpp:237]     Train net output #0: loss = 1.22316 (* 1 = 1.22316 loss)
I0410 00:43:29.428678 14511 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 00:43:34.341068 14511 solver.cpp:218] Iteration 3336 (2.44286 iter/s, 4.91227s/12 iters), loss = 1.10049
I0410 00:43:34.341114 14511 solver.cpp:237]     Train net output #0: loss = 1.10049 (* 1 = 1.10049 loss)
I0410 00:43:34.341125 14511 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 00:43:34.808681 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:39.404902 14511 solver.cpp:218] Iteration 3348 (2.36983 iter/s, 5.06365s/12 iters), loss = 1.38354
I0410 00:43:39.405040 14511 solver.cpp:237]     Train net output #0: loss = 1.38354 (* 1 = 1.38354 loss)
I0410 00:43:39.405052 14511 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 00:43:44.147053 14511 solver.cpp:218] Iteration 3360 (2.53064 iter/s, 4.74189s/12 iters), loss = 0.873432
I0410 00:43:44.147111 14511 solver.cpp:237]     Train net output #0: loss = 0.873432 (* 1 = 0.873432 loss)
I0410 00:43:44.147122 14511 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 00:43:46.223678 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 00:43:53.728998 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 00:44:01.715170 14511 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 00:44:01.715198 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:44:05.236546 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:06.587090 14511 solver.cpp:397]     Test net output #0: accuracy = 0.273284
I0410 00:44:06.587138 14511 solver.cpp:397]     Test net output #1: loss = 3.64011 (* 1 = 3.64011 loss)
I0410 00:44:08.301924 14511 solver.cpp:218] Iteration 3372 (0.496807 iter/s, 24.1542s/12 iters), loss = 1.02727
I0410 00:44:08.301983 14511 solver.cpp:237]     Train net output #0: loss = 1.02727 (* 1 = 1.02727 loss)
I0410 00:44:08.301993 14511 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 00:44:13.067086 14511 solver.cpp:218] Iteration 3384 (2.51838 iter/s, 4.76497s/12 iters), loss = 1.28177
I0410 00:44:13.067201 14511 solver.cpp:237]     Train net output #0: loss = 1.28177 (* 1 = 1.28177 loss)
I0410 00:44:13.067215 14511 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 00:44:18.213337 14511 solver.cpp:218] Iteration 3396 (2.33191 iter/s, 5.146s/12 iters), loss = 0.954511
I0410 00:44:18.213387 14511 solver.cpp:237]     Train net output #0: loss = 0.954511 (* 1 = 0.954511 loss)
I0410 00:44:18.213399 14511 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 00:44:23.097219 14511 solver.cpp:218] Iteration 3408 (2.45715 iter/s, 4.8837s/12 iters), loss = 1.29752
I0410 00:44:23.097262 14511 solver.cpp:237]     Train net output #0: loss = 1.29752 (* 1 = 1.29752 loss)
I0410 00:44:23.097271 14511 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 00:44:28.282394 14511 solver.cpp:218] Iteration 3420 (2.31437 iter/s, 5.18499s/12 iters), loss = 0.7529
I0410 00:44:28.282454 14511 solver.cpp:237]     Train net output #0: loss = 0.7529 (* 1 = 0.7529 loss)
I0410 00:44:28.282466 14511 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 00:44:33.548617 14511 solver.cpp:218] Iteration 3432 (2.27876 iter/s, 5.26603s/12 iters), loss = 0.877761
I0410 00:44:33.548668 14511 solver.cpp:237]     Train net output #0: loss = 0.877761 (* 1 = 0.877761 loss)
I0410 00:44:33.548681 14511 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 00:44:36.095608 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:38.522670 14511 solver.cpp:218] Iteration 3444 (2.41261 iter/s, 4.97387s/12 iters), loss = 0.619457
I0410 00:44:38.522716 14511 solver.cpp:237]     Train net output #0: loss = 0.619457 (* 1 = 0.619457 loss)
I0410 00:44:38.522725 14511 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 00:44:43.778887 14511 solver.cpp:218] Iteration 3456 (2.28309 iter/s, 5.25603s/12 iters), loss = 0.96403
I0410 00:44:43.779024 14511 solver.cpp:237]     Train net output #0: loss = 0.96403 (* 1 = 0.96403 loss)
I0410 00:44:43.779036 14511 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 00:44:48.532230 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 00:44:57.400554 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 00:45:06.203402 14511 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 00:45:06.203428 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:45:06.681072 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:45:09.313539 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:10.824772 14511 solver.cpp:397]     Test net output #0: accuracy = 0.280024
I0410 00:45:10.824815 14511 solver.cpp:397]     Test net output #1: loss = 3.69443 (* 1 = 3.69443 loss)
I0410 00:45:10.933410 14511 solver.cpp:218] Iteration 3468 (0.441928 iter/s, 27.1537s/12 iters), loss = 1.05206
I0410 00:45:10.933454 14511 solver.cpp:237]     Train net output #0: loss = 1.05206 (* 1 = 1.05206 loss)
I0410 00:45:10.933463 14511 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 00:45:14.870071 14511 solver.cpp:218] Iteration 3480 (3.04838 iter/s, 3.93651s/12 iters), loss = 1.07252
I0410 00:45:14.882017 14511 solver.cpp:237]     Train net output #0: loss = 1.07252 (* 1 = 1.07252 loss)
I0410 00:45:14.882027 14511 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 00:45:19.680335 14511 solver.cpp:218] Iteration 3492 (2.50094 iter/s, 4.79819s/12 iters), loss = 0.977851
I0410 00:45:19.680383 14511 solver.cpp:237]     Train net output #0: loss = 0.977851 (* 1 = 0.977851 loss)
I0410 00:45:19.680395 14511 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 00:45:24.427244 14511 solver.cpp:218] Iteration 3504 (2.52806 iter/s, 4.74673s/12 iters), loss = 0.857737
I0410 00:45:24.427292 14511 solver.cpp:237]     Train net output #0: loss = 0.857737 (* 1 = 0.857737 loss)
I0410 00:45:24.427304 14511 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 00:45:29.397528 14511 solver.cpp:218] Iteration 3516 (2.41444 iter/s, 4.9701s/12 iters), loss = 0.771025
I0410 00:45:29.397583 14511 solver.cpp:237]     Train net output #0: loss = 0.771025 (* 1 = 0.771025 loss)
I0410 00:45:29.397593 14511 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 00:45:34.143230 14511 solver.cpp:218] Iteration 3528 (2.5287 iter/s, 4.74552s/12 iters), loss = 1.00147
I0410 00:45:34.143276 14511 solver.cpp:237]     Train net output #0: loss = 1.00147 (* 1 = 1.00147 loss)
I0410 00:45:34.143287 14511 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 00:45:38.508936 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:38.798326 14511 solver.cpp:218] Iteration 3540 (2.57792 iter/s, 4.65492s/12 iters), loss = 0.78053
I0410 00:45:38.798379 14511 solver.cpp:237]     Train net output #0: loss = 0.78053 (* 1 = 0.78053 loss)
I0410 00:45:38.798393 14511 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 00:45:43.706070 14511 solver.cpp:218] Iteration 3552 (2.44521 iter/s, 4.90756s/12 iters), loss = 1.00043
I0410 00:45:43.706121 14511 solver.cpp:237]     Train net output #0: loss = 1.00043 (* 1 = 1.00043 loss)
I0410 00:45:43.706132 14511 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 00:45:48.404175 14511 solver.cpp:218] Iteration 3564 (2.55432 iter/s, 4.69793s/12 iters), loss = 0.657337
I0410 00:45:48.404268 14511 solver.cpp:237]     Train net output #0: loss = 0.657337 (* 1 = 0.657337 loss)
I0410 00:45:48.404279 14511 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 00:45:50.334862 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 00:46:02.761852 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 00:46:10.492272 14511 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 00:46:10.492297 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:46:13.829308 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:15.313098 14511 solver.cpp:397]     Test net output #0: accuracy = 0.316176
I0410 00:46:15.313144 14511 solver.cpp:397]     Test net output #1: loss = 3.43723 (* 1 = 3.43723 loss)
I0410 00:46:17.186966 14511 solver.cpp:218] Iteration 3576 (0.416927 iter/s, 28.782s/12 iters), loss = 0.923882
I0410 00:46:17.187019 14511 solver.cpp:237]     Train net output #0: loss = 0.923882 (* 1 = 0.923882 loss)
I0410 00:46:17.187029 14511 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 00:46:22.034905 14511 solver.cpp:218] Iteration 3588 (2.47537 iter/s, 4.84775s/12 iters), loss = 0.867172
I0410 00:46:22.035071 14511 solver.cpp:237]     Train net output #0: loss = 0.867172 (* 1 = 0.867172 loss)
I0410 00:46:22.035085 14511 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 00:46:26.700105 14511 solver.cpp:218] Iteration 3600 (2.5724 iter/s, 4.66491s/12 iters), loss = 0.652724
I0410 00:46:26.700150 14511 solver.cpp:237]     Train net output #0: loss = 0.652724 (* 1 = 0.652724 loss)
I0410 00:46:26.700160 14511 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 00:46:31.715603 14511 solver.cpp:218] Iteration 3612 (2.39267 iter/s, 5.01531s/12 iters), loss = 0.865579
I0410 00:46:31.715648 14511 solver.cpp:237]     Train net output #0: loss = 0.865579 (* 1 = 0.865579 loss)
I0410 00:46:31.715659 14511 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 00:46:36.698918 14511 solver.cpp:218] Iteration 3624 (2.40812 iter/s, 4.98313s/12 iters), loss = 0.803853
I0410 00:46:36.698967 14511 solver.cpp:237]     Train net output #0: loss = 0.803853 (* 1 = 0.803853 loss)
I0410 00:46:36.698976 14511 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 00:46:41.326972 14511 solver.cpp:218] Iteration 3636 (2.59298 iter/s, 4.62788s/12 iters), loss = 0.931519
I0410 00:46:41.327018 14511 solver.cpp:237]     Train net output #0: loss = 0.931519 (* 1 = 0.931519 loss)
I0410 00:46:41.327030 14511 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 00:46:43.054436 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:46.011292 14511 solver.cpp:218] Iteration 3648 (2.56183 iter/s, 4.68415s/12 iters), loss = 0.837952
I0410 00:46:46.011335 14511 solver.cpp:237]     Train net output #0: loss = 0.837952 (* 1 = 0.837952 loss)
I0410 00:46:46.011344 14511 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 00:46:50.654016 14511 solver.cpp:218] Iteration 3660 (2.5848 iter/s, 4.64252s/12 iters), loss = 0.492744
I0410 00:46:50.654059 14511 solver.cpp:237]     Train net output #0: loss = 0.492744 (* 1 = 0.492744 loss)
I0410 00:46:50.654073 14511 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 00:46:54.946285 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 00:47:03.292950 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 00:47:13.218364 14511 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 00:47:13.218389 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:47:16.386243 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:17.863509 14511 solver.cpp:397]     Test net output #0: accuracy = 0.323529
I0410 00:47:17.863559 14511 solver.cpp:397]     Test net output #1: loss = 3.46281 (* 1 = 3.46281 loss)
I0410 00:47:17.972590 14511 solver.cpp:218] Iteration 3672 (0.439273 iter/s, 27.3179s/12 iters), loss = 0.642952
I0410 00:47:17.974128 14511 solver.cpp:237]     Train net output #0: loss = 0.642952 (* 1 = 0.642952 loss)
I0410 00:47:17.974148 14511 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 00:47:22.121501 14511 solver.cpp:218] Iteration 3684 (2.89347 iter/s, 4.14727s/12 iters), loss = 0.799779
I0410 00:47:22.121558 14511 solver.cpp:237]     Train net output #0: loss = 0.799779 (* 1 = 0.799779 loss)
I0410 00:47:22.121572 14511 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 00:47:26.825608 14511 solver.cpp:218] Iteration 3696 (2.55106 iter/s, 4.70392s/12 iters), loss = 0.620576
I0410 00:47:26.825754 14511 solver.cpp:237]     Train net output #0: loss = 0.620576 (* 1 = 0.620576 loss)
I0410 00:47:26.825767 14511 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 00:47:31.462044 14511 solver.cpp:218] Iteration 3708 (2.58835 iter/s, 4.63617s/12 iters), loss = 0.883009
I0410 00:47:31.462092 14511 solver.cpp:237]     Train net output #0: loss = 0.883009 (* 1 = 0.883009 loss)
I0410 00:47:31.462103 14511 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 00:47:36.083338 14511 solver.cpp:218] Iteration 3720 (2.59677 iter/s, 4.62112s/12 iters), loss = 0.619681
I0410 00:47:36.083395 14511 solver.cpp:237]     Train net output #0: loss = 0.619681 (* 1 = 0.619681 loss)
I0410 00:47:36.083408 14511 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 00:47:41.045882 14511 solver.cpp:218] Iteration 3732 (2.41821 iter/s, 4.96236s/12 iters), loss = 0.800273
I0410 00:47:41.045933 14511 solver.cpp:237]     Train net output #0: loss = 0.800273 (* 1 = 0.800273 loss)
I0410 00:47:41.045944 14511 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 00:47:44.889803 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:45.876739 14511 solver.cpp:218] Iteration 3744 (2.48412 iter/s, 4.83068s/12 iters), loss = 1.02488
I0410 00:47:45.876783 14511 solver.cpp:237]     Train net output #0: loss = 1.02488 (* 1 = 1.02488 loss)
I0410 00:47:45.876792 14511 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 00:47:51.178896 14511 solver.cpp:218] Iteration 3756 (2.26331 iter/s, 5.30197s/12 iters), loss = 0.514013
I0410 00:47:51.178939 14511 solver.cpp:237]     Train net output #0: loss = 0.514013 (* 1 = 0.514013 loss)
I0410 00:47:51.178949 14511 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 00:47:56.584028 14511 solver.cpp:218] Iteration 3768 (2.22019 iter/s, 5.40495s/12 iters), loss = 0.693676
I0410 00:47:56.584069 14511 solver.cpp:237]     Train net output #0: loss = 0.693676 (* 1 = 0.693676 loss)
I0410 00:47:56.584079 14511 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 00:47:58.814524 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 00:48:09.989161 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 00:48:19.126541 14511 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 00:48:19.126562 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:48:22.215840 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:23.727660 14511 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0410 00:48:23.727697 14511 solver.cpp:397]     Test net output #1: loss = 3.44536 (* 1 = 3.44536 loss)
I0410 00:48:25.517395 14511 solver.cpp:218] Iteration 3780 (0.414757 iter/s, 28.9326s/12 iters), loss = 0.416987
I0410 00:48:25.517436 14511 solver.cpp:237]     Train net output #0: loss = 0.416987 (* 1 = 0.416987 loss)
I0410 00:48:25.517444 14511 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 00:48:30.247112 14511 solver.cpp:218] Iteration 3792 (2.53724 iter/s, 4.72955s/12 iters), loss = 0.641079
I0410 00:48:30.247201 14511 solver.cpp:237]     Train net output #0: loss = 0.641079 (* 1 = 0.641079 loss)
I0410 00:48:30.247211 14511 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 00:48:34.887918 14511 solver.cpp:218] Iteration 3804 (2.58588 iter/s, 4.64059s/12 iters), loss = 0.384866
I0410 00:48:34.887972 14511 solver.cpp:237]     Train net output #0: loss = 0.384867 (* 1 = 0.384867 loss)
I0410 00:48:34.887984 14511 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 00:48:39.716941 14511 solver.cpp:218] Iteration 3816 (2.48507 iter/s, 4.82884s/12 iters), loss = 0.652491
I0410 00:48:39.716985 14511 solver.cpp:237]     Train net output #0: loss = 0.652491 (* 1 = 0.652491 loss)
I0410 00:48:39.716995 14511 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 00:48:44.512313 14511 solver.cpp:218] Iteration 3828 (2.5025 iter/s, 4.7952s/12 iters), loss = 0.688363
I0410 00:48:44.512354 14511 solver.cpp:237]     Train net output #0: loss = 0.688363 (* 1 = 0.688363 loss)
I0410 00:48:44.512363 14511 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 00:48:49.150822 14511 solver.cpp:218] Iteration 3840 (2.58713 iter/s, 4.63834s/12 iters), loss = 1.03285
I0410 00:48:49.150874 14511 solver.cpp:237]     Train net output #0: loss = 1.03285 (* 1 = 1.03285 loss)
I0410 00:48:49.150885 14511 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 00:48:50.323650 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:53.986446 14511 solver.cpp:218] Iteration 3852 (2.48168 iter/s, 4.83544s/12 iters), loss = 0.901098
I0410 00:48:53.986490 14511 solver.cpp:237]     Train net output #0: loss = 0.901098 (* 1 = 0.901098 loss)
I0410 00:48:53.986500 14511 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 00:48:58.799618 14511 solver.cpp:218] Iteration 3864 (2.49325 iter/s, 4.813s/12 iters), loss = 0.769644
I0410 00:48:58.799657 14511 solver.cpp:237]     Train net output #0: loss = 0.769644 (* 1 = 0.769644 loss)
I0410 00:48:58.799666 14511 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 00:49:03.353410 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 00:49:14.185050 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 00:49:23.513280 14511 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 00:49:23.513305 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:49:26.459163 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:28.019199 14511 solver.cpp:397]     Test net output #0: accuracy = 0.300858
I0410 00:49:28.019243 14511 solver.cpp:397]     Test net output #1: loss = 3.69681 (* 1 = 3.69681 loss)
I0410 00:49:28.127786 14511 solver.cpp:218] Iteration 3876 (0.409173 iter/s, 29.3274s/12 iters), loss = 0.742439
I0410 00:49:28.127835 14511 solver.cpp:237]     Train net output #0: loss = 0.74244 (* 1 = 0.74244 loss)
I0410 00:49:28.127846 14511 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 00:49:32.540052 14511 solver.cpp:218] Iteration 3888 (2.7198 iter/s, 4.4121s/12 iters), loss = 0.526421
I0410 00:49:32.540103 14511 solver.cpp:237]     Train net output #0: loss = 0.526421 (* 1 = 0.526421 loss)
I0410 00:49:32.540115 14511 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 00:49:37.804723 14511 solver.cpp:218] Iteration 3900 (2.27943 iter/s, 5.26448s/12 iters), loss = 0.752994
I0410 00:49:37.804833 14511 solver.cpp:237]     Train net output #0: loss = 0.752994 (* 1 = 0.752994 loss)
I0410 00:49:37.804847 14511 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 00:49:42.982302 14511 solver.cpp:218] Iteration 3912 (2.31779 iter/s, 5.17734s/12 iters), loss = 0.499998
I0410 00:49:42.982347 14511 solver.cpp:237]     Train net output #0: loss = 0.499998 (* 1 = 0.499998 loss)
I0410 00:49:42.982357 14511 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 00:49:47.642002 14511 solver.cpp:218] Iteration 3924 (2.57537 iter/s, 4.65952s/12 iters), loss = 0.647053
I0410 00:49:47.642055 14511 solver.cpp:237]     Train net output #0: loss = 0.647053 (* 1 = 0.647053 loss)
I0410 00:49:47.642066 14511 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 00:49:52.452702 14511 solver.cpp:218] Iteration 3936 (2.49453 iter/s, 4.81052s/12 iters), loss = 0.436352
I0410 00:49:52.452746 14511 solver.cpp:237]     Train net output #0: loss = 0.436352 (* 1 = 0.436352 loss)
I0410 00:49:52.452755 14511 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 00:49:55.634157 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:57.341078 14511 solver.cpp:218] Iteration 3948 (2.45489 iter/s, 4.8882s/12 iters), loss = 0.720861
I0410 00:49:57.341130 14511 solver.cpp:237]     Train net output #0: loss = 0.720861 (* 1 = 0.720861 loss)
I0410 00:49:57.341141 14511 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 00:50:02.663450 14511 solver.cpp:218] Iteration 3960 (2.25472 iter/s, 5.32218s/12 iters), loss = 0.776589
I0410 00:50:02.663502 14511 solver.cpp:237]     Train net output #0: loss = 0.776589 (* 1 = 0.776589 loss)
I0410 00:50:02.663514 14511 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 00:50:07.828603 14511 solver.cpp:218] Iteration 3972 (2.32335 iter/s, 5.16496s/12 iters), loss = 0.581404
I0410 00:50:07.828717 14511 solver.cpp:237]     Train net output #0: loss = 0.581404 (* 1 = 0.581404 loss)
I0410 00:50:07.828730 14511 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 00:50:09.921424 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 00:50:18.960150 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 00:50:35.256794 14511 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 00:50:35.256816 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:50:38.130964 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:39.759781 14511 solver.cpp:397]     Test net output #0: accuracy = 0.306985
I0410 00:50:39.759814 14511 solver.cpp:397]     Test net output #1: loss = 3.64736 (* 1 = 3.64736 loss)
I0410 00:50:41.372151 14511 solver.cpp:218] Iteration 3984 (0.357754 iter/s, 33.5426s/12 iters), loss = 0.706185
I0410 00:50:41.372202 14511 solver.cpp:237]     Train net output #0: loss = 0.706185 (* 1 = 0.706185 loss)
I0410 00:50:41.372213 14511 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 00:50:46.165125 14511 solver.cpp:218] Iteration 3996 (2.50376 iter/s, 4.79279s/12 iters), loss = 0.457713
I0410 00:50:46.165170 14511 solver.cpp:237]     Train net output #0: loss = 0.457713 (* 1 = 0.457713 loss)
I0410 00:50:46.165180 14511 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 00:50:51.454699 14511 solver.cpp:218] Iteration 4008 (2.2687 iter/s, 5.28938s/12 iters), loss = 0.893279
I0410 00:50:51.454741 14511 solver.cpp:237]     Train net output #0: loss = 0.893279 (* 1 = 0.893279 loss)
I0410 00:50:51.454752 14511 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 00:50:56.170552 14511 solver.cpp:218] Iteration 4020 (2.5447 iter/s, 4.71568s/12 iters), loss = 0.858594
I0410 00:50:56.170601 14511 solver.cpp:237]     Train net output #0: loss = 0.858594 (* 1 = 0.858594 loss)
I0410 00:50:56.170611 14511 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 00:51:01.344275 14511 solver.cpp:218] Iteration 4032 (2.31949 iter/s, 5.17354s/12 iters), loss = 0.406426
I0410 00:51:01.344313 14511 solver.cpp:237]     Train net output #0: loss = 0.406426 (* 1 = 0.406426 loss)
I0410 00:51:01.344322 14511 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 00:51:06.325332 14511 solver.cpp:218] Iteration 4044 (2.40921 iter/s, 4.98088s/12 iters), loss = 0.528128
I0410 00:51:06.325381 14511 solver.cpp:237]     Train net output #0: loss = 0.528128 (* 1 = 0.528128 loss)
I0410 00:51:06.325392 14511 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 00:51:06.779942 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:11.447698 14511 solver.cpp:218] Iteration 4056 (2.34275 iter/s, 5.12218s/12 iters), loss = 0.556434
I0410 00:51:11.451457 14511 solver.cpp:237]     Train net output #0: loss = 0.556435 (* 1 = 0.556435 loss)
I0410 00:51:11.451470 14511 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 00:51:16.454547 14511 solver.cpp:218] Iteration 4068 (2.39858 iter/s, 5.00296s/12 iters), loss = 0.565131
I0410 00:51:16.454594 14511 solver.cpp:237]     Train net output #0: loss = 0.565131 (* 1 = 0.565131 loss)
I0410 00:51:16.454607 14511 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 00:51:21.180682 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 00:51:39.122709 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 00:51:50.308590 14511 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 00:51:50.308696 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:51:53.229933 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:55.028427 14511 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0410 00:51:55.028478 14511 solver.cpp:397]     Test net output #1: loss = 3.70418 (* 1 = 3.70418 loss)
I0410 00:51:55.136930 14511 solver.cpp:218] Iteration 4080 (0.310227 iter/s, 38.6814s/12 iters), loss = 0.508746
I0410 00:51:55.136976 14511 solver.cpp:237]     Train net output #0: loss = 0.508746 (* 1 = 0.508746 loss)
I0410 00:51:55.136986 14511 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 00:51:59.352674 14511 solver.cpp:218] Iteration 4092 (2.84658 iter/s, 4.21558s/12 iters), loss = 0.773555
I0410 00:51:59.352720 14511 solver.cpp:237]     Train net output #0: loss = 0.773555 (* 1 = 0.773555 loss)
I0410 00:51:59.352731 14511 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 00:52:04.102327 14511 solver.cpp:218] Iteration 4104 (2.52659 iter/s, 4.74948s/12 iters), loss = 0.396687
I0410 00:52:04.102372 14511 solver.cpp:237]     Train net output #0: loss = 0.396687 (* 1 = 0.396687 loss)
I0410 00:52:04.102383 14511 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 00:52:09.057246 14511 solver.cpp:218] Iteration 4116 (2.42192 iter/s, 4.95474s/12 iters), loss = 0.36646
I0410 00:52:09.057298 14511 solver.cpp:237]     Train net output #0: loss = 0.36646 (* 1 = 0.36646 loss)
I0410 00:52:09.057310 14511 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 00:52:14.229741 14511 solver.cpp:218] Iteration 4128 (2.32005 iter/s, 5.1723s/12 iters), loss = 0.467326
I0410 00:52:14.229785 14511 solver.cpp:237]     Train net output #0: loss = 0.467326 (* 1 = 0.467326 loss)
I0410 00:52:14.229795 14511 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 00:52:18.929119 14511 solver.cpp:218] Iteration 4140 (2.55363 iter/s, 4.6992s/12 iters), loss = 0.493479
I0410 00:52:18.929168 14511 solver.cpp:237]     Train net output #0: loss = 0.49348 (* 1 = 0.49348 loss)
I0410 00:52:18.929180 14511 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 00:52:21.352849 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:23.665441 14511 solver.cpp:218] Iteration 4152 (2.53371 iter/s, 4.73614s/12 iters), loss = 0.428933
I0410 00:52:23.665493 14511 solver.cpp:237]     Train net output #0: loss = 0.428933 (* 1 = 0.428933 loss)
I0410 00:52:23.665504 14511 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 00:52:25.295130 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:52:28.774298 14511 solver.cpp:218] Iteration 4164 (2.34895 iter/s, 5.10867s/12 iters), loss = 0.672824
I0410 00:52:28.774348 14511 solver.cpp:237]     Train net output #0: loss = 0.672824 (* 1 = 0.672824 loss)
I0410 00:52:28.774356 14511 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 00:52:33.711323 14511 solver.cpp:218] Iteration 4176 (2.4307 iter/s, 4.93684s/12 iters), loss = 0.638776
I0410 00:52:33.711377 14511 solver.cpp:237]     Train net output #0: loss = 0.638776 (* 1 = 0.638776 loss)
I0410 00:52:33.711390 14511 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 00:52:35.714812 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 00:52:54.334946 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 00:53:03.160019 14511 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 00:53:03.160044 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:53:06.001065 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:07.677162 14511 solver.cpp:397]     Test net output #0: accuracy = 0.323529
I0410 00:53:07.677198 14511 solver.cpp:397]     Test net output #1: loss = 3.67041 (* 1 = 3.67041 loss)
I0410 00:53:09.495317 14511 solver.cpp:218] Iteration 4188 (0.335354 iter/s, 35.7831s/12 iters), loss = 0.648817
I0410 00:53:09.495370 14511 solver.cpp:237]     Train net output #0: loss = 0.648818 (* 1 = 0.648818 loss)
I0410 00:53:09.495381 14511 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 00:53:14.544673 14511 solver.cpp:218] Iteration 4200 (2.37663 iter/s, 5.04917s/12 iters), loss = 0.51755
I0410 00:53:14.544719 14511 solver.cpp:237]     Train net output #0: loss = 0.51755 (* 1 = 0.51755 loss)
I0410 00:53:14.544729 14511 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 00:53:19.011652 14511 solver.cpp:218] Iteration 4212 (2.68648 iter/s, 4.46681s/12 iters), loss = 0.450257
I0410 00:53:19.011699 14511 solver.cpp:237]     Train net output #0: loss = 0.450257 (* 1 = 0.450257 loss)
I0410 00:53:19.011709 14511 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 00:53:23.958287 14511 solver.cpp:218] Iteration 4224 (2.42598 iter/s, 4.94646s/12 iters), loss = 0.75345
I0410 00:53:23.958335 14511 solver.cpp:237]     Train net output #0: loss = 0.75345 (* 1 = 0.75345 loss)
I0410 00:53:23.958346 14511 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 00:53:28.916352 14511 solver.cpp:218] Iteration 4236 (2.42039 iter/s, 4.95788s/12 iters), loss = 0.54895
I0410 00:53:28.916492 14511 solver.cpp:237]     Train net output #0: loss = 0.54895 (* 1 = 0.54895 loss)
I0410 00:53:28.916507 14511 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 00:53:33.313026 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:33.564627 14511 solver.cpp:218] Iteration 4248 (2.58175 iter/s, 4.64801s/12 iters), loss = 0.56855
I0410 00:53:33.564679 14511 solver.cpp:237]     Train net output #0: loss = 0.56855 (* 1 = 0.56855 loss)
I0410 00:53:33.564690 14511 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 00:53:38.464115 14511 solver.cpp:218] Iteration 4260 (2.44933 iter/s, 4.89931s/12 iters), loss = 0.670446
I0410 00:53:38.464165 14511 solver.cpp:237]     Train net output #0: loss = 0.670446 (* 1 = 0.670446 loss)
I0410 00:53:38.464177 14511 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 00:53:43.235546 14511 solver.cpp:218] Iteration 4272 (2.51506 iter/s, 4.77126s/12 iters), loss = 0.713913
I0410 00:53:43.235584 14511 solver.cpp:237]     Train net output #0: loss = 0.713913 (* 1 = 0.713913 loss)
I0410 00:53:43.235592 14511 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 00:53:47.560346 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 00:53:59.200747 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 00:54:05.051025 14511 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 00:54:05.051049 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:54:07.851797 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:09.606048 14511 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0410 00:54:09.606099 14511 solver.cpp:397]     Test net output #1: loss = 3.71072 (* 1 = 3.71072 loss)
I0410 00:54:09.714622 14511 solver.cpp:218] Iteration 4284 (0.4532 iter/s, 26.4784s/12 iters), loss = 0.579567
I0410 00:54:09.714676 14511 solver.cpp:237]     Train net output #0: loss = 0.579568 (* 1 = 0.579568 loss)
I0410 00:54:09.714687 14511 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 00:54:13.906795 14511 solver.cpp:218] Iteration 4296 (2.86259 iter/s, 4.192s/12 iters), loss = 0.600886
I0410 00:54:13.906850 14511 solver.cpp:237]     Train net output #0: loss = 0.600886 (* 1 = 0.600886 loss)
I0410 00:54:13.906862 14511 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 00:54:18.987669 14511 solver.cpp:218] Iteration 4308 (2.36189 iter/s, 5.08068s/12 iters), loss = 0.507745
I0410 00:54:18.987723 14511 solver.cpp:237]     Train net output #0: loss = 0.507745 (* 1 = 0.507745 loss)
I0410 00:54:18.987736 14511 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 00:54:24.038769 14511 solver.cpp:218] Iteration 4320 (2.37581 iter/s, 5.05091s/12 iters), loss = 0.368018
I0410 00:54:24.038812 14511 solver.cpp:237]     Train net output #0: loss = 0.368018 (* 1 = 0.368018 loss)
I0410 00:54:24.038822 14511 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 00:54:29.188777 14511 solver.cpp:218] Iteration 4332 (2.33018 iter/s, 5.14982s/12 iters), loss = 0.449566
I0410 00:54:29.188832 14511 solver.cpp:237]     Train net output #0: loss = 0.449566 (* 1 = 0.449566 loss)
I0410 00:54:29.188843 14511 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 00:54:34.305086 14511 solver.cpp:218] Iteration 4344 (2.34553 iter/s, 5.11612s/12 iters), loss = 0.433936
I0410 00:54:34.305202 14511 solver.cpp:237]     Train net output #0: loss = 0.433936 (* 1 = 0.433936 loss)
I0410 00:54:34.305212 14511 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 00:54:36.192463 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:39.404909 14511 solver.cpp:218] Iteration 4356 (2.35314 iter/s, 5.09958s/12 iters), loss = 0.437133
I0410 00:54:39.404954 14511 solver.cpp:237]     Train net output #0: loss = 0.437134 (* 1 = 0.437134 loss)
I0410 00:54:39.404965 14511 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 00:54:44.650869 14511 solver.cpp:218] Iteration 4368 (2.28755 iter/s, 5.24578s/12 iters), loss = 0.580665
I0410 00:54:44.650915 14511 solver.cpp:237]     Train net output #0: loss = 0.580665 (* 1 = 0.580665 loss)
I0410 00:54:44.650926 14511 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 00:54:50.018249 14511 solver.cpp:218] Iteration 4380 (2.23581 iter/s, 5.36719s/12 iters), loss = 0.457989
I0410 00:54:50.018292 14511 solver.cpp:237]     Train net output #0: loss = 0.457989 (* 1 = 0.457989 loss)
I0410 00:54:50.018301 14511 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 00:54:52.133653 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 00:54:59.593406 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 00:55:05.451493 14511 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 00:55:05.451565 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:55:08.258932 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:10.073690 14511 solver.cpp:397]     Test net output #0: accuracy = 0.316789
I0410 00:55:10.073740 14511 solver.cpp:397]     Test net output #1: loss = 3.85376 (* 1 = 3.85376 loss)
I0410 00:55:11.954936 14511 solver.cpp:218] Iteration 4392 (0.547043 iter/s, 21.9361s/12 iters), loss = 0.42988
I0410 00:55:11.954984 14511 solver.cpp:237]     Train net output #0: loss = 0.429881 (* 1 = 0.429881 loss)
I0410 00:55:11.954996 14511 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 00:55:17.160704 14511 solver.cpp:218] Iteration 4404 (2.30522 iter/s, 5.20558s/12 iters), loss = 0.524548
I0410 00:55:17.160753 14511 solver.cpp:237]     Train net output #0: loss = 0.524548 (* 1 = 0.524548 loss)
I0410 00:55:17.160764 14511 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 00:55:22.161017 14511 solver.cpp:218] Iteration 4416 (2.39994 iter/s, 5.00013s/12 iters), loss = 0.514943
I0410 00:55:22.161064 14511 solver.cpp:237]     Train net output #0: loss = 0.514943 (* 1 = 0.514943 loss)
I0410 00:55:22.161073 14511 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 00:55:27.224818 14511 solver.cpp:218] Iteration 4428 (2.36985 iter/s, 5.06361s/12 iters), loss = 0.37918
I0410 00:55:27.224869 14511 solver.cpp:237]     Train net output #0: loss = 0.379181 (* 1 = 0.379181 loss)
I0410 00:55:27.224880 14511 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 00:55:31.855118 14511 solver.cpp:218] Iteration 4440 (2.59172 iter/s, 4.63012s/12 iters), loss = 0.425406
I0410 00:55:31.855165 14511 solver.cpp:237]     Train net output #0: loss = 0.425406 (* 1 = 0.425406 loss)
I0410 00:55:31.855175 14511 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 00:55:35.578687 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:36.498662 14511 solver.cpp:218] Iteration 4452 (2.58433 iter/s, 4.64337s/12 iters), loss = 0.479943
I0410 00:55:36.498703 14511 solver.cpp:237]     Train net output #0: loss = 0.479944 (* 1 = 0.479944 loss)
I0410 00:55:36.498713 14511 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 00:55:41.292238 14511 solver.cpp:218] Iteration 4464 (2.50344 iter/s, 4.7934s/12 iters), loss = 0.41825
I0410 00:55:41.292289 14511 solver.cpp:237]     Train net output #0: loss = 0.41825 (* 1 = 0.41825 loss)
I0410 00:55:41.292300 14511 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 00:55:46.013183 14511 solver.cpp:218] Iteration 4476 (2.54196 iter/s, 4.72077s/12 iters), loss = 0.608643
I0410 00:55:46.013233 14511 solver.cpp:237]     Train net output #0: loss = 0.608644 (* 1 = 0.608644 loss)
I0410 00:55:46.013244 14511 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 00:55:50.273371 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 00:55:57.887596 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 00:56:03.817941 14511 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 00:56:03.817981 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:56:06.635406 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:08.501264 14511 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0410 00:56:08.501309 14511 solver.cpp:397]     Test net output #1: loss = 3.68648 (* 1 = 3.68648 loss)
I0410 00:56:08.609669 14511 solver.cpp:218] Iteration 4488 (0.53107 iter/s, 22.5959s/12 iters), loss = 0.498502
I0410 00:56:08.609730 14511 solver.cpp:237]     Train net output #0: loss = 0.498502 (* 1 = 0.498502 loss)
I0410 00:56:08.609743 14511 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 00:56:12.736971 14511 solver.cpp:218] Iteration 4500 (2.90759 iter/s, 4.12713s/12 iters), loss = 0.419318
I0410 00:56:12.737011 14511 solver.cpp:237]     Train net output #0: loss = 0.419319 (* 1 = 0.419319 loss)
I0410 00:56:12.737021 14511 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 00:56:17.517077 14511 solver.cpp:218] Iteration 4512 (2.51049 iter/s, 4.77993s/12 iters), loss = 0.439593
I0410 00:56:17.517132 14511 solver.cpp:237]     Train net output #0: loss = 0.439594 (* 1 = 0.439594 loss)
I0410 00:56:17.517143 14511 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 00:56:22.777835 14511 solver.cpp:218] Iteration 4524 (2.28112 iter/s, 5.26056s/12 iters), loss = 0.374155
I0410 00:56:22.777890 14511 solver.cpp:237]     Train net output #0: loss = 0.374155 (* 1 = 0.374155 loss)
I0410 00:56:22.777904 14511 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 00:56:27.696854 14511 solver.cpp:218] Iteration 4536 (2.43961 iter/s, 4.91883s/12 iters), loss = 0.591157
I0410 00:56:27.696909 14511 solver.cpp:237]     Train net output #0: loss = 0.591158 (* 1 = 0.591158 loss)
I0410 00:56:27.696920 14511 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 00:56:32.860648 14511 solver.cpp:218] Iteration 4548 (2.32396 iter/s, 5.1636s/12 iters), loss = 0.352703
I0410 00:56:32.860698 14511 solver.cpp:237]     Train net output #0: loss = 0.352703 (* 1 = 0.352703 loss)
I0410 00:56:32.860709 14511 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 00:56:34.152472 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:38.154685 14511 solver.cpp:218] Iteration 4560 (2.26678 iter/s, 5.29384s/12 iters), loss = 0.463867
I0410 00:56:38.154758 14511 solver.cpp:237]     Train net output #0: loss = 0.463867 (* 1 = 0.463867 loss)
I0410 00:56:38.154769 14511 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 00:56:43.040803 14511 solver.cpp:218] Iteration 4572 (2.45604 iter/s, 4.88591s/12 iters), loss = 0.500948
I0410 00:56:43.040856 14511 solver.cpp:237]     Train net output #0: loss = 0.500948 (* 1 = 0.500948 loss)
I0410 00:56:43.040868 14511 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 00:56:48.210307 14511 solver.cpp:218] Iteration 4584 (2.3214 iter/s, 5.1693s/12 iters), loss = 0.480408
I0410 00:56:48.210361 14511 solver.cpp:237]     Train net output #0: loss = 0.480408 (* 1 = 0.480408 loss)
I0410 00:56:48.210372 14511 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 00:56:50.224443 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 00:57:00.341692 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 00:57:07.800094 14511 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 00:57:07.800117 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:57:10.526899 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:12.405500 14511 solver.cpp:397]     Test net output #0: accuracy = 0.341912
I0410 00:57:12.405548 14511 solver.cpp:397]     Test net output #1: loss = 3.62078 (* 1 = 3.62078 loss)
I0410 00:57:14.136221 14511 solver.cpp:218] Iteration 4596 (0.46287 iter/s, 25.9252s/12 iters), loss = 0.505493
I0410 00:57:14.136265 14511 solver.cpp:237]     Train net output #0: loss = 0.505493 (* 1 = 0.505493 loss)
I0410 00:57:14.136276 14511 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 00:57:18.868924 14511 solver.cpp:218] Iteration 4608 (2.53564 iter/s, 4.73253s/12 iters), loss = 0.493464
I0410 00:57:18.868971 14511 solver.cpp:237]     Train net output #0: loss = 0.493464 (* 1 = 0.493464 loss)
I0410 00:57:18.868983 14511 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 00:57:23.553691 14511 solver.cpp:218] Iteration 4620 (2.56159 iter/s, 4.68459s/12 iters), loss = 0.312903
I0410 00:57:23.553761 14511 solver.cpp:237]     Train net output #0: loss = 0.312903 (* 1 = 0.312903 loss)
I0410 00:57:23.553776 14511 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 00:57:28.323827 14511 solver.cpp:218] Iteration 4632 (2.51575 iter/s, 4.76994s/12 iters), loss = 0.444285
I0410 00:57:28.323877 14511 solver.cpp:237]     Train net output #0: loss = 0.444285 (* 1 = 0.444285 loss)
I0410 00:57:28.323889 14511 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 00:57:33.492766 14511 solver.cpp:218] Iteration 4644 (2.32164 iter/s, 5.16876s/12 iters), loss = 0.312806
I0410 00:57:33.492805 14511 solver.cpp:237]     Train net output #0: loss = 0.312806 (* 1 = 0.312806 loss)
I0410 00:57:33.492813 14511 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 00:57:37.049860 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:38.766321 14511 solver.cpp:218] Iteration 4656 (2.27558 iter/s, 5.27337s/12 iters), loss = 0.324747
I0410 00:57:38.766371 14511 solver.cpp:237]     Train net output #0: loss = 0.324747 (* 1 = 0.324747 loss)
I0410 00:57:38.766383 14511 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 00:57:43.696127 14511 solver.cpp:218] Iteration 4668 (2.43426 iter/s, 4.92963s/12 iters), loss = 0.308804
I0410 00:57:43.696247 14511 solver.cpp:237]     Train net output #0: loss = 0.308804 (* 1 = 0.308804 loss)
I0410 00:57:43.696260 14511 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 00:57:48.580436 14511 solver.cpp:218] Iteration 4680 (2.45697 iter/s, 4.88406s/12 iters), loss = 0.241868
I0410 00:57:48.580504 14511 solver.cpp:237]     Train net output #0: loss = 0.241868 (* 1 = 0.241868 loss)
I0410 00:57:48.580523 14511 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 00:57:52.966567 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 00:58:00.371062 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 00:58:12.665194 14511 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 00:58:12.665217 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:58:15.212306 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:17.086009 14511 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0410 00:58:17.086055 14511 solver.cpp:397]     Test net output #1: loss = 3.66725 (* 1 = 3.66725 loss)
I0410 00:58:17.195740 14511 solver.cpp:218] Iteration 4692 (0.419367 iter/s, 28.6145s/12 iters), loss = 0.362871
I0410 00:58:17.197265 14511 solver.cpp:237]     Train net output #0: loss = 0.362871 (* 1 = 0.362871 loss)
I0410 00:58:17.197278 14511 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 00:58:21.484414 14511 solver.cpp:218] Iteration 4704 (2.79913 iter/s, 4.28704s/12 iters), loss = 0.354016
I0410 00:58:21.484457 14511 solver.cpp:237]     Train net output #0: loss = 0.354016 (* 1 = 0.354016 loss)
I0410 00:58:21.484467 14511 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 00:58:26.404256 14511 solver.cpp:218] Iteration 4716 (2.43919 iter/s, 4.91967s/12 iters), loss = 0.407879
I0410 00:58:26.404295 14511 solver.cpp:237]     Train net output #0: loss = 0.407879 (* 1 = 0.407879 loss)
I0410 00:58:26.404304 14511 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 00:58:31.363528 14511 solver.cpp:218] Iteration 4728 (2.4198 iter/s, 4.9591s/12 iters), loss = 0.470858
I0410 00:58:31.363581 14511 solver.cpp:237]     Train net output #0: loss = 0.470858 (* 1 = 0.470858 loss)
I0410 00:58:31.363592 14511 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 00:58:36.329638 14511 solver.cpp:218] Iteration 4740 (2.41647 iter/s, 4.96593s/12 iters), loss = 0.245626
I0410 00:58:36.329680 14511 solver.cpp:237]     Train net output #0: loss = 0.245626 (* 1 = 0.245626 loss)
I0410 00:58:36.329690 14511 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 00:58:41.097627 14511 solver.cpp:218] Iteration 4752 (2.51688 iter/s, 4.76782s/12 iters), loss = 0.366681
I0410 00:58:41.097684 14511 solver.cpp:237]     Train net output #0: loss = 0.366681 (* 1 = 0.366681 loss)
I0410 00:58:41.097697 14511 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 00:58:41.533394 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:45.572649 14511 solver.cpp:218] Iteration 4764 (2.68166 iter/s, 4.47484s/12 iters), loss = 0.443995
I0410 00:58:45.572794 14511 solver.cpp:237]     Train net output #0: loss = 0.443995 (* 1 = 0.443995 loss)
I0410 00:58:45.572806 14511 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 00:58:50.054546 14511 solver.cpp:218] Iteration 4776 (2.6776 iter/s, 4.48163s/12 iters), loss = 0.484967
I0410 00:58:50.054602 14511 solver.cpp:237]     Train net output #0: loss = 0.484967 (* 1 = 0.484967 loss)
I0410 00:58:50.054615 14511 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 00:58:54.527148 14511 solver.cpp:218] Iteration 4788 (2.68311 iter/s, 4.47243s/12 iters), loss = 0.366364
I0410 00:58:54.527202 14511 solver.cpp:237]     Train net output #0: loss = 0.366364 (* 1 = 0.366364 loss)
I0410 00:58:54.527215 14511 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 00:58:56.331928 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 00:59:05.427942 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 00:59:20.900406 14511 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 00:59:20.900473 14511 net.cpp:676] Ignoring source layer train-data
I0410 00:59:23.503424 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:25.549876 14511 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0410 00:59:25.549932 14511 solver.cpp:397]     Test net output #1: loss = 3.56686 (* 1 = 3.56686 loss)
I0410 00:59:27.234519 14511 solver.cpp:218] Iteration 4800 (0.366899 iter/s, 32.7065s/12 iters), loss = 0.449222
I0410 00:59:27.234560 14511 solver.cpp:237]     Train net output #0: loss = 0.449222 (* 1 = 0.449222 loss)
I0410 00:59:27.234568 14511 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 00:59:32.484006 14511 solver.cpp:218] Iteration 4812 (2.28602 iter/s, 5.2493s/12 iters), loss = 0.358356
I0410 00:59:32.484055 14511 solver.cpp:237]     Train net output #0: loss = 0.358356 (* 1 = 0.358356 loss)
I0410 00:59:32.484067 14511 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 00:59:37.470371 14511 solver.cpp:218] Iteration 4824 (2.40665 iter/s, 4.98618s/12 iters), loss = 0.250244
I0410 00:59:37.470419 14511 solver.cpp:237]     Train net output #0: loss = 0.250244 (* 1 = 0.250244 loss)
I0410 00:59:37.470430 14511 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 00:59:42.084192 14511 solver.cpp:218] Iteration 4836 (2.60098 iter/s, 4.61364s/12 iters), loss = 0.298911
I0410 00:59:42.084240 14511 solver.cpp:237]     Train net output #0: loss = 0.298911 (* 1 = 0.298911 loss)
I0410 00:59:42.084250 14511 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 00:59:43.913430 14511 blocking_queue.cpp:49] Waiting for data
I0410 00:59:46.737131 14511 solver.cpp:218] Iteration 4848 (2.57911 iter/s, 4.65276s/12 iters), loss = 0.424271
I0410 00:59:46.737193 14511 solver.cpp:237]     Train net output #0: loss = 0.424272 (* 1 = 0.424272 loss)
I0410 00:59:46.737208 14511 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 00:59:49.243818 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:51.466446 14511 solver.cpp:218] Iteration 4860 (2.53747 iter/s, 4.72913s/12 iters), loss = 0.317906
I0410 00:59:51.466573 14511 solver.cpp:237]     Train net output #0: loss = 0.317906 (* 1 = 0.317906 loss)
I0410 00:59:51.466584 14511 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 00:59:56.135337 14511 solver.cpp:218] Iteration 4872 (2.57034 iter/s, 4.66864s/12 iters), loss = 0.416533
I0410 00:59:56.135381 14511 solver.cpp:237]     Train net output #0: loss = 0.416533 (* 1 = 0.416533 loss)
I0410 00:59:56.135392 14511 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 01:00:00.866005 14511 solver.cpp:218] Iteration 4884 (2.53674 iter/s, 4.73049s/12 iters), loss = 0.228766
I0410 01:00:00.866063 14511 solver.cpp:237]     Train net output #0: loss = 0.228766 (* 1 = 0.228766 loss)
I0410 01:00:00.866075 14511 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 01:00:05.249086 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 01:00:15.703395 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 01:00:32.086365 14511 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 01:00:32.086432 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:00:34.721174 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:36.719791 14511 solver.cpp:397]     Test net output #0: accuracy = 0.35049
I0410 01:00:36.719856 14511 solver.cpp:397]     Test net output #1: loss = 3.62699 (* 1 = 3.62699 loss)
I0410 01:00:36.828331 14511 solver.cpp:218] Iteration 4896 (0.333691 iter/s, 35.9614s/12 iters), loss = 0.340583
I0410 01:00:36.829859 14511 solver.cpp:237]     Train net output #0: loss = 0.340583 (* 1 = 0.340583 loss)
I0410 01:00:36.829871 14511 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 01:00:40.777846 14511 solver.cpp:218] Iteration 4908 (3.03961 iter/s, 3.94788s/12 iters), loss = 0.335001
I0410 01:00:40.777889 14511 solver.cpp:237]     Train net output #0: loss = 0.335002 (* 1 = 0.335002 loss)
I0410 01:00:40.777897 14511 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 01:00:45.732985 14511 solver.cpp:218] Iteration 4920 (2.42182 iter/s, 4.95496s/12 iters), loss = 0.260853
I0410 01:00:45.733031 14511 solver.cpp:237]     Train net output #0: loss = 0.260853 (* 1 = 0.260853 loss)
I0410 01:00:45.733042 14511 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 01:00:50.524833 14511 solver.cpp:218] Iteration 4932 (2.50435 iter/s, 4.79167s/12 iters), loss = 0.162361
I0410 01:00:50.524881 14511 solver.cpp:237]     Train net output #0: loss = 0.162361 (* 1 = 0.162361 loss)
I0410 01:00:50.524891 14511 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 01:00:55.196272 14511 solver.cpp:218] Iteration 4944 (2.5689 iter/s, 4.67126s/12 iters), loss = 0.177767
I0410 01:00:55.196316 14511 solver.cpp:237]     Train net output #0: loss = 0.177767 (* 1 = 0.177767 loss)
I0410 01:00:55.196324 14511 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 01:00:59.652612 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:59.866466 14511 solver.cpp:218] Iteration 4956 (2.56958 iter/s, 4.67002s/12 iters), loss = 0.198407
I0410 01:00:59.866524 14511 solver.cpp:237]     Train net output #0: loss = 0.198407 (* 1 = 0.198407 loss)
I0410 01:00:59.866537 14511 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 01:01:04.406394 14511 solver.cpp:218] Iteration 4968 (2.64332 iter/s, 4.53974s/12 iters), loss = 0.365389
I0410 01:01:04.406497 14511 solver.cpp:237]     Train net output #0: loss = 0.365389 (* 1 = 0.365389 loss)
I0410 01:01:04.406507 14511 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 01:01:08.990155 14511 solver.cpp:218] Iteration 4980 (2.61807 iter/s, 4.58354s/12 iters), loss = 0.289394
I0410 01:01:08.990198 14511 solver.cpp:237]     Train net output #0: loss = 0.289394 (* 1 = 0.289394 loss)
I0410 01:01:08.990208 14511 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 01:01:13.981165 14511 solver.cpp:218] Iteration 4992 (2.40441 iter/s, 4.99083s/12 iters), loss = 0.353576
I0410 01:01:13.981215 14511 solver.cpp:237]     Train net output #0: loss = 0.353576 (* 1 = 0.353576 loss)
I0410 01:01:13.981225 14511 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 01:01:15.994669 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 01:01:30.738003 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 01:01:42.561609 14511 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 01:01:42.561668 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:01:45.057852 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:47.067273 14511 solver.cpp:397]     Test net output #0: accuracy = 0.348039
I0410 01:01:47.067314 14511 solver.cpp:397]     Test net output #1: loss = 3.67585 (* 1 = 3.67585 loss)
I0410 01:01:48.728724 14511 solver.cpp:218] Iteration 5004 (0.345357 iter/s, 34.7467s/12 iters), loss = 0.207453
I0410 01:01:48.728776 14511 solver.cpp:237]     Train net output #0: loss = 0.207453 (* 1 = 0.207453 loss)
I0410 01:01:48.728787 14511 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 01:01:53.295976 14511 solver.cpp:218] Iteration 5016 (2.6275 iter/s, 4.56707s/12 iters), loss = 0.364714
I0410 01:01:53.296025 14511 solver.cpp:237]     Train net output #0: loss = 0.364715 (* 1 = 0.364715 loss)
I0410 01:01:53.296036 14511 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 01:01:57.938273 14511 solver.cpp:218] Iteration 5028 (2.58503 iter/s, 4.64212s/12 iters), loss = 0.155531
I0410 01:01:57.938313 14511 solver.cpp:237]     Train net output #0: loss = 0.155531 (* 1 = 0.155531 loss)
I0410 01:01:57.938321 14511 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 01:02:02.586758 14511 solver.cpp:218] Iteration 5040 (2.58158 iter/s, 4.64832s/12 iters), loss = 0.168551
I0410 01:02:02.586800 14511 solver.cpp:237]     Train net output #0: loss = 0.168552 (* 1 = 0.168552 loss)
I0410 01:02:02.586808 14511 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 01:02:07.239420 14511 solver.cpp:218] Iteration 5052 (2.57926 iter/s, 4.65249s/12 iters), loss = 0.210584
I0410 01:02:07.239471 14511 solver.cpp:237]     Train net output #0: loss = 0.210584 (* 1 = 0.210584 loss)
I0410 01:02:07.239482 14511 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 01:02:08.972915 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:11.943626 14511 solver.cpp:218] Iteration 5064 (2.55101 iter/s, 4.70403s/12 iters), loss = 0.335567
I0410 01:02:11.943679 14511 solver.cpp:237]     Train net output #0: loss = 0.335568 (* 1 = 0.335568 loss)
I0410 01:02:11.943691 14511 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 01:02:17.164398 14511 solver.cpp:218] Iteration 5076 (2.2986 iter/s, 5.22057s/12 iters), loss = 0.471624
I0410 01:02:17.169476 14511 solver.cpp:237]     Train net output #0: loss = 0.471624 (* 1 = 0.471624 loss)
I0410 01:02:17.169490 14511 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 01:02:22.212281 14511 solver.cpp:218] Iteration 5088 (2.37969 iter/s, 5.04267s/12 iters), loss = 0.163145
I0410 01:02:22.212340 14511 solver.cpp:237]     Train net output #0: loss = 0.163145 (* 1 = 0.163145 loss)
I0410 01:02:22.212353 14511 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 01:02:26.682231 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 01:02:40.909487 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 01:02:50.825794 14511 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 01:02:50.825870 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:02:53.366684 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:55.404518 14511 solver.cpp:397]     Test net output #0: accuracy = 0.338235
I0410 01:02:55.404561 14511 solver.cpp:397]     Test net output #1: loss = 3.75455 (* 1 = 3.75455 loss)
I0410 01:02:55.513067 14511 solver.cpp:218] Iteration 5100 (0.360361 iter/s, 33.2999s/12 iters), loss = 0.216747
I0410 01:02:55.513113 14511 solver.cpp:237]     Train net output #0: loss = 0.216747 (* 1 = 0.216747 loss)
I0410 01:02:55.513123 14511 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 01:02:59.461211 14511 solver.cpp:218] Iteration 5112 (3.03953 iter/s, 3.94798s/12 iters), loss = 0.356792
I0410 01:02:59.461259 14511 solver.cpp:237]     Train net output #0: loss = 0.356792 (* 1 = 0.356792 loss)
I0410 01:02:59.461269 14511 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 01:03:04.526791 14511 solver.cpp:218] Iteration 5124 (2.36902 iter/s, 5.06539s/12 iters), loss = 0.294344
I0410 01:03:04.526849 14511 solver.cpp:237]     Train net output #0: loss = 0.294344 (* 1 = 0.294344 loss)
I0410 01:03:04.526860 14511 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 01:03:09.232038 14511 solver.cpp:218] Iteration 5136 (2.55045 iter/s, 4.70506s/12 iters), loss = 0.232445
I0410 01:03:09.232095 14511 solver.cpp:237]     Train net output #0: loss = 0.232445 (* 1 = 0.232445 loss)
I0410 01:03:09.232107 14511 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 01:03:14.309350 14511 solver.cpp:218] Iteration 5148 (2.36355 iter/s, 5.07712s/12 iters), loss = 0.360782
I0410 01:03:14.309396 14511 solver.cpp:237]     Train net output #0: loss = 0.360782 (* 1 = 0.360782 loss)
I0410 01:03:14.309409 14511 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 01:03:18.406945 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:19.385838 14511 solver.cpp:218] Iteration 5160 (2.36392 iter/s, 5.0763s/12 iters), loss = 0.16351
I0410 01:03:19.385890 14511 solver.cpp:237]     Train net output #0: loss = 0.16351 (* 1 = 0.16351 loss)
I0410 01:03:19.385901 14511 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 01:03:24.529104 14511 solver.cpp:218] Iteration 5172 (2.33323 iter/s, 5.14308s/12 iters), loss = 0.337131
I0410 01:03:24.529181 14511 solver.cpp:237]     Train net output #0: loss = 0.337131 (* 1 = 0.337131 loss)
I0410 01:03:24.529193 14511 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 01:03:29.441258 14511 solver.cpp:218] Iteration 5184 (2.44302 iter/s, 4.91195s/12 iters), loss = 0.230747
I0410 01:03:29.441310 14511 solver.cpp:237]     Train net output #0: loss = 0.230748 (* 1 = 0.230748 loss)
I0410 01:03:29.441323 14511 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 01:03:34.537068 14511 solver.cpp:218] Iteration 5196 (2.35496 iter/s, 5.09562s/12 iters), loss = 0.375874
I0410 01:03:34.537124 14511 solver.cpp:237]     Train net output #0: loss = 0.375874 (* 1 = 0.375874 loss)
I0410 01:03:34.537137 14511 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 01:03:36.383030 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 01:03:49.276247 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 01:03:56.291311 14511 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 01:03:56.291376 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:03:58.707362 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:00.780644 14511 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0410 01:04:00.780686 14511 solver.cpp:397]     Test net output #1: loss = 3.54987 (* 1 = 3.54987 loss)
I0410 01:04:02.541517 14511 solver.cpp:218] Iteration 5208 (0.428515 iter/s, 28.0037s/12 iters), loss = 0.300359
I0410 01:04:02.541565 14511 solver.cpp:237]     Train net output #0: loss = 0.300359 (* 1 = 0.300359 loss)
I0410 01:04:02.541576 14511 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 01:04:07.105530 14511 solver.cpp:218] Iteration 5220 (2.62936 iter/s, 4.56384s/12 iters), loss = 0.321853
I0410 01:04:07.105571 14511 solver.cpp:237]     Train net output #0: loss = 0.321853 (* 1 = 0.321853 loss)
I0410 01:04:07.105581 14511 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 01:04:11.567997 14511 solver.cpp:218] Iteration 5232 (2.68919 iter/s, 4.4623s/12 iters), loss = 0.368128
I0410 01:04:11.568037 14511 solver.cpp:237]     Train net output #0: loss = 0.368128 (* 1 = 0.368128 loss)
I0410 01:04:11.568046 14511 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 01:04:16.292984 14511 solver.cpp:218] Iteration 5244 (2.53978 iter/s, 4.72482s/12 iters), loss = 0.270355
I0410 01:04:16.293035 14511 solver.cpp:237]     Train net output #0: loss = 0.270355 (* 1 = 0.270355 loss)
I0410 01:04:16.293046 14511 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 01:04:20.961920 14511 solver.cpp:218] Iteration 5256 (2.57028 iter/s, 4.66876s/12 iters), loss = 0.274369
I0410 01:04:20.961990 14511 solver.cpp:237]     Train net output #0: loss = 0.274369 (* 1 = 0.274369 loss)
I0410 01:04:20.962004 14511 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 01:04:22.322449 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:25.855316 14511 solver.cpp:218] Iteration 5268 (2.45238 iter/s, 4.8932s/12 iters), loss = 0.382467
I0410 01:04:25.855365 14511 solver.cpp:237]     Train net output #0: loss = 0.382468 (* 1 = 0.382468 loss)
I0410 01:04:25.855376 14511 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 01:04:30.550693 14511 solver.cpp:218] Iteration 5280 (2.5558 iter/s, 4.6952s/12 iters), loss = 0.216848
I0410 01:04:30.550861 14511 solver.cpp:237]     Train net output #0: loss = 0.216848 (* 1 = 0.216848 loss)
I0410 01:04:30.550876 14511 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 01:04:35.238600 14511 solver.cpp:218] Iteration 5292 (2.55993 iter/s, 4.68762s/12 iters), loss = 0.199428
I0410 01:04:35.238644 14511 solver.cpp:237]     Train net output #0: loss = 0.199428 (* 1 = 0.199428 loss)
I0410 01:04:35.238656 14511 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 01:04:39.857364 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 01:04:50.321887 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 01:04:56.113865 14511 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 01:04:56.113886 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:04:58.475899 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:00.595777 14511 solver.cpp:397]     Test net output #0: accuracy = 0.359681
I0410 01:05:00.597074 14511 solver.cpp:397]     Test net output #1: loss = 3.68637 (* 1 = 3.68637 loss)
I0410 01:05:00.704424 14511 solver.cpp:218] Iteration 5304 (0.471232 iter/s, 25.4652s/12 iters), loss = 0.175064
I0410 01:05:00.705946 14511 solver.cpp:237]     Train net output #0: loss = 0.175064 (* 1 = 0.175064 loss)
I0410 01:05:00.705974 14511 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 01:05:04.654254 14511 solver.cpp:218] Iteration 5316 (3.03936 iter/s, 3.9482s/12 iters), loss = 0.39789
I0410 01:05:04.654310 14511 solver.cpp:237]     Train net output #0: loss = 0.39789 (* 1 = 0.39789 loss)
I0410 01:05:04.654323 14511 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 01:05:09.571622 14511 solver.cpp:218] Iteration 5328 (2.44042 iter/s, 4.91718s/12 iters), loss = 0.223137
I0410 01:05:09.571674 14511 solver.cpp:237]     Train net output #0: loss = 0.223138 (* 1 = 0.223138 loss)
I0410 01:05:09.571686 14511 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 01:05:14.809830 14511 solver.cpp:218] Iteration 5340 (2.29094 iter/s, 5.23801s/12 iters), loss = 0.187121
I0410 01:05:14.809870 14511 solver.cpp:237]     Train net output #0: loss = 0.187121 (* 1 = 0.187121 loss)
I0410 01:05:14.809878 14511 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 01:05:19.960083 14511 solver.cpp:218] Iteration 5352 (2.33006 iter/s, 5.15008s/12 iters), loss = 0.206645
I0410 01:05:19.960129 14511 solver.cpp:237]     Train net output #0: loss = 0.206645 (* 1 = 0.206645 loss)
I0410 01:05:19.960140 14511 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 01:05:23.336236 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:24.890077 14511 solver.cpp:218] Iteration 5364 (2.43417 iter/s, 4.92981s/12 iters), loss = 0.121008
I0410 01:05:24.890123 14511 solver.cpp:237]     Train net output #0: loss = 0.121009 (* 1 = 0.121009 loss)
I0410 01:05:24.890134 14511 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 01:05:29.556870 14511 solver.cpp:218] Iteration 5376 (2.57145 iter/s, 4.66662s/12 iters), loss = 0.228311
I0410 01:05:29.556917 14511 solver.cpp:237]     Train net output #0: loss = 0.228311 (* 1 = 0.228311 loss)
I0410 01:05:29.556931 14511 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 01:05:34.458056 14511 solver.cpp:218] Iteration 5388 (2.44848 iter/s, 4.90101s/12 iters), loss = 0.268222
I0410 01:05:34.458204 14511 solver.cpp:237]     Train net output #0: loss = 0.268222 (* 1 = 0.268222 loss)
I0410 01:05:34.458217 14511 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 01:05:39.300938 14511 solver.cpp:218] Iteration 5400 (2.478 iter/s, 4.84261s/12 iters), loss = 0.287998
I0410 01:05:39.300987 14511 solver.cpp:237]     Train net output #0: loss = 0.287999 (* 1 = 0.287999 loss)
I0410 01:05:39.300997 14511 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 01:05:41.447608 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 01:05:48.972501 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 01:05:54.781167 14511 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 01:05:54.781188 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:05:57.143654 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:59.341920 14511 solver.cpp:397]     Test net output #0: accuracy = 0.36152
I0410 01:05:59.342000 14511 solver.cpp:397]     Test net output #1: loss = 3.50339 (* 1 = 3.50339 loss)
I0410 01:06:00.970258 14511 solver.cpp:218] Iteration 5412 (0.553793 iter/s, 21.6687s/12 iters), loss = 0.258157
I0410 01:06:00.970312 14511 solver.cpp:237]     Train net output #0: loss = 0.258157 (* 1 = 0.258157 loss)
I0410 01:06:00.970324 14511 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 01:06:06.110154 14511 solver.cpp:218] Iteration 5424 (2.33476 iter/s, 5.1397s/12 iters), loss = 0.216429
I0410 01:06:06.110229 14511 solver.cpp:237]     Train net output #0: loss = 0.21643 (* 1 = 0.21643 loss)
I0410 01:06:06.110239 14511 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 01:06:11.383972 14511 solver.cpp:218] Iteration 5436 (2.27549 iter/s, 5.2736s/12 iters), loss = 0.228579
I0410 01:06:11.384032 14511 solver.cpp:237]     Train net output #0: loss = 0.228579 (* 1 = 0.228579 loss)
I0410 01:06:11.384043 14511 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 01:06:16.654417 14511 solver.cpp:218] Iteration 5448 (2.27693 iter/s, 5.27025s/12 iters), loss = 0.213924
I0410 01:06:16.654461 14511 solver.cpp:237]     Train net output #0: loss = 0.213924 (* 1 = 0.213924 loss)
I0410 01:06:16.654470 14511 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 01:06:21.128305 14511 solver.cpp:218] Iteration 5460 (2.68233 iter/s, 4.47372s/12 iters), loss = 0.125208
I0410 01:06:21.128346 14511 solver.cpp:237]     Train net output #0: loss = 0.125208 (* 1 = 0.125208 loss)
I0410 01:06:21.128355 14511 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 01:06:21.581924 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:25.876535 14511 solver.cpp:218] Iteration 5472 (2.52735 iter/s, 4.74806s/12 iters), loss = 0.293857
I0410 01:06:25.876590 14511 solver.cpp:237]     Train net output #0: loss = 0.293857 (* 1 = 0.293857 loss)
I0410 01:06:25.876602 14511 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 01:06:30.884312 14511 solver.cpp:218] Iteration 5484 (2.39636 iter/s, 5.00759s/12 iters), loss = 0.41018
I0410 01:06:30.884358 14511 solver.cpp:237]     Train net output #0: loss = 0.410181 (* 1 = 0.410181 loss)
I0410 01:06:30.884369 14511 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 01:06:35.950260 14511 solver.cpp:218] Iteration 5496 (2.36884 iter/s, 5.06577s/12 iters), loss = 0.190358
I0410 01:06:35.950309 14511 solver.cpp:237]     Train net output #0: loss = 0.190358 (* 1 = 0.190358 loss)
I0410 01:06:35.950320 14511 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 01:06:40.306181 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 01:06:47.690773 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 01:06:54.706717 14511 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 01:06:54.706743 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:06:57.003432 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:59.201817 14511 solver.cpp:397]     Test net output #0: accuracy = 0.367647
I0410 01:06:59.201867 14511 solver.cpp:397]     Test net output #1: loss = 3.61965 (* 1 = 3.61965 loss)
I0410 01:06:59.310293 14511 solver.cpp:218] Iteration 5508 (0.513711 iter/s, 23.3594s/12 iters), loss = 0.150176
I0410 01:06:59.310338 14511 solver.cpp:237]     Train net output #0: loss = 0.150176 (* 1 = 0.150176 loss)
I0410 01:06:59.310348 14511 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 01:07:03.677107 14511 solver.cpp:218] Iteration 5520 (2.7481 iter/s, 4.36665s/12 iters), loss = 0.257921
I0410 01:07:03.677153 14511 solver.cpp:237]     Train net output #0: loss = 0.257921 (* 1 = 0.257921 loss)
I0410 01:07:03.677163 14511 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 01:07:06.240365 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:07:08.902302 14511 solver.cpp:218] Iteration 5532 (2.29665 iter/s, 5.225s/12 iters), loss = 0.148393
I0410 01:07:08.902359 14511 solver.cpp:237]     Train net output #0: loss = 0.148393 (* 1 = 0.148393 loss)
I0410 01:07:08.902371 14511 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 01:07:13.876804 14511 solver.cpp:218] Iteration 5544 (2.41239 iter/s, 4.97431s/12 iters), loss = 0.268574
I0410 01:07:13.876866 14511 solver.cpp:237]     Train net output #0: loss = 0.268574 (* 1 = 0.268574 loss)
I0410 01:07:13.876874 14511 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 01:07:19.019217 14511 solver.cpp:218] Iteration 5556 (2.33362 iter/s, 5.14221s/12 iters), loss = 0.251269
I0410 01:07:19.019258 14511 solver.cpp:237]     Train net output #0: loss = 0.251269 (* 1 = 0.251269 loss)
I0410 01:07:19.019268 14511 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 01:07:21.561425 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:23.792402 14511 solver.cpp:218] Iteration 5568 (2.51413 iter/s, 4.77301s/12 iters), loss = 0.203557
I0410 01:07:23.792451 14511 solver.cpp:237]     Train net output #0: loss = 0.203557 (* 1 = 0.203557 loss)
I0410 01:07:23.792464 14511 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 01:07:28.792490 14511 solver.cpp:218] Iteration 5580 (2.40004 iter/s, 4.99991s/12 iters), loss = 0.157737
I0410 01:07:28.792534 14511 solver.cpp:237]     Train net output #0: loss = 0.157737 (* 1 = 0.157737 loss)
I0410 01:07:28.792543 14511 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 01:07:33.808230 14511 solver.cpp:218] Iteration 5592 (2.39256 iter/s, 5.01556s/12 iters), loss = 0.192767
I0410 01:07:33.808281 14511 solver.cpp:237]     Train net output #0: loss = 0.192767 (* 1 = 0.192767 loss)
I0410 01:07:33.808291 14511 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 01:07:38.713160 14511 solver.cpp:218] Iteration 5604 (2.44661 iter/s, 4.90475s/12 iters), loss = 0.266833
I0410 01:07:38.713204 14511 solver.cpp:237]     Train net output #0: loss = 0.266833 (* 1 = 0.266833 loss)
I0410 01:07:38.713215 14511 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 01:07:40.797361 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 01:07:48.261669 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 01:08:01.111538 14511 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 01:08:01.111563 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:08:03.380302 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:05.693401 14511 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0410 01:08:05.693449 14511 solver.cpp:397]     Test net output #1: loss = 3.65019 (* 1 = 3.65019 loss)
I0410 01:08:07.565152 14511 solver.cpp:218] Iteration 5616 (0.415927 iter/s, 28.8512s/12 iters), loss = 0.230938
I0410 01:08:07.565212 14511 solver.cpp:237]     Train net output #0: loss = 0.230938 (* 1 = 0.230938 loss)
I0410 01:08:07.565225 14511 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 01:08:12.844132 14511 solver.cpp:218] Iteration 5628 (2.27325 iter/s, 5.27878s/12 iters), loss = 0.165072
I0410 01:08:12.844184 14511 solver.cpp:237]     Train net output #0: loss = 0.165072 (* 1 = 0.165072 loss)
I0410 01:08:12.844195 14511 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 01:08:17.690140 14511 solver.cpp:218] Iteration 5640 (2.47636 iter/s, 4.84583s/12 iters), loss = 0.327864
I0410 01:08:17.690184 14511 solver.cpp:237]     Train net output #0: loss = 0.327864 (* 1 = 0.327864 loss)
I0410 01:08:17.690193 14511 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 01:08:22.682946 14511 solver.cpp:218] Iteration 5652 (2.40355 iter/s, 4.99262s/12 iters), loss = 0.371102
I0410 01:08:22.683046 14511 solver.cpp:237]     Train net output #0: loss = 0.371102 (* 1 = 0.371102 loss)
I0410 01:08:22.683058 14511 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 01:08:27.730491 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:27.920796 14511 solver.cpp:218] Iteration 5664 (2.29112 iter/s, 5.23761s/12 iters), loss = 0.208816
I0410 01:08:27.920840 14511 solver.cpp:237]     Train net output #0: loss = 0.208816 (* 1 = 0.208816 loss)
I0410 01:08:27.920850 14511 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 01:08:32.724720 14511 solver.cpp:218] Iteration 5676 (2.49805 iter/s, 4.80375s/12 iters), loss = 0.201159
I0410 01:08:32.724757 14511 solver.cpp:237]     Train net output #0: loss = 0.201159 (* 1 = 0.201159 loss)
I0410 01:08:32.724766 14511 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 01:08:37.605815 14511 solver.cpp:218] Iteration 5688 (2.45855 iter/s, 4.88092s/12 iters), loss = 0.105483
I0410 01:08:37.605867 14511 solver.cpp:237]     Train net output #0: loss = 0.105483 (* 1 = 0.105483 loss)
I0410 01:08:37.605880 14511 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 01:08:42.292009 14511 solver.cpp:218] Iteration 5700 (2.56081 iter/s, 4.68601s/12 iters), loss = 0.298178
I0410 01:08:42.292059 14511 solver.cpp:237]     Train net output #0: loss = 0.298178 (* 1 = 0.298178 loss)
I0410 01:08:42.292070 14511 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 01:08:46.584285 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 01:08:58.186414 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 01:09:15.062376 14511 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 01:09:15.062400 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:09:17.406103 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:19.708611 14511 solver.cpp:397]     Test net output #0: accuracy = 0.358456
I0410 01:09:19.708663 14511 solver.cpp:397]     Test net output #1: loss = 3.72149 (* 1 = 3.72149 loss)
I0410 01:09:19.817138 14511 solver.cpp:218] Iteration 5712 (0.319794 iter/s, 37.5242s/12 iters), loss = 0.188163
I0410 01:09:19.817185 14511 solver.cpp:237]     Train net output #0: loss = 0.188163 (* 1 = 0.188163 loss)
I0410 01:09:19.817196 14511 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 01:09:24.797775 14511 solver.cpp:218] Iteration 5724 (2.40943 iter/s, 4.98043s/12 iters), loss = 0.184398
I0410 01:09:24.797827 14511 solver.cpp:237]     Train net output #0: loss = 0.184398 (* 1 = 0.184398 loss)
I0410 01:09:24.797837 14511 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 01:09:29.470242 14511 solver.cpp:218] Iteration 5736 (2.56833 iter/s, 4.67229s/12 iters), loss = 0.233268
I0410 01:09:29.470378 14511 solver.cpp:237]     Train net output #0: loss = 0.233269 (* 1 = 0.233269 loss)
I0410 01:09:29.470389 14511 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 01:09:34.140512 14511 solver.cpp:218] Iteration 5748 (2.56959 iter/s, 4.67001s/12 iters), loss = 0.176292
I0410 01:09:34.140560 14511 solver.cpp:237]     Train net output #0: loss = 0.176292 (* 1 = 0.176292 loss)
I0410 01:09:34.140573 14511 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 01:09:38.754601 14511 solver.cpp:218] Iteration 5760 (2.60083 iter/s, 4.61391s/12 iters), loss = 0.166925
I0410 01:09:38.754648 14511 solver.cpp:237]     Train net output #0: loss = 0.166925 (* 1 = 0.166925 loss)
I0410 01:09:38.754658 14511 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 01:09:40.587455 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:43.507040 14511 solver.cpp:218] Iteration 5772 (2.52511 iter/s, 4.75226s/12 iters), loss = 0.0827197
I0410 01:09:43.507092 14511 solver.cpp:237]     Train net output #0: loss = 0.0827199 (* 1 = 0.0827199 loss)
I0410 01:09:43.507104 14511 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 01:09:48.465631 14511 solver.cpp:218] Iteration 5784 (2.42013 iter/s, 4.95841s/12 iters), loss = 0.163946
I0410 01:09:48.465672 14511 solver.cpp:237]     Train net output #0: loss = 0.163946 (* 1 = 0.163946 loss)
I0410 01:09:48.465682 14511 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 01:09:53.218657 14511 solver.cpp:218] Iteration 5796 (2.5248 iter/s, 4.75286s/12 iters), loss = 0.169513
I0410 01:09:53.218698 14511 solver.cpp:237]     Train net output #0: loss = 0.169513 (* 1 = 0.169513 loss)
I0410 01:09:53.218708 14511 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 01:09:57.852401 14511 solver.cpp:218] Iteration 5808 (2.5898 iter/s, 4.63357s/12 iters), loss = 0.116686
I0410 01:09:57.852455 14511 solver.cpp:237]     Train net output #0: loss = 0.116686 (* 1 = 0.116686 loss)
I0410 01:09:57.852468 14511 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 01:09:59.779498 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 01:10:14.594947 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 01:10:28.912925 14511 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 01:10:28.912947 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:10:31.221469 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:33.683668 14511 solver.cpp:397]     Test net output #0: accuracy = 0.377451
I0410 01:10:33.683717 14511 solver.cpp:397]     Test net output #1: loss = 3.57158 (* 1 = 3.57158 loss)
I0410 01:10:35.434041 14511 solver.cpp:218] Iteration 5820 (0.319313 iter/s, 37.5807s/12 iters), loss = 0.326795
I0410 01:10:35.434082 14511 solver.cpp:237]     Train net output #0: loss = 0.326795 (* 1 = 0.326795 loss)
I0410 01:10:35.434092 14511 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 01:10:40.109922 14511 solver.cpp:218] Iteration 5832 (2.56645 iter/s, 4.67571s/12 iters), loss = 0.196327
I0410 01:10:40.109989 14511 solver.cpp:237]     Train net output #0: loss = 0.196327 (* 1 = 0.196327 loss)
I0410 01:10:40.110002 14511 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 01:10:44.840183 14511 solver.cpp:218] Iteration 5844 (2.53696 iter/s, 4.73007s/12 iters), loss = 0.151725
I0410 01:10:44.840236 14511 solver.cpp:237]     Train net output #0: loss = 0.151725 (* 1 = 0.151725 loss)
I0410 01:10:44.840247 14511 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 01:10:49.622658 14511 solver.cpp:218] Iteration 5856 (2.50926 iter/s, 4.78229s/12 iters), loss = 0.142058
I0410 01:10:49.622705 14511 solver.cpp:237]     Train net output #0: loss = 0.142058 (* 1 = 0.142058 loss)
I0410 01:10:49.622717 14511 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 01:10:53.701980 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:54.510025 14511 solver.cpp:218] Iteration 5868 (2.4554 iter/s, 4.88719s/12 iters), loss = 0.233144
I0410 01:10:54.510069 14511 solver.cpp:237]     Train net output #0: loss = 0.233144 (* 1 = 0.233144 loss)
I0410 01:10:54.510079 14511 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 01:10:59.468966 14511 solver.cpp:218] Iteration 5880 (2.41996 iter/s, 4.95877s/12 iters), loss = 0.13498
I0410 01:10:59.469005 14511 solver.cpp:237]     Train net output #0: loss = 0.13498 (* 1 = 0.13498 loss)
I0410 01:10:59.469014 14511 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 01:11:04.107127 14511 solver.cpp:218] Iteration 5892 (2.58733 iter/s, 4.63799s/12 iters), loss = 0.118018
I0410 01:11:04.107273 14511 solver.cpp:237]     Train net output #0: loss = 0.118018 (* 1 = 0.118018 loss)
I0410 01:11:04.107286 14511 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 01:11:08.770228 14511 solver.cpp:218] Iteration 5904 (2.57354 iter/s, 4.66283s/12 iters), loss = 0.271511
I0410 01:11:08.770277 14511 solver.cpp:237]     Train net output #0: loss = 0.271511 (* 1 = 0.271511 loss)
I0410 01:11:08.770287 14511 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 01:11:13.001456 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 01:11:35.090948 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 01:11:40.906052 14511 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 01:11:40.906072 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:11:43.149864 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:45.498044 14511 solver.cpp:397]     Test net output #0: accuracy = 0.385417
I0410 01:11:45.498080 14511 solver.cpp:397]     Test net output #1: loss = 3.54946 (* 1 = 3.54946 loss)
I0410 01:11:45.600947 14511 solver.cpp:218] Iteration 5916 (0.325823 iter/s, 36.8298s/12 iters), loss = 0.10012
I0410 01:11:45.600993 14511 solver.cpp:237]     Train net output #0: loss = 0.10012 (* 1 = 0.10012 loss)
I0410 01:11:45.601002 14511 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 01:11:49.448297 14511 solver.cpp:218] Iteration 5928 (3.11916 iter/s, 3.84719s/12 iters), loss = 0.129712
I0410 01:11:49.448345 14511 solver.cpp:237]     Train net output #0: loss = 0.129712 (* 1 = 0.129712 loss)
I0410 01:11:49.448354 14511 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 01:11:54.089792 14511 solver.cpp:218] Iteration 5940 (2.58547 iter/s, 4.64132s/12 iters), loss = 0.178665
I0410 01:11:54.089838 14511 solver.cpp:237]     Train net output #0: loss = 0.178665 (* 1 = 0.178665 loss)
I0410 01:11:54.089849 14511 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 01:11:58.743453 14511 solver.cpp:218] Iteration 5952 (2.57871 iter/s, 4.65349s/12 iters), loss = 0.173497
I0410 01:11:58.743497 14511 solver.cpp:237]     Train net output #0: loss = 0.173497 (* 1 = 0.173497 loss)
I0410 01:11:58.743506 14511 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 01:12:03.809891 14511 solver.cpp:218] Iteration 5964 (2.36861 iter/s, 5.06625s/12 iters), loss = 0.150624
I0410 01:12:03.809938 14511 solver.cpp:237]     Train net output #0: loss = 0.150624 (* 1 = 0.150624 loss)
I0410 01:12:03.809949 14511 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 01:12:05.206674 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:08.990423 14511 solver.cpp:218] Iteration 5976 (2.31645 iter/s, 5.18034s/12 iters), loss = 0.187286
I0410 01:12:08.990479 14511 solver.cpp:237]     Train net output #0: loss = 0.187286 (* 1 = 0.187286 loss)
I0410 01:12:08.990491 14511 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 01:12:14.128427 14511 solver.cpp:218] Iteration 5988 (2.33563 iter/s, 5.13781s/12 iters), loss = 0.150326
I0410 01:12:14.128477 14511 solver.cpp:237]     Train net output #0: loss = 0.150326 (* 1 = 0.150326 loss)
I0410 01:12:14.128489 14511 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 01:12:19.390439 14511 solver.cpp:218] Iteration 6000 (2.28058 iter/s, 5.26182s/12 iters), loss = 0.216042
I0410 01:12:19.390482 14511 solver.cpp:237]     Train net output #0: loss = 0.216042 (* 1 = 0.216042 loss)
I0410 01:12:19.390491 14511 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 01:12:24.616400 14511 solver.cpp:218] Iteration 6012 (2.29631 iter/s, 5.22578s/12 iters), loss = 0.310853
I0410 01:12:24.616443 14511 solver.cpp:237]     Train net output #0: loss = 0.310853 (* 1 = 0.310853 loss)
I0410 01:12:24.616456 14511 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 01:12:26.461890 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 01:12:39.217243 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 01:12:49.475926 14511 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 01:12:49.475950 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:12:51.588263 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:54.061864 14511 solver.cpp:397]     Test net output #0: accuracy = 0.371936
I0410 01:12:54.061908 14511 solver.cpp:397]     Test net output #1: loss = 3.56672 (* 1 = 3.56672 loss)
I0410 01:12:55.804613 14511 solver.cpp:218] Iteration 6024 (0.384771 iter/s, 31.1874s/12 iters), loss = 0.191837
I0410 01:12:55.804657 14511 solver.cpp:237]     Train net output #0: loss = 0.191837 (* 1 = 0.191837 loss)
I0410 01:12:55.804666 14511 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 01:13:00.846741 14511 solver.cpp:218] Iteration 6036 (2.38003 iter/s, 5.04194s/12 iters), loss = 0.115339
I0410 01:13:00.846786 14511 solver.cpp:237]     Train net output #0: loss = 0.115339 (* 1 = 0.115339 loss)
I0410 01:13:00.846794 14511 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 01:13:06.049654 14511 solver.cpp:218] Iteration 6048 (2.30648 iter/s, 5.20272s/12 iters), loss = 0.19903
I0410 01:13:06.049711 14511 solver.cpp:237]     Train net output #0: loss = 0.19903 (* 1 = 0.19903 loss)
I0410 01:13:06.049724 14511 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 01:13:11.276993 14511 solver.cpp:218] Iteration 6060 (2.29571 iter/s, 5.22714s/12 iters), loss = 0.153412
I0410 01:13:11.277107 14511 solver.cpp:237]     Train net output #0: loss = 0.153412 (* 1 = 0.153412 loss)
I0410 01:13:11.277119 14511 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 01:13:14.886521 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:16.540992 14511 solver.cpp:218] Iteration 6072 (2.27974 iter/s, 5.26375s/12 iters), loss = 0.102203
I0410 01:13:16.541041 14511 solver.cpp:237]     Train net output #0: loss = 0.102203 (* 1 = 0.102203 loss)
I0410 01:13:16.541052 14511 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 01:13:21.760349 14511 solver.cpp:218] Iteration 6084 (2.29922 iter/s, 5.21916s/12 iters), loss = 0.155318
I0410 01:13:21.760407 14511 solver.cpp:237]     Train net output #0: loss = 0.155318 (* 1 = 0.155318 loss)
I0410 01:13:21.760419 14511 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 01:13:26.986002 14511 solver.cpp:218] Iteration 6096 (2.29645 iter/s, 5.22546s/12 iters), loss = 0.302396
I0410 01:13:26.986052 14511 solver.cpp:237]     Train net output #0: loss = 0.302396 (* 1 = 0.302396 loss)
I0410 01:13:26.986066 14511 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 01:13:31.987946 14511 solver.cpp:218] Iteration 6108 (2.39916 iter/s, 5.00176s/12 iters), loss = 0.0694924
I0410 01:13:31.987998 14511 solver.cpp:237]     Train net output #0: loss = 0.0694926 (* 1 = 0.0694926 loss)
I0410 01:13:31.988010 14511 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 01:13:36.511819 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 01:13:47.192934 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 01:13:54.354285 14511 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 01:13:54.354310 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:13:56.423243 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:58.911289 14511 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0410 01:13:58.911327 14511 solver.cpp:397]     Test net output #1: loss = 3.65497 (* 1 = 3.65497 loss)
I0410 01:13:59.020144 14511 solver.cpp:218] Iteration 6120 (0.443927 iter/s, 27.0315s/12 iters), loss = 0.158801
I0410 01:13:59.020192 14511 solver.cpp:237]     Train net output #0: loss = 0.158802 (* 1 = 0.158802 loss)
I0410 01:13:59.020202 14511 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 01:14:03.214680 14511 solver.cpp:218] Iteration 6132 (2.86098 iter/s, 4.19437s/12 iters), loss = 0.120574
I0410 01:14:03.214732 14511 solver.cpp:237]     Train net output #0: loss = 0.120574 (* 1 = 0.120574 loss)
I0410 01:14:03.214743 14511 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 01:14:08.101944 14511 solver.cpp:218] Iteration 6144 (2.45545 iter/s, 4.88708s/12 iters), loss = 0.190249
I0410 01:14:08.102011 14511 solver.cpp:237]     Train net output #0: loss = 0.190249 (* 1 = 0.190249 loss)
I0410 01:14:08.102023 14511 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 01:14:13.061127 14511 solver.cpp:218] Iteration 6156 (2.41985 iter/s, 4.95898s/12 iters), loss = 0.307339
I0410 01:14:13.061182 14511 solver.cpp:237]     Train net output #0: loss = 0.307339 (* 1 = 0.307339 loss)
I0410 01:14:13.061193 14511 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 01:14:17.809806 14511 solver.cpp:218] Iteration 6168 (2.52711 iter/s, 4.7485s/12 iters), loss = 0.0722835
I0410 01:14:17.809900 14511 solver.cpp:237]     Train net output #0: loss = 0.0722837 (* 1 = 0.0722837 loss)
I0410 01:14:17.809911 14511 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 01:14:18.321180 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:22.722013 14511 solver.cpp:218] Iteration 6180 (2.44301 iter/s, 4.91198s/12 iters), loss = 0.237896
I0410 01:14:22.722069 14511 solver.cpp:237]     Train net output #0: loss = 0.237896 (* 1 = 0.237896 loss)
I0410 01:14:22.722082 14511 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 01:14:28.079314 14511 solver.cpp:218] Iteration 6192 (2.24002 iter/s, 5.3571s/12 iters), loss = 0.225772
I0410 01:14:28.079362 14511 solver.cpp:237]     Train net output #0: loss = 0.225772 (* 1 = 0.225772 loss)
I0410 01:14:28.079375 14511 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 01:14:33.284238 14511 solver.cpp:218] Iteration 6204 (2.30559 iter/s, 5.20474s/12 iters), loss = 0.0460444
I0410 01:14:33.284286 14511 solver.cpp:237]     Train net output #0: loss = 0.0460446 (* 1 = 0.0460446 loss)
I0410 01:14:33.284296 14511 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 01:14:38.249878 14511 solver.cpp:218] Iteration 6216 (2.4167 iter/s, 4.96546s/12 iters), loss = 0.401869
I0410 01:14:38.249928 14511 solver.cpp:237]     Train net output #0: loss = 0.40187 (* 1 = 0.40187 loss)
I0410 01:14:38.249939 14511 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 01:14:40.216316 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 01:14:48.048238 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 01:14:54.131093 14511 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 01:14:54.131117 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:14:56.326704 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:57.640708 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:14:58.823215 14511 solver.cpp:397]     Test net output #0: accuracy = 0.389093
I0410 01:14:58.823254 14511 solver.cpp:397]     Test net output #1: loss = 3.54842 (* 1 = 3.54842 loss)
I0410 01:15:00.521533 14511 solver.cpp:218] Iteration 6228 (0.538816 iter/s, 22.2711s/12 iters), loss = 0.216904
I0410 01:15:00.521572 14511 solver.cpp:237]     Train net output #0: loss = 0.216904 (* 1 = 0.216904 loss)
I0410 01:15:00.521581 14511 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 01:15:05.148520 14511 solver.cpp:218] Iteration 6240 (2.59357 iter/s, 4.62682s/12 iters), loss = 0.147743
I0410 01:15:05.148567 14511 solver.cpp:237]     Train net output #0: loss = 0.147743 (* 1 = 0.147743 loss)
I0410 01:15:05.148576 14511 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 01:15:09.792371 14511 solver.cpp:218] Iteration 6252 (2.58416 iter/s, 4.64368s/12 iters), loss = 0.0907082
I0410 01:15:09.792415 14511 solver.cpp:237]     Train net output #0: loss = 0.0907084 (* 1 = 0.0907084 loss)
I0410 01:15:09.792425 14511 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 01:15:14.575296 14511 solver.cpp:218] Iteration 6264 (2.50902 iter/s, 4.78275s/12 iters), loss = 0.154559
I0410 01:15:14.575342 14511 solver.cpp:237]     Train net output #0: loss = 0.154559 (* 1 = 0.154559 loss)
I0410 01:15:14.575351 14511 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 01:15:17.169378 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:19.361387 14511 solver.cpp:218] Iteration 6276 (2.50736 iter/s, 4.78591s/12 iters), loss = 0.185759
I0410 01:15:19.361481 14511 solver.cpp:237]     Train net output #0: loss = 0.185759 (* 1 = 0.185759 loss)
I0410 01:15:19.361491 14511 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 01:15:24.210321 14511 solver.cpp:218] Iteration 6288 (2.47488 iter/s, 4.84871s/12 iters), loss = 0.264943
I0410 01:15:24.210363 14511 solver.cpp:237]     Train net output #0: loss = 0.264943 (* 1 = 0.264943 loss)
I0410 01:15:24.210372 14511 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 01:15:28.891916 14511 solver.cpp:218] Iteration 6300 (2.56332 iter/s, 4.68142s/12 iters), loss = 0.121106
I0410 01:15:28.891970 14511 solver.cpp:237]     Train net output #0: loss = 0.121106 (* 1 = 0.121106 loss)
I0410 01:15:28.891983 14511 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 01:15:33.846671 14511 solver.cpp:218] Iteration 6312 (2.42201 iter/s, 4.95457s/12 iters), loss = 0.134386
I0410 01:15:33.846722 14511 solver.cpp:237]     Train net output #0: loss = 0.134386 (* 1 = 0.134386 loss)
I0410 01:15:33.846733 14511 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 01:15:38.407002 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 01:15:45.876785 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 01:15:51.688088 14511 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 01:15:51.688133 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:15:53.699267 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:56.207962 14511 solver.cpp:397]     Test net output #0: accuracy = 0.394608
I0410 01:15:56.208006 14511 solver.cpp:397]     Test net output #1: loss = 3.56026 (* 1 = 3.56026 loss)
I0410 01:15:56.316959 14511 solver.cpp:218] Iteration 6324 (0.534053 iter/s, 22.4697s/12 iters), loss = 0.0630548
I0410 01:15:56.318536 14511 solver.cpp:237]     Train net output #0: loss = 0.0630549 (* 1 = 0.0630549 loss)
I0410 01:15:56.318547 14511 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 01:16:00.636538 14511 solver.cpp:218] Iteration 6336 (2.77914 iter/s, 4.31789s/12 iters), loss = 0.0810857
I0410 01:16:00.636590 14511 solver.cpp:237]     Train net output #0: loss = 0.0810859 (* 1 = 0.0810859 loss)
I0410 01:16:00.636600 14511 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 01:16:05.389758 14511 solver.cpp:218] Iteration 6348 (2.5247 iter/s, 4.75304s/12 iters), loss = 0.160965
I0410 01:16:05.389807 14511 solver.cpp:237]     Train net output #0: loss = 0.160965 (* 1 = 0.160965 loss)
I0410 01:16:05.389817 14511 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 01:16:10.634943 14511 solver.cpp:218] Iteration 6360 (2.2879 iter/s, 5.24499s/12 iters), loss = 0.268754
I0410 01:16:10.635004 14511 solver.cpp:237]     Train net output #0: loss = 0.268754 (* 1 = 0.268754 loss)
I0410 01:16:10.635017 14511 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 01:16:15.735394 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:15.905763 14511 solver.cpp:218] Iteration 6372 (2.27677 iter/s, 5.27062s/12 iters), loss = 0.139575
I0410 01:16:15.905804 14511 solver.cpp:237]     Train net output #0: loss = 0.139575 (* 1 = 0.139575 loss)
I0410 01:16:15.905813 14511 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 01:16:21.127262 14511 solver.cpp:218] Iteration 6384 (2.29827 iter/s, 5.22132s/12 iters), loss = 0.172362
I0410 01:16:21.127311 14511 solver.cpp:237]     Train net output #0: loss = 0.172363 (* 1 = 0.172363 loss)
I0410 01:16:21.127322 14511 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 01:16:26.177274 14511 solver.cpp:218] Iteration 6396 (2.37632 iter/s, 5.04982s/12 iters), loss = 0.0956941
I0410 01:16:26.177407 14511 solver.cpp:237]     Train net output #0: loss = 0.0956943 (* 1 = 0.0956943 loss)
I0410 01:16:26.177419 14511 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 01:16:31.399117 14511 solver.cpp:218] Iteration 6408 (2.29816 iter/s, 5.22158s/12 iters), loss = 0.0898564
I0410 01:16:31.399159 14511 solver.cpp:237]     Train net output #0: loss = 0.0898566 (* 1 = 0.0898566 loss)
I0410 01:16:31.399169 14511 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 01:16:36.382607 14511 solver.cpp:218] Iteration 6420 (2.40804 iter/s, 4.98331s/12 iters), loss = 0.129342
I0410 01:16:36.382663 14511 solver.cpp:237]     Train net output #0: loss = 0.129343 (* 1 = 0.129343 loss)
I0410 01:16:36.382673 14511 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 01:16:38.229715 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 01:16:46.587052 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 01:16:52.380561 14511 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 01:16:52.380582 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:16:54.290704 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:56.856994 14511 solver.cpp:397]     Test net output #0: accuracy = 0.376226
I0410 01:16:56.857117 14511 solver.cpp:397]     Test net output #1: loss = 3.78696 (* 1 = 3.78696 loss)
I0410 01:16:58.627465 14511 solver.cpp:218] Iteration 6432 (0.539465 iter/s, 22.2443s/12 iters), loss = 0.149321
I0410 01:16:58.627522 14511 solver.cpp:237]     Train net output #0: loss = 0.149321 (* 1 = 0.149321 loss)
I0410 01:16:58.627533 14511 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 01:17:03.112478 14511 solver.cpp:218] Iteration 6444 (2.67568 iter/s, 4.48484s/12 iters), loss = 0.0907661
I0410 01:17:03.112529 14511 solver.cpp:237]     Train net output #0: loss = 0.0907663 (* 1 = 0.0907663 loss)
I0410 01:17:03.112538 14511 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 01:17:07.984510 14511 solver.cpp:218] Iteration 6456 (2.46313 iter/s, 4.87185s/12 iters), loss = 0.0547705
I0410 01:17:07.984562 14511 solver.cpp:237]     Train net output #0: loss = 0.0547707 (* 1 = 0.0547707 loss)
I0410 01:17:07.984575 14511 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 01:17:12.858776 14511 solver.cpp:218] Iteration 6468 (2.462 iter/s, 4.87408s/12 iters), loss = 0.33056
I0410 01:17:12.858830 14511 solver.cpp:237]     Train net output #0: loss = 0.33056 (* 1 = 0.33056 loss)
I0410 01:17:12.858844 14511 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 01:17:14.771903 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:17.650504 14511 solver.cpp:218] Iteration 6480 (2.50441 iter/s, 4.79154s/12 iters), loss = 0.0525669
I0410 01:17:17.650557 14511 solver.cpp:237]     Train net output #0: loss = 0.052567 (* 1 = 0.052567 loss)
I0410 01:17:17.650569 14511 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 01:17:22.628295 14511 solver.cpp:218] Iteration 6492 (2.4108 iter/s, 4.9776s/12 iters), loss = 0.11913
I0410 01:17:22.628350 14511 solver.cpp:237]     Train net output #0: loss = 0.11913 (* 1 = 0.11913 loss)
I0410 01:17:22.628362 14511 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 01:17:27.855034 14511 solver.cpp:218] Iteration 6504 (2.29597 iter/s, 5.22655s/12 iters), loss = 0.127959
I0410 01:17:27.855152 14511 solver.cpp:237]     Train net output #0: loss = 0.127959 (* 1 = 0.127959 loss)
I0410 01:17:27.855162 14511 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 01:17:32.600406 14511 solver.cpp:218] Iteration 6516 (2.52891 iter/s, 4.74513s/12 iters), loss = 0.211104
I0410 01:17:32.600451 14511 solver.cpp:237]     Train net output #0: loss = 0.211104 (* 1 = 0.211104 loss)
I0410 01:17:32.600462 14511 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 01:17:36.820710 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 01:17:44.431079 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 01:17:50.713804 14511 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 01:17:50.713826 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:17:52.539213 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:55.180528 14511 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0410 01:17:55.180559 14511 solver.cpp:397]     Test net output #1: loss = 3.65615 (* 1 = 3.65615 loss)
I0410 01:17:55.289539 14511 solver.cpp:218] Iteration 6528 (0.528901 iter/s, 22.6885s/12 iters), loss = 0.118677
I0410 01:17:55.291067 14511 solver.cpp:237]     Train net output #0: loss = 0.118677 (* 1 = 0.118677 loss)
I0410 01:17:55.291077 14511 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 01:17:59.595067 14511 solver.cpp:218] Iteration 6540 (2.78818 iter/s, 4.30389s/12 iters), loss = 0.0391678
I0410 01:17:59.595168 14511 solver.cpp:237]     Train net output #0: loss = 0.039168 (* 1 = 0.039168 loss)
I0410 01:17:59.595183 14511 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 01:18:04.507328 14511 solver.cpp:218] Iteration 6552 (2.44298 iter/s, 4.91203s/12 iters), loss = 0.146886
I0410 01:18:04.507369 14511 solver.cpp:237]     Train net output #0: loss = 0.146887 (* 1 = 0.146887 loss)
I0410 01:18:04.507380 14511 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 01:18:09.429531 14511 solver.cpp:218] Iteration 6564 (2.43802 iter/s, 4.92203s/12 iters), loss = 0.187337
I0410 01:18:09.429570 14511 solver.cpp:237]     Train net output #0: loss = 0.187337 (* 1 = 0.187337 loss)
I0410 01:18:09.429579 14511 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 01:18:13.455047 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:14.226505 14511 solver.cpp:218] Iteration 6576 (2.50167 iter/s, 4.7968s/12 iters), loss = 0.249028
I0410 01:18:14.226557 14511 solver.cpp:237]     Train net output #0: loss = 0.249029 (* 1 = 0.249029 loss)
I0410 01:18:14.226569 14511 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 01:18:18.874240 14511 solver.cpp:218] Iteration 6588 (2.582 iter/s, 4.64756s/12 iters), loss = 0.249706
I0410 01:18:18.874294 14511 solver.cpp:237]     Train net output #0: loss = 0.249706 (* 1 = 0.249706 loss)
I0410 01:18:18.874306 14511 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 01:18:23.699214 14511 solver.cpp:218] Iteration 6600 (2.48715 iter/s, 4.82479s/12 iters), loss = 0.155144
I0410 01:18:23.699257 14511 solver.cpp:237]     Train net output #0: loss = 0.155144 (* 1 = 0.155144 loss)
I0410 01:18:23.699267 14511 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 01:18:28.564877 14511 solver.cpp:218] Iteration 6612 (2.46635 iter/s, 4.86549s/12 iters), loss = 0.108928
I0410 01:18:28.564926 14511 solver.cpp:237]     Train net output #0: loss = 0.108928 (* 1 = 0.108928 loss)
I0410 01:18:28.564937 14511 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 01:18:33.399830 14511 solver.cpp:218] Iteration 6624 (2.48202 iter/s, 4.83477s/12 iters), loss = 0.154194
I0410 01:18:33.399979 14511 solver.cpp:237]     Train net output #0: loss = 0.154194 (* 1 = 0.154194 loss)
I0410 01:18:33.399993 14511 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 01:18:35.340574 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 01:18:42.798466 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 01:18:48.613940 14511 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 01:18:48.613981 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:18:50.637486 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:53.270201 14511 solver.cpp:397]     Test net output #0: accuracy = 0.397059
I0410 01:18:53.270253 14511 solver.cpp:397]     Test net output #1: loss = 3.68285 (* 1 = 3.68285 loss)
I0410 01:18:55.115335 14511 solver.cpp:218] Iteration 6636 (0.552618 iter/s, 21.7148s/12 iters), loss = 0.207407
I0410 01:18:55.115388 14511 solver.cpp:237]     Train net output #0: loss = 0.207407 (* 1 = 0.207407 loss)
I0410 01:18:55.115401 14511 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 01:19:00.097318 14511 solver.cpp:218] Iteration 6648 (2.40877 iter/s, 4.98179s/12 iters), loss = 0.148809
I0410 01:19:00.097375 14511 solver.cpp:237]     Train net output #0: loss = 0.148809 (* 1 = 0.148809 loss)
I0410 01:19:00.097388 14511 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 01:19:05.112373 14511 solver.cpp:218] Iteration 6660 (2.39289 iter/s, 5.01486s/12 iters), loss = 0.116231
I0410 01:19:05.113220 14511 solver.cpp:237]     Train net output #0: loss = 0.116232 (* 1 = 0.116232 loss)
I0410 01:19:05.113234 14511 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 01:19:10.324424 14511 solver.cpp:218] Iteration 6672 (2.30279 iter/s, 5.21107s/12 iters), loss = 0.0651457
I0410 01:19:10.324473 14511 solver.cpp:237]     Train net output #0: loss = 0.0651459 (* 1 = 0.0651459 loss)
I0410 01:19:10.324483 14511 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 01:19:11.698076 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:15.460276 14511 solver.cpp:218] Iteration 6684 (2.3366 iter/s, 5.13566s/12 iters), loss = 0.136286
I0410 01:19:15.460340 14511 solver.cpp:237]     Train net output #0: loss = 0.136286 (* 1 = 0.136286 loss)
I0410 01:19:15.460355 14511 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 01:19:20.642163 14511 solver.cpp:218] Iteration 6696 (2.31585 iter/s, 5.18169s/12 iters), loss = 0.0908686
I0410 01:19:20.642210 14511 solver.cpp:237]     Train net output #0: loss = 0.0908688 (* 1 = 0.0908688 loss)
I0410 01:19:20.642218 14511 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 01:19:25.859056 14511 solver.cpp:218] Iteration 6708 (2.3003 iter/s, 5.21671s/12 iters), loss = 0.14962
I0410 01:19:25.859097 14511 solver.cpp:237]     Train net output #0: loss = 0.14962 (* 1 = 0.14962 loss)
I0410 01:19:25.859107 14511 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 01:19:30.476893 14511 solver.cpp:218] Iteration 6720 (2.59872 iter/s, 4.61767s/12 iters), loss = 0.0673935
I0410 01:19:30.476948 14511 solver.cpp:237]     Train net output #0: loss = 0.0673936 (* 1 = 0.0673936 loss)
I0410 01:19:30.476960 14511 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 01:19:34.643486 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 01:19:43.421713 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 01:19:52.604594 14511 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 01:19:52.604617 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:19:54.436323 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:57.100556 14511 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0410 01:19:57.100598 14511 solver.cpp:397]     Test net output #1: loss = 3.72117 (* 1 = 3.72117 loss)
I0410 01:19:57.209082 14511 solver.cpp:218] Iteration 6732 (0.448909 iter/s, 26.7315s/12 iters), loss = 0.0860817
I0410 01:19:57.209131 14511 solver.cpp:237]     Train net output #0: loss = 0.0860819 (* 1 = 0.0860819 loss)
I0410 01:19:57.209141 14511 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 01:20:01.585238 14511 solver.cpp:218] Iteration 6744 (2.74224 iter/s, 4.37599s/12 iters), loss = 0.123314
I0410 01:20:01.585281 14511 solver.cpp:237]     Train net output #0: loss = 0.123314 (* 1 = 0.123314 loss)
I0410 01:20:01.585290 14511 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 01:20:06.402578 14511 solver.cpp:218] Iteration 6756 (2.49109 iter/s, 4.81716s/12 iters), loss = 0.0707256
I0410 01:20:06.402626 14511 solver.cpp:237]     Train net output #0: loss = 0.0707258 (* 1 = 0.0707258 loss)
I0410 01:20:06.402638 14511 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 01:20:11.095685 14511 solver.cpp:218] Iteration 6768 (2.55704 iter/s, 4.69293s/12 iters), loss = 0.221743
I0410 01:20:11.095728 14511 solver.cpp:237]     Train net output #0: loss = 0.221743 (* 1 = 0.221743 loss)
I0410 01:20:11.095738 14511 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 01:20:14.282382 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:15.712801 14511 solver.cpp:218] Iteration 6780 (2.59912 iter/s, 4.61695s/12 iters), loss = 0.103461
I0410 01:20:15.712844 14511 solver.cpp:237]     Train net output #0: loss = 0.103461 (* 1 = 0.103461 loss)
I0410 01:20:15.712853 14511 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 01:20:20.433693 14511 solver.cpp:218] Iteration 6792 (2.54199 iter/s, 4.72072s/12 iters), loss = 0.189397
I0410 01:20:20.433746 14511 solver.cpp:237]     Train net output #0: loss = 0.189397 (* 1 = 0.189397 loss)
I0410 01:20:20.433758 14511 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 01:20:25.259352 14511 solver.cpp:218] Iteration 6804 (2.4868 iter/s, 4.82548s/12 iters), loss = 0.100625
I0410 01:20:25.259395 14511 solver.cpp:237]     Train net output #0: loss = 0.100625 (* 1 = 0.100625 loss)
I0410 01:20:25.259405 14511 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 01:20:29.978704 14511 solver.cpp:218] Iteration 6816 (2.54282 iter/s, 4.71917s/12 iters), loss = 0.174253
I0410 01:20:29.978752 14511 solver.cpp:237]     Train net output #0: loss = 0.174253 (* 1 = 0.174253 loss)
I0410 01:20:29.978762 14511 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 01:20:34.741943 14511 solver.cpp:218] Iteration 6828 (2.51939 iter/s, 4.76306s/12 iters), loss = 0.191391
I0410 01:20:34.742014 14511 solver.cpp:237]     Train net output #0: loss = 0.191391 (* 1 = 0.191391 loss)
I0410 01:20:34.742027 14511 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 01:20:36.657176 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 01:20:46.183899 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 01:20:55.673523 14511 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 01:20:55.673547 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:20:57.595506 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:00.309027 14511 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0410 01:21:00.309063 14511 solver.cpp:397]     Test net output #1: loss = 3.56761 (* 1 = 3.56761 loss)
I0410 01:21:02.299731 14511 solver.cpp:218] Iteration 6840 (0.43546 iter/s, 27.557s/12 iters), loss = 0.0760182
I0410 01:21:02.299785 14511 solver.cpp:237]     Train net output #0: loss = 0.0760184 (* 1 = 0.0760184 loss)
I0410 01:21:02.299796 14511 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 01:21:07.335875 14511 solver.cpp:218] Iteration 6852 (2.38286 iter/s, 5.03595s/12 iters), loss = 0.0853197
I0410 01:21:07.335922 14511 solver.cpp:237]     Train net output #0: loss = 0.0853198 (* 1 = 0.0853198 loss)
I0410 01:21:07.335934 14511 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 01:21:12.479926 14511 solver.cpp:218] Iteration 6864 (2.33288 iter/s, 5.14386s/12 iters), loss = 0.116936
I0410 01:21:12.479979 14511 solver.cpp:237]     Train net output #0: loss = 0.116936 (* 1 = 0.116936 loss)
I0410 01:21:12.479990 14511 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 01:21:17.737396 14511 solver.cpp:218] Iteration 6876 (2.28255 iter/s, 5.25727s/12 iters), loss = 0.110437
I0410 01:21:17.737547 14511 solver.cpp:237]     Train net output #0: loss = 0.110438 (* 1 = 0.110438 loss)
I0410 01:21:17.737561 14511 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 01:21:18.275811 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:22.777855 14511 solver.cpp:218] Iteration 6888 (2.38087 iter/s, 5.04018s/12 iters), loss = 0.192456
I0410 01:21:22.777899 14511 solver.cpp:237]     Train net output #0: loss = 0.192457 (* 1 = 0.192457 loss)
I0410 01:21:22.777909 14511 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 01:21:27.965054 14511 solver.cpp:218] Iteration 6900 (2.31347 iter/s, 5.18701s/12 iters), loss = 0.146783
I0410 01:21:27.965106 14511 solver.cpp:237]     Train net output #0: loss = 0.146783 (* 1 = 0.146783 loss)
I0410 01:21:27.965117 14511 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 01:21:32.952271 14511 solver.cpp:218] Iteration 6912 (2.40624 iter/s, 4.98703s/12 iters), loss = 0.110924
I0410 01:21:32.952312 14511 solver.cpp:237]     Train net output #0: loss = 0.110924 (* 1 = 0.110924 loss)
I0410 01:21:32.952322 14511 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 01:21:38.035531 14511 solver.cpp:218] Iteration 6924 (2.36078 iter/s, 5.08308s/12 iters), loss = 0.199128
I0410 01:21:38.035588 14511 solver.cpp:237]     Train net output #0: loss = 0.199128 (* 1 = 0.199128 loss)
I0410 01:21:38.035598 14511 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 01:21:42.598155 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 01:21:52.066357 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 01:21:57.962910 14511 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 01:21:57.962931 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:21:58.609365 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:21:59.691309 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:02.440462 14511 solver.cpp:397]     Test net output #0: accuracy = 0.395833
I0410 01:22:02.440492 14511 solver.cpp:397]     Test net output #1: loss = 3.6929 (* 1 = 3.6929 loss)
I0410 01:22:02.548957 14511 solver.cpp:218] Iteration 6936 (0.489541 iter/s, 24.5128s/12 iters), loss = 0.0583382
I0410 01:22:02.550480 14511 solver.cpp:237]     Train net output #0: loss = 0.0583383 (* 1 = 0.0583383 loss)
I0410 01:22:02.550490 14511 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 01:22:06.512296 14511 solver.cpp:218] Iteration 6948 (3.029 iter/s, 3.9617s/12 iters), loss = 0.25429
I0410 01:22:06.512356 14511 solver.cpp:237]     Train net output #0: loss = 0.25429 (* 1 = 0.25429 loss)
I0410 01:22:06.512369 14511 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 01:22:11.454144 14511 solver.cpp:218] Iteration 6960 (2.42834 iter/s, 4.94166s/12 iters), loss = 0.0637276
I0410 01:22:11.454193 14511 solver.cpp:237]     Train net output #0: loss = 0.0637278 (* 1 = 0.0637278 loss)
I0410 01:22:11.454205 14511 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 01:22:16.141800 14511 solver.cpp:218] Iteration 6972 (2.56001 iter/s, 4.68748s/12 iters), loss = 0.120704
I0410 01:22:16.141852 14511 solver.cpp:237]     Train net output #0: loss = 0.120704 (* 1 = 0.120704 loss)
I0410 01:22:16.141865 14511 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 01:22:18.696265 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:20.833353 14511 solver.cpp:218] Iteration 6984 (2.55789 iter/s, 4.69137s/12 iters), loss = 0.0746017
I0410 01:22:20.833395 14511 solver.cpp:237]     Train net output #0: loss = 0.0746018 (* 1 = 0.0746018 loss)
I0410 01:22:20.833403 14511 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 01:22:25.588888 14511 solver.cpp:218] Iteration 6996 (2.52347 iter/s, 4.75536s/12 iters), loss = 0.178766
I0410 01:22:25.589031 14511 solver.cpp:237]     Train net output #0: loss = 0.178766 (* 1 = 0.178766 loss)
I0410 01:22:25.589044 14511 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 01:22:30.371481 14511 solver.cpp:218] Iteration 7008 (2.50924 iter/s, 4.78232s/12 iters), loss = 0.085143
I0410 01:22:30.371532 14511 solver.cpp:237]     Train net output #0: loss = 0.0851432 (* 1 = 0.0851432 loss)
I0410 01:22:30.371542 14511 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 01:22:35.250747 14511 solver.cpp:218] Iteration 7020 (2.45948 iter/s, 4.87908s/12 iters), loss = 0.21066
I0410 01:22:35.250795 14511 solver.cpp:237]     Train net output #0: loss = 0.210661 (* 1 = 0.210661 loss)
I0410 01:22:35.250808 14511 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 01:22:40.234375 14511 solver.cpp:218] Iteration 7032 (2.40797 iter/s, 4.98345s/12 iters), loss = 0.0507522
I0410 01:22:40.234418 14511 solver.cpp:237]     Train net output #0: loss = 0.0507524 (* 1 = 0.0507524 loss)
I0410 01:22:40.234427 14511 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 01:22:42.099581 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 01:22:49.566489 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 01:22:55.476946 14511 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 01:22:55.476969 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:22:57.370914 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:00.241616 14511 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0410 01:23:00.241660 14511 solver.cpp:397]     Test net output #1: loss = 3.63059 (* 1 = 3.63059 loss)
I0410 01:23:01.915669 14511 solver.cpp:218] Iteration 7044 (0.553487 iter/s, 21.6807s/12 iters), loss = 0.124119
I0410 01:23:01.915715 14511 solver.cpp:237]     Train net output #0: loss = 0.12412 (* 1 = 0.12412 loss)
I0410 01:23:01.915725 14511 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 01:23:07.076397 14511 solver.cpp:218] Iteration 7056 (2.32533 iter/s, 5.16055s/12 iters), loss = 0.098101
I0410 01:23:07.076436 14511 solver.cpp:237]     Train net output #0: loss = 0.0981012 (* 1 = 0.0981012 loss)
I0410 01:23:07.076445 14511 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 01:23:11.936242 14511 solver.cpp:218] Iteration 7068 (2.4693 iter/s, 4.85967s/12 iters), loss = 0.12483
I0410 01:23:11.936290 14511 solver.cpp:237]     Train net output #0: loss = 0.12483 (* 1 = 0.12483 loss)
I0410 01:23:11.936301 14511 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 01:23:16.668725 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:16.806550 14511 solver.cpp:218] Iteration 7080 (2.464 iter/s, 4.87013s/12 iters), loss = 0.0494396
I0410 01:23:16.806597 14511 solver.cpp:237]     Train net output #0: loss = 0.0494398 (* 1 = 0.0494398 loss)
I0410 01:23:16.806609 14511 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 01:23:22.044991 14511 solver.cpp:218] Iteration 7092 (2.29084 iter/s, 5.23825s/12 iters), loss = 0.107906
I0410 01:23:22.045035 14511 solver.cpp:237]     Train net output #0: loss = 0.107907 (* 1 = 0.107907 loss)
I0410 01:23:22.045047 14511 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 01:23:27.055088 14511 solver.cpp:218] Iteration 7104 (2.39525 iter/s, 5.00992s/12 iters), loss = 0.0841985
I0410 01:23:27.055141 14511 solver.cpp:237]     Train net output #0: loss = 0.0841987 (* 1 = 0.0841987 loss)
I0410 01:23:27.055152 14511 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 01:23:32.079190 14511 solver.cpp:218] Iteration 7116 (2.38858 iter/s, 5.0239s/12 iters), loss = 0.113501
I0410 01:23:32.079344 14511 solver.cpp:237]     Train net output #0: loss = 0.113501 (* 1 = 0.113501 loss)
I0410 01:23:32.079361 14511 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 01:23:37.324607 14511 solver.cpp:218] Iteration 7128 (2.28783 iter/s, 5.24514s/12 iters), loss = 0.0413803
I0410 01:23:37.324656 14511 solver.cpp:237]     Train net output #0: loss = 0.0413805 (* 1 = 0.0413805 loss)
I0410 01:23:37.324667 14511 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 01:23:42.096478 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 01:23:51.515151 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 01:23:57.527532 14511 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 01:23:57.527555 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:23:59.157466 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:01.986205 14511 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0410 01:24:01.986246 14511 solver.cpp:397]     Test net output #1: loss = 3.71154 (* 1 = 3.71154 loss)
I0410 01:24:02.092470 14511 solver.cpp:218] Iteration 7140 (0.484512 iter/s, 24.7672s/12 iters), loss = 0.183651
I0410 01:24:02.092576 14511 solver.cpp:237]     Train net output #0: loss = 0.183651 (* 1 = 0.183651 loss)
I0410 01:24:02.092587 14511 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 01:24:06.186823 14511 solver.cpp:218] Iteration 7152 (2.93102 iter/s, 4.09413s/12 iters), loss = 0.0947669
I0410 01:24:06.186879 14511 solver.cpp:237]     Train net output #0: loss = 0.0947671 (* 1 = 0.0947671 loss)
I0410 01:24:06.186892 14511 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 01:24:11.389717 14511 solver.cpp:218] Iteration 7164 (2.3065 iter/s, 5.2027s/12 iters), loss = 0.0973219
I0410 01:24:11.389772 14511 solver.cpp:237]     Train net output #0: loss = 0.0973221 (* 1 = 0.0973221 loss)
I0410 01:24:11.389784 14511 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 01:24:16.418326 14511 solver.cpp:218] Iteration 7176 (2.38643 iter/s, 5.02842s/12 iters), loss = 0.132215
I0410 01:24:16.418367 14511 solver.cpp:237]     Train net output #0: loss = 0.132216 (* 1 = 0.132216 loss)
I0410 01:24:16.418376 14511 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 01:24:18.578668 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:21.300957 14511 solver.cpp:218] Iteration 7188 (2.45778 iter/s, 4.88246s/12 iters), loss = 0.0415025
I0410 01:24:21.301004 14511 solver.cpp:237]     Train net output #0: loss = 0.0415027 (* 1 = 0.0415027 loss)
I0410 01:24:21.301015 14511 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 01:24:26.340957 14511 solver.cpp:218] Iteration 7200 (2.38104 iter/s, 5.03982s/12 iters), loss = 0.0662281
I0410 01:24:26.341004 14511 solver.cpp:237]     Train net output #0: loss = 0.0662283 (* 1 = 0.0662283 loss)
I0410 01:24:26.341014 14511 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 01:24:31.507520 14511 solver.cpp:218] Iteration 7212 (2.32271 iter/s, 5.16638s/12 iters), loss = 0.134593
I0410 01:24:31.507577 14511 solver.cpp:237]     Train net output #0: loss = 0.134593 (* 1 = 0.134593 loss)
I0410 01:24:31.507589 14511 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 01:24:36.326498 14511 solver.cpp:218] Iteration 7224 (2.49025 iter/s, 4.81879s/12 iters), loss = 0.0867893
I0410 01:24:36.326591 14511 solver.cpp:237]     Train net output #0: loss = 0.0867894 (* 1 = 0.0867894 loss)
I0410 01:24:36.326602 14511 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 01:24:41.310479 14511 solver.cpp:218] Iteration 7236 (2.40782 iter/s, 4.98375s/12 iters), loss = 0.0840387
I0410 01:24:41.310535 14511 solver.cpp:237]     Train net output #0: loss = 0.0840389 (* 1 = 0.0840389 loss)
I0410 01:24:41.310549 14511 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 01:24:43.410288 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 01:24:50.863425 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 01:24:56.743970 14511 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 01:24:56.743997 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:24:58.361064 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:01.238988 14511 solver.cpp:397]     Test net output #0: accuracy = 0.39277
I0410 01:25:01.239027 14511 solver.cpp:397]     Test net output #1: loss = 3.66626 (* 1 = 3.66626 loss)
I0410 01:25:02.907932 14511 solver.cpp:218] Iteration 7248 (0.555636 iter/s, 21.5969s/12 iters), loss = 0.0782252
I0410 01:25:02.907985 14511 solver.cpp:237]     Train net output #0: loss = 0.0782254 (* 1 = 0.0782254 loss)
I0410 01:25:02.907999 14511 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 01:25:07.475514 14511 solver.cpp:218] Iteration 7260 (2.62731 iter/s, 4.5674s/12 iters), loss = 0.0618891
I0410 01:25:07.475653 14511 solver.cpp:237]     Train net output #0: loss = 0.0618892 (* 1 = 0.0618892 loss)
I0410 01:25:07.475663 14511 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 01:25:12.261626 14511 solver.cpp:218] Iteration 7272 (2.5074 iter/s, 4.78584s/12 iters), loss = 0.0888145
I0410 01:25:12.261687 14511 solver.cpp:237]     Train net output #0: loss = 0.0888147 (* 1 = 0.0888147 loss)
I0410 01:25:12.261700 14511 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 01:25:16.264847 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:17.027760 14511 solver.cpp:218] Iteration 7284 (2.51786 iter/s, 4.76595s/12 iters), loss = 0.07643
I0410 01:25:17.027812 14511 solver.cpp:237]     Train net output #0: loss = 0.0764302 (* 1 = 0.0764302 loss)
I0410 01:25:17.027824 14511 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 01:25:22.178381 14511 solver.cpp:218] Iteration 7296 (2.3299 iter/s, 5.15043s/12 iters), loss = 0.152827
I0410 01:25:22.178437 14511 solver.cpp:237]     Train net output #0: loss = 0.152827 (* 1 = 0.152827 loss)
I0410 01:25:22.178448 14511 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 01:25:26.928073 14511 solver.cpp:218] Iteration 7308 (2.52657 iter/s, 4.74951s/12 iters), loss = 0.0621298
I0410 01:25:26.928110 14511 solver.cpp:237]     Train net output #0: loss = 0.06213 (* 1 = 0.06213 loss)
I0410 01:25:26.928118 14511 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 01:25:32.183491 14511 solver.cpp:218] Iteration 7320 (2.28344 iter/s, 5.25524s/12 iters), loss = 0.143999
I0410 01:25:32.183545 14511 solver.cpp:237]     Train net output #0: loss = 0.143999 (* 1 = 0.143999 loss)
I0410 01:25:32.183557 14511 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 01:25:37.057641 14511 solver.cpp:218] Iteration 7332 (2.46206 iter/s, 4.87397s/12 iters), loss = 0.167884
I0410 01:25:37.057685 14511 solver.cpp:237]     Train net output #0: loss = 0.167885 (* 1 = 0.167885 loss)
I0410 01:25:37.057694 14511 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 01:25:41.567520 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 01:25:49.074144 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 01:25:55.073048 14511 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 01:25:55.073071 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:25:56.679890 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:59.579387 14511 solver.cpp:397]     Test net output #0: accuracy = 0.400123
I0410 01:25:59.579433 14511 solver.cpp:397]     Test net output #1: loss = 3.66209 (* 1 = 3.66209 loss)
I0410 01:25:59.688146 14511 solver.cpp:218] Iteration 7344 (0.530272 iter/s, 22.6299s/12 iters), loss = 0.076362
I0410 01:25:59.689664 14511 solver.cpp:237]     Train net output #0: loss = 0.0763622 (* 1 = 0.0763622 loss)
I0410 01:25:59.689677 14511 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 01:26:03.914539 14511 solver.cpp:218] Iteration 7356 (2.84039 iter/s, 4.22477s/12 iters), loss = 0.103104
I0410 01:26:03.914582 14511 solver.cpp:237]     Train net output #0: loss = 0.103104 (* 1 = 0.103104 loss)
I0410 01:26:03.914592 14511 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 01:26:08.938740 14511 solver.cpp:218] Iteration 7368 (2.38853 iter/s, 5.02402s/12 iters), loss = 0.138077
I0410 01:26:08.938786 14511 solver.cpp:237]     Train net output #0: loss = 0.138077 (* 1 = 0.138077 loss)
I0410 01:26:08.938796 14511 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 01:26:14.094164 14511 solver.cpp:218] Iteration 7380 (2.32773 iter/s, 5.15524s/12 iters), loss = 0.0789389
I0410 01:26:14.094262 14511 solver.cpp:237]     Train net output #0: loss = 0.0789391 (* 1 = 0.0789391 loss)
I0410 01:26:14.094274 14511 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 01:26:15.272858 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:18.783252 14511 solver.cpp:218] Iteration 7392 (2.55925 iter/s, 4.68887s/12 iters), loss = 0.177082
I0410 01:26:18.783298 14511 solver.cpp:237]     Train net output #0: loss = 0.177082 (* 1 = 0.177082 loss)
I0410 01:26:18.783310 14511 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 01:26:24.021551 14511 solver.cpp:218] Iteration 7404 (2.2909 iter/s, 5.23812s/12 iters), loss = 0.0422868
I0410 01:26:24.021603 14511 solver.cpp:237]     Train net output #0: loss = 0.042287 (* 1 = 0.042287 loss)
I0410 01:26:24.021615 14511 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 01:26:28.995977 14511 solver.cpp:218] Iteration 7416 (2.41243 iter/s, 4.97425s/12 iters), loss = 0.0829143
I0410 01:26:28.996009 14511 solver.cpp:237]     Train net output #0: loss = 0.0829145 (* 1 = 0.0829145 loss)
I0410 01:26:28.996018 14511 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 01:26:33.805371 14511 solver.cpp:218] Iteration 7428 (2.4952 iter/s, 4.80923s/12 iters), loss = 0.0711945
I0410 01:26:33.805421 14511 solver.cpp:237]     Train net output #0: loss = 0.0711947 (* 1 = 0.0711947 loss)
I0410 01:26:33.805434 14511 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 01:26:38.795234 14511 solver.cpp:218] Iteration 7440 (2.40496 iter/s, 4.98968s/12 iters), loss = 0.0864448
I0410 01:26:38.795279 14511 solver.cpp:237]     Train net output #0: loss = 0.086445 (* 1 = 0.086445 loss)
I0410 01:26:38.795289 14511 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 01:26:40.801452 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 01:26:48.288566 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 01:26:54.102622 14511 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 01:26:54.102643 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:26:55.572441 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:58.522068 14511 solver.cpp:397]     Test net output #0: accuracy = 0.395221
I0410 01:26:58.522114 14511 solver.cpp:397]     Test net output #1: loss = 3.67696 (* 1 = 3.67696 loss)
I0410 01:27:00.223611 14511 solver.cpp:218] Iteration 7452 (0.56002 iter/s, 21.4278s/12 iters), loss = 0.121042
I0410 01:27:00.223654 14511 solver.cpp:237]     Train net output #0: loss = 0.121042 (* 1 = 0.121042 loss)
I0410 01:27:00.223664 14511 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 01:27:04.932238 14511 solver.cpp:218] Iteration 7464 (2.54861 iter/s, 4.70846s/12 iters), loss = 0.031222
I0410 01:27:04.932286 14511 solver.cpp:237]     Train net output #0: loss = 0.0312222 (* 1 = 0.0312222 loss)
I0410 01:27:04.932297 14511 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 01:27:09.808631 14511 solver.cpp:218] Iteration 7476 (2.46093 iter/s, 4.87621s/12 iters), loss = 0.142405
I0410 01:27:09.808691 14511 solver.cpp:237]     Train net output #0: loss = 0.142406 (* 1 = 0.142406 loss)
I0410 01:27:09.808702 14511 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 01:27:13.397672 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:14.843124 14511 solver.cpp:218] Iteration 7488 (2.38365 iter/s, 5.0343s/12 iters), loss = 0.164133
I0410 01:27:14.843176 14511 solver.cpp:237]     Train net output #0: loss = 0.164134 (* 1 = 0.164134 loss)
I0410 01:27:14.843187 14511 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 01:27:19.457001 14511 solver.cpp:218] Iteration 7500 (2.60095 iter/s, 4.6137s/12 iters), loss = 0.0515898
I0410 01:27:19.457105 14511 solver.cpp:237]     Train net output #0: loss = 0.05159 (* 1 = 0.05159 loss)
I0410 01:27:19.457116 14511 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 01:27:24.606655 14511 solver.cpp:218] Iteration 7512 (2.33036 iter/s, 5.14942s/12 iters), loss = 0.0771146
I0410 01:27:24.606695 14511 solver.cpp:237]     Train net output #0: loss = 0.0771148 (* 1 = 0.0771148 loss)
I0410 01:27:24.606704 14511 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 01:27:29.742249 14511 solver.cpp:218] Iteration 7524 (2.33671 iter/s, 5.13542s/12 iters), loss = 0.0601704
I0410 01:27:29.742300 14511 solver.cpp:237]     Train net output #0: loss = 0.0601706 (* 1 = 0.0601706 loss)
I0410 01:27:29.742312 14511 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 01:27:34.930794 14511 solver.cpp:218] Iteration 7536 (2.31287 iter/s, 5.18836s/12 iters), loss = 0.106669
I0410 01:27:34.930836 14511 solver.cpp:237]     Train net output #0: loss = 0.106669 (* 1 = 0.106669 loss)
I0410 01:27:34.930845 14511 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 01:27:39.314517 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 01:27:46.815222 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 01:27:52.955826 14511 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 01:27:52.955876 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:27:54.443431 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:57.422961 14511 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0410 01:27:57.423007 14511 solver.cpp:397]     Test net output #1: loss = 3.59654 (* 1 = 3.59654 loss)
I0410 01:27:57.531513 14511 solver.cpp:218] Iteration 7548 (0.530971 iter/s, 22.6001s/12 iters), loss = 0.0151121
I0410 01:27:57.531567 14511 solver.cpp:237]     Train net output #0: loss = 0.0151123 (* 1 = 0.0151123 loss)
I0410 01:27:57.531579 14511 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 01:28:01.909479 14511 solver.cpp:218] Iteration 7560 (2.74111 iter/s, 4.3778s/12 iters), loss = 0.0424138
I0410 01:28:01.909519 14511 solver.cpp:237]     Train net output #0: loss = 0.0424141 (* 1 = 0.0424141 loss)
I0410 01:28:01.909529 14511 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 01:28:07.052273 14511 solver.cpp:218] Iteration 7572 (2.33344 iter/s, 5.14261s/12 iters), loss = 0.0747181
I0410 01:28:07.052311 14511 solver.cpp:237]     Train net output #0: loss = 0.0747183 (* 1 = 0.0747183 loss)
I0410 01:28:07.052320 14511 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 01:28:11.903509 14511 solver.cpp:218] Iteration 7584 (2.47369 iter/s, 4.85106s/12 iters), loss = 0.0298854
I0410 01:28:11.903565 14511 solver.cpp:237]     Train net output #0: loss = 0.0298856 (* 1 = 0.0298856 loss)
I0410 01:28:11.903578 14511 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 01:28:12.518394 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:17.053509 14511 solver.cpp:218] Iteration 7596 (2.33019 iter/s, 5.1498s/12 iters), loss = 0.0576031
I0410 01:28:17.053565 14511 solver.cpp:237]     Train net output #0: loss = 0.0576033 (* 1 = 0.0576033 loss)
I0410 01:28:17.053578 14511 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 01:28:21.864756 14511 solver.cpp:218] Iteration 7608 (2.49425 iter/s, 4.81106s/12 iters), loss = 0.0476774
I0410 01:28:21.864809 14511 solver.cpp:237]     Train net output #0: loss = 0.0476776 (* 1 = 0.0476776 loss)
I0410 01:28:21.864820 14511 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 01:28:27.129745 14511 solver.cpp:218] Iteration 7620 (2.27929 iter/s, 5.26479s/12 iters), loss = 0.127395
I0410 01:28:27.129897 14511 solver.cpp:237]     Train net output #0: loss = 0.127395 (* 1 = 0.127395 loss)
I0410 01:28:27.129909 14511 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 01:28:29.645763 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:28:32.347147 14511 solver.cpp:218] Iteration 7632 (2.30012 iter/s, 5.21711s/12 iters), loss = 0.0844455
I0410 01:28:32.347194 14511 solver.cpp:237]     Train net output #0: loss = 0.0844457 (* 1 = 0.0844457 loss)
I0410 01:28:32.347205 14511 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 01:28:37.567268 14511 solver.cpp:218] Iteration 7644 (2.29888 iter/s, 5.21993s/12 iters), loss = 0.0437529
I0410 01:28:37.567324 14511 solver.cpp:237]     Train net output #0: loss = 0.0437531 (* 1 = 0.0437531 loss)
I0410 01:28:37.567337 14511 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 01:28:39.639811 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 01:28:47.170334 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 01:28:53.037086 14511 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 01:28:53.037108 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:28:54.515326 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:57.670692 14511 solver.cpp:397]     Test net output #0: accuracy = 0.395833
I0410 01:28:57.670783 14511 solver.cpp:397]     Test net output #1: loss = 3.78988 (* 1 = 3.78988 loss)
I0410 01:28:59.539944 14511 solver.cpp:218] Iteration 7656 (0.546148 iter/s, 21.9721s/12 iters), loss = 0.199291
I0410 01:28:59.539996 14511 solver.cpp:237]     Train net output #0: loss = 0.199291 (* 1 = 0.199291 loss)
I0410 01:28:59.540007 14511 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 01:29:04.773216 14511 solver.cpp:218] Iteration 7668 (2.2931 iter/s, 5.23308s/12 iters), loss = 0.0472467
I0410 01:29:04.773262 14511 solver.cpp:237]     Train net output #0: loss = 0.0472469 (* 1 = 0.0472469 loss)
I0410 01:29:04.773270 14511 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 01:29:09.881664 14511 solver.cpp:218] Iteration 7680 (2.34913 iter/s, 5.10826s/12 iters), loss = 0.131774
I0410 01:29:09.881711 14511 solver.cpp:237]     Train net output #0: loss = 0.131775 (* 1 = 0.131775 loss)
I0410 01:29:09.881723 14511 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 01:29:12.746726 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:15.020967 14511 solver.cpp:218] Iteration 7692 (2.33503 iter/s, 5.13912s/12 iters), loss = 0.0708092
I0410 01:29:15.021008 14511 solver.cpp:237]     Train net output #0: loss = 0.0708094 (* 1 = 0.0708094 loss)
I0410 01:29:15.021016 14511 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 01:29:20.263716 14511 solver.cpp:218] Iteration 7704 (2.28895 iter/s, 5.24257s/12 iters), loss = 0.10844
I0410 01:29:20.263763 14511 solver.cpp:237]     Train net output #0: loss = 0.108441 (* 1 = 0.108441 loss)
I0410 01:29:20.263774 14511 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 01:29:25.339716 14511 solver.cpp:218] Iteration 7716 (2.36415 iter/s, 5.07582s/12 iters), loss = 0.0569025
I0410 01:29:25.339762 14511 solver.cpp:237]     Train net output #0: loss = 0.0569027 (* 1 = 0.0569027 loss)
I0410 01:29:25.339773 14511 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 01:29:30.565182 14511 solver.cpp:218] Iteration 7728 (2.29653 iter/s, 5.22528s/12 iters), loss = 0.0505005
I0410 01:29:30.565302 14511 solver.cpp:237]     Train net output #0: loss = 0.0505007 (* 1 = 0.0505007 loss)
I0410 01:29:30.565318 14511 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 01:29:35.287125 14511 solver.cpp:218] Iteration 7740 (2.54146 iter/s, 4.7217s/12 iters), loss = 0.0178081
I0410 01:29:35.287184 14511 solver.cpp:237]     Train net output #0: loss = 0.0178083 (* 1 = 0.0178083 loss)
I0410 01:29:35.287195 14511 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 01:29:39.541139 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 01:29:48.791196 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 01:29:54.604300 14511 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 01:29:54.604323 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:29:56.070544 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:59.191718 14511 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0410 01:29:59.191769 14511 solver.cpp:397]     Test net output #1: loss = 3.78336 (* 1 = 3.78336 loss)
I0410 01:29:59.300457 14511 solver.cpp:218] Iteration 7752 (0.499736 iter/s, 24.0127s/12 iters), loss = 0.070887
I0410 01:29:59.300511 14511 solver.cpp:237]     Train net output #0: loss = 0.0708872 (* 1 = 0.0708872 loss)
I0410 01:29:59.300523 14511 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 01:30:03.708317 14511 solver.cpp:218] Iteration 7764 (2.72252 iter/s, 4.40768s/12 iters), loss = 0.163737
I0410 01:30:03.708451 14511 solver.cpp:237]     Train net output #0: loss = 0.163737 (* 1 = 0.163737 loss)
I0410 01:30:03.708462 14511 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 01:30:08.532909 14511 solver.cpp:218] Iteration 7776 (2.4874 iter/s, 4.82432s/12 iters), loss = 0.0536325
I0410 01:30:08.532964 14511 solver.cpp:237]     Train net output #0: loss = 0.0536327 (* 1 = 0.0536327 loss)
I0410 01:30:08.532975 14511 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 01:30:13.668593 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:13.708356 14511 solver.cpp:218] Iteration 7788 (2.31873 iter/s, 5.17525s/12 iters), loss = 0.0278248
I0410 01:30:13.708401 14511 solver.cpp:237]     Train net output #0: loss = 0.027825 (* 1 = 0.027825 loss)
I0410 01:30:13.708411 14511 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 01:30:18.929651 14511 solver.cpp:218] Iteration 7800 (2.29836 iter/s, 5.22111s/12 iters), loss = 0.0462065
I0410 01:30:18.929706 14511 solver.cpp:237]     Train net output #0: loss = 0.0462068 (* 1 = 0.0462068 loss)
I0410 01:30:18.929718 14511 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 01:30:23.622735 14511 solver.cpp:218] Iteration 7812 (2.55705 iter/s, 4.6929s/12 iters), loss = 0.103807
I0410 01:30:23.622781 14511 solver.cpp:237]     Train net output #0: loss = 0.103808 (* 1 = 0.103808 loss)
I0410 01:30:23.622792 14511 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 01:30:28.296695 14511 solver.cpp:218] Iteration 7824 (2.56751 iter/s, 4.67378s/12 iters), loss = 0.0361418
I0410 01:30:28.296748 14511 solver.cpp:237]     Train net output #0: loss = 0.036142 (* 1 = 0.036142 loss)
I0410 01:30:28.296759 14511 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 01:30:33.509991 14511 solver.cpp:218] Iteration 7836 (2.3019 iter/s, 5.21309s/12 iters), loss = 0.0934651
I0410 01:30:33.510042 14511 solver.cpp:237]     Train net output #0: loss = 0.0934653 (* 1 = 0.0934653 loss)
I0410 01:30:33.510056 14511 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 01:30:38.420218 14511 solver.cpp:218] Iteration 7848 (2.44397 iter/s, 4.91005s/12 iters), loss = 0.0414276
I0410 01:30:38.422811 14511 solver.cpp:237]     Train net output #0: loss = 0.0414279 (* 1 = 0.0414279 loss)
I0410 01:30:38.422821 14511 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 01:30:40.546721 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 01:30:48.046797 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 01:31:01.809491 14511 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 01:31:01.809520 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:31:03.209492 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:06.357828 14511 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0410 01:31:06.357872 14511 solver.cpp:397]     Test net output #1: loss = 3.77652 (* 1 = 3.77652 loss)
I0410 01:31:07.997187 14511 solver.cpp:218] Iteration 7860 (0.405767 iter/s, 29.5737s/12 iters), loss = 0.250812
I0410 01:31:07.997239 14511 solver.cpp:237]     Train net output #0: loss = 0.250812 (* 1 = 0.250812 loss)
I0410 01:31:07.997251 14511 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 01:31:12.827106 14511 solver.cpp:218] Iteration 7872 (2.48461 iter/s, 4.82973s/12 iters), loss = 0.0147166
I0410 01:31:12.827234 14511 solver.cpp:237]     Train net output #0: loss = 0.0147168 (* 1 = 0.0147168 loss)
I0410 01:31:12.827244 14511 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 01:31:17.386112 14511 solver.cpp:218] Iteration 7884 (2.6323 iter/s, 4.55876s/12 iters), loss = 0.0761248
I0410 01:31:17.386168 14511 solver.cpp:237]     Train net output #0: loss = 0.0761251 (* 1 = 0.0761251 loss)
I0410 01:31:17.386178 14511 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 01:31:19.260283 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:21.868328 14511 solver.cpp:218] Iteration 7896 (2.67735 iter/s, 4.48204s/12 iters), loss = 0.0212301
I0410 01:31:21.868373 14511 solver.cpp:237]     Train net output #0: loss = 0.0212304 (* 1 = 0.0212304 loss)
I0410 01:31:21.868383 14511 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 01:31:26.349205 14511 solver.cpp:218] Iteration 7908 (2.67815 iter/s, 4.48071s/12 iters), loss = 0.0842359
I0410 01:31:26.349252 14511 solver.cpp:237]     Train net output #0: loss = 0.0842362 (* 1 = 0.0842362 loss)
I0410 01:31:26.349264 14511 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 01:31:30.826730 14511 solver.cpp:218] Iteration 7920 (2.68015 iter/s, 4.47736s/12 iters), loss = 0.0640089
I0410 01:31:30.826779 14511 solver.cpp:237]     Train net output #0: loss = 0.0640092 (* 1 = 0.0640092 loss)
I0410 01:31:30.826792 14511 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 01:31:35.305757 14511 solver.cpp:218] Iteration 7932 (2.67926 iter/s, 4.47886s/12 iters), loss = 0.0754776
I0410 01:31:35.305805 14511 solver.cpp:237]     Train net output #0: loss = 0.0754779 (* 1 = 0.0754779 loss)
I0410 01:31:35.305817 14511 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 01:31:39.837381 14511 solver.cpp:218] Iteration 7944 (2.64815 iter/s, 4.53146s/12 iters), loss = 0.0724574
I0410 01:31:39.837422 14511 solver.cpp:237]     Train net output #0: loss = 0.0724576 (* 1 = 0.0724576 loss)
I0410 01:31:39.837431 14511 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 01:31:44.207999 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 01:31:51.702481 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 01:31:57.527595 14511 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 01:31:57.527621 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:31:58.889241 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:02.033457 14511 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0410 01:32:02.033494 14511 solver.cpp:397]     Test net output #1: loss = 3.7792 (* 1 = 3.7792 loss)
I0410 01:32:02.142057 14511 solver.cpp:218] Iteration 7956 (0.538018 iter/s, 22.3041s/12 iters), loss = 0.201673
I0410 01:32:02.142115 14511 solver.cpp:237]     Train net output #0: loss = 0.201674 (* 1 = 0.201674 loss)
I0410 01:32:02.142127 14511 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 01:32:06.232134 14511 solver.cpp:218] Iteration 7968 (2.93406 iter/s, 4.0899s/12 iters), loss = 0.0556997
I0410 01:32:06.232190 14511 solver.cpp:237]     Train net output #0: loss = 0.0557 (* 1 = 0.0557 loss)
I0410 01:32:06.232201 14511 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 01:32:11.295946 14511 solver.cpp:218] Iteration 7980 (2.36985 iter/s, 5.06362s/12 iters), loss = 0.100096
I0410 01:32:11.296021 14511 solver.cpp:237]     Train net output #0: loss = 0.100097 (* 1 = 0.100097 loss)
I0410 01:32:11.296039 14511 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 01:32:15.383718 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:16.105365 14511 solver.cpp:218] Iteration 7992 (2.49521 iter/s, 4.80922s/12 iters), loss = 0.134211
I0410 01:32:16.105424 14511 solver.cpp:237]     Train net output #0: loss = 0.134211 (* 1 = 0.134211 loss)
I0410 01:32:16.105437 14511 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 01:32:20.868031 14511 solver.cpp:218] Iteration 8004 (2.5197 iter/s, 4.76248s/12 iters), loss = 0.156575
I0410 01:32:20.868084 14511 solver.cpp:237]     Train net output #0: loss = 0.156575 (* 1 = 0.156575 loss)
I0410 01:32:20.868095 14511 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 01:32:25.693539 14511 solver.cpp:218] Iteration 8016 (2.48688 iter/s, 4.82533s/12 iters), loss = 0.077509
I0410 01:32:25.693590 14511 solver.cpp:237]     Train net output #0: loss = 0.0775092 (* 1 = 0.0775092 loss)
I0410 01:32:25.693600 14511 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 01:32:30.918730 14511 solver.cpp:218] Iteration 8028 (2.29665 iter/s, 5.225s/12 iters), loss = 0.0676282
I0410 01:32:30.918777 14511 solver.cpp:237]     Train net output #0: loss = 0.0676284 (* 1 = 0.0676284 loss)
I0410 01:32:30.918785 14511 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 01:32:36.105674 14511 solver.cpp:218] Iteration 8040 (2.31359 iter/s, 5.18675s/12 iters), loss = 0.0241714
I0410 01:32:36.105724 14511 solver.cpp:237]     Train net output #0: loss = 0.0241717 (* 1 = 0.0241717 loss)
I0410 01:32:36.105734 14511 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 01:32:40.937801 14511 solver.cpp:218] Iteration 8052 (2.48347 iter/s, 4.83195s/12 iters), loss = 0.0260373
I0410 01:32:40.937844 14511 solver.cpp:237]     Train net output #0: loss = 0.0260376 (* 1 = 0.0260376 loss)
I0410 01:32:40.937852 14511 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 01:32:43.047513 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 01:32:52.820411 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 01:33:03.976548 14511 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 01:33:03.976568 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:33:05.203505 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:08.433259 14511 solver.cpp:397]     Test net output #0: accuracy = 0.408701
I0410 01:33:08.433308 14511 solver.cpp:397]     Test net output #1: loss = 3.77324 (* 1 = 3.77324 loss)
I0410 01:33:10.242233 14511 solver.cpp:218] Iteration 8064 (0.409505 iter/s, 29.3037s/12 iters), loss = 0.030103
I0410 01:33:10.242283 14511 solver.cpp:237]     Train net output #0: loss = 0.0301033 (* 1 = 0.0301033 loss)
I0410 01:33:10.242295 14511 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 01:33:15.401201 14511 solver.cpp:218] Iteration 8076 (2.32613 iter/s, 5.15877s/12 iters), loss = 0.146858
I0410 01:33:15.401258 14511 solver.cpp:237]     Train net output #0: loss = 0.146858 (* 1 = 0.146858 loss)
I0410 01:33:15.401270 14511 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 01:33:20.650568 14511 solver.cpp:218] Iteration 8088 (2.28608 iter/s, 5.24917s/12 iters), loss = 0.0843799
I0410 01:33:20.650630 14511 solver.cpp:237]     Train net output #0: loss = 0.0843801 (* 1 = 0.0843801 loss)
I0410 01:33:20.650643 14511 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 01:33:22.059875 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:25.551322 14511 solver.cpp:218] Iteration 8100 (2.4487 iter/s, 4.90056s/12 iters), loss = 0.153985
I0410 01:33:25.551432 14511 solver.cpp:237]     Train net output #0: loss = 0.153985 (* 1 = 0.153985 loss)
I0410 01:33:25.551445 14511 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 01:33:30.154561 14511 solver.cpp:218] Iteration 8112 (2.60699 iter/s, 4.603s/12 iters), loss = 0.095357
I0410 01:33:30.154613 14511 solver.cpp:237]     Train net output #0: loss = 0.0953573 (* 1 = 0.0953573 loss)
I0410 01:33:30.154624 14511 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 01:33:34.821561 14511 solver.cpp:218] Iteration 8124 (2.57134 iter/s, 4.66682s/12 iters), loss = 0.0393581
I0410 01:33:34.821614 14511 solver.cpp:237]     Train net output #0: loss = 0.0393584 (* 1 = 0.0393584 loss)
I0410 01:33:34.821626 14511 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 01:33:39.565425 14511 solver.cpp:218] Iteration 8136 (2.52968 iter/s, 4.74369s/12 iters), loss = 0.0919475
I0410 01:33:39.565479 14511 solver.cpp:237]     Train net output #0: loss = 0.0919478 (* 1 = 0.0919478 loss)
I0410 01:33:39.565491 14511 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 01:33:44.307425 14511 solver.cpp:218] Iteration 8148 (2.53067 iter/s, 4.74182s/12 iters), loss = 0.0420348
I0410 01:33:44.307472 14511 solver.cpp:237]     Train net output #0: loss = 0.042035 (* 1 = 0.042035 loss)
I0410 01:33:44.307482 14511 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 01:33:48.495144 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 01:34:09.698779 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 01:34:30.758134 14511 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 01:34:30.758159 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:34:32.266768 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:35.688123 14511 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0410 01:34:35.688164 14511 solver.cpp:397]     Test net output #1: loss = 3.70248 (* 1 = 3.70248 loss)
I0410 01:34:35.796550 14511 solver.cpp:218] Iteration 8160 (0.233065 iter/s, 51.4878s/12 iters), loss = 0.0640536
I0410 01:34:35.796609 14511 solver.cpp:237]     Train net output #0: loss = 0.0640538 (* 1 = 0.0640538 loss)
I0410 01:34:35.796622 14511 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 01:34:39.745515 14511 solver.cpp:218] Iteration 8172 (3.0389 iter/s, 3.9488s/12 iters), loss = 0.118812
I0410 01:34:39.745599 14511 solver.cpp:237]     Train net output #0: loss = 0.118812 (* 1 = 0.118812 loss)
I0410 01:34:39.745613 14511 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 01:34:44.992087 14511 solver.cpp:218] Iteration 8184 (2.28731 iter/s, 5.24634s/12 iters), loss = 0.0275728
I0410 01:34:44.992136 14511 solver.cpp:237]     Train net output #0: loss = 0.027573 (* 1 = 0.027573 loss)
I0410 01:34:44.992146 14511 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 01:34:48.513875 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:49.883656 14511 solver.cpp:218] Iteration 8196 (2.45329 iter/s, 4.89139s/12 iters), loss = 0.0818913
I0410 01:34:49.883703 14511 solver.cpp:237]     Train net output #0: loss = 0.0818916 (* 1 = 0.0818916 loss)
I0410 01:34:49.883714 14511 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 01:34:55.048813 14511 solver.cpp:218] Iteration 8208 (2.32334 iter/s, 5.16497s/12 iters), loss = 0.0367493
I0410 01:34:55.048863 14511 solver.cpp:237]     Train net output #0: loss = 0.0367496 (* 1 = 0.0367496 loss)
I0410 01:34:55.048875 14511 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 01:34:59.898372 14511 solver.cpp:218] Iteration 8220 (2.47455 iter/s, 4.84937s/12 iters), loss = 0.111971
I0410 01:34:59.898427 14511 solver.cpp:237]     Train net output #0: loss = 0.111971 (* 1 = 0.111971 loss)
I0410 01:34:59.898439 14511 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 01:35:04.691977 14511 solver.cpp:218] Iteration 8232 (2.50343 iter/s, 4.79342s/12 iters), loss = 0.103321
I0410 01:35:04.692028 14511 solver.cpp:237]     Train net output #0: loss = 0.103321 (* 1 = 0.103321 loss)
I0410 01:35:04.692039 14511 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 01:35:09.577529 14511 solver.cpp:218] Iteration 8244 (2.45631 iter/s, 4.88537s/12 iters), loss = 0.0941655
I0410 01:35:09.577584 14511 solver.cpp:237]     Train net output #0: loss = 0.0941658 (* 1 = 0.0941658 loss)
I0410 01:35:09.577595 14511 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 01:35:14.439146 14511 solver.cpp:218] Iteration 8256 (2.46842 iter/s, 4.86141s/12 iters), loss = 0.0651246
I0410 01:35:14.439294 14511 solver.cpp:237]     Train net output #0: loss = 0.0651249 (* 1 = 0.0651249 loss)
I0410 01:35:14.439306 14511 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 01:35:16.317641 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 01:35:37.457913 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 01:35:46.249742 14511 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 01:35:46.249802 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:35:47.500141 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:50.769235 14511 solver.cpp:397]     Test net output #0: accuracy = 0.412377
I0410 01:35:50.769266 14511 solver.cpp:397]     Test net output #1: loss = 3.70669 (* 1 = 3.70669 loss)
I0410 01:35:52.580602 14511 solver.cpp:218] Iteration 8268 (0.314627 iter/s, 38.1404s/12 iters), loss = 0.0546929
I0410 01:35:52.580654 14511 solver.cpp:237]     Train net output #0: loss = 0.0546932 (* 1 = 0.0546932 loss)
I0410 01:35:52.580665 14511 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 01:35:57.750417 14511 solver.cpp:218] Iteration 8280 (2.32125 iter/s, 5.16962s/12 iters), loss = 0.0220009
I0410 01:35:57.750465 14511 solver.cpp:237]     Train net output #0: loss = 0.0220012 (* 1 = 0.0220012 loss)
I0410 01:35:57.750476 14511 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 01:36:02.722016 14511 solver.cpp:218] Iteration 8292 (2.4138 iter/s, 4.97141s/12 iters), loss = 0.0405811
I0410 01:36:02.722069 14511 solver.cpp:237]     Train net output #0: loss = 0.0405814 (* 1 = 0.0405814 loss)
I0410 01:36:02.722080 14511 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 01:36:03.403718 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:07.708765 14511 solver.cpp:218] Iteration 8304 (2.40647 iter/s, 4.98656s/12 iters), loss = 0.0853366
I0410 01:36:07.708817 14511 solver.cpp:237]     Train net output #0: loss = 0.0853369 (* 1 = 0.0853369 loss)
I0410 01:36:07.708827 14511 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 01:36:10.257447 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:36:12.292136 14511 solver.cpp:218] Iteration 8316 (2.61826 iter/s, 4.5832s/12 iters), loss = 0.0993236
I0410 01:36:12.292177 14511 solver.cpp:237]     Train net output #0: loss = 0.0993239 (* 1 = 0.0993239 loss)
I0410 01:36:12.292188 14511 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 01:36:17.150184 14511 solver.cpp:218] Iteration 8328 (2.47022 iter/s, 4.85787s/12 iters), loss = 0.236176
I0410 01:36:17.150290 14511 solver.cpp:237]     Train net output #0: loss = 0.236177 (* 1 = 0.236177 loss)
I0410 01:36:17.150301 14511 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 01:36:21.607389 14511 solver.cpp:218] Iteration 8340 (2.69241 iter/s, 4.45698s/12 iters), loss = 0.157647
I0410 01:36:21.607439 14511 solver.cpp:237]     Train net output #0: loss = 0.157648 (* 1 = 0.157648 loss)
I0410 01:36:21.607448 14511 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 01:36:26.132678 14511 solver.cpp:218] Iteration 8352 (2.65187 iter/s, 4.52511s/12 iters), loss = 0.0732042
I0410 01:36:26.132727 14511 solver.cpp:237]     Train net output #0: loss = 0.0732045 (* 1 = 0.0732045 loss)
I0410 01:36:26.132738 14511 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 01:36:30.171001 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 01:36:37.790324 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 01:36:43.609720 14511 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 01:36:43.609743 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:36:44.808650 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:48.121032 14511 solver.cpp:397]     Test net output #0: accuracy = 0.417892
I0410 01:36:48.121145 14511 solver.cpp:397]     Test net output #1: loss = 3.68387 (* 1 = 3.68387 loss)
I0410 01:36:48.229787 14511 solver.cpp:218] Iteration 8364 (0.543072 iter/s, 22.0965s/12 iters), loss = 0.104953
I0410 01:36:48.229835 14511 solver.cpp:237]     Train net output #0: loss = 0.104954 (* 1 = 0.104954 loss)
I0410 01:36:48.229846 14511 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 01:36:52.415730 14511 solver.cpp:218] Iteration 8376 (2.86685 iter/s, 4.18577s/12 iters), loss = 0.0325106
I0410 01:36:52.415782 14511 solver.cpp:237]     Train net output #0: loss = 0.0325109 (* 1 = 0.0325109 loss)
I0410 01:36:52.415793 14511 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 01:36:57.509819 14511 solver.cpp:218] Iteration 8388 (2.35576 iter/s, 5.09389s/12 iters), loss = 0.0155386
I0410 01:36:57.509874 14511 solver.cpp:237]     Train net output #0: loss = 0.0155388 (* 1 = 0.0155388 loss)
I0410 01:36:57.509886 14511 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 01:37:00.107681 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:02.262440 14511 solver.cpp:218] Iteration 8400 (2.52502 iter/s, 4.75244s/12 iters), loss = 0.0313705
I0410 01:37:02.262490 14511 solver.cpp:237]     Train net output #0: loss = 0.0313708 (* 1 = 0.0313708 loss)
I0410 01:37:02.262499 14511 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 01:37:06.879463 14511 solver.cpp:218] Iteration 8412 (2.59918 iter/s, 4.61684s/12 iters), loss = 0.0957507
I0410 01:37:06.879515 14511 solver.cpp:237]     Train net output #0: loss = 0.095751 (* 1 = 0.095751 loss)
I0410 01:37:06.879526 14511 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 01:37:11.501516 14511 solver.cpp:218] Iteration 8424 (2.59635 iter/s, 4.62187s/12 iters), loss = 0.0324536
I0410 01:37:11.501572 14511 solver.cpp:237]     Train net output #0: loss = 0.0324539 (* 1 = 0.0324539 loss)
I0410 01:37:11.501585 14511 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 01:37:16.290705 14511 solver.cpp:218] Iteration 8436 (2.50574 iter/s, 4.789s/12 iters), loss = 0.0413065
I0410 01:37:16.290752 14511 solver.cpp:237]     Train net output #0: loss = 0.0413068 (* 1 = 0.0413068 loss)
I0410 01:37:16.290764 14511 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 01:37:21.344023 14511 solver.cpp:218] Iteration 8448 (2.37476 iter/s, 5.05313s/12 iters), loss = 0.0627582
I0410 01:37:21.344117 14511 solver.cpp:237]     Train net output #0: loss = 0.0627585 (* 1 = 0.0627585 loss)
I0410 01:37:21.344132 14511 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 01:37:26.489373 14511 solver.cpp:218] Iteration 8460 (2.33231 iter/s, 5.14512s/12 iters), loss = 0.0616171
I0410 01:37:26.489426 14511 solver.cpp:237]     Train net output #0: loss = 0.0616174 (* 1 = 0.0616174 loss)
I0410 01:37:26.489439 14511 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 01:37:28.626562 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 01:37:36.771695 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 01:37:42.587371 14511 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 01:37:42.587395 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:37:43.754765 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:47.177326 14511 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0410 01:37:47.177354 14511 solver.cpp:397]     Test net output #1: loss = 3.66359 (* 1 = 3.66359 loss)
I0410 01:37:48.849020 14511 solver.cpp:218] Iteration 8472 (0.536696 iter/s, 22.359s/12 iters), loss = 0.0729125
I0410 01:37:48.849076 14511 solver.cpp:237]     Train net output #0: loss = 0.0729128 (* 1 = 0.0729128 loss)
I0410 01:37:48.849086 14511 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 01:37:53.546516 14511 solver.cpp:218] Iteration 8484 (2.55465 iter/s, 4.69732s/12 iters), loss = 0.0443676
I0410 01:37:53.546658 14511 solver.cpp:237]     Train net output #0: loss = 0.0443679 (* 1 = 0.0443679 loss)
I0410 01:37:53.546672 14511 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 01:37:58.279639 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:58.281908 14511 solver.cpp:218] Iteration 8496 (2.53425 iter/s, 4.73513s/12 iters), loss = 0.0657774
I0410 01:37:58.281952 14511 solver.cpp:237]     Train net output #0: loss = 0.0657777 (* 1 = 0.0657777 loss)
I0410 01:37:58.281981 14511 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 01:38:03.327236 14511 solver.cpp:218] Iteration 8508 (2.37852 iter/s, 5.04514s/12 iters), loss = 0.0340627
I0410 01:38:03.327294 14511 solver.cpp:237]     Train net output #0: loss = 0.034063 (* 1 = 0.034063 loss)
I0410 01:38:03.327308 14511 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 01:38:08.480142 14511 solver.cpp:218] Iteration 8520 (2.32887 iter/s, 5.15271s/12 iters), loss = 0.150638
I0410 01:38:08.480180 14511 solver.cpp:237]     Train net output #0: loss = 0.150639 (* 1 = 0.150639 loss)
I0410 01:38:08.480188 14511 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 01:38:13.018033 14511 solver.cpp:218] Iteration 8532 (2.64449 iter/s, 4.53773s/12 iters), loss = 0.0417179
I0410 01:38:13.018070 14511 solver.cpp:237]     Train net output #0: loss = 0.0417182 (* 1 = 0.0417182 loss)
I0410 01:38:13.018079 14511 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 01:38:17.692010 14511 solver.cpp:218] Iteration 8544 (2.5675 iter/s, 4.67381s/12 iters), loss = 0.165105
I0410 01:38:17.692066 14511 solver.cpp:237]     Train net output #0: loss = 0.165105 (* 1 = 0.165105 loss)
I0410 01:38:17.692077 14511 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 01:38:22.884522 14511 solver.cpp:218] Iteration 8556 (2.31111 iter/s, 5.19232s/12 iters), loss = 0.072794
I0410 01:38:22.884567 14511 solver.cpp:237]     Train net output #0: loss = 0.0727943 (* 1 = 0.0727943 loss)
I0410 01:38:22.884578 14511 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 01:38:27.215816 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 01:38:43.359195 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 01:38:54.202458 14511 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 01:38:54.202483 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:38:55.309053 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:58.693140 14511 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0410 01:38:58.693210 14511 solver.cpp:397]     Test net output #1: loss = 3.77981 (* 1 = 3.77981 loss)
I0410 01:38:58.799612 14511 solver.cpp:218] Iteration 8568 (0.33413 iter/s, 35.9142s/12 iters), loss = 0.0598385
I0410 01:38:58.799651 14511 solver.cpp:237]     Train net output #0: loss = 0.0598388 (* 1 = 0.0598388 loss)
I0410 01:38:58.799660 14511 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 01:39:03.142493 14511 solver.cpp:218] Iteration 8580 (2.76325 iter/s, 4.34271s/12 iters), loss = 0.0195064
I0410 01:39:03.142539 14511 solver.cpp:237]     Train net output #0: loss = 0.0195067 (* 1 = 0.0195067 loss)
I0410 01:39:03.142549 14511 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 01:39:08.163362 14511 solver.cpp:218] Iteration 8592 (2.39011 iter/s, 5.02069s/12 iters), loss = 0.0306307
I0410 01:39:08.163398 14511 solver.cpp:237]     Train net output #0: loss = 0.030631 (* 1 = 0.030631 loss)
I0410 01:39:08.163405 14511 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 01:39:10.341850 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:13.321694 14511 solver.cpp:218] Iteration 8604 (2.32642 iter/s, 5.15815s/12 iters), loss = 0.0401655
I0410 01:39:13.321748 14511 solver.cpp:237]     Train net output #0: loss = 0.0401658 (* 1 = 0.0401658 loss)
I0410 01:39:13.321758 14511 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 01:39:18.396055 14511 solver.cpp:218] Iteration 8616 (2.36492 iter/s, 5.07417s/12 iters), loss = 0.0556782
I0410 01:39:18.396106 14511 solver.cpp:237]     Train net output #0: loss = 0.0556784 (* 1 = 0.0556784 loss)
I0410 01:39:18.396117 14511 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 01:39:23.041849 14511 solver.cpp:218] Iteration 8628 (2.58308 iter/s, 4.64562s/12 iters), loss = 0.0320843
I0410 01:39:23.041906 14511 solver.cpp:237]     Train net output #0: loss = 0.0320846 (* 1 = 0.0320846 loss)
I0410 01:39:23.041919 14511 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 01:39:28.214092 14511 solver.cpp:218] Iteration 8640 (2.32017 iter/s, 5.17204s/12 iters), loss = 0.0759546
I0410 01:39:28.214136 14511 solver.cpp:237]     Train net output #0: loss = 0.0759549 (* 1 = 0.0759549 loss)
I0410 01:39:28.214145 14511 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 01:39:33.494896 14511 solver.cpp:218] Iteration 8652 (2.27246 iter/s, 5.28062s/12 iters), loss = 0.0205007
I0410 01:39:33.495031 14511 solver.cpp:237]     Train net output #0: loss = 0.020501 (* 1 = 0.020501 loss)
I0410 01:39:33.495045 14511 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 01:39:38.667557 14511 solver.cpp:218] Iteration 8664 (2.32001 iter/s, 5.17239s/12 iters), loss = 0.123043
I0410 01:39:38.667605 14511 solver.cpp:237]     Train net output #0: loss = 0.123043 (* 1 = 0.123043 loss)
I0410 01:39:38.667618 14511 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 01:39:40.582919 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 01:39:54.869902 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 01:40:05.970782 14511 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 01:40:05.970830 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:40:07.055785 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:10.488499 14511 solver.cpp:397]     Test net output #0: accuracy = 0.417279
I0410 01:40:10.488550 14511 solver.cpp:397]     Test net output #1: loss = 3.66296 (* 1 = 3.66296 loss)
I0410 01:40:12.274277 14511 solver.cpp:218] Iteration 8676 (0.357081 iter/s, 33.6058s/12 iters), loss = 0.0330823
I0410 01:40:12.274333 14511 solver.cpp:237]     Train net output #0: loss = 0.0330826 (* 1 = 0.0330826 loss)
I0410 01:40:12.274346 14511 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 01:40:17.205662 14511 solver.cpp:218] Iteration 8688 (2.43349 iter/s, 4.93119s/12 iters), loss = 0.0253172
I0410 01:40:17.205721 14511 solver.cpp:237]     Train net output #0: loss = 0.0253176 (* 1 = 0.0253176 loss)
I0410 01:40:17.205734 14511 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 01:40:21.528787 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:22.269917 14511 solver.cpp:218] Iteration 8700 (2.36964 iter/s, 5.06406s/12 iters), loss = 0.0641045
I0410 01:40:22.269990 14511 solver.cpp:237]     Train net output #0: loss = 0.0641048 (* 1 = 0.0641048 loss)
I0410 01:40:22.270004 14511 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 01:40:27.310150 14511 solver.cpp:218] Iteration 8712 (2.38094 iter/s, 5.04002s/12 iters), loss = 0.00804789
I0410 01:40:27.310201 14511 solver.cpp:237]     Train net output #0: loss = 0.00804822 (* 1 = 0.00804822 loss)
I0410 01:40:27.310212 14511 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 01:40:32.502000 14511 solver.cpp:218] Iteration 8724 (2.3114 iter/s, 5.19165s/12 iters), loss = 0.0357166
I0410 01:40:32.502056 14511 solver.cpp:237]     Train net output #0: loss = 0.0357169 (* 1 = 0.0357169 loss)
I0410 01:40:32.502068 14511 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 01:40:37.741668 14511 solver.cpp:218] Iteration 8736 (2.29031 iter/s, 5.23947s/12 iters), loss = 0.0656369
I0410 01:40:37.741751 14511 solver.cpp:237]     Train net output #0: loss = 0.0656372 (* 1 = 0.0656372 loss)
I0410 01:40:37.741765 14511 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 01:40:42.352959 14511 solver.cpp:218] Iteration 8748 (2.60242 iter/s, 4.61109s/12 iters), loss = 0.015469
I0410 01:40:42.353009 14511 solver.cpp:237]     Train net output #0: loss = 0.0154693 (* 1 = 0.0154693 loss)
I0410 01:40:42.353024 14511 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 01:40:46.933728 14511 solver.cpp:218] Iteration 8760 (2.61975 iter/s, 4.58059s/12 iters), loss = 0.00602499
I0410 01:40:46.933782 14511 solver.cpp:237]     Train net output #0: loss = 0.00602531 (* 1 = 0.00602531 loss)
I0410 01:40:46.933794 14511 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 01:40:51.157557 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 01:40:59.703559 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 01:41:11.259785 14511 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 01:41:11.259866 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:41:12.229262 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:15.696645 14511 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0410 01:41:15.696688 14511 solver.cpp:397]     Test net output #1: loss = 3.71606 (* 1 = 3.71606 loss)
I0410 01:41:15.803352 14511 solver.cpp:218] Iteration 8772 (0.415673 iter/s, 28.8689s/12 iters), loss = 0.114118
I0410 01:41:15.803400 14511 solver.cpp:237]     Train net output #0: loss = 0.114118 (* 1 = 0.114118 loss)
I0410 01:41:15.803409 14511 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 01:41:20.183149 14511 solver.cpp:218] Iteration 8784 (2.73996 iter/s, 4.37963s/12 iters), loss = 0.0246122
I0410 01:41:20.183195 14511 solver.cpp:237]     Train net output #0: loss = 0.0246125 (* 1 = 0.0246125 loss)
I0410 01:41:20.183207 14511 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 01:41:25.427248 14511 solver.cpp:218] Iteration 8796 (2.28837 iter/s, 5.24391s/12 iters), loss = 0.0603299
I0410 01:41:25.427294 14511 solver.cpp:237]     Train net output #0: loss = 0.0603302 (* 1 = 0.0603302 loss)
I0410 01:41:25.427304 14511 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 01:41:26.914317 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:30.405699 14511 solver.cpp:218] Iteration 8808 (2.41048 iter/s, 4.97827s/12 iters), loss = 0.0872087
I0410 01:41:30.405753 14511 solver.cpp:237]     Train net output #0: loss = 0.087209 (* 1 = 0.087209 loss)
I0410 01:41:30.405766 14511 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 01:41:35.230576 14511 solver.cpp:218] Iteration 8820 (2.48721 iter/s, 4.82469s/12 iters), loss = 0.048854
I0410 01:41:35.230625 14511 solver.cpp:237]     Train net output #0: loss = 0.0488543 (* 1 = 0.0488543 loss)
I0410 01:41:35.230635 14511 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 01:41:40.085600 14511 solver.cpp:218] Iteration 8832 (2.47176 iter/s, 4.85484s/12 iters), loss = 0.0689201
I0410 01:41:40.085647 14511 solver.cpp:237]     Train net output #0: loss = 0.0689204 (* 1 = 0.0689204 loss)
I0410 01:41:40.085657 14511 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 01:41:44.799587 14511 solver.cpp:218] Iteration 8844 (2.54571 iter/s, 4.71381s/12 iters), loss = 0.093473
I0410 01:41:44.799701 14511 solver.cpp:237]     Train net output #0: loss = 0.0934733 (* 1 = 0.0934733 loss)
I0410 01:41:44.799715 14511 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 01:41:49.904556 14511 solver.cpp:218] Iteration 8856 (2.35077 iter/s, 5.10472s/12 iters), loss = 0.0234494
I0410 01:41:49.904603 14511 solver.cpp:237]     Train net output #0: loss = 0.0234497 (* 1 = 0.0234497 loss)
I0410 01:41:49.904614 14511 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 01:41:54.503950 14511 solver.cpp:218] Iteration 8868 (2.60914 iter/s, 4.59922s/12 iters), loss = 0.0186757
I0410 01:41:54.503993 14511 solver.cpp:237]     Train net output #0: loss = 0.018676 (* 1 = 0.018676 loss)
I0410 01:41:54.504002 14511 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 01:41:56.481637 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 01:42:04.024482 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 01:42:12.317049 14511 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 01:42:12.317072 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:42:13.326251 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:17.020081 14511 solver.cpp:397]     Test net output #0: accuracy = 0.425245
I0410 01:42:17.020185 14511 solver.cpp:397]     Test net output #1: loss = 3.66309 (* 1 = 3.66309 loss)
I0410 01:42:18.890035 14511 solver.cpp:218] Iteration 8880 (0.492097 iter/s, 24.3854s/12 iters), loss = 0.0969568
I0410 01:42:18.890089 14511 solver.cpp:237]     Train net output #0: loss = 0.0969571 (* 1 = 0.0969571 loss)
I0410 01:42:18.890101 14511 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 01:42:24.119415 14511 solver.cpp:218] Iteration 8892 (2.29481 iter/s, 5.22919s/12 iters), loss = 0.0681648
I0410 01:42:24.119465 14511 solver.cpp:237]     Train net output #0: loss = 0.0681651 (* 1 = 0.0681651 loss)
I0410 01:42:24.119475 14511 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 01:42:27.710911 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:29.131601 14511 solver.cpp:218] Iteration 8904 (2.39426 iter/s, 5.012s/12 iters), loss = 0.0158596
I0410 01:42:29.131654 14511 solver.cpp:237]     Train net output #0: loss = 0.0158599 (* 1 = 0.0158599 loss)
I0410 01:42:29.131666 14511 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 01:42:34.249871 14511 solver.cpp:218] Iteration 8916 (2.34463 iter/s, 5.11808s/12 iters), loss = 0.11556
I0410 01:42:34.249915 14511 solver.cpp:237]     Train net output #0: loss = 0.11556 (* 1 = 0.11556 loss)
I0410 01:42:34.249925 14511 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 01:42:39.152825 14511 solver.cpp:218] Iteration 8928 (2.44759 iter/s, 4.90278s/12 iters), loss = 0.0260119
I0410 01:42:39.152868 14511 solver.cpp:237]     Train net output #0: loss = 0.0260122 (* 1 = 0.0260122 loss)
I0410 01:42:39.152876 14511 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 01:42:44.015635 14511 solver.cpp:218] Iteration 8940 (2.4678 iter/s, 4.86263s/12 iters), loss = 0.0373703
I0410 01:42:44.015686 14511 solver.cpp:237]     Train net output #0: loss = 0.0373706 (* 1 = 0.0373706 loss)
I0410 01:42:44.015697 14511 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 01:42:48.974810 14511 solver.cpp:218] Iteration 8952 (2.41985 iter/s, 4.95899s/12 iters), loss = 0.0689553
I0410 01:42:48.974916 14511 solver.cpp:237]     Train net output #0: loss = 0.0689556 (* 1 = 0.0689556 loss)
I0410 01:42:48.974929 14511 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 01:42:53.656885 14511 solver.cpp:218] Iteration 8964 (2.5631 iter/s, 4.68184s/12 iters), loss = 0.0183449
I0410 01:42:53.656942 14511 solver.cpp:237]     Train net output #0: loss = 0.0183452 (* 1 = 0.0183452 loss)
I0410 01:42:53.656957 14511 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 01:42:58.079980 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 01:43:07.797320 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 01:43:15.057396 14511 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 01:43:15.057420 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:43:15.991413 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:19.627602 14511 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0410 01:43:19.627718 14511 solver.cpp:397]     Test net output #1: loss = 3.67572 (* 1 = 3.67572 loss)
I0410 01:43:19.737025 14511 solver.cpp:218] Iteration 8976 (0.460133 iter/s, 26.0794s/12 iters), loss = 0.0487117
I0410 01:43:19.738643 14511 solver.cpp:237]     Train net output #0: loss = 0.0487119 (* 1 = 0.0487119 loss)
I0410 01:43:19.738660 14511 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 01:43:24.095448 14511 solver.cpp:218] Iteration 8988 (2.75438 iter/s, 4.35669s/12 iters), loss = 0.0224057
I0410 01:43:24.095487 14511 solver.cpp:237]     Train net output #0: loss = 0.022406 (* 1 = 0.022406 loss)
I0410 01:43:24.095496 14511 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 01:43:27.537595 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:43:29.271525 14511 solver.cpp:218] Iteration 9000 (2.31844 iter/s, 5.1759s/12 iters), loss = 0.0655686
I0410 01:43:29.271569 14511 solver.cpp:237]     Train net output #0: loss = 0.0655689 (* 1 = 0.0655689 loss)
I0410 01:43:29.271579 14511 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 01:43:30.001116 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:34.376210 14511 solver.cpp:218] Iteration 9012 (2.35087 iter/s, 5.1045s/12 iters), loss = 0.0469926
I0410 01:43:34.376252 14511 solver.cpp:237]     Train net output #0: loss = 0.0469929 (* 1 = 0.0469929 loss)
I0410 01:43:34.376261 14511 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 01:43:39.392902 14511 solver.cpp:218] Iteration 9024 (2.3921 iter/s, 5.01651s/12 iters), loss = 0.0599162
I0410 01:43:39.392949 14511 solver.cpp:237]     Train net output #0: loss = 0.0599165 (* 1 = 0.0599165 loss)
I0410 01:43:39.392958 14511 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 01:43:44.495663 14511 solver.cpp:218] Iteration 9036 (2.35175 iter/s, 5.10258s/12 iters), loss = 0.063209
I0410 01:43:44.495708 14511 solver.cpp:237]     Train net output #0: loss = 0.0632093 (* 1 = 0.0632093 loss)
I0410 01:43:44.495718 14511 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 01:43:49.759480 14511 solver.cpp:218] Iteration 9048 (2.2798 iter/s, 5.26363s/12 iters), loss = 0.0939226
I0410 01:43:49.759630 14511 solver.cpp:237]     Train net output #0: loss = 0.0939229 (* 1 = 0.0939229 loss)
I0410 01:43:49.759644 14511 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 01:43:54.860510 14511 solver.cpp:218] Iteration 9060 (2.3526 iter/s, 5.10075s/12 iters), loss = 0.0193419
I0410 01:43:54.860554 14511 solver.cpp:237]     Train net output #0: loss = 0.0193422 (* 1 = 0.0193422 loss)
I0410 01:43:54.860563 14511 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 01:43:59.825837 14511 solver.cpp:218] Iteration 9072 (2.41685 iter/s, 4.96515s/12 iters), loss = 0.0920987
I0410 01:43:59.825879 14511 solver.cpp:237]     Train net output #0: loss = 0.092099 (* 1 = 0.092099 loss)
I0410 01:43:59.825889 14511 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 01:44:01.667929 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 01:44:16.609896 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 01:44:42.343293 14511 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 01:44:42.343369 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:44:43.272971 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:47.070083 14511 solver.cpp:397]     Test net output #0: accuracy = 0.433211
I0410 01:44:47.070127 14511 solver.cpp:397]     Test net output #1: loss = 3.60886 (* 1 = 3.60886 loss)
I0410 01:44:48.910580 14511 solver.cpp:218] Iteration 9084 (0.244481 iter/s, 49.0835s/12 iters), loss = 0.0212341
I0410 01:44:48.910627 14511 solver.cpp:237]     Train net output #0: loss = 0.0212344 (* 1 = 0.0212344 loss)
I0410 01:44:48.910638 14511 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 01:44:53.792493 14511 solver.cpp:218] Iteration 9096 (2.45814 iter/s, 4.88173s/12 iters), loss = 0.0275717
I0410 01:44:53.792534 14511 solver.cpp:237]     Train net output #0: loss = 0.027572 (* 1 = 0.027572 loss)
I0410 01:44:53.792544 14511 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 01:44:56.478729 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:58.448611 14511 solver.cpp:218] Iteration 9108 (2.57735 iter/s, 4.65594s/12 iters), loss = 0.0643957
I0410 01:44:58.448657 14511 solver.cpp:237]     Train net output #0: loss = 0.064396 (* 1 = 0.064396 loss)
I0410 01:44:58.448668 14511 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 01:45:03.044976 14511 solver.cpp:218] Iteration 9120 (2.61086 iter/s, 4.59619s/12 iters), loss = 0.0531507
I0410 01:45:03.045022 14511 solver.cpp:237]     Train net output #0: loss = 0.053151 (* 1 = 0.053151 loss)
I0410 01:45:03.045032 14511 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 01:45:07.721208 14511 solver.cpp:218] Iteration 9132 (2.56627 iter/s, 4.67606s/12 iters), loss = 0.0261653
I0410 01:45:07.721257 14511 solver.cpp:237]     Train net output #0: loss = 0.0261656 (* 1 = 0.0261656 loss)
I0410 01:45:07.721268 14511 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 01:45:12.577491 14511 solver.cpp:218] Iteration 9144 (2.47112 iter/s, 4.8561s/12 iters), loss = 0.0335327
I0410 01:45:12.577615 14511 solver.cpp:237]     Train net output #0: loss = 0.033533 (* 1 = 0.033533 loss)
I0410 01:45:12.577626 14511 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 01:45:17.794589 14511 solver.cpp:218] Iteration 9156 (2.30025 iter/s, 5.21683s/12 iters), loss = 0.0373674
I0410 01:45:17.794639 14511 solver.cpp:237]     Train net output #0: loss = 0.0373677 (* 1 = 0.0373677 loss)
I0410 01:45:17.794651 14511 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 01:45:22.583621 14511 solver.cpp:218] Iteration 9168 (2.50582 iter/s, 4.78885s/12 iters), loss = 0.0109595
I0410 01:45:22.583665 14511 solver.cpp:237]     Train net output #0: loss = 0.0109598 (* 1 = 0.0109598 loss)
I0410 01:45:22.583674 14511 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 01:45:26.778267 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 01:45:52.049899 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 01:46:01.832348 14511 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 01:46:01.832372 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:46:02.715808 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:06.528478 14511 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0410 01:46:06.528528 14511 solver.cpp:397]     Test net output #1: loss = 3.61201 (* 1 = 3.61201 loss)
I0410 01:46:06.637199 14511 solver.cpp:218] Iteration 9180 (0.272403 iter/s, 44.0524s/12 iters), loss = 0.0385123
I0410 01:46:06.638773 14511 solver.cpp:237]     Train net output #0: loss = 0.0385126 (* 1 = 0.0385126 loss)
I0410 01:46:06.638787 14511 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 01:46:10.821419 14511 solver.cpp:218] Iteration 9192 (2.86908 iter/s, 4.18253s/12 iters), loss = 0.151297
I0410 01:46:10.821471 14511 solver.cpp:237]     Train net output #0: loss = 0.151297 (* 1 = 0.151297 loss)
I0410 01:46:10.821482 14511 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 01:46:15.510280 14511 solver.cpp:218] Iteration 9204 (2.55936 iter/s, 4.68868s/12 iters), loss = 0.0488029
I0410 01:46:15.510334 14511 solver.cpp:237]     Train net output #0: loss = 0.0488032 (* 1 = 0.0488032 loss)
I0410 01:46:15.510344 14511 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 01:46:15.521659 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:20.040791 14511 solver.cpp:218] Iteration 9216 (2.64881 iter/s, 4.53033s/12 iters), loss = 0.0465582
I0410 01:46:20.040849 14511 solver.cpp:237]     Train net output #0: loss = 0.0465585 (* 1 = 0.0465585 loss)
I0410 01:46:20.040860 14511 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 01:46:24.508042 14511 solver.cpp:218] Iteration 9228 (2.68633 iter/s, 4.46706s/12 iters), loss = 0.0494244
I0410 01:46:24.508126 14511 solver.cpp:237]     Train net output #0: loss = 0.0494246 (* 1 = 0.0494246 loss)
I0410 01:46:24.508138 14511 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 01:46:29.185966 14511 solver.cpp:218] Iteration 9240 (2.56536 iter/s, 4.6777s/12 iters), loss = 0.0223648
I0410 01:46:29.186015 14511 solver.cpp:237]     Train net output #0: loss = 0.0223651 (* 1 = 0.0223651 loss)
I0410 01:46:29.186028 14511 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 01:46:33.967676 14511 solver.cpp:218] Iteration 9252 (2.50966 iter/s, 4.78153s/12 iters), loss = 0.0639198
I0410 01:46:33.967720 14511 solver.cpp:237]     Train net output #0: loss = 0.06392 (* 1 = 0.06392 loss)
I0410 01:46:33.967728 14511 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 01:46:38.983202 14511 solver.cpp:218] Iteration 9264 (2.39266 iter/s, 5.01535s/12 iters), loss = 0.0397389
I0410 01:46:38.983250 14511 solver.cpp:237]     Train net output #0: loss = 0.0397392 (* 1 = 0.0397392 loss)
I0410 01:46:38.983263 14511 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 01:46:43.893556 14511 solver.cpp:218] Iteration 9276 (2.44391 iter/s, 4.91017s/12 iters), loss = 0.0499364
I0410 01:46:43.893612 14511 solver.cpp:237]     Train net output #0: loss = 0.0499367 (* 1 = 0.0499367 loss)
I0410 01:46:43.893625 14511 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 01:46:45.816831 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 01:46:54.731848 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 01:47:00.581646 14511 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 01:47:00.581671 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:47:01.573056 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:05.298171 14511 solver.cpp:397]     Test net output #0: accuracy = 0.41299
I0410 01:47:05.298213 14511 solver.cpp:397]     Test net output #1: loss = 3.73112 (* 1 = 3.73112 loss)
I0410 01:47:07.064182 14511 solver.cpp:218] Iteration 9288 (0.517911 iter/s, 23.17s/12 iters), loss = 0.0232824
I0410 01:47:07.064229 14511 solver.cpp:237]     Train net output #0: loss = 0.0232827 (* 1 = 0.0232827 loss)
I0410 01:47:07.064240 14511 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 01:47:12.118662 14511 solver.cpp:218] Iteration 9300 (2.37422 iter/s, 5.05429s/12 iters), loss = 0.0258285
I0410 01:47:12.118713 14511 solver.cpp:237]     Train net output #0: loss = 0.0258288 (* 1 = 0.0258288 loss)
I0410 01:47:12.118724 14511 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 01:47:14.221781 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:16.840813 14511 solver.cpp:218] Iteration 9312 (2.54131 iter/s, 4.72197s/12 iters), loss = 0.0554997
I0410 01:47:16.840862 14511 solver.cpp:237]     Train net output #0: loss = 0.0555 (* 1 = 0.0555 loss)
I0410 01:47:16.840873 14511 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 01:47:21.823562 14511 solver.cpp:218] Iteration 9324 (2.4084 iter/s, 4.98257s/12 iters), loss = 0.0396526
I0410 01:47:21.823603 14511 solver.cpp:237]     Train net output #0: loss = 0.0396529 (* 1 = 0.0396529 loss)
I0410 01:47:21.823613 14511 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 01:47:26.680233 14511 solver.cpp:218] Iteration 9336 (2.47092 iter/s, 4.85649s/12 iters), loss = 0.0481817
I0410 01:47:26.680344 14511 solver.cpp:237]     Train net output #0: loss = 0.048182 (* 1 = 0.048182 loss)
I0410 01:47:26.680358 14511 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 01:47:31.366331 14511 solver.cpp:218] Iteration 9348 (2.5609 iter/s, 4.68586s/12 iters), loss = 0.0575243
I0410 01:47:31.366376 14511 solver.cpp:237]     Train net output #0: loss = 0.0575246 (* 1 = 0.0575246 loss)
I0410 01:47:31.366386 14511 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 01:47:36.090775 14511 solver.cpp:218] Iteration 9360 (2.54007 iter/s, 4.72427s/12 iters), loss = 0.0260104
I0410 01:47:36.090816 14511 solver.cpp:237]     Train net output #0: loss = 0.0260107 (* 1 = 0.0260107 loss)
I0410 01:47:36.090826 14511 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 01:47:41.240988 14511 solver.cpp:218] Iteration 9372 (2.33008 iter/s, 5.15003s/12 iters), loss = 0.126737
I0410 01:47:41.241024 14511 solver.cpp:237]     Train net output #0: loss = 0.126737 (* 1 = 0.126737 loss)
I0410 01:47:41.241034 14511 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 01:47:45.908659 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 01:47:55.512128 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 01:48:01.356140 14511 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 01:48:01.356218 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:48:02.147518 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:05.865361 14511 solver.cpp:397]     Test net output #0: accuracy = 0.416667
I0410 01:48:05.865412 14511 solver.cpp:397]     Test net output #1: loss = 3.66651 (* 1 = 3.66651 loss)
I0410 01:48:05.974282 14511 solver.cpp:218] Iteration 9384 (0.485189 iter/s, 24.7326s/12 iters), loss = 0.0689962
I0410 01:48:05.974334 14511 solver.cpp:237]     Train net output #0: loss = 0.0689965 (* 1 = 0.0689965 loss)
I0410 01:48:05.974344 14511 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 01:48:10.238358 14511 solver.cpp:218] Iteration 9396 (2.81432 iter/s, 4.2639s/12 iters), loss = 0.0197753
I0410 01:48:10.238407 14511 solver.cpp:237]     Train net output #0: loss = 0.0197756 (* 1 = 0.0197756 loss)
I0410 01:48:10.238420 14511 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 01:48:14.441017 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:15.104931 14511 solver.cpp:218] Iteration 9408 (2.46589 iter/s, 4.86639s/12 iters), loss = 0.0385792
I0410 01:48:15.104984 14511 solver.cpp:237]     Train net output #0: loss = 0.0385795 (* 1 = 0.0385795 loss)
I0410 01:48:15.104995 14511 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 01:48:20.178203 14511 solver.cpp:218] Iteration 9420 (2.36542 iter/s, 5.07308s/12 iters), loss = 0.0196742
I0410 01:48:20.178243 14511 solver.cpp:237]     Train net output #0: loss = 0.0196745 (* 1 = 0.0196745 loss)
I0410 01:48:20.178253 14511 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 01:48:24.877146 14511 solver.cpp:218] Iteration 9432 (2.55386 iter/s, 4.69877s/12 iters), loss = 0.0141193
I0410 01:48:24.877190 14511 solver.cpp:237]     Train net output #0: loss = 0.0141196 (* 1 = 0.0141196 loss)
I0410 01:48:24.877200 14511 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 01:48:29.652607 14511 solver.cpp:218] Iteration 9444 (2.51294 iter/s, 4.77528s/12 iters), loss = 0.0370834
I0410 01:48:29.652653 14511 solver.cpp:237]     Train net output #0: loss = 0.0370836 (* 1 = 0.0370836 loss)
I0410 01:48:29.652662 14511 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 01:48:34.769780 14511 solver.cpp:218] Iteration 9456 (2.34513 iter/s, 5.11699s/12 iters), loss = 0.0740002
I0410 01:48:34.769871 14511 solver.cpp:237]     Train net output #0: loss = 0.0740005 (* 1 = 0.0740005 loss)
I0410 01:48:34.769881 14511 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 01:48:39.474916 14511 solver.cpp:218] Iteration 9468 (2.55052 iter/s, 4.70492s/12 iters), loss = 0.00674189
I0410 01:48:39.474968 14511 solver.cpp:237]     Train net output #0: loss = 0.00674218 (* 1 = 0.00674218 loss)
I0410 01:48:39.474979 14511 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 01:48:44.262001 14511 solver.cpp:218] Iteration 9480 (2.50684 iter/s, 4.7869s/12 iters), loss = 0.0254204
I0410 01:48:44.262053 14511 solver.cpp:237]     Train net output #0: loss = 0.0254207 (* 1 = 0.0254207 loss)
I0410 01:48:44.262064 14511 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 01:48:46.192618 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 01:49:04.361496 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 01:49:10.214929 14511 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 01:49:10.214980 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:49:10.884685 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:14.794394 14511 solver.cpp:397]     Test net output #0: accuracy = 0.41299
I0410 01:49:14.794440 14511 solver.cpp:397]     Test net output #1: loss = 3.67344 (* 1 = 3.67344 loss)
I0410 01:49:16.421617 14511 solver.cpp:218] Iteration 9492 (0.373149 iter/s, 32.1588s/12 iters), loss = 0.0909193
I0410 01:49:16.421671 14511 solver.cpp:237]     Train net output #0: loss = 0.0909196 (* 1 = 0.0909196 loss)
I0410 01:49:16.421682 14511 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 01:49:21.048756 14511 solver.cpp:218] Iteration 9504 (2.5935 iter/s, 4.62696s/12 iters), loss = 0.0476331
I0410 01:49:21.048807 14511 solver.cpp:237]     Train net output #0: loss = 0.0476334 (* 1 = 0.0476334 loss)
I0410 01:49:21.048820 14511 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 01:49:22.410097 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:25.797159 14511 solver.cpp:218] Iteration 9516 (2.52726 iter/s, 4.74822s/12 iters), loss = 0.0594518
I0410 01:49:25.797207 14511 solver.cpp:237]     Train net output #0: loss = 0.0594521 (* 1 = 0.0594521 loss)
I0410 01:49:25.797219 14511 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 01:49:30.735349 14511 solver.cpp:218] Iteration 9528 (2.43013 iter/s, 4.93801s/12 iters), loss = 0.0363244
I0410 01:49:30.735400 14511 solver.cpp:237]     Train net output #0: loss = 0.0363247 (* 1 = 0.0363247 loss)
I0410 01:49:30.735410 14511 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 01:49:35.458613 14511 solver.cpp:218] Iteration 9540 (2.54071 iter/s, 4.72309s/12 iters), loss = 0.0409353
I0410 01:49:35.458660 14511 solver.cpp:237]     Train net output #0: loss = 0.0409356 (* 1 = 0.0409356 loss)
I0410 01:49:35.458671 14511 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 01:49:40.038534 14511 solver.cpp:218] Iteration 9552 (2.62023 iter/s, 4.57975s/12 iters), loss = 0.131796
I0410 01:49:40.038585 14511 solver.cpp:237]     Train net output #0: loss = 0.131797 (* 1 = 0.131797 loss)
I0410 01:49:40.038597 14511 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 01:49:44.747493 14511 solver.cpp:218] Iteration 9564 (2.54843 iter/s, 4.70878s/12 iters), loss = 0.0803453
I0410 01:49:44.747607 14511 solver.cpp:237]     Train net output #0: loss = 0.0803456 (* 1 = 0.0803456 loss)
I0410 01:49:44.747620 14511 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 01:49:49.532985 14511 solver.cpp:218] Iteration 9576 (2.50771 iter/s, 4.78525s/12 iters), loss = 0.0216084
I0410 01:49:49.533039 14511 solver.cpp:237]     Train net output #0: loss = 0.0216087 (* 1 = 0.0216087 loss)
I0410 01:49:49.533052 14511 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 01:49:54.353168 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 01:50:13.183877 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 01:50:25.427793 14511 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 01:50:25.427845 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:50:26.161180 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:30.340692 14511 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0410 01:50:30.340739 14511 solver.cpp:397]     Test net output #1: loss = 3.61663 (* 1 = 3.61663 loss)
I0410 01:50:30.449442 14511 solver.cpp:218] Iteration 9588 (0.293288 iter/s, 40.9154s/12 iters), loss = 0.0507451
I0410 01:50:30.449501 14511 solver.cpp:237]     Train net output #0: loss = 0.0507453 (* 1 = 0.0507453 loss)
I0410 01:50:30.449512 14511 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 01:50:34.667040 14511 solver.cpp:218] Iteration 9600 (2.84534 iter/s, 4.21743s/12 iters), loss = 0.0287916
I0410 01:50:34.667081 14511 solver.cpp:237]     Train net output #0: loss = 0.0287919 (* 1 = 0.0287919 loss)
I0410 01:50:34.667093 14511 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 01:50:38.416926 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:39.887133 14511 solver.cpp:218] Iteration 9612 (2.29889 iter/s, 5.21991s/12 iters), loss = 0.0253574
I0410 01:50:39.887187 14511 solver.cpp:237]     Train net output #0: loss = 0.0253577 (* 1 = 0.0253577 loss)
I0410 01:50:39.887199 14511 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 01:50:44.904031 14511 solver.cpp:218] Iteration 9624 (2.39201 iter/s, 5.01671s/12 iters), loss = 0.0857383
I0410 01:50:44.904075 14511 solver.cpp:237]     Train net output #0: loss = 0.0857386 (* 1 = 0.0857386 loss)
I0410 01:50:44.904084 14511 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 01:50:50.053225 14511 solver.cpp:218] Iteration 9636 (2.33054 iter/s, 5.14901s/12 iters), loss = 0.022056
I0410 01:50:50.053268 14511 solver.cpp:237]     Train net output #0: loss = 0.0220563 (* 1 = 0.0220563 loss)
I0410 01:50:50.053277 14511 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 01:50:55.295027 14511 solver.cpp:218] Iteration 9648 (2.28937 iter/s, 5.24162s/12 iters), loss = 0.138606
I0410 01:50:55.295068 14511 solver.cpp:237]     Train net output #0: loss = 0.138606 (* 1 = 0.138606 loss)
I0410 01:50:55.295078 14511 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 01:51:00.334573 14511 solver.cpp:218] Iteration 9660 (2.38125 iter/s, 5.03937s/12 iters), loss = 0.076328
I0410 01:51:00.334708 14511 solver.cpp:237]     Train net output #0: loss = 0.0763283 (* 1 = 0.0763283 loss)
I0410 01:51:00.334722 14511 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 01:51:05.559253 14511 solver.cpp:218] Iteration 9672 (2.29691 iter/s, 5.22441s/12 iters), loss = 0.0636452
I0410 01:51:05.559304 14511 solver.cpp:237]     Train net output #0: loss = 0.0636455 (* 1 = 0.0636455 loss)
I0410 01:51:05.559315 14511 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 01:51:10.739217 14511 solver.cpp:218] Iteration 9684 (2.3167 iter/s, 5.17978s/12 iters), loss = 0.0647226
I0410 01:51:10.739260 14511 solver.cpp:237]     Train net output #0: loss = 0.0647229 (* 1 = 0.0647229 loss)
I0410 01:51:10.739270 14511 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 01:51:12.824090 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 01:51:24.952240 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 01:51:36.182374 14511 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 01:51:36.182443 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:51:36.858286 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:39.877353 14511 blocking_queue.cpp:49] Waiting for data
I0410 01:51:40.876161 14511 solver.cpp:397]     Test net output #0: accuracy = 0.422181
I0410 01:51:40.876226 14511 solver.cpp:397]     Test net output #1: loss = 3.68978 (* 1 = 3.68978 loss)
I0410 01:51:42.646148 14511 solver.cpp:218] Iteration 9696 (0.376104 iter/s, 31.9061s/12 iters), loss = 0.0173582
I0410 01:51:42.646203 14511 solver.cpp:237]     Train net output #0: loss = 0.0173584 (* 1 = 0.0173584 loss)
I0410 01:51:42.646215 14511 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 01:51:47.724661 14511 solver.cpp:218] Iteration 9708 (2.36299 iter/s, 5.07831s/12 iters), loss = 0.0576336
I0410 01:51:47.724716 14511 solver.cpp:237]     Train net output #0: loss = 0.0576339 (* 1 = 0.0576339 loss)
I0410 01:51:47.724728 14511 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 01:51:48.503808 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:52.965801 14511 solver.cpp:218] Iteration 9720 (2.28966 iter/s, 5.24094s/12 iters), loss = 0.0563025
I0410 01:51:52.965852 14511 solver.cpp:237]     Train net output #0: loss = 0.0563027 (* 1 = 0.0563027 loss)
I0410 01:51:52.965862 14511 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 01:51:58.027254 14511 solver.cpp:218] Iteration 9732 (2.37095 iter/s, 5.06127s/12 iters), loss = 0.0254102
I0410 01:51:58.027293 14511 solver.cpp:237]     Train net output #0: loss = 0.0254105 (* 1 = 0.0254105 loss)
I0410 01:51:58.027302 14511 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 01:52:03.189167 14511 solver.cpp:218] Iteration 9744 (2.3248 iter/s, 5.16173s/12 iters), loss = 0.0606173
I0410 01:52:03.189218 14511 solver.cpp:237]     Train net output #0: loss = 0.0606176 (* 1 = 0.0606176 loss)
I0410 01:52:03.189230 14511 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 01:52:08.414412 14511 solver.cpp:218] Iteration 9756 (2.29663 iter/s, 5.22505s/12 iters), loss = 0.0287361
I0410 01:52:08.414517 14511 solver.cpp:237]     Train net output #0: loss = 0.0287364 (* 1 = 0.0287364 loss)
I0410 01:52:08.414530 14511 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 01:52:13.595317 14511 solver.cpp:218] Iteration 9768 (2.31631 iter/s, 5.18066s/12 iters), loss = 0.00361691
I0410 01:52:13.595369 14511 solver.cpp:237]     Train net output #0: loss = 0.00361718 (* 1 = 0.00361718 loss)
I0410 01:52:13.595382 14511 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 01:52:18.672839 14511 solver.cpp:218] Iteration 9780 (2.36345 iter/s, 5.07733s/12 iters), loss = 0.0394962
I0410 01:52:18.672891 14511 solver.cpp:237]     Train net output #0: loss = 0.0394965 (* 1 = 0.0394965 loss)
I0410 01:52:18.672904 14511 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 01:52:23.220898 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 01:52:30.782465 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 01:52:36.584723 14511 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 01:52:36.584745 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:52:37.110335 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:41.030431 14511 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0410 01:52:41.030561 14511 solver.cpp:397]     Test net output #1: loss = 3.65121 (* 1 = 3.65121 loss)
I0410 01:52:41.139266 14511 solver.cpp:218] Iteration 9792 (0.534145 iter/s, 22.4658s/12 iters), loss = 0.0504588
I0410 01:52:41.139317 14511 solver.cpp:237]     Train net output #0: loss = 0.0504591 (* 1 = 0.0504591 loss)
I0410 01:52:41.139329 14511 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 01:52:45.710490 14511 solver.cpp:218] Iteration 9804 (2.62522 iter/s, 4.57105s/12 iters), loss = 0.0123966
I0410 01:52:45.710541 14511 solver.cpp:237]     Train net output #0: loss = 0.0123969 (* 1 = 0.0123969 loss)
I0410 01:52:45.710552 14511 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 01:52:48.625667 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:50.784797 14511 solver.cpp:218] Iteration 9816 (2.36494 iter/s, 5.07412s/12 iters), loss = 0.037051
I0410 01:52:50.784843 14511 solver.cpp:237]     Train net output #0: loss = 0.0370513 (* 1 = 0.0370513 loss)
I0410 01:52:50.784852 14511 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 01:52:55.804004 14511 solver.cpp:218] Iteration 9828 (2.3909 iter/s, 5.01903s/12 iters), loss = 0.0711726
I0410 01:52:55.804051 14511 solver.cpp:237]     Train net output #0: loss = 0.0711729 (* 1 = 0.0711729 loss)
I0410 01:52:55.804061 14511 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 01:53:00.894246 14511 solver.cpp:218] Iteration 9840 (2.35754 iter/s, 5.09006s/12 iters), loss = 0.028155
I0410 01:53:00.894299 14511 solver.cpp:237]     Train net output #0: loss = 0.0281552 (* 1 = 0.0281552 loss)
I0410 01:53:00.894311 14511 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 01:53:05.771783 14511 solver.cpp:218] Iteration 9852 (2.46035 iter/s, 4.87735s/12 iters), loss = 0.0188667
I0410 01:53:05.771836 14511 solver.cpp:237]     Train net output #0: loss = 0.018867 (* 1 = 0.018867 loss)
I0410 01:53:05.771847 14511 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 01:53:10.487442 14511 solver.cpp:218] Iteration 9864 (2.54481 iter/s, 4.71548s/12 iters), loss = 0.0240596
I0410 01:53:10.487495 14511 solver.cpp:237]     Train net output #0: loss = 0.0240598 (* 1 = 0.0240598 loss)
I0410 01:53:10.487507 14511 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 01:53:15.269196 14511 solver.cpp:218] Iteration 9876 (2.50964 iter/s, 4.78157s/12 iters), loss = 0.0368885
I0410 01:53:15.269306 14511 solver.cpp:237]     Train net output #0: loss = 0.0368888 (* 1 = 0.0368888 loss)
I0410 01:53:15.269318 14511 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 01:53:19.989118 14511 solver.cpp:218] Iteration 9888 (2.54254 iter/s, 4.71969s/12 iters), loss = 0.0636221
I0410 01:53:19.989166 14511 solver.cpp:237]     Train net output #0: loss = 0.0636224 (* 1 = 0.0636224 loss)
I0410 01:53:19.989181 14511 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 01:53:21.889991 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 01:53:34.679736 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 01:53:46.725139 14511 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 01:53:46.725219 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:53:47.311414 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:51.326052 14511 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0410 01:53:51.326104 14511 solver.cpp:397]     Test net output #1: loss = 3.66074 (* 1 = 3.66074 loss)
I0410 01:53:53.053151 14511 solver.cpp:218] Iteration 9900 (0.362942 iter/s, 33.0632s/12 iters), loss = 0.0214724
I0410 01:53:53.053200 14511 solver.cpp:237]     Train net output #0: loss = 0.0214727 (* 1 = 0.0214727 loss)
I0410 01:53:53.053212 14511 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 01:53:58.317418 14511 solver.cpp:218] Iteration 9912 (2.2796 iter/s, 5.26407s/12 iters), loss = 0.0305002
I0410 01:53:58.317472 14511 solver.cpp:237]     Train net output #0: loss = 0.0305005 (* 1 = 0.0305005 loss)
I0410 01:53:58.317485 14511 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 01:53:58.404922 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:03.394387 14511 solver.cpp:218] Iteration 9924 (2.3637 iter/s, 5.07678s/12 iters), loss = 0.0214185
I0410 01:54:03.394439 14511 solver.cpp:237]     Train net output #0: loss = 0.0214188 (* 1 = 0.0214188 loss)
I0410 01:54:03.394460 14511 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 01:54:08.300392 14511 solver.cpp:218] Iteration 9936 (2.44607 iter/s, 4.90582s/12 iters), loss = 0.00543991
I0410 01:54:08.300441 14511 solver.cpp:237]     Train net output #0: loss = 0.00544019 (* 1 = 0.00544019 loss)
I0410 01:54:08.300453 14511 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 01:54:13.346385 14511 solver.cpp:218] Iteration 9948 (2.37821 iter/s, 5.04581s/12 iters), loss = 0.058245
I0410 01:54:13.346426 14511 solver.cpp:237]     Train net output #0: loss = 0.0582452 (* 1 = 0.0582452 loss)
I0410 01:54:13.346434 14511 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 01:54:18.527657 14511 solver.cpp:218] Iteration 9960 (2.31612 iter/s, 5.18109s/12 iters), loss = 0.0692319
I0410 01:54:18.527789 14511 solver.cpp:237]     Train net output #0: loss = 0.0692322 (* 1 = 0.0692322 loss)
I0410 01:54:18.527802 14511 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 01:54:23.602504 14511 solver.cpp:218] Iteration 9972 (2.36473 iter/s, 5.07458s/12 iters), loss = 0.0396575
I0410 01:54:23.602558 14511 solver.cpp:237]     Train net output #0: loss = 0.0396578 (* 1 = 0.0396578 loss)
I0410 01:54:23.602571 14511 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 01:54:28.829771 14511 solver.cpp:218] Iteration 9984 (2.29574 iter/s, 5.22707s/12 iters), loss = 0.0513404
I0410 01:54:28.829826 14511 solver.cpp:237]     Train net output #0: loss = 0.0513407 (* 1 = 0.0513407 loss)
I0410 01:54:28.829843 14511 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 01:54:33.629446 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 01:54:51.558900 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 01:55:10.768458 14511 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 01:55:10.768486 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:55:11.306646 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:15.277658 14511 solver.cpp:397]     Test net output #0: accuracy = 0.418505
I0410 01:55:15.277709 14511 solver.cpp:397]     Test net output #1: loss = 3.6134 (* 1 = 3.6134 loss)
I0410 01:55:15.386162 14511 solver.cpp:218] Iteration 9996 (0.257759 iter/s, 46.5552s/12 iters), loss = 0.0365731
I0410 01:55:15.386205 14511 solver.cpp:237]     Train net output #0: loss = 0.0365734 (* 1 = 0.0365734 loss)
I0410 01:55:15.386215 14511 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 01:55:19.239835 14511 solver.cpp:218] Iteration 10008 (3.11404 iter/s, 3.85352s/12 iters), loss = 0.0197963
I0410 01:55:19.239882 14511 solver.cpp:237]     Train net output #0: loss = 0.0197966 (* 1 = 0.0197966 loss)
I0410 01:55:19.239890 14511 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 01:55:21.279809 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:23.999686 14511 solver.cpp:218] Iteration 10020 (2.52118 iter/s, 4.75967s/12 iters), loss = 0.0246959
I0410 01:55:23.999827 14511 solver.cpp:237]     Train net output #0: loss = 0.0246962 (* 1 = 0.0246962 loss)
I0410 01:55:23.999840 14511 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 01:55:29.147573 14511 solver.cpp:218] Iteration 10032 (2.33118 iter/s, 5.14761s/12 iters), loss = 0.0171693
I0410 01:55:29.147619 14511 solver.cpp:237]     Train net output #0: loss = 0.0171696 (* 1 = 0.0171696 loss)
I0410 01:55:29.147629 14511 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 01:55:34.269320 14511 solver.cpp:218] Iteration 10044 (2.34304 iter/s, 5.12156s/12 iters), loss = 0.0195929
I0410 01:55:34.269372 14511 solver.cpp:237]     Train net output #0: loss = 0.0195932 (* 1 = 0.0195932 loss)
I0410 01:55:34.269383 14511 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 01:55:39.463579 14511 solver.cpp:218] Iteration 10056 (2.31033 iter/s, 5.19406s/12 iters), loss = 0.0798011
I0410 01:55:39.463636 14511 solver.cpp:237]     Train net output #0: loss = 0.0798014 (* 1 = 0.0798014 loss)
I0410 01:55:39.463649 14511 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 01:55:44.671964 14511 solver.cpp:218] Iteration 10068 (2.30406 iter/s, 5.20819s/12 iters), loss = 0.00864784
I0410 01:55:44.672011 14511 solver.cpp:237]     Train net output #0: loss = 0.00864814 (* 1 = 0.00864814 loss)
I0410 01:55:44.672020 14511 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 01:55:49.166435 14511 solver.cpp:218] Iteration 10080 (2.67005 iter/s, 4.4943s/12 iters), loss = 0.114409
I0410 01:55:49.166476 14511 solver.cpp:237]     Train net output #0: loss = 0.114409 (* 1 = 0.114409 loss)
I0410 01:55:49.166486 14511 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 01:55:54.025020 14511 solver.cpp:218] Iteration 10092 (2.46994 iter/s, 4.85841s/12 iters), loss = 0.0774432
I0410 01:55:54.025115 14511 solver.cpp:237]     Train net output #0: loss = 0.0774436 (* 1 = 0.0774436 loss)
I0410 01:55:54.025125 14511 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 01:55:56.134207 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 01:56:21.271328 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 01:56:32.748535 14511 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 01:56:32.748592 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:56:33.214462 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:37.501724 14511 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0410 01:56:37.501765 14511 solver.cpp:397]     Test net output #1: loss = 3.63802 (* 1 = 3.63802 loss)
I0410 01:56:39.289256 14511 solver.cpp:218] Iteration 10104 (0.265117 iter/s, 45.263s/12 iters), loss = 0.02537
I0410 01:56:39.289316 14511 solver.cpp:237]     Train net output #0: loss = 0.0253703 (* 1 = 0.0253703 loss)
I0410 01:56:39.289328 14511 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 01:56:43.693931 14515 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:44.292338 14511 solver.cpp:218] Iteration 10116 (2.39862 iter/s, 5.00289s/12 iters), loss = 0.00831572
I0410 01:56:44.292387 14511 solver.cpp:237]     Train net output #0: loss = 0.00831605 (* 1 = 0.00831605 loss)
I0410 01:56:44.292397 14511 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 01:56:49.111889 14511 solver.cpp:218] Iteration 10128 (2.48995 iter/s, 4.81937s/12 iters), loss = 0.0180909
I0410 01:56:49.111936 14511 solver.cpp:237]     Train net output #0: loss = 0.0180912 (* 1 = 0.0180912 loss)
I0410 01:56:49.111945 14511 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 01:56:54.144259 14511 solver.cpp:218] Iteration 10140 (2.38465 iter/s, 5.03218s/12 iters), loss = 0.0282985
I0410 01:56:54.144320 14511 solver.cpp:237]     Train net output #0: loss = 0.0282988 (* 1 = 0.0282988 loss)
I0410 01:56:54.144333 14511 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 01:56:58.984520 14511 solver.cpp:218] Iteration 10152 (2.4793 iter/s, 4.84007s/12 iters), loss = 0.00673928
I0410 01:56:58.984575 14511 solver.cpp:237]     Train net output #0: loss = 0.0067396 (* 1 = 0.0067396 loss)
I0410 01:56:58.984587 14511 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 01:57:03.853857 14511 solver.cpp:218] Iteration 10164 (2.4645 iter/s, 4.86915s/12 iters), loss = 0.0313819
I0410 01:57:03.854804 14511 solver.cpp:237]     Train net output #0: loss = 0.0313822 (* 1 = 0.0313822 loss)
I0410 01:57:03.854816 14511 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 01:57:08.642479 14511 solver.cpp:218] Iteration 10176 (2.5065 iter/s, 4.78754s/12 iters), loss = 0.0574193
I0410 01:57:08.642526 14511 solver.cpp:237]     Train net output #0: loss = 0.0574196 (* 1 = 0.0574196 loss)
I0410 01:57:08.642535 14511 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 01:57:13.717411 14511 solver.cpp:218] Iteration 10188 (2.36465 iter/s, 5.07474s/12 iters), loss = 0.0146885
I0410 01:57:13.717463 14511 solver.cpp:237]     Train net output #0: loss = 0.0146889 (* 1 = 0.0146889 loss)
I0410 01:57:13.717475 14511 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 01:57:18.325016 14511 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 01:57:30.043900 14511 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 01:57:35.943599 14511 solver.cpp:310] Iteration 10200, loss = 0.0621398
I0410 01:57:35.943673 14511 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 01:57:35.943681 14511 net.cpp:676] Ignoring source layer train-data
I0410 01:57:36.349555 14516 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:40.598178 14511 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0410 01:57:40.598230 14511 solver.cpp:397]     Test net output #1: loss = 3.60061 (* 1 = 3.60061 loss)
I0410 01:57:40.598242 14511 solver.cpp:315] Optimization Done.
I0410 01:57:40.598249 14511 caffe.cpp:259] Optimization Done.