DIGITS-CNN/cars/architecture-investigations/fc/3-layers/1024/caffe_output.log

I0409 19:57:54.747947 15108 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-195753-4f12/solver.prototxt
I0409 19:57:54.748095 15108 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 19:57:54.748101 15108 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 19:57:54.748160 15108 caffe.cpp:218] Using GPUs 2
I0409 19:57:54.762773 15108 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0409 19:57:55.023713 15108 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 19:57:55.024361 15108 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 19:57:55.024933 15108 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 19:57:55.024951 15108 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 19:57:55.025104 15108 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: 1024
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: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 19:57:55.025202 15108 layer_factory.hpp:77] Creating layer train-data
I0409 19:57:55.027349 15108 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 19:57:55.027554 15108 net.cpp:84] Creating Layer train-data
I0409 19:57:55.027565 15108 net.cpp:380] train-data -> data
I0409 19:57:55.027585 15108 net.cpp:380] train-data -> label
I0409 19:57:55.027596 15108 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:57:55.032243 15108 data_layer.cpp:45] output data size: 128,3,227,227
I0409 19:57:55.155830 15108 net.cpp:122] Setting up train-data
I0409 19:57:55.155856 15108 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 19:57:55.155861 15108 net.cpp:129] Top shape: 128 (128)
I0409 19:57:55.155865 15108 net.cpp:137] Memory required for data: 79149056
I0409 19:57:55.155874 15108 layer_factory.hpp:77] Creating layer conv1
I0409 19:57:55.155896 15108 net.cpp:84] Creating Layer conv1
I0409 19:57:55.155902 15108 net.cpp:406] conv1 <- data
I0409 19:57:55.155915 15108 net.cpp:380] conv1 -> conv1
I0409 19:57:55.696290 15108 net.cpp:122] Setting up conv1
I0409 19:57:55.696314 15108 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:57:55.696318 15108 net.cpp:137] Memory required for data: 227833856
I0409 19:57:55.696338 15108 layer_factory.hpp:77] Creating layer relu1
I0409 19:57:55.696368 15108 net.cpp:84] Creating Layer relu1
I0409 19:57:55.696373 15108 net.cpp:406] relu1 <- conv1
I0409 19:57:55.696379 15108 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:57:55.696671 15108 net.cpp:122] Setting up relu1
I0409 19:57:55.696681 15108 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:57:55.696684 15108 net.cpp:137] Memory required for data: 376518656
I0409 19:57:55.696688 15108 layer_factory.hpp:77] Creating layer norm1
I0409 19:57:55.696697 15108 net.cpp:84] Creating Layer norm1
I0409 19:57:55.696700 15108 net.cpp:406] norm1 <- conv1
I0409 19:57:55.696707 15108 net.cpp:380] norm1 -> norm1
I0409 19:57:55.697149 15108 net.cpp:122] Setting up norm1
I0409 19:57:55.697160 15108 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 19:57:55.697163 15108 net.cpp:137] Memory required for data: 525203456
I0409 19:57:55.697167 15108 layer_factory.hpp:77] Creating layer pool1
I0409 19:57:55.697175 15108 net.cpp:84] Creating Layer pool1
I0409 19:57:55.697180 15108 net.cpp:406] pool1 <- norm1
I0409 19:57:55.697185 15108 net.cpp:380] pool1 -> pool1
I0409 19:57:55.697221 15108 net.cpp:122] Setting up pool1
I0409 19:57:55.697227 15108 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 19:57:55.697229 15108 net.cpp:137] Memory required for data: 561035264
I0409 19:57:55.697233 15108 layer_factory.hpp:77] Creating layer conv2
I0409 19:57:55.697243 15108 net.cpp:84] Creating Layer conv2
I0409 19:57:55.697247 15108 net.cpp:406] conv2 <- pool1
I0409 19:57:55.697252 15108 net.cpp:380] conv2 -> conv2
I0409 19:57:55.703755 15108 net.cpp:122] Setting up conv2
I0409 19:57:55.703771 15108 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:57:55.703774 15108 net.cpp:137] Memory required for data: 656586752
I0409 19:57:55.703785 15108 layer_factory.hpp:77] Creating layer relu2
I0409 19:57:55.703794 15108 net.cpp:84] Creating Layer relu2
I0409 19:57:55.703797 15108 net.cpp:406] relu2 <- conv2
I0409 19:57:55.703804 15108 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:57:55.704232 15108 net.cpp:122] Setting up relu2
I0409 19:57:55.704241 15108 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:57:55.704246 15108 net.cpp:137] Memory required for data: 752138240
I0409 19:57:55.704249 15108 layer_factory.hpp:77] Creating layer norm2
I0409 19:57:55.704257 15108 net.cpp:84] Creating Layer norm2
I0409 19:57:55.704260 15108 net.cpp:406] norm2 <- conv2
I0409 19:57:55.704267 15108 net.cpp:380] norm2 -> norm2
I0409 19:57:55.704555 15108 net.cpp:122] Setting up norm2
I0409 19:57:55.704563 15108 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 19:57:55.704566 15108 net.cpp:137] Memory required for data: 847689728
I0409 19:57:55.704571 15108 layer_factory.hpp:77] Creating layer pool2
I0409 19:57:55.704577 15108 net.cpp:84] Creating Layer pool2
I0409 19:57:55.704581 15108 net.cpp:406] pool2 <- norm2
I0409 19:57:55.704586 15108 net.cpp:380] pool2 -> pool2
I0409 19:57:55.704613 15108 net.cpp:122] Setting up pool2
I0409 19:57:55.704618 15108 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:57:55.704622 15108 net.cpp:137] Memory required for data: 869840896
I0409 19:57:55.704624 15108 layer_factory.hpp:77] Creating layer conv3
I0409 19:57:55.704634 15108 net.cpp:84] Creating Layer conv3
I0409 19:57:55.704638 15108 net.cpp:406] conv3 <- pool2
I0409 19:57:55.704643 15108 net.cpp:380] conv3 -> conv3
I0409 19:57:55.714392 15108 net.cpp:122] Setting up conv3
I0409 19:57:55.714406 15108 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:57:55.714411 15108 net.cpp:137] Memory required for data: 903067648
I0409 19:57:55.714419 15108 layer_factory.hpp:77] Creating layer relu3
I0409 19:57:55.714426 15108 net.cpp:84] Creating Layer relu3
I0409 19:57:55.714430 15108 net.cpp:406] relu3 <- conv3
I0409 19:57:55.714435 15108 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:57:55.714888 15108 net.cpp:122] Setting up relu3
I0409 19:57:55.714897 15108 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:57:55.714900 15108 net.cpp:137] Memory required for data: 936294400
I0409 19:57:55.714905 15108 layer_factory.hpp:77] Creating layer conv4
I0409 19:57:55.714932 15108 net.cpp:84] Creating Layer conv4
I0409 19:57:55.714936 15108 net.cpp:406] conv4 <- conv3
I0409 19:57:55.714943 15108 net.cpp:380] conv4 -> conv4
I0409 19:57:55.725224 15108 net.cpp:122] Setting up conv4
I0409 19:57:55.725239 15108 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:57:55.725242 15108 net.cpp:137] Memory required for data: 969521152
I0409 19:57:55.725251 15108 layer_factory.hpp:77] Creating layer relu4
I0409 19:57:55.725260 15108 net.cpp:84] Creating Layer relu4
I0409 19:57:55.725265 15108 net.cpp:406] relu4 <- conv4
I0409 19:57:55.725270 15108 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:57:55.725603 15108 net.cpp:122] Setting up relu4
I0409 19:57:55.725611 15108 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 19:57:55.725615 15108 net.cpp:137] Memory required for data: 1002747904
I0409 19:57:55.725617 15108 layer_factory.hpp:77] Creating layer conv5
I0409 19:57:55.725628 15108 net.cpp:84] Creating Layer conv5
I0409 19:57:55.725631 15108 net.cpp:406] conv5 <- conv4
I0409 19:57:55.725641 15108 net.cpp:380] conv5 -> conv5
I0409 19:57:55.733907 15108 net.cpp:122] Setting up conv5
I0409 19:57:55.733922 15108 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:57:55.733925 15108 net.cpp:137] Memory required for data: 1024899072
I0409 19:57:55.733938 15108 layer_factory.hpp:77] Creating layer relu5
I0409 19:57:55.733945 15108 net.cpp:84] Creating Layer relu5
I0409 19:57:55.733949 15108 net.cpp:406] relu5 <- conv5
I0409 19:57:55.733964 15108 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:57:55.734445 15108 net.cpp:122] Setting up relu5
I0409 19:57:55.734454 15108 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 19:57:55.734458 15108 net.cpp:137] Memory required for data: 1047050240
I0409 19:57:55.734462 15108 layer_factory.hpp:77] Creating layer pool5
I0409 19:57:55.734469 15108 net.cpp:84] Creating Layer pool5
I0409 19:57:55.734472 15108 net.cpp:406] pool5 <- conv5
I0409 19:57:55.734479 15108 net.cpp:380] pool5 -> pool5
I0409 19:57:55.734515 15108 net.cpp:122] Setting up pool5
I0409 19:57:55.734521 15108 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 19:57:55.734524 15108 net.cpp:137] Memory required for data: 1051768832
I0409 19:57:55.734527 15108 layer_factory.hpp:77] Creating layer fc6
I0409 19:57:55.734539 15108 net.cpp:84] Creating Layer fc6
I0409 19:57:55.734541 15108 net.cpp:406] fc6 <- pool5
I0409 19:57:55.734547 15108 net.cpp:380] fc6 -> fc6
I0409 19:57:55.822893 15108 net.cpp:122] Setting up fc6
I0409 19:57:55.822916 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.822921 15108 net.cpp:137] Memory required for data: 1052293120
I0409 19:57:55.822930 15108 layer_factory.hpp:77] Creating layer relu6
I0409 19:57:55.822940 15108 net.cpp:84] Creating Layer relu6
I0409 19:57:55.822947 15108 net.cpp:406] relu6 <- fc6
I0409 19:57:55.822955 15108 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:57:55.823562 15108 net.cpp:122] Setting up relu6
I0409 19:57:55.823572 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.823576 15108 net.cpp:137] Memory required for data: 1052817408
I0409 19:57:55.823581 15108 layer_factory.hpp:77] Creating layer drop6
I0409 19:57:55.823588 15108 net.cpp:84] Creating Layer drop6
I0409 19:57:55.823592 15108 net.cpp:406] drop6 <- fc6
I0409 19:57:55.823598 15108 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:57:55.823626 15108 net.cpp:122] Setting up drop6
I0409 19:57:55.823632 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.823637 15108 net.cpp:137] Memory required for data: 1053341696
I0409 19:57:55.823642 15108 layer_factory.hpp:77] Creating layer fc7
I0409 19:57:55.823649 15108 net.cpp:84] Creating Layer fc7
I0409 19:57:55.823654 15108 net.cpp:406] fc7 <- fc6
I0409 19:57:55.823660 15108 net.cpp:380] fc7 -> fc7
I0409 19:57:55.833600 15108 net.cpp:122] Setting up fc7
I0409 19:57:55.833611 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.833616 15108 net.cpp:137] Memory required for data: 1053865984
I0409 19:57:55.833623 15108 layer_factory.hpp:77] Creating layer relu7
I0409 19:57:55.833648 15108 net.cpp:84] Creating Layer relu7
I0409 19:57:55.833653 15108 net.cpp:406] relu7 <- fc7
I0409 19:57:55.833659 15108 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:57:55.834664 15108 net.cpp:122] Setting up relu7
I0409 19:57:55.834674 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.834678 15108 net.cpp:137] Memory required for data: 1054390272
I0409 19:57:55.834683 15108 layer_factory.hpp:77] Creating layer drop7
I0409 19:57:55.834689 15108 net.cpp:84] Creating Layer drop7
I0409 19:57:55.834694 15108 net.cpp:406] drop7 <- fc7
I0409 19:57:55.834700 15108 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:57:55.834726 15108 net.cpp:122] Setting up drop7
I0409 19:57:55.834731 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.834735 15108 net.cpp:137] Memory required for data: 1054914560
I0409 19:57:55.834739 15108 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:57:55.834746 15108 net.cpp:84] Creating Layer fc7.5
I0409 19:57:55.834750 15108 net.cpp:406] fc7.5 <- fc7
I0409 19:57:55.834758 15108 net.cpp:380] fc7.5 -> fc7.5
I0409 19:57:55.844607 15108 net.cpp:122] Setting up fc7.5
I0409 19:57:55.844619 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.844624 15108 net.cpp:137] Memory required for data: 1055438848
I0409 19:57:55.844631 15108 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:57:55.844640 15108 net.cpp:84] Creating Layer relu7.5
I0409 19:57:55.844645 15108 net.cpp:406] relu7.5 <- fc7.5
I0409 19:57:55.844650 15108 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:57:55.845168 15108 net.cpp:122] Setting up relu7.5
I0409 19:57:55.845177 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.845181 15108 net.cpp:137] Memory required for data: 1055963136
I0409 19:57:55.845185 15108 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:57:55.845192 15108 net.cpp:84] Creating Layer drop7.5
I0409 19:57:55.845196 15108 net.cpp:406] drop7.5 <- fc7.5
I0409 19:57:55.845202 15108 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:57:55.845227 15108 net.cpp:122] Setting up drop7.5
I0409 19:57:55.845232 15108 net.cpp:129] Top shape: 128 1024 (131072)
I0409 19:57:55.845235 15108 net.cpp:137] Memory required for data: 1056487424
I0409 19:57:55.845239 15108 layer_factory.hpp:77] Creating layer fc8
I0409 19:57:55.845247 15108 net.cpp:84] Creating Layer fc8
I0409 19:57:55.845252 15108 net.cpp:406] fc8 <- fc7.5
I0409 19:57:55.845258 15108 net.cpp:380] fc8 -> fc8
I0409 19:57:55.847059 15108 net.cpp:122] Setting up fc8
I0409 19:57:55.847064 15108 net.cpp:129] Top shape: 128 196 (25088)
I0409 19:57:55.847069 15108 net.cpp:137] Memory required for data: 1056587776
I0409 19:57:55.847079 15108 layer_factory.hpp:77] Creating layer loss
I0409 19:57:55.847086 15108 net.cpp:84] Creating Layer loss
I0409 19:57:55.847090 15108 net.cpp:406] loss <- fc8
I0409 19:57:55.847095 15108 net.cpp:406] loss <- label
I0409 19:57:55.847103 15108 net.cpp:380] loss -> loss
I0409 19:57:55.847115 15108 layer_factory.hpp:77] Creating layer loss
I0409 19:57:55.847692 15108 net.cpp:122] Setting up loss
I0409 19:57:55.847700 15108 net.cpp:129] Top shape: (1)
I0409 19:57:55.847704 15108 net.cpp:132]     with loss weight 1
I0409 19:57:55.847723 15108 net.cpp:137] Memory required for data: 1056587780
I0409 19:57:55.847726 15108 net.cpp:198] loss needs backward computation.
I0409 19:57:55.847733 15108 net.cpp:198] fc8 needs backward computation.
I0409 19:57:55.847738 15108 net.cpp:198] drop7.5 needs backward computation.
I0409 19:57:55.847741 15108 net.cpp:198] relu7.5 needs backward computation.
I0409 19:57:55.847745 15108 net.cpp:198] fc7.5 needs backward computation.
I0409 19:57:55.847749 15108 net.cpp:198] drop7 needs backward computation.
I0409 19:57:55.847754 15108 net.cpp:198] relu7 needs backward computation.
I0409 19:57:55.847757 15108 net.cpp:198] fc7 needs backward computation.
I0409 19:57:55.847761 15108 net.cpp:198] drop6 needs backward computation.
I0409 19:57:55.847766 15108 net.cpp:198] relu6 needs backward computation.
I0409 19:57:55.847770 15108 net.cpp:198] fc6 needs backward computation.
I0409 19:57:55.847788 15108 net.cpp:198] pool5 needs backward computation.
I0409 19:57:55.847793 15108 net.cpp:198] relu5 needs backward computation.
I0409 19:57:55.847797 15108 net.cpp:198] conv5 needs backward computation.
I0409 19:57:55.847801 15108 net.cpp:198] relu4 needs backward computation.
I0409 19:57:55.847805 15108 net.cpp:198] conv4 needs backward computation.
I0409 19:57:55.847810 15108 net.cpp:198] relu3 needs backward computation.
I0409 19:57:55.847813 15108 net.cpp:198] conv3 needs backward computation.
I0409 19:57:55.847818 15108 net.cpp:198] pool2 needs backward computation.
I0409 19:57:55.847822 15108 net.cpp:198] norm2 needs backward computation.
I0409 19:57:55.847826 15108 net.cpp:198] relu2 needs backward computation.
I0409 19:57:55.847831 15108 net.cpp:198] conv2 needs backward computation.
I0409 19:57:55.847834 15108 net.cpp:198] pool1 needs backward computation.
I0409 19:57:55.847838 15108 net.cpp:198] norm1 needs backward computation.
I0409 19:57:55.847842 15108 net.cpp:198] relu1 needs backward computation.
I0409 19:57:55.847846 15108 net.cpp:198] conv1 needs backward computation.
I0409 19:57:55.847852 15108 net.cpp:200] train-data does not need backward computation.
I0409 19:57:55.847857 15108 net.cpp:242] This network produces output loss
I0409 19:57:55.847872 15108 net.cpp:255] Network initialization done.
I0409 19:57:55.848417 15108 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 19:57:55.848453 15108 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 19:57:55.848611 15108 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: 1024
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: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 19:57:55.848706 15108 layer_factory.hpp:77] Creating layer val-data
I0409 19:57:55.850576 15108 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 19:57:55.850780 15108 net.cpp:84] Creating Layer val-data
I0409 19:57:55.850790 15108 net.cpp:380] val-data -> data
I0409 19:57:55.850800 15108 net.cpp:380] val-data -> label
I0409 19:57:55.850806 15108 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 19:57:55.854756 15108 data_layer.cpp:45] output data size: 32,3,227,227
I0409 19:57:55.896126 15108 net.cpp:122] Setting up val-data
I0409 19:57:55.896147 15108 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 19:57:55.896152 15108 net.cpp:129] Top shape: 32 (32)
I0409 19:57:55.896155 15108 net.cpp:137] Memory required for data: 19787264
I0409 19:57:55.896180 15108 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 19:57:55.896193 15108 net.cpp:84] Creating Layer label_val-data_1_split
I0409 19:57:55.896198 15108 net.cpp:406] label_val-data_1_split <- label
I0409 19:57:55.896204 15108 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 19:57:55.896214 15108 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 19:57:55.896279 15108 net.cpp:122] Setting up label_val-data_1_split
I0409 19:57:55.896286 15108 net.cpp:129] Top shape: 32 (32)
I0409 19:57:55.896289 15108 net.cpp:129] Top shape: 32 (32)
I0409 19:57:55.896292 15108 net.cpp:137] Memory required for data: 19787520
I0409 19:57:55.896296 15108 layer_factory.hpp:77] Creating layer conv1
I0409 19:57:55.896307 15108 net.cpp:84] Creating Layer conv1
I0409 19:57:55.896311 15108 net.cpp:406] conv1 <- data
I0409 19:57:55.896317 15108 net.cpp:380] conv1 -> conv1
I0409 19:57:55.898161 15108 net.cpp:122] Setting up conv1
I0409 19:57:55.898172 15108 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:57:55.898176 15108 net.cpp:137] Memory required for data: 56958720
I0409 19:57:55.898186 15108 layer_factory.hpp:77] Creating layer relu1
I0409 19:57:55.898193 15108 net.cpp:84] Creating Layer relu1
I0409 19:57:55.898197 15108 net.cpp:406] relu1 <- conv1
I0409 19:57:55.898202 15108 net.cpp:367] relu1 -> conv1 (in-place)
I0409 19:57:55.898640 15108 net.cpp:122] Setting up relu1
I0409 19:57:55.898649 15108 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:57:55.898653 15108 net.cpp:137] Memory required for data: 94129920
I0409 19:57:55.898656 15108 layer_factory.hpp:77] Creating layer norm1
I0409 19:57:55.898665 15108 net.cpp:84] Creating Layer norm1
I0409 19:57:55.898669 15108 net.cpp:406] norm1 <- conv1
I0409 19:57:55.898674 15108 net.cpp:380] norm1 -> norm1
I0409 19:57:55.903264 15108 net.cpp:122] Setting up norm1
I0409 19:57:55.903276 15108 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 19:57:55.903280 15108 net.cpp:137] Memory required for data: 131301120
I0409 19:57:55.903285 15108 layer_factory.hpp:77] Creating layer pool1
I0409 19:57:55.903292 15108 net.cpp:84] Creating Layer pool1
I0409 19:57:55.903296 15108 net.cpp:406] pool1 <- norm1
I0409 19:57:55.903302 15108 net.cpp:380] pool1 -> pool1
I0409 19:57:55.903332 15108 net.cpp:122] Setting up pool1
I0409 19:57:55.903337 15108 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 19:57:55.903340 15108 net.cpp:137] Memory required for data: 140259072
I0409 19:57:55.903344 15108 layer_factory.hpp:77] Creating layer conv2
I0409 19:57:55.903352 15108 net.cpp:84] Creating Layer conv2
I0409 19:57:55.903355 15108 net.cpp:406] conv2 <- pool1
I0409 19:57:55.903360 15108 net.cpp:380] conv2 -> conv2
I0409 19:57:55.909505 15108 net.cpp:122] Setting up conv2
I0409 19:57:55.909518 15108 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:57:55.909523 15108 net.cpp:137] Memory required for data: 164146944
I0409 19:57:55.909533 15108 layer_factory.hpp:77] Creating layer relu2
I0409 19:57:55.909540 15108 net.cpp:84] Creating Layer relu2
I0409 19:57:55.909544 15108 net.cpp:406] relu2 <- conv2
I0409 19:57:55.909549 15108 net.cpp:367] relu2 -> conv2 (in-place)
I0409 19:57:55.911204 15108 net.cpp:122] Setting up relu2
I0409 19:57:55.911214 15108 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:57:55.911218 15108 net.cpp:137] Memory required for data: 188034816
I0409 19:57:55.911221 15108 layer_factory.hpp:77] Creating layer norm2
I0409 19:57:55.911231 15108 net.cpp:84] Creating Layer norm2
I0409 19:57:55.911235 15108 net.cpp:406] norm2 <- conv2
I0409 19:57:55.911242 15108 net.cpp:380] norm2 -> norm2
I0409 19:57:55.911602 15108 net.cpp:122] Setting up norm2
I0409 19:57:55.911610 15108 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 19:57:55.911613 15108 net.cpp:137] Memory required for data: 211922688
I0409 19:57:55.911618 15108 layer_factory.hpp:77] Creating layer pool2
I0409 19:57:55.911625 15108 net.cpp:84] Creating Layer pool2
I0409 19:57:55.911629 15108 net.cpp:406] pool2 <- norm2
I0409 19:57:55.911653 15108 net.cpp:380] pool2 -> pool2
I0409 19:57:55.911684 15108 net.cpp:122] Setting up pool2
I0409 19:57:55.911689 15108 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:57:55.911692 15108 net.cpp:137] Memory required for data: 217460480
I0409 19:57:55.911696 15108 layer_factory.hpp:77] Creating layer conv3
I0409 19:57:55.911705 15108 net.cpp:84] Creating Layer conv3
I0409 19:57:55.911708 15108 net.cpp:406] conv3 <- pool2
I0409 19:57:55.911715 15108 net.cpp:380] conv3 -> conv3
I0409 19:57:55.924403 15108 net.cpp:122] Setting up conv3
I0409 19:57:55.924422 15108 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:57:55.924427 15108 net.cpp:137] Memory required for data: 225767168
I0409 19:57:55.924438 15108 layer_factory.hpp:77] Creating layer relu3
I0409 19:57:55.924448 15108 net.cpp:84] Creating Layer relu3
I0409 19:57:55.924453 15108 net.cpp:406] relu3 <- conv3
I0409 19:57:55.924459 15108 net.cpp:367] relu3 -> conv3 (in-place)
I0409 19:57:55.924808 15108 net.cpp:122] Setting up relu3
I0409 19:57:55.924816 15108 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:57:55.924820 15108 net.cpp:137] Memory required for data: 234073856
I0409 19:57:55.924823 15108 layer_factory.hpp:77] Creating layer conv4
I0409 19:57:55.924834 15108 net.cpp:84] Creating Layer conv4
I0409 19:57:55.924839 15108 net.cpp:406] conv4 <- conv3
I0409 19:57:55.924845 15108 net.cpp:380] conv4 -> conv4
I0409 19:57:55.936669 15108 net.cpp:122] Setting up conv4
I0409 19:57:55.936684 15108 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:57:55.936688 15108 net.cpp:137] Memory required for data: 242380544
I0409 19:57:55.936697 15108 layer_factory.hpp:77] Creating layer relu4
I0409 19:57:55.936704 15108 net.cpp:84] Creating Layer relu4
I0409 19:57:55.936709 15108 net.cpp:406] relu4 <- conv4
I0409 19:57:55.936717 15108 net.cpp:367] relu4 -> conv4 (in-place)
I0409 19:57:55.937207 15108 net.cpp:122] Setting up relu4
I0409 19:57:55.937217 15108 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 19:57:55.937222 15108 net.cpp:137] Memory required for data: 250687232
I0409 19:57:55.937225 15108 layer_factory.hpp:77] Creating layer conv5
I0409 19:57:55.937235 15108 net.cpp:84] Creating Layer conv5
I0409 19:57:55.937239 15108 net.cpp:406] conv5 <- conv4
I0409 19:57:55.937247 15108 net.cpp:380] conv5 -> conv5
I0409 19:57:55.945652 15108 net.cpp:122] Setting up conv5
I0409 19:57:55.945665 15108 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:57:55.945669 15108 net.cpp:137] Memory required for data: 256225024
I0409 19:57:55.945683 15108 layer_factory.hpp:77] Creating layer relu5
I0409 19:57:55.945689 15108 net.cpp:84] Creating Layer relu5
I0409 19:57:55.945694 15108 net.cpp:406] relu5 <- conv5
I0409 19:57:55.945700 15108 net.cpp:367] relu5 -> conv5 (in-place)
I0409 19:57:55.946406 15108 net.cpp:122] Setting up relu5
I0409 19:57:55.946416 15108 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 19:57:55.946419 15108 net.cpp:137] Memory required for data: 261762816
I0409 19:57:55.946424 15108 layer_factory.hpp:77] Creating layer pool5
I0409 19:57:55.946434 15108 net.cpp:84] Creating Layer pool5
I0409 19:57:55.946437 15108 net.cpp:406] pool5 <- conv5
I0409 19:57:55.946444 15108 net.cpp:380] pool5 -> pool5
I0409 19:57:55.946483 15108 net.cpp:122] Setting up pool5
I0409 19:57:55.946488 15108 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 19:57:55.946492 15108 net.cpp:137] Memory required for data: 262942464
I0409 19:57:55.946496 15108 layer_factory.hpp:77] Creating layer fc6
I0409 19:57:55.946502 15108 net.cpp:84] Creating Layer fc6
I0409 19:57:55.946506 15108 net.cpp:406] fc6 <- pool5
I0409 19:57:55.946512 15108 net.cpp:380] fc6 -> fc6
I0409 19:57:56.035044 15108 net.cpp:122] Setting up fc6
I0409 19:57:56.035063 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.035068 15108 net.cpp:137] Memory required for data: 263073536
I0409 19:57:56.035076 15108 layer_factory.hpp:77] Creating layer relu6
I0409 19:57:56.035085 15108 net.cpp:84] Creating Layer relu6
I0409 19:57:56.035090 15108 net.cpp:406] relu6 <- fc6
I0409 19:57:56.035117 15108 net.cpp:367] relu6 -> fc6 (in-place)
I0409 19:57:56.035535 15108 net.cpp:122] Setting up relu6
I0409 19:57:56.035544 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.035548 15108 net.cpp:137] Memory required for data: 263204608
I0409 19:57:56.035552 15108 layer_factory.hpp:77] Creating layer drop6
I0409 19:57:56.035558 15108 net.cpp:84] Creating Layer drop6
I0409 19:57:56.035562 15108 net.cpp:406] drop6 <- fc6
I0409 19:57:56.035567 15108 net.cpp:367] drop6 -> fc6 (in-place)
I0409 19:57:56.035591 15108 net.cpp:122] Setting up drop6
I0409 19:57:56.035596 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.035599 15108 net.cpp:137] Memory required for data: 263335680
I0409 19:57:56.035602 15108 layer_factory.hpp:77] Creating layer fc7
I0409 19:57:56.035610 15108 net.cpp:84] Creating Layer fc7
I0409 19:57:56.035614 15108 net.cpp:406] fc7 <- fc6
I0409 19:57:56.035619 15108 net.cpp:380] fc7 -> fc7
I0409 19:57:56.045564 15108 net.cpp:122] Setting up fc7
I0409 19:57:56.045578 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.045581 15108 net.cpp:137] Memory required for data: 263466752
I0409 19:57:56.045589 15108 layer_factory.hpp:77] Creating layer relu7
I0409 19:57:56.045598 15108 net.cpp:84] Creating Layer relu7
I0409 19:57:56.045601 15108 net.cpp:406] relu7 <- fc7
I0409 19:57:56.045608 15108 net.cpp:367] relu7 -> fc7 (in-place)
I0409 19:57:56.046336 15108 net.cpp:122] Setting up relu7
I0409 19:57:56.046345 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.046350 15108 net.cpp:137] Memory required for data: 263597824
I0409 19:57:56.046353 15108 layer_factory.hpp:77] Creating layer drop7
I0409 19:57:56.046361 15108 net.cpp:84] Creating Layer drop7
I0409 19:57:56.046365 15108 net.cpp:406] drop7 <- fc7
I0409 19:57:56.046370 15108 net.cpp:367] drop7 -> fc7 (in-place)
I0409 19:57:56.046394 15108 net.cpp:122] Setting up drop7
I0409 19:57:56.046399 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.046402 15108 net.cpp:137] Memory required for data: 263728896
I0409 19:57:56.046406 15108 layer_factory.hpp:77] Creating layer fc7.5
I0409 19:57:56.046413 15108 net.cpp:84] Creating Layer fc7.5
I0409 19:57:56.046416 15108 net.cpp:406] fc7.5 <- fc7
I0409 19:57:56.046422 15108 net.cpp:380] fc7.5 -> fc7.5
I0409 19:57:56.056704 15108 net.cpp:122] Setting up fc7.5
I0409 19:57:56.056720 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.056723 15108 net.cpp:137] Memory required for data: 263859968
I0409 19:57:56.056732 15108 layer_factory.hpp:77] Creating layer relu7.5
I0409 19:57:56.056741 15108 net.cpp:84] Creating Layer relu7.5
I0409 19:57:56.056746 15108 net.cpp:406] relu7.5 <- fc7.5
I0409 19:57:56.056752 15108 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 19:57:56.057355 15108 net.cpp:122] Setting up relu7.5
I0409 19:57:56.057364 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.057368 15108 net.cpp:137] Memory required for data: 263991040
I0409 19:57:56.057371 15108 layer_factory.hpp:77] Creating layer drop7.5
I0409 19:57:56.057379 15108 net.cpp:84] Creating Layer drop7.5
I0409 19:57:56.057382 15108 net.cpp:406] drop7.5 <- fc7.5
I0409 19:57:56.057389 15108 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 19:57:56.057412 15108 net.cpp:122] Setting up drop7.5
I0409 19:57:56.057418 15108 net.cpp:129] Top shape: 32 1024 (32768)
I0409 19:57:56.057421 15108 net.cpp:137] Memory required for data: 264122112
I0409 19:57:56.057425 15108 layer_factory.hpp:77] Creating layer fc8
I0409 19:57:56.057432 15108 net.cpp:84] Creating Layer fc8
I0409 19:57:56.057435 15108 net.cpp:406] fc8 <- fc7.5
I0409 19:57:56.057442 15108 net.cpp:380] fc8 -> fc8
I0409 19:57:56.059258 15108 net.cpp:122] Setting up fc8
I0409 19:57:56.059265 15108 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:57:56.059268 15108 net.cpp:137] Memory required for data: 264147200
I0409 19:57:56.059279 15108 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 19:57:56.059285 15108 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 19:57:56.059289 15108 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 19:57:56.059311 15108 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 19:57:56.059319 15108 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 19:57:56.059351 15108 net.cpp:122] Setting up fc8_fc8_0_split
I0409 19:57:56.059356 15108 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:57:56.059360 15108 net.cpp:129] Top shape: 32 196 (6272)
I0409 19:57:56.059363 15108 net.cpp:137] Memory required for data: 264197376
I0409 19:57:56.059366 15108 layer_factory.hpp:77] Creating layer accuracy
I0409 19:57:56.059374 15108 net.cpp:84] Creating Layer accuracy
I0409 19:57:56.059377 15108 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 19:57:56.059382 15108 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 19:57:56.059387 15108 net.cpp:380] accuracy -> accuracy
I0409 19:57:56.059394 15108 net.cpp:122] Setting up accuracy
I0409 19:57:56.059398 15108 net.cpp:129] Top shape: (1)
I0409 19:57:56.059401 15108 net.cpp:137] Memory required for data: 264197380
I0409 19:57:56.059406 15108 layer_factory.hpp:77] Creating layer loss
I0409 19:57:56.059412 15108 net.cpp:84] Creating Layer loss
I0409 19:57:56.059415 15108 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 19:57:56.059419 15108 net.cpp:406] loss <- label_val-data_1_split_1
I0409 19:57:56.059423 15108 net.cpp:380] loss -> loss
I0409 19:57:56.059432 15108 layer_factory.hpp:77] Creating layer loss
I0409 19:57:56.061022 15108 net.cpp:122] Setting up loss
I0409 19:57:56.061033 15108 net.cpp:129] Top shape: (1)
I0409 19:57:56.061036 15108 net.cpp:132]     with loss weight 1
I0409 19:57:56.061046 15108 net.cpp:137] Memory required for data: 264197384
I0409 19:57:56.061050 15108 net.cpp:198] loss needs backward computation.
I0409 19:57:56.061055 15108 net.cpp:200] accuracy does not need backward computation.
I0409 19:57:56.061060 15108 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 19:57:56.061064 15108 net.cpp:198] fc8 needs backward computation.
I0409 19:57:56.061067 15108 net.cpp:198] drop7.5 needs backward computation.
I0409 19:57:56.061070 15108 net.cpp:198] relu7.5 needs backward computation.
I0409 19:57:56.061074 15108 net.cpp:198] fc7.5 needs backward computation.
I0409 19:57:56.061077 15108 net.cpp:198] drop7 needs backward computation.
I0409 19:57:56.061081 15108 net.cpp:198] relu7 needs backward computation.
I0409 19:57:56.061084 15108 net.cpp:198] fc7 needs backward computation.
I0409 19:57:56.061089 15108 net.cpp:198] drop6 needs backward computation.
I0409 19:57:56.061091 15108 net.cpp:198] relu6 needs backward computation.
I0409 19:57:56.061095 15108 net.cpp:198] fc6 needs backward computation.
I0409 19:57:56.061098 15108 net.cpp:198] pool5 needs backward computation.
I0409 19:57:56.061102 15108 net.cpp:198] relu5 needs backward computation.
I0409 19:57:56.061105 15108 net.cpp:198] conv5 needs backward computation.
I0409 19:57:56.061110 15108 net.cpp:198] relu4 needs backward computation.
I0409 19:57:56.061112 15108 net.cpp:198] conv4 needs backward computation.
I0409 19:57:56.061115 15108 net.cpp:198] relu3 needs backward computation.
I0409 19:57:56.061120 15108 net.cpp:198] conv3 needs backward computation.
I0409 19:57:56.061123 15108 net.cpp:198] pool2 needs backward computation.
I0409 19:57:56.061126 15108 net.cpp:198] norm2 needs backward computation.
I0409 19:57:56.061131 15108 net.cpp:198] relu2 needs backward computation.
I0409 19:57:56.061133 15108 net.cpp:198] conv2 needs backward computation.
I0409 19:57:56.061137 15108 net.cpp:198] pool1 needs backward computation.
I0409 19:57:56.061141 15108 net.cpp:198] norm1 needs backward computation.
I0409 19:57:56.061144 15108 net.cpp:198] relu1 needs backward computation.
I0409 19:57:56.061147 15108 net.cpp:198] conv1 needs backward computation.
I0409 19:57:56.061151 15108 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 19:57:56.061156 15108 net.cpp:200] val-data does not need backward computation.
I0409 19:57:56.061159 15108 net.cpp:242] This network produces output accuracy
I0409 19:57:56.061163 15108 net.cpp:242] This network produces output loss
I0409 19:57:56.061188 15108 net.cpp:255] Network initialization done.
I0409 19:57:56.061300 15108 solver.cpp:56] Solver scaffolding done.
I0409 19:57:56.061803 15108 caffe.cpp:248] Starting Optimization
I0409 19:57:56.061812 15108 solver.cpp:272] Solving
I0409 19:57:56.061816 15108 solver.cpp:273] Learning Rate Policy: exp
I0409 19:57:56.063040 15108 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 19:57:56.063048 15108 net.cpp:676] Ignoring source layer train-data
I0409 19:57:56.083271 15108 blocking_queue.cpp:49] Waiting for data
I0409 19:58:00.482707 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:00.526854 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0409 19:58:00.526902 15108 solver.cpp:397]     Test net output #1: loss = 5.27837 (* 1 = 5.27837 loss)
I0409 19:58:00.640125 15108 solver.cpp:218] Iteration 0 (-1.30421e-39 iter/s, 4.57811s/12 iters), loss = 5.27739
I0409 19:58:00.641640 15108 solver.cpp:237]     Train net output #0: loss = 5.27739 (* 1 = 5.27739 loss)
I0409 19:58:00.641661 15108 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 19:58:04.453996 15108 solver.cpp:218] Iteration 12 (3.14778 iter/s, 3.81221s/12 iters), loss = 5.28086
I0409 19:58:04.454046 15108 solver.cpp:237]     Train net output #0: loss = 5.28086 (* 1 = 5.28086 loss)
I0409 19:58:04.454057 15108 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 19:58:09.234490 15108 solver.cpp:218] Iteration 24 (2.51032 iter/s, 4.78026s/12 iters), loss = 5.28482
I0409 19:58:09.234549 15108 solver.cpp:237]     Train net output #0: loss = 5.28482 (* 1 = 5.28482 loss)
I0409 19:58:09.234562 15108 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 19:58:14.075265 15108 solver.cpp:218] Iteration 36 (2.47906 iter/s, 4.84054s/12 iters), loss = 5.27613
I0409 19:58:14.075305 15108 solver.cpp:237]     Train net output #0: loss = 5.27613 (* 1 = 5.27613 loss)
I0409 19:58:14.075315 15108 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 19:58:18.959717 15108 solver.cpp:218] Iteration 48 (2.45689 iter/s, 4.88423s/12 iters), loss = 5.28639
I0409 19:58:18.959760 15108 solver.cpp:237]     Train net output #0: loss = 5.28639 (* 1 = 5.28639 loss)
I0409 19:58:18.959770 15108 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 19:58:23.859591 15108 solver.cpp:218] Iteration 60 (2.44916 iter/s, 4.89964s/12 iters), loss = 5.28219
I0409 19:58:23.859633 15108 solver.cpp:237]     Train net output #0: loss = 5.28219 (* 1 = 5.28219 loss)
I0409 19:58:23.859642 15108 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 19:58:28.701036 15108 solver.cpp:218] Iteration 72 (2.47872 iter/s, 4.84122s/12 iters), loss = 5.27677
I0409 19:58:28.701144 15108 solver.cpp:237]     Train net output #0: loss = 5.27677 (* 1 = 5.27677 loss)
I0409 19:58:28.701160 15108 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 19:58:33.531246 15108 solver.cpp:218] Iteration 84 (2.48451 iter/s, 4.82992s/12 iters), loss = 5.28017
I0409 19:58:33.531286 15108 solver.cpp:237]     Train net output #0: loss = 5.28017 (* 1 = 5.28017 loss)
I0409 19:58:33.531296 15108 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 19:58:38.333117 15108 solver.cpp:218] Iteration 96 (2.49915 iter/s, 4.80164s/12 iters), loss = 5.29165
I0409 19:58:38.333175 15108 solver.cpp:237]     Train net output #0: loss = 5.29165 (* 1 = 5.29165 loss)
I0409 19:58:38.333189 15108 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 19:58:39.981379 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:40.288921 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 19:58:41.315652 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 19:58:43.477905 15108 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 19:58:43.477926 15108 net.cpp:676] Ignoring source layer train-data
I0409 19:58:47.762535 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:58:47.838589 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:58:47.838636 15108 solver.cpp:397]     Test net output #1: loss = 5.28084 (* 1 = 5.28084 loss)
I0409 19:58:49.585667 15108 solver.cpp:218] Iteration 108 (1.06647 iter/s, 11.2521s/12 iters), loss = 5.27689
I0409 19:58:49.585716 15108 solver.cpp:237]     Train net output #0: loss = 5.27689 (* 1 = 5.27689 loss)
I0409 19:58:49.585727 15108 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 19:58:54.564234 15108 solver.cpp:218] Iteration 120 (2.41045 iter/s, 4.97833s/12 iters), loss = 5.27862
I0409 19:58:54.564275 15108 solver.cpp:237]     Train net output #0: loss = 5.27862 (* 1 = 5.27862 loss)
I0409 19:58:54.564283 15108 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 19:58:59.356156 15108 solver.cpp:218] Iteration 132 (2.50434 iter/s, 4.79169s/12 iters), loss = 5.2457
I0409 19:58:59.356290 15108 solver.cpp:237]     Train net output #0: loss = 5.2457 (* 1 = 5.2457 loss)
I0409 19:58:59.356304 15108 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 19:59:04.268000 15108 solver.cpp:218] Iteration 144 (2.44323 iter/s, 4.91153s/12 iters), loss = 5.29661
I0409 19:59:04.268050 15108 solver.cpp:237]     Train net output #0: loss = 5.29661 (* 1 = 5.29661 loss)
I0409 19:59:04.268061 15108 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 19:59:09.053623 15108 solver.cpp:218] Iteration 156 (2.50763 iter/s, 4.78539s/12 iters), loss = 5.26324
I0409 19:59:09.053673 15108 solver.cpp:237]     Train net output #0: loss = 5.26324 (* 1 = 5.26324 loss)
I0409 19:59:09.053683 15108 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 19:59:13.816828 15108 solver.cpp:218] Iteration 168 (2.51944 iter/s, 4.76296s/12 iters), loss = 5.27639
I0409 19:59:13.816874 15108 solver.cpp:237]     Train net output #0: loss = 5.27639 (* 1 = 5.27639 loss)
I0409 19:59:13.816884 15108 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 19:59:18.617470 15108 solver.cpp:218] Iteration 180 (2.4998 iter/s, 4.80038s/12 iters), loss = 5.27486
I0409 19:59:18.617543 15108 solver.cpp:237]     Train net output #0: loss = 5.27486 (* 1 = 5.27486 loss)
I0409 19:59:18.617561 15108 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 19:59:23.434505 15108 solver.cpp:218] Iteration 192 (2.49129 iter/s, 4.81678s/12 iters), loss = 5.27582
I0409 19:59:23.434554 15108 solver.cpp:237]     Train net output #0: loss = 5.27582 (* 1 = 5.27582 loss)
I0409 19:59:23.434567 15108 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 19:59:27.124122 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:27.830140 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 19:59:30.172930 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 19:59:31.201436 15108 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 19:59:31.201464 15108 net.cpp:676] Ignoring source layer train-data
I0409 19:59:35.540511 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 19:59:35.662326 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 19:59:35.662375 15108 solver.cpp:397]     Test net output #1: loss = 5.28361 (* 1 = 5.28361 loss)
I0409 19:59:35.745592 15108 solver.cpp:218] Iteration 204 (0.974772 iter/s, 12.3106s/12 iters), loss = 5.27245
I0409 19:59:35.745671 15108 solver.cpp:237]     Train net output #0: loss = 5.27245 (* 1 = 5.27245 loss)
I0409 19:59:35.745687 15108 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 19:59:39.837940 15108 solver.cpp:218] Iteration 216 (2.93247 iter/s, 4.09211s/12 iters), loss = 5.28015
I0409 19:59:39.838004 15108 solver.cpp:237]     Train net output #0: loss = 5.28015 (* 1 = 5.28015 loss)
I0409 19:59:39.838016 15108 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 19:59:44.791891 15108 solver.cpp:218] Iteration 228 (2.42244 iter/s, 4.95369s/12 iters), loss = 5.25695
I0409 19:59:44.791936 15108 solver.cpp:237]     Train net output #0: loss = 5.25695 (* 1 = 5.25695 loss)
I0409 19:59:44.791945 15108 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 19:59:49.690400 15108 solver.cpp:218] Iteration 240 (2.44985 iter/s, 4.89827s/12 iters), loss = 5.29112
I0409 19:59:49.690457 15108 solver.cpp:237]     Train net output #0: loss = 5.29112 (* 1 = 5.29112 loss)
I0409 19:59:49.690470 15108 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 19:59:54.559705 15108 solver.cpp:218] Iteration 252 (2.46454 iter/s, 4.86906s/12 iters), loss = 5.2663
I0409 19:59:54.559741 15108 solver.cpp:237]     Train net output #0: loss = 5.2663 (* 1 = 5.2663 loss)
I0409 19:59:54.559748 15108 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 19:59:59.345048 15108 solver.cpp:218] Iteration 264 (2.50778 iter/s, 4.78511s/12 iters), loss = 5.27403
I0409 19:59:59.345099 15108 solver.cpp:237]     Train net output #0: loss = 5.27403 (* 1 = 5.27403 loss)
I0409 19:59:59.345111 15108 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 20:00:04.171818 15108 solver.cpp:218] Iteration 276 (2.48626 iter/s, 4.82653s/12 iters), loss = 5.28878
I0409 20:00:04.171942 15108 solver.cpp:237]     Train net output #0: loss = 5.28878 (* 1 = 5.28878 loss)
I0409 20:00:04.171952 15108 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 20:00:08.995633 15108 solver.cpp:218] Iteration 288 (2.48782 iter/s, 4.8235s/12 iters), loss = 5.27767
I0409 20:00:08.995684 15108 solver.cpp:237]     Train net output #0: loss = 5.27767 (* 1 = 5.27767 loss)
I0409 20:00:08.995697 15108 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 20:00:13.853201 15108 solver.cpp:218] Iteration 300 (2.4705 iter/s, 4.85732s/12 iters), loss = 5.27876
I0409 20:00:13.853257 15108 solver.cpp:237]     Train net output #0: loss = 5.27876 (* 1 = 5.27876 loss)
I0409 20:00:13.853268 15108 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 20:00:14.812572 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:15.823065 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 20:00:16.667232 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 20:00:17.245993 15108 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 20:00:17.246019 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:00:21.477583 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:00:21.636391 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:00:21.636440 15108 solver.cpp:397]     Test net output #1: loss = 5.28501 (* 1 = 5.28501 loss)
I0409 20:00:23.417013 15108 solver.cpp:218] Iteration 312 (1.25479 iter/s, 9.56339s/12 iters), loss = 5.27876
I0409 20:00:23.417057 15108 solver.cpp:237]     Train net output #0: loss = 5.27876 (* 1 = 5.27876 loss)
I0409 20:00:23.417066 15108 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 20:00:28.297853 15108 solver.cpp:218] Iteration 324 (2.45872 iter/s, 4.88059s/12 iters), loss = 5.24931
I0409 20:00:28.297900 15108 solver.cpp:237]     Train net output #0: loss = 5.24931 (* 1 = 5.24931 loss)
I0409 20:00:28.297910 15108 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 20:00:33.101423 15108 solver.cpp:218] Iteration 336 (2.49827 iter/s, 4.80333s/12 iters), loss = 5.26006
I0409 20:00:33.101469 15108 solver.cpp:237]     Train net output #0: loss = 5.26006 (* 1 = 5.26006 loss)
I0409 20:00:33.101480 15108 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 20:00:37.995997 15108 solver.cpp:218] Iteration 348 (2.45182 iter/s, 4.89433s/12 iters), loss = 5.27156
I0409 20:00:37.996088 15108 solver.cpp:237]     Train net output #0: loss = 5.27156 (* 1 = 5.27156 loss)
I0409 20:00:37.996101 15108 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 20:00:42.813694 15108 solver.cpp:218] Iteration 360 (2.49096 iter/s, 4.81742s/12 iters), loss = 5.29322
I0409 20:00:42.813731 15108 solver.cpp:237]     Train net output #0: loss = 5.29322 (* 1 = 5.29322 loss)
I0409 20:00:42.813740 15108 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 20:00:47.683470 15108 solver.cpp:218] Iteration 372 (2.4643 iter/s, 4.86953s/12 iters), loss = 5.26979
I0409 20:00:47.683531 15108 solver.cpp:237]     Train net output #0: loss = 5.26979 (* 1 = 5.26979 loss)
I0409 20:00:47.683542 15108 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 20:00:52.614915 15108 solver.cpp:218] Iteration 384 (2.43349 iter/s, 4.93119s/12 iters), loss = 5.28399
I0409 20:00:52.614960 15108 solver.cpp:237]     Train net output #0: loss = 5.28399 (* 1 = 5.28399 loss)
I0409 20:00:52.614969 15108 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 20:00:57.476101 15108 solver.cpp:218] Iteration 396 (2.46866 iter/s, 4.86094s/12 iters), loss = 5.26852
I0409 20:00:57.476155 15108 solver.cpp:237]     Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
I0409 20:00:57.476168 15108 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 20:01:00.499593 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:01.852983 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 20:01:03.425153 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 20:01:05.518620 15108 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 20:01:05.518641 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:01:09.664566 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:09.868932 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:01:09.868971 15108 solver.cpp:397]     Test net output #1: loss = 5.28643 (* 1 = 5.28643 loss)
I0409 20:01:09.952286 15108 solver.cpp:218] Iteration 408 (0.961874 iter/s, 12.4756s/12 iters), loss = 5.28315
I0409 20:01:09.952347 15108 solver.cpp:237]     Train net output #0: loss = 5.28315 (* 1 = 5.28315 loss)
I0409 20:01:09.952358 15108 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 20:01:14.123843 15108 solver.cpp:218] Iteration 420 (2.87679 iter/s, 4.17132s/12 iters), loss = 5.27327
I0409 20:01:14.123888 15108 solver.cpp:237]     Train net output #0: loss = 5.27327 (* 1 = 5.27327 loss)
I0409 20:01:14.123896 15108 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 20:01:18.925565 15108 solver.cpp:218] Iteration 432 (2.49923 iter/s, 4.80149s/12 iters), loss = 5.26994
I0409 20:01:18.925596 15108 solver.cpp:237]     Train net output #0: loss = 5.26994 (* 1 = 5.26994 loss)
I0409 20:01:18.925604 15108 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 20:01:23.744083 15108 solver.cpp:218] Iteration 444 (2.49051 iter/s, 4.81829s/12 iters), loss = 5.28925
I0409 20:01:23.744132 15108 solver.cpp:237]     Train net output #0: loss = 5.28925 (* 1 = 5.28925 loss)
I0409 20:01:23.744140 15108 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 20:01:28.598860 15108 solver.cpp:218] Iteration 456 (2.47192 iter/s, 4.85453s/12 iters), loss = 5.28169
I0409 20:01:28.598903 15108 solver.cpp:237]     Train net output #0: loss = 5.28169 (* 1 = 5.28169 loss)
I0409 20:01:28.598913 15108 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 20:01:33.543304 15108 solver.cpp:218] Iteration 468 (2.42709 iter/s, 4.9442s/12 iters), loss = 5.28792
I0409 20:01:33.543350 15108 solver.cpp:237]     Train net output #0: loss = 5.28792 (* 1 = 5.28792 loss)
I0409 20:01:33.543359 15108 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 20:01:38.421082 15108 solver.cpp:218] Iteration 480 (2.46026 iter/s, 4.87753s/12 iters), loss = 5.26656
I0409 20:01:38.421131 15108 solver.cpp:237]     Train net output #0: loss = 5.26656 (* 1 = 5.26656 loss)
I0409 20:01:38.421145 15108 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 20:01:43.281971 15108 solver.cpp:218] Iteration 492 (2.46882 iter/s, 4.86062s/12 iters), loss = 5.29221
I0409 20:01:43.282079 15108 solver.cpp:237]     Train net output #0: loss = 5.29221 (* 1 = 5.29221 loss)
I0409 20:01:43.282089 15108 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 20:01:48.189254 15108 solver.cpp:218] Iteration 504 (2.4455 iter/s, 4.90697s/12 iters), loss = 5.26677
I0409 20:01:48.189307 15108 solver.cpp:237]     Train net output #0: loss = 5.26677 (* 1 = 5.26677 loss)
I0409 20:01:48.189321 15108 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 20:01:48.443194 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:50.172935 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 20:01:50.945753 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 20:01:51.513511 15108 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 20:01:51.513540 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:01:55.721575 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:01:55.958040 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 20:01:55.958092 15108 solver.cpp:397]     Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
I0409 20:01:57.682494 15108 solver.cpp:218] Iteration 516 (1.26411 iter/s, 9.49281s/12 iters), loss = 5.28309
I0409 20:01:57.682538 15108 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0409 20:01:57.682547 15108 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 20:02:02.574108 15108 solver.cpp:218] Iteration 528 (2.4533 iter/s, 4.89136s/12 iters), loss = 5.2739
I0409 20:02:02.574165 15108 solver.cpp:237]     Train net output #0: loss = 5.2739 (* 1 = 5.2739 loss)
I0409 20:02:02.574177 15108 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 20:02:07.455420 15108 solver.cpp:218] Iteration 540 (2.45848 iter/s, 4.88106s/12 iters), loss = 5.27421
I0409 20:02:07.455474 15108 solver.cpp:237]     Train net output #0: loss = 5.27421 (* 1 = 5.27421 loss)
I0409 20:02:07.455487 15108 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 20:02:12.353731 15108 solver.cpp:218] Iteration 552 (2.44995 iter/s, 4.89806s/12 iters), loss = 5.27454
I0409 20:02:12.353787 15108 solver.cpp:237]     Train net output #0: loss = 5.27454 (* 1 = 5.27454 loss)
I0409 20:02:12.353801 15108 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 20:02:17.242722 15108 solver.cpp:218] Iteration 564 (2.45462 iter/s, 4.88874s/12 iters), loss = 5.26133
I0409 20:02:17.242808 15108 solver.cpp:237]     Train net output #0: loss = 5.26133 (* 1 = 5.26133 loss)
I0409 20:02:17.242818 15108 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 20:02:22.237448 15108 solver.cpp:218] Iteration 576 (2.40267 iter/s, 4.99444s/12 iters), loss = 5.27647
I0409 20:02:22.237495 15108 solver.cpp:237]     Train net output #0: loss = 5.27647 (* 1 = 5.27647 loss)
I0409 20:02:22.237504 15108 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 20:02:27.147608 15108 solver.cpp:218] Iteration 588 (2.44404 iter/s, 4.90991s/12 iters), loss = 5.2662
I0409 20:02:27.147653 15108 solver.cpp:237]     Train net output #0: loss = 5.2662 (* 1 = 5.2662 loss)
I0409 20:02:27.147662 15108 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 20:02:32.001931 15108 solver.cpp:218] Iteration 600 (2.47215 iter/s, 4.85408s/12 iters), loss = 5.26021
I0409 20:02:32.002002 15108 solver.cpp:237]     Train net output #0: loss = 5.26021 (* 1 = 5.26021 loss)
I0409 20:02:32.002015 15108 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 20:02:34.307209 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:36.394943 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 20:02:38.244630 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 20:02:39.743835 15108 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 20:02:39.743865 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:02:43.915589 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:02:44.198973 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:02:44.199014 15108 solver.cpp:397]     Test net output #1: loss = 5.2858 (* 1 = 5.2858 loss)
I0409 20:02:44.282069 15108 solver.cpp:218] Iteration 612 (0.977231 iter/s, 12.2796s/12 iters), loss = 5.27564
I0409 20:02:44.282112 15108 solver.cpp:237]     Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
I0409 20:02:44.282122 15108 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 20:02:48.394457 15108 solver.cpp:218] Iteration 624 (2.91816 iter/s, 4.11218s/12 iters), loss = 5.29242
I0409 20:02:48.394613 15108 solver.cpp:237]     Train net output #0: loss = 5.29242 (* 1 = 5.29242 loss)
I0409 20:02:48.394623 15108 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 20:02:53.201828 15108 solver.cpp:218] Iteration 636 (2.49635 iter/s, 4.80702s/12 iters), loss = 5.28653
I0409 20:02:53.201879 15108 solver.cpp:237]     Train net output #0: loss = 5.28653 (* 1 = 5.28653 loss)
I0409 20:02:53.201890 15108 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 20:02:58.075320 15108 solver.cpp:218] Iteration 648 (2.46243 iter/s, 4.87324s/12 iters), loss = 5.276
I0409 20:02:58.075377 15108 solver.cpp:237]     Train net output #0: loss = 5.276 (* 1 = 5.276 loss)
I0409 20:02:58.075390 15108 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 20:03:02.942564 15108 solver.cpp:218] Iteration 660 (2.46559 iter/s, 4.86699s/12 iters), loss = 5.27002
I0409 20:03:02.942622 15108 solver.cpp:237]     Train net output #0: loss = 5.27002 (* 1 = 5.27002 loss)
I0409 20:03:02.942636 15108 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 20:03:07.806591 15108 solver.cpp:218] Iteration 672 (2.46722 iter/s, 4.86377s/12 iters), loss = 5.27769
I0409 20:03:07.806651 15108 solver.cpp:237]     Train net output #0: loss = 5.27769 (* 1 = 5.27769 loss)
I0409 20:03:07.806664 15108 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 20:03:12.273895 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:03:12.726884 15108 solver.cpp:218] Iteration 684 (2.43901 iter/s, 4.92003s/12 iters), loss = 5.27564
I0409 20:03:12.726938 15108 solver.cpp:237]     Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
I0409 20:03:12.726948 15108 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 20:03:17.567268 15108 solver.cpp:218] Iteration 696 (2.47927 iter/s, 4.84013s/12 iters), loss = 5.27406
I0409 20:03:17.567323 15108 solver.cpp:237]     Train net output #0: loss = 5.27406 (* 1 = 5.27406 loss)
I0409 20:03:17.567337 15108 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 20:03:22.035181 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:22.404317 15108 solver.cpp:218] Iteration 708 (2.48098 iter/s, 4.8368s/12 iters), loss = 5.2596
I0409 20:03:22.404368 15108 solver.cpp:237]     Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
I0409 20:03:22.404382 15108 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 20:03:24.382650 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 20:03:27.086977 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 20:03:27.839725 15108 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 20:03:27.839743 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:03:31.972369 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:03:32.292304 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:03:32.292356 15108 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0409 20:03:34.113101 15108 solver.cpp:218] Iteration 720 (1.02492 iter/s, 11.7083s/12 iters), loss = 5.26885
I0409 20:03:34.113142 15108 solver.cpp:237]     Train net output #0: loss = 5.26885 (* 1 = 5.26885 loss)
I0409 20:03:34.113150 15108 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 20:03:39.090396 15108 solver.cpp:218] Iteration 732 (2.41107 iter/s, 4.97705s/12 iters), loss = 5.27625
I0409 20:03:39.090440 15108 solver.cpp:237]     Train net output #0: loss = 5.27625 (* 1 = 5.27625 loss)
I0409 20:03:39.090451 15108 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 20:03:43.915997 15108 solver.cpp:218] Iteration 744 (2.48686 iter/s, 4.82535s/12 iters), loss = 5.27858
I0409 20:03:43.916045 15108 solver.cpp:237]     Train net output #0: loss = 5.27858 (* 1 = 5.27858 loss)
I0409 20:03:43.916054 15108 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 20:03:48.931969 15108 solver.cpp:218] Iteration 756 (2.39248 iter/s, 5.01572s/12 iters), loss = 5.27804
I0409 20:03:48.932018 15108 solver.cpp:237]     Train net output #0: loss = 5.27804 (* 1 = 5.27804 loss)
I0409 20:03:48.932029 15108 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 20:03:53.896622 15108 solver.cpp:218] Iteration 768 (2.41721 iter/s, 4.9644s/12 iters), loss = 5.27918
I0409 20:03:53.896734 15108 solver.cpp:237]     Train net output #0: loss = 5.27918 (* 1 = 5.27918 loss)
I0409 20:03:53.896746 15108 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 20:03:58.895889 15108 solver.cpp:218] Iteration 780 (2.4005 iter/s, 4.99895s/12 iters), loss = 5.26615
I0409 20:03:58.895937 15108 solver.cpp:237]     Train net output #0: loss = 5.26615 (* 1 = 5.26615 loss)
I0409 20:03:58.895946 15108 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 20:04:03.755086 15108 solver.cpp:218] Iteration 792 (2.46967 iter/s, 4.85895s/12 iters), loss = 5.26891
I0409 20:04:03.755136 15108 solver.cpp:237]     Train net output #0: loss = 5.26891 (* 1 = 5.26891 loss)
I0409 20:04:03.755146 15108 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 20:04:08.591239 15108 solver.cpp:218] Iteration 804 (2.48144 iter/s, 4.8359s/12 iters), loss = 5.29173
I0409 20:04:08.591297 15108 solver.cpp:237]     Train net output #0: loss = 5.29173 (* 1 = 5.29173 loss)
I0409 20:04:08.591310 15108 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 20:04:10.268237 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:13.289570 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 20:04:15.228525 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 20:04:18.167598 15108 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 20:04:18.167623 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:04:22.281111 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:04:22.634680 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:04:22.634729 15108 solver.cpp:397]     Test net output #1: loss = 5.2862 (* 1 = 5.2862 loss)
I0409 20:04:22.718060 15108 solver.cpp:218] Iteration 816 (0.849485 iter/s, 14.1262s/12 iters), loss = 5.27277
I0409 20:04:22.718125 15108 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0409 20:04:22.718139 15108 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 20:04:26.937503 15108 solver.cpp:218] Iteration 828 (2.84414 iter/s, 4.21921s/12 iters), loss = 5.28144
I0409 20:04:26.937592 15108 solver.cpp:237]     Train net output #0: loss = 5.28144 (* 1 = 5.28144 loss)
I0409 20:04:26.937602 15108 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 20:04:31.811821 15108 solver.cpp:218] Iteration 840 (2.46203 iter/s, 4.87403s/12 iters), loss = 5.23071
I0409 20:04:31.811869 15108 solver.cpp:237]     Train net output #0: loss = 5.23071 (* 1 = 5.23071 loss)
I0409 20:04:31.811882 15108 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 20:04:36.658427 15108 solver.cpp:218] Iteration 852 (2.47609 iter/s, 4.84636s/12 iters), loss = 5.30196
I0409 20:04:36.658480 15108 solver.cpp:237]     Train net output #0: loss = 5.30196 (* 1 = 5.30196 loss)
I0409 20:04:36.658494 15108 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 20:04:41.523895 15108 solver.cpp:218] Iteration 864 (2.46649 iter/s, 4.86522s/12 iters), loss = 5.26122
I0409 20:04:41.523939 15108 solver.cpp:237]     Train net output #0: loss = 5.26122 (* 1 = 5.26122 loss)
I0409 20:04:41.523948 15108 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 20:04:46.352135 15108 solver.cpp:218] Iteration 876 (2.4855 iter/s, 4.82799s/12 iters), loss = 5.27054
I0409 20:04:46.352195 15108 solver.cpp:237]     Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
I0409 20:04:46.352207 15108 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 20:04:51.408943 15108 solver.cpp:218] Iteration 888 (2.37316 iter/s, 5.05654s/12 iters), loss = 5.26676
I0409 20:04:51.409003 15108 solver.cpp:237]     Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
I0409 20:04:51.409016 15108 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 20:04:56.254030 15108 solver.cpp:218] Iteration 900 (2.47687 iter/s, 4.84483s/12 iters), loss = 5.2757
I0409 20:04:56.254084 15108 solver.cpp:237]     Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
I0409 20:04:56.254096 15108 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 20:05:00.015663 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:01.099751 15108 solver.cpp:218] Iteration 912 (2.47654 iter/s, 4.84547s/12 iters), loss = 5.26415
I0409 20:05:01.099802 15108 solver.cpp:237]     Train net output #0: loss = 5.26415 (* 1 = 5.26415 loss)
I0409 20:05:01.099815 15108 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 20:05:03.075652 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 20:05:03.848743 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 20:05:04.413112 15108 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 20:05:04.413133 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:05:08.528916 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:08.962479 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:05:08.962520 15108 solver.cpp:397]     Test net output #1: loss = 5.28598 (* 1 = 5.28598 loss)
I0409 20:05:10.724840 15108 solver.cpp:218] Iteration 924 (1.2468 iter/s, 9.62466s/12 iters), loss = 5.28452
I0409 20:05:10.724890 15108 solver.cpp:237]     Train net output #0: loss = 5.28452 (* 1 = 5.28452 loss)
I0409 20:05:10.724902 15108 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 20:05:15.550014 15108 solver.cpp:218] Iteration 936 (2.48708 iter/s, 4.82493s/12 iters), loss = 5.26057
I0409 20:05:15.550073 15108 solver.cpp:237]     Train net output #0: loss = 5.26057 (* 1 = 5.26057 loss)
I0409 20:05:15.550086 15108 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 20:05:20.391049 15108 solver.cpp:218] Iteration 948 (2.47894 iter/s, 4.84078s/12 iters), loss = 5.28689
I0409 20:05:20.391103 15108 solver.cpp:237]     Train net output #0: loss = 5.28689 (* 1 = 5.28689 loss)
I0409 20:05:20.391119 15108 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 20:05:25.275945 15108 solver.cpp:218] Iteration 960 (2.45668 iter/s, 4.88464s/12 iters), loss = 5.25783
I0409 20:05:25.276000 15108 solver.cpp:237]     Train net output #0: loss = 5.25783 (* 1 = 5.25783 loss)
I0409 20:05:25.276010 15108 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 20:05:30.410868 15108 solver.cpp:218] Iteration 972 (2.33706 iter/s, 5.13466s/12 iters), loss = 5.27431
I0409 20:05:30.410966 15108 solver.cpp:237]     Train net output #0: loss = 5.27431 (* 1 = 5.27431 loss)
I0409 20:05:30.410979 15108 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 20:05:35.416543 15108 solver.cpp:218] Iteration 984 (2.39742 iter/s, 5.00537s/12 iters), loss = 5.28997
I0409 20:05:35.416587 15108 solver.cpp:237]     Train net output #0: loss = 5.28997 (* 1 = 5.28997 loss)
I0409 20:05:35.416597 15108 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 20:05:40.281040 15108 solver.cpp:218] Iteration 996 (2.46697 iter/s, 4.86426s/12 iters), loss = 5.2774
I0409 20:05:40.281080 15108 solver.cpp:237]     Train net output #0: loss = 5.2774 (* 1 = 5.2774 loss)
I0409 20:05:40.281090 15108 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 20:05:45.188190 15108 solver.cpp:218] Iteration 1008 (2.44553 iter/s, 4.9069s/12 iters), loss = 5.28271
I0409 20:05:45.188243 15108 solver.cpp:237]     Train net output #0: loss = 5.28271 (* 1 = 5.28271 loss)
I0409 20:05:45.188256 15108 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 20:05:46.180819 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:49.565300 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 20:05:50.332520 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 20:05:50.894703 15108 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 20:05:50.894721 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:05:54.887776 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:05:55.319288 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:05:55.319339 15108 solver.cpp:397]     Test net output #1: loss = 5.28567 (* 1 = 5.28567 loss)
I0409 20:05:55.400933 15108 solver.cpp:218] Iteration 1020 (1.17506 iter/s, 10.2123s/12 iters), loss = 5.28817
I0409 20:05:55.400996 15108 solver.cpp:237]     Train net output #0: loss = 5.28817 (* 1 = 5.28817 loss)
I0409 20:05:55.401010 15108 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 20:05:59.394016 15108 solver.cpp:218] Iteration 1032 (3.00537 iter/s, 3.99285s/12 iters), loss = 5.24885
I0409 20:05:59.394080 15108 solver.cpp:237]     Train net output #0: loss = 5.24885 (* 1 = 5.24885 loss)
I0409 20:05:59.394093 15108 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 20:06:04.320684 15108 solver.cpp:218] Iteration 1044 (2.43586 iter/s, 4.9264s/12 iters), loss = 5.25895
I0409 20:06:04.320870 15108 solver.cpp:237]     Train net output #0: loss = 5.25895 (* 1 = 5.25895 loss)
I0409 20:06:04.320889 15108 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 20:06:09.178813 15108 solver.cpp:218] Iteration 1056 (2.47028 iter/s, 4.85775s/12 iters), loss = 5.26222
I0409 20:06:09.178884 15108 solver.cpp:237]     Train net output #0: loss = 5.26222 (* 1 = 5.26222 loss)
I0409 20:06:09.178900 15108 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 20:06:13.970916 15108 solver.cpp:218] Iteration 1068 (2.50426 iter/s, 4.79184s/12 iters), loss = 5.29087
I0409 20:06:13.970961 15108 solver.cpp:237]     Train net output #0: loss = 5.29087 (* 1 = 5.29087 loss)
I0409 20:06:13.970970 15108 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 20:06:18.823107 15108 solver.cpp:218] Iteration 1080 (2.47323 iter/s, 4.85195s/12 iters), loss = 5.27032
I0409 20:06:18.823161 15108 solver.cpp:237]     Train net output #0: loss = 5.27032 (* 1 = 5.27032 loss)
I0409 20:06:18.823171 15108 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 20:06:23.682988 15108 solver.cpp:218] Iteration 1092 (2.46932 iter/s, 4.85963s/12 iters), loss = 5.28297
I0409 20:06:23.683039 15108 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0409 20:06:23.683053 15108 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 20:06:28.584803 15108 solver.cpp:218] Iteration 1104 (2.4482 iter/s, 4.90156s/12 iters), loss = 5.26901
I0409 20:06:28.584859 15108 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0409 20:06:28.584872 15108 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 20:06:31.605206 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:33.378911 15108 solver.cpp:218] Iteration 1116 (2.5032 iter/s, 4.79386s/12 iters), loss = 5.27182
I0409 20:06:33.378968 15108 solver.cpp:237]     Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
I0409 20:06:33.378981 15108 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 20:06:35.365480 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 20:06:36.156214 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 20:06:36.744619 15108 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 20:06:36.744644 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:06:40.716094 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:06:41.191339 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 20:06:41.191376 15108 solver.cpp:397]     Test net output #1: loss = 5.28465 (* 1 = 5.28465 loss)
I0409 20:06:43.001441 15108 solver.cpp:218] Iteration 1128 (1.24713 iter/s, 9.6221s/12 iters), loss = 5.27274
I0409 20:06:43.001482 15108 solver.cpp:237]     Train net output #0: loss = 5.27274 (* 1 = 5.27274 loss)
I0409 20:06:43.001492 15108 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 20:06:48.034013 15108 solver.cpp:218] Iteration 1140 (2.38458 iter/s, 5.03232s/12 iters), loss = 5.26511
I0409 20:06:48.034065 15108 solver.cpp:237]     Train net output #0: loss = 5.26511 (* 1 = 5.26511 loss)
I0409 20:06:48.034075 15108 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 20:06:52.828395 15108 solver.cpp:218] Iteration 1152 (2.50306 iter/s, 4.79412s/12 iters), loss = 5.27826
I0409 20:06:52.828447 15108 solver.cpp:237]     Train net output #0: loss = 5.27826 (* 1 = 5.27826 loss)
I0409 20:06:52.828459 15108 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 20:06:57.691185 15108 solver.cpp:218] Iteration 1164 (2.46785 iter/s, 4.86254s/12 iters), loss = 5.27526
I0409 20:06:57.691238 15108 solver.cpp:237]     Train net output #0: loss = 5.27526 (* 1 = 5.27526 loss)
I0409 20:06:57.691251 15108 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 20:07:02.547410 15108 solver.cpp:218] Iteration 1176 (2.47118 iter/s, 4.85597s/12 iters), loss = 5.28269
I0409 20:07:02.547464 15108 solver.cpp:237]     Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
I0409 20:07:02.547477 15108 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 20:07:07.417006 15108 solver.cpp:218] Iteration 1188 (2.4644 iter/s, 4.86934s/12 iters), loss = 5.26797
I0409 20:07:07.417129 15108 solver.cpp:237]     Train net output #0: loss = 5.26797 (* 1 = 5.26797 loss)
I0409 20:07:07.417141 15108 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 20:07:12.255964 15108 solver.cpp:218] Iteration 1200 (2.48003 iter/s, 4.83864s/12 iters), loss = 5.28651
I0409 20:07:12.256009 15108 solver.cpp:237]     Train net output #0: loss = 5.28651 (* 1 = 5.28651 loss)
I0409 20:07:12.256018 15108 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 20:07:17.087743 15108 solver.cpp:218] Iteration 1212 (2.48368 iter/s, 4.83154s/12 iters), loss = 5.25967
I0409 20:07:17.087792 15108 solver.cpp:237]     Train net output #0: loss = 5.25967 (* 1 = 5.25967 loss)
I0409 20:07:17.087805 15108 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 20:07:17.365253 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:21.537843 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 20:07:22.321820 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 20:07:22.886930 15108 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 20:07:22.886955 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:07:26.686285 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:07:27.197208 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 20:07:27.197247 15108 solver.cpp:397]     Test net output #1: loss = 5.27904 (* 1 = 5.27904 loss)
I0409 20:07:27.280465 15108 solver.cpp:218] Iteration 1224 (1.17736 iter/s, 10.1923s/12 iters), loss = 5.27751
I0409 20:07:27.280510 15108 solver.cpp:237]     Train net output #0: loss = 5.27751 (* 1 = 5.27751 loss)
I0409 20:07:27.280519 15108 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 20:07:31.528945 15108 solver.cpp:218] Iteration 1236 (2.82469 iter/s, 4.24826s/12 iters), loss = 5.26563
I0409 20:07:31.528988 15108 solver.cpp:237]     Train net output #0: loss = 5.26563 (* 1 = 5.26563 loss)
I0409 20:07:31.528997 15108 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 20:07:36.412056 15108 solver.cpp:218] Iteration 1248 (2.45758 iter/s, 4.88286s/12 iters), loss = 5.26471
I0409 20:07:36.412117 15108 solver.cpp:237]     Train net output #0: loss = 5.26471 (* 1 = 5.26471 loss)
I0409 20:07:36.412130 15108 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 20:07:41.440748 15108 solver.cpp:218] Iteration 1260 (2.38643 iter/s, 5.02843s/12 iters), loss = 5.25777
I0409 20:07:41.440863 15108 solver.cpp:237]     Train net output #0: loss = 5.25777 (* 1 = 5.25777 loss)
I0409 20:07:41.440872 15108 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 20:07:46.316565 15108 solver.cpp:218] Iteration 1272 (2.46128 iter/s, 4.87551s/12 iters), loss = 5.21806
I0409 20:07:46.316612 15108 solver.cpp:237]     Train net output #0: loss = 5.21806 (* 1 = 5.21806 loss)
I0409 20:07:46.316623 15108 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 20:07:51.174937 15108 solver.cpp:218] Iteration 1284 (2.47009 iter/s, 4.85813s/12 iters), loss = 5.22706
I0409 20:07:51.174976 15108 solver.cpp:237]     Train net output #0: loss = 5.22706 (* 1 = 5.22706 loss)
I0409 20:07:51.174986 15108 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 20:07:56.015887 15108 solver.cpp:218] Iteration 1296 (2.47897 iter/s, 4.84071s/12 iters), loss = 5.19648
I0409 20:07:56.015929 15108 solver.cpp:237]     Train net output #0: loss = 5.19648 (* 1 = 5.19648 loss)
I0409 20:07:56.015939 15108 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 20:08:00.878762 15108 solver.cpp:218] Iteration 1308 (2.4678 iter/s, 4.86263s/12 iters), loss = 5.20831
I0409 20:08:00.878806 15108 solver.cpp:237]     Train net output #0: loss = 5.20831 (* 1 = 5.20831 loss)
I0409 20:08:00.878815 15108 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 20:08:03.335156 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:05.749455 15108 solver.cpp:218] Iteration 1320 (2.46384 iter/s, 4.87045s/12 iters), loss = 5.21788
I0409 20:08:05.749505 15108 solver.cpp:237]     Train net output #0: loss = 5.21788 (* 1 = 5.21788 loss)
I0409 20:08:05.749518 15108 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 20:08:07.748533 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 20:08:09.151984 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 20:08:10.224884 15108 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 20:08:10.224912 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:08:14.124336 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:14.680614 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0409 20:08:14.680656 15108 solver.cpp:397]     Test net output #1: loss = 5.20189 (* 1 = 5.20189 loss)
I0409 20:08:16.527132 15108 solver.cpp:218] Iteration 1332 (1.11346 iter/s, 10.7772s/12 iters), loss = 5.20938
I0409 20:08:16.527197 15108 solver.cpp:237]     Train net output #0: loss = 5.20938 (* 1 = 5.20938 loss)
I0409 20:08:16.527211 15108 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 20:08:21.422185 15108 solver.cpp:218] Iteration 1344 (2.45159 iter/s, 4.89479s/12 iters), loss = 5.08509
I0409 20:08:21.422233 15108 solver.cpp:237]     Train net output #0: loss = 5.08509 (* 1 = 5.08509 loss)
I0409 20:08:21.422245 15108 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 20:08:26.463979 15108 solver.cpp:218] Iteration 1356 (2.38022 iter/s, 5.04154s/12 iters), loss = 5.18312
I0409 20:08:26.464026 15108 solver.cpp:237]     Train net output #0: loss = 5.18312 (* 1 = 5.18312 loss)
I0409 20:08:26.464038 15108 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 20:08:31.329255 15108 solver.cpp:218] Iteration 1368 (2.46658 iter/s, 4.86503s/12 iters), loss = 5.16823
I0409 20:08:31.329308 15108 solver.cpp:237]     Train net output #0: loss = 5.16823 (* 1 = 5.16823 loss)
I0409 20:08:31.329321 15108 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 20:08:31.329638 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:08:36.181262 15108 solver.cpp:218] Iteration 1380 (2.47333 iter/s, 4.85175s/12 iters), loss = 5.17357
I0409 20:08:36.181322 15108 solver.cpp:237]     Train net output #0: loss = 5.17357 (* 1 = 5.17357 loss)
I0409 20:08:36.181335 15108 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 20:08:41.029229 15108 solver.cpp:218] Iteration 1392 (2.4754 iter/s, 4.84771s/12 iters), loss = 5.02912
I0409 20:08:41.029286 15108 solver.cpp:237]     Train net output #0: loss = 5.02912 (* 1 = 5.02912 loss)
I0409 20:08:41.029297 15108 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 20:08:45.904772 15108 solver.cpp:218] Iteration 1404 (2.4614 iter/s, 4.87528s/12 iters), loss = 5.16659
I0409 20:08:45.904927 15108 solver.cpp:237]     Train net output #0: loss = 5.16659 (* 1 = 5.16659 loss)
I0409 20:08:45.904938 15108 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 20:08:50.424397 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:08:50.766368 15108 solver.cpp:218] Iteration 1416 (2.4685 iter/s, 4.86125s/12 iters), loss = 5.16423
I0409 20:08:50.766415 15108 solver.cpp:237]     Train net output #0: loss = 5.16423 (* 1 = 5.16423 loss)
I0409 20:08:50.766424 15108 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 20:08:55.189548 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 20:08:56.123731 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 20:08:57.323290 15108 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 20:08:57.323320 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:09:01.396780 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:02.004717 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0409 20:09:02.004765 15108 solver.cpp:397]     Test net output #1: loss = 5.16125 (* 1 = 5.16125 loss)
I0409 20:09:02.088013 15108 solver.cpp:218] Iteration 1428 (1.05996 iter/s, 11.3212s/12 iters), loss = 5.19792
I0409 20:09:02.088070 15108 solver.cpp:237]     Train net output #0: loss = 5.19792 (* 1 = 5.19792 loss)
I0409 20:09:02.088083 15108 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 20:09:06.087157 15108 solver.cpp:218] Iteration 1440 (3.00081 iter/s, 3.99892s/12 iters), loss = 5.12488
I0409 20:09:06.087216 15108 solver.cpp:237]     Train net output #0: loss = 5.12488 (* 1 = 5.12488 loss)
I0409 20:09:06.087229 15108 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 20:09:10.956734 15108 solver.cpp:218] Iteration 1452 (2.46441 iter/s, 4.86932s/12 iters), loss = 5.15146
I0409 20:09:10.956789 15108 solver.cpp:237]     Train net output #0: loss = 5.15146 (* 1 = 5.15146 loss)
I0409 20:09:10.956800 15108 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 20:09:15.782806 15108 solver.cpp:218] Iteration 1464 (2.48662 iter/s, 4.82582s/12 iters), loss = 5.17952
I0409 20:09:15.782848 15108 solver.cpp:237]     Train net output #0: loss = 5.17952 (* 1 = 5.17952 loss)
I0409 20:09:15.782857 15108 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 20:09:20.659155 15108 solver.cpp:218] Iteration 1476 (2.46098 iter/s, 4.8761s/12 iters), loss = 5.14773
I0409 20:09:20.659277 15108 solver.cpp:237]     Train net output #0: loss = 5.14773 (* 1 = 5.14773 loss)
I0409 20:09:20.659292 15108 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 20:09:25.527209 15108 solver.cpp:218] Iteration 1488 (2.46521 iter/s, 4.86774s/12 iters), loss = 5.17366
I0409 20:09:25.527261 15108 solver.cpp:237]     Train net output #0: loss = 5.17366 (* 1 = 5.17366 loss)
I0409 20:09:25.527273 15108 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 20:09:30.722307 15108 solver.cpp:218] Iteration 1500 (2.30998 iter/s, 5.19484s/12 iters), loss = 5.10289
I0409 20:09:30.722349 15108 solver.cpp:237]     Train net output #0: loss = 5.10289 (* 1 = 5.10289 loss)
I0409 20:09:30.722359 15108 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 20:09:35.602476 15108 solver.cpp:218] Iteration 1512 (2.45905 iter/s, 4.87993s/12 iters), loss = 5.14679
I0409 20:09:35.602520 15108 solver.cpp:237]     Train net output #0: loss = 5.14679 (* 1 = 5.14679 loss)
I0409 20:09:35.602530 15108 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 20:09:37.363585 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:40.449190 15108 solver.cpp:218] Iteration 1524 (2.47603 iter/s, 4.84647s/12 iters), loss = 5.16073
I0409 20:09:40.449235 15108 solver.cpp:237]     Train net output #0: loss = 5.16073 (* 1 = 5.16073 loss)
I0409 20:09:40.449244 15108 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 20:09:42.425412 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 20:09:43.223930 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 20:09:43.808216 15108 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 20:09:43.808246 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:09:47.827181 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:09:48.475402 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 20:09:48.475452 15108 solver.cpp:397]     Test net output #1: loss = 5.15116 (* 1 = 5.15116 loss)
I0409 20:09:50.296785 15108 solver.cpp:218] Iteration 1536 (1.21863 iter/s, 9.84716s/12 iters), loss = 5.17156
I0409 20:09:50.296836 15108 solver.cpp:237]     Train net output #0: loss = 5.17156 (* 1 = 5.17156 loss)
I0409 20:09:50.296850 15108 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 20:09:55.157210 15108 solver.cpp:218] Iteration 1548 (2.46905 iter/s, 4.86018s/12 iters), loss = 5.1607
I0409 20:09:55.157363 15108 solver.cpp:237]     Train net output #0: loss = 5.1607 (* 1 = 5.1607 loss)
I0409 20:09:55.157377 15108 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 20:10:00.164853 15108 solver.cpp:218] Iteration 1560 (2.39651 iter/s, 5.00729s/12 iters), loss = 5.11139
I0409 20:10:00.164908 15108 solver.cpp:237]     Train net output #0: loss = 5.11139 (* 1 = 5.11139 loss)
I0409 20:10:00.164921 15108 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 20:10:05.192442 15108 solver.cpp:218] Iteration 1572 (2.38695 iter/s, 5.02734s/12 iters), loss = 5.12864
I0409 20:10:05.192484 15108 solver.cpp:237]     Train net output #0: loss = 5.12864 (* 1 = 5.12864 loss)
I0409 20:10:05.192493 15108 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 20:10:10.293179 15108 solver.cpp:218] Iteration 1584 (2.35271 iter/s, 5.10049s/12 iters), loss = 5.19702
I0409 20:10:10.293224 15108 solver.cpp:237]     Train net output #0: loss = 5.19702 (* 1 = 5.19702 loss)
I0409 20:10:10.293234 15108 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 20:10:15.201994 15108 solver.cpp:218] Iteration 1596 (2.44471 iter/s, 4.90857s/12 iters), loss = 5.09344
I0409 20:10:15.202044 15108 solver.cpp:237]     Train net output #0: loss = 5.09344 (* 1 = 5.09344 loss)
I0409 20:10:15.202054 15108 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 20:10:20.174129 15108 solver.cpp:218] Iteration 1608 (2.41357 iter/s, 4.97188s/12 iters), loss = 5.19508
I0409 20:10:20.174173 15108 solver.cpp:237]     Train net output #0: loss = 5.19508 (* 1 = 5.19508 loss)
I0409 20:10:20.174182 15108 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 20:10:24.036375 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:25.096522 15108 solver.cpp:218] Iteration 1620 (2.43796 iter/s, 4.92215s/12 iters), loss = 5.05
I0409 20:10:25.096565 15108 solver.cpp:237]     Train net output #0: loss = 5.05 (* 1 = 5.05 loss)
I0409 20:10:25.096575 15108 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 20:10:29.518469 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 20:10:30.307837 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 20:10:30.890493 15108 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 20:10:30.890520 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:10:34.800863 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:10:35.483649 15108 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 20:10:35.483697 15108 solver.cpp:397]     Test net output #1: loss = 5.14444 (* 1 = 5.14444 loss)
I0409 20:10:35.567055 15108 solver.cpp:218] Iteration 1632 (1.14612 iter/s, 10.4701s/12 iters), loss = 5.17531
I0409 20:10:35.567111 15108 solver.cpp:237]     Train net output #0: loss = 5.17531 (* 1 = 5.17531 loss)
I0409 20:10:35.567122 15108 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 20:10:39.695138 15108 solver.cpp:218] Iteration 1644 (2.90708 iter/s, 4.12785s/12 iters), loss = 5.185
I0409 20:10:39.695200 15108 solver.cpp:237]     Train net output #0: loss = 5.185 (* 1 = 5.185 loss)
I0409 20:10:39.695214 15108 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 20:10:44.631691 15108 solver.cpp:218] Iteration 1656 (2.43097 iter/s, 4.9363s/12 iters), loss = 5.12225
I0409 20:10:44.631731 15108 solver.cpp:237]     Train net output #0: loss = 5.12225 (* 1 = 5.12225 loss)
I0409 20:10:44.631740 15108 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 20:10:49.511380 15108 solver.cpp:218] Iteration 1668 (2.4593 iter/s, 4.87945s/12 iters), loss = 5.06657
I0409 20:10:49.511426 15108 solver.cpp:237]     Train net output #0: loss = 5.06657 (* 1 = 5.06657 loss)
I0409 20:10:49.511437 15108 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 20:10:54.351413 15108 solver.cpp:218] Iteration 1680 (2.47945 iter/s, 4.83978s/12 iters), loss = 5.17014
I0409 20:10:54.351475 15108 solver.cpp:237]     Train net output #0: loss = 5.17014 (* 1 = 5.17014 loss)
I0409 20:10:54.351490 15108 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 20:10:59.290311 15108 solver.cpp:218] Iteration 1692 (2.42982 iter/s, 4.93864s/12 iters), loss = 5.20158
I0409 20:10:59.290360 15108 solver.cpp:237]     Train net output #0: loss = 5.20158 (* 1 = 5.20158 loss)
I0409 20:10:59.290370 15108 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 20:11:04.186184 15108 solver.cpp:218] Iteration 1704 (2.45117 iter/s, 4.89562s/12 iters), loss = 5.03105
I0409 20:11:04.186329 15108 solver.cpp:237]     Train net output #0: loss = 5.03105 (* 1 = 5.03105 loss)
I0409 20:11:04.186343 15108 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 20:11:09.078112 15108 solver.cpp:218] Iteration 1716 (2.45319 iter/s, 4.89159s/12 iters), loss = 5.19555
I0409 20:11:09.078155 15108 solver.cpp:237]     Train net output #0: loss = 5.19555 (* 1 = 5.19555 loss)
I0409 20:11:09.078163 15108 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 20:11:10.115878 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:14.021661 15108 solver.cpp:218] Iteration 1728 (2.42753 iter/s, 4.9433s/12 iters), loss = 5.07701
I0409 20:11:14.021705 15108 solver.cpp:237]     Train net output #0: loss = 5.07701 (* 1 = 5.07701 loss)
I0409 20:11:14.021714 15108 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 20:11:16.009315 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 20:11:16.789317 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 20:11:17.360435 15108 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 20:11:17.360467 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:11:21.251492 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:21.953760 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0409 20:11:21.953811 15108 solver.cpp:397]     Test net output #1: loss = 5.12815 (* 1 = 5.12815 loss)
I0409 20:11:23.791251 15108 solver.cpp:218] Iteration 1740 (1.22836 iter/s, 9.76916s/12 iters), loss = 5.11462
I0409 20:11:23.791309 15108 solver.cpp:237]     Train net output #0: loss = 5.11462 (* 1 = 5.11462 loss)
I0409 20:11:23.791322 15108 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 20:11:28.745908 15108 solver.cpp:218] Iteration 1752 (2.42209 iter/s, 4.95439s/12 iters), loss = 5.12633
I0409 20:11:28.745980 15108 solver.cpp:237]     Train net output #0: loss = 5.12633 (* 1 = 5.12633 loss)
I0409 20:11:28.745993 15108 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 20:11:33.659788 15108 solver.cpp:218] Iteration 1764 (2.44219 iter/s, 4.91362s/12 iters), loss = 5.10143
I0409 20:11:33.659848 15108 solver.cpp:237]     Train net output #0: loss = 5.10143 (* 1 = 5.10143 loss)
I0409 20:11:33.659864 15108 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 20:11:38.587446 15108 solver.cpp:218] Iteration 1776 (2.43536 iter/s, 4.9274s/12 iters), loss = 5.13797
I0409 20:11:38.587571 15108 solver.cpp:237]     Train net output #0: loss = 5.13797 (* 1 = 5.13797 loss)
I0409 20:11:38.587585 15108 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 20:11:43.511507 15108 solver.cpp:218] Iteration 1788 (2.43718 iter/s, 4.92373s/12 iters), loss = 5.15643
I0409 20:11:43.511564 15108 solver.cpp:237]     Train net output #0: loss = 5.15643 (* 1 = 5.15643 loss)
I0409 20:11:43.511576 15108 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 20:11:48.437889 15108 solver.cpp:218] Iteration 1800 (2.43599 iter/s, 4.92612s/12 iters), loss = 5.06694
I0409 20:11:48.437947 15108 solver.cpp:237]     Train net output #0: loss = 5.06694 (* 1 = 5.06694 loss)
I0409 20:11:48.437976 15108 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 20:11:53.538414 15108 solver.cpp:218] Iteration 1812 (2.35282 iter/s, 5.10026s/12 iters), loss = 5.04111
I0409 20:11:53.538467 15108 solver.cpp:237]     Train net output #0: loss = 5.04111 (* 1 = 5.04111 loss)
I0409 20:11:53.538480 15108 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 20:11:56.811856 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:11:58.587536 15108 solver.cpp:218] Iteration 1824 (2.37677 iter/s, 5.04886s/12 iters), loss = 5.10861
I0409 20:11:58.587596 15108 solver.cpp:237]     Train net output #0: loss = 5.10861 (* 1 = 5.10861 loss)
I0409 20:11:58.587610 15108 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 20:12:03.072741 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 20:12:05.297647 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 20:12:06.171803 15108 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 20:12:06.171833 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:12:09.894404 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:10.641506 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0409 20:12:10.641552 15108 solver.cpp:397]     Test net output #1: loss = 5.094 (* 1 = 5.094 loss)
I0409 20:12:10.724964 15108 solver.cpp:218] Iteration 1836 (0.988721 iter/s, 12.1369s/12 iters), loss = 5.1555
I0409 20:12:10.725013 15108 solver.cpp:237]     Train net output #0: loss = 5.1555 (* 1 = 5.1555 loss)
I0409 20:12:10.725023 15108 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 20:12:14.934921 15108 solver.cpp:218] Iteration 1848 (2.85053 iter/s, 4.20974s/12 iters), loss = 5.17836
I0409 20:12:14.934959 15108 solver.cpp:237]     Train net output #0: loss = 5.17836 (* 1 = 5.17836 loss)
I0409 20:12:14.934968 15108 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 20:12:19.767650 15108 solver.cpp:218] Iteration 1860 (2.48319 iter/s, 4.83249s/12 iters), loss = 5.08088
I0409 20:12:19.767699 15108 solver.cpp:237]     Train net output #0: loss = 5.08088 (* 1 = 5.08088 loss)
I0409 20:12:19.767709 15108 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 20:12:24.662019 15108 solver.cpp:218] Iteration 1872 (2.45192 iter/s, 4.89412s/12 iters), loss = 5.07594
I0409 20:12:24.662070 15108 solver.cpp:237]     Train net output #0: loss = 5.07594 (* 1 = 5.07594 loss)
I0409 20:12:24.662081 15108 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 20:12:29.608678 15108 solver.cpp:218] Iteration 1884 (2.426 iter/s, 4.94641s/12 iters), loss = 5.10157
I0409 20:12:29.608736 15108 solver.cpp:237]     Train net output #0: loss = 5.10157 (* 1 = 5.10157 loss)
I0409 20:12:29.608747 15108 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 20:12:34.500790 15108 solver.cpp:218] Iteration 1896 (2.45305 iter/s, 4.89186s/12 iters), loss = 5.08462
I0409 20:12:34.500834 15108 solver.cpp:237]     Train net output #0: loss = 5.08462 (* 1 = 5.08462 loss)
I0409 20:12:34.500844 15108 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 20:12:39.474157 15108 solver.cpp:218] Iteration 1908 (2.41297 iter/s, 4.97312s/12 iters), loss = 5.15385
I0409 20:12:39.474200 15108 solver.cpp:237]     Train net output #0: loss = 5.15385 (* 1 = 5.15385 loss)
I0409 20:12:39.474210 15108 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 20:12:44.352103 15108 solver.cpp:218] Iteration 1920 (2.46017 iter/s, 4.87771s/12 iters), loss = 5.10009
I0409 20:12:44.352228 15108 solver.cpp:237]     Train net output #0: loss = 5.10009 (* 1 = 5.10009 loss)
I0409 20:12:44.352238 15108 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 20:12:44.659478 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:49.527374 15108 solver.cpp:218] Iteration 1932 (2.31887 iter/s, 5.17493s/12 iters), loss = 5.01695
I0409 20:12:49.527421 15108 solver.cpp:237]     Train net output #0: loss = 5.01695 (* 1 = 5.01695 loss)
I0409 20:12:49.527431 15108 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 20:12:51.499217 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 20:12:52.296854 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 20:12:52.875174 15108 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 20:12:52.875202 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:12:56.474463 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:12:57.297180 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0409 20:12:57.297231 15108 solver.cpp:397]     Test net output #1: loss = 5.07218 (* 1 = 5.07218 loss)
I0409 20:12:59.197181 15108 solver.cpp:218] Iteration 1944 (1.24103 iter/s, 9.66937s/12 iters), loss = 5.14783
I0409 20:12:59.197235 15108 solver.cpp:237]     Train net output #0: loss = 5.14783 (* 1 = 5.14783 loss)
I0409 20:12:59.197247 15108 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 20:13:04.211386 15108 solver.cpp:218] Iteration 1956 (2.39332 iter/s, 5.01395s/12 iters), loss = 5.09448
I0409 20:13:04.211432 15108 solver.cpp:237]     Train net output #0: loss = 5.09448 (* 1 = 5.09448 loss)
I0409 20:13:04.211441 15108 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 20:13:09.148737 15108 solver.cpp:218] Iteration 1968 (2.43058 iter/s, 4.9371s/12 iters), loss = 5.02153
I0409 20:13:09.148792 15108 solver.cpp:237]     Train net output #0: loss = 5.02153 (* 1 = 5.02153 loss)
I0409 20:13:09.148804 15108 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 20:13:14.060063 15108 solver.cpp:218] Iteration 1980 (2.44346 iter/s, 4.91107s/12 iters), loss = 5.04859
I0409 20:13:14.060109 15108 solver.cpp:237]     Train net output #0: loss = 5.04859 (* 1 = 5.04859 loss)
I0409 20:13:14.060120 15108 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 20:13:18.964840 15108 solver.cpp:218] Iteration 1992 (2.44672 iter/s, 4.90452s/12 iters), loss = 5.0537
I0409 20:13:18.964977 15108 solver.cpp:237]     Train net output #0: loss = 5.0537 (* 1 = 5.0537 loss)
I0409 20:13:18.964991 15108 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 20:13:23.843820 15108 solver.cpp:218] Iteration 2004 (2.4597 iter/s, 4.87865s/12 iters), loss = 5.02192
I0409 20:13:23.843873 15108 solver.cpp:237]     Train net output #0: loss = 5.02192 (* 1 = 5.02192 loss)
I0409 20:13:23.843884 15108 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 20:13:28.795359 15108 solver.cpp:218] Iteration 2016 (2.42361 iter/s, 4.95129s/12 iters), loss = 5.01379
I0409 20:13:28.795397 15108 solver.cpp:237]     Train net output #0: loss = 5.01379 (* 1 = 5.01379 loss)
I0409 20:13:28.795404 15108 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 20:13:31.280853 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:33.704272 15108 solver.cpp:218] Iteration 2028 (2.44465 iter/s, 4.90868s/12 iters), loss = 5.02863
I0409 20:13:33.704311 15108 solver.cpp:237]     Train net output #0: loss = 5.02863 (* 1 = 5.02863 loss)
I0409 20:13:33.704319 15108 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 20:13:38.120800 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 20:13:38.904985 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 20:13:39.474834 15108 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 20:13:39.474860 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:13:43.107317 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:13:43.934159 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0409 20:13:43.934222 15108 solver.cpp:397]     Test net output #1: loss = 5.05702 (* 1 = 5.05702 loss)
I0409 20:13:44.017735 15108 solver.cpp:218] Iteration 2040 (1.16358 iter/s, 10.313s/12 iters), loss = 5.03858
I0409 20:13:44.017794 15108 solver.cpp:237]     Train net output #0: loss = 5.03858 (* 1 = 5.03858 loss)
I0409 20:13:44.017808 15108 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 20:13:48.235242 15108 solver.cpp:218] Iteration 2052 (2.84544 iter/s, 4.21727s/12 iters), loss = 4.97188
I0409 20:13:48.235302 15108 solver.cpp:237]     Train net output #0: loss = 4.97188 (* 1 = 4.97188 loss)
I0409 20:13:48.235314 15108 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 20:13:48.606889 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:13:53.173097 15108 solver.cpp:218] Iteration 2064 (2.43034 iter/s, 4.93759s/12 iters), loss = 5.0129
I0409 20:13:53.173221 15108 solver.cpp:237]     Train net output #0: loss = 5.0129 (* 1 = 5.0129 loss)
I0409 20:13:53.173231 15108 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 20:13:58.067349 15108 solver.cpp:218] Iteration 2076 (2.45202 iter/s, 4.89393s/12 iters), loss = 5.12947
I0409 20:13:58.067392 15108 solver.cpp:237]     Train net output #0: loss = 5.12947 (* 1 = 5.12947 loss)
I0409 20:13:58.067401 15108 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 20:14:02.973433 15108 solver.cpp:218] Iteration 2088 (2.44607 iter/s, 4.90584s/12 iters), loss = 5.0192
I0409 20:14:02.973480 15108 solver.cpp:237]     Train net output #0: loss = 5.0192 (* 1 = 5.0192 loss)
I0409 20:14:02.973491 15108 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 20:14:07.837424 15108 solver.cpp:218] Iteration 2100 (2.46724 iter/s, 4.86374s/12 iters), loss = 4.80834
I0409 20:14:07.837481 15108 solver.cpp:237]     Train net output #0: loss = 4.80834 (* 1 = 4.80834 loss)
I0409 20:14:07.837492 15108 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 20:14:12.805239 15108 solver.cpp:218] Iteration 2112 (2.41568 iter/s, 4.96755s/12 iters), loss = 5.03553
I0409 20:14:12.805282 15108 solver.cpp:237]     Train net output #0: loss = 5.03553 (* 1 = 5.03553 loss)
I0409 20:14:12.805291 15108 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 20:14:17.541288 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:17.859987 15108 solver.cpp:218] Iteration 2124 (2.37412 iter/s, 5.0545s/12 iters), loss = 4.99931
I0409 20:14:17.860034 15108 solver.cpp:237]     Train net output #0: loss = 4.99931 (* 1 = 4.99931 loss)
I0409 20:14:17.860047 15108 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 20:14:22.847910 15108 solver.cpp:218] Iteration 2136 (2.40593 iter/s, 4.98767s/12 iters), loss = 5.05346
I0409 20:14:22.847965 15108 solver.cpp:237]     Train net output #0: loss = 5.05346 (* 1 = 5.05346 loss)
I0409 20:14:22.847977 15108 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 20:14:24.852654 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 20:14:25.688742 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 20:14:26.281354 15108 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 20:14:26.281385 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:14:29.997305 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:14:30.863700 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0409 20:14:30.863751 15108 solver.cpp:397]     Test net output #1: loss = 5.03761 (* 1 = 5.03761 loss)
I0409 20:14:32.658677 15108 solver.cpp:218] Iteration 2148 (1.2232 iter/s, 9.81032s/12 iters), loss = 4.95809
I0409 20:14:32.658726 15108 solver.cpp:237]     Train net output #0: loss = 4.95809 (* 1 = 4.95809 loss)
I0409 20:14:32.658736 15108 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 20:14:37.541034 15108 solver.cpp:218] Iteration 2160 (2.45796 iter/s, 4.8821s/12 iters), loss = 5.0009
I0409 20:14:37.541090 15108 solver.cpp:237]     Train net output #0: loss = 5.0009 (* 1 = 5.0009 loss)
I0409 20:14:37.541102 15108 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 20:14:42.369194 15108 solver.cpp:218] Iteration 2172 (2.48555 iter/s, 4.8279s/12 iters), loss = 5.0171
I0409 20:14:42.369254 15108 solver.cpp:237]     Train net output #0: loss = 5.0171 (* 1 = 5.0171 loss)
I0409 20:14:42.369267 15108 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 20:14:47.248978 15108 solver.cpp:218] Iteration 2184 (2.45926 iter/s, 4.87952s/12 iters), loss = 4.99085
I0409 20:14:47.249033 15108 solver.cpp:237]     Train net output #0: loss = 4.99085 (* 1 = 4.99085 loss)
I0409 20:14:47.249047 15108 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 20:14:52.167666 15108 solver.cpp:218] Iteration 2196 (2.4398 iter/s, 4.91843s/12 iters), loss = 5.05689
I0409 20:14:52.167716 15108 solver.cpp:237]     Train net output #0: loss = 5.05689 (* 1 = 5.05689 loss)
I0409 20:14:52.167726 15108 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 20:14:57.016160 15108 solver.cpp:218] Iteration 2208 (2.47512 iter/s, 4.84825s/12 iters), loss = 4.94006
I0409 20:14:57.016265 15108 solver.cpp:237]     Train net output #0: loss = 4.94006 (* 1 = 4.94006 loss)
I0409 20:14:57.016276 15108 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 20:15:02.001678 15108 solver.cpp:218] Iteration 2220 (2.40712 iter/s, 4.98521s/12 iters), loss = 5.05085
I0409 20:15:02.001735 15108 solver.cpp:237]     Train net output #0: loss = 5.05085 (* 1 = 5.05085 loss)
I0409 20:15:02.001749 15108 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 20:15:03.796561 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:06.919442 15108 solver.cpp:218] Iteration 2232 (2.44026 iter/s, 4.9175s/12 iters), loss = 5.0588
I0409 20:15:06.919499 15108 solver.cpp:237]     Train net output #0: loss = 5.0588 (* 1 = 5.0588 loss)
I0409 20:15:06.919517 15108 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 20:15:11.357093 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 20:15:13.254549 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 20:15:14.778949 15108 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 20:15:14.778980 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:15:18.218919 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:19.123929 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0409 20:15:19.123976 15108 solver.cpp:397]     Test net output #1: loss = 4.98921 (* 1 = 4.98921 loss)
I0409 20:15:19.206161 15108 solver.cpp:218] Iteration 2244 (0.976709 iter/s, 12.2862s/12 iters), loss = 5.05958
I0409 20:15:19.206223 15108 solver.cpp:237]     Train net output #0: loss = 5.05958 (* 1 = 5.05958 loss)
I0409 20:15:19.206238 15108 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 20:15:23.467664 15108 solver.cpp:218] Iteration 2256 (2.81607 iter/s, 4.26127s/12 iters), loss = 4.96215
I0409 20:15:23.467722 15108 solver.cpp:237]     Train net output #0: loss = 4.96215 (* 1 = 4.96215 loss)
I0409 20:15:23.467734 15108 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 20:15:28.375813 15108 solver.cpp:218] Iteration 2268 (2.44504 iter/s, 4.90789s/12 iters), loss = 4.9763
I0409 20:15:28.375919 15108 solver.cpp:237]     Train net output #0: loss = 4.9763 (* 1 = 4.9763 loss)
I0409 20:15:28.375931 15108 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 20:15:33.307883 15108 solver.cpp:218] Iteration 2280 (2.43321 iter/s, 4.93176s/12 iters), loss = 4.95596
I0409 20:15:33.307942 15108 solver.cpp:237]     Train net output #0: loss = 4.95596 (* 1 = 4.95596 loss)
I0409 20:15:33.307955 15108 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 20:15:38.209237 15108 solver.cpp:218] Iteration 2292 (2.44843 iter/s, 4.90109s/12 iters), loss = 4.97784
I0409 20:15:38.209285 15108 solver.cpp:237]     Train net output #0: loss = 4.97784 (* 1 = 4.97784 loss)
I0409 20:15:38.209295 15108 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 20:15:43.109805 15108 solver.cpp:218] Iteration 2304 (2.44882 iter/s, 4.90032s/12 iters), loss = 4.95329
I0409 20:15:43.109851 15108 solver.cpp:237]     Train net output #0: loss = 4.95329 (* 1 = 4.95329 loss)
I0409 20:15:43.109861 15108 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 20:15:48.061246 15108 solver.cpp:218] Iteration 2316 (2.42366 iter/s, 4.95119s/12 iters), loss = 4.96805
I0409 20:15:48.061292 15108 solver.cpp:237]     Train net output #0: loss = 4.96805 (* 1 = 4.96805 loss)
I0409 20:15:48.061302 15108 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 20:15:51.933501 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:15:52.974258 15108 solver.cpp:218] Iteration 2328 (2.44262 iter/s, 4.91277s/12 iters), loss = 4.97155
I0409 20:15:52.974306 15108 solver.cpp:237]     Train net output #0: loss = 4.97155 (* 1 = 4.97155 loss)
I0409 20:15:52.974316 15108 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 20:15:57.854414 15108 solver.cpp:218] Iteration 2340 (2.45906 iter/s, 4.8799s/12 iters), loss = 4.97815
I0409 20:15:57.854461 15108 solver.cpp:237]     Train net output #0: loss = 4.97815 (* 1 = 4.97815 loss)
I0409 20:15:57.854470 15108 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 20:15:59.882251 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 20:16:01.438195 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 20:16:03.252552 15108 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 20:16:03.252583 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:16:06.766741 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:07.706143 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0409 20:16:07.706192 15108 solver.cpp:397]     Test net output #1: loss = 4.95675 (* 1 = 4.95675 loss)
I0409 20:16:09.626580 15108 solver.cpp:218] Iteration 2352 (1.0194 iter/s, 11.7716s/12 iters), loss = 4.9894
I0409 20:16:09.626641 15108 solver.cpp:237]     Train net output #0: loss = 4.9894 (* 1 = 4.9894 loss)
I0409 20:16:09.626654 15108 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 20:16:14.463150 15108 solver.cpp:218] Iteration 2364 (2.48123 iter/s, 4.8363s/12 iters), loss = 4.94094
I0409 20:16:14.463212 15108 solver.cpp:237]     Train net output #0: loss = 4.94094 (* 1 = 4.94094 loss)
I0409 20:16:14.463223 15108 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 20:16:19.369212 15108 solver.cpp:218] Iteration 2376 (2.44609 iter/s, 4.90579s/12 iters), loss = 4.82778
I0409 20:16:19.369269 15108 solver.cpp:237]     Train net output #0: loss = 4.82778 (* 1 = 4.82778 loss)
I0409 20:16:19.369284 15108 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 20:16:24.313737 15108 solver.cpp:218] Iteration 2388 (2.42705 iter/s, 4.94427s/12 iters), loss = 4.93695
I0409 20:16:24.313784 15108 solver.cpp:237]     Train net output #0: loss = 4.93695 (* 1 = 4.93695 loss)
I0409 20:16:24.313794 15108 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 20:16:29.259423 15108 solver.cpp:218] Iteration 2400 (2.42648 iter/s, 4.94543s/12 iters), loss = 4.99982
I0409 20:16:29.259477 15108 solver.cpp:237]     Train net output #0: loss = 4.99982 (* 1 = 4.99982 loss)
I0409 20:16:29.259490 15108 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 20:16:34.114753 15108 solver.cpp:218] Iteration 2412 (2.47164 iter/s, 4.85508s/12 iters), loss = 4.79648
I0409 20:16:34.114887 15108 solver.cpp:237]     Train net output #0: loss = 4.79648 (* 1 = 4.79648 loss)
I0409 20:16:34.114897 15108 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 20:16:39.037024 15108 solver.cpp:218] Iteration 2424 (2.43806 iter/s, 4.92194s/12 iters), loss = 4.97284
I0409 20:16:39.037063 15108 solver.cpp:237]     Train net output #0: loss = 4.97284 (* 1 = 4.97284 loss)
I0409 20:16:39.037072 15108 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 20:16:40.199034 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:44.022599 15108 solver.cpp:218] Iteration 2436 (2.40706 iter/s, 4.98533s/12 iters), loss = 4.82594
I0409 20:16:44.022657 15108 solver.cpp:237]     Train net output #0: loss = 4.82594 (* 1 = 4.82594 loss)
I0409 20:16:44.022670 15108 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 20:16:48.471272 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 20:16:49.208914 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 20:16:49.773427 15108 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 20:16:49.773449 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:16:53.697276 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:16:54.740974 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0409 20:16:54.741019 15108 solver.cpp:397]     Test net output #1: loss = 4.86195 (* 1 = 4.86195 loss)
I0409 20:16:54.824321 15108 solver.cpp:218] Iteration 2448 (1.11098 iter/s, 10.8012s/12 iters), loss = 4.79354
I0409 20:16:54.824371 15108 solver.cpp:237]     Train net output #0: loss = 4.79354 (* 1 = 4.79354 loss)
I0409 20:16:54.824381 15108 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 20:16:59.004192 15108 solver.cpp:218] Iteration 2460 (2.87106 iter/s, 4.17965s/12 iters), loss = 4.89157
I0409 20:16:59.004248 15108 solver.cpp:237]     Train net output #0: loss = 4.89157 (* 1 = 4.89157 loss)
I0409 20:16:59.004261 15108 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 20:17:04.104982 15108 solver.cpp:218] Iteration 2472 (2.3527 iter/s, 5.10053s/12 iters), loss = 4.92366
I0409 20:17:04.105031 15108 solver.cpp:237]     Train net output #0: loss = 4.92366 (* 1 = 4.92366 loss)
I0409 20:17:04.105039 15108 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 20:17:08.950944 15108 solver.cpp:218] Iteration 2484 (2.47642 iter/s, 4.84571s/12 iters), loss = 4.93041
I0409 20:17:08.951064 15108 solver.cpp:237]     Train net output #0: loss = 4.93041 (* 1 = 4.93041 loss)
I0409 20:17:08.951078 15108 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 20:17:13.892746 15108 solver.cpp:218] Iteration 2496 (2.42842 iter/s, 4.94149s/12 iters), loss = 5.05132
I0409 20:17:13.892791 15108 solver.cpp:237]     Train net output #0: loss = 5.05132 (* 1 = 5.05132 loss)
I0409 20:17:13.892800 15108 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 20:17:18.774132 15108 solver.cpp:218] Iteration 2508 (2.45845 iter/s, 4.88113s/12 iters), loss = 4.86755
I0409 20:17:18.774188 15108 solver.cpp:237]     Train net output #0: loss = 4.86755 (* 1 = 4.86755 loss)
I0409 20:17:18.774201 15108 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 20:17:23.767096 15108 solver.cpp:218] Iteration 2520 (2.40351 iter/s, 4.9927s/12 iters), loss = 4.85153
I0409 20:17:23.767144 15108 solver.cpp:237]     Train net output #0: loss = 4.85153 (* 1 = 4.85153 loss)
I0409 20:17:23.767155 15108 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 20:17:26.891422 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:28.615460 15108 solver.cpp:218] Iteration 2532 (2.47519 iter/s, 4.84812s/12 iters), loss = 4.85782
I0409 20:17:28.615510 15108 solver.cpp:237]     Train net output #0: loss = 4.85782 (* 1 = 4.85782 loss)
I0409 20:17:28.615520 15108 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 20:17:33.572299 15108 solver.cpp:218] Iteration 2544 (2.42102 iter/s, 4.95658s/12 iters), loss = 4.96556
I0409 20:17:33.572355 15108 solver.cpp:237]     Train net output #0: loss = 4.96556 (* 1 = 4.96556 loss)
I0409 20:17:33.572367 15108 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 20:17:35.539213 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 20:17:36.345765 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 20:17:36.973611 15108 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 20:17:36.973642 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:17:40.581178 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:17:41.604449 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0409 20:17:41.604498 15108 solver.cpp:397]     Test net output #1: loss = 4.85217 (* 1 = 4.85217 loss)
I0409 20:17:43.637382 15108 solver.cpp:218] Iteration 2556 (1.1923 iter/s, 10.0646s/12 iters), loss = 4.84553
I0409 20:17:43.637442 15108 solver.cpp:237]     Train net output #0: loss = 4.84553 (* 1 = 4.84553 loss)
I0409 20:17:43.637455 15108 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 20:17:48.701997 15108 solver.cpp:218] Iteration 2568 (2.3695 iter/s, 5.06435s/12 iters), loss = 4.74533
I0409 20:17:48.702040 15108 solver.cpp:237]     Train net output #0: loss = 4.74533 (* 1 = 4.74533 loss)
I0409 20:17:48.702049 15108 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 20:17:53.621702 15108 solver.cpp:218] Iteration 2580 (2.4393 iter/s, 4.91945s/12 iters), loss = 4.75463
I0409 20:17:53.621762 15108 solver.cpp:237]     Train net output #0: loss = 4.75463 (* 1 = 4.75463 loss)
I0409 20:17:53.621773 15108 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 20:17:58.548172 15108 solver.cpp:218] Iteration 2592 (2.43595 iter/s, 4.92621s/12 iters), loss = 4.771
I0409 20:17:58.548219 15108 solver.cpp:237]     Train net output #0: loss = 4.771 (* 1 = 4.771 loss)
I0409 20:17:58.548229 15108 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 20:18:03.635113 15108 solver.cpp:218] Iteration 2604 (2.3591 iter/s, 5.08669s/12 iters), loss = 4.79135
I0409 20:18:03.635154 15108 solver.cpp:237]     Train net output #0: loss = 4.79135 (* 1 = 4.79135 loss)
I0409 20:18:03.635162 15108 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 20:18:08.540491 15108 solver.cpp:218] Iteration 2616 (2.44642 iter/s, 4.90513s/12 iters), loss = 4.88553
I0409 20:18:08.540551 15108 solver.cpp:237]     Train net output #0: loss = 4.88553 (* 1 = 4.88553 loss)
I0409 20:18:08.540565 15108 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 20:18:13.463866 15108 solver.cpp:218] Iteration 2628 (2.43748 iter/s, 4.92311s/12 iters), loss = 4.81568
I0409 20:18:13.463974 15108 solver.cpp:237]     Train net output #0: loss = 4.81568 (* 1 = 4.81568 loss)
I0409 20:18:13.463987 15108 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 20:18:13.906327 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:18.382572 15108 solver.cpp:218] Iteration 2640 (2.43982 iter/s, 4.9184s/12 iters), loss = 4.78347
I0409 20:18:18.382627 15108 solver.cpp:237]     Train net output #0: loss = 4.78347 (* 1 = 4.78347 loss)
I0409 20:18:18.382637 15108 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 20:18:22.832772 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 20:18:23.634783 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 20:18:24.209476 15108 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 20:18:24.209506 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:18:27.623926 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:18:28.680114 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0409 20:18:28.680160 15108 solver.cpp:397]     Test net output #1: loss = 4.72697 (* 1 = 4.72697 loss)
I0409 20:18:28.763317 15108 solver.cpp:218] Iteration 2652 (1.15604 iter/s, 10.3803s/12 iters), loss = 4.84197
I0409 20:18:28.763371 15108 solver.cpp:237]     Train net output #0: loss = 4.84197 (* 1 = 4.84197 loss)
I0409 20:18:28.763382 15108 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 20:18:33.112144 15108 solver.cpp:218] Iteration 2664 (2.75952 iter/s, 4.34859s/12 iters), loss = 4.68208
I0409 20:18:33.112203 15108 solver.cpp:237]     Train net output #0: loss = 4.68208 (* 1 = 4.68208 loss)
I0409 20:18:33.112215 15108 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 20:18:37.978989 15108 solver.cpp:218] Iteration 2676 (2.46579 iter/s, 4.86659s/12 iters), loss = 4.63585
I0409 20:18:37.979027 15108 solver.cpp:237]     Train net output #0: loss = 4.63585 (* 1 = 4.63585 loss)
I0409 20:18:37.979034 15108 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 20:18:42.880551 15108 solver.cpp:218] Iteration 2688 (2.44832 iter/s, 4.90132s/12 iters), loss = 4.61666
I0409 20:18:42.880604 15108 solver.cpp:237]     Train net output #0: loss = 4.61666 (* 1 = 4.61666 loss)
I0409 20:18:42.880616 15108 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 20:18:47.946255 15108 solver.cpp:218] Iteration 2700 (2.369 iter/s, 5.06544s/12 iters), loss = 4.72917
I0409 20:18:47.946374 15108 solver.cpp:237]     Train net output #0: loss = 4.72917 (* 1 = 4.72917 loss)
I0409 20:18:47.946389 15108 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 20:18:52.927001 15108 solver.cpp:218] Iteration 2712 (2.40943 iter/s, 4.98043s/12 iters), loss = 4.64478
I0409 20:18:52.927048 15108 solver.cpp:237]     Train net output #0: loss = 4.64478 (* 1 = 4.64478 loss)
I0409 20:18:52.927062 15108 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 20:18:57.988312 15108 solver.cpp:218] Iteration 2724 (2.37105 iter/s, 5.06106s/12 iters), loss = 4.79767
I0409 20:18:57.988360 15108 solver.cpp:237]     Train net output #0: loss = 4.79767 (* 1 = 4.79767 loss)
I0409 20:18:57.988373 15108 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 20:19:00.559283 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:02.938230 15108 solver.cpp:218] Iteration 2736 (2.42441 iter/s, 4.94966s/12 iters), loss = 4.57662
I0409 20:19:02.938284 15108 solver.cpp:237]     Train net output #0: loss = 4.57662 (* 1 = 4.57662 loss)
I0409 20:19:02.938297 15108 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 20:19:07.792667 15108 solver.cpp:218] Iteration 2748 (2.47209 iter/s, 4.85418s/12 iters), loss = 4.71459
I0409 20:19:07.792719 15108 solver.cpp:237]     Train net output #0: loss = 4.71459 (* 1 = 4.71459 loss)
I0409 20:19:07.792732 15108 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 20:19:09.752517 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 20:19:10.972012 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 20:19:11.565481 15108 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 20:19:11.565512 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:19:14.547751 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:19:15.065404 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:16.508807 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0409 20:19:16.508838 15108 solver.cpp:397]     Test net output #1: loss = 4.66854 (* 1 = 4.66854 loss)
I0409 20:19:18.307819 15108 solver.cpp:218] Iteration 2760 (1.14126 iter/s, 10.5147s/12 iters), loss = 4.57812
I0409 20:19:18.307914 15108 solver.cpp:237]     Train net output #0: loss = 4.57812 (* 1 = 4.57812 loss)
I0409 20:19:18.307924 15108 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 20:19:23.305189 15108 solver.cpp:218] Iteration 2772 (2.40141 iter/s, 4.99707s/12 iters), loss = 4.64648
I0409 20:19:23.305244 15108 solver.cpp:237]     Train net output #0: loss = 4.64648 (* 1 = 4.64648 loss)
I0409 20:19:23.305256 15108 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 20:19:28.236724 15108 solver.cpp:218] Iteration 2784 (2.43345 iter/s, 4.93127s/12 iters), loss = 4.70233
I0409 20:19:28.236776 15108 solver.cpp:237]     Train net output #0: loss = 4.70233 (* 1 = 4.70233 loss)
I0409 20:19:28.236788 15108 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 20:19:33.155706 15108 solver.cpp:218] Iteration 2796 (2.43965 iter/s, 4.91873s/12 iters), loss = 4.59218
I0409 20:19:33.155751 15108 solver.cpp:237]     Train net output #0: loss = 4.59218 (* 1 = 4.59218 loss)
I0409 20:19:33.155761 15108 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 20:19:38.218288 15108 solver.cpp:218] Iteration 2808 (2.37045 iter/s, 5.06233s/12 iters), loss = 4.49002
I0409 20:19:38.218334 15108 solver.cpp:237]     Train net output #0: loss = 4.49002 (* 1 = 4.49002 loss)
I0409 20:19:38.218344 15108 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 20:19:43.201122 15108 solver.cpp:218] Iteration 2820 (2.40839 iter/s, 4.98258s/12 iters), loss = 4.57731
I0409 20:19:43.201170 15108 solver.cpp:237]     Train net output #0: loss = 4.57731 (* 1 = 4.57731 loss)
I0409 20:19:43.201182 15108 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 20:19:47.821344 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:19:48.103722 15108 solver.cpp:218] Iteration 2832 (2.44781 iter/s, 4.90235s/12 iters), loss = 4.54789
I0409 20:19:48.103765 15108 solver.cpp:237]     Train net output #0: loss = 4.54789 (* 1 = 4.54789 loss)
I0409 20:19:48.103773 15108 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 20:19:53.000660 15108 solver.cpp:218] Iteration 2844 (2.45063 iter/s, 4.89669s/12 iters), loss = 4.68326
I0409 20:19:53.000783 15108 solver.cpp:237]     Train net output #0: loss = 4.68326 (* 1 = 4.68326 loss)
I0409 20:19:53.000798 15108 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 20:19:57.461603 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 20:19:58.907356 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 20:20:00.817414 15108 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 20:20:00.817443 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:20:04.077122 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:05.218967 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0447304
I0409 20:20:05.219017 15108 solver.cpp:397]     Test net output #1: loss = 4.58162 (* 1 = 4.58162 loss)
I0409 20:20:05.302248 15108 solver.cpp:218] Iteration 2856 (0.975532 iter/s, 12.301s/12 iters), loss = 4.51054
I0409 20:20:05.302295 15108 solver.cpp:237]     Train net output #0: loss = 4.51054 (* 1 = 4.51054 loss)
I0409 20:20:05.302306 15108 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 20:20:09.395648 15108 solver.cpp:218] Iteration 2868 (2.9317 iter/s, 4.09319s/12 iters), loss = 4.67066
I0409 20:20:09.395687 15108 solver.cpp:237]     Train net output #0: loss = 4.67066 (* 1 = 4.67066 loss)
I0409 20:20:09.395696 15108 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 20:20:14.289067 15108 solver.cpp:218] Iteration 2880 (2.45241 iter/s, 4.89315s/12 iters), loss = 4.59505
I0409 20:20:14.289117 15108 solver.cpp:237]     Train net output #0: loss = 4.59505 (* 1 = 4.59505 loss)
I0409 20:20:14.289127 15108 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 20:20:19.216305 15108 solver.cpp:218] Iteration 2892 (2.43557 iter/s, 4.92698s/12 iters), loss = 4.51382
I0409 20:20:19.216351 15108 solver.cpp:237]     Train net output #0: loss = 4.51382 (* 1 = 4.51382 loss)
I0409 20:20:19.216361 15108 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 20:20:24.178565 15108 solver.cpp:218] Iteration 2904 (2.41837 iter/s, 4.96201s/12 iters), loss = 4.63281
I0409 20:20:24.178671 15108 solver.cpp:237]     Train net output #0: loss = 4.63281 (* 1 = 4.63281 loss)
I0409 20:20:24.178684 15108 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 20:20:29.155275 15108 solver.cpp:218] Iteration 2916 (2.41138 iter/s, 4.97639s/12 iters), loss = 4.48841
I0409 20:20:29.155339 15108 solver.cpp:237]     Train net output #0: loss = 4.48841 (* 1 = 4.48841 loss)
I0409 20:20:29.155354 15108 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 20:20:34.108301 15108 solver.cpp:218] Iteration 2928 (2.42289 iter/s, 4.95275s/12 iters), loss = 4.61664
I0409 20:20:34.108355 15108 solver.cpp:237]     Train net output #0: loss = 4.61664 (* 1 = 4.61664 loss)
I0409 20:20:34.108367 15108 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 20:20:35.896391 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:39.007792 15108 solver.cpp:218] Iteration 2940 (2.44936 iter/s, 4.89923s/12 iters), loss = 4.54653
I0409 20:20:39.007844 15108 solver.cpp:237]     Train net output #0: loss = 4.54653 (* 1 = 4.54653 loss)
I0409 20:20:39.007855 15108 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 20:20:43.967073 15108 solver.cpp:218] Iteration 2952 (2.41983 iter/s, 4.95903s/12 iters), loss = 4.59037
I0409 20:20:43.967126 15108 solver.cpp:237]     Train net output #0: loss = 4.59037 (* 1 = 4.59037 loss)
I0409 20:20:43.967139 15108 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 20:20:45.940603 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 20:20:46.702515 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 20:20:47.265977 15108 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 20:20:47.266002 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:20:50.497153 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:20:51.697798 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0477941
I0409 20:20:51.697849 15108 solver.cpp:397]     Test net output #1: loss = 4.55916 (* 1 = 4.55916 loss)
I0409 20:20:53.626055 15108 solver.cpp:218] Iteration 2964 (1.24242 iter/s, 9.65854s/12 iters), loss = 4.26411
I0409 20:20:53.626107 15108 solver.cpp:237]     Train net output #0: loss = 4.26411 (* 1 = 4.26411 loss)
I0409 20:20:53.626121 15108 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 20:20:58.486541 15108 solver.cpp:218] Iteration 2976 (2.46902 iter/s, 4.86024s/12 iters), loss = 4.57698
I0409 20:20:58.486694 15108 solver.cpp:237]     Train net output #0: loss = 4.57698 (* 1 = 4.57698 loss)
I0409 20:20:58.486711 15108 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 20:21:03.411010 15108 solver.cpp:218] Iteration 2988 (2.43699 iter/s, 4.92411s/12 iters), loss = 4.60325
I0409 20:21:03.411056 15108 solver.cpp:237]     Train net output #0: loss = 4.60325 (* 1 = 4.60325 loss)
I0409 20:21:03.411068 15108 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 20:21:08.422323 15108 solver.cpp:218] Iteration 3000 (2.39471 iter/s, 5.01105s/12 iters), loss = 4.45279
I0409 20:21:08.422384 15108 solver.cpp:237]     Train net output #0: loss = 4.45279 (* 1 = 4.45279 loss)
I0409 20:21:08.422397 15108 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 20:21:13.447947 15108 solver.cpp:218] Iteration 3012 (2.38789 iter/s, 5.02535s/12 iters), loss = 4.50341
I0409 20:21:13.448009 15108 solver.cpp:237]     Train net output #0: loss = 4.50341 (* 1 = 4.50341 loss)
I0409 20:21:13.448022 15108 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 20:21:18.405306 15108 solver.cpp:218] Iteration 3024 (2.42077 iter/s, 4.95709s/12 iters), loss = 4.43888
I0409 20:21:18.405350 15108 solver.cpp:237]     Train net output #0: loss = 4.43888 (* 1 = 4.43888 loss)
I0409 20:21:18.405360 15108 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 20:21:22.298840 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:23.339100 15108 solver.cpp:218] Iteration 3036 (2.43233 iter/s, 4.93355s/12 iters), loss = 4.39579
I0409 20:21:23.339143 15108 solver.cpp:237]     Train net output #0: loss = 4.39579 (* 1 = 4.39579 loss)
I0409 20:21:23.339151 15108 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 20:21:28.240008 15108 solver.cpp:218] Iteration 3048 (2.44865 iter/s, 4.90066s/12 iters), loss = 4.55749
I0409 20:21:28.240061 15108 solver.cpp:237]     Train net output #0: loss = 4.55749 (* 1 = 4.55749 loss)
I0409 20:21:28.240072 15108 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 20:21:32.708073 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 20:21:34.719339 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 20:21:37.232897 15108 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 20:21:37.232930 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:21:40.467422 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:21:41.717662 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0508578
I0409 20:21:41.717706 15108 solver.cpp:397]     Test net output #1: loss = 4.47552 (* 1 = 4.47552 loss)
I0409 20:21:41.800869 15108 solver.cpp:218] Iteration 3060 (0.884938 iter/s, 13.5603s/12 iters), loss = 4.43807
I0409 20:21:41.800915 15108 solver.cpp:237]     Train net output #0: loss = 4.43807 (* 1 = 4.43807 loss)
I0409 20:21:41.800923 15108 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 20:21:46.467643 15108 solver.cpp:218] Iteration 3072 (2.5715 iter/s, 4.66654s/12 iters), loss = 4.30958
I0409 20:21:46.467686 15108 solver.cpp:237]     Train net output #0: loss = 4.30958 (* 1 = 4.30958 loss)
I0409 20:21:46.467694 15108 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 20:21:51.405723 15108 solver.cpp:218] Iteration 3084 (2.43022 iter/s, 4.93782s/12 iters), loss = 4.37485
I0409 20:21:51.405782 15108 solver.cpp:237]     Train net output #0: loss = 4.37485 (* 1 = 4.37485 loss)
I0409 20:21:51.405794 15108 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 20:21:56.306015 15108 solver.cpp:218] Iteration 3096 (2.44897 iter/s, 4.90002s/12 iters), loss = 4.39264
I0409 20:21:56.306069 15108 solver.cpp:237]     Train net output #0: loss = 4.39264 (* 1 = 4.39264 loss)
I0409 20:21:56.306082 15108 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 20:22:01.208827 15108 solver.cpp:218] Iteration 3108 (2.4477 iter/s, 4.90256s/12 iters), loss = 4.34223
I0409 20:22:01.208868 15108 solver.cpp:237]     Train net output #0: loss = 4.34223 (* 1 = 4.34223 loss)
I0409 20:22:01.208876 15108 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 20:22:06.174909 15108 solver.cpp:218] Iteration 3120 (2.41651 iter/s, 4.96584s/12 iters), loss = 4.24083
I0409 20:22:06.174998 15108 solver.cpp:237]     Train net output #0: loss = 4.24083 (* 1 = 4.24083 loss)
I0409 20:22:06.175007 15108 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 20:22:11.183583 15108 solver.cpp:218] Iteration 3132 (2.39598 iter/s, 5.00838s/12 iters), loss = 4.48674
I0409 20:22:11.183617 15108 solver.cpp:237]     Train net output #0: loss = 4.48674 (* 1 = 4.48674 loss)
I0409 20:22:11.183626 15108 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 20:22:12.289969 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:16.220188 15108 solver.cpp:218] Iteration 3144 (2.38267 iter/s, 5.03636s/12 iters), loss = 4.25208
I0409 20:22:16.220242 15108 solver.cpp:237]     Train net output #0: loss = 4.25208 (* 1 = 4.25208 loss)
I0409 20:22:16.220254 15108 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 20:22:21.142525 15108 solver.cpp:218] Iteration 3156 (2.438 iter/s, 4.92208s/12 iters), loss = 4.29049
I0409 20:22:21.142582 15108 solver.cpp:237]     Train net output #0: loss = 4.29049 (* 1 = 4.29049 loss)
I0409 20:22:21.142596 15108 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 20:22:23.117803 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 20:22:23.880225 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 20:22:24.456282 15108 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 20:22:24.456311 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:22:27.549443 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:22:28.843047 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0526961
I0409 20:22:28.843091 15108 solver.cpp:397]     Test net output #1: loss = 4.37829 (* 1 = 4.37829 loss)
I0409 20:22:30.593516 15108 solver.cpp:218] Iteration 3168 (1.26977 iter/s, 9.45055s/12 iters), loss = 4.38447
I0409 20:22:30.593560 15108 solver.cpp:237]     Train net output #0: loss = 4.38447 (* 1 = 4.38447 loss)
I0409 20:22:30.593570 15108 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 20:22:35.407667 15108 solver.cpp:218] Iteration 3180 (2.49278 iter/s, 4.8139s/12 iters), loss = 4.28613
I0409 20:22:35.407716 15108 solver.cpp:237]     Train net output #0: loss = 4.28613 (* 1 = 4.28613 loss)
I0409 20:22:35.407727 15108 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 20:22:40.567137 15108 solver.cpp:218] Iteration 3192 (2.32594 iter/s, 5.15921s/12 iters), loss = 4.25351
I0409 20:22:40.567234 15108 solver.cpp:237]     Train net output #0: loss = 4.25351 (* 1 = 4.25351 loss)
I0409 20:22:40.567245 15108 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 20:22:45.568825 15108 solver.cpp:218] Iteration 3204 (2.39934 iter/s, 5.00138s/12 iters), loss = 4.4479
I0409 20:22:45.568869 15108 solver.cpp:237]     Train net output #0: loss = 4.4479 (* 1 = 4.4479 loss)
I0409 20:22:45.568879 15108 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 20:22:50.465850 15108 solver.cpp:218] Iteration 3216 (2.45059 iter/s, 4.89678s/12 iters), loss = 4.36931
I0409 20:22:50.465898 15108 solver.cpp:237]     Train net output #0: loss = 4.36931 (* 1 = 4.36931 loss)
I0409 20:22:50.465909 15108 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 20:22:55.328405 15108 solver.cpp:218] Iteration 3228 (2.46797 iter/s, 4.8623s/12 iters), loss = 4.33332
I0409 20:22:55.328464 15108 solver.cpp:237]     Train net output #0: loss = 4.33332 (* 1 = 4.33332 loss)
I0409 20:22:55.328477 15108 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 20:22:58.600879 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:00.367231 15108 solver.cpp:218] Iteration 3240 (2.38163 iter/s, 5.03856s/12 iters), loss = 4.44537
I0409 20:23:00.367286 15108 solver.cpp:237]     Train net output #0: loss = 4.44537 (* 1 = 4.44537 loss)
I0409 20:23:00.367300 15108 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 20:23:05.417140 15108 solver.cpp:218] Iteration 3252 (2.3764 iter/s, 5.04965s/12 iters), loss = 4.35169
I0409 20:23:05.417188 15108 solver.cpp:237]     Train net output #0: loss = 4.35169 (* 1 = 4.35169 loss)
I0409 20:23:05.417198 15108 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 20:23:09.855574 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 20:23:10.699683 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 20:23:11.270493 15108 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 20:23:11.270514 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:23:14.385226 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:15.732288 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0409 20:23:15.732317 15108 solver.cpp:397]     Test net output #1: loss = 4.24673 (* 1 = 4.24673 loss)
I0409 20:23:15.815646 15108 solver.cpp:218] Iteration 3264 (1.15406 iter/s, 10.398s/12 iters), loss = 4.34623
I0409 20:23:15.815693 15108 solver.cpp:237]     Train net output #0: loss = 4.34623 (* 1 = 4.34623 loss)
I0409 20:23:15.815703 15108 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 20:23:20.011370 15108 solver.cpp:218] Iteration 3276 (2.8602 iter/s, 4.19551s/12 iters), loss = 4.31997
I0409 20:23:20.011406 15108 solver.cpp:237]     Train net output #0: loss = 4.31997 (* 1 = 4.31997 loss)
I0409 20:23:20.011417 15108 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 20:23:24.976516 15108 solver.cpp:218] Iteration 3288 (2.41697 iter/s, 4.9649s/12 iters), loss = 4.28257
I0409 20:23:24.976560 15108 solver.cpp:237]     Train net output #0: loss = 4.28257 (* 1 = 4.28257 loss)
I0409 20:23:24.976568 15108 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 20:23:29.886409 15108 solver.cpp:218] Iteration 3300 (2.44417 iter/s, 4.90964s/12 iters), loss = 4.23601
I0409 20:23:29.886464 15108 solver.cpp:237]     Train net output #0: loss = 4.23601 (* 1 = 4.23601 loss)
I0409 20:23:29.886477 15108 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 20:23:34.783497 15108 solver.cpp:218] Iteration 3312 (2.45056 iter/s, 4.89683s/12 iters), loss = 4.24327
I0409 20:23:34.783547 15108 solver.cpp:237]     Train net output #0: loss = 4.24327 (* 1 = 4.24327 loss)
I0409 20:23:34.783560 15108 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 20:23:39.652379 15108 solver.cpp:218] Iteration 3324 (2.46476 iter/s, 4.86863s/12 iters), loss = 4.36071
I0409 20:23:39.652432 15108 solver.cpp:237]     Train net output #0: loss = 4.36071 (* 1 = 4.36071 loss)
I0409 20:23:39.652447 15108 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 20:23:44.594419 15108 solver.cpp:218] Iteration 3336 (2.42827 iter/s, 4.94178s/12 iters), loss = 4.3868
I0409 20:23:44.594519 15108 solver.cpp:237]     Train net output #0: loss = 4.3868 (* 1 = 4.3868 loss)
I0409 20:23:44.594528 15108 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 20:23:45.056690 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:23:49.532832 15108 solver.cpp:218] Iteration 3348 (2.43008 iter/s, 4.93811s/12 iters), loss = 4.21359
I0409 20:23:49.532881 15108 solver.cpp:237]     Train net output #0: loss = 4.21359 (* 1 = 4.21359 loss)
I0409 20:23:49.532889 15108 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 20:23:54.389271 15108 solver.cpp:218] Iteration 3360 (2.47108 iter/s, 4.85618s/12 iters), loss = 4.26656
I0409 20:23:54.389323 15108 solver.cpp:237]     Train net output #0: loss = 4.26656 (* 1 = 4.26656 loss)
I0409 20:23:54.389335 15108 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 20:23:56.344842 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 20:23:57.158223 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 20:23:57.732163 15108 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 20:23:57.732184 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:24:00.813879 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:02.144830 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0409 20:24:02.144879 15108 solver.cpp:397]     Test net output #1: loss = 4.19795 (* 1 = 4.19795 loss)
I0409 20:24:04.040454 15108 solver.cpp:218] Iteration 3372 (1.24343 iter/s, 9.65075s/12 iters), loss = 4.00263
I0409 20:24:04.040506 15108 solver.cpp:237]     Train net output #0: loss = 4.00263 (* 1 = 4.00263 loss)
I0409 20:24:04.040519 15108 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 20:24:09.024940 15108 solver.cpp:218] Iteration 3384 (2.4076 iter/s, 4.98422s/12 iters), loss = 4.2155
I0409 20:24:09.024994 15108 solver.cpp:237]     Train net output #0: loss = 4.2155 (* 1 = 4.2155 loss)
I0409 20:24:09.025008 15108 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 20:24:13.907389 15108 solver.cpp:218] Iteration 3396 (2.45791 iter/s, 4.88219s/12 iters), loss = 4.10837
I0409 20:24:13.907444 15108 solver.cpp:237]     Train net output #0: loss = 4.10837 (* 1 = 4.10837 loss)
I0409 20:24:13.907457 15108 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 20:24:18.812613 15108 solver.cpp:218] Iteration 3408 (2.4465 iter/s, 4.90497s/12 iters), loss = 4.12969
I0409 20:24:18.812711 15108 solver.cpp:237]     Train net output #0: loss = 4.12969 (* 1 = 4.12969 loss)
I0409 20:24:18.812721 15108 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 20:24:23.714061 15108 solver.cpp:218] Iteration 3420 (2.44841 iter/s, 4.90115s/12 iters), loss = 4.09257
I0409 20:24:23.714116 15108 solver.cpp:237]     Train net output #0: loss = 4.09257 (* 1 = 4.09257 loss)
I0409 20:24:23.714129 15108 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 20:24:28.794045 15108 solver.cpp:218] Iteration 3432 (2.36234 iter/s, 5.07972s/12 iters), loss = 4.18695
I0409 20:24:28.794097 15108 solver.cpp:237]     Train net output #0: loss = 4.18695 (* 1 = 4.18695 loss)
I0409 20:24:28.794111 15108 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 20:24:31.351527 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:33.687885 15108 solver.cpp:218] Iteration 3444 (2.45219 iter/s, 4.89358s/12 iters), loss = 3.99935
I0409 20:24:33.687935 15108 solver.cpp:237]     Train net output #0: loss = 3.99935 (* 1 = 3.99935 loss)
I0409 20:24:33.687945 15108 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 20:24:38.532413 15108 solver.cpp:218] Iteration 3456 (2.47715 iter/s, 4.84427s/12 iters), loss = 4.10081
I0409 20:24:38.532465 15108 solver.cpp:237]     Train net output #0: loss = 4.10081 (* 1 = 4.10081 loss)
I0409 20:24:38.532477 15108 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 20:24:42.986364 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 20:24:44.620726 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 20:24:45.382244 15108 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 20:24:45.382273 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:24:45.539945 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:24:48.473068 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:24:49.854823 15108 solver.cpp:397]     Test net output #0: accuracy = 0.0716912
I0409 20:24:49.854952 15108 solver.cpp:397]     Test net output #1: loss = 4.12096 (* 1 = 4.12096 loss)
I0409 20:24:49.938339 15108 solver.cpp:218] Iteration 3468 (1.05213 iter/s, 11.4054s/12 iters), loss = 4.04725
I0409 20:24:49.938387 15108 solver.cpp:237]     Train net output #0: loss = 4.04725 (* 1 = 4.04725 loss)
I0409 20:24:49.938397 15108 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 20:24:54.092550 15108 solver.cpp:218] Iteration 3480 (2.88879 iter/s, 4.15399s/12 iters), loss = 4.1693
I0409 20:24:54.092605 15108 solver.cpp:237]     Train net output #0: loss = 4.1693 (* 1 = 4.1693 loss)
I0409 20:24:54.092619 15108 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 20:24:58.991789 15108 solver.cpp:218] Iteration 3492 (2.44949 iter/s, 4.89898s/12 iters), loss = 4.27144
I0409 20:24:58.991837 15108 solver.cpp:237]     Train net output #0: loss = 4.27144 (* 1 = 4.27144 loss)
I0409 20:24:58.991845 15108 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 20:25:03.889914 15108 solver.cpp:218] Iteration 3504 (2.45004 iter/s, 4.89787s/12 iters), loss = 3.92757
I0409 20:25:03.889973 15108 solver.cpp:237]     Train net output #0: loss = 3.92757 (* 1 = 3.92757 loss)
I0409 20:25:03.889983 15108 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 20:25:08.810034 15108 solver.cpp:218] Iteration 3516 (2.43909 iter/s, 4.91988s/12 iters), loss = 3.99537
I0409 20:25:08.810078 15108 solver.cpp:237]     Train net output #0: loss = 3.99537 (* 1 = 3.99537 loss)
I0409 20:25:08.810087 15108 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 20:25:13.742820 15108 solver.cpp:218] Iteration 3528 (2.43283 iter/s, 4.93254s/12 iters), loss = 3.98412
I0409 20:25:13.742861 15108 solver.cpp:237]     Train net output #0: loss = 3.98412 (* 1 = 3.98412 loss)
I0409 20:25:13.742870 15108 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 20:25:18.383098 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:18.637192 15108 solver.cpp:218] Iteration 3540 (2.45192 iter/s, 4.89413s/12 iters), loss = 4.0969
I0409 20:25:18.637233 15108 solver.cpp:237]     Train net output #0: loss = 4.0969 (* 1 = 4.0969 loss)
I0409 20:25:18.637243 15108 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 20:25:23.554150 15108 solver.cpp:218] Iteration 3552 (2.44065 iter/s, 4.91671s/12 iters), loss = 3.99276
I0409 20:25:23.554273 15108 solver.cpp:237]     Train net output #0: loss = 3.99276 (* 1 = 3.99276 loss)
I0409 20:25:23.554287 15108 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 20:25:28.465245 15108 solver.cpp:218] Iteration 3564 (2.44361 iter/s, 4.91077s/12 iters), loss = 3.93447
I0409 20:25:28.465301 15108 solver.cpp:237]     Train net output #0: loss = 3.93447 (* 1 = 3.93447 loss)
I0409 20:25:28.465312 15108 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 20:25:30.446017 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 20:25:31.707731 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 20:25:34.001911 15108 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 20:25:34.001936 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:25:37.144325 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:25:38.586079 15108 solver.cpp:397]     Test net output #0: accuracy = 0.091299
I0409 20:25:38.586129 15108 solver.cpp:397]     Test net output #1: loss = 4.00169 (* 1 = 4.00169 loss)
I0409 20:25:40.329134 15108 solver.cpp:218] Iteration 3576 (1.01152 iter/s, 11.8634s/12 iters), loss = 4.30115
I0409 20:25:40.329197 15108 solver.cpp:237]     Train net output #0: loss = 4.30115 (* 1 = 4.30115 loss)
I0409 20:25:40.329211 15108 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 20:25:45.221025 15108 solver.cpp:218] Iteration 3588 (2.45317 iter/s, 4.89162s/12 iters), loss = 3.96615
I0409 20:25:45.221079 15108 solver.cpp:237]     Train net output #0: loss = 3.96615 (* 1 = 3.96615 loss)
I0409 20:25:45.221091 15108 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 20:25:50.109180 15108 solver.cpp:218] Iteration 3600 (2.45504 iter/s, 4.8879s/12 iters), loss = 4.02735
I0409 20:25:50.109230 15108 solver.cpp:237]     Train net output #0: loss = 4.02735 (* 1 = 4.02735 loss)
I0409 20:25:50.109242 15108 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 20:25:55.096307 15108 solver.cpp:218] Iteration 3612 (2.40632 iter/s, 4.98687s/12 iters), loss = 4.06047
I0409 20:25:55.096438 15108 solver.cpp:237]     Train net output #0: loss = 4.06047 (* 1 = 4.06047 loss)
I0409 20:25:55.096451 15108 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 20:26:00.061375 15108 solver.cpp:218] Iteration 3624 (2.41705 iter/s, 4.96473s/12 iters), loss = 3.92935
I0409 20:26:00.061437 15108 solver.cpp:237]     Train net output #0: loss = 3.92935 (* 1 = 3.92935 loss)
I0409 20:26:00.061450 15108 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 20:26:05.032698 15108 solver.cpp:218] Iteration 3636 (2.41397 iter/s, 4.97106s/12 iters), loss = 4.26811
I0409 20:26:05.032742 15108 solver.cpp:237]     Train net output #0: loss = 4.26811 (* 1 = 4.26811 loss)
I0409 20:26:05.032750 15108 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 20:26:06.897764 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:09.975754 15108 solver.cpp:218] Iteration 3648 (2.42777 iter/s, 4.94281s/12 iters), loss = 4.00946
I0409 20:26:09.975793 15108 solver.cpp:237]     Train net output #0: loss = 4.00946 (* 1 = 4.00946 loss)
I0409 20:26:09.975802 15108 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 20:26:14.923938 15108 solver.cpp:218] Iteration 3660 (2.42525 iter/s, 4.94794s/12 iters), loss = 4.13018
I0409 20:26:14.923988 15108 solver.cpp:237]     Train net output #0: loss = 4.13018 (* 1 = 4.13018 loss)
I0409 20:26:14.924000 15108 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 20:26:19.461120 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 20:26:20.249929 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 20:26:20.834475 15108 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 20:26:20.834506 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:26:23.795156 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:25.251652 15108 solver.cpp:397]     Test net output #0: accuracy = 0.09375
I0409 20:26:25.254112 15108 solver.cpp:397]     Test net output #1: loss = 3.95447 (* 1 = 3.95447 loss)
I0409 20:26:25.337754 15108 solver.cpp:218] Iteration 3672 (1.15237 iter/s, 10.4133s/12 iters), loss = 3.7028
I0409 20:26:25.337806 15108 solver.cpp:237]     Train net output #0: loss = 3.7028 (* 1 = 3.7028 loss)
I0409 20:26:25.337818 15108 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 20:26:29.529760 15108 solver.cpp:218] Iteration 3684 (2.86275 iter/s, 4.19177s/12 iters), loss = 4.12958
I0409 20:26:29.529824 15108 solver.cpp:237]     Train net output #0: loss = 4.12958 (* 1 = 4.12958 loss)
I0409 20:26:29.529840 15108 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 20:26:34.476065 15108 solver.cpp:218] Iteration 3696 (2.42618 iter/s, 4.94604s/12 iters), loss = 3.83672
I0409 20:26:34.476122 15108 solver.cpp:237]     Train net output #0: loss = 3.83672 (* 1 = 3.83672 loss)
I0409 20:26:34.476133 15108 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 20:26:39.400311 15108 solver.cpp:218] Iteration 3708 (2.43706 iter/s, 4.92397s/12 iters), loss = 3.98633
I0409 20:26:39.400380 15108 solver.cpp:237]     Train net output #0: loss = 3.98633 (* 1 = 3.98633 loss)
I0409 20:26:39.400398 15108 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 20:26:44.316452 15108 solver.cpp:218] Iteration 3720 (2.44107 iter/s, 4.91587s/12 iters), loss = 3.92737
I0409 20:26:44.316495 15108 solver.cpp:237]     Train net output #0: loss = 3.92737 (* 1 = 3.92737 loss)
I0409 20:26:44.316504 15108 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 20:26:49.155894 15108 solver.cpp:218] Iteration 3732 (2.47975 iter/s, 4.8392s/12 iters), loss = 3.94788
I0409 20:26:49.155943 15108 solver.cpp:237]     Train net output #0: loss = 3.94788 (* 1 = 3.94788 loss)
I0409 20:26:49.155952 15108 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 20:26:53.112591 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:26:54.109218 15108 solver.cpp:218] Iteration 3744 (2.42274 iter/s, 4.95307s/12 iters), loss = 3.92683
I0409 20:26:54.109267 15108 solver.cpp:237]     Train net output #0: loss = 3.92683 (* 1 = 3.92683 loss)
I0409 20:26:54.109278 15108 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 20:26:59.049242 15108 solver.cpp:218] Iteration 3756 (2.42926 iter/s, 4.93977s/12 iters), loss = 3.8703
I0409 20:26:59.049381 15108 solver.cpp:237]     Train net output #0: loss = 3.8703 (* 1 = 3.8703 loss)
I0409 20:26:59.049392 15108 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 20:27:03.950196 15108 solver.cpp:218] Iteration 3768 (2.44867 iter/s, 4.90061s/12 iters), loss = 3.77675
I0409 20:27:03.950254 15108 solver.cpp:237]     Train net output #0: loss = 3.77675 (* 1 = 3.77675 loss)
I0409 20:27:03.950266 15108 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 20:27:05.930068 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 20:27:07.367550 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 20:27:08.460027 15108 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 20:27:08.460052 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:27:11.399242 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:12.989681 15108 solver.cpp:397]     Test net output #0: accuracy = 0.098652
I0409 20:27:12.989729 15108 solver.cpp:397]     Test net output #1: loss = 3.87427 (* 1 = 3.87427 loss)
I0409 20:27:14.778338 15108 solver.cpp:218] Iteration 3780 (1.10827 iter/s, 10.8277s/12 iters), loss = 3.77926
I0409 20:27:14.778383 15108 solver.cpp:237]     Train net output #0: loss = 3.77926 (* 1 = 3.77926 loss)
I0409 20:27:14.778393 15108 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 20:27:19.707285 15108 solver.cpp:218] Iteration 3792 (2.43472 iter/s, 4.92869s/12 iters), loss = 3.81795
I0409 20:27:19.707335 15108 solver.cpp:237]     Train net output #0: loss = 3.81795 (* 1 = 3.81795 loss)
I0409 20:27:19.707347 15108 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 20:27:24.599503 15108 solver.cpp:218] Iteration 3804 (2.453 iter/s, 4.89196s/12 iters), loss = 3.85869
I0409 20:27:24.599551 15108 solver.cpp:237]     Train net output #0: loss = 3.85869 (* 1 = 3.85869 loss)
I0409 20:27:24.599563 15108 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 20:27:29.530838 15108 solver.cpp:218] Iteration 3816 (2.43354 iter/s, 4.93108s/12 iters), loss = 3.57771
I0409 20:27:29.530984 15108 solver.cpp:237]     Train net output #0: loss = 3.57771 (* 1 = 3.57771 loss)
I0409 20:27:29.530995 15108 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 20:27:34.440126 15108 solver.cpp:218] Iteration 3828 (2.44452 iter/s, 4.90894s/12 iters), loss = 3.51865
I0409 20:27:34.440181 15108 solver.cpp:237]     Train net output #0: loss = 3.51865 (* 1 = 3.51865 loss)
I0409 20:27:34.440192 15108 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 20:27:39.349786 15108 solver.cpp:218] Iteration 3840 (2.44429 iter/s, 4.90939s/12 iters), loss = 3.88887
I0409 20:27:39.349850 15108 solver.cpp:237]     Train net output #0: loss = 3.88887 (* 1 = 3.88887 loss)
I0409 20:27:39.349867 15108 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 20:27:40.477411 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:44.306608 15108 solver.cpp:218] Iteration 3852 (2.42103 iter/s, 4.95656s/12 iters), loss = 3.69145
I0409 20:27:44.306655 15108 solver.cpp:237]     Train net output #0: loss = 3.69145 (* 1 = 3.69145 loss)
I0409 20:27:44.306665 15108 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 20:27:49.232956 15108 solver.cpp:218] Iteration 3864 (2.43601 iter/s, 4.9261s/12 iters), loss = 3.79374
I0409 20:27:49.232997 15108 solver.cpp:237]     Train net output #0: loss = 3.79374 (* 1 = 3.79374 loss)
I0409 20:27:49.233006 15108 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 20:27:53.730188 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 20:27:54.514039 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 20:27:55.077653 15108 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 20:27:55.077677 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:27:57.846804 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:27:59.386874 15108 solver.cpp:397]     Test net output #0: accuracy = 0.10723
I0409 20:27:59.386924 15108 solver.cpp:397]     Test net output #1: loss = 3.83752 (* 1 = 3.83752 loss)
I0409 20:27:59.470098 15108 solver.cpp:218] Iteration 3876 (1.17225 iter/s, 10.2367s/12 iters), loss = 3.87368
I0409 20:27:59.470149 15108 solver.cpp:237]     Train net output #0: loss = 3.87368 (* 1 = 3.87368 loss)
I0409 20:27:59.470160 15108 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 20:28:03.588536 15108 solver.cpp:218] Iteration 3888 (2.91389 iter/s, 4.11821s/12 iters), loss = 3.71877
I0409 20:28:03.588660 15108 solver.cpp:237]     Train net output #0: loss = 3.71877 (* 1 = 3.71877 loss)
I0409 20:28:03.588672 15108 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 20:28:08.609122 15108 solver.cpp:218] Iteration 3900 (2.39031 iter/s, 5.02026s/12 iters), loss = 3.72209
I0409 20:28:08.609165 15108 solver.cpp:237]     Train net output #0: loss = 3.72209 (* 1 = 3.72209 loss)
I0409 20:28:08.609175 15108 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 20:28:13.592398 15108 solver.cpp:218] Iteration 3912 (2.40817 iter/s, 4.98303s/12 iters), loss = 3.75186
I0409 20:28:13.592443 15108 solver.cpp:237]     Train net output #0: loss = 3.75186 (* 1 = 3.75186 loss)
I0409 20:28:13.592454 15108 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 20:28:18.510481 15108 solver.cpp:218] Iteration 3924 (2.4401 iter/s, 4.91783s/12 iters), loss = 3.67253
I0409 20:28:18.510537 15108 solver.cpp:237]     Train net output #0: loss = 3.67253 (* 1 = 3.67253 loss)
I0409 20:28:18.510550 15108 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 20:28:23.547730 15108 solver.cpp:218] Iteration 3936 (2.38238 iter/s, 5.03698s/12 iters), loss = 3.65101
I0409 20:28:23.547785 15108 solver.cpp:237]     Train net output #0: loss = 3.65101 (* 1 = 3.65101 loss)
I0409 20:28:23.547798 15108 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 20:28:26.898715 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:28.529465 15108 solver.cpp:218] Iteration 3948 (2.40893 iter/s, 4.98147s/12 iters), loss = 3.86197
I0409 20:28:28.529518 15108 solver.cpp:237]     Train net output #0: loss = 3.86197 (* 1 = 3.86197 loss)
I0409 20:28:28.529528 15108 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 20:28:33.465277 15108 solver.cpp:218] Iteration 3960 (2.43133 iter/s, 4.93556s/12 iters), loss = 3.82768
I0409 20:28:33.465319 15108 solver.cpp:237]     Train net output #0: loss = 3.82768 (* 1 = 3.82768 loss)
I0409 20:28:33.465330 15108 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 20:28:38.399623 15108 solver.cpp:218] Iteration 3972 (2.43205 iter/s, 4.9341s/12 iters), loss = 3.86169
I0409 20:28:38.399741 15108 solver.cpp:237]     Train net output #0: loss = 3.86169 (* 1 = 3.86169 loss)
I0409 20:28:38.399751 15108 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 20:28:40.558102 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 20:28:41.313839 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 20:28:41.875777 15108 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 20:28:41.875797 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:28:44.687672 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:28:46.263284 15108 solver.cpp:397]     Test net output #0: accuracy = 0.11826
I0409 20:28:46.263324 15108 solver.cpp:397]     Test net output #1: loss = 3.72611 (* 1 = 3.72611 loss)
I0409 20:28:48.163519 15108 solver.cpp:218] Iteration 3984 (1.22908 iter/s, 9.76339s/12 iters), loss = 3.70715
I0409 20:28:48.163561 15108 solver.cpp:237]     Train net output #0: loss = 3.70715 (* 1 = 3.70715 loss)
I0409 20:28:48.163571 15108 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 20:28:53.273031 15108 solver.cpp:218] Iteration 3996 (2.34868 iter/s, 5.10926s/12 iters), loss = 3.81973
I0409 20:28:53.273085 15108 solver.cpp:237]     Train net output #0: loss = 3.81973 (* 1 = 3.81973 loss)
I0409 20:28:53.273098 15108 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 20:28:58.450037 15108 solver.cpp:218] Iteration 4008 (2.31806 iter/s, 5.17674s/12 iters), loss = 3.77599
I0409 20:28:58.450083 15108 solver.cpp:237]     Train net output #0: loss = 3.77599 (* 1 = 3.77599 loss)
I0409 20:28:58.450093 15108 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 20:29:03.351744 15108 solver.cpp:218] Iteration 4020 (2.44825 iter/s, 4.90145s/12 iters), loss = 3.66437
I0409 20:29:03.351800 15108 solver.cpp:237]     Train net output #0: loss = 3.66437 (* 1 = 3.66437 loss)
I0409 20:29:03.351814 15108 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 20:29:08.288280 15108 solver.cpp:218] Iteration 4032 (2.43098 iter/s, 4.93628s/12 iters), loss = 3.73807
I0409 20:29:08.288332 15108 solver.cpp:237]     Train net output #0: loss = 3.73807 (* 1 = 3.73807 loss)
I0409 20:29:08.288343 15108 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 20:29:13.198076 15108 solver.cpp:218] Iteration 4044 (2.44422 iter/s, 4.90954s/12 iters), loss = 3.77197
I0409 20:29:13.198174 15108 solver.cpp:237]     Train net output #0: loss = 3.77197 (* 1 = 3.77197 loss)
I0409 20:29:13.198185 15108 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 20:29:13.694866 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:18.170784 15108 solver.cpp:218] Iteration 4056 (2.41332 iter/s, 4.97241s/12 iters), loss = 3.62134
I0409 20:29:18.170840 15108 solver.cpp:237]     Train net output #0: loss = 3.62134 (* 1 = 3.62134 loss)
I0409 20:29:18.170852 15108 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 20:29:23.186501 15108 solver.cpp:218] Iteration 4068 (2.3926 iter/s, 5.01546s/12 iters), loss = 3.47211
I0409 20:29:23.186558 15108 solver.cpp:237]     Train net output #0: loss = 3.47211 (* 1 = 3.47211 loss)
I0409 20:29:23.186569 15108 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 20:29:27.682446 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 20:29:29.822125 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 20:29:30.591354 15108 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 20:29:30.591384 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:29:33.455055 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:29:35.088989 15108 solver.cpp:397]     Test net output #0: accuracy = 0.129902
I0409 20:29:35.089025 15108 solver.cpp:397]     Test net output #1: loss = 3.60544 (* 1 = 3.60544 loss)
I0409 20:29:35.171957 15108 solver.cpp:218] Iteration 4080 (1.00126 iter/s, 11.9849s/12 iters), loss = 3.67089
I0409 20:29:35.171999 15108 solver.cpp:237]     Train net output #0: loss = 3.67089 (* 1 = 3.67089 loss)
I0409 20:29:35.172009 15108 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 20:29:39.480381 15108 solver.cpp:218] Iteration 4092 (2.78539 iter/s, 4.3082s/12 iters), loss = 3.51396
I0409 20:29:39.480435 15108 solver.cpp:237]     Train net output #0: loss = 3.51396 (* 1 = 3.51396 loss)
I0409 20:29:39.480448 15108 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 20:29:44.436353 15108 solver.cpp:218] Iteration 4104 (2.42145 iter/s, 4.95571s/12 iters), loss = 3.59936
I0409 20:29:44.436511 15108 solver.cpp:237]     Train net output #0: loss = 3.59936 (* 1 = 3.59936 loss)
I0409 20:29:44.436523 15108 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 20:29:49.416486 15108 solver.cpp:218] Iteration 4116 (2.40975 iter/s, 4.97977s/12 iters), loss = 3.59264
I0409 20:29:49.416532 15108 solver.cpp:237]     Train net output #0: loss = 3.59264 (* 1 = 3.59264 loss)
I0409 20:29:49.416543 15108 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 20:29:54.322116 15108 solver.cpp:218] Iteration 4128 (2.44629 iter/s, 4.90538s/12 iters), loss = 3.39833
I0409 20:29:54.322167 15108 solver.cpp:237]     Train net output #0: loss = 3.39833 (* 1 = 3.39833 loss)
I0409 20:29:54.322180 15108 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 20:29:59.174885 15108 solver.cpp:218] Iteration 4140 (2.47294 iter/s, 4.85252s/12 iters), loss = 3.68538
I0409 20:29:59.174944 15108 solver.cpp:237]     Train net output #0: loss = 3.68538 (* 1 = 3.68538 loss)
I0409 20:29:59.174958 15108 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 20:30:01.768748 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:04.098071 15108 solver.cpp:218] Iteration 4152 (2.43758 iter/s, 4.92292s/12 iters), loss = 3.40141
I0409 20:30:04.098129 15108 solver.cpp:237]     Train net output #0: loss = 3.40141 (* 1 = 3.40141 loss)
I0409 20:30:04.098142 15108 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 20:30:04.454104 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:30:08.994024 15108 solver.cpp:218] Iteration 4164 (2.45113 iter/s, 4.89569s/12 iters), loss = 3.4496
I0409 20:30:08.994076 15108 solver.cpp:237]     Train net output #0: loss = 3.4496 (* 1 = 3.4496 loss)
I0409 20:30:08.994088 15108 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 20:30:13.876144 15108 solver.cpp:218] Iteration 4176 (2.45808 iter/s, 4.88186s/12 iters), loss = 3.44649
I0409 20:30:13.876201 15108 solver.cpp:237]     Train net output #0: loss = 3.44649 (* 1 = 3.44649 loss)
I0409 20:30:13.876215 15108 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 20:30:15.844627 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 20:30:16.620832 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 20:30:17.193641 15108 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 20:30:17.193679 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:30:20.117764 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:21.771437 15108 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0409 20:30:21.771476 15108 solver.cpp:397]     Test net output #1: loss = 3.4991 (* 1 = 3.4991 loss)
I0409 20:30:23.678755 15108 solver.cpp:218] Iteration 4188 (1.22422 iter/s, 9.80217s/12 iters), loss = 3.71374
I0409 20:30:23.678797 15108 solver.cpp:237]     Train net output #0: loss = 3.71374 (* 1 = 3.71374 loss)
I0409 20:30:23.678805 15108 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 20:30:28.584573 15108 solver.cpp:218] Iteration 4200 (2.4462 iter/s, 4.90557s/12 iters), loss = 3.41626
I0409 20:30:28.584615 15108 solver.cpp:237]     Train net output #0: loss = 3.41626 (* 1 = 3.41626 loss)
I0409 20:30:28.584625 15108 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 20:30:33.435431 15108 solver.cpp:218] Iteration 4212 (2.47391 iter/s, 4.85061s/12 iters), loss = 3.33869
I0409 20:30:33.435474 15108 solver.cpp:237]     Train net output #0: loss = 3.33869 (* 1 = 3.33869 loss)
I0409 20:30:33.435484 15108 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 20:30:38.345768 15108 solver.cpp:218] Iteration 4224 (2.44395 iter/s, 4.91009s/12 iters), loss = 3.63364
I0409 20:30:38.345814 15108 solver.cpp:237]     Train net output #0: loss = 3.63364 (* 1 = 3.63364 loss)
I0409 20:30:38.345824 15108 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 20:30:43.294183 15108 solver.cpp:218] Iteration 4236 (2.42514 iter/s, 4.94816s/12 iters), loss = 3.51737
I0409 20:30:43.294234 15108 solver.cpp:237]     Train net output #0: loss = 3.51737 (* 1 = 3.51737 loss)
I0409 20:30:43.294245 15108 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 20:30:47.966576 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:30:48.199434 15108 solver.cpp:218] Iteration 4248 (2.44648 iter/s, 4.905s/12 iters), loss = 3.50318
I0409 20:30:48.199486 15108 solver.cpp:237]     Train net output #0: loss = 3.50318 (* 1 = 3.50318 loss)
I0409 20:30:48.199499 15108 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 20:30:53.154166 15108 solver.cpp:218] Iteration 4260 (2.42205 iter/s, 4.95448s/12 iters), loss = 3.36646
I0409 20:30:53.154215 15108 solver.cpp:237]     Train net output #0: loss = 3.36646 (* 1 = 3.36646 loss)
I0409 20:30:53.154224 15108 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 20:30:58.077384 15108 solver.cpp:218] Iteration 4272 (2.43756 iter/s, 4.92296s/12 iters), loss = 3.34736
I0409 20:30:58.077437 15108 solver.cpp:237]     Train net output #0: loss = 3.34736 (* 1 = 3.34736 loss)
I0409 20:30:58.077450 15108 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 20:31:02.576555 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 20:31:04.244717 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 20:31:04.819638 15108 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 20:31:04.819658 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:31:07.734192 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:09.554859 15108 solver.cpp:397]     Test net output #0: accuracy = 0.136029
I0409 20:31:09.554909 15108 solver.cpp:397]     Test net output #1: loss = 3.54722 (* 1 = 3.54722 loss)
I0409 20:31:09.638074 15108 solver.cpp:218] Iteration 4284 (1.03805 iter/s, 11.5602s/12 iters), loss = 3.70365
I0409 20:31:09.638126 15108 solver.cpp:237]     Train net output #0: loss = 3.70365 (* 1 = 3.70365 loss)
I0409 20:31:09.638139 15108 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 20:31:13.783982 15108 solver.cpp:218] Iteration 4296 (2.89458 iter/s, 4.14568s/12 iters), loss = 3.46107
I0409 20:31:13.784042 15108 solver.cpp:237]     Train net output #0: loss = 3.46107 (* 1 = 3.46107 loss)
I0409 20:31:13.784054 15108 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 20:31:18.837389 15108 solver.cpp:218] Iteration 4308 (2.37476 iter/s, 5.05314s/12 iters), loss = 3.4389
I0409 20:31:18.837471 15108 solver.cpp:237]     Train net output #0: loss = 3.4389 (* 1 = 3.4389 loss)
I0409 20:31:18.837481 15108 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 20:31:23.819237 15108 solver.cpp:218] Iteration 4320 (2.40888 iter/s, 4.98156s/12 iters), loss = 3.47984
I0409 20:31:23.819281 15108 solver.cpp:237]     Train net output #0: loss = 3.47984 (* 1 = 3.47984 loss)
I0409 20:31:23.819289 15108 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 20:31:28.716125 15108 solver.cpp:218] Iteration 4332 (2.45066 iter/s, 4.89664s/12 iters), loss = 3.29394
I0409 20:31:28.716181 15108 solver.cpp:237]     Train net output #0: loss = 3.29394 (* 1 = 3.29394 loss)
I0409 20:31:28.716192 15108 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 20:31:33.620434 15108 solver.cpp:218] Iteration 4344 (2.44695 iter/s, 4.90406s/12 iters), loss = 3.52016
I0409 20:31:33.620471 15108 solver.cpp:237]     Train net output #0: loss = 3.52016 (* 1 = 3.52016 loss)
I0409 20:31:33.620481 15108 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 20:31:35.508103 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:38.592559 15108 solver.cpp:218] Iteration 4356 (2.41357 iter/s, 4.97189s/12 iters), loss = 3.36234
I0409 20:31:38.592594 15108 solver.cpp:237]     Train net output #0: loss = 3.36234 (* 1 = 3.36234 loss)
I0409 20:31:38.592603 15108 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 20:31:43.500778 15108 solver.cpp:218] Iteration 4368 (2.445 iter/s, 4.90797s/12 iters), loss = 3.37725
I0409 20:31:43.500836 15108 solver.cpp:237]     Train net output #0: loss = 3.37725 (* 1 = 3.37725 loss)
I0409 20:31:43.500849 15108 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 20:31:48.401631 15108 solver.cpp:218] Iteration 4380 (2.44869 iter/s, 4.90059s/12 iters), loss = 3.15521
I0409 20:31:48.401688 15108 solver.cpp:237]     Train net output #0: loss = 3.15521 (* 1 = 3.15521 loss)
I0409 20:31:48.401700 15108 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 20:31:50.381820 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 20:31:51.165733 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 20:31:51.740528 15108 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 20:31:51.740554 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:31:54.349326 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:31:56.123399 15108 solver.cpp:397]     Test net output #0: accuracy = 0.169118
I0409 20:31:56.123451 15108 solver.cpp:397]     Test net output #1: loss = 3.42039 (* 1 = 3.42039 loss)
I0409 20:31:57.993141 15108 solver.cpp:218] Iteration 4392 (1.25116 iter/s, 9.59107s/12 iters), loss = 3.40935
I0409 20:31:57.993198 15108 solver.cpp:237]     Train net output #0: loss = 3.40935 (* 1 = 3.40935 loss)
I0409 20:31:57.993212 15108 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 20:32:02.912400 15108 solver.cpp:218] Iteration 4404 (2.43952 iter/s, 4.919s/12 iters), loss = 3.36635
I0409 20:32:02.912451 15108 solver.cpp:237]     Train net output #0: loss = 3.36635 (* 1 = 3.36635 loss)
I0409 20:32:02.912463 15108 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 20:32:08.035828 15108 solver.cpp:218] Iteration 4416 (2.3423 iter/s, 5.12316s/12 iters), loss = 3.43663
I0409 20:32:08.035887 15108 solver.cpp:237]     Train net output #0: loss = 3.43663 (* 1 = 3.43663 loss)
I0409 20:32:08.035899 15108 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 20:32:13.040488 15108 solver.cpp:218] Iteration 4428 (2.39789 iter/s, 5.00439s/12 iters), loss = 3.19636
I0409 20:32:13.040536 15108 solver.cpp:237]     Train net output #0: loss = 3.19636 (* 1 = 3.19636 loss)
I0409 20:32:13.040545 15108 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 20:32:18.015676 15108 solver.cpp:218] Iteration 4440 (2.41209 iter/s, 4.97494s/12 iters), loss = 3.1581
I0409 20:32:18.015717 15108 solver.cpp:237]     Train net output #0: loss = 3.1581 (* 1 = 3.1581 loss)
I0409 20:32:18.015725 15108 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 20:32:21.952379 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:22.876040 15108 solver.cpp:218] Iteration 4452 (2.46908 iter/s, 4.86012s/12 iters), loss = 3.35377
I0409 20:32:22.876096 15108 solver.cpp:237]     Train net output #0: loss = 3.35377 (* 1 = 3.35377 loss)
I0409 20:32:22.876109 15108 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 20:32:27.763406 15108 solver.cpp:218] Iteration 4464 (2.45544 iter/s, 4.88711s/12 iters), loss = 3.56176
I0409 20:32:27.763474 15108 solver.cpp:237]     Train net output #0: loss = 3.56176 (* 1 = 3.56176 loss)
I0409 20:32:27.763486 15108 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 20:32:32.797845 15108 solver.cpp:218] Iteration 4476 (2.38371 iter/s, 5.03416s/12 iters), loss = 3.25816
I0409 20:32:32.797900 15108 solver.cpp:237]     Train net output #0: loss = 3.25816 (* 1 = 3.25816 loss)
I0409 20:32:32.797914 15108 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 20:32:37.264446 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 20:32:38.027074 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 20:32:38.621990 15108 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 20:32:38.622020 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:32:41.448303 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:32:43.219064 15108 solver.cpp:397]     Test net output #0: accuracy = 0.170343
I0409 20:32:43.219115 15108 solver.cpp:397]     Test net output #1: loss = 3.37732 (* 1 = 3.37732 loss)
I0409 20:32:43.305929 15108 solver.cpp:218] Iteration 4488 (1.14203 iter/s, 10.5076s/12 iters), loss = 3.16597
I0409 20:32:43.305989 15108 solver.cpp:237]     Train net output #0: loss = 3.16597 (* 1 = 3.16597 loss)
I0409 20:32:43.305999 15108 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 20:32:47.754670 15108 solver.cpp:218] Iteration 4500 (2.69754 iter/s, 4.4485s/12 iters), loss = 3.2033
I0409 20:32:47.754711 15108 solver.cpp:237]     Train net output #0: loss = 3.2033 (* 1 = 3.2033 loss)
I0409 20:32:47.754720 15108 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 20:32:52.642374 15108 solver.cpp:218] Iteration 4512 (2.45527 iter/s, 4.88745s/12 iters), loss = 3.2652
I0409 20:32:52.642489 15108 solver.cpp:237]     Train net output #0: loss = 3.2652 (* 1 = 3.2652 loss)
I0409 20:32:52.642503 15108 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 20:32:57.637363 15108 solver.cpp:218] Iteration 4524 (2.40256 iter/s, 4.99467s/12 iters), loss = 3.18276
I0409 20:32:57.637408 15108 solver.cpp:237]     Train net output #0: loss = 3.18276 (* 1 = 3.18276 loss)
I0409 20:32:57.637420 15108 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 20:33:02.522406 15108 solver.cpp:218] Iteration 4536 (2.4566 iter/s, 4.88479s/12 iters), loss = 3.01157
I0409 20:33:02.522466 15108 solver.cpp:237]     Train net output #0: loss = 3.01157 (* 1 = 3.01157 loss)
I0409 20:33:02.522478 15108 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 20:33:07.497373 15108 solver.cpp:218] Iteration 4548 (2.41221 iter/s, 4.9747s/12 iters), loss = 3.16234
I0409 20:33:07.497431 15108 solver.cpp:237]     Train net output #0: loss = 3.16234 (* 1 = 3.16234 loss)
I0409 20:33:07.497445 15108 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 20:33:08.732110 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:12.473409 15108 solver.cpp:218] Iteration 4560 (2.41169 iter/s, 4.97577s/12 iters), loss = 2.94501
I0409 20:33:12.473464 15108 solver.cpp:237]     Train net output #0: loss = 2.94501 (* 1 = 2.94501 loss)
I0409 20:33:12.473474 15108 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 20:33:17.685216 15108 solver.cpp:218] Iteration 4572 (2.30259 iter/s, 5.21153s/12 iters), loss = 3.23661
I0409 20:33:17.685271 15108 solver.cpp:237]     Train net output #0: loss = 3.23661 (* 1 = 3.23661 loss)
I0409 20:33:17.685281 15108 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 20:33:22.574334 15108 solver.cpp:218] Iteration 4584 (2.45456 iter/s, 4.88886s/12 iters), loss = 3.23965
I0409 20:33:22.574383 15108 solver.cpp:237]     Train net output #0: loss = 3.23965 (* 1 = 3.23965 loss)
I0409 20:33:22.574393 15108 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 20:33:24.550585 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 20:33:25.319392 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 20:33:26.151379 15108 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 20:33:26.151407 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:33:28.777247 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:30.590270 15108 solver.cpp:397]     Test net output #0: accuracy = 0.170343
I0409 20:33:30.590315 15108 solver.cpp:397]     Test net output #1: loss = 3.31384 (* 1 = 3.31384 loss)
I0409 20:33:32.522794 15108 solver.cpp:218] Iteration 4596 (1.20627 iter/s, 9.94801s/12 iters), loss = 3.22595
I0409 20:33:32.522842 15108 solver.cpp:237]     Train net output #0: loss = 3.22595 (* 1 = 3.22595 loss)
I0409 20:33:32.522855 15108 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 20:33:37.501142 15108 solver.cpp:218] Iteration 4608 (2.41056 iter/s, 4.97809s/12 iters), loss = 3.12035
I0409 20:33:37.501202 15108 solver.cpp:237]     Train net output #0: loss = 3.12035 (* 1 = 3.12035 loss)
I0409 20:33:37.501214 15108 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 20:33:42.333597 15108 solver.cpp:218] Iteration 4620 (2.48334 iter/s, 4.8322s/12 iters), loss = 3.31871
I0409 20:33:42.333644 15108 solver.cpp:237]     Train net output #0: loss = 3.31871 (* 1 = 3.31871 loss)
I0409 20:33:42.333654 15108 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 20:33:47.248454 15108 solver.cpp:218] Iteration 4632 (2.4417 iter/s, 4.9146s/12 iters), loss = 3.1612
I0409 20:33:47.248507 15108 solver.cpp:237]     Train net output #0: loss = 3.1612 (* 1 = 3.1612 loss)
I0409 20:33:47.248520 15108 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 20:33:52.153640 15108 solver.cpp:218] Iteration 4644 (2.44652 iter/s, 4.90493s/12 iters), loss = 2.99867
I0409 20:33:52.153699 15108 solver.cpp:237]     Train net output #0: loss = 2.99867 (* 1 = 2.99867 loss)
I0409 20:33:52.153712 15108 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 20:33:55.470597 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:33:57.042438 15108 solver.cpp:218] Iteration 4656 (2.45472 iter/s, 4.88854s/12 iters), loss = 3.36509
I0409 20:33:57.042491 15108 solver.cpp:237]     Train net output #0: loss = 3.36509 (* 1 = 3.36509 loss)
I0409 20:33:57.042503 15108 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 20:34:01.946350 15108 solver.cpp:218] Iteration 4668 (2.44715 iter/s, 4.90366s/12 iters), loss = 3.18987
I0409 20:34:01.946398 15108 solver.cpp:237]     Train net output #0: loss = 3.18987 (* 1 = 3.18987 loss)
I0409 20:34:01.946408 15108 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 20:34:06.856418 15108 solver.cpp:218] Iteration 4680 (2.44409 iter/s, 4.90981s/12 iters), loss = 3.29657
I0409 20:34:06.856470 15108 solver.cpp:237]     Train net output #0: loss = 3.29657 (* 1 = 3.29657 loss)
I0409 20:34:06.856482 15108 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 20:34:11.352267 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 20:34:12.812422 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 20:34:13.988296 15108 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 20:34:13.988328 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:34:16.534654 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:18.404649 15108 solver.cpp:397]     Test net output #0: accuracy = 0.199142
I0409 20:34:18.404696 15108 solver.cpp:397]     Test net output #1: loss = 3.1799 (* 1 = 3.1799 loss)
I0409 20:34:18.487764 15108 solver.cpp:218] Iteration 4692 (1.03174 iter/s, 11.6308s/12 iters), loss = 3.17303
I0409 20:34:18.487821 15108 solver.cpp:237]     Train net output #0: loss = 3.17303 (* 1 = 3.17303 loss)
I0409 20:34:18.487833 15108 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 20:34:22.947472 15108 solver.cpp:218] Iteration 4704 (2.6909 iter/s, 4.45947s/12 iters), loss = 3.23231
I0409 20:34:22.947525 15108 solver.cpp:237]     Train net output #0: loss = 3.23231 (* 1 = 3.23231 loss)
I0409 20:34:22.947538 15108 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 20:34:28.039119 15108 solver.cpp:218] Iteration 4716 (2.35693 iter/s, 5.09138s/12 iters), loss = 3.24432
I0409 20:34:28.039288 15108 solver.cpp:237]     Train net output #0: loss = 3.24432 (* 1 = 3.24432 loss)
I0409 20:34:28.039302 15108 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 20:34:32.937348 15108 solver.cpp:218] Iteration 4728 (2.45005 iter/s, 4.89786s/12 iters), loss = 3.26196
I0409 20:34:32.937402 15108 solver.cpp:237]     Train net output #0: loss = 3.26196 (* 1 = 3.26196 loss)
I0409 20:34:32.937413 15108 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 20:34:37.804812 15108 solver.cpp:218] Iteration 4740 (2.46548 iter/s, 4.86721s/12 iters), loss = 2.93056
I0409 20:34:37.804867 15108 solver.cpp:237]     Train net output #0: loss = 2.93056 (* 1 = 2.93056 loss)
I0409 20:34:37.804877 15108 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 20:34:42.701786 15108 solver.cpp:218] Iteration 4752 (2.45062 iter/s, 4.89672s/12 iters), loss = 3.04102
I0409 20:34:42.701833 15108 solver.cpp:237]     Train net output #0: loss = 3.04102 (* 1 = 3.04102 loss)
I0409 20:34:42.701844 15108 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 20:34:43.211580 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:34:47.621451 15108 solver.cpp:218] Iteration 4764 (2.43932 iter/s, 4.91941s/12 iters), loss = 2.881
I0409 20:34:47.621515 15108 solver.cpp:237]     Train net output #0: loss = 2.881 (* 1 = 2.881 loss)
I0409 20:34:47.621526 15108 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 20:34:52.499435 15108 solver.cpp:218] Iteration 4776 (2.46017 iter/s, 4.87772s/12 iters), loss = 2.94824
I0409 20:34:52.499493 15108 solver.cpp:237]     Train net output #0: loss = 2.94824 (* 1 = 2.94824 loss)
I0409 20:34:52.499505 15108 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 20:34:57.454900 15108 solver.cpp:218] Iteration 4788 (2.4217 iter/s, 4.9552s/12 iters), loss = 3.09737
I0409 20:34:57.454950 15108 solver.cpp:237]     Train net output #0: loss = 3.09737 (* 1 = 3.09737 loss)
I0409 20:34:57.454962 15108 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 20:34:59.434742 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 20:35:00.231194 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 20:35:00.806494 15108 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 20:35:00.806522 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:35:03.435665 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:05.331389 15108 solver.cpp:397]     Test net output #0: accuracy = 0.210784
I0409 20:35:05.331430 15108 solver.cpp:397]     Test net output #1: loss = 3.16607 (* 1 = 3.16607 loss)
I0409 20:35:07.135306 15108 solver.cpp:218] Iteration 4800 (1.23967 iter/s, 9.67997s/12 iters), loss = 2.69479
I0409 20:35:07.135360 15108 solver.cpp:237]     Train net output #0: loss = 2.69479 (* 1 = 2.69479 loss)
I0409 20:35:07.135372 15108 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 20:35:12.057886 15108 solver.cpp:218] Iteration 4812 (2.43788 iter/s, 4.92232s/12 iters), loss = 3.00472
I0409 20:35:12.057935 15108 solver.cpp:237]     Train net output #0: loss = 3.00472 (* 1 = 3.00472 loss)
I0409 20:35:12.057946 15108 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 20:35:16.891175 15108 solver.cpp:218] Iteration 4824 (2.48291 iter/s, 4.83304s/12 iters), loss = 3.06229
I0409 20:35:16.891216 15108 solver.cpp:237]     Train net output #0: loss = 3.06229 (* 1 = 3.06229 loss)
I0409 20:35:16.891225 15108 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 20:35:21.818562 15108 solver.cpp:218] Iteration 4836 (2.43549 iter/s, 4.92714s/12 iters), loss = 2.85769
I0409 20:35:21.818615 15108 solver.cpp:237]     Train net output #0: loss = 2.85769 (* 1 = 2.85769 loss)
I0409 20:35:21.818627 15108 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 20:35:22.603574 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:35:26.793715 15108 solver.cpp:218] Iteration 4848 (2.41211 iter/s, 4.9749s/12 iters), loss = 3.21353
I0409 20:35:26.793766 15108 solver.cpp:237]     Train net output #0: loss = 3.21353 (* 1 = 3.21353 loss)
I0409 20:35:26.793778 15108 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 20:35:29.411254 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:31.721186 15108 solver.cpp:218] Iteration 4860 (2.43545 iter/s, 4.92721s/12 iters), loss = 2.57656
I0409 20:35:31.721773 15108 solver.cpp:237]     Train net output #0: loss = 2.57656 (* 1 = 2.57656 loss)
I0409 20:35:31.721786 15108 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 20:35:36.630060 15108 solver.cpp:218] Iteration 4872 (2.44494 iter/s, 4.90809s/12 iters), loss = 2.8701
I0409 20:35:36.630105 15108 solver.cpp:237]     Train net output #0: loss = 2.8701 (* 1 = 2.8701 loss)
I0409 20:35:36.630115 15108 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 20:35:41.523310 15108 solver.cpp:218] Iteration 4884 (2.45248 iter/s, 4.893s/12 iters), loss = 2.89004
I0409 20:35:41.523370 15108 solver.cpp:237]     Train net output #0: loss = 2.89004 (* 1 = 2.89004 loss)
I0409 20:35:41.523382 15108 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 20:35:45.974845 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 20:35:46.736016 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 20:35:47.300782 15108 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 20:35:47.300802 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:35:49.747166 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:35:51.679559 15108 solver.cpp:397]     Test net output #0: accuracy = 0.208946
I0409 20:35:51.679600 15108 solver.cpp:397]     Test net output #1: loss = 3.13897 (* 1 = 3.13897 loss)
I0409 20:35:51.762799 15108 solver.cpp:218] Iteration 4896 (1.17199 iter/s, 10.239s/12 iters), loss = 2.97814
I0409 20:35:51.762861 15108 solver.cpp:237]     Train net output #0: loss = 2.97814 (* 1 = 2.97814 loss)
I0409 20:35:51.762872 15108 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 20:35:56.004151 15108 solver.cpp:218] Iteration 4908 (2.82944 iter/s, 4.24112s/12 iters), loss = 2.95737
I0409 20:35:56.004194 15108 solver.cpp:237]     Train net output #0: loss = 2.95737 (* 1 = 2.95737 loss)
I0409 20:35:56.004204 15108 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 20:36:00.912485 15108 solver.cpp:218] Iteration 4920 (2.44495 iter/s, 4.90809s/12 iters), loss = 2.82882
I0409 20:36:00.912533 15108 solver.cpp:237]     Train net output #0: loss = 2.82882 (* 1 = 2.82882 loss)
I0409 20:36:00.912542 15108 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 20:36:06.132156 15108 solver.cpp:218] Iteration 4932 (2.29911 iter/s, 5.21941s/12 iters), loss = 2.79527
I0409 20:36:06.132243 15108 solver.cpp:237]     Train net output #0: loss = 2.79527 (* 1 = 2.79527 loss)
I0409 20:36:06.132252 15108 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 20:36:11.034684 15108 solver.cpp:218] Iteration 4944 (2.44786 iter/s, 4.90224s/12 iters), loss = 2.69214
I0409 20:36:11.034737 15108 solver.cpp:237]     Train net output #0: loss = 2.69214 (* 1 = 2.69214 loss)
I0409 20:36:11.034749 15108 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 20:36:15.736424 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:15.925532 15108 solver.cpp:218] Iteration 4956 (2.45369 iter/s, 4.8906s/12 iters), loss = 3.07816
I0409 20:36:15.925568 15108 solver.cpp:237]     Train net output #0: loss = 3.07816 (* 1 = 3.07816 loss)
I0409 20:36:15.925576 15108 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 20:36:20.812875 15108 solver.cpp:218] Iteration 4968 (2.45544 iter/s, 4.88711s/12 iters), loss = 2.76597
I0409 20:36:20.812911 15108 solver.cpp:237]     Train net output #0: loss = 2.76597 (* 1 = 2.76597 loss)
I0409 20:36:20.812920 15108 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 20:36:25.772423 15108 solver.cpp:218] Iteration 4980 (2.4197 iter/s, 4.9593s/12 iters), loss = 2.70938
I0409 20:36:25.772467 15108 solver.cpp:237]     Train net output #0: loss = 2.70938 (* 1 = 2.70938 loss)
I0409 20:36:25.772475 15108 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 20:36:30.694597 15108 solver.cpp:218] Iteration 4992 (2.43807 iter/s, 4.92192s/12 iters), loss = 3.01385
I0409 20:36:30.694655 15108 solver.cpp:237]     Train net output #0: loss = 3.01385 (* 1 = 3.01385 loss)
I0409 20:36:30.694669 15108 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 20:36:32.774403 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 20:36:34.875182 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 20:36:35.545001 15108 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 20:36:35.545030 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:36:37.957401 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:36:40.037184 15108 solver.cpp:397]     Test net output #0: accuracy = 0.234681
I0409 20:36:40.037231 15108 solver.cpp:397]     Test net output #1: loss = 2.9544 (* 1 = 2.9544 loss)
I0409 20:36:41.864442 15108 solver.cpp:218] Iteration 5004 (1.07437 iter/s, 11.1693s/12 iters), loss = 2.55531
I0409 20:36:41.864495 15108 solver.cpp:237]     Train net output #0: loss = 2.55531 (* 1 = 2.55531 loss)
I0409 20:36:41.864507 15108 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 20:36:46.794651 15108 solver.cpp:218] Iteration 5016 (2.4341 iter/s, 4.92995s/12 iters), loss = 2.80953
I0409 20:36:46.794701 15108 solver.cpp:237]     Train net output #0: loss = 2.80953 (* 1 = 2.80953 loss)
I0409 20:36:46.794713 15108 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 20:36:51.744299 15108 solver.cpp:218] Iteration 5028 (2.42454 iter/s, 4.94939s/12 iters), loss = 2.98665
I0409 20:36:51.744349 15108 solver.cpp:237]     Train net output #0: loss = 2.98665 (* 1 = 2.98665 loss)
I0409 20:36:51.744360 15108 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 20:36:56.621676 15108 solver.cpp:218] Iteration 5040 (2.46047 iter/s, 4.87713s/12 iters), loss = 2.80191
I0409 20:36:56.621732 15108 solver.cpp:237]     Train net output #0: loss = 2.80191 (* 1 = 2.80191 loss)
I0409 20:36:56.621744 15108 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 20:37:01.479391 15108 solver.cpp:218] Iteration 5052 (2.47043 iter/s, 4.85746s/12 iters), loss = 2.77881
I0409 20:37:01.479451 15108 solver.cpp:237]     Train net output #0: loss = 2.77881 (* 1 = 2.77881 loss)
I0409 20:37:01.479463 15108 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 20:37:03.358999 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:06.314594 15108 solver.cpp:218] Iteration 5064 (2.48193 iter/s, 4.83494s/12 iters), loss = 2.78357
I0409 20:37:06.314651 15108 solver.cpp:237]     Train net output #0: loss = 2.78357 (* 1 = 2.78357 loss)
I0409 20:37:06.314663 15108 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 20:37:11.150862 15108 solver.cpp:218] Iteration 5076 (2.48139 iter/s, 4.83601s/12 iters), loss = 2.8471
I0409 20:37:11.150966 15108 solver.cpp:237]     Train net output #0: loss = 2.8471 (* 1 = 2.8471 loss)
I0409 20:37:11.150979 15108 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 20:37:16.048358 15108 solver.cpp:218] Iteration 5088 (2.45039 iter/s, 4.89719s/12 iters), loss = 2.59858
I0409 20:37:16.048408 15108 solver.cpp:237]     Train net output #0: loss = 2.59858 (* 1 = 2.59858 loss)
I0409 20:37:16.048419 15108 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 20:37:20.569375 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 20:37:21.395313 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 20:37:21.967367 15108 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 20:37:21.967386 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:37:24.335618 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:26.345803 15108 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0409 20:37:26.345852 15108 solver.cpp:397]     Test net output #1: loss = 2.91999 (* 1 = 2.91999 loss)
I0409 20:37:26.428557 15108 solver.cpp:218] Iteration 5100 (1.1561 iter/s, 10.3797s/12 iters), loss = 2.85866
I0409 20:37:26.428611 15108 solver.cpp:237]     Train net output #0: loss = 2.85866 (* 1 = 2.85866 loss)
I0409 20:37:26.428622 15108 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 20:37:30.623145 15108 solver.cpp:218] Iteration 5112 (2.86098 iter/s, 4.19436s/12 iters), loss = 2.62727
I0409 20:37:30.623194 15108 solver.cpp:237]     Train net output #0: loss = 2.62727 (* 1 = 2.62727 loss)
I0409 20:37:30.623206 15108 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 20:37:35.527297 15108 solver.cpp:218] Iteration 5124 (2.44703 iter/s, 4.9039s/12 iters), loss = 2.7124
I0409 20:37:35.527345 15108 solver.cpp:237]     Train net output #0: loss = 2.7124 (* 1 = 2.7124 loss)
I0409 20:37:35.527354 15108 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 20:37:40.465173 15108 solver.cpp:218] Iteration 5136 (2.43032 iter/s, 4.93762s/12 iters), loss = 2.51993
I0409 20:37:40.465219 15108 solver.cpp:237]     Train net output #0: loss = 2.51993 (* 1 = 2.51993 loss)
I0409 20:37:40.465227 15108 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 20:37:45.425323 15108 solver.cpp:218] Iteration 5148 (2.41941 iter/s, 4.95989s/12 iters), loss = 2.53315
I0409 20:37:45.425484 15108 solver.cpp:237]     Train net output #0: loss = 2.53315 (* 1 = 2.53315 loss)
I0409 20:37:45.425498 15108 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 20:37:49.375018 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:37:50.303912 15108 solver.cpp:218] Iteration 5160 (2.45991 iter/s, 4.87823s/12 iters), loss = 2.46694
I0409 20:37:50.303956 15108 solver.cpp:237]     Train net output #0: loss = 2.46694 (* 1 = 2.46694 loss)
I0409 20:37:50.303966 15108 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 20:37:55.213416 15108 solver.cpp:218] Iteration 5172 (2.44437 iter/s, 4.90925s/12 iters), loss = 2.75194
I0409 20:37:55.213471 15108 solver.cpp:237]     Train net output #0: loss = 2.75194 (* 1 = 2.75194 loss)
I0409 20:37:55.213482 15108 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 20:38:00.114450 15108 solver.cpp:218] Iteration 5184 (2.44859 iter/s, 4.90078s/12 iters), loss = 2.73995
I0409 20:38:00.114491 15108 solver.cpp:237]     Train net output #0: loss = 2.73995 (* 1 = 2.73995 loss)
I0409 20:38:00.114502 15108 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 20:38:05.135795 15108 solver.cpp:218] Iteration 5196 (2.38992 iter/s, 5.02109s/12 iters), loss = 2.79158
I0409 20:38:05.135839 15108 solver.cpp:237]     Train net output #0: loss = 2.79158 (* 1 = 2.79158 loss)
I0409 20:38:05.135848 15108 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 20:38:07.188030 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 20:38:08.151551 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 20:38:09.650063 15108 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 20:38:09.650087 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:38:12.065632 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:14.111907 15108 solver.cpp:397]     Test net output #0: accuracy = 0.270833
I0409 20:38:14.111954 15108 solver.cpp:397]     Test net output #1: loss = 2.82186 (* 1 = 2.82186 loss)
I0409 20:38:15.975608 15108 solver.cpp:218] Iteration 5208 (1.10708 iter/s, 10.8393s/12 iters), loss = 2.48815
I0409 20:38:15.975762 15108 solver.cpp:237]     Train net output #0: loss = 2.48815 (* 1 = 2.48815 loss)
I0409 20:38:15.975773 15108 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 20:38:20.919497 15108 solver.cpp:218] Iteration 5220 (2.42742 iter/s, 4.94353s/12 iters), loss = 2.80405
I0409 20:38:20.919549 15108 solver.cpp:237]     Train net output #0: loss = 2.80405 (* 1 = 2.80405 loss)
I0409 20:38:20.919564 15108 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 20:38:25.874752 15108 solver.cpp:218] Iteration 5232 (2.4218 iter/s, 4.955s/12 iters), loss = 2.64481
I0409 20:38:25.874807 15108 solver.cpp:237]     Train net output #0: loss = 2.64481 (* 1 = 2.64481 loss)
I0409 20:38:25.874819 15108 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 20:38:30.818781 15108 solver.cpp:218] Iteration 5244 (2.4273 iter/s, 4.94377s/12 iters), loss = 2.65512
I0409 20:38:30.818833 15108 solver.cpp:237]     Train net output #0: loss = 2.65512 (* 1 = 2.65512 loss)
I0409 20:38:30.818845 15108 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 20:38:35.749074 15108 solver.cpp:218] Iteration 5256 (2.43406 iter/s, 4.93004s/12 iters), loss = 2.77623
I0409 20:38:35.749114 15108 solver.cpp:237]     Train net output #0: loss = 2.77623 (* 1 = 2.77623 loss)
I0409 20:38:35.749123 15108 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 20:38:37.055166 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:38:40.707859 15108 solver.cpp:218] Iteration 5268 (2.42007 iter/s, 4.95854s/12 iters), loss = 2.4696
I0409 20:38:40.707906 15108 solver.cpp:237]     Train net output #0: loss = 2.4696 (* 1 = 2.4696 loss)
I0409 20:38:40.707916 15108 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 20:38:45.580469 15108 solver.cpp:218] Iteration 5280 (2.46287 iter/s, 4.87236s/12 iters), loss = 2.62029
I0409 20:38:45.580528 15108 solver.cpp:237]     Train net output #0: loss = 2.62029 (* 1 = 2.62029 loss)
I0409 20:38:45.580539 15108 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 20:38:50.431416 15108 solver.cpp:218] Iteration 5292 (2.47388 iter/s, 4.85068s/12 iters), loss = 2.49858
I0409 20:38:50.431540 15108 solver.cpp:237]     Train net output #0: loss = 2.49858 (* 1 = 2.49858 loss)
I0409 20:38:50.431551 15108 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 20:38:54.783700 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 20:38:55.635874 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 20:38:56.215811 15108 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 20:38:56.215838 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:38:58.464489 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:00.551381 15108 solver.cpp:397]     Test net output #0: accuracy = 0.268995
I0409 20:39:00.551419 15108 solver.cpp:397]     Test net output #1: loss = 2.82048 (* 1 = 2.82048 loss)
I0409 20:39:00.634481 15108 solver.cpp:218] Iteration 5304 (1.17618 iter/s, 10.2025s/12 iters), loss = 2.73511
I0409 20:39:00.634531 15108 solver.cpp:237]     Train net output #0: loss = 2.73511 (* 1 = 2.73511 loss)
I0409 20:39:00.634541 15108 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 20:39:04.873607 15108 solver.cpp:218] Iteration 5316 (2.83092 iter/s, 4.2389s/12 iters), loss = 2.71798
I0409 20:39:04.873647 15108 solver.cpp:237]     Train net output #0: loss = 2.71798 (* 1 = 2.71798 loss)
I0409 20:39:04.873656 15108 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 20:39:09.727730 15108 solver.cpp:218] Iteration 5328 (2.47225 iter/s, 4.85388s/12 iters), loss = 2.57775
I0409 20:39:09.727777 15108 solver.cpp:237]     Train net output #0: loss = 2.57775 (* 1 = 2.57775 loss)
I0409 20:39:09.727787 15108 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 20:39:14.678879 15108 solver.cpp:218] Iteration 5340 (2.42381 iter/s, 4.95089s/12 iters), loss = 2.74353
I0409 20:39:14.678936 15108 solver.cpp:237]     Train net output #0: loss = 2.74353 (* 1 = 2.74353 loss)
I0409 20:39:14.678948 15108 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 20:39:19.603909 15108 solver.cpp:218] Iteration 5352 (2.43666 iter/s, 4.92477s/12 iters), loss = 2.42059
I0409 20:39:19.603961 15108 solver.cpp:237]     Train net output #0: loss = 2.42059 (* 1 = 2.42059 loss)
I0409 20:39:19.603971 15108 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 20:39:22.993356 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:24.504463 15108 solver.cpp:218] Iteration 5364 (2.44883 iter/s, 4.90029s/12 iters), loss = 2.52498
I0409 20:39:24.504523 15108 solver.cpp:237]     Train net output #0: loss = 2.52498 (* 1 = 2.52498 loss)
I0409 20:39:24.504534 15108 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 20:39:29.429941 15108 solver.cpp:218] Iteration 5376 (2.43644 iter/s, 4.92521s/12 iters), loss = 2.52002
I0409 20:39:29.430013 15108 solver.cpp:237]     Train net output #0: loss = 2.52002 (* 1 = 2.52002 loss)
I0409 20:39:29.430024 15108 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 20:39:34.341187 15108 solver.cpp:218] Iteration 5388 (2.44351 iter/s, 4.91097s/12 iters), loss = 2.78628
I0409 20:39:34.341244 15108 solver.cpp:237]     Train net output #0: loss = 2.78628 (* 1 = 2.78628 loss)
I0409 20:39:34.341255 15108 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 20:39:39.220715 15108 solver.cpp:218] Iteration 5400 (2.45939 iter/s, 4.87927s/12 iters), loss = 2.7327
I0409 20:39:39.220760 15108 solver.cpp:237]     Train net output #0: loss = 2.7327 (* 1 = 2.7327 loss)
I0409 20:39:39.220769 15108 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 20:39:41.264268 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 20:39:42.648036 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 20:39:43.757789 15108 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 20:39:43.757818 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:39:46.039072 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:39:48.165753 15108 solver.cpp:397]     Test net output #0: accuracy = 0.281863
I0409 20:39:48.165807 15108 solver.cpp:397]     Test net output #1: loss = 2.67896 (* 1 = 2.67896 loss)
I0409 20:39:50.091022 15108 solver.cpp:218] Iteration 5412 (1.10397 iter/s, 10.8698s/12 iters), loss = 2.50328
I0409 20:39:50.091065 15108 solver.cpp:237]     Train net output #0: loss = 2.50328 (* 1 = 2.50328 loss)
I0409 20:39:50.091074 15108 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 20:39:54.968222 15108 solver.cpp:218] Iteration 5424 (2.46055 iter/s, 4.87695s/12 iters), loss = 2.54796
I0409 20:39:54.968317 15108 solver.cpp:237]     Train net output #0: loss = 2.54796 (* 1 = 2.54796 loss)
I0409 20:39:54.968327 15108 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 20:39:59.839202 15108 solver.cpp:218] Iteration 5436 (2.46372 iter/s, 4.87068s/12 iters), loss = 2.5669
I0409 20:39:59.839262 15108 solver.cpp:237]     Train net output #0: loss = 2.5669 (* 1 = 2.5669 loss)
I0409 20:39:59.839275 15108 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 20:40:04.883165 15108 solver.cpp:218] Iteration 5448 (2.37921 iter/s, 5.04369s/12 iters), loss = 2.56414
I0409 20:40:04.883221 15108 solver.cpp:237]     Train net output #0: loss = 2.56414 (* 1 = 2.56414 loss)
I0409 20:40:04.883232 15108 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 20:40:09.752956 15108 solver.cpp:218] Iteration 5460 (2.4643 iter/s, 4.86954s/12 iters), loss = 2.61935
I0409 20:40:09.752996 15108 solver.cpp:237]     Train net output #0: loss = 2.61935 (* 1 = 2.61935 loss)
I0409 20:40:09.753007 15108 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 20:40:10.319864 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:14.664845 15108 solver.cpp:218] Iteration 5472 (2.44318 iter/s, 4.91164s/12 iters), loss = 2.57845
I0409 20:40:14.664891 15108 solver.cpp:237]     Train net output #0: loss = 2.57845 (* 1 = 2.57845 loss)
I0409 20:40:14.664901 15108 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 20:40:19.555537 15108 solver.cpp:218] Iteration 5484 (2.45377 iter/s, 4.89044s/12 iters), loss = 2.4657
I0409 20:40:19.555583 15108 solver.cpp:237]     Train net output #0: loss = 2.4657 (* 1 = 2.4657 loss)
I0409 20:40:19.555591 15108 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 20:40:24.551040 15108 solver.cpp:218] Iteration 5496 (2.40228 iter/s, 4.99524s/12 iters), loss = 2.59136
I0409 20:40:24.551095 15108 solver.cpp:237]     Train net output #0: loss = 2.59136 (* 1 = 2.59136 loss)
I0409 20:40:24.551105 15108 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 20:40:28.937373 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 20:40:29.717269 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 20:40:30.526221 15108 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 20:40:30.526247 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:40:32.812811 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:34.982524 15108 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0409 20:40:34.982569 15108 solver.cpp:397]     Test net output #1: loss = 2.64938 (* 1 = 2.64938 loss)
I0409 20:40:35.065971 15108 solver.cpp:218] Iteration 5508 (1.14129 iter/s, 10.5144s/12 iters), loss = 2.31456
I0409 20:40:35.066030 15108 solver.cpp:237]     Train net output #0: loss = 2.31456 (* 1 = 2.31456 loss)
I0409 20:40:35.066042 15108 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 20:40:39.150362 15108 solver.cpp:218] Iteration 5520 (2.93818 iter/s, 4.08416s/12 iters), loss = 2.49949
I0409 20:40:39.150408 15108 solver.cpp:237]     Train net output #0: loss = 2.49949 (* 1 = 2.49949 loss)
I0409 20:40:39.150418 15108 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 20:40:40.316092 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:40:44.059995 15108 solver.cpp:218] Iteration 5532 (2.4443 iter/s, 4.90938s/12 iters), loss = 2.61425
I0409 20:40:44.060046 15108 solver.cpp:237]     Train net output #0: loss = 2.61425 (* 1 = 2.61425 loss)
I0409 20:40:44.060058 15108 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 20:40:48.961556 15108 solver.cpp:218] Iteration 5544 (2.44833 iter/s, 4.90131s/12 iters), loss = 2.291
I0409 20:40:48.961598 15108 solver.cpp:237]     Train net output #0: loss = 2.291 (* 1 = 2.291 loss)
I0409 20:40:48.961607 15108 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 20:40:53.948541 15108 solver.cpp:218] Iteration 5556 (2.40638 iter/s, 4.98674s/12 iters), loss = 2.53735
I0409 20:40:53.948582 15108 solver.cpp:237]     Train net output #0: loss = 2.53735 (* 1 = 2.53735 loss)
I0409 20:40:53.948591 15108 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 20:40:56.650671 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:40:59.061197 15108 solver.cpp:218] Iteration 5568 (2.34723 iter/s, 5.1124s/12 iters), loss = 2.32822
I0409 20:40:59.061293 15108 solver.cpp:237]     Train net output #0: loss = 2.32822 (* 1 = 2.32822 loss)
I0409 20:40:59.061305 15108 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 20:41:04.098901 15108 solver.cpp:218] Iteration 5580 (2.38218 iter/s, 5.0374s/12 iters), loss = 2.19883
I0409 20:41:04.098955 15108 solver.cpp:237]     Train net output #0: loss = 2.19883 (* 1 = 2.19883 loss)
I0409 20:41:04.098968 15108 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 20:41:08.960716 15108 solver.cpp:218] Iteration 5592 (2.46834 iter/s, 4.86156s/12 iters), loss = 2.3651
I0409 20:41:08.960765 15108 solver.cpp:237]     Train net output #0: loss = 2.3651 (* 1 = 2.3651 loss)
I0409 20:41:08.960775 15108 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 20:41:13.876247 15108 solver.cpp:218] Iteration 5604 (2.44137 iter/s, 4.91528s/12 iters), loss = 2.43241
I0409 20:41:13.876296 15108 solver.cpp:237]     Train net output #0: loss = 2.43241 (* 1 = 2.43241 loss)
I0409 20:41:13.876305 15108 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 20:41:15.884408 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 20:41:16.693481 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 20:41:17.274757 15108 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 20:41:17.274783 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:41:19.667191 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:21.884522 15108 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0409 20:41:21.884572 15108 solver.cpp:397]     Test net output #1: loss = 2.65066 (* 1 = 2.65066 loss)
I0409 20:41:23.675204 15108 solver.cpp:218] Iteration 5616 (1.22467 iter/s, 9.79852s/12 iters), loss = 2.31143
I0409 20:41:23.675256 15108 solver.cpp:237]     Train net output #0: loss = 2.31143 (* 1 = 2.31143 loss)
I0409 20:41:23.675268 15108 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 20:41:28.552665 15108 solver.cpp:218] Iteration 5628 (2.46043 iter/s, 4.8772s/12 iters), loss = 2.51861
I0409 20:41:28.552722 15108 solver.cpp:237]     Train net output #0: loss = 2.51861 (* 1 = 2.51861 loss)
I0409 20:41:28.552734 15108 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 20:41:33.532034 15108 solver.cpp:218] Iteration 5640 (2.41007 iter/s, 4.97911s/12 iters), loss = 2.42744
I0409 20:41:33.534605 15108 solver.cpp:237]     Train net output #0: loss = 2.42744 (* 1 = 2.42744 loss)
I0409 20:41:33.534615 15108 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 20:41:38.457989 15108 solver.cpp:218] Iteration 5652 (2.43745 iter/s, 4.92318s/12 iters), loss = 2.38537
I0409 20:41:38.458034 15108 solver.cpp:237]     Train net output #0: loss = 2.38537 (* 1 = 2.38537 loss)
I0409 20:41:38.458043 15108 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 20:41:43.176795 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:41:43.342337 15108 solver.cpp:218] Iteration 5664 (2.45695 iter/s, 4.88409s/12 iters), loss = 2.36705
I0409 20:41:43.342392 15108 solver.cpp:237]     Train net output #0: loss = 2.36705 (* 1 = 2.36705 loss)
I0409 20:41:43.342406 15108 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 20:41:48.226989 15108 solver.cpp:218] Iteration 5676 (2.45681 iter/s, 4.88439s/12 iters), loss = 2.205
I0409 20:41:48.227036 15108 solver.cpp:237]     Train net output #0: loss = 2.205 (* 1 = 2.205 loss)
I0409 20:41:48.227047 15108 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 20:41:53.165850 15108 solver.cpp:218] Iteration 5688 (2.42983 iter/s, 4.93861s/12 iters), loss = 2.05535
I0409 20:41:53.165906 15108 solver.cpp:237]     Train net output #0: loss = 2.05535 (* 1 = 2.05535 loss)
I0409 20:41:53.165921 15108 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 20:41:58.056448 15108 solver.cpp:218] Iteration 5700 (2.45382 iter/s, 4.89034s/12 iters), loss = 2.38116
I0409 20:41:58.056493 15108 solver.cpp:237]     Train net output #0: loss = 2.38116 (* 1 = 2.38116 loss)
I0409 20:41:58.056502 15108 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 20:42:02.509429 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 20:42:03.321231 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 20:42:03.909301 15108 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 20:42:03.909371 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:42:06.071964 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:08.312126 15108 solver.cpp:397]     Test net output #0: accuracy = 0.300245
I0409 20:42:08.312175 15108 solver.cpp:397]     Test net output #1: loss = 2.65322 (* 1 = 2.65322 loss)
I0409 20:42:08.395493 15108 solver.cpp:218] Iteration 5712 (1.1607 iter/s, 10.3386s/12 iters), loss = 2.24509
I0409 20:42:08.395550 15108 solver.cpp:237]     Train net output #0: loss = 2.24509 (* 1 = 2.24509 loss)
I0409 20:42:08.395563 15108 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 20:42:12.581296 15108 solver.cpp:218] Iteration 5724 (2.86699 iter/s, 4.18557s/12 iters), loss = 2.24881
I0409 20:42:12.581351 15108 solver.cpp:237]     Train net output #0: loss = 2.24881 (* 1 = 2.24881 loss)
I0409 20:42:12.581362 15108 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 20:42:17.484108 15108 solver.cpp:218] Iteration 5736 (2.44771 iter/s, 4.90255s/12 iters), loss = 2.41512
I0409 20:42:17.484161 15108 solver.cpp:237]     Train net output #0: loss = 2.41512 (* 1 = 2.41512 loss)
I0409 20:42:17.484172 15108 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 20:42:22.311677 15108 solver.cpp:218] Iteration 5748 (2.48585 iter/s, 4.82731s/12 iters), loss = 2.39926
I0409 20:42:22.311730 15108 solver.cpp:237]     Train net output #0: loss = 2.39926 (* 1 = 2.39926 loss)
I0409 20:42:22.311741 15108 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 20:42:27.183251 15108 solver.cpp:218] Iteration 5760 (2.4634 iter/s, 4.87132s/12 iters), loss = 2.2831
I0409 20:42:27.183301 15108 solver.cpp:237]     Train net output #0: loss = 2.2831 (* 1 = 2.2831 loss)
I0409 20:42:27.183310 15108 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 20:42:29.142364 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:32.159579 15108 solver.cpp:218] Iteration 5772 (2.41155 iter/s, 4.97606s/12 iters), loss = 2.18593
I0409 20:42:32.159636 15108 solver.cpp:237]     Train net output #0: loss = 2.18593 (* 1 = 2.18593 loss)
I0409 20:42:32.159651 15108 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 20:42:37.073843 15108 solver.cpp:218] Iteration 5784 (2.442 iter/s, 4.914s/12 iters), loss = 2.36687
I0409 20:42:37.074012 15108 solver.cpp:237]     Train net output #0: loss = 2.36687 (* 1 = 2.36687 loss)
I0409 20:42:37.074028 15108 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 20:42:41.957291 15108 solver.cpp:218] Iteration 5796 (2.45746 iter/s, 4.88309s/12 iters), loss = 2.21491
I0409 20:42:41.957336 15108 solver.cpp:237]     Train net output #0: loss = 2.21491 (* 1 = 2.21491 loss)
I0409 20:42:41.957347 15108 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 20:42:46.849423 15108 solver.cpp:218] Iteration 5808 (2.45304 iter/s, 4.89188s/12 iters), loss = 2.31137
I0409 20:42:46.849475 15108 solver.cpp:237]     Train net output #0: loss = 2.31137 (* 1 = 2.31137 loss)
I0409 20:42:46.849493 15108 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 20:42:49.078573 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 20:42:50.557862 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 20:42:51.239430 15108 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 20:42:51.239459 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:42:53.648607 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:42:56.080583 15108 solver.cpp:397]     Test net output #0: accuracy = 0.311887
I0409 20:42:56.080615 15108 solver.cpp:397]     Test net output #1: loss = 2.62199 (* 1 = 2.62199 loss)
I0409 20:42:57.828488 15108 solver.cpp:218] Iteration 5820 (1.09304 iter/s, 10.9786s/12 iters), loss = 2.00492
I0409 20:42:57.828532 15108 solver.cpp:237]     Train net output #0: loss = 2.00492 (* 1 = 2.00492 loss)
I0409 20:42:57.828541 15108 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 20:43:02.748775 15108 solver.cpp:218] Iteration 5832 (2.43901 iter/s, 4.92003s/12 iters), loss = 2.25311
I0409 20:43:02.748831 15108 solver.cpp:237]     Train net output #0: loss = 2.25311 (* 1 = 2.25311 loss)
I0409 20:43:02.748844 15108 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 20:43:07.658416 15108 solver.cpp:218] Iteration 5844 (2.4443 iter/s, 4.90939s/12 iters), loss = 2.1318
I0409 20:43:07.658485 15108 solver.cpp:237]     Train net output #0: loss = 2.1318 (* 1 = 2.1318 loss)
I0409 20:43:07.658493 15108 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 20:43:12.508092 15108 solver.cpp:218] Iteration 5856 (2.47453 iter/s, 4.84941s/12 iters), loss = 2.29994
I0409 20:43:12.508144 15108 solver.cpp:237]     Train net output #0: loss = 2.29994 (* 1 = 2.29994 loss)
I0409 20:43:12.508155 15108 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 20:43:16.571823 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:17.369940 15108 solver.cpp:218] Iteration 5868 (2.46833 iter/s, 4.86159s/12 iters), loss = 1.95068
I0409 20:43:17.370012 15108 solver.cpp:237]     Train net output #0: loss = 1.95068 (* 1 = 1.95068 loss)
I0409 20:43:17.370025 15108 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 20:43:22.271296 15108 solver.cpp:218] Iteration 5880 (2.44844 iter/s, 4.90108s/12 iters), loss = 2.40554
I0409 20:43:22.271350 15108 solver.cpp:237]     Train net output #0: loss = 2.40554 (* 1 = 2.40554 loss)
I0409 20:43:22.271364 15108 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 20:43:27.184000 15108 solver.cpp:218] Iteration 5892 (2.44278 iter/s, 4.91244s/12 iters), loss = 2.56325
I0409 20:43:27.184047 15108 solver.cpp:237]     Train net output #0: loss = 2.56325 (* 1 = 2.56325 loss)
I0409 20:43:27.184056 15108 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 20:43:32.088456 15108 solver.cpp:218] Iteration 5904 (2.44688 iter/s, 4.9042s/12 iters), loss = 2.01142
I0409 20:43:32.088502 15108 solver.cpp:237]     Train net output #0: loss = 2.01142 (* 1 = 2.01142 loss)
I0409 20:43:32.088515 15108 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 20:43:36.597321 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 20:43:38.156060 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 20:43:39.014395 15108 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 20:43:39.014425 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:43:41.136123 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:43:43.458678 15108 solver.cpp:397]     Test net output #0: accuracy = 0.333946
I0409 20:43:43.458721 15108 solver.cpp:397]     Test net output #1: loss = 2.50817 (* 1 = 2.50817 loss)
I0409 20:43:43.541558 15108 solver.cpp:218] Iteration 5916 (1.0478 iter/s, 11.4526s/12 iters), loss = 2.09643
I0409 20:43:43.541606 15108 solver.cpp:237]     Train net output #0: loss = 2.09643 (* 1 = 2.09643 loss)
I0409 20:43:43.541617 15108 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 20:43:47.641618 15108 solver.cpp:218] Iteration 5928 (2.92695 iter/s, 4.09984s/12 iters), loss = 2.19551
I0409 20:43:47.641664 15108 solver.cpp:237]     Train net output #0: loss = 2.19551 (* 1 = 2.19551 loss)
I0409 20:43:47.641672 15108 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 20:43:52.480010 15108 solver.cpp:218] Iteration 5940 (2.48029 iter/s, 4.83814s/12 iters), loss = 1.87732
I0409 20:43:52.480059 15108 solver.cpp:237]     Train net output #0: loss = 1.87732 (* 1 = 1.87732 loss)
I0409 20:43:52.480073 15108 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 20:43:57.489991 15108 solver.cpp:218] Iteration 5952 (2.39534 iter/s, 5.00973s/12 iters), loss = 2.01405
I0409 20:43:57.490036 15108 solver.cpp:237]     Train net output #0: loss = 2.01405 (* 1 = 2.01405 loss)
I0409 20:43:57.490046 15108 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 20:44:02.438227 15108 solver.cpp:218] Iteration 5964 (2.42523 iter/s, 4.94799s/12 iters), loss = 2.17332
I0409 20:44:02.438277 15108 solver.cpp:237]     Train net output #0: loss = 2.17332 (* 1 = 2.17332 loss)
I0409 20:44:02.438287 15108 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 20:44:03.743141 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:07.349526 15108 solver.cpp:218] Iteration 5976 (2.44347 iter/s, 4.91105s/12 iters), loss = 1.83459
I0409 20:44:07.349572 15108 solver.cpp:237]     Train net output #0: loss = 1.83459 (* 1 = 1.83459 loss)
I0409 20:44:07.349582 15108 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 20:44:12.295859 15108 solver.cpp:218] Iteration 5988 (2.42616 iter/s, 4.94608s/12 iters), loss = 2.23703
I0409 20:44:12.296033 15108 solver.cpp:237]     Train net output #0: loss = 2.23703 (* 1 = 2.23703 loss)
I0409 20:44:12.296048 15108 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 20:44:17.283356 15108 solver.cpp:218] Iteration 6000 (2.4062 iter/s, 4.98712s/12 iters), loss = 2.11396
I0409 20:44:17.283412 15108 solver.cpp:237]     Train net output #0: loss = 2.11396 (* 1 = 2.11396 loss)
I0409 20:44:17.283424 15108 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 20:44:22.288094 15108 solver.cpp:218] Iteration 6012 (2.39785 iter/s, 5.00448s/12 iters), loss = 1.9815
I0409 20:44:22.288146 15108 solver.cpp:237]     Train net output #0: loss = 1.9815 (* 1 = 1.9815 loss)
I0409 20:44:22.288157 15108 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 20:44:24.302768 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 20:44:25.694373 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 20:44:26.910722 15108 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 20:44:26.910753 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:44:29.066597 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:31.485139 15108 solver.cpp:397]     Test net output #0: accuracy = 0.341912
I0409 20:44:31.485185 15108 solver.cpp:397]     Test net output #1: loss = 2.52224 (* 1 = 2.52224 loss)
I0409 20:44:33.259713 15108 solver.cpp:218] Iteration 6024 (1.09378 iter/s, 10.9711s/12 iters), loss = 2.1833
I0409 20:44:33.259778 15108 solver.cpp:237]     Train net output #0: loss = 2.1833 (* 1 = 2.1833 loss)
I0409 20:44:33.259789 15108 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 20:44:38.058123 15108 solver.cpp:218] Iteration 6036 (2.50097 iter/s, 4.79815s/12 iters), loss = 2.04907
I0409 20:44:38.058178 15108 solver.cpp:237]     Train net output #0: loss = 2.04907 (* 1 = 2.04907 loss)
I0409 20:44:38.058189 15108 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 20:44:42.923216 15108 solver.cpp:218] Iteration 6048 (2.46668 iter/s, 4.86484s/12 iters), loss = 2.23543
I0409 20:44:42.923296 15108 solver.cpp:237]     Train net output #0: loss = 2.23543 (* 1 = 2.23543 loss)
I0409 20:44:42.923308 15108 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 20:44:47.773412 15108 solver.cpp:218] Iteration 6060 (2.47427 iter/s, 4.84991s/12 iters), loss = 2.14488
I0409 20:44:47.773471 15108 solver.cpp:237]     Train net output #0: loss = 2.14488 (* 1 = 2.14488 loss)
I0409 20:44:47.773484 15108 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 20:44:51.115634 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:44:52.602227 15108 solver.cpp:218] Iteration 6072 (2.48522 iter/s, 4.82855s/12 iters), loss = 2.26814
I0409 20:44:52.602288 15108 solver.cpp:237]     Train net output #0: loss = 2.26814 (* 1 = 2.26814 loss)
I0409 20:44:52.602299 15108 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 20:44:57.473822 15108 solver.cpp:218] Iteration 6084 (2.46339 iter/s, 4.87133s/12 iters), loss = 2.29611
I0409 20:44:57.473882 15108 solver.cpp:237]     Train net output #0: loss = 2.29611 (* 1 = 2.29611 loss)
I0409 20:44:57.473896 15108 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 20:45:02.325491 15108 solver.cpp:218] Iteration 6096 (2.47351 iter/s, 4.8514s/12 iters), loss = 2.16177
I0409 20:45:02.325547 15108 solver.cpp:237]     Train net output #0: loss = 2.16177 (* 1 = 2.16177 loss)
I0409 20:45:02.325559 15108 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 20:45:07.238443 15108 solver.cpp:218] Iteration 6108 (2.44265 iter/s, 4.91269s/12 iters), loss = 2.36952
I0409 20:45:07.238489 15108 solver.cpp:237]     Train net output #0: loss = 2.36952 (* 1 = 2.36952 loss)
I0409 20:45:07.238497 15108 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 20:45:11.833863 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 20:45:12.626814 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 20:45:13.904739 15108 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 20:45:13.904863 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:45:15.955940 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:18.359658 15108 solver.cpp:397]     Test net output #0: accuracy = 0.34375
I0409 20:45:18.359699 15108 solver.cpp:397]     Test net output #1: loss = 2.53516 (* 1 = 2.53516 loss)
I0409 20:45:18.442999 15108 solver.cpp:218] Iteration 6120 (1.07104 iter/s, 11.2041s/12 iters), loss = 2.05837
I0409 20:45:18.443046 15108 solver.cpp:237]     Train net output #0: loss = 2.05837 (* 1 = 2.05837 loss)
I0409 20:45:18.443056 15108 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 20:45:22.619277 15108 solver.cpp:218] Iteration 6132 (2.87353 iter/s, 4.17605s/12 iters), loss = 2.02751
I0409 20:45:22.619319 15108 solver.cpp:237]     Train net output #0: loss = 2.02751 (* 1 = 2.02751 loss)
I0409 20:45:22.619328 15108 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 20:45:27.837926 15108 solver.cpp:218] Iteration 6144 (2.29956 iter/s, 5.21839s/12 iters), loss = 2.40686
I0409 20:45:27.838011 15108 solver.cpp:237]     Train net output #0: loss = 2.40686 (* 1 = 2.40686 loss)
I0409 20:45:27.838021 15108 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 20:45:32.718664 15108 solver.cpp:218] Iteration 6156 (2.45879 iter/s, 4.88045s/12 iters), loss = 1.91811
I0409 20:45:32.718717 15108 solver.cpp:237]     Train net output #0: loss = 1.91811 (* 1 = 1.91811 loss)
I0409 20:45:32.718729 15108 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 20:45:37.661852 15108 solver.cpp:218] Iteration 6168 (2.42771 iter/s, 4.94292s/12 iters), loss = 2.0557
I0409 20:45:37.661908 15108 solver.cpp:237]     Train net output #0: loss = 2.0557 (* 1 = 2.0557 loss)
I0409 20:45:37.661919 15108 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 20:45:38.257602 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:45:42.558698 15108 solver.cpp:218] Iteration 6180 (2.45069 iter/s, 4.89658s/12 iters), loss = 2.12297
I0409 20:45:42.558759 15108 solver.cpp:237]     Train net output #0: loss = 2.12297 (* 1 = 2.12297 loss)
I0409 20:45:42.558771 15108 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 20:45:47.477109 15108 solver.cpp:218] Iteration 6192 (2.43995 iter/s, 4.91814s/12 iters), loss = 2.04666
I0409 20:45:47.477226 15108 solver.cpp:237]     Train net output #0: loss = 2.04666 (* 1 = 2.04666 loss)
I0409 20:45:47.477237 15108 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 20:45:52.399489 15108 solver.cpp:218] Iteration 6204 (2.43801 iter/s, 4.92206s/12 iters), loss = 1.87032
I0409 20:45:52.399544 15108 solver.cpp:237]     Train net output #0: loss = 1.87032 (* 1 = 1.87032 loss)
I0409 20:45:52.399555 15108 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 20:45:57.305012 15108 solver.cpp:218] Iteration 6216 (2.44635 iter/s, 4.90527s/12 iters), loss = 1.90765
I0409 20:45:57.305058 15108 solver.cpp:237]     Train net output #0: loss = 1.90765 (* 1 = 1.90765 loss)
I0409 20:45:57.305065 15108 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 20:45:59.317023 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 20:46:00.126282 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 20:46:00.698879 15108 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 20:46:00.698904 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:46:02.582589 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:03.582316 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:46:05.059012 15108 solver.cpp:397]     Test net output #0: accuracy = 0.363358
I0409 20:46:05.059057 15108 solver.cpp:397]     Test net output #1: loss = 2.43092 (* 1 = 2.43092 loss)
I0409 20:46:06.909019 15108 solver.cpp:218] Iteration 6228 (1.24954 iter/s, 9.60357s/12 iters), loss = 1.82335
I0409 20:46:06.909080 15108 solver.cpp:237]     Train net output #0: loss = 1.82335 (* 1 = 1.82335 loss)
I0409 20:46:06.909092 15108 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 20:46:11.825197 15108 solver.cpp:218] Iteration 6240 (2.44105 iter/s, 4.91592s/12 iters), loss = 2.13842
I0409 20:46:11.825237 15108 solver.cpp:237]     Train net output #0: loss = 2.13842 (* 1 = 2.13842 loss)
I0409 20:46:11.825245 15108 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 20:46:16.694037 15108 solver.cpp:218] Iteration 6252 (2.46478 iter/s, 4.8686s/12 iters), loss = 1.77076
I0409 20:46:16.694088 15108 solver.cpp:237]     Train net output #0: loss = 1.77076 (* 1 = 1.77076 loss)
I0409 20:46:16.694101 15108 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 20:46:21.669104 15108 solver.cpp:218] Iteration 6264 (2.41215 iter/s, 4.97481s/12 iters), loss = 1.84505
I0409 20:46:21.669229 15108 solver.cpp:237]     Train net output #0: loss = 1.84505 (* 1 = 1.84505 loss)
I0409 20:46:21.669240 15108 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 20:46:24.400946 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:26.613999 15108 solver.cpp:218] Iteration 6276 (2.4269 iter/s, 4.94457s/12 iters), loss = 1.81764
I0409 20:46:26.614042 15108 solver.cpp:237]     Train net output #0: loss = 1.81764 (* 1 = 1.81764 loss)
I0409 20:46:26.614051 15108 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 20:46:31.524055 15108 solver.cpp:218] Iteration 6288 (2.44406 iter/s, 4.90986s/12 iters), loss = 2.24599
I0409 20:46:31.524102 15108 solver.cpp:237]     Train net output #0: loss = 2.24599 (* 1 = 2.24599 loss)
I0409 20:46:31.524111 15108 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 20:46:36.531762 15108 solver.cpp:218] Iteration 6300 (2.39638 iter/s, 5.00755s/12 iters), loss = 1.92399
I0409 20:46:36.531821 15108 solver.cpp:237]     Train net output #0: loss = 1.92399 (* 1 = 1.92399 loss)
I0409 20:46:36.531834 15108 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 20:46:41.360533 15108 solver.cpp:218] Iteration 6312 (2.48519 iter/s, 4.82861s/12 iters), loss = 1.72384
I0409 20:46:41.360579 15108 solver.cpp:237]     Train net output #0: loss = 1.72384 (* 1 = 1.72384 loss)
I0409 20:46:41.360589 15108 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 20:46:45.826242 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 20:46:46.911056 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 20:46:49.153241 15108 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 20:46:49.153271 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:46:51.319162 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:46:53.796108 15108 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0409 20:46:53.796234 15108 solver.cpp:397]     Test net output #1: loss = 2.46461 (* 1 = 2.46461 loss)
I0409 20:46:53.879452 15108 solver.cpp:218] Iteration 6324 (0.958572 iter/s, 12.5186s/12 iters), loss = 1.78422
I0409 20:46:53.879509 15108 solver.cpp:237]     Train net output #0: loss = 1.78422 (* 1 = 1.78422 loss)
I0409 20:46:53.879520 15108 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 20:46:57.962662 15108 solver.cpp:218] Iteration 6336 (2.93897 iter/s, 4.08307s/12 iters), loss = 1.93651
I0409 20:46:57.962704 15108 solver.cpp:237]     Train net output #0: loss = 1.93651 (* 1 = 1.93651 loss)
I0409 20:46:57.962713 15108 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 20:47:02.847324 15108 solver.cpp:218] Iteration 6348 (2.45675 iter/s, 4.88451s/12 iters), loss = 1.81684
I0409 20:47:02.847379 15108 solver.cpp:237]     Train net output #0: loss = 1.81684 (* 1 = 1.81684 loss)
I0409 20:47:02.847391 15108 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 20:47:07.791925 15108 solver.cpp:218] Iteration 6360 (2.42697 iter/s, 4.94444s/12 iters), loss = 1.86527
I0409 20:47:07.791976 15108 solver.cpp:237]     Train net output #0: loss = 1.86527 (* 1 = 1.86527 loss)
I0409 20:47:07.791990 15108 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 20:47:12.546483 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:12.682835 15108 solver.cpp:218] Iteration 6372 (2.45361 iter/s, 4.89075s/12 iters), loss = 2.04696
I0409 20:47:12.682886 15108 solver.cpp:237]     Train net output #0: loss = 2.04696 (* 1 = 2.04696 loss)
I0409 20:47:12.682898 15108 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 20:47:17.591614 15108 solver.cpp:218] Iteration 6384 (2.44468 iter/s, 4.90862s/12 iters), loss = 1.72657
I0409 20:47:17.591668 15108 solver.cpp:237]     Train net output #0: loss = 1.72657 (* 1 = 1.72657 loss)
I0409 20:47:17.591681 15108 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 20:47:22.490962 15108 solver.cpp:218] Iteration 6396 (2.44939 iter/s, 4.89918s/12 iters), loss = 1.62818
I0409 20:47:22.491015 15108 solver.cpp:237]     Train net output #0: loss = 1.62818 (* 1 = 1.62818 loss)
I0409 20:47:22.491030 15108 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 20:47:27.427450 15108 solver.cpp:218] Iteration 6408 (2.43096 iter/s, 4.93632s/12 iters), loss = 2.02856
I0409 20:47:27.427569 15108 solver.cpp:237]     Train net output #0: loss = 2.02856 (* 1 = 2.02856 loss)
I0409 20:47:27.427578 15108 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 20:47:32.472748 15108 solver.cpp:218] Iteration 6420 (2.37856 iter/s, 5.04506s/12 iters), loss = 1.95668
I0409 20:47:32.472803 15108 solver.cpp:237]     Train net output #0: loss = 1.95668 (* 1 = 1.95668 loss)
I0409 20:47:32.472816 15108 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 20:47:34.517189 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 20:47:35.533414 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 20:47:36.971650 15108 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 20:47:36.971683 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:47:38.769114 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:47:41.317464 15108 solver.cpp:397]     Test net output #0: accuracy = 0.384804
I0409 20:47:41.317509 15108 solver.cpp:397]     Test net output #1: loss = 2.39681 (* 1 = 2.39681 loss)
I0409 20:47:43.127954 15108 solver.cpp:218] Iteration 6432 (1.12624 iter/s, 10.6549s/12 iters), loss = 1.62924
I0409 20:47:43.128021 15108 solver.cpp:237]     Train net output #0: loss = 1.62924 (* 1 = 1.62924 loss)
I0409 20:47:43.128033 15108 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 20:47:48.084007 15108 solver.cpp:218] Iteration 6444 (2.42137 iter/s, 4.95587s/12 iters), loss = 2.04881
I0409 20:47:48.084050 15108 solver.cpp:237]     Train net output #0: loss = 2.04881 (* 1 = 2.04881 loss)
I0409 20:47:48.084061 15108 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 20:47:52.973706 15108 solver.cpp:218] Iteration 6456 (2.45422 iter/s, 4.88955s/12 iters), loss = 1.93394
I0409 20:47:52.973745 15108 solver.cpp:237]     Train net output #0: loss = 1.93394 (* 1 = 1.93394 loss)
I0409 20:47:52.973755 15108 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 20:47:57.856060 15108 solver.cpp:218] Iteration 6468 (2.45791 iter/s, 4.8822s/12 iters), loss = 1.55828
I0409 20:47:57.856138 15108 solver.cpp:237]     Train net output #0: loss = 1.55828 (* 1 = 1.55828 loss)
I0409 20:47:57.856149 15108 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 20:47:59.797818 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:02.784627 15108 solver.cpp:218] Iteration 6480 (2.43488 iter/s, 4.92837s/12 iters), loss = 1.85926
I0409 20:48:02.784683 15108 solver.cpp:237]     Train net output #0: loss = 1.85926 (* 1 = 1.85926 loss)
I0409 20:48:02.784698 15108 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 20:48:07.813145 15108 solver.cpp:218] Iteration 6492 (2.38647 iter/s, 5.02834s/12 iters), loss = 2.01067
I0409 20:48:07.813201 15108 solver.cpp:237]     Train net output #0: loss = 2.01067 (* 1 = 2.01067 loss)
I0409 20:48:07.813215 15108 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 20:48:12.676949 15108 solver.cpp:218] Iteration 6504 (2.46729 iter/s, 4.86363s/12 iters), loss = 1.92497
I0409 20:48:12.676992 15108 solver.cpp:237]     Train net output #0: loss = 1.92497 (* 1 = 1.92497 loss)
I0409 20:48:12.677002 15108 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 20:48:17.541060 15108 solver.cpp:218] Iteration 6516 (2.46713 iter/s, 4.86395s/12 iters), loss = 1.89177
I0409 20:48:17.541110 15108 solver.cpp:237]     Train net output #0: loss = 1.89177 (* 1 = 1.89177 loss)
I0409 20:48:17.541122 15108 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 20:48:21.992792 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 20:48:22.766552 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 20:48:23.340328 15108 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 20:48:23.340358 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:48:25.235432 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:27.794253 15108 solver.cpp:397]     Test net output #0: accuracy = 0.360907
I0409 20:48:27.794289 15108 solver.cpp:397]     Test net output #1: loss = 2.41334 (* 1 = 2.41334 loss)
I0409 20:48:27.877151 15108 solver.cpp:218] Iteration 6528 (1.16101 iter/s, 10.3358s/12 iters), loss = 1.51201
I0409 20:48:27.877249 15108 solver.cpp:237]     Train net output #0: loss = 1.51201 (* 1 = 1.51201 loss)
I0409 20:48:27.877260 15108 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 20:48:32.077180 15108 solver.cpp:218] Iteration 6540 (2.85726 iter/s, 4.19982s/12 iters), loss = 1.942
I0409 20:48:32.077227 15108 solver.cpp:237]     Train net output #0: loss = 1.942 (* 1 = 1.942 loss)
I0409 20:48:32.077237 15108 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 20:48:36.966522 15108 solver.cpp:218] Iteration 6552 (2.45441 iter/s, 4.88917s/12 iters), loss = 1.94451
I0409 20:48:36.966580 15108 solver.cpp:237]     Train net output #0: loss = 1.94451 (* 1 = 1.94451 loss)
I0409 20:48:36.966591 15108 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 20:48:41.819739 15108 solver.cpp:218] Iteration 6564 (2.47268 iter/s, 4.85303s/12 iters), loss = 1.758
I0409 20:48:41.819797 15108 solver.cpp:237]     Train net output #0: loss = 1.758 (* 1 = 1.758 loss)
I0409 20:48:41.819808 15108 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 20:48:45.931639 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:48:46.693998 15108 solver.cpp:218] Iteration 6576 (2.46201 iter/s, 4.87407s/12 iters), loss = 1.85175
I0409 20:48:46.694057 15108 solver.cpp:237]     Train net output #0: loss = 1.85175 (* 1 = 1.85175 loss)
I0409 20:48:46.694068 15108 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 20:48:51.635593 15108 solver.cpp:218] Iteration 6588 (2.42846 iter/s, 4.94141s/12 iters), loss = 1.78282
I0409 20:48:51.635641 15108 solver.cpp:237]     Train net output #0: loss = 1.78282 (* 1 = 1.78282 loss)
I0409 20:48:51.635650 15108 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 20:48:56.512240 15108 solver.cpp:218] Iteration 6600 (2.4608 iter/s, 4.87647s/12 iters), loss = 1.88368
I0409 20:48:56.512293 15108 solver.cpp:237]     Train net output #0: loss = 1.88368 (* 1 = 1.88368 loss)
I0409 20:48:56.512305 15108 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 20:49:01.441531 15108 solver.cpp:218] Iteration 6612 (2.43451 iter/s, 4.92912s/12 iters), loss = 1.60227
I0409 20:49:01.441649 15108 solver.cpp:237]     Train net output #0: loss = 1.60227 (* 1 = 1.60227 loss)
I0409 20:49:01.441663 15108 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 20:49:06.352427 15108 solver.cpp:218] Iteration 6624 (2.44366 iter/s, 4.91066s/12 iters), loss = 1.83532
I0409 20:49:06.352475 15108 solver.cpp:237]     Train net output #0: loss = 1.83532 (* 1 = 1.83532 loss)
I0409 20:49:06.352484 15108 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 20:49:08.375808 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 20:49:09.182363 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 20:49:09.756906 15108 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 20:49:09.756937 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:49:12.008421 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:14.608966 15108 solver.cpp:397]     Test net output #0: accuracy = 0.365196
I0409 20:49:14.609002 15108 solver.cpp:397]     Test net output #1: loss = 2.42273 (* 1 = 2.42273 loss)
I0409 20:49:16.374269 15108 solver.cpp:218] Iteration 6636 (1.19742 iter/s, 10.0216s/12 iters), loss = 1.919
I0409 20:49:16.374320 15108 solver.cpp:237]     Train net output #0: loss = 1.919 (* 1 = 1.919 loss)
I0409 20:49:16.374332 15108 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 20:49:21.509210 15108 solver.cpp:218] Iteration 6648 (2.33702 iter/s, 5.13475s/12 iters), loss = 1.72473
I0409 20:49:21.509266 15108 solver.cpp:237]     Train net output #0: loss = 1.72473 (* 1 = 1.72473 loss)
I0409 20:49:21.509280 15108 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 20:49:26.421501 15108 solver.cpp:218] Iteration 6660 (2.44294 iter/s, 4.9121s/12 iters), loss = 1.67953
I0409 20:49:26.421559 15108 solver.cpp:237]     Train net output #0: loss = 1.67953 (* 1 = 1.67953 loss)
I0409 20:49:26.421571 15108 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 20:49:31.334595 15108 solver.cpp:218] Iteration 6672 (2.44254 iter/s, 4.91291s/12 iters), loss = 1.62034
I0409 20:49:31.334645 15108 solver.cpp:237]     Train net output #0: loss = 1.62034 (* 1 = 1.62034 loss)
I0409 20:49:31.334654 15108 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 20:49:32.664275 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:49:36.256857 15108 solver.cpp:218] Iteration 6684 (2.43799 iter/s, 4.92208s/12 iters), loss = 1.38838
I0409 20:49:36.256904 15108 solver.cpp:237]     Train net output #0: loss = 1.38838 (* 1 = 1.38838 loss)
I0409 20:49:36.256914 15108 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 20:49:41.160432 15108 solver.cpp:218] Iteration 6696 (2.44728 iter/s, 4.9034s/12 iters), loss = 1.82885
I0409 20:49:41.160492 15108 solver.cpp:237]     Train net output #0: loss = 1.82885 (* 1 = 1.82885 loss)
I0409 20:49:41.160506 15108 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 20:49:46.086357 15108 solver.cpp:218] Iteration 6708 (2.43618 iter/s, 4.92574s/12 iters), loss = 1.71874
I0409 20:49:46.086407 15108 solver.cpp:237]     Train net output #0: loss = 1.71874 (* 1 = 1.71874 loss)
I0409 20:49:46.086419 15108 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 20:49:50.983530 15108 solver.cpp:218] Iteration 6720 (2.45048 iter/s, 4.897s/12 iters), loss = 1.74443
I0409 20:49:50.983570 15108 solver.cpp:237]     Train net output #0: loss = 1.74443 (* 1 = 1.74443 loss)
I0409 20:49:50.983579 15108 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 20:49:55.418195 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 20:49:56.546196 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 20:49:57.120379 15108 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 20:49:57.120409 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:49:59.349172 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:02.046813 15108 solver.cpp:397]     Test net output #0: accuracy = 0.387868
I0409 20:50:02.046854 15108 solver.cpp:397]     Test net output #1: loss = 2.40166 (* 1 = 2.40166 loss)
I0409 20:50:02.130082 15108 solver.cpp:218] Iteration 6732 (1.0766 iter/s, 11.1462s/12 iters), loss = 1.59936
I0409 20:50:02.130132 15108 solver.cpp:237]     Train net output #0: loss = 1.59936 (* 1 = 1.59936 loss)
I0409 20:50:02.130143 15108 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 20:50:06.323863 15108 solver.cpp:218] Iteration 6744 (2.86149 iter/s, 4.19362s/12 iters), loss = 1.64704
I0409 20:50:06.324004 15108 solver.cpp:237]     Train net output #0: loss = 1.64704 (* 1 = 1.64704 loss)
I0409 20:50:06.324016 15108 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 20:50:11.283828 15108 solver.cpp:218] Iteration 6756 (2.41951 iter/s, 4.95969s/12 iters), loss = 1.88266
I0409 20:50:11.283883 15108 solver.cpp:237]     Train net output #0: loss = 1.88266 (* 1 = 1.88266 loss)
I0409 20:50:11.283896 15108 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 20:50:16.354056 15108 solver.cpp:218] Iteration 6768 (2.36685 iter/s, 5.07004s/12 iters), loss = 1.73642
I0409 20:50:16.354112 15108 solver.cpp:237]     Train net output #0: loss = 1.73642 (* 1 = 1.73642 loss)
I0409 20:50:16.354125 15108 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 20:50:19.855101 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:21.325645 15108 solver.cpp:218] Iteration 6780 (2.41381 iter/s, 4.9714s/12 iters), loss = 1.85238
I0409 20:50:21.325690 15108 solver.cpp:237]     Train net output #0: loss = 1.85238 (* 1 = 1.85238 loss)
I0409 20:50:21.325701 15108 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 20:50:26.230185 15108 solver.cpp:218] Iteration 6792 (2.4468 iter/s, 4.90436s/12 iters), loss = 1.73255
I0409 20:50:26.230239 15108 solver.cpp:237]     Train net output #0: loss = 1.73255 (* 1 = 1.73255 loss)
I0409 20:50:26.230250 15108 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 20:50:31.220903 15108 solver.cpp:218] Iteration 6804 (2.40456 iter/s, 4.99053s/12 iters), loss = 1.71399
I0409 20:50:31.220945 15108 solver.cpp:237]     Train net output #0: loss = 1.71399 (* 1 = 1.71399 loss)
I0409 20:50:31.220955 15108 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 20:50:36.052973 15108 solver.cpp:218] Iteration 6816 (2.4835 iter/s, 4.83189s/12 iters), loss = 1.69499
I0409 20:50:36.053016 15108 solver.cpp:237]     Train net output #0: loss = 1.69499 (* 1 = 1.69499 loss)
I0409 20:50:36.053025 15108 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 20:50:41.224300 15108 solver.cpp:218] Iteration 6828 (2.32057 iter/s, 5.17114s/12 iters), loss = 1.39189
I0409 20:50:41.224424 15108 solver.cpp:237]     Train net output #0: loss = 1.39189 (* 1 = 1.39189 loss)
I0409 20:50:41.224439 15108 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 20:50:43.216074 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 20:50:44.666838 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 20:50:46.722445 15108 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 20:50:46.722472 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:50:48.588436 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:50:51.255864 15108 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0409 20:50:51.255897 15108 solver.cpp:397]     Test net output #1: loss = 2.399 (* 1 = 2.399 loss)
I0409 20:50:53.134191 15108 solver.cpp:218] Iteration 6840 (1.0076 iter/s, 11.9095s/12 iters), loss = 1.65633
I0409 20:50:53.134255 15108 solver.cpp:237]     Train net output #0: loss = 1.65633 (* 1 = 1.65633 loss)
I0409 20:50:53.134268 15108 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 20:50:57.971099 15108 solver.cpp:218] Iteration 6852 (2.48102 iter/s, 4.83671s/12 iters), loss = 1.75573
I0409 20:50:57.971145 15108 solver.cpp:237]     Train net output #0: loss = 1.75573 (* 1 = 1.75573 loss)
I0409 20:50:57.971156 15108 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 20:51:02.860461 15108 solver.cpp:218] Iteration 6864 (2.4544 iter/s, 4.88918s/12 iters), loss = 1.47983
I0409 20:51:02.860513 15108 solver.cpp:237]     Train net output #0: loss = 1.47983 (* 1 = 1.47983 loss)
I0409 20:51:02.860523 15108 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 20:51:07.769840 15108 solver.cpp:218] Iteration 6876 (2.4444 iter/s, 4.90919s/12 iters), loss = 1.57165
I0409 20:51:07.769889 15108 solver.cpp:237]     Train net output #0: loss = 1.57165 (* 1 = 1.57165 loss)
I0409 20:51:07.769899 15108 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 20:51:08.374275 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:12.684087 15108 solver.cpp:218] Iteration 6888 (2.44198 iter/s, 4.91405s/12 iters), loss = 1.56638
I0409 20:51:12.684247 15108 solver.cpp:237]     Train net output #0: loss = 1.56638 (* 1 = 1.56638 loss)
I0409 20:51:12.684260 15108 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 20:51:17.595528 15108 solver.cpp:218] Iteration 6900 (2.44342 iter/s, 4.91115s/12 iters), loss = 1.67682
I0409 20:51:17.595571 15108 solver.cpp:237]     Train net output #0: loss = 1.67682 (* 1 = 1.67682 loss)
I0409 20:51:17.595579 15108 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 20:51:22.505630 15108 solver.cpp:218] Iteration 6912 (2.44403 iter/s, 4.90992s/12 iters), loss = 1.57729
I0409 20:51:22.505681 15108 solver.cpp:237]     Train net output #0: loss = 1.57729 (* 1 = 1.57729 loss)
I0409 20:51:22.505693 15108 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 20:51:27.473997 15108 solver.cpp:218] Iteration 6924 (2.41538 iter/s, 4.96815s/12 iters), loss = 1.74018
I0409 20:51:27.474043 15108 solver.cpp:237]     Train net output #0: loss = 1.74018 (* 1 = 1.74018 loss)
I0409 20:51:27.474053 15108 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 20:51:31.934851 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 20:51:32.791779 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 20:51:34.026844 15108 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 20:51:34.026871 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:51:34.374384 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:51:35.857833 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:38.570389 15108 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0409 20:51:38.570438 15108 solver.cpp:397]     Test net output #1: loss = 2.33044 (* 1 = 2.33044 loss)
I0409 20:51:38.653565 15108 solver.cpp:218] Iteration 6936 (1.07342 iter/s, 11.1792s/12 iters), loss = 1.53187
I0409 20:51:38.653615 15108 solver.cpp:237]     Train net output #0: loss = 1.53187 (* 1 = 1.53187 loss)
I0409 20:51:38.653627 15108 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 20:51:42.715256 15108 solver.cpp:218] Iteration 6948 (2.95456 iter/s, 4.06152s/12 iters), loss = 1.5724
I0409 20:51:42.715373 15108 solver.cpp:237]     Train net output #0: loss = 1.5724 (* 1 = 1.5724 loss)
I0409 20:51:42.715387 15108 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 20:51:47.806931 15108 solver.cpp:218] Iteration 6960 (2.35691 iter/s, 5.09141s/12 iters), loss = 1.42468
I0409 20:51:47.806988 15108 solver.cpp:237]     Train net output #0: loss = 1.42468 (* 1 = 1.42468 loss)
I0409 20:51:47.807000 15108 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 20:51:52.711408 15108 solver.cpp:218] Iteration 6972 (2.44684 iter/s, 4.90428s/12 iters), loss = 1.5491
I0409 20:51:52.711459 15108 solver.cpp:237]     Train net output #0: loss = 1.5491 (* 1 = 1.5491 loss)
I0409 20:51:52.711473 15108 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 20:51:55.425027 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:51:57.633118 15108 solver.cpp:218] Iteration 6984 (2.43827 iter/s, 4.92152s/12 iters), loss = 1.41341
I0409 20:51:57.633168 15108 solver.cpp:237]     Train net output #0: loss = 1.41341 (* 1 = 1.41341 loss)
I0409 20:51:57.633180 15108 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 20:52:02.490468 15108 solver.cpp:218] Iteration 6996 (2.47058 iter/s, 4.85716s/12 iters), loss = 1.42306
I0409 20:52:02.490526 15108 solver.cpp:237]     Train net output #0: loss = 1.42306 (* 1 = 1.42306 loss)
I0409 20:52:02.490540 15108 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 20:52:07.392017 15108 solver.cpp:218] Iteration 7008 (2.44831 iter/s, 4.90134s/12 iters), loss = 1.40456
I0409 20:52:07.392076 15108 solver.cpp:237]     Train net output #0: loss = 1.40456 (* 1 = 1.40456 loss)
I0409 20:52:07.392089 15108 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 20:52:12.295961 15108 solver.cpp:218] Iteration 7020 (2.44711 iter/s, 4.90374s/12 iters), loss = 1.62333
I0409 20:52:12.296017 15108 solver.cpp:237]     Train net output #0: loss = 1.62333 (* 1 = 1.62333 loss)
I0409 20:52:12.296030 15108 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 20:52:17.325773 15108 solver.cpp:218] Iteration 7032 (2.38587 iter/s, 5.02961s/12 iters), loss = 1.46983
I0409 20:52:17.325892 15108 solver.cpp:237]     Train net output #0: loss = 1.46983 (* 1 = 1.46983 loss)
I0409 20:52:17.325907 15108 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 20:52:19.326714 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 20:52:20.112201 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 20:52:20.690311 15108 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 20:52:20.690340 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:52:22.296075 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:25.083771 15108 solver.cpp:397]     Test net output #0: accuracy = 0.428309
I0409 20:52:25.083803 15108 solver.cpp:397]     Test net output #1: loss = 2.28129 (* 1 = 2.28129 loss)
I0409 20:52:26.878206 15108 solver.cpp:218] Iteration 7044 (1.25628 iter/s, 9.55204s/12 iters), loss = 1.52508
I0409 20:52:26.878259 15108 solver.cpp:237]     Train net output #0: loss = 1.52508 (* 1 = 1.52508 loss)
I0409 20:52:26.878270 15108 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 20:52:31.782367 15108 solver.cpp:218] Iteration 7056 (2.447 iter/s, 4.90396s/12 iters), loss = 1.43829
I0409 20:52:31.782413 15108 solver.cpp:237]     Train net output #0: loss = 1.43829 (* 1 = 1.43829 loss)
I0409 20:52:31.782423 15108 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 20:52:36.847677 15108 solver.cpp:218] Iteration 7068 (2.36915 iter/s, 5.06511s/12 iters), loss = 1.50657
I0409 20:52:36.847725 15108 solver.cpp:237]     Train net output #0: loss = 1.50657 (* 1 = 1.50657 loss)
I0409 20:52:36.847738 15108 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 20:52:41.703980 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:52:41.807175 15108 solver.cpp:218] Iteration 7080 (2.41969 iter/s, 4.9593s/12 iters), loss = 1.53359
I0409 20:52:41.807221 15108 solver.cpp:237]     Train net output #0: loss = 1.53359 (* 1 = 1.53359 loss)
I0409 20:52:41.807230 15108 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 20:52:46.688324 15108 solver.cpp:218] Iteration 7092 (2.45853 iter/s, 4.88096s/12 iters), loss = 1.37916
I0409 20:52:46.688370 15108 solver.cpp:237]     Train net output #0: loss = 1.37916 (* 1 = 1.37916 loss)
I0409 20:52:46.688380 15108 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 20:52:51.662981 15108 solver.cpp:218] Iteration 7104 (2.41232 iter/s, 4.97446s/12 iters), loss = 1.29722
I0409 20:52:51.663100 15108 solver.cpp:237]     Train net output #0: loss = 1.29722 (* 1 = 1.29722 loss)
I0409 20:52:51.663115 15108 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 20:52:56.540405 15108 solver.cpp:218] Iteration 7116 (2.46045 iter/s, 4.87716s/12 iters), loss = 1.72065
I0409 20:52:56.540458 15108 solver.cpp:237]     Train net output #0: loss = 1.72065 (* 1 = 1.72065 loss)
I0409 20:52:56.540472 15108 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 20:53:01.479733 15108 solver.cpp:218] Iteration 7128 (2.42958 iter/s, 4.93912s/12 iters), loss = 1.43126
I0409 20:53:01.479789 15108 solver.cpp:237]     Train net output #0: loss = 1.43126 (* 1 = 1.43126 loss)
I0409 20:53:01.479804 15108 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 20:53:05.914916 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 20:53:09.386723 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 20:53:10.629235 15108 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 20:53:10.629262 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:53:12.289568 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:15.081995 15108 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0409 20:53:15.082047 15108 solver.cpp:397]     Test net output #1: loss = 2.40052 (* 1 = 2.40052 loss)
I0409 20:53:15.165217 15108 solver.cpp:218] Iteration 7140 (0.87687 iter/s, 13.685s/12 iters), loss = 1.50991
I0409 20:53:15.165272 15108 solver.cpp:237]     Train net output #0: loss = 1.50991 (* 1 = 1.50991 loss)
I0409 20:53:15.165285 15108 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 20:53:19.231689 15108 solver.cpp:218] Iteration 7152 (2.95109 iter/s, 4.06629s/12 iters), loss = 1.46753
I0409 20:53:19.231737 15108 solver.cpp:237]     Train net output #0: loss = 1.46753 (* 1 = 1.46753 loss)
I0409 20:53:19.231746 15108 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 20:53:24.104820 15108 solver.cpp:218] Iteration 7164 (2.46258 iter/s, 4.87293s/12 iters), loss = 1.54227
I0409 20:53:24.104975 15108 solver.cpp:237]     Train net output #0: loss = 1.54227 (* 1 = 1.54227 loss)
I0409 20:53:24.104990 15108 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 20:53:28.956732 15108 solver.cpp:218] Iteration 7176 (2.47341 iter/s, 4.85161s/12 iters), loss = 1.52145
I0409 20:53:28.956789 15108 solver.cpp:237]     Train net output #0: loss = 1.52145 (* 1 = 1.52145 loss)
I0409 20:53:28.956801 15108 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 20:53:30.998711 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:53:33.826587 15108 solver.cpp:218] Iteration 7188 (2.46425 iter/s, 4.86965s/12 iters), loss = 1.50211
I0409 20:53:33.826647 15108 solver.cpp:237]     Train net output #0: loss = 1.50211 (* 1 = 1.50211 loss)
I0409 20:53:33.826659 15108 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 20:53:38.726662 15108 solver.cpp:218] Iteration 7200 (2.44904 iter/s, 4.89987s/12 iters), loss = 1.40302
I0409 20:53:38.726697 15108 solver.cpp:237]     Train net output #0: loss = 1.40302 (* 1 = 1.40302 loss)
I0409 20:53:38.726706 15108 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 20:53:43.641131 15108 solver.cpp:218] Iteration 7212 (2.44186 iter/s, 4.91428s/12 iters), loss = 1.43222
I0409 20:53:43.641177 15108 solver.cpp:237]     Train net output #0: loss = 1.43222 (* 1 = 1.43222 loss)
I0409 20:53:43.641188 15108 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 20:53:48.545652 15108 solver.cpp:218] Iteration 7224 (2.44682 iter/s, 4.90432s/12 iters), loss = 1.32422
I0409 20:53:48.545708 15108 solver.cpp:237]     Train net output #0: loss = 1.32422 (* 1 = 1.32422 loss)
I0409 20:53:48.545720 15108 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 20:53:53.442188 15108 solver.cpp:218] Iteration 7236 (2.45082 iter/s, 4.89633s/12 iters), loss = 1.2668
I0409 20:53:53.442247 15108 solver.cpp:237]     Train net output #0: loss = 1.2668 (* 1 = 1.2668 loss)
I0409 20:53:53.442263 15108 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 20:53:55.462421 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 20:53:56.256901 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 20:53:57.471992 15108 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 20:53:57.472021 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:53:59.125586 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:02.128621 15108 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0409 20:54:02.128654 15108 solver.cpp:397]     Test net output #1: loss = 2.29584 (* 1 = 2.29584 loss)
I0409 20:54:04.003720 15108 solver.cpp:218] Iteration 7248 (1.13624 iter/s, 10.5612s/12 iters), loss = 1.54444
I0409 20:54:04.003775 15108 solver.cpp:237]     Train net output #0: loss = 1.54444 (* 1 = 1.54444 loss)
I0409 20:54:04.003787 15108 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 20:54:08.867483 15108 solver.cpp:218] Iteration 7260 (2.46733 iter/s, 4.86355s/12 iters), loss = 1.39172
I0409 20:54:08.867535 15108 solver.cpp:237]     Train net output #0: loss = 1.39172 (* 1 = 1.39172 loss)
I0409 20:54:08.867548 15108 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 20:54:13.800271 15108 solver.cpp:218] Iteration 7272 (2.4328 iter/s, 4.93258s/12 iters), loss = 1.38491
I0409 20:54:13.800318 15108 solver.cpp:237]     Train net output #0: loss = 1.38491 (* 1 = 1.38491 loss)
I0409 20:54:13.800328 15108 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 20:54:18.039589 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:18.794574 15108 solver.cpp:218] Iteration 7284 (2.40284 iter/s, 4.9941s/12 iters), loss = 1.16822
I0409 20:54:18.794621 15108 solver.cpp:237]     Train net output #0: loss = 1.16822 (* 1 = 1.16822 loss)
I0409 20:54:18.794631 15108 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 20:54:23.755314 15108 solver.cpp:218] Iteration 7296 (2.41909 iter/s, 4.96054s/12 iters), loss = 1.64873
I0409 20:54:23.755367 15108 solver.cpp:237]     Train net output #0: loss = 1.64873 (* 1 = 1.64873 loss)
I0409 20:54:23.755380 15108 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 20:54:28.662369 15108 solver.cpp:218] Iteration 7308 (2.44556 iter/s, 4.90685s/12 iters), loss = 1.59153
I0409 20:54:28.662472 15108 solver.cpp:237]     Train net output #0: loss = 1.59153 (* 1 = 1.59153 loss)
I0409 20:54:28.662482 15108 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 20:54:33.613560 15108 solver.cpp:218] Iteration 7320 (2.42379 iter/s, 4.95093s/12 iters), loss = 1.25386
I0409 20:54:33.613613 15108 solver.cpp:237]     Train net output #0: loss = 1.25386 (* 1 = 1.25386 loss)
I0409 20:54:33.613626 15108 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 20:54:38.716096 15108 solver.cpp:218] Iteration 7332 (2.35187 iter/s, 5.10232s/12 iters), loss = 1.44023
I0409 20:54:38.716150 15108 solver.cpp:237]     Train net output #0: loss = 1.44023 (* 1 = 1.44023 loss)
I0409 20:54:38.716162 15108 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 20:54:43.335144 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 20:54:44.116078 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 20:54:44.687616 15108 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 20:54:44.687645 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:54:46.238245 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:54:49.104811 15108 solver.cpp:397]     Test net output #0: accuracy = 0.446691
I0409 20:54:49.104862 15108 solver.cpp:397]     Test net output #1: loss = 2.27972 (* 1 = 2.27972 loss)
I0409 20:54:49.186905 15108 solver.cpp:218] Iteration 7344 (1.14608 iter/s, 10.4704s/12 iters), loss = 1.13212
I0409 20:54:49.186962 15108 solver.cpp:237]     Train net output #0: loss = 1.13212 (* 1 = 1.13212 loss)
I0409 20:54:49.186973 15108 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 20:54:53.500440 15108 solver.cpp:218] Iteration 7356 (2.78207 iter/s, 4.31334s/12 iters), loss = 1.38162
I0409 20:54:53.500486 15108 solver.cpp:237]     Train net output #0: loss = 1.38162 (* 1 = 1.38162 loss)
I0409 20:54:53.500496 15108 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 20:54:58.407313 15108 solver.cpp:218] Iteration 7368 (2.44565 iter/s, 4.90667s/12 iters), loss = 1.34626
I0409 20:54:58.407367 15108 solver.cpp:237]     Train net output #0: loss = 1.34626 (* 1 = 1.34626 loss)
I0409 20:54:58.407378 15108 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 20:55:03.324126 15108 solver.cpp:218] Iteration 7380 (2.44071 iter/s, 4.91661s/12 iters), loss = 1.23822
I0409 20:55:03.328246 15108 solver.cpp:237]     Train net output #0: loss = 1.23822 (* 1 = 1.23822 loss)
I0409 20:55:03.328256 15108 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 20:55:04.716611 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:08.262183 15108 solver.cpp:218] Iteration 7392 (2.43221 iter/s, 4.93378s/12 iters), loss = 1.29983
I0409 20:55:08.262238 15108 solver.cpp:237]     Train net output #0: loss = 1.29983 (* 1 = 1.29983 loss)
I0409 20:55:08.262248 15108 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 20:55:13.175673 15108 solver.cpp:218] Iteration 7404 (2.44236 iter/s, 4.91327s/12 iters), loss = 1.32287
I0409 20:55:13.175716 15108 solver.cpp:237]     Train net output #0: loss = 1.32287 (* 1 = 1.32287 loss)
I0409 20:55:13.175727 15108 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 20:55:18.059417 15108 solver.cpp:218] Iteration 7416 (2.45723 iter/s, 4.88354s/12 iters), loss = 1.39172
I0409 20:55:18.059463 15108 solver.cpp:237]     Train net output #0: loss = 1.39172 (* 1 = 1.39172 loss)
I0409 20:55:18.059471 15108 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 20:55:22.978801 15108 solver.cpp:218] Iteration 7428 (2.43943 iter/s, 4.91918s/12 iters), loss = 1.15929
I0409 20:55:22.978849 15108 solver.cpp:237]     Train net output #0: loss = 1.15929 (* 1 = 1.15929 loss)
I0409 20:55:22.978858 15108 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 20:55:27.863493 15108 solver.cpp:218] Iteration 7440 (2.45676 iter/s, 4.88447s/12 iters), loss = 1.29704
I0409 20:55:27.863557 15108 solver.cpp:237]     Train net output #0: loss = 1.29704 (* 1 = 1.29704 loss)
I0409 20:55:27.863572 15108 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 20:55:29.856124 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 20:55:30.609591 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 20:55:31.174762 15108 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 20:55:31.174787 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:55:32.702033 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:35.723206 15108 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0409 20:55:35.723354 15108 solver.cpp:397]     Test net output #1: loss = 2.29837 (* 1 = 2.29837 loss)
I0409 20:55:37.552956 15108 solver.cpp:218] Iteration 7452 (1.2385 iter/s, 9.6891s/12 iters), loss = 1.26286
I0409 20:55:37.553004 15108 solver.cpp:237]     Train net output #0: loss = 1.26286 (* 1 = 1.26286 loss)
I0409 20:55:37.553014 15108 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 20:55:42.520642 15108 solver.cpp:218] Iteration 7464 (2.41571 iter/s, 4.96747s/12 iters), loss = 1.41646
I0409 20:55:42.520687 15108 solver.cpp:237]     Train net output #0: loss = 1.41646 (* 1 = 1.41646 loss)
I0409 20:55:42.520697 15108 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 20:55:47.584826 15108 solver.cpp:218] Iteration 7476 (2.36968 iter/s, 5.06397s/12 iters), loss = 1.44854
I0409 20:55:47.584882 15108 solver.cpp:237]     Train net output #0: loss = 1.44854 (* 1 = 1.44854 loss)
I0409 20:55:47.584893 15108 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 20:55:51.021762 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:55:52.499033 15108 solver.cpp:218] Iteration 7488 (2.44201 iter/s, 4.91399s/12 iters), loss = 1.39851
I0409 20:55:52.499087 15108 solver.cpp:237]     Train net output #0: loss = 1.39851 (* 1 = 1.39851 loss)
I0409 20:55:52.499099 15108 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 20:55:57.367372 15108 solver.cpp:218] Iteration 7500 (2.46502 iter/s, 4.86812s/12 iters), loss = 1.37887
I0409 20:55:57.367422 15108 solver.cpp:237]     Train net output #0: loss = 1.37887 (* 1 = 1.37887 loss)
I0409 20:55:57.367434 15108 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 20:56:02.255501 15108 solver.cpp:218] Iteration 7512 (2.45503 iter/s, 4.88792s/12 iters), loss = 1.26207
I0409 20:56:02.255558 15108 solver.cpp:237]     Train net output #0: loss = 1.26207 (* 1 = 1.26207 loss)
I0409 20:56:02.255571 15108 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 20:56:07.106317 15108 solver.cpp:218] Iteration 7524 (2.47392 iter/s, 4.85059s/12 iters), loss = 1.229
I0409 20:56:07.106523 15108 solver.cpp:237]     Train net output #0: loss = 1.229 (* 1 = 1.229 loss)
I0409 20:56:07.106541 15108 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 20:56:11.987637 15108 solver.cpp:218] Iteration 7536 (2.45853 iter/s, 4.88097s/12 iters), loss = 1.39874
I0409 20:56:11.987689 15108 solver.cpp:237]     Train net output #0: loss = 1.39874 (* 1 = 1.39874 loss)
I0409 20:56:11.987701 15108 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 20:56:16.597705 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 20:56:17.385672 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 20:56:17.970652 15108 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 20:56:17.970680 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:56:19.466488 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:22.535331 15108 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0409 20:56:22.535377 15108 solver.cpp:397]     Test net output #1: loss = 2.26963 (* 1 = 2.26963 loss)
I0409 20:56:22.618468 15108 solver.cpp:218] Iteration 7548 (1.12883 iter/s, 10.6304s/12 iters), loss = 1.14726
I0409 20:56:22.618517 15108 solver.cpp:237]     Train net output #0: loss = 1.14726 (* 1 = 1.14726 loss)
I0409 20:56:22.618528 15108 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 20:56:26.825508 15108 solver.cpp:218] Iteration 7560 (2.85249 iter/s, 4.20685s/12 iters), loss = 1.47746
I0409 20:56:26.825559 15108 solver.cpp:237]     Train net output #0: loss = 1.47746 (* 1 = 1.47746 loss)
I0409 20:56:26.825570 15108 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 20:56:31.888650 15108 solver.cpp:218] Iteration 7572 (2.37017 iter/s, 5.06293s/12 iters), loss = 1.14798
I0409 20:56:31.888692 15108 solver.cpp:237]     Train net output #0: loss = 1.14798 (* 1 = 1.14798 loss)
I0409 20:56:31.888701 15108 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 20:56:36.799477 15108 solver.cpp:218] Iteration 7584 (2.44369 iter/s, 4.9106s/12 iters), loss = 1.40496
I0409 20:56:36.799551 15108 solver.cpp:237]     Train net output #0: loss = 1.40496 (* 1 = 1.40496 loss)
I0409 20:56:36.799567 15108 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 20:56:37.446224 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:56:41.775179 15108 solver.cpp:218] Iteration 7596 (2.41183 iter/s, 4.97547s/12 iters), loss = 1.32318
I0409 20:56:41.775234 15108 solver.cpp:237]     Train net output #0: loss = 1.32318 (* 1 = 1.32318 loss)
I0409 20:56:41.775247 15108 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 20:56:46.709731 15108 solver.cpp:218] Iteration 7608 (2.43194 iter/s, 4.93433s/12 iters), loss = 1.33263
I0409 20:56:46.709787 15108 solver.cpp:237]     Train net output #0: loss = 1.33263 (* 1 = 1.33263 loss)
I0409 20:56:46.709800 15108 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 20:56:51.828754 15108 solver.cpp:218] Iteration 7620 (2.3443 iter/s, 5.11879s/12 iters), loss = 1.12859
I0409 20:56:51.828810 15108 solver.cpp:237]     Train net output #0: loss = 1.12859 (* 1 = 1.12859 loss)
I0409 20:56:51.828822 15108 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 20:56:53.044696 15108 blocking_queue.cpp:49] Waiting for data
I0409 20:56:56.755970 15108 solver.cpp:218] Iteration 7632 (2.43556 iter/s, 4.927s/12 iters), loss = 1.29778
I0409 20:56:56.756021 15108 solver.cpp:237]     Train net output #0: loss = 1.29778 (* 1 = 1.29778 loss)
I0409 20:56:56.756033 15108 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 20:57:01.674172 15108 solver.cpp:218] Iteration 7644 (2.44002 iter/s, 4.91798s/12 iters), loss = 1.19265
I0409 20:57:01.674234 15108 solver.cpp:237]     Train net output #0: loss = 1.19265 (* 1 = 1.19265 loss)
I0409 20:57:01.674250 15108 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 20:57:03.652217 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 20:57:05.093228 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 20:57:05.970489 15108 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 20:57:05.970510 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:57:07.467394 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:10.455147 15108 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0409 20:57:10.455186 15108 solver.cpp:397]     Test net output #1: loss = 2.234 (* 1 = 2.234 loss)
I0409 20:57:12.270136 15108 solver.cpp:218] Iteration 7656 (1.13255 iter/s, 10.5956s/12 iters), loss = 1.31407
I0409 20:57:12.270192 15108 solver.cpp:237]     Train net output #0: loss = 1.31407 (* 1 = 1.31407 loss)
I0409 20:57:12.270205 15108 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 20:57:17.179456 15108 solver.cpp:218] Iteration 7668 (2.44444 iter/s, 4.9091s/12 iters), loss = 1.2496
I0409 20:57:17.179507 15108 solver.cpp:237]     Train net output #0: loss = 1.2496 (* 1 = 1.2496 loss)
I0409 20:57:17.179519 15108 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 20:57:22.417872 15108 solver.cpp:218] Iteration 7680 (2.29087 iter/s, 5.23818s/12 iters), loss = 1.21416
I0409 20:57:22.417929 15108 solver.cpp:237]     Train net output #0: loss = 1.21416 (* 1 = 1.21416 loss)
I0409 20:57:22.417943 15108 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 20:57:25.345893 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:57:27.503190 15108 solver.cpp:218] Iteration 7692 (2.35984 iter/s, 5.08509s/12 iters), loss = 1.16154
I0409 20:57:27.503247 15108 solver.cpp:237]     Train net output #0: loss = 1.16154 (* 1 = 1.16154 loss)
I0409 20:57:27.503259 15108 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 20:57:32.414839 15108 solver.cpp:218] Iteration 7704 (2.44328 iter/s, 4.91142s/12 iters), loss = 1.2533
I0409 20:57:32.414886 15108 solver.cpp:237]     Train net output #0: loss = 1.2533 (* 1 = 1.2533 loss)
I0409 20:57:32.414896 15108 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 20:57:37.405608 15108 solver.cpp:218] Iteration 7716 (2.40454 iter/s, 4.99055s/12 iters), loss = 1.26825
I0409 20:57:37.405663 15108 solver.cpp:237]     Train net output #0: loss = 1.26825 (* 1 = 1.26825 loss)
I0409 20:57:37.405678 15108 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 20:57:42.276790 15108 solver.cpp:218] Iteration 7728 (2.46358 iter/s, 4.87097s/12 iters), loss = 1.14449
I0409 20:57:42.276892 15108 solver.cpp:237]     Train net output #0: loss = 1.14449 (* 1 = 1.14449 loss)
I0409 20:57:42.276904 15108 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 20:57:47.417368 15108 solver.cpp:218] Iteration 7740 (2.33449 iter/s, 5.1403s/12 iters), loss = 1.23798
I0409 20:57:47.417419 15108 solver.cpp:237]     Train net output #0: loss = 1.23798 (* 1 = 1.23798 loss)
I0409 20:57:47.417431 15108 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 20:57:51.990886 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 20:57:55.373653 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 20:57:56.596266 15108 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 20:57:56.596295 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:57:58.019356 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:01.040292 15108 solver.cpp:397]     Test net output #0: accuracy = 0.430147
I0409 20:58:01.040342 15108 solver.cpp:397]     Test net output #1: loss = 2.35742 (* 1 = 2.35742 loss)
I0409 20:58:01.124187 15108 solver.cpp:218] Iteration 7752 (0.875509 iter/s, 13.7063s/12 iters), loss = 1.11722
I0409 20:58:01.124265 15108 solver.cpp:237]     Train net output #0: loss = 1.11722 (* 1 = 1.11722 loss)
I0409 20:58:01.124282 15108 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 20:58:05.245031 15108 solver.cpp:218] Iteration 7764 (2.91218 iter/s, 4.12062s/12 iters), loss = 1.12137
I0409 20:58:05.245087 15108 solver.cpp:237]     Train net output #0: loss = 1.12137 (* 1 = 1.12137 loss)
I0409 20:58:05.245100 15108 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 20:58:10.141831 15108 solver.cpp:218] Iteration 7776 (2.45069 iter/s, 4.89657s/12 iters), loss = 1.19232
I0409 20:58:10.141880 15108 solver.cpp:237]     Train net output #0: loss = 1.19232 (* 1 = 1.19232 loss)
I0409 20:58:10.141889 15108 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 20:58:15.041353 15108 solver.cpp:218] Iteration 7788 (2.44933 iter/s, 4.8993s/12 iters), loss = 1.31519
I0409 20:58:15.041515 15108 solver.cpp:237]     Train net output #0: loss = 1.31519 (* 1 = 1.31519 loss)
I0409 20:58:15.041527 15108 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 20:58:15.049470 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:19.960613 15108 solver.cpp:218] Iteration 7800 (2.43956 iter/s, 4.91893s/12 iters), loss = 1.36293
I0409 20:58:19.960678 15108 solver.cpp:237]     Train net output #0: loss = 1.36293 (* 1 = 1.36293 loss)
I0409 20:58:19.960692 15108 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 20:58:24.857983 15108 solver.cpp:218] Iteration 7812 (2.45042 iter/s, 4.89712s/12 iters), loss = 1.21623
I0409 20:58:24.858017 15108 solver.cpp:237]     Train net output #0: loss = 1.21623 (* 1 = 1.21623 loss)
I0409 20:58:24.858026 15108 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 20:58:29.760635 15108 solver.cpp:218] Iteration 7824 (2.44775 iter/s, 4.90245s/12 iters), loss = 1.25074
I0409 20:58:29.760677 15108 solver.cpp:237]     Train net output #0: loss = 1.25074 (* 1 = 1.25074 loss)
I0409 20:58:29.760687 15108 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 20:58:34.646296 15108 solver.cpp:218] Iteration 7836 (2.45628 iter/s, 4.88545s/12 iters), loss = 1.3872
I0409 20:58:34.646343 15108 solver.cpp:237]     Train net output #0: loss = 1.3872 (* 1 = 1.3872 loss)
I0409 20:58:34.646351 15108 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 20:58:39.525362 15108 solver.cpp:218] Iteration 7848 (2.45959 iter/s, 4.87885s/12 iters), loss = 1.06499
I0409 20:58:39.525408 15108 solver.cpp:237]     Train net output #0: loss = 1.06499 (* 1 = 1.06499 loss)
I0409 20:58:39.525418 15108 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 20:58:41.507140 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 20:58:42.295027 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 20:58:42.863430 15108 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 20:58:42.863456 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:58:44.203976 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:58:47.259268 15108 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0409 20:58:47.259385 15108 solver.cpp:397]     Test net output #1: loss = 2.31148 (* 1 = 2.31148 loss)
I0409 20:58:49.144440 15108 solver.cpp:218] Iteration 7860 (1.24757 iter/s, 9.61872s/12 iters), loss = 1.19217
I0409 20:58:49.144476 15108 solver.cpp:237]     Train net output #0: loss = 1.19217 (* 1 = 1.19217 loss)
I0409 20:58:49.144485 15108 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 20:58:54.012048 15108 solver.cpp:218] Iteration 7872 (2.46538 iter/s, 4.8674s/12 iters), loss = 1.33921
I0409 20:58:54.012097 15108 solver.cpp:237]     Train net output #0: loss = 1.33921 (* 1 = 1.33921 loss)
I0409 20:58:54.012110 15108 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 20:58:58.852106 15108 solver.cpp:218] Iteration 7884 (2.47942 iter/s, 4.83985s/12 iters), loss = 1.03145
I0409 20:58:58.852154 15108 solver.cpp:237]     Train net output #0: loss = 1.03145 (* 1 = 1.03145 loss)
I0409 20:58:58.852166 15108 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 20:59:00.965991 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:03.826254 15108 solver.cpp:218] Iteration 7896 (2.41258 iter/s, 4.97393s/12 iters), loss = 1.31688
I0409 20:59:03.826309 15108 solver.cpp:237]     Train net output #0: loss = 1.31688 (* 1 = 1.31688 loss)
I0409 20:59:03.826323 15108 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 20:59:08.709918 15108 solver.cpp:218] Iteration 7908 (2.45728 iter/s, 4.88344s/12 iters), loss = 1.17833
I0409 20:59:08.709995 15108 solver.cpp:237]     Train net output #0: loss = 1.17833 (* 1 = 1.17833 loss)
I0409 20:59:08.710008 15108 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 20:59:13.657912 15108 solver.cpp:218] Iteration 7920 (2.42535 iter/s, 4.94774s/12 iters), loss = 1.13263
I0409 20:59:13.657994 15108 solver.cpp:237]     Train net output #0: loss = 1.13263 (* 1 = 1.13263 loss)
I0409 20:59:13.658008 15108 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 20:59:18.517202 15108 solver.cpp:218] Iteration 7932 (2.46962 iter/s, 4.85904s/12 iters), loss = 1.35176
I0409 20:59:18.517326 15108 solver.cpp:237]     Train net output #0: loss = 1.35176 (* 1 = 1.35176 loss)
I0409 20:59:18.517339 15108 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 20:59:23.462920 15108 solver.cpp:218] Iteration 7944 (2.42649 iter/s, 4.94542s/12 iters), loss = 0.845269
I0409 20:59:23.462980 15108 solver.cpp:237]     Train net output #0: loss = 0.845269 (* 1 = 0.845269 loss)
I0409 20:59:23.462992 15108 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 20:59:27.930112 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 20:59:28.734269 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 20:59:29.302729 15108 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 20:59:29.302753 15108 net.cpp:676] Ignoring source layer train-data
I0409 20:59:30.506229 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:33.613720 15108 solver.cpp:397]     Test net output #0: accuracy = 0.460172
I0409 20:59:33.613776 15108 solver.cpp:397]     Test net output #1: loss = 2.17963 (* 1 = 2.17963 loss)
I0409 20:59:33.696936 15108 solver.cpp:218] Iteration 7956 (1.17261 iter/s, 10.2336s/12 iters), loss = 1.08137
I0409 20:59:33.697006 15108 solver.cpp:237]     Train net output #0: loss = 1.08137 (* 1 = 1.08137 loss)
I0409 20:59:33.697021 15108 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 20:59:37.833048 15108 solver.cpp:218] Iteration 7968 (2.90142 iter/s, 4.1359s/12 iters), loss = 1.21965
I0409 20:59:37.833096 15108 solver.cpp:237]     Train net output #0: loss = 1.21965 (* 1 = 1.21965 loss)
I0409 20:59:37.833106 15108 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 20:59:42.760382 15108 solver.cpp:218] Iteration 7980 (2.4355 iter/s, 4.92711s/12 iters), loss = 1.13565
I0409 20:59:42.760434 15108 solver.cpp:237]     Train net output #0: loss = 1.13565 (* 1 = 1.13565 loss)
I0409 20:59:42.760445 15108 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 20:59:46.987785 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 20:59:47.695978 15108 solver.cpp:218] Iteration 7992 (2.43143 iter/s, 4.93537s/12 iters), loss = 1.16675
I0409 20:59:47.696039 15108 solver.cpp:237]     Train net output #0: loss = 1.16675 (* 1 = 1.16675 loss)
I0409 20:59:47.696053 15108 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 20:59:52.900120 15108 solver.cpp:218] Iteration 8004 (2.30596 iter/s, 5.20391s/12 iters), loss = 1.19232
I0409 20:59:52.900197 15108 solver.cpp:237]     Train net output #0: loss = 1.19232 (* 1 = 1.19232 loss)
I0409 20:59:52.900208 15108 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 20:59:58.031286 15108 solver.cpp:218] Iteration 8016 (2.33877 iter/s, 5.13091s/12 iters), loss = 1.42589
I0409 20:59:58.031342 15108 solver.cpp:237]     Train net output #0: loss = 1.42589 (* 1 = 1.42589 loss)
I0409 20:59:58.031352 15108 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 21:00:02.939358 15108 solver.cpp:218] Iteration 8028 (2.44507 iter/s, 4.90784s/12 iters), loss = 1.07023
I0409 21:00:02.939406 15108 solver.cpp:237]     Train net output #0: loss = 1.07023 (* 1 = 1.07023 loss)
I0409 21:00:02.939416 15108 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 21:00:07.893368 15108 solver.cpp:218] Iteration 8040 (2.42239 iter/s, 4.95379s/12 iters), loss = 1.12322
I0409 21:00:07.893417 15108 solver.cpp:237]     Train net output #0: loss = 1.12322 (* 1 = 1.12322 loss)
I0409 21:00:07.893427 15108 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 21:00:12.785580 15108 solver.cpp:218] Iteration 8052 (2.45299 iter/s, 4.89198s/12 iters), loss = 1.02784
I0409 21:00:12.785652 15108 solver.cpp:237]     Train net output #0: loss = 1.02784 (* 1 = 1.02784 loss)
I0409 21:00:12.785668 15108 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 21:00:14.804029 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 21:00:15.597218 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 21:00:16.165205 15108 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 21:00:16.165225 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:00:17.370345 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:20.584455 15108 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0409 21:00:20.584502 15108 solver.cpp:397]     Test net output #1: loss = 2.24632 (* 1 = 2.24632 loss)
I0409 21:00:22.449163 15108 solver.cpp:218] Iteration 8064 (1.24183 iter/s, 9.66319s/12 iters), loss = 1.20664
I0409 21:00:22.449223 15108 solver.cpp:237]     Train net output #0: loss = 1.20664 (* 1 = 1.20664 loss)
I0409 21:00:22.449235 15108 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 21:00:27.335268 15108 solver.cpp:218] Iteration 8076 (2.45606 iter/s, 4.88587s/12 iters), loss = 1.0649
I0409 21:00:27.335418 15108 solver.cpp:237]     Train net output #0: loss = 1.0649 (* 1 = 1.0649 loss)
I0409 21:00:27.335430 15108 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 21:00:32.239090 15108 solver.cpp:218] Iteration 8088 (2.44723 iter/s, 4.9035s/12 iters), loss = 1.30738
I0409 21:00:32.239146 15108 solver.cpp:237]     Train net output #0: loss = 1.30738 (* 1 = 1.30738 loss)
I0409 21:00:32.239158 15108 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 21:00:33.641012 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:00:37.176703 15108 solver.cpp:218] Iteration 8100 (2.43044 iter/s, 4.93739s/12 iters), loss = 1.00188
I0409 21:00:37.176744 15108 solver.cpp:237]     Train net output #0: loss = 1.00188 (* 1 = 1.00188 loss)
I0409 21:00:37.176753 15108 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 21:00:42.162036 15108 solver.cpp:218] Iteration 8112 (2.40717 iter/s, 4.98511s/12 iters), loss = 1.14872
I0409 21:00:42.162092 15108 solver.cpp:237]     Train net output #0: loss = 1.14872 (* 1 = 1.14872 loss)
I0409 21:00:42.162106 15108 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 21:00:47.007990 15108 solver.cpp:218] Iteration 8124 (2.47641 iter/s, 4.84573s/12 iters), loss = 0.941695
I0409 21:00:47.008049 15108 solver.cpp:237]     Train net output #0: loss = 0.941695 (* 1 = 0.941695 loss)
I0409 21:00:47.008060 15108 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 21:00:51.874104 15108 solver.cpp:218] Iteration 8136 (2.46615 iter/s, 4.86589s/12 iters), loss = 0.965789
I0409 21:00:51.874145 15108 solver.cpp:237]     Train net output #0: loss = 0.965789 (* 1 = 0.965789 loss)
I0409 21:00:51.874153 15108 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 21:00:56.828192 15108 solver.cpp:218] Iteration 8148 (2.42235 iter/s, 4.95387s/12 iters), loss = 1.00999
I0409 21:00:56.828250 15108 solver.cpp:237]     Train net output #0: loss = 1.00999 (* 1 = 1.00999 loss)
I0409 21:00:56.828263 15108 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 21:01:01.245014 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 21:01:02.072161 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 21:01:03.476840 15108 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 21:01:03.476871 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:01:04.625183 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:07.977663 15108 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0409 21:01:07.977692 15108 solver.cpp:397]     Test net output #1: loss = 2.21292 (* 1 = 2.21292 loss)
I0409 21:01:08.060791 15108 solver.cpp:218] Iteration 8160 (1.06836 iter/s, 11.2322s/12 iters), loss = 1.05268
I0409 21:01:08.060856 15108 solver.cpp:237]     Train net output #0: loss = 1.05268 (* 1 = 1.05268 loss)
I0409 21:01:08.060870 15108 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 21:01:12.371743 15108 solver.cpp:218] Iteration 8172 (2.78375 iter/s, 4.31073s/12 iters), loss = 1.02291
I0409 21:01:12.371793 15108 solver.cpp:237]     Train net output #0: loss = 1.02291 (* 1 = 1.02291 loss)
I0409 21:01:12.371804 15108 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 21:01:17.333984 15108 solver.cpp:218] Iteration 8184 (2.41838 iter/s, 4.96201s/12 iters), loss = 1.18207
I0409 21:01:17.334038 15108 solver.cpp:237]     Train net output #0: loss = 1.18207 (* 1 = 1.18207 loss)
I0409 21:01:17.334048 15108 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 21:01:20.790869 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:22.225006 15108 solver.cpp:218] Iteration 8196 (2.45359 iter/s, 4.8908s/12 iters), loss = 1.20085
I0409 21:01:22.225059 15108 solver.cpp:237]     Train net output #0: loss = 1.20085 (* 1 = 1.20085 loss)
I0409 21:01:22.225070 15108 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 21:01:27.155575 15108 solver.cpp:218] Iteration 8208 (2.43391 iter/s, 4.93034s/12 iters), loss = 0.936146
I0409 21:01:27.155633 15108 solver.cpp:237]     Train net output #0: loss = 0.936146 (* 1 = 0.936146 loss)
I0409 21:01:27.155647 15108 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 21:01:32.112283 15108 solver.cpp:218] Iteration 8220 (2.42108 iter/s, 4.95647s/12 iters), loss = 1.11031
I0409 21:01:32.112421 15108 solver.cpp:237]     Train net output #0: loss = 1.11031 (* 1 = 1.11031 loss)
I0409 21:01:32.112433 15108 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 21:01:37.006778 15108 solver.cpp:218] Iteration 8232 (2.45189 iter/s, 4.89418s/12 iters), loss = 1.04657
I0409 21:01:37.006835 15108 solver.cpp:237]     Train net output #0: loss = 1.04657 (* 1 = 1.04657 loss)
I0409 21:01:37.006850 15108 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 21:01:41.918859 15108 solver.cpp:218] Iteration 8244 (2.44307 iter/s, 4.91185s/12 iters), loss = 0.954139
I0409 21:01:41.918905 15108 solver.cpp:237]     Train net output #0: loss = 0.954139 (* 1 = 0.954139 loss)
I0409 21:01:41.918916 15108 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 21:01:46.829937 15108 solver.cpp:218] Iteration 8256 (2.44356 iter/s, 4.91086s/12 iters), loss = 1.12656
I0409 21:01:46.830006 15108 solver.cpp:237]     Train net output #0: loss = 1.12656 (* 1 = 1.12656 loss)
I0409 21:01:46.830018 15108 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 21:01:48.829949 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 21:01:49.637614 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 21:01:50.229832 15108 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 21:01:50.229854 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:01:51.444643 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:01:54.662685 15108 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0409 21:01:54.662734 15108 solver.cpp:397]     Test net output #1: loss = 2.1685 (* 1 = 2.1685 loss)
I0409 21:01:56.585413 15108 solver.cpp:218] Iteration 8268 (1.23013 iter/s, 9.75507s/12 iters), loss = 1.01139
I0409 21:01:56.585469 15108 solver.cpp:237]     Train net output #0: loss = 1.01139 (* 1 = 1.01139 loss)
I0409 21:01:56.585480 15108 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 21:02:01.623288 15108 solver.cpp:218] Iteration 8280 (2.38207 iter/s, 5.03763s/12 iters), loss = 0.93941
I0409 21:02:01.623354 15108 solver.cpp:237]     Train net output #0: loss = 0.93941 (* 1 = 0.93941 loss)
I0409 21:02:01.623368 15108 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 21:02:06.554883 15108 solver.cpp:218] Iteration 8292 (2.43341 iter/s, 4.93135s/12 iters), loss = 1.10044
I0409 21:02:06.555032 15108 solver.cpp:237]     Train net output #0: loss = 1.10044 (* 1 = 1.10044 loss)
I0409 21:02:06.555043 15108 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 21:02:07.217690 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:11.476684 15108 solver.cpp:218] Iteration 8304 (2.43829 iter/s, 4.92148s/12 iters), loss = 0.938714
I0409 21:02:11.476728 15108 solver.cpp:237]     Train net output #0: loss = 0.938714 (* 1 = 0.938714 loss)
I0409 21:02:11.476737 15108 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 21:02:13.144134 15108 blocking_queue.cpp:49] Waiting for data
I0409 21:02:16.504705 15108 solver.cpp:218] Iteration 8316 (2.38673 iter/s, 5.02779s/12 iters), loss = 1.33192
I0409 21:02:16.504771 15108 solver.cpp:237]     Train net output #0: loss = 1.33192 (* 1 = 1.33192 loss)
I0409 21:02:16.504784 15108 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 21:02:21.421167 15108 solver.cpp:218] Iteration 8328 (2.4409 iter/s, 4.91622s/12 iters), loss = 0.833305
I0409 21:02:21.421226 15108 solver.cpp:237]     Train net output #0: loss = 0.833305 (* 1 = 0.833305 loss)
I0409 21:02:21.421238 15108 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 21:02:26.369513 15108 solver.cpp:218] Iteration 8340 (2.42517 iter/s, 4.94811s/12 iters), loss = 1.07555
I0409 21:02:26.369565 15108 solver.cpp:237]     Train net output #0: loss = 1.07555 (* 1 = 1.07555 loss)
I0409 21:02:26.369578 15108 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 21:02:31.292618 15108 solver.cpp:218] Iteration 8352 (2.4376 iter/s, 4.92287s/12 iters), loss = 1.03076
I0409 21:02:31.292692 15108 solver.cpp:237]     Train net output #0: loss = 1.03076 (* 1 = 1.03076 loss)
I0409 21:02:31.292711 15108 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 21:02:35.801337 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 21:02:38.684682 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 21:02:41.748068 15108 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 21:02:41.748100 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:02:42.928289 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:02:46.243153 15108 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0409 21:02:46.243203 15108 solver.cpp:397]     Test net output #1: loss = 2.21984 (* 1 = 2.21984 loss)
I0409 21:02:46.325932 15108 solver.cpp:218] Iteration 8364 (0.798258 iter/s, 15.0327s/12 iters), loss = 0.970116
I0409 21:02:46.326037 15108 solver.cpp:237]     Train net output #0: loss = 0.970116 (* 1 = 0.970116 loss)
I0409 21:02:46.326056 15108 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 21:02:50.614109 15108 solver.cpp:218] Iteration 8376 (2.79856 iter/s, 4.28792s/12 iters), loss = 0.821004
I0409 21:02:50.614156 15108 solver.cpp:237]     Train net output #0: loss = 0.821004 (* 1 = 0.821004 loss)
I0409 21:02:50.614166 15108 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 21:02:55.810722 15108 solver.cpp:218] Iteration 8388 (2.3093 iter/s, 5.19638s/12 iters), loss = 0.897038
I0409 21:02:55.810770 15108 solver.cpp:237]     Train net output #0: loss = 0.897038 (* 1 = 0.897038 loss)
I0409 21:02:55.810779 15108 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 21:02:58.685968 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:00.988517 15108 solver.cpp:218] Iteration 8400 (2.3177 iter/s, 5.17756s/12 iters), loss = 1.07226
I0409 21:03:00.988574 15108 solver.cpp:237]     Train net output #0: loss = 1.07226 (* 1 = 1.07226 loss)
I0409 21:03:00.988586 15108 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 21:03:06.049645 15108 solver.cpp:218] Iteration 8412 (2.37113 iter/s, 5.06089s/12 iters), loss = 0.987914
I0409 21:03:06.049688 15108 solver.cpp:237]     Train net output #0: loss = 0.987914 (* 1 = 0.987914 loss)
I0409 21:03:06.049700 15108 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 21:03:10.947989 15108 solver.cpp:218] Iteration 8424 (2.44992 iter/s, 4.89811s/12 iters), loss = 1.12698
I0409 21:03:10.948149 15108 solver.cpp:237]     Train net output #0: loss = 1.12698 (* 1 = 1.12698 loss)
I0409 21:03:10.948163 15108 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 21:03:15.858625 15108 solver.cpp:218] Iteration 8436 (2.44385 iter/s, 4.91029s/12 iters), loss = 1.02323
I0409 21:03:15.858678 15108 solver.cpp:237]     Train net output #0: loss = 1.02323 (* 1 = 1.02323 loss)
I0409 21:03:15.858692 15108 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 21:03:20.784955 15108 solver.cpp:218] Iteration 8448 (2.436 iter/s, 4.9261s/12 iters), loss = 1.03119
I0409 21:03:20.784998 15108 solver.cpp:237]     Train net output #0: loss = 1.03119 (* 1 = 1.03119 loss)
I0409 21:03:20.785008 15108 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 21:03:25.706074 15108 solver.cpp:218] Iteration 8460 (2.43858 iter/s, 4.9209s/12 iters), loss = 0.874146
I0409 21:03:25.706120 15108 solver.cpp:237]     Train net output #0: loss = 0.874146 (* 1 = 0.874146 loss)
I0409 21:03:25.706128 15108 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 21:03:27.699368 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 21:03:28.610812 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 21:03:29.616359 15108 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 21:03:29.616385 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:03:30.782810 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:34.359861 15108 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0409 21:03:34.359892 15108 solver.cpp:397]     Test net output #1: loss = 2.21458 (* 1 = 2.21458 loss)
I0409 21:03:36.242574 15108 solver.cpp:218] Iteration 8472 (1.13894 iter/s, 10.5361s/12 iters), loss = 1.11547
I0409 21:03:36.242628 15108 solver.cpp:237]     Train net output #0: loss = 1.11547 (* 1 = 1.11547 loss)
I0409 21:03:36.242641 15108 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 21:03:41.096536 15108 solver.cpp:218] Iteration 8484 (2.47232 iter/s, 4.85373s/12 iters), loss = 1.05541
I0409 21:03:41.096650 15108 solver.cpp:237]     Train net output #0: loss = 1.05541 (* 1 = 1.05541 loss)
I0409 21:03:41.096666 15108 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 21:03:45.974841 15108 solver.cpp:218] Iteration 8496 (2.46002 iter/s, 4.87802s/12 iters), loss = 1.00359
I0409 21:03:45.974889 15108 solver.cpp:237]     Train net output #0: loss = 1.00359 (* 1 = 1.00359 loss)
I0409 21:03:45.974897 15108 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 21:03:46.021761 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:03:50.882292 15108 solver.cpp:218] Iteration 8508 (2.44538 iter/s, 4.90722s/12 iters), loss = 0.720486
I0409 21:03:50.882337 15108 solver.cpp:237]     Train net output #0: loss = 0.720486 (* 1 = 0.720486 loss)
I0409 21:03:50.882346 15108 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 21:03:55.802821 15108 solver.cpp:218] Iteration 8520 (2.43888 iter/s, 4.9203s/12 iters), loss = 0.776489
I0409 21:03:55.802866 15108 solver.cpp:237]     Train net output #0: loss = 0.776489 (* 1 = 0.776489 loss)
I0409 21:03:55.802875 15108 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 21:04:00.746006 15108 solver.cpp:218] Iteration 8532 (2.4277 iter/s, 4.94295s/12 iters), loss = 0.852351
I0409 21:04:00.746063 15108 solver.cpp:237]     Train net output #0: loss = 0.852351 (* 1 = 0.852351 loss)
I0409 21:04:00.746078 15108 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 21:04:05.771657 15108 solver.cpp:218] Iteration 8544 (2.38787 iter/s, 5.02541s/12 iters), loss = 1.14275
I0409 21:04:05.771715 15108 solver.cpp:237]     Train net output #0: loss = 1.14275 (* 1 = 1.14275 loss)
I0409 21:04:05.771728 15108 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 21:04:10.624836 15108 solver.cpp:218] Iteration 8556 (2.47273 iter/s, 4.85294s/12 iters), loss = 0.885549
I0409 21:04:10.624894 15108 solver.cpp:237]     Train net output #0: loss = 0.885549 (* 1 = 0.885549 loss)
I0409 21:04:10.624907 15108 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 21:04:15.022161 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 21:04:15.792068 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 21:04:17.670723 15108 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 21:04:17.670753 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:04:18.858152 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:22.404821 15108 solver.cpp:397]     Test net output #0: accuracy = 0.490196
I0409 21:04:22.404855 15108 solver.cpp:397]     Test net output #1: loss = 2.17794 (* 1 = 2.17794 loss)
I0409 21:04:22.487910 15108 solver.cpp:218] Iteration 8568 (1.01158 iter/s, 11.8626s/12 iters), loss = 0.77962
I0409 21:04:22.487958 15108 solver.cpp:237]     Train net output #0: loss = 0.77962 (* 1 = 0.77962 loss)
I0409 21:04:22.487967 15108 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 21:04:26.708292 15108 solver.cpp:218] Iteration 8580 (2.84349 iter/s, 4.22017s/12 iters), loss = 0.93502
I0409 21:04:26.708345 15108 solver.cpp:237]     Train net output #0: loss = 0.93502 (* 1 = 0.93502 loss)
I0409 21:04:26.708356 15108 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 21:04:31.664484 15108 solver.cpp:218] Iteration 8592 (2.42133 iter/s, 4.95596s/12 iters), loss = 0.813759
I0409 21:04:31.664531 15108 solver.cpp:237]     Train net output #0: loss = 0.813759 (* 1 = 0.813759 loss)
I0409 21:04:31.664544 15108 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 21:04:33.794576 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:04:36.604856 15108 solver.cpp:218] Iteration 8604 (2.42908 iter/s, 4.94015s/12 iters), loss = 0.984741
I0409 21:04:36.604902 15108 solver.cpp:237]     Train net output #0: loss = 0.984741 (* 1 = 0.984741 loss)
I0409 21:04:36.604914 15108 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 21:04:41.517203 15108 solver.cpp:218] Iteration 8616 (2.44294 iter/s, 4.91212s/12 iters), loss = 0.742669
I0409 21:04:41.517266 15108 solver.cpp:237]     Train net output #0: loss = 0.742669 (* 1 = 0.742669 loss)
I0409 21:04:41.517278 15108 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 21:04:46.383250 15108 solver.cpp:218] Iteration 8628 (2.46619 iter/s, 4.8658s/12 iters), loss = 0.854161
I0409 21:04:46.383375 15108 solver.cpp:237]     Train net output #0: loss = 0.854161 (* 1 = 0.854161 loss)
I0409 21:04:46.383389 15108 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 21:04:51.316988 15108 solver.cpp:218] Iteration 8640 (2.43238 iter/s, 4.93344s/12 iters), loss = 0.986893
I0409 21:04:51.317034 15108 solver.cpp:237]     Train net output #0: loss = 0.986893 (* 1 = 0.986893 loss)
I0409 21:04:51.317044 15108 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 21:04:56.275573 15108 solver.cpp:218] Iteration 8652 (2.42016 iter/s, 4.95835s/12 iters), loss = 1.06903
I0409 21:04:56.275616 15108 solver.cpp:237]     Train net output #0: loss = 1.06903 (* 1 = 1.06903 loss)
I0409 21:04:56.275626 15108 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 21:05:01.232957 15108 solver.cpp:218] Iteration 8664 (2.42074 iter/s, 4.95715s/12 iters), loss = 1.15461
I0409 21:05:01.233018 15108 solver.cpp:237]     Train net output #0: loss = 1.15461 (* 1 = 1.15461 loss)
I0409 21:05:01.233031 15108 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 21:05:03.252081 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 21:05:04.044190 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 21:05:04.612773 15108 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 21:05:04.612793 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:05:05.575937 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:08.952159 15108 solver.cpp:397]     Test net output #0: accuracy = 0.474877
I0409 21:05:08.952205 15108 solver.cpp:397]     Test net output #1: loss = 2.19439 (* 1 = 2.19439 loss)
I0409 21:05:10.741181 15108 solver.cpp:218] Iteration 8676 (1.26212 iter/s, 9.50783s/12 iters), loss = 0.86561
I0409 21:05:10.741235 15108 solver.cpp:237]     Train net output #0: loss = 0.86561 (* 1 = 0.86561 loss)
I0409 21:05:10.741247 15108 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 21:05:15.871354 15108 solver.cpp:218] Iteration 8688 (2.33921 iter/s, 5.12993s/12 iters), loss = 0.886051
I0409 21:05:15.871409 15108 solver.cpp:237]     Train net output #0: loss = 0.886051 (* 1 = 0.886051 loss)
I0409 21:05:15.871421 15108 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 21:05:20.104043 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:20.772773 15108 solver.cpp:218] Iteration 8700 (2.44839 iter/s, 4.90118s/12 iters), loss = 1.00032
I0409 21:05:20.772828 15108 solver.cpp:237]     Train net output #0: loss = 1.00032 (* 1 = 1.00032 loss)
I0409 21:05:20.772840 15108 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 21:05:25.938874 15108 solver.cpp:218] Iteration 8712 (2.32295 iter/s, 5.16585s/12 iters), loss = 0.881721
I0409 21:05:25.938932 15108 solver.cpp:237]     Train net output #0: loss = 0.881721 (* 1 = 0.881721 loss)
I0409 21:05:25.938946 15108 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 21:05:31.082739 15108 solver.cpp:218] Iteration 8724 (2.33299 iter/s, 5.14362s/12 iters), loss = 1.13003
I0409 21:05:31.082783 15108 solver.cpp:237]     Train net output #0: loss = 1.13003 (* 1 = 1.13003 loss)
I0409 21:05:31.082793 15108 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 21:05:35.949800 15108 solver.cpp:218] Iteration 8736 (2.46567 iter/s, 4.86683s/12 iters), loss = 0.825608
I0409 21:05:35.949858 15108 solver.cpp:237]     Train net output #0: loss = 0.825608 (* 1 = 0.825608 loss)
I0409 21:05:35.949873 15108 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 21:05:40.999650 15108 solver.cpp:218] Iteration 8748 (2.37642 iter/s, 5.04962s/12 iters), loss = 0.894555
I0409 21:05:40.999687 15108 solver.cpp:237]     Train net output #0: loss = 0.894555 (* 1 = 0.894555 loss)
I0409 21:05:40.999696 15108 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 21:05:45.981242 15108 solver.cpp:218] Iteration 8760 (2.40898 iter/s, 4.98137s/12 iters), loss = 0.841613
I0409 21:05:45.981298 15108 solver.cpp:237]     Train net output #0: loss = 0.841613 (* 1 = 0.841613 loss)
I0409 21:05:45.981310 15108 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 21:05:50.456893 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 21:05:51.335131 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 21:05:51.919103 15108 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 21:05:51.919131 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:05:52.837549 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:05:56.413983 15108 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0409 21:05:56.414036 15108 solver.cpp:397]     Test net output #1: loss = 2.15997 (* 1 = 2.15997 loss)
I0409 21:05:56.497437 15108 solver.cpp:218] Iteration 8772 (1.14114 iter/s, 10.5158s/12 iters), loss = 0.917884
I0409 21:05:56.497483 15108 solver.cpp:237]     Train net output #0: loss = 0.917884 (* 1 = 0.917884 loss)
I0409 21:05:56.497495 15108 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 21:06:00.661713 15108 solver.cpp:218] Iteration 8784 (2.88179 iter/s, 4.16408s/12 iters), loss = 0.787123
I0409 21:06:00.661765 15108 solver.cpp:237]     Train net output #0: loss = 0.787123 (* 1 = 0.787123 loss)
I0409 21:06:00.661777 15108 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 21:06:05.644577 15108 solver.cpp:218] Iteration 8796 (2.40837 iter/s, 4.98262s/12 iters), loss = 0.624992
I0409 21:06:05.644639 15108 solver.cpp:237]     Train net output #0: loss = 0.624992 (* 1 = 0.624992 loss)
I0409 21:06:05.644651 15108 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 21:06:07.075455 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:10.592475 15108 solver.cpp:218] Iteration 8808 (2.42539 iter/s, 4.94765s/12 iters), loss = 1.14175
I0409 21:06:10.592530 15108 solver.cpp:237]     Train net output #0: loss = 1.14175 (* 1 = 1.14175 loss)
I0409 21:06:10.592541 15108 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 21:06:15.525091 15108 solver.cpp:218] Iteration 8820 (2.4329 iter/s, 4.93238s/12 iters), loss = 0.720101
I0409 21:06:15.525142 15108 solver.cpp:237]     Train net output #0: loss = 0.720101 (* 1 = 0.720101 loss)
I0409 21:06:15.525156 15108 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 21:06:20.411527 15108 solver.cpp:218] Iteration 8832 (2.45589 iter/s, 4.88621s/12 iters), loss = 0.612137
I0409 21:06:20.411572 15108 solver.cpp:237]     Train net output #0: loss = 0.612137 (* 1 = 0.612137 loss)
I0409 21:06:20.411583 15108 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 21:06:25.289716 15108 solver.cpp:218] Iteration 8844 (2.46005 iter/s, 4.87796s/12 iters), loss = 0.774297
I0409 21:06:25.289850 15108 solver.cpp:237]     Train net output #0: loss = 0.774297 (* 1 = 0.774297 loss)
I0409 21:06:25.289865 15108 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 21:06:30.384466 15108 solver.cpp:218] Iteration 8856 (2.35551 iter/s, 5.09443s/12 iters), loss = 0.753349
I0409 21:06:30.384526 15108 solver.cpp:237]     Train net output #0: loss = 0.753349 (* 1 = 0.753349 loss)
I0409 21:06:30.384539 15108 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 21:06:35.366415 15108 solver.cpp:218] Iteration 8868 (2.40881 iter/s, 4.98171s/12 iters), loss = 0.957277
I0409 21:06:35.366470 15108 solver.cpp:237]     Train net output #0: loss = 0.957277 (* 1 = 0.957277 loss)
I0409 21:06:35.366482 15108 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 21:06:37.397850 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 21:06:38.562136 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 21:06:39.180891 15108 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 21:06:39.180912 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:06:40.153067 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:43.694015 15108 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0409 21:06:43.694057 15108 solver.cpp:397]     Test net output #1: loss = 2.1718 (* 1 = 2.1718 loss)
I0409 21:06:45.493615 15108 solver.cpp:218] Iteration 8880 (1.18498 iter/s, 10.1268s/12 iters), loss = 0.679592
I0409 21:06:45.493660 15108 solver.cpp:237]     Train net output #0: loss = 0.679592 (* 1 = 0.679592 loss)
I0409 21:06:45.493669 15108 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 21:06:50.322181 15108 solver.cpp:218] Iteration 8892 (2.48532 iter/s, 4.82834s/12 iters), loss = 0.61204
I0409 21:06:50.322224 15108 solver.cpp:237]     Train net output #0: loss = 0.61204 (* 1 = 0.61204 loss)
I0409 21:06:50.322234 15108 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 21:06:53.865557 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:06:55.247501 15108 solver.cpp:218] Iteration 8904 (2.4365 iter/s, 4.9251s/12 iters), loss = 0.826653
I0409 21:06:55.247548 15108 solver.cpp:237]     Train net output #0: loss = 0.826653 (* 1 = 0.826653 loss)
I0409 21:06:55.247557 15108 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 21:07:00.561904 15108 solver.cpp:218] Iteration 8916 (2.25812 iter/s, 5.31415s/12 iters), loss = 0.738382
I0409 21:07:00.562059 15108 solver.cpp:237]     Train net output #0: loss = 0.738382 (* 1 = 0.738382 loss)
I0409 21:07:00.562074 15108 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 21:07:05.511766 15108 solver.cpp:218] Iteration 8928 (2.42447 iter/s, 4.94953s/12 iters), loss = 1.01672
I0409 21:07:05.511812 15108 solver.cpp:237]     Train net output #0: loss = 1.01672 (* 1 = 1.01672 loss)
I0409 21:07:05.511821 15108 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 21:07:10.423676 15108 solver.cpp:218] Iteration 8940 (2.44316 iter/s, 4.91167s/12 iters), loss = 0.905189
I0409 21:07:10.423738 15108 solver.cpp:237]     Train net output #0: loss = 0.905189 (* 1 = 0.905189 loss)
I0409 21:07:10.423750 15108 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 21:07:15.326865 15108 solver.cpp:218] Iteration 8952 (2.44751 iter/s, 4.90295s/12 iters), loss = 0.860962
I0409 21:07:15.326912 15108 solver.cpp:237]     Train net output #0: loss = 0.860962 (* 1 = 0.860962 loss)
I0409 21:07:15.326921 15108 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 21:07:20.213763 15108 solver.cpp:218] Iteration 8964 (2.45566 iter/s, 4.88667s/12 iters), loss = 0.587987
I0409 21:07:20.213824 15108 solver.cpp:237]     Train net output #0: loss = 0.587987 (* 1 = 0.587987 loss)
I0409 21:07:20.213836 15108 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 21:07:24.692085 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 21:07:26.353484 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 21:07:28.766530 15108 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 21:07:28.766561 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:07:29.592908 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:33.200817 15108 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0409 21:07:33.200906 15108 solver.cpp:397]     Test net output #1: loss = 2.0799 (* 1 = 2.0799 loss)
I0409 21:07:33.282932 15108 solver.cpp:218] Iteration 8976 (0.918228 iter/s, 13.0686s/12 iters), loss = 0.883235
I0409 21:07:33.282995 15108 solver.cpp:237]     Train net output #0: loss = 0.883235 (* 1 = 0.883235 loss)
I0409 21:07:33.283010 15108 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 21:07:37.530838 15108 solver.cpp:218] Iteration 8988 (2.82507 iter/s, 4.24768s/12 iters), loss = 0.879893
I0409 21:07:37.530900 15108 solver.cpp:237]     Train net output #0: loss = 0.879893 (* 1 = 0.879893 loss)
I0409 21:07:37.530915 15108 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 21:07:39.490111 15108 blocking_queue.cpp:49] Waiting for data
I0409 21:07:42.361696 15108 solver.cpp:218] Iteration 9000 (2.48416 iter/s, 4.83062s/12 iters), loss = 0.856789
I0409 21:07:42.361747 15108 solver.cpp:237]     Train net output #0: loss = 0.856789 (* 1 = 0.856789 loss)
I0409 21:07:42.361758 15108 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 21:07:43.051154 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:07:47.254559 15108 solver.cpp:218] Iteration 9012 (2.45267 iter/s, 4.89263s/12 iters), loss = 0.893041
I0409 21:07:47.254619 15108 solver.cpp:237]     Train net output #0: loss = 0.893041 (* 1 = 0.893041 loss)
I0409 21:07:47.254632 15108 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 21:07:52.176450 15108 solver.cpp:218] Iteration 9024 (2.43821 iter/s, 4.92164s/12 iters), loss = 0.755606
I0409 21:07:52.176502 15108 solver.cpp:237]     Train net output #0: loss = 0.755606 (* 1 = 0.755606 loss)
I0409 21:07:52.176513 15108 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 21:07:57.064707 15108 solver.cpp:218] Iteration 9036 (2.45498 iter/s, 4.88802s/12 iters), loss = 0.855669
I0409 21:07:57.064756 15108 solver.cpp:237]     Train net output #0: loss = 0.855669 (* 1 = 0.855669 loss)
I0409 21:07:57.064765 15108 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 21:08:01.990167 15108 solver.cpp:218] Iteration 9048 (2.43644 iter/s, 4.92522s/12 iters), loss = 0.770598
I0409 21:08:01.990212 15108 solver.cpp:237]     Train net output #0: loss = 0.770598 (* 1 = 0.770598 loss)
I0409 21:08:01.990222 15108 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 21:08:06.914264 15108 solver.cpp:218] Iteration 9060 (2.43711 iter/s, 4.92387s/12 iters), loss = 0.806482
I0409 21:08:06.914394 15108 solver.cpp:237]     Train net output #0: loss = 0.806482 (* 1 = 0.806482 loss)
I0409 21:08:06.914405 15108 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 21:08:11.781224 15108 solver.cpp:218] Iteration 9072 (2.46576 iter/s, 4.86665s/12 iters), loss = 0.935464
I0409 21:08:11.781268 15108 solver.cpp:237]     Train net output #0: loss = 0.935464 (* 1 = 0.935464 loss)
I0409 21:08:11.781277 15108 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 21:08:13.763017 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 21:08:14.587584 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 21:08:15.198210 15108 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 21:08:15.198233 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:08:16.160807 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:19.822100 15108 solver.cpp:397]     Test net output #0: accuracy = 0.488358
I0409 21:08:19.822150 15108 solver.cpp:397]     Test net output #1: loss = 2.12206 (* 1 = 2.12206 loss)
I0409 21:08:21.624927 15108 solver.cpp:218] Iteration 9084 (1.2191 iter/s, 9.8433s/12 iters), loss = 0.815671
I0409 21:08:21.624989 15108 solver.cpp:237]     Train net output #0: loss = 0.815671 (* 1 = 0.815671 loss)
I0409 21:08:21.625000 15108 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 21:08:26.520882 15108 solver.cpp:218] Iteration 9096 (2.45112 iter/s, 4.89571s/12 iters), loss = 0.894857
I0409 21:08:26.520941 15108 solver.cpp:237]     Train net output #0: loss = 0.894857 (* 1 = 0.894857 loss)
I0409 21:08:26.520954 15108 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 21:08:29.392910 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:08:31.424921 15108 solver.cpp:218] Iteration 9108 (2.44709 iter/s, 4.90379s/12 iters), loss = 0.721869
I0409 21:08:31.424978 15108 solver.cpp:237]     Train net output #0: loss = 0.721869 (* 1 = 0.721869 loss)
I0409 21:08:31.424990 15108 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 21:08:36.288580 15108 solver.cpp:218] Iteration 9120 (2.4674 iter/s, 4.86342s/12 iters), loss = 0.692409
I0409 21:08:36.288626 15108 solver.cpp:237]     Train net output #0: loss = 0.692409 (* 1 = 0.692409 loss)
I0409 21:08:36.288635 15108 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 21:08:41.127091 15108 solver.cpp:218] Iteration 9132 (2.48022 iter/s, 4.83829s/12 iters), loss = 0.987344
I0409 21:08:41.127164 15108 solver.cpp:237]     Train net output #0: loss = 0.987344 (* 1 = 0.987344 loss)
I0409 21:08:41.127175 15108 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 21:08:46.025352 15108 solver.cpp:218] Iteration 9144 (2.44998 iter/s, 4.898s/12 iters), loss = 0.766703
I0409 21:08:46.025410 15108 solver.cpp:237]     Train net output #0: loss = 0.766703 (* 1 = 0.766703 loss)
I0409 21:08:46.025422 15108 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 21:08:50.994629 15108 solver.cpp:218] Iteration 9156 (2.41496 iter/s, 4.96903s/12 iters), loss = 0.863446
I0409 21:08:50.994686 15108 solver.cpp:237]     Train net output #0: loss = 0.863446 (* 1 = 0.863446 loss)
I0409 21:08:50.994699 15108 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 21:08:55.925187 15108 solver.cpp:218] Iteration 9168 (2.43392 iter/s, 4.93032s/12 iters), loss = 0.716914
I0409 21:08:55.925226 15108 solver.cpp:237]     Train net output #0: loss = 0.716914 (* 1 = 0.716914 loss)
I0409 21:08:55.925236 15108 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 21:09:00.427119 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 21:09:02.739882 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 21:09:03.905485 15108 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 21:09:03.905508 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:09:04.692126 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:08.309700 15108 solver.cpp:397]     Test net output #0: accuracy = 0.504902
I0409 21:09:08.309748 15108 solver.cpp:397]     Test net output #1: loss = 2.12763 (* 1 = 2.12763 loss)
I0409 21:09:08.392952 15108 solver.cpp:218] Iteration 9180 (0.962519 iter/s, 12.4673s/12 iters), loss = 0.886297
I0409 21:09:08.393004 15108 solver.cpp:237]     Train net output #0: loss = 0.886297 (* 1 = 0.886297 loss)
I0409 21:09:08.393015 15108 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 21:09:12.611075 15108 solver.cpp:218] Iteration 9192 (2.84501 iter/s, 4.21791s/12 iters), loss = 0.784462
I0409 21:09:12.611198 15108 solver.cpp:237]     Train net output #0: loss = 0.784462 (* 1 = 0.784462 loss)
I0409 21:09:12.611208 15108 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 21:09:17.539958 15108 solver.cpp:218] Iteration 9204 (2.43478 iter/s, 4.92857s/12 iters), loss = 0.694779
I0409 21:09:17.540014 15108 solver.cpp:237]     Train net output #0: loss = 0.694779 (* 1 = 0.694779 loss)
I0409 21:09:17.540025 15108 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 21:09:17.610131 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:22.521140 15108 solver.cpp:218] Iteration 9216 (2.40918 iter/s, 4.98094s/12 iters), loss = 0.945486
I0409 21:09:22.521198 15108 solver.cpp:237]     Train net output #0: loss = 0.945486 (* 1 = 0.945486 loss)
I0409 21:09:22.521211 15108 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 21:09:27.426265 15108 solver.cpp:218] Iteration 9228 (2.44654 iter/s, 4.90488s/12 iters), loss = 0.695016
I0409 21:09:27.426329 15108 solver.cpp:237]     Train net output #0: loss = 0.695016 (* 1 = 0.695016 loss)
I0409 21:09:27.426343 15108 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 21:09:32.282356 15108 solver.cpp:218] Iteration 9240 (2.47125 iter/s, 4.85584s/12 iters), loss = 0.70546
I0409 21:09:32.282397 15108 solver.cpp:237]     Train net output #0: loss = 0.70546 (* 1 = 0.70546 loss)
I0409 21:09:32.282407 15108 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 21:09:37.118403 15108 solver.cpp:218] Iteration 9252 (2.48148 iter/s, 4.83582s/12 iters), loss = 0.786349
I0409 21:09:37.118453 15108 solver.cpp:237]     Train net output #0: loss = 0.786349 (* 1 = 0.786349 loss)
I0409 21:09:37.118463 15108 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 21:09:41.985453 15108 solver.cpp:218] Iteration 9264 (2.46568 iter/s, 4.86682s/12 iters), loss = 0.745856
I0409 21:09:41.985503 15108 solver.cpp:237]     Train net output #0: loss = 0.745856 (* 1 = 0.745856 loss)
I0409 21:09:41.985513 15108 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 21:09:46.843112 15108 solver.cpp:218] Iteration 9276 (2.47045 iter/s, 4.85742s/12 iters), loss = 0.770217
I0409 21:09:46.843217 15108 solver.cpp:237]     Train net output #0: loss = 0.770217 (* 1 = 0.770217 loss)
I0409 21:09:46.843230 15108 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 21:09:48.834528 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 21:09:49.614941 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 21:09:50.184180 15108 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 21:09:50.184201 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:09:51.007247 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:09:54.934724 15108 solver.cpp:397]     Test net output #0: accuracy = 0.481005
I0409 21:09:54.934772 15108 solver.cpp:397]     Test net output #1: loss = 2.24354 (* 1 = 2.24354 loss)
I0409 21:09:56.738867 15108 solver.cpp:218] Iteration 9288 (1.2127 iter/s, 9.89529s/12 iters), loss = 0.829036
I0409 21:09:56.738929 15108 solver.cpp:237]     Train net output #0: loss = 0.829036 (* 1 = 0.829036 loss)
I0409 21:09:56.738942 15108 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 21:10:01.639312 15108 solver.cpp:218] Iteration 9300 (2.44888 iter/s, 4.9002s/12 iters), loss = 0.636045
I0409 21:10:01.639369 15108 solver.cpp:237]     Train net output #0: loss = 0.636045 (* 1 = 0.636045 loss)
I0409 21:10:01.639381 15108 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 21:10:03.814072 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:06.539633 15108 solver.cpp:218] Iteration 9312 (2.44894 iter/s, 4.90009s/12 iters), loss = 0.660405
I0409 21:10:06.539674 15108 solver.cpp:237]     Train net output #0: loss = 0.660405 (* 1 = 0.660405 loss)
I0409 21:10:06.539683 15108 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 21:10:11.489116 15108 solver.cpp:218] Iteration 9324 (2.4246 iter/s, 4.94926s/12 iters), loss = 0.663497
I0409 21:10:11.489162 15108 solver.cpp:237]     Train net output #0: loss = 0.663497 (* 1 = 0.663497 loss)
I0409 21:10:11.489174 15108 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 21:10:16.438508 15108 solver.cpp:218] Iteration 9336 (2.42465 iter/s, 4.94916s/12 iters), loss = 0.759459
I0409 21:10:16.438561 15108 solver.cpp:237]     Train net output #0: loss = 0.759459 (* 1 = 0.759459 loss)
I0409 21:10:16.438572 15108 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 21:10:21.345840 15108 solver.cpp:218] Iteration 9348 (2.44544 iter/s, 4.90709s/12 iters), loss = 0.664587
I0409 21:10:21.346021 15108 solver.cpp:237]     Train net output #0: loss = 0.664587 (* 1 = 0.664587 loss)
I0409 21:10:21.346036 15108 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 21:10:26.268488 15108 solver.cpp:218] Iteration 9360 (2.43789 iter/s, 4.92229s/12 iters), loss = 0.54326
I0409 21:10:26.268532 15108 solver.cpp:237]     Train net output #0: loss = 0.54326 (* 1 = 0.54326 loss)
I0409 21:10:26.268539 15108 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 21:10:31.295612 15108 solver.cpp:218] Iteration 9372 (2.38716 iter/s, 5.02689s/12 iters), loss = 0.821648
I0409 21:10:31.295660 15108 solver.cpp:237]     Train net output #0: loss = 0.821648 (* 1 = 0.821648 loss)
I0409 21:10:31.295672 15108 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 21:10:35.828091 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 21:10:36.605870 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 21:10:37.178685 15108 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 21:10:37.178715 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:10:37.868697 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:41.807976 15108 solver.cpp:397]     Test net output #0: accuracy = 0.494485
I0409 21:10:41.808037 15108 solver.cpp:397]     Test net output #1: loss = 2.18151 (* 1 = 2.18151 loss)
I0409 21:10:41.891314 15108 solver.cpp:218] Iteration 9384 (1.13258 iter/s, 10.5953s/12 iters), loss = 0.633955
I0409 21:10:41.891377 15108 solver.cpp:237]     Train net output #0: loss = 0.633955 (* 1 = 0.633955 loss)
I0409 21:10:41.891396 15108 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 21:10:46.084885 15108 solver.cpp:218] Iteration 9396 (2.86167 iter/s, 4.19335s/12 iters), loss = 0.645564
I0409 21:10:46.084939 15108 solver.cpp:237]     Train net output #0: loss = 0.645564 (* 1 = 0.645564 loss)
I0409 21:10:46.084949 15108 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 21:10:50.471166 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:10:51.127719 15108 solver.cpp:218] Iteration 9408 (2.37973 iter/s, 5.04259s/12 iters), loss = 0.841828
I0409 21:10:51.127781 15108 solver.cpp:237]     Train net output #0: loss = 0.841828 (* 1 = 0.841828 loss)
I0409 21:10:51.127794 15108 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 21:10:56.171715 15108 solver.cpp:218] Iteration 9420 (2.37918 iter/s, 5.04375s/12 iters), loss = 0.816132
I0409 21:10:56.171846 15108 solver.cpp:237]     Train net output #0: loss = 0.816132 (* 1 = 0.816132 loss)
I0409 21:10:56.171857 15108 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 21:11:01.312826 15108 solver.cpp:218] Iteration 9432 (2.33427 iter/s, 5.14079s/12 iters), loss = 0.744205
I0409 21:11:01.312875 15108 solver.cpp:237]     Train net output #0: loss = 0.744205 (* 1 = 0.744205 loss)
I0409 21:11:01.312885 15108 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 21:11:06.182528 15108 solver.cpp:218] Iteration 9444 (2.46434 iter/s, 4.86946s/12 iters), loss = 0.648949
I0409 21:11:06.182598 15108 solver.cpp:237]     Train net output #0: loss = 0.648949 (* 1 = 0.648949 loss)
I0409 21:11:06.182615 15108 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 21:11:11.080338 15108 solver.cpp:218] Iteration 9456 (2.4502 iter/s, 4.89756s/12 iters), loss = 0.725129
I0409 21:11:11.080380 15108 solver.cpp:237]     Train net output #0: loss = 0.725129 (* 1 = 0.725129 loss)
I0409 21:11:11.080389 15108 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 21:11:16.038977 15108 solver.cpp:218] Iteration 9468 (2.42014 iter/s, 4.9584s/12 iters), loss = 0.589665
I0409 21:11:16.039050 15108 solver.cpp:237]     Train net output #0: loss = 0.589665 (* 1 = 0.589665 loss)
I0409 21:11:16.039067 15108 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 21:11:20.977785 15108 solver.cpp:218] Iteration 9480 (2.42986 iter/s, 4.93855s/12 iters), loss = 0.574018
I0409 21:11:20.977836 15108 solver.cpp:237]     Train net output #0: loss = 0.574018 (* 1 = 0.574018 loss)
I0409 21:11:20.977847 15108 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 21:11:23.096415 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 21:11:24.109295 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 21:11:25.039947 15108 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 21:11:25.039966 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:11:25.724923 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:29.726441 15108 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0409 21:11:29.726501 15108 solver.cpp:397]     Test net output #1: loss = 2.23537 (* 1 = 2.23537 loss)
I0409 21:11:31.621618 15108 solver.cpp:218] Iteration 9492 (1.12746 iter/s, 10.6434s/12 iters), loss = 0.882545
I0409 21:11:31.621685 15108 solver.cpp:237]     Train net output #0: loss = 0.882545 (* 1 = 0.882545 loss)
I0409 21:11:31.621698 15108 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 21:11:36.622184 15108 solver.cpp:218] Iteration 9504 (2.39986 iter/s, 5.00029s/12 iters), loss = 0.747791
I0409 21:11:36.622236 15108 solver.cpp:237]     Train net output #0: loss = 0.747791 (* 1 = 0.747791 loss)
I0409 21:11:36.622246 15108 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 21:11:38.092501 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:11:41.574553 15108 solver.cpp:218] Iteration 9516 (2.4232 iter/s, 4.95214s/12 iters), loss = 0.704812
I0409 21:11:41.574597 15108 solver.cpp:237]     Train net output #0: loss = 0.704812 (* 1 = 0.704812 loss)
I0409 21:11:41.574609 15108 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 21:11:46.609452 15108 solver.cpp:218] Iteration 9528 (2.38348 iter/s, 5.03466s/12 iters), loss = 0.481522
I0409 21:11:46.609504 15108 solver.cpp:237]     Train net output #0: loss = 0.481522 (* 1 = 0.481522 loss)
I0409 21:11:46.609517 15108 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 21:11:51.554028 15108 solver.cpp:218] Iteration 9540 (2.42702 iter/s, 4.94433s/12 iters), loss = 0.611398
I0409 21:11:51.554087 15108 solver.cpp:237]     Train net output #0: loss = 0.611398 (* 1 = 0.611398 loss)
I0409 21:11:51.554100 15108 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 21:11:56.491788 15108 solver.cpp:218] Iteration 9552 (2.43037 iter/s, 4.93751s/12 iters), loss = 0.665642
I0409 21:11:56.491840 15108 solver.cpp:237]     Train net output #0: loss = 0.665642 (* 1 = 0.665642 loss)
I0409 21:11:56.491852 15108 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 21:12:01.441735 15108 solver.cpp:218] Iteration 9564 (2.42438 iter/s, 4.94971s/12 iters), loss = 0.62757
I0409 21:12:01.441852 15108 solver.cpp:237]     Train net output #0: loss = 0.62757 (* 1 = 0.62757 loss)
I0409 21:12:01.441862 15108 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 21:12:06.345873 15108 solver.cpp:218] Iteration 9576 (2.44706 iter/s, 4.90383s/12 iters), loss = 0.659694
I0409 21:12:06.345925 15108 solver.cpp:237]     Train net output #0: loss = 0.659694 (* 1 = 0.659694 loss)
I0409 21:12:06.345935 15108 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 21:12:10.802093 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 21:12:11.593467 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 21:12:12.196679 15108 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 21:12:12.196702 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:12:12.892697 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:16.640652 15108 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0409 21:12:16.640686 15108 solver.cpp:397]     Test net output #1: loss = 2.25284 (* 1 = 2.25284 loss)
I0409 21:12:16.721951 15108 solver.cpp:218] Iteration 9588 (1.15655 iter/s, 10.3756s/12 iters), loss = 0.678733
I0409 21:12:16.722028 15108 solver.cpp:237]     Train net output #0: loss = 0.678733 (* 1 = 0.678733 loss)
I0409 21:12:16.722039 15108 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 21:12:20.815868 15108 solver.cpp:218] Iteration 9600 (2.93134 iter/s, 4.09369s/12 iters), loss = 0.69332
I0409 21:12:20.815915 15108 solver.cpp:237]     Train net output #0: loss = 0.69332 (* 1 = 0.69332 loss)
I0409 21:12:20.815925 15108 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 21:12:24.332207 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:12:25.705885 15108 solver.cpp:218] Iteration 9612 (2.4541 iter/s, 4.88978s/12 iters), loss = 0.602582
I0409 21:12:25.705935 15108 solver.cpp:237]     Train net output #0: loss = 0.602582 (* 1 = 0.602582 loss)
I0409 21:12:25.705947 15108 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 21:12:30.600725 15108 solver.cpp:218] Iteration 9624 (2.45168 iter/s, 4.89461s/12 iters), loss = 0.748045
I0409 21:12:30.600775 15108 solver.cpp:237]     Train net output #0: loss = 0.748045 (* 1 = 0.748045 loss)
I0409 21:12:30.600785 15108 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 21:12:35.525792 15108 solver.cpp:218] Iteration 9636 (2.43663 iter/s, 4.92484s/12 iters), loss = 0.788559
I0409 21:12:35.525858 15108 solver.cpp:237]     Train net output #0: loss = 0.788559 (* 1 = 0.788559 loss)
I0409 21:12:35.525868 15108 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 21:12:40.349303 15108 solver.cpp:218] Iteration 9648 (2.48794 iter/s, 4.82326s/12 iters), loss = 1.00181
I0409 21:12:40.349352 15108 solver.cpp:237]     Train net output #0: loss = 1.00181 (* 1 = 1.00181 loss)
I0409 21:12:40.349362 15108 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 21:12:45.291379 15108 solver.cpp:218] Iteration 9660 (2.42825 iter/s, 4.94184s/12 iters), loss = 0.726413
I0409 21:12:45.291432 15108 solver.cpp:237]     Train net output #0: loss = 0.726413 (* 1 = 0.726413 loss)
I0409 21:12:45.291443 15108 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 21:12:50.188913 15108 solver.cpp:218] Iteration 9672 (2.45033 iter/s, 4.8973s/12 iters), loss = 0.443297
I0409 21:12:50.188961 15108 solver.cpp:237]     Train net output #0: loss = 0.443297 (* 1 = 0.443297 loss)
I0409 21:12:50.188972 15108 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 21:12:55.174810 15108 solver.cpp:218] Iteration 9684 (2.4069 iter/s, 4.98566s/12 iters), loss = 0.64246
I0409 21:12:55.174860 15108 solver.cpp:237]     Train net output #0: loss = 0.64246 (* 1 = 0.64246 loss)
I0409 21:12:55.174870 15108 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 21:12:57.319196 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 21:12:58.140705 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 21:12:58.714159 15108 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 21:12:58.714184 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:12:59.255539 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:01.820433 15108 blocking_queue.cpp:49] Waiting for data
I0409 21:13:03.089061 15108 solver.cpp:397]     Test net output #0: accuracy = 0.507353
I0409 21:13:03.089097 15108 solver.cpp:397]     Test net output #1: loss = 2.23972 (* 1 = 2.23972 loss)
I0409 21:13:04.946738 15108 solver.cpp:218] Iteration 9696 (1.22806 iter/s, 9.77152s/12 iters), loss = 0.620688
I0409 21:13:04.946790 15108 solver.cpp:237]     Train net output #0: loss = 0.620688 (* 1 = 0.620688 loss)
I0409 21:13:04.946800 15108 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 21:13:09.839366 15108 solver.cpp:218] Iteration 9708 (2.45279 iter/s, 4.89239s/12 iters), loss = 0.710118
I0409 21:13:09.839488 15108 solver.cpp:237]     Train net output #0: loss = 0.710118 (* 1 = 0.710118 loss)
I0409 21:13:09.839498 15108 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 21:13:10.573921 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:14.767573 15108 solver.cpp:218] Iteration 9720 (2.43511 iter/s, 4.9279s/12 iters), loss = 0.549409
I0409 21:13:14.767616 15108 solver.cpp:237]     Train net output #0: loss = 0.549409 (* 1 = 0.549409 loss)
I0409 21:13:14.767624 15108 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 21:13:19.893810 15108 solver.cpp:218] Iteration 9732 (2.34101 iter/s, 5.126s/12 iters), loss = 0.654698
I0409 21:13:19.893870 15108 solver.cpp:237]     Train net output #0: loss = 0.654698 (* 1 = 0.654698 loss)
I0409 21:13:19.893885 15108 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 21:13:24.926448 15108 solver.cpp:218] Iteration 9744 (2.38455 iter/s, 5.03239s/12 iters), loss = 0.693082
I0409 21:13:24.926501 15108 solver.cpp:237]     Train net output #0: loss = 0.693082 (* 1 = 0.693082 loss)
I0409 21:13:24.926512 15108 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 21:13:30.035554 15108 solver.cpp:218] Iteration 9756 (2.34886 iter/s, 5.10886s/12 iters), loss = 0.674306
I0409 21:13:30.035614 15108 solver.cpp:237]     Train net output #0: loss = 0.674306 (* 1 = 0.674306 loss)
I0409 21:13:30.035627 15108 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 21:13:35.225843 15108 solver.cpp:218] Iteration 9768 (2.31212 iter/s, 5.19003s/12 iters), loss = 0.583128
I0409 21:13:35.225899 15108 solver.cpp:237]     Train net output #0: loss = 0.583128 (* 1 = 0.583128 loss)
I0409 21:13:35.225912 15108 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 21:13:40.177343 15108 solver.cpp:218] Iteration 9780 (2.42363 iter/s, 4.95126s/12 iters), loss = 0.778062
I0409 21:13:40.177455 15108 solver.cpp:237]     Train net output #0: loss = 0.778062 (* 1 = 0.778062 loss)
I0409 21:13:40.177469 15108 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 21:13:44.631878 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 21:13:45.451407 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 21:13:46.037124 15108 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 21:13:46.037155 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:13:46.641033 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:50.483490 15108 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0409 21:13:50.483527 15108 solver.cpp:397]     Test net output #1: loss = 2.21623 (* 1 = 2.21623 loss)
I0409 21:13:50.566687 15108 solver.cpp:218] Iteration 9792 (1.15508 iter/s, 10.3889s/12 iters), loss = 0.503551
I0409 21:13:50.566746 15108 solver.cpp:237]     Train net output #0: loss = 0.503551 (* 1 = 0.503551 loss)
I0409 21:13:50.566758 15108 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 21:13:54.894201 15108 solver.cpp:218] Iteration 9804 (2.7731 iter/s, 4.32729s/12 iters), loss = 0.590275
I0409 21:13:54.894261 15108 solver.cpp:237]     Train net output #0: loss = 0.590275 (* 1 = 0.590275 loss)
I0409 21:13:54.894274 15108 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 21:13:57.801055 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:13:59.799482 15108 solver.cpp:218] Iteration 9816 (2.44647 iter/s, 4.90503s/12 iters), loss = 0.794975
I0409 21:13:59.799543 15108 solver.cpp:237]     Train net output #0: loss = 0.794975 (* 1 = 0.794975 loss)
I0409 21:13:59.799558 15108 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 21:14:04.748869 15108 solver.cpp:218] Iteration 9828 (2.42466 iter/s, 4.94914s/12 iters), loss = 0.592199
I0409 21:14:04.748914 15108 solver.cpp:237]     Train net output #0: loss = 0.592199 (* 1 = 0.592199 loss)
I0409 21:14:04.748924 15108 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 21:14:09.613375 15108 solver.cpp:218] Iteration 9840 (2.46696 iter/s, 4.86428s/12 iters), loss = 0.567859
I0409 21:14:09.613417 15108 solver.cpp:237]     Train net output #0: loss = 0.567859 (* 1 = 0.567859 loss)
I0409 21:14:09.613426 15108 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 21:14:14.521247 15108 solver.cpp:218] Iteration 9852 (2.44517 iter/s, 4.90764s/12 iters), loss = 0.575176
I0409 21:14:14.521353 15108 solver.cpp:237]     Train net output #0: loss = 0.575176 (* 1 = 0.575176 loss)
I0409 21:14:14.521363 15108 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 21:14:19.453687 15108 solver.cpp:218] Iteration 9864 (2.43302 iter/s, 4.93214s/12 iters), loss = 0.676765
I0409 21:14:19.453754 15108 solver.cpp:237]     Train net output #0: loss = 0.676765 (* 1 = 0.676765 loss)
I0409 21:14:19.453769 15108 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 21:14:24.370028 15108 solver.cpp:218] Iteration 9876 (2.44096 iter/s, 4.91609s/12 iters), loss = 0.594919
I0409 21:14:24.370076 15108 solver.cpp:237]     Train net output #0: loss = 0.594919 (* 1 = 0.594919 loss)
I0409 21:14:24.370086 15108 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 21:14:29.287494 15108 solver.cpp:218] Iteration 9888 (2.44039 iter/s, 4.91724s/12 iters), loss = 0.663865
I0409 21:14:29.287524 15108 solver.cpp:237]     Train net output #0: loss = 0.663865 (* 1 = 0.663865 loss)
I0409 21:14:29.287533 15108 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 21:14:31.294768 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 21:14:32.809152 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 21:14:33.394640 15108 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 21:14:33.394668 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:14:33.871354 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:37.820175 15108 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0409 21:14:37.820209 15108 solver.cpp:397]     Test net output #1: loss = 2.24225 (* 1 = 2.24225 loss)
I0409 21:14:39.648380 15108 solver.cpp:218] Iteration 9900 (1.15825 iter/s, 10.3605s/12 iters), loss = 0.673869
I0409 21:14:39.648437 15108 solver.cpp:237]     Train net output #0: loss = 0.673869 (* 1 = 0.673869 loss)
I0409 21:14:39.648448 15108 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 21:14:44.512631 15108 solver.cpp:218] Iteration 9912 (2.4671 iter/s, 4.86401s/12 iters), loss = 0.610698
I0409 21:14:44.512684 15108 solver.cpp:237]     Train net output #0: loss = 0.610698 (* 1 = 0.610698 loss)
I0409 21:14:44.512696 15108 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 21:14:44.610849 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:14:49.371253 15108 solver.cpp:218] Iteration 9924 (2.46996 iter/s, 4.85837s/12 iters), loss = 0.59244
I0409 21:14:49.371320 15108 solver.cpp:237]     Train net output #0: loss = 0.59244 (* 1 = 0.59244 loss)
I0409 21:14:49.371336 15108 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 21:14:54.287755 15108 solver.cpp:218] Iteration 9936 (2.44088 iter/s, 4.91625s/12 iters), loss = 0.56685
I0409 21:14:54.287804 15108 solver.cpp:237]     Train net output #0: loss = 0.56685 (* 1 = 0.56685 loss)
I0409 21:14:54.287818 15108 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 21:14:59.396296 15108 solver.cpp:218] Iteration 9948 (2.34912 iter/s, 5.1083s/12 iters), loss = 0.492758
I0409 21:14:59.396342 15108 solver.cpp:237]     Train net output #0: loss = 0.492758 (* 1 = 0.492758 loss)
I0409 21:14:59.396353 15108 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 21:15:04.395045 15108 solver.cpp:218] Iteration 9960 (2.40071 iter/s, 4.99851s/12 iters), loss = 0.615134
I0409 21:15:04.395095 15108 solver.cpp:237]     Train net output #0: loss = 0.615134 (* 1 = 0.615134 loss)
I0409 21:15:04.395107 15108 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 21:15:09.258190 15108 solver.cpp:218] Iteration 9972 (2.46766 iter/s, 4.8629s/12 iters), loss = 0.544312
I0409 21:15:09.258249 15108 solver.cpp:237]     Train net output #0: loss = 0.544312 (* 1 = 0.544312 loss)
I0409 21:15:09.258263 15108 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 21:15:14.192529 15108 solver.cpp:218] Iteration 9984 (2.43206 iter/s, 4.93409s/12 iters), loss = 0.551331
I0409 21:15:14.192582 15108 solver.cpp:237]     Train net output #0: loss = 0.551331 (* 1 = 0.551331 loss)
I0409 21:15:14.192595 15108 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 21:15:18.858675 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 21:15:20.132182 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 21:15:20.700951 15108 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 21:15:20.700973 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:15:21.207842 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:25.133085 15108 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0409 21:15:25.133123 15108 solver.cpp:397]     Test net output #1: loss = 2.24924 (* 1 = 2.24924 loss)
I0409 21:15:25.216111 15108 solver.cpp:218] Iteration 9996 (1.08862 iter/s, 11.0231s/12 iters), loss = 0.607461
I0409 21:15:25.216163 15108 solver.cpp:237]     Train net output #0: loss = 0.607461 (* 1 = 0.607461 loss)
I0409 21:15:25.216174 15108 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 21:15:29.444664 15108 solver.cpp:218] Iteration 10008 (2.83799 iter/s, 4.22834s/12 iters), loss = 0.532523
I0409 21:15:29.444705 15108 solver.cpp:237]     Train net output #0: loss = 0.532523 (* 1 = 0.532523 loss)
I0409 21:15:29.444713 15108 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 21:15:31.647279 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:15:34.377389 15108 solver.cpp:218] Iteration 10020 (2.43285 iter/s, 4.93249s/12 iters), loss = 0.431648
I0409 21:15:34.377446 15108 solver.cpp:237]     Train net output #0: loss = 0.431648 (* 1 = 0.431648 loss)
I0409 21:15:34.377460 15108 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 21:15:39.354686 15108 solver.cpp:218] Iteration 10032 (2.41107 iter/s, 4.97705s/12 iters), loss = 0.45576
I0409 21:15:39.354734 15108 solver.cpp:237]     Train net output #0: loss = 0.45576 (* 1 = 0.45576 loss)
I0409 21:15:39.354744 15108 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 21:15:44.248160 15108 solver.cpp:218] Iteration 10044 (2.45236 iter/s, 4.89324s/12 iters), loss = 0.538092
I0409 21:15:44.248220 15108 solver.cpp:237]     Train net output #0: loss = 0.538092 (* 1 = 0.538092 loss)
I0409 21:15:44.248234 15108 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 21:15:49.159695 15108 solver.cpp:218] Iteration 10056 (2.44335 iter/s, 4.91129s/12 iters), loss = 0.476002
I0409 21:15:49.159826 15108 solver.cpp:237]     Train net output #0: loss = 0.476002 (* 1 = 0.476002 loss)
I0409 21:15:49.159837 15108 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 21:15:54.066094 15108 solver.cpp:218] Iteration 10068 (2.44594 iter/s, 4.90608s/12 iters), loss = 0.566372
I0409 21:15:54.066146 15108 solver.cpp:237]     Train net output #0: loss = 0.566372 (* 1 = 0.566372 loss)
I0409 21:15:54.066159 15108 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 21:15:58.922677 15108 solver.cpp:218] Iteration 10080 (2.47099 iter/s, 4.85634s/12 iters), loss = 0.683272
I0409 21:15:58.922734 15108 solver.cpp:237]     Train net output #0: loss = 0.683272 (* 1 = 0.683272 loss)
I0409 21:15:58.922747 15108 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 21:16:03.842224 15108 solver.cpp:218] Iteration 10092 (2.43937 iter/s, 4.9193s/12 iters), loss = 0.563699
I0409 21:16:03.842280 15108 solver.cpp:237]     Train net output #0: loss = 0.563699 (* 1 = 0.563699 loss)
I0409 21:16:03.842293 15108 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 21:16:05.854575 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 21:16:07.657776 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 21:16:09.466271 15108 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 21:16:09.466301 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:16:09.940183 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:13.945681 15108 solver.cpp:397]     Test net output #0: accuracy = 0.498162
I0409 21:16:13.945731 15108 solver.cpp:397]     Test net output #1: loss = 2.27034 (* 1 = 2.27034 loss)
I0409 21:16:15.775964 15108 solver.cpp:218] Iteration 10104 (1.00559 iter/s, 11.9333s/12 iters), loss = 0.670067
I0409 21:16:15.776011 15108 solver.cpp:237]     Train net output #0: loss = 0.670067 (* 1 = 0.670067 loss)
I0409 21:16:15.776021 15108 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 21:16:20.082479 15112 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:16:20.706854 15108 solver.cpp:218] Iteration 10116 (2.43375 iter/s, 4.93065s/12 iters), loss = 0.585454
I0409 21:16:20.706910 15108 solver.cpp:237]     Train net output #0: loss = 0.585454 (* 1 = 0.585454 loss)
I0409 21:16:20.706924 15108 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 21:16:25.646994 15108 solver.cpp:218] Iteration 10128 (2.4292 iter/s, 4.9399s/12 iters), loss = 0.659261
I0409 21:16:25.647051 15108 solver.cpp:237]     Train net output #0: loss = 0.659261 (* 1 = 0.659261 loss)
I0409 21:16:25.647064 15108 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 21:16:30.547545 15108 solver.cpp:218] Iteration 10140 (2.44883 iter/s, 4.9003s/12 iters), loss = 0.609744
I0409 21:16:30.547610 15108 solver.cpp:237]     Train net output #0: loss = 0.609744 (* 1 = 0.609744 loss)
I0409 21:16:30.547623 15108 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 21:16:35.451246 15108 solver.cpp:218] Iteration 10152 (2.44726 iter/s, 4.90345s/12 iters), loss = 0.503218
I0409 21:16:35.451290 15108 solver.cpp:237]     Train net output #0: loss = 0.503218 (* 1 = 0.503218 loss)
I0409 21:16:35.451299 15108 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 21:16:40.459657 15108 solver.cpp:218] Iteration 10164 (2.39608 iter/s, 5.00817s/12 iters), loss = 0.573777
I0409 21:16:40.459710 15108 solver.cpp:237]     Train net output #0: loss = 0.573777 (* 1 = 0.573777 loss)
I0409 21:16:40.459723 15108 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 21:16:45.399155 15108 solver.cpp:218] Iteration 10176 (2.42951 iter/s, 4.93926s/12 iters), loss = 0.678787
I0409 21:16:45.399195 15108 solver.cpp:237]     Train net output #0: loss = 0.678787 (* 1 = 0.678787 loss)
I0409 21:16:45.399205 15108 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 21:16:50.408697 15108 solver.cpp:218] Iteration 10188 (2.39554 iter/s, 5.00931s/12 iters), loss = 0.577685
I0409 21:16:50.408877 15108 solver.cpp:237]     Train net output #0: loss = 0.577685 (* 1 = 0.577685 loss)
I0409 21:16:50.408891 15108 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 21:16:54.904126 15108 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 21:16:55.706332 15108 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 21:16:56.318011 15108 solver.cpp:310] Iteration 10200, loss = 0.429073
I0409 21:16:56.318048 15108 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 21:16:56.318058 15108 net.cpp:676] Ignoring source layer train-data
I0409 21:16:56.796306 15113 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:01.062069 15108 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0409 21:17:01.062119 15108 solver.cpp:397]     Test net output #1: loss = 2.2961 (* 1 = 2.2961 loss)
I0409 21:17:01.062130 15108 solver.cpp:315] Optimization Done.
I0409 21:17:01.062137 15108 caffe.cpp:259] Optimization Done.