DIGITS-CNN/cars/architecture-investigations/fc/2-layers/512/caffe_output.log

I0409 23:04:49.787638  6396 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-210951-7f10/solver.prototxt
I0409 23:04:49.787843  6396 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 23:04:49.787851  6396 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 23:04:49.787930  6396 caffe.cpp:218] Using GPUs 1
I0409 23:04:49.846495  6396 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0409 23:04:50.139474  6396 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 23:04:50.218997  6396 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 23:04:50.270009  6396 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 23:04:50.270038  6396 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 23:04:50.270239  6396 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 23:04:50.270352  6396 layer_factory.hpp:77] Creating layer train-data
I0409 23:04:50.516469  6396 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 23:04:50.517014  6396 net.cpp:84] Creating Layer train-data
I0409 23:04:50.517041  6396 net.cpp:380] train-data -> data
I0409 23:04:50.517081  6396 net.cpp:380] train-data -> label
I0409 23:04:50.517105  6396 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 23:04:50.590185  6396 data_layer.cpp:45] output data size: 128,3,227,227
I0409 23:04:50.759325  6396 net.cpp:122] Setting up train-data
I0409 23:04:50.759351  6396 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 23:04:50.759356  6396 net.cpp:129] Top shape: 128 (128)
I0409 23:04:50.759359  6396 net.cpp:137] Memory required for data: 79149056
I0409 23:04:50.759371  6396 layer_factory.hpp:77] Creating layer conv1
I0409 23:04:50.759392  6396 net.cpp:84] Creating Layer conv1
I0409 23:04:50.759399  6396 net.cpp:406] conv1 <- data
I0409 23:04:50.759413  6396 net.cpp:380] conv1 -> conv1
I0409 23:04:51.343858  6396 net.cpp:122] Setting up conv1
I0409 23:04:51.343883  6396 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:04:51.343888  6396 net.cpp:137] Memory required for data: 227833856
I0409 23:04:51.343909  6396 layer_factory.hpp:77] Creating layer relu1
I0409 23:04:51.343919  6396 net.cpp:84] Creating Layer relu1
I0409 23:04:51.343924  6396 net.cpp:406] relu1 <- conv1
I0409 23:04:51.343931  6396 net.cpp:367] relu1 -> conv1 (in-place)
I0409 23:04:51.344242  6396 net.cpp:122] Setting up relu1
I0409 23:04:51.344252  6396 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:04:51.344256  6396 net.cpp:137] Memory required for data: 376518656
I0409 23:04:51.344260  6396 layer_factory.hpp:77] Creating layer norm1
I0409 23:04:51.344269  6396 net.cpp:84] Creating Layer norm1
I0409 23:04:51.344274  6396 net.cpp:406] norm1 <- conv1
I0409 23:04:51.344305  6396 net.cpp:380] norm1 -> norm1
I0409 23:04:51.344785  6396 net.cpp:122] Setting up norm1
I0409 23:04:51.344797  6396 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:04:51.344802  6396 net.cpp:137] Memory required for data: 525203456
I0409 23:04:51.344808  6396 layer_factory.hpp:77] Creating layer pool1
I0409 23:04:51.344816  6396 net.cpp:84] Creating Layer pool1
I0409 23:04:51.344821  6396 net.cpp:406] pool1 <- norm1
I0409 23:04:51.344828  6396 net.cpp:380] pool1 -> pool1
I0409 23:04:51.344867  6396 net.cpp:122] Setting up pool1
I0409 23:04:51.344875  6396 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 23:04:51.344879  6396 net.cpp:137] Memory required for data: 561035264
I0409 23:04:51.344883  6396 layer_factory.hpp:77] Creating layer conv2
I0409 23:04:51.344894  6396 net.cpp:84] Creating Layer conv2
I0409 23:04:51.344897  6396 net.cpp:406] conv2 <- pool1
I0409 23:04:51.344903  6396 net.cpp:380] conv2 -> conv2
I0409 23:04:51.351981  6396 net.cpp:122] Setting up conv2
I0409 23:04:51.351996  6396 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:04:51.352000  6396 net.cpp:137] Memory required for data: 656586752
I0409 23:04:51.352010  6396 layer_factory.hpp:77] Creating layer relu2
I0409 23:04:51.352017  6396 net.cpp:84] Creating Layer relu2
I0409 23:04:51.352021  6396 net.cpp:406] relu2 <- conv2
I0409 23:04:51.352028  6396 net.cpp:367] relu2 -> conv2 (in-place)
I0409 23:04:51.352473  6396 net.cpp:122] Setting up relu2
I0409 23:04:51.352484  6396 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:04:51.352488  6396 net.cpp:137] Memory required for data: 752138240
I0409 23:04:51.352494  6396 layer_factory.hpp:77] Creating layer norm2
I0409 23:04:51.352501  6396 net.cpp:84] Creating Layer norm2
I0409 23:04:51.352506  6396 net.cpp:406] norm2 <- conv2
I0409 23:04:51.352514  6396 net.cpp:380] norm2 -> norm2
I0409 23:04:51.352819  6396 net.cpp:122] Setting up norm2
I0409 23:04:51.352829  6396 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:04:51.352833  6396 net.cpp:137] Memory required for data: 847689728
I0409 23:04:51.352838  6396 layer_factory.hpp:77] Creating layer pool2
I0409 23:04:51.352845  6396 net.cpp:84] Creating Layer pool2
I0409 23:04:51.352849  6396 net.cpp:406] pool2 <- norm2
I0409 23:04:51.352854  6396 net.cpp:380] pool2 -> pool2
I0409 23:04:51.352882  6396 net.cpp:122] Setting up pool2
I0409 23:04:51.352888  6396 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:04:51.352891  6396 net.cpp:137] Memory required for data: 869840896
I0409 23:04:51.352895  6396 layer_factory.hpp:77] Creating layer conv3
I0409 23:04:51.352903  6396 net.cpp:84] Creating Layer conv3
I0409 23:04:51.352908  6396 net.cpp:406] conv3 <- pool2
I0409 23:04:51.352914  6396 net.cpp:380] conv3 -> conv3
I0409 23:04:51.363785  6396 net.cpp:122] Setting up conv3
I0409 23:04:51.363798  6396 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:04:51.363802  6396 net.cpp:137] Memory required for data: 903067648
I0409 23:04:51.363812  6396 layer_factory.hpp:77] Creating layer relu3
I0409 23:04:51.363821  6396 net.cpp:84] Creating Layer relu3
I0409 23:04:51.363826  6396 net.cpp:406] relu3 <- conv3
I0409 23:04:51.363832  6396 net.cpp:367] relu3 -> conv3 (in-place)
I0409 23:04:51.364352  6396 net.cpp:122] Setting up relu3
I0409 23:04:51.364362  6396 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:04:51.364365  6396 net.cpp:137] Memory required for data: 936294400
I0409 23:04:51.364369  6396 layer_factory.hpp:77] Creating layer conv4
I0409 23:04:51.364380  6396 net.cpp:84] Creating Layer conv4
I0409 23:04:51.364384  6396 net.cpp:406] conv4 <- conv3
I0409 23:04:51.364392  6396 net.cpp:380] conv4 -> conv4
I0409 23:04:51.375677  6396 net.cpp:122] Setting up conv4
I0409 23:04:51.375695  6396 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:04:51.375699  6396 net.cpp:137] Memory required for data: 969521152
I0409 23:04:51.375708  6396 layer_factory.hpp:77] Creating layer relu4
I0409 23:04:51.375716  6396 net.cpp:84] Creating Layer relu4
I0409 23:04:51.375739  6396 net.cpp:406] relu4 <- conv4
I0409 23:04:51.375747  6396 net.cpp:367] relu4 -> conv4 (in-place)
I0409 23:04:51.376114  6396 net.cpp:122] Setting up relu4
I0409 23:04:51.376124  6396 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:04:51.376128  6396 net.cpp:137] Memory required for data: 1002747904
I0409 23:04:51.376132  6396 layer_factory.hpp:77] Creating layer conv5
I0409 23:04:51.376143  6396 net.cpp:84] Creating Layer conv5
I0409 23:04:51.376147  6396 net.cpp:406] conv5 <- conv4
I0409 23:04:51.376157  6396 net.cpp:380] conv5 -> conv5
I0409 23:04:51.390148  6396 net.cpp:122] Setting up conv5
I0409 23:04:51.390172  6396 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:04:51.390177  6396 net.cpp:137] Memory required for data: 1024899072
I0409 23:04:51.390206  6396 layer_factory.hpp:77] Creating layer relu5
I0409 23:04:51.390223  6396 net.cpp:84] Creating Layer relu5
I0409 23:04:51.390233  6396 net.cpp:406] relu5 <- conv5
I0409 23:04:51.390249  6396 net.cpp:367] relu5 -> conv5 (in-place)
I0409 23:04:51.391422  6396 net.cpp:122] Setting up relu5
I0409 23:04:51.391443  6396 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:04:51.391450  6396 net.cpp:137] Memory required for data: 1047050240
I0409 23:04:51.391458  6396 layer_factory.hpp:77] Creating layer pool5
I0409 23:04:51.391475  6396 net.cpp:84] Creating Layer pool5
I0409 23:04:51.391484  6396 net.cpp:406] pool5 <- conv5
I0409 23:04:51.391496  6396 net.cpp:380] pool5 -> pool5
I0409 23:04:51.391587  6396 net.cpp:122] Setting up pool5
I0409 23:04:51.391599  6396 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 23:04:51.391606  6396 net.cpp:137] Memory required for data: 1051768832
I0409 23:04:51.391613  6396 layer_factory.hpp:77] Creating layer fc6
I0409 23:04:51.391633  6396 net.cpp:84] Creating Layer fc6
I0409 23:04:51.391640  6396 net.cpp:406] fc6 <- pool5
I0409 23:04:51.391652  6396 net.cpp:380] fc6 -> fc6
I0409 23:04:51.486677  6396 net.cpp:122] Setting up fc6
I0409 23:04:51.486703  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.486708  6396 net.cpp:137] Memory required for data: 1052030976
I0409 23:04:51.486721  6396 layer_factory.hpp:77] Creating layer relu6
I0409 23:04:51.486733  6396 net.cpp:84] Creating Layer relu6
I0409 23:04:51.486739  6396 net.cpp:406] relu6 <- fc6
I0409 23:04:51.486748  6396 net.cpp:367] relu6 -> fc6 (in-place)
I0409 23:04:51.487648  6396 net.cpp:122] Setting up relu6
I0409 23:04:51.487660  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.487666  6396 net.cpp:137] Memory required for data: 1052293120
I0409 23:04:51.487671  6396 layer_factory.hpp:77] Creating layer drop6
I0409 23:04:51.487681  6396 net.cpp:84] Creating Layer drop6
I0409 23:04:51.487686  6396 net.cpp:406] drop6 <- fc6
I0409 23:04:51.487696  6396 net.cpp:367] drop6 -> fc6 (in-place)
I0409 23:04:51.487736  6396 net.cpp:122] Setting up drop6
I0409 23:04:51.487743  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.487748  6396 net.cpp:137] Memory required for data: 1052555264
I0409 23:04:51.487753  6396 layer_factory.hpp:77] Creating layer fc7
I0409 23:04:51.487766  6396 net.cpp:84] Creating Layer fc7
I0409 23:04:51.487771  6396 net.cpp:406] fc7 <- fc6
I0409 23:04:51.487778  6396 net.cpp:380] fc7 -> fc7
I0409 23:04:51.491689  6396 net.cpp:122] Setting up fc7
I0409 23:04:51.491699  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.491704  6396 net.cpp:137] Memory required for data: 1052817408
I0409 23:04:51.491714  6396 layer_factory.hpp:77] Creating layer relu7
I0409 23:04:51.491721  6396 net.cpp:84] Creating Layer relu7
I0409 23:04:51.491725  6396 net.cpp:406] relu7 <- fc7
I0409 23:04:51.491732  6396 net.cpp:367] relu7 -> fc7 (in-place)
I0409 23:04:51.493413  6396 net.cpp:122] Setting up relu7
I0409 23:04:51.493425  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.493430  6396 net.cpp:137] Memory required for data: 1053079552
I0409 23:04:51.493435  6396 layer_factory.hpp:77] Creating layer drop7
I0409 23:04:51.493443  6396 net.cpp:84] Creating Layer drop7
I0409 23:04:51.493448  6396 net.cpp:406] drop7 <- fc7
I0409 23:04:51.493481  6396 net.cpp:367] drop7 -> fc7 (in-place)
I0409 23:04:51.493516  6396 net.cpp:122] Setting up drop7
I0409 23:04:51.493523  6396 net.cpp:129] Top shape: 128 512 (65536)
I0409 23:04:51.493527  6396 net.cpp:137] Memory required for data: 1053341696
I0409 23:04:51.493532  6396 layer_factory.hpp:77] Creating layer fc8
I0409 23:04:51.493542  6396 net.cpp:84] Creating Layer fc8
I0409 23:04:51.493547  6396 net.cpp:406] fc8 <- fc7
I0409 23:04:51.493556  6396 net.cpp:380] fc8 -> fc8
I0409 23:04:51.495100  6396 net.cpp:122] Setting up fc8
I0409 23:04:51.495108  6396 net.cpp:129] Top shape: 128 196 (25088)
I0409 23:04:51.495113  6396 net.cpp:137] Memory required for data: 1053442048
I0409 23:04:51.495121  6396 layer_factory.hpp:77] Creating layer loss
I0409 23:04:51.495129  6396 net.cpp:84] Creating Layer loss
I0409 23:04:51.495134  6396 net.cpp:406] loss <- fc8
I0409 23:04:51.495141  6396 net.cpp:406] loss <- label
I0409 23:04:51.495148  6396 net.cpp:380] loss -> loss
I0409 23:04:51.495159  6396 layer_factory.hpp:77] Creating layer loss
I0409 23:04:51.496053  6396 net.cpp:122] Setting up loss
I0409 23:04:51.496068  6396 net.cpp:129] Top shape: (1)
I0409 23:04:51.496073  6396 net.cpp:132]     with loss weight 1
I0409 23:04:51.496094  6396 net.cpp:137] Memory required for data: 1053442052
I0409 23:04:51.496100  6396 net.cpp:198] loss needs backward computation.
I0409 23:04:51.496110  6396 net.cpp:198] fc8 needs backward computation.
I0409 23:04:51.496115  6396 net.cpp:198] drop7 needs backward computation.
I0409 23:04:51.496120  6396 net.cpp:198] relu7 needs backward computation.
I0409 23:04:51.496125  6396 net.cpp:198] fc7 needs backward computation.
I0409 23:04:51.496130  6396 net.cpp:198] drop6 needs backward computation.
I0409 23:04:51.496134  6396 net.cpp:198] relu6 needs backward computation.
I0409 23:04:51.496140  6396 net.cpp:198] fc6 needs backward computation.
I0409 23:04:51.496145  6396 net.cpp:198] pool5 needs backward computation.
I0409 23:04:51.496150  6396 net.cpp:198] relu5 needs backward computation.
I0409 23:04:51.496155  6396 net.cpp:198] conv5 needs backward computation.
I0409 23:04:51.496160  6396 net.cpp:198] relu4 needs backward computation.
I0409 23:04:51.496165  6396 net.cpp:198] conv4 needs backward computation.
I0409 23:04:51.496170  6396 net.cpp:198] relu3 needs backward computation.
I0409 23:04:51.496176  6396 net.cpp:198] conv3 needs backward computation.
I0409 23:04:51.496181  6396 net.cpp:198] pool2 needs backward computation.
I0409 23:04:51.496186  6396 net.cpp:198] norm2 needs backward computation.
I0409 23:04:51.496191  6396 net.cpp:198] relu2 needs backward computation.
I0409 23:04:51.496196  6396 net.cpp:198] conv2 needs backward computation.
I0409 23:04:51.496201  6396 net.cpp:198] pool1 needs backward computation.
I0409 23:04:51.496206  6396 net.cpp:198] norm1 needs backward computation.
I0409 23:04:51.496210  6396 net.cpp:198] relu1 needs backward computation.
I0409 23:04:51.496215  6396 net.cpp:198] conv1 needs backward computation.
I0409 23:04:51.496222  6396 net.cpp:200] train-data does not need backward computation.
I0409 23:04:51.496227  6396 net.cpp:242] This network produces output loss
I0409 23:04:51.496246  6396 net.cpp:255] Network initialization done.
I0409 23:04:51.526417  6396 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 23:04:51.526487  6396 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 23:04:51.526746  6396 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 512
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 23:04:51.526924  6396 layer_factory.hpp:77] Creating layer val-data
I0409 23:04:51.908569  6396 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 23:04:51.909112  6396 net.cpp:84] Creating Layer val-data
I0409 23:04:51.909137  6396 net.cpp:380] val-data -> data
I0409 23:04:51.909159  6396 net.cpp:380] val-data -> label
I0409 23:04:51.909174  6396 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 23:04:51.960507  6396 data_layer.cpp:45] output data size: 32,3,227,227
I0409 23:04:52.004259  6396 net.cpp:122] Setting up val-data
I0409 23:04:52.004284  6396 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 23:04:52.004292  6396 net.cpp:129] Top shape: 32 (32)
I0409 23:04:52.004295  6396 net.cpp:137] Memory required for data: 19787264
I0409 23:04:52.004303  6396 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 23:04:52.004318  6396 net.cpp:84] Creating Layer label_val-data_1_split
I0409 23:04:52.004324  6396 net.cpp:406] label_val-data_1_split <- label
I0409 23:04:52.004333  6396 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 23:04:52.004346  6396 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 23:04:52.004489  6396 net.cpp:122] Setting up label_val-data_1_split
I0409 23:04:52.004498  6396 net.cpp:129] Top shape: 32 (32)
I0409 23:04:52.004503  6396 net.cpp:129] Top shape: 32 (32)
I0409 23:04:52.004508  6396 net.cpp:137] Memory required for data: 19787520
I0409 23:04:52.004513  6396 layer_factory.hpp:77] Creating layer conv1
I0409 23:04:52.004528  6396 net.cpp:84] Creating Layer conv1
I0409 23:04:52.004532  6396 net.cpp:406] conv1 <- data
I0409 23:04:52.004541  6396 net.cpp:380] conv1 -> conv1
I0409 23:04:52.007539  6396 net.cpp:122] Setting up conv1
I0409 23:04:52.007555  6396 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:04:52.007560  6396 net.cpp:137] Memory required for data: 56958720
I0409 23:04:52.007573  6396 layer_factory.hpp:77] Creating layer relu1
I0409 23:04:52.007582  6396 net.cpp:84] Creating Layer relu1
I0409 23:04:52.007587  6396 net.cpp:406] relu1 <- conv1
I0409 23:04:52.007594  6396 net.cpp:367] relu1 -> conv1 (in-place)
I0409 23:04:52.008026  6396 net.cpp:122] Setting up relu1
I0409 23:04:52.008038  6396 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:04:52.008042  6396 net.cpp:137] Memory required for data: 94129920
I0409 23:04:52.008047  6396 layer_factory.hpp:77] Creating layer norm1
I0409 23:04:52.008057  6396 net.cpp:84] Creating Layer norm1
I0409 23:04:52.008062  6396 net.cpp:406] norm1 <- conv1
I0409 23:04:52.008069  6396 net.cpp:380] norm1 -> norm1
I0409 23:04:52.008869  6396 net.cpp:122] Setting up norm1
I0409 23:04:52.008883  6396 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:04:52.008888  6396 net.cpp:137] Memory required for data: 131301120
I0409 23:04:52.008893  6396 layer_factory.hpp:77] Creating layer pool1
I0409 23:04:52.008903  6396 net.cpp:84] Creating Layer pool1
I0409 23:04:52.008908  6396 net.cpp:406] pool1 <- norm1
I0409 23:04:52.008914  6396 net.cpp:380] pool1 -> pool1
I0409 23:04:52.008957  6396 net.cpp:122] Setting up pool1
I0409 23:04:52.008965  6396 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 23:04:52.008968  6396 net.cpp:137] Memory required for data: 140259072
I0409 23:04:52.008973  6396 layer_factory.hpp:77] Creating layer conv2
I0409 23:04:52.008985  6396 net.cpp:84] Creating Layer conv2
I0409 23:04:52.008988  6396 net.cpp:406] conv2 <- pool1
I0409 23:04:52.009021  6396 net.cpp:380] conv2 -> conv2
I0409 23:04:52.019079  6396 net.cpp:122] Setting up conv2
I0409 23:04:52.019102  6396 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:04:52.019107  6396 net.cpp:137] Memory required for data: 164146944
I0409 23:04:52.019121  6396 layer_factory.hpp:77] Creating layer relu2
I0409 23:04:52.019132  6396 net.cpp:84] Creating Layer relu2
I0409 23:04:52.019137  6396 net.cpp:406] relu2 <- conv2
I0409 23:04:52.019145  6396 net.cpp:367] relu2 -> conv2 (in-place)
I0409 23:04:52.019842  6396 net.cpp:122] Setting up relu2
I0409 23:04:52.019855  6396 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:04:52.019860  6396 net.cpp:137] Memory required for data: 188034816
I0409 23:04:52.019865  6396 layer_factory.hpp:77] Creating layer norm2
I0409 23:04:52.019878  6396 net.cpp:84] Creating Layer norm2
I0409 23:04:52.019883  6396 net.cpp:406] norm2 <- conv2
I0409 23:04:52.019891  6396 net.cpp:380] norm2 -> norm2
I0409 23:04:52.020607  6396 net.cpp:122] Setting up norm2
I0409 23:04:52.020620  6396 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:04:52.020625  6396 net.cpp:137] Memory required for data: 211922688
I0409 23:04:52.020630  6396 layer_factory.hpp:77] Creating layer pool2
I0409 23:04:52.020640  6396 net.cpp:84] Creating Layer pool2
I0409 23:04:52.020645  6396 net.cpp:406] pool2 <- norm2
I0409 23:04:52.020653  6396 net.cpp:380] pool2 -> pool2
I0409 23:04:52.020697  6396 net.cpp:122] Setting up pool2
I0409 23:04:52.020704  6396 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:04:52.020709  6396 net.cpp:137] Memory required for data: 217460480
I0409 23:04:52.020714  6396 layer_factory.hpp:77] Creating layer conv3
I0409 23:04:52.020727  6396 net.cpp:84] Creating Layer conv3
I0409 23:04:52.020731  6396 net.cpp:406] conv3 <- pool2
I0409 23:04:52.020740  6396 net.cpp:380] conv3 -> conv3
I0409 23:04:52.037477  6396 net.cpp:122] Setting up conv3
I0409 23:04:52.037498  6396 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:04:52.037503  6396 net.cpp:137] Memory required for data: 225767168
I0409 23:04:52.037518  6396 layer_factory.hpp:77] Creating layer relu3
I0409 23:04:52.037529  6396 net.cpp:84] Creating Layer relu3
I0409 23:04:52.037535  6396 net.cpp:406] relu3 <- conv3
I0409 23:04:52.037542  6396 net.cpp:367] relu3 -> conv3 (in-place)
I0409 23:04:52.038245  6396 net.cpp:122] Setting up relu3
I0409 23:04:52.038259  6396 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:04:52.038262  6396 net.cpp:137] Memory required for data: 234073856
I0409 23:04:52.038267  6396 layer_factory.hpp:77] Creating layer conv4
I0409 23:04:52.038282  6396 net.cpp:84] Creating Layer conv4
I0409 23:04:52.038287  6396 net.cpp:406] conv4 <- conv3
I0409 23:04:52.038297  6396 net.cpp:380] conv4 -> conv4
I0409 23:04:52.051004  6396 net.cpp:122] Setting up conv4
I0409 23:04:52.051024  6396 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:04:52.051028  6396 net.cpp:137] Memory required for data: 242380544
I0409 23:04:52.051038  6396 layer_factory.hpp:77] Creating layer relu4
I0409 23:04:52.051048  6396 net.cpp:84] Creating Layer relu4
I0409 23:04:52.051054  6396 net.cpp:406] relu4 <- conv4
I0409 23:04:52.051060  6396 net.cpp:367] relu4 -> conv4 (in-place)
I0409 23:04:52.051496  6396 net.cpp:122] Setting up relu4
I0409 23:04:52.051506  6396 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:04:52.051509  6396 net.cpp:137] Memory required for data: 250687232
I0409 23:04:52.051514  6396 layer_factory.hpp:77] Creating layer conv5
I0409 23:04:52.051527  6396 net.cpp:84] Creating Layer conv5
I0409 23:04:52.051530  6396 net.cpp:406] conv5 <- conv4
I0409 23:04:52.051539  6396 net.cpp:380] conv5 -> conv5
I0409 23:04:52.062309  6396 net.cpp:122] Setting up conv5
I0409 23:04:52.062328  6396 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:04:52.062332  6396 net.cpp:137] Memory required for data: 256225024
I0409 23:04:52.062350  6396 layer_factory.hpp:77] Creating layer relu5
I0409 23:04:52.062361  6396 net.cpp:84] Creating Layer relu5
I0409 23:04:52.062366  6396 net.cpp:406] relu5 <- conv5
I0409 23:04:52.062392  6396 net.cpp:367] relu5 -> conv5 (in-place)
I0409 23:04:52.063001  6396 net.cpp:122] Setting up relu5
I0409 23:04:52.063014  6396 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:04:52.063017  6396 net.cpp:137] Memory required for data: 261762816
I0409 23:04:52.063022  6396 layer_factory.hpp:77] Creating layer pool5
I0409 23:04:52.063033  6396 net.cpp:84] Creating Layer pool5
I0409 23:04:52.063037  6396 net.cpp:406] pool5 <- conv5
I0409 23:04:52.063045  6396 net.cpp:380] pool5 -> pool5
I0409 23:04:52.063089  6396 net.cpp:122] Setting up pool5
I0409 23:04:52.063097  6396 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 23:04:52.063099  6396 net.cpp:137] Memory required for data: 262942464
I0409 23:04:52.063103  6396 layer_factory.hpp:77] Creating layer fc6
I0409 23:04:52.063112  6396 net.cpp:84] Creating Layer fc6
I0409 23:04:52.063117  6396 net.cpp:406] fc6 <- pool5
I0409 23:04:52.063123  6396 net.cpp:380] fc6 -> fc6
I0409 23:04:52.115468  6396 net.cpp:122] Setting up fc6
I0409 23:04:52.115487  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.115491  6396 net.cpp:137] Memory required for data: 263008000
I0409 23:04:52.115502  6396 layer_factory.hpp:77] Creating layer relu6
I0409 23:04:52.115512  6396 net.cpp:84] Creating Layer relu6
I0409 23:04:52.115517  6396 net.cpp:406] relu6 <- fc6
I0409 23:04:52.115526  6396 net.cpp:367] relu6 -> fc6 (in-place)
I0409 23:04:52.116503  6396 net.cpp:122] Setting up relu6
I0409 23:04:52.116513  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.116518  6396 net.cpp:137] Memory required for data: 263073536
I0409 23:04:52.116521  6396 layer_factory.hpp:77] Creating layer drop6
I0409 23:04:52.116530  6396 net.cpp:84] Creating Layer drop6
I0409 23:04:52.116534  6396 net.cpp:406] drop6 <- fc6
I0409 23:04:52.116544  6396 net.cpp:367] drop6 -> fc6 (in-place)
I0409 23:04:52.116573  6396 net.cpp:122] Setting up drop6
I0409 23:04:52.116580  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.116585  6396 net.cpp:137] Memory required for data: 263139072
I0409 23:04:52.116588  6396 layer_factory.hpp:77] Creating layer fc7
I0409 23:04:52.116596  6396 net.cpp:84] Creating Layer fc7
I0409 23:04:52.116601  6396 net.cpp:406] fc7 <- fc6
I0409 23:04:52.116611  6396 net.cpp:380] fc7 -> fc7
I0409 23:04:52.119923  6396 net.cpp:122] Setting up fc7
I0409 23:04:52.119935  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.119938  6396 net.cpp:137] Memory required for data: 263204608
I0409 23:04:52.119946  6396 layer_factory.hpp:77] Creating layer relu7
I0409 23:04:52.119954  6396 net.cpp:84] Creating Layer relu7
I0409 23:04:52.119959  6396 net.cpp:406] relu7 <- fc7
I0409 23:04:52.119966  6396 net.cpp:367] relu7 -> fc7 (in-place)
I0409 23:04:52.120349  6396 net.cpp:122] Setting up relu7
I0409 23:04:52.120358  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.120363  6396 net.cpp:137] Memory required for data: 263270144
I0409 23:04:52.120368  6396 layer_factory.hpp:77] Creating layer drop7
I0409 23:04:52.120374  6396 net.cpp:84] Creating Layer drop7
I0409 23:04:52.120378  6396 net.cpp:406] drop7 <- fc7
I0409 23:04:52.120386  6396 net.cpp:367] drop7 -> fc7 (in-place)
I0409 23:04:52.120411  6396 net.cpp:122] Setting up drop7
I0409 23:04:52.120417  6396 net.cpp:129] Top shape: 32 512 (16384)
I0409 23:04:52.120422  6396 net.cpp:137] Memory required for data: 263335680
I0409 23:04:52.120427  6396 layer_factory.hpp:77] Creating layer fc8
I0409 23:04:52.120434  6396 net.cpp:84] Creating Layer fc8
I0409 23:04:52.120440  6396 net.cpp:406] fc8 <- fc7
I0409 23:04:52.120447  6396 net.cpp:380] fc8 -> fc8
I0409 23:04:52.121482  6396 net.cpp:122] Setting up fc8
I0409 23:04:52.121490  6396 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:04:52.121495  6396 net.cpp:137] Memory required for data: 263360768
I0409 23:04:52.121503  6396 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 23:04:52.121511  6396 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 23:04:52.121516  6396 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 23:04:52.121522  6396 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 23:04:52.121547  6396 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 23:04:52.121580  6396 net.cpp:122] Setting up fc8_fc8_0_split
I0409 23:04:52.121585  6396 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:04:52.121592  6396 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:04:52.121595  6396 net.cpp:137] Memory required for data: 263410944
I0409 23:04:52.121600  6396 layer_factory.hpp:77] Creating layer accuracy
I0409 23:04:52.121608  6396 net.cpp:84] Creating Layer accuracy
I0409 23:04:52.121613  6396 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 23:04:52.121618  6396 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 23:04:52.121624  6396 net.cpp:380] accuracy -> accuracy
I0409 23:04:52.121632  6396 net.cpp:122] Setting up accuracy
I0409 23:04:52.121636  6396 net.cpp:129] Top shape: (1)
I0409 23:04:52.121640  6396 net.cpp:137] Memory required for data: 263410948
I0409 23:04:52.121645  6396 layer_factory.hpp:77] Creating layer loss
I0409 23:04:52.121654  6396 net.cpp:84] Creating Layer loss
I0409 23:04:52.121657  6396 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 23:04:52.121663  6396 net.cpp:406] loss <- label_val-data_1_split_1
I0409 23:04:52.121670  6396 net.cpp:380] loss -> loss
I0409 23:04:52.121678  6396 layer_factory.hpp:77] Creating layer loss
I0409 23:04:52.122332  6396 net.cpp:122] Setting up loss
I0409 23:04:52.122342  6396 net.cpp:129] Top shape: (1)
I0409 23:04:52.122347  6396 net.cpp:132]     with loss weight 1
I0409 23:04:52.122359  6396 net.cpp:137] Memory required for data: 263410952
I0409 23:04:52.122364  6396 net.cpp:198] loss needs backward computation.
I0409 23:04:52.122370  6396 net.cpp:200] accuracy does not need backward computation.
I0409 23:04:52.122375  6396 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 23:04:52.122380  6396 net.cpp:198] fc8 needs backward computation.
I0409 23:04:52.122385  6396 net.cpp:198] drop7 needs backward computation.
I0409 23:04:52.122390  6396 net.cpp:198] relu7 needs backward computation.
I0409 23:04:52.122393  6396 net.cpp:198] fc7 needs backward computation.
I0409 23:04:52.122400  6396 net.cpp:198] drop6 needs backward computation.
I0409 23:04:52.122404  6396 net.cpp:198] relu6 needs backward computation.
I0409 23:04:52.122408  6396 net.cpp:198] fc6 needs backward computation.
I0409 23:04:52.122413  6396 net.cpp:198] pool5 needs backward computation.
I0409 23:04:52.122417  6396 net.cpp:198] relu5 needs backward computation.
I0409 23:04:52.122422  6396 net.cpp:198] conv5 needs backward computation.
I0409 23:04:52.122426  6396 net.cpp:198] relu4 needs backward computation.
I0409 23:04:52.122431  6396 net.cpp:198] conv4 needs backward computation.
I0409 23:04:52.122437  6396 net.cpp:198] relu3 needs backward computation.
I0409 23:04:52.122442  6396 net.cpp:198] conv3 needs backward computation.
I0409 23:04:52.122447  6396 net.cpp:198] pool2 needs backward computation.
I0409 23:04:52.122452  6396 net.cpp:198] norm2 needs backward computation.
I0409 23:04:52.122455  6396 net.cpp:198] relu2 needs backward computation.
I0409 23:04:52.122460  6396 net.cpp:198] conv2 needs backward computation.
I0409 23:04:52.122465  6396 net.cpp:198] pool1 needs backward computation.
I0409 23:04:52.122469  6396 net.cpp:198] norm1 needs backward computation.
I0409 23:04:52.122474  6396 net.cpp:198] relu1 needs backward computation.
I0409 23:04:52.122479  6396 net.cpp:198] conv1 needs backward computation.
I0409 23:04:52.122484  6396 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 23:04:52.122489  6396 net.cpp:200] val-data does not need backward computation.
I0409 23:04:52.122494  6396 net.cpp:242] This network produces output accuracy
I0409 23:04:52.122499  6396 net.cpp:242] This network produces output loss
I0409 23:04:52.122517  6396 net.cpp:255] Network initialization done.
I0409 23:04:52.122592  6396 solver.cpp:56] Solver scaffolding done.
I0409 23:04:52.123062  6396 caffe.cpp:248] Starting Optimization
I0409 23:04:52.123072  6396 solver.cpp:272] Solving
I0409 23:04:52.123085  6396 solver.cpp:273] Learning Rate Policy: exp
I0409 23:04:52.124389  6396 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 23:04:52.124400  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:04:52.128414  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:04:56.686985  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:56.731577  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 23:04:56.731622  6396 solver.cpp:397]     Test net output #1: loss = 5.27807 (* 1 = 5.27807 loss)
I0409 23:04:56.818626  6396 solver.cpp:218] Iteration 0 (-2.81661e-43 iter/s, 4.69537s/12 iters), loss = 5.27455
I0409 23:04:56.818673  6396 solver.cpp:237]     Train net output #0: loss = 5.27455 (* 1 = 5.27455 loss)
I0409 23:04:56.818701  6396 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 23:05:00.818203  6396 solver.cpp:218] Iteration 12 (3.00045 iter/s, 3.99939s/12 iters), loss = 5.27929
I0409 23:05:00.818249  6396 solver.cpp:237]     Train net output #0: loss = 5.27929 (* 1 = 5.27929 loss)
I0409 23:05:00.818260  6396 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 23:05:05.753098  6396 solver.cpp:218] Iteration 24 (2.43176 iter/s, 4.93469s/12 iters), loss = 5.2787
I0409 23:05:05.753139  6396 solver.cpp:237]     Train net output #0: loss = 5.2787 (* 1 = 5.2787 loss)
I0409 23:05:05.753149  6396 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 23:05:10.625360  6396 solver.cpp:218] Iteration 36 (2.46302 iter/s, 4.87206s/12 iters), loss = 5.28537
I0409 23:05:10.625419  6396 solver.cpp:237]     Train net output #0: loss = 5.28537 (* 1 = 5.28537 loss)
I0409 23:05:10.625432  6396 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 23:05:15.579774  6396 solver.cpp:218] Iteration 48 (2.42219 iter/s, 4.9542s/12 iters), loss = 5.28018
I0409 23:05:15.579818  6396 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0409 23:05:15.579828  6396 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 23:05:20.499451  6396 solver.cpp:218] Iteration 60 (2.43928 iter/s, 4.91947s/12 iters), loss = 5.27536
I0409 23:05:20.499606  6396 solver.cpp:237]     Train net output #0: loss = 5.27536 (* 1 = 5.27536 loss)
I0409 23:05:20.499622  6396 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 23:05:25.575270  6396 solver.cpp:218] Iteration 72 (2.3643 iter/s, 5.07551s/12 iters), loss = 5.27605
I0409 23:05:25.575312  6396 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0409 23:05:25.575321  6396 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 23:05:30.478696  6396 solver.cpp:218] Iteration 84 (2.44737 iter/s, 4.90322s/12 iters), loss = 5.28184
I0409 23:05:30.478751  6396 solver.cpp:237]     Train net output #0: loss = 5.28184 (* 1 = 5.28184 loss)
I0409 23:05:30.478763  6396 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 23:05:35.387403  6396 solver.cpp:218] Iteration 96 (2.44474 iter/s, 4.90849s/12 iters), loss = 5.2859
I0409 23:05:35.387456  6396 solver.cpp:237]     Train net output #0: loss = 5.2859 (* 1 = 5.2859 loss)
I0409 23:05:35.387467  6396 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 23:05:37.057448  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:37.364966  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 23:05:37.954406  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 23:05:40.057876  6396 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 23:05:40.057898  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:05:44.445679  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:44.521896  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:05:44.521947  6396 solver.cpp:397]     Test net output #1: loss = 5.27944 (* 1 = 5.27944 loss)
I0409 23:05:46.403074  6396 solver.cpp:218] Iteration 108 (1.08939 iter/s, 11.0153s/12 iters), loss = 5.28126
I0409 23:05:46.403115  6396 solver.cpp:237]     Train net output #0: loss = 5.28126 (* 1 = 5.28126 loss)
I0409 23:05:46.403123  6396 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 23:05:51.560257  6396 solver.cpp:218] Iteration 120 (2.32695 iter/s, 5.15697s/12 iters), loss = 5.27769
I0409 23:05:51.560406  6396 solver.cpp:237]     Train net output #0: loss = 5.27769 (* 1 = 5.27769 loss)
I0409 23:05:51.560416  6396 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 23:05:56.549398  6396 solver.cpp:218] Iteration 132 (2.40537 iter/s, 4.98884s/12 iters), loss = 5.25276
I0409 23:05:56.549441  6396 solver.cpp:237]     Train net output #0: loss = 5.25276 (* 1 = 5.25276 loss)
I0409 23:05:56.549451  6396 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 23:06:01.398185  6396 solver.cpp:218] Iteration 144 (2.47495 iter/s, 4.84859s/12 iters), loss = 5.28589
I0409 23:06:01.398245  6396 solver.cpp:237]     Train net output #0: loss = 5.28589 (* 1 = 5.28589 loss)
I0409 23:06:01.398257  6396 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 23:06:06.469995  6396 solver.cpp:218] Iteration 156 (2.36612 iter/s, 5.07159s/12 iters), loss = 5.26399
I0409 23:06:06.470037  6396 solver.cpp:237]     Train net output #0: loss = 5.26399 (* 1 = 5.26399 loss)
I0409 23:06:06.470047  6396 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 23:06:11.510557  6396 solver.cpp:218] Iteration 168 (2.38078 iter/s, 5.04036s/12 iters), loss = 5.27704
I0409 23:06:11.510607  6396 solver.cpp:237]     Train net output #0: loss = 5.27704 (* 1 = 5.27704 loss)
I0409 23:06:11.510618  6396 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 23:06:16.442725  6396 solver.cpp:218] Iteration 180 (2.43311 iter/s, 4.93196s/12 iters), loss = 5.2723
I0409 23:06:16.442778  6396 solver.cpp:237]     Train net output #0: loss = 5.2723 (* 1 = 5.2723 loss)
I0409 23:06:16.442790  6396 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 23:06:21.474241  6396 solver.cpp:218] Iteration 192 (2.38507 iter/s, 5.0313s/12 iters), loss = 5.2814
I0409 23:06:21.474280  6396 solver.cpp:237]     Train net output #0: loss = 5.2814 (* 1 = 5.2814 loss)
I0409 23:06:21.474288  6396 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 23:06:25.326884  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:25.984145  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 23:06:27.344069  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 23:06:27.665225  6396 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 23:06:27.665244  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:06:31.994709  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:32.116238  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:06:32.116293  6396 solver.cpp:397]     Test net output #1: loss = 5.28107 (* 1 = 5.28107 loss)
I0409 23:06:32.199648  6396 solver.cpp:218] Iteration 204 (1.11888 iter/s, 10.725s/12 iters), loss = 5.27436
I0409 23:06:32.199698  6396 solver.cpp:237]     Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
I0409 23:06:32.199708  6396 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 23:06:36.417697  6396 solver.cpp:218] Iteration 216 (2.84504 iter/s, 4.21786s/12 iters), loss = 5.2777
I0409 23:06:36.417743  6396 solver.cpp:237]     Train net output #0: loss = 5.2777 (* 1 = 5.2777 loss)
I0409 23:06:36.417753  6396 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 23:06:41.320812  6396 solver.cpp:218] Iteration 228 (2.44753 iter/s, 4.90291s/12 iters), loss = 5.25923
I0409 23:06:41.320868  6396 solver.cpp:237]     Train net output #0: loss = 5.25923 (* 1 = 5.25923 loss)
I0409 23:06:41.320881  6396 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 23:06:46.224095  6396 solver.cpp:218] Iteration 240 (2.44745 iter/s, 4.90307s/12 iters), loss = 5.28509
I0409 23:06:46.224144  6396 solver.cpp:237]     Train net output #0: loss = 5.28509 (* 1 = 5.28509 loss)
I0409 23:06:46.224153  6396 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 23:06:51.241735  6396 solver.cpp:218] Iteration 252 (2.39166 iter/s, 5.01743s/12 iters), loss = 5.27584
I0409 23:06:51.241789  6396 solver.cpp:237]     Train net output #0: loss = 5.27584 (* 1 = 5.27584 loss)
I0409 23:06:51.241801  6396 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 23:06:56.286557  6396 solver.cpp:218] Iteration 264 (2.37878 iter/s, 5.04461s/12 iters), loss = 5.27004
I0409 23:06:56.286700  6396 solver.cpp:237]     Train net output #0: loss = 5.27004 (* 1 = 5.27004 loss)
I0409 23:06:56.286711  6396 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 23:07:01.266630  6396 solver.cpp:218] Iteration 276 (2.40975 iter/s, 4.97977s/12 iters), loss = 5.29071
I0409 23:07:01.266686  6396 solver.cpp:237]     Train net output #0: loss = 5.29071 (* 1 = 5.29071 loss)
I0409 23:07:01.266696  6396 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 23:07:06.287289  6396 solver.cpp:218] Iteration 288 (2.39022 iter/s, 5.02045s/12 iters), loss = 5.27801
I0409 23:07:06.287329  6396 solver.cpp:237]     Train net output #0: loss = 5.27801 (* 1 = 5.27801 loss)
I0409 23:07:06.287338  6396 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 23:07:11.268934  6396 solver.cpp:218] Iteration 300 (2.40894 iter/s, 4.98144s/12 iters), loss = 5.2789
I0409 23:07:11.268992  6396 solver.cpp:237]     Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
I0409 23:07:11.269003  6396 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 23:07:12.225855  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:13.268409  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 23:07:13.719501  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 23:07:14.044073  6396 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 23:07:14.044092  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:07:18.424403  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:18.581279  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:07:18.581328  6396 solver.cpp:397]     Test net output #1: loss = 5.28201 (* 1 = 5.28201 loss)
I0409 23:07:20.459380  6396 solver.cpp:218] Iteration 312 (1.30575 iter/s, 9.19011s/12 iters), loss = 5.28612
I0409 23:07:20.459432  6396 solver.cpp:237]     Train net output #0: loss = 5.28612 (* 1 = 5.28612 loss)
I0409 23:07:20.459444  6396 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 23:07:25.387271  6396 solver.cpp:218] Iteration 324 (2.43522 iter/s, 4.92769s/12 iters), loss = 5.2522
I0409 23:07:25.387318  6396 solver.cpp:237]     Train net output #0: loss = 5.2522 (* 1 = 5.2522 loss)
I0409 23:07:25.387328  6396 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 23:07:30.326529  6396 solver.cpp:218] Iteration 336 (2.42962 iter/s, 4.93905s/12 iters), loss = 5.26202
I0409 23:07:30.326648  6396 solver.cpp:237]     Train net output #0: loss = 5.26202 (* 1 = 5.26202 loss)
I0409 23:07:30.326663  6396 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 23:07:35.248317  6396 solver.cpp:218] Iteration 348 (2.43827 iter/s, 4.92151s/12 iters), loss = 5.26278
I0409 23:07:35.248368  6396 solver.cpp:237]     Train net output #0: loss = 5.26278 (* 1 = 5.26278 loss)
I0409 23:07:35.248380  6396 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 23:07:40.159725  6396 solver.cpp:218] Iteration 360 (2.44339 iter/s, 4.9112s/12 iters), loss = 5.28856
I0409 23:07:40.159772  6396 solver.cpp:237]     Train net output #0: loss = 5.28856 (* 1 = 5.28856 loss)
I0409 23:07:40.159782  6396 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 23:07:45.100924  6396 solver.cpp:218] Iteration 372 (2.42866 iter/s, 4.941s/12 iters), loss = 5.27144
I0409 23:07:45.100980  6396 solver.cpp:237]     Train net output #0: loss = 5.27144 (* 1 = 5.27144 loss)
I0409 23:07:45.100993  6396 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 23:07:50.172209  6396 solver.cpp:218] Iteration 384 (2.36637 iter/s, 5.07107s/12 iters), loss = 5.27793
I0409 23:07:50.172259  6396 solver.cpp:237]     Train net output #0: loss = 5.27793 (* 1 = 5.27793 loss)
I0409 23:07:50.172269  6396 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 23:07:55.102274  6396 solver.cpp:218] Iteration 396 (2.43415 iter/s, 4.92986s/12 iters), loss = 5.25999
I0409 23:07:55.102330  6396 solver.cpp:237]     Train net output #0: loss = 5.25999 (* 1 = 5.25999 loss)
I0409 23:07:55.102341  6396 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 23:07:58.232841  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:59.639489  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 23:08:00.208518  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 23:08:00.517051  6396 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 23:08:00.517158  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:08:04.915843  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:05.117702  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:08:05.117748  6396 solver.cpp:397]     Test net output #1: loss = 5.27655 (* 1 = 5.27655 loss)
I0409 23:08:05.201004  6396 solver.cpp:218] Iteration 408 (1.18831 iter/s, 10.0984s/12 iters), loss = 5.27337
I0409 23:08:05.201056  6396 solver.cpp:237]     Train net output #0: loss = 5.27337 (* 1 = 5.27337 loss)
I0409 23:08:05.201066  6396 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 23:08:09.453930  6396 solver.cpp:218] Iteration 420 (2.82171 iter/s, 4.25274s/12 iters), loss = 5.27275
I0409 23:08:09.453987  6396 solver.cpp:237]     Train net output #0: loss = 5.27275 (* 1 = 5.27275 loss)
I0409 23:08:09.453999  6396 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 23:08:14.579460  6396 solver.cpp:218] Iteration 432 (2.34132 iter/s, 5.12531s/12 iters), loss = 5.21435
I0409 23:08:14.579506  6396 solver.cpp:237]     Train net output #0: loss = 5.21435 (* 1 = 5.21435 loss)
I0409 23:08:14.579516  6396 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 23:08:19.450215  6396 solver.cpp:218] Iteration 444 (2.46378 iter/s, 4.87056s/12 iters), loss = 5.20044
I0409 23:08:19.450260  6396 solver.cpp:237]     Train net output #0: loss = 5.20044 (* 1 = 5.20044 loss)
I0409 23:08:19.450268  6396 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 23:08:24.400693  6396 solver.cpp:218] Iteration 456 (2.42411 iter/s, 4.95027s/12 iters), loss = 5.24697
I0409 23:08:24.400746  6396 solver.cpp:237]     Train net output #0: loss = 5.24697 (* 1 = 5.24697 loss)
I0409 23:08:24.400758  6396 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 23:08:29.296018  6396 solver.cpp:218] Iteration 468 (2.45142 iter/s, 4.89512s/12 iters), loss = 5.16572
I0409 23:08:29.296068  6396 solver.cpp:237]     Train net output #0: loss = 5.16572 (* 1 = 5.16572 loss)
I0409 23:08:29.296080  6396 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 23:08:34.246402  6396 solver.cpp:218] Iteration 480 (2.42416 iter/s, 4.95017s/12 iters), loss = 5.17617
I0409 23:08:34.246495  6396 solver.cpp:237]     Train net output #0: loss = 5.17617 (* 1 = 5.17617 loss)
I0409 23:08:34.246506  6396 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 23:08:39.216249  6396 solver.cpp:218] Iteration 492 (2.41468 iter/s, 4.9696s/12 iters), loss = 5.19176
I0409 23:08:39.216305  6396 solver.cpp:237]     Train net output #0: loss = 5.19176 (* 1 = 5.19176 loss)
I0409 23:08:39.216316  6396 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 23:08:44.172780  6396 solver.cpp:218] Iteration 504 (2.42115 iter/s, 4.95632s/12 iters), loss = 5.18939
I0409 23:08:44.172818  6396 solver.cpp:237]     Train net output #0: loss = 5.18939 (* 1 = 5.18939 loss)
I0409 23:08:44.172828  6396 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 23:08:44.419914  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:46.180029  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 23:08:46.675359  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 23:08:46.993366  6396 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 23:08:46.993386  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:08:51.263931  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:51.500875  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0409 23:08:51.500921  6396 solver.cpp:397]     Test net output #1: loss = 5.17309 (* 1 = 5.17309 loss)
I0409 23:08:53.453568  6396 solver.cpp:218] Iteration 516 (1.29304 iter/s, 9.28047s/12 iters), loss = 5.15812
I0409 23:08:53.453613  6396 solver.cpp:237]     Train net output #0: loss = 5.15812 (* 1 = 5.15812 loss)
I0409 23:08:53.453621  6396 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 23:08:58.383664  6396 solver.cpp:218] Iteration 528 (2.43413 iter/s, 4.92989s/12 iters), loss = 5.21556
I0409 23:08:58.383710  6396 solver.cpp:237]     Train net output #0: loss = 5.21556 (* 1 = 5.21556 loss)
I0409 23:08:58.383719  6396 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 23:09:03.438783  6396 solver.cpp:218] Iteration 540 (2.37393 iter/s, 5.05491s/12 iters), loss = 5.16067
I0409 23:09:03.438830  6396 solver.cpp:237]     Train net output #0: loss = 5.16067 (* 1 = 5.16067 loss)
I0409 23:09:03.438840  6396 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 23:09:08.430047  6396 solver.cpp:218] Iteration 552 (2.4043 iter/s, 4.99106s/12 iters), loss = 5.10233
I0409 23:09:08.430193  6396 solver.cpp:237]     Train net output #0: loss = 5.10233 (* 1 = 5.10233 loss)
I0409 23:09:08.430207  6396 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 23:09:13.356006  6396 solver.cpp:218] Iteration 564 (2.43622 iter/s, 4.92566s/12 iters), loss = 5.18275
I0409 23:09:13.356046  6396 solver.cpp:237]     Train net output #0: loss = 5.18275 (* 1 = 5.18275 loss)
I0409 23:09:13.356055  6396 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 23:09:18.412827  6396 solver.cpp:218] Iteration 576 (2.37312 iter/s, 5.05662s/12 iters), loss = 5.14567
I0409 23:09:18.412869  6396 solver.cpp:237]     Train net output #0: loss = 5.14567 (* 1 = 5.14567 loss)
I0409 23:09:18.412879  6396 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 23:09:23.542287  6396 solver.cpp:218] Iteration 588 (2.33953 iter/s, 5.12924s/12 iters), loss = 5.15818
I0409 23:09:23.542341  6396 solver.cpp:237]     Train net output #0: loss = 5.15818 (* 1 = 5.15818 loss)
I0409 23:09:23.542353  6396 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 23:09:28.672533  6396 solver.cpp:218] Iteration 600 (2.33917 iter/s, 5.13003s/12 iters), loss = 5.17018
I0409 23:09:28.672587  6396 solver.cpp:237]     Train net output #0: loss = 5.17018 (* 1 = 5.17018 loss)
I0409 23:09:28.672600  6396 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 23:09:31.050797  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:33.187615  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 23:09:33.726255  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 23:09:34.047101  6396 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 23:09:34.047128  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:09:38.380204  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:38.681798  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 23:09:38.681874  6396 solver.cpp:397]     Test net output #1: loss = 5.15308 (* 1 = 5.15308 loss)
I0409 23:09:38.765374  6396 solver.cpp:218] Iteration 612 (1.189 iter/s, 10.0925s/12 iters), loss = 5.19614
I0409 23:09:38.765420  6396 solver.cpp:237]     Train net output #0: loss = 5.19614 (* 1 = 5.19614 loss)
I0409 23:09:38.765430  6396 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 23:09:43.021528  6396 solver.cpp:218] Iteration 624 (2.81957 iter/s, 4.25597s/12 iters), loss = 5.17875
I0409 23:09:43.021580  6396 solver.cpp:237]     Train net output #0: loss = 5.17875 (* 1 = 5.17875 loss)
I0409 23:09:43.021593  6396 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 23:09:48.006983  6396 solver.cpp:218] Iteration 636 (2.4071 iter/s, 4.98525s/12 iters), loss = 5.06564
I0409 23:09:48.007014  6396 solver.cpp:237]     Train net output #0: loss = 5.06564 (* 1 = 5.06564 loss)
I0409 23:09:48.007021  6396 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 23:09:53.000921  6396 solver.cpp:218] Iteration 648 (2.403 iter/s, 4.99375s/12 iters), loss = 5.18829
I0409 23:09:53.000957  6396 solver.cpp:237]     Train net output #0: loss = 5.18829 (* 1 = 5.18829 loss)
I0409 23:09:53.000967  6396 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 23:09:58.187526  6396 solver.cpp:218] Iteration 660 (2.31374 iter/s, 5.1864s/12 iters), loss = 5.16724
I0409 23:09:58.187574  6396 solver.cpp:237]     Train net output #0: loss = 5.16724 (* 1 = 5.16724 loss)
I0409 23:09:58.187583  6396 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 23:10:03.135460  6396 solver.cpp:218] Iteration 672 (2.42535 iter/s, 4.94773s/12 iters), loss = 5.12689
I0409 23:10:03.135499  6396 solver.cpp:237]     Train net output #0: loss = 5.12689 (* 1 = 5.12689 loss)
I0409 23:10:03.135507  6396 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 23:10:07.096614  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:10:07.961032  6396 solver.cpp:218] Iteration 684 (2.48685 iter/s, 4.82538s/12 iters), loss = 5.00176
I0409 23:10:07.961087  6396 solver.cpp:237]     Train net output #0: loss = 5.00176 (* 1 = 5.00176 loss)
I0409 23:10:07.961099  6396 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 23:10:12.847134  6396 solver.cpp:218] Iteration 696 (2.45605 iter/s, 4.88589s/12 iters), loss = 5.16521
I0409 23:10:12.847290  6396 solver.cpp:237]     Train net output #0: loss = 5.16521 (* 1 = 5.16521 loss)
I0409 23:10:12.847302  6396 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 23:10:17.381686  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:17.760285  6396 solver.cpp:218] Iteration 708 (2.44257 iter/s, 4.91285s/12 iters), loss = 5.16187
I0409 23:10:17.760337  6396 solver.cpp:237]     Train net output #0: loss = 5.16187 (* 1 = 5.16187 loss)
I0409 23:10:17.760352  6396 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 23:10:19.738740  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 23:10:21.973515  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 23:10:22.824321  6396 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 23:10:22.824352  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:10:27.192025  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:27.509991  6396 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 23:10:27.510022  6396 solver.cpp:397]     Test net output #1: loss = 5.14474 (* 1 = 5.14474 loss)
I0409 23:10:29.433158  6396 solver.cpp:218] Iteration 720 (1.02806 iter/s, 11.6725s/12 iters), loss = 5.17338
I0409 23:10:29.433210  6396 solver.cpp:237]     Train net output #0: loss = 5.17338 (* 1 = 5.17338 loss)
I0409 23:10:29.433223  6396 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 23:10:34.451440  6396 solver.cpp:218] Iteration 732 (2.39136 iter/s, 5.01807s/12 iters), loss = 5.11556
I0409 23:10:34.451488  6396 solver.cpp:237]     Train net output #0: loss = 5.11556 (* 1 = 5.11556 loss)
I0409 23:10:34.451498  6396 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 23:10:39.334790  6396 solver.cpp:218] Iteration 744 (2.45743 iter/s, 4.88315s/12 iters), loss = 5.11764
I0409 23:10:39.334836  6396 solver.cpp:237]     Train net output #0: loss = 5.11764 (* 1 = 5.11764 loss)
I0409 23:10:39.334847  6396 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 23:10:44.261577  6396 solver.cpp:218] Iteration 756 (2.43577 iter/s, 4.92658s/12 iters), loss = 5.11053
I0409 23:10:44.263350  6396 solver.cpp:237]     Train net output #0: loss = 5.11053 (* 1 = 5.11053 loss)
I0409 23:10:44.263368  6396 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 23:10:49.212472  6396 solver.cpp:218] Iteration 768 (2.42474 iter/s, 4.94898s/12 iters), loss = 5.14857
I0409 23:10:49.212522  6396 solver.cpp:237]     Train net output #0: loss = 5.14857 (* 1 = 5.14857 loss)
I0409 23:10:49.212533  6396 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 23:10:54.105814  6396 solver.cpp:218] Iteration 780 (2.45241 iter/s, 4.89315s/12 iters), loss = 5.18963
I0409 23:10:54.105854  6396 solver.cpp:237]     Train net output #0: loss = 5.18963 (* 1 = 5.18963 loss)
I0409 23:10:54.105863  6396 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 23:10:59.018446  6396 solver.cpp:218] Iteration 792 (2.44278 iter/s, 4.91243s/12 iters), loss = 5.0572
I0409 23:10:59.018498  6396 solver.cpp:237]     Train net output #0: loss = 5.0572 (* 1 = 5.0572 loss)
I0409 23:10:59.018512  6396 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 23:11:04.005082  6396 solver.cpp:218] Iteration 804 (2.40653 iter/s, 4.98643s/12 iters), loss = 5.12295
I0409 23:11:04.005131  6396 solver.cpp:237]     Train net output #0: loss = 5.12295 (* 1 = 5.12295 loss)
I0409 23:11:04.005143  6396 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 23:11:05.717293  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:08.525118  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 23:11:09.007797  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 23:11:09.326476  6396 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 23:11:09.326499  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:11:13.609239  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:13.991477  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0409 23:11:13.991508  6396 solver.cpp:397]     Test net output #1: loss = 5.11371 (* 1 = 5.11371 loss)
I0409 23:11:14.074678  6396 solver.cpp:218] Iteration 816 (1.19175 iter/s, 10.0692s/12 iters), loss = 5.12455
I0409 23:11:14.074723  6396 solver.cpp:237]     Train net output #0: loss = 5.12455 (* 1 = 5.12455 loss)
I0409 23:11:14.074733  6396 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 23:11:18.372727  6396 solver.cpp:218] Iteration 828 (2.79208 iter/s, 4.29787s/12 iters), loss = 5.15723
I0409 23:11:18.374475  6396 solver.cpp:237]     Train net output #0: loss = 5.15723 (* 1 = 5.15723 loss)
I0409 23:11:18.374488  6396 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 23:11:23.330358  6396 solver.cpp:218] Iteration 840 (2.42144 iter/s, 4.95572s/12 iters), loss = 5.06996
I0409 23:11:23.330410  6396 solver.cpp:237]     Train net output #0: loss = 5.06996 (* 1 = 5.06996 loss)
I0409 23:11:23.330422  6396 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 23:11:28.588757  6396 solver.cpp:218] Iteration 852 (2.28216 iter/s, 5.25818s/12 iters), loss = 5.07888
I0409 23:11:28.588805  6396 solver.cpp:237]     Train net output #0: loss = 5.07888 (* 1 = 5.07888 loss)
I0409 23:11:28.588815  6396 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 23:11:33.706398  6396 solver.cpp:218] Iteration 864 (2.34493 iter/s, 5.11743s/12 iters), loss = 5.10176
I0409 23:11:33.706449  6396 solver.cpp:237]     Train net output #0: loss = 5.10176 (* 1 = 5.10176 loss)
I0409 23:11:33.706462  6396 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 23:11:39.106531  6396 solver.cpp:218] Iteration 876 (2.22226 iter/s, 5.39991s/12 iters), loss = 5.12121
I0409 23:11:39.106580  6396 solver.cpp:237]     Train net output #0: loss = 5.12121 (* 1 = 5.12121 loss)
I0409 23:11:39.106591  6396 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 23:11:44.202370  6396 solver.cpp:218] Iteration 888 (2.35496 iter/s, 5.09563s/12 iters), loss = 4.97436
I0409 23:11:44.202423  6396 solver.cpp:237]     Train net output #0: loss = 4.97436 (* 1 = 4.97436 loss)
I0409 23:11:44.202435  6396 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 23:11:49.439241  6396 solver.cpp:218] Iteration 900 (2.29154 iter/s, 5.23665s/12 iters), loss = 5.14885
I0409 23:11:49.439404  6396 solver.cpp:237]     Train net output #0: loss = 5.14885 (* 1 = 5.14885 loss)
I0409 23:11:49.439419  6396 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 23:11:53.412437  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:54.582702  6396 solver.cpp:218] Iteration 912 (2.3332 iter/s, 5.14314s/12 iters), loss = 4.94136
I0409 23:11:54.582744  6396 solver.cpp:237]     Train net output #0: loss = 4.94136 (* 1 = 4.94136 loss)
I0409 23:11:54.582754  6396 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 23:11:56.544286  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 23:11:57.030730  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 23:11:57.354992  6396 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 23:11:57.355023  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:12:01.379563  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:01.778394  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0409 23:12:01.778435  6396 solver.cpp:397]     Test net output #1: loss = 5.04566 (* 1 = 5.04566 loss)
I0409 23:12:03.647375  6396 solver.cpp:218] Iteration 924 (1.32387 iter/s, 9.06435s/12 iters), loss = 5.09454
I0409 23:12:03.647431  6396 solver.cpp:237]     Train net output #0: loss = 5.09454 (* 1 = 5.09454 loss)
I0409 23:12:03.647444  6396 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 23:12:08.624416  6396 solver.cpp:218] Iteration 936 (2.41118 iter/s, 4.97682s/12 iters), loss = 5.15762
I0409 23:12:08.624471  6396 solver.cpp:237]     Train net output #0: loss = 5.15762 (* 1 = 5.15762 loss)
I0409 23:12:08.624485  6396 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 23:12:13.545991  6396 solver.cpp:218] Iteration 948 (2.43835 iter/s, 4.92135s/12 iters), loss = 5.04396
I0409 23:12:13.546041  6396 solver.cpp:237]     Train net output #0: loss = 5.04396 (* 1 = 5.04396 loss)
I0409 23:12:13.546051  6396 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 23:12:18.511624  6396 solver.cpp:218] Iteration 960 (2.41671 iter/s, 4.96543s/12 iters), loss = 5.00934
I0409 23:12:18.511672  6396 solver.cpp:237]     Train net output #0: loss = 5.00934 (* 1 = 5.00934 loss)
I0409 23:12:18.511679  6396 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 23:12:23.546980  6396 solver.cpp:218] Iteration 972 (2.38325 iter/s, 5.03515s/12 iters), loss = 5.08225
I0409 23:12:23.547080  6396 solver.cpp:237]     Train net output #0: loss = 5.08225 (* 1 = 5.08225 loss)
I0409 23:12:23.547091  6396 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 23:12:28.684842  6396 solver.cpp:218] Iteration 984 (2.33572 iter/s, 5.1376s/12 iters), loss = 5.06988
I0409 23:12:28.684892  6396 solver.cpp:237]     Train net output #0: loss = 5.06988 (* 1 = 5.06988 loss)
I0409 23:12:28.684904  6396 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 23:12:33.641265  6396 solver.cpp:218] Iteration 996 (2.4212 iter/s, 4.95622s/12 iters), loss = 4.92916
I0409 23:12:33.641319  6396 solver.cpp:237]     Train net output #0: loss = 4.92916 (* 1 = 4.92916 loss)
I0409 23:12:33.641330  6396 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 23:12:38.553884  6396 solver.cpp:218] Iteration 1008 (2.44279 iter/s, 4.91241s/12 iters), loss = 5.05439
I0409 23:12:38.553938  6396 solver.cpp:237]     Train net output #0: loss = 5.05439 (* 1 = 5.05439 loss)
I0409 23:12:38.553949  6396 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 23:12:39.550246  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:43.027499  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 23:12:43.875685  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 23:12:44.568985  6396 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 23:12:44.569018  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:12:48.846451  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:49.283015  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0409 23:12:49.283066  6396 solver.cpp:397]     Test net output #1: loss = 5.01896 (* 1 = 5.01896 loss)
I0409 23:12:49.366451  6396 solver.cpp:218] Iteration 1020 (1.10986 iter/s, 10.8122s/12 iters), loss = 5.00835
I0409 23:12:49.366510  6396 solver.cpp:237]     Train net output #0: loss = 5.00835 (* 1 = 5.00835 loss)
I0409 23:12:49.366521  6396 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 23:12:53.671056  6396 solver.cpp:218] Iteration 1032 (2.78784 iter/s, 4.30441s/12 iters), loss = 5.03018
I0409 23:12:53.671223  6396 solver.cpp:237]     Train net output #0: loss = 5.03018 (* 1 = 5.03018 loss)
I0409 23:12:53.671239  6396 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 23:12:58.637876  6396 solver.cpp:218] Iteration 1044 (2.41619 iter/s, 4.9665s/12 iters), loss = 5.04159
I0409 23:12:58.637925  6396 solver.cpp:237]     Train net output #0: loss = 5.04159 (* 1 = 5.04159 loss)
I0409 23:12:58.637938  6396 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 23:13:03.517397  6396 solver.cpp:218] Iteration 1056 (2.45936 iter/s, 4.87931s/12 iters), loss = 5.01864
I0409 23:13:03.517454  6396 solver.cpp:237]     Train net output #0: loss = 5.01864 (* 1 = 5.01864 loss)
I0409 23:13:03.517467  6396 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 23:13:08.506633  6396 solver.cpp:218] Iteration 1068 (2.40528 iter/s, 4.98902s/12 iters), loss = 5.04865
I0409 23:13:08.506681  6396 solver.cpp:237]     Train net output #0: loss = 5.04865 (* 1 = 5.04865 loss)
I0409 23:13:08.506693  6396 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 23:13:13.349357  6396 solver.cpp:218] Iteration 1080 (2.47804 iter/s, 4.84253s/12 iters), loss = 5.00438
I0409 23:13:13.349402  6396 solver.cpp:237]     Train net output #0: loss = 5.00438 (* 1 = 5.00438 loss)
I0409 23:13:13.349412  6396 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 23:13:18.298717  6396 solver.cpp:218] Iteration 1092 (2.42466 iter/s, 4.94915s/12 iters), loss = 5.01233
I0409 23:13:18.298774  6396 solver.cpp:237]     Train net output #0: loss = 5.01233 (* 1 = 5.01233 loss)
I0409 23:13:18.298786  6396 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 23:13:23.220844  6396 solver.cpp:218] Iteration 1104 (2.43807 iter/s, 4.92192s/12 iters), loss = 4.95184
I0409 23:13:23.220892  6396 solver.cpp:237]     Train net output #0: loss = 4.95184 (* 1 = 4.95184 loss)
I0409 23:13:23.220903  6396 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 23:13:26.307430  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:28.129155  6396 solver.cpp:218] Iteration 1116 (2.44493 iter/s, 4.90811s/12 iters), loss = 5.06477
I0409 23:13:28.129197  6396 solver.cpp:237]     Train net output #0: loss = 5.06477 (* 1 = 5.06477 loss)
I0409 23:13:28.129205  6396 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 23:13:30.140933  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 23:13:31.180573  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 23:13:32.329027  6396 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 23:13:32.329052  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:13:36.353238  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:36.827795  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0208333
I0409 23:13:36.827836  6396 solver.cpp:397]     Test net output #1: loss = 4.98487 (* 1 = 4.98487 loss)
I0409 23:13:38.527520  6396 solver.cpp:218] Iteration 1128 (1.15407 iter/s, 10.398s/12 iters), loss = 5.09501
I0409 23:13:38.527575  6396 solver.cpp:237]     Train net output #0: loss = 5.09501 (* 1 = 5.09501 loss)
I0409 23:13:38.527586  6396 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 23:13:43.488430  6396 solver.cpp:218] Iteration 1140 (2.41901 iter/s, 4.9607s/12 iters), loss = 5.00849
I0409 23:13:43.488476  6396 solver.cpp:237]     Train net output #0: loss = 5.00849 (* 1 = 5.00849 loss)
I0409 23:13:43.488487  6396 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 23:13:48.396610  6396 solver.cpp:218] Iteration 1152 (2.445 iter/s, 4.90798s/12 iters), loss = 4.87203
I0409 23:13:48.396656  6396 solver.cpp:237]     Train net output #0: loss = 4.87203 (* 1 = 4.87203 loss)
I0409 23:13:48.396667  6396 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 23:13:53.322038  6396 solver.cpp:218] Iteration 1164 (2.43643 iter/s, 4.92523s/12 iters), loss = 4.93016
I0409 23:13:53.322091  6396 solver.cpp:237]     Train net output #0: loss = 4.93016 (* 1 = 4.93016 loss)
I0409 23:13:53.322105  6396 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 23:13:58.448644  6396 solver.cpp:218] Iteration 1176 (2.34083 iter/s, 5.12639s/12 iters), loss = 4.95692
I0409 23:13:58.448763  6396 solver.cpp:237]     Train net output #0: loss = 4.95692 (* 1 = 4.95692 loss)
I0409 23:13:58.448776  6396 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 23:14:03.308151  6396 solver.cpp:218] Iteration 1188 (2.46952 iter/s, 4.85924s/12 iters), loss = 4.98052
I0409 23:14:03.308194  6396 solver.cpp:237]     Train net output #0: loss = 4.98052 (* 1 = 4.98052 loss)
I0409 23:14:03.308203  6396 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 23:14:08.438724  6396 solver.cpp:218] Iteration 1200 (2.33901 iter/s, 5.13037s/12 iters), loss = 5.02275
I0409 23:14:08.438771  6396 solver.cpp:237]     Train net output #0: loss = 5.02275 (* 1 = 5.02275 loss)
I0409 23:14:08.438782  6396 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 23:14:13.384367  6396 solver.cpp:218] Iteration 1212 (2.42648 iter/s, 4.94544s/12 iters), loss = 5.00519
I0409 23:14:13.384414  6396 solver.cpp:237]     Train net output #0: loss = 5.00519 (* 1 = 5.00519 loss)
I0409 23:14:13.384428  6396 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 23:14:13.662586  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:17.849836  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 23:14:18.282027  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 23:14:18.608780  6396 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 23:14:18.608805  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:14:22.569582  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:23.173488  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0409 23:14:23.173525  6396 solver.cpp:397]     Test net output #1: loss = 4.9107 (* 1 = 4.9107 loss)
I0409 23:14:23.256559  6396 solver.cpp:218] Iteration 1224 (1.21558 iter/s, 9.87185s/12 iters), loss = 4.87699
I0409 23:14:23.256619  6396 solver.cpp:237]     Train net output #0: loss = 4.87699 (* 1 = 4.87699 loss)
I0409 23:14:23.256631  6396 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 23:14:27.579262  6396 solver.cpp:218] Iteration 1236 (2.77617 iter/s, 4.32251s/12 iters), loss = 5.04432
I0409 23:14:27.579309  6396 solver.cpp:237]     Train net output #0: loss = 5.04432 (* 1 = 5.04432 loss)
I0409 23:14:27.579319  6396 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 23:14:32.533716  6396 solver.cpp:218] Iteration 1248 (2.42216 iter/s, 4.95425s/12 iters), loss = 4.90206
I0409 23:14:32.533808  6396 solver.cpp:237]     Train net output #0: loss = 4.90206 (* 1 = 4.90206 loss)
I0409 23:14:32.533823  6396 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 23:14:37.476972  6396 solver.cpp:218] Iteration 1260 (2.42767 iter/s, 4.94301s/12 iters), loss = 4.87208
I0409 23:14:37.477025  6396 solver.cpp:237]     Train net output #0: loss = 4.87208 (* 1 = 4.87208 loss)
I0409 23:14:37.477037  6396 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 23:14:42.509850  6396 solver.cpp:218] Iteration 1272 (2.38442 iter/s, 5.03266s/12 iters), loss = 4.81342
I0409 23:14:42.509898  6396 solver.cpp:237]     Train net output #0: loss = 4.81342 (* 1 = 4.81342 loss)
I0409 23:14:42.509907  6396 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 23:14:47.617373  6396 solver.cpp:218] Iteration 1284 (2.34957 iter/s, 5.10731s/12 iters), loss = 4.91452
I0409 23:14:47.617432  6396 solver.cpp:237]     Train net output #0: loss = 4.91452 (* 1 = 4.91452 loss)
I0409 23:14:47.617444  6396 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 23:14:52.583127  6396 solver.cpp:218] Iteration 1296 (2.41666 iter/s, 4.96554s/12 iters), loss = 4.71023
I0409 23:14:52.583181  6396 solver.cpp:237]     Train net output #0: loss = 4.71023 (* 1 = 4.71023 loss)
I0409 23:14:52.583194  6396 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 23:14:57.575850  6396 solver.cpp:218] Iteration 1308 (2.4036 iter/s, 4.99251s/12 iters), loss = 4.85882
I0409 23:14:57.575912  6396 solver.cpp:237]     Train net output #0: loss = 4.85882 (* 1 = 4.85882 loss)
I0409 23:14:57.575928  6396 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 23:15:00.062047  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:02.561372  6396 solver.cpp:218] Iteration 1320 (2.40707 iter/s, 4.98531s/12 iters), loss = 4.83453
I0409 23:15:02.561504  6396 solver.cpp:237]     Train net output #0: loss = 4.83453 (* 1 = 4.83453 loss)
I0409 23:15:02.561518  6396 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 23:15:04.726630  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 23:15:05.181049  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 23:15:05.497941  6396 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 23:15:05.497992  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:15:09.406265  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:09.960430  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0409 23:15:09.960480  6396 solver.cpp:397]     Test net output #1: loss = 4.83378 (* 1 = 4.83378 loss)
I0409 23:15:11.845858  6396 solver.cpp:218] Iteration 1332 (1.29254 iter/s, 9.28408s/12 iters), loss = 4.80834
I0409 23:15:11.845906  6396 solver.cpp:237]     Train net output #0: loss = 4.80834 (* 1 = 4.80834 loss)
I0409 23:15:11.845918  6396 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 23:15:16.891418  6396 solver.cpp:218] Iteration 1344 (2.37843 iter/s, 5.04535s/12 iters), loss = 4.66146
I0409 23:15:16.891471  6396 solver.cpp:237]     Train net output #0: loss = 4.66146 (* 1 = 4.66146 loss)
I0409 23:15:16.891482  6396 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 23:15:22.133783  6396 solver.cpp:218] Iteration 1356 (2.28914 iter/s, 5.24215s/12 iters), loss = 4.87689
I0409 23:15:22.133833  6396 solver.cpp:237]     Train net output #0: loss = 4.87689 (* 1 = 4.87689 loss)
I0409 23:15:22.133846  6396 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 23:15:26.868096  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:15:27.318863  6396 solver.cpp:218] Iteration 1368 (2.31443 iter/s, 5.18486s/12 iters), loss = 4.85403
I0409 23:15:27.318933  6396 solver.cpp:237]     Train net output #0: loss = 4.85403 (* 1 = 4.85403 loss)
I0409 23:15:27.318949  6396 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 23:15:32.232381  6396 solver.cpp:218] Iteration 1380 (2.44235 iter/s, 4.9133s/12 iters), loss = 4.70112
I0409 23:15:32.232421  6396 solver.cpp:237]     Train net output #0: loss = 4.70112 (* 1 = 4.70112 loss)
I0409 23:15:32.232429  6396 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 23:15:37.124405  6396 solver.cpp:218] Iteration 1392 (2.45307 iter/s, 4.89183s/12 iters), loss = 4.56327
I0409 23:15:37.124519  6396 solver.cpp:237]     Train net output #0: loss = 4.56327 (* 1 = 4.56327 loss)
I0409 23:15:37.124532  6396 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 23:15:42.072276  6396 solver.cpp:218] Iteration 1404 (2.42542 iter/s, 4.94761s/12 iters), loss = 4.78942
I0409 23:15:42.072324  6396 solver.cpp:237]     Train net output #0: loss = 4.78942 (* 1 = 4.78942 loss)
I0409 23:15:42.072335  6396 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 23:15:46.891553  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:47.245496  6396 solver.cpp:218] Iteration 1416 (2.31974 iter/s, 5.173s/12 iters), loss = 4.87584
I0409 23:15:47.245548  6396 solver.cpp:237]     Train net output #0: loss = 4.87584 (* 1 = 4.87584 loss)
I0409 23:15:47.245559  6396 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 23:15:51.779784  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 23:15:52.660035  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 23:15:53.435796  6396 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 23:15:53.435815  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:15:57.448776  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:58.037214  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0392157
I0409 23:15:58.037264  6396 solver.cpp:397]     Test net output #1: loss = 4.80582 (* 1 = 4.80582 loss)
I0409 23:15:58.120535  6396 solver.cpp:218] Iteration 1428 (1.10348 iter/s, 10.8747s/12 iters), loss = 4.89245
I0409 23:15:58.120594  6396 solver.cpp:237]     Train net output #0: loss = 4.89245 (* 1 = 4.89245 loss)
I0409 23:15:58.120605  6396 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 23:16:02.433589  6396 solver.cpp:218] Iteration 1440 (2.78238 iter/s, 4.31286s/12 iters), loss = 4.58012
I0409 23:16:02.433632  6396 solver.cpp:237]     Train net output #0: loss = 4.58012 (* 1 = 4.58012 loss)
I0409 23:16:02.433645  6396 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 23:16:07.535856  6396 solver.cpp:218] Iteration 1452 (2.35199 iter/s, 5.10206s/12 iters), loss = 4.75584
I0409 23:16:07.536021  6396 solver.cpp:237]     Train net output #0: loss = 4.75584 (* 1 = 4.75584 loss)
I0409 23:16:07.536041  6396 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 23:16:12.538585  6396 solver.cpp:218] Iteration 1464 (2.39884 iter/s, 5.00242s/12 iters), loss = 4.79226
I0409 23:16:12.538640  6396 solver.cpp:237]     Train net output #0: loss = 4.79226 (* 1 = 4.79226 loss)
I0409 23:16:12.538651  6396 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 23:16:17.593446  6396 solver.cpp:218] Iteration 1476 (2.37406 iter/s, 5.05463s/12 iters), loss = 4.75938
I0409 23:16:17.593504  6396 solver.cpp:237]     Train net output #0: loss = 4.75938 (* 1 = 4.75938 loss)
I0409 23:16:17.593516  6396 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 23:16:22.557855  6396 solver.cpp:218] Iteration 1488 (2.41731 iter/s, 4.96419s/12 iters), loss = 4.78902
I0409 23:16:22.557906  6396 solver.cpp:237]     Train net output #0: loss = 4.78902 (* 1 = 4.78902 loss)
I0409 23:16:22.557917  6396 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 23:16:27.535130  6396 solver.cpp:218] Iteration 1500 (2.41106 iter/s, 4.97706s/12 iters), loss = 4.50856
I0409 23:16:27.535187  6396 solver.cpp:237]     Train net output #0: loss = 4.50856 (* 1 = 4.50856 loss)
I0409 23:16:27.535198  6396 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 23:16:32.480310  6396 solver.cpp:218] Iteration 1512 (2.42671 iter/s, 4.94496s/12 iters), loss = 4.75476
I0409 23:16:32.480355  6396 solver.cpp:237]     Train net output #0: loss = 4.75476 (* 1 = 4.75476 loss)
I0409 23:16:32.480362  6396 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 23:16:34.188673  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:37.365240  6396 solver.cpp:218] Iteration 1524 (2.45664 iter/s, 4.88473s/12 iters), loss = 4.76191
I0409 23:16:37.365296  6396 solver.cpp:237]     Train net output #0: loss = 4.76191 (* 1 = 4.76191 loss)
I0409 23:16:37.365309  6396 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 23:16:39.387161  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 23:16:39.856946  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 23:16:40.254982  6396 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 23:16:40.255002  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:16:44.056459  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:44.710235  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0409 23:16:44.710273  6396 solver.cpp:397]     Test net output #1: loss = 4.63122 (* 1 = 4.63122 loss)
I0409 23:16:46.478646  6396 solver.cpp:218] Iteration 1536 (1.31679 iter/s, 9.11307s/12 iters), loss = 4.8042
I0409 23:16:46.478700  6396 solver.cpp:237]     Train net output #0: loss = 4.8042 (* 1 = 4.8042 loss)
I0409 23:16:46.478711  6396 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 23:16:51.465226  6396 solver.cpp:218] Iteration 1548 (2.40656 iter/s, 4.98637s/12 iters), loss = 4.39548
I0409 23:16:51.465270  6396 solver.cpp:237]     Train net output #0: loss = 4.39548 (* 1 = 4.39548 loss)
I0409 23:16:51.465279  6396 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 23:16:56.490134  6396 solver.cpp:218] Iteration 1560 (2.3882 iter/s, 5.0247s/12 iters), loss = 4.57998
I0409 23:16:56.490178  6396 solver.cpp:237]     Train net output #0: loss = 4.57998 (* 1 = 4.57998 loss)
I0409 23:16:56.490188  6396 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 23:17:01.540081  6396 solver.cpp:218] Iteration 1572 (2.37636 iter/s, 5.04975s/12 iters), loss = 4.46439
I0409 23:17:01.540122  6396 solver.cpp:237]     Train net output #0: loss = 4.46439 (* 1 = 4.46439 loss)
I0409 23:17:01.540132  6396 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 23:17:06.425592  6396 solver.cpp:218] Iteration 1584 (2.45634 iter/s, 4.88531s/12 iters), loss = 4.69496
I0409 23:17:06.425643  6396 solver.cpp:237]     Train net output #0: loss = 4.69496 (* 1 = 4.69496 loss)
I0409 23:17:06.425657  6396 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 23:17:11.337610  6396 solver.cpp:218] Iteration 1596 (2.44309 iter/s, 4.91181s/12 iters), loss = 4.51098
I0409 23:17:11.337690  6396 solver.cpp:237]     Train net output #0: loss = 4.51098 (* 1 = 4.51098 loss)
I0409 23:17:11.337699  6396 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 23:17:16.311223  6396 solver.cpp:218] Iteration 1608 (2.41285 iter/s, 4.97337s/12 iters), loss = 4.68196
I0409 23:17:16.311281  6396 solver.cpp:237]     Train net output #0: loss = 4.68196 (* 1 = 4.68196 loss)
I0409 23:17:16.311292  6396 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 23:17:20.102902  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:21.165529  6396 solver.cpp:218] Iteration 1620 (2.47214 iter/s, 4.85409s/12 iters), loss = 4.5233
I0409 23:17:21.165578  6396 solver.cpp:237]     Train net output #0: loss = 4.5233 (* 1 = 4.5233 loss)
I0409 23:17:21.165587  6396 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 23:17:25.586316  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 23:17:26.024292  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 23:17:26.333432  6396 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 23:17:26.333465  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:17:30.169979  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:30.840970  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0409 23:17:30.841017  6396 solver.cpp:397]     Test net output #1: loss = 4.57721 (* 1 = 4.57721 loss)
I0409 23:17:30.924233  6396 solver.cpp:218] Iteration 1632 (1.22972 iter/s, 9.75836s/12 iters), loss = 4.66223
I0409 23:17:30.924291  6396 solver.cpp:237]     Train net output #0: loss = 4.66223 (* 1 = 4.66223 loss)
I0409 23:17:30.924302  6396 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 23:17:35.202394  6396 solver.cpp:218] Iteration 1644 (2.80507 iter/s, 4.27797s/12 iters), loss = 4.53743
I0409 23:17:35.202446  6396 solver.cpp:237]     Train net output #0: loss = 4.53743 (* 1 = 4.53743 loss)
I0409 23:17:35.202456  6396 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 23:17:40.153878  6396 solver.cpp:218] Iteration 1656 (2.42362 iter/s, 4.95127s/12 iters), loss = 4.50593
I0409 23:17:40.153936  6396 solver.cpp:237]     Train net output #0: loss = 4.50593 (* 1 = 4.50593 loss)
I0409 23:17:40.153949  6396 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 23:17:45.333623  6396 solver.cpp:218] Iteration 1668 (2.31682 iter/s, 5.17952s/12 iters), loss = 4.33459
I0409 23:17:45.333791  6396 solver.cpp:237]     Train net output #0: loss = 4.33459 (* 1 = 4.33459 loss)
I0409 23:17:45.333804  6396 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 23:17:50.468796  6396 solver.cpp:218] Iteration 1680 (2.33697 iter/s, 5.13485s/12 iters), loss = 4.4101
I0409 23:17:50.468837  6396 solver.cpp:237]     Train net output #0: loss = 4.4101 (* 1 = 4.4101 loss)
I0409 23:17:50.468845  6396 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 23:17:55.340991  6396 solver.cpp:218] Iteration 1692 (2.46305 iter/s, 4.872s/12 iters), loss = 4.40928
I0409 23:17:55.341045  6396 solver.cpp:237]     Train net output #0: loss = 4.40928 (* 1 = 4.40928 loss)
I0409 23:17:55.341056  6396 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 23:18:00.218047  6396 solver.cpp:218] Iteration 1704 (2.46061 iter/s, 4.87685s/12 iters), loss = 4.41133
I0409 23:18:00.218108  6396 solver.cpp:237]     Train net output #0: loss = 4.41133 (* 1 = 4.41133 loss)
I0409 23:18:00.218124  6396 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 23:18:05.122891  6396 solver.cpp:218] Iteration 1716 (2.44667 iter/s, 4.90463s/12 iters), loss = 4.46178
I0409 23:18:05.122948  6396 solver.cpp:237]     Train net output #0: loss = 4.46178 (* 1 = 4.46178 loss)
I0409 23:18:05.122961  6396 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 23:18:06.142616  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:10.014753  6396 solver.cpp:218] Iteration 1728 (2.45316 iter/s, 4.89165s/12 iters), loss = 4.41805
I0409 23:18:10.014816  6396 solver.cpp:237]     Train net output #0: loss = 4.41805 (* 1 = 4.41805 loss)
I0409 23:18:10.014827  6396 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 23:18:12.078881  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 23:18:13.353004  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 23:18:16.555765  6396 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 23:18:16.555863  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:18:20.484387  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:21.186722  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0508578
I0409 23:18:21.186787  6396 solver.cpp:397]     Test net output #1: loss = 4.45494 (* 1 = 4.45494 loss)
I0409 23:18:23.079223  6396 solver.cpp:218] Iteration 1740 (0.918554 iter/s, 13.064s/12 iters), loss = 4.44893
I0409 23:18:23.079311  6396 solver.cpp:237]     Train net output #0: loss = 4.44893 (* 1 = 4.44893 loss)
I0409 23:18:23.079322  6396 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 23:18:28.020570  6396 solver.cpp:218] Iteration 1752 (2.42861 iter/s, 4.9411s/12 iters), loss = 4.30604
I0409 23:18:28.020615  6396 solver.cpp:237]     Train net output #0: loss = 4.30604 (* 1 = 4.30604 loss)
I0409 23:18:28.020625  6396 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 23:18:33.023504  6396 solver.cpp:218] Iteration 1764 (2.39869 iter/s, 5.00273s/12 iters), loss = 4.40201
I0409 23:18:33.023550  6396 solver.cpp:237]     Train net output #0: loss = 4.40201 (* 1 = 4.40201 loss)
I0409 23:18:33.023558  6396 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 23:18:38.031085  6396 solver.cpp:218] Iteration 1776 (2.39647 iter/s, 5.00737s/12 iters), loss = 4.40017
I0409 23:18:38.031128  6396 solver.cpp:237]     Train net output #0: loss = 4.40017 (* 1 = 4.40017 loss)
I0409 23:18:38.031138  6396 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 23:18:42.891469  6396 solver.cpp:218] Iteration 1788 (2.46904 iter/s, 4.86018s/12 iters), loss = 4.40695
I0409 23:18:42.891516  6396 solver.cpp:237]     Train net output #0: loss = 4.40695 (* 1 = 4.40695 loss)
I0409 23:18:42.891525  6396 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 23:18:47.910559  6396 solver.cpp:218] Iteration 1800 (2.39097 iter/s, 5.01888s/12 iters), loss = 4.18079
I0409 23:18:47.910701  6396 solver.cpp:237]     Train net output #0: loss = 4.18079 (* 1 = 4.18079 loss)
I0409 23:18:47.910713  6396 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 23:18:52.937618  6396 solver.cpp:218] Iteration 1812 (2.38722 iter/s, 5.02676s/12 iters), loss = 4.29196
I0409 23:18:52.937667  6396 solver.cpp:237]     Train net output #0: loss = 4.29196 (* 1 = 4.29196 loss)
I0409 23:18:52.937678  6396 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 23:18:56.095976  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:57.864013  6396 solver.cpp:218] Iteration 1824 (2.43596 iter/s, 4.92619s/12 iters), loss = 4.3334
I0409 23:18:57.864076  6396 solver.cpp:237]     Train net output #0: loss = 4.3334 (* 1 = 4.3334 loss)
I0409 23:18:57.864089  6396 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 23:19:02.511440  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 23:19:02.943543  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 23:19:03.251252  6396 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 23:19:03.251277  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:19:07.145830  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:07.899140  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0784314
I0409 23:19:07.899170  6396 solver.cpp:397]     Test net output #1: loss = 4.29694 (* 1 = 4.29694 loss)
I0409 23:19:07.983600  6396 solver.cpp:218] Iteration 1836 (1.18586 iter/s, 10.1192s/12 iters), loss = 4.26144
I0409 23:19:07.983660  6396 solver.cpp:237]     Train net output #0: loss = 4.26144 (* 1 = 4.26144 loss)
I0409 23:19:07.983672  6396 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 23:19:12.062814  6396 solver.cpp:218] Iteration 1848 (2.94189 iter/s, 4.07902s/12 iters), loss = 4.34542
I0409 23:19:12.062877  6396 solver.cpp:237]     Train net output #0: loss = 4.34542 (* 1 = 4.34542 loss)
I0409 23:19:12.062891  6396 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 23:19:17.033352  6396 solver.cpp:218] Iteration 1860 (2.41433 iter/s, 4.97032s/12 iters), loss = 4.3279
I0409 23:19:17.033401  6396 solver.cpp:237]     Train net output #0: loss = 4.3279 (* 1 = 4.3279 loss)
I0409 23:19:17.033409  6396 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 23:19:22.134953  6396 solver.cpp:218] Iteration 1872 (2.3523 iter/s, 5.10139s/12 iters), loss = 4.3064
I0409 23:19:22.135071  6396 solver.cpp:237]     Train net output #0: loss = 4.3064 (* 1 = 4.3064 loss)
I0409 23:19:22.135084  6396 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 23:19:27.372979  6396 solver.cpp:218] Iteration 1884 (2.29106 iter/s, 5.23775s/12 iters), loss = 4.34952
I0409 23:19:27.373029  6396 solver.cpp:237]     Train net output #0: loss = 4.34952 (* 1 = 4.34952 loss)
I0409 23:19:27.373041  6396 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 23:19:32.239475  6396 solver.cpp:218] Iteration 1896 (2.46595 iter/s, 4.86629s/12 iters), loss = 4.2163
I0409 23:19:32.239521  6396 solver.cpp:237]     Train net output #0: loss = 4.2163 (* 1 = 4.2163 loss)
I0409 23:19:32.239529  6396 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 23:19:37.303911  6396 solver.cpp:218] Iteration 1908 (2.36956 iter/s, 5.06423s/12 iters), loss = 4.28224
I0409 23:19:37.303963  6396 solver.cpp:237]     Train net output #0: loss = 4.28224 (* 1 = 4.28224 loss)
I0409 23:19:37.303977  6396 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 23:19:42.793198  6396 solver.cpp:218] Iteration 1920 (2.18617 iter/s, 5.48906s/12 iters), loss = 4.35912
I0409 23:19:42.793252  6396 solver.cpp:237]     Train net output #0: loss = 4.35912 (* 1 = 4.35912 loss)
I0409 23:19:42.793263  6396 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 23:19:43.124256  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:47.816746  6396 solver.cpp:218] Iteration 1932 (2.38886 iter/s, 5.02332s/12 iters), loss = 4.23774
I0409 23:19:47.816804  6396 solver.cpp:237]     Train net output #0: loss = 4.23774 (* 1 = 4.23774 loss)
I0409 23:19:47.816815  6396 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 23:19:49.792270  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 23:19:50.235394  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 23:19:50.533365  6396 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 23:19:50.533382  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:19:54.174852  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:54.959771  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0765931
I0409 23:19:54.959813  6396 solver.cpp:397]     Test net output #1: loss = 4.15033 (* 1 = 4.15033 loss)
I0409 23:19:56.875648  6396 solver.cpp:218] Iteration 1944 (1.32471 iter/s, 9.05857s/12 iters), loss = 4.26458
I0409 23:19:56.875694  6396 solver.cpp:237]     Train net output #0: loss = 4.26458 (* 1 = 4.26458 loss)
I0409 23:19:56.875702  6396 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 23:20:01.825706  6396 solver.cpp:218] Iteration 1956 (2.42432 iter/s, 4.94985s/12 iters), loss = 4.32529
I0409 23:20:01.825760  6396 solver.cpp:237]     Train net output #0: loss = 4.32529 (* 1 = 4.32529 loss)
I0409 23:20:01.825770  6396 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 23:20:06.776881  6396 solver.cpp:218] Iteration 1968 (2.42377 iter/s, 4.95096s/12 iters), loss = 4.2211
I0409 23:20:06.776930  6396 solver.cpp:237]     Train net output #0: loss = 4.2211 (* 1 = 4.2211 loss)
I0409 23:20:06.776939  6396 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 23:20:11.752470  6396 solver.cpp:218] Iteration 1980 (2.41188 iter/s, 4.97538s/12 iters), loss = 4.0977
I0409 23:20:11.752511  6396 solver.cpp:237]     Train net output #0: loss = 4.0977 (* 1 = 4.0977 loss)
I0409 23:20:11.752519  6396 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 23:20:16.666509  6396 solver.cpp:218] Iteration 1992 (2.44208 iter/s, 4.91384s/12 iters), loss = 4.17903
I0409 23:20:16.666550  6396 solver.cpp:237]     Train net output #0: loss = 4.17903 (* 1 = 4.17903 loss)
I0409 23:20:16.666558  6396 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 23:20:21.528373  6396 solver.cpp:218] Iteration 2004 (2.46829 iter/s, 4.86167s/12 iters), loss = 4.06815
I0409 23:20:21.528424  6396 solver.cpp:237]     Train net output #0: loss = 4.06815 (* 1 = 4.06815 loss)
I0409 23:20:21.528434  6396 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 23:20:26.554777  6396 solver.cpp:218] Iteration 2016 (2.38749 iter/s, 5.0262s/12 iters), loss = 4.24403
I0409 23:20:26.554877  6396 solver.cpp:237]     Train net output #0: loss = 4.24403 (* 1 = 4.24403 loss)
I0409 23:20:26.554886  6396 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 23:20:29.084246  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:31.509711  6396 solver.cpp:218] Iteration 2028 (2.42195 iter/s, 4.95468s/12 iters), loss = 4.00201
I0409 23:20:31.509757  6396 solver.cpp:237]     Train net output #0: loss = 4.00201 (* 1 = 4.00201 loss)
I0409 23:20:31.509765  6396 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 23:20:36.060308  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 23:20:36.495018  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 23:20:36.793994  6396 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 23:20:36.794023  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:20:40.373848  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:41.218719  6396 solver.cpp:397]     Test net output #0: accuracy = 0.098652
I0409 23:20:41.218752  6396 solver.cpp:397]     Test net output #1: loss = 3.96935 (* 1 = 3.96935 loss)
I0409 23:20:41.302090  6396 solver.cpp:218] Iteration 2040 (1.22549 iter/s, 9.79203s/12 iters), loss = 3.91138
I0409 23:20:41.302137  6396 solver.cpp:237]     Train net output #0: loss = 3.91138 (* 1 = 3.91138 loss)
I0409 23:20:41.302146  6396 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 23:20:45.418421  6396 solver.cpp:218] Iteration 2052 (2.91534 iter/s, 4.11615s/12 iters), loss = 4.02643
I0409 23:20:45.418464  6396 solver.cpp:237]     Train net output #0: loss = 4.02643 (* 1 = 4.02643 loss)
I0409 23:20:45.418473  6396 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 23:20:45.418679  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:20:50.340124  6396 solver.cpp:218] Iteration 2064 (2.43828 iter/s, 4.92149s/12 iters), loss = 4.17505
I0409 23:20:50.340178  6396 solver.cpp:237]     Train net output #0: loss = 4.17505 (* 1 = 4.17505 loss)
I0409 23:20:50.340188  6396 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 23:20:55.379323  6396 solver.cpp:218] Iteration 2076 (2.38143 iter/s, 5.03898s/12 iters), loss = 4.13792
I0409 23:20:55.379374  6396 solver.cpp:237]     Train net output #0: loss = 4.13792 (* 1 = 4.13792 loss)
I0409 23:20:55.379382  6396 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 23:21:00.320042  6396 solver.cpp:218] Iteration 2088 (2.4289 iter/s, 4.94051s/12 iters), loss = 3.94799
I0409 23:21:00.321516  6396 solver.cpp:237]     Train net output #0: loss = 3.94799 (* 1 = 3.94799 loss)
I0409 23:21:00.321532  6396 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 23:21:05.237515  6396 solver.cpp:218] Iteration 2100 (2.44109 iter/s, 4.91585s/12 iters), loss = 4.05609
I0409 23:21:05.237582  6396 solver.cpp:237]     Train net output #0: loss = 4.05609 (* 1 = 4.05609 loss)
I0409 23:21:05.237597  6396 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 23:21:10.191763  6396 solver.cpp:218] Iteration 2112 (2.42227 iter/s, 4.95402s/12 iters), loss = 4.06933
I0409 23:21:10.191807  6396 solver.cpp:237]     Train net output #0: loss = 4.06933 (* 1 = 4.06933 loss)
I0409 23:21:10.191817  6396 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 23:21:14.953332  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:15.274793  6396 solver.cpp:218] Iteration 2124 (2.36089 iter/s, 5.08282s/12 iters), loss = 3.64441
I0409 23:21:15.274849  6396 solver.cpp:237]     Train net output #0: loss = 3.64441 (* 1 = 3.64441 loss)
I0409 23:21:15.274859  6396 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 23:21:20.281240  6396 solver.cpp:218] Iteration 2136 (2.39701 iter/s, 5.00623s/12 iters), loss = 4.07856
I0409 23:21:20.281286  6396 solver.cpp:237]     Train net output #0: loss = 4.07856 (* 1 = 4.07856 loss)
I0409 23:21:20.281294  6396 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 23:21:22.336412  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 23:21:23.170626  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 23:21:23.494549  6396 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 23:21:23.494580  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:21:27.345259  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:28.216073  6396 solver.cpp:397]     Test net output #0: accuracy = 0.0980392
I0409 23:21:28.216112  6396 solver.cpp:397]     Test net output #1: loss = 3.90421 (* 1 = 3.90421 loss)
I0409 23:21:30.015677  6396 solver.cpp:218] Iteration 2148 (1.23278 iter/s, 9.73409s/12 iters), loss = 3.90262
I0409 23:21:30.015723  6396 solver.cpp:237]     Train net output #0: loss = 3.90262 (* 1 = 3.90262 loss)
I0409 23:21:30.015733  6396 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 23:21:35.018576  6396 solver.cpp:218] Iteration 2160 (2.39871 iter/s, 5.00269s/12 iters), loss = 4.22384
I0409 23:21:35.018719  6396 solver.cpp:237]     Train net output #0: loss = 4.22384 (* 1 = 4.22384 loss)
I0409 23:21:35.018729  6396 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 23:21:40.031054  6396 solver.cpp:218] Iteration 2172 (2.39417 iter/s, 5.01217s/12 iters), loss = 3.83919
I0409 23:21:40.031107  6396 solver.cpp:237]     Train net output #0: loss = 3.83919 (* 1 = 3.83919 loss)
I0409 23:21:40.031117  6396 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 23:21:45.012954  6396 solver.cpp:218] Iteration 2184 (2.40882 iter/s, 4.98169s/12 iters), loss = 3.73305
I0409 23:21:45.013000  6396 solver.cpp:237]     Train net output #0: loss = 3.73305 (* 1 = 3.73305 loss)
I0409 23:21:45.013010  6396 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 23:21:50.341827  6396 solver.cpp:218] Iteration 2196 (2.25198 iter/s, 5.32865s/12 iters), loss = 3.83489
I0409 23:21:50.341883  6396 solver.cpp:237]     Train net output #0: loss = 3.83489 (* 1 = 3.83489 loss)
I0409 23:21:50.341895  6396 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 23:21:55.305423  6396 solver.cpp:218] Iteration 2208 (2.41771 iter/s, 4.96338s/12 iters), loss = 3.84688
I0409 23:21:55.305477  6396 solver.cpp:237]     Train net output #0: loss = 3.84688 (* 1 = 3.84688 loss)
I0409 23:21:55.305488  6396 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 23:22:00.267741  6396 solver.cpp:218] Iteration 2220 (2.41833 iter/s, 4.96211s/12 iters), loss = 3.8275
I0409 23:22:00.267781  6396 solver.cpp:237]     Train net output #0: loss = 3.8275 (* 1 = 3.8275 loss)
I0409 23:22:00.267788  6396 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 23:22:02.064924  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:05.241541  6396 solver.cpp:218] Iteration 2232 (2.41274 iter/s, 4.9736s/12 iters), loss = 3.90747
I0409 23:22:05.241639  6396 solver.cpp:237]     Train net output #0: loss = 3.90747 (* 1 = 3.90747 loss)
I0409 23:22:05.241648  6396 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 23:22:09.818936  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 23:22:10.283102  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 23:22:10.577862  6396 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 23:22:10.577880  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:22:14.330479  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:15.258924  6396 solver.cpp:397]     Test net output #0: accuracy = 0.115809
I0409 23:22:15.258952  6396 solver.cpp:397]     Test net output #1: loss = 3.85089 (* 1 = 3.85089 loss)
I0409 23:22:15.343456  6396 solver.cpp:218] Iteration 2244 (1.18794 iter/s, 10.1015s/12 iters), loss = 4.07175
I0409 23:22:15.343499  6396 solver.cpp:237]     Train net output #0: loss = 4.07175 (* 1 = 4.07175 loss)
I0409 23:22:15.343508  6396 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 23:22:19.926609  6396 solver.cpp:218] Iteration 2256 (2.6184 iter/s, 4.58296s/12 iters), loss = 3.71838
I0409 23:22:19.926649  6396 solver.cpp:237]     Train net output #0: loss = 3.71838 (* 1 = 3.71838 loss)
I0409 23:22:19.926657  6396 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 23:22:24.922219  6396 solver.cpp:218] Iteration 2268 (2.4022 iter/s, 4.99541s/12 iters), loss = 3.7817
I0409 23:22:24.922259  6396 solver.cpp:237]     Train net output #0: loss = 3.7817 (* 1 = 3.7817 loss)
I0409 23:22:24.922268  6396 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 23:22:29.810324  6396 solver.cpp:218] Iteration 2280 (2.45504 iter/s, 4.8879s/12 iters), loss = 3.65404
I0409 23:22:29.810372  6396 solver.cpp:237]     Train net output #0: loss = 3.65404 (* 1 = 3.65404 loss)
I0409 23:22:29.810380  6396 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 23:22:34.993151  6396 solver.cpp:218] Iteration 2292 (2.31544 iter/s, 5.18261s/12 iters), loss = 3.71967
I0409 23:22:34.993206  6396 solver.cpp:237]     Train net output #0: loss = 3.71967 (* 1 = 3.71967 loss)
I0409 23:22:34.993217  6396 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 23:22:40.059214  6396 solver.cpp:218] Iteration 2304 (2.3688 iter/s, 5.06585s/12 iters), loss = 3.95429
I0409 23:22:40.059348  6396 solver.cpp:237]     Train net output #0: loss = 3.95429 (* 1 = 3.95429 loss)
I0409 23:22:40.059358  6396 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 23:22:44.995191  6396 solver.cpp:218] Iteration 2316 (2.43127 iter/s, 4.93568s/12 iters), loss = 3.85445
I0409 23:22:44.995236  6396 solver.cpp:237]     Train net output #0: loss = 3.85445 (* 1 = 3.85445 loss)
I0409 23:22:44.995245  6396 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 23:22:48.855512  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:49.899006  6396 solver.cpp:218] Iteration 2328 (2.44718 iter/s, 4.90361s/12 iters), loss = 3.53099
I0409 23:22:49.899051  6396 solver.cpp:237]     Train net output #0: loss = 3.53099 (* 1 = 3.53099 loss)
I0409 23:22:49.899060  6396 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 23:22:54.804775  6396 solver.cpp:218] Iteration 2340 (2.4462 iter/s, 4.90557s/12 iters), loss = 3.75378
I0409 23:22:54.804814  6396 solver.cpp:237]     Train net output #0: loss = 3.75378 (* 1 = 3.75378 loss)
I0409 23:22:54.804822  6396 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 23:22:56.784858  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 23:22:57.234802  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 23:22:57.541805  6396 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 23:22:57.541828  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:23:00.981339  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:01.939874  6396 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0409 23:23:01.939937  6396 solver.cpp:397]     Test net output #1: loss = 3.68796 (* 1 = 3.68796 loss)
I0409 23:23:04.070427  6396 solver.cpp:218] Iteration 2352 (1.29515 iter/s, 9.26533s/12 iters), loss = 3.63336
I0409 23:23:04.070474  6396 solver.cpp:237]     Train net output #0: loss = 3.63336 (* 1 = 3.63336 loss)
I0409 23:23:04.070483  6396 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 23:23:09.050709  6396 solver.cpp:218] Iteration 2364 (2.40961 iter/s, 4.98007s/12 iters), loss = 3.70532
I0409 23:23:09.050774  6396 solver.cpp:237]     Train net output #0: loss = 3.70532 (* 1 = 3.70532 loss)
I0409 23:23:09.050787  6396 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 23:23:14.010890  6396 solver.cpp:218] Iteration 2376 (2.41937 iter/s, 4.95996s/12 iters), loss = 3.5374
I0409 23:23:14.010998  6396 solver.cpp:237]     Train net output #0: loss = 3.5374 (* 1 = 3.5374 loss)
I0409 23:23:14.011008  6396 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 23:23:19.013919  6396 solver.cpp:218] Iteration 2388 (2.39868 iter/s, 5.00276s/12 iters), loss = 3.70028
I0409 23:23:19.014000  6396 solver.cpp:237]     Train net output #0: loss = 3.70028 (* 1 = 3.70028 loss)
I0409 23:23:19.014012  6396 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 23:23:23.914187  6396 solver.cpp:218] Iteration 2400 (2.44896 iter/s, 4.90003s/12 iters), loss = 3.683
I0409 23:23:23.914233  6396 solver.cpp:237]     Train net output #0: loss = 3.683 (* 1 = 3.683 loss)
I0409 23:23:23.914242  6396 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 23:23:28.912034  6396 solver.cpp:218] Iteration 2412 (2.40114 iter/s, 4.99763s/12 iters), loss = 3.45108
I0409 23:23:28.912094  6396 solver.cpp:237]     Train net output #0: loss = 3.45108 (* 1 = 3.45108 loss)
I0409 23:23:28.912106  6396 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 23:23:34.319797  6396 solver.cpp:218] Iteration 2424 (2.21913 iter/s, 5.40753s/12 iters), loss = 3.68421
I0409 23:23:34.319856  6396 solver.cpp:237]     Train net output #0: loss = 3.68421 (* 1 = 3.68421 loss)
I0409 23:23:34.319870  6396 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 23:23:35.409070  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:39.307972  6396 solver.cpp:218] Iteration 2436 (2.4058 iter/s, 4.98795s/12 iters), loss = 3.7046
I0409 23:23:39.308029  6396 solver.cpp:237]     Train net output #0: loss = 3.7046 (* 1 = 3.7046 loss)
I0409 23:23:39.308041  6396 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 23:23:43.936532  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 23:23:44.969053  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 23:23:45.883288  6396 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 23:23:45.883319  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:23:49.501157  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:50.917886  6396 solver.cpp:397]     Test net output #0: accuracy = 0.164828
I0409 23:23:50.917933  6396 solver.cpp:397]     Test net output #1: loss = 3.63631 (* 1 = 3.63631 loss)
I0409 23:23:51.001318  6396 solver.cpp:218] Iteration 2448 (1.02626 iter/s, 11.6929s/12 iters), loss = 3.77965
I0409 23:23:51.001371  6396 solver.cpp:237]     Train net output #0: loss = 3.77965 (* 1 = 3.77965 loss)
I0409 23:23:51.001382  6396 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 23:23:55.114737  6396 solver.cpp:218] Iteration 2460 (2.91741 iter/s, 4.11323s/12 iters), loss = 3.51167
I0409 23:23:55.114784  6396 solver.cpp:237]     Train net output #0: loss = 3.51167 (* 1 = 3.51167 loss)
I0409 23:23:55.114792  6396 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 23:24:00.724881  6396 solver.cpp:218] Iteration 2472 (2.13907 iter/s, 5.60992s/12 iters), loss = 3.67142
I0409 23:24:00.724949  6396 solver.cpp:237]     Train net output #0: loss = 3.67142 (* 1 = 3.67142 loss)
I0409 23:24:00.724964  6396 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 23:24:05.690819  6396 solver.cpp:218] Iteration 2484 (2.41657 iter/s, 4.96571s/12 iters), loss = 3.55337
I0409 23:24:05.690874  6396 solver.cpp:237]     Train net output #0: loss = 3.55337 (* 1 = 3.55337 loss)
I0409 23:24:05.690886  6396 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 23:24:10.606384  6396 solver.cpp:218] Iteration 2496 (2.44133 iter/s, 4.91535s/12 iters), loss = 3.60504
I0409 23:24:10.606438  6396 solver.cpp:237]     Train net output #0: loss = 3.60504 (* 1 = 3.60504 loss)
I0409 23:24:10.606449  6396 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 23:24:15.459794  6396 solver.cpp:218] Iteration 2508 (2.47259 iter/s, 4.85321s/12 iters), loss = 3.64453
I0409 23:24:15.459894  6396 solver.cpp:237]     Train net output #0: loss = 3.64453 (* 1 = 3.64453 loss)
I0409 23:24:15.459903  6396 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 23:24:20.497493  6396 solver.cpp:218] Iteration 2520 (2.38216 iter/s, 5.03744s/12 iters), loss = 3.60964
I0409 23:24:20.497545  6396 solver.cpp:237]     Train net output #0: loss = 3.60964 (* 1 = 3.60964 loss)
I0409 23:24:20.497555  6396 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 23:24:23.690412  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:25.470041  6396 solver.cpp:218] Iteration 2532 (2.41335 iter/s, 4.97234s/12 iters), loss = 3.60237
I0409 23:24:25.470088  6396 solver.cpp:237]     Train net output #0: loss = 3.60237 (* 1 = 3.60237 loss)
I0409 23:24:25.470095  6396 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 23:24:30.504165  6396 solver.cpp:218] Iteration 2544 (2.38383 iter/s, 5.03391s/12 iters), loss = 3.45615
I0409 23:24:30.504233  6396 solver.cpp:237]     Train net output #0: loss = 3.45615 (* 1 = 3.45615 loss)
I0409 23:24:30.504246  6396 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 23:24:32.519446  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 23:24:32.974117  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 23:24:33.293267  6396 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 23:24:33.293287  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:24:36.685676  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:37.707994  6396 solver.cpp:397]     Test net output #0: accuracy = 0.170956
I0409 23:24:37.708034  6396 solver.cpp:397]     Test net output #1: loss = 3.48558 (* 1 = 3.48558 loss)
I0409 23:24:40.051139  6396 solver.cpp:218] Iteration 2556 (1.25699 iter/s, 9.54662s/12 iters), loss = 3.52544
I0409 23:24:40.051182  6396 solver.cpp:237]     Train net output #0: loss = 3.52544 (* 1 = 3.52544 loss)
I0409 23:24:40.051192  6396 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 23:24:45.365500  6396 solver.cpp:218] Iteration 2568 (2.25812 iter/s, 5.31415s/12 iters), loss = 3.27331
I0409 23:24:45.365562  6396 solver.cpp:237]     Train net output #0: loss = 3.27331 (* 1 = 3.27331 loss)
I0409 23:24:45.365574  6396 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 23:24:50.459228  6396 solver.cpp:218] Iteration 2580 (2.35594 iter/s, 5.0935s/12 iters), loss = 3.36237
I0409 23:24:50.460436  6396 solver.cpp:237]     Train net output #0: loss = 3.36237 (* 1 = 3.36237 loss)
I0409 23:24:50.460448  6396 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 23:24:55.455416  6396 solver.cpp:218] Iteration 2592 (2.40249 iter/s, 4.99482s/12 iters), loss = 3.75403
I0409 23:24:55.455462  6396 solver.cpp:237]     Train net output #0: loss = 3.75403 (* 1 = 3.75403 loss)
I0409 23:24:55.455471  6396 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 23:25:00.575925  6396 solver.cpp:218] Iteration 2604 (2.34361 iter/s, 5.1203s/12 iters), loss = 3.45138
I0409 23:25:00.575981  6396 solver.cpp:237]     Train net output #0: loss = 3.45138 (* 1 = 3.45138 loss)
I0409 23:25:00.575994  6396 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 23:25:05.525979  6396 solver.cpp:218] Iteration 2616 (2.42433 iter/s, 4.94982s/12 iters), loss = 3.58074
I0409 23:25:05.526033  6396 solver.cpp:237]     Train net output #0: loss = 3.58074 (* 1 = 3.58074 loss)
I0409 23:25:05.526043  6396 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 23:25:10.392393  6396 solver.cpp:218] Iteration 2628 (2.46599 iter/s, 4.8662s/12 iters), loss = 3.30902
I0409 23:25:10.392441  6396 solver.cpp:237]     Train net output #0: loss = 3.30902 (* 1 = 3.30902 loss)
I0409 23:25:10.392450  6396 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 23:25:10.841361  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:15.330222  6396 solver.cpp:218] Iteration 2640 (2.43032 iter/s, 4.93762s/12 iters), loss = 3.58375
I0409 23:25:15.330271  6396 solver.cpp:237]     Train net output #0: loss = 3.58375 (* 1 = 3.58375 loss)
I0409 23:25:15.330278  6396 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 23:25:20.089196  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 23:25:20.558179  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 23:25:20.873579  6396 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 23:25:20.873598  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:25:24.368548  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:25.589336  6396 solver.cpp:397]     Test net output #0: accuracy = 0.191789
I0409 23:25:25.589372  6396 solver.cpp:397]     Test net output #1: loss = 3.37555 (* 1 = 3.37555 loss)
I0409 23:25:25.676592  6396 solver.cpp:218] Iteration 2652 (1.15987 iter/s, 10.346s/12 iters), loss = 3.35528
I0409 23:25:25.676632  6396 solver.cpp:237]     Train net output #0: loss = 3.35528 (* 1 = 3.35528 loss)
I0409 23:25:25.676640  6396 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 23:25:29.802512  6396 solver.cpp:218] Iteration 2664 (2.90857 iter/s, 4.12574s/12 iters), loss = 3.27167
I0409 23:25:29.802567  6396 solver.cpp:237]     Train net output #0: loss = 3.27167 (* 1 = 3.27167 loss)
I0409 23:25:29.802577  6396 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 23:25:34.687947  6396 solver.cpp:218] Iteration 2676 (2.45639 iter/s, 4.88523s/12 iters), loss = 3.38674
I0409 23:25:34.687994  6396 solver.cpp:237]     Train net output #0: loss = 3.38674 (* 1 = 3.38674 loss)
I0409 23:25:34.688004  6396 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 23:25:39.699800  6396 solver.cpp:218] Iteration 2688 (2.39442 iter/s, 5.01165s/12 iters), loss = 3.32528
I0409 23:25:39.699852  6396 solver.cpp:237]     Train net output #0: loss = 3.32528 (* 1 = 3.32528 loss)
I0409 23:25:39.699863  6396 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 23:25:44.878245  6396 solver.cpp:218] Iteration 2700 (2.31739 iter/s, 5.17823s/12 iters), loss = 3.43758
I0409 23:25:44.878288  6396 solver.cpp:237]     Train net output #0: loss = 3.43758 (* 1 = 3.43758 loss)
I0409 23:25:44.878296  6396 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 23:25:49.865252  6396 solver.cpp:218] Iteration 2712 (2.40635 iter/s, 4.98681s/12 iters), loss = 3.23327
I0409 23:25:49.865291  6396 solver.cpp:237]     Train net output #0: loss = 3.23327 (* 1 = 3.23327 loss)
I0409 23:25:49.865303  6396 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 23:25:54.819701  6396 solver.cpp:218] Iteration 2724 (2.42216 iter/s, 4.95425s/12 iters), loss = 3.50207
I0409 23:25:54.819859  6396 solver.cpp:237]     Train net output #0: loss = 3.50207 (* 1 = 3.50207 loss)
I0409 23:25:54.819869  6396 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 23:25:57.325637  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:59.691696  6396 solver.cpp:218] Iteration 2736 (2.46321 iter/s, 4.87169s/12 iters), loss = 2.99152
I0409 23:25:59.691751  6396 solver.cpp:237]     Train net output #0: loss = 2.99152 (* 1 = 2.99152 loss)
I0409 23:25:59.691763  6396 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 23:26:04.618518  6396 solver.cpp:218] Iteration 2748 (2.43575 iter/s, 4.92661s/12 iters), loss = 3.4643
I0409 23:26:04.618579  6396 solver.cpp:237]     Train net output #0: loss = 3.4643 (* 1 = 3.4643 loss)
I0409 23:26:04.618592  6396 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 23:26:06.601256  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 23:26:07.361196  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 23:26:08.325081  6396 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 23:26:08.325103  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:26:10.986815  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:26:11.584913  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:12.686039  6396 solver.cpp:397]     Test net output #0: accuracy = 0.193627
I0409 23:26:12.686069  6396 solver.cpp:397]     Test net output #1: loss = 3.33955 (* 1 = 3.33955 loss)
I0409 23:26:14.559674  6396 solver.cpp:218] Iteration 2760 (1.20715 iter/s, 9.9408s/12 iters), loss = 3.24949
I0409 23:26:14.559722  6396 solver.cpp:237]     Train net output #0: loss = 3.24949 (* 1 = 3.24949 loss)
I0409 23:26:14.559731  6396 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 23:26:19.541671  6396 solver.cpp:218] Iteration 2772 (2.40878 iter/s, 4.98179s/12 iters), loss = 3.2756
I0409 23:26:19.541733  6396 solver.cpp:237]     Train net output #0: loss = 3.2756 (* 1 = 3.2756 loss)
I0409 23:26:19.541745  6396 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 23:26:24.447470  6396 solver.cpp:218] Iteration 2784 (2.44619 iter/s, 4.90558s/12 iters), loss = 3.45906
I0409 23:26:24.447535  6396 solver.cpp:237]     Train net output #0: loss = 3.45906 (* 1 = 3.45906 loss)
I0409 23:26:24.447547  6396 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 23:26:29.563442  6396 solver.cpp:218] Iteration 2796 (2.3457 iter/s, 5.11575s/12 iters), loss = 3.27261
I0409 23:26:29.563556  6396 solver.cpp:237]     Train net output #0: loss = 3.27261 (* 1 = 3.27261 loss)
I0409 23:26:29.563565  6396 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 23:26:34.715662  6396 solver.cpp:218] Iteration 2808 (2.32922 iter/s, 5.15194s/12 iters), loss = 3.3906
I0409 23:26:34.715711  6396 solver.cpp:237]     Train net output #0: loss = 3.3906 (* 1 = 3.3906 loss)
I0409 23:26:34.715721  6396 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 23:26:39.686753  6396 solver.cpp:218] Iteration 2820 (2.41406 iter/s, 4.97088s/12 iters), loss = 3.20911
I0409 23:26:39.686810  6396 solver.cpp:237]     Train net output #0: loss = 3.20911 (* 1 = 3.20911 loss)
I0409 23:26:39.686821  6396 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 23:26:44.497700  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:44.786018  6396 solver.cpp:218] Iteration 2832 (2.35338 iter/s, 5.09905s/12 iters), loss = 3.21195
I0409 23:26:44.786074  6396 solver.cpp:237]     Train net output #0: loss = 3.21195 (* 1 = 3.21195 loss)
I0409 23:26:44.786087  6396 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 23:26:49.698231  6396 solver.cpp:218] Iteration 2844 (2.443 iter/s, 4.912s/12 iters), loss = 3.2163
I0409 23:26:49.698277  6396 solver.cpp:237]     Train net output #0: loss = 3.2163 (* 1 = 3.2163 loss)
I0409 23:26:49.698285  6396 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 23:26:54.140373  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 23:26:54.586123  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 23:26:54.898814  6396 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 23:26:54.898842  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:26:58.148963  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:59.555423  6396 solver.cpp:397]     Test net output #0: accuracy = 0.216912
I0409 23:26:59.555467  6396 solver.cpp:397]     Test net output #1: loss = 3.22982 (* 1 = 3.22982 loss)
I0409 23:26:59.638754  6396 solver.cpp:218] Iteration 2856 (1.20722 iter/s, 9.94018s/12 iters), loss = 2.8985
I0409 23:26:59.638860  6396 solver.cpp:237]     Train net output #0: loss = 2.8985 (* 1 = 2.8985 loss)
I0409 23:26:59.638871  6396 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 23:27:03.787151  6396 solver.cpp:218] Iteration 2868 (2.89286 iter/s, 4.14815s/12 iters), loss = 3.20744
I0409 23:27:03.787211  6396 solver.cpp:237]     Train net output #0: loss = 3.20744 (* 1 = 3.20744 loss)
I0409 23:27:03.787225  6396 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 23:27:08.683121  6396 solver.cpp:218] Iteration 2880 (2.4511 iter/s, 4.89575s/12 iters), loss = 3.0272
I0409 23:27:08.683182  6396 solver.cpp:237]     Train net output #0: loss = 3.0272 (* 1 = 3.0272 loss)
I0409 23:27:08.683194  6396 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 23:27:13.774137  6396 solver.cpp:218] Iteration 2892 (2.3572 iter/s, 5.0908s/12 iters), loss = 3.21877
I0409 23:27:13.774181  6396 solver.cpp:237]     Train net output #0: loss = 3.21877 (* 1 = 3.21877 loss)
I0409 23:27:13.774190  6396 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 23:27:18.803162  6396 solver.cpp:218] Iteration 2904 (2.38625 iter/s, 5.02882s/12 iters), loss = 3.06068
I0409 23:27:18.803215  6396 solver.cpp:237]     Train net output #0: loss = 3.06068 (* 1 = 3.06068 loss)
I0409 23:27:18.803226  6396 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 23:27:23.741020  6396 solver.cpp:218] Iteration 2916 (2.43031 iter/s, 4.93765s/12 iters), loss = 3.12346
I0409 23:27:23.741067  6396 solver.cpp:237]     Train net output #0: loss = 3.12346 (* 1 = 3.12346 loss)
I0409 23:27:23.741076  6396 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 23:27:28.874442  6396 solver.cpp:218] Iteration 2928 (2.33772 iter/s, 5.13321s/12 iters), loss = 3.1726
I0409 23:27:28.874487  6396 solver.cpp:237]     Train net output #0: loss = 3.1726 (* 1 = 3.1726 loss)
I0409 23:27:28.874495  6396 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 23:27:30.658066  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:33.745502  6396 solver.cpp:218] Iteration 2940 (2.46363 iter/s, 4.87085s/12 iters), loss = 3.0785
I0409 23:27:33.745561  6396 solver.cpp:237]     Train net output #0: loss = 3.0785 (* 1 = 3.0785 loss)
I0409 23:27:33.745573  6396 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 23:27:38.689675  6396 solver.cpp:218] Iteration 2952 (2.42721 iter/s, 4.94395s/12 iters), loss = 3.00427
I0409 23:27:38.689726  6396 solver.cpp:237]     Train net output #0: loss = 3.00427 (* 1 = 3.00427 loss)
I0409 23:27:38.689736  6396 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 23:27:40.731464  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 23:27:41.184720  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 23:27:41.489234  6396 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 23:27:41.489257  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:27:44.702224  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:46.003178  6396 solver.cpp:397]     Test net output #0: accuracy = 0.222426
I0409 23:27:46.003221  6396 solver.cpp:397]     Test net output #1: loss = 3.16018 (* 1 = 3.16018 loss)
I0409 23:27:47.746841  6396 solver.cpp:218] Iteration 2964 (1.32497 iter/s, 9.05684s/12 iters), loss = 3.01048
I0409 23:27:47.746891  6396 solver.cpp:237]     Train net output #0: loss = 3.01048 (* 1 = 3.01048 loss)
I0409 23:27:47.746899  6396 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 23:27:52.697995  6396 solver.cpp:218] Iteration 2976 (2.42378 iter/s, 4.95095s/12 iters), loss = 2.93954
I0409 23:27:52.698037  6396 solver.cpp:237]     Train net output #0: loss = 2.93954 (* 1 = 2.93954 loss)
I0409 23:27:52.698045  6396 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 23:27:57.701207  6396 solver.cpp:218] Iteration 2988 (2.39856 iter/s, 5.00301s/12 iters), loss = 3.25113
I0409 23:27:57.701265  6396 solver.cpp:237]     Train net output #0: loss = 3.25113 (* 1 = 3.25113 loss)
I0409 23:27:57.701277  6396 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 23:28:02.607034  6396 solver.cpp:218] Iteration 3000 (2.44618 iter/s, 4.90561s/12 iters), loss = 3.2066
I0409 23:28:02.607129  6396 solver.cpp:237]     Train net output #0: loss = 3.2066 (* 1 = 3.2066 loss)
I0409 23:28:02.607142  6396 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 23:28:07.559376  6396 solver.cpp:218] Iteration 3012 (2.42322 iter/s, 4.95209s/12 iters), loss = 3.2979
I0409 23:28:07.559423  6396 solver.cpp:237]     Train net output #0: loss = 3.2979 (* 1 = 3.2979 loss)
I0409 23:28:07.559432  6396 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 23:28:12.454695  6396 solver.cpp:218] Iteration 3024 (2.45142 iter/s, 4.89511s/12 iters), loss = 2.95977
I0409 23:28:12.454743  6396 solver.cpp:237]     Train net output #0: loss = 2.95977 (* 1 = 2.95977 loss)
I0409 23:28:12.454753  6396 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 23:28:16.456532  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:17.469342  6396 solver.cpp:218] Iteration 3036 (2.39309 iter/s, 5.01444s/12 iters), loss = 2.6581
I0409 23:28:17.469405  6396 solver.cpp:237]     Train net output #0: loss = 2.6581 (* 1 = 2.6581 loss)
I0409 23:28:17.469417  6396 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 23:28:22.386585  6396 solver.cpp:218] Iteration 3048 (2.4405 iter/s, 4.91702s/12 iters), loss = 2.90884
I0409 23:28:22.386637  6396 solver.cpp:237]     Train net output #0: loss = 2.90884 (* 1 = 2.90884 loss)
I0409 23:28:22.386648  6396 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 23:28:26.904120  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 23:28:27.356971  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 23:28:27.654367  6396 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 23:28:27.654386  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:28:30.774912  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:32.154649  6396 solver.cpp:397]     Test net output #0: accuracy = 0.260417
I0409 23:28:32.154690  6396 solver.cpp:397]     Test net output #1: loss = 3.02677 (* 1 = 3.02677 loss)
I0409 23:28:32.237820  6396 solver.cpp:218] Iteration 3060 (1.21816 iter/s, 9.85089s/12 iters), loss = 3.02081
I0409 23:28:32.237871  6396 solver.cpp:237]     Train net output #0: loss = 3.02081 (* 1 = 3.02081 loss)
I0409 23:28:32.237882  6396 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 23:28:36.406498  6396 solver.cpp:218] Iteration 3072 (2.87874 iter/s, 4.16849s/12 iters), loss = 2.93876
I0409 23:28:36.406646  6396 solver.cpp:237]     Train net output #0: loss = 2.93876 (* 1 = 2.93876 loss)
I0409 23:28:36.406659  6396 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 23:28:41.356833  6396 solver.cpp:218] Iteration 3084 (2.42423 iter/s, 4.95003s/12 iters), loss = 2.94269
I0409 23:28:41.356882  6396 solver.cpp:237]     Train net output #0: loss = 2.94269 (* 1 = 2.94269 loss)
I0409 23:28:41.356891  6396 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 23:28:46.265175  6396 solver.cpp:218] Iteration 3096 (2.44492 iter/s, 4.90814s/12 iters), loss = 3.09695
I0409 23:28:46.265218  6396 solver.cpp:237]     Train net output #0: loss = 3.09695 (* 1 = 3.09695 loss)
I0409 23:28:46.265228  6396 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 23:28:51.196815  6396 solver.cpp:218] Iteration 3108 (2.43337 iter/s, 4.93144s/12 iters), loss = 2.78946
I0409 23:28:51.196866  6396 solver.cpp:237]     Train net output #0: loss = 2.78946 (* 1 = 2.78946 loss)
I0409 23:28:51.196877  6396 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 23:28:56.214243  6396 solver.cpp:218] Iteration 3120 (2.39176 iter/s, 5.01722s/12 iters), loss = 2.83518
I0409 23:28:56.214296  6396 solver.cpp:237]     Train net output #0: loss = 2.83518 (* 1 = 2.83518 loss)
I0409 23:28:56.214305  6396 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 23:29:01.196305  6396 solver.cpp:218] Iteration 3132 (2.40874 iter/s, 4.98185s/12 iters), loss = 2.79641
I0409 23:29:01.196353  6396 solver.cpp:237]     Train net output #0: loss = 2.79641 (* 1 = 2.79641 loss)
I0409 23:29:01.196364  6396 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 23:29:02.293349  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:06.171682  6396 solver.cpp:218] Iteration 3144 (2.41198 iter/s, 4.97517s/12 iters), loss = 2.54074
I0409 23:29:06.171738  6396 solver.cpp:237]     Train net output #0: loss = 2.54074 (* 1 = 2.54074 loss)
I0409 23:29:06.171751  6396 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 23:29:11.147612  6396 solver.cpp:218] Iteration 3156 (2.41171 iter/s, 4.97572s/12 iters), loss = 2.86028
I0409 23:29:11.147686  6396 solver.cpp:237]     Train net output #0: loss = 2.86028 (* 1 = 2.86028 loss)
I0409 23:29:11.147696  6396 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 23:29:13.179744  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 23:29:14.641804  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 23:29:14.966953  6396 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 23:29:14.966975  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:29:18.050451  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:19.310766  6396 solver.cpp:397]     Test net output #0: accuracy = 0.271446
I0409 23:29:19.310799  6396 solver.cpp:397]     Test net output #1: loss = 3.00344 (* 1 = 3.00344 loss)
I0409 23:29:21.102608  6396 solver.cpp:218] Iteration 3168 (1.20547 iter/s, 9.95462s/12 iters), loss = 2.58983
I0409 23:29:21.102658  6396 solver.cpp:237]     Train net output #0: loss = 2.58983 (* 1 = 2.58983 loss)
I0409 23:29:21.102666  6396 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 23:29:25.949996  6396 solver.cpp:218] Iteration 3180 (2.47566 iter/s, 4.84719s/12 iters), loss = 2.87345
I0409 23:29:25.950037  6396 solver.cpp:237]     Train net output #0: loss = 2.87345 (* 1 = 2.87345 loss)
I0409 23:29:25.950044  6396 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 23:29:30.868533  6396 solver.cpp:218] Iteration 3192 (2.43985 iter/s, 4.91834s/12 iters), loss = 2.80032
I0409 23:29:30.868579  6396 solver.cpp:237]     Train net output #0: loss = 2.80032 (* 1 = 2.80032 loss)
I0409 23:29:30.868589  6396 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 23:29:35.794553  6396 solver.cpp:218] Iteration 3204 (2.43614 iter/s, 4.92582s/12 iters), loss = 2.74604
I0409 23:29:35.794595  6396 solver.cpp:237]     Train net output #0: loss = 2.74604 (* 1 = 2.74604 loss)
I0409 23:29:35.794603  6396 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 23:29:40.940201  6396 solver.cpp:218] Iteration 3216 (2.33216 iter/s, 5.14544s/12 iters), loss = 2.85592
I0409 23:29:40.940258  6396 solver.cpp:237]     Train net output #0: loss = 2.85592 (* 1 = 2.85592 loss)
I0409 23:29:40.940270  6396 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 23:29:45.858140  6396 solver.cpp:218] Iteration 3228 (2.44015 iter/s, 4.91773s/12 iters), loss = 2.66524
I0409 23:29:45.858264  6396 solver.cpp:237]     Train net output #0: loss = 2.66524 (* 1 = 2.66524 loss)
I0409 23:29:45.858274  6396 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 23:29:49.013859  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:50.774327  6396 solver.cpp:218] Iteration 3240 (2.44105 iter/s, 4.91591s/12 iters), loss = 2.98872
I0409 23:29:50.774379  6396 solver.cpp:237]     Train net output #0: loss = 2.98872 (* 1 = 2.98872 loss)
I0409 23:29:50.774387  6396 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 23:29:55.653903  6396 solver.cpp:218] Iteration 3252 (2.45933 iter/s, 4.87937s/12 iters), loss = 2.7101
I0409 23:29:55.653975  6396 solver.cpp:237]     Train net output #0: loss = 2.7101 (* 1 = 2.7101 loss)
I0409 23:29:55.653988  6396 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 23:30:00.125454  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 23:30:00.658059  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 23:30:00.975849  6396 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 23:30:00.975873  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:30:04.206970  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:05.557492  6396 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0409 23:30:05.557520  6396 solver.cpp:397]     Test net output #1: loss = 2.75662 (* 1 = 2.75662 loss)
I0409 23:30:05.640612  6396 solver.cpp:218] Iteration 3264 (1.20164 iter/s, 9.98636s/12 iters), loss = 2.75577
I0409 23:30:05.640650  6396 solver.cpp:237]     Train net output #0: loss = 2.75577 (* 1 = 2.75577 loss)
I0409 23:30:05.640658  6396 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 23:30:09.916091  6396 solver.cpp:218] Iteration 3276 (2.80682 iter/s, 4.2753s/12 iters), loss = 2.75935
I0409 23:30:09.916134  6396 solver.cpp:237]     Train net output #0: loss = 2.75935 (* 1 = 2.75935 loss)
I0409 23:30:09.916144  6396 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 23:30:14.884045  6396 solver.cpp:218] Iteration 3288 (2.41558 iter/s, 4.96775s/12 iters), loss = 2.78987
I0409 23:30:14.884095  6396 solver.cpp:237]     Train net output #0: loss = 2.78987 (* 1 = 2.78987 loss)
I0409 23:30:14.884107  6396 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 23:30:19.841665  6396 solver.cpp:218] Iteration 3300 (2.42062 iter/s, 4.95741s/12 iters), loss = 2.75551
I0409 23:30:19.841776  6396 solver.cpp:237]     Train net output #0: loss = 2.75551 (* 1 = 2.75551 loss)
I0409 23:30:19.841789  6396 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 23:30:24.797587  6396 solver.cpp:218] Iteration 3312 (2.42147 iter/s, 4.95566s/12 iters), loss = 2.65682
I0409 23:30:24.797626  6396 solver.cpp:237]     Train net output #0: loss = 2.65682 (* 1 = 2.65682 loss)
I0409 23:30:24.797636  6396 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 23:30:29.693454  6396 solver.cpp:218] Iteration 3324 (2.45115 iter/s, 4.89567s/12 iters), loss = 2.47431
I0409 23:30:29.693497  6396 solver.cpp:237]     Train net output #0: loss = 2.47431 (* 1 = 2.47431 loss)
I0409 23:30:29.693506  6396 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 23:30:34.591689  6396 solver.cpp:218] Iteration 3336 (2.44996 iter/s, 4.89804s/12 iters), loss = 2.61373
I0409 23:30:34.591738  6396 solver.cpp:237]     Train net output #0: loss = 2.61373 (* 1 = 2.61373 loss)
I0409 23:30:34.591749  6396 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 23:30:35.052076  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:39.500286  6396 solver.cpp:218] Iteration 3348 (2.44479 iter/s, 4.90839s/12 iters), loss = 2.75752
I0409 23:30:39.500337  6396 solver.cpp:237]     Train net output #0: loss = 2.75752 (* 1 = 2.75752 loss)
I0409 23:30:39.500349  6396 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 23:30:44.442811  6396 solver.cpp:218] Iteration 3360 (2.42801 iter/s, 4.94232s/12 iters), loss = 2.66446
I0409 23:30:44.442858  6396 solver.cpp:237]     Train net output #0: loss = 2.66446 (* 1 = 2.66446 loss)
I0409 23:30:44.442869  6396 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 23:30:46.650827  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 23:30:47.109643  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 23:30:47.428175  6396 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 23:30:47.428205  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:30:50.436151  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:51.768546  6396 solver.cpp:397]     Test net output #0: accuracy = 0.30576
I0409 23:30:51.768577  6396 solver.cpp:397]     Test net output #1: loss = 2.75935 (* 1 = 2.75935 loss)
I0409 23:30:53.490573  6396 solver.cpp:218] Iteration 3372 (1.32634 iter/s, 9.04744s/12 iters), loss = 2.64847
I0409 23:30:53.490625  6396 solver.cpp:237]     Train net output #0: loss = 2.64847 (* 1 = 2.64847 loss)
I0409 23:30:53.490636  6396 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 23:30:58.364307  6396 solver.cpp:218] Iteration 3384 (2.46228 iter/s, 4.87353s/12 iters), loss = 2.69692
I0409 23:30:58.364353  6396 solver.cpp:237]     Train net output #0: loss = 2.69692 (* 1 = 2.69692 loss)
I0409 23:30:58.364362  6396 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 23:31:03.281452  6396 solver.cpp:218] Iteration 3396 (2.44054 iter/s, 4.91694s/12 iters), loss = 2.59377
I0409 23:31:03.281494  6396 solver.cpp:237]     Train net output #0: loss = 2.59377 (* 1 = 2.59377 loss)
I0409 23:31:03.281503  6396 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 23:31:08.185282  6396 solver.cpp:218] Iteration 3408 (2.44717 iter/s, 4.90363s/12 iters), loss = 2.82858
I0409 23:31:08.185321  6396 solver.cpp:237]     Train net output #0: loss = 2.82858 (* 1 = 2.82858 loss)
I0409 23:31:08.185330  6396 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 23:31:13.120580  6396 solver.cpp:218] Iteration 3420 (2.43156 iter/s, 4.9351s/12 iters), loss = 2.46627
I0409 23:31:13.120633  6396 solver.cpp:237]     Train net output #0: loss = 2.46627 (* 1 = 2.46627 loss)
I0409 23:31:13.120646  6396 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 23:31:18.112628  6396 solver.cpp:218] Iteration 3432 (2.40392 iter/s, 4.99184s/12 iters), loss = 2.75714
I0409 23:31:18.112685  6396 solver.cpp:237]     Train net output #0: loss = 2.75714 (* 1 = 2.75714 loss)
I0409 23:31:18.112699  6396 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 23:31:20.758184  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:23.142920  6396 solver.cpp:218] Iteration 3444 (2.38565 iter/s, 5.03008s/12 iters), loss = 2.31122
I0409 23:31:23.142961  6396 solver.cpp:237]     Train net output #0: loss = 2.31122 (* 1 = 2.31122 loss)
I0409 23:31:23.142971  6396 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 23:31:28.021097  6396 solver.cpp:218] Iteration 3456 (2.46003 iter/s, 4.87798s/12 iters), loss = 2.59902
I0409 23:31:28.021142  6396 solver.cpp:237]     Train net output #0: loss = 2.59902 (* 1 = 2.59902 loss)
I0409 23:31:28.021150  6396 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 23:31:32.543267  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 23:31:32.977324  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 23:31:33.288199  6396 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 23:31:33.288221  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:31:33.319831  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:31:36.250692  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:37.656399  6396 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0409 23:31:37.656430  6396 solver.cpp:397]     Test net output #1: loss = 2.65153 (* 1 = 2.65153 loss)
I0409 23:31:37.739708  6396 solver.cpp:218] Iteration 3468 (1.23479 iter/s, 9.71828s/12 iters), loss = 2.43406
I0409 23:31:37.739750  6396 solver.cpp:237]     Train net output #0: loss = 2.43406 (* 1 = 2.43406 loss)
I0409 23:31:37.739758  6396 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 23:31:41.881083  6396 solver.cpp:218] Iteration 3480 (2.89771 iter/s, 4.1412s/12 iters), loss = 2.41365
I0409 23:31:41.881124  6396 solver.cpp:237]     Train net output #0: loss = 2.41365 (* 1 = 2.41365 loss)
I0409 23:31:41.881131  6396 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 23:31:46.895648  6396 solver.cpp:218] Iteration 3492 (2.39312 iter/s, 5.01437s/12 iters), loss = 2.67123
I0409 23:31:46.895696  6396 solver.cpp:237]     Train net output #0: loss = 2.67123 (* 1 = 2.67123 loss)
I0409 23:31:46.895705  6396 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 23:31:51.813032  6396 solver.cpp:218] Iteration 3504 (2.44042 iter/s, 4.91718s/12 iters), loss = 2.56306
I0409 23:31:51.813154  6396 solver.cpp:237]     Train net output #0: loss = 2.56306 (* 1 = 2.56306 loss)
I0409 23:31:51.813166  6396 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 23:31:56.673264  6396 solver.cpp:218] Iteration 3516 (2.46916 iter/s, 4.85996s/12 iters), loss = 2.50654
I0409 23:31:56.673321  6396 solver.cpp:237]     Train net output #0: loss = 2.50654 (* 1 = 2.50654 loss)
I0409 23:31:56.673332  6396 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 23:32:01.633610  6396 solver.cpp:218] Iteration 3528 (2.41929 iter/s, 4.96014s/12 iters), loss = 2.46962
I0409 23:32:01.633652  6396 solver.cpp:237]     Train net output #0: loss = 2.46962 (* 1 = 2.46962 loss)
I0409 23:32:01.633661  6396 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 23:32:06.510089  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:06.768647  6396 solver.cpp:218] Iteration 3540 (2.33698 iter/s, 5.13483s/12 iters), loss = 2.43491
I0409 23:32:06.768698  6396 solver.cpp:237]     Train net output #0: loss = 2.43491 (* 1 = 2.43491 loss)
I0409 23:32:06.768710  6396 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 23:32:11.753778  6396 solver.cpp:218] Iteration 3552 (2.40726 iter/s, 4.98492s/12 iters), loss = 2.42762
I0409 23:32:11.753827  6396 solver.cpp:237]     Train net output #0: loss = 2.42762 (* 1 = 2.42762 loss)
I0409 23:32:11.753840  6396 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 23:32:16.710485  6396 solver.cpp:218] Iteration 3564 (2.42106 iter/s, 4.9565s/12 iters), loss = 2.22869
I0409 23:32:16.710539  6396 solver.cpp:237]     Train net output #0: loss = 2.22869 (* 1 = 2.22869 loss)
I0409 23:32:16.710551  6396 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 23:32:18.883941  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 23:32:19.357728  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 23:32:19.690624  6396 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 23:32:19.690661  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:32:22.862548  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:24.371155  6396 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0409 23:32:24.371191  6396 solver.cpp:397]     Test net output #1: loss = 2.58929 (* 1 = 2.58929 loss)
I0409 23:32:26.236177  6396 solver.cpp:218] Iteration 3576 (1.2598 iter/s, 9.52535s/12 iters), loss = 2.65176
I0409 23:32:26.236222  6396 solver.cpp:237]     Train net output #0: loss = 2.65176 (* 1 = 2.65176 loss)
I0409 23:32:26.236230  6396 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 23:32:31.104327  6396 solver.cpp:218] Iteration 3588 (2.4651 iter/s, 4.86795s/12 iters), loss = 2.38618
I0409 23:32:31.104377  6396 solver.cpp:237]     Train net output #0: loss = 2.38618 (* 1 = 2.38618 loss)
I0409 23:32:31.104390  6396 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 23:32:36.097313  6396 solver.cpp:218] Iteration 3600 (2.40347 iter/s, 4.99278s/12 iters), loss = 2.53459
I0409 23:32:36.097369  6396 solver.cpp:237]     Train net output #0: loss = 2.53459 (* 1 = 2.53459 loss)
I0409 23:32:36.097381  6396 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 23:32:40.941974  6396 solver.cpp:218] Iteration 3612 (2.47707 iter/s, 4.84443s/12 iters), loss = 2.50511
I0409 23:32:40.942040  6396 solver.cpp:237]     Train net output #0: loss = 2.50511 (* 1 = 2.50511 loss)
I0409 23:32:40.942051  6396 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 23:32:45.949292  6396 solver.cpp:218] Iteration 3624 (2.3966 iter/s, 5.0071s/12 iters), loss = 2.5658
I0409 23:32:45.949333  6396 solver.cpp:237]     Train net output #0: loss = 2.5658 (* 1 = 2.5658 loss)
I0409 23:32:45.949342  6396 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 23:32:50.937438  6396 solver.cpp:218] Iteration 3636 (2.4058 iter/s, 4.98794s/12 iters), loss = 2.36077
I0409 23:32:50.937489  6396 solver.cpp:237]     Train net output #0: loss = 2.36077 (* 1 = 2.36077 loss)
I0409 23:32:50.937500  6396 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 23:32:52.790081  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:55.905297  6396 solver.cpp:218] Iteration 3648 (2.41563 iter/s, 4.96765s/12 iters), loss = 2.04617
I0409 23:32:55.906711  6396 solver.cpp:237]     Train net output #0: loss = 2.04617 (* 1 = 2.04617 loss)
I0409 23:32:55.906725  6396 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 23:33:01.010923  6396 solver.cpp:218] Iteration 3660 (2.35107 iter/s, 5.10405s/12 iters), loss = 2.37293
I0409 23:33:01.010979  6396 solver.cpp:237]     Train net output #0: loss = 2.37293 (* 1 = 2.37293 loss)
I0409 23:33:01.010990  6396 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 23:33:05.510445  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 23:33:06.374545  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 23:33:08.487803  6396 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 23:33:08.487833  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:33:11.459666  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:12.915874  6396 solver.cpp:397]     Test net output #0: accuracy = 0.370098
I0409 23:33:12.915910  6396 solver.cpp:397]     Test net output #1: loss = 2.42089 (* 1 = 2.42089 loss)
I0409 23:33:12.998996  6396 solver.cpp:218] Iteration 3672 (1.00103 iter/s, 11.9877s/12 iters), loss = 1.98345
I0409 23:33:12.999044  6396 solver.cpp:237]     Train net output #0: loss = 1.98345 (* 1 = 1.98345 loss)
I0409 23:33:12.999058  6396 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 23:33:17.035991  6396 solver.cpp:218] Iteration 3684 (2.97264 iter/s, 4.03682s/12 iters), loss = 2.34353
I0409 23:33:17.036038  6396 solver.cpp:237]     Train net output #0: loss = 2.34353 (* 1 = 2.34353 loss)
I0409 23:33:17.036048  6396 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 23:33:22.042064  6396 solver.cpp:218] Iteration 3696 (2.39718 iter/s, 5.00587s/12 iters), loss = 1.84847
I0409 23:33:22.042098  6396 solver.cpp:237]     Train net output #0: loss = 1.84847 (* 1 = 1.84847 loss)
I0409 23:33:22.042106  6396 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 23:33:27.036886  6396 solver.cpp:218] Iteration 3708 (2.40259 iter/s, 4.99462s/12 iters), loss = 2.31439
I0409 23:33:27.037037  6396 solver.cpp:237]     Train net output #0: loss = 2.31439 (* 1 = 2.31439 loss)
I0409 23:33:27.037051  6396 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 23:33:32.025446  6396 solver.cpp:218] Iteration 3720 (2.40565 iter/s, 4.98826s/12 iters), loss = 2.33099
I0409 23:33:32.025488  6396 solver.cpp:237]     Train net output #0: loss = 2.33099 (* 1 = 2.33099 loss)
I0409 23:33:32.025497  6396 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 23:33:36.899219  6396 solver.cpp:218] Iteration 3732 (2.46226 iter/s, 4.87357s/12 iters), loss = 2.22179
I0409 23:33:36.899267  6396 solver.cpp:237]     Train net output #0: loss = 2.22179 (* 1 = 2.22179 loss)
I0409 23:33:36.899276  6396 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 23:33:40.969275  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:42.038000  6396 solver.cpp:218] Iteration 3744 (2.33528 iter/s, 5.13856s/12 iters), loss = 2.24235
I0409 23:33:42.038056  6396 solver.cpp:237]     Train net output #0: loss = 2.24235 (* 1 = 2.24235 loss)
I0409 23:33:42.038069  6396 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 23:33:47.058269  6396 solver.cpp:218] Iteration 3756 (2.39041 iter/s, 5.02006s/12 iters), loss = 2.43215
I0409 23:33:47.058313  6396 solver.cpp:237]     Train net output #0: loss = 2.43215 (* 1 = 2.43215 loss)
I0409 23:33:47.058322  6396 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 23:33:52.111732  6396 solver.cpp:218] Iteration 3768 (2.37471 iter/s, 5.05326s/12 iters), loss = 2.34006
I0409 23:33:52.111774  6396 solver.cpp:237]     Train net output #0: loss = 2.34006 (* 1 = 2.34006 loss)
I0409 23:33:52.111783  6396 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 23:33:54.132010  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 23:33:54.549702  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 23:33:54.856447  6396 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 23:33:54.856477  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:33:57.868743  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:59.361579  6396 solver.cpp:397]     Test net output #0: accuracy = 0.362745
I0409 23:33:59.361632  6396 solver.cpp:397]     Test net output #1: loss = 2.48612 (* 1 = 2.48612 loss)
I0409 23:34:01.060631  6396 solver.cpp:218] Iteration 3780 (1.34099 iter/s, 8.94859s/12 iters), loss = 2.24261
I0409 23:34:01.060678  6396 solver.cpp:237]     Train net output #0: loss = 2.24261 (* 1 = 2.24261 loss)
I0409 23:34:01.060689  6396 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 23:34:05.971174  6396 solver.cpp:218] Iteration 3792 (2.44382 iter/s, 4.91035s/12 iters), loss = 2.19902
I0409 23:34:05.971218  6396 solver.cpp:237]     Train net output #0: loss = 2.19902 (* 1 = 2.19902 loss)
I0409 23:34:05.971227  6396 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 23:34:10.907732  6396 solver.cpp:218] Iteration 3804 (2.43094 iter/s, 4.93636s/12 iters), loss = 2.32504
I0409 23:34:10.907768  6396 solver.cpp:237]     Train net output #0: loss = 2.32504 (* 1 = 2.32504 loss)
I0409 23:34:10.907775  6396 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 23:34:15.892066  6396 solver.cpp:218] Iteration 3816 (2.40764 iter/s, 4.98413s/12 iters), loss = 2.13417
I0409 23:34:15.892117  6396 solver.cpp:237]     Train net output #0: loss = 2.13417 (* 1 = 2.13417 loss)
I0409 23:34:15.892128  6396 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 23:34:20.848233  6396 solver.cpp:218] Iteration 3828 (2.42133 iter/s, 4.95596s/12 iters), loss = 2.05313
I0409 23:34:20.848289  6396 solver.cpp:237]     Train net output #0: loss = 2.05313 (* 1 = 2.05313 loss)
I0409 23:34:20.848300  6396 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 23:34:25.807925  6396 solver.cpp:218] Iteration 3840 (2.41961 iter/s, 4.95948s/12 iters), loss = 2.15897
I0409 23:34:25.807974  6396 solver.cpp:237]     Train net output #0: loss = 2.15897 (* 1 = 2.15897 loss)
I0409 23:34:25.807984  6396 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 23:34:26.915323  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:30.741466  6396 solver.cpp:218] Iteration 3852 (2.43243 iter/s, 4.93333s/12 iters), loss = 2.07998
I0409 23:34:30.741614  6396 solver.cpp:237]     Train net output #0: loss = 2.07998 (* 1 = 2.07998 loss)
I0409 23:34:30.741627  6396 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 23:34:35.674064  6396 solver.cpp:218] Iteration 3864 (2.43295 iter/s, 4.93229s/12 iters), loss = 2.29533
I0409 23:34:35.674120  6396 solver.cpp:237]     Train net output #0: loss = 2.29533 (* 1 = 2.29533 loss)
I0409 23:34:35.674132  6396 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 23:34:40.158047  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 23:34:40.628317  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 23:34:40.948771  6396 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 23:34:40.948799  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:34:43.812628  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:45.352830  6396 solver.cpp:397]     Test net output #0: accuracy = 0.367034
I0409 23:34:45.352875  6396 solver.cpp:397]     Test net output #1: loss = 2.52871 (* 1 = 2.52871 loss)
I0409 23:34:45.436288  6396 solver.cpp:218] Iteration 3876 (1.22927 iter/s, 9.76187s/12 iters), loss = 2.20479
I0409 23:34:45.436358  6396 solver.cpp:237]     Train net output #0: loss = 2.20479 (* 1 = 2.20479 loss)
I0409 23:34:45.436374  6396 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 23:34:49.551844  6396 solver.cpp:218] Iteration 3888 (2.91591 iter/s, 4.11535s/12 iters), loss = 2.12435
I0409 23:34:49.551905  6396 solver.cpp:237]     Train net output #0: loss = 2.12435 (* 1 = 2.12435 loss)
I0409 23:34:49.551918  6396 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 23:34:54.484526  6396 solver.cpp:218] Iteration 3900 (2.43286 iter/s, 4.93246s/12 iters), loss = 2.13643
I0409 23:34:54.484573  6396 solver.cpp:237]     Train net output #0: loss = 2.13643 (* 1 = 2.13643 loss)
I0409 23:34:54.484583  6396 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 23:34:59.421525  6396 solver.cpp:218] Iteration 3912 (2.43073 iter/s, 4.93679s/12 iters), loss = 1.96929
I0409 23:34:59.421571  6396 solver.cpp:237]     Train net output #0: loss = 1.96929 (* 1 = 1.96929 loss)
I0409 23:34:59.421581  6396 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 23:35:04.347028  6396 solver.cpp:218] Iteration 3924 (2.4364 iter/s, 4.9253s/12 iters), loss = 2.30534
I0409 23:35:04.347158  6396 solver.cpp:237]     Train net output #0: loss = 2.30534 (* 1 = 2.30534 loss)
I0409 23:35:04.347173  6396 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 23:35:09.307339  6396 solver.cpp:218] Iteration 3936 (2.41934 iter/s, 4.96003s/12 iters), loss = 2.10263
I0409 23:35:09.307390  6396 solver.cpp:237]     Train net output #0: loss = 2.10263 (* 1 = 2.10263 loss)
I0409 23:35:09.307402  6396 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 23:35:12.663535  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:14.285925  6396 solver.cpp:218] Iteration 3948 (2.41042 iter/s, 4.97838s/12 iters), loss = 1.994
I0409 23:35:14.285995  6396 solver.cpp:237]     Train net output #0: loss = 1.994 (* 1 = 1.994 loss)
I0409 23:35:14.286008  6396 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 23:35:19.295078  6396 solver.cpp:218] Iteration 3960 (2.39572 iter/s, 5.00893s/12 iters), loss = 2.40154
I0409 23:35:19.295126  6396 solver.cpp:237]     Train net output #0: loss = 2.40154 (* 1 = 2.40154 loss)
I0409 23:35:19.295135  6396 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 23:35:24.194074  6396 solver.cpp:218] Iteration 3972 (2.44958 iter/s, 4.89879s/12 iters), loss = 2.20827
I0409 23:35:24.194120  6396 solver.cpp:237]     Train net output #0: loss = 2.20827 (* 1 = 2.20827 loss)
I0409 23:35:24.194129  6396 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 23:35:26.241912  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 23:35:26.665354  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 23:35:26.973456  6396 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 23:35:26.973479  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:35:29.895996  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:31.671955  6396 solver.cpp:397]     Test net output #0: accuracy = 0.387255
I0409 23:35:31.671998  6396 solver.cpp:397]     Test net output #1: loss = 2.38403 (* 1 = 2.38403 loss)
I0409 23:35:33.603036  6396 solver.cpp:218] Iteration 3984 (1.27542 iter/s, 9.40863s/12 iters), loss = 2.30946
I0409 23:35:33.603082  6396 solver.cpp:237]     Train net output #0: loss = 2.30946 (* 1 = 2.30946 loss)
I0409 23:35:33.603091  6396 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 23:35:38.536051  6396 solver.cpp:218] Iteration 3996 (2.43269 iter/s, 4.93281s/12 iters), loss = 2.14429
I0409 23:35:38.536187  6396 solver.cpp:237]     Train net output #0: loss = 2.14429 (* 1 = 2.14429 loss)
I0409 23:35:38.536197  6396 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 23:35:43.449183  6396 solver.cpp:218] Iteration 4008 (2.44258 iter/s, 4.91284s/12 iters), loss = 2.22945
I0409 23:35:43.449240  6396 solver.cpp:237]     Train net output #0: loss = 2.22945 (* 1 = 2.22945 loss)
I0409 23:35:43.449252  6396 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 23:35:48.357161  6396 solver.cpp:218] Iteration 4020 (2.44511 iter/s, 4.90776s/12 iters), loss = 2.24465
I0409 23:35:48.357206  6396 solver.cpp:237]     Train net output #0: loss = 2.24465 (* 1 = 2.24465 loss)
I0409 23:35:48.357215  6396 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 23:35:53.292090  6396 solver.cpp:218] Iteration 4032 (2.43175 iter/s, 4.93472s/12 iters), loss = 2.2244
I0409 23:35:53.292135  6396 solver.cpp:237]     Train net output #0: loss = 2.2244 (* 1 = 2.2244 loss)
I0409 23:35:53.292145  6396 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 23:35:58.181742  6396 solver.cpp:218] Iteration 4044 (2.45426 iter/s, 4.88945s/12 iters), loss = 2.00603
I0409 23:35:58.181797  6396 solver.cpp:237]     Train net output #0: loss = 2.00603 (* 1 = 2.00603 loss)
I0409 23:35:58.181807  6396 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 23:35:58.662981  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:03.108047  6396 solver.cpp:218] Iteration 4056 (2.43601 iter/s, 4.92609s/12 iters), loss = 2.24287
I0409 23:36:03.108100  6396 solver.cpp:237]     Train net output #0: loss = 2.24287 (* 1 = 2.24287 loss)
I0409 23:36:03.108112  6396 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 23:36:08.046268  6396 solver.cpp:218] Iteration 4068 (2.43013 iter/s, 4.93801s/12 iters), loss = 1.96323
I0409 23:36:08.046326  6396 solver.cpp:237]     Train net output #0: loss = 1.96323 (* 1 = 1.96323 loss)
I0409 23:36:08.046339  6396 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 23:36:12.511226  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 23:36:13.382416  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 23:36:13.778892  6396 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 23:36:13.778913  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:36:16.554854  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:18.185047  6396 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0409 23:36:18.185091  6396 solver.cpp:397]     Test net output #1: loss = 2.38865 (* 1 = 2.38865 loss)
I0409 23:36:18.268390  6396 solver.cpp:218] Iteration 4080 (1.17397 iter/s, 10.2218s/12 iters), loss = 1.90834
I0409 23:36:18.268440  6396 solver.cpp:237]     Train net output #0: loss = 1.90834 (* 1 = 1.90834 loss)
I0409 23:36:18.268451  6396 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 23:36:22.477504  6396 solver.cpp:218] Iteration 4092 (2.85108 iter/s, 4.20893s/12 iters), loss = 2.25098
I0409 23:36:22.477558  6396 solver.cpp:237]     Train net output #0: loss = 2.25098 (* 1 = 2.25098 loss)
I0409 23:36:22.477571  6396 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 23:36:27.595609  6396 solver.cpp:218] Iteration 4104 (2.34472 iter/s, 5.11789s/12 iters), loss = 2.21235
I0409 23:36:27.595661  6396 solver.cpp:237]     Train net output #0: loss = 2.21235 (* 1 = 2.21235 loss)
I0409 23:36:27.595672  6396 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 23:36:32.777334  6396 solver.cpp:218] Iteration 4116 (2.31593 iter/s, 5.1815s/12 iters), loss = 2.11744
I0409 23:36:32.777392  6396 solver.cpp:237]     Train net output #0: loss = 2.11744 (* 1 = 2.11744 loss)
I0409 23:36:32.777405  6396 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 23:36:37.698987  6396 solver.cpp:218] Iteration 4128 (2.43831 iter/s, 4.92144s/12 iters), loss = 1.88276
I0409 23:36:37.699039  6396 solver.cpp:237]     Train net output #0: loss = 1.88276 (* 1 = 1.88276 loss)
I0409 23:36:37.699051  6396 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 23:36:42.639921  6396 solver.cpp:218] Iteration 4140 (2.42879 iter/s, 4.94073s/12 iters), loss = 1.93207
I0409 23:36:42.640041  6396 solver.cpp:237]     Train net output #0: loss = 1.93207 (* 1 = 1.93207 loss)
I0409 23:36:42.640051  6396 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 23:36:45.355258  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:47.773627  6396 solver.cpp:218] Iteration 4152 (2.33762 iter/s, 5.13342s/12 iters), loss = 1.7884
I0409 23:36:47.773684  6396 solver.cpp:237]     Train net output #0: loss = 1.7884 (* 1 = 1.7884 loss)
I0409 23:36:47.773695  6396 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 23:36:47.774013  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:36:52.672773  6396 solver.cpp:218] Iteration 4164 (2.44951 iter/s, 4.89893s/12 iters), loss = 2.11157
I0409 23:36:52.672830  6396 solver.cpp:237]     Train net output #0: loss = 2.11157 (* 1 = 2.11157 loss)
I0409 23:36:52.672843  6396 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 23:36:57.580739  6396 solver.cpp:218] Iteration 4176 (2.44511 iter/s, 4.90776s/12 iters), loss = 1.95386
I0409 23:36:57.580790  6396 solver.cpp:237]     Train net output #0: loss = 1.95386 (* 1 = 1.95386 loss)
I0409 23:36:57.580801  6396 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 23:36:59.584210  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 23:37:00.082202  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 23:37:00.712510  6396 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 23:37:00.712535  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:37:03.535848  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:05.201402  6396 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0409 23:37:05.201449  6396 solver.cpp:397]     Test net output #1: loss = 2.29466 (* 1 = 2.29466 loss)
I0409 23:37:07.077759  6396 solver.cpp:218] Iteration 4188 (1.2636 iter/s, 9.49668s/12 iters), loss = 1.84102
I0409 23:37:07.077811  6396 solver.cpp:237]     Train net output #0: loss = 1.84102 (* 1 = 1.84102 loss)
I0409 23:37:07.077822  6396 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 23:37:12.088758  6396 solver.cpp:218] Iteration 4200 (2.39483 iter/s, 5.01079s/12 iters), loss = 1.92706
I0409 23:37:12.088809  6396 solver.cpp:237]     Train net output #0: loss = 1.92706 (* 1 = 1.92706 loss)
I0409 23:37:12.088819  6396 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 23:37:17.070518  6396 solver.cpp:218] Iteration 4212 (2.40889 iter/s, 4.98155s/12 iters), loss = 1.87248
I0409 23:37:17.070683  6396 solver.cpp:237]     Train net output #0: loss = 1.87248 (* 1 = 1.87248 loss)
I0409 23:37:17.070698  6396 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 23:37:22.173370  6396 solver.cpp:218] Iteration 4224 (2.35177 iter/s, 5.10254s/12 iters), loss = 1.98834
I0409 23:37:22.173411  6396 solver.cpp:237]     Train net output #0: loss = 1.98834 (* 1 = 1.98834 loss)
I0409 23:37:22.173420  6396 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 23:37:27.071841  6396 solver.cpp:218] Iteration 4236 (2.44984 iter/s, 4.89828s/12 iters), loss = 1.98223
I0409 23:37:27.071884  6396 solver.cpp:237]     Train net output #0: loss = 1.98223 (* 1 = 1.98223 loss)
I0409 23:37:27.071893  6396 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 23:37:31.731400  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:31.959520  6396 solver.cpp:218] Iteration 4248 (2.45525 iter/s, 4.88748s/12 iters), loss = 1.96889
I0409 23:37:31.959575  6396 solver.cpp:237]     Train net output #0: loss = 1.96889 (* 1 = 1.96889 loss)
I0409 23:37:31.959587  6396 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 23:37:36.869053  6396 solver.cpp:218] Iteration 4260 (2.44433 iter/s, 4.90932s/12 iters), loss = 1.84662
I0409 23:37:36.869110  6396 solver.cpp:237]     Train net output #0: loss = 1.84662 (* 1 = 1.84662 loss)
I0409 23:37:36.869122  6396 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 23:37:41.787611  6396 solver.cpp:218] Iteration 4272 (2.43984 iter/s, 4.91835s/12 iters), loss = 1.88822
I0409 23:37:41.787659  6396 solver.cpp:237]     Train net output #0: loss = 1.88822 (* 1 = 1.88822 loss)
I0409 23:37:41.787670  6396 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 23:37:46.326473  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 23:37:46.806380  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 23:37:47.437104  6396 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 23:37:47.437191  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:37:50.091257  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:51.796758  6396 solver.cpp:397]     Test net output #0: accuracy = 0.425245
I0409 23:37:51.796808  6396 solver.cpp:397]     Test net output #1: loss = 2.22491 (* 1 = 2.22491 loss)
I0409 23:37:51.880434  6396 solver.cpp:218] Iteration 4284 (1.189 iter/s, 10.0925s/12 iters), loss = 1.9019
I0409 23:37:51.880482  6396 solver.cpp:237]     Train net output #0: loss = 1.9019 (* 1 = 1.9019 loss)
I0409 23:37:51.880493  6396 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 23:37:56.150620  6396 solver.cpp:218] Iteration 4296 (2.8103 iter/s, 4.27s/12 iters), loss = 1.88204
I0409 23:37:56.150665  6396 solver.cpp:237]     Train net output #0: loss = 1.88204 (* 1 = 1.88204 loss)
I0409 23:37:56.150673  6396 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 23:38:01.225839  6396 solver.cpp:218] Iteration 4308 (2.36452 iter/s, 5.07502s/12 iters), loss = 1.6653
I0409 23:38:01.225886  6396 solver.cpp:237]     Train net output #0: loss = 1.6653 (* 1 = 1.6653 loss)
I0409 23:38:01.225898  6396 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 23:38:06.203702  6396 solver.cpp:218] Iteration 4320 (2.41077 iter/s, 4.97766s/12 iters), loss = 2.0276
I0409 23:38:06.203755  6396 solver.cpp:237]     Train net output #0: loss = 2.0276 (* 1 = 2.0276 loss)
I0409 23:38:06.203768  6396 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 23:38:11.212116  6396 solver.cpp:218] Iteration 4332 (2.39607 iter/s, 5.0082s/12 iters), loss = 1.92924
I0409 23:38:11.212170  6396 solver.cpp:237]     Train net output #0: loss = 1.92924 (* 1 = 1.92924 loss)
I0409 23:38:11.212182  6396 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 23:38:16.242043  6396 solver.cpp:218] Iteration 4344 (2.38582 iter/s, 5.02972s/12 iters), loss = 1.80821
I0409 23:38:16.242107  6396 solver.cpp:237]     Train net output #0: loss = 1.80821 (* 1 = 1.80821 loss)
I0409 23:38:16.242120  6396 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 23:38:18.142449  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:21.203181  6396 solver.cpp:218] Iteration 4356 (2.4189 iter/s, 4.96093s/12 iters), loss = 1.73378
I0409 23:38:21.203230  6396 solver.cpp:237]     Train net output #0: loss = 1.73378 (* 1 = 1.73378 loss)
I0409 23:38:21.203241  6396 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 23:38:26.260812  6396 solver.cpp:218] Iteration 4368 (2.37275 iter/s, 5.05742s/12 iters), loss = 1.75265
I0409 23:38:26.260867  6396 solver.cpp:237]     Train net output #0: loss = 1.75265 (* 1 = 1.75265 loss)
I0409 23:38:26.260879  6396 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 23:38:31.276007  6396 solver.cpp:218] Iteration 4380 (2.39283 iter/s, 5.01498s/12 iters), loss = 1.73971
I0409 23:38:31.276059  6396 solver.cpp:237]     Train net output #0: loss = 1.73971 (* 1 = 1.73971 loss)
I0409 23:38:31.276072  6396 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 23:38:33.281033  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 23:38:33.797456  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 23:38:34.401669  6396 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 23:38:34.401700  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:38:37.014873  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:38.765575  6396 solver.cpp:397]     Test net output #0: accuracy = 0.432598
I0409 23:38:38.765623  6396 solver.cpp:397]     Test net output #1: loss = 2.1914 (* 1 = 2.1914 loss)
I0409 23:38:40.697240  6396 solver.cpp:218] Iteration 4392 (1.27376 iter/s, 9.4209s/12 iters), loss = 1.89606
I0409 23:38:40.697288  6396 solver.cpp:237]     Train net output #0: loss = 1.89606 (* 1 = 1.89606 loss)
I0409 23:38:40.697297  6396 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 23:38:45.646157  6396 solver.cpp:218] Iteration 4404 (2.42487 iter/s, 4.94871s/12 iters), loss = 1.55932
I0409 23:38:45.646209  6396 solver.cpp:237]     Train net output #0: loss = 1.55932 (* 1 = 1.55932 loss)
I0409 23:38:45.646220  6396 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 23:38:50.668445  6396 solver.cpp:218] Iteration 4416 (2.38945 iter/s, 5.02208s/12 iters), loss = 1.82919
I0409 23:38:50.668553  6396 solver.cpp:237]     Train net output #0: loss = 1.82919 (* 1 = 1.82919 loss)
I0409 23:38:50.668565  6396 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 23:38:55.885342  6396 solver.cpp:218] Iteration 4428 (2.30034 iter/s, 5.21663s/12 iters), loss = 1.91874
I0409 23:38:55.885398  6396 solver.cpp:237]     Train net output #0: loss = 1.91874 (* 1 = 1.91874 loss)
I0409 23:38:55.885411  6396 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 23:39:00.888667  6396 solver.cpp:218] Iteration 4440 (2.39851 iter/s, 5.00312s/12 iters), loss = 1.5552
I0409 23:39:00.888715  6396 solver.cpp:237]     Train net output #0: loss = 1.5552 (* 1 = 1.5552 loss)
I0409 23:39:00.888727  6396 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 23:39:04.841382  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:05.809969  6396 solver.cpp:218] Iteration 4452 (2.43849 iter/s, 4.92108s/12 iters), loss = 1.66639
I0409 23:39:05.810025  6396 solver.cpp:237]     Train net output #0: loss = 1.66639 (* 1 = 1.66639 loss)
I0409 23:39:05.810037  6396 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 23:39:10.718080  6396 solver.cpp:218] Iteration 4464 (2.44503 iter/s, 4.90791s/12 iters), loss = 1.98536
I0409 23:39:10.718137  6396 solver.cpp:237]     Train net output #0: loss = 1.98536 (* 1 = 1.98536 loss)
I0409 23:39:10.718148  6396 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 23:39:15.597328  6396 solver.cpp:218] Iteration 4476 (2.4595 iter/s, 4.87904s/12 iters), loss = 1.75028
I0409 23:39:15.597383  6396 solver.cpp:237]     Train net output #0: loss = 1.75028 (* 1 = 1.75028 loss)
I0409 23:39:15.597394  6396 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 23:39:20.077693  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 23:39:20.804855  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 23:39:21.361821  6396 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 23:39:21.361845  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:39:23.921548  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:25.692267  6396 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0409 23:39:25.692304  6396 solver.cpp:397]     Test net output #1: loss = 2.23635 (* 1 = 2.23635 loss)
I0409 23:39:25.775730  6396 solver.cpp:218] Iteration 4488 (1.17901 iter/s, 10.1781s/12 iters), loss = 1.76075
I0409 23:39:25.775784  6396 solver.cpp:237]     Train net output #0: loss = 1.76075 (* 1 = 1.76075 loss)
I0409 23:39:25.775794  6396 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 23:39:30.086803  6396 solver.cpp:218] Iteration 4500 (2.78365 iter/s, 4.31088s/12 iters), loss = 1.67328
I0409 23:39:30.086854  6396 solver.cpp:237]     Train net output #0: loss = 1.67328 (* 1 = 1.67328 loss)
I0409 23:39:30.086866  6396 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 23:39:35.025619  6396 solver.cpp:218] Iteration 4512 (2.42983 iter/s, 4.93861s/12 iters), loss = 1.77123
I0409 23:39:35.025665  6396 solver.cpp:237]     Train net output #0: loss = 1.77123 (* 1 = 1.77123 loss)
I0409 23:39:35.025674  6396 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 23:39:40.032374  6396 solver.cpp:218] Iteration 4524 (2.39686 iter/s, 5.00655s/12 iters), loss = 1.73755
I0409 23:39:40.032416  6396 solver.cpp:237]     Train net output #0: loss = 1.73755 (* 1 = 1.73755 loss)
I0409 23:39:40.032424  6396 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 23:39:45.096984  6396 solver.cpp:218] Iteration 4536 (2.36948 iter/s, 5.06441s/12 iters), loss = 1.6224
I0409 23:39:45.097029  6396 solver.cpp:237]     Train net output #0: loss = 1.6224 (* 1 = 1.6224 loss)
I0409 23:39:45.097040  6396 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 23:39:50.041045  6396 solver.cpp:218] Iteration 4548 (2.42725 iter/s, 4.94386s/12 iters), loss = 1.82898
I0409 23:39:50.041090  6396 solver.cpp:237]     Train net output #0: loss = 1.82898 (* 1 = 1.82898 loss)
I0409 23:39:50.041098  6396 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 23:39:51.277822  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:54.954428  6396 solver.cpp:218] Iteration 4560 (2.44241 iter/s, 4.91318s/12 iters), loss = 1.54523
I0409 23:39:54.954473  6396 solver.cpp:237]     Train net output #0: loss = 1.54523 (* 1 = 1.54523 loss)
I0409 23:39:54.954481  6396 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 23:39:59.893108  6396 solver.cpp:218] Iteration 4572 (2.4299 iter/s, 4.93848s/12 iters), loss = 1.62023
I0409 23:39:59.893153  6396 solver.cpp:237]     Train net output #0: loss = 1.62023 (* 1 = 1.62023 loss)
I0409 23:39:59.893164  6396 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 23:40:04.789886  6396 solver.cpp:218] Iteration 4584 (2.45069 iter/s, 4.89658s/12 iters), loss = 1.50383
I0409 23:40:04.789932  6396 solver.cpp:237]     Train net output #0: loss = 1.50383 (* 1 = 1.50383 loss)
I0409 23:40:04.789939  6396 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 23:40:06.771414  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 23:40:09.112443  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 23:40:09.833427  6396 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 23:40:09.833447  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:40:12.534955  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:14.351265  6396 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0409 23:40:14.351295  6396 solver.cpp:397]     Test net output #1: loss = 2.06363 (* 1 = 2.06363 loss)
I0409 23:40:16.234881  6396 solver.cpp:218] Iteration 4596 (1.04853 iter/s, 11.4446s/12 iters), loss = 1.53011
I0409 23:40:16.234943  6396 solver.cpp:237]     Train net output #0: loss = 1.53011 (* 1 = 1.53011 loss)
I0409 23:40:16.234956  6396 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 23:40:21.181660  6396 solver.cpp:218] Iteration 4608 (2.42593 iter/s, 4.94657s/12 iters), loss = 1.35859
I0409 23:40:21.181704  6396 solver.cpp:237]     Train net output #0: loss = 1.35859 (* 1 = 1.35859 loss)
I0409 23:40:21.181715  6396 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 23:40:26.070713  6396 solver.cpp:218] Iteration 4620 (2.45456 iter/s, 4.88886s/12 iters), loss = 1.31442
I0409 23:40:26.070869  6396 solver.cpp:237]     Train net output #0: loss = 1.31442 (* 1 = 1.31442 loss)
I0409 23:40:26.070885  6396 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 23:40:31.068348  6396 solver.cpp:218] Iteration 4632 (2.40128 iter/s, 4.99733s/12 iters), loss = 1.47417
I0409 23:40:31.068395  6396 solver.cpp:237]     Train net output #0: loss = 1.47417 (* 1 = 1.47417 loss)
I0409 23:40:31.068404  6396 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 23:40:36.127948  6396 solver.cpp:218] Iteration 4644 (2.37182 iter/s, 5.0594s/12 iters), loss = 1.68549
I0409 23:40:36.127995  6396 solver.cpp:237]     Train net output #0: loss = 1.68549 (* 1 = 1.68549 loss)
I0409 23:40:36.128005  6396 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 23:40:39.623687  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:41.197274  6396 solver.cpp:218] Iteration 4656 (2.36727 iter/s, 5.06912s/12 iters), loss = 1.57728
I0409 23:40:41.197324  6396 solver.cpp:237]     Train net output #0: loss = 1.57728 (* 1 = 1.57728 loss)
I0409 23:40:41.197335  6396 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 23:40:46.215263  6396 solver.cpp:218] Iteration 4668 (2.3915 iter/s, 5.01778s/12 iters), loss = 1.64207
I0409 23:40:46.215322  6396 solver.cpp:237]     Train net output #0: loss = 1.64207 (* 1 = 1.64207 loss)
I0409 23:40:46.215335  6396 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 23:40:51.153918  6396 solver.cpp:218] Iteration 4680 (2.42992 iter/s, 4.93844s/12 iters), loss = 1.68641
I0409 23:40:51.153995  6396 solver.cpp:237]     Train net output #0: loss = 1.68641 (* 1 = 1.68641 loss)
I0409 23:40:51.154009  6396 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 23:40:55.716061  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 23:40:56.130509  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 23:40:56.432812  6396 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 23:40:56.432837  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:40:58.938733  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:00.802918  6396 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0409 23:41:00.802966  6396 solver.cpp:397]     Test net output #1: loss = 2.18758 (* 1 = 2.18758 loss)
I0409 23:41:00.886438  6396 solver.cpp:218] Iteration 4692 (1.23303 iter/s, 9.73214s/12 iters), loss = 1.42562
I0409 23:41:00.886490  6396 solver.cpp:237]     Train net output #0: loss = 1.42562 (* 1 = 1.42562 loss)
I0409 23:41:00.886500  6396 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 23:41:05.200695  6396 solver.cpp:218] Iteration 4704 (2.7816 iter/s, 4.31406s/12 iters), loss = 1.72659
I0409 23:41:05.200754  6396 solver.cpp:237]     Train net output #0: loss = 1.72659 (* 1 = 1.72659 loss)
I0409 23:41:05.200767  6396 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 23:41:10.069742  6396 solver.cpp:218] Iteration 4716 (2.46466 iter/s, 4.86883s/12 iters), loss = 1.69849
I0409 23:41:10.069795  6396 solver.cpp:237]     Train net output #0: loss = 1.69849 (* 1 = 1.69849 loss)
I0409 23:41:10.069808  6396 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 23:41:15.009306  6396 solver.cpp:218] Iteration 4728 (2.42946 iter/s, 4.93937s/12 iters), loss = 1.59687
I0409 23:41:15.009338  6396 solver.cpp:237]     Train net output #0: loss = 1.59687 (* 1 = 1.59687 loss)
I0409 23:41:15.009347  6396 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 23:41:19.939828  6396 solver.cpp:218] Iteration 4740 (2.43391 iter/s, 4.93033s/12 iters), loss = 1.42146
I0409 23:41:19.939870  6396 solver.cpp:237]     Train net output #0: loss = 1.42146 (* 1 = 1.42146 loss)
I0409 23:41:19.939879  6396 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 23:41:24.881698  6396 solver.cpp:218] Iteration 4752 (2.42833 iter/s, 4.94167s/12 iters), loss = 1.66931
I0409 23:41:24.881752  6396 solver.cpp:237]     Train net output #0: loss = 1.66931 (* 1 = 1.66931 loss)
I0409 23:41:24.881764  6396 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 23:41:25.402940  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:29.797991  6396 solver.cpp:218] Iteration 4764 (2.44097 iter/s, 4.91608s/12 iters), loss = 1.6675
I0409 23:41:29.798152  6396 solver.cpp:237]     Train net output #0: loss = 1.6675 (* 1 = 1.6675 loss)
I0409 23:41:29.798166  6396 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 23:41:34.663902  6396 solver.cpp:218] Iteration 4776 (2.4663 iter/s, 4.8656s/12 iters), loss = 1.55913
I0409 23:41:34.663956  6396 solver.cpp:237]     Train net output #0: loss = 1.55913 (* 1 = 1.55913 loss)
I0409 23:41:34.663969  6396 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 23:41:39.595480  6396 solver.cpp:218] Iteration 4788 (2.4334 iter/s, 4.93137s/12 iters), loss = 1.28777
I0409 23:41:39.595535  6396 solver.cpp:237]     Train net output #0: loss = 1.28777 (* 1 = 1.28777 loss)
I0409 23:41:39.595546  6396 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 23:41:41.590755  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 23:41:42.069672  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 23:41:42.383643  6396 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 23:41:42.383661  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:41:44.944722  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:46.838264  6396 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0409 23:41:46.838299  6396 solver.cpp:397]     Test net output #1: loss = 2.09064 (* 1 = 2.09064 loss)
I0409 23:41:48.720752  6396 solver.cpp:218] Iteration 4800 (1.31508 iter/s, 9.12495s/12 iters), loss = 1.44423
I0409 23:41:48.720803  6396 solver.cpp:237]     Train net output #0: loss = 1.44423 (* 1 = 1.44423 loss)
I0409 23:41:48.720815  6396 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 23:41:53.698823  6396 solver.cpp:218] Iteration 4812 (2.41067 iter/s, 4.97787s/12 iters), loss = 1.78568
I0409 23:41:53.698873  6396 solver.cpp:237]     Train net output #0: loss = 1.78568 (* 1 = 1.78568 loss)
I0409 23:41:53.698885  6396 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 23:41:58.674353  6396 solver.cpp:218] Iteration 4824 (2.4119 iter/s, 4.97533s/12 iters), loss = 1.41975
I0409 23:41:58.674407  6396 solver.cpp:237]     Train net output #0: loss = 1.41975 (* 1 = 1.41975 loss)
I0409 23:41:58.674419  6396 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 23:42:03.643152  6396 solver.cpp:218] Iteration 4836 (2.41517 iter/s, 4.96859s/12 iters), loss = 1.45016
I0409 23:42:03.643257  6396 solver.cpp:237]     Train net output #0: loss = 1.45016 (* 1 = 1.45016 loss)
I0409 23:42:03.643266  6396 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 23:42:04.036335  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:42:08.697185  6396 solver.cpp:218] Iteration 4848 (2.37447 iter/s, 5.05377s/12 iters), loss = 1.7338
I0409 23:42:08.697237  6396 solver.cpp:237]     Train net output #0: loss = 1.7338 (* 1 = 1.7338 loss)
I0409 23:42:08.697249  6396 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 23:42:11.335813  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:13.595362  6396 solver.cpp:218] Iteration 4860 (2.44999 iter/s, 4.89797s/12 iters), loss = 1.31022
I0409 23:42:13.595418  6396 solver.cpp:237]     Train net output #0: loss = 1.31022 (* 1 = 1.31022 loss)
I0409 23:42:13.595430  6396 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 23:42:18.477286  6396 solver.cpp:218] Iteration 4872 (2.45815 iter/s, 4.88172s/12 iters), loss = 1.44428
I0409 23:42:18.477349  6396 solver.cpp:237]     Train net output #0: loss = 1.44428 (* 1 = 1.44428 loss)
I0409 23:42:18.477361  6396 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 23:42:23.345371  6396 solver.cpp:218] Iteration 4884 (2.46514 iter/s, 4.86787s/12 iters), loss = 1.58386
I0409 23:42:23.345427  6396 solver.cpp:237]     Train net output #0: loss = 1.58386 (* 1 = 1.58386 loss)
I0409 23:42:23.345438  6396 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 23:42:27.754204  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 23:42:28.212955  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 23:42:28.516018  6396 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 23:42:28.516042  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:42:30.900669  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:32.934656  6396 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0409 23:42:32.934700  6396 solver.cpp:397]     Test net output #1: loss = 2.0654 (* 1 = 2.0654 loss)
I0409 23:42:33.017901  6396 solver.cpp:218] Iteration 4896 (1.24067 iter/s, 9.67219s/12 iters), loss = 1.38006
I0409 23:42:33.017951  6396 solver.cpp:237]     Train net output #0: loss = 1.38006 (* 1 = 1.38006 loss)
I0409 23:42:33.017979  6396 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 23:42:37.368402  6396 solver.cpp:218] Iteration 4908 (2.75842 iter/s, 4.35031s/12 iters), loss = 1.37288
I0409 23:42:37.368503  6396 solver.cpp:237]     Train net output #0: loss = 1.37288 (* 1 = 1.37288 loss)
I0409 23:42:37.368513  6396 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 23:42:42.252970  6396 solver.cpp:218] Iteration 4920 (2.45684 iter/s, 4.88432s/12 iters), loss = 1.28975
I0409 23:42:42.253006  6396 solver.cpp:237]     Train net output #0: loss = 1.28975 (* 1 = 1.28975 loss)
I0409 23:42:42.253015  6396 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 23:42:47.607815  6396 solver.cpp:218] Iteration 4932 (2.24105 iter/s, 5.35464s/12 iters), loss = 1.46738
I0409 23:42:47.607856  6396 solver.cpp:237]     Train net output #0: loss = 1.46738 (* 1 = 1.46738 loss)
I0409 23:42:47.607864  6396 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 23:42:52.722395  6396 solver.cpp:218] Iteration 4944 (2.34633 iter/s, 5.11438s/12 iters), loss = 1.33787
I0409 23:42:52.722451  6396 solver.cpp:237]     Train net output #0: loss = 1.33787 (* 1 = 1.33787 loss)
I0409 23:42:52.722465  6396 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 23:42:57.392863  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:57.589288  6396 solver.cpp:218] Iteration 4956 (2.46574 iter/s, 4.86668s/12 iters), loss = 1.51169
I0409 23:42:57.589339  6396 solver.cpp:237]     Train net output #0: loss = 1.51169 (* 1 = 1.51169 loss)
I0409 23:42:57.589350  6396 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 23:43:02.575052  6396 solver.cpp:218] Iteration 4968 (2.40696 iter/s, 4.98555s/12 iters), loss = 1.63893
I0409 23:43:02.575103  6396 solver.cpp:237]     Train net output #0: loss = 1.63893 (* 1 = 1.63893 loss)
I0409 23:43:02.575114  6396 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 23:43:07.564990  6396 solver.cpp:218] Iteration 4980 (2.40494 iter/s, 4.98973s/12 iters), loss = 1.35185
I0409 23:43:07.565172  6396 solver.cpp:237]     Train net output #0: loss = 1.35185 (* 1 = 1.35185 loss)
I0409 23:43:07.565186  6396 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 23:43:12.579942  6396 solver.cpp:218] Iteration 4992 (2.393 iter/s, 5.01462s/12 iters), loss = 1.57119
I0409 23:43:12.579993  6396 solver.cpp:237]     Train net output #0: loss = 1.57119 (* 1 = 1.57119 loss)
I0409 23:43:12.580005  6396 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 23:43:14.616120  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 23:43:16.171270  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 23:43:19.097430  6396 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 23:43:19.097457  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:43:21.507513  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:23.549865  6396 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0409 23:43:23.549912  6396 solver.cpp:397]     Test net output #1: loss = 2.1465 (* 1 = 2.1465 loss)
I0409 23:43:25.414203  6396 solver.cpp:218] Iteration 5004 (0.935028 iter/s, 12.8338s/12 iters), loss = 1.34208
I0409 23:43:25.414252  6396 solver.cpp:237]     Train net output #0: loss = 1.34208 (* 1 = 1.34208 loss)
I0409 23:43:25.414261  6396 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 23:43:30.357372  6396 solver.cpp:218] Iteration 5016 (2.42769 iter/s, 4.94297s/12 iters), loss = 1.54273
I0409 23:43:30.357419  6396 solver.cpp:237]     Train net output #0: loss = 1.54273 (* 1 = 1.54273 loss)
I0409 23:43:30.357430  6396 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 23:43:35.259367  6396 solver.cpp:218] Iteration 5028 (2.44808 iter/s, 4.9018s/12 iters), loss = 1.33465
I0409 23:43:35.259408  6396 solver.cpp:237]     Train net output #0: loss = 1.33465 (* 1 = 1.33465 loss)
I0409 23:43:35.259418  6396 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 23:43:40.732924  6396 solver.cpp:218] Iteration 5040 (2.19244 iter/s, 5.47335s/12 iters), loss = 1.53542
I0409 23:43:40.733029  6396 solver.cpp:237]     Train net output #0: loss = 1.53542 (* 1 = 1.53542 loss)
I0409 23:43:40.733040  6396 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 23:43:45.752785  6396 solver.cpp:218] Iteration 5052 (2.39063 iter/s, 5.0196s/12 iters), loss = 1.33773
I0409 23:43:45.752830  6396 solver.cpp:237]     Train net output #0: loss = 1.33773 (* 1 = 1.33773 loss)
I0409 23:43:45.752840  6396 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 23:43:47.668295  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:50.727340  6396 solver.cpp:218] Iteration 5064 (2.41237 iter/s, 4.97435s/12 iters), loss = 1.32446
I0409 23:43:50.727393  6396 solver.cpp:237]     Train net output #0: loss = 1.32446 (* 1 = 1.32446 loss)
I0409 23:43:50.727406  6396 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 23:43:55.576844  6396 solver.cpp:218] Iteration 5076 (2.47458 iter/s, 4.8493s/12 iters), loss = 1.52258
I0409 23:43:55.576886  6396 solver.cpp:237]     Train net output #0: loss = 1.52258 (* 1 = 1.52258 loss)
I0409 23:43:55.576896  6396 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 23:44:00.483932  6396 solver.cpp:218] Iteration 5088 (2.44554 iter/s, 4.90689s/12 iters), loss = 1.21961
I0409 23:44:00.483987  6396 solver.cpp:237]     Train net output #0: loss = 1.21961 (* 1 = 1.21961 loss)
I0409 23:44:00.484000  6396 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 23:44:04.981174  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 23:44:05.427546  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 23:44:05.758401  6396 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 23:44:05.758431  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:44:08.302244  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:10.338086  6396 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0409 23:44:10.338115  6396 solver.cpp:397]     Test net output #1: loss = 2.08713 (* 1 = 2.08713 loss)
I0409 23:44:10.421272  6396 solver.cpp:218] Iteration 5100 (1.20761 iter/s, 9.937s/12 iters), loss = 1.40138
I0409 23:44:10.421311  6396 solver.cpp:237]     Train net output #0: loss = 1.40138 (* 1 = 1.40138 loss)
I0409 23:44:10.421320  6396 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 23:44:14.656049  6396 solver.cpp:218] Iteration 5112 (2.8338 iter/s, 4.2346s/12 iters), loss = 1.21988
I0409 23:44:14.656150  6396 solver.cpp:237]     Train net output #0: loss = 1.21988 (* 1 = 1.21988 loss)
I0409 23:44:14.656160  6396 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 23:44:19.583092  6396 solver.cpp:218] Iteration 5124 (2.43567 iter/s, 4.92678s/12 iters), loss = 1.28602
I0409 23:44:19.583145  6396 solver.cpp:237]     Train net output #0: loss = 1.28602 (* 1 = 1.28602 loss)
I0409 23:44:19.583156  6396 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 23:44:24.470885  6396 solver.cpp:218] Iteration 5136 (2.4552 iter/s, 4.88759s/12 iters), loss = 1.20332
I0409 23:44:24.470937  6396 solver.cpp:237]     Train net output #0: loss = 1.20332 (* 1 = 1.20332 loss)
I0409 23:44:24.470949  6396 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 23:44:29.477636  6396 solver.cpp:218] Iteration 5148 (2.39686 iter/s, 5.00655s/12 iters), loss = 1.56055
I0409 23:44:29.477676  6396 solver.cpp:237]     Train net output #0: loss = 1.56055 (* 1 = 1.56055 loss)
I0409 23:44:29.477684  6396 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 23:44:33.552160  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:34.488935  6396 solver.cpp:218] Iteration 5160 (2.39468 iter/s, 5.0111s/12 iters), loss = 1.29191
I0409 23:44:34.488982  6396 solver.cpp:237]     Train net output #0: loss = 1.29191 (* 1 = 1.29191 loss)
I0409 23:44:34.488993  6396 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 23:44:39.463025  6396 solver.cpp:218] Iteration 5172 (2.4126 iter/s, 4.97389s/12 iters), loss = 1.26019
I0409 23:44:39.463078  6396 solver.cpp:237]     Train net output #0: loss = 1.26019 (* 1 = 1.26019 loss)
I0409 23:44:39.463089  6396 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 23:44:44.385620  6396 solver.cpp:218] Iteration 5184 (2.43784 iter/s, 4.92238s/12 iters), loss = 1.35202
I0409 23:44:44.385675  6396 solver.cpp:237]     Train net output #0: loss = 1.35202 (* 1 = 1.35202 loss)
I0409 23:44:44.385687  6396 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 23:44:49.452234  6396 solver.cpp:218] Iteration 5196 (2.36854 iter/s, 5.0664s/12 iters), loss = 1.21079
I0409 23:44:49.452304  6396 solver.cpp:237]     Train net output #0: loss = 1.21079 (* 1 = 1.21079 loss)
I0409 23:44:49.452314  6396 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 23:44:51.447501  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 23:44:52.300827  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 23:44:53.107239  6396 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 23:44:53.107270  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:44:55.518594  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:57.566684  6396 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0409 23:44:57.566735  6396 solver.cpp:397]     Test net output #1: loss = 2.10685 (* 1 = 2.10685 loss)
I0409 23:44:59.327812  6396 solver.cpp:218] Iteration 5208 (1.21516 iter/s, 9.87522s/12 iters), loss = 1.28189
I0409 23:44:59.327857  6396 solver.cpp:237]     Train net output #0: loss = 1.28189 (* 1 = 1.28189 loss)
I0409 23:44:59.327867  6396 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 23:45:04.574476  6396 solver.cpp:218] Iteration 5220 (2.28726 iter/s, 5.24645s/12 iters), loss = 1.31913
I0409 23:45:04.574530  6396 solver.cpp:237]     Train net output #0: loss = 1.31913 (* 1 = 1.31913 loss)
I0409 23:45:04.574542  6396 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 23:45:09.535621  6396 solver.cpp:218] Iteration 5232 (2.4189 iter/s, 4.96094s/12 iters), loss = 1.04105
I0409 23:45:09.535678  6396 solver.cpp:237]     Train net output #0: loss = 1.04105 (* 1 = 1.04105 loss)
I0409 23:45:09.535691  6396 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 23:45:14.467449  6396 solver.cpp:218] Iteration 5244 (2.43328 iter/s, 4.93162s/12 iters), loss = 1.25135
I0409 23:45:14.467490  6396 solver.cpp:237]     Train net output #0: loss = 1.25135 (* 1 = 1.25135 loss)
I0409 23:45:14.467500  6396 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 23:45:19.696308  6396 solver.cpp:218] Iteration 5256 (2.29504 iter/s, 5.22866s/12 iters), loss = 1.07518
I0409 23:45:19.696451  6396 solver.cpp:237]     Train net output #0: loss = 1.07518 (* 1 = 1.07518 loss)
I0409 23:45:19.696465  6396 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 23:45:21.092583  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:24.878989  6396 solver.cpp:218] Iteration 5268 (2.31554 iter/s, 5.18238s/12 iters), loss = 1.29942
I0409 23:45:24.879045  6396 solver.cpp:237]     Train net output #0: loss = 1.29942 (* 1 = 1.29942 loss)
I0409 23:45:24.879056  6396 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 23:45:29.824676  6396 solver.cpp:218] Iteration 5280 (2.42646 iter/s, 4.94548s/12 iters), loss = 1.27317
I0409 23:45:29.824718  6396 solver.cpp:237]     Train net output #0: loss = 1.27317 (* 1 = 1.27317 loss)
I0409 23:45:29.824728  6396 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 23:45:34.726204  6396 solver.cpp:218] Iteration 5292 (2.44831 iter/s, 4.90134s/12 iters), loss = 1.16052
I0409 23:45:34.726246  6396 solver.cpp:237]     Train net output #0: loss = 1.16052 (* 1 = 1.16052 loss)
I0409 23:45:34.726254  6396 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 23:45:39.175698  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 23:45:39.619940  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 23:45:39.936585  6396 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 23:45:39.936602  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:45:42.501847  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:44.600886  6396 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0409 23:45:44.600934  6396 solver.cpp:397]     Test net output #1: loss = 2.06457 (* 1 = 2.06457 loss)
I0409 23:45:44.684166  6396 solver.cpp:218] Iteration 5304 (1.20511 iter/s, 9.95762s/12 iters), loss = 1.23886
I0409 23:45:44.684218  6396 solver.cpp:237]     Train net output #0: loss = 1.23886 (* 1 = 1.23886 loss)
I0409 23:45:44.684229  6396 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 23:45:49.103423  6396 solver.cpp:218] Iteration 5316 (2.71551 iter/s, 4.41906s/12 iters), loss = 1.07565
I0409 23:45:49.103469  6396 solver.cpp:237]     Train net output #0: loss = 1.07565 (* 1 = 1.07565 loss)
I0409 23:45:49.103480  6396 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 23:45:53.974779  6396 solver.cpp:218] Iteration 5328 (2.46348 iter/s, 4.87116s/12 iters), loss = 1.12109
I0409 23:45:53.974854  6396 solver.cpp:237]     Train net output #0: loss = 1.12109 (* 1 = 1.12109 loss)
I0409 23:45:53.974864  6396 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 23:45:59.008924  6396 solver.cpp:218] Iteration 5340 (2.38383 iter/s, 5.03391s/12 iters), loss = 1.23459
I0409 23:45:59.008981  6396 solver.cpp:237]     Train net output #0: loss = 1.23459 (* 1 = 1.23459 loss)
I0409 23:45:59.008992  6396 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 23:46:03.996592  6396 solver.cpp:218] Iteration 5352 (2.40604 iter/s, 4.98745s/12 iters), loss = 1.26893
I0409 23:46:03.996634  6396 solver.cpp:237]     Train net output #0: loss = 1.26893 (* 1 = 1.26893 loss)
I0409 23:46:03.996644  6396 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 23:46:07.326485  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:08.877097  6396 solver.cpp:218] Iteration 5364 (2.45886 iter/s, 4.88031s/12 iters), loss = 1.20722
I0409 23:46:08.877154  6396 solver.cpp:237]     Train net output #0: loss = 1.20722 (* 1 = 1.20722 loss)
I0409 23:46:08.877166  6396 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 23:46:13.848398  6396 solver.cpp:218] Iteration 5376 (2.41396 iter/s, 4.97109s/12 iters), loss = 1.32018
I0409 23:46:13.848454  6396 solver.cpp:237]     Train net output #0: loss = 1.32018 (* 1 = 1.32018 loss)
I0409 23:46:13.848465  6396 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 23:46:18.771906  6396 solver.cpp:218] Iteration 5388 (2.43739 iter/s, 4.9233s/12 iters), loss = 1.36778
I0409 23:46:18.771965  6396 solver.cpp:237]     Train net output #0: loss = 1.36778 (* 1 = 1.36778 loss)
I0409 23:46:18.771976  6396 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 23:46:23.697522  6396 solver.cpp:218] Iteration 5400 (2.43635 iter/s, 4.9254s/12 iters), loss = 1.30054
I0409 23:46:23.697580  6396 solver.cpp:237]     Train net output #0: loss = 1.30054 (* 1 = 1.30054 loss)
I0409 23:46:23.697593  6396 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 23:46:25.657006  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 23:46:26.107805  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 23:46:26.424870  6396 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 23:46:26.424890  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:46:28.684685  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:30.814730  6396 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0409 23:46:30.814779  6396 solver.cpp:397]     Test net output #1: loss = 2.02388 (* 1 = 2.02388 loss)
I0409 23:46:32.611711  6396 solver.cpp:218] Iteration 5412 (1.34622 iter/s, 8.91386s/12 iters), loss = 1.37871
I0409 23:46:32.611764  6396 solver.cpp:237]     Train net output #0: loss = 1.37871 (* 1 = 1.37871 loss)
I0409 23:46:32.611775  6396 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 23:46:37.597203  6396 solver.cpp:218] Iteration 5424 (2.40708 iter/s, 4.98529s/12 iters), loss = 1.28707
I0409 23:46:37.597249  6396 solver.cpp:237]     Train net output #0: loss = 1.28707 (* 1 = 1.28707 loss)
I0409 23:46:37.597259  6396 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 23:46:42.512411  6396 solver.cpp:218] Iteration 5436 (2.4415 iter/s, 4.91501s/12 iters), loss = 1.14722
I0409 23:46:42.512470  6396 solver.cpp:237]     Train net output #0: loss = 1.14722 (* 1 = 1.14722 loss)
I0409 23:46:42.512482  6396 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 23:46:47.459014  6396 solver.cpp:218] Iteration 5448 (2.42601 iter/s, 4.94639s/12 iters), loss = 0.98418
I0409 23:46:47.459064  6396 solver.cpp:237]     Train net output #0: loss = 0.98418 (* 1 = 0.98418 loss)
I0409 23:46:47.459075  6396 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 23:46:52.345592  6396 solver.cpp:218] Iteration 5460 (2.45581 iter/s, 4.88637s/12 iters), loss = 1.20199
I0409 23:46:52.345644  6396 solver.cpp:237]     Train net output #0: loss = 1.20199 (* 1 = 1.20199 loss)
I0409 23:46:52.345657  6396 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 23:46:52.916646  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:57.399863  6396 solver.cpp:218] Iteration 5472 (2.37433 iter/s, 5.05406s/12 iters), loss = 1.22487
I0409 23:46:57.399982  6396 solver.cpp:237]     Train net output #0: loss = 1.22487 (* 1 = 1.22487 loss)
I0409 23:46:57.399996  6396 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 23:47:02.351845  6396 solver.cpp:218] Iteration 5484 (2.42341 iter/s, 4.95171s/12 iters), loss = 1.11418
I0409 23:47:02.351903  6396 solver.cpp:237]     Train net output #0: loss = 1.11418 (* 1 = 1.11418 loss)
I0409 23:47:02.351917  6396 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 23:47:07.425994  6396 solver.cpp:218] Iteration 5496 (2.36503 iter/s, 5.07394s/12 iters), loss = 1.14154
I0409 23:47:07.426044  6396 solver.cpp:237]     Train net output #0: loss = 1.14154 (* 1 = 1.14154 loss)
I0409 23:47:07.426057  6396 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 23:47:11.954000  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 23:47:13.431457  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 23:47:14.557116  6396 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 23:47:14.557140  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:47:16.841966  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:19.031122  6396 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0409 23:47:19.031162  6396 solver.cpp:397]     Test net output #1: loss = 2.02636 (* 1 = 2.02636 loss)
I0409 23:47:19.119766  6396 solver.cpp:218] Iteration 5508 (1.02622 iter/s, 11.6934s/12 iters), loss = 1.35926
I0409 23:47:19.119812  6396 solver.cpp:237]     Train net output #0: loss = 1.35926 (* 1 = 1.35926 loss)
I0409 23:47:19.119822  6396 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 23:47:23.412274  6396 solver.cpp:218] Iteration 5520 (2.79569 iter/s, 4.29232s/12 iters), loss = 1.01343
I0409 23:47:23.412330  6396 solver.cpp:237]     Train net output #0: loss = 1.01343 (* 1 = 1.01343 loss)
I0409 23:47:23.412341  6396 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 23:47:24.192956  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:47:28.387045  6396 solver.cpp:218] Iteration 5532 (2.41227 iter/s, 4.97456s/12 iters), loss = 1.29008
I0409 23:47:28.387166  6396 solver.cpp:237]     Train net output #0: loss = 1.29008 (* 1 = 1.29008 loss)
I0409 23:47:28.387177  6396 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 23:47:33.422134  6396 solver.cpp:218] Iteration 5544 (2.38341 iter/s, 5.03481s/12 iters), loss = 1.06908
I0409 23:47:33.422186  6396 solver.cpp:237]     Train net output #0: loss = 1.06908 (* 1 = 1.06908 loss)
I0409 23:47:33.422197  6396 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 23:47:38.452695  6396 solver.cpp:218] Iteration 5556 (2.38552 iter/s, 5.03035s/12 iters), loss = 1.23074
I0409 23:47:38.452740  6396 solver.cpp:237]     Train net output #0: loss = 1.23074 (* 1 = 1.23074 loss)
I0409 23:47:38.452749  6396 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 23:47:41.079183  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:43.369825  6396 solver.cpp:218] Iteration 5568 (2.44055 iter/s, 4.91693s/12 iters), loss = 1.16131
I0409 23:47:43.369875  6396 solver.cpp:237]     Train net output #0: loss = 1.16131 (* 1 = 1.16131 loss)
I0409 23:47:43.369889  6396 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 23:47:48.279623  6396 solver.cpp:218] Iteration 5580 (2.44419 iter/s, 4.90959s/12 iters), loss = 1.21144
I0409 23:47:48.279673  6396 solver.cpp:237]     Train net output #0: loss = 1.21144 (* 1 = 1.21144 loss)
I0409 23:47:48.279686  6396 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 23:47:53.315661  6396 solver.cpp:218] Iteration 5592 (2.38292 iter/s, 5.03584s/12 iters), loss = 1.1609
I0409 23:47:53.315711  6396 solver.cpp:237]     Train net output #0: loss = 1.1609 (* 1 = 1.1609 loss)
I0409 23:47:53.315719  6396 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 23:47:58.428105  6396 solver.cpp:218] Iteration 5604 (2.34731 iter/s, 5.11223s/12 iters), loss = 0.886791
I0409 23:47:58.428217  6396 solver.cpp:237]     Train net output #0: loss = 0.886791 (* 1 = 0.886791 loss)
I0409 23:47:58.428229  6396 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 23:48:00.437288  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 23:48:00.860831  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 23:48:01.158358  6396 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 23:48:01.158378  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:48:03.341715  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:05.783078  6396 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0409 23:48:05.783125  6396 solver.cpp:397]     Test net output #1: loss = 2.09805 (* 1 = 2.09805 loss)
I0409 23:48:07.664916  6396 solver.cpp:218] Iteration 5616 (1.2992 iter/s, 9.23643s/12 iters), loss = 1.28246
I0409 23:48:07.664964  6396 solver.cpp:237]     Train net output #0: loss = 1.28246 (* 1 = 1.28246 loss)
I0409 23:48:07.664975  6396 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 23:48:12.650821  6396 solver.cpp:218] Iteration 5628 (2.40688 iter/s, 4.9857s/12 iters), loss = 1.07345
I0409 23:48:12.650869  6396 solver.cpp:237]     Train net output #0: loss = 1.07345 (* 1 = 1.07345 loss)
I0409 23:48:12.650882  6396 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 23:48:17.554878  6396 solver.cpp:218] Iteration 5640 (2.44706 iter/s, 4.90385s/12 iters), loss = 1.0793
I0409 23:48:17.554935  6396 solver.cpp:237]     Train net output #0: loss = 1.0793 (* 1 = 1.0793 loss)
I0409 23:48:17.554947  6396 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 23:48:22.588385  6396 solver.cpp:218] Iteration 5652 (2.38413 iter/s, 5.03329s/12 iters), loss = 1.1851
I0409 23:48:22.588443  6396 solver.cpp:237]     Train net output #0: loss = 1.1851 (* 1 = 1.1851 loss)
I0409 23:48:22.588454  6396 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 23:48:27.389619  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:27.551592  6396 solver.cpp:218] Iteration 5664 (2.41789 iter/s, 4.963s/12 iters), loss = 1.03357
I0409 23:48:27.551628  6396 solver.cpp:237]     Train net output #0: loss = 1.03357 (* 1 = 1.03357 loss)
I0409 23:48:27.551636  6396 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 23:48:32.482208  6396 solver.cpp:218] Iteration 5676 (2.43387 iter/s, 4.93042s/12 iters), loss = 0.994841
I0409 23:48:32.482375  6396 solver.cpp:237]     Train net output #0: loss = 0.994841 (* 1 = 0.994841 loss)
I0409 23:48:32.482390  6396 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 23:48:37.394071  6396 solver.cpp:218] Iteration 5688 (2.44322 iter/s, 4.91154s/12 iters), loss = 0.784141
I0409 23:48:37.394121  6396 solver.cpp:237]     Train net output #0: loss = 0.784141 (* 1 = 0.784141 loss)
I0409 23:48:37.394131  6396 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 23:48:42.299687  6396 solver.cpp:218] Iteration 5700 (2.44628 iter/s, 4.90541s/12 iters), loss = 1.12322
I0409 23:48:42.299744  6396 solver.cpp:237]     Train net output #0: loss = 1.12322 (* 1 = 1.12322 loss)
I0409 23:48:42.299757  6396 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 23:48:46.773326  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 23:48:47.196101  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 23:48:47.499562  6396 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 23:48:47.499581  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:48:49.726727  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:51.985849  6396 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0409 23:48:51.985893  6396 solver.cpp:397]     Test net output #1: loss = 1.95575 (* 1 = 1.95575 loss)
I0409 23:48:52.069000  6396 solver.cpp:218] Iteration 5712 (1.22838 iter/s, 9.76897s/12 iters), loss = 1.13294
I0409 23:48:52.069046  6396 solver.cpp:237]     Train net output #0: loss = 1.13294 (* 1 = 1.13294 loss)
I0409 23:48:52.069058  6396 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 23:48:56.283720  6396 solver.cpp:218] Iteration 5724 (2.84729 iter/s, 4.21454s/12 iters), loss = 1.25827
I0409 23:48:56.283772  6396 solver.cpp:237]     Train net output #0: loss = 1.25827 (* 1 = 1.25827 loss)
I0409 23:48:56.283785  6396 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 23:49:01.385385  6396 solver.cpp:218] Iteration 5736 (2.35227 iter/s, 5.10145s/12 iters), loss = 0.772492
I0409 23:49:01.385437  6396 solver.cpp:237]     Train net output #0: loss = 0.772492 (* 1 = 0.772492 loss)
I0409 23:49:01.385448  6396 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 23:49:06.243407  6396 solver.cpp:218] Iteration 5748 (2.47025 iter/s, 4.85782s/12 iters), loss = 0.972716
I0409 23:49:06.243566  6396 solver.cpp:237]     Train net output #0: loss = 0.972716 (* 1 = 0.972716 loss)
I0409 23:49:06.243582  6396 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 23:49:11.193011  6396 solver.cpp:218] Iteration 5760 (2.42459 iter/s, 4.94929s/12 iters), loss = 0.890603
I0409 23:49:11.193070  6396 solver.cpp:237]     Train net output #0: loss = 0.890603 (* 1 = 0.890603 loss)
I0409 23:49:11.193081  6396 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 23:49:13.158334  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:16.112597  6396 solver.cpp:218] Iteration 5772 (2.43934 iter/s, 4.91937s/12 iters), loss = 1.27382
I0409 23:49:16.112648  6396 solver.cpp:237]     Train net output #0: loss = 1.27382 (* 1 = 1.27382 loss)
I0409 23:49:16.112658  6396 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 23:49:21.039188  6396 solver.cpp:218] Iteration 5784 (2.43587 iter/s, 4.92638s/12 iters), loss = 1.22907
I0409 23:49:21.039239  6396 solver.cpp:237]     Train net output #0: loss = 1.22907 (* 1 = 1.22907 loss)
I0409 23:49:21.039250  6396 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 23:49:25.941267  6396 solver.cpp:218] Iteration 5796 (2.44804 iter/s, 4.90187s/12 iters), loss = 1.02648
I0409 23:49:25.941313  6396 solver.cpp:237]     Train net output #0: loss = 1.02648 (* 1 = 1.02648 loss)
I0409 23:49:25.941321  6396 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 23:49:30.853169  6396 solver.cpp:218] Iteration 5808 (2.44314 iter/s, 4.91171s/12 iters), loss = 1.10264
I0409 23:49:30.853212  6396 solver.cpp:237]     Train net output #0: loss = 1.10264 (* 1 = 1.10264 loss)
I0409 23:49:30.853222  6396 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 23:49:32.843935  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 23:49:33.818534  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 23:49:34.947930  6396 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 23:49:34.947955  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:49:37.093533  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:39.379472  6396 solver.cpp:397]     Test net output #0: accuracy = 0.51777
I0409 23:49:39.379520  6396 solver.cpp:397]     Test net output #1: loss = 1.99054 (* 1 = 1.99054 loss)
I0409 23:49:41.232180  6396 solver.cpp:218] Iteration 5820 (1.15622 iter/s, 10.3787s/12 iters), loss = 0.974285
I0409 23:49:41.232234  6396 solver.cpp:237]     Train net output #0: loss = 0.974285 (* 1 = 0.974285 loss)
I0409 23:49:41.232244  6396 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 23:49:46.160537  6396 solver.cpp:218] Iteration 5832 (2.43499 iter/s, 4.92815s/12 iters), loss = 1.21351
I0409 23:49:46.160591  6396 solver.cpp:237]     Train net output #0: loss = 1.21351 (* 1 = 1.21351 loss)
I0409 23:49:46.160604  6396 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 23:49:51.136642  6396 solver.cpp:218] Iteration 5844 (2.41162 iter/s, 4.9759s/12 iters), loss = 1.14345
I0409 23:49:51.136679  6396 solver.cpp:237]     Train net output #0: loss = 1.14345 (* 1 = 1.14345 loss)
I0409 23:49:51.136687  6396 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 23:49:56.078155  6396 solver.cpp:218] Iteration 5856 (2.4285 iter/s, 4.94132s/12 iters), loss = 0.906926
I0409 23:49:56.078205  6396 solver.cpp:237]     Train net output #0: loss = 0.906926 (* 1 = 0.906926 loss)
I0409 23:49:56.078214  6396 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 23:50:00.223799  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:01.035733  6396 solver.cpp:218] Iteration 5868 (2.42064 iter/s, 4.95737s/12 iters), loss = 0.882607
I0409 23:50:01.035779  6396 solver.cpp:237]     Train net output #0: loss = 0.882607 (* 1 = 0.882607 loss)
I0409 23:50:01.035789  6396 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 23:50:06.083715  6396 solver.cpp:218] Iteration 5880 (2.37728 iter/s, 5.04778s/12 iters), loss = 1.10167
I0409 23:50:06.083765  6396 solver.cpp:237]     Train net output #0: loss = 1.10167 (* 1 = 1.10167 loss)
I0409 23:50:06.083776  6396 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 23:50:10.988432  6396 solver.cpp:218] Iteration 5892 (2.44672 iter/s, 4.90452s/12 iters), loss = 1.17707
I0409 23:50:10.988557  6396 solver.cpp:237]     Train net output #0: loss = 1.17707 (* 1 = 1.17707 loss)
I0409 23:50:10.988567  6396 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 23:50:15.901196  6396 solver.cpp:218] Iteration 5904 (2.44276 iter/s, 4.91248s/12 iters), loss = 0.919068
I0409 23:50:15.901248  6396 solver.cpp:237]     Train net output #0: loss = 0.919068 (* 1 = 0.919068 loss)
I0409 23:50:15.901258  6396 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 23:50:20.398375  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 23:50:20.855705  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 23:50:21.175101  6396 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 23:50:21.175130  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:50:23.406951  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:25.804229  6396 solver.cpp:397]     Test net output #0: accuracy = 0.518995
I0409 23:50:25.804273  6396 solver.cpp:397]     Test net output #1: loss = 2.02677 (* 1 = 2.02677 loss)
I0409 23:50:25.887398  6396 solver.cpp:218] Iteration 5916 (1.2017 iter/s, 9.98585s/12 iters), loss = 1.19185
I0409 23:50:25.887444  6396 solver.cpp:237]     Train net output #0: loss = 1.19185 (* 1 = 1.19185 loss)
I0409 23:50:25.887454  6396 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 23:50:30.184517  6396 solver.cpp:218] Iteration 5928 (2.79269 iter/s, 4.29694s/12 iters), loss = 1.00369
I0409 23:50:30.184561  6396 solver.cpp:237]     Train net output #0: loss = 1.00369 (* 1 = 1.00369 loss)
I0409 23:50:30.184569  6396 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 23:50:35.126665  6396 solver.cpp:218] Iteration 5940 (2.42819 iter/s, 4.94195s/12 iters), loss = 1.35095
I0409 23:50:35.126710  6396 solver.cpp:237]     Train net output #0: loss = 1.35095 (* 1 = 1.35095 loss)
I0409 23:50:35.126719  6396 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 23:50:40.094236  6396 solver.cpp:218] Iteration 5952 (2.41577 iter/s, 4.96736s/12 iters), loss = 0.970139
I0409 23:50:40.094291  6396 solver.cpp:237]     Train net output #0: loss = 0.970139 (* 1 = 0.970139 loss)
I0409 23:50:40.094301  6396 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 23:50:44.994009  6396 solver.cpp:218] Iteration 5964 (2.4492 iter/s, 4.89956s/12 iters), loss = 1.02891
I0409 23:50:44.994127  6396 solver.cpp:237]     Train net output #0: loss = 1.02891 (* 1 = 1.02891 loss)
I0409 23:50:44.994140  6396 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 23:50:46.274405  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:49.918916  6396 solver.cpp:218] Iteration 5976 (2.43673 iter/s, 4.92463s/12 iters), loss = 0.776308
I0409 23:50:49.918967  6396 solver.cpp:237]     Train net output #0: loss = 0.776308 (* 1 = 0.776308 loss)
I0409 23:50:49.918975  6396 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 23:50:54.817873  6396 solver.cpp:218] Iteration 5988 (2.4496 iter/s, 4.89875s/12 iters), loss = 1.19161
I0409 23:50:54.817925  6396 solver.cpp:237]     Train net output #0: loss = 1.19161 (* 1 = 1.19161 loss)
I0409 23:50:54.817937  6396 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 23:50:59.790978  6396 solver.cpp:218] Iteration 6000 (2.41308 iter/s, 4.9729s/12 iters), loss = 0.833682
I0409 23:50:59.791024  6396 solver.cpp:237]     Train net output #0: loss = 0.833682 (* 1 = 0.833682 loss)
I0409 23:50:59.791033  6396 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 23:51:04.653532  6396 solver.cpp:218] Iteration 6012 (2.46794 iter/s, 4.86235s/12 iters), loss = 0.790592
I0409 23:51:04.653594  6396 solver.cpp:237]     Train net output #0: loss = 0.790592 (* 1 = 0.790592 loss)
I0409 23:51:04.653607  6396 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 23:51:06.660596  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 23:51:07.366806  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 23:51:07.685896  6396 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 23:51:07.685926  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:51:09.681866  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:12.085785  6396 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0409 23:51:12.085824  6396 solver.cpp:397]     Test net output #1: loss = 1.98972 (* 1 = 1.98972 loss)
I0409 23:51:13.949035  6396 solver.cpp:218] Iteration 6024 (1.29099 iter/s, 9.29516s/12 iters), loss = 0.88475
I0409 23:51:13.949090  6396 solver.cpp:237]     Train net output #0: loss = 0.88475 (* 1 = 0.88475 loss)
I0409 23:51:13.949101  6396 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 23:51:18.800797  6396 solver.cpp:218] Iteration 6036 (2.47343 iter/s, 4.85155s/12 iters), loss = 0.99033
I0409 23:51:18.800945  6396 solver.cpp:237]     Train net output #0: loss = 0.99033 (* 1 = 0.99033 loss)
I0409 23:51:18.800959  6396 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 23:51:23.710374  6396 solver.cpp:218] Iteration 6048 (2.44435 iter/s, 4.90928s/12 iters), loss = 1.1571
I0409 23:51:23.710427  6396 solver.cpp:237]     Train net output #0: loss = 1.1571 (* 1 = 1.1571 loss)
I0409 23:51:23.710438  6396 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 23:51:28.632125  6396 solver.cpp:218] Iteration 6060 (2.43826 iter/s, 4.92154s/12 iters), loss = 1.04913
I0409 23:51:28.632174  6396 solver.cpp:237]     Train net output #0: loss = 1.04913 (* 1 = 1.04913 loss)
I0409 23:51:28.632184  6396 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 23:51:32.038890  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:33.572576  6396 solver.cpp:218] Iteration 6072 (2.42903 iter/s, 4.94025s/12 iters), loss = 1.15423
I0409 23:51:33.572625  6396 solver.cpp:237]     Train net output #0: loss = 1.15423 (* 1 = 1.15423 loss)
I0409 23:51:33.572638  6396 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 23:51:38.471980  6396 solver.cpp:218] Iteration 6084 (2.44938 iter/s, 4.8992s/12 iters), loss = 1.16375
I0409 23:51:38.472023  6396 solver.cpp:237]     Train net output #0: loss = 1.16375 (* 1 = 1.16375 loss)
I0409 23:51:38.472033  6396 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 23:51:43.376546  6396 solver.cpp:218] Iteration 6096 (2.4468 iter/s, 4.90437s/12 iters), loss = 1.05462
I0409 23:51:43.376591  6396 solver.cpp:237]     Train net output #0: loss = 1.05462 (* 1 = 1.05462 loss)
I0409 23:51:43.376601  6396 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 23:51:48.320433  6396 solver.cpp:218] Iteration 6108 (2.42734 iter/s, 4.94368s/12 iters), loss = 0.767485
I0409 23:51:48.320490  6396 solver.cpp:237]     Train net output #0: loss = 0.767485 (* 1 = 0.767485 loss)
I0409 23:51:48.320503  6396 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 23:51:52.787204  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 23:51:53.218420  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 23:51:53.517920  6396 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 23:51:53.517940  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:51:55.580144  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:57.975822  6396 solver.cpp:397]     Test net output #0: accuracy = 0.525735
I0409 23:51:57.975860  6396 solver.cpp:397]     Test net output #1: loss = 1.96531 (* 1 = 1.96531 loss)
I0409 23:51:58.059106  6396 solver.cpp:218] Iteration 6120 (1.23224 iter/s, 9.73833s/12 iters), loss = 0.848845
I0409 23:51:58.059170  6396 solver.cpp:237]     Train net output #0: loss = 0.848845 (* 1 = 0.848845 loss)
I0409 23:51:58.059181  6396 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 23:52:02.161110  6396 solver.cpp:218] Iteration 6132 (2.92553 iter/s, 4.10182s/12 iters), loss = 0.963003
I0409 23:52:02.161149  6396 solver.cpp:237]     Train net output #0: loss = 0.963003 (* 1 = 0.963003 loss)
I0409 23:52:02.161159  6396 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 23:52:07.150951  6396 solver.cpp:218] Iteration 6144 (2.40498 iter/s, 4.98965s/12 iters), loss = 0.995381
I0409 23:52:07.150998  6396 solver.cpp:237]     Train net output #0: loss = 0.995381 (* 1 = 0.995381 loss)
I0409 23:52:07.151008  6396 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 23:52:12.079670  6396 solver.cpp:218] Iteration 6156 (2.43481 iter/s, 4.92852s/12 iters), loss = 0.873192
I0409 23:52:12.079716  6396 solver.cpp:237]     Train net output #0: loss = 0.873192 (* 1 = 0.873192 loss)
I0409 23:52:12.079728  6396 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 23:52:16.996079  6396 solver.cpp:218] Iteration 6168 (2.44091 iter/s, 4.91621s/12 iters), loss = 0.818559
I0409 23:52:16.996136  6396 solver.cpp:237]     Train net output #0: loss = 0.818559 (* 1 = 0.818559 loss)
I0409 23:52:16.996150  6396 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 23:52:17.582381  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:21.880861  6396 solver.cpp:218] Iteration 6180 (2.45672 iter/s, 4.88457s/12 iters), loss = 0.839055
I0409 23:52:21.880918  6396 solver.cpp:237]     Train net output #0: loss = 0.839055 (* 1 = 0.839055 loss)
I0409 23:52:21.880930  6396 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 23:52:26.748980  6396 solver.cpp:218] Iteration 6192 (2.46512 iter/s, 4.86791s/12 iters), loss = 0.984422
I0409 23:52:26.749123  6396 solver.cpp:237]     Train net output #0: loss = 0.984422 (* 1 = 0.984422 loss)
I0409 23:52:26.749136  6396 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 23:52:31.647132  6396 solver.cpp:218] Iteration 6204 (2.45005 iter/s, 4.89786s/12 iters), loss = 0.901569
I0409 23:52:31.647179  6396 solver.cpp:237]     Train net output #0: loss = 0.901569 (* 1 = 0.901569 loss)
I0409 23:52:31.647190  6396 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 23:52:36.516243  6396 solver.cpp:218] Iteration 6216 (2.46462 iter/s, 4.86891s/12 iters), loss = 1.2934
I0409 23:52:36.516294  6396 solver.cpp:237]     Train net output #0: loss = 1.2934 (* 1 = 1.2934 loss)
I0409 23:52:36.516306  6396 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 23:52:38.505594  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 23:52:38.981986  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 23:52:39.299414  6396 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 23:52:39.299443  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:52:41.433678  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:42.345520  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:52:43.875663  6396 solver.cpp:397]     Test net output #0: accuracy = 0.505515
I0409 23:52:43.875727  6396 solver.cpp:397]     Test net output #1: loss = 2.04526 (* 1 = 2.04526 loss)
I0409 23:52:45.815692  6396 solver.cpp:218] Iteration 6228 (1.29044 iter/s, 9.29912s/12 iters), loss = 0.994679
I0409 23:52:45.815737  6396 solver.cpp:237]     Train net output #0: loss = 0.994679 (* 1 = 0.994679 loss)
I0409 23:52:45.815747  6396 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 23:52:50.781051  6396 solver.cpp:218] Iteration 6240 (2.41684 iter/s, 4.96516s/12 iters), loss = 0.782719
I0409 23:52:50.781093  6396 solver.cpp:237]     Train net output #0: loss = 0.782719 (* 1 = 0.782719 loss)
I0409 23:52:50.781101  6396 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 23:52:55.677510  6396 solver.cpp:218] Iteration 6252 (2.45085 iter/s, 4.89625s/12 iters), loss = 0.99411
I0409 23:52:55.677572  6396 solver.cpp:237]     Train net output #0: loss = 0.99411 (* 1 = 0.99411 loss)
I0409 23:52:55.677584  6396 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 23:53:00.640974  6396 solver.cpp:218] Iteration 6264 (2.41777 iter/s, 4.96325s/12 iters), loss = 1.02187
I0409 23:53:00.641093  6396 solver.cpp:237]     Train net output #0: loss = 1.02187 (* 1 = 1.02187 loss)
I0409 23:53:00.641103  6396 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 23:53:03.321924  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:05.507732  6396 solver.cpp:218] Iteration 6276 (2.46585 iter/s, 4.86649s/12 iters), loss = 0.847722
I0409 23:53:05.507779  6396 solver.cpp:237]     Train net output #0: loss = 0.847722 (* 1 = 0.847722 loss)
I0409 23:53:05.507788  6396 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 23:53:10.447453  6396 solver.cpp:218] Iteration 6288 (2.42939 iter/s, 4.93951s/12 iters), loss = 0.962475
I0409 23:53:10.447510  6396 solver.cpp:237]     Train net output #0: loss = 0.962475 (* 1 = 0.962475 loss)
I0409 23:53:10.447523  6396 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 23:53:15.453933  6396 solver.cpp:218] Iteration 6300 (2.397 iter/s, 5.00627s/12 iters), loss = 0.988435
I0409 23:53:15.454005  6396 solver.cpp:237]     Train net output #0: loss = 0.988435 (* 1 = 0.988435 loss)
I0409 23:53:15.454016  6396 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 23:53:20.420783  6396 solver.cpp:218] Iteration 6312 (2.41613 iter/s, 4.96662s/12 iters), loss = 0.871215
I0409 23:53:20.420841  6396 solver.cpp:237]     Train net output #0: loss = 0.871215 (* 1 = 0.871215 loss)
I0409 23:53:20.420853  6396 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 23:53:24.909654  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 23:53:25.929073  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 23:53:26.244889  6396 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 23:53:26.244907  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:53:28.332283  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:30.807737  6396 solver.cpp:397]     Test net output #0: accuracy = 0.52451
I0409 23:53:30.807858  6396 solver.cpp:397]     Test net output #1: loss = 2.01769 (* 1 = 2.01769 loss)
I0409 23:53:30.891368  6396 solver.cpp:218] Iteration 6324 (1.14611 iter/s, 10.4702s/12 iters), loss = 0.992378
I0409 23:53:30.891417  6396 solver.cpp:237]     Train net output #0: loss = 0.992378 (* 1 = 0.992378 loss)
I0409 23:53:30.891428  6396 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 23:53:35.112056  6396 solver.cpp:218] Iteration 6336 (2.84326 iter/s, 4.22051s/12 iters), loss = 0.842865
I0409 23:53:35.112102  6396 solver.cpp:237]     Train net output #0: loss = 0.842865 (* 1 = 0.842865 loss)
I0409 23:53:35.112110  6396 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 23:53:40.079713  6396 solver.cpp:218] Iteration 6348 (2.41572 iter/s, 4.96746s/12 iters), loss = 0.923089
I0409 23:53:40.079761  6396 solver.cpp:237]     Train net output #0: loss = 0.923089 (* 1 = 0.923089 loss)
I0409 23:53:40.079774  6396 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 23:53:45.018323  6396 solver.cpp:218] Iteration 6360 (2.42994 iter/s, 4.9384s/12 iters), loss = 0.843535
I0409 23:53:45.018381  6396 solver.cpp:237]     Train net output #0: loss = 0.843535 (* 1 = 0.843535 loss)
I0409 23:53:45.018393  6396 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 23:53:49.791493  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:49.929862  6396 solver.cpp:218] Iteration 6372 (2.44333 iter/s, 4.91133s/12 iters), loss = 0.754288
I0409 23:53:49.929908  6396 solver.cpp:237]     Train net output #0: loss = 0.754288 (* 1 = 0.754288 loss)
I0409 23:53:49.929916  6396 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 23:53:54.816990  6396 solver.cpp:218] Iteration 6384 (2.45553 iter/s, 4.88693s/12 iters), loss = 0.760821
I0409 23:53:54.817039  6396 solver.cpp:237]     Train net output #0: loss = 0.760821 (* 1 = 0.760821 loss)
I0409 23:53:54.817050  6396 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 23:53:59.908077  6396 solver.cpp:218] Iteration 6396 (2.35716 iter/s, 5.09088s/12 iters), loss = 0.701372
I0409 23:53:59.908131  6396 solver.cpp:237]     Train net output #0: loss = 0.701372 (* 1 = 0.701372 loss)
I0409 23:53:59.908143  6396 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 23:54:04.824442  6396 solver.cpp:218] Iteration 6408 (2.44093 iter/s, 4.91616s/12 iters), loss = 0.934794
I0409 23:54:04.824591  6396 solver.cpp:237]     Train net output #0: loss = 0.934794 (* 1 = 0.934794 loss)
I0409 23:54:04.824604  6396 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 23:54:09.760883  6396 solver.cpp:218] Iteration 6420 (2.43105 iter/s, 4.93614s/12 iters), loss = 0.979845
I0409 23:54:09.760937  6396 solver.cpp:237]     Train net output #0: loss = 0.979845 (* 1 = 0.979845 loss)
I0409 23:54:09.760951  6396 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 23:54:11.771391  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 23:54:12.211131  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 23:54:12.513878  6396 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 23:54:12.513903  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:54:14.374596  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:16.894865  6396 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0409 23:54:16.894907  6396 solver.cpp:397]     Test net output #1: loss = 1.93795 (* 1 = 1.93795 loss)
I0409 23:54:18.840032  6396 solver.cpp:218] Iteration 6432 (1.32176 iter/s, 9.07882s/12 iters), loss = 0.681316
I0409 23:54:18.840090  6396 solver.cpp:237]     Train net output #0: loss = 0.681316 (* 1 = 0.681316 loss)
I0409 23:54:18.840101  6396 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 23:54:23.816783  6396 solver.cpp:218] Iteration 6444 (2.41132 iter/s, 4.97653s/12 iters), loss = 0.650329
I0409 23:54:23.816843  6396 solver.cpp:237]     Train net output #0: loss = 0.650329 (* 1 = 0.650329 loss)
I0409 23:54:23.816854  6396 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 23:54:28.756048  6396 solver.cpp:218] Iteration 6456 (2.42962 iter/s, 4.93905s/12 iters), loss = 0.978873
I0409 23:54:28.756100  6396 solver.cpp:237]     Train net output #0: loss = 0.978873 (* 1 = 0.978873 loss)
I0409 23:54:28.756114  6396 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 23:54:33.713376  6396 solver.cpp:218] Iteration 6468 (2.42076 iter/s, 4.95712s/12 iters), loss = 0.655662
I0409 23:54:33.713428  6396 solver.cpp:237]     Train net output #0: loss = 0.655662 (* 1 = 0.655662 loss)
I0409 23:54:33.713438  6396 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 23:54:35.711524  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:38.635504  6396 solver.cpp:218] Iteration 6480 (2.43807 iter/s, 4.92192s/12 iters), loss = 0.950461
I0409 23:54:38.635560  6396 solver.cpp:237]     Train net output #0: loss = 0.950461 (* 1 = 0.950461 loss)
I0409 23:54:38.635573  6396 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 23:54:43.567827  6396 solver.cpp:218] Iteration 6492 (2.43303 iter/s, 4.93212s/12 iters), loss = 0.947059
I0409 23:54:43.567879  6396 solver.cpp:237]     Train net output #0: loss = 0.947059 (* 1 = 0.947059 loss)
I0409 23:54:43.567891  6396 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 23:54:48.501179  6396 solver.cpp:218] Iteration 6504 (2.43252 iter/s, 4.93315s/12 iters), loss = 0.801746
I0409 23:54:48.501224  6396 solver.cpp:237]     Train net output #0: loss = 0.801746 (* 1 = 0.801746 loss)
I0409 23:54:48.501232  6396 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 23:54:53.384951  6396 solver.cpp:218] Iteration 6516 (2.45722 iter/s, 4.88357s/12 iters), loss = 0.780661
I0409 23:54:53.384997  6396 solver.cpp:237]     Train net output #0: loss = 0.780661 (* 1 = 0.780661 loss)
I0409 23:54:53.385007  6396 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 23:54:57.834748  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 23:54:58.282397  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 23:54:58.598738  6396 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 23:54:58.598762  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:55:00.505369  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:03.095752  6396 solver.cpp:397]     Test net output #0: accuracy = 0.519608
I0409 23:55:03.095795  6396 solver.cpp:397]     Test net output #1: loss = 1.97383 (* 1 = 1.97383 loss)
I0409 23:55:03.179100  6396 solver.cpp:218] Iteration 6528 (1.22526 iter/s, 9.79381s/12 iters), loss = 0.793367
I0409 23:55:03.179149  6396 solver.cpp:237]     Train net output #0: loss = 0.793367 (* 1 = 0.793367 loss)
I0409 23:55:03.179160  6396 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 23:55:07.276386  6396 solver.cpp:218] Iteration 6540 (2.9289 iter/s, 4.0971s/12 iters), loss = 0.764667
I0409 23:55:07.276559  6396 solver.cpp:237]     Train net output #0: loss = 0.764667 (* 1 = 0.764667 loss)
I0409 23:55:07.276573  6396 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 23:55:12.265012  6396 solver.cpp:218] Iteration 6552 (2.40564 iter/s, 4.98829s/12 iters), loss = 0.799266
I0409 23:55:12.265084  6396 solver.cpp:237]     Train net output #0: loss = 0.799266 (* 1 = 0.799266 loss)
I0409 23:55:12.265100  6396 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 23:55:17.215070  6396 solver.cpp:218] Iteration 6564 (2.42432 iter/s, 4.94984s/12 iters), loss = 1.01906
I0409 23:55:17.215112  6396 solver.cpp:237]     Train net output #0: loss = 1.01906 (* 1 = 1.01906 loss)
I0409 23:55:17.215121  6396 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 23:55:21.368147  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:22.139032  6396 solver.cpp:218] Iteration 6576 (2.43716 iter/s, 4.92376s/12 iters), loss = 0.87882
I0409 23:55:22.139083  6396 solver.cpp:237]     Train net output #0: loss = 0.87882 (* 1 = 0.87882 loss)
I0409 23:55:22.139094  6396 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 23:55:27.149272  6396 solver.cpp:218] Iteration 6588 (2.3952 iter/s, 5.01003s/12 iters), loss = 0.942534
I0409 23:55:27.149333  6396 solver.cpp:237]     Train net output #0: loss = 0.942534 (* 1 = 0.942534 loss)
I0409 23:55:27.149344  6396 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 23:55:32.089977  6396 solver.cpp:218] Iteration 6600 (2.42892 iter/s, 4.94046s/12 iters), loss = 0.828873
I0409 23:55:32.090039  6396 solver.cpp:237]     Train net output #0: loss = 0.828873 (* 1 = 0.828873 loss)
I0409 23:55:32.090051  6396 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 23:55:37.032243  6396 solver.cpp:218] Iteration 6612 (2.42814 iter/s, 4.94205s/12 iters), loss = 0.681193
I0409 23:55:37.032299  6396 solver.cpp:237]     Train net output #0: loss = 0.681193 (* 1 = 0.681193 loss)
I0409 23:55:37.032310  6396 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 23:55:42.005010  6396 solver.cpp:218] Iteration 6624 (2.41325 iter/s, 4.97255s/12 iters), loss = 0.806195
I0409 23:55:42.005115  6396 solver.cpp:237]     Train net output #0: loss = 0.806195 (* 1 = 0.806195 loss)
I0409 23:55:42.005125  6396 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 23:55:44.013720  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 23:55:44.464143  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 23:55:44.789587  6396 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 23:55:44.789606  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:55:46.675779  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:49.274042  6396 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0409 23:55:49.274091  6396 solver.cpp:397]     Test net output #1: loss = 1.97565 (* 1 = 1.97565 loss)
I0409 23:55:51.060660  6396 solver.cpp:218] Iteration 6636 (1.3252 iter/s, 9.05527s/12 iters), loss = 0.761862
I0409 23:55:51.060719  6396 solver.cpp:237]     Train net output #0: loss = 0.761862 (* 1 = 0.761862 loss)
I0409 23:55:51.060730  6396 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 23:55:55.985746  6396 solver.cpp:218] Iteration 6648 (2.43661 iter/s, 4.92487s/12 iters), loss = 0.619172
I0409 23:55:55.985805  6396 solver.cpp:237]     Train net output #0: loss = 0.619172 (* 1 = 0.619172 loss)
I0409 23:55:55.985817  6396 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 23:56:00.922060  6396 solver.cpp:218] Iteration 6660 (2.43107 iter/s, 4.9361s/12 iters), loss = 0.960879
I0409 23:56:00.922122  6396 solver.cpp:237]     Train net output #0: loss = 0.960879 (* 1 = 0.960879 loss)
I0409 23:56:00.922134  6396 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 23:56:05.886056  6396 solver.cpp:218] Iteration 6672 (2.41751 iter/s, 4.96378s/12 iters), loss = 0.88338
I0409 23:56:05.886117  6396 solver.cpp:237]     Train net output #0: loss = 0.88338 (* 1 = 0.88338 loss)
I0409 23:56:05.886129  6396 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 23:56:07.239257  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:10.841478  6396 solver.cpp:218] Iteration 6684 (2.4217 iter/s, 4.9552s/12 iters), loss = 0.597468
I0409 23:56:10.841536  6396 solver.cpp:237]     Train net output #0: loss = 0.597468 (* 1 = 0.597468 loss)
I0409 23:56:10.841547  6396 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 23:56:15.772181  6396 solver.cpp:218] Iteration 6696 (2.43384 iter/s, 4.93049s/12 iters), loss = 0.768611
I0409 23:56:15.772337  6396 solver.cpp:237]     Train net output #0: loss = 0.768611 (* 1 = 0.768611 loss)
I0409 23:56:15.772351  6396 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 23:56:20.685463  6396 solver.cpp:218] Iteration 6708 (2.44251 iter/s, 4.91297s/12 iters), loss = 0.788428
I0409 23:56:20.685513  6396 solver.cpp:237]     Train net output #0: loss = 0.788428 (* 1 = 0.788428 loss)
I0409 23:56:20.685523  6396 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 23:56:25.596266  6396 solver.cpp:218] Iteration 6720 (2.4437 iter/s, 4.9106s/12 iters), loss = 0.67188
I0409 23:56:25.596313  6396 solver.cpp:237]     Train net output #0: loss = 0.67188 (* 1 = 0.67188 loss)
I0409 23:56:25.596326  6396 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 23:56:30.042732  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 23:56:30.699683  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 23:56:33.099964  6396 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 23:56:33.099985  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:56:34.778334  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:37.409770  6396 solver.cpp:397]     Test net output #0: accuracy = 0.518995
I0409 23:56:37.409809  6396 solver.cpp:397]     Test net output #1: loss = 1.95163 (* 1 = 1.95163 loss)
I0409 23:56:37.492944  6396 solver.cpp:218] Iteration 6732 (1.00872 iter/s, 11.8963s/12 iters), loss = 0.675438
I0409 23:56:37.492990  6396 solver.cpp:237]     Train net output #0: loss = 0.675438 (* 1 = 0.675438 loss)
I0409 23:56:37.493000  6396 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 23:56:41.703135  6396 solver.cpp:218] Iteration 6744 (2.85035 iter/s, 4.21001s/12 iters), loss = 0.640104
I0409 23:56:41.703191  6396 solver.cpp:237]     Train net output #0: loss = 0.640104 (* 1 = 0.640104 loss)
I0409 23:56:41.703202  6396 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 23:56:46.645893  6396 solver.cpp:218] Iteration 6756 (2.4279 iter/s, 4.94254s/12 iters), loss = 0.923736
I0409 23:56:46.646041  6396 solver.cpp:237]     Train net output #0: loss = 0.923736 (* 1 = 0.923736 loss)
I0409 23:56:46.646054  6396 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 23:56:51.933611  6396 solver.cpp:218] Iteration 6768 (2.26955 iter/s, 5.2874s/12 iters), loss = 0.66228
I0409 23:56:51.933666  6396 solver.cpp:237]     Train net output #0: loss = 0.66228 (* 1 = 0.66228 loss)
I0409 23:56:51.933678  6396 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 23:56:55.403510  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:56.896687  6396 solver.cpp:218] Iteration 6780 (2.41796 iter/s, 4.96287s/12 iters), loss = 0.870623
I0409 23:56:56.896739  6396 solver.cpp:237]     Train net output #0: loss = 0.870623 (* 1 = 0.870623 loss)
I0409 23:56:56.896751  6396 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 23:57:01.876158  6396 solver.cpp:218] Iteration 6792 (2.40999 iter/s, 4.97927s/12 iters), loss = 0.919935
I0409 23:57:01.876202  6396 solver.cpp:237]     Train net output #0: loss = 0.919935 (* 1 = 0.919935 loss)
I0409 23:57:01.876211  6396 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 23:57:06.739792  6396 solver.cpp:218] Iteration 6804 (2.46739 iter/s, 4.86344s/12 iters), loss = 0.888565
I0409 23:57:06.739841  6396 solver.cpp:237]     Train net output #0: loss = 0.888565 (* 1 = 0.888565 loss)
I0409 23:57:06.739851  6396 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 23:57:11.638533  6396 solver.cpp:218] Iteration 6816 (2.44971 iter/s, 4.89854s/12 iters), loss = 0.687512
I0409 23:57:11.638566  6396 solver.cpp:237]     Train net output #0: loss = 0.687512 (* 1 = 0.687512 loss)
I0409 23:57:11.638573  6396 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 23:57:16.573742  6396 solver.cpp:218] Iteration 6828 (2.4316 iter/s, 4.93502s/12 iters), loss = 0.619806
I0409 23:57:16.573801  6396 solver.cpp:237]     Train net output #0: loss = 0.619806 (* 1 = 0.619806 loss)
I0409 23:57:16.573814  6396 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 23:57:18.600248  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 23:57:19.182363  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 23:57:19.513450  6396 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 23:57:19.513478  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:57:21.391649  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:24.102596  6396 solver.cpp:397]     Test net output #0: accuracy = 0.545956
I0409 23:57:24.102638  6396 solver.cpp:397]     Test net output #1: loss = 1.93661 (* 1 = 1.93661 loss)
I0409 23:57:25.861629  6396 solver.cpp:218] Iteration 6840 (1.29205 iter/s, 9.28755s/12 iters), loss = 0.478748
I0409 23:57:25.861680  6396 solver.cpp:237]     Train net output #0: loss = 0.478748 (* 1 = 0.478748 loss)
I0409 23:57:25.861692  6396 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 23:57:30.708218  6396 solver.cpp:218] Iteration 6852 (2.47607 iter/s, 4.84638s/12 iters), loss = 0.710092
I0409 23:57:30.708276  6396 solver.cpp:237]     Train net output #0: loss = 0.710092 (* 1 = 0.710092 loss)
I0409 23:57:30.708289  6396 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 23:57:35.667367  6396 solver.cpp:218] Iteration 6864 (2.41987 iter/s, 4.95894s/12 iters), loss = 0.929079
I0409 23:57:35.667412  6396 solver.cpp:237]     Train net output #0: loss = 0.929079 (* 1 = 0.929079 loss)
I0409 23:57:35.667421  6396 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 23:57:40.536710  6396 solver.cpp:218] Iteration 6876 (2.4645 iter/s, 4.86914s/12 iters), loss = 0.812541
I0409 23:57:40.536756  6396 solver.cpp:237]     Train net output #0: loss = 0.812541 (* 1 = 0.812541 loss)
I0409 23:57:40.536765  6396 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 23:57:41.168723  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:45.449782  6396 solver.cpp:218] Iteration 6888 (2.44257 iter/s, 4.91287s/12 iters), loss = 0.797272
I0409 23:57:45.449841  6396 solver.cpp:237]     Train net output #0: loss = 0.797272 (* 1 = 0.797272 loss)
I0409 23:57:45.449854  6396 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 23:57:50.370096  6396 solver.cpp:218] Iteration 6900 (2.43898 iter/s, 4.92009s/12 iters), loss = 0.743687
I0409 23:57:50.370270  6396 solver.cpp:237]     Train net output #0: loss = 0.743687 (* 1 = 0.743687 loss)
I0409 23:57:50.370285  6396 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 23:57:55.252480  6396 solver.cpp:218] Iteration 6912 (2.45798 iter/s, 4.88206s/12 iters), loss = 0.857599
I0409 23:57:55.252535  6396 solver.cpp:237]     Train net output #0: loss = 0.857599 (* 1 = 0.857599 loss)
I0409 23:57:55.252549  6396 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 23:58:00.196331  6396 solver.cpp:218] Iteration 6924 (2.42736 iter/s, 4.94364s/12 iters), loss = 0.994493
I0409 23:58:00.196382  6396 solver.cpp:237]     Train net output #0: loss = 0.994493 (* 1 = 0.994493 loss)
I0409 23:58:00.196393  6396 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 23:58:04.668382  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 23:58:05.094630  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 23:58:05.397547  6396 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 23:58:05.397570  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:58:05.680248  6396 blocking_queue.cpp:49] Waiting for data
I0409 23:58:07.164506  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:09.967836  6396 solver.cpp:397]     Test net output #0: accuracy = 0.511029
I0409 23:58:09.967881  6396 solver.cpp:397]     Test net output #1: loss = 2.01742 (* 1 = 2.01742 loss)
I0409 23:58:10.051323  6396 solver.cpp:218] Iteration 6936 (1.2177 iter/s, 9.85465s/12 iters), loss = 0.613284
I0409 23:58:10.051371  6396 solver.cpp:237]     Train net output #0: loss = 0.613284 (* 1 = 0.613284 loss)
I0409 23:58:10.051381  6396 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 23:58:14.234899  6396 solver.cpp:218] Iteration 6948 (2.86848 iter/s, 4.18339s/12 iters), loss = 0.859434
I0409 23:58:14.234946  6396 solver.cpp:237]     Train net output #0: loss = 0.859434 (* 1 = 0.859434 loss)
I0409 23:58:14.234953  6396 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 23:58:19.222745  6396 solver.cpp:218] Iteration 6960 (2.40595 iter/s, 4.98764s/12 iters), loss = 0.713329
I0409 23:58:19.222796  6396 solver.cpp:237]     Train net output #0: loss = 0.713329 (* 1 = 0.713329 loss)
I0409 23:58:19.222807  6396 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 23:58:24.277379  6396 solver.cpp:218] Iteration 6972 (2.37416 iter/s, 5.05442s/12 iters), loss = 0.711089
I0409 23:58:24.277467  6396 solver.cpp:237]     Train net output #0: loss = 0.711089 (* 1 = 0.711089 loss)
I0409 23:58:24.277475  6396 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 23:58:27.032757  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:29.254009  6396 solver.cpp:218] Iteration 6984 (2.41139 iter/s, 4.97639s/12 iters), loss = 0.913944
I0409 23:58:29.254052  6396 solver.cpp:237]     Train net output #0: loss = 0.913944 (* 1 = 0.913944 loss)
I0409 23:58:29.254063  6396 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 23:58:34.236649  6396 solver.cpp:218] Iteration 6996 (2.40846 iter/s, 4.98244s/12 iters), loss = 0.787754
I0409 23:58:34.236690  6396 solver.cpp:237]     Train net output #0: loss = 0.787754 (* 1 = 0.787754 loss)
I0409 23:58:34.236698  6396 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 23:58:39.159011  6396 solver.cpp:218] Iteration 7008 (2.43795 iter/s, 4.92217s/12 iters), loss = 0.660303
I0409 23:58:39.159057  6396 solver.cpp:237]     Train net output #0: loss = 0.660303 (* 1 = 0.660303 loss)
I0409 23:58:39.159067  6396 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 23:58:44.185220  6396 solver.cpp:218] Iteration 7020 (2.38759 iter/s, 5.026s/12 iters), loss = 0.73754
I0409 23:58:44.185274  6396 solver.cpp:237]     Train net output #0: loss = 0.73754 (* 1 = 0.73754 loss)
I0409 23:58:44.185286  6396 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 23:58:49.138160  6396 solver.cpp:218] Iteration 7032 (2.42291 iter/s, 4.95273s/12 iters), loss = 0.831974
I0409 23:58:49.138207  6396 solver.cpp:237]     Train net output #0: loss = 0.831974 (* 1 = 0.831974 loss)
I0409 23:58:49.138216  6396 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 23:58:51.122125  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 23:58:51.576687  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 23:58:51.893054  6396 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 23:58:51.893080  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:58:53.590219  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:56.334398  6396 solver.cpp:397]     Test net output #0: accuracy = 0.531863
I0409 23:58:56.334568  6396 solver.cpp:397]     Test net output #1: loss = 1.96661 (* 1 = 1.96661 loss)
I0409 23:58:58.180212  6396 solver.cpp:218] Iteration 7044 (1.32718 iter/s, 9.04173s/12 iters), loss = 0.788345
I0409 23:58:58.180263  6396 solver.cpp:237]     Train net output #0: loss = 0.788345 (* 1 = 0.788345 loss)
I0409 23:58:58.180274  6396 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 23:59:03.297711  6396 solver.cpp:218] Iteration 7056 (2.34499 iter/s, 5.11729s/12 iters), loss = 0.504124
I0409 23:59:03.297763  6396 solver.cpp:237]     Train net output #0: loss = 0.504124 (* 1 = 0.504124 loss)
I0409 23:59:03.297775  6396 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 23:59:08.273528  6396 solver.cpp:218] Iteration 7068 (2.41177 iter/s, 4.9756s/12 iters), loss = 0.74639
I0409 23:59:08.273581  6396 solver.cpp:237]     Train net output #0: loss = 0.74639 (* 1 = 0.74639 loss)
I0409 23:59:08.273593  6396 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 23:59:13.082933  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:13.192281  6396 solver.cpp:218] Iteration 7080 (2.43975 iter/s, 4.91855s/12 iters), loss = 0.682843
I0409 23:59:13.192332  6396 solver.cpp:237]     Train net output #0: loss = 0.682843 (* 1 = 0.682843 loss)
I0409 23:59:13.192342  6396 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 23:59:18.157563  6396 solver.cpp:218] Iteration 7092 (2.41689 iter/s, 4.96507s/12 iters), loss = 0.581838
I0409 23:59:18.157613  6396 solver.cpp:237]     Train net output #0: loss = 0.581838 (* 1 = 0.581838 loss)
I0409 23:59:18.157622  6396 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 23:59:23.102349  6396 solver.cpp:218] Iteration 7104 (2.4269 iter/s, 4.94458s/12 iters), loss = 0.470003
I0409 23:59:23.102393  6396 solver.cpp:237]     Train net output #0: loss = 0.470003 (* 1 = 0.470003 loss)
I0409 23:59:23.102402  6396 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 23:59:28.128913  6396 solver.cpp:218] Iteration 7116 (2.38741 iter/s, 5.02636s/12 iters), loss = 0.6785
I0409 23:59:28.129032  6396 solver.cpp:237]     Train net output #0: loss = 0.6785 (* 1 = 0.6785 loss)
I0409 23:59:28.129047  6396 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 23:59:33.179520  6396 solver.cpp:218] Iteration 7128 (2.37608 iter/s, 5.05033s/12 iters), loss = 0.577561
I0409 23:59:33.179566  6396 solver.cpp:237]     Train net output #0: loss = 0.577561 (* 1 = 0.577561 loss)
I0409 23:59:33.179577  6396 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 23:59:37.686298  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 23:59:38.160656  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 23:59:38.482975  6396 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 23:59:38.483002  6396 net.cpp:676] Ignoring source layer train-data
I0409 23:59:40.059579  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:42.869243  6396 solver.cpp:397]     Test net output #0: accuracy = 0.541054
I0409 23:59:42.869292  6396 solver.cpp:397]     Test net output #1: loss = 2.03287 (* 1 = 2.03287 loss)
I0409 23:59:42.952859  6396 solver.cpp:218] Iteration 7140 (1.22787 iter/s, 9.773s/12 iters), loss = 0.626334
I0409 23:59:42.952913  6396 solver.cpp:237]     Train net output #0: loss = 0.626334 (* 1 = 0.626334 loss)
I0409 23:59:42.952925  6396 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 23:59:47.135026  6396 solver.cpp:218] Iteration 7152 (2.86946 iter/s, 4.18197s/12 iters), loss = 0.454517
I0409 23:59:47.135076  6396 solver.cpp:237]     Train net output #0: loss = 0.454517 (* 1 = 0.454517 loss)
I0409 23:59:47.135085  6396 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 23:59:52.038527  6396 solver.cpp:218] Iteration 7164 (2.44733 iter/s, 4.9033s/12 iters), loss = 0.730396
I0409 23:59:52.038574  6396 solver.cpp:237]     Train net output #0: loss = 0.730396 (* 1 = 0.730396 loss)
I0409 23:59:52.038583  6396 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 23:59:57.042497  6396 solver.cpp:218] Iteration 7176 (2.3982 iter/s, 5.00376s/12 iters), loss = 0.720915
I0409 23:59:57.042551  6396 solver.cpp:237]     Train net output #0: loss = 0.720915 (* 1 = 0.720915 loss)
I0409 23:59:57.042563  6396 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 23:59:59.152545  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:02.026098  6396 solver.cpp:218] Iteration 7188 (2.408 iter/s, 4.98339s/12 iters), loss = 0.818263
I0410 00:00:02.026146  6396 solver.cpp:237]     Train net output #0: loss = 0.818263 (* 1 = 0.818263 loss)
I0410 00:00:02.026155  6396 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 00:00:06.981920  6396 solver.cpp:218] Iteration 7200 (2.4215 iter/s, 4.95562s/12 iters), loss = 0.60214
I0410 00:00:06.981995  6396 solver.cpp:237]     Train net output #0: loss = 0.60214 (* 1 = 0.60214 loss)
I0410 00:00:06.982005  6396 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 00:00:11.925021  6396 solver.cpp:218] Iteration 7212 (2.42774 iter/s, 4.94287s/12 iters), loss = 0.571428
I0410 00:00:11.925078  6396 solver.cpp:237]     Train net output #0: loss = 0.571428 (* 1 = 0.571428 loss)
I0410 00:00:11.925091  6396 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 00:00:16.928952  6396 solver.cpp:218] Iteration 7224 (2.39822 iter/s, 5.00371s/12 iters), loss = 0.64254
I0410 00:00:16.929003  6396 solver.cpp:237]     Train net output #0: loss = 0.64254 (* 1 = 0.64254 loss)
I0410 00:00:16.929013  6396 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 00:00:21.927843  6396 solver.cpp:218] Iteration 7236 (2.40064 iter/s, 4.99868s/12 iters), loss = 0.437528
I0410 00:00:21.927912  6396 solver.cpp:237]     Train net output #0: loss = 0.437528 (* 1 = 0.437528 loss)
I0410 00:00:21.927929  6396 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 00:00:23.960222  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 00:00:25.485577  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 00:00:26.591641  6396 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 00:00:26.591670  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:00:28.225821  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:31.125572  6396 solver.cpp:397]     Test net output #0: accuracy = 0.544118
I0410 00:00:31.125697  6396 solver.cpp:397]     Test net output #1: loss = 1.9328 (* 1 = 1.9328 loss)
I0410 00:00:33.054443  6396 solver.cpp:218] Iteration 7248 (1.07854 iter/s, 11.1262s/12 iters), loss = 0.643116
I0410 00:00:33.054503  6396 solver.cpp:237]     Train net output #0: loss = 0.643116 (* 1 = 0.643116 loss)
I0410 00:00:33.054515  6396 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 00:00:37.993185  6396 solver.cpp:218] Iteration 7260 (2.42987 iter/s, 4.93853s/12 iters), loss = 0.731656
I0410 00:00:37.993233  6396 solver.cpp:237]     Train net output #0: loss = 0.731656 (* 1 = 0.731656 loss)
I0410 00:00:37.993244  6396 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 00:00:42.927254  6396 solver.cpp:218] Iteration 7272 (2.43217 iter/s, 4.93386s/12 iters), loss = 0.664226
I0410 00:00:42.927311  6396 solver.cpp:237]     Train net output #0: loss = 0.664226 (* 1 = 0.664226 loss)
I0410 00:00:42.927325  6396 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 00:00:47.139536  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:47.875774  6396 solver.cpp:218] Iteration 7284 (2.42507 iter/s, 4.94831s/12 iters), loss = 0.720213
I0410 00:00:47.875833  6396 solver.cpp:237]     Train net output #0: loss = 0.720213 (* 1 = 0.720213 loss)
I0410 00:00:47.875845  6396 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 00:00:52.848039  6396 solver.cpp:218] Iteration 7296 (2.41349 iter/s, 4.97205s/12 iters), loss = 0.608607
I0410 00:00:52.848085  6396 solver.cpp:237]     Train net output #0: loss = 0.608607 (* 1 = 0.608607 loss)
I0410 00:00:52.848096  6396 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 00:00:57.758594  6396 solver.cpp:218] Iteration 7308 (2.44382 iter/s, 4.91035s/12 iters), loss = 0.613965
I0410 00:00:57.758646  6396 solver.cpp:237]     Train net output #0: loss = 0.613965 (* 1 = 0.613965 loss)
I0410 00:00:57.758657  6396 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 00:01:02.656047  6396 solver.cpp:218] Iteration 7320 (2.45036 iter/s, 4.89725s/12 iters), loss = 0.444402
I0410 00:01:02.658304  6396 solver.cpp:237]     Train net output #0: loss = 0.444402 (* 1 = 0.444402 loss)
I0410 00:01:02.658316  6396 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 00:01:07.562597  6396 solver.cpp:218] Iteration 7332 (2.44691 iter/s, 4.90414s/12 iters), loss = 0.577648
I0410 00:01:07.562646  6396 solver.cpp:237]     Train net output #0: loss = 0.577648 (* 1 = 0.577648 loss)
I0410 00:01:07.562656  6396 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 00:01:12.017565  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 00:01:12.434422  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 00:01:12.732647  6396 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 00:01:12.732676  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:01:14.338770  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:17.254411  6396 solver.cpp:397]     Test net output #0: accuracy = 0.556373
I0410 00:01:17.254458  6396 solver.cpp:397]     Test net output #1: loss = 1.89776 (* 1 = 1.89776 loss)
I0410 00:01:17.337860  6396 solver.cpp:218] Iteration 7344 (1.22763 iter/s, 9.77491s/12 iters), loss = 0.580602
I0410 00:01:17.337911  6396 solver.cpp:237]     Train net output #0: loss = 0.580602 (* 1 = 0.580602 loss)
I0410 00:01:17.337924  6396 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 00:01:21.482645  6396 solver.cpp:218] Iteration 7356 (2.89533 iter/s, 4.1446s/12 iters), loss = 0.397144
I0410 00:01:21.482686  6396 solver.cpp:237]     Train net output #0: loss = 0.397144 (* 1 = 0.397144 loss)
I0410 00:01:21.482694  6396 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 00:01:26.370476  6396 solver.cpp:218] Iteration 7368 (2.45518 iter/s, 4.88762s/12 iters), loss = 0.594387
I0410 00:01:26.370545  6396 solver.cpp:237]     Train net output #0: loss = 0.594387 (* 1 = 0.594387 loss)
I0410 00:01:26.370563  6396 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 00:01:31.328253  6396 solver.cpp:218] Iteration 7380 (2.42055 iter/s, 4.95756s/12 iters), loss = 0.511821
I0410 00:01:31.328294  6396 solver.cpp:237]     Train net output #0: loss = 0.511821 (* 1 = 0.511821 loss)
I0410 00:01:31.328303  6396 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 00:01:32.701251  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:36.244905  6396 solver.cpp:218] Iteration 7392 (2.44079 iter/s, 4.91645s/12 iters), loss = 0.744804
I0410 00:01:36.244966  6396 solver.cpp:237]     Train net output #0: loss = 0.744804 (* 1 = 0.744804 loss)
I0410 00:01:36.244979  6396 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 00:01:41.177532  6396 solver.cpp:218] Iteration 7404 (2.43289 iter/s, 4.93241s/12 iters), loss = 0.617495
I0410 00:01:41.177589  6396 solver.cpp:237]     Train net output #0: loss = 0.617495 (* 1 = 0.617495 loss)
I0410 00:01:41.177603  6396 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 00:01:46.136521  6396 solver.cpp:218] Iteration 7416 (2.41995 iter/s, 4.95878s/12 iters), loss = 0.490691
I0410 00:01:46.136579  6396 solver.cpp:237]     Train net output #0: loss = 0.490691 (* 1 = 0.490691 loss)
I0410 00:01:46.136590  6396 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 00:01:51.026942  6396 solver.cpp:218] Iteration 7428 (2.45388 iter/s, 4.89021s/12 iters), loss = 0.589477
I0410 00:01:51.027002  6396 solver.cpp:237]     Train net output #0: loss = 0.589477 (* 1 = 0.589477 loss)
I0410 00:01:51.027014  6396 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 00:01:55.949924  6396 solver.cpp:218] Iteration 7440 (2.43765 iter/s, 4.92276s/12 iters), loss = 0.531264
I0410 00:01:55.949981  6396 solver.cpp:237]     Train net output #0: loss = 0.531264 (* 1 = 0.531264 loss)
I0410 00:01:55.949990  6396 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 00:01:57.905782  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 00:01:59.164742  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 00:01:59.476202  6396 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 00:01:59.476231  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:02:01.029978  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:03.936115  6396 solver.cpp:397]     Test net output #0: accuracy = 0.544118
I0410 00:02:03.936233  6396 solver.cpp:397]     Test net output #1: loss = 2.00447 (* 1 = 2.00447 loss)
I0410 00:02:05.830524  6396 solver.cpp:218] Iteration 7452 (1.21455 iter/s, 9.88024s/12 iters), loss = 0.521374
I0410 00:02:05.830576  6396 solver.cpp:237]     Train net output #0: loss = 0.521374 (* 1 = 0.521374 loss)
I0410 00:02:05.830587  6396 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 00:02:10.713801  6396 solver.cpp:218] Iteration 7464 (2.45747 iter/s, 4.88306s/12 iters), loss = 0.658872
I0410 00:02:10.713857  6396 solver.cpp:237]     Train net output #0: loss = 0.658872 (* 1 = 0.658872 loss)
I0410 00:02:10.713871  6396 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 00:02:15.672991  6396 solver.cpp:218] Iteration 7476 (2.41985 iter/s, 4.95898s/12 iters), loss = 0.695809
I0410 00:02:15.673048  6396 solver.cpp:237]     Train net output #0: loss = 0.695809 (* 1 = 0.695809 loss)
I0410 00:02:15.673063  6396 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 00:02:19.125316  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:20.612300  6396 solver.cpp:218] Iteration 7488 (2.4296 iter/s, 4.93909s/12 iters), loss = 0.69765
I0410 00:02:20.612354  6396 solver.cpp:237]     Train net output #0: loss = 0.69765 (* 1 = 0.69765 loss)
I0410 00:02:20.612366  6396 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 00:02:25.686451  6396 solver.cpp:218] Iteration 7500 (2.36503 iter/s, 5.07394s/12 iters), loss = 0.589432
I0410 00:02:25.686504  6396 solver.cpp:237]     Train net output #0: loss = 0.589432 (* 1 = 0.589432 loss)
I0410 00:02:25.686514  6396 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 00:02:30.567566  6396 solver.cpp:218] Iteration 7512 (2.45856 iter/s, 4.88091s/12 iters), loss = 0.600003
I0410 00:02:30.567616  6396 solver.cpp:237]     Train net output #0: loss = 0.600003 (* 1 = 0.600003 loss)
I0410 00:02:30.567625  6396 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 00:02:35.446323  6396 solver.cpp:218] Iteration 7524 (2.45975 iter/s, 4.87855s/12 iters), loss = 0.601211
I0410 00:02:35.446461  6396 solver.cpp:237]     Train net output #0: loss = 0.601211 (* 1 = 0.601211 loss)
I0410 00:02:35.446470  6396 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 00:02:40.363965  6396 solver.cpp:218] Iteration 7536 (2.44034 iter/s, 4.91735s/12 iters), loss = 0.608871
I0410 00:02:40.364013  6396 solver.cpp:237]     Train net output #0: loss = 0.608871 (* 1 = 0.608871 loss)
I0410 00:02:40.364022  6396 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 00:02:44.834403  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 00:02:45.290529  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 00:02:45.607193  6396 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 00:02:45.607218  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:02:47.088316  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:50.029294  6396 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0410 00:02:50.029345  6396 solver.cpp:397]     Test net output #1: loss = 1.94891 (* 1 = 1.94891 loss)
I0410 00:02:50.112569  6396 solver.cpp:218] Iteration 7548 (1.23099 iter/s, 9.74826s/12 iters), loss = 0.48554
I0410 00:02:50.112623  6396 solver.cpp:237]     Train net output #0: loss = 0.48554 (* 1 = 0.48554 loss)
I0410 00:02:50.112634  6396 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 00:02:54.304950  6396 solver.cpp:218] Iteration 7560 (2.86246 iter/s, 4.19219s/12 iters), loss = 0.718588
I0410 00:02:54.304993  6396 solver.cpp:237]     Train net output #0: loss = 0.718588 (* 1 = 0.718588 loss)
I0410 00:02:54.305003  6396 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 00:02:59.254824  6396 solver.cpp:218] Iteration 7572 (2.4244 iter/s, 4.94967s/12 iters), loss = 0.631978
I0410 00:02:59.254878  6396 solver.cpp:237]     Train net output #0: loss = 0.631978 (* 1 = 0.631978 loss)
I0410 00:02:59.254887  6396 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 00:03:04.170388  6396 solver.cpp:218] Iteration 7584 (2.44133 iter/s, 4.91535s/12 iters), loss = 0.682018
I0410 00:03:04.170445  6396 solver.cpp:237]     Train net output #0: loss = 0.682018 (* 1 = 0.682018 loss)
I0410 00:03:04.170459  6396 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 00:03:04.813354  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:09.225067  6396 solver.cpp:218] Iteration 7596 (2.37414 iter/s, 5.05446s/12 iters), loss = 0.479076
I0410 00:03:09.225188  6396 solver.cpp:237]     Train net output #0: loss = 0.479076 (* 1 = 0.479076 loss)
I0410 00:03:09.225200  6396 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 00:03:14.219568  6396 solver.cpp:218] Iteration 7608 (2.40278 iter/s, 4.99422s/12 iters), loss = 0.70589
I0410 00:03:14.219624  6396 solver.cpp:237]     Train net output #0: loss = 0.70589 (* 1 = 0.70589 loss)
I0410 00:03:14.219637  6396 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 00:03:19.211242  6396 solver.cpp:218] Iteration 7620 (2.4041 iter/s, 4.99147s/12 iters), loss = 0.573745
I0410 00:03:19.211288  6396 solver.cpp:237]     Train net output #0: loss = 0.573745 (* 1 = 0.573745 loss)
I0410 00:03:19.211298  6396 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 00:03:19.964200  6396 blocking_queue.cpp:49] Waiting for data
I0410 00:03:24.156847  6396 solver.cpp:218] Iteration 7632 (2.42649 iter/s, 4.94541s/12 iters), loss = 0.593114
I0410 00:03:24.156898  6396 solver.cpp:237]     Train net output #0: loss = 0.593114 (* 1 = 0.593114 loss)
I0410 00:03:24.156909  6396 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 00:03:29.103682  6396 solver.cpp:218] Iteration 7644 (2.4259 iter/s, 4.94663s/12 iters), loss = 0.526375
I0410 00:03:29.103735  6396 solver.cpp:237]     Train net output #0: loss = 0.526375 (* 1 = 0.526375 loss)
I0410 00:03:29.103747  6396 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 00:03:31.103133  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 00:03:31.577872  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 00:03:31.886904  6396 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 00:03:31.886920  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:03:33.354883  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:36.343739  6396 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0410 00:03:36.343791  6396 solver.cpp:397]     Test net output #1: loss = 1.96015 (* 1 = 1.96015 loss)
I0410 00:03:38.095127  6396 solver.cpp:218] Iteration 7656 (1.33465 iter/s, 8.99112s/12 iters), loss = 0.571128
I0410 00:03:38.095171  6396 solver.cpp:237]     Train net output #0: loss = 0.571128 (* 1 = 0.571128 loss)
I0410 00:03:38.095180  6396 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 00:03:43.052511  6396 solver.cpp:218] Iteration 7668 (2.42073 iter/s, 4.95718s/12 iters), loss = 0.55561
I0410 00:03:43.052635  6396 solver.cpp:237]     Train net output #0: loss = 0.55561 (* 1 = 0.55561 loss)
I0410 00:03:43.052646  6396 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 00:03:47.962146  6396 solver.cpp:218] Iteration 7680 (2.44431 iter/s, 4.90935s/12 iters), loss = 0.529483
I0410 00:03:47.962203  6396 solver.cpp:237]     Train net output #0: loss = 0.529483 (* 1 = 0.529483 loss)
I0410 00:03:47.962214  6396 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 00:03:50.734066  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:52.903115  6396 solver.cpp:218] Iteration 7692 (2.42878 iter/s, 4.94076s/12 iters), loss = 0.528502
I0410 00:03:52.903156  6396 solver.cpp:237]     Train net output #0: loss = 0.528502 (* 1 = 0.528502 loss)
I0410 00:03:52.903165  6396 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 00:03:57.919350  6396 solver.cpp:218] Iteration 7704 (2.39233 iter/s, 5.01603s/12 iters), loss = 0.559512
I0410 00:03:57.919392  6396 solver.cpp:237]     Train net output #0: loss = 0.559512 (* 1 = 0.559512 loss)
I0410 00:03:57.919401  6396 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 00:04:02.893317  6396 solver.cpp:218] Iteration 7716 (2.41266 iter/s, 4.97376s/12 iters), loss = 0.614838
I0410 00:04:02.893393  6396 solver.cpp:237]     Train net output #0: loss = 0.614838 (* 1 = 0.614838 loss)
I0410 00:04:02.893407  6396 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 00:04:07.810540  6396 solver.cpp:218] Iteration 7728 (2.44051 iter/s, 4.917s/12 iters), loss = 0.397453
I0410 00:04:07.810586  6396 solver.cpp:237]     Train net output #0: loss = 0.397453 (* 1 = 0.397453 loss)
I0410 00:04:07.810595  6396 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 00:04:12.722501  6396 solver.cpp:218] Iteration 7740 (2.44312 iter/s, 4.91175s/12 iters), loss = 0.530043
I0410 00:04:12.722559  6396 solver.cpp:237]     Train net output #0: loss = 0.530043 (* 1 = 0.530043 loss)
I0410 00:04:12.722571  6396 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 00:04:17.179324  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 00:04:17.636726  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 00:04:19.083039  6396 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 00:04:19.083070  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:04:20.481665  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:23.506044  6396 solver.cpp:397]     Test net output #0: accuracy = 0.553309
I0410 00:04:23.506096  6396 solver.cpp:397]     Test net output #1: loss = 1.9487 (* 1 = 1.9487 loss)
I0410 00:04:23.589452  6396 solver.cpp:218] Iteration 7752 (1.1043 iter/s, 10.8666s/12 iters), loss = 0.492757
I0410 00:04:23.589507  6396 solver.cpp:237]     Train net output #0: loss = 0.492757 (* 1 = 0.492757 loss)
I0410 00:04:23.589519  6396 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 00:04:27.675400  6396 solver.cpp:218] Iteration 7764 (2.93703 iter/s, 4.08576s/12 iters), loss = 0.564788
I0410 00:04:27.675468  6396 solver.cpp:237]     Train net output #0: loss = 0.564788 (* 1 = 0.564788 loss)
I0410 00:04:27.675480  6396 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 00:04:32.545477  6396 solver.cpp:218] Iteration 7776 (2.46414 iter/s, 4.86985s/12 iters), loss = 0.437101
I0410 00:04:32.545537  6396 solver.cpp:237]     Train net output #0: loss = 0.437101 (* 1 = 0.437101 loss)
I0410 00:04:32.545548  6396 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 00:04:37.562237  6396 solver.cpp:218] Iteration 7788 (2.39209 iter/s, 5.01654s/12 iters), loss = 0.500784
I0410 00:04:37.562297  6396 solver.cpp:237]     Train net output #0: loss = 0.500784 (* 1 = 0.500784 loss)
I0410 00:04:37.562310  6396 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 00:04:37.574162  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:42.576879  6396 solver.cpp:218] Iteration 7800 (2.3931 iter/s, 5.01442s/12 iters), loss = 0.463285
I0410 00:04:42.576928  6396 solver.cpp:237]     Train net output #0: loss = 0.463285 (* 1 = 0.463285 loss)
I0410 00:04:42.576938  6396 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 00:04:47.552256  6396 solver.cpp:218] Iteration 7812 (2.41198 iter/s, 4.97517s/12 iters), loss = 0.358046
I0410 00:04:47.552397  6396 solver.cpp:237]     Train net output #0: loss = 0.358046 (* 1 = 0.358046 loss)
I0410 00:04:47.552409  6396 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 00:04:52.453280  6396 solver.cpp:218] Iteration 7824 (2.44862 iter/s, 4.90073s/12 iters), loss = 0.438287
I0410 00:04:52.453328  6396 solver.cpp:237]     Train net output #0: loss = 0.438287 (* 1 = 0.438287 loss)
I0410 00:04:52.453338  6396 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 00:04:57.433306  6396 solver.cpp:218] Iteration 7836 (2.40973 iter/s, 4.97982s/12 iters), loss = 0.568691
I0410 00:04:57.433351  6396 solver.cpp:237]     Train net output #0: loss = 0.568691 (* 1 = 0.568691 loss)
I0410 00:04:57.433359  6396 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 00:05:02.341286  6396 solver.cpp:218] Iteration 7848 (2.4451 iter/s, 4.90777s/12 iters), loss = 0.386934
I0410 00:05:02.341364  6396 solver.cpp:237]     Train net output #0: loss = 0.386934 (* 1 = 0.386934 loss)
I0410 00:05:02.341382  6396 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 00:05:04.336887  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 00:05:05.173714  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 00:05:05.602349  6396 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 00:05:05.602365  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:05:06.991987  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:10.059276  6396 solver.cpp:397]     Test net output #0: accuracy = 0.576593
I0410 00:05:10.059325  6396 solver.cpp:397]     Test net output #1: loss = 1.87098 (* 1 = 1.87098 loss)
I0410 00:05:12.008946  6396 solver.cpp:218] Iteration 7860 (1.2413 iter/s, 9.66729s/12 iters), loss = 0.517304
I0410 00:05:12.008998  6396 solver.cpp:237]     Train net output #0: loss = 0.517304 (* 1 = 0.517304 loss)
I0410 00:05:12.009009  6396 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 00:05:16.989269  6396 solver.cpp:218] Iteration 7872 (2.40958 iter/s, 4.98011s/12 iters), loss = 0.407837
I0410 00:05:16.989320  6396 solver.cpp:237]     Train net output #0: loss = 0.407837 (* 1 = 0.407837 loss)
I0410 00:05:16.989329  6396 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 00:05:21.990563  6396 solver.cpp:218] Iteration 7884 (2.39948 iter/s, 5.00109s/12 iters), loss = 0.397466
I0410 00:05:21.990684  6396 solver.cpp:237]     Train net output #0: loss = 0.397466 (* 1 = 0.397466 loss)
I0410 00:05:21.990697  6396 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 00:05:24.092942  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:26.887303  6396 solver.cpp:218] Iteration 7896 (2.45074 iter/s, 4.89647s/12 iters), loss = 0.531649
I0410 00:05:26.887343  6396 solver.cpp:237]     Train net output #0: loss = 0.531649 (* 1 = 0.531649 loss)
I0410 00:05:26.887351  6396 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 00:05:31.806394  6396 solver.cpp:218] Iteration 7908 (2.43958 iter/s, 4.91889s/12 iters), loss = 0.461568
I0410 00:05:31.806452  6396 solver.cpp:237]     Train net output #0: loss = 0.461568 (* 1 = 0.461568 loss)
I0410 00:05:31.806464  6396 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 00:05:36.749994  6396 solver.cpp:218] Iteration 7920 (2.4275 iter/s, 4.94336s/12 iters), loss = 0.601116
I0410 00:05:36.750047  6396 solver.cpp:237]     Train net output #0: loss = 0.601116 (* 1 = 0.601116 loss)
I0410 00:05:36.750057  6396 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 00:05:41.777727  6396 solver.cpp:218] Iteration 7932 (2.38686 iter/s, 5.02752s/12 iters), loss = 0.607718
I0410 00:05:41.777774  6396 solver.cpp:237]     Train net output #0: loss = 0.607718 (* 1 = 0.607718 loss)
I0410 00:05:41.777786  6396 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 00:05:46.912987  6396 solver.cpp:218] Iteration 7944 (2.33688 iter/s, 5.13505s/12 iters), loss = 0.471929
I0410 00:05:46.913033  6396 solver.cpp:237]     Train net output #0: loss = 0.471929 (* 1 = 0.471929 loss)
I0410 00:05:46.913043  6396 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 00:05:51.336571  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 00:05:52.061283  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 00:05:53.704035  6396 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 00:05:53.704066  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:05:54.981454  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:58.199651  6396 solver.cpp:397]     Test net output #0: accuracy = 0.56924
I0410 00:05:58.199681  6396 solver.cpp:397]     Test net output #1: loss = 1.91525 (* 1 = 1.91525 loss)
I0410 00:05:58.282640  6396 solver.cpp:218] Iteration 7956 (1.05548 iter/s, 11.3693s/12 iters), loss = 0.396886
I0410 00:05:58.282687  6396 solver.cpp:237]     Train net output #0: loss = 0.396886 (* 1 = 0.396886 loss)
I0410 00:05:58.282696  6396 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 00:06:02.651715  6396 solver.cpp:218] Iteration 7968 (2.74669 iter/s, 4.36889s/12 iters), loss = 0.460044
I0410 00:06:02.651757  6396 solver.cpp:237]     Train net output #0: loss = 0.460044 (* 1 = 0.460044 loss)
I0410 00:06:02.651764  6396 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 00:06:07.609383  6396 solver.cpp:218] Iteration 7980 (2.42059 iter/s, 4.95747s/12 iters), loss = 0.480798
I0410 00:06:07.609441  6396 solver.cpp:237]     Train net output #0: loss = 0.480798 (* 1 = 0.480798 loss)
I0410 00:06:07.609454  6396 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 00:06:11.849902  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:12.553833  6396 solver.cpp:218] Iteration 7992 (2.42707 iter/s, 4.94424s/12 iters), loss = 0.388416
I0410 00:06:12.553879  6396 solver.cpp:237]     Train net output #0: loss = 0.388416 (* 1 = 0.388416 loss)
I0410 00:06:12.553889  6396 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 00:06:17.627029  6396 solver.cpp:218] Iteration 8004 (2.36547 iter/s, 5.07299s/12 iters), loss = 0.677141
I0410 00:06:17.627084  6396 solver.cpp:237]     Train net output #0: loss = 0.677141 (* 1 = 0.677141 loss)
I0410 00:06:17.627095  6396 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 00:06:22.614588  6396 solver.cpp:218] Iteration 8016 (2.40609 iter/s, 4.98735s/12 iters), loss = 0.582079
I0410 00:06:22.614729  6396 solver.cpp:237]     Train net output #0: loss = 0.582079 (* 1 = 0.582079 loss)
I0410 00:06:22.614737  6396 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 00:06:27.546828  6396 solver.cpp:218] Iteration 8028 (2.43312 iter/s, 4.93194s/12 iters), loss = 0.365035
I0410 00:06:27.546882  6396 solver.cpp:237]     Train net output #0: loss = 0.365035 (* 1 = 0.365035 loss)
I0410 00:06:27.546895  6396 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 00:06:32.891311  6396 solver.cpp:218] Iteration 8040 (2.2454 iter/s, 5.34426s/12 iters), loss = 0.678818
I0410 00:06:32.891363  6396 solver.cpp:237]     Train net output #0: loss = 0.678818 (* 1 = 0.678818 loss)
I0410 00:06:32.891376  6396 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 00:06:38.097669  6396 solver.cpp:218] Iteration 8052 (2.30497 iter/s, 5.20614s/12 iters), loss = 0.393924
I0410 00:06:38.097726  6396 solver.cpp:237]     Train net output #0: loss = 0.393924 (* 1 = 0.393924 loss)
I0410 00:06:38.097739  6396 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 00:06:40.126593  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 00:06:40.550215  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 00:06:40.850068  6396 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 00:06:40.850093  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:06:42.098682  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:45.245569  6396 solver.cpp:397]     Test net output #0: accuracy = 0.570466
I0410 00:06:45.245607  6396 solver.cpp:397]     Test net output #1: loss = 1.95526 (* 1 = 1.95526 loss)
I0410 00:06:46.986936  6396 solver.cpp:218] Iteration 8064 (1.34999 iter/s, 8.88894s/12 iters), loss = 0.530227
I0410 00:06:46.986984  6396 solver.cpp:237]     Train net output #0: loss = 0.530227 (* 1 = 0.530227 loss)
I0410 00:06:46.986994  6396 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 00:06:51.831825  6396 solver.cpp:218] Iteration 8076 (2.47694 iter/s, 4.84469s/12 iters), loss = 0.349906
I0410 00:06:51.831863  6396 solver.cpp:237]     Train net output #0: loss = 0.349906 (* 1 = 0.349906 loss)
I0410 00:06:51.831872  6396 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 00:06:56.723253  6396 solver.cpp:218] Iteration 8088 (2.45337 iter/s, 4.89123s/12 iters), loss = 0.387069
I0410 00:06:56.723352  6396 solver.cpp:237]     Train net output #0: loss = 0.387069 (* 1 = 0.387069 loss)
I0410 00:06:56.723362  6396 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 00:06:58.095882  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:01.628275  6396 solver.cpp:218] Iteration 8100 (2.4466 iter/s, 4.90477s/12 iters), loss = 0.52828
I0410 00:07:01.628326  6396 solver.cpp:237]     Train net output #0: loss = 0.52828 (* 1 = 0.52828 loss)
I0410 00:07:01.628338  6396 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 00:07:06.515169  6396 solver.cpp:218] Iteration 8112 (2.45565 iter/s, 4.88668s/12 iters), loss = 0.42516
I0410 00:07:06.515228  6396 solver.cpp:237]     Train net output #0: loss = 0.42516 (* 1 = 0.42516 loss)
I0410 00:07:06.515239  6396 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 00:07:11.500157  6396 solver.cpp:218] Iteration 8124 (2.40733 iter/s, 4.98477s/12 iters), loss = 0.299437
I0410 00:07:11.500216  6396 solver.cpp:237]     Train net output #0: loss = 0.299437 (* 1 = 0.299437 loss)
I0410 00:07:11.500227  6396 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 00:07:16.427662  6396 solver.cpp:218] Iteration 8136 (2.43541 iter/s, 4.9273s/12 iters), loss = 0.40451
I0410 00:07:16.427706  6396 solver.cpp:237]     Train net output #0: loss = 0.40451 (* 1 = 0.40451 loss)
I0410 00:07:16.427716  6396 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 00:07:21.508090  6396 solver.cpp:218] Iteration 8148 (2.3621 iter/s, 5.08022s/12 iters), loss = 0.402816
I0410 00:07:21.508142  6396 solver.cpp:237]     Train net output #0: loss = 0.402816 (* 1 = 0.402816 loss)
I0410 00:07:21.508152  6396 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 00:07:26.063889  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 00:07:26.486358  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 00:07:26.792093  6396 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 00:07:26.792217  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:07:27.927893  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:31.109035  6396 solver.cpp:397]     Test net output #0: accuracy = 0.578431
I0410 00:07:31.109083  6396 solver.cpp:397]     Test net output #1: loss = 1.93416 (* 1 = 1.93416 loss)
I0410 00:07:31.192672  6396 solver.cpp:218] Iteration 8160 (1.23913 iter/s, 9.68423s/12 iters), loss = 0.41202
I0410 00:07:31.192744  6396 solver.cpp:237]     Train net output #0: loss = 0.41202 (* 1 = 0.41202 loss)
I0410 00:07:31.192759  6396 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 00:07:35.306380  6396 solver.cpp:218] Iteration 8172 (2.91722 iter/s, 4.11351s/12 iters), loss = 0.569727
I0410 00:07:35.306433  6396 solver.cpp:237]     Train net output #0: loss = 0.569727 (* 1 = 0.569727 loss)
I0410 00:07:35.306447  6396 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 00:07:40.173933  6396 solver.cpp:218] Iteration 8184 (2.46541 iter/s, 4.86734s/12 iters), loss = 0.415882
I0410 00:07:40.174013  6396 solver.cpp:237]     Train net output #0: loss = 0.415882 (* 1 = 0.415882 loss)
I0410 00:07:40.174026  6396 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 00:07:43.628414  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:45.047102  6396 solver.cpp:218] Iteration 8196 (2.46258 iter/s, 4.87293s/12 iters), loss = 0.475072
I0410 00:07:45.047168  6396 solver.cpp:237]     Train net output #0: loss = 0.475072 (* 1 = 0.475072 loss)
I0410 00:07:45.047179  6396 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 00:07:49.932523  6396 solver.cpp:218] Iteration 8208 (2.4564 iter/s, 4.8852s/12 iters), loss = 0.542122
I0410 00:07:49.932581  6396 solver.cpp:237]     Train net output #0: loss = 0.542122 (* 1 = 0.542122 loss)
I0410 00:07:49.932595  6396 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 00:07:54.753012  6396 solver.cpp:218] Iteration 8220 (2.48948 iter/s, 4.82028s/12 iters), loss = 0.58407
I0410 00:07:54.753070  6396 solver.cpp:237]     Train net output #0: loss = 0.58407 (* 1 = 0.58407 loss)
I0410 00:07:54.753082  6396 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 00:07:59.602155  6396 solver.cpp:218] Iteration 8232 (2.47477 iter/s, 4.84893s/12 iters), loss = 0.432641
I0410 00:07:59.602241  6396 solver.cpp:237]     Train net output #0: loss = 0.432641 (* 1 = 0.432641 loss)
I0410 00:07:59.602252  6396 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 00:08:04.655858  6396 solver.cpp:218] Iteration 8244 (2.37461 iter/s, 5.05345s/12 iters), loss = 0.564514
I0410 00:08:04.655915  6396 solver.cpp:237]     Train net output #0: loss = 0.564514 (* 1 = 0.564514 loss)
I0410 00:08:04.655927  6396 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 00:08:09.681438  6396 solver.cpp:218] Iteration 8256 (2.38789 iter/s, 5.02536s/12 iters), loss = 0.337274
I0410 00:08:09.681495  6396 solver.cpp:237]     Train net output #0: loss = 0.337274 (* 1 = 0.337274 loss)
I0410 00:08:09.681509  6396 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 00:08:11.673821  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 00:08:12.142056  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 00:08:12.463647  6396 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 00:08:12.463675  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:08:13.652036  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:16.866932  6396 solver.cpp:397]     Test net output #0: accuracy = 0.572917
I0410 00:08:16.866982  6396 solver.cpp:397]     Test net output #1: loss = 1.99994 (* 1 = 1.99994 loss)
I0410 00:08:18.730360  6396 solver.cpp:218] Iteration 8268 (1.32617 iter/s, 9.04859s/12 iters), loss = 0.423492
I0410 00:08:18.730407  6396 solver.cpp:237]     Train net output #0: loss = 0.423492 (* 1 = 0.423492 loss)
I0410 00:08:18.730417  6396 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 00:08:23.620959  6396 solver.cpp:218] Iteration 8280 (2.45379 iter/s, 4.89039s/12 iters), loss = 0.605371
I0410 00:08:23.621019  6396 solver.cpp:237]     Train net output #0: loss = 0.605371 (* 1 = 0.605371 loss)
I0410 00:08:23.621031  6396 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 00:08:28.536170  6396 solver.cpp:218] Iteration 8292 (2.44151 iter/s, 4.915s/12 iters), loss = 0.469845
I0410 00:08:28.536216  6396 solver.cpp:237]     Train net output #0: loss = 0.469845 (* 1 = 0.469845 loss)
I0410 00:08:28.536226  6396 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 00:08:29.224299  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:33.470839  6396 solver.cpp:218] Iteration 8304 (2.43188 iter/s, 4.93446s/12 iters), loss = 0.38628
I0410 00:08:33.470939  6396 solver.cpp:237]     Train net output #0: loss = 0.38628 (* 1 = 0.38628 loss)
I0410 00:08:33.470949  6396 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 00:08:34.640234  6396 blocking_queue.cpp:49] Waiting for data
I0410 00:08:38.357290  6396 solver.cpp:218] Iteration 8316 (2.4559 iter/s, 4.8862s/12 iters), loss = 0.365104
I0410 00:08:38.357334  6396 solver.cpp:237]     Train net output #0: loss = 0.365104 (* 1 = 0.365104 loss)
I0410 00:08:38.357347  6396 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 00:08:43.296741  6396 solver.cpp:218] Iteration 8328 (2.42952 iter/s, 4.93925s/12 iters), loss = 0.637254
I0410 00:08:43.296790  6396 solver.cpp:237]     Train net output #0: loss = 0.637254 (* 1 = 0.637254 loss)
I0410 00:08:43.296799  6396 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 00:08:48.216491  6396 solver.cpp:218] Iteration 8340 (2.43925 iter/s, 4.91954s/12 iters), loss = 0.428658
I0410 00:08:48.216545  6396 solver.cpp:237]     Train net output #0: loss = 0.428658 (* 1 = 0.428658 loss)
I0410 00:08:48.216558  6396 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 00:08:53.117655  6396 solver.cpp:218] Iteration 8352 (2.4485 iter/s, 4.90096s/12 iters), loss = 0.450404
I0410 00:08:53.117712  6396 solver.cpp:237]     Train net output #0: loss = 0.450404 (* 1 = 0.450404 loss)
I0410 00:08:53.117724  6396 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 00:08:57.579622  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 00:08:58.063048  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 00:08:58.382337  6396 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 00:08:58.382367  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:08:59.563184  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:02.927919  6396 solver.cpp:397]     Test net output #0: accuracy = 0.585172
I0410 00:09:02.927961  6396 solver.cpp:397]     Test net output #1: loss = 1.90502 (* 1 = 1.90502 loss)
I0410 00:09:03.011157  6396 solver.cpp:218] Iteration 8364 (1.21296 iter/s, 9.89315s/12 iters), loss = 0.521031
I0410 00:09:03.011206  6396 solver.cpp:237]     Train net output #0: loss = 0.521031 (* 1 = 0.521031 loss)
I0410 00:09:03.011216  6396 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 00:09:07.050709  6396 solver.cpp:218] Iteration 8376 (2.97076 iter/s, 4.03937s/12 iters), loss = 0.335354
I0410 00:09:07.050810  6396 solver.cpp:237]     Train net output #0: loss = 0.335354 (* 1 = 0.335354 loss)
I0410 00:09:07.050819  6396 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 00:09:11.979878  6396 solver.cpp:218] Iteration 8388 (2.43461 iter/s, 4.92891s/12 iters), loss = 0.356941
I0410 00:09:11.979928  6396 solver.cpp:237]     Train net output #0: loss = 0.356941 (* 1 = 0.356941 loss)
I0410 00:09:11.979938  6396 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 00:09:14.718103  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:16.896919  6396 solver.cpp:218] Iteration 8400 (2.44059 iter/s, 4.91684s/12 iters), loss = 0.364593
I0410 00:09:16.896970  6396 solver.cpp:237]     Train net output #0: loss = 0.364593 (* 1 = 0.364593 loss)
I0410 00:09:16.896981  6396 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 00:09:22.045809  6396 solver.cpp:218] Iteration 8412 (2.33069 iter/s, 5.14868s/12 iters), loss = 0.316095
I0410 00:09:22.045855  6396 solver.cpp:237]     Train net output #0: loss = 0.316095 (* 1 = 0.316095 loss)
I0410 00:09:22.045867  6396 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 00:09:26.958256  6396 solver.cpp:218] Iteration 8424 (2.44287 iter/s, 4.91224s/12 iters), loss = 0.470128
I0410 00:09:26.958309  6396 solver.cpp:237]     Train net output #0: loss = 0.470128 (* 1 = 0.470128 loss)
I0410 00:09:26.958321  6396 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 00:09:31.806356  6396 solver.cpp:218] Iteration 8436 (2.4753 iter/s, 4.84789s/12 iters), loss = 0.463089
I0410 00:09:31.806414  6396 solver.cpp:237]     Train net output #0: loss = 0.463089 (* 1 = 0.463089 loss)
I0410 00:09:31.806427  6396 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 00:09:36.698513  6396 solver.cpp:218] Iteration 8448 (2.45301 iter/s, 4.89194s/12 iters), loss = 0.660579
I0410 00:09:36.698567  6396 solver.cpp:237]     Train net output #0: loss = 0.660579 (* 1 = 0.660579 loss)
I0410 00:09:36.698580  6396 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 00:09:41.638962  6396 solver.cpp:218] Iteration 8460 (2.42903 iter/s, 4.94024s/12 iters), loss = 0.374917
I0410 00:09:41.639122  6396 solver.cpp:237]     Train net output #0: loss = 0.374917 (* 1 = 0.374917 loss)
I0410 00:09:41.639135  6396 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 00:09:43.655508  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 00:09:44.111865  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 00:09:45.121467  6396 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 00:09:45.121488  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:09:46.166056  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:49.481554  6396 solver.cpp:397]     Test net output #0: accuracy = 0.588848
I0410 00:09:49.481602  6396 solver.cpp:397]     Test net output #1: loss = 1.90164 (* 1 = 1.90164 loss)
I0410 00:09:51.409667  6396 solver.cpp:218] Iteration 8472 (1.22822 iter/s, 9.77025s/12 iters), loss = 0.308575
I0410 00:09:51.409723  6396 solver.cpp:237]     Train net output #0: loss = 0.308575 (* 1 = 0.308575 loss)
I0410 00:09:51.409735  6396 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 00:09:56.413303  6396 solver.cpp:218] Iteration 8484 (2.39836 iter/s, 5.00342s/12 iters), loss = 0.445113
I0410 00:09:56.413354  6396 solver.cpp:237]     Train net output #0: loss = 0.445113 (* 1 = 0.445113 loss)
I0410 00:09:56.413365  6396 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 00:10:01.316525  6396 solver.cpp:218] Iteration 8496 (2.44748 iter/s, 4.90301s/12 iters), loss = 0.485582
I0410 00:10:01.316584  6396 solver.cpp:237]     Train net output #0: loss = 0.485582 (* 1 = 0.485582 loss)
I0410 00:10:01.316596  6396 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 00:10:01.364382  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:06.241578  6396 solver.cpp:218] Iteration 8508 (2.43663 iter/s, 4.92484s/12 iters), loss = 0.371981
I0410 00:10:06.241634  6396 solver.cpp:237]     Train net output #0: loss = 0.371981 (* 1 = 0.371981 loss)
I0410 00:10:06.241647  6396 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 00:10:11.158877  6396 solver.cpp:218] Iteration 8520 (2.44047 iter/s, 4.91709s/12 iters), loss = 0.349761
I0410 00:10:11.158929  6396 solver.cpp:237]     Train net output #0: loss = 0.349761 (* 1 = 0.349761 loss)
I0410 00:10:11.158941  6396 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 00:10:16.084738  6396 solver.cpp:218] Iteration 8532 (2.43623 iter/s, 4.92565s/12 iters), loss = 0.386573
I0410 00:10:16.084949  6396 solver.cpp:237]     Train net output #0: loss = 0.386573 (* 1 = 0.386573 loss)
I0410 00:10:16.084962  6396 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 00:10:20.992347  6396 solver.cpp:218] Iteration 8544 (2.44536 iter/s, 4.90725s/12 iters), loss = 0.493034
I0410 00:10:20.992385  6396 solver.cpp:237]     Train net output #0: loss = 0.493034 (* 1 = 0.493034 loss)
I0410 00:10:20.992394  6396 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 00:10:25.903465  6396 solver.cpp:218] Iteration 8556 (2.44353 iter/s, 4.91093s/12 iters), loss = 0.317279
I0410 00:10:25.903502  6396 solver.cpp:237]     Train net output #0: loss = 0.317279 (* 1 = 0.317279 loss)
I0410 00:10:25.903510  6396 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 00:10:30.378567  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 00:10:32.773603  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 00:10:33.591302  6396 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 00:10:33.591323  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:10:34.606254  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:38.131099  6396 solver.cpp:397]     Test net output #0: accuracy = 0.577819
I0410 00:10:38.131129  6396 solver.cpp:397]     Test net output #1: loss = 1.97421 (* 1 = 1.97421 loss)
I0410 00:10:38.214097  6396 solver.cpp:218] Iteration 8568 (0.974799 iter/s, 12.3102s/12 iters), loss = 0.369813
I0410 00:10:38.214143  6396 solver.cpp:237]     Train net output #0: loss = 0.369813 (* 1 = 0.369813 loss)
I0410 00:10:38.214152  6396 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 00:10:42.288489  6396 solver.cpp:218] Iteration 8580 (2.94536 iter/s, 4.07421s/12 iters), loss = 0.315365
I0410 00:10:42.288547  6396 solver.cpp:237]     Train net output #0: loss = 0.315365 (* 1 = 0.315365 loss)
I0410 00:10:42.288560  6396 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 00:10:47.401667  6396 solver.cpp:218] Iteration 8592 (2.34698 iter/s, 5.11296s/12 iters), loss = 0.342833
I0410 00:10:47.401762  6396 solver.cpp:237]     Train net output #0: loss = 0.342833 (* 1 = 0.342833 loss)
I0410 00:10:47.401773  6396 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 00:10:49.580457  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:52.403015  6396 solver.cpp:218] Iteration 8604 (2.39947 iter/s, 5.0011s/12 iters), loss = 0.491314
I0410 00:10:52.403055  6396 solver.cpp:237]     Train net output #0: loss = 0.491314 (* 1 = 0.491314 loss)
I0410 00:10:52.403064  6396 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 00:10:57.495164  6396 solver.cpp:218] Iteration 8616 (2.35666 iter/s, 5.09194s/12 iters), loss = 0.332876
I0410 00:10:57.495216  6396 solver.cpp:237]     Train net output #0: loss = 0.332876 (* 1 = 0.332876 loss)
I0410 00:10:57.495227  6396 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 00:11:02.376142  6396 solver.cpp:218] Iteration 8628 (2.45863 iter/s, 4.88077s/12 iters), loss = 0.430373
I0410 00:11:02.376183  6396 solver.cpp:237]     Train net output #0: loss = 0.430373 (* 1 = 0.430373 loss)
I0410 00:11:02.376190  6396 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 00:11:07.421723  6396 solver.cpp:218] Iteration 8640 (2.37841 iter/s, 5.04538s/12 iters), loss = 0.384442
I0410 00:11:07.421770  6396 solver.cpp:237]     Train net output #0: loss = 0.384442 (* 1 = 0.384442 loss)
I0410 00:11:07.421780  6396 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 00:11:12.428439  6396 solver.cpp:218] Iteration 8652 (2.39688 iter/s, 5.00651s/12 iters), loss = 0.344907
I0410 00:11:12.428494  6396 solver.cpp:237]     Train net output #0: loss = 0.344907 (* 1 = 0.344907 loss)
I0410 00:11:12.428508  6396 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 00:11:17.351442  6396 solver.cpp:218] Iteration 8664 (2.43764 iter/s, 4.92279s/12 iters), loss = 0.272319
I0410 00:11:17.351491  6396 solver.cpp:237]     Train net output #0: loss = 0.272319 (* 1 = 0.272319 loss)
I0410 00:11:17.351502  6396 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 00:11:19.367138  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 00:11:21.060982  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 00:11:21.382299  6396 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 00:11:21.382328  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:11:22.369428  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:26.016113  6396 solver.cpp:397]     Test net output #0: accuracy = 0.568627
I0410 00:11:26.016147  6396 solver.cpp:397]     Test net output #1: loss = 2.03448 (* 1 = 2.03448 loss)
I0410 00:11:27.864353  6396 solver.cpp:218] Iteration 8676 (1.14149 iter/s, 10.5125s/12 iters), loss = 0.451089
I0410 00:11:27.864406  6396 solver.cpp:237]     Train net output #0: loss = 0.451089 (* 1 = 0.451089 loss)
I0410 00:11:27.864418  6396 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 00:11:32.870273  6396 solver.cpp:218] Iteration 8688 (2.39726 iter/s, 5.00572s/12 iters), loss = 0.595131
I0410 00:11:32.870321  6396 solver.cpp:237]     Train net output #0: loss = 0.595131 (* 1 = 0.595131 loss)
I0410 00:11:32.870332  6396 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 00:11:37.148720  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:37.816661  6396 solver.cpp:218] Iteration 8700 (2.42611 iter/s, 4.94618s/12 iters), loss = 0.42975
I0410 00:11:37.816718  6396 solver.cpp:237]     Train net output #0: loss = 0.42975 (* 1 = 0.42975 loss)
I0410 00:11:37.816731  6396 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 00:11:42.723575  6396 solver.cpp:218] Iteration 8712 (2.44563 iter/s, 4.90671s/12 iters), loss = 0.596835
I0410 00:11:42.723621  6396 solver.cpp:237]     Train net output #0: loss = 0.596835 (* 1 = 0.596835 loss)
I0410 00:11:42.723634  6396 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 00:11:47.685669  6396 solver.cpp:218] Iteration 8724 (2.41843 iter/s, 4.96189s/12 iters), loss = 0.445356
I0410 00:11:47.685724  6396 solver.cpp:237]     Train net output #0: loss = 0.445356 (* 1 = 0.445356 loss)
I0410 00:11:47.685736  6396 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 00:11:52.632117  6396 solver.cpp:218] Iteration 8736 (2.42609 iter/s, 4.94623s/12 iters), loss = 0.380434
I0410 00:11:52.632239  6396 solver.cpp:237]     Train net output #0: loss = 0.380434 (* 1 = 0.380434 loss)
I0410 00:11:52.632252  6396 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 00:11:57.626664  6396 solver.cpp:218] Iteration 8748 (2.40275 iter/s, 4.99427s/12 iters), loss = 0.335815
I0410 00:11:57.626718  6396 solver.cpp:237]     Train net output #0: loss = 0.335815 (* 1 = 0.335815 loss)
I0410 00:11:57.626729  6396 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 00:12:02.565513  6396 solver.cpp:218] Iteration 8760 (2.42982 iter/s, 4.93864s/12 iters), loss = 0.382096
I0410 00:12:02.565570  6396 solver.cpp:237]     Train net output #0: loss = 0.382096 (* 1 = 0.382096 loss)
I0410 00:12:02.565580  6396 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 00:12:07.055663  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 00:12:07.892357  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 00:12:08.303040  6396 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 00:12:08.303057  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:12:09.230808  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:12.810276  6396 solver.cpp:397]     Test net output #0: accuracy = 0.587623
I0410 00:12:12.810304  6396 solver.cpp:397]     Test net output #1: loss = 1.90743 (* 1 = 1.90743 loss)
I0410 00:12:12.893365  6396 solver.cpp:218] Iteration 8772 (1.16195 iter/s, 10.3275s/12 iters), loss = 0.346937
I0410 00:12:12.893406  6396 solver.cpp:237]     Train net output #0: loss = 0.346937 (* 1 = 0.346937 loss)
I0410 00:12:12.893415  6396 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 00:12:17.031539  6396 solver.cpp:218] Iteration 8784 (2.89995 iter/s, 4.138s/12 iters), loss = 0.301242
I0410 00:12:17.031584  6396 solver.cpp:237]     Train net output #0: loss = 0.301242 (* 1 = 0.301242 loss)
I0410 00:12:17.031594  6396 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 00:12:21.998991  6396 solver.cpp:218] Iteration 8796 (2.41582 iter/s, 4.96726s/12 iters), loss = 0.416966
I0410 00:12:21.999034  6396 solver.cpp:237]     Train net output #0: loss = 0.416966 (* 1 = 0.416966 loss)
I0410 00:12:21.999043  6396 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 00:12:23.428459  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:26.948529  6396 solver.cpp:218] Iteration 8808 (2.42457 iter/s, 4.94933s/12 iters), loss = 0.467832
I0410 00:12:26.948571  6396 solver.cpp:237]     Train net output #0: loss = 0.467832 (* 1 = 0.467832 loss)
I0410 00:12:26.948580  6396 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 00:12:31.862856  6396 solver.cpp:218] Iteration 8820 (2.44194 iter/s, 4.91413s/12 iters), loss = 0.314849
I0410 00:12:31.862905  6396 solver.cpp:237]     Train net output #0: loss = 0.314849 (* 1 = 0.314849 loss)
I0410 00:12:31.862915  6396 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 00:12:36.790035  6396 solver.cpp:218] Iteration 8832 (2.43557 iter/s, 4.92698s/12 iters), loss = 0.343688
I0410 00:12:36.790091  6396 solver.cpp:237]     Train net output #0: loss = 0.343688 (* 1 = 0.343688 loss)
I0410 00:12:36.790104  6396 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 00:12:41.673601  6396 solver.cpp:218] Iteration 8844 (2.45733 iter/s, 4.88336s/12 iters), loss = 0.389762
I0410 00:12:41.673658  6396 solver.cpp:237]     Train net output #0: loss = 0.389762 (* 1 = 0.389762 loss)
I0410 00:12:41.673671  6396 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 00:12:46.568892  6396 solver.cpp:218] Iteration 8856 (2.45144 iter/s, 4.89508s/12 iters), loss = 0.35646
I0410 00:12:46.568948  6396 solver.cpp:237]     Train net output #0: loss = 0.35646 (* 1 = 0.35646 loss)
I0410 00:12:46.568959  6396 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 00:12:51.506934  6396 solver.cpp:218] Iteration 8868 (2.43022 iter/s, 4.93783s/12 iters), loss = 0.321884
I0410 00:12:51.506995  6396 solver.cpp:237]     Train net output #0: loss = 0.321884 (* 1 = 0.321884 loss)
I0410 00:12:51.507007  6396 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 00:12:53.517149  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 00:12:54.885995  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 00:12:55.590562  6396 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 00:12:55.590591  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:12:56.642469  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:00.119251  6396 solver.cpp:397]     Test net output #0: accuracy = 0.57598
I0410 00:13:00.119302  6396 solver.cpp:397]     Test net output #1: loss = 2.02685 (* 1 = 2.02685 loss)
I0410 00:13:01.908854  6396 solver.cpp:218] Iteration 8880 (1.15367 iter/s, 10.4016s/12 iters), loss = 0.344603
I0410 00:13:01.908916  6396 solver.cpp:237]     Train net output #0: loss = 0.344603 (* 1 = 0.344603 loss)
I0410 00:13:01.908928  6396 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 00:13:06.919478  6396 solver.cpp:218] Iteration 8892 (2.39501 iter/s, 5.01041s/12 iters), loss = 0.404381
I0410 00:13:06.919523  6396 solver.cpp:237]     Train net output #0: loss = 0.404381 (* 1 = 0.404381 loss)
I0410 00:13:06.919531  6396 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 00:13:10.543962  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:11.954643  6396 solver.cpp:218] Iteration 8904 (2.38334 iter/s, 5.03496s/12 iters), loss = 0.293729
I0410 00:13:11.954689  6396 solver.cpp:237]     Train net output #0: loss = 0.293729 (* 1 = 0.293729 loss)
I0410 00:13:11.954697  6396 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 00:13:16.922925  6396 solver.cpp:218] Iteration 8916 (2.41542 iter/s, 4.96808s/12 iters), loss = 0.352945
I0410 00:13:16.922976  6396 solver.cpp:237]     Train net output #0: loss = 0.352945 (* 1 = 0.352945 loss)
I0410 00:13:16.922987  6396 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 00:13:21.851711  6396 solver.cpp:218] Iteration 8928 (2.43478 iter/s, 4.92858s/12 iters), loss = 0.331451
I0410 00:13:21.851769  6396 solver.cpp:237]     Train net output #0: loss = 0.331451 (* 1 = 0.331451 loss)
I0410 00:13:21.851781  6396 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 00:13:26.756417  6396 solver.cpp:218] Iteration 8940 (2.44674 iter/s, 4.90449s/12 iters), loss = 0.471949
I0410 00:13:26.756558  6396 solver.cpp:237]     Train net output #0: loss = 0.471949 (* 1 = 0.471949 loss)
I0410 00:13:26.756569  6396 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 00:13:31.672936  6396 solver.cpp:218] Iteration 8952 (2.4409 iter/s, 4.91622s/12 iters), loss = 0.556343
I0410 00:13:31.672996  6396 solver.cpp:237]     Train net output #0: loss = 0.556343 (* 1 = 0.556343 loss)
I0410 00:13:31.673007  6396 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 00:13:36.544576  6396 solver.cpp:218] Iteration 8964 (2.46334 iter/s, 4.87143s/12 iters), loss = 0.367866
I0410 00:13:36.544631  6396 solver.cpp:237]     Train net output #0: loss = 0.367866 (* 1 = 0.367866 loss)
I0410 00:13:36.544643  6396 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 00:13:41.015273  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 00:13:41.868012  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 00:13:42.598122  6396 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 00:13:42.598150  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:13:43.552790  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:47.135288  6396 solver.cpp:397]     Test net output #0: accuracy = 0.585784
I0410 00:13:47.135327  6396 solver.cpp:397]     Test net output #1: loss = 1.91383 (* 1 = 1.91383 loss)
I0410 00:13:47.218454  6396 solver.cpp:218] Iteration 8976 (1.12428 iter/s, 10.6735s/12 iters), loss = 0.342751
I0410 00:13:47.218514  6396 solver.cpp:237]     Train net output #0: loss = 0.342751 (* 1 = 0.342751 loss)
I0410 00:13:47.218526  6396 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 00:13:51.514147  6396 solver.cpp:218] Iteration 8988 (2.79362 iter/s, 4.2955s/12 iters), loss = 0.242347
I0410 00:13:51.514199  6396 solver.cpp:237]     Train net output #0: loss = 0.242347 (* 1 = 0.242347 loss)
I0410 00:13:51.514209  6396 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 00:13:53.123708  6396 blocking_queue.cpp:49] Waiting for data
I0410 00:13:56.462961  6396 solver.cpp:218] Iteration 9000 (2.42493 iter/s, 4.94861s/12 iters), loss = 0.338435
I0410 00:13:56.463004  6396 solver.cpp:237]     Train net output #0: loss = 0.338435 (* 1 = 0.338435 loss)
I0410 00:13:56.463014  6396 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 00:13:57.161679  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:01.360677  6396 solver.cpp:218] Iteration 9012 (2.45022 iter/s, 4.89751s/12 iters), loss = 0.368831
I0410 00:14:01.360738  6396 solver.cpp:237]     Train net output #0: loss = 0.368831 (* 1 = 0.368831 loss)
I0410 00:14:01.360751  6396 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 00:14:06.342826  6396 solver.cpp:218] Iteration 9024 (2.4087 iter/s, 4.98193s/12 iters), loss = 0.194407
I0410 00:14:06.342869  6396 solver.cpp:237]     Train net output #0: loss = 0.194407 (* 1 = 0.194407 loss)
I0410 00:14:06.342880  6396 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 00:14:11.289824  6396 solver.cpp:218] Iteration 9036 (2.42581 iter/s, 4.94679s/12 iters), loss = 0.359875
I0410 00:14:11.289886  6396 solver.cpp:237]     Train net output #0: loss = 0.359875 (* 1 = 0.359875 loss)
I0410 00:14:11.289898  6396 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 00:14:16.230299  6396 solver.cpp:218] Iteration 9048 (2.42902 iter/s, 4.94026s/12 iters), loss = 0.266714
I0410 00:14:16.230351  6396 solver.cpp:237]     Train net output #0: loss = 0.266714 (* 1 = 0.266714 loss)
I0410 00:14:16.230362  6396 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 00:14:21.162751  6396 solver.cpp:218] Iteration 9060 (2.43297 iter/s, 4.93225s/12 iters), loss = 0.365277
I0410 00:14:21.162793  6396 solver.cpp:237]     Train net output #0: loss = 0.365277 (* 1 = 0.365277 loss)
I0410 00:14:21.162802  6396 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 00:14:26.119132  6396 solver.cpp:218] Iteration 9072 (2.42122 iter/s, 4.95618s/12 iters), loss = 0.372175
I0410 00:14:26.119184  6396 solver.cpp:237]     Train net output #0: loss = 0.372175 (* 1 = 0.372175 loss)
I0410 00:14:26.119194  6396 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 00:14:28.127213  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 00:14:28.547144  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 00:14:28.851264  6396 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 00:14:28.851291  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:14:29.658507  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:33.374421  6396 solver.cpp:397]     Test net output #0: accuracy = 0.591299
I0410 00:14:33.374462  6396 solver.cpp:397]     Test net output #1: loss = 2.01067 (* 1 = 2.01067 loss)
I0410 00:14:35.333117  6396 solver.cpp:218] Iteration 9084 (1.30241 iter/s, 9.21366s/12 iters), loss = 0.267445
I0410 00:14:35.333153  6396 solver.cpp:237]     Train net output #0: loss = 0.267445 (* 1 = 0.267445 loss)
I0410 00:14:35.333163  6396 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 00:14:40.314560  6396 solver.cpp:218] Iteration 9096 (2.40903 iter/s, 4.98125s/12 iters), loss = 0.316134
I0410 00:14:40.314607  6396 solver.cpp:237]     Train net output #0: loss = 0.316134 (* 1 = 0.316134 loss)
I0410 00:14:40.314616  6396 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 00:14:43.169747  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:45.161088  6396 solver.cpp:218] Iteration 9108 (2.4761 iter/s, 4.84633s/12 iters), loss = 0.433043
I0410 00:14:45.161134  6396 solver.cpp:237]     Train net output #0: loss = 0.433043 (* 1 = 0.433043 loss)
I0410 00:14:45.161145  6396 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 00:14:50.107589  6396 solver.cpp:218] Iteration 9120 (2.42605 iter/s, 4.9463s/12 iters), loss = 0.250071
I0410 00:14:50.107642  6396 solver.cpp:237]     Train net output #0: loss = 0.250071 (* 1 = 0.250071 loss)
I0410 00:14:50.107654  6396 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 00:14:55.018559  6396 solver.cpp:218] Iteration 9132 (2.44361 iter/s, 4.91077s/12 iters), loss = 0.302604
I0410 00:14:55.018601  6396 solver.cpp:237]     Train net output #0: loss = 0.302604 (* 1 = 0.302604 loss)
I0410 00:14:55.018610  6396 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 00:15:00.043408  6396 solver.cpp:218] Iteration 9144 (2.38822 iter/s, 5.02465s/12 iters), loss = 0.194635
I0410 00:15:00.043478  6396 solver.cpp:237]     Train net output #0: loss = 0.194635 (* 1 = 0.194635 loss)
I0410 00:15:00.043488  6396 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 00:15:04.959128  6396 solver.cpp:218] Iteration 9156 (2.44126 iter/s, 4.91549s/12 iters), loss = 0.210477
I0410 00:15:04.959182  6396 solver.cpp:237]     Train net output #0: loss = 0.210477 (* 1 = 0.210477 loss)
I0410 00:15:04.959194  6396 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 00:15:09.842293  6396 solver.cpp:218] Iteration 9168 (2.45753 iter/s, 4.88296s/12 iters), loss = 0.273947
I0410 00:15:09.842342  6396 solver.cpp:237]     Train net output #0: loss = 0.273947 (* 1 = 0.273947 loss)
I0410 00:15:09.842351  6396 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 00:15:14.397244  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 00:15:15.402431  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 00:15:16.000537  6396 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 00:15:16.000561  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:15:16.811435  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:20.419971  6396 solver.cpp:397]     Test net output #0: accuracy = 0.582108
I0410 00:15:20.420011  6396 solver.cpp:397]     Test net output #1: loss = 1.88264 (* 1 = 1.88264 loss)
I0410 00:15:20.503207  6396 solver.cpp:218] Iteration 9180 (1.12565 iter/s, 10.6605s/12 iters), loss = 0.257854
I0410 00:15:20.503262  6396 solver.cpp:237]     Train net output #0: loss = 0.257854 (* 1 = 0.257854 loss)
I0410 00:15:20.503271  6396 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 00:15:24.714336  6396 solver.cpp:218] Iteration 9192 (2.84972 iter/s, 4.21094s/12 iters), loss = 0.312886
I0410 00:15:24.714395  6396 solver.cpp:237]     Train net output #0: loss = 0.312886 (* 1 = 0.312886 loss)
I0410 00:15:24.714407  6396 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 00:15:29.628618  6396 solver.cpp:218] Iteration 9204 (2.44197 iter/s, 4.91407s/12 iters), loss = 0.308744
I0410 00:15:29.628670  6396 solver.cpp:237]     Train net output #0: loss = 0.308744 (* 1 = 0.308744 loss)
I0410 00:15:29.628684  6396 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 00:15:29.711163  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:34.566426  6396 solver.cpp:218] Iteration 9216 (2.43033 iter/s, 4.9376s/12 iters), loss = 0.437619
I0410 00:15:34.566740  6396 solver.cpp:237]     Train net output #0: loss = 0.437619 (* 1 = 0.437619 loss)
I0410 00:15:34.566753  6396 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 00:15:39.602350  6396 solver.cpp:218] Iteration 9228 (2.3831 iter/s, 5.03545s/12 iters), loss = 0.318563
I0410 00:15:39.602401  6396 solver.cpp:237]     Train net output #0: loss = 0.318563 (* 1 = 0.318563 loss)
I0410 00:15:39.602413  6396 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 00:15:44.559433  6396 solver.cpp:218] Iteration 9240 (2.42088 iter/s, 4.95687s/12 iters), loss = 0.467073
I0410 00:15:44.559489  6396 solver.cpp:237]     Train net output #0: loss = 0.467073 (* 1 = 0.467073 loss)
I0410 00:15:44.559504  6396 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 00:15:49.523311  6396 solver.cpp:218] Iteration 9252 (2.41757 iter/s, 4.96367s/12 iters), loss = 0.318748
I0410 00:15:49.523366  6396 solver.cpp:237]     Train net output #0: loss = 0.318748 (* 1 = 0.318748 loss)
I0410 00:15:49.523378  6396 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 00:15:54.437443  6396 solver.cpp:218] Iteration 9264 (2.44204 iter/s, 4.91393s/12 iters), loss = 0.344712
I0410 00:15:54.437489  6396 solver.cpp:237]     Train net output #0: loss = 0.344712 (* 1 = 0.344712 loss)
I0410 00:15:54.437501  6396 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 00:15:59.393448  6396 solver.cpp:218] Iteration 9276 (2.4214 iter/s, 4.9558s/12 iters), loss = 0.482375
I0410 00:15:59.393501  6396 solver.cpp:237]     Train net output #0: loss = 0.482375 (* 1 = 0.482375 loss)
I0410 00:15:59.393512  6396 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 00:16:01.413583  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 00:16:01.867919  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 00:16:02.183306  6396 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 00:16:02.183331  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:16:02.902556  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:06.624948  6396 solver.cpp:397]     Test net output #0: accuracy = 0.590686
I0410 00:16:06.625185  6396 solver.cpp:397]     Test net output #1: loss = 1.91906 (* 1 = 1.91906 loss)
I0410 00:16:08.389098  6396 solver.cpp:218] Iteration 9288 (1.33403 iter/s, 8.99533s/12 iters), loss = 0.340156
I0410 00:16:08.389151  6396 solver.cpp:237]     Train net output #0: loss = 0.340156 (* 1 = 0.340156 loss)
I0410 00:16:08.389163  6396 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 00:16:13.335496  6396 solver.cpp:218] Iteration 9300 (2.42611 iter/s, 4.94619s/12 iters), loss = 0.237741
I0410 00:16:13.335561  6396 solver.cpp:237]     Train net output #0: loss = 0.237741 (* 1 = 0.237741 loss)
I0410 00:16:13.335573  6396 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 00:16:15.524364  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:18.650324  6396 solver.cpp:218] Iteration 9312 (2.25793 iter/s, 5.3146s/12 iters), loss = 0.359171
I0410 00:16:18.650382  6396 solver.cpp:237]     Train net output #0: loss = 0.359171 (* 1 = 0.359171 loss)
I0410 00:16:18.650394  6396 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 00:16:24.937219  6396 solver.cpp:218] Iteration 9324 (1.90881 iter/s, 6.28664s/12 iters), loss = 0.213887
I0410 00:16:24.937279  6396 solver.cpp:237]     Train net output #0: loss = 0.213887 (* 1 = 0.213887 loss)
I0410 00:16:24.937291  6396 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 00:16:30.182960  6396 solver.cpp:218] Iteration 9336 (2.28767 iter/s, 5.24552s/12 iters), loss = 0.486827
I0410 00:16:30.183015  6396 solver.cpp:237]     Train net output #0: loss = 0.486827 (* 1 = 0.486827 loss)
I0410 00:16:30.183028  6396 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 00:16:35.132249  6396 solver.cpp:218] Iteration 9348 (2.42469 iter/s, 4.94908s/12 iters), loss = 0.272722
I0410 00:16:35.132308  6396 solver.cpp:237]     Train net output #0: loss = 0.272722 (* 1 = 0.272722 loss)
I0410 00:16:35.132320  6396 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 00:16:40.077342  6396 solver.cpp:218] Iteration 9360 (2.42675 iter/s, 4.94488s/12 iters), loss = 0.346792
I0410 00:16:40.077448  6396 solver.cpp:237]     Train net output #0: loss = 0.346792 (* 1 = 0.346792 loss)
I0410 00:16:40.077459  6396 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 00:16:45.003368  6396 solver.cpp:218] Iteration 9372 (2.43617 iter/s, 4.92577s/12 iters), loss = 0.360427
I0410 00:16:45.003415  6396 solver.cpp:237]     Train net output #0: loss = 0.360427 (* 1 = 0.360427 loss)
I0410 00:16:45.003428  6396 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 00:16:49.479207  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 00:16:50.104101  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 00:16:50.824465  6396 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 00:16:50.824483  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:16:51.572676  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:55.244220  6396 solver.cpp:397]     Test net output #0: accuracy = 0.584559
I0410 00:16:55.244264  6396 solver.cpp:397]     Test net output #1: loss = 1.99377 (* 1 = 1.99377 loss)
I0410 00:16:55.327518  6396 solver.cpp:218] Iteration 9384 (1.16236 iter/s, 10.3238s/12 iters), loss = 0.273328
I0410 00:16:55.327569  6396 solver.cpp:237]     Train net output #0: loss = 0.273328 (* 1 = 0.273328 loss)
I0410 00:16:55.327580  6396 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 00:16:59.630846  6396 solver.cpp:218] Iteration 9396 (2.78866 iter/s, 4.30313s/12 iters), loss = 0.360437
I0410 00:16:59.630900  6396 solver.cpp:237]     Train net output #0: loss = 0.360437 (* 1 = 0.360437 loss)
I0410 00:16:59.630914  6396 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 00:17:03.982820  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:04.636047  6396 solver.cpp:218] Iteration 9408 (2.39761 iter/s, 5.00499s/12 iters), loss = 0.28216
I0410 00:17:04.636101  6396 solver.cpp:237]     Train net output #0: loss = 0.28216 (* 1 = 0.28216 loss)
I0410 00:17:04.636113  6396 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 00:17:09.625209  6396 solver.cpp:218] Iteration 9420 (2.40531 iter/s, 4.98895s/12 iters), loss = 0.299242
I0410 00:17:09.625262  6396 solver.cpp:237]     Train net output #0: loss = 0.299242 (* 1 = 0.299242 loss)
I0410 00:17:09.625275  6396 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 00:17:14.666551  6396 solver.cpp:218] Iteration 9432 (2.38042 iter/s, 5.04114s/12 iters), loss = 0.329691
I0410 00:17:14.666703  6396 solver.cpp:237]     Train net output #0: loss = 0.329691 (* 1 = 0.329691 loss)
I0410 00:17:14.666713  6396 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 00:17:19.588968  6396 solver.cpp:218] Iteration 9444 (2.43798 iter/s, 4.92211s/12 iters), loss = 0.212146
I0410 00:17:19.589020  6396 solver.cpp:237]     Train net output #0: loss = 0.212146 (* 1 = 0.212146 loss)
I0410 00:17:19.589030  6396 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 00:17:24.664875  6396 solver.cpp:218] Iteration 9456 (2.36421 iter/s, 5.0757s/12 iters), loss = 0.204075
I0410 00:17:24.664929  6396 solver.cpp:237]     Train net output #0: loss = 0.204075 (* 1 = 0.204075 loss)
I0410 00:17:24.664942  6396 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 00:17:29.616412  6396 solver.cpp:218] Iteration 9468 (2.42359 iter/s, 4.95133s/12 iters), loss = 0.364914
I0410 00:17:29.616462  6396 solver.cpp:237]     Train net output #0: loss = 0.364914 (* 1 = 0.364914 loss)
I0410 00:17:29.616473  6396 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 00:17:34.679483  6396 solver.cpp:218] Iteration 9480 (2.3702 iter/s, 5.06287s/12 iters), loss = 0.278094
I0410 00:17:34.679520  6396 solver.cpp:237]     Train net output #0: loss = 0.278094 (* 1 = 0.278094 loss)
I0410 00:17:34.679530  6396 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 00:17:36.859290  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 00:17:37.727900  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 00:17:38.412685  6396 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 00:17:38.412710  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:17:39.182260  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:43.119410  6396 solver.cpp:397]     Test net output #0: accuracy = 0.602328
I0410 00:17:43.119457  6396 solver.cpp:397]     Test net output #1: loss = 1.95104 (* 1 = 1.95104 loss)
I0410 00:17:45.016870  6396 solver.cpp:218] Iteration 9492 (1.16087 iter/s, 10.337s/12 iters), loss = 0.498526
I0410 00:17:45.016952  6396 solver.cpp:237]     Train net output #0: loss = 0.498526 (* 1 = 0.498526 loss)
I0410 00:17:45.016964  6396 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 00:17:49.908056  6396 solver.cpp:218] Iteration 9504 (2.45351 iter/s, 4.89095s/12 iters), loss = 0.254243
I0410 00:17:49.908099  6396 solver.cpp:237]     Train net output #0: loss = 0.254243 (* 1 = 0.254243 loss)
I0410 00:17:49.908108  6396 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 00:17:51.392474  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:54.925875  6396 solver.cpp:218] Iteration 9516 (2.39157 iter/s, 5.01762s/12 iters), loss = 0.269046
I0410 00:17:54.925923  6396 solver.cpp:237]     Train net output #0: loss = 0.269046 (* 1 = 0.269046 loss)
I0410 00:17:54.925935  6396 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 00:17:59.950393  6396 solver.cpp:218] Iteration 9528 (2.38839 iter/s, 5.02431s/12 iters), loss = 0.397849
I0410 00:17:59.950438  6396 solver.cpp:237]     Train net output #0: loss = 0.397849 (* 1 = 0.397849 loss)
I0410 00:17:59.950446  6396 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 00:18:04.936559  6396 solver.cpp:218] Iteration 9540 (2.40676 iter/s, 4.98596s/12 iters), loss = 0.287938
I0410 00:18:04.936617  6396 solver.cpp:237]     Train net output #0: loss = 0.287938 (* 1 = 0.287938 loss)
I0410 00:18:04.936630  6396 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 00:18:09.840886  6396 solver.cpp:218] Iteration 9552 (2.44693 iter/s, 4.90411s/12 iters), loss = 0.236524
I0410 00:18:09.840942  6396 solver.cpp:237]     Train net output #0: loss = 0.236524 (* 1 = 0.236524 loss)
I0410 00:18:09.840956  6396 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 00:18:14.774518  6396 solver.cpp:218] Iteration 9564 (2.43239 iter/s, 4.93342s/12 iters), loss = 0.369479
I0410 00:18:14.774566  6396 solver.cpp:237]     Train net output #0: loss = 0.369479 (* 1 = 0.369479 loss)
I0410 00:18:14.774577  6396 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 00:18:19.771984  6396 solver.cpp:218] Iteration 9576 (2.40132 iter/s, 4.99726s/12 iters), loss = 0.233391
I0410 00:18:19.772119  6396 solver.cpp:237]     Train net output #0: loss = 0.233391 (* 1 = 0.233391 loss)
I0410 00:18:19.772131  6396 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 00:18:24.300995  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 00:18:24.863373  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 00:18:25.447408  6396 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 00:18:25.447429  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:18:26.157671  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:30.030586  6396 solver.cpp:397]     Test net output #0: accuracy = 0.598652
I0410 00:18:30.030622  6396 solver.cpp:397]     Test net output #1: loss = 1.82979 (* 1 = 1.82979 loss)
I0410 00:18:30.113903  6396 solver.cpp:218] Iteration 9588 (1.16038 iter/s, 10.3415s/12 iters), loss = 0.235028
I0410 00:18:30.113952  6396 solver.cpp:237]     Train net output #0: loss = 0.235028 (* 1 = 0.235028 loss)
I0410 00:18:30.113977  6396 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 00:18:34.318742  6396 solver.cpp:218] Iteration 9600 (2.85398 iter/s, 4.20466s/12 iters), loss = 0.23668
I0410 00:18:34.318786  6396 solver.cpp:237]     Train net output #0: loss = 0.23668 (* 1 = 0.23668 loss)
I0410 00:18:34.318799  6396 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 00:18:37.883088  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:39.272212  6396 solver.cpp:218] Iteration 9612 (2.42264 iter/s, 4.95327s/12 iters), loss = 0.168449
I0410 00:18:39.272260  6396 solver.cpp:237]     Train net output #0: loss = 0.168449 (* 1 = 0.168449 loss)
I0410 00:18:39.272271  6396 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 00:18:44.378024  6396 solver.cpp:218] Iteration 9624 (2.35036 iter/s, 5.10561s/12 iters), loss = 0.196578
I0410 00:18:44.378075  6396 solver.cpp:237]     Train net output #0: loss = 0.196578 (* 1 = 0.196578 loss)
I0410 00:18:44.378087  6396 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 00:18:49.325606  6396 solver.cpp:218] Iteration 9636 (2.42553 iter/s, 4.94738s/12 iters), loss = 0.191904
I0410 00:18:49.325655  6396 solver.cpp:237]     Train net output #0: loss = 0.191904 (* 1 = 0.191904 loss)
I0410 00:18:49.325667  6396 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 00:18:54.191556  6396 solver.cpp:218] Iteration 9648 (2.46622 iter/s, 4.86574s/12 iters), loss = 0.434557
I0410 00:18:54.191648  6396 solver.cpp:237]     Train net output #0: loss = 0.434557 (* 1 = 0.434557 loss)
I0410 00:18:54.191658  6396 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 00:18:59.151273  6396 solver.cpp:218] Iteration 9660 (2.41961 iter/s, 4.95947s/12 iters), loss = 0.402251
I0410 00:18:59.151326  6396 solver.cpp:237]     Train net output #0: loss = 0.402251 (* 1 = 0.402251 loss)
I0410 00:18:59.151340  6396 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 00:19:04.135964  6396 solver.cpp:218] Iteration 9672 (2.40747 iter/s, 4.98448s/12 iters), loss = 0.268414
I0410 00:19:04.136018  6396 solver.cpp:237]     Train net output #0: loss = 0.268414 (* 1 = 0.268414 loss)
I0410 00:19:04.136030  6396 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 00:19:09.149112  6396 solver.cpp:218] Iteration 9684 (2.39381 iter/s, 5.01294s/12 iters), loss = 0.132732
I0410 00:19:09.149168  6396 solver.cpp:237]     Train net output #0: loss = 0.132732 (* 1 = 0.132732 loss)
I0410 00:19:09.149178  6396 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 00:19:11.203940  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 00:19:12.765493  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 00:19:13.083618  6396 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 00:19:13.083649  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:19:13.695060  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:16.272696  6396 blocking_queue.cpp:49] Waiting for data
I0410 00:19:17.612666  6396 solver.cpp:397]     Test net output #0: accuracy = 0.593137
I0410 00:19:17.612704  6396 solver.cpp:397]     Test net output #1: loss = 2.06587 (* 1 = 2.06587 loss)
I0410 00:19:19.502679  6396 solver.cpp:218] Iteration 9696 (1.15906 iter/s, 10.3532s/12 iters), loss = 0.236343
I0410 00:19:19.502733  6396 solver.cpp:237]     Train net output #0: loss = 0.236343 (* 1 = 0.236343 loss)
I0410 00:19:19.502746  6396 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 00:19:24.434227  6396 solver.cpp:218] Iteration 9708 (2.43341 iter/s, 4.93134s/12 iters), loss = 0.282385
I0410 00:19:24.434348  6396 solver.cpp:237]     Train net output #0: loss = 0.282385 (* 1 = 0.282385 loss)
I0410 00:19:24.434358  6396 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 00:19:25.168294  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:29.421656  6396 solver.cpp:218] Iteration 9720 (2.40618 iter/s, 4.98716s/12 iters), loss = 0.431054
I0410 00:19:29.421697  6396 solver.cpp:237]     Train net output #0: loss = 0.431054 (* 1 = 0.431054 loss)
I0410 00:19:29.421706  6396 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 00:19:34.361536  6396 solver.cpp:218] Iteration 9732 (2.42931 iter/s, 4.93968s/12 iters), loss = 0.298937
I0410 00:19:34.361594  6396 solver.cpp:237]     Train net output #0: loss = 0.298937 (* 1 = 0.298937 loss)
I0410 00:19:34.361606  6396 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 00:19:39.350464  6396 solver.cpp:218] Iteration 9744 (2.40543 iter/s, 4.98871s/12 iters), loss = 0.304827
I0410 00:19:39.350515  6396 solver.cpp:237]     Train net output #0: loss = 0.304827 (* 1 = 0.304827 loss)
I0410 00:19:39.350528  6396 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 00:19:44.194856  6396 solver.cpp:218] Iteration 9756 (2.4772 iter/s, 4.84419s/12 iters), loss = 0.451593
I0410 00:19:44.194911  6396 solver.cpp:237]     Train net output #0: loss = 0.451593 (* 1 = 0.451593 loss)
I0410 00:19:44.194921  6396 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 00:19:49.146050  6396 solver.cpp:218] Iteration 9768 (2.42376 iter/s, 4.95098s/12 iters), loss = 0.231429
I0410 00:19:49.146106  6396 solver.cpp:237]     Train net output #0: loss = 0.231429 (* 1 = 0.231429 loss)
I0410 00:19:49.146117  6396 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 00:19:54.061326  6396 solver.cpp:218] Iteration 9780 (2.44147 iter/s, 4.91506s/12 iters), loss = 0.395176
I0410 00:19:54.061378  6396 solver.cpp:237]     Train net output #0: loss = 0.395176 (* 1 = 0.395176 loss)
I0410 00:19:54.061391  6396 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 00:19:58.516482  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 00:19:58.968020  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 00:19:59.295851  6396 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 00:19:59.295877  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:19:59.900741  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:03.786464  6396 solver.cpp:397]     Test net output #0: accuracy = 0.599265
I0410 00:20:03.786511  6396 solver.cpp:397]     Test net output #1: loss = 1.95589 (* 1 = 1.95589 loss)
I0410 00:20:03.870143  6396 solver.cpp:218] Iteration 9792 (1.22343 iter/s, 9.80847s/12 iters), loss = 0.161826
I0410 00:20:03.870193  6396 solver.cpp:237]     Train net output #0: loss = 0.161826 (* 1 = 0.161826 loss)
I0410 00:20:03.870204  6396 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 00:20:08.400173  6396 solver.cpp:218] Iteration 9804 (2.6491 iter/s, 4.52983s/12 iters), loss = 0.187464
I0410 00:20:08.400228  6396 solver.cpp:237]     Train net output #0: loss = 0.187464 (* 1 = 0.187464 loss)
I0410 00:20:08.400239  6396 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 00:20:11.457254  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:13.481290  6396 solver.cpp:218] Iteration 9816 (2.36179 iter/s, 5.0809s/12 iters), loss = 0.261944
I0410 00:20:13.481349  6396 solver.cpp:237]     Train net output #0: loss = 0.261944 (* 1 = 0.261944 loss)
I0410 00:20:13.481361  6396 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 00:20:18.433984  6396 solver.cpp:218] Iteration 9828 (2.42303 iter/s, 4.95247s/12 iters), loss = 0.297139
I0410 00:20:18.434032  6396 solver.cpp:237]     Train net output #0: loss = 0.297139 (* 1 = 0.297139 loss)
I0410 00:20:18.434042  6396 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 00:20:23.341804  6396 solver.cpp:218] Iteration 9840 (2.44518 iter/s, 4.90762s/12 iters), loss = 0.2925
I0410 00:20:23.341847  6396 solver.cpp:237]     Train net output #0: loss = 0.2925 (* 1 = 0.2925 loss)
I0410 00:20:23.341857  6396 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 00:20:28.340561  6396 solver.cpp:218] Iteration 9852 (2.40069 iter/s, 4.99855s/12 iters), loss = 0.31005
I0410 00:20:28.340608  6396 solver.cpp:237]     Train net output #0: loss = 0.31005 (* 1 = 0.31005 loss)
I0410 00:20:28.340618  6396 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 00:20:33.326498  6396 solver.cpp:218] Iteration 9864 (2.40687 iter/s, 4.98573s/12 iters), loss = 0.195311
I0410 00:20:33.326645  6396 solver.cpp:237]     Train net output #0: loss = 0.195311 (* 1 = 0.195311 loss)
I0410 00:20:33.326658  6396 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 00:20:38.238315  6396 solver.cpp:218] Iteration 9876 (2.44324 iter/s, 4.91152s/12 iters), loss = 0.240621
I0410 00:20:38.238365  6396 solver.cpp:237]     Train net output #0: loss = 0.240621 (* 1 = 0.240621 loss)
I0410 00:20:38.238377  6396 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 00:20:43.285228  6396 solver.cpp:218] Iteration 9888 (2.37779 iter/s, 5.04671s/12 iters), loss = 0.284928
I0410 00:20:43.285284  6396 solver.cpp:237]     Train net output #0: loss = 0.284928 (* 1 = 0.284928 loss)
I0410 00:20:43.285295  6396 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 00:20:45.294121  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 00:20:46.172189  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 00:20:46.900033  6396 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 00:20:46.900063  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:20:47.468470  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:51.342661  6396 solver.cpp:397]     Test net output #0: accuracy = 0.599877
I0410 00:20:51.342701  6396 solver.cpp:397]     Test net output #1: loss = 1.87582 (* 1 = 1.87582 loss)
I0410 00:20:53.089365  6396 solver.cpp:218] Iteration 9900 (1.22402 iter/s, 9.80379s/12 iters), loss = 0.253212
I0410 00:20:53.089418  6396 solver.cpp:237]     Train net output #0: loss = 0.253212 (* 1 = 0.253212 loss)
I0410 00:20:53.089429  6396 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 00:20:57.914072  6396 solver.cpp:218] Iteration 9912 (2.4873 iter/s, 4.8245s/12 iters), loss = 0.238367
I0410 00:20:57.914122  6396 solver.cpp:237]     Train net output #0: loss = 0.238367 (* 1 = 0.238367 loss)
I0410 00:20:57.914134  6396 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 00:20:58.012249  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:03.035797  6396 solver.cpp:218] Iteration 9924 (2.34306 iter/s, 5.12151s/12 iters), loss = 0.121326
I0410 00:21:03.035852  6396 solver.cpp:237]     Train net output #0: loss = 0.121326 (* 1 = 0.121326 loss)
I0410 00:21:03.035864  6396 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 00:21:08.095734  6396 solver.cpp:218] Iteration 9936 (2.37167 iter/s, 5.05972s/12 iters), loss = 0.269581
I0410 00:21:08.095866  6396 solver.cpp:237]     Train net output #0: loss = 0.269581 (* 1 = 0.269581 loss)
I0410 00:21:08.095882  6396 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 00:21:13.090895  6396 solver.cpp:218] Iteration 9948 (2.40246 iter/s, 4.99488s/12 iters), loss = 0.262196
I0410 00:21:13.090941  6396 solver.cpp:237]     Train net output #0: loss = 0.262196 (* 1 = 0.262196 loss)
I0410 00:21:13.090953  6396 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 00:21:18.038066  6396 solver.cpp:218] Iteration 9960 (2.42573 iter/s, 4.94697s/12 iters), loss = 0.231219
I0410 00:21:18.038113  6396 solver.cpp:237]     Train net output #0: loss = 0.231219 (* 1 = 0.231219 loss)
I0410 00:21:18.038125  6396 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 00:21:22.942950  6396 solver.cpp:218] Iteration 9972 (2.44664 iter/s, 4.90468s/12 iters), loss = 0.361502
I0410 00:21:22.942996  6396 solver.cpp:237]     Train net output #0: loss = 0.361502 (* 1 = 0.361502 loss)
I0410 00:21:22.943006  6396 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 00:21:27.862638  6396 solver.cpp:218] Iteration 9984 (2.43928 iter/s, 4.91949s/12 iters), loss = 0.232692
I0410 00:21:27.862687  6396 solver.cpp:237]     Train net output #0: loss = 0.232692 (* 1 = 0.232692 loss)
I0410 00:21:27.862695  6396 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 00:21:32.380268  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 00:21:33.394409  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 00:21:33.717134  6396 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 00:21:33.717162  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:21:34.295199  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:38.235960  6396 solver.cpp:397]     Test net output #0: accuracy = 0.598039
I0410 00:21:38.236095  6396 solver.cpp:397]     Test net output #1: loss = 1.93357 (* 1 = 1.93357 loss)
I0410 00:21:38.319384  6396 solver.cpp:218] Iteration 9996 (1.14762 iter/s, 10.4564s/12 iters), loss = 0.138446
I0410 00:21:38.319442  6396 solver.cpp:237]     Train net output #0: loss = 0.138446 (* 1 = 0.138446 loss)
I0410 00:21:38.319455  6396 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 00:21:42.692276  6396 solver.cpp:218] Iteration 10008 (2.7443 iter/s, 4.3727s/12 iters), loss = 0.328357
I0410 00:21:42.692325  6396 solver.cpp:237]     Train net output #0: loss = 0.328357 (* 1 = 0.328357 loss)
I0410 00:21:42.692335  6396 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 00:21:44.946401  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:47.680586  6396 solver.cpp:218] Iteration 10020 (2.40572 iter/s, 4.9881s/12 iters), loss = 0.301588
I0410 00:21:47.680636  6396 solver.cpp:237]     Train net output #0: loss = 0.301588 (* 1 = 0.301588 loss)
I0410 00:21:47.680647  6396 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 00:21:52.672595  6396 solver.cpp:218] Iteration 10032 (2.40394 iter/s, 4.9918s/12 iters), loss = 0.231714
I0410 00:21:52.672648  6396 solver.cpp:237]     Train net output #0: loss = 0.231714 (* 1 = 0.231714 loss)
I0410 00:21:52.672662  6396 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 00:21:57.632632  6396 solver.cpp:218] Iteration 10044 (2.41944 iter/s, 4.95983s/12 iters), loss = 0.27375
I0410 00:21:57.632684  6396 solver.cpp:237]     Train net output #0: loss = 0.27375 (* 1 = 0.27375 loss)
I0410 00:21:57.632699  6396 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 00:22:02.656038  6396 solver.cpp:218] Iteration 10056 (2.38892 iter/s, 5.02319s/12 iters), loss = 0.171432
I0410 00:22:02.656092  6396 solver.cpp:237]     Train net output #0: loss = 0.171432 (* 1 = 0.171432 loss)
I0410 00:22:02.656103  6396 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 00:22:07.808368  6396 solver.cpp:218] Iteration 10068 (2.32914 iter/s, 5.15212s/12 iters), loss = 0.224659
I0410 00:22:07.808409  6396 solver.cpp:237]     Train net output #0: loss = 0.224659 (* 1 = 0.224659 loss)
I0410 00:22:07.808418  6396 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 00:22:13.204452  6396 solver.cpp:218] Iteration 10080 (2.22392 iter/s, 5.39588s/12 iters), loss = 0.198015
I0410 00:22:13.218039  6396 solver.cpp:237]     Train net output #0: loss = 0.198015 (* 1 = 0.198015 loss)
I0410 00:22:13.218052  6396 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 00:22:18.160141  6396 solver.cpp:218] Iteration 10092 (2.42819 iter/s, 4.94195s/12 iters), loss = 0.22793
I0410 00:22:18.160192  6396 solver.cpp:237]     Train net output #0: loss = 0.22793 (* 1 = 0.22793 loss)
I0410 00:22:18.160202  6396 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 00:22:20.263829  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 00:22:22.395021  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 00:22:22.951604  6396 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 00:22:22.951623  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:22:23.378715  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:27.364400  6396 solver.cpp:397]     Test net output #0: accuracy = 0.595588
I0410 00:22:27.364449  6396 solver.cpp:397]     Test net output #1: loss = 1.9279 (* 1 = 1.9279 loss)
I0410 00:22:29.213155  6396 solver.cpp:218] Iteration 10104 (1.08571 iter/s, 11.0526s/12 iters), loss = 0.233035
I0410 00:22:29.213197  6396 solver.cpp:237]     Train net output #0: loss = 0.233035 (* 1 = 0.233035 loss)
I0410 00:22:29.213205  6396 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 00:22:33.553717  6400 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:34.183845  6396 solver.cpp:218] Iteration 10116 (2.41425 iter/s, 4.97049s/12 iters), loss = 0.231064
I0410 00:22:34.183895  6396 solver.cpp:237]     Train net output #0: loss = 0.231064 (* 1 = 0.231064 loss)
I0410 00:22:34.183907  6396 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 00:22:39.098832  6396 solver.cpp:218] Iteration 10128 (2.44162 iter/s, 4.91478s/12 iters), loss = 0.251912
I0410 00:22:39.098888  6396 solver.cpp:237]     Train net output #0: loss = 0.251912 (* 1 = 0.251912 loss)
I0410 00:22:39.098901  6396 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 00:22:43.993654  6396 solver.cpp:218] Iteration 10140 (2.45167 iter/s, 4.89462s/12 iters), loss = 0.217957
I0410 00:22:43.993764  6396 solver.cpp:237]     Train net output #0: loss = 0.217957 (* 1 = 0.217957 loss)
I0410 00:22:43.993773  6396 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 00:22:49.002866  6396 solver.cpp:218] Iteration 10152 (2.39571 iter/s, 5.00895s/12 iters), loss = 0.229969
I0410 00:22:49.002912  6396 solver.cpp:237]     Train net output #0: loss = 0.229969 (* 1 = 0.229969 loss)
I0410 00:22:49.002924  6396 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 00:22:54.037717  6396 solver.cpp:218] Iteration 10164 (2.38349 iter/s, 5.03464s/12 iters), loss = 0.193928
I0410 00:22:54.037778  6396 solver.cpp:237]     Train net output #0: loss = 0.193928 (* 1 = 0.193928 loss)
I0410 00:22:54.037791  6396 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 00:22:59.010346  6396 solver.cpp:218] Iteration 10176 (2.41332 iter/s, 4.97241s/12 iters), loss = 0.212104
I0410 00:22:59.010396  6396 solver.cpp:237]     Train net output #0: loss = 0.212104 (* 1 = 0.212104 loss)
I0410 00:22:59.010407  6396 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 00:23:03.960711  6396 solver.cpp:218] Iteration 10188 (2.42416 iter/s, 4.95016s/12 iters), loss = 0.240575
I0410 00:23:03.960752  6396 solver.cpp:237]     Train net output #0: loss = 0.240575 (* 1 = 0.240575 loss)
I0410 00:23:03.960762  6396 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 00:23:08.519349  6396 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 00:23:08.950157  6396 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 00:23:09.287446  6396 solver.cpp:310] Iteration 10200, loss = 0.179988
I0410 00:23:09.287487  6396 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 00:23:09.287497  6396 net.cpp:676] Ignoring source layer train-data
I0410 00:23:09.712990  6401 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:13.919181  6396 solver.cpp:397]     Test net output #0: accuracy = 0.602328
I0410 00:23:13.919232  6396 solver.cpp:397]     Test net output #1: loss = 1.94631 (* 1 = 1.94631 loss)
I0410 00:23:13.919245  6396 solver.cpp:315] Optimization Done.
I0410 00:23:13.919252  6396 caffe.cpp:259] Optimization Done.