DIGITS-CNN/cars/lr-investigations/sigmoid/1e-2/50_0.15/caffe_output.log

I0408 19:18:24.449684  5931 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210408-191822-3c4c/solver.prototxt
I0408 19:18:24.449854  5931 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 19:18:24.449860  5931 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 19:18:24.449932  5931 caffe.cpp:218] Using GPUs 0
I0408 19:18:24.497622  5931 caffe.cpp:223] GPU 0: GeForce RTX 2080
I0408 19:18:24.941783  5931 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "sigmoid"
gamma: -0.0014705883
momentum: 0.9
weight_decay: 0.0001
stepsize: 5100
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 19:18:24.942917  5931 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 19:18:24.944129  5931 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 19:18:24.944144  5931 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 19:18:24.944264  5931 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-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 19:18:24.944351  5931 layer_factory.hpp:77] Creating layer train-data
I0408 19:18:25.053862  5931 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db
I0408 19:18:25.055430  5931 net.cpp:84] Creating Layer train-data
I0408 19:18:25.055464  5931 net.cpp:380] train-data -> data
I0408 19:18:25.055506  5931 net.cpp:380] train-data -> label
I0408 19:18:25.055532  5931 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0408 19:18:25.065255  5931 data_layer.cpp:45] output data size: 128,3,227,227
I0408 19:18:25.220326  5931 net.cpp:122] Setting up train-data
I0408 19:18:25.220352  5931 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 19:18:25.220357  5931 net.cpp:129] Top shape: 128 (128)
I0408 19:18:25.220360  5931 net.cpp:137] Memory required for data: 79149056
I0408 19:18:25.220371  5931 layer_factory.hpp:77] Creating layer conv1
I0408 19:18:25.220393  5931 net.cpp:84] Creating Layer conv1
I0408 19:18:25.220399  5931 net.cpp:406] conv1 <- data
I0408 19:18:25.220412  5931 net.cpp:380] conv1 -> conv1
I0408 19:18:26.108603  5931 net.cpp:122] Setting up conv1
I0408 19:18:26.108624  5931 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:18:26.108628  5931 net.cpp:137] Memory required for data: 227833856
I0408 19:18:26.108647  5931 layer_factory.hpp:77] Creating layer relu1
I0408 19:18:26.108656  5931 net.cpp:84] Creating Layer relu1
I0408 19:18:26.108660  5931 net.cpp:406] relu1 <- conv1
I0408 19:18:26.108665  5931 net.cpp:367] relu1 -> conv1 (in-place)
I0408 19:18:26.108984  5931 net.cpp:122] Setting up relu1
I0408 19:18:26.108994  5931 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:18:26.108996  5931 net.cpp:137] Memory required for data: 376518656
I0408 19:18:26.108999  5931 layer_factory.hpp:77] Creating layer norm1
I0408 19:18:26.109007  5931 net.cpp:84] Creating Layer norm1
I0408 19:18:26.109040  5931 net.cpp:406] norm1 <- conv1
I0408 19:18:26.109045  5931 net.cpp:380] norm1 -> norm1
I0408 19:18:26.109565  5931 net.cpp:122] Setting up norm1
I0408 19:18:26.109575  5931 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:18:26.109577  5931 net.cpp:137] Memory required for data: 525203456
I0408 19:18:26.109580  5931 layer_factory.hpp:77] Creating layer pool1
I0408 19:18:26.109586  5931 net.cpp:84] Creating Layer pool1
I0408 19:18:26.109589  5931 net.cpp:406] pool1 <- norm1
I0408 19:18:26.109594  5931 net.cpp:380] pool1 -> pool1
I0408 19:18:26.109624  5931 net.cpp:122] Setting up pool1
I0408 19:18:26.109629  5931 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 19:18:26.109632  5931 net.cpp:137] Memory required for data: 561035264
I0408 19:18:26.109634  5931 layer_factory.hpp:77] Creating layer conv2
I0408 19:18:26.109643  5931 net.cpp:84] Creating Layer conv2
I0408 19:18:26.109647  5931 net.cpp:406] conv2 <- pool1
I0408 19:18:26.109650  5931 net.cpp:380] conv2 -> conv2
I0408 19:18:26.117425  5931 net.cpp:122] Setting up conv2
I0408 19:18:26.117436  5931 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:18:26.117439  5931 net.cpp:137] Memory required for data: 656586752
I0408 19:18:26.117447  5931 layer_factory.hpp:77] Creating layer relu2
I0408 19:18:26.117453  5931 net.cpp:84] Creating Layer relu2
I0408 19:18:26.117456  5931 net.cpp:406] relu2 <- conv2
I0408 19:18:26.117460  5931 net.cpp:367] relu2 -> conv2 (in-place)
I0408 19:18:26.118005  5931 net.cpp:122] Setting up relu2
I0408 19:18:26.118014  5931 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:18:26.118017  5931 net.cpp:137] Memory required for data: 752138240
I0408 19:18:26.118021  5931 layer_factory.hpp:77] Creating layer norm2
I0408 19:18:26.118027  5931 net.cpp:84] Creating Layer norm2
I0408 19:18:26.118031  5931 net.cpp:406] norm2 <- conv2
I0408 19:18:26.118034  5931 net.cpp:380] norm2 -> norm2
I0408 19:18:26.118404  5931 net.cpp:122] Setting up norm2
I0408 19:18:26.118413  5931 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:18:26.118415  5931 net.cpp:137] Memory required for data: 847689728
I0408 19:18:26.118418  5931 layer_factory.hpp:77] Creating layer pool2
I0408 19:18:26.118427  5931 net.cpp:84] Creating Layer pool2
I0408 19:18:26.118429  5931 net.cpp:406] pool2 <- norm2
I0408 19:18:26.118433  5931 net.cpp:380] pool2 -> pool2
I0408 19:18:26.118459  5931 net.cpp:122] Setting up pool2
I0408 19:18:26.118464  5931 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:18:26.118466  5931 net.cpp:137] Memory required for data: 869840896
I0408 19:18:26.118469  5931 layer_factory.hpp:77] Creating layer conv3
I0408 19:18:26.118477  5931 net.cpp:84] Creating Layer conv3
I0408 19:18:26.118480  5931 net.cpp:406] conv3 <- pool2
I0408 19:18:26.118484  5931 net.cpp:380] conv3 -> conv3
I0408 19:18:26.128641  5931 net.cpp:122] Setting up conv3
I0408 19:18:26.128651  5931 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:18:26.128654  5931 net.cpp:137] Memory required for data: 903067648
I0408 19:18:26.128662  5931 layer_factory.hpp:77] Creating layer relu3
I0408 19:18:26.128667  5931 net.cpp:84] Creating Layer relu3
I0408 19:18:26.128670  5931 net.cpp:406] relu3 <- conv3
I0408 19:18:26.128676  5931 net.cpp:367] relu3 -> conv3 (in-place)
I0408 19:18:26.129232  5931 net.cpp:122] Setting up relu3
I0408 19:18:26.129243  5931 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:18:26.129245  5931 net.cpp:137] Memory required for data: 936294400
I0408 19:18:26.129248  5931 layer_factory.hpp:77] Creating layer conv4
I0408 19:18:26.129257  5931 net.cpp:84] Creating Layer conv4
I0408 19:18:26.129261  5931 net.cpp:406] conv4 <- conv3
I0408 19:18:26.129266  5931 net.cpp:380] conv4 -> conv4
I0408 19:18:26.140365  5931 net.cpp:122] Setting up conv4
I0408 19:18:26.140377  5931 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:18:26.140380  5931 net.cpp:137] Memory required for data: 969521152
I0408 19:18:26.140386  5931 layer_factory.hpp:77] Creating layer relu4
I0408 19:18:26.140393  5931 net.cpp:84] Creating Layer relu4
I0408 19:18:26.140413  5931 net.cpp:406] relu4 <- conv4
I0408 19:18:26.140419  5931 net.cpp:367] relu4 -> conv4 (in-place)
I0408 19:18:26.140949  5931 net.cpp:122] Setting up relu4
I0408 19:18:26.140956  5931 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:18:26.140959  5931 net.cpp:137] Memory required for data: 1002747904
I0408 19:18:26.140962  5931 layer_factory.hpp:77] Creating layer conv5
I0408 19:18:26.140971  5931 net.cpp:84] Creating Layer conv5
I0408 19:18:26.140974  5931 net.cpp:406] conv5 <- conv4
I0408 19:18:26.140980  5931 net.cpp:380] conv5 -> conv5
I0408 19:18:26.150013  5931 net.cpp:122] Setting up conv5
I0408 19:18:26.150025  5931 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:18:26.150028  5931 net.cpp:137] Memory required for data: 1024899072
I0408 19:18:26.150038  5931 layer_factory.hpp:77] Creating layer relu5
I0408 19:18:26.150043  5931 net.cpp:84] Creating Layer relu5
I0408 19:18:26.150046  5931 net.cpp:406] relu5 <- conv5
I0408 19:18:26.150053  5931 net.cpp:367] relu5 -> conv5 (in-place)
I0408 19:18:26.150632  5931 net.cpp:122] Setting up relu5
I0408 19:18:26.150642  5931 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:18:26.150645  5931 net.cpp:137] Memory required for data: 1047050240
I0408 19:18:26.150648  5931 layer_factory.hpp:77] Creating layer pool5
I0408 19:18:26.150653  5931 net.cpp:84] Creating Layer pool5
I0408 19:18:26.150656  5931 net.cpp:406] pool5 <- conv5
I0408 19:18:26.150663  5931 net.cpp:380] pool5 -> pool5
I0408 19:18:26.150697  5931 net.cpp:122] Setting up pool5
I0408 19:18:26.150702  5931 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 19:18:26.150704  5931 net.cpp:137] Memory required for data: 1051768832
I0408 19:18:26.150707  5931 layer_factory.hpp:77] Creating layer fc6
I0408 19:18:26.150717  5931 net.cpp:84] Creating Layer fc6
I0408 19:18:26.150720  5931 net.cpp:406] fc6 <- pool5
I0408 19:18:26.150725  5931 net.cpp:380] fc6 -> fc6
I0408 19:18:26.492550  5931 net.cpp:122] Setting up fc6
I0408 19:18:26.492571  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.492574  5931 net.cpp:137] Memory required for data: 1053865984
I0408 19:18:26.492583  5931 layer_factory.hpp:77] Creating layer relu6
I0408 19:18:26.492590  5931 net.cpp:84] Creating Layer relu6
I0408 19:18:26.492594  5931 net.cpp:406] relu6 <- fc6
I0408 19:18:26.492599  5931 net.cpp:367] relu6 -> fc6 (in-place)
I0408 19:18:26.493383  5931 net.cpp:122] Setting up relu6
I0408 19:18:26.493393  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.493396  5931 net.cpp:137] Memory required for data: 1055963136
I0408 19:18:26.493399  5931 layer_factory.hpp:77] Creating layer drop6
I0408 19:18:26.493405  5931 net.cpp:84] Creating Layer drop6
I0408 19:18:26.493408  5931 net.cpp:406] drop6 <- fc6
I0408 19:18:26.493412  5931 net.cpp:367] drop6 -> fc6 (in-place)
I0408 19:18:26.493438  5931 net.cpp:122] Setting up drop6
I0408 19:18:26.493443  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.493444  5931 net.cpp:137] Memory required for data: 1058060288
I0408 19:18:26.493448  5931 layer_factory.hpp:77] Creating layer fc7
I0408 19:18:26.493454  5931 net.cpp:84] Creating Layer fc7
I0408 19:18:26.493458  5931 net.cpp:406] fc7 <- fc6
I0408 19:18:26.493461  5931 net.cpp:380] fc7 -> fc7
I0408 19:18:26.644878  5931 net.cpp:122] Setting up fc7
I0408 19:18:26.644897  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.644901  5931 net.cpp:137] Memory required for data: 1060157440
I0408 19:18:26.644909  5931 layer_factory.hpp:77] Creating layer relu7
I0408 19:18:26.644917  5931 net.cpp:84] Creating Layer relu7
I0408 19:18:26.644920  5931 net.cpp:406] relu7 <- fc7
I0408 19:18:26.644928  5931 net.cpp:367] relu7 -> fc7 (in-place)
I0408 19:18:26.645399  5931 net.cpp:122] Setting up relu7
I0408 19:18:26.645408  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.645411  5931 net.cpp:137] Memory required for data: 1062254592
I0408 19:18:26.645413  5931 layer_factory.hpp:77] Creating layer drop7
I0408 19:18:26.645419  5931 net.cpp:84] Creating Layer drop7
I0408 19:18:26.645442  5931 net.cpp:406] drop7 <- fc7
I0408 19:18:26.645447  5931 net.cpp:367] drop7 -> fc7 (in-place)
I0408 19:18:26.645468  5931 net.cpp:122] Setting up drop7
I0408 19:18:26.645474  5931 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:18:26.645478  5931 net.cpp:137] Memory required for data: 1064351744
I0408 19:18:26.645479  5931 layer_factory.hpp:77] Creating layer fc8
I0408 19:18:26.645485  5931 net.cpp:84] Creating Layer fc8
I0408 19:18:26.645488  5931 net.cpp:406] fc8 <- fc7
I0408 19:18:26.645493  5931 net.cpp:380] fc8 -> fc8
I0408 19:18:26.652873  5931 net.cpp:122] Setting up fc8
I0408 19:18:26.652881  5931 net.cpp:129] Top shape: 128 196 (25088)
I0408 19:18:26.652884  5931 net.cpp:137] Memory required for data: 1064452096
I0408 19:18:26.652889  5931 layer_factory.hpp:77] Creating layer loss
I0408 19:18:26.652894  5931 net.cpp:84] Creating Layer loss
I0408 19:18:26.652897  5931 net.cpp:406] loss <- fc8
I0408 19:18:26.652900  5931 net.cpp:406] loss <- label
I0408 19:18:26.652906  5931 net.cpp:380] loss -> loss
I0408 19:18:26.652915  5931 layer_factory.hpp:77] Creating layer loss
I0408 19:18:26.653544  5931 net.cpp:122] Setting up loss
I0408 19:18:26.653553  5931 net.cpp:129] Top shape: (1)
I0408 19:18:26.653554  5931 net.cpp:132]     with loss weight 1
I0408 19:18:26.653573  5931 net.cpp:137] Memory required for data: 1064452100
I0408 19:18:26.653575  5931 net.cpp:198] loss needs backward computation.
I0408 19:18:26.653581  5931 net.cpp:198] fc8 needs backward computation.
I0408 19:18:26.653584  5931 net.cpp:198] drop7 needs backward computation.
I0408 19:18:26.653586  5931 net.cpp:198] relu7 needs backward computation.
I0408 19:18:26.653589  5931 net.cpp:198] fc7 needs backward computation.
I0408 19:18:26.653591  5931 net.cpp:198] drop6 needs backward computation.
I0408 19:18:26.653594  5931 net.cpp:198] relu6 needs backward computation.
I0408 19:18:26.653596  5931 net.cpp:198] fc6 needs backward computation.
I0408 19:18:26.653599  5931 net.cpp:198] pool5 needs backward computation.
I0408 19:18:26.653601  5931 net.cpp:198] relu5 needs backward computation.
I0408 19:18:26.653604  5931 net.cpp:198] conv5 needs backward computation.
I0408 19:18:26.653607  5931 net.cpp:198] relu4 needs backward computation.
I0408 19:18:26.653609  5931 net.cpp:198] conv4 needs backward computation.
I0408 19:18:26.653612  5931 net.cpp:198] relu3 needs backward computation.
I0408 19:18:26.653615  5931 net.cpp:198] conv3 needs backward computation.
I0408 19:18:26.653617  5931 net.cpp:198] pool2 needs backward computation.
I0408 19:18:26.653620  5931 net.cpp:198] norm2 needs backward computation.
I0408 19:18:26.653623  5931 net.cpp:198] relu2 needs backward computation.
I0408 19:18:26.653625  5931 net.cpp:198] conv2 needs backward computation.
I0408 19:18:26.653628  5931 net.cpp:198] pool1 needs backward computation.
I0408 19:18:26.653632  5931 net.cpp:198] norm1 needs backward computation.
I0408 19:18:26.653635  5931 net.cpp:198] relu1 needs backward computation.
I0408 19:18:26.653637  5931 net.cpp:198] conv1 needs backward computation.
I0408 19:18:26.653641  5931 net.cpp:200] train-data does not need backward computation.
I0408 19:18:26.653643  5931 net.cpp:242] This network produces output loss
I0408 19:18:26.653656  5931 net.cpp:255] Network initialization done.
I0408 19:18:26.654724  5931 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 19:18:26.654752  5931 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 19:18:26.654878  5931 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-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 19:18:26.654975  5931 layer_factory.hpp:77] Creating layer val-data
I0408 19:18:26.679214  5931 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db
I0408 19:18:26.682032  5931 net.cpp:84] Creating Layer val-data
I0408 19:18:26.682065  5931 net.cpp:380] val-data -> data
I0408 19:18:26.682086  5931 net.cpp:380] val-data -> label
I0408 19:18:26.682106  5931 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0408 19:18:26.689946  5931 data_layer.cpp:45] output data size: 32,3,227,227
I0408 19:18:26.726176  5931 net.cpp:122] Setting up val-data
I0408 19:18:26.726198  5931 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 19:18:26.726202  5931 net.cpp:129] Top shape: 32 (32)
I0408 19:18:26.726204  5931 net.cpp:137] Memory required for data: 19787264
I0408 19:18:26.726210  5931 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 19:18:26.726222  5931 net.cpp:84] Creating Layer label_val-data_1_split
I0408 19:18:26.726225  5931 net.cpp:406] label_val-data_1_split <- label
I0408 19:18:26.726231  5931 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 19:18:26.726240  5931 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 19:18:26.726277  5931 net.cpp:122] Setting up label_val-data_1_split
I0408 19:18:26.726282  5931 net.cpp:129] Top shape: 32 (32)
I0408 19:18:26.726285  5931 net.cpp:129] Top shape: 32 (32)
I0408 19:18:26.726287  5931 net.cpp:137] Memory required for data: 19787520
I0408 19:18:26.726289  5931 layer_factory.hpp:77] Creating layer conv1
I0408 19:18:26.726300  5931 net.cpp:84] Creating Layer conv1
I0408 19:18:26.726302  5931 net.cpp:406] conv1 <- data
I0408 19:18:26.726307  5931 net.cpp:380] conv1 -> conv1
I0408 19:18:26.729629  5931 net.cpp:122] Setting up conv1
I0408 19:18:26.729640  5931 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:18:26.729642  5931 net.cpp:137] Memory required for data: 56958720
I0408 19:18:26.729651  5931 layer_factory.hpp:77] Creating layer relu1
I0408 19:18:26.729657  5931 net.cpp:84] Creating Layer relu1
I0408 19:18:26.729660  5931 net.cpp:406] relu1 <- conv1
I0408 19:18:26.729665  5931 net.cpp:367] relu1 -> conv1 (in-place)
I0408 19:18:26.729977  5931 net.cpp:122] Setting up relu1
I0408 19:18:26.729986  5931 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:18:26.729990  5931 net.cpp:137] Memory required for data: 94129920
I0408 19:18:26.729992  5931 layer_factory.hpp:77] Creating layer norm1
I0408 19:18:26.730000  5931 net.cpp:84] Creating Layer norm1
I0408 19:18:26.730002  5931 net.cpp:406] norm1 <- conv1
I0408 19:18:26.730007  5931 net.cpp:380] norm1 -> norm1
I0408 19:18:26.730517  5931 net.cpp:122] Setting up norm1
I0408 19:18:26.730527  5931 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:18:26.730530  5931 net.cpp:137] Memory required for data: 131301120
I0408 19:18:26.730532  5931 layer_factory.hpp:77] Creating layer pool1
I0408 19:18:26.730538  5931 net.cpp:84] Creating Layer pool1
I0408 19:18:26.730541  5931 net.cpp:406] pool1 <- norm1
I0408 19:18:26.730546  5931 net.cpp:380] pool1 -> pool1
I0408 19:18:26.730571  5931 net.cpp:122] Setting up pool1
I0408 19:18:26.730574  5931 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 19:18:26.730577  5931 net.cpp:137] Memory required for data: 140259072
I0408 19:18:26.730579  5931 layer_factory.hpp:77] Creating layer conv2
I0408 19:18:26.730587  5931 net.cpp:84] Creating Layer conv2
I0408 19:18:26.730589  5931 net.cpp:406] conv2 <- pool1
I0408 19:18:26.730613  5931 net.cpp:380] conv2 -> conv2
I0408 19:18:26.740005  5931 net.cpp:122] Setting up conv2
I0408 19:18:26.740015  5931 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:18:26.740018  5931 net.cpp:137] Memory required for data: 164146944
I0408 19:18:26.740026  5931 layer_factory.hpp:77] Creating layer relu2
I0408 19:18:26.740033  5931 net.cpp:84] Creating Layer relu2
I0408 19:18:26.740036  5931 net.cpp:406] relu2 <- conv2
I0408 19:18:26.740041  5931 net.cpp:367] relu2 -> conv2 (in-place)
I0408 19:18:26.740607  5931 net.cpp:122] Setting up relu2
I0408 19:18:26.740615  5931 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:18:26.740617  5931 net.cpp:137] Memory required for data: 188034816
I0408 19:18:26.740620  5931 layer_factory.hpp:77] Creating layer norm2
I0408 19:18:26.740628  5931 net.cpp:84] Creating Layer norm2
I0408 19:18:26.740631  5931 net.cpp:406] norm2 <- conv2
I0408 19:18:26.740638  5931 net.cpp:380] norm2 -> norm2
I0408 19:18:26.741400  5931 net.cpp:122] Setting up norm2
I0408 19:18:26.741410  5931 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:18:26.741412  5931 net.cpp:137] Memory required for data: 211922688
I0408 19:18:26.741415  5931 layer_factory.hpp:77] Creating layer pool2
I0408 19:18:26.741421  5931 net.cpp:84] Creating Layer pool2
I0408 19:18:26.741425  5931 net.cpp:406] pool2 <- norm2
I0408 19:18:26.741428  5931 net.cpp:380] pool2 -> pool2
I0408 19:18:26.741456  5931 net.cpp:122] Setting up pool2
I0408 19:18:26.741461  5931 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:18:26.741462  5931 net.cpp:137] Memory required for data: 217460480
I0408 19:18:26.741466  5931 layer_factory.hpp:77] Creating layer conv3
I0408 19:18:26.741474  5931 net.cpp:84] Creating Layer conv3
I0408 19:18:26.741477  5931 net.cpp:406] conv3 <- pool2
I0408 19:18:26.741482  5931 net.cpp:380] conv3 -> conv3
I0408 19:18:26.752660  5931 net.cpp:122] Setting up conv3
I0408 19:18:26.752671  5931 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:18:26.752674  5931 net.cpp:137] Memory required for data: 225767168
I0408 19:18:26.752686  5931 layer_factory.hpp:77] Creating layer relu3
I0408 19:18:26.752691  5931 net.cpp:84] Creating Layer relu3
I0408 19:18:26.752694  5931 net.cpp:406] relu3 <- conv3
I0408 19:18:26.752699  5931 net.cpp:367] relu3 -> conv3 (in-place)
I0408 19:18:26.753315  5931 net.cpp:122] Setting up relu3
I0408 19:18:26.753324  5931 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:18:26.753327  5931 net.cpp:137] Memory required for data: 234073856
I0408 19:18:26.753330  5931 layer_factory.hpp:77] Creating layer conv4
I0408 19:18:26.753340  5931 net.cpp:84] Creating Layer conv4
I0408 19:18:26.753342  5931 net.cpp:406] conv4 <- conv3
I0408 19:18:26.753350  5931 net.cpp:380] conv4 -> conv4
I0408 19:18:26.763305  5931 net.cpp:122] Setting up conv4
I0408 19:18:26.763316  5931 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:18:26.763319  5931 net.cpp:137] Memory required for data: 242380544
I0408 19:18:26.763325  5931 layer_factory.hpp:77] Creating layer relu4
I0408 19:18:26.763331  5931 net.cpp:84] Creating Layer relu4
I0408 19:18:26.763334  5931 net.cpp:406] relu4 <- conv4
I0408 19:18:26.763340  5931 net.cpp:367] relu4 -> conv4 (in-place)
I0408 19:18:26.763721  5931 net.cpp:122] Setting up relu4
I0408 19:18:26.763729  5931 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:18:26.763732  5931 net.cpp:137] Memory required for data: 250687232
I0408 19:18:26.763736  5931 layer_factory.hpp:77] Creating layer conv5
I0408 19:18:26.763747  5931 net.cpp:84] Creating Layer conv5
I0408 19:18:26.763751  5931 net.cpp:406] conv5 <- conv4
I0408 19:18:26.763756  5931 net.cpp:380] conv5 -> conv5
I0408 19:18:26.772959  5931 net.cpp:122] Setting up conv5
I0408 19:18:26.772970  5931 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:18:26.772974  5931 net.cpp:137] Memory required for data: 256225024
I0408 19:18:26.772984  5931 layer_factory.hpp:77] Creating layer relu5
I0408 19:18:26.772989  5931 net.cpp:84] Creating Layer relu5
I0408 19:18:26.773010  5931 net.cpp:406] relu5 <- conv5
I0408 19:18:26.773015  5931 net.cpp:367] relu5 -> conv5 (in-place)
I0408 19:18:26.773561  5931 net.cpp:122] Setting up relu5
I0408 19:18:26.773571  5931 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:18:26.773573  5931 net.cpp:137] Memory required for data: 261762816
I0408 19:18:26.773576  5931 layer_factory.hpp:77] Creating layer pool5
I0408 19:18:26.773586  5931 net.cpp:84] Creating Layer pool5
I0408 19:18:26.773589  5931 net.cpp:406] pool5 <- conv5
I0408 19:18:26.773594  5931 net.cpp:380] pool5 -> pool5
I0408 19:18:26.773627  5931 net.cpp:122] Setting up pool5
I0408 19:18:26.773633  5931 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 19:18:26.773635  5931 net.cpp:137] Memory required for data: 262942464
I0408 19:18:26.773638  5931 layer_factory.hpp:77] Creating layer fc6
I0408 19:18:26.773643  5931 net.cpp:84] Creating Layer fc6
I0408 19:18:26.773646  5931 net.cpp:406] fc6 <- pool5
I0408 19:18:26.773651  5931 net.cpp:380] fc6 -> fc6
I0408 19:18:27.114794  5931 net.cpp:122] Setting up fc6
I0408 19:18:27.114814  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.114816  5931 net.cpp:137] Memory required for data: 263466752
I0408 19:18:27.114825  5931 layer_factory.hpp:77] Creating layer relu6
I0408 19:18:27.114833  5931 net.cpp:84] Creating Layer relu6
I0408 19:18:27.114837  5931 net.cpp:406] relu6 <- fc6
I0408 19:18:27.114842  5931 net.cpp:367] relu6 -> fc6 (in-place)
I0408 19:18:27.115625  5931 net.cpp:122] Setting up relu6
I0408 19:18:27.115634  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.115638  5931 net.cpp:137] Memory required for data: 263991040
I0408 19:18:27.115640  5931 layer_factory.hpp:77] Creating layer drop6
I0408 19:18:27.115648  5931 net.cpp:84] Creating Layer drop6
I0408 19:18:27.115650  5931 net.cpp:406] drop6 <- fc6
I0408 19:18:27.115655  5931 net.cpp:367] drop6 -> fc6 (in-place)
I0408 19:18:27.115677  5931 net.cpp:122] Setting up drop6
I0408 19:18:27.115681  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.115684  5931 net.cpp:137] Memory required for data: 264515328
I0408 19:18:27.115686  5931 layer_factory.hpp:77] Creating layer fc7
I0408 19:18:27.115694  5931 net.cpp:84] Creating Layer fc7
I0408 19:18:27.115696  5931 net.cpp:406] fc7 <- fc6
I0408 19:18:27.115700  5931 net.cpp:380] fc7 -> fc7
I0408 19:18:27.265542  5931 net.cpp:122] Setting up fc7
I0408 19:18:27.265564  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.265568  5931 net.cpp:137] Memory required for data: 265039616
I0408 19:18:27.265575  5931 layer_factory.hpp:77] Creating layer relu7
I0408 19:18:27.265583  5931 net.cpp:84] Creating Layer relu7
I0408 19:18:27.265588  5931 net.cpp:406] relu7 <- fc7
I0408 19:18:27.265594  5931 net.cpp:367] relu7 -> fc7 (in-place)
I0408 19:18:27.266093  5931 net.cpp:122] Setting up relu7
I0408 19:18:27.266101  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.266103  5931 net.cpp:137] Memory required for data: 265563904
I0408 19:18:27.266106  5931 layer_factory.hpp:77] Creating layer drop7
I0408 19:18:27.266113  5931 net.cpp:84] Creating Layer drop7
I0408 19:18:27.266115  5931 net.cpp:406] drop7 <- fc7
I0408 19:18:27.266121  5931 net.cpp:367] drop7 -> fc7 (in-place)
I0408 19:18:27.266142  5931 net.cpp:122] Setting up drop7
I0408 19:18:27.266147  5931 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:18:27.266149  5931 net.cpp:137] Memory required for data: 266088192
I0408 19:18:27.266152  5931 layer_factory.hpp:77] Creating layer fc8
I0408 19:18:27.266160  5931 net.cpp:84] Creating Layer fc8
I0408 19:18:27.266162  5931 net.cpp:406] fc8 <- fc7
I0408 19:18:27.266167  5931 net.cpp:380] fc8 -> fc8
I0408 19:18:27.273520  5931 net.cpp:122] Setting up fc8
I0408 19:18:27.273530  5931 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:18:27.273532  5931 net.cpp:137] Memory required for data: 266113280
I0408 19:18:27.273537  5931 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 19:18:27.273542  5931 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 19:18:27.273547  5931 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 19:18:27.273569  5931 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 19:18:27.273576  5931 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 19:18:27.273602  5931 net.cpp:122] Setting up fc8_fc8_0_split
I0408 19:18:27.273607  5931 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:18:27.273609  5931 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:18:27.273612  5931 net.cpp:137] Memory required for data: 266163456
I0408 19:18:27.273614  5931 layer_factory.hpp:77] Creating layer accuracy
I0408 19:18:27.273620  5931 net.cpp:84] Creating Layer accuracy
I0408 19:18:27.273622  5931 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 19:18:27.273627  5931 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 19:18:27.273631  5931 net.cpp:380] accuracy -> accuracy
I0408 19:18:27.273638  5931 net.cpp:122] Setting up accuracy
I0408 19:18:27.273640  5931 net.cpp:129] Top shape: (1)
I0408 19:18:27.273643  5931 net.cpp:137] Memory required for data: 266163460
I0408 19:18:27.273645  5931 layer_factory.hpp:77] Creating layer loss
I0408 19:18:27.273649  5931 net.cpp:84] Creating Layer loss
I0408 19:18:27.273651  5931 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 19:18:27.273655  5931 net.cpp:406] loss <- label_val-data_1_split_1
I0408 19:18:27.273658  5931 net.cpp:380] loss -> loss
I0408 19:18:27.273664  5931 layer_factory.hpp:77] Creating layer loss
I0408 19:18:27.274297  5931 net.cpp:122] Setting up loss
I0408 19:18:27.274305  5931 net.cpp:129] Top shape: (1)
I0408 19:18:27.274308  5931 net.cpp:132]     with loss weight 1
I0408 19:18:27.274317  5931 net.cpp:137] Memory required for data: 266163464
I0408 19:18:27.274320  5931 net.cpp:198] loss needs backward computation.
I0408 19:18:27.274324  5931 net.cpp:200] accuracy does not need backward computation.
I0408 19:18:27.274327  5931 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 19:18:27.274330  5931 net.cpp:198] fc8 needs backward computation.
I0408 19:18:27.274333  5931 net.cpp:198] drop7 needs backward computation.
I0408 19:18:27.274335  5931 net.cpp:198] relu7 needs backward computation.
I0408 19:18:27.274338  5931 net.cpp:198] fc7 needs backward computation.
I0408 19:18:27.274340  5931 net.cpp:198] drop6 needs backward computation.
I0408 19:18:27.274343  5931 net.cpp:198] relu6 needs backward computation.
I0408 19:18:27.274344  5931 net.cpp:198] fc6 needs backward computation.
I0408 19:18:27.274348  5931 net.cpp:198] pool5 needs backward computation.
I0408 19:18:27.274350  5931 net.cpp:198] relu5 needs backward computation.
I0408 19:18:27.274353  5931 net.cpp:198] conv5 needs backward computation.
I0408 19:18:27.274355  5931 net.cpp:198] relu4 needs backward computation.
I0408 19:18:27.274358  5931 net.cpp:198] conv4 needs backward computation.
I0408 19:18:27.274360  5931 net.cpp:198] relu3 needs backward computation.
I0408 19:18:27.274363  5931 net.cpp:198] conv3 needs backward computation.
I0408 19:18:27.274366  5931 net.cpp:198] pool2 needs backward computation.
I0408 19:18:27.274369  5931 net.cpp:198] norm2 needs backward computation.
I0408 19:18:27.274371  5931 net.cpp:198] relu2 needs backward computation.
I0408 19:18:27.274374  5931 net.cpp:198] conv2 needs backward computation.
I0408 19:18:27.274376  5931 net.cpp:198] pool1 needs backward computation.
I0408 19:18:27.274379  5931 net.cpp:198] norm1 needs backward computation.
I0408 19:18:27.274381  5931 net.cpp:198] relu1 needs backward computation.
I0408 19:18:27.274384  5931 net.cpp:198] conv1 needs backward computation.
I0408 19:18:27.274389  5931 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 19:18:27.274392  5931 net.cpp:200] val-data does not need backward computation.
I0408 19:18:27.274394  5931 net.cpp:242] This network produces output accuracy
I0408 19:18:27.274397  5931 net.cpp:242] This network produces output loss
I0408 19:18:27.274412  5931 net.cpp:255] Network initialization done.
I0408 19:18:27.274475  5931 solver.cpp:56] Solver scaffolding done.
I0408 19:18:27.274785  5931 caffe.cpp:248] Starting Optimization
I0408 19:18:27.274792  5931 solver.cpp:272] Solving
I0408 19:18:27.274803  5931 solver.cpp:273] Learning Rate Policy: sigmoid
I0408 19:18:27.276445  5931 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 19:18:27.276455  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:18:27.361034  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:18:31.480471  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:18:31.524021  5931 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 19:18:31.524065  5931 solver.cpp:397]     Test net output #1: loss = 5.28176 (* 1 = 5.28176 loss)
I0408 19:18:31.622799  5931 solver.cpp:218] Iteration 0 (0 iter/s, 4.34797s/12 iters), loss = 5.28295
I0408 19:18:31.624333  5931 solver.cpp:237]     Train net output #0: loss = 5.28295 (* 1 = 5.28295 loss)
I0408 19:18:31.624364  5931 sgd_solver.cpp:105] Iteration 0, lr = 0.00999447
I0408 19:18:35.279364  5931 solver.cpp:218] Iteration 12 (3.28315 iter/s, 3.65502s/12 iters), loss = 5.27251
I0408 19:18:35.279397  5931 solver.cpp:237]     Train net output #0: loss = 5.27251 (* 1 = 5.27251 loss)
I0408 19:18:35.279403  5931 sgd_solver.cpp:105] Iteration 12, lr = 0.00999437
I0408 19:18:40.076592  5931 solver.cpp:218] Iteration 24 (2.50147 iter/s, 4.79718s/12 iters), loss = 5.28835
I0408 19:18:40.076625  5931 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0408 19:18:40.076632  5931 sgd_solver.cpp:105] Iteration 24, lr = 0.00999427
I0408 19:18:44.938189  5931 solver.cpp:218] Iteration 36 (2.46835 iter/s, 4.86155s/12 iters), loss = 5.28317
I0408 19:18:44.938223  5931 solver.cpp:237]     Train net output #0: loss = 5.28317 (* 1 = 5.28317 loss)
I0408 19:18:44.938231  5931 sgd_solver.cpp:105] Iteration 36, lr = 0.00999417
I0408 19:18:49.746843  5931 solver.cpp:218] Iteration 48 (2.49552 iter/s, 4.80861s/12 iters), loss = 5.2705
I0408 19:18:49.746876  5931 solver.cpp:237]     Train net output #0: loss = 5.2705 (* 1 = 5.2705 loss)
I0408 19:18:49.746883  5931 sgd_solver.cpp:105] Iteration 48, lr = 0.00999407
I0408 19:18:54.571940  5931 solver.cpp:218] Iteration 60 (2.48702 iter/s, 4.82506s/12 iters), loss = 5.28049
I0408 19:18:54.572134  5931 solver.cpp:237]     Train net output #0: loss = 5.28049 (* 1 = 5.28049 loss)
I0408 19:18:54.572144  5931 sgd_solver.cpp:105] Iteration 60, lr = 0.00999396
I0408 19:18:59.412802  5931 solver.cpp:218] Iteration 72 (2.479 iter/s, 4.84066s/12 iters), loss = 5.29263
I0408 19:18:59.412834  5931 solver.cpp:237]     Train net output #0: loss = 5.29263 (* 1 = 5.29263 loss)
I0408 19:18:59.412842  5931 sgd_solver.cpp:105] Iteration 72, lr = 0.00999385
I0408 19:19:04.212456  5931 solver.cpp:218] Iteration 84 (2.5002 iter/s, 4.79961s/12 iters), loss = 5.30485
I0408 19:19:04.212488  5931 solver.cpp:237]     Train net output #0: loss = 5.30485 (* 1 = 5.30485 loss)
I0408 19:19:04.212496  5931 sgd_solver.cpp:105] Iteration 84, lr = 0.00999375
I0408 19:19:09.074090  5931 solver.cpp:218] Iteration 96 (2.46833 iter/s, 4.86159s/12 iters), loss = 5.28237
I0408 19:19:09.074122  5931 solver.cpp:237]     Train net output #0: loss = 5.28237 (* 1 = 5.28237 loss)
I0408 19:19:09.074129  5931 sgd_solver.cpp:105] Iteration 96, lr = 0.00999363
I0408 19:19:10.680305  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:19:10.966989  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 19:19:14.918813  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 19:19:18.626622  5931 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 19:19:18.626652  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:19:23.331385  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:19:23.418673  5931 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 19:19:23.418720  5931 solver.cpp:397]     Test net output #1: loss = 5.2855 (* 1 = 5.2855 loss)
I0408 19:19:25.223667  5931 solver.cpp:218] Iteration 108 (0.743056 iter/s, 16.1495s/12 iters), loss = 5.29071
I0408 19:19:25.223839  5931 solver.cpp:237]     Train net output #0: loss = 5.29071 (* 1 = 5.29071 loss)
I0408 19:19:25.223847  5931 sgd_solver.cpp:105] Iteration 108, lr = 0.00999352
I0408 19:19:30.006146  5931 solver.cpp:218] Iteration 120 (2.50926 iter/s, 4.78229s/12 iters), loss = 5.26548
I0408 19:19:30.006178  5931 solver.cpp:237]     Train net output #0: loss = 5.26548 (* 1 = 5.26548 loss)
I0408 19:19:30.006186  5931 sgd_solver.cpp:105] Iteration 120, lr = 0.00999341
I0408 19:19:34.754223  5931 solver.cpp:218] Iteration 132 (2.52736 iter/s, 4.74803s/12 iters), loss = 5.28124
I0408 19:19:34.754256  5931 solver.cpp:237]     Train net output #0: loss = 5.28124 (* 1 = 5.28124 loss)
I0408 19:19:34.754262  5931 sgd_solver.cpp:105] Iteration 132, lr = 0.00999329
I0408 19:19:39.628005  5931 solver.cpp:218] Iteration 144 (2.46218 iter/s, 4.87374s/12 iters), loss = 5.25093
I0408 19:19:39.628041  5931 solver.cpp:237]     Train net output #0: loss = 5.25093 (* 1 = 5.25093 loss)
I0408 19:19:39.628048  5931 sgd_solver.cpp:105] Iteration 144, lr = 0.00999317
I0408 19:19:44.452972  5931 solver.cpp:218] Iteration 156 (2.48709 iter/s, 4.82492s/12 iters), loss = 5.20436
I0408 19:19:44.453006  5931 solver.cpp:237]     Train net output #0: loss = 5.20436 (* 1 = 5.20436 loss)
I0408 19:19:44.453013  5931 sgd_solver.cpp:105] Iteration 156, lr = 0.00999305
I0408 19:19:49.198729  5931 solver.cpp:218] Iteration 168 (2.5286 iter/s, 4.74571s/12 iters), loss = 5.24019
I0408 19:19:49.198760  5931 solver.cpp:237]     Train net output #0: loss = 5.24019 (* 1 = 5.24019 loss)
I0408 19:19:49.198768  5931 sgd_solver.cpp:105] Iteration 168, lr = 0.00999292
I0408 19:19:53.912940  5931 solver.cpp:218] Iteration 180 (2.54552 iter/s, 4.71416s/12 iters), loss = 5.29033
I0408 19:19:53.912971  5931 solver.cpp:237]     Train net output #0: loss = 5.29033 (* 1 = 5.29033 loss)
I0408 19:19:53.912979  5931 sgd_solver.cpp:105] Iteration 180, lr = 0.0099928
I0408 19:19:58.812431  5931 solver.cpp:218] Iteration 192 (2.44926 iter/s, 4.89944s/12 iters), loss = 5.28427
I0408 19:19:58.812494  5931 solver.cpp:237]     Train net output #0: loss = 5.28427 (* 1 = 5.28427 loss)
I0408 19:19:58.812501  5931 sgd_solver.cpp:105] Iteration 192, lr = 0.00999267
I0408 19:20:02.444236  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:03.076654  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 19:20:06.254990  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 19:20:11.386488  5931 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 19:20:11.386515  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:20:16.055007  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:16.190394  5931 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0408 19:20:16.190443  5931 solver.cpp:397]     Test net output #1: loss = 5.18632 (* 1 = 5.18632 loss)
I0408 19:20:16.287061  5931 solver.cpp:218] Iteration 204 (0.686714 iter/s, 17.4745s/12 iters), loss = 5.23933
I0408 19:20:16.287097  5931 solver.cpp:237]     Train net output #0: loss = 5.23933 (* 1 = 5.23933 loss)
I0408 19:20:16.287104  5931 sgd_solver.cpp:105] Iteration 204, lr = 0.00999254
I0408 19:20:20.233229  5931 solver.cpp:218] Iteration 216 (3.04097 iter/s, 3.94611s/12 iters), loss = 5.22555
I0408 19:20:20.233261  5931 solver.cpp:237]     Train net output #0: loss = 5.22555 (* 1 = 5.22555 loss)
I0408 19:20:20.233269  5931 sgd_solver.cpp:105] Iteration 216, lr = 0.00999241
I0408 19:20:25.074522  5931 solver.cpp:218] Iteration 228 (2.4787 iter/s, 4.84124s/12 iters), loss = 5.16378
I0408 19:20:25.074554  5931 solver.cpp:237]     Train net output #0: loss = 5.16378 (* 1 = 5.16378 loss)
I0408 19:20:25.074561  5931 sgd_solver.cpp:105] Iteration 228, lr = 0.00999227
I0408 19:20:29.879467  5931 solver.cpp:218] Iteration 240 (2.49745 iter/s, 4.8049s/12 iters), loss = 5.17308
I0408 19:20:29.879568  5931 solver.cpp:237]     Train net output #0: loss = 5.17308 (* 1 = 5.17308 loss)
I0408 19:20:29.879577  5931 sgd_solver.cpp:105] Iteration 240, lr = 0.00999213
I0408 19:20:34.743187  5931 solver.cpp:218] Iteration 252 (2.46731 iter/s, 4.8636s/12 iters), loss = 5.17189
I0408 19:20:34.743230  5931 solver.cpp:237]     Train net output #0: loss = 5.17189 (* 1 = 5.17189 loss)
I0408 19:20:34.743238  5931 sgd_solver.cpp:105] Iteration 252, lr = 0.00999199
I0408 19:20:39.547736  5931 solver.cpp:218] Iteration 264 (2.49766 iter/s, 4.80449s/12 iters), loss = 5.16305
I0408 19:20:39.547771  5931 solver.cpp:237]     Train net output #0: loss = 5.16305 (* 1 = 5.16305 loss)
I0408 19:20:39.547778  5931 sgd_solver.cpp:105] Iteration 264, lr = 0.00999185
I0408 19:20:44.371253  5931 solver.cpp:218] Iteration 276 (2.48784 iter/s, 4.82347s/12 iters), loss = 5.10665
I0408 19:20:44.371286  5931 solver.cpp:237]     Train net output #0: loss = 5.10665 (* 1 = 5.10665 loss)
I0408 19:20:44.371294  5931 sgd_solver.cpp:105] Iteration 276, lr = 0.00999171
I0408 19:20:49.226384  5931 solver.cpp:218] Iteration 288 (2.47164 iter/s, 4.85508s/12 iters), loss = 5.14506
I0408 19:20:49.226415  5931 solver.cpp:237]     Train net output #0: loss = 5.14506 (* 1 = 5.14506 loss)
I0408 19:20:49.226423  5931 sgd_solver.cpp:105] Iteration 288, lr = 0.00999156
I0408 19:20:54.035475  5931 solver.cpp:218] Iteration 300 (2.4953 iter/s, 4.80904s/12 iters), loss = 5.10509
I0408 19:20:54.035509  5931 solver.cpp:237]     Train net output #0: loss = 5.10509 (* 1 = 5.10509 loss)
I0408 19:20:54.035517  5931 sgd_solver.cpp:105] Iteration 300, lr = 0.00999141
I0408 19:20:54.972564  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:55.973624  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 19:20:59.798215  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 19:21:03.743253  5931 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 19:21:03.743312  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:21:08.346855  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:21:08.521420  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0408 19:21:08.521468  5931 solver.cpp:397]     Test net output #1: loss = 5.13568 (* 1 = 5.13568 loss)
I0408 19:21:10.260635  5931 solver.cpp:218] Iteration 312 (0.739595 iter/s, 16.2251s/12 iters), loss = 5.16162
I0408 19:21:10.260668  5931 solver.cpp:237]     Train net output #0: loss = 5.16162 (* 1 = 5.16162 loss)
I0408 19:21:10.260676  5931 sgd_solver.cpp:105] Iteration 312, lr = 0.00999126
I0408 19:21:15.022380  5931 solver.cpp:218] Iteration 324 (2.52011 iter/s, 4.7617s/12 iters), loss = 5.08379
I0408 19:21:15.022413  5931 solver.cpp:237]     Train net output #0: loss = 5.08379 (* 1 = 5.08379 loss)
I0408 19:21:15.022420  5931 sgd_solver.cpp:105] Iteration 324, lr = 0.0099911
I0408 19:21:19.710613  5931 solver.cpp:218] Iteration 336 (2.55963 iter/s, 4.68818s/12 iters), loss = 5.11962
I0408 19:21:19.710644  5931 solver.cpp:237]     Train net output #0: loss = 5.11962 (* 1 = 5.11962 loss)
I0408 19:21:19.710652  5931 sgd_solver.cpp:105] Iteration 336, lr = 0.00999094
I0408 19:21:24.437058  5931 solver.cpp:218] Iteration 348 (2.53893 iter/s, 4.72639s/12 iters), loss = 5.13996
I0408 19:21:24.437091  5931 solver.cpp:237]     Train net output #0: loss = 5.13996 (* 1 = 5.13996 loss)
I0408 19:21:24.437098  5931 sgd_solver.cpp:105] Iteration 348, lr = 0.00999078
I0408 19:21:29.245507  5931 solver.cpp:218] Iteration 360 (2.49563 iter/s, 4.8084s/12 iters), loss = 5.07686
I0408 19:21:29.245541  5931 solver.cpp:237]     Train net output #0: loss = 5.07686 (* 1 = 5.07686 loss)
I0408 19:21:29.245548  5931 sgd_solver.cpp:105] Iteration 360, lr = 0.00999062
I0408 19:21:34.064729  5931 solver.cpp:218] Iteration 372 (2.49006 iter/s, 4.81917s/12 iters), loss = 5.18514
I0408 19:21:34.064841  5931 solver.cpp:237]     Train net output #0: loss = 5.18514 (* 1 = 5.18514 loss)
I0408 19:21:34.064849  5931 sgd_solver.cpp:105] Iteration 372, lr = 0.00999045
I0408 19:21:38.920732  5931 solver.cpp:218] Iteration 384 (2.47123 iter/s, 4.85588s/12 iters), loss = 5.086
I0408 19:21:38.920764  5931 solver.cpp:237]     Train net output #0: loss = 5.086 (* 1 = 5.086 loss)
I0408 19:21:38.920773  5931 sgd_solver.cpp:105] Iteration 384, lr = 0.00999028
I0408 19:21:43.717406  5931 solver.cpp:218] Iteration 396 (2.50176 iter/s, 4.79662s/12 iters), loss = 5.20769
I0408 19:21:43.717439  5931 solver.cpp:237]     Train net output #0: loss = 5.20769 (* 1 = 5.20769 loss)
I0408 19:21:43.717447  5931 sgd_solver.cpp:105] Iteration 396, lr = 0.00999011
I0408 19:21:46.675863  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:21:47.996348  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 19:21:51.933189  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 19:21:55.538669  5931 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 19:21:55.538694  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:22:00.023007  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:22:00.220916  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0408 19:22:00.220963  5931 solver.cpp:397]     Test net output #1: loss = 5.0719 (* 1 = 5.0719 loss)
I0408 19:22:00.317713  5931 solver.cpp:218] Iteration 408 (0.722881 iter/s, 16.6002s/12 iters), loss = 5.11971
I0408 19:22:00.317749  5931 solver.cpp:237]     Train net output #0: loss = 5.11971 (* 1 = 5.11971 loss)
I0408 19:22:00.317757  5931 sgd_solver.cpp:105] Iteration 408, lr = 0.00998993
I0408 19:22:04.270629  5931 solver.cpp:218] Iteration 420 (3.03577 iter/s, 3.95286s/12 iters), loss = 4.97193
I0408 19:22:04.270743  5931 solver.cpp:237]     Train net output #0: loss = 4.97193 (* 1 = 4.97193 loss)
I0408 19:22:04.270751  5931 sgd_solver.cpp:105] Iteration 420, lr = 0.00998975
I0408 19:22:09.049351  5931 solver.cpp:218] Iteration 432 (2.5112 iter/s, 4.77859s/12 iters), loss = 5.19053
I0408 19:22:09.049384  5931 solver.cpp:237]     Train net output #0: loss = 5.19053 (* 1 = 5.19053 loss)
I0408 19:22:09.049391  5931 sgd_solver.cpp:105] Iteration 432, lr = 0.00998957
I0408 19:22:13.900709  5931 solver.cpp:218] Iteration 444 (2.47356 iter/s, 4.8513s/12 iters), loss = 4.94635
I0408 19:22:13.900743  5931 solver.cpp:237]     Train net output #0: loss = 4.94635 (* 1 = 4.94635 loss)
I0408 19:22:13.900749  5931 sgd_solver.cpp:105] Iteration 444, lr = 0.00998939
I0408 19:22:18.695047  5931 solver.cpp:218] Iteration 456 (2.50298 iter/s, 4.79428s/12 iters), loss = 5.04596
I0408 19:22:18.695080  5931 solver.cpp:237]     Train net output #0: loss = 5.04596 (* 1 = 5.04596 loss)
I0408 19:22:18.695088  5931 sgd_solver.cpp:105] Iteration 456, lr = 0.0099892
I0408 19:22:23.567930  5931 solver.cpp:218] Iteration 468 (2.46263 iter/s, 4.87283s/12 iters), loss = 5.18983
I0408 19:22:23.567965  5931 solver.cpp:237]     Train net output #0: loss = 5.18983 (* 1 = 5.18983 loss)
I0408 19:22:23.567972  5931 sgd_solver.cpp:105] Iteration 468, lr = 0.009989
I0408 19:22:28.354305  5931 solver.cpp:218] Iteration 480 (2.50715 iter/s, 4.78632s/12 iters), loss = 5.14115
I0408 19:22:28.354339  5931 solver.cpp:237]     Train net output #0: loss = 5.14115 (* 1 = 5.14115 loss)
I0408 19:22:28.354346  5931 sgd_solver.cpp:105] Iteration 480, lr = 0.00998881
I0408 19:22:33.187806  5931 solver.cpp:218] Iteration 492 (2.4827 iter/s, 4.83345s/12 iters), loss = 4.98217
I0408 19:22:33.187840  5931 solver.cpp:237]     Train net output #0: loss = 4.98217 (* 1 = 4.98217 loss)
I0408 19:22:33.187849  5931 sgd_solver.cpp:105] Iteration 492, lr = 0.00998861
I0408 19:22:37.910627  5931 solver.cpp:218] Iteration 504 (2.54088 iter/s, 4.72277s/12 iters), loss = 5.09303
I0408 19:22:37.910740  5931 solver.cpp:237]     Train net output #0: loss = 5.09303 (* 1 = 5.09303 loss)
I0408 19:22:37.910749  5931 sgd_solver.cpp:105] Iteration 504, lr = 0.00998841
I0408 19:22:38.143754  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:22:39.814267  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 19:22:42.878676  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 19:22:45.222062  5931 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 19:22:45.222088  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:22:49.748190  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:22:50.006605  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0408 19:22:50.006655  5931 solver.cpp:397]     Test net output #1: loss = 5.0364 (* 1 = 5.0364 loss)
I0408 19:22:51.750349  5931 solver.cpp:218] Iteration 516 (0.867079 iter/s, 13.8396s/12 iters), loss = 5.03107
I0408 19:22:51.750383  5931 solver.cpp:237]     Train net output #0: loss = 5.03107 (* 1 = 5.03107 loss)
I0408 19:22:51.750391  5931 sgd_solver.cpp:105] Iteration 516, lr = 0.0099882
I0408 19:22:56.681389  5931 solver.cpp:218] Iteration 528 (2.43359 iter/s, 4.93099s/12 iters), loss = 5.02173
I0408 19:22:56.681422  5931 solver.cpp:237]     Train net output #0: loss = 5.02173 (* 1 = 5.02173 loss)
I0408 19:22:56.681429  5931 sgd_solver.cpp:105] Iteration 528, lr = 0.00998799
I0408 19:23:01.372756  5931 solver.cpp:218] Iteration 540 (2.55792 iter/s, 4.69132s/12 iters), loss = 4.91073
I0408 19:23:01.372787  5931 solver.cpp:237]     Train net output #0: loss = 4.91073 (* 1 = 4.91073 loss)
I0408 19:23:01.372794  5931 sgd_solver.cpp:105] Iteration 540, lr = 0.00998778
I0408 19:23:06.196326  5931 solver.cpp:218] Iteration 552 (2.48781 iter/s, 4.82352s/12 iters), loss = 4.9294
I0408 19:23:06.196359  5931 solver.cpp:237]     Train net output #0: loss = 4.9294 (* 1 = 4.9294 loss)
I0408 19:23:06.196367  5931 sgd_solver.cpp:105] Iteration 552, lr = 0.00998756
I0408 19:23:11.011468  5931 solver.cpp:218] Iteration 564 (2.49217 iter/s, 4.81509s/12 iters), loss = 4.97653
I0408 19:23:11.011628  5931 solver.cpp:237]     Train net output #0: loss = 4.97653 (* 1 = 4.97653 loss)
I0408 19:23:11.011638  5931 sgd_solver.cpp:105] Iteration 564, lr = 0.00998734
I0408 19:23:15.825018  5931 solver.cpp:218] Iteration 576 (2.49305 iter/s, 4.81337s/12 iters), loss = 5.0284
I0408 19:23:15.825050  5931 solver.cpp:237]     Train net output #0: loss = 5.0284 (* 1 = 5.0284 loss)
I0408 19:23:15.825057  5931 sgd_solver.cpp:105] Iteration 576, lr = 0.00998711
I0408 19:23:20.682231  5931 solver.cpp:218] Iteration 588 (2.47058 iter/s, 4.85716s/12 iters), loss = 5.00584
I0408 19:23:20.682265  5931 solver.cpp:237]     Train net output #0: loss = 5.00584 (* 1 = 5.00584 loss)
I0408 19:23:20.682272  5931 sgd_solver.cpp:105] Iteration 588, lr = 0.00998688
I0408 19:23:25.456974  5931 solver.cpp:218] Iteration 600 (2.51325 iter/s, 4.77469s/12 iters), loss = 4.98288
I0408 19:23:25.457005  5931 solver.cpp:237]     Train net output #0: loss = 4.98288 (* 1 = 4.98288 loss)
I0408 19:23:25.457012  5931 sgd_solver.cpp:105] Iteration 600, lr = 0.00998665
I0408 19:23:27.761816  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:23:29.842723  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 19:23:32.922132  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 19:23:35.313571  5931 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 19:23:35.313597  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:23:39.431644  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:23:39.706440  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0408 19:23:39.706486  5931 solver.cpp:397]     Test net output #1: loss = 4.974 (* 1 = 4.974 loss)
I0408 19:23:39.802424  5931 solver.cpp:218] Iteration 612 (0.836506 iter/s, 14.3454s/12 iters), loss = 4.9078
I0408 19:23:39.802459  5931 solver.cpp:237]     Train net output #0: loss = 4.9078 (* 1 = 4.9078 loss)
I0408 19:23:39.802465  5931 sgd_solver.cpp:105] Iteration 612, lr = 0.00998641
I0408 19:23:43.752210  5931 solver.cpp:218] Iteration 624 (3.03818 iter/s, 3.94974s/12 iters), loss = 4.96202
I0408 19:23:43.752331  5931 solver.cpp:237]     Train net output #0: loss = 4.96202 (* 1 = 4.96202 loss)
I0408 19:23:43.752339  5931 sgd_solver.cpp:105] Iteration 624, lr = 0.00998617
I0408 19:23:48.610968  5931 solver.cpp:218] Iteration 636 (2.46984 iter/s, 4.85862s/12 iters), loss = 4.91441
I0408 19:23:48.611001  5931 solver.cpp:237]     Train net output #0: loss = 4.91441 (* 1 = 4.91441 loss)
I0408 19:23:48.611008  5931 sgd_solver.cpp:105] Iteration 636, lr = 0.00998593
I0408 19:23:53.405874  5931 solver.cpp:218] Iteration 648 (2.50268 iter/s, 4.79485s/12 iters), loss = 4.86356
I0408 19:23:53.405907  5931 solver.cpp:237]     Train net output #0: loss = 4.86356 (* 1 = 4.86356 loss)
I0408 19:23:53.405915  5931 sgd_solver.cpp:105] Iteration 648, lr = 0.00998568
I0408 19:23:58.145263  5931 solver.cpp:218] Iteration 660 (2.532 iter/s, 4.73934s/12 iters), loss = 4.92028
I0408 19:23:58.145295  5931 solver.cpp:237]     Train net output #0: loss = 4.92028 (* 1 = 4.92028 loss)
I0408 19:23:58.145303  5931 sgd_solver.cpp:105] Iteration 660, lr = 0.00998542
I0408 19:24:02.881536  5931 solver.cpp:218] Iteration 672 (2.53367 iter/s, 4.73622s/12 iters), loss = 5.03567
I0408 19:24:02.881568  5931 solver.cpp:237]     Train net output #0: loss = 5.03567 (* 1 = 5.03567 loss)
I0408 19:24:02.881575  5931 sgd_solver.cpp:105] Iteration 672, lr = 0.00998516
I0408 19:24:07.724680  5931 solver.cpp:218] Iteration 684 (2.47776 iter/s, 4.84309s/12 iters), loss = 4.91484
I0408 19:24:07.724710  5931 solver.cpp:237]     Train net output #0: loss = 4.91484 (* 1 = 4.91484 loss)
I0408 19:24:07.724718  5931 sgd_solver.cpp:105] Iteration 684, lr = 0.0099849
I0408 19:24:08.479023  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:24:12.546566  5931 solver.cpp:218] Iteration 696 (2.48868 iter/s, 4.82183s/12 iters), loss = 4.79349
I0408 19:24:12.546597  5931 solver.cpp:237]     Train net output #0: loss = 4.79349 (* 1 = 4.79349 loss)
I0408 19:24:12.546605  5931 sgd_solver.cpp:105] Iteration 696, lr = 0.00998463
I0408 19:24:16.985522  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:24:17.344061  5931 solver.cpp:218] Iteration 708 (2.50133 iter/s, 4.79745s/12 iters), loss = 4.92898
I0408 19:24:17.344095  5931 solver.cpp:237]     Train net output #0: loss = 4.92898 (* 1 = 4.92898 loss)
I0408 19:24:17.344102  5931 sgd_solver.cpp:105] Iteration 708, lr = 0.00998436
I0408 19:24:19.250128  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 19:24:22.327769  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 19:24:24.716590  5931 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 19:24:24.716616  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:24:29.146328  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:24:29.492427  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0379902
I0408 19:24:29.492476  5931 solver.cpp:397]     Test net output #1: loss = 4.8577 (* 1 = 4.8577 loss)
I0408 19:24:31.225651  5931 solver.cpp:218] Iteration 720 (0.864459 iter/s, 13.8815s/12 iters), loss = 4.90879
I0408 19:24:31.225687  5931 solver.cpp:237]     Train net output #0: loss = 4.90879 (* 1 = 4.90879 loss)
I0408 19:24:31.225693  5931 sgd_solver.cpp:105] Iteration 720, lr = 0.00998408
I0408 19:24:36.191231  5931 solver.cpp:218] Iteration 732 (2.41666 iter/s, 4.96553s/12 iters), loss = 4.87671
I0408 19:24:36.191263  5931 solver.cpp:237]     Train net output #0: loss = 4.87671 (* 1 = 4.87671 loss)
I0408 19:24:36.191272  5931 sgd_solver.cpp:105] Iteration 732, lr = 0.0099838
I0408 19:24:40.888144  5931 solver.cpp:218] Iteration 744 (2.5549 iter/s, 4.69686s/12 iters), loss = 4.79004
I0408 19:24:40.888176  5931 solver.cpp:237]     Train net output #0: loss = 4.79004 (* 1 = 4.79004 loss)
I0408 19:24:40.888185  5931 sgd_solver.cpp:105] Iteration 744, lr = 0.00998351
I0408 19:24:45.717201  5931 solver.cpp:218] Iteration 756 (2.48498 iter/s, 4.82901s/12 iters), loss = 4.66244
I0408 19:24:45.717236  5931 solver.cpp:237]     Train net output #0: loss = 4.66244 (* 1 = 4.66244 loss)
I0408 19:24:45.717242  5931 sgd_solver.cpp:105] Iteration 756, lr = 0.00998322
I0408 19:24:50.528672  5931 solver.cpp:218] Iteration 768 (2.49407 iter/s, 4.81142s/12 iters), loss = 4.77981
I0408 19:24:50.528767  5931 solver.cpp:237]     Train net output #0: loss = 4.77981 (* 1 = 4.77981 loss)
I0408 19:24:50.528776  5931 sgd_solver.cpp:105] Iteration 768, lr = 0.00998292
I0408 19:24:55.357666  5931 solver.cpp:218] Iteration 780 (2.48505 iter/s, 4.82888s/12 iters), loss = 4.63616
I0408 19:24:55.357698  5931 solver.cpp:237]     Train net output #0: loss = 4.63616 (* 1 = 4.63616 loss)
I0408 19:24:55.357705  5931 sgd_solver.cpp:105] Iteration 780, lr = 0.00998261
I0408 19:25:00.137574  5931 solver.cpp:218] Iteration 792 (2.51054 iter/s, 4.77986s/12 iters), loss = 4.91283
I0408 19:25:00.137607  5931 solver.cpp:237]     Train net output #0: loss = 4.91283 (* 1 = 4.91283 loss)
I0408 19:25:00.137615  5931 sgd_solver.cpp:105] Iteration 792, lr = 0.0099823
I0408 19:25:04.845707  5931 solver.cpp:218] Iteration 804 (2.54881 iter/s, 4.70808s/12 iters), loss = 4.5747
I0408 19:25:04.845741  5931 solver.cpp:237]     Train net output #0: loss = 4.5747 (* 1 = 4.5747 loss)
I0408 19:25:04.845748  5931 sgd_solver.cpp:105] Iteration 804, lr = 0.00998199
I0408 19:25:06.497603  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:09.153486  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 19:25:12.720693  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 19:25:15.837437  5931 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 19:25:15.837462  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:25:20.236233  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:20.625757  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0484069
I0408 19:25:20.625912  5931 solver.cpp:397]     Test net output #1: loss = 4.7259 (* 1 = 4.7259 loss)
I0408 19:25:20.722509  5931 solver.cpp:218] Iteration 816 (0.755823 iter/s, 15.8767s/12 iters), loss = 4.54968
I0408 19:25:20.722545  5931 solver.cpp:237]     Train net output #0: loss = 4.54968 (* 1 = 4.54968 loss)
I0408 19:25:20.722553  5931 sgd_solver.cpp:105] Iteration 816, lr = 0.00998167
I0408 19:25:24.725463  5931 solver.cpp:218] Iteration 828 (2.99783 iter/s, 4.0029s/12 iters), loss = 4.72748
I0408 19:25:24.725497  5931 solver.cpp:237]     Train net output #0: loss = 4.72748 (* 1 = 4.72748 loss)
I0408 19:25:24.725503  5931 sgd_solver.cpp:105] Iteration 828, lr = 0.00998134
I0408 19:25:29.566557  5931 solver.cpp:218] Iteration 840 (2.47881 iter/s, 4.84104s/12 iters), loss = 4.75735
I0408 19:25:29.566589  5931 solver.cpp:237]     Train net output #0: loss = 4.75735 (* 1 = 4.75735 loss)
I0408 19:25:29.566597  5931 sgd_solver.cpp:105] Iteration 840, lr = 0.00998101
I0408 19:25:34.401502  5931 solver.cpp:218] Iteration 852 (2.48196 iter/s, 4.83489s/12 iters), loss = 4.60554
I0408 19:25:34.401535  5931 solver.cpp:237]     Train net output #0: loss = 4.60554 (* 1 = 4.60554 loss)
I0408 19:25:34.401541  5931 sgd_solver.cpp:105] Iteration 852, lr = 0.00998068
I0408 19:25:39.185503  5931 solver.cpp:218] Iteration 864 (2.50838 iter/s, 4.78396s/12 iters), loss = 4.61728
I0408 19:25:39.185534  5931 solver.cpp:237]     Train net output #0: loss = 4.61728 (* 1 = 4.61728 loss)
I0408 19:25:39.185542  5931 sgd_solver.cpp:105] Iteration 864, lr = 0.00998033
I0408 19:25:44.053187  5931 solver.cpp:218] Iteration 876 (2.46526 iter/s, 4.86764s/12 iters), loss = 4.79955
I0408 19:25:44.053221  5931 solver.cpp:237]     Train net output #0: loss = 4.79955 (* 1 = 4.79955 loss)
I0408 19:25:44.053228  5931 sgd_solver.cpp:105] Iteration 876, lr = 0.00997998
I0408 19:25:48.871539  5931 solver.cpp:218] Iteration 888 (2.4905 iter/s, 4.81831s/12 iters), loss = 4.68932
I0408 19:25:48.871577  5931 solver.cpp:237]     Train net output #0: loss = 4.68932 (* 1 = 4.68932 loss)
I0408 19:25:48.871583  5931 sgd_solver.cpp:105] Iteration 888, lr = 0.00997963
I0408 19:25:53.694054  5931 solver.cpp:218] Iteration 900 (2.48835 iter/s, 4.82246s/12 iters), loss = 4.69012
I0408 19:25:53.694150  5931 solver.cpp:237]     Train net output #0: loss = 4.69012 (* 1 = 4.69012 loss)
I0408 19:25:53.694159  5931 sgd_solver.cpp:105] Iteration 900, lr = 0.00997927
I0408 19:25:57.404742  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:58.480036  5931 solver.cpp:218] Iteration 912 (2.50738 iter/s, 4.78587s/12 iters), loss = 4.74772
I0408 19:25:58.480067  5931 solver.cpp:237]     Train net output #0: loss = 4.74772 (* 1 = 4.74772 loss)
I0408 19:25:58.480073  5931 sgd_solver.cpp:105] Iteration 912, lr = 0.0099789
I0408 19:26:00.446987  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 19:26:04.944500  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 19:26:08.822530  5931 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 19:26:08.822556  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:26:13.159917  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:26:13.597429  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0594363
I0408 19:26:13.597478  5931 solver.cpp:397]     Test net output #1: loss = 4.59275 (* 1 = 4.59275 loss)
I0408 19:26:15.348654  5931 solver.cpp:218] Iteration 924 (0.711382 iter/s, 16.8686s/12 iters), loss = 4.54142
I0408 19:26:15.348688  5931 solver.cpp:237]     Train net output #0: loss = 4.54142 (* 1 = 4.54142 loss)
I0408 19:26:15.348696  5931 sgd_solver.cpp:105] Iteration 924, lr = 0.00997852
I0408 19:26:20.200681  5931 solver.cpp:218] Iteration 936 (2.47322 iter/s, 4.85198s/12 iters), loss = 4.51866
I0408 19:26:20.200713  5931 solver.cpp:237]     Train net output #0: loss = 4.51866 (* 1 = 4.51866 loss)
I0408 19:26:20.200721  5931 sgd_solver.cpp:105] Iteration 936, lr = 0.00997814
I0408 19:26:25.017448  5931 solver.cpp:218] Iteration 948 (2.49132 iter/s, 4.81672s/12 iters), loss = 4.6412
I0408 19:26:25.017563  5931 solver.cpp:237]     Train net output #0: loss = 4.6412 (* 1 = 4.6412 loss)
I0408 19:26:25.017572  5931 sgd_solver.cpp:105] Iteration 948, lr = 0.00997775
I0408 19:26:29.824128  5931 solver.cpp:218] Iteration 960 (2.49659 iter/s, 4.80655s/12 iters), loss = 4.44454
I0408 19:26:29.824162  5931 solver.cpp:237]     Train net output #0: loss = 4.44454 (* 1 = 4.44454 loss)
I0408 19:26:29.824168  5931 sgd_solver.cpp:105] Iteration 960, lr = 0.00997736
I0408 19:26:34.682168  5931 solver.cpp:218] Iteration 972 (2.47016 iter/s, 4.85799s/12 iters), loss = 4.76845
I0408 19:26:34.682201  5931 solver.cpp:237]     Train net output #0: loss = 4.76845 (* 1 = 4.76845 loss)
I0408 19:26:34.682209  5931 sgd_solver.cpp:105] Iteration 972, lr = 0.00997696
I0408 19:26:39.445036  5931 solver.cpp:218] Iteration 984 (2.51952 iter/s, 4.76282s/12 iters), loss = 4.44885
I0408 19:26:39.445070  5931 solver.cpp:237]     Train net output #0: loss = 4.44885 (* 1 = 4.44885 loss)
I0408 19:26:39.445076  5931 sgd_solver.cpp:105] Iteration 984, lr = 0.00997655
I0408 19:26:44.169898  5931 solver.cpp:218] Iteration 996 (2.53978 iter/s, 4.72481s/12 iters), loss = 4.53855
I0408 19:26:44.169931  5931 solver.cpp:237]     Train net output #0: loss = 4.53855 (* 1 = 4.53855 loss)
I0408 19:26:44.169939  5931 sgd_solver.cpp:105] Iteration 996, lr = 0.00997613
I0408 19:26:48.878381  5931 solver.cpp:218] Iteration 1008 (2.54862 iter/s, 4.70843s/12 iters), loss = 4.18505
I0408 19:26:48.878413  5931 solver.cpp:237]     Train net output #0: loss = 4.18505 (* 1 = 4.18505 loss)
I0408 19:26:48.878420  5931 sgd_solver.cpp:105] Iteration 1008, lr = 0.00997571
I0408 19:26:49.816838  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:26:53.120780  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 19:26:57.055611  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 19:27:00.477900  5931 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 19:27:00.477926  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:27:04.784061  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:27:05.260186  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0747549
I0408 19:27:05.260236  5931 solver.cpp:397]     Test net output #1: loss = 4.40519 (* 1 = 4.40519 loss)
I0408 19:27:05.356878  5931 solver.cpp:218] Iteration 1020 (0.728225 iter/s, 16.4784s/12 iters), loss = 4.49982
I0408 19:27:05.356943  5931 solver.cpp:237]     Train net output #0: loss = 4.49982 (* 1 = 4.49982 loss)
I0408 19:27:05.356959  5931 sgd_solver.cpp:105] Iteration 1020, lr = 0.00997527
I0408 19:27:09.317698  5931 solver.cpp:218] Iteration 1032 (3.02973 iter/s, 3.96075s/12 iters), loss = 4.26781
I0408 19:27:09.317731  5931 solver.cpp:237]     Train net output #0: loss = 4.26781 (* 1 = 4.26781 loss)
I0408 19:27:09.317739  5931 sgd_solver.cpp:105] Iteration 1032, lr = 0.00997483
I0408 19:27:14.076663  5931 solver.cpp:218] Iteration 1044 (2.52158 iter/s, 4.75892s/12 iters), loss = 4.45317
I0408 19:27:14.076696  5931 solver.cpp:237]     Train net output #0: loss = 4.45317 (* 1 = 4.45317 loss)
I0408 19:27:14.076704  5931 sgd_solver.cpp:105] Iteration 1044, lr = 0.00997439
I0408 19:27:18.810415  5931 solver.cpp:218] Iteration 1056 (2.53501 iter/s, 4.7337s/12 iters), loss = 4.21486
I0408 19:27:18.810446  5931 solver.cpp:237]     Train net output #0: loss = 4.21486 (* 1 = 4.21486 loss)
I0408 19:27:18.810453  5931 sgd_solver.cpp:105] Iteration 1056, lr = 0.00997393
I0408 19:27:23.534380  5931 solver.cpp:218] Iteration 1068 (2.54026 iter/s, 4.72392s/12 iters), loss = 4.14842
I0408 19:27:23.534413  5931 solver.cpp:237]     Train net output #0: loss = 4.14842 (* 1 = 4.14842 loss)
I0408 19:27:23.534420  5931 sgd_solver.cpp:105] Iteration 1068, lr = 0.00997347
I0408 19:27:28.362879  5931 solver.cpp:218] Iteration 1080 (2.48527 iter/s, 4.82845s/12 iters), loss = 4.40467
I0408 19:27:28.363003  5931 solver.cpp:237]     Train net output #0: loss = 4.40467 (* 1 = 4.40467 loss)
I0408 19:27:28.363013  5931 sgd_solver.cpp:105] Iteration 1080, lr = 0.009973
I0408 19:27:33.067231  5931 solver.cpp:218] Iteration 1092 (2.5509 iter/s, 4.70422s/12 iters), loss = 3.98863
I0408 19:27:33.067265  5931 solver.cpp:237]     Train net output #0: loss = 3.98863 (* 1 = 3.98863 loss)
I0408 19:27:33.067271  5931 sgd_solver.cpp:105] Iteration 1092, lr = 0.00997252
I0408 19:27:37.823047  5931 solver.cpp:218] Iteration 1104 (2.52325 iter/s, 4.75577s/12 iters), loss = 4.40003
I0408 19:27:37.823081  5931 solver.cpp:237]     Train net output #0: loss = 4.40003 (* 1 = 4.40003 loss)
I0408 19:27:37.823087  5931 sgd_solver.cpp:105] Iteration 1104, lr = 0.00997203
I0408 19:27:40.877550  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:27:42.666611  5931 solver.cpp:218] Iteration 1116 (2.47754 iter/s, 4.84352s/12 iters), loss = 4.22214
I0408 19:27:42.666643  5931 solver.cpp:237]     Train net output #0: loss = 4.22214 (* 1 = 4.22214 loss)
I0408 19:27:42.666651  5931 sgd_solver.cpp:105] Iteration 1116, lr = 0.00997153
I0408 19:27:44.644829  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 19:27:47.716501  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 19:27:50.066706  5931 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 19:27:50.066731  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:27:54.317313  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:27:54.843360  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0784314
I0408 19:27:54.843410  5931 solver.cpp:397]     Test net output #1: loss = 4.3162 (* 1 = 4.3162 loss)
I0408 19:27:56.585356  5931 solver.cpp:218] Iteration 1128 (0.86215 iter/s, 13.9187s/12 iters), loss = 4.01441
I0408 19:27:56.585391  5931 solver.cpp:237]     Train net output #0: loss = 4.01441 (* 1 = 4.01441 loss)
I0408 19:27:56.585398  5931 sgd_solver.cpp:105] Iteration 1128, lr = 0.00997103
I0408 19:28:01.254228  5931 solver.cpp:218] Iteration 1140 (2.57024 iter/s, 4.66882s/12 iters), loss = 4.28376
I0408 19:28:01.254341  5931 solver.cpp:237]     Train net output #0: loss = 4.28376 (* 1 = 4.28376 loss)
I0408 19:28:01.254350  5931 sgd_solver.cpp:105] Iteration 1140, lr = 0.00997052
I0408 19:28:05.964165  5931 solver.cpp:218] Iteration 1152 (2.54787 iter/s, 4.70981s/12 iters), loss = 4.05672
I0408 19:28:05.964198  5931 solver.cpp:237]     Train net output #0: loss = 4.05672 (* 1 = 4.05672 loss)
I0408 19:28:05.964206  5931 sgd_solver.cpp:105] Iteration 1152, lr = 0.00996999
I0408 19:28:10.807466  5931 solver.cpp:218] Iteration 1164 (2.47767 iter/s, 4.84325s/12 iters), loss = 3.97635
I0408 19:28:10.807499  5931 solver.cpp:237]     Train net output #0: loss = 3.97635 (* 1 = 3.97635 loss)
I0408 19:28:10.807507  5931 sgd_solver.cpp:105] Iteration 1164, lr = 0.00996946
I0408 19:28:15.634007  5931 solver.cpp:218] Iteration 1176 (2.48628 iter/s, 4.82649s/12 iters), loss = 4.24351
I0408 19:28:15.634042  5931 solver.cpp:237]     Train net output #0: loss = 4.24351 (* 1 = 4.24351 loss)
I0408 19:28:15.634048  5931 sgd_solver.cpp:105] Iteration 1176, lr = 0.00996892
I0408 19:28:20.310830  5931 solver.cpp:218] Iteration 1188 (2.56587 iter/s, 4.67678s/12 iters), loss = 4.11137
I0408 19:28:20.310863  5931 solver.cpp:237]     Train net output #0: loss = 4.11137 (* 1 = 4.11137 loss)
I0408 19:28:20.310870  5931 sgd_solver.cpp:105] Iteration 1188, lr = 0.00996837
I0408 19:28:25.095845  5931 solver.cpp:218] Iteration 1200 (2.50785 iter/s, 4.78497s/12 iters), loss = 4.17805
I0408 19:28:25.095880  5931 solver.cpp:237]     Train net output #0: loss = 4.17805 (* 1 = 4.17805 loss)
I0408 19:28:25.095886  5931 sgd_solver.cpp:105] Iteration 1200, lr = 0.0099678
I0408 19:28:29.917023  5931 solver.cpp:218] Iteration 1212 (2.48904 iter/s, 4.82113s/12 iters), loss = 4.00441
I0408 19:28:29.917057  5931 solver.cpp:237]     Train net output #0: loss = 4.00441 (* 1 = 4.00441 loss)
I0408 19:28:29.917065  5931 sgd_solver.cpp:105] Iteration 1212, lr = 0.00996723
I0408 19:28:30.167395  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:28:34.273356  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 19:28:37.399178  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 19:28:40.098701  5931 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 19:28:40.098726  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:28:44.134161  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:28:44.632582  5931 solver.cpp:397]     Test net output #0: accuracy = 0.0992647
I0408 19:28:44.632620  5931 solver.cpp:397]     Test net output #1: loss = 4.12533 (* 1 = 4.12533 loss)
I0408 19:28:44.726790  5931 solver.cpp:218] Iteration 1224 (0.810279 iter/s, 14.8097s/12 iters), loss = 3.99823
I0408 19:28:44.726825  5931 solver.cpp:237]     Train net output #0: loss = 3.99823 (* 1 = 3.99823 loss)
I0408 19:28:44.726833  5931 sgd_solver.cpp:105] Iteration 1224, lr = 0.00996665
I0408 19:28:48.689040  5931 solver.cpp:218] Iteration 1236 (3.02862 iter/s, 3.9622s/12 iters), loss = 4.017
I0408 19:28:48.689074  5931 solver.cpp:237]     Train net output #0: loss = 4.017 (* 1 = 4.017 loss)
I0408 19:28:48.689081  5931 sgd_solver.cpp:105] Iteration 1236, lr = 0.00996606
I0408 19:28:53.398548  5931 solver.cpp:218] Iteration 1248 (2.54807 iter/s, 4.70946s/12 iters), loss = 3.93097
I0408 19:28:53.398581  5931 solver.cpp:237]     Train net output #0: loss = 3.93097 (* 1 = 3.93097 loss)
I0408 19:28:53.398589  5931 sgd_solver.cpp:105] Iteration 1248, lr = 0.00996546
I0408 19:28:58.114282  5931 solver.cpp:218] Iteration 1260 (2.5447 iter/s, 4.71568s/12 iters), loss = 3.84744
I0408 19:28:58.114315  5931 solver.cpp:237]     Train net output #0: loss = 3.84744 (* 1 = 3.84744 loss)
I0408 19:28:58.114323  5931 sgd_solver.cpp:105] Iteration 1260, lr = 0.00996485
I0408 19:29:02.877190  5931 solver.cpp:218] Iteration 1272 (2.5195 iter/s, 4.76286s/12 iters), loss = 3.93239
I0408 19:29:02.877223  5931 solver.cpp:237]     Train net output #0: loss = 3.93239 (* 1 = 3.93239 loss)
I0408 19:29:02.877231  5931 sgd_solver.cpp:105] Iteration 1272, lr = 0.00996422
I0408 19:29:07.687041  5931 solver.cpp:218] Iteration 1284 (2.49491 iter/s, 4.80979s/12 iters), loss = 3.95677
I0408 19:29:07.687211  5931 solver.cpp:237]     Train net output #0: loss = 3.95677 (* 1 = 3.95677 loss)
I0408 19:29:07.687220  5931 sgd_solver.cpp:105] Iteration 1284, lr = 0.00996359
I0408 19:29:12.539284  5931 solver.cpp:218] Iteration 1296 (2.47317 iter/s, 4.85206s/12 iters), loss = 3.9259
I0408 19:29:12.539316  5931 solver.cpp:237]     Train net output #0: loss = 3.9259 (* 1 = 3.9259 loss)
I0408 19:29:12.539324  5931 sgd_solver.cpp:105] Iteration 1296, lr = 0.00996294
I0408 19:29:17.358345  5931 solver.cpp:218] Iteration 1308 (2.49014 iter/s, 4.81901s/12 iters), loss = 4.21108
I0408 19:29:17.358376  5931 solver.cpp:237]     Train net output #0: loss = 4.21108 (* 1 = 4.21108 loss)
I0408 19:29:17.358384  5931 sgd_solver.cpp:105] Iteration 1308, lr = 0.00996228
I0408 19:29:19.779569  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:29:22.175256  5931 solver.cpp:218] Iteration 1320 (2.49125 iter/s, 4.81686s/12 iters), loss = 3.90153
I0408 19:29:22.175287  5931 solver.cpp:237]     Train net output #0: loss = 3.90153 (* 1 = 3.90153 loss)
I0408 19:29:22.175295  5931 sgd_solver.cpp:105] Iteration 1320, lr = 0.00996161
I0408 19:29:24.158465  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 19:29:27.417311  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 19:29:29.780164  5931 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 19:29:29.780189  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:29:33.940455  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:29:34.560994  5931 solver.cpp:397]     Test net output #0: accuracy = 0.110907
I0408 19:29:34.561043  5931 solver.cpp:397]     Test net output #1: loss = 4.0578 (* 1 = 4.0578 loss)
I0408 19:29:36.313087  5931 solver.cpp:218] Iteration 1332 (0.84879 iter/s, 14.1378s/12 iters), loss = 3.98789
I0408 19:29:36.313122  5931 solver.cpp:237]     Train net output #0: loss = 3.98789 (* 1 = 3.98789 loss)
I0408 19:29:36.313129  5931 sgd_solver.cpp:105] Iteration 1332, lr = 0.00996093
I0408 19:29:41.041668  5931 solver.cpp:218] Iteration 1344 (2.53779 iter/s, 4.72853s/12 iters), loss = 3.82301
I0408 19:29:41.041780  5931 solver.cpp:237]     Train net output #0: loss = 3.82301 (* 1 = 3.82301 loss)
I0408 19:29:41.041788  5931 sgd_solver.cpp:105] Iteration 1344, lr = 0.00996024
I0408 19:29:45.875771  5931 solver.cpp:218] Iteration 1356 (2.48243 iter/s, 4.83398s/12 iters), loss = 3.95784
I0408 19:29:45.875804  5931 solver.cpp:237]     Train net output #0: loss = 3.95784 (* 1 = 3.95784 loss)
I0408 19:29:45.875811  5931 sgd_solver.cpp:105] Iteration 1356, lr = 0.00995954
I0408 19:29:50.694695  5931 solver.cpp:218] Iteration 1368 (2.49021 iter/s, 4.81887s/12 iters), loss = 3.92292
I0408 19:29:50.694728  5931 solver.cpp:237]     Train net output #0: loss = 3.92292 (* 1 = 3.92292 loss)
I0408 19:29:50.694736  5931 sgd_solver.cpp:105] Iteration 1368, lr = 0.00995882
I0408 19:29:51.847590  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:29:55.541841  5931 solver.cpp:218] Iteration 1380 (2.47571 iter/s, 4.8471s/12 iters), loss = 3.93092
I0408 19:29:55.541872  5931 solver.cpp:237]     Train net output #0: loss = 3.93092 (* 1 = 3.93092 loss)
I0408 19:29:55.541879  5931 sgd_solver.cpp:105] Iteration 1380, lr = 0.00995809
I0408 19:30:00.364908  5931 solver.cpp:218] Iteration 1392 (2.48807 iter/s, 4.82302s/12 iters), loss = 3.91716
I0408 19:30:00.364941  5931 solver.cpp:237]     Train net output #0: loss = 3.91716 (* 1 = 3.91716 loss)
I0408 19:30:00.364949  5931 sgd_solver.cpp:105] Iteration 1392, lr = 0.00995735
I0408 19:30:05.166183  5931 solver.cpp:218] Iteration 1404 (2.49936 iter/s, 4.80123s/12 iters), loss = 3.8919
I0408 19:30:05.166216  5931 solver.cpp:237]     Train net output #0: loss = 3.8919 (* 1 = 3.8919 loss)
I0408 19:30:05.166224  5931 sgd_solver.cpp:105] Iteration 1404, lr = 0.00995659
I0408 19:30:09.664080  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:30:09.995765  5931 solver.cpp:218] Iteration 1416 (2.48471 iter/s, 4.82954s/12 iters), loss = 3.79788
I0408 19:30:09.995796  5931 solver.cpp:237]     Train net output #0: loss = 3.79788 (* 1 = 3.79788 loss)
I0408 19:30:09.995805  5931 sgd_solver.cpp:105] Iteration 1416, lr = 0.00995582
I0408 19:30:14.373322  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 19:30:17.932387  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 19:30:21.376196  5931 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 19:30:21.376222  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:30:25.092309  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:30:25.672089  5931 solver.cpp:397]     Test net output #0: accuracy = 0.147672
I0408 19:30:25.672135  5931 solver.cpp:397]     Test net output #1: loss = 3.93032 (* 1 = 3.93032 loss)
I0408 19:30:25.768970  5931 solver.cpp:218] Iteration 1428 (0.760787 iter/s, 15.7732s/12 iters), loss = 3.58445
I0408 19:30:25.769007  5931 solver.cpp:237]     Train net output #0: loss = 3.58445 (* 1 = 3.58445 loss)
I0408 19:30:25.769016  5931 sgd_solver.cpp:105] Iteration 1428, lr = 0.00995504
I0408 19:30:29.690510  5931 solver.cpp:218] Iteration 1440 (3.06006 iter/s, 3.92149s/12 iters), loss = 3.86636
I0408 19:30:29.690541  5931 solver.cpp:237]     Train net output #0: loss = 3.86636 (* 1 = 3.86636 loss)
I0408 19:30:29.690548  5931 sgd_solver.cpp:105] Iteration 1440, lr = 0.00995424
I0408 19:30:34.516654  5931 solver.cpp:218] Iteration 1452 (2.48648 iter/s, 4.8261s/12 iters), loss = 3.72832
I0408 19:30:34.516685  5931 solver.cpp:237]     Train net output #0: loss = 3.72832 (* 1 = 3.72832 loss)
I0408 19:30:34.516692  5931 sgd_solver.cpp:105] Iteration 1452, lr = 0.00995343
I0408 19:30:39.351946  5931 solver.cpp:218] Iteration 1464 (2.48178 iter/s, 4.83525s/12 iters), loss = 3.48909
I0408 19:30:39.351979  5931 solver.cpp:237]     Train net output #0: loss = 3.48909 (* 1 = 3.48909 loss)
I0408 19:30:39.351986  5931 sgd_solver.cpp:105] Iteration 1464, lr = 0.0099526
I0408 19:30:44.138279  5931 solver.cpp:218] Iteration 1476 (2.50716 iter/s, 4.78628s/12 iters), loss = 3.24151
I0408 19:30:44.138309  5931 solver.cpp:237]     Train net output #0: loss = 3.24151 (* 1 = 3.24151 loss)
I0408 19:30:44.138316  5931 sgd_solver.cpp:105] Iteration 1476, lr = 0.00995176
I0408 19:30:48.998698  5931 solver.cpp:218] Iteration 1488 (2.46895 iter/s, 4.86037s/12 iters), loss = 3.81638
I0408 19:30:48.998778  5931 solver.cpp:237]     Train net output #0: loss = 3.81638 (* 1 = 3.81638 loss)
I0408 19:30:48.998787  5931 sgd_solver.cpp:105] Iteration 1488, lr = 0.00995091
I0408 19:30:53.806434  5931 solver.cpp:218] Iteration 1500 (2.49603 iter/s, 4.80764s/12 iters), loss = 3.75439
I0408 19:30:53.806466  5931 solver.cpp:237]     Train net output #0: loss = 3.75439 (* 1 = 3.75439 loss)
I0408 19:30:53.806473  5931 sgd_solver.cpp:105] Iteration 1500, lr = 0.00995004
I0408 19:30:58.645150  5931 solver.cpp:218] Iteration 1512 (2.48002 iter/s, 4.83867s/12 iters), loss = 3.55017
I0408 19:30:58.645182  5931 solver.cpp:237]     Train net output #0: loss = 3.55017 (* 1 = 3.55017 loss)
I0408 19:30:58.645190  5931 sgd_solver.cpp:105] Iteration 1512, lr = 0.00994916
I0408 19:31:00.358456  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:31:03.461797  5931 solver.cpp:218] Iteration 1524 (2.49138 iter/s, 4.8166s/12 iters), loss = 3.38095
I0408 19:31:03.461830  5931 solver.cpp:237]     Train net output #0: loss = 3.38095 (* 1 = 3.38095 loss)
I0408 19:31:03.461838  5931 sgd_solver.cpp:105] Iteration 1524, lr = 0.00994825
I0408 19:31:05.401121  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 19:31:08.559868  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 19:31:11.015883  5931 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 19:31:11.015908  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:31:15.093099  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:31:15.801059  5931 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0408 19:31:15.801107  5931 solver.cpp:397]     Test net output #1: loss = 3.8383 (* 1 = 3.8383 loss)
I0408 19:31:17.549612  5931 solver.cpp:218] Iteration 1536 (0.851803 iter/s, 14.0878s/12 iters), loss = 3.32331
I0408 19:31:17.549647  5931 solver.cpp:237]     Train net output #0: loss = 3.32331 (* 1 = 3.32331 loss)
I0408 19:31:17.549655  5931 sgd_solver.cpp:105] Iteration 1536, lr = 0.00994734
I0408 19:31:22.316493  5931 solver.cpp:218] Iteration 1548 (2.5174 iter/s, 4.76683s/12 iters), loss = 3.63964
I0408 19:31:22.316589  5931 solver.cpp:237]     Train net output #0: loss = 3.63964 (* 1 = 3.63964 loss)
I0408 19:31:22.316597  5931 sgd_solver.cpp:105] Iteration 1548, lr = 0.00994641
I0408 19:31:27.176549  5931 solver.cpp:218] Iteration 1560 (2.46916 iter/s, 4.85994s/12 iters), loss = 3.61762
I0408 19:31:27.176589  5931 solver.cpp:237]     Train net output #0: loss = 3.61762 (* 1 = 3.61762 loss)
I0408 19:31:27.176599  5931 sgd_solver.cpp:105] Iteration 1560, lr = 0.00994546
I0408 19:31:31.988277  5931 solver.cpp:218] Iteration 1572 (2.49393 iter/s, 4.81168s/12 iters), loss = 3.54953
I0408 19:31:31.988310  5931 solver.cpp:237]     Train net output #0: loss = 3.54953 (* 1 = 3.54953 loss)
I0408 19:31:31.988317  5931 sgd_solver.cpp:105] Iteration 1572, lr = 0.00994449
I0408 19:31:36.815892  5931 solver.cpp:218] Iteration 1584 (2.48572 iter/s, 4.82757s/12 iters), loss = 3.58744
I0408 19:31:36.815925  5931 solver.cpp:237]     Train net output #0: loss = 3.58744 (* 1 = 3.58744 loss)
I0408 19:31:36.815932  5931 sgd_solver.cpp:105] Iteration 1584, lr = 0.00994351
I0408 19:31:41.654845  5931 solver.cpp:218] Iteration 1596 (2.4799 iter/s, 4.8389s/12 iters), loss = 3.3441
I0408 19:31:41.654878  5931 solver.cpp:237]     Train net output #0: loss = 3.3441 (* 1 = 3.3441 loss)
I0408 19:31:41.654886  5931 sgd_solver.cpp:105] Iteration 1596, lr = 0.00994251
I0408 19:31:46.467761  5931 solver.cpp:218] Iteration 1608 (2.49332 iter/s, 4.81287s/12 iters), loss = 3.51845
I0408 19:31:46.467793  5931 solver.cpp:237]     Train net output #0: loss = 3.51845 (* 1 = 3.51845 loss)
I0408 19:31:46.467800  5931 sgd_solver.cpp:105] Iteration 1608, lr = 0.00994149
I0408 19:31:50.230473  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:31:51.266613  5931 solver.cpp:218] Iteration 1620 (2.50062 iter/s, 4.7988s/12 iters), loss = 3.50903
I0408 19:31:51.266644  5931 solver.cpp:237]     Train net output #0: loss = 3.50903 (* 1 = 3.50903 loss)
I0408 19:31:51.266650  5931 sgd_solver.cpp:105] Iteration 1620, lr = 0.00994046
I0408 19:31:55.654479  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 19:31:59.062194  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 19:32:01.440238  5931 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 19:32:01.440263  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:32:05.372561  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:32:06.031466  5931 solver.cpp:397]     Test net output #0: accuracy = 0.169118
I0408 19:32:06.031518  5931 solver.cpp:397]     Test net output #1: loss = 3.69719 (* 1 = 3.69719 loss)
I0408 19:32:06.128342  5931 solver.cpp:218] Iteration 1632 (0.807446 iter/s, 14.8617s/12 iters), loss = 3.43845
I0408 19:32:06.128376  5931 solver.cpp:237]     Train net output #0: loss = 3.43845 (* 1 = 3.43845 loss)
I0408 19:32:06.128383  5931 sgd_solver.cpp:105] Iteration 1632, lr = 0.0099394
I0408 19:32:10.138854  5931 solver.cpp:218] Iteration 1644 (2.99217 iter/s, 4.01046s/12 iters), loss = 3.37178
I0408 19:32:10.138888  5931 solver.cpp:237]     Train net output #0: loss = 3.37178 (* 1 = 3.37178 loss)
I0408 19:32:10.138895  5931 sgd_solver.cpp:105] Iteration 1644, lr = 0.00993833
I0408 19:32:14.988745  5931 solver.cpp:218] Iteration 1656 (2.47431 iter/s, 4.84984s/12 iters), loss = 3.56193
I0408 19:32:14.988777  5931 solver.cpp:237]     Train net output #0: loss = 3.56193 (* 1 = 3.56193 loss)
I0408 19:32:14.988785  5931 sgd_solver.cpp:105] Iteration 1656, lr = 0.00993724
I0408 19:32:19.787761  5931 solver.cpp:218] Iteration 1668 (2.50054 iter/s, 4.79897s/12 iters), loss = 3.47631
I0408 19:32:19.787796  5931 solver.cpp:237]     Train net output #0: loss = 3.47631 (* 1 = 3.47631 loss)
I0408 19:32:19.787802  5931 sgd_solver.cpp:105] Iteration 1668, lr = 0.00993613
I0408 19:32:24.646354  5931 solver.cpp:218] Iteration 1680 (2.46988 iter/s, 4.85854s/12 iters), loss = 3.37645
I0408 19:32:24.646387  5931 solver.cpp:237]     Train net output #0: loss = 3.37645 (* 1 = 3.37645 loss)
I0408 19:32:24.646394  5931 sgd_solver.cpp:105] Iteration 1680, lr = 0.009935
I0408 19:32:29.447304  5931 solver.cpp:218] Iteration 1692 (2.49953 iter/s, 4.80091s/12 iters), loss = 3.39719
I0408 19:32:29.447455  5931 solver.cpp:237]     Train net output #0: loss = 3.39719 (* 1 = 3.39719 loss)
I0408 19:32:29.447464  5931 sgd_solver.cpp:105] Iteration 1692, lr = 0.00993385
I0408 19:32:34.264837  5931 solver.cpp:218] Iteration 1704 (2.49099 iter/s, 4.81737s/12 iters), loss = 3.34932
I0408 19:32:34.264868  5931 solver.cpp:237]     Train net output #0: loss = 3.34932 (* 1 = 3.34932 loss)
I0408 19:32:34.264876  5931 sgd_solver.cpp:105] Iteration 1704, lr = 0.00993268
I0408 19:32:39.113759  5931 solver.cpp:218] Iteration 1716 (2.4748 iter/s, 4.84888s/12 iters), loss = 3.22958
I0408 19:32:39.113791  5931 solver.cpp:237]     Train net output #0: loss = 3.22958 (* 1 = 3.22958 loss)
I0408 19:32:39.113799  5931 sgd_solver.cpp:105] Iteration 1716, lr = 0.00993149
I0408 19:32:40.085196  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:32:43.809278  5931 solver.cpp:218] Iteration 1728 (2.55565 iter/s, 4.69547s/12 iters), loss = 3.49653
I0408 19:32:43.809309  5931 solver.cpp:237]     Train net output #0: loss = 3.49653 (* 1 = 3.49653 loss)
I0408 19:32:43.809316  5931 sgd_solver.cpp:105] Iteration 1728, lr = 0.00993028
I0408 19:32:45.728770  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 19:32:49.598235  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 19:32:52.771811  5931 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 19:32:52.771843  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:32:56.773375  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:32:57.491618  5931 solver.cpp:397]     Test net output #0: accuracy = 0.171569
I0408 19:32:57.491664  5931 solver.cpp:397]     Test net output #1: loss = 3.65161 (* 1 = 3.65161 loss)
I0408 19:32:59.232687  5931 solver.cpp:218] Iteration 1740 (0.778041 iter/s, 15.4233s/12 iters), loss = 3.07183
I0408 19:32:59.232722  5931 solver.cpp:237]     Train net output #0: loss = 3.07183 (* 1 = 3.07183 loss)
I0408 19:32:59.232728  5931 sgd_solver.cpp:105] Iteration 1740, lr = 0.00992905
I0408 19:33:04.007655  5931 solver.cpp:218] Iteration 1752 (2.51313 iter/s, 4.77492s/12 iters), loss = 3.11234
I0408 19:33:04.007719  5931 solver.cpp:237]     Train net output #0: loss = 3.11234 (* 1 = 3.11234 loss)
I0408 19:33:04.007726  5931 sgd_solver.cpp:105] Iteration 1752, lr = 0.00992779
I0408 19:33:08.821727  5931 solver.cpp:218] Iteration 1764 (2.49273 iter/s, 4.81399s/12 iters), loss = 3.10957
I0408 19:33:08.821759  5931 solver.cpp:237]     Train net output #0: loss = 3.10957 (* 1 = 3.10957 loss)
I0408 19:33:08.821768  5931 sgd_solver.cpp:105] Iteration 1764, lr = 0.00992651
I0408 19:33:13.658185  5931 solver.cpp:218] Iteration 1776 (2.48118 iter/s, 4.8364s/12 iters), loss = 3.0906
I0408 19:33:13.658222  5931 solver.cpp:237]     Train net output #0: loss = 3.0906 (* 1 = 3.0906 loss)
I0408 19:33:13.658232  5931 sgd_solver.cpp:105] Iteration 1776, lr = 0.00992522
I0408 19:33:18.465909  5931 solver.cpp:218] Iteration 1788 (2.49601 iter/s, 4.80767s/12 iters), loss = 3.15253
I0408 19:33:18.465941  5931 solver.cpp:237]     Train net output #0: loss = 3.15253 (* 1 = 3.15253 loss)
I0408 19:33:18.465948  5931 sgd_solver.cpp:105] Iteration 1788, lr = 0.00992389
I0408 19:33:23.173699  5931 solver.cpp:218] Iteration 1800 (2.54899 iter/s, 4.70774s/12 iters), loss = 3.20365
I0408 19:33:23.173732  5931 solver.cpp:237]     Train net output #0: loss = 3.20365 (* 1 = 3.20365 loss)
I0408 19:33:23.173739  5931 sgd_solver.cpp:105] Iteration 1800, lr = 0.00992255
I0408 19:33:27.911000  5931 solver.cpp:218] Iteration 1812 (2.53311 iter/s, 4.73725s/12 iters), loss = 3.05909
I0408 19:33:27.911033  5931 solver.cpp:237]     Train net output #0: loss = 3.05909 (* 1 = 3.05909 loss)
I0408 19:33:27.911041  5931 sgd_solver.cpp:105] Iteration 1812, lr = 0.00992118
I0408 19:33:30.985661  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:33:32.758126  5931 solver.cpp:218] Iteration 1824 (2.47572 iter/s, 4.84707s/12 iters), loss = 3.19891
I0408 19:33:32.758165  5931 solver.cpp:237]     Train net output #0: loss = 3.19891 (* 1 = 3.19891 loss)
I0408 19:33:32.758174  5931 sgd_solver.cpp:105] Iteration 1824, lr = 0.00991979
I0408 19:33:37.122134  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 19:33:40.229362  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 19:33:42.619299  5931 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 19:33:42.619326  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:33:46.569561  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:33:47.401554  5931 solver.cpp:397]     Test net output #0: accuracy = 0.215074
I0408 19:33:47.401603  5931 solver.cpp:397]     Test net output #1: loss = 3.46741 (* 1 = 3.46741 loss)
I0408 19:33:47.498178  5931 solver.cpp:218] Iteration 1836 (0.814112 iter/s, 14.74s/12 iters), loss = 2.86189
I0408 19:33:47.498212  5931 solver.cpp:237]     Train net output #0: loss = 2.86189 (* 1 = 2.86189 loss)
I0408 19:33:47.498220  5931 sgd_solver.cpp:105] Iteration 1836, lr = 0.00991837
I0408 19:33:51.509824  5931 solver.cpp:218] Iteration 1848 (2.99133 iter/s, 4.01159s/12 iters), loss = 3.34784
I0408 19:33:51.509856  5931 solver.cpp:237]     Train net output #0: loss = 3.34784 (* 1 = 3.34784 loss)
I0408 19:33:51.509863  5931 sgd_solver.cpp:105] Iteration 1848, lr = 0.00991693
I0408 19:33:56.234838  5931 solver.cpp:218] Iteration 1860 (2.5397 iter/s, 4.72496s/12 iters), loss = 2.74712
I0408 19:33:56.234870  5931 solver.cpp:237]     Train net output #0: loss = 2.74712 (* 1 = 2.74712 loss)
I0408 19:33:56.234877  5931 sgd_solver.cpp:105] Iteration 1860, lr = 0.00991547
I0408 19:34:01.099488  5931 solver.cpp:218] Iteration 1872 (2.4668 iter/s, 4.8646s/12 iters), loss = 3.01523
I0408 19:34:01.099522  5931 solver.cpp:237]     Train net output #0: loss = 3.01523 (* 1 = 3.01523 loss)
I0408 19:34:01.099529  5931 sgd_solver.cpp:105] Iteration 1872, lr = 0.00991397
I0408 19:34:05.898860  5931 solver.cpp:218] Iteration 1884 (2.50035 iter/s, 4.79932s/12 iters), loss = 2.90075
I0408 19:34:05.898892  5931 solver.cpp:237]     Train net output #0: loss = 2.90075 (* 1 = 2.90075 loss)
I0408 19:34:05.898900  5931 sgd_solver.cpp:105] Iteration 1884, lr = 0.00991246
I0408 19:34:10.701092  5931 solver.cpp:218] Iteration 1896 (2.49886 iter/s, 4.80218s/12 iters), loss = 3.16211
I0408 19:34:10.701175  5931 solver.cpp:237]     Train net output #0: loss = 3.16211 (* 1 = 3.16211 loss)
I0408 19:34:10.701184  5931 sgd_solver.cpp:105] Iteration 1896, lr = 0.00991091
I0408 19:34:15.439218  5931 solver.cpp:218] Iteration 1908 (2.5327 iter/s, 4.73802s/12 iters), loss = 2.95688
I0408 19:34:15.439251  5931 solver.cpp:237]     Train net output #0: loss = 2.95688 (* 1 = 2.95688 loss)
I0408 19:34:15.439258  5931 sgd_solver.cpp:105] Iteration 1908, lr = 0.00990934
I0408 19:34:20.175232  5931 solver.cpp:218] Iteration 1920 (2.5338 iter/s, 4.73597s/12 iters), loss = 2.60159
I0408 19:34:20.175266  5931 solver.cpp:237]     Train net output #0: loss = 2.60159 (* 1 = 2.60159 loss)
I0408 19:34:20.175273  5931 sgd_solver.cpp:105] Iteration 1920, lr = 0.00990774
I0408 19:34:20.454672  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:34:24.921419  5931 solver.cpp:218] Iteration 1932 (2.52837 iter/s, 4.74614s/12 iters), loss = 2.84615
I0408 19:34:24.921450  5931 solver.cpp:237]     Train net output #0: loss = 2.84615 (* 1 = 2.84615 loss)
I0408 19:34:24.921458  5931 sgd_solver.cpp:105] Iteration 1932, lr = 0.00990611
I0408 19:34:26.842355  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 19:34:31.252169  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 19:34:34.107241  5931 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 19:34:34.107267  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:34:37.813169  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:34:38.583055  5931 solver.cpp:397]     Test net output #0: accuracy = 0.250613
I0408 19:34:38.583102  5931 solver.cpp:397]     Test net output #1: loss = 3.32955 (* 1 = 3.32955 loss)
I0408 19:34:40.330612  5931 solver.cpp:218] Iteration 1944 (0.778759 iter/s, 15.4091s/12 iters), loss = 2.86624
I0408 19:34:40.330647  5931 solver.cpp:237]     Train net output #0: loss = 2.86624 (* 1 = 2.86624 loss)
I0408 19:34:40.330655  5931 sgd_solver.cpp:105] Iteration 1944, lr = 0.00990446
I0408 19:34:45.128799  5931 solver.cpp:218] Iteration 1956 (2.50097 iter/s, 4.79813s/12 iters), loss = 2.95158
I0408 19:34:45.128897  5931 solver.cpp:237]     Train net output #0: loss = 2.95158 (* 1 = 2.95158 loss)
I0408 19:34:45.128906  5931 sgd_solver.cpp:105] Iteration 1956, lr = 0.00990277
I0408 19:34:49.935156  5931 solver.cpp:218] Iteration 1968 (2.49675 iter/s, 4.80624s/12 iters), loss = 2.89945
I0408 19:34:49.935189  5931 solver.cpp:237]     Train net output #0: loss = 2.89945 (* 1 = 2.89945 loss)
I0408 19:34:49.935201  5931 sgd_solver.cpp:105] Iteration 1968, lr = 0.00990106
I0408 19:34:54.782119  5931 solver.cpp:218] Iteration 1980 (2.4758 iter/s, 4.84691s/12 iters), loss = 3.08024
I0408 19:34:54.782150  5931 solver.cpp:237]     Train net output #0: loss = 3.08024 (* 1 = 3.08024 loss)
I0408 19:34:54.782157  5931 sgd_solver.cpp:105] Iteration 1980, lr = 0.00989932
I0408 19:34:59.596925  5931 solver.cpp:218] Iteration 1992 (2.49234 iter/s, 4.81476s/12 iters), loss = 2.72169
I0408 19:34:59.596957  5931 solver.cpp:237]     Train net output #0: loss = 2.72169 (* 1 = 2.72169 loss)
I0408 19:34:59.596964  5931 sgd_solver.cpp:105] Iteration 1992, lr = 0.00989754
I0408 19:35:04.417912  5931 solver.cpp:218] Iteration 2004 (2.48914 iter/s, 4.82094s/12 iters), loss = 3.02805
I0408 19:35:04.417945  5931 solver.cpp:237]     Train net output #0: loss = 3.02805 (* 1 = 3.02805 loss)
I0408 19:35:04.417953  5931 sgd_solver.cpp:105] Iteration 2004, lr = 0.00989574
I0408 19:35:09.239106  5931 solver.cpp:218] Iteration 2016 (2.48904 iter/s, 4.82114s/12 iters), loss = 2.80974
I0408 19:35:09.239137  5931 solver.cpp:237]     Train net output #0: loss = 2.80974 (* 1 = 2.80974 loss)
I0408 19:35:09.239145  5931 sgd_solver.cpp:105] Iteration 2016, lr = 0.0098939
I0408 19:35:11.691733  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:35:14.059460  5931 solver.cpp:218] Iteration 2028 (2.48947 iter/s, 4.82031s/12 iters), loss = 2.81692
I0408 19:35:14.059494  5931 solver.cpp:237]     Train net output #0: loss = 2.81692 (* 1 = 2.81692 loss)
I0408 19:35:14.059501  5931 sgd_solver.cpp:105] Iteration 2028, lr = 0.00989203
I0408 19:35:18.420747  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 19:35:21.535476  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 19:35:23.890939  5931 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 19:35:23.890964  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:35:27.673660  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:35:28.489058  5931 solver.cpp:397]     Test net output #0: accuracy = 0.247549
I0408 19:35:28.489105  5931 solver.cpp:397]     Test net output #1: loss = 3.2654 (* 1 = 3.2654 loss)
I0408 19:35:28.585580  5931 solver.cpp:218] Iteration 2040 (0.826102 iter/s, 14.5261s/12 iters), loss = 2.46278
I0408 19:35:28.585616  5931 solver.cpp:237]     Train net output #0: loss = 2.46278 (* 1 = 2.46278 loss)
I0408 19:35:28.585624  5931 sgd_solver.cpp:105] Iteration 2040, lr = 0.00989013
I0408 19:35:32.526542  5931 solver.cpp:218] Iteration 2052 (3.04498 iter/s, 3.94091s/12 iters), loss = 2.50297
I0408 19:35:32.526576  5931 solver.cpp:237]     Train net output #0: loss = 2.50297 (* 1 = 2.50297 loss)
I0408 19:35:32.526583  5931 sgd_solver.cpp:105] Iteration 2052, lr = 0.0098882
I0408 19:35:34.071233  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:35:37.352569  5931 solver.cpp:218] Iteration 2064 (2.48654 iter/s, 4.82597s/12 iters), loss = 2.77328
I0408 19:35:37.352603  5931 solver.cpp:237]     Train net output #0: loss = 2.77328 (* 1 = 2.77328 loss)
I0408 19:35:37.352612  5931 sgd_solver.cpp:105] Iteration 2064, lr = 0.00988623
I0408 19:35:42.184135  5931 solver.cpp:218] Iteration 2076 (2.48369 iter/s, 4.83151s/12 iters), loss = 3.06107
I0408 19:35:42.184167  5931 solver.cpp:237]     Train net output #0: loss = 3.06107 (* 1 = 3.06107 loss)
I0408 19:35:42.184175  5931 sgd_solver.cpp:105] Iteration 2076, lr = 0.00988423
I0408 19:35:47.002928  5931 solver.cpp:218] Iteration 2088 (2.49028 iter/s, 4.81874s/12 iters), loss = 2.7561
I0408 19:35:47.002959  5931 solver.cpp:237]     Train net output #0: loss = 2.7561 (* 1 = 2.7561 loss)
I0408 19:35:47.002966  5931 sgd_solver.cpp:105] Iteration 2088, lr = 0.00988219
I0408 19:35:51.845197  5931 solver.cpp:218] Iteration 2100 (2.4782 iter/s, 4.84222s/12 iters), loss = 3.08235
I0408 19:35:51.845311  5931 solver.cpp:237]     Train net output #0: loss = 3.08235 (* 1 = 3.08235 loss)
I0408 19:35:51.845320  5931 sgd_solver.cpp:105] Iteration 2100, lr = 0.00988012
I0408 19:35:56.654162  5931 solver.cpp:218] Iteration 2112 (2.49541 iter/s, 4.80884s/12 iters), loss = 2.67728
I0408 19:35:56.654194  5931 solver.cpp:237]     Train net output #0: loss = 2.67728 (* 1 = 2.67728 loss)
I0408 19:35:56.654202  5931 sgd_solver.cpp:105] Iteration 2112, lr = 0.00987801
I0408 19:36:01.161051  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:36:01.465315  5931 solver.cpp:218] Iteration 2124 (2.49423 iter/s, 4.8111s/12 iters), loss = 2.80512
I0408 19:36:01.465348  5931 solver.cpp:237]     Train net output #0: loss = 2.80512 (* 1 = 2.80512 loss)
I0408 19:36:01.465355  5931 sgd_solver.cpp:105] Iteration 2124, lr = 0.00987586
I0408 19:36:06.319355  5931 solver.cpp:218] Iteration 2136 (2.47219 iter/s, 4.85399s/12 iters), loss = 2.77417
I0408 19:36:06.319389  5931 solver.cpp:237]     Train net output #0: loss = 2.77417 (* 1 = 2.77417 loss)
I0408 19:36:06.319397  5931 sgd_solver.cpp:105] Iteration 2136, lr = 0.00987368
I0408 19:36:08.286396  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 19:36:12.828195  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 19:36:16.146903  5931 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 19:36:16.146927  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:36:19.974332  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:36:20.933254  5931 solver.cpp:397]     Test net output #0: accuracy = 0.260417
I0408 19:36:20.933305  5931 solver.cpp:397]     Test net output #1: loss = 3.20332 (* 1 = 3.20332 loss)
I0408 19:36:22.666127  5931 solver.cpp:218] Iteration 2148 (0.734093 iter/s, 16.3467s/12 iters), loss = 2.68131
I0408 19:36:22.666224  5931 solver.cpp:237]     Train net output #0: loss = 2.68131 (* 1 = 2.68131 loss)
I0408 19:36:22.666231  5931 sgd_solver.cpp:105] Iteration 2148, lr = 0.00987146
I0408 19:36:27.497283  5931 solver.cpp:218] Iteration 2160 (2.48394 iter/s, 4.83104s/12 iters), loss = 2.86201
I0408 19:36:27.497313  5931 solver.cpp:237]     Train net output #0: loss = 2.86201 (* 1 = 2.86201 loss)
I0408 19:36:27.497321  5931 sgd_solver.cpp:105] Iteration 2160, lr = 0.0098692
I0408 19:36:32.218503  5931 solver.cpp:218] Iteration 2172 (2.54174 iter/s, 4.72117s/12 iters), loss = 2.36904
I0408 19:36:32.218535  5931 solver.cpp:237]     Train net output #0: loss = 2.36904 (* 1 = 2.36904 loss)
I0408 19:36:32.218542  5931 sgd_solver.cpp:105] Iteration 2172, lr = 0.00986691
I0408 19:36:37.064347  5931 solver.cpp:218] Iteration 2184 (2.47637 iter/s, 4.8458s/12 iters), loss = 2.74273
I0408 19:36:37.064381  5931 solver.cpp:237]     Train net output #0: loss = 2.74273 (* 1 = 2.74273 loss)
I0408 19:36:37.064388  5931 sgd_solver.cpp:105] Iteration 2184, lr = 0.00986457
I0408 19:36:41.861943  5931 solver.cpp:218] Iteration 2196 (2.50128 iter/s, 4.79754s/12 iters), loss = 2.70727
I0408 19:36:41.861982  5931 solver.cpp:237]     Train net output #0: loss = 2.70727 (* 1 = 2.70727 loss)
I0408 19:36:41.861989  5931 sgd_solver.cpp:105] Iteration 2196, lr = 0.00986219
I0408 19:36:46.725137  5931 solver.cpp:218] Iteration 2208 (2.46754 iter/s, 4.86314s/12 iters), loss = 2.45504
I0408 19:36:46.725169  5931 solver.cpp:237]     Train net output #0: loss = 2.45504 (* 1 = 2.45504 loss)
I0408 19:36:46.725176  5931 sgd_solver.cpp:105] Iteration 2208, lr = 0.00985977
I0408 19:36:51.556500  5931 solver.cpp:218] Iteration 2220 (2.4838 iter/s, 4.83131s/12 iters), loss = 2.17187
I0408 19:36:51.556535  5931 solver.cpp:237]     Train net output #0: loss = 2.17187 (* 1 = 2.17187 loss)
I0408 19:36:51.556541  5931 sgd_solver.cpp:105] Iteration 2220, lr = 0.00985731
I0408 19:36:53.334318  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:36:56.352470  5931 solver.cpp:218] Iteration 2232 (2.50213 iter/s, 4.79592s/12 iters), loss = 2.53691
I0408 19:36:56.352502  5931 solver.cpp:237]     Train net output #0: loss = 2.53691 (* 1 = 2.53691 loss)
I0408 19:36:56.352510  5931 sgd_solver.cpp:105] Iteration 2232, lr = 0.00985481
I0408 19:37:00.755615  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 19:37:04.455587  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 19:37:08.158442  5931 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 19:37:08.158468  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:37:11.810256  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:37:12.704125  5931 solver.cpp:397]     Test net output #0: accuracy = 0.268382
I0408 19:37:12.704172  5931 solver.cpp:397]     Test net output #1: loss = 3.21659 (* 1 = 3.21659 loss)
I0408 19:37:12.800781  5931 solver.cpp:218] Iteration 2244 (0.729561 iter/s, 16.4482s/12 iters), loss = 2.4143
I0408 19:37:12.800817  5931 solver.cpp:237]     Train net output #0: loss = 2.4143 (* 1 = 2.4143 loss)
I0408 19:37:12.800823  5931 sgd_solver.cpp:105] Iteration 2244, lr = 0.00985226
I0408 19:37:16.744585  5931 solver.cpp:218] Iteration 2256 (3.04279 iter/s, 3.94375s/12 iters), loss = 2.91359
I0408 19:37:16.744616  5931 solver.cpp:237]     Train net output #0: loss = 2.91359 (* 1 = 2.91359 loss)
I0408 19:37:16.744623  5931 sgd_solver.cpp:105] Iteration 2256, lr = 0.00984967
I0408 19:37:21.441716  5931 solver.cpp:218] Iteration 2268 (2.55478 iter/s, 4.69709s/12 iters), loss = 2.59858
I0408 19:37:21.441749  5931 solver.cpp:237]     Train net output #0: loss = 2.59858 (* 1 = 2.59858 loss)
I0408 19:37:21.441757  5931 sgd_solver.cpp:105] Iteration 2268, lr = 0.00984703
I0408 19:37:26.183737  5931 solver.cpp:218] Iteration 2280 (2.53059 iter/s, 4.74197s/12 iters), loss = 2.49528
I0408 19:37:26.183881  5931 solver.cpp:237]     Train net output #0: loss = 2.49528 (* 1 = 2.49528 loss)
I0408 19:37:26.183888  5931 sgd_solver.cpp:105] Iteration 2280, lr = 0.00984435
I0408 19:37:30.962338  5931 solver.cpp:218] Iteration 2292 (2.51128 iter/s, 4.77844s/12 iters), loss = 2.65525
I0408 19:37:30.962371  5931 solver.cpp:237]     Train net output #0: loss = 2.65525 (* 1 = 2.65525 loss)
I0408 19:37:30.962378  5931 sgd_solver.cpp:105] Iteration 2292, lr = 0.00984162
I0408 19:37:35.800585  5931 solver.cpp:218] Iteration 2304 (2.48026 iter/s, 4.83819s/12 iters), loss = 2.33193
I0408 19:37:35.800616  5931 solver.cpp:237]     Train net output #0: loss = 2.33193 (* 1 = 2.33193 loss)
I0408 19:37:35.800623  5931 sgd_solver.cpp:105] Iteration 2304, lr = 0.00983885
I0408 19:37:40.615244  5931 solver.cpp:218] Iteration 2316 (2.49241 iter/s, 4.81461s/12 iters), loss = 2.69005
I0408 19:37:40.615276  5931 solver.cpp:237]     Train net output #0: loss = 2.69005 (* 1 = 2.69005 loss)
I0408 19:37:40.615283  5931 sgd_solver.cpp:105] Iteration 2316, lr = 0.00983603
I0408 19:37:44.414597  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:37:45.413044  5931 solver.cpp:218] Iteration 2328 (2.50117 iter/s, 4.79775s/12 iters), loss = 2.54279
I0408 19:37:45.413077  5931 solver.cpp:237]     Train net output #0: loss = 2.54279 (* 1 = 2.54279 loss)
I0408 19:37:45.413085  5931 sgd_solver.cpp:105] Iteration 2328, lr = 0.00983316
I0408 19:37:50.190591  5931 solver.cpp:218] Iteration 2340 (2.51178 iter/s, 4.7775s/12 iters), loss = 2.13108
I0408 19:37:50.190623  5931 solver.cpp:237]     Train net output #0: loss = 2.13108 (* 1 = 2.13108 loss)
I0408 19:37:50.190631  5931 sgd_solver.cpp:105] Iteration 2340, lr = 0.00983024
I0408 19:37:52.105490  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 19:37:56.466341  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 19:38:00.425770  5931 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 19:38:00.425797  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:38:04.165217  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:38:05.218755  5931 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0408 19:38:05.218803  5931 solver.cpp:397]     Test net output #1: loss = 3.09828 (* 1 = 3.09828 loss)
I0408 19:38:06.946848  5931 solver.cpp:218] Iteration 2352 (0.716153 iter/s, 16.7562s/12 iters), loss = 2.09273
I0408 19:38:06.946883  5931 solver.cpp:237]     Train net output #0: loss = 2.09273 (* 1 = 2.09273 loss)
I0408 19:38:06.946890  5931 sgd_solver.cpp:105] Iteration 2352, lr = 0.00982727
I0408 19:38:11.682466  5931 solver.cpp:218] Iteration 2364 (2.53401 iter/s, 4.73557s/12 iters), loss = 2.62565
I0408 19:38:11.682499  5931 solver.cpp:237]     Train net output #0: loss = 2.62565 (* 1 = 2.62565 loss)
I0408 19:38:11.682507  5931 sgd_solver.cpp:105] Iteration 2364, lr = 0.00982425
I0408 19:38:16.418247  5931 solver.cpp:218] Iteration 2376 (2.53393 iter/s, 4.73573s/12 iters), loss = 2.42592
I0408 19:38:16.418280  5931 solver.cpp:237]     Train net output #0: loss = 2.42592 (* 1 = 2.42592 loss)
I0408 19:38:16.418288  5931 sgd_solver.cpp:105] Iteration 2376, lr = 0.00982117
I0408 19:38:21.135447  5931 solver.cpp:218] Iteration 2388 (2.54391 iter/s, 4.71715s/12 iters), loss = 2.04437
I0408 19:38:21.135479  5931 solver.cpp:237]     Train net output #0: loss = 2.04437 (* 1 = 2.04437 loss)
I0408 19:38:21.135488  5931 sgd_solver.cpp:105] Iteration 2388, lr = 0.00981805
I0408 19:38:25.843243  5931 solver.cpp:218] Iteration 2400 (2.54899 iter/s, 4.70775s/12 iters), loss = 2.67607
I0408 19:38:25.843276  5931 solver.cpp:237]     Train net output #0: loss = 2.67607 (* 1 = 2.67607 loss)
I0408 19:38:25.843282  5931 sgd_solver.cpp:105] Iteration 2400, lr = 0.00981487
I0408 19:38:30.684999  5931 solver.cpp:218] Iteration 2412 (2.47846 iter/s, 4.84171s/12 iters), loss = 2.51547
I0408 19:38:30.685060  5931 solver.cpp:237]     Train net output #0: loss = 2.51547 (* 1 = 2.51547 loss)
I0408 19:38:30.685068  5931 sgd_solver.cpp:105] Iteration 2412, lr = 0.00981163
I0408 19:38:35.506407  5931 solver.cpp:218] Iteration 2424 (2.48894 iter/s, 4.82133s/12 iters), loss = 2.0959
I0408 19:38:35.506438  5931 solver.cpp:237]     Train net output #0: loss = 2.0959 (* 1 = 2.0959 loss)
I0408 19:38:35.506446  5931 sgd_solver.cpp:105] Iteration 2424, lr = 0.00980834
I0408 19:38:36.524883  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:38:40.325289  5931 solver.cpp:218] Iteration 2436 (2.49023 iter/s, 4.81883s/12 iters), loss = 2.52854
I0408 19:38:40.325320  5931 solver.cpp:237]     Train net output #0: loss = 2.52854 (* 1 = 2.52854 loss)
I0408 19:38:40.325327  5931 sgd_solver.cpp:105] Iteration 2436, lr = 0.009805
I0408 19:38:44.699910  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 19:38:47.811446  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 19:38:51.583385  5931 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 19:38:51.583411  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:38:55.286252  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:38:56.372520  5931 solver.cpp:397]     Test net output #0: accuracy = 0.261029
I0408 19:38:56.372570  5931 solver.cpp:397]     Test net output #1: loss = 3.26117 (* 1 = 3.26117 loss)
I0408 19:38:56.469086  5931 solver.cpp:218] Iteration 2448 (0.743322 iter/s, 16.1437s/12 iters), loss = 2.36564
I0408 19:38:56.469121  5931 solver.cpp:237]     Train net output #0: loss = 2.36564 (* 1 = 2.36564 loss)
I0408 19:38:56.469130  5931 sgd_solver.cpp:105] Iteration 2448, lr = 0.0098016
I0408 19:39:00.398000  5931 solver.cpp:218] Iteration 2460 (3.05432 iter/s, 3.92886s/12 iters), loss = 2.11892
I0408 19:39:00.398033  5931 solver.cpp:237]     Train net output #0: loss = 2.11892 (* 1 = 2.11892 loss)
I0408 19:39:00.398041  5931 sgd_solver.cpp:105] Iteration 2460, lr = 0.00979814
I0408 19:39:05.224330  5931 solver.cpp:218] Iteration 2472 (2.48639 iter/s, 4.82628s/12 iters), loss = 2.2783
I0408 19:39:05.224480  5931 solver.cpp:237]     Train net output #0: loss = 2.2783 (* 1 = 2.2783 loss)
I0408 19:39:05.224489  5931 sgd_solver.cpp:105] Iteration 2472, lr = 0.00979462
I0408 19:39:10.044690  5931 solver.cpp:218] Iteration 2484 (2.48953 iter/s, 4.8202s/12 iters), loss = 2.22805
I0408 19:39:10.044723  5931 solver.cpp:237]     Train net output #0: loss = 2.22805 (* 1 = 2.22805 loss)
I0408 19:39:10.044731  5931 sgd_solver.cpp:105] Iteration 2484, lr = 0.00979104
I0408 19:39:14.883322  5931 solver.cpp:218] Iteration 2496 (2.48007 iter/s, 4.83858s/12 iters), loss = 2.4572
I0408 19:39:14.883355  5931 solver.cpp:237]     Train net output #0: loss = 2.4572 (* 1 = 2.4572 loss)
I0408 19:39:14.883363  5931 sgd_solver.cpp:105] Iteration 2496, lr = 0.00978739
I0408 19:39:19.597734  5931 solver.cpp:218] Iteration 2508 (2.54541 iter/s, 4.71436s/12 iters), loss = 2.26248
I0408 19:39:19.597765  5931 solver.cpp:237]     Train net output #0: loss = 2.26248 (* 1 = 2.26248 loss)
I0408 19:39:19.597774  5931 sgd_solver.cpp:105] Iteration 2508, lr = 0.00978369
I0408 19:39:24.329479  5931 solver.cpp:218] Iteration 2520 (2.53609 iter/s, 4.7317s/12 iters), loss = 2.41503
I0408 19:39:24.329511  5931 solver.cpp:237]     Train net output #0: loss = 2.41503 (* 1 = 2.41503 loss)
I0408 19:39:24.329519  5931 sgd_solver.cpp:105] Iteration 2520, lr = 0.00977992
I0408 19:39:27.438206  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:39:29.116416  5931 solver.cpp:218] Iteration 2532 (2.50685 iter/s, 4.78689s/12 iters), loss = 2.17744
I0408 19:39:29.116447  5931 solver.cpp:237]     Train net output #0: loss = 2.17744 (* 1 = 2.17744 loss)
I0408 19:39:29.116456  5931 sgd_solver.cpp:105] Iteration 2532, lr = 0.00977609
I0408 19:39:33.828629  5931 solver.cpp:218] Iteration 2544 (2.5466 iter/s, 4.71216s/12 iters), loss = 2.1308
I0408 19:39:33.828660  5931 solver.cpp:237]     Train net output #0: loss = 2.1308 (* 1 = 2.1308 loss)
I0408 19:39:33.828668  5931 sgd_solver.cpp:105] Iteration 2544, lr = 0.0097722
I0408 19:39:35.752049  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 19:39:39.296643  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 19:39:43.053663  5931 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 19:39:43.053689  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:39:46.706526  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:39:47.837779  5931 solver.cpp:397]     Test net output #0: accuracy = 0.279412
I0408 19:39:47.837814  5931 solver.cpp:397]     Test net output #1: loss = 3.14504 (* 1 = 3.14504 loss)
I0408 19:39:49.572825  5931 solver.cpp:218] Iteration 2556 (0.762189 iter/s, 15.7441s/12 iters), loss = 2.27666
I0408 19:39:49.572860  5931 solver.cpp:237]     Train net output #0: loss = 2.27666 (* 1 = 2.27666 loss)
I0408 19:39:49.572866  5931 sgd_solver.cpp:105] Iteration 2556, lr = 0.00976824
I0408 19:39:54.265280  5931 solver.cpp:218] Iteration 2568 (2.55733 iter/s, 4.6924s/12 iters), loss = 1.93333
I0408 19:39:54.265313  5931 solver.cpp:237]     Train net output #0: loss = 1.93333 (* 1 = 1.93333 loss)
I0408 19:39:54.265321  5931 sgd_solver.cpp:105] Iteration 2568, lr = 0.00976421
I0408 19:39:59.105406  5931 solver.cpp:218] Iteration 2580 (2.4793 iter/s, 4.84008s/12 iters), loss = 2.34647
I0408 19:39:59.105438  5931 solver.cpp:237]     Train net output #0: loss = 2.34647 (* 1 = 2.34647 loss)
I0408 19:39:59.105446  5931 sgd_solver.cpp:105] Iteration 2580, lr = 0.00976011
I0408 19:40:03.930449  5931 solver.cpp:218] Iteration 2592 (2.48705 iter/s, 4.82499s/12 iters), loss = 2.18032
I0408 19:40:03.930482  5931 solver.cpp:237]     Train net output #0: loss = 2.18032 (* 1 = 2.18032 loss)
I0408 19:40:03.930490  5931 sgd_solver.cpp:105] Iteration 2592, lr = 0.00975594
I0408 19:40:08.696230  5931 solver.cpp:218] Iteration 2604 (2.51798 iter/s, 4.76573s/12 iters), loss = 1.88009
I0408 19:40:08.696292  5931 solver.cpp:237]     Train net output #0: loss = 1.88009 (* 1 = 1.88009 loss)
I0408 19:40:08.696300  5931 sgd_solver.cpp:105] Iteration 2604, lr = 0.00975171
I0408 19:40:13.425879  5931 solver.cpp:218] Iteration 2616 (2.53723 iter/s, 4.72957s/12 iters), loss = 2.32095
I0408 19:40:13.425911  5931 solver.cpp:237]     Train net output #0: loss = 2.32095 (* 1 = 2.32095 loss)
I0408 19:40:13.425920  5931 sgd_solver.cpp:105] Iteration 2616, lr = 0.0097474
I0408 19:40:18.192402  5931 solver.cpp:218] Iteration 2628 (2.51759 iter/s, 4.76647s/12 iters), loss = 1.6661
I0408 19:40:18.192435  5931 solver.cpp:237]     Train net output #0: loss = 1.6661 (* 1 = 1.6661 loss)
I0408 19:40:18.192445  5931 sgd_solver.cpp:105] Iteration 2628, lr = 0.00974302
I0408 19:40:18.605332  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:40:22.923465  5931 solver.cpp:218] Iteration 2640 (2.53645 iter/s, 4.73101s/12 iters), loss = 2.2403
I0408 19:40:22.923498  5931 solver.cpp:237]     Train net output #0: loss = 2.2403 (* 1 = 2.2403 loss)
I0408 19:40:22.923506  5931 sgd_solver.cpp:105] Iteration 2640, lr = 0.00973856
I0408 19:40:27.205996  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 19:40:30.316349  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 19:40:32.719694  5931 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 19:40:32.719720  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:40:36.343957  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:40:37.515556  5931 solver.cpp:397]     Test net output #0: accuracy = 0.299632
I0408 19:40:37.515604  5931 solver.cpp:397]     Test net output #1: loss = 3.10295 (* 1 = 3.10295 loss)
I0408 19:40:37.612187  5931 solver.cpp:218] Iteration 2652 (0.816957 iter/s, 14.6887s/12 iters), loss = 2.08837
I0408 19:40:37.612224  5931 solver.cpp:237]     Train net output #0: loss = 2.08837 (* 1 = 2.08837 loss)
I0408 19:40:37.612233  5931 sgd_solver.cpp:105] Iteration 2652, lr = 0.00973403
I0408 19:40:41.535809  5931 solver.cpp:218] Iteration 2664 (3.05844 iter/s, 3.92357s/12 iters), loss = 2.09268
I0408 19:40:41.535895  5931 solver.cpp:237]     Train net output #0: loss = 2.09268 (* 1 = 2.09268 loss)
I0408 19:40:41.535904  5931 sgd_solver.cpp:105] Iteration 2664, lr = 0.00972942
I0408 19:40:46.220741  5931 solver.cpp:218] Iteration 2676 (2.56146 iter/s, 4.68483s/12 iters), loss = 2.20134
I0408 19:40:46.220779  5931 solver.cpp:237]     Train net output #0: loss = 2.20134 (* 1 = 2.20134 loss)
I0408 19:40:46.220786  5931 sgd_solver.cpp:105] Iteration 2676, lr = 0.00972474
I0408 19:40:50.991250  5931 solver.cpp:218] Iteration 2688 (2.51548 iter/s, 4.77045s/12 iters), loss = 1.93849
I0408 19:40:50.991283  5931 solver.cpp:237]     Train net output #0: loss = 1.93849 (* 1 = 1.93849 loss)
I0408 19:40:50.991290  5931 sgd_solver.cpp:105] Iteration 2688, lr = 0.00971997
I0408 19:40:55.791440  5931 solver.cpp:218] Iteration 2700 (2.49993 iter/s, 4.80014s/12 iters), loss = 2.49127
I0408 19:40:55.791472  5931 solver.cpp:237]     Train net output #0: loss = 2.49127 (* 1 = 2.49127 loss)
I0408 19:40:55.791481  5931 sgd_solver.cpp:105] Iteration 2700, lr = 0.00971513
I0408 19:41:00.645083  5931 solver.cpp:218] Iteration 2712 (2.4724 iter/s, 4.85359s/12 iters), loss = 1.76925
I0408 19:41:00.645114  5931 solver.cpp:237]     Train net output #0: loss = 1.76925 (* 1 = 1.76925 loss)
I0408 19:41:00.645121  5931 sgd_solver.cpp:105] Iteration 2712, lr = 0.00971021
I0408 19:41:05.480443  5931 solver.cpp:218] Iteration 2724 (2.48174 iter/s, 4.83531s/12 iters), loss = 2.10723
I0408 19:41:05.480477  5931 solver.cpp:237]     Train net output #0: loss = 2.10723 (* 1 = 2.10723 loss)
I0408 19:41:05.480485  5931 sgd_solver.cpp:105] Iteration 2724, lr = 0.0097052
I0408 19:41:07.949951  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:41:10.299273  5931 solver.cpp:218] Iteration 2736 (2.49026 iter/s, 4.81878s/12 iters), loss = 2.00742
I0408 19:41:10.299306  5931 solver.cpp:237]     Train net output #0: loss = 2.00742 (* 1 = 2.00742 loss)
I0408 19:41:10.299314  5931 sgd_solver.cpp:105] Iteration 2736, lr = 0.00970011
I0408 19:41:15.002141  5931 solver.cpp:218] Iteration 2748 (2.55166 iter/s, 4.70282s/12 iters), loss = 2.10363
I0408 19:41:15.002265  5931 solver.cpp:237]     Train net output #0: loss = 2.10363 (* 1 = 2.10363 loss)
I0408 19:41:15.002276  5931 sgd_solver.cpp:105] Iteration 2748, lr = 0.00969493
I0408 19:41:16.908144  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 19:41:20.002602  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 19:41:22.363296  5931 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 19:41:22.363322  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:41:25.672354  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:41:25.941510  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:41:27.165488  5931 solver.cpp:397]     Test net output #0: accuracy = 0.304534
I0408 19:41:27.165537  5931 solver.cpp:397]     Test net output #1: loss = 3.14128 (* 1 = 3.14128 loss)
I0408 19:41:28.895112  5931 solver.cpp:218] Iteration 2760 (0.863755 iter/s, 13.8928s/12 iters), loss = 2.21353
I0408 19:41:28.895148  5931 solver.cpp:237]     Train net output #0: loss = 2.21353 (* 1 = 2.21353 loss)
I0408 19:41:28.895156  5931 sgd_solver.cpp:105] Iteration 2760, lr = 0.00968967
I0408 19:41:33.600862  5931 solver.cpp:218] Iteration 2772 (2.5501 iter/s, 4.70569s/12 iters), loss = 2.35685
I0408 19:41:33.600893  5931 solver.cpp:237]     Train net output #0: loss = 2.35685 (* 1 = 2.35685 loss)
I0408 19:41:33.600899  5931 sgd_solver.cpp:105] Iteration 2772, lr = 0.00968432
I0408 19:41:38.433198  5931 solver.cpp:218] Iteration 2784 (2.4833 iter/s, 4.83229s/12 iters), loss = 2.39505
I0408 19:41:38.433231  5931 solver.cpp:237]     Train net output #0: loss = 2.39505 (* 1 = 2.39505 loss)
I0408 19:41:38.433239  5931 sgd_solver.cpp:105] Iteration 2784, lr = 0.00967888
I0408 19:41:43.245333  5931 solver.cpp:218] Iteration 2796 (2.49372 iter/s, 4.81208s/12 iters), loss = 2.36989
I0408 19:41:43.245365  5931 solver.cpp:237]     Train net output #0: loss = 2.36989 (* 1 = 2.36989 loss)
I0408 19:41:43.245373  5931 sgd_solver.cpp:105] Iteration 2796, lr = 0.00967335
I0408 19:41:48.024652  5931 solver.cpp:218] Iteration 2808 (2.51084 iter/s, 4.77927s/12 iters), loss = 2.06494
I0408 19:41:48.024744  5931 solver.cpp:237]     Train net output #0: loss = 2.06494 (* 1 = 2.06494 loss)
I0408 19:41:48.024751  5931 sgd_solver.cpp:105] Iteration 2808, lr = 0.00966773
I0408 19:41:52.757622  5931 solver.cpp:218] Iteration 2820 (2.53546 iter/s, 4.73286s/12 iters), loss = 1.86764
I0408 19:41:52.757655  5931 solver.cpp:237]     Train net output #0: loss = 1.86764 (* 1 = 1.86764 loss)
I0408 19:41:52.757663  5931 sgd_solver.cpp:105] Iteration 2820, lr = 0.00966201
I0408 19:41:57.236042  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:41:57.524037  5931 solver.cpp:218] Iteration 2832 (2.51764 iter/s, 4.76636s/12 iters), loss = 2.05133
I0408 19:41:57.524070  5931 solver.cpp:237]     Train net output #0: loss = 2.05133 (* 1 = 2.05133 loss)
I0408 19:41:57.524078  5931 sgd_solver.cpp:105] Iteration 2832, lr = 0.0096562
I0408 19:42:02.363499  5931 solver.cpp:218] Iteration 2844 (2.47964 iter/s, 4.83941s/12 iters), loss = 1.95991
I0408 19:42:02.363533  5931 solver.cpp:237]     Train net output #0: loss = 1.95991 (* 1 = 1.95991 loss)
I0408 19:42:02.363539  5931 sgd_solver.cpp:105] Iteration 2844, lr = 0.00965029
I0408 19:42:06.726016  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 19:42:09.833176  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 19:42:12.193868  5931 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 19:42:12.193894  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:42:15.723096  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:42:16.981695  5931 solver.cpp:397]     Test net output #0: accuracy = 0.297181
I0408 19:42:16.981742  5931 solver.cpp:397]     Test net output #1: loss = 3.13355 (* 1 = 3.13355 loss)
I0408 19:42:17.078207  5931 solver.cpp:218] Iteration 2856 (0.815514 iter/s, 14.7146s/12 iters), loss = 2.0758
I0408 19:42:17.078250  5931 solver.cpp:237]     Train net output #0: loss = 2.0758 (* 1 = 2.0758 loss)
I0408 19:42:17.078258  5931 sgd_solver.cpp:105] Iteration 2856, lr = 0.00964429
I0408 19:42:21.052358  5931 solver.cpp:218] Iteration 2868 (3.01956 iter/s, 3.97409s/12 iters), loss = 1.90546
I0408 19:42:21.052421  5931 solver.cpp:237]     Train net output #0: loss = 1.90546 (* 1 = 1.90546 loss)
I0408 19:42:21.052429  5931 sgd_solver.cpp:105] Iteration 2868, lr = 0.00963818
I0408 19:42:25.862953  5931 solver.cpp:218] Iteration 2880 (2.49453 iter/s, 4.81052s/12 iters), loss = 1.88721
I0408 19:42:25.862985  5931 solver.cpp:237]     Train net output #0: loss = 1.88721 (* 1 = 1.88721 loss)
I0408 19:42:25.862993  5931 sgd_solver.cpp:105] Iteration 2880, lr = 0.00963198
I0408 19:42:30.681690  5931 solver.cpp:218] Iteration 2892 (2.49031 iter/s, 4.81869s/12 iters), loss = 2.44913
I0408 19:42:30.681722  5931 solver.cpp:237]     Train net output #0: loss = 2.44913 (* 1 = 2.44913 loss)
I0408 19:42:30.681730  5931 sgd_solver.cpp:105] Iteration 2892, lr = 0.00962567
I0408 19:42:35.491722  5931 solver.cpp:218] Iteration 2904 (2.49481 iter/s, 4.80998s/12 iters), loss = 1.8989
I0408 19:42:35.491755  5931 solver.cpp:237]     Train net output #0: loss = 1.8989 (* 1 = 1.8989 loss)
I0408 19:42:35.491762  5931 sgd_solver.cpp:105] Iteration 2904, lr = 0.00961926
I0408 19:42:40.336444  5931 solver.cpp:218] Iteration 2916 (2.47695 iter/s, 4.84467s/12 iters), loss = 1.77333
I0408 19:42:40.336477  5931 solver.cpp:237]     Train net output #0: loss = 1.77333 (* 1 = 1.77333 loss)
I0408 19:42:40.336484  5931 sgd_solver.cpp:105] Iteration 2916, lr = 0.00961275
I0408 19:42:45.114996  5931 solver.cpp:218] Iteration 2928 (2.51125 iter/s, 4.7785s/12 iters), loss = 1.6426
I0408 19:42:45.115029  5931 solver.cpp:237]     Train net output #0: loss = 1.6426 (* 1 = 1.6426 loss)
I0408 19:42:45.115037  5931 sgd_solver.cpp:105] Iteration 2928, lr = 0.00960612
I0408 19:42:46.835109  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:42:49.806344  5931 solver.cpp:218] Iteration 2940 (2.55793 iter/s, 4.6913s/12 iters), loss = 1.95866
I0408 19:42:49.806378  5931 solver.cpp:237]     Train net output #0: loss = 1.95866 (* 1 = 1.95866 loss)
I0408 19:42:49.806385  5931 sgd_solver.cpp:105] Iteration 2940, lr = 0.00959939
I0408 19:42:54.622810  5931 solver.cpp:218] Iteration 2952 (2.49148 iter/s, 4.81641s/12 iters), loss = 1.89963
I0408 19:42:54.622961  5931 solver.cpp:237]     Train net output #0: loss = 1.89963 (* 1 = 1.89963 loss)
I0408 19:42:54.622969  5931 sgd_solver.cpp:105] Iteration 2952, lr = 0.00959255
I0408 19:42:56.586161  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 19:43:01.326535  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 19:43:05.371796  5931 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 19:43:05.371821  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:43:08.856606  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:43:10.169764  5931 solver.cpp:397]     Test net output #0: accuracy = 0.315564
I0408 19:43:10.169813  5931 solver.cpp:397]     Test net output #1: loss = 3.04869 (* 1 = 3.04869 loss)
I0408 19:43:11.911417  5931 solver.cpp:218] Iteration 2964 (0.694106 iter/s, 17.2884s/12 iters), loss = 2.17748
I0408 19:43:11.911453  5931 solver.cpp:237]     Train net output #0: loss = 2.17748 (* 1 = 2.17748 loss)
I0408 19:43:11.911459  5931 sgd_solver.cpp:105] Iteration 2964, lr = 0.0095856
I0408 19:43:16.705067  5931 solver.cpp:218] Iteration 2976 (2.50334 iter/s, 4.7936s/12 iters), loss = 1.79911
I0408 19:43:16.705098  5931 solver.cpp:237]     Train net output #0: loss = 1.79911 (* 1 = 1.79911 loss)
I0408 19:43:16.705106  5931 sgd_solver.cpp:105] Iteration 2976, lr = 0.00957853
I0408 19:43:21.536164  5931 solver.cpp:218] Iteration 2988 (2.48393 iter/s, 4.83105s/12 iters), loss = 2.20402
I0408 19:43:21.536198  5931 solver.cpp:237]     Train net output #0: loss = 2.20402 (* 1 = 2.20402 loss)
I0408 19:43:21.536206  5931 sgd_solver.cpp:105] Iteration 2988, lr = 0.00957135
I0408 19:43:26.366717  5931 solver.cpp:218] Iteration 3000 (2.48422 iter/s, 4.8305s/12 iters), loss = 2.03664
I0408 19:43:26.366832  5931 solver.cpp:237]     Train net output #0: loss = 2.03664 (* 1 = 2.03664 loss)
I0408 19:43:26.366840  5931 sgd_solver.cpp:105] Iteration 3000, lr = 0.00956405
I0408 19:43:31.202358  5931 solver.cpp:218] Iteration 3012 (2.48164 iter/s, 4.83551s/12 iters), loss = 1.75509
I0408 19:43:31.202389  5931 solver.cpp:237]     Train net output #0: loss = 1.75509 (* 1 = 1.75509 loss)
I0408 19:43:31.202395  5931 sgd_solver.cpp:105] Iteration 3012, lr = 0.00955663
I0408 19:43:36.020627  5931 solver.cpp:218] Iteration 3024 (2.49055 iter/s, 4.81822s/12 iters), loss = 2.015
I0408 19:43:36.020660  5931 solver.cpp:237]     Train net output #0: loss = 2.015 (* 1 = 2.015 loss)
I0408 19:43:36.020668  5931 sgd_solver.cpp:105] Iteration 3024, lr = 0.00954909
I0408 19:43:39.807835  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:43:40.797749  5931 solver.cpp:218] Iteration 3036 (2.512 iter/s, 4.77707s/12 iters), loss = 1.56338
I0408 19:43:40.797780  5931 solver.cpp:237]     Train net output #0: loss = 1.56338 (* 1 = 1.56338 loss)
I0408 19:43:40.797787  5931 sgd_solver.cpp:105] Iteration 3036, lr = 0.00954143
I0408 19:43:45.662631  5931 solver.cpp:218] Iteration 3048 (2.46668 iter/s, 4.86483s/12 iters), loss = 1.99556
I0408 19:43:45.662663  5931 solver.cpp:237]     Train net output #0: loss = 1.99556 (* 1 = 1.99556 loss)
I0408 19:43:45.662670  5931 sgd_solver.cpp:105] Iteration 3048, lr = 0.00953365
I0408 19:43:50.008245  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 19:43:53.197582  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 19:43:55.828387  5931 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 19:43:55.828413  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:43:59.266371  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:44:00.614713  5931 solver.cpp:397]     Test net output #0: accuracy = 0.304534
I0408 19:44:00.614761  5931 solver.cpp:397]     Test net output #1: loss = 3.11495 (* 1 = 3.11495 loss)
I0408 19:44:00.711243  5931 solver.cpp:218] Iteration 3060 (0.797419 iter/s, 15.0485s/12 iters), loss = 1.87507
I0408 19:44:00.711282  5931 solver.cpp:237]     Train net output #0: loss = 1.87507 (* 1 = 1.87507 loss)
I0408 19:44:00.711289  5931 sgd_solver.cpp:105] Iteration 3060, lr = 0.00952574
I0408 19:44:04.732054  5931 solver.cpp:218] Iteration 3072 (2.98451 iter/s, 4.02076s/12 iters), loss = 1.79878
I0408 19:44:04.732087  5931 solver.cpp:237]     Train net output #0: loss = 1.79878 (* 1 = 1.79878 loss)
I0408 19:44:04.732095  5931 sgd_solver.cpp:105] Iteration 3072, lr = 0.00951771
I0408 19:44:09.751900  5931 solver.cpp:218] Iteration 3084 (2.39054 iter/s, 5.0198s/12 iters), loss = 1.55169
I0408 19:44:09.751935  5931 solver.cpp:237]     Train net output #0: loss = 1.55169 (* 1 = 1.55169 loss)
I0408 19:44:09.751942  5931 sgd_solver.cpp:105] Iteration 3084, lr = 0.00950954
I0408 19:44:14.572896  5931 solver.cpp:218] Iteration 3096 (2.48914 iter/s, 4.82094s/12 iters), loss = 1.60376
I0408 19:44:14.572928  5931 solver.cpp:237]     Train net output #0: loss = 1.60376 (* 1 = 1.60376 loss)
I0408 19:44:14.572935  5931 sgd_solver.cpp:105] Iteration 3096, lr = 0.00950124
I0408 19:44:19.430220  5931 solver.cpp:218] Iteration 3108 (2.47052 iter/s, 4.85727s/12 iters), loss = 1.71824
I0408 19:44:19.430253  5931 solver.cpp:237]     Train net output #0: loss = 1.71824 (* 1 = 1.71824 loss)
I0408 19:44:19.430259  5931 sgd_solver.cpp:105] Iteration 3108, lr = 0.00949281
I0408 19:44:24.271450  5931 solver.cpp:218] Iteration 3120 (2.47873 iter/s, 4.84118s/12 iters), loss = 1.6159
I0408 19:44:24.271484  5931 solver.cpp:237]     Train net output #0: loss = 1.6159 (* 1 = 1.6159 loss)
I0408 19:44:24.271492  5931 sgd_solver.cpp:105] Iteration 3120, lr = 0.00948425
I0408 19:44:29.070111  5931 solver.cpp:218] Iteration 3132 (2.50073 iter/s, 4.79861s/12 iters), loss = 1.71648
I0408 19:44:29.070153  5931 solver.cpp:237]     Train net output #0: loss = 1.71648 (* 1 = 1.71648 loss)
I0408 19:44:29.070164  5931 sgd_solver.cpp:105] Iteration 3132, lr = 0.00947555
I0408 19:44:30.114615  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:44:33.914194  5931 solver.cpp:218] Iteration 3144 (2.47728 iter/s, 4.84402s/12 iters), loss = 1.68917
I0408 19:44:33.914227  5931 solver.cpp:237]     Train net output #0: loss = 1.68917 (* 1 = 1.68917 loss)
I0408 19:44:33.914234  5931 sgd_solver.cpp:105] Iteration 3144, lr = 0.00946671
I0408 19:44:38.717455  5931 solver.cpp:218] Iteration 3156 (2.49833 iter/s, 4.80321s/12 iters), loss = 1.64058
I0408 19:44:38.717487  5931 solver.cpp:237]     Train net output #0: loss = 1.64058 (* 1 = 1.64058 loss)
I0408 19:44:38.717495  5931 sgd_solver.cpp:105] Iteration 3156, lr = 0.00945773
I0408 19:44:40.669191  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 19:44:43.773061  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 19:44:46.127104  5931 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 19:44:46.127130  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:44:49.515609  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:44:50.915791  5931 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0408 19:44:50.915838  5931 solver.cpp:397]     Test net output #1: loss = 3.20776 (* 1 = 3.20776 loss)
I0408 19:44:52.678568  5931 solver.cpp:218] Iteration 3168 (0.859534 iter/s, 13.961s/12 iters), loss = 1.9325
I0408 19:44:52.678601  5931 solver.cpp:237]     Train net output #0: loss = 1.9325 (* 1 = 1.9325 loss)
I0408 19:44:52.678608  5931 sgd_solver.cpp:105] Iteration 3168, lr = 0.00944861
I0408 19:44:57.391702  5931 solver.cpp:218] Iteration 3180 (2.5461 iter/s, 4.71308s/12 iters), loss = 1.52427
I0408 19:44:57.391734  5931 solver.cpp:237]     Train net output #0: loss = 1.52427 (* 1 = 1.52427 loss)
I0408 19:44:57.391742  5931 sgd_solver.cpp:105] Iteration 3180, lr = 0.00943934
I0408 19:45:02.252027  5931 solver.cpp:218] Iteration 3192 (2.469 iter/s, 4.86028s/12 iters), loss = 1.31882
I0408 19:45:02.252187  5931 solver.cpp:237]     Train net output #0: loss = 1.31882 (* 1 = 1.31882 loss)
I0408 19:45:02.252197  5931 sgd_solver.cpp:105] Iteration 3192, lr = 0.00942993
I0408 19:45:07.063972  5931 solver.cpp:218] Iteration 3204 (2.49388 iter/s, 4.81177s/12 iters), loss = 1.77936
I0408 19:45:07.064008  5931 solver.cpp:237]     Train net output #0: loss = 1.77936 (* 1 = 1.77936 loss)
I0408 19:45:07.064016  5931 sgd_solver.cpp:105] Iteration 3204, lr = 0.00942037
I0408 19:45:11.773883  5931 solver.cpp:218] Iteration 3216 (2.54785 iter/s, 4.70986s/12 iters), loss = 2.16366
I0408 19:45:11.773916  5931 solver.cpp:237]     Train net output #0: loss = 2.16366 (* 1 = 2.16366 loss)
I0408 19:45:11.773922  5931 sgd_solver.cpp:105] Iteration 3216, lr = 0.00941066
I0408 19:45:16.527292  5931 solver.cpp:218] Iteration 3228 (2.52453 iter/s, 4.75336s/12 iters), loss = 1.4334
I0408 19:45:16.527325  5931 solver.cpp:237]     Train net output #0: loss = 1.4334 (* 1 = 1.4334 loss)
I0408 19:45:16.527333  5931 sgd_solver.cpp:105] Iteration 3228, lr = 0.00940079
I0408 19:45:19.651507  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:45:21.341858  5931 solver.cpp:218] Iteration 3240 (2.49246 iter/s, 4.81451s/12 iters), loss = 1.77559
I0408 19:45:21.341892  5931 solver.cpp:237]     Train net output #0: loss = 1.77559 (* 1 = 1.77559 loss)
I0408 19:45:21.341899  5931 sgd_solver.cpp:105] Iteration 3240, lr = 0.00939077
I0408 19:45:26.192190  5931 solver.cpp:218] Iteration 3252 (2.47408 iter/s, 4.85028s/12 iters), loss = 1.76575
I0408 19:45:26.192222  5931 solver.cpp:237]     Train net output #0: loss = 1.76575 (* 1 = 1.76575 loss)
I0408 19:45:26.192229  5931 sgd_solver.cpp:105] Iteration 3252, lr = 0.0093806
I0408 19:45:30.558578  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 19:45:33.679528  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 19:45:36.046247  5931 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 19:45:36.046272  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:45:39.288388  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:45:40.548239  5931 solver.cpp:397]     Test net output #0: accuracy = 0.305147
I0408 19:45:40.548285  5931 solver.cpp:397]     Test net output #1: loss = 3.17492 (* 1 = 3.17492 loss)
I0408 19:45:40.644657  5931 solver.cpp:218] Iteration 3264 (0.830312 iter/s, 14.4524s/12 iters), loss = 1.67337
I0408 19:45:40.644690  5931 solver.cpp:237]     Train net output #0: loss = 1.67337 (* 1 = 1.67337 loss)
I0408 19:45:40.644698  5931 sgd_solver.cpp:105] Iteration 3264, lr = 0.00937027
I0408 19:45:44.655725  5931 solver.cpp:218] Iteration 3276 (2.99176 iter/s, 4.01102s/12 iters), loss = 1.63598
I0408 19:45:44.655757  5931 solver.cpp:237]     Train net output #0: loss = 1.63598 (* 1 = 1.63598 loss)
I0408 19:45:44.655766  5931 sgd_solver.cpp:105] Iteration 3276, lr = 0.00935977
I0408 19:45:49.491142  5931 solver.cpp:218] Iteration 3288 (2.48172 iter/s, 4.83536s/12 iters), loss = 2.06538
I0408 19:45:49.491174  5931 solver.cpp:237]     Train net output #0: loss = 2.06538 (* 1 = 2.06538 loss)
I0408 19:45:49.491183  5931 sgd_solver.cpp:105] Iteration 3288, lr = 0.00934912
I0408 19:45:54.329195  5931 solver.cpp:218] Iteration 3300 (2.48036 iter/s, 4.838s/12 iters), loss = 1.66721
I0408 19:45:54.329227  5931 solver.cpp:237]     Train net output #0: loss = 1.66721 (* 1 = 1.66721 loss)
I0408 19:45:54.329234  5931 sgd_solver.cpp:105] Iteration 3300, lr = 0.0093383
I0408 19:45:59.100371  5931 solver.cpp:218] Iteration 3312 (2.51513 iter/s, 4.77113s/12 iters), loss = 1.60572
I0408 19:45:59.100404  5931 solver.cpp:237]     Train net output #0: loss = 1.60572 (* 1 = 1.60572 loss)
I0408 19:45:59.100411  5931 sgd_solver.cpp:105] Iteration 3312, lr = 0.00932731
I0408 19:46:03.838589  5931 solver.cpp:218] Iteration 3324 (2.53263 iter/s, 4.73817s/12 iters), loss = 1.81534
I0408 19:46:03.838733  5931 solver.cpp:237]     Train net output #0: loss = 1.81534 (* 1 = 1.81534 loss)
I0408 19:46:03.838742  5931 sgd_solver.cpp:105] Iteration 3324, lr = 0.00931615
I0408 19:46:08.586114  5931 solver.cpp:218] Iteration 3336 (2.52772 iter/s, 4.74736s/12 iters), loss = 1.4701
I0408 19:46:08.586148  5931 solver.cpp:237]     Train net output #0: loss = 1.4701 (* 1 = 1.4701 loss)
I0408 19:46:08.586154  5931 sgd_solver.cpp:105] Iteration 3336, lr = 0.00930482
I0408 19:46:09.028456  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:46:13.432662  5931 solver.cpp:218] Iteration 3348 (2.47602 iter/s, 4.8465s/12 iters), loss = 1.9405
I0408 19:46:13.432693  5931 solver.cpp:237]     Train net output #0: loss = 1.9405 (* 1 = 1.9405 loss)
I0408 19:46:13.432701  5931 sgd_solver.cpp:105] Iteration 3348, lr = 0.00929332
I0408 19:46:18.253808  5931 solver.cpp:218] Iteration 3360 (2.48906 iter/s, 4.8211s/12 iters), loss = 1.74063
I0408 19:46:18.253841  5931 solver.cpp:237]     Train net output #0: loss = 1.74063 (* 1 = 1.74063 loss)
I0408 19:46:18.253849  5931 sgd_solver.cpp:105] Iteration 3360, lr = 0.00928164
I0408 19:46:20.218148  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 19:46:25.278501  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 19:46:27.712607  5931 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 19:46:27.712633  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:46:31.026484  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:46:32.495137  5931 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0408 19:46:32.495187  5931 solver.cpp:397]     Test net output #1: loss = 3.11988 (* 1 = 3.11988 loss)
I0408 19:46:34.225756  5931 solver.cpp:218] Iteration 3372 (0.75132 iter/s, 15.9719s/12 iters), loss = 1.5596
I0408 19:46:34.225870  5931 solver.cpp:237]     Train net output #0: loss = 1.5596 (* 1 = 1.5596 loss)
I0408 19:46:34.225879  5931 sgd_solver.cpp:105] Iteration 3372, lr = 0.00926979
I0408 19:46:38.943619  5931 solver.cpp:218] Iteration 3384 (2.54359 iter/s, 4.71773s/12 iters), loss = 1.47987
I0408 19:46:38.943650  5931 solver.cpp:237]     Train net output #0: loss = 1.47987 (* 1 = 1.47987 loss)
I0408 19:46:38.943658  5931 sgd_solver.cpp:105] Iteration 3384, lr = 0.00925775
I0408 19:46:43.769683  5931 solver.cpp:218] Iteration 3396 (2.48652 iter/s, 4.82601s/12 iters), loss = 1.71752
I0408 19:46:43.769716  5931 solver.cpp:237]     Train net output #0: loss = 1.71752 (* 1 = 1.71752 loss)
I0408 19:46:43.769723  5931 sgd_solver.cpp:105] Iteration 3396, lr = 0.00924553
I0408 19:46:48.562824  5931 solver.cpp:218] Iteration 3408 (2.50361 iter/s, 4.79309s/12 iters), loss = 1.7134
I0408 19:46:48.562856  5931 solver.cpp:237]     Train net output #0: loss = 1.7134 (* 1 = 1.7134 loss)
I0408 19:46:48.562865  5931 sgd_solver.cpp:105] Iteration 3408, lr = 0.00923313
I0408 19:46:53.416544  5931 solver.cpp:218] Iteration 3420 (2.47236 iter/s, 4.85367s/12 iters), loss = 1.47257
I0408 19:46:53.416576  5931 solver.cpp:237]     Train net output #0: loss = 1.47257 (* 1 = 1.47257 loss)
I0408 19:46:53.416584  5931 sgd_solver.cpp:105] Iteration 3420, lr = 0.00922054
I0408 19:46:58.229673  5931 solver.cpp:218] Iteration 3432 (2.49321 iter/s, 4.81308s/12 iters), loss = 1.59194
I0408 19:46:58.229705  5931 solver.cpp:237]     Train net output #0: loss = 1.59194 (* 1 = 1.59194 loss)
I0408 19:46:58.229712  5931 sgd_solver.cpp:105] Iteration 3432, lr = 0.00920776
I0408 19:47:00.729575  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:47:03.033624  5931 solver.cpp:218] Iteration 3444 (2.49797 iter/s, 4.8039s/12 iters), loss = 1.63615
I0408 19:47:03.033656  5931 solver.cpp:237]     Train net output #0: loss = 1.63615 (* 1 = 1.63615 loss)
I0408 19:47:03.033664  5931 sgd_solver.cpp:105] Iteration 3444, lr = 0.00919479
I0408 19:47:07.872620  5931 solver.cpp:218] Iteration 3456 (2.47988 iter/s, 4.83894s/12 iters), loss = 1.51587
I0408 19:47:07.872754  5931 solver.cpp:237]     Train net output #0: loss = 1.51587 (* 1 = 1.51587 loss)
I0408 19:47:07.872764  5931 sgd_solver.cpp:105] Iteration 3456, lr = 0.00918163
I0408 19:47:12.244850  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 19:47:15.369820  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 19:47:18.078097  5931 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 19:47:18.078123  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:47:18.488811  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:47:21.336710  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:47:22.859947  5931 solver.cpp:397]     Test net output #0: accuracy = 0.307598
I0408 19:47:22.859997  5931 solver.cpp:397]     Test net output #1: loss = 3.1532 (* 1 = 3.1532 loss)
I0408 19:47:22.956562  5931 solver.cpp:218] Iteration 3468 (0.795557 iter/s, 15.0838s/12 iters), loss = 1.41851
I0408 19:47:22.956595  5931 solver.cpp:237]     Train net output #0: loss = 1.41851 (* 1 = 1.41851 loss)
I0408 19:47:22.956604  5931 sgd_solver.cpp:105] Iteration 3468, lr = 0.00916827
I0408 19:47:26.932937  5931 solver.cpp:218] Iteration 3480 (3.01786 iter/s, 3.97632s/12 iters), loss = 1.7508
I0408 19:47:26.932971  5931 solver.cpp:237]     Train net output #0: loss = 1.7508 (* 1 = 1.7508 loss)
I0408 19:47:26.932978  5931 sgd_solver.cpp:105] Iteration 3480, lr = 0.00915472
I0408 19:47:31.775795  5931 solver.cpp:218] Iteration 3492 (2.4779 iter/s, 4.84281s/12 iters), loss = 1.47012
I0408 19:47:31.775828  5931 solver.cpp:237]     Train net output #0: loss = 1.47012 (* 1 = 1.47012 loss)
I0408 19:47:31.775835  5931 sgd_solver.cpp:105] Iteration 3492, lr = 0.00914096
I0408 19:47:36.610395  5931 solver.cpp:218] Iteration 3504 (2.48213 iter/s, 4.83455s/12 iters), loss = 1.83011
I0408 19:47:36.610428  5931 solver.cpp:237]     Train net output #0: loss = 1.83011 (* 1 = 1.83011 loss)
I0408 19:47:36.610436  5931 sgd_solver.cpp:105] Iteration 3504, lr = 0.009127
I0408 19:47:41.412057  5931 solver.cpp:218] Iteration 3516 (2.49916 iter/s, 4.80161s/12 iters), loss = 1.63459
I0408 19:47:41.412117  5931 solver.cpp:237]     Train net output #0: loss = 1.63459 (* 1 = 1.63459 loss)
I0408 19:47:41.412125  5931 sgd_solver.cpp:105] Iteration 3516, lr = 0.00911284
I0408 19:47:46.240335  5931 solver.cpp:218] Iteration 3528 (2.4854 iter/s, 4.8282s/12 iters), loss = 1.61359
I0408 19:47:46.240367  5931 solver.cpp:237]     Train net output #0: loss = 1.61359 (* 1 = 1.61359 loss)
I0408 19:47:46.240375  5931 sgd_solver.cpp:105] Iteration 3528, lr = 0.00909847
I0408 19:47:50.826333  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:47:51.086673  5931 solver.cpp:218] Iteration 3540 (2.47612 iter/s, 4.84628s/12 iters), loss = 1.62708
I0408 19:47:51.086705  5931 solver.cpp:237]     Train net output #0: loss = 1.62708 (* 1 = 1.62708 loss)
I0408 19:47:51.086712  5931 sgd_solver.cpp:105] Iteration 3540, lr = 0.00908389
I0408 19:47:55.882212  5931 solver.cpp:218] Iteration 3552 (2.50235 iter/s, 4.79549s/12 iters), loss = 1.44678
I0408 19:47:55.882243  5931 solver.cpp:237]     Train net output #0: loss = 1.44678 (* 1 = 1.44678 loss)
I0408 19:47:55.882251  5931 sgd_solver.cpp:105] Iteration 3552, lr = 0.0090691
I0408 19:48:00.728989  5931 solver.cpp:218] Iteration 3564 (2.4759 iter/s, 4.84673s/12 iters), loss = 1.43815
I0408 19:48:00.729022  5931 solver.cpp:237]     Train net output #0: loss = 1.43815 (* 1 = 1.43815 loss)
I0408 19:48:00.729029  5931 sgd_solver.cpp:105] Iteration 3564, lr = 0.00905409
I0408 19:48:02.638224  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 19:48:05.781563  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 19:48:08.133029  5931 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 19:48:08.133054  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:48:11.157994  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:48:12.570896  5931 solver.cpp:397]     Test net output #0: accuracy = 0.310049
I0408 19:48:12.571038  5931 solver.cpp:397]     Test net output #1: loss = 3.2581 (* 1 = 3.2581 loss)
I0408 19:48:14.329645  5931 solver.cpp:218] Iteration 3576 (0.882315 iter/s, 13.6006s/12 iters), loss = 1.72021
I0408 19:48:14.329681  5931 solver.cpp:237]     Train net output #0: loss = 1.72021 (* 1 = 1.72021 loss)
I0408 19:48:14.329689  5931 sgd_solver.cpp:105] Iteration 3576, lr = 0.00903887
I0408 19:48:19.218842  5931 solver.cpp:218] Iteration 3588 (2.45442 iter/s, 4.88914s/12 iters), loss = 1.27849
I0408 19:48:19.218873  5931 solver.cpp:237]     Train net output #0: loss = 1.27849 (* 1 = 1.27849 loss)
I0408 19:48:19.218881  5931 sgd_solver.cpp:105] Iteration 3588, lr = 0.00902343
I0408 19:48:24.058223  5931 solver.cpp:218] Iteration 3600 (2.47968 iter/s, 4.83933s/12 iters), loss = 1.77975
I0408 19:48:24.058259  5931 solver.cpp:237]     Train net output #0: loss = 1.77975 (* 1 = 1.77975 loss)
I0408 19:48:24.058267  5931 sgd_solver.cpp:105] Iteration 3600, lr = 0.00900776
I0408 19:48:28.948431  5931 solver.cpp:218] Iteration 3612 (2.45391 iter/s, 4.89015s/12 iters), loss = 1.53952
I0408 19:48:28.948463  5931 solver.cpp:237]     Train net output #0: loss = 1.53952 (* 1 = 1.53952 loss)
I0408 19:48:28.948470  5931 sgd_solver.cpp:105] Iteration 3612, lr = 0.00899188
I0408 19:48:33.720458  5931 solver.cpp:218] Iteration 3624 (2.51468 iter/s, 4.77198s/12 iters), loss = 1.45971
I0408 19:48:33.720490  5931 solver.cpp:237]     Train net output #0: loss = 1.45971 (* 1 = 1.45971 loss)
I0408 19:48:33.720499  5931 sgd_solver.cpp:105] Iteration 3624, lr = 0.00897577
I0408 19:48:38.524768  5931 solver.cpp:218] Iteration 3636 (2.49778 iter/s, 4.80426s/12 iters), loss = 1.53678
I0408 19:48:38.524801  5931 solver.cpp:237]     Train net output #0: loss = 1.53678 (* 1 = 1.53678 loss)
I0408 19:48:38.524809  5931 sgd_solver.cpp:105] Iteration 3636, lr = 0.00895943
I0408 19:48:40.311872  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:48:43.320030  5931 solver.cpp:218] Iteration 3648 (2.5025 iter/s, 4.79521s/12 iters), loss = 1.70507
I0408 19:48:43.320152  5931 solver.cpp:237]     Train net output #0: loss = 1.70507 (* 1 = 1.70507 loss)
I0408 19:48:43.320160  5931 sgd_solver.cpp:105] Iteration 3648, lr = 0.00894287
I0408 19:48:48.164324  5931 solver.cpp:218] Iteration 3660 (2.47721 iter/s, 4.84416s/12 iters), loss = 1.51504
I0408 19:48:48.164356  5931 solver.cpp:237]     Train net output #0: loss = 1.51504 (* 1 = 1.51504 loss)
I0408 19:48:48.164364  5931 sgd_solver.cpp:105] Iteration 3660, lr = 0.00892607
I0408 19:48:52.532120  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 19:48:55.760816  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 19:48:59.361902  5931 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 19:48:59.361930  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:49:02.542757  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:49:04.150705  5931 solver.cpp:397]     Test net output #0: accuracy = 0.301471
I0408 19:49:04.150753  5931 solver.cpp:397]     Test net output #1: loss = 3.16574 (* 1 = 3.16574 loss)
I0408 19:49:04.247330  5931 solver.cpp:218] Iteration 3672 (0.746132 iter/s, 16.0829s/12 iters), loss = 1.50581
I0408 19:49:04.247367  5931 solver.cpp:237]     Train net output #0: loss = 1.50581 (* 1 = 1.50581 loss)
I0408 19:49:04.247375  5931 sgd_solver.cpp:105] Iteration 3672, lr = 0.00890903
I0408 19:49:08.198350  5931 solver.cpp:218] Iteration 3684 (3.03723 iter/s, 3.95097s/12 iters), loss = 1.53007
I0408 19:49:08.198383  5931 solver.cpp:237]     Train net output #0: loss = 1.53007 (* 1 = 1.53007 loss)
I0408 19:49:08.198390  5931 sgd_solver.cpp:105] Iteration 3684, lr = 0.00889176
I0408 19:49:13.105240  5931 solver.cpp:218] Iteration 3696 (2.44557 iter/s, 4.90684s/12 iters), loss = 1.48696
I0408 19:49:13.105274  5931 solver.cpp:237]     Train net output #0: loss = 1.48696 (* 1 = 1.48696 loss)
I0408 19:49:13.105281  5931 sgd_solver.cpp:105] Iteration 3696, lr = 0.00887425
I0408 19:49:18.319669  5931 solver.cpp:218] Iteration 3708 (2.30133 iter/s, 5.21438s/12 iters), loss = 1.29337
I0408 19:49:18.319816  5931 solver.cpp:237]     Train net output #0: loss = 1.29337 (* 1 = 1.29337 loss)
I0408 19:49:18.319825  5931 sgd_solver.cpp:105] Iteration 3708, lr = 0.0088565
I0408 19:49:23.147174  5931 solver.cpp:218] Iteration 3720 (2.48584 iter/s, 4.82734s/12 iters), loss = 1.27008
I0408 19:49:23.147210  5931 solver.cpp:237]     Train net output #0: loss = 1.27008 (* 1 = 1.27008 loss)
I0408 19:49:23.147218  5931 sgd_solver.cpp:105] Iteration 3720, lr = 0.00883851
I0408 19:49:28.101531  5931 solver.cpp:218] Iteration 3732 (2.42214 iter/s, 4.9543s/12 iters), loss = 1.60349
I0408 19:49:28.101565  5931 solver.cpp:237]     Train net output #0: loss = 1.60349 (* 1 = 1.60349 loss)
I0408 19:49:28.101572  5931 sgd_solver.cpp:105] Iteration 3732, lr = 0.00882027
I0408 19:49:32.338606  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:49:33.317809  5931 solver.cpp:218] Iteration 3744 (2.30051 iter/s, 5.21622s/12 iters), loss = 1.43698
I0408 19:49:33.317842  5931 solver.cpp:237]     Train net output #0: loss = 1.43698 (* 1 = 1.43698 loss)
I0408 19:49:33.317848  5931 sgd_solver.cpp:105] Iteration 3744, lr = 0.00880178
I0408 19:49:38.240744  5931 solver.cpp:218] Iteration 3756 (2.4376 iter/s, 4.92288s/12 iters), loss = 1.32667
I0408 19:49:38.240777  5931 solver.cpp:237]     Train net output #0: loss = 1.32667 (* 1 = 1.32667 loss)
I0408 19:49:38.240784  5931 sgd_solver.cpp:105] Iteration 3756, lr = 0.00878304
I0408 19:49:43.331405  5931 solver.cpp:218] Iteration 3768 (2.35728 iter/s, 5.09061s/12 iters), loss = 1.68272
I0408 19:49:43.331440  5931 solver.cpp:237]     Train net output #0: loss = 1.68272 (* 1 = 1.68272 loss)
I0408 19:49:43.331449  5931 sgd_solver.cpp:105] Iteration 3768, lr = 0.00876406
I0408 19:49:45.337543  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 19:49:48.459075  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 19:49:50.860807  5931 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 19:49:50.860832  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:49:53.999948  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:49:55.646855  5931 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0408 19:49:55.646905  5931 solver.cpp:397]     Test net output #1: loss = 3.21819 (* 1 = 3.21819 loss)
I0408 19:49:57.460376  5931 solver.cpp:218] Iteration 3780 (0.849323 iter/s, 14.1289s/12 iters), loss = 1.88812
I0408 19:49:57.460409  5931 solver.cpp:237]     Train net output #0: loss = 1.88812 (* 1 = 1.88812 loss)
I0408 19:49:57.460417  5931 sgd_solver.cpp:105] Iteration 3780, lr = 0.00874481
I0408 19:50:02.648438  5931 solver.cpp:218] Iteration 3792 (2.31303 iter/s, 5.18801s/12 iters), loss = 1.4342
I0408 19:50:02.648470  5931 solver.cpp:237]     Train net output #0: loss = 1.4342 (* 1 = 1.4342 loss)
I0408 19:50:02.648478  5931 sgd_solver.cpp:105] Iteration 3792, lr = 0.00872531
I0408 19:50:07.744280  5931 solver.cpp:218] Iteration 3804 (2.35489 iter/s, 5.09579s/12 iters), loss = 1.38925
I0408 19:50:07.744313  5931 solver.cpp:237]     Train net output #0: loss = 1.38925 (* 1 = 1.38925 loss)
I0408 19:50:07.744320  5931 sgd_solver.cpp:105] Iteration 3804, lr = 0.00870556
I0408 19:50:12.683784  5931 solver.cpp:218] Iteration 3816 (2.42942 iter/s, 4.93945s/12 iters), loss = 1.59515
I0408 19:50:12.683817  5931 solver.cpp:237]     Train net output #0: loss = 1.59515 (* 1 = 1.59515 loss)
I0408 19:50:12.683825  5931 sgd_solver.cpp:105] Iteration 3816, lr = 0.00868554
I0408 19:50:17.720166  5931 solver.cpp:218] Iteration 3828 (2.38269 iter/s, 5.03633s/12 iters), loss = 1.28719
I0408 19:50:17.720198  5931 solver.cpp:237]     Train net output #0: loss = 1.28719 (* 1 = 1.28719 loss)
I0408 19:50:17.720206  5931 sgd_solver.cpp:105] Iteration 3828, lr = 0.00866526
I0408 19:50:22.454062  5931 solver.cpp:218] Iteration 3840 (2.53494 iter/s, 4.73385s/12 iters), loss = 1.09634
I0408 19:50:22.454161  5931 solver.cpp:237]     Train net output #0: loss = 1.09634 (* 1 = 1.09634 loss)
I0408 19:50:22.454170  5931 sgd_solver.cpp:105] Iteration 3840, lr = 0.00864472
I0408 19:50:23.531124  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:50:27.468495  5931 solver.cpp:218] Iteration 3852 (2.39315 iter/s, 5.01432s/12 iters), loss = 1.3781
I0408 19:50:27.468528  5931 solver.cpp:237]     Train net output #0: loss = 1.3781 (* 1 = 1.3781 loss)
I0408 19:50:27.468535  5931 sgd_solver.cpp:105] Iteration 3852, lr = 0.00862391
I0408 19:50:32.496354  5931 solver.cpp:218] Iteration 3864 (2.38673 iter/s, 5.02781s/12 iters), loss = 1.64849
I0408 19:50:32.496387  5931 solver.cpp:237]     Train net output #0: loss = 1.64849 (* 1 = 1.64849 loss)
I0408 19:50:32.496393  5931 sgd_solver.cpp:105] Iteration 3864, lr = 0.00860284
I0408 19:50:36.962940  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 19:50:43.248865  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 19:50:46.601116  5931 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 19:50:46.601143  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:50:49.554091  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:50:51.058842  5931 solver.cpp:397]     Test net output #0: accuracy = 0.309436
I0408 19:50:51.058890  5931 solver.cpp:397]     Test net output #1: loss = 3.10056 (* 1 = 3.10056 loss)
I0408 19:50:51.154953  5931 solver.cpp:218] Iteration 3876 (0.643138 iter/s, 18.6585s/12 iters), loss = 1.65166
I0408 19:50:51.154989  5931 solver.cpp:237]     Train net output #0: loss = 1.65166 (* 1 = 1.65166 loss)
I0408 19:50:51.154996  5931 sgd_solver.cpp:105] Iteration 3876, lr = 0.00858149
I0408 19:50:55.377822  5931 solver.cpp:218] Iteration 3888 (2.84171 iter/s, 4.22282s/12 iters), loss = 1.34599
I0408 19:50:55.377936  5931 solver.cpp:237]     Train net output #0: loss = 1.34599 (* 1 = 1.34599 loss)
I0408 19:50:55.377945  5931 sgd_solver.cpp:105] Iteration 3888, lr = 0.00855987
I0408 19:51:00.326395  5931 solver.cpp:218] Iteration 3900 (2.42501 iter/s, 4.94844s/12 iters), loss = 1.4676
I0408 19:51:00.326428  5931 solver.cpp:237]     Train net output #0: loss = 1.4676 (* 1 = 1.4676 loss)
I0408 19:51:00.326436  5931 sgd_solver.cpp:105] Iteration 3900, lr = 0.00853798
I0408 19:51:05.088506  5931 solver.cpp:218] Iteration 3912 (2.51992 iter/s, 4.76206s/12 iters), loss = 1.47221
I0408 19:51:05.088539  5931 solver.cpp:237]     Train net output #0: loss = 1.47221 (* 1 = 1.47221 loss)
I0408 19:51:05.088547  5931 sgd_solver.cpp:105] Iteration 3912, lr = 0.00851581
I0408 19:51:09.952258  5931 solver.cpp:218] Iteration 3924 (2.46726 iter/s, 4.8637s/12 iters), loss = 1.06335
I0408 19:51:09.952292  5931 solver.cpp:237]     Train net output #0: loss = 1.06335 (* 1 = 1.06335 loss)
I0408 19:51:09.952299  5931 sgd_solver.cpp:105] Iteration 3924, lr = 0.00849337
I0408 19:51:14.762267  5931 solver.cpp:218] Iteration 3936 (2.49483 iter/s, 4.80996s/12 iters), loss = 1.12105
I0408 19:51:14.762300  5931 solver.cpp:237]     Train net output #0: loss = 1.12105 (* 1 = 1.12105 loss)
I0408 19:51:14.762306  5931 sgd_solver.cpp:105] Iteration 3936, lr = 0.00847065
I0408 19:51:18.042289  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:51:19.569290  5931 solver.cpp:218] Iteration 3948 (2.49638 iter/s, 4.80697s/12 iters), loss = 1.08098
I0408 19:51:19.569322  5931 solver.cpp:237]     Train net output #0: loss = 1.08098 (* 1 = 1.08098 loss)
I0408 19:51:19.569329  5931 sgd_solver.cpp:105] Iteration 3948, lr = 0.00844765
I0408 19:51:24.511744  5931 solver.cpp:218] Iteration 3960 (2.42797 iter/s, 4.9424s/12 iters), loss = 1.03392
I0408 19:51:24.511776  5931 solver.cpp:237]     Train net output #0: loss = 1.03392 (* 1 = 1.03392 loss)
I0408 19:51:24.511785  5931 sgd_solver.cpp:105] Iteration 3960, lr = 0.00842437
I0408 19:51:29.277631  5931 solver.cpp:218] Iteration 3972 (2.51792 iter/s, 4.76583s/12 iters), loss = 1.32092
I0408 19:51:29.277729  5931 solver.cpp:237]     Train net output #0: loss = 1.32092 (* 1 = 1.32092 loss)
I0408 19:51:29.277737  5931 sgd_solver.cpp:105] Iteration 3972, lr = 0.0084008
I0408 19:51:31.184481  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 19:51:35.462579  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 19:51:39.070483  5931 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 19:51:39.070508  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:51:42.083915  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:51:43.615447  5931 solver.cpp:397]     Test net output #0: accuracy = 0.319853
I0408 19:51:43.615491  5931 solver.cpp:397]     Test net output #1: loss = 3.16041 (* 1 = 3.16041 loss)
I0408 19:51:45.347700  5931 solver.cpp:218] Iteration 3984 (0.746736 iter/s, 16.0699s/12 iters), loss = 1.26151
I0408 19:51:45.347735  5931 solver.cpp:237]     Train net output #0: loss = 1.26151 (* 1 = 1.26151 loss)
I0408 19:51:45.347743  5931 sgd_solver.cpp:105] Iteration 3984, lr = 0.00837695
I0408 19:51:50.108656  5931 solver.cpp:218] Iteration 3996 (2.52053 iter/s, 4.7609s/12 iters), loss = 1.12289
I0408 19:51:50.108688  5931 solver.cpp:237]     Train net output #0: loss = 1.12289 (* 1 = 1.12289 loss)
I0408 19:51:50.108697  5931 sgd_solver.cpp:105] Iteration 3996, lr = 0.00835281
I0408 19:51:54.968400  5931 solver.cpp:218] Iteration 4008 (2.46929 iter/s, 4.85969s/12 iters), loss = 1.05794
I0408 19:51:54.968433  5931 solver.cpp:237]     Train net output #0: loss = 1.05794 (* 1 = 1.05794 loss)
I0408 19:51:54.968441  5931 sgd_solver.cpp:105] Iteration 4008, lr = 0.00832839
I0408 19:51:59.785013  5931 solver.cpp:218] Iteration 4020 (2.49141 iter/s, 4.81655s/12 iters), loss = 1.72157
I0408 19:51:59.785156  5931 solver.cpp:237]     Train net output #0: loss = 1.72157 (* 1 = 1.72157 loss)
I0408 19:51:59.785166  5931 sgd_solver.cpp:105] Iteration 4020, lr = 0.00830368
I0408 19:52:04.579777  5931 solver.cpp:218] Iteration 4032 (2.50281 iter/s, 4.79461s/12 iters), loss = 1.51982
I0408 19:52:04.579810  5931 solver.cpp:237]     Train net output #0: loss = 1.51982 (* 1 = 1.51982 loss)
I0408 19:52:04.579818  5931 sgd_solver.cpp:105] Iteration 4032, lr = 0.00827867
I0408 19:52:09.430395  5931 solver.cpp:218] Iteration 4044 (2.47394 iter/s, 4.85057s/12 iters), loss = 1.51046
I0408 19:52:09.430428  5931 solver.cpp:237]     Train net output #0: loss = 1.51046 (* 1 = 1.51046 loss)
I0408 19:52:09.430436  5931 sgd_solver.cpp:105] Iteration 4044, lr = 0.00825338
I0408 19:52:09.891220  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:52:14.253439  5931 solver.cpp:218] Iteration 4056 (2.48808 iter/s, 4.82299s/12 iters), loss = 1.26421
I0408 19:52:14.253471  5931 solver.cpp:237]     Train net output #0: loss = 1.26421 (* 1 = 1.26421 loss)
I0408 19:52:14.253479  5931 sgd_solver.cpp:105] Iteration 4056, lr = 0.0082278
I0408 19:52:19.074704  5931 solver.cpp:218] Iteration 4068 (2.489 iter/s, 4.82121s/12 iters), loss = 1.41095
I0408 19:52:19.074736  5931 solver.cpp:237]     Train net output #0: loss = 1.41095 (* 1 = 1.41095 loss)
I0408 19:52:19.074743  5931 sgd_solver.cpp:105] Iteration 4068, lr = 0.00820192
I0408 19:52:23.444603  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 19:52:27.382596  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 19:52:30.613588  5931 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 19:52:30.613692  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:52:33.622864  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:52:35.396471  5931 solver.cpp:397]     Test net output #0: accuracy = 0.318627
I0408 19:52:35.396518  5931 solver.cpp:397]     Test net output #1: loss = 3.22433 (* 1 = 3.22433 loss)
I0408 19:52:35.491971  5931 solver.cpp:218] Iteration 4080 (0.730941 iter/s, 16.4172s/12 iters), loss = 1.20716
I0408 19:52:35.492003  5931 solver.cpp:237]     Train net output #0: loss = 1.20716 (* 1 = 1.20716 loss)
I0408 19:52:35.492012  5931 sgd_solver.cpp:105] Iteration 4080, lr = 0.00817574
I0408 19:52:39.484777  5931 solver.cpp:218] Iteration 4092 (3.00544 iter/s, 3.99276s/12 iters), loss = 1.16726
I0408 19:52:39.484810  5931 solver.cpp:237]     Train net output #0: loss = 1.16726 (* 1 = 1.16726 loss)
I0408 19:52:39.484817  5931 sgd_solver.cpp:105] Iteration 4092, lr = 0.00814928
I0408 19:52:44.207520  5931 solver.cpp:218] Iteration 4104 (2.54092 iter/s, 4.72269s/12 iters), loss = 1.12696
I0408 19:52:44.207551  5931 solver.cpp:237]     Train net output #0: loss = 1.12696 (* 1 = 1.12696 loss)
I0408 19:52:44.207558  5931 sgd_solver.cpp:105] Iteration 4104, lr = 0.00812251
I0408 19:52:49.030146  5931 solver.cpp:218] Iteration 4116 (2.4883 iter/s, 4.82258s/12 iters), loss = 1.44622
I0408 19:52:49.030179  5931 solver.cpp:237]     Train net output #0: loss = 1.44622 (* 1 = 1.44622 loss)
I0408 19:52:49.030186  5931 sgd_solver.cpp:105] Iteration 4116, lr = 0.00809545
I0408 19:52:53.846629  5931 solver.cpp:218] Iteration 4128 (2.49147 iter/s, 4.81643s/12 iters), loss = 1.02868
I0408 19:52:53.846660  5931 solver.cpp:237]     Train net output #0: loss = 1.02868 (* 1 = 1.02868 loss)
I0408 19:52:53.846668  5931 sgd_solver.cpp:105] Iteration 4128, lr = 0.0080681
I0408 19:52:58.672061  5931 solver.cpp:218] Iteration 4140 (2.48685 iter/s, 4.82538s/12 iters), loss = 0.857603
I0408 19:52:58.672096  5931 solver.cpp:237]     Train net output #0: loss = 0.857603 (* 1 = 0.857603 loss)
I0408 19:52:58.672103  5931 sgd_solver.cpp:105] Iteration 4140, lr = 0.00804044
I0408 19:53:01.218135  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:03.513885  5931 solver.cpp:218] Iteration 4152 (2.47843 iter/s, 4.84177s/12 iters), loss = 1.2021
I0408 19:53:03.513918  5931 solver.cpp:237]     Train net output #0: loss = 1.2021 (* 1 = 1.2021 loss)
I0408 19:53:03.513926  5931 sgd_solver.cpp:105] Iteration 4152, lr = 0.00801249
I0408 19:53:05.051857  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:53:08.323308  5931 solver.cpp:218] Iteration 4164 (2.49513 iter/s, 4.80937s/12 iters), loss = 1.05113
I0408 19:53:08.323340  5931 solver.cpp:237]     Train net output #0: loss = 1.05113 (* 1 = 1.05113 loss)
I0408 19:53:08.323348  5931 sgd_solver.cpp:105] Iteration 4164, lr = 0.00798423
I0408 19:53:13.149473  5931 solver.cpp:218] Iteration 4176 (2.48647 iter/s, 4.82611s/12 iters), loss = 1.49781
I0408 19:53:13.149504  5931 solver.cpp:237]     Train net output #0: loss = 1.49781 (* 1 = 1.49781 loss)
I0408 19:53:13.149513  5931 sgd_solver.cpp:105] Iteration 4176, lr = 0.00795568
I0408 19:53:15.104741  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 19:53:18.211241  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 19:53:21.356909  5931 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 19:53:21.356935  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:53:24.319149  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:26.146239  5931 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0408 19:53:26.146286  5931 solver.cpp:397]     Test net output #1: loss = 3.12765 (* 1 = 3.12765 loss)
I0408 19:53:27.883332  5931 solver.cpp:218] Iteration 4188 (0.814454 iter/s, 14.7338s/12 iters), loss = 1.58775
I0408 19:53:27.883365  5931 solver.cpp:237]     Train net output #0: loss = 1.58775 (* 1 = 1.58775 loss)
I0408 19:53:27.883373  5931 sgd_solver.cpp:105] Iteration 4188, lr = 0.00792683
I0408 19:53:32.615044  5931 solver.cpp:218] Iteration 4200 (2.53611 iter/s, 4.73166s/12 iters), loss = 1.11497
I0408 19:53:32.615140  5931 solver.cpp:237]     Train net output #0: loss = 1.11497 (* 1 = 1.11497 loss)
I0408 19:53:32.615149  5931 sgd_solver.cpp:105] Iteration 4200, lr = 0.00789768
I0408 19:53:37.447885  5931 solver.cpp:218] Iteration 4212 (2.48307 iter/s, 4.83273s/12 iters), loss = 1.23404
I0408 19:53:37.447923  5931 solver.cpp:237]     Train net output #0: loss = 1.23404 (* 1 = 1.23404 loss)
I0408 19:53:37.447930  5931 sgd_solver.cpp:105] Iteration 4212, lr = 0.00786823
I0408 19:53:42.283274  5931 solver.cpp:218] Iteration 4224 (2.48173 iter/s, 4.83533s/12 iters), loss = 1.34776
I0408 19:53:42.283313  5931 solver.cpp:237]     Train net output #0: loss = 1.34776 (* 1 = 1.34776 loss)
I0408 19:53:42.283322  5931 sgd_solver.cpp:105] Iteration 4224, lr = 0.00783848
I0408 19:53:47.100708  5931 solver.cpp:218] Iteration 4236 (2.49098 iter/s, 4.81738s/12 iters), loss = 1.16645
I0408 19:53:47.100744  5931 solver.cpp:237]     Train net output #0: loss = 1.16645 (* 1 = 1.16645 loss)
I0408 19:53:47.100752  5931 sgd_solver.cpp:105] Iteration 4236, lr = 0.00780843
I0408 19:53:51.677166  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:51.894517  5931 solver.cpp:218] Iteration 4248 (2.50326 iter/s, 4.79375s/12 iters), loss = 1.18824
I0408 19:53:51.894551  5931 solver.cpp:237]     Train net output #0: loss = 1.18824 (* 1 = 1.18824 loss)
I0408 19:53:51.894559  5931 sgd_solver.cpp:105] Iteration 4248, lr = 0.00777809
I0408 19:53:56.592785  5931 solver.cpp:218] Iteration 4260 (2.55416 iter/s, 4.69821s/12 iters), loss = 0.948969
I0408 19:53:56.592819  5931 solver.cpp:237]     Train net output #0: loss = 0.948969 (* 1 = 0.948969 loss)
I0408 19:53:56.592828  5931 sgd_solver.cpp:105] Iteration 4260, lr = 0.00774744
I0408 19:54:01.379490  5931 solver.cpp:218] Iteration 4272 (2.50697 iter/s, 4.78665s/12 iters), loss = 1.01914
I0408 19:54:01.379523  5931 solver.cpp:237]     Train net output #0: loss = 1.01914 (* 1 = 1.01914 loss)
I0408 19:54:01.379537  5931 sgd_solver.cpp:105] Iteration 4272, lr = 0.00771649
I0408 19:54:05.751072  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 19:54:08.864266  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 19:54:11.218518  5931 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 19:54:11.218544  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:54:14.140738  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:54:15.999917  5931 solver.cpp:397]     Test net output #0: accuracy = 0.333333
I0408 19:54:15.999966  5931 solver.cpp:397]     Test net output #1: loss = 3.15961 (* 1 = 3.15961 loss)
I0408 19:54:16.096501  5931 solver.cpp:218] Iteration 4284 (0.815387 iter/s, 14.7169s/12 iters), loss = 1.12854
I0408 19:54:16.096539  5931 solver.cpp:237]     Train net output #0: loss = 1.12854 (* 1 = 1.12854 loss)
I0408 19:54:16.096546  5931 sgd_solver.cpp:105] Iteration 4284, lr = 0.00768525
I0408 19:54:20.038765  5931 solver.cpp:218] Iteration 4296 (3.04398 iter/s, 3.94221s/12 iters), loss = 0.818015
I0408 19:54:20.038800  5931 solver.cpp:237]     Train net output #0: loss = 0.818015 (* 1 = 0.818015 loss)
I0408 19:54:20.038807  5931 sgd_solver.cpp:105] Iteration 4296, lr = 0.00765371
I0408 19:54:24.915174  5931 solver.cpp:218] Iteration 4308 (2.46085 iter/s, 4.87636s/12 iters), loss = 1.00404
I0408 19:54:24.915211  5931 solver.cpp:237]     Train net output #0: loss = 1.00404 (* 1 = 1.00404 loss)
I0408 19:54:24.915220  5931 sgd_solver.cpp:105] Iteration 4308, lr = 0.00762187
I0408 19:54:29.693827  5931 solver.cpp:218] Iteration 4320 (2.5112 iter/s, 4.7786s/12 iters), loss = 1.52882
I0408 19:54:29.693866  5931 solver.cpp:237]     Train net output #0: loss = 1.52882 (* 1 = 1.52882 loss)
I0408 19:54:29.693873  5931 sgd_solver.cpp:105] Iteration 4320, lr = 0.00758973
I0408 19:54:34.529711  5931 solver.cpp:218] Iteration 4332 (2.48148 iter/s, 4.83583s/12 iters), loss = 0.943072
I0408 19:54:34.529744  5931 solver.cpp:237]     Train net output #0: loss = 0.943072 (* 1 = 0.943072 loss)
I0408 19:54:34.529752  5931 sgd_solver.cpp:105] Iteration 4332, lr = 0.0075573
I0408 19:54:39.350107  5931 solver.cpp:218] Iteration 4344 (2.48945 iter/s, 4.82034s/12 iters), loss = 0.936029
I0408 19:54:39.350203  5931 solver.cpp:237]     Train net output #0: loss = 0.936029 (* 1 = 0.936029 loss)
I0408 19:54:39.350210  5931 sgd_solver.cpp:105] Iteration 4344, lr = 0.00752458
I0408 19:54:41.174686  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:54:44.188874  5931 solver.cpp:218] Iteration 4356 (2.48003 iter/s, 4.83866s/12 iters), loss = 0.903117
I0408 19:54:44.188907  5931 solver.cpp:237]     Train net output #0: loss = 0.903117 (* 1 = 0.903117 loss)
I0408 19:54:44.188915  5931 sgd_solver.cpp:105] Iteration 4356, lr = 0.00749156
I0408 19:54:49.023360  5931 solver.cpp:218] Iteration 4368 (2.48219 iter/s, 4.83444s/12 iters), loss = 0.822737
I0408 19:54:49.023396  5931 solver.cpp:237]     Train net output #0: loss = 0.822737 (* 1 = 0.822737 loss)
I0408 19:54:49.023403  5931 sgd_solver.cpp:105] Iteration 4368, lr = 0.00745825
I0408 19:54:53.842201  5931 solver.cpp:218] Iteration 4380 (2.49025 iter/s, 4.81879s/12 iters), loss = 1.03773
I0408 19:54:53.842233  5931 solver.cpp:237]     Train net output #0: loss = 1.03773 (* 1 = 1.03773 loss)
I0408 19:54:53.842242  5931 sgd_solver.cpp:105] Iteration 4380, lr = 0.00742466
I0408 19:54:55.795584  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 19:54:58.951737  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 19:55:01.310107  5931 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 19:55:01.310133  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:55:04.183652  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:55:06.091280  5931 solver.cpp:397]     Test net output #0: accuracy = 0.356005
I0408 19:55:06.091329  5931 solver.cpp:397]     Test net output #1: loss = 3.1704 (* 1 = 3.1704 loss)
I0408 19:55:07.817795  5931 solver.cpp:218] Iteration 4392 (0.858644 iter/s, 13.9755s/12 iters), loss = 1.31783
I0408 19:55:07.817829  5931 solver.cpp:237]     Train net output #0: loss = 1.31783 (* 1 = 1.31783 loss)
I0408 19:55:07.817836  5931 sgd_solver.cpp:105] Iteration 4392, lr = 0.00739077
I0408 19:55:12.546609  5931 solver.cpp:218] Iteration 4404 (2.53766 iter/s, 4.72876s/12 iters), loss = 1.02119
I0408 19:55:12.546726  5931 solver.cpp:237]     Train net output #0: loss = 1.02119 (* 1 = 1.02119 loss)
I0408 19:55:12.546736  5931 sgd_solver.cpp:105] Iteration 4404, lr = 0.00735659
I0408 19:55:17.353472  5931 solver.cpp:218] Iteration 4416 (2.4965 iter/s, 4.80673s/12 iters), loss = 0.814722
I0408 19:55:17.353507  5931 solver.cpp:237]     Train net output #0: loss = 0.814722 (* 1 = 0.814722 loss)
I0408 19:55:17.353513  5931 sgd_solver.cpp:105] Iteration 4416, lr = 0.00732214
I0408 19:55:22.153848  5931 solver.cpp:218] Iteration 4428 (2.49983 iter/s, 4.80032s/12 iters), loss = 1.015
I0408 19:55:22.153882  5931 solver.cpp:237]     Train net output #0: loss = 1.015 (* 1 = 1.015 loss)
I0408 19:55:22.153889  5931 sgd_solver.cpp:105] Iteration 4428, lr = 0.00728739
I0408 19:55:27.004606  5931 solver.cpp:218] Iteration 4440 (2.47387 iter/s, 4.8507s/12 iters), loss = 0.763155
I0408 19:55:27.004639  5931 solver.cpp:237]     Train net output #0: loss = 0.763155 (* 1 = 0.763155 loss)
I0408 19:55:27.004647  5931 sgd_solver.cpp:105] Iteration 4440, lr = 0.00725237
I0408 19:55:30.844806  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:55:31.781582  5931 solver.cpp:218] Iteration 4452 (2.51208 iter/s, 4.77693s/12 iters), loss = 1.22367
I0408 19:55:31.781615  5931 solver.cpp:237]     Train net output #0: loss = 1.22367 (* 1 = 1.22367 loss)
I0408 19:55:31.781622  5931 sgd_solver.cpp:105] Iteration 4452, lr = 0.00721706
I0408 19:55:36.626356  5931 solver.cpp:218] Iteration 4464 (2.47692 iter/s, 4.84472s/12 iters), loss = 0.884745
I0408 19:55:36.626389  5931 solver.cpp:237]     Train net output #0: loss = 0.884745 (* 1 = 0.884745 loss)
I0408 19:55:36.626396  5931 sgd_solver.cpp:105] Iteration 4464, lr = 0.00718148
I0408 19:55:41.471554  5931 solver.cpp:218] Iteration 4476 (2.4767 iter/s, 4.84515s/12 iters), loss = 1.01609
I0408 19:55:41.471591  5931 solver.cpp:237]     Train net output #0: loss = 1.01609 (* 1 = 1.01609 loss)
I0408 19:55:41.471598  5931 sgd_solver.cpp:105] Iteration 4476, lr = 0.00714562
I0408 19:55:45.798264  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 19:55:50.364996  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 19:55:54.690732  5931 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 19:55:54.690758  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:55:57.342164  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:55:59.059446  5931 solver.cpp:397]     Test net output #0: accuracy = 0.355392
I0408 19:55:59.059492  5931 solver.cpp:397]     Test net output #1: loss = 3.15389 (* 1 = 3.15389 loss)
I0408 19:55:59.155997  5931 solver.cpp:218] Iteration 4488 (0.678566 iter/s, 17.6844s/12 iters), loss = 0.974825
I0408 19:55:59.156036  5931 solver.cpp:237]     Train net output #0: loss = 0.974825 (* 1 = 0.974825 loss)
I0408 19:55:59.156044  5931 sgd_solver.cpp:105] Iteration 4488, lr = 0.00710949
I0408 19:56:03.140056  5931 solver.cpp:218] Iteration 4500 (3.01204 iter/s, 3.98401s/12 iters), loss = 0.633073
I0408 19:56:03.140089  5931 solver.cpp:237]     Train net output #0: loss = 0.633073 (* 1 = 0.633073 loss)
I0408 19:56:03.140096  5931 sgd_solver.cpp:105] Iteration 4500, lr = 0.0070731
I0408 19:56:07.953107  5931 solver.cpp:218] Iteration 4512 (2.49325 iter/s, 4.813s/12 iters), loss = 1.14185
I0408 19:56:07.953140  5931 solver.cpp:237]     Train net output #0: loss = 1.14185 (* 1 = 1.14185 loss)
I0408 19:56:07.953146  5931 sgd_solver.cpp:105] Iteration 4512, lr = 0.00703643
I0408 19:56:12.695410  5931 solver.cpp:218] Iteration 4524 (2.53044 iter/s, 4.74226s/12 iters), loss = 0.898067
I0408 19:56:12.695443  5931 solver.cpp:237]     Train net output #0: loss = 0.898067 (* 1 = 0.898067 loss)
I0408 19:56:12.695451  5931 sgd_solver.cpp:105] Iteration 4524, lr = 0.0069995
I0408 19:56:17.414057  5931 solver.cpp:218] Iteration 4536 (2.54313 iter/s, 4.71859s/12 iters), loss = 0.961567
I0408 19:56:17.414170  5931 solver.cpp:237]     Train net output #0: loss = 0.961567 (* 1 = 0.961567 loss)
I0408 19:56:17.414178  5931 sgd_solver.cpp:105] Iteration 4536, lr = 0.00696231
I0408 19:56:22.266469  5931 solver.cpp:218] Iteration 4548 (2.47306 iter/s, 4.85228s/12 iters), loss = 0.867192
I0408 19:56:22.266503  5931 solver.cpp:237]     Train net output #0: loss = 0.867192 (* 1 = 0.867192 loss)
I0408 19:56:22.266511  5931 sgd_solver.cpp:105] Iteration 4548, lr = 0.00692485
I0408 19:56:23.464895  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:56:27.076748  5931 solver.cpp:218] Iteration 4560 (2.49468 iter/s, 4.81023s/12 iters), loss = 0.710486
I0408 19:56:27.076783  5931 solver.cpp:237]     Train net output #0: loss = 0.710486 (* 1 = 0.710486 loss)
I0408 19:56:27.076790  5931 sgd_solver.cpp:105] Iteration 4560, lr = 0.00688715
I0408 19:56:31.882838  5931 solver.cpp:218] Iteration 4572 (2.49686 iter/s, 4.80604s/12 iters), loss = 1.12257
I0408 19:56:31.882871  5931 solver.cpp:237]     Train net output #0: loss = 1.12257 (* 1 = 1.12257 loss)
I0408 19:56:31.882879  5931 sgd_solver.cpp:105] Iteration 4572, lr = 0.00684919
I0408 19:56:36.728086  5931 solver.cpp:218] Iteration 4584 (2.47668 iter/s, 4.8452s/12 iters), loss = 0.863892
I0408 19:56:36.728121  5931 solver.cpp:237]     Train net output #0: loss = 0.863892 (* 1 = 0.863892 loss)
I0408 19:56:36.728129  5931 sgd_solver.cpp:105] Iteration 4584, lr = 0.00681098
I0408 19:56:38.636687  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 19:56:41.710852  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 19:56:44.117866  5931 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 19:56:44.117893  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:56:46.795609  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:56:48.566730  5931 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0408 19:56:48.566929  5931 solver.cpp:397]     Test net output #1: loss = 3.32809 (* 1 = 3.32809 loss)
I0408 19:56:50.305078  5931 solver.cpp:218] Iteration 4596 (0.883853 iter/s, 13.5769s/12 iters), loss = 0.809408
I0408 19:56:50.305111  5931 solver.cpp:237]     Train net output #0: loss = 0.809408 (* 1 = 0.809408 loss)
I0408 19:56:50.305119  5931 sgd_solver.cpp:105] Iteration 4596, lr = 0.00677253
I0408 19:56:55.022451  5931 solver.cpp:218] Iteration 4608 (2.54382 iter/s, 4.71732s/12 iters), loss = 0.797493
I0408 19:56:55.022485  5931 solver.cpp:237]     Train net output #0: loss = 0.797493 (* 1 = 0.797493 loss)
I0408 19:56:55.022493  5931 sgd_solver.cpp:105] Iteration 4608, lr = 0.00673384
I0408 19:56:59.836619  5931 solver.cpp:218] Iteration 4620 (2.49267 iter/s, 4.81411s/12 iters), loss = 0.639119
I0408 19:56:59.836655  5931 solver.cpp:237]     Train net output #0: loss = 0.639119 (* 1 = 0.639119 loss)
I0408 19:56:59.836663  5931 sgd_solver.cpp:105] Iteration 4620, lr = 0.00669491
I0408 19:57:04.672621  5931 solver.cpp:218] Iteration 4632 (2.48142 iter/s, 4.83595s/12 iters), loss = 0.945835
I0408 19:57:04.672653  5931 solver.cpp:237]     Train net output #0: loss = 0.945835 (* 1 = 0.945835 loss)
I0408 19:57:04.672660  5931 sgd_solver.cpp:105] Iteration 4632, lr = 0.00665574
I0408 19:57:09.468278  5931 solver.cpp:218] Iteration 4644 (2.50229 iter/s, 4.79561s/12 iters), loss = 0.889238
I0408 19:57:09.468318  5931 solver.cpp:237]     Train net output #0: loss = 0.889238 (* 1 = 0.889238 loss)
I0408 19:57:09.468325  5931 sgd_solver.cpp:105] Iteration 4644, lr = 0.00661635
I0408 19:57:12.734026  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:57:14.285780  5931 solver.cpp:218] Iteration 4656 (2.49095 iter/s, 4.81744s/12 iters), loss = 0.531205
I0408 19:57:14.285813  5931 solver.cpp:237]     Train net output #0: loss = 0.531205 (* 1 = 0.531205 loss)
I0408 19:57:14.285821  5931 sgd_solver.cpp:105] Iteration 4656, lr = 0.00657673
I0408 19:57:19.116061  5931 solver.cpp:218] Iteration 4668 (2.48435 iter/s, 4.83023s/12 iters), loss = 0.886806
I0408 19:57:19.116168  5931 solver.cpp:237]     Train net output #0: loss = 0.886806 (* 1 = 0.886806 loss)
I0408 19:57:19.116176  5931 sgd_solver.cpp:105] Iteration 4668, lr = 0.00653689
I0408 19:57:23.938036  5931 solver.cpp:218] Iteration 4680 (2.48867 iter/s, 4.82185s/12 iters), loss = 0.736363
I0408 19:57:23.938071  5931 solver.cpp:237]     Train net output #0: loss = 0.736363 (* 1 = 0.736363 loss)
I0408 19:57:23.938079  5931 sgd_solver.cpp:105] Iteration 4680, lr = 0.00649683
I0408 19:57:28.323472  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 19:57:31.685166  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 19:57:35.143018  5931 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 19:57:35.143043  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:57:37.875829  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:57:39.681412  5931 solver.cpp:397]     Test net output #0: accuracy = 0.356005
I0408 19:57:39.681458  5931 solver.cpp:397]     Test net output #1: loss = 3.22998 (* 1 = 3.22998 loss)
I0408 19:57:39.777456  5931 solver.cpp:218] Iteration 4692 (0.757607 iter/s, 15.8393s/12 iters), loss = 0.746475
I0408 19:57:39.777491  5931 solver.cpp:237]     Train net output #0: loss = 0.746475 (* 1 = 0.746475 loss)
I0408 19:57:39.777499  5931 sgd_solver.cpp:105] Iteration 4692, lr = 0.00645656
I0408 19:57:43.748168  5931 solver.cpp:218] Iteration 4704 (3.02217 iter/s, 3.97066s/12 iters), loss = 0.52389
I0408 19:57:43.748203  5931 solver.cpp:237]     Train net output #0: loss = 0.52389 (* 1 = 0.52389 loss)
I0408 19:57:43.748211  5931 sgd_solver.cpp:105] Iteration 4704, lr = 0.00641609
I0408 19:57:48.453466  5931 solver.cpp:218] Iteration 4716 (2.55035 iter/s, 4.70525s/12 iters), loss = 0.84353
I0408 19:57:48.453501  5931 solver.cpp:237]     Train net output #0: loss = 0.84353 (* 1 = 0.84353 loss)
I0408 19:57:48.453508  5931 sgd_solver.cpp:105] Iteration 4716, lr = 0.00637541
I0408 19:57:53.071825  5931 solver.cpp:218] Iteration 4728 (2.59835 iter/s, 4.61831s/12 iters), loss = 0.841287
I0408 19:57:53.071966  5931 solver.cpp:237]     Train net output #0: loss = 0.841287 (* 1 = 0.841287 loss)
I0408 19:57:53.071975  5931 sgd_solver.cpp:105] Iteration 4728, lr = 0.00633453
I0408 19:57:57.738499  5931 solver.cpp:218] Iteration 4740 (2.57151 iter/s, 4.66652s/12 iters), loss = 1.11174
I0408 19:57:57.738533  5931 solver.cpp:237]     Train net output #0: loss = 1.11174 (* 1 = 1.11174 loss)
I0408 19:57:57.738539  5931 sgd_solver.cpp:105] Iteration 4740, lr = 0.00629346
I0408 19:58:02.526955  5931 solver.cpp:218] Iteration 4752 (2.50605 iter/s, 4.7884s/12 iters), loss = 0.946293
I0408 19:58:02.526988  5931 solver.cpp:237]     Train net output #0: loss = 0.946293 (* 1 = 0.946293 loss)
I0408 19:58:02.526995  5931 sgd_solver.cpp:105] Iteration 4752, lr = 0.0062522
I0408 19:58:03.022832  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:58:07.375183  5931 solver.cpp:218] Iteration 4764 (2.47516 iter/s, 4.84818s/12 iters), loss = 0.730234
I0408 19:58:07.375226  5931 solver.cpp:237]     Train net output #0: loss = 0.730234 (* 1 = 0.730234 loss)
I0408 19:58:07.375234  5931 sgd_solver.cpp:105] Iteration 4764, lr = 0.00621076
I0408 19:58:12.188254  5931 solver.cpp:218] Iteration 4776 (2.49324 iter/s, 4.81301s/12 iters), loss = 0.814758
I0408 19:58:12.188288  5931 solver.cpp:237]     Train net output #0: loss = 0.814758 (* 1 = 0.814758 loss)
I0408 19:58:12.188297  5931 sgd_solver.cpp:105] Iteration 4776, lr = 0.00616914
I0408 19:58:16.879282  5931 solver.cpp:218] Iteration 4788 (2.55811 iter/s, 4.69097s/12 iters), loss = 0.799296
I0408 19:58:16.879320  5931 solver.cpp:237]     Train net output #0: loss = 0.799296 (* 1 = 0.799296 loss)
I0408 19:58:16.879328  5931 sgd_solver.cpp:105] Iteration 4788, lr = 0.00612735
I0408 19:58:18.734681  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 19:58:21.814431  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 19:58:24.162583  5931 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 19:58:24.162703  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:58:26.857852  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:58:28.950800  5931 solver.cpp:397]     Test net output #0: accuracy = 0.381127
I0408 19:58:28.950847  5931 solver.cpp:397]     Test net output #1: loss = 3.08625 (* 1 = 3.08625 loss)
I0408 19:58:30.704238  5931 solver.cpp:218] Iteration 4800 (0.868 iter/s, 13.8249s/12 iters), loss = 0.727426
I0408 19:58:30.704275  5931 solver.cpp:237]     Train net output #0: loss = 0.727426 (* 1 = 0.727426 loss)
I0408 19:58:30.704283  5931 sgd_solver.cpp:105] Iteration 4800, lr = 0.00608539
I0408 19:58:35.521260  5931 solver.cpp:218] Iteration 4812 (2.49119 iter/s, 4.81697s/12 iters), loss = 0.721816
I0408 19:58:35.521292  5931 solver.cpp:237]     Train net output #0: loss = 0.721816 (* 1 = 0.721816 loss)
I0408 19:58:35.521299  5931 sgd_solver.cpp:105] Iteration 4812, lr = 0.00604327
I0408 19:58:40.327932  5931 solver.cpp:218] Iteration 4824 (2.49656 iter/s, 4.80662s/12 iters), loss = 0.820893
I0408 19:58:40.327966  5931 solver.cpp:237]     Train net output #0: loss = 0.820893 (* 1 = 0.820893 loss)
I0408 19:58:40.327973  5931 sgd_solver.cpp:105] Iteration 4824, lr = 0.006001
I0408 19:58:45.205153  5931 solver.cpp:218] Iteration 4836 (2.46044 iter/s, 4.87717s/12 iters), loss = 0.868211
I0408 19:58:45.205188  5931 solver.cpp:237]     Train net output #0: loss = 0.868211 (* 1 = 0.868211 loss)
I0408 19:58:45.205195  5931 sgd_solver.cpp:105] Iteration 4836, lr = 0.00595858
I0408 19:58:47.119526  5931 blocking_queue.cpp:49] Waiting for data
I0408 19:58:49.932314  5931 solver.cpp:218] Iteration 4848 (2.53855 iter/s, 4.72711s/12 iters), loss = 0.686241
I0408 19:58:49.932351  5931 solver.cpp:237]     Train net output #0: loss = 0.686241 (* 1 = 0.686241 loss)
I0408 19:58:49.932358  5931 sgd_solver.cpp:105] Iteration 4848, lr = 0.00591601
I0408 19:58:52.499554  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:58:54.765101  5931 solver.cpp:218] Iteration 4860 (2.48307 iter/s, 4.83273s/12 iters), loss = 0.885092
I0408 19:58:54.765272  5931 solver.cpp:237]     Train net output #0: loss = 0.885092 (* 1 = 0.885092 loss)
I0408 19:58:54.765281  5931 sgd_solver.cpp:105] Iteration 4860, lr = 0.00587331
I0408 19:58:59.631913  5931 solver.cpp:218] Iteration 4872 (2.46577 iter/s, 4.86663s/12 iters), loss = 0.51506
I0408 19:58:59.631947  5931 solver.cpp:237]     Train net output #0: loss = 0.51506 (* 1 = 0.51506 loss)
I0408 19:58:59.631954  5931 sgd_solver.cpp:105] Iteration 4872, lr = 0.00583047
I0408 19:59:04.325546  5931 solver.cpp:218] Iteration 4884 (2.55668 iter/s, 4.69358s/12 iters), loss = 0.599404
I0408 19:59:04.325579  5931 solver.cpp:237]     Train net output #0: loss = 0.599404 (* 1 = 0.599404 loss)
I0408 19:59:04.325587  5931 sgd_solver.cpp:105] Iteration 4884, lr = 0.00578751
I0408 19:59:08.589504  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 19:59:11.674010  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 19:59:14.433045  5931 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 19:59:14.433071  5931 net.cpp:676] Ignoring source layer train-data
I0408 19:59:17.024758  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:59:18.908504  5931 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0408 19:59:18.908550  5931 solver.cpp:397]     Test net output #1: loss = 3.09063 (* 1 = 3.09063 loss)
I0408 19:59:19.004987  5931 solver.cpp:218] Iteration 4896 (0.817474 iter/s, 14.6794s/12 iters), loss = 0.846195
I0408 19:59:19.005020  5931 solver.cpp:237]     Train net output #0: loss = 0.846195 (* 1 = 0.846195 loss)
I0408 19:59:19.005029  5931 sgd_solver.cpp:105] Iteration 4896, lr = 0.00574443
I0408 19:59:22.952045  5931 solver.cpp:218] Iteration 4908 (3.04028 iter/s, 3.94701s/12 iters), loss = 0.778551
I0408 19:59:22.952078  5931 solver.cpp:237]     Train net output #0: loss = 0.778551 (* 1 = 0.778551 loss)
I0408 19:59:22.952086  5931 sgd_solver.cpp:105] Iteration 4908, lr = 0.00570123
I0408 19:59:27.776707  5931 solver.cpp:218] Iteration 4920 (2.48725 iter/s, 4.82461s/12 iters), loss = 0.583149
I0408 19:59:27.776824  5931 solver.cpp:237]     Train net output #0: loss = 0.583149 (* 1 = 0.583149 loss)
I0408 19:59:27.776834  5931 sgd_solver.cpp:105] Iteration 4920, lr = 0.00565793
I0408 19:59:32.576397  5931 solver.cpp:218] Iteration 4932 (2.50023 iter/s, 4.79956s/12 iters), loss = 0.656009
I0408 19:59:32.576431  5931 solver.cpp:237]     Train net output #0: loss = 0.656009 (* 1 = 0.656009 loss)
I0408 19:59:32.576438  5931 sgd_solver.cpp:105] Iteration 4932, lr = 0.00561452
I0408 19:59:37.400527  5931 solver.cpp:218] Iteration 4944 (2.48752 iter/s, 4.82408s/12 iters), loss = 0.690486
I0408 19:59:37.400559  5931 solver.cpp:237]     Train net output #0: loss = 0.690486 (* 1 = 0.690486 loss)
I0408 19:59:37.400566  5931 sgd_solver.cpp:105] Iteration 4944, lr = 0.00557103
I0408 19:59:42.040854  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:59:42.241802  5931 solver.cpp:218] Iteration 4956 (2.47871 iter/s, 4.84123s/12 iters), loss = 0.462403
I0408 19:59:42.241835  5931 solver.cpp:237]     Train net output #0: loss = 0.462403 (* 1 = 0.462403 loss)
I0408 19:59:42.241842  5931 sgd_solver.cpp:105] Iteration 4956, lr = 0.00552744
I0408 19:59:47.055979  5931 solver.cpp:218] Iteration 4968 (2.49267 iter/s, 4.81412s/12 iters), loss = 0.487629
I0408 19:59:47.056013  5931 solver.cpp:237]     Train net output #0: loss = 0.487629 (* 1 = 0.487629 loss)
I0408 19:59:47.056020  5931 sgd_solver.cpp:105] Iteration 4968, lr = 0.00548378
I0408 19:59:51.887815  5931 solver.cpp:218] Iteration 4980 (2.48356 iter/s, 4.83178s/12 iters), loss = 0.614614
I0408 19:59:51.887849  5931 solver.cpp:237]     Train net output #0: loss = 0.614614 (* 1 = 0.614614 loss)
I0408 19:59:51.887856  5931 sgd_solver.cpp:105] Iteration 4980, lr = 0.00544003
I0408 19:59:56.714779  5931 solver.cpp:218] Iteration 4992 (2.48606 iter/s, 4.82691s/12 iters), loss = 0.696353
I0408 19:59:56.714812  5931 solver.cpp:237]     Train net output #0: loss = 0.696353 (* 1 = 0.696353 loss)
I0408 19:59:56.714819  5931 sgd_solver.cpp:105] Iteration 4992, lr = 0.00539623
I0408 19:59:58.674933  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 20:00:02.304445  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 20:00:05.357527  5931 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 20:00:05.357553  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:00:07.975019  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:00:10.141289  5931 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0408 20:00:10.141338  5931 solver.cpp:397]     Test net output #1: loss = 2.94133 (* 1 = 2.94133 loss)
I0408 20:00:11.897152  5931 solver.cpp:218] Iteration 5004 (0.790394 iter/s, 15.1823s/12 iters), loss = 0.448587
I0408 20:00:11.897186  5931 solver.cpp:237]     Train net output #0: loss = 0.448587 (* 1 = 0.448587 loss)
I0408 20:00:11.897194  5931 sgd_solver.cpp:105] Iteration 5004, lr = 0.00535236
I0408 20:00:16.596283  5931 solver.cpp:218] Iteration 5016 (2.55369 iter/s, 4.69908s/12 iters), loss = 0.512112
I0408 20:00:16.596315  5931 solver.cpp:237]     Train net output #0: loss = 0.512112 (* 1 = 0.512112 loss)
I0408 20:00:16.596323  5931 sgd_solver.cpp:105] Iteration 5016, lr = 0.00530843
I0408 20:00:21.437103  5931 solver.cpp:218] Iteration 5028 (2.47895 iter/s, 4.84076s/12 iters), loss = 0.606361
I0408 20:00:21.437135  5931 solver.cpp:237]     Train net output #0: loss = 0.606361 (* 1 = 0.606361 loss)
I0408 20:00:21.437142  5931 sgd_solver.cpp:105] Iteration 5028, lr = 0.00526446
I0408 20:00:26.239049  5931 solver.cpp:218] Iteration 5040 (2.49901 iter/s, 4.80189s/12 iters), loss = 0.518664
I0408 20:00:26.239081  5931 solver.cpp:237]     Train net output #0: loss = 0.518664 (* 1 = 0.518664 loss)
I0408 20:00:26.239089  5931 sgd_solver.cpp:105] Iteration 5040, lr = 0.00522045
I0408 20:00:31.066817  5931 solver.cpp:218] Iteration 5052 (2.48565 iter/s, 4.82772s/12 iters), loss = 0.538114
I0408 20:00:31.066933  5931 solver.cpp:237]     Train net output #0: loss = 0.538114 (* 1 = 0.538114 loss)
I0408 20:00:31.066942  5931 sgd_solver.cpp:105] Iteration 5052, lr = 0.0051764
I0408 20:00:32.919497  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:00:35.912753  5931 solver.cpp:218] Iteration 5064 (2.47637 iter/s, 4.8458s/12 iters), loss = 0.602735
I0408 20:00:35.912787  5931 solver.cpp:237]     Train net output #0: loss = 0.602735 (* 1 = 0.602735 loss)
I0408 20:00:35.912794  5931 sgd_solver.cpp:105] Iteration 5064, lr = 0.00513232
I0408 20:00:40.712847  5931 solver.cpp:218] Iteration 5076 (2.49998 iter/s, 4.80004s/12 iters), loss = 0.549402
I0408 20:00:40.712882  5931 solver.cpp:237]     Train net output #0: loss = 0.549402 (* 1 = 0.549402 loss)
I0408 20:00:40.712889  5931 sgd_solver.cpp:105] Iteration 5076, lr = 0.00508823
I0408 20:00:45.574056  5931 solver.cpp:218] Iteration 5088 (2.46855 iter/s, 4.86115s/12 iters), loss = 0.573379
I0408 20:00:45.574090  5931 solver.cpp:237]     Train net output #0: loss = 0.573379 (* 1 = 0.573379 loss)
I0408 20:00:45.574097  5931 sgd_solver.cpp:105] Iteration 5088, lr = 0.00504412
I0408 20:00:49.930560  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 20:00:53.030486  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 20:00:55.385972  5931 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 20:00:55.385998  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:00:57.971072  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:00.195363  5931 solver.cpp:397]     Test net output #0: accuracy = 0.389093
I0408 20:01:00.195411  5931 solver.cpp:397]     Test net output #1: loss = 3.09837 (* 1 = 3.09837 loss)
I0408 20:01:00.291934  5931 solver.cpp:218] Iteration 5100 (0.815339 iter/s, 14.7178s/12 iters), loss = 0.460532
I0408 20:01:00.291967  5931 solver.cpp:237]     Train net output #0: loss = 0.460532 (* 1 = 0.460532 loss)
I0408 20:01:00.291975  5931 sgd_solver.cpp:105] Iteration 5100, lr = 0.005
I0408 20:01:04.238128  5931 solver.cpp:218] Iteration 5112 (3.04095 iter/s, 3.94614s/12 iters), loss = 0.593658
I0408 20:01:04.238225  5931 solver.cpp:237]     Train net output #0: loss = 0.593658 (* 1 = 0.593658 loss)
I0408 20:01:04.238234  5931 sgd_solver.cpp:105] Iteration 5112, lr = 0.00495588
I0408 20:01:09.069818  5931 solver.cpp:218] Iteration 5124 (2.48366 iter/s, 4.83157s/12 iters), loss = 0.505945
I0408 20:01:09.069852  5931 solver.cpp:237]     Train net output #0: loss = 0.505945 (* 1 = 0.505945 loss)
I0408 20:01:09.069859  5931 sgd_solver.cpp:105] Iteration 5124, lr = 0.00491177
I0408 20:01:13.903571  5931 solver.cpp:218] Iteration 5136 (2.48257 iter/s, 4.8337s/12 iters), loss = 0.41879
I0408 20:01:13.903610  5931 solver.cpp:237]     Train net output #0: loss = 0.41879 (* 1 = 0.41879 loss)
I0408 20:01:13.903618  5931 sgd_solver.cpp:105] Iteration 5136, lr = 0.00486768
I0408 20:01:18.712028  5931 solver.cpp:218] Iteration 5148 (2.49563 iter/s, 4.8084s/12 iters), loss = 0.677808
I0408 20:01:18.712064  5931 solver.cpp:237]     Train net output #0: loss = 0.677808 (* 1 = 0.677808 loss)
I0408 20:01:18.712071  5931 sgd_solver.cpp:105] Iteration 5148, lr = 0.0048236
I0408 20:01:22.598358  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:23.493361  5931 solver.cpp:218] Iteration 5160 (2.50979 iter/s, 4.78128s/12 iters), loss = 0.488419
I0408 20:01:23.493396  5931 solver.cpp:237]     Train net output #0: loss = 0.488419 (* 1 = 0.488419 loss)
I0408 20:01:23.493403  5931 sgd_solver.cpp:105] Iteration 5160, lr = 0.00477955
I0408 20:01:28.229866  5931 solver.cpp:218] Iteration 5172 (2.53354 iter/s, 4.73645s/12 iters), loss = 0.445747
I0408 20:01:28.229899  5931 solver.cpp:237]     Train net output #0: loss = 0.445747 (* 1 = 0.445747 loss)
I0408 20:01:28.229907  5931 sgd_solver.cpp:105] Iteration 5172, lr = 0.00473554
I0408 20:01:32.978571  5931 solver.cpp:218] Iteration 5184 (2.52703 iter/s, 4.74865s/12 iters), loss = 0.474229
I0408 20:01:32.978605  5931 solver.cpp:237]     Train net output #0: loss = 0.474229 (* 1 = 0.474229 loss)
I0408 20:01:32.978612  5931 sgd_solver.cpp:105] Iteration 5184, lr = 0.00469157
I0408 20:01:37.835505  5931 solver.cpp:218] Iteration 5196 (2.47072 iter/s, 4.85688s/12 iters), loss = 0.384813
I0408 20:01:37.835578  5931 solver.cpp:237]     Train net output #0: loss = 0.384813 (* 1 = 0.384813 loss)
I0408 20:01:37.835587  5931 sgd_solver.cpp:105] Iteration 5196, lr = 0.00464764
I0408 20:01:39.758060  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 20:01:43.882413  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 20:01:46.231945  5931 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 20:01:46.231971  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:01:48.557762  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:50.546787  5931 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0408 20:01:50.546830  5931 solver.cpp:397]     Test net output #1: loss = 2.96037 (* 1 = 2.96037 loss)
I0408 20:01:52.254446  5931 solver.cpp:218] Iteration 5208 (0.832245 iter/s, 14.4188s/12 iters), loss = 0.342959
I0408 20:01:52.254482  5931 solver.cpp:237]     Train net output #0: loss = 0.342959 (* 1 = 0.342959 loss)
I0408 20:01:52.254489  5931 sgd_solver.cpp:105] Iteration 5208, lr = 0.00460377
I0408 20:01:57.084659  5931 solver.cpp:218] Iteration 5220 (2.48439 iter/s, 4.83016s/12 iters), loss = 0.519105
I0408 20:01:57.084692  5931 solver.cpp:237]     Train net output #0: loss = 0.519105 (* 1 = 0.519105 loss)
I0408 20:01:57.084699  5931 sgd_solver.cpp:105] Iteration 5220, lr = 0.00455996
I0408 20:02:01.927886  5931 solver.cpp:218] Iteration 5232 (2.47772 iter/s, 4.84317s/12 iters), loss = 0.528687
I0408 20:02:01.927917  5931 solver.cpp:237]     Train net output #0: loss = 0.528687 (* 1 = 0.528687 loss)
I0408 20:02:01.927925  5931 sgd_solver.cpp:105] Iteration 5232, lr = 0.00451622
I0408 20:02:06.745537  5931 solver.cpp:218] Iteration 5244 (2.49087 iter/s, 4.8176s/12 iters), loss = 0.379963
I0408 20:02:06.745573  5931 solver.cpp:237]     Train net output #0: loss = 0.379963 (* 1 = 0.379963 loss)
I0408 20:02:06.745580  5931 sgd_solver.cpp:105] Iteration 5244, lr = 0.00447256
I0408 20:02:11.576947  5931 solver.cpp:218] Iteration 5256 (2.48378 iter/s, 4.83135s/12 iters), loss = 0.365287
I0408 20:02:11.577044  5931 solver.cpp:237]     Train net output #0: loss = 0.365287 (* 1 = 0.365287 loss)
I0408 20:02:11.577052  5931 sgd_solver.cpp:105] Iteration 5256, lr = 0.00442897
I0408 20:02:12.810026  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:02:16.390740  5931 solver.cpp:218] Iteration 5268 (2.4929 iter/s, 4.81368s/12 iters), loss = 0.504454
I0408 20:02:16.390772  5931 solver.cpp:237]     Train net output #0: loss = 0.504454 (* 1 = 0.504454 loss)
I0408 20:02:16.390779  5931 sgd_solver.cpp:105] Iteration 5268, lr = 0.00438548
I0408 20:02:21.235987  5931 solver.cpp:218] Iteration 5280 (2.47668 iter/s, 4.8452s/12 iters), loss = 0.491741
I0408 20:02:21.236021  5931 solver.cpp:237]     Train net output #0: loss = 0.491741 (* 1 = 0.491741 loss)
I0408 20:02:21.236028  5931 sgd_solver.cpp:105] Iteration 5280, lr = 0.00434207
I0408 20:02:26.121444  5931 solver.cpp:218] Iteration 5292 (2.4563 iter/s, 4.8854s/12 iters), loss = 0.278349
I0408 20:02:26.121479  5931 solver.cpp:237]     Train net output #0: loss = 0.278349 (* 1 = 0.278349 loss)
I0408 20:02:26.121485  5931 sgd_solver.cpp:105] Iteration 5292, lr = 0.00429877
I0408 20:02:30.450222  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 20:02:33.558655  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 20:02:36.403622  5931 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 20:02:36.403647  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:02:38.815709  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:02:40.847920  5931 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0408 20:02:40.847965  5931 solver.cpp:397]     Test net output #1: loss = 2.96069 (* 1 = 2.96069 loss)
I0408 20:02:40.944459  5931 solver.cpp:218] Iteration 5304 (0.809556 iter/s, 14.8229s/12 iters), loss = 0.346396
I0408 20:02:40.944495  5931 solver.cpp:237]     Train net output #0: loss = 0.346396 (* 1 = 0.346396 loss)
I0408 20:02:40.944502  5931 sgd_solver.cpp:105] Iteration 5304, lr = 0.00425557
I0408 20:02:44.917454  5931 solver.cpp:218] Iteration 5316 (3.02043 iter/s, 3.97294s/12 iters), loss = 0.626065
I0408 20:02:44.917577  5931 solver.cpp:237]     Train net output #0: loss = 0.626065 (* 1 = 0.626065 loss)
I0408 20:02:44.917587  5931 sgd_solver.cpp:105] Iteration 5316, lr = 0.00421249
I0408 20:02:49.761301  5931 solver.cpp:218] Iteration 5328 (2.47744 iter/s, 4.8437s/12 iters), loss = 0.433611
I0408 20:02:49.761333  5931 solver.cpp:237]     Train net output #0: loss = 0.433611 (* 1 = 0.433611 loss)
I0408 20:02:49.761341  5931 sgd_solver.cpp:105] Iteration 5328, lr = 0.00416953
I0408 20:02:54.616887  5931 solver.cpp:218] Iteration 5340 (2.47141 iter/s, 4.85553s/12 iters), loss = 0.35842
I0408 20:02:54.616920  5931 solver.cpp:237]     Train net output #0: loss = 0.35842 (* 1 = 0.35842 loss)
I0408 20:02:54.616927  5931 sgd_solver.cpp:105] Iteration 5340, lr = 0.00412669
I0408 20:02:59.486536  5931 solver.cpp:218] Iteration 5352 (2.46427 iter/s, 4.86959s/12 iters), loss = 0.56464
I0408 20:02:59.486569  5931 solver.cpp:237]     Train net output #0: loss = 0.56464 (* 1 = 0.56464 loss)
I0408 20:02:59.486577  5931 sgd_solver.cpp:105] Iteration 5352, lr = 0.00408399
I0408 20:03:02.958838  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:03:04.533740  5931 solver.cpp:218] Iteration 5364 (2.37758 iter/s, 5.04715s/12 iters), loss = 0.295886
I0408 20:03:04.533774  5931 solver.cpp:237]     Train net output #0: loss = 0.295886 (* 1 = 0.295886 loss)
I0408 20:03:04.533782  5931 sgd_solver.cpp:105] Iteration 5364, lr = 0.00404142
I0408 20:03:09.331681  5931 solver.cpp:218] Iteration 5376 (2.5011 iter/s, 4.79789s/12 iters), loss = 0.46485
I0408 20:03:09.331713  5931 solver.cpp:237]     Train net output #0: loss = 0.46485 (* 1 = 0.46485 loss)
I0408 20:03:09.331720  5931 sgd_solver.cpp:105] Iteration 5376, lr = 0.003999
I0408 20:03:14.089387  5931 solver.cpp:218] Iteration 5388 (2.52225 iter/s, 4.75765s/12 iters), loss = 0.409785
I0408 20:03:14.089427  5931 solver.cpp:237]     Train net output #0: loss = 0.409785 (* 1 = 0.409785 loss)
I0408 20:03:14.089435  5931 sgd_solver.cpp:105] Iteration 5388, lr = 0.00395672
I0408 20:03:18.976068  5931 solver.cpp:218] Iteration 5400 (2.45568 iter/s, 4.88662s/12 iters), loss = 0.420254
I0408 20:03:18.976166  5931 solver.cpp:237]     Train net output #0: loss = 0.420254 (* 1 = 0.420254 loss)
I0408 20:03:18.976174  5931 sgd_solver.cpp:105] Iteration 5400, lr = 0.00391461
I0408 20:03:20.995224  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 20:03:26.019547  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 20:03:28.409253  5931 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 20:03:28.409279  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:03:30.809617  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:03:32.884824  5931 solver.cpp:397]     Test net output #0: accuracy = 0.425858
I0408 20:03:32.884868  5931 solver.cpp:397]     Test net output #1: loss = 2.97192 (* 1 = 2.97192 loss)
I0408 20:03:34.627386  5931 solver.cpp:218] Iteration 5412 (0.766715 iter/s, 15.6512s/12 iters), loss = 0.174743
I0408 20:03:34.627424  5931 solver.cpp:237]     Train net output #0: loss = 0.174743 (* 1 = 0.174743 loss)
I0408 20:03:34.627430  5931 sgd_solver.cpp:105] Iteration 5412, lr = 0.00387265
I0408 20:03:39.410715  5931 solver.cpp:218] Iteration 5424 (2.50874 iter/s, 4.78328s/12 iters), loss = 0.30676
I0408 20:03:39.410749  5931 solver.cpp:237]     Train net output #0: loss = 0.30676 (* 1 = 0.30676 loss)
I0408 20:03:39.410756  5931 sgd_solver.cpp:105] Iteration 5424, lr = 0.00383086
I0408 20:03:44.213932  5931 solver.cpp:218] Iteration 5436 (2.49836 iter/s, 4.80316s/12 iters), loss = 0.451519
I0408 20:03:44.213963  5931 solver.cpp:237]     Train net output #0: loss = 0.451519 (* 1 = 0.451519 loss)
I0408 20:03:44.213971  5931 sgd_solver.cpp:105] Iteration 5436, lr = 0.00378924
I0408 20:03:49.087755  5931 solver.cpp:218] Iteration 5448 (2.46216 iter/s, 4.87377s/12 iters), loss = 0.460821
I0408 20:03:49.087873  5931 solver.cpp:237]     Train net output #0: loss = 0.460821 (* 1 = 0.460821 loss)
I0408 20:03:49.087882  5931 sgd_solver.cpp:105] Iteration 5448, lr = 0.0037478
I0408 20:03:53.884119  5931 solver.cpp:218] Iteration 5460 (2.50196 iter/s, 4.79623s/12 iters), loss = 0.266154
I0408 20:03:53.884153  5931 solver.cpp:237]     Train net output #0: loss = 0.266154 (* 1 = 0.266154 loss)
I0408 20:03:53.884160  5931 sgd_solver.cpp:105] Iteration 5460, lr = 0.00370654
I0408 20:03:54.407403  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:03:58.746989  5931 solver.cpp:218] Iteration 5472 (2.4677 iter/s, 4.86282s/12 iters), loss = 0.157723
I0408 20:03:58.747025  5931 solver.cpp:237]     Train net output #0: loss = 0.157723 (* 1 = 0.157723 loss)
I0408 20:03:58.747032  5931 sgd_solver.cpp:105] Iteration 5472, lr = 0.00366547
I0408 20:04:03.634888  5931 solver.cpp:218] Iteration 5484 (2.45507 iter/s, 4.88784s/12 iters), loss = 0.346608
I0408 20:04:03.634922  5931 solver.cpp:237]     Train net output #0: loss = 0.346608 (* 1 = 0.346608 loss)
I0408 20:04:03.634930  5931 sgd_solver.cpp:105] Iteration 5484, lr = 0.00362459
I0408 20:04:08.462983  5931 solver.cpp:218] Iteration 5496 (2.48548 iter/s, 4.82804s/12 iters), loss = 0.400039
I0408 20:04:08.463017  5931 solver.cpp:237]     Train net output #0: loss = 0.400039 (* 1 = 0.400039 loss)
I0408 20:04:08.463025  5931 sgd_solver.cpp:105] Iteration 5496, lr = 0.00358391
I0408 20:04:13.081629  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 20:04:17.910331  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 20:04:21.824476  5931 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 20:04:21.824579  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:04:24.084301  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:04:26.196768  5931 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0408 20:04:26.196815  5931 solver.cpp:397]     Test net output #1: loss = 3.01402 (* 1 = 3.01402 loss)
I0408 20:04:26.293203  5931 solver.cpp:218] Iteration 5508 (0.673018 iter/s, 17.8301s/12 iters), loss = 0.346051
I0408 20:04:26.293237  5931 solver.cpp:237]     Train net output #0: loss = 0.346051 (* 1 = 0.346051 loss)
I0408 20:04:26.293246  5931 sgd_solver.cpp:105] Iteration 5508, lr = 0.00354344
I0408 20:04:30.263195  5931 solver.cpp:218] Iteration 5520 (3.02272 iter/s, 3.96994s/12 iters), loss = 0.30861
I0408 20:04:30.263234  5931 solver.cpp:237]     Train net output #0: loss = 0.30861 (* 1 = 0.30861 loss)
I0408 20:04:30.263242  5931 sgd_solver.cpp:105] Iteration 5520, lr = 0.00350317
I0408 20:04:32.619812  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:04:35.097795  5931 solver.cpp:218] Iteration 5532 (2.48214 iter/s, 4.83454s/12 iters), loss = 0.256705
I0408 20:04:35.097831  5931 solver.cpp:237]     Train net output #0: loss = 0.256705 (* 1 = 0.256705 loss)
I0408 20:04:35.097838  5931 sgd_solver.cpp:105] Iteration 5532, lr = 0.00346311
I0408 20:04:39.922616  5931 solver.cpp:218] Iteration 5544 (2.48717 iter/s, 4.82476s/12 iters), loss = 0.22492
I0408 20:04:39.922650  5931 solver.cpp:237]     Train net output #0: loss = 0.22492 (* 1 = 0.22492 loss)
I0408 20:04:39.922657  5931 sgd_solver.cpp:105] Iteration 5544, lr = 0.00342327
I0408 20:04:44.774231  5931 solver.cpp:218] Iteration 5556 (2.47343 iter/s, 4.85156s/12 iters), loss = 0.265254
I0408 20:04:44.774267  5931 solver.cpp:237]     Train net output #0: loss = 0.265254 (* 1 = 0.265254 loss)
I0408 20:04:44.774274  5931 sgd_solver.cpp:105] Iteration 5556, lr = 0.00338365
I0408 20:04:47.413537  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:04:49.695631  5931 solver.cpp:218] Iteration 5568 (2.43836 iter/s, 4.92135s/12 iters), loss = 0.337113
I0408 20:04:49.695665  5931 solver.cpp:237]     Train net output #0: loss = 0.337113 (* 1 = 0.337113 loss)
I0408 20:04:49.695673  5931 sgd_solver.cpp:105] Iteration 5568, lr = 0.00334426
I0408 20:04:54.732162  5931 solver.cpp:218] Iteration 5580 (2.38262 iter/s, 5.03648s/12 iters), loss = 0.372072
I0408 20:04:54.732277  5931 solver.cpp:237]     Train net output #0: loss = 0.372072 (* 1 = 0.372072 loss)
I0408 20:04:54.732285  5931 sgd_solver.cpp:105] Iteration 5580, lr = 0.00330509
I0408 20:04:59.478559  5931 solver.cpp:218] Iteration 5592 (2.5283 iter/s, 4.74626s/12 iters), loss = 0.271403
I0408 20:04:59.478591  5931 solver.cpp:237]     Train net output #0: loss = 0.271403 (* 1 = 0.271403 loss)
I0408 20:04:59.478600  5931 sgd_solver.cpp:105] Iteration 5592, lr = 0.00326616
I0408 20:05:04.204401  5931 solver.cpp:218] Iteration 5604 (2.53926 iter/s, 4.72579s/12 iters), loss = 0.422858
I0408 20:05:04.204433  5931 solver.cpp:237]     Train net output #0: loss = 0.422858 (* 1 = 0.422858 loss)
I0408 20:05:04.204442  5931 sgd_solver.cpp:105] Iteration 5604, lr = 0.00322747
I0408 20:05:06.134773  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 20:05:09.229415  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 20:05:12.079964  5931 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 20:05:12.079990  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:05:14.428975  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:05:16.864751  5931 solver.cpp:397]     Test net output #0: accuracy = 0.43076
I0408 20:05:16.864795  5931 solver.cpp:397]     Test net output #1: loss = 3.02431 (* 1 = 3.02431 loss)
I0408 20:05:18.668787  5931 solver.cpp:218] Iteration 5616 (0.829628 iter/s, 14.4643s/12 iters), loss = 0.315521
I0408 20:05:18.668820  5931 solver.cpp:237]     Train net output #0: loss = 0.315521 (* 1 = 0.315521 loss)
I0408 20:05:18.668828  5931 sgd_solver.cpp:105] Iteration 5616, lr = 0.00318902
I0408 20:05:23.579398  5931 solver.cpp:218] Iteration 5628 (2.44371 iter/s, 4.91056s/12 iters), loss = 0.263526
I0408 20:05:23.579432  5931 solver.cpp:237]     Train net output #0: loss = 0.263526 (* 1 = 0.263526 loss)
I0408 20:05:23.579439  5931 sgd_solver.cpp:105] Iteration 5628, lr = 0.00315081
I0408 20:05:28.361833  5931 solver.cpp:218] Iteration 5640 (2.50921 iter/s, 4.78238s/12 iters), loss = 0.4336
I0408 20:05:28.361928  5931 solver.cpp:237]     Train net output #0: loss = 0.4336 (* 1 = 0.4336 loss)
I0408 20:05:28.361937  5931 sgd_solver.cpp:105] Iteration 5640, lr = 0.00311285
I0408 20:05:33.196039  5931 solver.cpp:218] Iteration 5652 (2.48237 iter/s, 4.83409s/12 iters), loss = 0.296835
I0408 20:05:33.196074  5931 solver.cpp:237]     Train net output #0: loss = 0.296835 (* 1 = 0.296835 loss)
I0408 20:05:33.196082  5931 sgd_solver.cpp:105] Iteration 5652, lr = 0.00307515
I0408 20:05:37.867424  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:05:38.030833  5931 solver.cpp:218] Iteration 5664 (2.48204 iter/s, 4.83474s/12 iters), loss = 0.219908
I0408 20:05:38.030866  5931 solver.cpp:237]     Train net output #0: loss = 0.219908 (* 1 = 0.219908 loss)
I0408 20:05:38.030874  5931 sgd_solver.cpp:105] Iteration 5664, lr = 0.00303769
I0408 20:05:42.785985  5931 solver.cpp:218] Iteration 5676 (2.52361 iter/s, 4.75509s/12 iters), loss = 0.22732
I0408 20:05:42.786017  5931 solver.cpp:237]     Train net output #0: loss = 0.22732 (* 1 = 0.22732 loss)
I0408 20:05:42.786024  5931 sgd_solver.cpp:105] Iteration 5676, lr = 0.0030005
I0408 20:05:47.514586  5931 solver.cpp:218] Iteration 5688 (2.53778 iter/s, 4.72855s/12 iters), loss = 0.247547
I0408 20:05:47.514621  5931 solver.cpp:237]     Train net output #0: loss = 0.247547 (* 1 = 0.247547 loss)
I0408 20:05:47.514627  5931 sgd_solver.cpp:105] Iteration 5688, lr = 0.00296357
I0408 20:05:52.313370  5931 solver.cpp:218] Iteration 5700 (2.50066 iter/s, 4.79873s/12 iters), loss = 0.507992
I0408 20:05:52.313402  5931 solver.cpp:237]     Train net output #0: loss = 0.507992 (* 1 = 0.507992 loss)
I0408 20:05:52.313410  5931 sgd_solver.cpp:105] Iteration 5700, lr = 0.0029269
I0408 20:05:56.715853  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 20:05:59.803385  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 20:06:02.159986  5931 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 20:06:02.160013  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:06:04.469571  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:06:06.941562  5931 solver.cpp:397]     Test net output #0: accuracy = 0.432598
I0408 20:06:06.941609  5931 solver.cpp:397]     Test net output #1: loss = 2.99721 (* 1 = 2.99721 loss)
I0408 20:06:07.038086  5931 solver.cpp:218] Iteration 5712 (0.81496 iter/s, 14.7246s/12 iters), loss = 0.273816
I0408 20:06:07.038122  5931 solver.cpp:237]     Train net output #0: loss = 0.273816 (* 1 = 0.273816 loss)
I0408 20:06:07.038130  5931 sgd_solver.cpp:105] Iteration 5712, lr = 0.0028905
I0408 20:06:10.987726  5931 solver.cpp:218] Iteration 5724 (3.03829 iter/s, 3.94959s/12 iters), loss = 0.308905
I0408 20:06:10.987766  5931 solver.cpp:237]     Train net output #0: loss = 0.308905 (* 1 = 0.308905 loss)
I0408 20:06:10.987773  5931 sgd_solver.cpp:105] Iteration 5724, lr = 0.00285438
I0408 20:06:15.823117  5931 solver.cpp:218] Iteration 5736 (2.48174 iter/s, 4.83533s/12 iters), loss = 0.238564
I0408 20:06:15.823148  5931 solver.cpp:237]     Train net output #0: loss = 0.238564 (* 1 = 0.238564 loss)
I0408 20:06:15.823155  5931 sgd_solver.cpp:105] Iteration 5736, lr = 0.00281852
I0408 20:06:20.558178  5931 solver.cpp:218] Iteration 5748 (2.53431 iter/s, 4.73501s/12 iters), loss = 0.37392
I0408 20:06:20.558212  5931 solver.cpp:237]     Train net output #0: loss = 0.37392 (* 1 = 0.37392 loss)
I0408 20:06:20.558218  5931 sgd_solver.cpp:105] Iteration 5748, lr = 0.00278294
I0408 20:06:25.366329  5931 solver.cpp:218] Iteration 5760 (2.49579 iter/s, 4.8081s/12 iters), loss = 0.236158
I0408 20:06:25.366362  5931 solver.cpp:237]     Train net output #0: loss = 0.236158 (* 1 = 0.236158 loss)
I0408 20:06:25.366370  5931 sgd_solver.cpp:105] Iteration 5760, lr = 0.00274763
I0408 20:06:27.197660  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:06:30.052968  5931 solver.cpp:218] Iteration 5772 (2.5605 iter/s, 4.68658s/12 iters), loss = 0.269355
I0408 20:06:30.053064  5931 solver.cpp:237]     Train net output #0: loss = 0.269355 (* 1 = 0.269355 loss)
I0408 20:06:30.053073  5931 sgd_solver.cpp:105] Iteration 5772, lr = 0.00271261
I0408 20:06:34.871819  5931 solver.cpp:218] Iteration 5784 (2.49028 iter/s, 4.81874s/12 iters), loss = 0.193116
I0408 20:06:34.871857  5931 solver.cpp:237]     Train net output #0: loss = 0.193116 (* 1 = 0.193116 loss)
I0408 20:06:34.871865  5931 sgd_solver.cpp:105] Iteration 5784, lr = 0.00267786
I0408 20:06:39.647927  5931 solver.cpp:218] Iteration 5796 (2.51254 iter/s, 4.77605s/12 iters), loss = 0.346414
I0408 20:06:39.647960  5931 solver.cpp:237]     Train net output #0: loss = 0.346414 (* 1 = 0.346414 loss)
I0408 20:06:39.647969  5931 sgd_solver.cpp:105] Iteration 5796, lr = 0.0026434
I0408 20:06:44.419777  5931 solver.cpp:218] Iteration 5808 (2.51478 iter/s, 4.7718s/12 iters), loss = 0.126896
I0408 20:06:44.419811  5931 solver.cpp:237]     Train net output #0: loss = 0.126896 (* 1 = 0.126896 loss)
I0408 20:06:44.419819  5931 sgd_solver.cpp:105] Iteration 5808, lr = 0.00260923
I0408 20:06:46.393946  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 20:06:49.537226  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 20:06:51.899366  5931 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 20:06:51.899394  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:06:54.154704  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:06:56.675269  5931 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0408 20:06:56.675318  5931 solver.cpp:397]     Test net output #1: loss = 3.00631 (* 1 = 3.00631 loss)
I0408 20:06:58.406922  5931 solver.cpp:218] Iteration 5820 (0.857935 iter/s, 13.9871s/12 iters), loss = 0.235076
I0408 20:06:58.406956  5931 solver.cpp:237]     Train net output #0: loss = 0.235076 (* 1 = 0.235076 loss)
I0408 20:06:58.406963  5931 sgd_solver.cpp:105] Iteration 5820, lr = 0.00257534
I0408 20:07:03.232364  5931 solver.cpp:218] Iteration 5832 (2.48685 iter/s, 4.82539s/12 iters), loss = 0.257338
I0408 20:07:03.232488  5931 solver.cpp:237]     Train net output #0: loss = 0.257338 (* 1 = 0.257338 loss)
I0408 20:07:03.232497  5931 sgd_solver.cpp:105] Iteration 5832, lr = 0.00254174
I0408 20:07:07.941685  5931 solver.cpp:218] Iteration 5844 (2.54821 iter/s, 4.70918s/12 iters), loss = 0.215022
I0408 20:07:07.941718  5931 solver.cpp:237]     Train net output #0: loss = 0.215022 (* 1 = 0.215022 loss)
I0408 20:07:07.941725  5931 sgd_solver.cpp:105] Iteration 5844, lr = 0.00250844
I0408 20:07:12.774667  5931 solver.cpp:218] Iteration 5856 (2.48297 iter/s, 4.83293s/12 iters), loss = 0.332472
I0408 20:07:12.774700  5931 solver.cpp:237]     Train net output #0: loss = 0.332472 (* 1 = 0.332472 loss)
I0408 20:07:12.774708  5931 sgd_solver.cpp:105] Iteration 5856, lr = 0.00247542
I0408 20:07:16.788264  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:07:17.561910  5931 solver.cpp:218] Iteration 5868 (2.50669 iter/s, 4.78719s/12 iters), loss = 0.208182
I0408 20:07:17.561944  5931 solver.cpp:237]     Train net output #0: loss = 0.208182 (* 1 = 0.208182 loss)
I0408 20:07:17.561950  5931 sgd_solver.cpp:105] Iteration 5868, lr = 0.0024427
I0408 20:07:22.426555  5931 solver.cpp:218] Iteration 5880 (2.46681 iter/s, 4.86459s/12 iters), loss = 0.115693
I0408 20:07:22.426589  5931 solver.cpp:237]     Train net output #0: loss = 0.115693 (* 1 = 0.115693 loss)
I0408 20:07:22.426597  5931 sgd_solver.cpp:105] Iteration 5880, lr = 0.00241027
I0408 20:07:27.271683  5931 solver.cpp:218] Iteration 5892 (2.47674 iter/s, 4.84508s/12 iters), loss = 0.207036
I0408 20:07:27.271718  5931 solver.cpp:237]     Train net output #0: loss = 0.207036 (* 1 = 0.207036 loss)
I0408 20:07:27.271725  5931 sgd_solver.cpp:105] Iteration 5892, lr = 0.00237813
I0408 20:07:31.975659  5931 solver.cpp:218] Iteration 5904 (2.55106 iter/s, 4.70392s/12 iters), loss = 0.200547
I0408 20:07:31.975694  5931 solver.cpp:237]     Train net output #0: loss = 0.200547 (* 1 = 0.200547 loss)
I0408 20:07:31.975701  5931 sgd_solver.cpp:105] Iteration 5904, lr = 0.00234629
I0408 20:07:36.291177  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 20:07:39.393429  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 20:07:41.748649  5931 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 20:07:41.748672  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:07:43.738060  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:07:45.996183  5931 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0408 20:07:45.996230  5931 solver.cpp:397]     Test net output #1: loss = 2.95003 (* 1 = 2.95003 loss)
I0408 20:07:46.091876  5931 solver.cpp:218] Iteration 5916 (0.85009 iter/s, 14.1161s/12 iters), loss = 0.168647
I0408 20:07:46.091912  5931 solver.cpp:237]     Train net output #0: loss = 0.168647 (* 1 = 0.168647 loss)
I0408 20:07:46.091919  5931 sgd_solver.cpp:105] Iteration 5916, lr = 0.00231475
I0408 20:07:50.096832  5931 solver.cpp:218] Iteration 5928 (2.99633 iter/s, 4.0049s/12 iters), loss = 0.225836
I0408 20:07:50.096865  5931 solver.cpp:237]     Train net output #0: loss = 0.225836 (* 1 = 0.225836 loss)
I0408 20:07:50.096873  5931 sgd_solver.cpp:105] Iteration 5928, lr = 0.00228351
I0408 20:07:54.831508  5931 solver.cpp:218] Iteration 5940 (2.53452 iter/s, 4.73462s/12 iters), loss = 0.144558
I0408 20:07:54.831547  5931 solver.cpp:237]     Train net output #0: loss = 0.144558 (* 1 = 0.144558 loss)
I0408 20:07:54.831555  5931 sgd_solver.cpp:105] Iteration 5940, lr = 0.00225256
I0408 20:07:59.687925  5931 solver.cpp:218] Iteration 5952 (2.47098 iter/s, 4.85637s/12 iters), loss = 0.186715
I0408 20:07:59.687958  5931 solver.cpp:237]     Train net output #0: loss = 0.186715 (* 1 = 0.186715 loss)
I0408 20:07:59.687965  5931 sgd_solver.cpp:105] Iteration 5952, lr = 0.00222191
I0408 20:08:04.500212  5931 solver.cpp:218] Iteration 5964 (2.49364 iter/s, 4.81224s/12 iters), loss = 0.196992
I0408 20:08:04.500248  5931 solver.cpp:237]     Train net output #0: loss = 0.196992 (* 1 = 0.196992 loss)
I0408 20:08:04.500257  5931 sgd_solver.cpp:105] Iteration 5964, lr = 0.00219157
I0408 20:08:05.781056  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:09.352057  5931 solver.cpp:218] Iteration 5976 (2.47332 iter/s, 4.85179s/12 iters), loss = 0.200582
I0408 20:08:09.352170  5931 solver.cpp:237]     Train net output #0: loss = 0.200582 (* 1 = 0.200582 loss)
I0408 20:08:09.352180  5931 sgd_solver.cpp:105] Iteration 5976, lr = 0.00216152
I0408 20:08:14.136185  5931 solver.cpp:218] Iteration 5988 (2.50836 iter/s, 4.784s/12 iters), loss = 0.277873
I0408 20:08:14.136219  5931 solver.cpp:237]     Train net output #0: loss = 0.277873 (* 1 = 0.277873 loss)
I0408 20:08:14.136227  5931 sgd_solver.cpp:105] Iteration 5988, lr = 0.00213177
I0408 20:08:18.979646  5931 solver.cpp:218] Iteration 6000 (2.47759 iter/s, 4.84341s/12 iters), loss = 0.259368
I0408 20:08:18.979686  5931 solver.cpp:237]     Train net output #0: loss = 0.259368 (* 1 = 0.259368 loss)
I0408 20:08:18.979693  5931 sgd_solver.cpp:105] Iteration 6000, lr = 0.00210232
I0408 20:08:23.802606  5931 solver.cpp:218] Iteration 6012 (2.48813 iter/s, 4.8229s/12 iters), loss = 0.0745169
I0408 20:08:23.802641  5931 solver.cpp:237]     Train net output #0: loss = 0.0745169 (* 1 = 0.0745169 loss)
I0408 20:08:23.802649  5931 sgd_solver.cpp:105] Iteration 6012, lr = 0.00207317
I0408 20:08:25.763334  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 20:08:28.903297  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 20:08:31.259670  5931 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 20:08:31.259696  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:08:33.308152  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:35.620275  5931 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0408 20:08:35.620321  5931 solver.cpp:397]     Test net output #1: loss = 2.99605 (* 1 = 2.99605 loss)
I0408 20:08:37.351923  5931 solver.cpp:218] Iteration 6024 (0.885658 iter/s, 13.5492s/12 iters), loss = 0.246775
I0408 20:08:37.351958  5931 solver.cpp:237]     Train net output #0: loss = 0.246775 (* 1 = 0.246775 loss)
I0408 20:08:37.351965  5931 sgd_solver.cpp:105] Iteration 6024, lr = 0.00204432
I0408 20:08:42.124806  5931 solver.cpp:218] Iteration 6036 (2.51423 iter/s, 4.77283s/12 iters), loss = 0.3277
I0408 20:08:42.124923  5931 solver.cpp:237]     Train net output #0: loss = 0.3277 (* 1 = 0.3277 loss)
I0408 20:08:42.124933  5931 sgd_solver.cpp:105] Iteration 6036, lr = 0.00201576
I0408 20:08:46.855738  5931 solver.cpp:218] Iteration 6048 (2.53657 iter/s, 4.7308s/12 iters), loss = 0.256331
I0408 20:08:46.855770  5931 solver.cpp:237]     Train net output #0: loss = 0.256331 (* 1 = 0.256331 loss)
I0408 20:08:46.855777  5931 sgd_solver.cpp:105] Iteration 6048, lr = 0.00198751
I0408 20:08:51.761030  5931 solver.cpp:218] Iteration 6060 (2.44636 iter/s, 4.90524s/12 iters), loss = 0.116351
I0408 20:08:51.761065  5931 solver.cpp:237]     Train net output #0: loss = 0.116351 (* 1 = 0.116351 loss)
I0408 20:08:51.761072  5931 sgd_solver.cpp:105] Iteration 6060, lr = 0.00195956
I0408 20:08:55.074321  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:56.574128  5931 solver.cpp:218] Iteration 6072 (2.49322 iter/s, 4.81304s/12 iters), loss = 0.162984
I0408 20:08:56.574162  5931 solver.cpp:237]     Train net output #0: loss = 0.162984 (* 1 = 0.162984 loss)
I0408 20:08:56.574168  5931 sgd_solver.cpp:105] Iteration 6072, lr = 0.0019319
I0408 20:09:01.385977  5931 solver.cpp:218] Iteration 6084 (2.49387 iter/s, 4.81179s/12 iters), loss = 0.124318
I0408 20:09:01.386009  5931 solver.cpp:237]     Train net output #0: loss = 0.124318 (* 1 = 0.124318 loss)
I0408 20:09:01.386018  5931 sgd_solver.cpp:105] Iteration 6084, lr = 0.00190455
I0408 20:09:06.322439  5931 solver.cpp:218] Iteration 6096 (2.43092 iter/s, 4.93641s/12 iters), loss = 0.245757
I0408 20:09:06.322474  5931 solver.cpp:237]     Train net output #0: loss = 0.245757 (* 1 = 0.245757 loss)
I0408 20:09:06.322480  5931 sgd_solver.cpp:105] Iteration 6096, lr = 0.00187749
I0408 20:09:11.060858  5931 solver.cpp:218] Iteration 6108 (2.53252 iter/s, 4.73836s/12 iters), loss = 0.205739
I0408 20:09:11.060889  5931 solver.cpp:237]     Train net output #0: loss = 0.205739 (* 1 = 0.205739 loss)
I0408 20:09:11.060897  5931 sgd_solver.cpp:105] Iteration 6108, lr = 0.00185072
I0408 20:09:15.463348  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 20:09:20.048437  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 20:09:22.424505  5931 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 20:09:22.424536  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:09:24.564239  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:09:27.215373  5931 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0408 20:09:27.215422  5931 solver.cpp:397]     Test net output #1: loss = 2.9398 (* 1 = 2.9398 loss)
I0408 20:09:27.312129  5931 solver.cpp:218] Iteration 6120 (0.738407 iter/s, 16.2512s/12 iters), loss = 0.191523
I0408 20:09:27.312165  5931 solver.cpp:237]     Train net output #0: loss = 0.191523 (* 1 = 0.191523 loss)
I0408 20:09:27.312172  5931 sgd_solver.cpp:105] Iteration 6120, lr = 0.00182426
I0408 20:09:31.356608  5931 solver.cpp:218] Iteration 6132 (2.96705 iter/s, 4.04443s/12 iters), loss = 0.266697
I0408 20:09:31.356642  5931 solver.cpp:237]     Train net output #0: loss = 0.266697 (* 1 = 0.266697 loss)
I0408 20:09:31.356650  5931 sgd_solver.cpp:105] Iteration 6132, lr = 0.00179808
I0408 20:09:36.217468  5931 solver.cpp:218] Iteration 6144 (2.46873 iter/s, 4.86081s/12 iters), loss = 0.370711
I0408 20:09:36.217499  5931 solver.cpp:237]     Train net output #0: loss = 0.370711 (* 1 = 0.370711 loss)
I0408 20:09:36.217507  5931 sgd_solver.cpp:105] Iteration 6144, lr = 0.0017722
I0408 20:09:41.051517  5931 solver.cpp:218] Iteration 6156 (2.48242 iter/s, 4.834s/12 iters), loss = 0.210087
I0408 20:09:41.051555  5931 solver.cpp:237]     Train net output #0: loss = 0.210087 (* 1 = 0.210087 loss)
I0408 20:09:41.051563  5931 sgd_solver.cpp:105] Iteration 6156, lr = 0.00174662
I0408 20:09:45.965445  5931 solver.cpp:218] Iteration 6168 (2.44207 iter/s, 4.91387s/12 iters), loss = 0.181852
I0408 20:09:45.965509  5931 solver.cpp:237]     Train net output #0: loss = 0.181852 (* 1 = 0.181852 loss)
I0408 20:09:45.965517  5931 sgd_solver.cpp:105] Iteration 6168, lr = 0.00172133
I0408 20:09:46.504793  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:09:50.758802  5931 solver.cpp:218] Iteration 6180 (2.50351 iter/s, 4.79328s/12 iters), loss = 0.147821
I0408 20:09:50.758837  5931 solver.cpp:237]     Train net output #0: loss = 0.147821 (* 1 = 0.147821 loss)
I0408 20:09:50.758846  5931 sgd_solver.cpp:105] Iteration 6180, lr = 0.00169632
I0408 20:09:55.686964  5931 solver.cpp:218] Iteration 6192 (2.43501 iter/s, 4.92811s/12 iters), loss = 0.123501
I0408 20:09:55.687000  5931 solver.cpp:237]     Train net output #0: loss = 0.123501 (* 1 = 0.123501 loss)
I0408 20:09:55.687006  5931 sgd_solver.cpp:105] Iteration 6192, lr = 0.00167161
I0408 20:10:00.510259  5931 solver.cpp:218] Iteration 6204 (2.48795 iter/s, 4.82324s/12 iters), loss = 0.128774
I0408 20:10:00.510293  5931 solver.cpp:237]     Train net output #0: loss = 0.128774 (* 1 = 0.128774 loss)
I0408 20:10:00.510300  5931 sgd_solver.cpp:105] Iteration 6204, lr = 0.00164719
I0408 20:10:05.372558  5931 solver.cpp:218] Iteration 6216 (2.468 iter/s, 4.86225s/12 iters), loss = 0.334978
I0408 20:10:05.372594  5931 solver.cpp:237]     Train net output #0: loss = 0.334978 (* 1 = 0.334978 loss)
I0408 20:10:05.372602  5931 sgd_solver.cpp:105] Iteration 6216, lr = 0.00162305
I0408 20:10:07.284966  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 20:10:10.440666  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 20:10:12.825141  5931 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 20:10:12.825167  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:10:14.862123  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:10:16.104339  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:10:17.241958  5931 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0408 20:10:17.242003  5931 solver.cpp:397]     Test net output #1: loss = 2.95634 (* 1 = 2.95634 loss)
I0408 20:10:18.974587  5931 solver.cpp:218] Iteration 6228 (0.882226 iter/s, 13.602s/12 iters), loss = 0.153808
I0408 20:10:18.974622  5931 solver.cpp:237]     Train net output #0: loss = 0.153808 (* 1 = 0.153808 loss)
I0408 20:10:18.974629  5931 sgd_solver.cpp:105] Iteration 6228, lr = 0.0015992
I0408 20:10:23.851501  5931 solver.cpp:218] Iteration 6240 (2.4606 iter/s, 4.87686s/12 iters), loss = 0.0711555
I0408 20:10:23.851536  5931 solver.cpp:237]     Train net output #0: loss = 0.0711555 (* 1 = 0.0711555 loss)
I0408 20:10:23.851544  5931 sgd_solver.cpp:105] Iteration 6240, lr = 0.00157563
I0408 20:10:28.754727  5931 solver.cpp:218] Iteration 6252 (2.4474 iter/s, 4.90317s/12 iters), loss = 0.170034
I0408 20:10:28.754760  5931 solver.cpp:237]     Train net output #0: loss = 0.170034 (* 1 = 0.170034 loss)
I0408 20:10:28.754767  5931 sgd_solver.cpp:105] Iteration 6252, lr = 0.00155235
I0408 20:10:33.615926  5931 solver.cpp:218] Iteration 6264 (2.46855 iter/s, 4.86115s/12 iters), loss = 0.160324
I0408 20:10:33.615958  5931 solver.cpp:237]     Train net output #0: loss = 0.160324 (* 1 = 0.160324 loss)
I0408 20:10:33.615967  5931 sgd_solver.cpp:105] Iteration 6264, lr = 0.00152935
I0408 20:10:36.205178  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:10:38.488222  5931 solver.cpp:218] Iteration 6276 (2.46293 iter/s, 4.87224s/12 iters), loss = 0.0757003
I0408 20:10:38.488255  5931 solver.cpp:237]     Train net output #0: loss = 0.0757003 (* 1 = 0.0757003 loss)
I0408 20:10:38.488262  5931 sgd_solver.cpp:105] Iteration 6276, lr = 0.00150663
I0408 20:10:43.354019  5931 solver.cpp:218] Iteration 6288 (2.46622 iter/s, 4.86575s/12 iters), loss = 0.136363
I0408 20:10:43.354053  5931 solver.cpp:237]     Train net output #0: loss = 0.136363 (* 1 = 0.136363 loss)
I0408 20:10:43.354061  5931 sgd_solver.cpp:105] Iteration 6288, lr = 0.00148419
I0408 20:10:48.155094  5931 solver.cpp:218] Iteration 6300 (2.49947 iter/s, 4.80102s/12 iters), loss = 0.14871
I0408 20:10:48.155211  5931 solver.cpp:237]     Train net output #0: loss = 0.14871 (* 1 = 0.14871 loss)
I0408 20:10:48.155220  5931 sgd_solver.cpp:105] Iteration 6300, lr = 0.00146202
I0408 20:10:53.068392  5931 solver.cpp:218] Iteration 6312 (2.44242 iter/s, 4.91316s/12 iters), loss = 0.180014
I0408 20:10:53.068423  5931 solver.cpp:237]     Train net output #0: loss = 0.180014 (* 1 = 0.180014 loss)
I0408 20:10:53.068431  5931 sgd_solver.cpp:105] Iteration 6312, lr = 0.00144013
I0408 20:10:57.431567  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 20:11:01.525633  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 20:11:04.830422  5931 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 20:11:04.830447  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:11:06.871363  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:11:09.605623  5931 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0408 20:11:09.605669  5931 solver.cpp:397]     Test net output #1: loss = 2.96024 (* 1 = 2.96024 loss)
I0408 20:11:09.702230  5931 solver.cpp:218] Iteration 6324 (0.721424 iter/s, 16.6338s/12 iters), loss = 0.168762
I0408 20:11:09.702267  5931 solver.cpp:237]     Train net output #0: loss = 0.168762 (* 1 = 0.168762 loss)
I0408 20:11:09.702275  5931 sgd_solver.cpp:105] Iteration 6324, lr = 0.00141851
I0408 20:11:13.726189  5931 solver.cpp:218] Iteration 6336 (2.98218 iter/s, 4.0239s/12 iters), loss = 0.102495
I0408 20:11:13.726224  5931 solver.cpp:237]     Train net output #0: loss = 0.102495 (* 1 = 0.102495 loss)
I0408 20:11:13.726231  5931 sgd_solver.cpp:105] Iteration 6336, lr = 0.00139716
I0408 20:11:18.546707  5931 solver.cpp:218] Iteration 6348 (2.48939 iter/s, 4.82046s/12 iters), loss = 0.230752
I0408 20:11:18.546805  5931 solver.cpp:237]     Train net output #0: loss = 0.230752 (* 1 = 0.230752 loss)
I0408 20:11:18.546814  5931 sgd_solver.cpp:105] Iteration 6348, lr = 0.00137609
I0408 20:11:23.428619  5931 solver.cpp:218] Iteration 6360 (2.45811 iter/s, 4.8818s/12 iters), loss = 0.318958
I0408 20:11:23.428651  5931 solver.cpp:237]     Train net output #0: loss = 0.318958 (* 1 = 0.318958 loss)
I0408 20:11:23.428659  5931 sgd_solver.cpp:105] Iteration 6360, lr = 0.00135528
I0408 20:11:28.053357  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:11:28.190143  5931 solver.cpp:218] Iteration 6372 (2.52023 iter/s, 4.76147s/12 iters), loss = 0.15347
I0408 20:11:28.190173  5931 solver.cpp:237]     Train net output #0: loss = 0.15347 (* 1 = 0.15347 loss)
I0408 20:11:28.190181  5931 sgd_solver.cpp:105] Iteration 6372, lr = 0.00133474
I0408 20:11:33.026971  5931 solver.cpp:218] Iteration 6384 (2.48099 iter/s, 4.83678s/12 iters), loss = 0.0793962
I0408 20:11:33.027004  5931 solver.cpp:237]     Train net output #0: loss = 0.0793962 (* 1 = 0.0793962 loss)
I0408 20:11:33.027011  5931 sgd_solver.cpp:105] Iteration 6384, lr = 0.00131446
I0408 20:11:37.895071  5931 solver.cpp:218] Iteration 6396 (2.46505 iter/s, 4.86805s/12 iters), loss = 0.177293
I0408 20:11:37.895103  5931 solver.cpp:237]     Train net output #0: loss = 0.177293 (* 1 = 0.177293 loss)
I0408 20:11:37.895112  5931 sgd_solver.cpp:105] Iteration 6396, lr = 0.00129444
I0408 20:11:42.710003  5931 solver.cpp:218] Iteration 6408 (2.49227 iter/s, 4.81488s/12 iters), loss = 0.165544
I0408 20:11:42.710036  5931 solver.cpp:237]     Train net output #0: loss = 0.165544 (* 1 = 0.165544 loss)
I0408 20:11:42.710043  5931 sgd_solver.cpp:105] Iteration 6408, lr = 0.00127468
I0408 20:11:47.520005  5931 solver.cpp:218] Iteration 6420 (2.49483 iter/s, 4.80995s/12 iters), loss = 0.125816
I0408 20:11:47.520040  5931 solver.cpp:237]     Train net output #0: loss = 0.125816 (* 1 = 0.125816 loss)
I0408 20:11:47.520047  5931 sgd_solver.cpp:105] Iteration 6420, lr = 0.00125519
I0408 20:11:49.422883  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 20:11:53.252032  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 20:11:56.711493  5931 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 20:11:56.711521  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:11:58.702052  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:12:01.492951  5931 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0408 20:12:01.492997  5931 solver.cpp:397]     Test net output #1: loss = 3.00246 (* 1 = 3.00246 loss)
I0408 20:12:03.239104  5931 solver.cpp:218] Iteration 6432 (0.763406 iter/s, 15.719s/12 iters), loss = 0.196917
I0408 20:12:03.239140  5931 solver.cpp:237]     Train net output #0: loss = 0.196917 (* 1 = 0.196917 loss)
I0408 20:12:03.239148  5931 sgd_solver.cpp:105] Iteration 6432, lr = 0.00123594
I0408 20:12:08.040228  5931 solver.cpp:218] Iteration 6444 (2.49944 iter/s, 4.80107s/12 iters), loss = 0.264956
I0408 20:12:08.040262  5931 solver.cpp:237]     Train net output #0: loss = 0.264956 (* 1 = 0.264956 loss)
I0408 20:12:08.040271  5931 sgd_solver.cpp:105] Iteration 6444, lr = 0.00121696
I0408 20:12:12.863951  5931 solver.cpp:218] Iteration 6456 (2.48773 iter/s, 4.82367s/12 iters), loss = 0.305538
I0408 20:12:12.863986  5931 solver.cpp:237]     Train net output #0: loss = 0.305538 (* 1 = 0.305538 loss)
I0408 20:12:12.863993  5931 sgd_solver.cpp:105] Iteration 6456, lr = 0.00119822
I0408 20:12:17.758031  5931 solver.cpp:218] Iteration 6468 (2.45197 iter/s, 4.89402s/12 iters), loss = 0.0797478
I0408 20:12:17.758066  5931 solver.cpp:237]     Train net output #0: loss = 0.0797478 (* 1 = 0.0797478 loss)
I0408 20:12:17.758074  5931 sgd_solver.cpp:105] Iteration 6468, lr = 0.00117973
I0408 20:12:19.778565  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:12:22.656345  5931 solver.cpp:218] Iteration 6480 (2.44985 iter/s, 4.89826s/12 iters), loss = 0.0982578
I0408 20:12:22.656380  5931 solver.cpp:237]     Train net output #0: loss = 0.0982579 (* 1 = 0.0982579 loss)
I0408 20:12:22.656388  5931 sgd_solver.cpp:105] Iteration 6480, lr = 0.00116149
I0408 20:12:27.474653  5931 solver.cpp:218] Iteration 6492 (2.49053 iter/s, 4.81825s/12 iters), loss = 0.104125
I0408 20:12:27.474686  5931 solver.cpp:237]     Train net output #0: loss = 0.104125 (* 1 = 0.104125 loss)
I0408 20:12:27.474694  5931 sgd_solver.cpp:105] Iteration 6492, lr = 0.0011435
I0408 20:12:32.354048  5931 solver.cpp:218] Iteration 6504 (2.45935 iter/s, 4.87934s/12 iters), loss = 0.184643
I0408 20:12:32.354081  5931 solver.cpp:237]     Train net output #0: loss = 0.184643 (* 1 = 0.184643 loss)
I0408 20:12:32.354089  5931 sgd_solver.cpp:105] Iteration 6504, lr = 0.00112575
I0408 20:12:37.232302  5931 solver.cpp:218] Iteration 6516 (2.45992 iter/s, 4.8782s/12 iters), loss = 0.0606845
I0408 20:12:37.232337  5931 solver.cpp:237]     Train net output #0: loss = 0.0606845 (* 1 = 0.0606845 loss)
I0408 20:12:37.232344  5931 sgd_solver.cpp:105] Iteration 6516, lr = 0.00110824
I0408 20:12:41.641340  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 20:12:45.429971  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 20:12:47.839398  5931 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 20:12:47.839422  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:12:49.688155  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:12:52.194080  5931 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0408 20:12:52.194218  5931 solver.cpp:397]     Test net output #1: loss = 2.96061 (* 1 = 2.96061 loss)
I0408 20:12:52.290647  5931 solver.cpp:218] Iteration 6528 (0.796904 iter/s, 15.0583s/12 iters), loss = 0.0645053
I0408 20:12:52.290684  5931 solver.cpp:237]     Train net output #0: loss = 0.0645054 (* 1 = 0.0645054 loss)
I0408 20:12:52.290693  5931 sgd_solver.cpp:105] Iteration 6528, lr = 0.00109097
I0408 20:12:56.250638  5931 solver.cpp:218] Iteration 6540 (3.03035 iter/s, 3.95994s/12 iters), loss = 0.119277
I0408 20:12:56.250674  5931 solver.cpp:237]     Train net output #0: loss = 0.119277 (* 1 = 0.119277 loss)
I0408 20:12:56.250681  5931 sgd_solver.cpp:105] Iteration 6540, lr = 0.00107393
I0408 20:13:01.046283  5931 solver.cpp:218] Iteration 6552 (2.5023 iter/s, 4.79559s/12 iters), loss = 0.0869053
I0408 20:13:01.046316  5931 solver.cpp:237]     Train net output #0: loss = 0.0869053 (* 1 = 0.0869053 loss)
I0408 20:13:01.046324  5931 sgd_solver.cpp:105] Iteration 6552, lr = 0.00105713
I0408 20:13:06.057199  5931 solver.cpp:218] Iteration 6564 (2.3948 iter/s, 5.01086s/12 iters), loss = 0.124849
I0408 20:13:06.057231  5931 solver.cpp:237]     Train net output #0: loss = 0.124849 (* 1 = 0.124849 loss)
I0408 20:13:06.057240  5931 sgd_solver.cpp:105] Iteration 6564, lr = 0.00104057
I0408 20:13:10.039264  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:13:10.767402  5931 solver.cpp:218] Iteration 6576 (2.54769 iter/s, 4.71015s/12 iters), loss = 0.187502
I0408 20:13:10.767434  5931 solver.cpp:237]     Train net output #0: loss = 0.187502 (* 1 = 0.187502 loss)
I0408 20:13:10.767441  5931 sgd_solver.cpp:105] Iteration 6576, lr = 0.00102423
I0408 20:13:15.552714  5931 solver.cpp:218] Iteration 6588 (2.5077 iter/s, 4.78526s/12 iters), loss = 0.101033
I0408 20:13:15.552748  5931 solver.cpp:237]     Train net output #0: loss = 0.101033 (* 1 = 0.101033 loss)
I0408 20:13:15.552757  5931 sgd_solver.cpp:105] Iteration 6588, lr = 0.00100812
I0408 20:13:20.493610  5931 solver.cpp:218] Iteration 6600 (2.42874 iter/s, 4.94084s/12 iters), loss = 0.136095
I0408 20:13:20.493644  5931 solver.cpp:237]     Train net output #0: loss = 0.136095 (* 1 = 0.136095 loss)
I0408 20:13:20.493651  5931 sgd_solver.cpp:105] Iteration 6600, lr = 0.000992235
I0408 20:13:25.458819  5931 solver.cpp:218] Iteration 6612 (2.41684 iter/s, 4.96515s/12 iters), loss = 0.115821
I0408 20:13:25.458936  5931 solver.cpp:237]     Train net output #0: loss = 0.115821 (* 1 = 0.115821 loss)
I0408 20:13:25.458945  5931 sgd_solver.cpp:105] Iteration 6612, lr = 0.000976574
I0408 20:13:30.456481  5931 solver.cpp:218] Iteration 6624 (2.40119 iter/s, 4.99752s/12 iters), loss = 0.278434
I0408 20:13:30.456513  5931 solver.cpp:237]     Train net output #0: loss = 0.278434 (* 1 = 0.278434 loss)
I0408 20:13:30.456521  5931 sgd_solver.cpp:105] Iteration 6624, lr = 0.000961133
I0408 20:13:32.526356  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 20:13:36.006157  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 20:13:38.355458  5931 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 20:13:38.355482  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:13:40.274756  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:13:42.873092  5931 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0408 20:13:42.873137  5931 solver.cpp:397]     Test net output #1: loss = 2.94478 (* 1 = 2.94478 loss)
I0408 20:13:44.616324  5931 solver.cpp:218] Iteration 6636 (0.847471 iter/s, 14.1598s/12 iters), loss = 0.133299
I0408 20:13:44.616359  5931 solver.cpp:237]     Train net output #0: loss = 0.133299 (* 1 = 0.133299 loss)
I0408 20:13:44.616366  5931 sgd_solver.cpp:105] Iteration 6636, lr = 0.000945911
I0408 20:13:49.346843  5931 solver.cpp:218] Iteration 6648 (2.53675 iter/s, 4.73046s/12 iters), loss = 0.301478
I0408 20:13:49.346876  5931 solver.cpp:237]     Train net output #0: loss = 0.301478 (* 1 = 0.301478 loss)
I0408 20:13:49.346884  5931 sgd_solver.cpp:105] Iteration 6648, lr = 0.000930905
I0408 20:13:54.184154  5931 solver.cpp:218] Iteration 6660 (2.48074 iter/s, 4.83726s/12 iters), loss = 0.110594
I0408 20:13:54.184187  5931 solver.cpp:237]     Train net output #0: loss = 0.110594 (* 1 = 0.110594 loss)
I0408 20:13:54.184195  5931 sgd_solver.cpp:105] Iteration 6660, lr = 0.000916113
I0408 20:13:58.985841  5931 solver.cpp:218] Iteration 6672 (2.49915 iter/s, 4.80163s/12 iters), loss = 0.0967719
I0408 20:13:58.985936  5931 solver.cpp:237]     Train net output #0: loss = 0.0967719 (* 1 = 0.0967719 loss)
I0408 20:13:58.985944  5931 sgd_solver.cpp:105] Iteration 6672, lr = 0.000901533
I0408 20:14:00.287806  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:14:03.751572  5931 solver.cpp:218] Iteration 6684 (2.51804 iter/s, 4.76562s/12 iters), loss = 0.176942
I0408 20:14:03.751607  5931 solver.cpp:237]     Train net output #0: loss = 0.176942 (* 1 = 0.176942 loss)
I0408 20:14:03.751616  5931 sgd_solver.cpp:105] Iteration 6684, lr = 0.000887162
I0408 20:14:08.475462  5931 solver.cpp:218] Iteration 6696 (2.54031 iter/s, 4.72384s/12 iters), loss = 0.0631857
I0408 20:14:08.475497  5931 solver.cpp:237]     Train net output #0: loss = 0.0631858 (* 1 = 0.0631858 loss)
I0408 20:14:08.475504  5931 sgd_solver.cpp:105] Iteration 6696, lr = 0.000872998
I0408 20:14:13.273082  5931 solver.cpp:218] Iteration 6708 (2.50127 iter/s, 4.79757s/12 iters), loss = 0.166637
I0408 20:14:13.273115  5931 solver.cpp:237]     Train net output #0: loss = 0.166637 (* 1 = 0.166637 loss)
I0408 20:14:13.273123  5931 sgd_solver.cpp:105] Iteration 6708, lr = 0.000859039
I0408 20:14:17.936640  5931 solver.cpp:218] Iteration 6720 (2.57317 iter/s, 4.6635s/12 iters), loss = 0.137803
I0408 20:14:17.936671  5931 solver.cpp:237]     Train net output #0: loss = 0.137803 (* 1 = 0.137803 loss)
I0408 20:14:17.936678  5931 sgd_solver.cpp:105] Iteration 6720, lr = 0.000845283
I0408 20:14:22.252166  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 20:14:25.308012  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 20:14:27.723134  5931 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 20:14:27.723160  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:14:29.600313  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:14:32.533883  5931 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0408 20:14:32.533931  5931 solver.cpp:397]     Test net output #1: loss = 2.92792 (* 1 = 2.92792 loss)
I0408 20:14:32.630347  5931 solver.cpp:218] Iteration 6732 (0.81668 iter/s, 14.6936s/12 iters), loss = 0.0591866
I0408 20:14:32.630384  5931 solver.cpp:237]     Train net output #0: loss = 0.0591867 (* 1 = 0.0591867 loss)
I0408 20:14:32.630393  5931 sgd_solver.cpp:105] Iteration 6732, lr = 0.000831727
I0408 20:14:36.601056  5931 solver.cpp:218] Iteration 6744 (3.02217 iter/s, 3.97066s/12 iters), loss = 0.163432
I0408 20:14:36.601091  5931 solver.cpp:237]     Train net output #0: loss = 0.163432 (* 1 = 0.163432 loss)
I0408 20:14:36.601099  5931 sgd_solver.cpp:105] Iteration 6744, lr = 0.000818369
I0408 20:14:41.400969  5931 solver.cpp:218] Iteration 6756 (2.50008 iter/s, 4.79986s/12 iters), loss = 0.134583
I0408 20:14:41.401002  5931 solver.cpp:237]     Train net output #0: loss = 0.134583 (* 1 = 0.134583 loss)
I0408 20:14:41.401010  5931 sgd_solver.cpp:105] Iteration 6756, lr = 0.000805206
I0408 20:14:46.205837  5931 solver.cpp:218] Iteration 6768 (2.4975 iter/s, 4.80481s/12 iters), loss = 0.0954983
I0408 20:14:46.205868  5931 solver.cpp:237]     Train net output #0: loss = 0.0954984 (* 1 = 0.0954984 loss)
I0408 20:14:46.205876  5931 sgd_solver.cpp:105] Iteration 6768, lr = 0.000792237
I0408 20:14:49.582054  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:14:51.064855  5931 solver.cpp:218] Iteration 6780 (2.46966 iter/s, 4.85897s/12 iters), loss = 0.13027
I0408 20:14:51.064888  5931 solver.cpp:237]     Train net output #0: loss = 0.13027 (* 1 = 0.13027 loss)
I0408 20:14:51.064895  5931 sgd_solver.cpp:105] Iteration 6780, lr = 0.000779459
I0408 20:14:55.858438  5931 solver.cpp:218] Iteration 6792 (2.50337 iter/s, 4.79353s/12 iters), loss = 0.200689
I0408 20:14:55.858471  5931 solver.cpp:237]     Train net output #0: loss = 0.200689 (* 1 = 0.200689 loss)
I0408 20:14:55.858479  5931 sgd_solver.cpp:105] Iteration 6792, lr = 0.000766871
I0408 20:15:00.700433  5931 solver.cpp:218] Iteration 6804 (2.47834 iter/s, 4.84194s/12 iters), loss = 0.085935
I0408 20:15:00.700548  5931 solver.cpp:237]     Train net output #0: loss = 0.085935 (* 1 = 0.085935 loss)
I0408 20:15:00.700557  5931 sgd_solver.cpp:105] Iteration 6804, lr = 0.000754468
I0408 20:15:05.547202  5931 solver.cpp:218] Iteration 6816 (2.47595 iter/s, 4.84663s/12 iters), loss = 0.128109
I0408 20:15:05.547236  5931 solver.cpp:237]     Train net output #0: loss = 0.128109 (* 1 = 0.128109 loss)
I0408 20:15:05.547243  5931 sgd_solver.cpp:105] Iteration 6816, lr = 0.00074225
I0408 20:15:10.352576  5931 solver.cpp:218] Iteration 6828 (2.49723 iter/s, 4.80532s/12 iters), loss = 0.120586
I0408 20:15:10.352608  5931 solver.cpp:237]     Train net output #0: loss = 0.120586 (* 1 = 0.120586 loss)
I0408 20:15:10.352617  5931 sgd_solver.cpp:105] Iteration 6828, lr = 0.000730215
I0408 20:15:12.281247  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 20:15:15.355904  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 20:15:17.709599  5931 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 20:15:17.709625  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:15:19.549518  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:15:22.466540  5931 solver.cpp:397]     Test net output #0: accuracy = 0.469363
I0408 20:15:22.466588  5931 solver.cpp:397]     Test net output #1: loss = 2.92281 (* 1 = 2.92281 loss)
I0408 20:15:24.207881  5931 solver.cpp:218] Iteration 6840 (0.866098 iter/s, 13.8552s/12 iters), loss = 0.164247
I0408 20:15:24.207917  5931 solver.cpp:237]     Train net output #0: loss = 0.164247 (* 1 = 0.164247 loss)
I0408 20:15:24.207926  5931 sgd_solver.cpp:105] Iteration 6840, lr = 0.000718359
I0408 20:15:29.007839  5931 solver.cpp:218] Iteration 6852 (2.50005 iter/s, 4.7999s/12 iters), loss = 0.154856
I0408 20:15:29.007871  5931 solver.cpp:237]     Train net output #0: loss = 0.154856 (* 1 = 0.154856 loss)
I0408 20:15:29.007879  5931 sgd_solver.cpp:105] Iteration 6852, lr = 0.000706682
I0408 20:15:33.844710  5931 solver.cpp:218] Iteration 6864 (2.48097 iter/s, 4.83682s/12 iters), loss = 0.129887
I0408 20:15:33.844862  5931 solver.cpp:237]     Train net output #0: loss = 0.129887 (* 1 = 0.129887 loss)
I0408 20:15:33.844871  5931 sgd_solver.cpp:105] Iteration 6864, lr = 0.00069518
I0408 20:15:38.682919  5931 solver.cpp:218] Iteration 6876 (2.48034 iter/s, 4.83804s/12 iters), loss = 0.074003
I0408 20:15:38.682952  5931 solver.cpp:237]     Train net output #0: loss = 0.0740031 (* 1 = 0.0740031 loss)
I0408 20:15:38.682960  5931 sgd_solver.cpp:105] Iteration 6876, lr = 0.000683851
I0408 20:15:39.260697  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:15:43.503979  5931 solver.cpp:218] Iteration 6888 (2.4891 iter/s, 4.82101s/12 iters), loss = 0.198173
I0408 20:15:43.504011  5931 solver.cpp:237]     Train net output #0: loss = 0.198173 (* 1 = 0.198173 loss)
I0408 20:15:43.504019  5931 sgd_solver.cpp:105] Iteration 6888, lr = 0.000672694
I0408 20:15:48.301620  5931 solver.cpp:218] Iteration 6900 (2.50126 iter/s, 4.79759s/12 iters), loss = 0.181807
I0408 20:15:48.301654  5931 solver.cpp:237]     Train net output #0: loss = 0.181807 (* 1 = 0.181807 loss)
I0408 20:15:48.301661  5931 sgd_solver.cpp:105] Iteration 6900, lr = 0.000661705
I0408 20:15:53.151407  5931 solver.cpp:218] Iteration 6912 (2.47436 iter/s, 4.84974s/12 iters), loss = 0.113427
I0408 20:15:53.151443  5931 solver.cpp:237]     Train net output #0: loss = 0.113427 (* 1 = 0.113427 loss)
I0408 20:15:53.151451  5931 sgd_solver.cpp:105] Iteration 6912, lr = 0.000650884
I0408 20:15:57.975843  5931 solver.cpp:218] Iteration 6924 (2.48737 iter/s, 4.82438s/12 iters), loss = 0.10663
I0408 20:15:57.975875  5931 solver.cpp:237]     Train net output #0: loss = 0.10663 (* 1 = 0.10663 loss)
I0408 20:15:57.975883  5931 sgd_solver.cpp:105] Iteration 6924, lr = 0.000640227
I0408 20:16:02.336524  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 20:16:06.629683  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 20:16:08.980520  5931 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 20:16:08.980546  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:16:09.593680  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:16:10.679060  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:16:13.348839  5931 solver.cpp:397]     Test net output #0: accuracy = 0.459559
I0408 20:16:13.348884  5931 solver.cpp:397]     Test net output #1: loss = 2.94355 (* 1 = 2.94355 loss)
I0408 20:16:13.445363  5931 solver.cpp:218] Iteration 6936 (0.775722 iter/s, 15.4695s/12 iters), loss = 0.114165
I0408 20:16:13.445396  5931 solver.cpp:237]     Train net output #0: loss = 0.114165 (* 1 = 0.114165 loss)
I0408 20:16:13.445405  5931 sgd_solver.cpp:105] Iteration 6936, lr = 0.000629733
I0408 20:16:17.429514  5931 solver.cpp:218] Iteration 6948 (3.01197 iter/s, 3.9841s/12 iters), loss = 0.131762
I0408 20:16:17.429548  5931 solver.cpp:237]     Train net output #0: loss = 0.131762 (* 1 = 0.131762 loss)
I0408 20:16:17.429556  5931 sgd_solver.cpp:105] Iteration 6948, lr = 0.0006194
I0408 20:16:22.282490  5931 solver.cpp:218] Iteration 6960 (2.47274 iter/s, 4.85292s/12 iters), loss = 0.280908
I0408 20:16:22.282522  5931 solver.cpp:237]     Train net output #0: loss = 0.280908 (* 1 = 0.280908 loss)
I0408 20:16:22.282531  5931 sgd_solver.cpp:105] Iteration 6960, lr = 0.000609226
I0408 20:16:27.083074  5931 solver.cpp:218] Iteration 6972 (2.49972 iter/s, 4.80053s/12 iters), loss = 0.0834992
I0408 20:16:27.083107  5931 solver.cpp:237]     Train net output #0: loss = 0.0834993 (* 1 = 0.0834993 loss)
I0408 20:16:27.083115  5931 sgd_solver.cpp:105] Iteration 6972, lr = 0.000599208
I0408 20:16:29.739754  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:16:31.932974  5931 solver.cpp:218] Iteration 6984 (2.4743 iter/s, 4.84985s/12 iters), loss = 0.164028
I0408 20:16:31.933009  5931 solver.cpp:237]     Train net output #0: loss = 0.164028 (* 1 = 0.164028 loss)
I0408 20:16:31.933017  5931 sgd_solver.cpp:105] Iteration 6984, lr = 0.000589344
I0408 20:16:36.755919  5931 solver.cpp:218] Iteration 6996 (2.48813 iter/s, 4.82289s/12 iters), loss = 0.169343
I0408 20:16:36.756073  5931 solver.cpp:237]     Train net output #0: loss = 0.169343 (* 1 = 0.169343 loss)
I0408 20:16:36.756083  5931 sgd_solver.cpp:105] Iteration 6996, lr = 0.000579632
I0408 20:16:41.536451  5931 solver.cpp:218] Iteration 7008 (2.51027 iter/s, 4.78036s/12 iters), loss = 0.10899
I0408 20:16:41.536484  5931 solver.cpp:237]     Train net output #0: loss = 0.10899 (* 1 = 0.10899 loss)
I0408 20:16:41.536492  5931 sgd_solver.cpp:105] Iteration 7008, lr = 0.000570071
I0408 20:16:46.375644  5931 solver.cpp:218] Iteration 7020 (2.47978 iter/s, 4.83914s/12 iters), loss = 0.206646
I0408 20:16:46.375679  5931 solver.cpp:237]     Train net output #0: loss = 0.206646 (* 1 = 0.206646 loss)
I0408 20:16:46.375686  5931 sgd_solver.cpp:105] Iteration 7020, lr = 0.000560659
I0408 20:16:51.199347  5931 solver.cpp:218] Iteration 7032 (2.48774 iter/s, 4.82365s/12 iters), loss = 0.0845994
I0408 20:16:51.199383  5931 solver.cpp:237]     Train net output #0: loss = 0.0845994 (* 1 = 0.0845994 loss)
I0408 20:16:51.199389  5931 sgd_solver.cpp:105] Iteration 7032, lr = 0.000551392
I0408 20:16:53.162933  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 20:16:57.693749  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 20:17:01.519523  5931 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 20:17:01.519548  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:17:03.273010  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:17:06.315460  5931 solver.cpp:397]     Test net output #0: accuracy = 0.462623
I0408 20:17:06.315510  5931 solver.cpp:397]     Test net output #1: loss = 2.95039 (* 1 = 2.95039 loss)
I0408 20:17:08.055099  5931 solver.cpp:218] Iteration 7044 (0.711926 iter/s, 16.8557s/12 iters), loss = 0.178482
I0408 20:17:08.055223  5931 solver.cpp:237]     Train net output #0: loss = 0.178482 (* 1 = 0.178482 loss)
I0408 20:17:08.055231  5931 sgd_solver.cpp:105] Iteration 7044, lr = 0.00054227
I0408 20:17:12.865379  5931 solver.cpp:218] Iteration 7056 (2.49473 iter/s, 4.81014s/12 iters), loss = 0.103092
I0408 20:17:12.865413  5931 solver.cpp:237]     Train net output #0: loss = 0.103092 (* 1 = 0.103092 loss)
I0408 20:17:12.865420  5931 sgd_solver.cpp:105] Iteration 7056, lr = 0.00053329
I0408 20:17:17.736277  5931 solver.cpp:218] Iteration 7068 (2.46364 iter/s, 4.87084s/12 iters), loss = 0.188787
I0408 20:17:17.736310  5931 solver.cpp:237]     Train net output #0: loss = 0.188787 (* 1 = 0.188787 loss)
I0408 20:17:17.736317  5931 sgd_solver.cpp:105] Iteration 7068, lr = 0.000524451
I0408 20:17:22.414850  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:17:22.536586  5931 solver.cpp:218] Iteration 7080 (2.49986 iter/s, 4.80026s/12 iters), loss = 0.194107
I0408 20:17:22.536615  5931 solver.cpp:237]     Train net output #0: loss = 0.194107 (* 1 = 0.194107 loss)
I0408 20:17:22.536623  5931 sgd_solver.cpp:105] Iteration 7080, lr = 0.00051575
I0408 20:17:27.378692  5931 solver.cpp:218] Iteration 7092 (2.47829 iter/s, 4.84206s/12 iters), loss = 0.0827191
I0408 20:17:27.378726  5931 solver.cpp:237]     Train net output #0: loss = 0.0827191 (* 1 = 0.0827191 loss)
I0408 20:17:27.378734  5931 sgd_solver.cpp:105] Iteration 7092, lr = 0.000507186
I0408 20:17:32.198681  5931 solver.cpp:218] Iteration 7104 (2.48966 iter/s, 4.81993s/12 iters), loss = 0.16073
I0408 20:17:32.198716  5931 solver.cpp:237]     Train net output #0: loss = 0.16073 (* 1 = 0.16073 loss)
I0408 20:17:32.198724  5931 sgd_solver.cpp:105] Iteration 7104, lr = 0.000498757
I0408 20:17:37.021219  5931 solver.cpp:218] Iteration 7116 (2.48834 iter/s, 4.82248s/12 iters), loss = 0.18256
I0408 20:17:37.021252  5931 solver.cpp:237]     Train net output #0: loss = 0.18256 (* 1 = 0.18256 loss)
I0408 20:17:37.021260  5931 sgd_solver.cpp:105] Iteration 7116, lr = 0.00049046
I0408 20:17:41.838027  5931 solver.cpp:218] Iteration 7128 (2.4913 iter/s, 4.81675s/12 iters), loss = 0.125816
I0408 20:17:41.838140  5931 solver.cpp:237]     Train net output #0: loss = 0.125816 (* 1 = 0.125816 loss)
I0408 20:17:41.838148  5931 sgd_solver.cpp:105] Iteration 7128, lr = 0.000482295
I0408 20:17:46.209100  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 20:17:49.364675  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 20:17:51.727552  5931 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 20:17:51.727579  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:17:53.296329  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:17:56.051793  5931 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0408 20:17:56.051839  5931 solver.cpp:397]     Test net output #1: loss = 2.94129 (* 1 = 2.94129 loss)
I0408 20:17:56.148749  5931 solver.cpp:218] Iteration 7140 (0.838541 iter/s, 14.3106s/12 iters), loss = 0.0895932
I0408 20:17:56.148800  5931 solver.cpp:237]     Train net output #0: loss = 0.0895933 (* 1 = 0.0895933 loss)
I0408 20:17:56.148813  5931 sgd_solver.cpp:105] Iteration 7140, lr = 0.000474259
I0408 20:18:00.106354  5931 solver.cpp:218] Iteration 7152 (3.03219 iter/s, 3.95754s/12 iters), loss = 0.18653
I0408 20:18:00.106387  5931 solver.cpp:237]     Train net output #0: loss = 0.18653 (* 1 = 0.18653 loss)
I0408 20:18:00.106395  5931 sgd_solver.cpp:105] Iteration 7152, lr = 0.00046635
I0408 20:18:04.809603  5931 solver.cpp:218] Iteration 7164 (2.55146 iter/s, 4.7032s/12 iters), loss = 0.0887692
I0408 20:18:04.809638  5931 solver.cpp:237]     Train net output #0: loss = 0.0887693 (* 1 = 0.0887693 loss)
I0408 20:18:04.809646  5931 sgd_solver.cpp:105] Iteration 7164, lr = 0.000458566
I0408 20:18:09.553859  5931 solver.cpp:218] Iteration 7176 (2.5294 iter/s, 4.7442s/12 iters), loss = 0.0805091
I0408 20:18:09.553894  5931 solver.cpp:237]     Train net output #0: loss = 0.0805092 (* 1 = 0.0805092 loss)
I0408 20:18:09.553901  5931 sgd_solver.cpp:105] Iteration 7176, lr = 0.000450907
I0408 20:18:11.601563  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:18:14.399296  5931 solver.cpp:218] Iteration 7188 (2.47658 iter/s, 4.84538s/12 iters), loss = 0.232216
I0408 20:18:14.399361  5931 solver.cpp:237]     Train net output #0: loss = 0.232216 (* 1 = 0.232216 loss)
I0408 20:18:14.399369  5931 sgd_solver.cpp:105] Iteration 7188, lr = 0.000443369
I0408 20:18:19.212534  5931 solver.cpp:218] Iteration 7200 (2.49317 iter/s, 4.81315s/12 iters), loss = 0.0812828
I0408 20:18:19.212568  5931 solver.cpp:237]     Train net output #0: loss = 0.0812828 (* 1 = 0.0812828 loss)
I0408 20:18:19.212575  5931 sgd_solver.cpp:105] Iteration 7200, lr = 0.000435951
I0408 20:18:24.076754  5931 solver.cpp:218] Iteration 7212 (2.46702 iter/s, 4.86417s/12 iters), loss = 0.125965
I0408 20:18:24.076788  5931 solver.cpp:237]     Train net output #0: loss = 0.125965 (* 1 = 0.125965 loss)
I0408 20:18:24.076795  5931 sgd_solver.cpp:105] Iteration 7212, lr = 0.000428653
I0408 20:18:28.859877  5931 solver.cpp:218] Iteration 7224 (2.50885 iter/s, 4.78307s/12 iters), loss = 0.06889
I0408 20:18:28.859916  5931 solver.cpp:237]     Train net output #0: loss = 0.06889 (* 1 = 0.06889 loss)
I0408 20:18:28.859923  5931 sgd_solver.cpp:105] Iteration 7224, lr = 0.000421471
I0408 20:18:33.699026  5931 solver.cpp:218] Iteration 7236 (2.47981 iter/s, 4.83909s/12 iters), loss = 0.0516151
I0408 20:18:33.699061  5931 solver.cpp:237]     Train net output #0: loss = 0.0516151 (* 1 = 0.0516151 loss)
I0408 20:18:33.699069  5931 sgd_solver.cpp:105] Iteration 7236, lr = 0.000414404
I0408 20:18:35.659523  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 20:18:38.754662  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 20:18:41.115761  5931 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 20:18:41.115787  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:18:42.776041  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:18:45.920527  5931 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0408 20:18:45.920724  5931 solver.cpp:397]     Test net output #1: loss = 2.9379 (* 1 = 2.9379 loss)
I0408 20:18:47.674877  5931 solver.cpp:218] Iteration 7248 (0.858628 iter/s, 13.9758s/12 iters), loss = 0.0657108
I0408 20:18:47.674913  5931 solver.cpp:237]     Train net output #0: loss = 0.0657108 (* 1 = 0.0657108 loss)
I0408 20:18:47.674921  5931 sgd_solver.cpp:105] Iteration 7248, lr = 0.00040745
I0408 20:18:52.370834  5931 solver.cpp:218] Iteration 7260 (2.55542 iter/s, 4.6959s/12 iters), loss = 0.151435
I0408 20:18:52.370869  5931 solver.cpp:237]     Train net output #0: loss = 0.151435 (* 1 = 0.151435 loss)
I0408 20:18:52.370877  5931 sgd_solver.cpp:105] Iteration 7260, lr = 0.000400608
I0408 20:18:57.212363  5931 solver.cpp:218] Iteration 7272 (2.47858 iter/s, 4.84147s/12 iters), loss = 0.0784705
I0408 20:18:57.212394  5931 solver.cpp:237]     Train net output #0: loss = 0.0784705 (* 1 = 0.0784705 loss)
I0408 20:18:57.212402  5931 sgd_solver.cpp:105] Iteration 7272, lr = 0.000393877
I0408 20:19:01.300945  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:19:02.022949  5931 solver.cpp:218] Iteration 7284 (2.49452 iter/s, 4.81054s/12 iters), loss = 0.0987218
I0408 20:19:02.022984  5931 solver.cpp:237]     Train net output #0: loss = 0.0987218 (* 1 = 0.0987218 loss)
I0408 20:19:02.022991  5931 sgd_solver.cpp:105] Iteration 7284, lr = 0.000387254
I0408 20:19:06.865867  5931 solver.cpp:218] Iteration 7296 (2.47787 iter/s, 4.84286s/12 iters), loss = 0.0326328
I0408 20:19:06.865900  5931 solver.cpp:237]     Train net output #0: loss = 0.0326328 (* 1 = 0.0326328 loss)
I0408 20:19:06.865906  5931 sgd_solver.cpp:105] Iteration 7296, lr = 0.000380738
I0408 20:19:11.649075  5931 solver.cpp:218] Iteration 7308 (2.5088 iter/s, 4.78316s/12 iters), loss = 0.0749877
I0408 20:19:11.649108  5931 solver.cpp:237]     Train net output #0: loss = 0.0749877 (* 1 = 0.0749877 loss)
I0408 20:19:11.649116  5931 sgd_solver.cpp:105] Iteration 7308, lr = 0.000374327
I0408 20:19:16.496217  5931 solver.cpp:218] Iteration 7320 (2.47571 iter/s, 4.84709s/12 iters), loss = 0.12528
I0408 20:19:16.496301  5931 solver.cpp:237]     Train net output #0: loss = 0.12528 (* 1 = 0.12528 loss)
I0408 20:19:16.496310  5931 sgd_solver.cpp:105] Iteration 7320, lr = 0.00036802
I0408 20:19:21.310253  5931 solver.cpp:218] Iteration 7332 (2.49276 iter/s, 4.81394s/12 iters), loss = 0.134648
I0408 20:19:21.310284  5931 solver.cpp:237]     Train net output #0: loss = 0.134648 (* 1 = 0.134648 loss)
I0408 20:19:21.310292  5931 sgd_solver.cpp:105] Iteration 7332, lr = 0.000361816
I0408 20:19:25.699064  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 20:19:29.611052  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 20:19:31.970430  5931 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 20:19:31.970455  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:19:33.650229  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:19:36.817358  5931 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0408 20:19:36.817406  5931 solver.cpp:397]     Test net output #1: loss = 2.92733 (* 1 = 2.92733 loss)
I0408 20:19:36.913908  5931 solver.cpp:218] Iteration 7344 (0.769054 iter/s, 15.6036s/12 iters), loss = 0.033353
I0408 20:19:36.913940  5931 solver.cpp:237]     Train net output #0: loss = 0.033353 (* 1 = 0.033353 loss)
I0408 20:19:36.913949  5931 sgd_solver.cpp:105] Iteration 7344, lr = 0.000355712
I0408 20:19:40.861305  5931 solver.cpp:218] Iteration 7356 (3.04002 iter/s, 3.94735s/12 iters), loss = 0.0802436
I0408 20:19:40.861337  5931 solver.cpp:237]     Train net output #0: loss = 0.0802436 (* 1 = 0.0802436 loss)
I0408 20:19:40.861346  5931 sgd_solver.cpp:105] Iteration 7356, lr = 0.000349707
I0408 20:19:45.655977  5931 solver.cpp:218] Iteration 7368 (2.5028 iter/s, 4.79462s/12 iters), loss = 0.0973827
I0408 20:19:45.656013  5931 solver.cpp:237]     Train net output #0: loss = 0.0973827 (* 1 = 0.0973827 loss)
I0408 20:19:45.656020  5931 sgd_solver.cpp:105] Iteration 7368, lr = 0.0003438
I0408 20:19:50.462960  5931 solver.cpp:218] Iteration 7380 (2.4964 iter/s, 4.80693s/12 iters), loss = 0.10154
I0408 20:19:50.463057  5931 solver.cpp:237]     Train net output #0: loss = 0.10154 (* 1 = 0.10154 loss)
I0408 20:19:50.463066  5931 sgd_solver.cpp:105] Iteration 7380, lr = 0.00033799
I0408 20:19:51.787308  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:19:55.296522  5931 solver.cpp:218] Iteration 7392 (2.4827 iter/s, 4.83345s/12 iters), loss = 0.205759
I0408 20:19:55.296561  5931 solver.cpp:237]     Train net output #0: loss = 0.205759 (* 1 = 0.205759 loss)
I0408 20:19:55.296569  5931 sgd_solver.cpp:105] Iteration 7392, lr = 0.000332274
I0408 20:20:00.119652  5931 solver.cpp:218] Iteration 7404 (2.48804 iter/s, 4.82307s/12 iters), loss = 0.209126
I0408 20:20:00.119686  5931 solver.cpp:237]     Train net output #0: loss = 0.209126 (* 1 = 0.209126 loss)
I0408 20:20:00.119694  5931 sgd_solver.cpp:105] Iteration 7404, lr = 0.000326652
I0408 20:20:04.919106  5931 solver.cpp:218] Iteration 7416 (2.50031 iter/s, 4.7994s/12 iters), loss = 0.0922531
I0408 20:20:04.919139  5931 solver.cpp:237]     Train net output #0: loss = 0.0922531 (* 1 = 0.0922531 loss)
I0408 20:20:04.919147  5931 sgd_solver.cpp:105] Iteration 7416, lr = 0.000321121
I0408 20:20:09.778239  5931 solver.cpp:218] Iteration 7428 (2.4696 iter/s, 4.85908s/12 iters), loss = 0.14191
I0408 20:20:09.778277  5931 solver.cpp:237]     Train net output #0: loss = 0.14191 (* 1 = 0.14191 loss)
I0408 20:20:09.778285  5931 sgd_solver.cpp:105] Iteration 7428, lr = 0.000315682
I0408 20:20:14.635035  5931 solver.cpp:218] Iteration 7440 (2.4708 iter/s, 4.85674s/12 iters), loss = 0.136621
I0408 20:20:14.635066  5931 solver.cpp:237]     Train net output #0: loss = 0.136621 (* 1 = 0.136621 loss)
I0408 20:20:14.635074  5931 sgd_solver.cpp:105] Iteration 7440, lr = 0.000310331
I0408 20:20:16.542326  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 20:20:19.931020  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 20:20:23.936364  5931 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 20:20:23.936429  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:20:25.507534  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:20:28.730105  5931 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0408 20:20:28.730154  5931 solver.cpp:397]     Test net output #1: loss = 2.95751 (* 1 = 2.95751 loss)
I0408 20:20:30.418192  5931 solver.cpp:218] Iteration 7452 (0.760308 iter/s, 15.7831s/12 iters), loss = 0.278244
I0408 20:20:30.418228  5931 solver.cpp:237]     Train net output #0: loss = 0.278244 (* 1 = 0.278244 loss)
I0408 20:20:30.418236  5931 sgd_solver.cpp:105] Iteration 7452, lr = 0.000305068
I0408 20:20:35.144546  5931 solver.cpp:218] Iteration 7464 (2.53899 iter/s, 4.7263s/12 iters), loss = 0.176602
I0408 20:20:35.144580  5931 solver.cpp:237]     Train net output #0: loss = 0.176602 (* 1 = 0.176602 loss)
I0408 20:20:35.144587  5931 sgd_solver.cpp:105] Iteration 7464, lr = 0.000299892
I0408 20:20:39.885480  5931 solver.cpp:218] Iteration 7476 (2.53118 iter/s, 4.74088s/12 iters), loss = 0.115417
I0408 20:20:39.885514  5931 solver.cpp:237]     Train net output #0: loss = 0.115417 (* 1 = 0.115417 loss)
I0408 20:20:39.885522  5931 sgd_solver.cpp:105] Iteration 7476, lr = 0.000294801
I0408 20:20:43.289029  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:20:44.707486  5931 solver.cpp:218] Iteration 7488 (2.48862 iter/s, 4.82196s/12 iters), loss = 0.0603507
I0408 20:20:44.707520  5931 solver.cpp:237]     Train net output #0: loss = 0.0603507 (* 1 = 0.0603507 loss)
I0408 20:20:44.707527  5931 sgd_solver.cpp:105] Iteration 7488, lr = 0.000289793
I0408 20:20:49.577452  5931 solver.cpp:218] Iteration 7500 (2.46411 iter/s, 4.86991s/12 iters), loss = 0.0762331
I0408 20:20:49.577486  5931 solver.cpp:237]     Train net output #0: loss = 0.0762331 (* 1 = 0.0762331 loss)
I0408 20:20:49.577494  5931 sgd_solver.cpp:105] Iteration 7500, lr = 0.000284869
I0408 20:20:54.378831  5931 solver.cpp:218] Iteration 7512 (2.49931 iter/s, 4.80132s/12 iters), loss = 0.171264
I0408 20:20:54.378928  5931 solver.cpp:237]     Train net output #0: loss = 0.171264 (* 1 = 0.171264 loss)
I0408 20:20:54.378938  5931 sgd_solver.cpp:105] Iteration 7512, lr = 0.000280025
I0408 20:20:59.213133  5931 solver.cpp:218] Iteration 7524 (2.48232 iter/s, 4.83419s/12 iters), loss = 0.0863697
I0408 20:20:59.213167  5931 solver.cpp:237]     Train net output #0: loss = 0.0863698 (* 1 = 0.0863698 loss)
I0408 20:20:59.213176  5931 sgd_solver.cpp:105] Iteration 7524, lr = 0.000275262
I0408 20:21:04.035259  5931 solver.cpp:218] Iteration 7536 (2.48855 iter/s, 4.82208s/12 iters), loss = 0.0900084
I0408 20:21:04.035293  5931 solver.cpp:237]     Train net output #0: loss = 0.0900084 (* 1 = 0.0900084 loss)
I0408 20:21:04.035301  5931 sgd_solver.cpp:105] Iteration 7536, lr = 0.000270577
I0408 20:21:08.386983  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 20:21:11.474941  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 20:21:13.848488  5931 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 20:21:13.848515  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:21:15.375437  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:21:18.636113  5931 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0408 20:21:18.636162  5931 solver.cpp:397]     Test net output #1: loss = 2.93221 (* 1 = 2.93221 loss)
I0408 20:21:18.731993  5931 solver.cpp:218] Iteration 7548 (0.816512 iter/s, 14.6967s/12 iters), loss = 0.110486
I0408 20:21:18.732028  5931 solver.cpp:237]     Train net output #0: loss = 0.110486 (* 1 = 0.110486 loss)
I0408 20:21:18.732038  5931 sgd_solver.cpp:105] Iteration 7548, lr = 0.00026597
I0408 20:21:22.667505  5931 solver.cpp:218] Iteration 7560 (3.0492 iter/s, 3.93546s/12 iters), loss = 0.054214
I0408 20:21:22.667544  5931 solver.cpp:237]     Train net output #0: loss = 0.054214 (* 1 = 0.054214 loss)
I0408 20:21:22.667551  5931 sgd_solver.cpp:105] Iteration 7560, lr = 0.000261439
I0408 20:21:27.544577  5931 solver.cpp:218] Iteration 7572 (2.46052 iter/s, 4.87701s/12 iters), loss = 0.0605282
I0408 20:21:27.544695  5931 solver.cpp:237]     Train net output #0: loss = 0.0605282 (* 1 = 0.0605282 loss)
I0408 20:21:27.544704  5931 sgd_solver.cpp:105] Iteration 7572, lr = 0.000256983
I0408 20:21:32.375460  5931 solver.cpp:218] Iteration 7584 (2.48409 iter/s, 4.83075s/12 iters), loss = 0.0444338
I0408 20:21:32.375499  5931 solver.cpp:237]     Train net output #0: loss = 0.0444338 (* 1 = 0.0444338 loss)
I0408 20:21:32.375507  5931 sgd_solver.cpp:105] Iteration 7584, lr = 0.000252602
I0408 20:21:32.981845  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:21:37.189952  5931 solver.cpp:218] Iteration 7596 (2.49251 iter/s, 4.81443s/12 iters), loss = 0.07387
I0408 20:21:37.189985  5931 solver.cpp:237]     Train net output #0: loss = 0.07387 (* 1 = 0.07387 loss)
I0408 20:21:37.189992  5931 sgd_solver.cpp:105] Iteration 7596, lr = 0.000248293
I0408 20:21:41.994925  5931 solver.cpp:218] Iteration 7608 (2.49744 iter/s, 4.80492s/12 iters), loss = 0.11755
I0408 20:21:41.994958  5931 solver.cpp:237]     Train net output #0: loss = 0.11755 (* 1 = 0.11755 loss)
I0408 20:21:41.994966  5931 sgd_solver.cpp:105] Iteration 7608, lr = 0.000244056
I0408 20:21:46.805831  5931 solver.cpp:218] Iteration 7620 (2.49436 iter/s, 4.81085s/12 iters), loss = 0.0318004
I0408 20:21:46.805868  5931 solver.cpp:237]     Train net output #0: loss = 0.0318004 (* 1 = 0.0318004 loss)
I0408 20:21:46.805876  5931 sgd_solver.cpp:105] Iteration 7620, lr = 0.000239889
I0408 20:21:49.141270  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:21:51.573714  5931 solver.cpp:218] Iteration 7632 (2.51687 iter/s, 4.76783s/12 iters), loss = 0.117765
I0408 20:21:51.573746  5931 solver.cpp:237]     Train net output #0: loss = 0.117765 (* 1 = 0.117765 loss)
I0408 20:21:51.573755  5931 sgd_solver.cpp:105] Iteration 7632, lr = 0.000235792
I0408 20:21:56.285547  5931 solver.cpp:218] Iteration 7644 (2.54681 iter/s, 4.71178s/12 iters), loss = 0.094675
I0408 20:21:56.285579  5931 solver.cpp:237]     Train net output #0: loss = 0.094675 (* 1 = 0.094675 loss)
I0408 20:21:56.285588  5931 sgd_solver.cpp:105] Iteration 7644, lr = 0.000231763
I0408 20:21:58.238368  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 20:22:03.017647  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 20:22:05.652349  5931 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 20:22:05.652375  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:22:07.126479  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:22:10.222456  5931 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0408 20:22:10.222502  5931 solver.cpp:397]     Test net output #1: loss = 2.94049 (* 1 = 2.94049 loss)
I0408 20:22:11.955940  5931 solver.cpp:218] Iteration 7656 (0.765778 iter/s, 15.6703s/12 iters), loss = 0.0435643
I0408 20:22:11.955976  5931 solver.cpp:237]     Train net output #0: loss = 0.0435643 (* 1 = 0.0435643 loss)
I0408 20:22:11.955983  5931 sgd_solver.cpp:105] Iteration 7656, lr = 0.000227801
I0408 20:22:16.681826  5931 solver.cpp:218] Iteration 7668 (2.53923 iter/s, 4.72583s/12 iters), loss = 0.123313
I0408 20:22:16.681859  5931 solver.cpp:237]     Train net output #0: loss = 0.123313 (* 1 = 0.123313 loss)
I0408 20:22:16.681867  5931 sgd_solver.cpp:105] Iteration 7668, lr = 0.000223906
I0408 20:22:21.405337  5931 solver.cpp:218] Iteration 7680 (2.54051 iter/s, 4.72346s/12 iters), loss = 0.0731207
I0408 20:22:21.405370  5931 solver.cpp:237]     Train net output #0: loss = 0.0731207 (* 1 = 0.0731207 loss)
I0408 20:22:21.405377  5931 sgd_solver.cpp:105] Iteration 7680, lr = 0.000220075
I0408 20:22:24.082844  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:22:26.240177  5931 solver.cpp:218] Iteration 7692 (2.48201 iter/s, 4.83479s/12 iters), loss = 0.0993189
I0408 20:22:26.240211  5931 solver.cpp:237]     Train net output #0: loss = 0.0993189 (* 1 = 0.0993189 loss)
I0408 20:22:26.240219  5931 sgd_solver.cpp:105] Iteration 7692, lr = 0.000216309
I0408 20:22:31.039340  5931 solver.cpp:218] Iteration 7704 (2.50046 iter/s, 4.79911s/12 iters), loss = 0.101911
I0408 20:22:31.039407  5931 solver.cpp:237]     Train net output #0: loss = 0.101911 (* 1 = 0.101911 loss)
I0408 20:22:31.039415  5931 sgd_solver.cpp:105] Iteration 7704, lr = 0.000212606
I0408 20:22:35.875198  5931 solver.cpp:218] Iteration 7716 (2.48151 iter/s, 4.83577s/12 iters), loss = 0.0725028
I0408 20:22:35.875231  5931 solver.cpp:237]     Train net output #0: loss = 0.0725029 (* 1 = 0.0725029 loss)
I0408 20:22:35.875239  5931 sgd_solver.cpp:105] Iteration 7716, lr = 0.000208964
I0408 20:22:40.676853  5931 solver.cpp:218] Iteration 7728 (2.49916 iter/s, 4.8016s/12 iters), loss = 0.115026
I0408 20:22:40.676887  5931 solver.cpp:237]     Train net output #0: loss = 0.115026 (* 1 = 0.115026 loss)
I0408 20:22:40.676893  5931 sgd_solver.cpp:105] Iteration 7728, lr = 0.000205384
I0408 20:22:45.528357  5931 solver.cpp:218] Iteration 7740 (2.47349 iter/s, 4.85145s/12 iters), loss = 0.0881686
I0408 20:22:45.528390  5931 solver.cpp:237]     Train net output #0: loss = 0.0881687 (* 1 = 0.0881687 loss)
I0408 20:22:45.528398  5931 sgd_solver.cpp:105] Iteration 7740, lr = 0.000201864
I0408 20:22:49.881412  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 20:22:52.990099  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 20:22:55.355211  5931 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 20:22:55.355237  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:22:56.796537  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:23:00.137374  5931 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0408 20:23:00.137423  5931 solver.cpp:397]     Test net output #1: loss = 2.92803 (* 1 = 2.92803 loss)
I0408 20:23:00.233801  5931 solver.cpp:218] Iteration 7752 (0.816028 iter/s, 14.7054s/12 iters), loss = 0.0743262
I0408 20:23:00.233837  5931 solver.cpp:237]     Train net output #0: loss = 0.0743262 (* 1 = 0.0743262 loss)
I0408 20:23:00.233845  5931 sgd_solver.cpp:105] Iteration 7752, lr = 0.000198403
I0408 20:23:04.154002  5931 solver.cpp:218] Iteration 7764 (3.06111 iter/s, 3.92015s/12 iters), loss = 0.223223
I0408 20:23:04.154098  5931 solver.cpp:237]     Train net output #0: loss = 0.223223 (* 1 = 0.223223 loss)
I0408 20:23:04.154107  5931 sgd_solver.cpp:105] Iteration 7764, lr = 0.000195
I0408 20:23:08.886755  5931 solver.cpp:218] Iteration 7776 (2.53558 iter/s, 4.73264s/12 iters), loss = 0.0771
I0408 20:23:08.886790  5931 solver.cpp:237]     Train net output #0: loss = 0.0771001 (* 1 = 0.0771001 loss)
I0408 20:23:08.886797  5931 sgd_solver.cpp:105] Iteration 7776, lr = 0.000191655
I0408 20:23:13.660429  5931 solver.cpp:218] Iteration 7788 (2.51381 iter/s, 4.77362s/12 iters), loss = 0.131822
I0408 20:23:13.660462  5931 solver.cpp:237]     Train net output #0: loss = 0.131822 (* 1 = 0.131822 loss)
I0408 20:23:13.660470  5931 sgd_solver.cpp:105] Iteration 7788, lr = 0.000188365
I0408 20:23:13.666374  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:23:18.473021  5931 solver.cpp:218] Iteration 7800 (2.49349 iter/s, 4.81254s/12 iters), loss = 0.102104
I0408 20:23:18.473054  5931 solver.cpp:237]     Train net output #0: loss = 0.102104 (* 1 = 0.102104 loss)
I0408 20:23:18.473062  5931 sgd_solver.cpp:105] Iteration 7800, lr = 0.000185131
I0408 20:23:23.284548  5931 solver.cpp:218] Iteration 7812 (2.49404 iter/s, 4.81147s/12 iters), loss = 0.165851
I0408 20:23:23.284579  5931 solver.cpp:237]     Train net output #0: loss = 0.165851 (* 1 = 0.165851 loss)
I0408 20:23:23.284586  5931 sgd_solver.cpp:105] Iteration 7812, lr = 0.000181952
I0408 20:23:28.096400  5931 solver.cpp:218] Iteration 7824 (2.49387 iter/s, 4.8118s/12 iters), loss = 0.116059
I0408 20:23:28.096433  5931 solver.cpp:237]     Train net output #0: loss = 0.116059 (* 1 = 0.116059 loss)
I0408 20:23:28.096441  5931 sgd_solver.cpp:105] Iteration 7824, lr = 0.000178826
I0408 20:23:32.954372  5931 solver.cpp:218] Iteration 7836 (2.47019 iter/s, 4.85792s/12 iters), loss = 0.110737
I0408 20:23:32.954406  5931 solver.cpp:237]     Train net output #0: loss = 0.110737 (* 1 = 0.110737 loss)
I0408 20:23:32.954414  5931 sgd_solver.cpp:105] Iteration 7836, lr = 0.000175753
I0408 20:23:37.781884  5931 solver.cpp:218] Iteration 7848 (2.48578 iter/s, 4.82746s/12 iters), loss = 0.19917
I0408 20:23:37.781942  5931 solver.cpp:237]     Train net output #0: loss = 0.19917 (* 1 = 0.19917 loss)
I0408 20:23:37.781950  5931 sgd_solver.cpp:105] Iteration 7848, lr = 0.000172732
I0408 20:23:39.740697  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 20:23:44.558678  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 20:23:46.919368  5931 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 20:23:46.919394  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:23:48.309034  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:23:51.709477  5931 solver.cpp:397]     Test net output #0: accuracy = 0.465074
I0408 20:23:51.709525  5931 solver.cpp:397]     Test net output #1: loss = 2.9478 (* 1 = 2.9478 loss)
I0408 20:23:53.455191  5931 solver.cpp:218] Iteration 7860 (0.765637 iter/s, 15.6732s/12 iters), loss = 0.125866
I0408 20:23:53.455235  5931 solver.cpp:237]     Train net output #0: loss = 0.125866 (* 1 = 0.125866 loss)
I0408 20:23:53.455243  5931 sgd_solver.cpp:105] Iteration 7860, lr = 0.000169762
I0408 20:23:58.128324  5931 solver.cpp:218] Iteration 7872 (2.56791 iter/s, 4.67307s/12 iters), loss = 0.151375
I0408 20:23:58.128357  5931 solver.cpp:237]     Train net output #0: loss = 0.151375 (* 1 = 0.151375 loss)
I0408 20:23:58.128365  5931 sgd_solver.cpp:105] Iteration 7872, lr = 0.000166842
I0408 20:24:02.844486  5931 solver.cpp:218] Iteration 7884 (2.54447 iter/s, 4.71611s/12 iters), loss = 0.0382169
I0408 20:24:02.844519  5931 solver.cpp:237]     Train net output #0: loss = 0.0382169 (* 1 = 0.0382169 loss)
I0408 20:24:02.844527  5931 sgd_solver.cpp:105] Iteration 7884, lr = 0.000163971
I0408 20:24:04.911635  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:24:07.601445  5931 solver.cpp:218] Iteration 7896 (2.52265 iter/s, 4.7569s/12 iters), loss = 0.0646236
I0408 20:24:07.601478  5931 solver.cpp:237]     Train net output #0: loss = 0.0646236 (* 1 = 0.0646236 loss)
I0408 20:24:07.601485  5931 sgd_solver.cpp:105] Iteration 7896, lr = 0.000161149
I0408 20:24:12.320919  5931 solver.cpp:218] Iteration 7908 (2.54268 iter/s, 4.71942s/12 iters), loss = 0.182402
I0408 20:24:12.321022  5931 solver.cpp:237]     Train net output #0: loss = 0.182402 (* 1 = 0.182402 loss)
I0408 20:24:12.321030  5931 sgd_solver.cpp:105] Iteration 7908, lr = 0.000158375
I0408 20:24:17.179579  5931 solver.cpp:218] Iteration 7920 (2.46988 iter/s, 4.85854s/12 iters), loss = 0.19271
I0408 20:24:17.179612  5931 solver.cpp:237]     Train net output #0: loss = 0.19271 (* 1 = 0.19271 loss)
I0408 20:24:17.179620  5931 sgd_solver.cpp:105] Iteration 7920, lr = 0.000155648
I0408 20:24:22.006021  5931 solver.cpp:218] Iteration 7932 (2.48633 iter/s, 4.82639s/12 iters), loss = 0.0952818
I0408 20:24:22.006053  5931 solver.cpp:237]     Train net output #0: loss = 0.0952818 (* 1 = 0.0952818 loss)
I0408 20:24:22.006062  5931 sgd_solver.cpp:105] Iteration 7932, lr = 0.000152967
I0408 20:24:26.796352  5931 solver.cpp:218] Iteration 7944 (2.50507 iter/s, 4.79028s/12 iters), loss = 0.146613
I0408 20:24:26.796387  5931 solver.cpp:237]     Train net output #0: loss = 0.146613 (* 1 = 0.146613 loss)
I0408 20:24:26.796394  5931 sgd_solver.cpp:105] Iteration 7944, lr = 0.000150331
I0408 20:24:31.200110  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 20:24:34.338088  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 20:24:37.527542  5931 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 20:24:37.527567  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:24:38.880158  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:24:42.319628  5931 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0408 20:24:42.319679  5931 solver.cpp:397]     Test net output #1: loss = 2.94018 (* 1 = 2.94018 loss)
I0408 20:24:42.415591  5931 solver.cpp:218] Iteration 7956 (0.768287 iter/s, 15.6192s/12 iters), loss = 0.0466145
I0408 20:24:42.415721  5931 solver.cpp:237]     Train net output #0: loss = 0.0466145 (* 1 = 0.0466145 loss)
I0408 20:24:42.415731  5931 sgd_solver.cpp:105] Iteration 7956, lr = 0.00014774
I0408 20:24:46.337007  5931 solver.cpp:218] Iteration 7968 (3.06023 iter/s, 3.92127s/12 iters), loss = 0.0817163
I0408 20:24:46.337041  5931 solver.cpp:237]     Train net output #0: loss = 0.0817163 (* 1 = 0.0817163 loss)
I0408 20:24:46.337049  5931 sgd_solver.cpp:105] Iteration 7968, lr = 0.000145194
I0408 20:24:51.135247  5931 solver.cpp:218] Iteration 7980 (2.50094 iter/s, 4.79819s/12 iters), loss = 0.0511982
I0408 20:24:51.135282  5931 solver.cpp:237]     Train net output #0: loss = 0.0511982 (* 1 = 0.0511982 loss)
I0408 20:24:51.135289  5931 sgd_solver.cpp:105] Iteration 7980, lr = 0.00014269
I0408 20:24:55.242931  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:24:55.923118  5931 solver.cpp:218] Iteration 7992 (2.50636 iter/s, 4.78782s/12 iters), loss = 0.0730105
I0408 20:24:55.923152  5931 solver.cpp:237]     Train net output #0: loss = 0.0730105 (* 1 = 0.0730105 loss)
I0408 20:24:55.923159  5931 sgd_solver.cpp:105] Iteration 7992, lr = 0.000140229
I0408 20:25:00.782428  5931 solver.cpp:218] Iteration 8004 (2.46951 iter/s, 4.85926s/12 iters), loss = 0.0519604
I0408 20:25:00.782461  5931 solver.cpp:237]     Train net output #0: loss = 0.0519604 (* 1 = 0.0519604 loss)
I0408 20:25:00.782469  5931 sgd_solver.cpp:105] Iteration 8004, lr = 0.00013781
I0408 20:25:05.574198  5931 solver.cpp:218] Iteration 8016 (2.50432 iter/s, 4.79171s/12 iters), loss = 0.0522229
I0408 20:25:05.574231  5931 solver.cpp:237]     Train net output #0: loss = 0.0522229 (* 1 = 0.0522229 loss)
I0408 20:25:05.574239  5931 sgd_solver.cpp:105] Iteration 8016, lr = 0.000135432
I0408 20:25:10.331296  5931 solver.cpp:218] Iteration 8028 (2.52257 iter/s, 4.75704s/12 iters), loss = 0.139277
I0408 20:25:10.331329  5931 solver.cpp:237]     Train net output #0: loss = 0.139277 (* 1 = 0.139277 loss)
I0408 20:25:10.331337  5931 sgd_solver.cpp:105] Iteration 8028, lr = 0.000133094
I0408 20:25:15.040107  5931 solver.cpp:218] Iteration 8040 (2.54844 iter/s, 4.70876s/12 iters), loss = 0.164568
I0408 20:25:15.040202  5931 solver.cpp:237]     Train net output #0: loss = 0.164568 (* 1 = 0.164568 loss)
I0408 20:25:15.040211  5931 sgd_solver.cpp:105] Iteration 8040, lr = 0.000130797
I0408 20:25:19.880785  5931 solver.cpp:218] Iteration 8052 (2.47905 iter/s, 4.84057s/12 iters), loss = 0.1742
I0408 20:25:19.880820  5931 solver.cpp:237]     Train net output #0: loss = 0.1742 (* 1 = 0.1742 loss)
I0408 20:25:19.880826  5931 sgd_solver.cpp:105] Iteration 8052, lr = 0.000128538
I0408 20:25:21.837352  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 20:25:24.963810  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 20:25:27.320695  5931 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 20:25:27.320720  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:25:28.619453  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:25:32.110468  5931 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0408 20:25:32.110517  5931 solver.cpp:397]     Test net output #1: loss = 2.94616 (* 1 = 2.94616 loss)
I0408 20:25:33.832515  5931 solver.cpp:218] Iteration 8064 (0.860113 iter/s, 13.9517s/12 iters), loss = 0.168703
I0408 20:25:33.832549  5931 solver.cpp:237]     Train net output #0: loss = 0.168703 (* 1 = 0.168703 loss)
I0408 20:25:33.832557  5931 sgd_solver.cpp:105] Iteration 8064, lr = 0.000126318
I0408 20:25:38.563421  5931 solver.cpp:218] Iteration 8076 (2.53654 iter/s, 4.73085s/12 iters), loss = 0.134421
I0408 20:25:38.563454  5931 solver.cpp:237]     Train net output #0: loss = 0.134421 (* 1 = 0.134421 loss)
I0408 20:25:38.563462  5931 sgd_solver.cpp:105] Iteration 8076, lr = 0.000124136
I0408 20:25:43.309962  5931 solver.cpp:218] Iteration 8088 (2.52819 iter/s, 4.74648s/12 iters), loss = 0.0561604
I0408 20:25:43.309993  5931 solver.cpp:237]     Train net output #0: loss = 0.0561605 (* 1 = 0.0561605 loss)
I0408 20:25:43.310000  5931 sgd_solver.cpp:105] Iteration 8088, lr = 0.000121991
I0408 20:25:44.625670  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:25:47.990897  5931 solver.cpp:218] Iteration 8100 (2.56362 iter/s, 4.68088s/12 iters), loss = 0.0781737
I0408 20:25:47.990960  5931 solver.cpp:237]     Train net output #0: loss = 0.0781737 (* 1 = 0.0781737 loss)
I0408 20:25:47.990968  5931 sgd_solver.cpp:105] Iteration 8100, lr = 0.000119883
I0408 20:25:52.831816  5931 solver.cpp:218] Iteration 8112 (2.47891 iter/s, 4.84084s/12 iters), loss = 0.124162
I0408 20:25:52.831851  5931 solver.cpp:237]     Train net output #0: loss = 0.124162 (* 1 = 0.124162 loss)
I0408 20:25:52.831858  5931 sgd_solver.cpp:105] Iteration 8112, lr = 0.000117811
I0408 20:25:57.656028  5931 solver.cpp:218] Iteration 8124 (2.48748 iter/s, 4.82416s/12 iters), loss = 0.0961411
I0408 20:25:57.656060  5931 solver.cpp:237]     Train net output #0: loss = 0.0961411 (* 1 = 0.0961411 loss)
I0408 20:25:57.656069  5931 sgd_solver.cpp:105] Iteration 8124, lr = 0.000115774
I0408 20:26:02.498335  5931 solver.cpp:218] Iteration 8136 (2.47818 iter/s, 4.84225s/12 iters), loss = 0.0787658
I0408 20:26:02.498368  5931 solver.cpp:237]     Train net output #0: loss = 0.0787658 (* 1 = 0.0787658 loss)
I0408 20:26:02.498376  5931 sgd_solver.cpp:105] Iteration 8136, lr = 0.000113772
I0408 20:26:07.322288  5931 solver.cpp:218] Iteration 8148 (2.48761 iter/s, 4.8239s/12 iters), loss = 0.064925
I0408 20:26:07.322320  5931 solver.cpp:237]     Train net output #0: loss = 0.0649251 (* 1 = 0.0649251 loss)
I0408 20:26:07.322329  5931 sgd_solver.cpp:105] Iteration 8148, lr = 0.000111804
I0408 20:26:11.680806  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 20:26:15.559020  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 20:26:17.915998  5931 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 20:26:17.916024  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:26:19.107070  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:26:22.232733  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0408 20:26:22.232777  5931 solver.cpp:397]     Test net output #1: loss = 2.93942 (* 1 = 2.93942 loss)
I0408 20:26:22.327145  5931 solver.cpp:218] Iteration 8160 (0.799744 iter/s, 15.0048s/12 iters), loss = 0.0508529
I0408 20:26:22.327178  5931 solver.cpp:237]     Train net output #0: loss = 0.0508529 (* 1 = 0.0508529 loss)
I0408 20:26:22.327185  5931 sgd_solver.cpp:105] Iteration 8160, lr = 0.000109869
I0408 20:26:26.459023  5931 solver.cpp:218] Iteration 8172 (2.90428 iter/s, 4.13183s/12 iters), loss = 0.0753525
I0408 20:26:26.459059  5931 solver.cpp:237]     Train net output #0: loss = 0.0753525 (* 1 = 0.0753525 loss)
I0408 20:26:26.459065  5931 sgd_solver.cpp:105] Iteration 8172, lr = 0.000107968
I0408 20:26:31.516129  5931 solver.cpp:218] Iteration 8184 (2.37292 iter/s, 5.05705s/12 iters), loss = 0.101495
I0408 20:26:31.516163  5931 solver.cpp:237]     Train net output #0: loss = 0.101495 (* 1 = 0.101495 loss)
I0408 20:26:31.516170  5931 sgd_solver.cpp:105] Iteration 8184, lr = 0.0001061
I0408 20:26:34.918092  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:26:36.319432  5931 solver.cpp:218] Iteration 8196 (2.49831 iter/s, 4.80325s/12 iters), loss = 0.085153
I0408 20:26:36.319468  5931 solver.cpp:237]     Train net output #0: loss = 0.0851531 (* 1 = 0.0851531 loss)
I0408 20:26:36.319476  5931 sgd_solver.cpp:105] Iteration 8196, lr = 0.000104263
I0408 20:26:41.268072  5931 solver.cpp:218] Iteration 8208 (2.42493 iter/s, 4.94859s/12 iters), loss = 0.112394
I0408 20:26:41.268105  5931 solver.cpp:237]     Train net output #0: loss = 0.112394 (* 1 = 0.112394 loss)
I0408 20:26:41.268113  5931 sgd_solver.cpp:105] Iteration 8208, lr = 0.000102458
I0408 20:26:46.106567  5931 solver.cpp:218] Iteration 8220 (2.48014 iter/s, 4.83844s/12 iters), loss = 0.154063
I0408 20:26:46.106600  5931 solver.cpp:237]     Train net output #0: loss = 0.154063 (* 1 = 0.154063 loss)
I0408 20:26:46.106608  5931 sgd_solver.cpp:105] Iteration 8220, lr = 0.000100684
I0408 20:26:50.947154  5931 solver.cpp:218] Iteration 8232 (2.47907 iter/s, 4.84053s/12 iters), loss = 0.105271
I0408 20:26:50.947284  5931 solver.cpp:237]     Train net output #0: loss = 0.105271 (* 1 = 0.105271 loss)
I0408 20:26:50.947293  5931 sgd_solver.cpp:105] Iteration 8232, lr = 9.89401e-05
I0408 20:26:56.002797  5931 solver.cpp:218] Iteration 8244 (2.37365 iter/s, 5.0555s/12 iters), loss = 0.134947
I0408 20:26:56.002830  5931 solver.cpp:237]     Train net output #0: loss = 0.134947 (* 1 = 0.134947 loss)
I0408 20:26:56.002837  5931 sgd_solver.cpp:105] Iteration 8244, lr = 9.72262e-05
I0408 20:27:00.783254  5931 solver.cpp:218] Iteration 8256 (2.51025 iter/s, 4.78041s/12 iters), loss = 0.176201
I0408 20:27:00.783288  5931 solver.cpp:237]     Train net output #0: loss = 0.176201 (* 1 = 0.176201 loss)
I0408 20:27:00.783295  5931 sgd_solver.cpp:105] Iteration 8256, lr = 9.55418e-05
I0408 20:27:02.754855  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 20:27:05.881983  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 20:27:09.374007  5931 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 20:27:09.374032  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:27:10.601816  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:27:14.163220  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:27:14.163269  5931 solver.cpp:397]     Test net output #1: loss = 2.93941 (* 1 = 2.93941 loss)
I0408 20:27:15.953040  5931 solver.cpp:218] Iteration 8268 (0.79105 iter/s, 15.1697s/12 iters), loss = 0.0735358
I0408 20:27:15.953075  5931 solver.cpp:237]     Train net output #0: loss = 0.0735358 (* 1 = 0.0735358 loss)
I0408 20:27:15.953083  5931 sgd_solver.cpp:105] Iteration 8268, lr = 9.38862e-05
I0408 20:27:20.682509  5931 solver.cpp:218] Iteration 8280 (2.53731 iter/s, 4.72941s/12 iters), loss = 0.0912038
I0408 20:27:20.682543  5931 solver.cpp:237]     Train net output #0: loss = 0.0912038 (* 1 = 0.0912038 loss)
I0408 20:27:20.682549  5931 sgd_solver.cpp:105] Iteration 8280, lr = 9.22591e-05
I0408 20:27:25.504879  5931 solver.cpp:218] Iteration 8292 (2.48843 iter/s, 4.82232s/12 iters), loss = 0.114888
I0408 20:27:25.505025  5931 solver.cpp:237]     Train net output #0: loss = 0.114888 (* 1 = 0.114888 loss)
I0408 20:27:25.505034  5931 sgd_solver.cpp:105] Iteration 8292, lr = 9.06599e-05
I0408 20:27:26.138120  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:27:30.352633  5931 solver.cpp:218] Iteration 8304 (2.47546 iter/s, 4.84759s/12 iters), loss = 0.0824306
I0408 20:27:30.352665  5931 solver.cpp:237]     Train net output #0: loss = 0.0824306 (* 1 = 0.0824306 loss)
I0408 20:27:30.352672  5931 sgd_solver.cpp:105] Iteration 8304, lr = 8.90882e-05
I0408 20:27:33.073662  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:27:35.130246  5931 solver.cpp:218] Iteration 8316 (2.51174 iter/s, 4.77756s/12 iters), loss = 0.0808418
I0408 20:27:35.130280  5931 solver.cpp:237]     Train net output #0: loss = 0.0808418 (* 1 = 0.0808418 loss)
I0408 20:27:35.130287  5931 sgd_solver.cpp:105] Iteration 8316, lr = 8.75435e-05
I0408 20:27:40.008551  5931 solver.cpp:218] Iteration 8328 (2.4599 iter/s, 4.87825s/12 iters), loss = 0.0297925
I0408 20:27:40.008586  5931 solver.cpp:237]     Train net output #0: loss = 0.0297925 (* 1 = 0.0297925 loss)
I0408 20:27:40.008594  5931 sgd_solver.cpp:105] Iteration 8328, lr = 8.60253e-05
I0408 20:27:44.911015  5931 solver.cpp:218] Iteration 8340 (2.44778 iter/s, 4.90241s/12 iters), loss = 0.101278
I0408 20:27:44.911047  5931 solver.cpp:237]     Train net output #0: loss = 0.101278 (* 1 = 0.101278 loss)
I0408 20:27:44.911054  5931 sgd_solver.cpp:105] Iteration 8340, lr = 8.45333e-05
I0408 20:27:49.745927  5931 solver.cpp:218] Iteration 8352 (2.48197 iter/s, 4.83486s/12 iters), loss = 0.10988
I0408 20:27:49.745960  5931 solver.cpp:237]     Train net output #0: loss = 0.10988 (* 1 = 0.10988 loss)
I0408 20:27:49.745968  5931 sgd_solver.cpp:105] Iteration 8352, lr = 8.30669e-05
I0408 20:27:54.103435  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 20:27:57.227099  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 20:27:59.647097  5931 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 20:27:59.647125  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:28:00.816737  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:28:04.415927  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:28:04.415974  5931 solver.cpp:397]     Test net output #1: loss = 2.94869 (* 1 = 2.94869 loss)
I0408 20:28:04.512482  5931 solver.cpp:218] Iteration 8364 (0.812651 iter/s, 14.7665s/12 iters), loss = 0.0300906
I0408 20:28:04.512516  5931 solver.cpp:237]     Train net output #0: loss = 0.0300906 (* 1 = 0.0300906 loss)
I0408 20:28:04.512523  5931 sgd_solver.cpp:105] Iteration 8364, lr = 8.16257e-05
I0408 20:28:08.512995  5931 solver.cpp:218] Iteration 8376 (2.99966 iter/s, 4.00046s/12 iters), loss = 0.0399983
I0408 20:28:08.513027  5931 solver.cpp:237]     Train net output #0: loss = 0.0399983 (* 1 = 0.0399983 loss)
I0408 20:28:08.513036  5931 sgd_solver.cpp:105] Iteration 8376, lr = 8.02093e-05
I0408 20:28:13.315222  5931 solver.cpp:218] Iteration 8388 (2.49887 iter/s, 4.80218s/12 iters), loss = 0.0287316
I0408 20:28:13.315255  5931 solver.cpp:237]     Train net output #0: loss = 0.0287316 (* 1 = 0.0287316 loss)
I0408 20:28:13.315263  5931 sgd_solver.cpp:105] Iteration 8388, lr = 7.88173e-05
I0408 20:28:16.044642  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:28:18.179610  5931 solver.cpp:218] Iteration 8400 (2.46694 iter/s, 4.86434s/12 iters), loss = 0.172697
I0408 20:28:18.179644  5931 solver.cpp:237]     Train net output #0: loss = 0.172696 (* 1 = 0.172696 loss)
I0408 20:28:18.179652  5931 sgd_solver.cpp:105] Iteration 8400, lr = 7.74494e-05
I0408 20:28:23.128772  5931 solver.cpp:218] Iteration 8412 (2.42468 iter/s, 4.9491s/12 iters), loss = 0.0878625
I0408 20:28:23.128818  5931 solver.cpp:237]     Train net output #0: loss = 0.0878624 (* 1 = 0.0878624 loss)
I0408 20:28:23.128841  5931 sgd_solver.cpp:105] Iteration 8412, lr = 7.61049e-05
I0408 20:28:28.049643  5931 solver.cpp:218] Iteration 8424 (2.43862 iter/s, 4.92081s/12 iters), loss = 0.0928251
I0408 20:28:28.049742  5931 solver.cpp:237]     Train net output #0: loss = 0.0928251 (* 1 = 0.0928251 loss)
I0408 20:28:28.049751  5931 sgd_solver.cpp:105] Iteration 8424, lr = 7.47836e-05
I0408 20:28:32.882720  5931 solver.cpp:218] Iteration 8436 (2.48295 iter/s, 4.83296s/12 iters), loss = 0.137636
I0408 20:28:32.882752  5931 solver.cpp:237]     Train net output #0: loss = 0.137636 (* 1 = 0.137636 loss)
I0408 20:28:32.882761  5931 sgd_solver.cpp:105] Iteration 8436, lr = 7.34851e-05
I0408 20:28:37.880565  5931 solver.cpp:218] Iteration 8448 (2.40106 iter/s, 4.99779s/12 iters), loss = 0.143883
I0408 20:28:37.880599  5931 solver.cpp:237]     Train net output #0: loss = 0.143883 (* 1 = 0.143883 loss)
I0408 20:28:37.880605  5931 sgd_solver.cpp:105] Iteration 8448, lr = 7.22089e-05
I0408 20:28:42.894275  5931 solver.cpp:218] Iteration 8460 (2.39346 iter/s, 5.01365s/12 iters), loss = 0.136481
I0408 20:28:42.894309  5931 solver.cpp:237]     Train net output #0: loss = 0.136481 (* 1 = 0.136481 loss)
I0408 20:28:42.894316  5931 sgd_solver.cpp:105] Iteration 8460, lr = 7.09548e-05
I0408 20:28:44.878473  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 20:28:48.067986  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 20:28:50.431948  5931 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 20:28:50.431974  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:28:51.569953  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:28:55.220135  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:28:55.220185  5931 solver.cpp:397]     Test net output #1: loss = 2.93773 (* 1 = 2.93773 loss)
I0408 20:28:56.962355  5931 solver.cpp:218] Iteration 8472 (0.852999 iter/s, 14.068s/12 iters), loss = 0.0511453
I0408 20:28:56.962390  5931 solver.cpp:237]     Train net output #0: loss = 0.0511453 (* 1 = 0.0511453 loss)
I0408 20:28:56.962399  5931 sgd_solver.cpp:105] Iteration 8472, lr = 6.97223e-05
I0408 20:29:01.840673  5931 solver.cpp:218] Iteration 8484 (2.45989 iter/s, 4.87826s/12 iters), loss = 0.0473533
I0408 20:29:01.840792  5931 solver.cpp:237]     Train net output #0: loss = 0.0473532 (* 1 = 0.0473532 loss)
I0408 20:29:01.840801  5931 sgd_solver.cpp:105] Iteration 8484, lr = 6.85111e-05
I0408 20:29:06.645707  5931 solver.cpp:218] Iteration 8496 (2.49745 iter/s, 4.8049s/12 iters), loss = 0.108647
I0408 20:29:06.645741  5931 solver.cpp:237]     Train net output #0: loss = 0.108647 (* 1 = 0.108647 loss)
I0408 20:29:06.645748  5931 sgd_solver.cpp:105] Iteration 8496, lr = 6.73207e-05
I0408 20:29:06.680244  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:29:11.507843  5931 solver.cpp:218] Iteration 8508 (2.46808 iter/s, 4.86209s/12 iters), loss = 0.0733235
I0408 20:29:11.507876  5931 solver.cpp:237]     Train net output #0: loss = 0.0733235 (* 1 = 0.0733235 loss)
I0408 20:29:11.507884  5931 sgd_solver.cpp:105] Iteration 8508, lr = 6.61509e-05
I0408 20:29:16.345728  5931 solver.cpp:218] Iteration 8520 (2.48045 iter/s, 4.83783s/12 iters), loss = 0.0643699
I0408 20:29:16.345762  5931 solver.cpp:237]     Train net output #0: loss = 0.0643699 (* 1 = 0.0643699 loss)
I0408 20:29:16.345768  5931 sgd_solver.cpp:105] Iteration 8520, lr = 6.50013e-05
I0408 20:29:21.103876  5931 solver.cpp:218] Iteration 8532 (2.52202 iter/s, 4.7581s/12 iters), loss = 0.0633643
I0408 20:29:21.103909  5931 solver.cpp:237]     Train net output #0: loss = 0.0633643 (* 1 = 0.0633643 loss)
I0408 20:29:21.103917  5931 sgd_solver.cpp:105] Iteration 8532, lr = 6.38715e-05
I0408 20:29:26.053018  5931 solver.cpp:218] Iteration 8544 (2.42469 iter/s, 4.94909s/12 iters), loss = 0.0946614
I0408 20:29:26.053057  5931 solver.cpp:237]     Train net output #0: loss = 0.0946614 (* 1 = 0.0946614 loss)
I0408 20:29:26.053064  5931 sgd_solver.cpp:105] Iteration 8544, lr = 6.27613e-05
I0408 20:29:30.829038  5931 solver.cpp:218] Iteration 8556 (2.51258 iter/s, 4.77596s/12 iters), loss = 0.111092
I0408 20:29:30.829071  5931 solver.cpp:237]     Train net output #0: loss = 0.111092 (* 1 = 0.111092 loss)
I0408 20:29:30.829078  5931 sgd_solver.cpp:105] Iteration 8556, lr = 6.16702e-05
I0408 20:29:35.121054  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 20:29:39.336314  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 20:29:42.511834  5931 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 20:29:42.511859  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:29:43.594988  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:29:47.112916  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0408 20:29:47.112962  5931 solver.cpp:397]     Test net output #1: loss = 2.94576 (* 1 = 2.94576 loss)
I0408 20:29:47.207698  5931 solver.cpp:218] Iteration 8568 (0.732664 iter/s, 16.3786s/12 iters), loss = 0.149233
I0408 20:29:47.207748  5931 solver.cpp:237]     Train net output #0: loss = 0.149233 (* 1 = 0.149233 loss)
I0408 20:29:47.207759  5931 sgd_solver.cpp:105] Iteration 8568, lr = 6.0598e-05
I0408 20:29:51.206379  5931 solver.cpp:218] Iteration 8580 (3.00104 iter/s, 3.99862s/12 iters), loss = 0.0988229
I0408 20:29:51.206414  5931 solver.cpp:237]     Train net output #0: loss = 0.0988229 (* 1 = 0.0988229 loss)
I0408 20:29:51.206421  5931 sgd_solver.cpp:105] Iteration 8580, lr = 5.95443e-05
I0408 20:29:56.132553  5931 solver.cpp:218] Iteration 8592 (2.436 iter/s, 4.92612s/12 iters), loss = 0.153437
I0408 20:29:56.132586  5931 solver.cpp:237]     Train net output #0: loss = 0.153437 (* 1 = 0.153437 loss)
I0408 20:29:56.132593  5931 sgd_solver.cpp:105] Iteration 8592, lr = 5.85088e-05
I0408 20:29:58.273859  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:30:00.957770  5931 solver.cpp:218] Iteration 8604 (2.48696 iter/s, 4.82516s/12 iters), loss = 0.0346651
I0408 20:30:00.957804  5931 solver.cpp:237]     Train net output #0: loss = 0.034665 (* 1 = 0.034665 loss)
I0408 20:30:00.957811  5931 sgd_solver.cpp:105] Iteration 8604, lr = 5.74913e-05
I0408 20:30:05.844019  5931 solver.cpp:218] Iteration 8616 (2.4559 iter/s, 4.8862s/12 iters), loss = 0.088288
I0408 20:30:05.844134  5931 solver.cpp:237]     Train net output #0: loss = 0.0882879 (* 1 = 0.0882879 loss)
I0408 20:30:05.844143  5931 sgd_solver.cpp:105] Iteration 8616, lr = 5.64913e-05
I0408 20:30:10.772385  5931 solver.cpp:218] Iteration 8628 (2.43495 iter/s, 4.92824s/12 iters), loss = 0.148244
I0408 20:30:10.772418  5931 solver.cpp:237]     Train net output #0: loss = 0.148244 (* 1 = 0.148244 loss)
I0408 20:30:10.772425  5931 sgd_solver.cpp:105] Iteration 8628, lr = 5.55086e-05
I0408 20:30:15.619257  5931 solver.cpp:218] Iteration 8640 (2.47585 iter/s, 4.84682s/12 iters), loss = 0.0402559
I0408 20:30:15.619292  5931 solver.cpp:237]     Train net output #0: loss = 0.0402559 (* 1 = 0.0402559 loss)
I0408 20:30:15.619300  5931 sgd_solver.cpp:105] Iteration 8640, lr = 5.4543e-05
I0408 20:30:20.432282  5931 solver.cpp:218] Iteration 8652 (2.49326 iter/s, 4.81297s/12 iters), loss = 0.148454
I0408 20:30:20.432315  5931 solver.cpp:237]     Train net output #0: loss = 0.148454 (* 1 = 0.148454 loss)
I0408 20:30:20.432323  5931 sgd_solver.cpp:105] Iteration 8652, lr = 5.3594e-05
I0408 20:30:25.361495  5931 solver.cpp:218] Iteration 8664 (2.43449 iter/s, 4.92916s/12 iters), loss = 0.117744
I0408 20:30:25.361528  5931 solver.cpp:237]     Train net output #0: loss = 0.117744 (* 1 = 0.117744 loss)
I0408 20:30:25.361536  5931 sgd_solver.cpp:105] Iteration 8664, lr = 5.26614e-05
I0408 20:30:27.283310  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 20:30:30.444983  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 20:30:32.804564  5931 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 20:30:32.804589  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:30:33.845715  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:30:37.590982  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0408 20:30:37.591125  5931 solver.cpp:397]     Test net output #1: loss = 2.94176 (* 1 = 2.94176 loss)
I0408 20:30:39.324362  5931 solver.cpp:218] Iteration 8676 (0.859427 iter/s, 13.9628s/12 iters), loss = 0.0733706
I0408 20:30:39.324395  5931 solver.cpp:237]     Train net output #0: loss = 0.0733706 (* 1 = 0.0733706 loss)
I0408 20:30:39.324402  5931 sgd_solver.cpp:105] Iteration 8676, lr = 5.17451e-05
I0408 20:30:44.171057  5931 solver.cpp:218] Iteration 8688 (2.47594 iter/s, 4.84664s/12 iters), loss = 0.0753945
I0408 20:30:44.171089  5931 solver.cpp:237]     Train net output #0: loss = 0.0753945 (* 1 = 0.0753945 loss)
I0408 20:30:44.171097  5931 sgd_solver.cpp:105] Iteration 8688, lr = 5.08445e-05
I0408 20:30:48.316236  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:30:48.970965  5931 solver.cpp:218] Iteration 8700 (2.50008 iter/s, 4.79986s/12 iters), loss = 0.0575705
I0408 20:30:48.970999  5931 solver.cpp:237]     Train net output #0: loss = 0.0575705 (* 1 = 0.0575705 loss)
I0408 20:30:48.971007  5931 sgd_solver.cpp:105] Iteration 8700, lr = 4.99596e-05
I0408 20:30:53.821552  5931 solver.cpp:218] Iteration 8712 (2.47396 iter/s, 4.85053s/12 iters), loss = 0.0795703
I0408 20:30:53.821583  5931 solver.cpp:237]     Train net output #0: loss = 0.0795702 (* 1 = 0.0795702 loss)
I0408 20:30:53.821591  5931 sgd_solver.cpp:105] Iteration 8712, lr = 4.909e-05
I0408 20:30:58.628325  5931 solver.cpp:218] Iteration 8724 (2.4965 iter/s, 4.80672s/12 iters), loss = 0.0746322
I0408 20:30:58.628360  5931 solver.cpp:237]     Train net output #0: loss = 0.0746321 (* 1 = 0.0746321 loss)
I0408 20:30:58.628367  5931 sgd_solver.cpp:105] Iteration 8724, lr = 4.82354e-05
I0408 20:31:03.472627  5931 solver.cpp:218] Iteration 8736 (2.47716 iter/s, 4.84425s/12 iters), loss = 0.12831
I0408 20:31:03.472659  5931 solver.cpp:237]     Train net output #0: loss = 0.12831 (* 1 = 0.12831 loss)
I0408 20:31:03.472667  5931 sgd_solver.cpp:105] Iteration 8736, lr = 4.73957e-05
I0408 20:31:08.296367  5931 solver.cpp:218] Iteration 8748 (2.48772 iter/s, 4.82369s/12 iters), loss = 0.120845
I0408 20:31:08.296424  5931 solver.cpp:237]     Train net output #0: loss = 0.120845 (* 1 = 0.120845 loss)
I0408 20:31:08.296432  5931 sgd_solver.cpp:105] Iteration 8748, lr = 4.65705e-05
I0408 20:31:13.092270  5931 solver.cpp:218] Iteration 8760 (2.50217 iter/s, 4.79583s/12 iters), loss = 0.0858041
I0408 20:31:13.092304  5931 solver.cpp:237]     Train net output #0: loss = 0.0858041 (* 1 = 0.0858041 loss)
I0408 20:31:13.092311  5931 sgd_solver.cpp:105] Iteration 8760, lr = 4.57596e-05
I0408 20:31:17.531430  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 20:31:21.013509  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 20:31:24.816133  5931 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 20:31:24.816157  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:31:25.814661  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:31:29.278798  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0408 20:31:29.278846  5931 solver.cpp:397]     Test net output #1: loss = 2.9544 (* 1 = 2.9544 loss)
I0408 20:31:29.375337  5931 solver.cpp:218] Iteration 8772 (0.736965 iter/s, 16.283s/12 iters), loss = 0.0411556
I0408 20:31:29.375370  5931 solver.cpp:237]     Train net output #0: loss = 0.0411556 (* 1 = 0.0411556 loss)
I0408 20:31:29.375378  5931 sgd_solver.cpp:105] Iteration 8772, lr = 4.49627e-05
I0408 20:31:33.539240  5931 solver.cpp:218] Iteration 8784 (2.88194 iter/s, 4.16385s/12 iters), loss = 0.134866
I0408 20:31:33.539275  5931 solver.cpp:237]     Train net output #0: loss = 0.134866 (* 1 = 0.134866 loss)
I0408 20:31:33.539283  5931 sgd_solver.cpp:105] Iteration 8784, lr = 4.41797e-05
I0408 20:31:38.444595  5931 solver.cpp:218] Iteration 8796 (2.44633 iter/s, 4.9053s/12 iters), loss = 0.10287
I0408 20:31:38.444695  5931 solver.cpp:237]     Train net output #0: loss = 0.10287 (* 1 = 0.10287 loss)
I0408 20:31:38.444703  5931 sgd_solver.cpp:105] Iteration 8796, lr = 4.34103e-05
I0408 20:31:39.868703  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:31:43.273849  5931 solver.cpp:218] Iteration 8808 (2.48492 iter/s, 4.82913s/12 iters), loss = 0.0556044
I0408 20:31:43.273881  5931 solver.cpp:237]     Train net output #0: loss = 0.0556044 (* 1 = 0.0556044 loss)
I0408 20:31:43.273890  5931 sgd_solver.cpp:105] Iteration 8808, lr = 4.26541e-05
I0408 20:31:48.101758  5931 solver.cpp:218] Iteration 8820 (2.48558 iter/s, 4.82786s/12 iters), loss = 0.0501046
I0408 20:31:48.101791  5931 solver.cpp:237]     Train net output #0: loss = 0.0501046 (* 1 = 0.0501046 loss)
I0408 20:31:48.101799  5931 sgd_solver.cpp:105] Iteration 8820, lr = 4.19112e-05
I0408 20:31:52.938191  5931 solver.cpp:218] Iteration 8832 (2.4812 iter/s, 4.83638s/12 iters), loss = 0.0740233
I0408 20:31:52.938226  5931 solver.cpp:237]     Train net output #0: loss = 0.0740233 (* 1 = 0.0740233 loss)
I0408 20:31:52.938235  5931 sgd_solver.cpp:105] Iteration 8832, lr = 4.11811e-05
I0408 20:31:57.894986  5931 solver.cpp:218] Iteration 8844 (2.42094 iter/s, 4.95674s/12 iters), loss = 0.181416
I0408 20:31:57.895025  5931 solver.cpp:237]     Train net output #0: loss = 0.181416 (* 1 = 0.181416 loss)
I0408 20:31:57.895032  5931 sgd_solver.cpp:105] Iteration 8844, lr = 4.04636e-05
I0408 20:32:02.857275  5931 solver.cpp:218] Iteration 8856 (2.41827 iter/s, 4.96223s/12 iters), loss = 0.0361681
I0408 20:32:02.857306  5931 solver.cpp:237]     Train net output #0: loss = 0.0361681 (* 1 = 0.0361681 loss)
I0408 20:32:02.857314  5931 sgd_solver.cpp:105] Iteration 8856, lr = 3.97586e-05
I0408 20:32:08.101279  5931 solver.cpp:218] Iteration 8868 (2.28835 iter/s, 5.24395s/12 iters), loss = 0.0931413
I0408 20:32:08.101310  5931 solver.cpp:237]     Train net output #0: loss = 0.0931413 (* 1 = 0.0931413 loss)
I0408 20:32:08.101317  5931 sgd_solver.cpp:105] Iteration 8868, lr = 3.90659e-05
I0408 20:32:10.105515  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 20:32:13.221325  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 20:32:15.575300  5931 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 20:32:15.575326  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:32:16.502672  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:32:19.884495  5931 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0408 20:32:19.884541  5931 solver.cpp:397]     Test net output #1: loss = 2.95092 (* 1 = 2.95092 loss)
I0408 20:32:21.676535  5931 solver.cpp:218] Iteration 8880 (0.883965 iter/s, 13.5752s/12 iters), loss = 0.0893554
I0408 20:32:21.676570  5931 solver.cpp:237]     Train net output #0: loss = 0.0893554 (* 1 = 0.0893554 loss)
I0408 20:32:21.676578  5931 sgd_solver.cpp:105] Iteration 8880, lr = 3.83852e-05
I0408 20:32:26.565443  5931 solver.cpp:218] Iteration 8892 (2.45456 iter/s, 4.88885s/12 iters), loss = 0.19656
I0408 20:32:26.565476  5931 solver.cpp:237]     Train net output #0: loss = 0.19656 (* 1 = 0.19656 loss)
I0408 20:32:26.565485  5931 sgd_solver.cpp:105] Iteration 8892, lr = 3.77162e-05
I0408 20:32:29.991446  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:32:31.315542  5931 solver.cpp:218] Iteration 8904 (2.52629 iter/s, 4.75005s/12 iters), loss = 0.184462
I0408 20:32:31.315575  5931 solver.cpp:237]     Train net output #0: loss = 0.184462 (* 1 = 0.184462 loss)
I0408 20:32:31.315583  5931 sgd_solver.cpp:105] Iteration 8904, lr = 3.7059e-05
I0408 20:32:36.149960  5931 solver.cpp:218] Iteration 8916 (2.48223 iter/s, 4.83436s/12 iters), loss = 0.0538377
I0408 20:32:36.149991  5931 solver.cpp:237]     Train net output #0: loss = 0.0538377 (* 1 = 0.0538377 loss)
I0408 20:32:36.149999  5931 sgd_solver.cpp:105] Iteration 8916, lr = 3.64131e-05
I0408 20:32:40.977504  5931 solver.cpp:218] Iteration 8928 (2.48576 iter/s, 4.82749s/12 iters), loss = 0.0963427
I0408 20:32:40.977653  5931 solver.cpp:237]     Train net output #0: loss = 0.0963427 (* 1 = 0.0963427 loss)
I0408 20:32:40.977663  5931 sgd_solver.cpp:105] Iteration 8928, lr = 3.57784e-05
I0408 20:32:45.782821  5931 solver.cpp:218] Iteration 8940 (2.49732 iter/s, 4.80515s/12 iters), loss = 0.120136
I0408 20:32:45.782855  5931 solver.cpp:237]     Train net output #0: loss = 0.120136 (* 1 = 0.120136 loss)
I0408 20:32:45.782862  5931 sgd_solver.cpp:105] Iteration 8940, lr = 3.51547e-05
I0408 20:32:50.719826  5931 solver.cpp:218] Iteration 8952 (2.43065 iter/s, 4.93695s/12 iters), loss = 0.0655056
I0408 20:32:50.719859  5931 solver.cpp:237]     Train net output #0: loss = 0.0655056 (* 1 = 0.0655056 loss)
I0408 20:32:50.719866  5931 sgd_solver.cpp:105] Iteration 8952, lr = 3.45419e-05
I0408 20:32:55.436892  5931 solver.cpp:218] Iteration 8964 (2.54398 iter/s, 4.71701s/12 iters), loss = 0.156777
I0408 20:32:55.436925  5931 solver.cpp:237]     Train net output #0: loss = 0.156777 (* 1 = 0.156777 loss)
I0408 20:32:55.436933  5931 sgd_solver.cpp:105] Iteration 8964, lr = 3.39398e-05
I0408 20:32:59.763679  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 20:33:02.857662  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 20:33:05.211658  5931 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 20:33:05.211683  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:33:06.132351  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:33:10.005319  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0408 20:33:10.005368  5931 solver.cpp:397]     Test net output #1: loss = 2.95804 (* 1 = 2.95804 loss)
I0408 20:33:10.102162  5931 solver.cpp:218] Iteration 8976 (0.818264 iter/s, 14.6652s/12 iters), loss = 0.0280937
I0408 20:33:10.102201  5931 solver.cpp:237]     Train net output #0: loss = 0.0280937 (* 1 = 0.0280937 loss)
I0408 20:33:10.102210  5931 sgd_solver.cpp:105] Iteration 8976, lr = 3.33481e-05
I0408 20:33:14.166538  5931 solver.cpp:218] Iteration 8988 (2.95253 iter/s, 4.06432s/12 iters), loss = 0.0495558
I0408 20:33:14.166617  5931 solver.cpp:237]     Train net output #0: loss = 0.0495558 (* 1 = 0.0495558 loss)
I0408 20:33:14.166626  5931 sgd_solver.cpp:105] Iteration 8988, lr = 3.27666e-05
I0408 20:33:17.340737  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:33:18.966545  5931 solver.cpp:218] Iteration 9000 (2.50005 iter/s, 4.79991s/12 iters), loss = 0.029268
I0408 20:33:18.966578  5931 solver.cpp:237]     Train net output #0: loss = 0.029268 (* 1 = 0.029268 loss)
I0408 20:33:18.966585  5931 sgd_solver.cpp:105] Iteration 9000, lr = 3.21953e-05
I0408 20:33:19.615051  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:33:23.697597  5931 solver.cpp:218] Iteration 9012 (2.53646 iter/s, 4.731s/12 iters), loss = 0.0514562
I0408 20:33:23.697628  5931 solver.cpp:237]     Train net output #0: loss = 0.0514562 (* 1 = 0.0514562 loss)
I0408 20:33:23.697636  5931 sgd_solver.cpp:105] Iteration 9012, lr = 3.16339e-05
I0408 20:33:28.421757  5931 solver.cpp:218] Iteration 9024 (2.54016 iter/s, 4.72411s/12 iters), loss = 0.119511
I0408 20:33:28.421789  5931 solver.cpp:237]     Train net output #0: loss = 0.119511 (* 1 = 0.119511 loss)
I0408 20:33:28.421797  5931 sgd_solver.cpp:105] Iteration 9024, lr = 3.10823e-05
I0408 20:33:33.231767  5931 solver.cpp:218] Iteration 9036 (2.49482 iter/s, 4.80996s/12 iters), loss = 0.0514225
I0408 20:33:33.231801  5931 solver.cpp:237]     Train net output #0: loss = 0.0514225 (* 1 = 0.0514225 loss)
I0408 20:33:33.231808  5931 sgd_solver.cpp:105] Iteration 9036, lr = 3.05403e-05
I0408 20:33:38.100306  5931 solver.cpp:218] Iteration 9048 (2.46483 iter/s, 4.86849s/12 iters), loss = 0.0690097
I0408 20:33:38.100340  5931 solver.cpp:237]     Train net output #0: loss = 0.0690097 (* 1 = 0.0690097 loss)
I0408 20:33:38.100348  5931 sgd_solver.cpp:105] Iteration 9048, lr = 3.00077e-05
I0408 20:33:42.925173  5931 solver.cpp:218] Iteration 9060 (2.48714 iter/s, 4.82481s/12 iters), loss = 0.114602
I0408 20:33:42.925204  5931 solver.cpp:237]     Train net output #0: loss = 0.114602 (* 1 = 0.114602 loss)
I0408 20:33:42.925212  5931 sgd_solver.cpp:105] Iteration 9060, lr = 2.94843e-05
I0408 20:33:47.746598  5931 solver.cpp:218] Iteration 9072 (2.48892 iter/s, 4.82137s/12 iters), loss = 0.0583481
I0408 20:33:47.746695  5931 solver.cpp:237]     Train net output #0: loss = 0.0583481 (* 1 = 0.0583481 loss)
I0408 20:33:47.746704  5931 sgd_solver.cpp:105] Iteration 9072, lr = 2.89701e-05
I0408 20:33:49.688468  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 20:33:52.807066  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 20:33:55.163450  5931 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 20:33:55.163475  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:33:56.031569  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:33:59.675956  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0408 20:33:59.676000  5931 solver.cpp:397]     Test net output #1: loss = 2.94564 (* 1 = 2.94564 loss)
I0408 20:34:01.425879  5931 solver.cpp:218] Iteration 9084 (0.877248 iter/s, 13.6791s/12 iters), loss = 0.0723732
I0408 20:34:01.425915  5931 solver.cpp:237]     Train net output #0: loss = 0.0723732 (* 1 = 0.0723732 loss)
I0408 20:34:01.425923  5931 sgd_solver.cpp:105] Iteration 9084, lr = 2.84647e-05
I0408 20:34:06.204130  5931 solver.cpp:218] Iteration 9096 (2.51141 iter/s, 4.77819s/12 iters), loss = 0.0574748
I0408 20:34:06.204164  5931 solver.cpp:237]     Train net output #0: loss = 0.0574748 (* 1 = 0.0574748 loss)
I0408 20:34:06.204172  5931 sgd_solver.cpp:105] Iteration 9096, lr = 2.79682e-05
I0408 20:34:09.065004  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:34:10.996745  5931 solver.cpp:218] Iteration 9108 (2.50388 iter/s, 4.79255s/12 iters), loss = 0.0838419
I0408 20:34:10.996779  5931 solver.cpp:237]     Train net output #0: loss = 0.0838419 (* 1 = 0.0838419 loss)
I0408 20:34:10.996786  5931 sgd_solver.cpp:105] Iteration 9108, lr = 2.74803e-05
I0408 20:34:15.736411  5931 solver.cpp:218] Iteration 9120 (2.53186 iter/s, 4.73961s/12 iters), loss = 0.117803
I0408 20:34:15.736445  5931 solver.cpp:237]     Train net output #0: loss = 0.117803 (* 1 = 0.117803 loss)
I0408 20:34:15.736452  5931 sgd_solver.cpp:105] Iteration 9120, lr = 2.70009e-05
I0408 20:34:20.517153  5931 solver.cpp:218] Iteration 9132 (2.5101 iter/s, 4.78068s/12 iters), loss = 0.110181
I0408 20:34:20.517271  5931 solver.cpp:237]     Train net output #0: loss = 0.110181 (* 1 = 0.110181 loss)
I0408 20:34:20.517280  5931 sgd_solver.cpp:105] Iteration 9132, lr = 2.65299e-05
I0408 20:34:25.332458  5931 solver.cpp:218] Iteration 9144 (2.49213 iter/s, 4.81516s/12 iters), loss = 0.0930482
I0408 20:34:25.332496  5931 solver.cpp:237]     Train net output #0: loss = 0.0930482 (* 1 = 0.0930482 loss)
I0408 20:34:25.332504  5931 sgd_solver.cpp:105] Iteration 9144, lr = 2.6067e-05
I0408 20:34:30.132419  5931 solver.cpp:218] Iteration 9156 (2.50005 iter/s, 4.7999s/12 iters), loss = 0.0490891
I0408 20:34:30.132452  5931 solver.cpp:237]     Train net output #0: loss = 0.0490891 (* 1 = 0.0490891 loss)
I0408 20:34:30.132460  5931 sgd_solver.cpp:105] Iteration 9156, lr = 2.56122e-05
I0408 20:34:34.975716  5931 solver.cpp:218] Iteration 9168 (2.47768 iter/s, 4.84324s/12 iters), loss = 0.127033
I0408 20:34:34.975751  5931 solver.cpp:237]     Train net output #0: loss = 0.127033 (* 1 = 0.127033 loss)
I0408 20:34:34.975759  5931 sgd_solver.cpp:105] Iteration 9168, lr = 2.51653e-05
I0408 20:34:39.332674  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 20:34:43.032655  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 20:34:46.995499  5931 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 20:34:46.995524  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:34:47.832314  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:34:51.787060  5931 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0408 20:34:51.787160  5931 solver.cpp:397]     Test net output #1: loss = 2.94609 (* 1 = 2.94609 loss)
I0408 20:34:51.883633  5931 solver.cpp:218] Iteration 9180 (0.70973 iter/s, 16.9078s/12 iters), loss = 0.0608644
I0408 20:34:51.883671  5931 solver.cpp:237]     Train net output #0: loss = 0.0608644 (* 1 = 0.0608644 loss)
I0408 20:34:51.883679  5931 sgd_solver.cpp:105] Iteration 9180, lr = 2.47262e-05
I0408 20:34:55.841660  5931 solver.cpp:218] Iteration 9192 (3.03186 iter/s, 3.95797s/12 iters), loss = 0.138006
I0408 20:34:55.841693  5931 solver.cpp:237]     Train net output #0: loss = 0.138006 (* 1 = 0.138006 loss)
I0408 20:34:55.841701  5931 sgd_solver.cpp:105] Iteration 9192, lr = 2.42948e-05
I0408 20:35:00.668448  5931 solver.cpp:218] Iteration 9204 (2.48616 iter/s, 4.82673s/12 iters), loss = 0.0751007
I0408 20:35:00.668483  5931 solver.cpp:237]     Train net output #0: loss = 0.0751007 (* 1 = 0.0751007 loss)
I0408 20:35:00.668489  5931 sgd_solver.cpp:105] Iteration 9204, lr = 2.38708e-05
I0408 20:35:00.728327  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:35:05.510135  5931 solver.cpp:218] Iteration 9216 (2.4785 iter/s, 4.84163s/12 iters), loss = 0.122912
I0408 20:35:05.510170  5931 solver.cpp:237]     Train net output #0: loss = 0.122912 (* 1 = 0.122912 loss)
I0408 20:35:05.510177  5931 sgd_solver.cpp:105] Iteration 9216, lr = 2.34542e-05
I0408 20:35:10.318519  5931 solver.cpp:218] Iteration 9228 (2.49567 iter/s, 4.80833s/12 iters), loss = 0.0800206
I0408 20:35:10.318552  5931 solver.cpp:237]     Train net output #0: loss = 0.0800206 (* 1 = 0.0800206 loss)
I0408 20:35:10.318559  5931 sgd_solver.cpp:105] Iteration 9228, lr = 2.30449e-05
I0408 20:35:15.108515  5931 solver.cpp:218] Iteration 9240 (2.50525 iter/s, 4.78994s/12 iters), loss = 0.0737501
I0408 20:35:15.108549  5931 solver.cpp:237]     Train net output #0: loss = 0.0737502 (* 1 = 0.0737502 loss)
I0408 20:35:15.108556  5931 sgd_solver.cpp:105] Iteration 9240, lr = 2.26427e-05
I0408 20:35:19.973369  5931 solver.cpp:218] Iteration 9252 (2.4667 iter/s, 4.8648s/12 iters), loss = 0.0617412
I0408 20:35:19.973402  5931 solver.cpp:237]     Train net output #0: loss = 0.0617412 (* 1 = 0.0617412 loss)
I0408 20:35:19.973408  5931 sgd_solver.cpp:105] Iteration 9252, lr = 2.22475e-05
I0408 20:35:24.804848  5931 solver.cpp:218] Iteration 9264 (2.48374 iter/s, 4.83142s/12 iters), loss = 0.131618
I0408 20:35:24.804996  5931 solver.cpp:237]     Train net output #0: loss = 0.131618 (* 1 = 0.131618 loss)
I0408 20:35:24.805004  5931 sgd_solver.cpp:105] Iteration 9264, lr = 2.18592e-05
I0408 20:35:29.627671  5931 solver.cpp:218] Iteration 9276 (2.48825 iter/s, 4.82266s/12 iters), loss = 0.225857
I0408 20:35:29.627707  5931 solver.cpp:237]     Train net output #0: loss = 0.225857 (* 1 = 0.225857 loss)
I0408 20:35:29.627715  5931 sgd_solver.cpp:105] Iteration 9276, lr = 2.14777e-05
I0408 20:35:31.577963  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 20:35:34.691906  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 20:35:37.051455  5931 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 20:35:37.051482  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:35:37.830374  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:35:41.821640  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:35:41.821686  5931 solver.cpp:397]     Test net output #1: loss = 2.95209 (* 1 = 2.95209 loss)
I0408 20:35:43.569531  5931 solver.cpp:218] Iteration 9288 (0.860722 iter/s, 13.9418s/12 iters), loss = 0.120213
I0408 20:35:43.569566  5931 solver.cpp:237]     Train net output #0: loss = 0.120213 (* 1 = 0.120213 loss)
I0408 20:35:43.569572  5931 sgd_solver.cpp:105] Iteration 9288, lr = 2.11028e-05
I0408 20:35:48.282476  5931 solver.cpp:218] Iteration 9300 (2.54621 iter/s, 4.71289s/12 iters), loss = 0.0497918
I0408 20:35:48.282510  5931 solver.cpp:237]     Train net output #0: loss = 0.0497918 (* 1 = 0.0497918 loss)
I0408 20:35:48.282516  5931 sgd_solver.cpp:105] Iteration 9300, lr = 2.07344e-05
I0408 20:35:50.398639  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:35:53.122256  5931 solver.cpp:218] Iteration 9312 (2.47948 iter/s, 4.83972s/12 iters), loss = 0.0553774
I0408 20:35:53.122287  5931 solver.cpp:237]     Train net output #0: loss = 0.0553775 (* 1 = 0.0553775 loss)
I0408 20:35:53.122295  5931 sgd_solver.cpp:105] Iteration 9312, lr = 2.03725e-05
I0408 20:35:57.935631  5931 solver.cpp:218] Iteration 9324 (2.49308 iter/s, 4.81332s/12 iters), loss = 0.0643109
I0408 20:35:57.935747  5931 solver.cpp:237]     Train net output #0: loss = 0.064311 (* 1 = 0.064311 loss)
I0408 20:35:57.935756  5931 sgd_solver.cpp:105] Iteration 9324, lr = 2.00168e-05
I0408 20:36:02.791230  5931 solver.cpp:218] Iteration 9336 (2.47144 iter/s, 4.85546s/12 iters), loss = 0.107235
I0408 20:36:02.791265  5931 solver.cpp:237]     Train net output #0: loss = 0.107235 (* 1 = 0.107235 loss)
I0408 20:36:02.791271  5931 sgd_solver.cpp:105] Iteration 9336, lr = 1.96673e-05
I0408 20:36:07.588019  5931 solver.cpp:218] Iteration 9348 (2.5017 iter/s, 4.79674s/12 iters), loss = 0.123679
I0408 20:36:07.588053  5931 solver.cpp:237]     Train net output #0: loss = 0.123679 (* 1 = 0.123679 loss)
I0408 20:36:07.588060  5931 sgd_solver.cpp:105] Iteration 9348, lr = 1.9324e-05
I0408 20:36:12.414711  5931 solver.cpp:218] Iteration 9360 (2.4862 iter/s, 4.82663s/12 iters), loss = 0.156089
I0408 20:36:12.414743  5931 solver.cpp:237]     Train net output #0: loss = 0.156089 (* 1 = 0.156089 loss)
I0408 20:36:12.414750  5931 sgd_solver.cpp:105] Iteration 9360, lr = 1.89866e-05
I0408 20:36:17.251804  5931 solver.cpp:218] Iteration 9372 (2.48086 iter/s, 4.83704s/12 iters), loss = 0.0873874
I0408 20:36:17.251839  5931 solver.cpp:237]     Train net output #0: loss = 0.0873875 (* 1 = 0.0873875 loss)
I0408 20:36:17.251847  5931 sgd_solver.cpp:105] Iteration 9372, lr = 1.86551e-05
I0408 20:36:21.609894  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 20:36:24.723522  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 20:36:27.078889  5931 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 20:36:27.078914  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:36:27.819653  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:36:31.861135  5931 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0408 20:36:31.861289  5931 solver.cpp:397]     Test net output #1: loss = 2.94438 (* 1 = 2.94438 loss)
I0408 20:36:31.957691  5931 solver.cpp:218] Iteration 9384 (0.816004 iter/s, 14.7058s/12 iters), loss = 0.25483
I0408 20:36:31.957724  5931 solver.cpp:237]     Train net output #0: loss = 0.25483 (* 1 = 0.25483 loss)
I0408 20:36:31.957733  5931 sgd_solver.cpp:105] Iteration 9384, lr = 1.83294e-05
I0408 20:36:35.913532  5931 solver.cpp:218] Iteration 9396 (3.03353 iter/s, 3.95578s/12 iters), loss = 0.181478
I0408 20:36:35.913565  5931 solver.cpp:237]     Train net output #0: loss = 0.181478 (* 1 = 0.181478 loss)
I0408 20:36:35.913573  5931 sgd_solver.cpp:105] Iteration 9396, lr = 1.80093e-05
I0408 20:36:40.077522  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:36:40.718521  5931 solver.cpp:218] Iteration 9408 (2.49743 iter/s, 4.80493s/12 iters), loss = 0.11943
I0408 20:36:40.718554  5931 solver.cpp:237]     Train net output #0: loss = 0.11943 (* 1 = 0.11943 loss)
I0408 20:36:40.718561  5931 sgd_solver.cpp:105] Iteration 9408, lr = 1.76949e-05
I0408 20:36:45.575469  5931 solver.cpp:218] Iteration 9420 (2.47072 iter/s, 4.85689s/12 iters), loss = 0.0547342
I0408 20:36:45.575503  5931 solver.cpp:237]     Train net output #0: loss = 0.0547343 (* 1 = 0.0547343 loss)
I0408 20:36:45.575510  5931 sgd_solver.cpp:105] Iteration 9420, lr = 1.73859e-05
I0408 20:36:50.381433  5931 solver.cpp:218] Iteration 9432 (2.49693 iter/s, 4.80591s/12 iters), loss = 0.0512802
I0408 20:36:50.381469  5931 solver.cpp:237]     Train net output #0: loss = 0.0512803 (* 1 = 0.0512803 loss)
I0408 20:36:50.381475  5931 sgd_solver.cpp:105] Iteration 9432, lr = 1.70823e-05
I0408 20:36:55.196823  5931 solver.cpp:218] Iteration 9444 (2.49204 iter/s, 4.81533s/12 iters), loss = 0.0732759
I0408 20:36:55.196858  5931 solver.cpp:237]     Train net output #0: loss = 0.0732759 (* 1 = 0.0732759 loss)
I0408 20:36:55.196866  5931 sgd_solver.cpp:105] Iteration 9444, lr = 1.6784e-05
I0408 20:37:00.030663  5931 solver.cpp:218] Iteration 9456 (2.48253 iter/s, 4.83378s/12 iters), loss = 0.17094
I0408 20:37:00.030697  5931 solver.cpp:237]     Train net output #0: loss = 0.17094 (* 1 = 0.17094 loss)
I0408 20:37:00.030704  5931 sgd_solver.cpp:105] Iteration 9456, lr = 1.64909e-05
I0408 20:37:04.870842  5931 solver.cpp:218] Iteration 9468 (2.47928 iter/s, 4.84012s/12 iters), loss = 0.137621
I0408 20:37:04.870963  5931 solver.cpp:237]     Train net output #0: loss = 0.137621 (* 1 = 0.137621 loss)
I0408 20:37:04.870972  5931 sgd_solver.cpp:105] Iteration 9468, lr = 1.62029e-05
I0408 20:37:09.671526  5931 solver.cpp:218] Iteration 9480 (2.49972 iter/s, 4.80054s/12 iters), loss = 0.179658
I0408 20:37:09.671561  5931 solver.cpp:237]     Train net output #0: loss = 0.179658 (* 1 = 0.179658 loss)
I0408 20:37:09.671569  5931 sgd_solver.cpp:105] Iteration 9480, lr = 1.59199e-05
I0408 20:37:11.625967  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 20:37:16.086841  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 20:37:18.439859  5931 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 20:37:18.439883  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:37:19.134279  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:37:23.224915  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:37:23.224964  5931 solver.cpp:397]     Test net output #1: loss = 2.9512 (* 1 = 2.9512 loss)
I0408 20:37:24.966917  5931 solver.cpp:218] Iteration 9492 (0.784554 iter/s, 15.2953s/12 iters), loss = 0.213301
I0408 20:37:24.966953  5931 solver.cpp:237]     Train net output #0: loss = 0.213301 (* 1 = 0.213301 loss)
I0408 20:37:24.966960  5931 sgd_solver.cpp:105] Iteration 9492, lr = 1.56419e-05
I0408 20:37:29.779623  5931 solver.cpp:218] Iteration 9504 (2.49343 iter/s, 4.81265s/12 iters), loss = 0.198088
I0408 20:37:29.779654  5931 solver.cpp:237]     Train net output #0: loss = 0.198088 (* 1 = 0.198088 loss)
I0408 20:37:29.779662  5931 sgd_solver.cpp:105] Iteration 9504, lr = 1.53687e-05
I0408 20:37:31.173892  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:37:34.600750  5931 solver.cpp:218] Iteration 9516 (2.48907 iter/s, 4.82107s/12 iters), loss = 0.0584544
I0408 20:37:34.600785  5931 solver.cpp:237]     Train net output #0: loss = 0.0584545 (* 1 = 0.0584545 loss)
I0408 20:37:34.600791  5931 sgd_solver.cpp:105] Iteration 9516, lr = 1.51003e-05
I0408 20:37:39.433826  5931 solver.cpp:218] Iteration 9528 (2.48292 iter/s, 4.83302s/12 iters), loss = 0.0587378
I0408 20:37:39.433925  5931 solver.cpp:237]     Train net output #0: loss = 0.0587378 (* 1 = 0.0587378 loss)
I0408 20:37:39.433933  5931 sgd_solver.cpp:105] Iteration 9528, lr = 1.48365e-05
I0408 20:37:44.215971  5931 solver.cpp:218] Iteration 9540 (2.5094 iter/s, 4.78203s/12 iters), loss = 0.0592625
I0408 20:37:44.216004  5931 solver.cpp:237]     Train net output #0: loss = 0.0592626 (* 1 = 0.0592626 loss)
I0408 20:37:44.216012  5931 sgd_solver.cpp:105] Iteration 9540, lr = 1.45774e-05
I0408 20:37:49.061978  5931 solver.cpp:218] Iteration 9552 (2.47629 iter/s, 4.84595s/12 iters), loss = 0.0672157
I0408 20:37:49.062011  5931 solver.cpp:237]     Train net output #0: loss = 0.0672158 (* 1 = 0.0672158 loss)
I0408 20:37:49.062019  5931 sgd_solver.cpp:105] Iteration 9552, lr = 1.43227e-05
I0408 20:37:53.895902  5931 solver.cpp:218] Iteration 9564 (2.48248 iter/s, 4.83387s/12 iters), loss = 0.046416
I0408 20:37:53.895936  5931 solver.cpp:237]     Train net output #0: loss = 0.046416 (* 1 = 0.046416 loss)
I0408 20:37:53.895943  5931 sgd_solver.cpp:105] Iteration 9564, lr = 1.40726e-05
I0408 20:37:58.727025  5931 solver.cpp:218] Iteration 9576 (2.48392 iter/s, 4.83107s/12 iters), loss = 0.144244
I0408 20:37:58.727059  5931 solver.cpp:237]     Train net output #0: loss = 0.144244 (* 1 = 0.144244 loss)
I0408 20:37:58.727066  5931 sgd_solver.cpp:105] Iteration 9576, lr = 1.38267e-05
I0408 20:38:03.077329  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 20:38:06.183543  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 20:38:10.067361  5931 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 20:38:10.067428  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:38:10.722975  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:38:14.855226  5931 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0408 20:38:14.855273  5931 solver.cpp:397]     Test net output #1: loss = 2.96082 (* 1 = 2.96082 loss)
I0408 20:38:14.951717  5931 solver.cpp:218] Iteration 9588 (0.739617 iter/s, 16.2246s/12 iters), loss = 0.0613223
I0408 20:38:14.951754  5931 solver.cpp:237]     Train net output #0: loss = 0.0613223 (* 1 = 0.0613223 loss)
I0408 20:38:14.951762  5931 sgd_solver.cpp:105] Iteration 9588, lr = 1.35852e-05
I0408 20:38:18.958339  5931 solver.cpp:218] Iteration 9600 (2.99509 iter/s, 4.00656s/12 iters), loss = 0.132205
I0408 20:38:18.958374  5931 solver.cpp:237]     Train net output #0: loss = 0.132205 (* 1 = 0.132205 loss)
I0408 20:38:18.958380  5931 sgd_solver.cpp:105] Iteration 9600, lr = 1.33479e-05
I0408 20:38:22.440071  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:38:23.787370  5931 solver.cpp:218] Iteration 9612 (2.485 iter/s, 4.82898s/12 iters), loss = 0.0688998
I0408 20:38:23.787405  5931 solver.cpp:237]     Train net output #0: loss = 0.0688998 (* 1 = 0.0688998 loss)
I0408 20:38:23.787412  5931 sgd_solver.cpp:105] Iteration 9612, lr = 1.31147e-05
I0408 20:38:28.643677  5931 solver.cpp:218] Iteration 9624 (2.47104 iter/s, 4.85625s/12 iters), loss = 0.119419
I0408 20:38:28.643712  5931 solver.cpp:237]     Train net output #0: loss = 0.119419 (* 1 = 0.119419 loss)
I0408 20:38:28.643720  5931 sgd_solver.cpp:105] Iteration 9624, lr = 1.28856e-05
I0408 20:38:33.460417  5931 solver.cpp:218] Iteration 9636 (2.49134 iter/s, 4.81668s/12 iters), loss = 0.0237301
I0408 20:38:33.460450  5931 solver.cpp:237]     Train net output #0: loss = 0.0237302 (* 1 = 0.0237302 loss)
I0408 20:38:33.460458  5931 sgd_solver.cpp:105] Iteration 9636, lr = 1.26605e-05
I0408 20:38:38.266090  5931 solver.cpp:218] Iteration 9648 (2.49708 iter/s, 4.80562s/12 iters), loss = 0.125619
I0408 20:38:38.266126  5931 solver.cpp:237]     Train net output #0: loss = 0.125619 (* 1 = 0.125619 loss)
I0408 20:38:38.266134  5931 sgd_solver.cpp:105] Iteration 9648, lr = 1.24393e-05
I0408 20:38:43.136318  5931 solver.cpp:218] Iteration 9660 (2.46398 iter/s, 4.87017s/12 iters), loss = 0.0678197
I0408 20:38:43.136462  5931 solver.cpp:237]     Train net output #0: loss = 0.0678197 (* 1 = 0.0678197 loss)
I0408 20:38:43.136471  5931 sgd_solver.cpp:105] Iteration 9660, lr = 1.2222e-05
I0408 20:38:47.958387  5931 solver.cpp:218] Iteration 9672 (2.48864 iter/s, 4.82191s/12 iters), loss = 0.0385928
I0408 20:38:47.958421  5931 solver.cpp:237]     Train net output #0: loss = 0.0385928 (* 1 = 0.0385928 loss)
I0408 20:38:47.958428  5931 sgd_solver.cpp:105] Iteration 9672, lr = 1.20084e-05
I0408 20:38:52.750205  5931 solver.cpp:218] Iteration 9684 (2.5043 iter/s, 4.79176s/12 iters), loss = 0.134879
I0408 20:38:52.750238  5931 solver.cpp:237]     Train net output #0: loss = 0.134879 (* 1 = 0.134879 loss)
I0408 20:38:52.750247  5931 sgd_solver.cpp:105] Iteration 9684, lr = 1.17986e-05
I0408 20:38:54.718261  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 20:38:57.833307  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 20:39:00.186273  5931 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 20:39:00.186300  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:39:00.789927  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:39:03.874589  5931 blocking_queue.cpp:49] Waiting for data
I0408 20:39:04.967350  5931 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0408 20:39:04.967398  5931 solver.cpp:397]     Test net output #1: loss = 2.95407 (* 1 = 2.95407 loss)
I0408 20:39:06.717764  5931 solver.cpp:218] Iteration 9696 (0.859138 iter/s, 13.9675s/12 iters), loss = 0.0833017
I0408 20:39:06.717798  5931 solver.cpp:237]     Train net output #0: loss = 0.0833017 (* 1 = 0.0833017 loss)
I0408 20:39:06.717806  5931 sgd_solver.cpp:105] Iteration 9696, lr = 1.15925e-05
I0408 20:39:11.425753  5931 solver.cpp:218] Iteration 9708 (2.54889 iter/s, 4.70794s/12 iters), loss = 0.142654
I0408 20:39:11.425787  5931 solver.cpp:237]     Train net output #0: loss = 0.142655 (* 1 = 0.142655 loss)
I0408 20:39:11.425794  5931 sgd_solver.cpp:105] Iteration 9708, lr = 1.13899e-05
I0408 20:39:12.116508  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:39:16.269891  5931 solver.cpp:218] Iteration 9720 (2.47725 iter/s, 4.84408s/12 iters), loss = 0.0678505
I0408 20:39:16.270009  5931 solver.cpp:237]     Train net output #0: loss = 0.0678505 (* 1 = 0.0678505 loss)
I0408 20:39:16.270016  5931 sgd_solver.cpp:105] Iteration 9720, lr = 1.11909e-05
I0408 20:39:21.082855  5931 solver.cpp:218] Iteration 9732 (2.49334 iter/s, 4.81283s/12 iters), loss = 0.0502067
I0408 20:39:21.082890  5931 solver.cpp:237]     Train net output #0: loss = 0.0502068 (* 1 = 0.0502068 loss)
I0408 20:39:21.082897  5931 sgd_solver.cpp:105] Iteration 9732, lr = 1.09954e-05
I0408 20:39:25.938031  5931 solver.cpp:218] Iteration 9744 (2.47162 iter/s, 4.85512s/12 iters), loss = 0.0657831
I0408 20:39:25.938066  5931 solver.cpp:237]     Train net output #0: loss = 0.0657832 (* 1 = 0.0657832 loss)
I0408 20:39:25.938073  5931 sgd_solver.cpp:105] Iteration 9744, lr = 1.08033e-05
I0408 20:39:30.765408  5931 solver.cpp:218] Iteration 9756 (2.48585 iter/s, 4.82732s/12 iters), loss = 0.085278
I0408 20:39:30.765439  5931 solver.cpp:237]     Train net output #0: loss = 0.0852781 (* 1 = 0.0852781 loss)
I0408 20:39:30.765446  5931 sgd_solver.cpp:105] Iteration 9756, lr = 1.06145e-05
I0408 20:39:35.604094  5931 solver.cpp:218] Iteration 9768 (2.48004 iter/s, 4.83864s/12 iters), loss = 0.0588029
I0408 20:39:35.604130  5931 solver.cpp:237]     Train net output #0: loss = 0.0588029 (* 1 = 0.0588029 loss)
I0408 20:39:35.604137  5931 sgd_solver.cpp:105] Iteration 9768, lr = 1.0429e-05
I0408 20:39:40.421936  5931 solver.cpp:218] Iteration 9780 (2.49077 iter/s, 4.81779s/12 iters), loss = 0.0834084
I0408 20:39:40.421968  5931 solver.cpp:237]     Train net output #0: loss = 0.0834084 (* 1 = 0.0834084 loss)
I0408 20:39:40.421977  5931 sgd_solver.cpp:105] Iteration 9780, lr = 1.02468e-05
I0408 20:39:44.683256  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 20:39:47.806821  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 20:39:50.162832  5931 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 20:39:50.162858  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:39:50.727265  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:39:54.953281  5931 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0408 20:39:54.953331  5931 solver.cpp:397]     Test net output #1: loss = 2.95754 (* 1 = 2.95754 loss)
I0408 20:39:55.049803  5931 solver.cpp:218] Iteration 9792 (0.820356 iter/s, 14.6278s/12 iters), loss = 0.0912696
I0408 20:39:55.049839  5931 solver.cpp:237]     Train net output #0: loss = 0.0912696 (* 1 = 0.0912696 loss)
I0408 20:39:55.049846  5931 sgd_solver.cpp:105] Iteration 9792, lr = 1.00677e-05
I0408 20:39:59.054729  5931 solver.cpp:218] Iteration 9804 (2.99635 iter/s, 4.00487s/12 iters), loss = 0.044689
I0408 20:39:59.054762  5931 solver.cpp:237]     Train net output #0: loss = 0.0446891 (* 1 = 0.0446891 loss)
I0408 20:39:59.054770  5931 sgd_solver.cpp:105] Iteration 9804, lr = 9.89177e-06
I0408 20:40:01.928100  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:40:03.910099  5931 solver.cpp:218] Iteration 9816 (2.47152 iter/s, 4.85531s/12 iters), loss = 0.0518716
I0408 20:40:03.910132  5931 solver.cpp:237]     Train net output #0: loss = 0.0518717 (* 1 = 0.0518717 loss)
I0408 20:40:03.910140  5931 sgd_solver.cpp:105] Iteration 9816, lr = 9.71891e-06
I0408 20:40:08.733824  5931 solver.cpp:218] Iteration 9828 (2.48773 iter/s, 4.82367s/12 iters), loss = 0.035862
I0408 20:40:08.733857  5931 solver.cpp:237]     Train net output #0: loss = 0.035862 (* 1 = 0.035862 loss)
I0408 20:40:08.733865  5931 sgd_solver.cpp:105] Iteration 9828, lr = 9.54907e-06
I0408 20:40:13.563439  5931 solver.cpp:218] Iteration 9840 (2.4847 iter/s, 4.82956s/12 iters), loss = 0.132918
I0408 20:40:13.563472  5931 solver.cpp:237]     Train net output #0: loss = 0.132918 (* 1 = 0.132918 loss)
I0408 20:40:13.563480  5931 sgd_solver.cpp:105] Iteration 9840, lr = 9.38219e-06
I0408 20:40:18.379511  5931 solver.cpp:218] Iteration 9852 (2.49168 iter/s, 4.81602s/12 iters), loss = 0.125383
I0408 20:40:18.379583  5931 solver.cpp:237]     Train net output #0: loss = 0.125383 (* 1 = 0.125383 loss)
I0408 20:40:18.379591  5931 sgd_solver.cpp:105] Iteration 9852, lr = 9.21823e-06
I0408 20:40:23.221319  5931 solver.cpp:218] Iteration 9864 (2.47846 iter/s, 4.84171s/12 iters), loss = 0.07437
I0408 20:40:23.221354  5931 solver.cpp:237]     Train net output #0: loss = 0.07437 (* 1 = 0.07437 loss)
I0408 20:40:23.221361  5931 sgd_solver.cpp:105] Iteration 9864, lr = 9.05713e-06
I0408 20:40:28.036972  5931 solver.cpp:218] Iteration 9876 (2.4919 iter/s, 4.8156s/12 iters), loss = 0.0945036
I0408 20:40:28.037007  5931 solver.cpp:237]     Train net output #0: loss = 0.0945036 (* 1 = 0.0945036 loss)
I0408 20:40:28.037015  5931 sgd_solver.cpp:105] Iteration 9876, lr = 8.89884e-06
I0408 20:40:32.861271  5931 solver.cpp:218] Iteration 9888 (2.48744 iter/s, 4.82424s/12 iters), loss = 0.194543
I0408 20:40:32.861305  5931 solver.cpp:237]     Train net output #0: loss = 0.194543 (* 1 = 0.194543 loss)
I0408 20:40:32.861313  5931 sgd_solver.cpp:105] Iteration 9888, lr = 8.74331e-06
I0408 20:40:34.828349  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 20:40:37.923693  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 20:40:40.279616  5931 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 20:40:40.279642  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:40:40.744858  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:40:44.506418  5931 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0408 20:40:44.506464  5931 solver.cpp:397]     Test net output #1: loss = 2.93378 (* 1 = 2.93378 loss)
I0408 20:40:46.251792  5931 solver.cpp:218] Iteration 9900 (0.896161 iter/s, 13.3905s/12 iters), loss = 0.12264
I0408 20:40:46.251828  5931 solver.cpp:237]     Train net output #0: loss = 0.12264 (* 1 = 0.12264 loss)
I0408 20:40:46.251837  5931 sgd_solver.cpp:105] Iteration 9900, lr = 8.5905e-06
I0408 20:40:50.982254  5931 solver.cpp:218] Iteration 9912 (2.53678 iter/s, 4.7304s/12 iters), loss = 0.0552435
I0408 20:40:50.982350  5931 solver.cpp:237]     Train net output #0: loss = 0.0552435 (* 1 = 0.0552435 loss)
I0408 20:40:50.982359  5931 sgd_solver.cpp:105] Iteration 9912, lr = 8.44036e-06
I0408 20:40:51.070289  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:40:55.760116  5931 solver.cpp:218] Iteration 9924 (2.51164 iter/s, 4.77775s/12 iters), loss = 0.152204
I0408 20:40:55.760149  5931 solver.cpp:237]     Train net output #0: loss = 0.152204 (* 1 = 0.152204 loss)
I0408 20:40:55.760157  5931 sgd_solver.cpp:105] Iteration 9924, lr = 8.29284e-06
I0408 20:41:00.578701  5931 solver.cpp:218] Iteration 9936 (2.49039 iter/s, 4.81853s/12 iters), loss = 0.0711884
I0408 20:41:00.578733  5931 solver.cpp:237]     Train net output #0: loss = 0.0711885 (* 1 = 0.0711885 loss)
I0408 20:41:00.578742  5931 sgd_solver.cpp:105] Iteration 9936, lr = 8.1479e-06
I0408 20:41:05.403081  5931 solver.cpp:218] Iteration 9948 (2.48739 iter/s, 4.82432s/12 iters), loss = 0.128263
I0408 20:41:05.403115  5931 solver.cpp:237]     Train net output #0: loss = 0.128263 (* 1 = 0.128263 loss)
I0408 20:41:05.403122  5931 sgd_solver.cpp:105] Iteration 9948, lr = 8.00549e-06
I0408 20:41:10.234453  5931 solver.cpp:218] Iteration 9960 (2.48379 iter/s, 4.83132s/12 iters), loss = 0.151204
I0408 20:41:10.234486  5931 solver.cpp:237]     Train net output #0: loss = 0.151204 (* 1 = 0.151204 loss)
I0408 20:41:10.234493  5931 sgd_solver.cpp:105] Iteration 9960, lr = 7.86557e-06
I0408 20:41:15.075289  5931 solver.cpp:218] Iteration 9972 (2.47894 iter/s, 4.84078s/12 iters), loss = 0.113763
I0408 20:41:15.075322  5931 solver.cpp:237]     Train net output #0: loss = 0.113763 (* 1 = 0.113763 loss)
I0408 20:41:15.075330  5931 sgd_solver.cpp:105] Iteration 9972, lr = 7.72808e-06
I0408 20:41:19.879783  5931 solver.cpp:218] Iteration 9984 (2.49769 iter/s, 4.80444s/12 iters), loss = 0.0795457
I0408 20:41:19.879818  5931 solver.cpp:237]     Train net output #0: loss = 0.0795457 (* 1 = 0.0795457 loss)
I0408 20:41:19.879827  5931 sgd_solver.cpp:105] Iteration 9984, lr = 7.59301e-06
I0408 20:41:24.260288  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 20:41:27.370132  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 20:41:29.787765  5931 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 20:41:29.787791  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:41:30.261198  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:41:34.452486  5931 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0408 20:41:34.452533  5931 solver.cpp:397]     Test net output #1: loss = 2.95554 (* 1 = 2.95554 loss)
I0408 20:41:34.549006  5931 solver.cpp:218] Iteration 9996 (0.818044 iter/s, 14.6691s/12 iters), loss = 0.135623
I0408 20:41:34.549041  5931 solver.cpp:237]     Train net output #0: loss = 0.135623 (* 1 = 0.135623 loss)
I0408 20:41:34.549049  5931 sgd_solver.cpp:105] Iteration 9996, lr = 7.46029e-06
I0408 20:41:38.544926  5931 solver.cpp:218] Iteration 10008 (3.00311 iter/s, 3.99586s/12 iters), loss = 0.0737608
I0408 20:41:38.544958  5931 solver.cpp:237]     Train net output #0: loss = 0.0737609 (* 1 = 0.0737609 loss)
I0408 20:41:38.544965  5931 sgd_solver.cpp:105] Iteration 10008, lr = 7.32989e-06
I0408 20:41:40.704476  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:41:43.386376  5931 solver.cpp:218] Iteration 10020 (2.47862 iter/s, 4.8414s/12 iters), loss = 0.0687955
I0408 20:41:43.386411  5931 solver.cpp:237]     Train net output #0: loss = 0.0687956 (* 1 = 0.0687956 loss)
I0408 20:41:43.386418  5931 sgd_solver.cpp:105] Iteration 10020, lr = 7.20176e-06
I0408 20:41:48.209853  5931 solver.cpp:218] Iteration 10032 (2.48786 iter/s, 4.82342s/12 iters), loss = 0.171151
I0408 20:41:48.209887  5931 solver.cpp:237]     Train net output #0: loss = 0.171151 (* 1 = 0.171151 loss)
I0408 20:41:48.209893  5931 sgd_solver.cpp:105] Iteration 10032, lr = 7.07588e-06
I0408 20:41:53.035887  5931 solver.cpp:218] Iteration 10044 (2.48654 iter/s, 4.82598s/12 iters), loss = 0.0817633
I0408 20:41:53.035921  5931 solver.cpp:237]     Train net output #0: loss = 0.0817634 (* 1 = 0.0817634 loss)
I0408 20:41:53.035929  5931 sgd_solver.cpp:105] Iteration 10044, lr = 6.95219e-06
I0408 20:41:57.857344  5931 solver.cpp:218] Iteration 10056 (2.4889 iter/s, 4.8214s/12 iters), loss = 0.115068
I0408 20:41:57.857439  5931 solver.cpp:237]     Train net output #0: loss = 0.115068 (* 1 = 0.115068 loss)
I0408 20:41:57.857448  5931 sgd_solver.cpp:105] Iteration 10056, lr = 6.83066e-06
I0408 20:42:02.654227  5931 solver.cpp:218] Iteration 10068 (2.50168 iter/s, 4.79677s/12 iters), loss = 0.167017
I0408 20:42:02.654260  5931 solver.cpp:237]     Train net output #0: loss = 0.167017 (* 1 = 0.167017 loss)
I0408 20:42:02.654268  5931 sgd_solver.cpp:105] Iteration 10068, lr = 6.71126e-06
I0408 20:42:07.511828  5931 solver.cpp:218] Iteration 10080 (2.47038 iter/s, 4.85755s/12 iters), loss = 0.162304
I0408 20:42:07.511859  5931 solver.cpp:237]     Train net output #0: loss = 0.162304 (* 1 = 0.162304 loss)
I0408 20:42:07.511868  5931 sgd_solver.cpp:105] Iteration 10080, lr = 6.59394e-06
I0408 20:42:12.320920  5931 solver.cpp:218] Iteration 10092 (2.4953 iter/s, 4.80904s/12 iters), loss = 0.0981655
I0408 20:42:12.320955  5931 solver.cpp:237]     Train net output #0: loss = 0.0981656 (* 1 = 0.0981656 loss)
I0408 20:42:12.320962  5931 sgd_solver.cpp:105] Iteration 10092, lr = 6.47867e-06
I0408 20:42:14.289875  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 20:42:19.269456  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 20:42:21.621007  5931 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 20:42:21.621034  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:42:22.057817  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:42:26.406313  5931 solver.cpp:397]     Test net output #0: accuracy = 0.47549
I0408 20:42:26.406352  5931 solver.cpp:397]     Test net output #1: loss = 2.94543 (* 1 = 2.94543 loss)
I0408 20:42:28.155103  5931 solver.cpp:218] Iteration 10104 (0.757858 iter/s, 15.8341s/12 iters), loss = 0.034996
I0408 20:42:28.155170  5931 solver.cpp:237]     Train net output #0: loss = 0.0349961 (* 1 = 0.0349961 loss)
I0408 20:42:28.155179  5931 sgd_solver.cpp:105] Iteration 10104, lr = 6.36542e-06
I0408 20:42:32.267426  5936 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:42:32.857280  5931 solver.cpp:218] Iteration 10116 (2.55206 iter/s, 4.70209s/12 iters), loss = 0.0455537
I0408 20:42:32.857316  5931 solver.cpp:237]     Train net output #0: loss = 0.0455538 (* 1 = 0.0455538 loss)
I0408 20:42:32.857323  5931 sgd_solver.cpp:105] Iteration 10116, lr = 6.25414e-06
I0408 20:42:37.622426  5931 solver.cpp:218] Iteration 10128 (2.51832 iter/s, 4.76509s/12 iters), loss = 0.11589
I0408 20:42:37.622457  5931 solver.cpp:237]     Train net output #0: loss = 0.11589 (* 1 = 0.11589 loss)
I0408 20:42:37.622465  5931 sgd_solver.cpp:105] Iteration 10128, lr = 6.14481e-06
I0408 20:42:42.429641  5931 solver.cpp:218] Iteration 10140 (2.49627 iter/s, 4.80717s/12 iters), loss = 0.0707577
I0408 20:42:42.429675  5931 solver.cpp:237]     Train net output #0: loss = 0.0707578 (* 1 = 0.0707578 loss)
I0408 20:42:42.429683  5931 sgd_solver.cpp:105] Iteration 10140, lr = 6.03739e-06
I0408 20:42:47.289350  5931 solver.cpp:218] Iteration 10152 (2.46931 iter/s, 4.85965s/12 iters), loss = 0.164566
I0408 20:42:47.289386  5931 solver.cpp:237]     Train net output #0: loss = 0.164566 (* 1 = 0.164566 loss)
I0408 20:42:47.289393  5931 sgd_solver.cpp:105] Iteration 10152, lr = 5.93184e-06
I0408 20:42:52.098048  5931 solver.cpp:218] Iteration 10164 (2.49551 iter/s, 4.80864s/12 iters), loss = 0.214253
I0408 20:42:52.098083  5931 solver.cpp:237]     Train net output #0: loss = 0.214253 (* 1 = 0.214253 loss)
I0408 20:42:52.098090  5931 sgd_solver.cpp:105] Iteration 10164, lr = 5.82814e-06
I0408 20:42:56.939460  5931 solver.cpp:218] Iteration 10176 (2.47864 iter/s, 4.84136s/12 iters), loss = 0.0714591
I0408 20:42:56.939500  5931 solver.cpp:237]     Train net output #0: loss = 0.0714592 (* 1 = 0.0714592 loss)
I0408 20:42:56.939507  5931 sgd_solver.cpp:105] Iteration 10176, lr = 5.72625e-06
I0408 20:43:01.749601  5931 solver.cpp:218] Iteration 10188 (2.49476 iter/s, 4.81009s/12 iters), loss = 0.0401616
I0408 20:43:01.749706  5931 solver.cpp:237]     Train net output #0: loss = 0.0401617 (* 1 = 0.0401617 loss)
I0408 20:43:01.749713  5931 sgd_solver.cpp:105] Iteration 10188, lr = 5.62614e-06
I0408 20:43:06.128652  5931 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 20:43:09.309223  5931 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 20:43:13.292811  5931 solver.cpp:310] Iteration 10200, loss = 0.064615
I0408 20:43:13.292837  5931 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 20:43:13.292841  5931 net.cpp:676] Ignoring source layer train-data
I0408 20:43:13.677668  5937 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:43:18.069763  5931 solver.cpp:397]     Test net output #0: accuracy = 0.474877
I0408 20:43:18.069809  5931 solver.cpp:397]     Test net output #1: loss = 2.93863 (* 1 = 2.93863 loss)
I0408 20:43:18.069819  5931 solver.cpp:315] Optimization Done.
I0408 20:43:18.069826  5931 caffe.cpp:259] Optimization Done.