DIGITS-CNN/cars/split-investigations/50.5.45/base/caffe_output.log

I0401 12:27:21.117691 21213 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-122717-69ee/solver.prototxt
I0401 12:27:21.117913 21213 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0401 12:27:21.117919 21213 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0401 12:27:21.118006 21213 caffe.cpp:218] Using GPUs 2
I0401 12:27:21.143129 21213 caffe.cpp:223] GPU 2: GeForce GTX TITAN X
I0401 12:27:21.374975 21213 solver.cpp:44] Initializing solver from parameters:
test_iter: 26
test_interval: 64
base_lr: 0.001
display: 8
max_iter: 6400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1.0000001e-05
snapshot: 64
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0401 12:27:21.477335 21213 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0401 12:27:21.561105 21213 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0401 12:27:21.561126 21213 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0401 12:27:21.561280 21213 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-AIN-3/digits/jobs/20210401-115942-8d19/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/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"
}
I0401 12:27:21.561403 21213 layer_factory.hpp:77] Creating layer train-data
I0401 12:27:21.569025 21213 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/train_db
I0401 12:27:21.569305 21213 net.cpp:84] Creating Layer train-data
I0401 12:27:21.569334 21213 net.cpp:380] train-data -> data
I0401 12:27:21.569375 21213 net.cpp:380] train-data -> label
I0401 12:27:21.569396 21213 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/mean.binaryproto
I0401 12:27:21.578495 21213 data_layer.cpp:45] output data size: 128,3,227,227
I0401 12:27:21.724570 21213 net.cpp:122] Setting up train-data
I0401 12:27:21.724591 21213 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0401 12:27:21.724596 21213 net.cpp:129] Top shape: 128 (128)
I0401 12:27:21.724597 21213 net.cpp:137] Memory required for data: 79149056
I0401 12:27:21.724606 21213 layer_factory.hpp:77] Creating layer conv1
I0401 12:27:21.724625 21213 net.cpp:84] Creating Layer conv1
I0401 12:27:21.724630 21213 net.cpp:406] conv1 <- data
I0401 12:27:21.724640 21213 net.cpp:380] conv1 -> conv1
I0401 12:27:22.161954 21213 net.cpp:122] Setting up conv1
I0401 12:27:22.161978 21213 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 12:27:22.161980 21213 net.cpp:137] Memory required for data: 227833856
I0401 12:27:22.161999 21213 layer_factory.hpp:77] Creating layer relu1
I0401 12:27:22.162009 21213 net.cpp:84] Creating Layer relu1
I0401 12:27:22.162012 21213 net.cpp:406] relu1 <- conv1
I0401 12:27:22.162016 21213 net.cpp:367] relu1 -> conv1 (in-place)
I0401 12:27:22.162271 21213 net.cpp:122] Setting up relu1
I0401 12:27:22.162279 21213 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 12:27:22.162282 21213 net.cpp:137] Memory required for data: 376518656
I0401 12:27:22.162284 21213 layer_factory.hpp:77] Creating layer norm1
I0401 12:27:22.162292 21213 net.cpp:84] Creating Layer norm1
I0401 12:27:22.162295 21213 net.cpp:406] norm1 <- conv1
I0401 12:27:22.162322 21213 net.cpp:380] norm1 -> norm1
I0401 12:27:22.162732 21213 net.cpp:122] Setting up norm1
I0401 12:27:22.162741 21213 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 12:27:22.162744 21213 net.cpp:137] Memory required for data: 525203456
I0401 12:27:22.162746 21213 layer_factory.hpp:77] Creating layer pool1
I0401 12:27:22.162752 21213 net.cpp:84] Creating Layer pool1
I0401 12:27:22.162755 21213 net.cpp:406] pool1 <- norm1
I0401 12:27:22.162760 21213 net.cpp:380] pool1 -> pool1
I0401 12:27:22.162791 21213 net.cpp:122] Setting up pool1
I0401 12:27:22.162796 21213 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0401 12:27:22.162798 21213 net.cpp:137] Memory required for data: 561035264
I0401 12:27:22.162801 21213 layer_factory.hpp:77] Creating layer conv2
I0401 12:27:22.162809 21213 net.cpp:84] Creating Layer conv2
I0401 12:27:22.162812 21213 net.cpp:406] conv2 <- pool1
I0401 12:27:22.162817 21213 net.cpp:380] conv2 -> conv2
I0401 12:27:22.168175 21213 net.cpp:122] Setting up conv2
I0401 12:27:22.168195 21213 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 12:27:22.168196 21213 net.cpp:137] Memory required for data: 656586752
I0401 12:27:22.168207 21213 layer_factory.hpp:77] Creating layer relu2
I0401 12:27:22.168215 21213 net.cpp:84] Creating Layer relu2
I0401 12:27:22.168216 21213 net.cpp:406] relu2 <- conv2
I0401 12:27:22.168221 21213 net.cpp:367] relu2 -> conv2 (in-place)
I0401 12:27:22.168615 21213 net.cpp:122] Setting up relu2
I0401 12:27:22.168624 21213 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 12:27:22.168627 21213 net.cpp:137] Memory required for data: 752138240
I0401 12:27:22.168629 21213 layer_factory.hpp:77] Creating layer norm2
I0401 12:27:22.168635 21213 net.cpp:84] Creating Layer norm2
I0401 12:27:22.168638 21213 net.cpp:406] norm2 <- conv2
I0401 12:27:22.168642 21213 net.cpp:380] norm2 -> norm2
I0401 12:27:22.168908 21213 net.cpp:122] Setting up norm2
I0401 12:27:22.168916 21213 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 12:27:22.168918 21213 net.cpp:137] Memory required for data: 847689728
I0401 12:27:22.168921 21213 layer_factory.hpp:77] Creating layer pool2
I0401 12:27:22.168927 21213 net.cpp:84] Creating Layer pool2
I0401 12:27:22.168931 21213 net.cpp:406] pool2 <- norm2
I0401 12:27:22.168933 21213 net.cpp:380] pool2 -> pool2
I0401 12:27:22.168958 21213 net.cpp:122] Setting up pool2
I0401 12:27:22.168962 21213 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 12:27:22.168964 21213 net.cpp:137] Memory required for data: 869840896
I0401 12:27:22.168967 21213 layer_factory.hpp:77] Creating layer conv3
I0401 12:27:22.168975 21213 net.cpp:84] Creating Layer conv3
I0401 12:27:22.168977 21213 net.cpp:406] conv3 <- pool2
I0401 12:27:22.168980 21213 net.cpp:380] conv3 -> conv3
I0401 12:27:22.178553 21213 net.cpp:122] Setting up conv3
I0401 12:27:22.178572 21213 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 12:27:22.178575 21213 net.cpp:137] Memory required for data: 903067648
I0401 12:27:22.178587 21213 layer_factory.hpp:77] Creating layer relu3
I0401 12:27:22.178594 21213 net.cpp:84] Creating Layer relu3
I0401 12:27:22.178597 21213 net.cpp:406] relu3 <- conv3
I0401 12:27:22.178603 21213 net.cpp:367] relu3 -> conv3 (in-place)
I0401 12:27:22.178992 21213 net.cpp:122] Setting up relu3
I0401 12:27:22.179001 21213 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 12:27:22.179003 21213 net.cpp:137] Memory required for data: 936294400
I0401 12:27:22.179005 21213 layer_factory.hpp:77] Creating layer conv4
I0401 12:27:22.179015 21213 net.cpp:84] Creating Layer conv4
I0401 12:27:22.179018 21213 net.cpp:406] conv4 <- conv3
I0401 12:27:22.179023 21213 net.cpp:380] conv4 -> conv4
I0401 12:27:22.188249 21213 net.cpp:122] Setting up conv4
I0401 12:27:22.188271 21213 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 12:27:22.188272 21213 net.cpp:137] Memory required for data: 969521152
I0401 12:27:22.188282 21213 layer_factory.hpp:77] Creating layer relu4
I0401 12:27:22.188289 21213 net.cpp:84] Creating Layer relu4
I0401 12:27:22.188310 21213 net.cpp:406] relu4 <- conv4
I0401 12:27:22.188315 21213 net.cpp:367] relu4 -> conv4 (in-place)
I0401 12:27:22.188622 21213 net.cpp:122] Setting up relu4
I0401 12:27:22.188629 21213 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 12:27:22.188632 21213 net.cpp:137] Memory required for data: 1002747904
I0401 12:27:22.188634 21213 layer_factory.hpp:77] Creating layer conv5
I0401 12:27:22.188644 21213 net.cpp:84] Creating Layer conv5
I0401 12:27:22.188647 21213 net.cpp:406] conv5 <- conv4
I0401 12:27:22.188652 21213 net.cpp:380] conv5 -> conv5
I0401 12:27:22.195936 21213 net.cpp:122] Setting up conv5
I0401 12:27:22.195957 21213 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 12:27:22.195960 21213 net.cpp:137] Memory required for data: 1024899072
I0401 12:27:22.195973 21213 layer_factory.hpp:77] Creating layer relu5
I0401 12:27:22.195981 21213 net.cpp:84] Creating Layer relu5
I0401 12:27:22.195984 21213 net.cpp:406] relu5 <- conv5
I0401 12:27:22.195989 21213 net.cpp:367] relu5 -> conv5 (in-place)
I0401 12:27:22.196437 21213 net.cpp:122] Setting up relu5
I0401 12:27:22.196445 21213 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 12:27:22.196447 21213 net.cpp:137] Memory required for data: 1047050240
I0401 12:27:22.196449 21213 layer_factory.hpp:77] Creating layer pool5
I0401 12:27:22.196456 21213 net.cpp:84] Creating Layer pool5
I0401 12:27:22.196458 21213 net.cpp:406] pool5 <- conv5
I0401 12:27:22.196463 21213 net.cpp:380] pool5 -> pool5
I0401 12:27:22.196496 21213 net.cpp:122] Setting up pool5
I0401 12:27:22.196501 21213 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0401 12:27:22.196502 21213 net.cpp:137] Memory required for data: 1051768832
I0401 12:27:22.196504 21213 layer_factory.hpp:77] Creating layer fc6
I0401 12:27:22.196514 21213 net.cpp:84] Creating Layer fc6
I0401 12:27:22.196516 21213 net.cpp:406] fc6 <- pool5
I0401 12:27:22.196521 21213 net.cpp:380] fc6 -> fc6
I0401 12:27:22.527060 21213 net.cpp:122] Setting up fc6
I0401 12:27:22.527078 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.527081 21213 net.cpp:137] Memory required for data: 1053865984
I0401 12:27:22.527088 21213 layer_factory.hpp:77] Creating layer relu6
I0401 12:27:22.527096 21213 net.cpp:84] Creating Layer relu6
I0401 12:27:22.527098 21213 net.cpp:406] relu6 <- fc6
I0401 12:27:22.527104 21213 net.cpp:367] relu6 -> fc6 (in-place)
I0401 12:27:22.530951 21213 net.cpp:122] Setting up relu6
I0401 12:27:22.530963 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.530966 21213 net.cpp:137] Memory required for data: 1055963136
I0401 12:27:22.530969 21213 layer_factory.hpp:77] Creating layer drop6
I0401 12:27:22.530977 21213 net.cpp:84] Creating Layer drop6
I0401 12:27:22.530980 21213 net.cpp:406] drop6 <- fc6
I0401 12:27:22.530984 21213 net.cpp:367] drop6 -> fc6 (in-place)
I0401 12:27:22.531013 21213 net.cpp:122] Setting up drop6
I0401 12:27:22.531018 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.531020 21213 net.cpp:137] Memory required for data: 1058060288
I0401 12:27:22.531023 21213 layer_factory.hpp:77] Creating layer fc7
I0401 12:27:22.531028 21213 net.cpp:84] Creating Layer fc7
I0401 12:27:22.531030 21213 net.cpp:406] fc7 <- fc6
I0401 12:27:22.531034 21213 net.cpp:380] fc7 -> fc7
I0401 12:27:22.685163 21213 net.cpp:122] Setting up fc7
I0401 12:27:22.685181 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.685184 21213 net.cpp:137] Memory required for data: 1060157440
I0401 12:27:22.685191 21213 layer_factory.hpp:77] Creating layer relu7
I0401 12:27:22.685200 21213 net.cpp:84] Creating Layer relu7
I0401 12:27:22.685204 21213 net.cpp:406] relu7 <- fc7
I0401 12:27:22.685209 21213 net.cpp:367] relu7 -> fc7 (in-place)
I0401 12:27:22.685562 21213 net.cpp:122] Setting up relu7
I0401 12:27:22.685570 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.685571 21213 net.cpp:137] Memory required for data: 1062254592
I0401 12:27:22.685575 21213 layer_factory.hpp:77] Creating layer drop7
I0401 12:27:22.685578 21213 net.cpp:84] Creating Layer drop7
I0401 12:27:22.685581 21213 net.cpp:406] drop7 <- fc7
I0401 12:27:22.685602 21213 net.cpp:367] drop7 -> fc7 (in-place)
I0401 12:27:22.685622 21213 net.cpp:122] Setting up drop7
I0401 12:27:22.685627 21213 net.cpp:129] Top shape: 128 4096 (524288)
I0401 12:27:22.685631 21213 net.cpp:137] Memory required for data: 1064351744
I0401 12:27:22.685632 21213 layer_factory.hpp:77] Creating layer fc8
I0401 12:27:22.685637 21213 net.cpp:84] Creating Layer fc8
I0401 12:27:22.685639 21213 net.cpp:406] fc8 <- fc7
I0401 12:27:22.685644 21213 net.cpp:380] fc8 -> fc8
I0401 12:27:22.692747 21213 net.cpp:122] Setting up fc8
I0401 12:27:22.692760 21213 net.cpp:129] Top shape: 128 196 (25088)
I0401 12:27:22.692762 21213 net.cpp:137] Memory required for data: 1064452096
I0401 12:27:22.692768 21213 layer_factory.hpp:77] Creating layer loss
I0401 12:27:22.692775 21213 net.cpp:84] Creating Layer loss
I0401 12:27:22.692778 21213 net.cpp:406] loss <- fc8
I0401 12:27:22.692782 21213 net.cpp:406] loss <- label
I0401 12:27:22.692788 21213 net.cpp:380] loss -> loss
I0401 12:27:22.692796 21213 layer_factory.hpp:77] Creating layer loss
I0401 12:27:22.694321 21213 net.cpp:122] Setting up loss
I0401 12:27:22.694330 21213 net.cpp:129] Top shape: (1)
I0401 12:27:22.694332 21213 net.cpp:132]     with loss weight 1
I0401 12:27:22.694347 21213 net.cpp:137] Memory required for data: 1064452100
I0401 12:27:22.694350 21213 net.cpp:198] loss needs backward computation.
I0401 12:27:22.694355 21213 net.cpp:198] fc8 needs backward computation.
I0401 12:27:22.694358 21213 net.cpp:198] drop7 needs backward computation.
I0401 12:27:22.694360 21213 net.cpp:198] relu7 needs backward computation.
I0401 12:27:22.694362 21213 net.cpp:198] fc7 needs backward computation.
I0401 12:27:22.694365 21213 net.cpp:198] drop6 needs backward computation.
I0401 12:27:22.694366 21213 net.cpp:198] relu6 needs backward computation.
I0401 12:27:22.694370 21213 net.cpp:198] fc6 needs backward computation.
I0401 12:27:22.694371 21213 net.cpp:198] pool5 needs backward computation.
I0401 12:27:22.694375 21213 net.cpp:198] relu5 needs backward computation.
I0401 12:27:22.694376 21213 net.cpp:198] conv5 needs backward computation.
I0401 12:27:22.694380 21213 net.cpp:198] relu4 needs backward computation.
I0401 12:27:22.694381 21213 net.cpp:198] conv4 needs backward computation.
I0401 12:27:22.694383 21213 net.cpp:198] relu3 needs backward computation.
I0401 12:27:22.694386 21213 net.cpp:198] conv3 needs backward computation.
I0401 12:27:22.694388 21213 net.cpp:198] pool2 needs backward computation.
I0401 12:27:22.694391 21213 net.cpp:198] norm2 needs backward computation.
I0401 12:27:22.694394 21213 net.cpp:198] relu2 needs backward computation.
I0401 12:27:22.694396 21213 net.cpp:198] conv2 needs backward computation.
I0401 12:27:22.694399 21213 net.cpp:198] pool1 needs backward computation.
I0401 12:27:22.694401 21213 net.cpp:198] norm1 needs backward computation.
I0401 12:27:22.694403 21213 net.cpp:198] relu1 needs backward computation.
I0401 12:27:22.694406 21213 net.cpp:198] conv1 needs backward computation.
I0401 12:27:22.694408 21213 net.cpp:200] train-data does not need backward computation.
I0401 12:27:22.694411 21213 net.cpp:242] This network produces output loss
I0401 12:27:22.694422 21213 net.cpp:255] Network initialization done.
I0401 12:27:22.695024 21213 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0401 12:27:22.695057 21213 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0401 12:27:22.695204 21213 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-AIN-3/digits/jobs/20210401-115942-8d19/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/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"
}
I0401 12:27:22.695303 21213 layer_factory.hpp:77] Creating layer val-data
I0401 12:27:22.698077 21213 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/val_db
I0401 12:27:22.698302 21213 net.cpp:84] Creating Layer val-data
I0401 12:27:22.698310 21213 net.cpp:380] val-data -> data
I0401 12:27:22.698318 21213 net.cpp:380] val-data -> label
I0401 12:27:22.698324 21213 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115942-8d19/mean.binaryproto
I0401 12:27:22.701804 21213 data_layer.cpp:45] output data size: 32,3,227,227
I0401 12:27:22.733502 21213 net.cpp:122] Setting up val-data
I0401 12:27:22.733522 21213 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0401 12:27:22.733525 21213 net.cpp:129] Top shape: 32 (32)
I0401 12:27:22.733527 21213 net.cpp:137] Memory required for data: 19787264
I0401 12:27:22.733533 21213 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0401 12:27:22.733544 21213 net.cpp:84] Creating Layer label_val-data_1_split
I0401 12:27:22.733547 21213 net.cpp:406] label_val-data_1_split <- label
I0401 12:27:22.733553 21213 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0401 12:27:22.733561 21213 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0401 12:27:22.733649 21213 net.cpp:122] Setting up label_val-data_1_split
I0401 12:27:22.733655 21213 net.cpp:129] Top shape: 32 (32)
I0401 12:27:22.733657 21213 net.cpp:129] Top shape: 32 (32)
I0401 12:27:22.733659 21213 net.cpp:137] Memory required for data: 19787520
I0401 12:27:22.733661 21213 layer_factory.hpp:77] Creating layer conv1
I0401 12:27:22.733672 21213 net.cpp:84] Creating Layer conv1
I0401 12:27:22.733675 21213 net.cpp:406] conv1 <- data
I0401 12:27:22.733680 21213 net.cpp:380] conv1 -> conv1
I0401 12:27:22.735970 21213 net.cpp:122] Setting up conv1
I0401 12:27:22.735980 21213 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 12:27:22.735982 21213 net.cpp:137] Memory required for data: 56958720
I0401 12:27:22.735991 21213 layer_factory.hpp:77] Creating layer relu1
I0401 12:27:22.735997 21213 net.cpp:84] Creating Layer relu1
I0401 12:27:22.735999 21213 net.cpp:406] relu1 <- conv1
I0401 12:27:22.736003 21213 net.cpp:367] relu1 -> conv1 (in-place)
I0401 12:27:22.736260 21213 net.cpp:122] Setting up relu1
I0401 12:27:22.736268 21213 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 12:27:22.736269 21213 net.cpp:137] Memory required for data: 94129920
I0401 12:27:22.736271 21213 layer_factory.hpp:77] Creating layer norm1
I0401 12:27:22.736279 21213 net.cpp:84] Creating Layer norm1
I0401 12:27:22.736281 21213 net.cpp:406] norm1 <- conv1
I0401 12:27:22.736285 21213 net.cpp:380] norm1 -> norm1
I0401 12:27:22.736701 21213 net.cpp:122] Setting up norm1
I0401 12:27:22.736711 21213 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 12:27:22.736712 21213 net.cpp:137] Memory required for data: 131301120
I0401 12:27:22.736714 21213 layer_factory.hpp:77] Creating layer pool1
I0401 12:27:22.736721 21213 net.cpp:84] Creating Layer pool1
I0401 12:27:22.736722 21213 net.cpp:406] pool1 <- norm1
I0401 12:27:22.736726 21213 net.cpp:380] pool1 -> pool1
I0401 12:27:22.736750 21213 net.cpp:122] Setting up pool1
I0401 12:27:22.736754 21213 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0401 12:27:22.736757 21213 net.cpp:137] Memory required for data: 140259072
I0401 12:27:22.736758 21213 layer_factory.hpp:77] Creating layer conv2
I0401 12:27:22.736766 21213 net.cpp:84] Creating Layer conv2
I0401 12:27:22.736768 21213 net.cpp:406] conv2 <- pool1
I0401 12:27:22.736789 21213 net.cpp:380] conv2 -> conv2
I0401 12:27:22.742633 21213 net.cpp:122] Setting up conv2
I0401 12:27:22.742650 21213 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 12:27:22.742653 21213 net.cpp:137] Memory required for data: 164146944
I0401 12:27:22.742664 21213 layer_factory.hpp:77] Creating layer relu2
I0401 12:27:22.742671 21213 net.cpp:84] Creating Layer relu2
I0401 12:27:22.742674 21213 net.cpp:406] relu2 <- conv2
I0401 12:27:22.742681 21213 net.cpp:367] relu2 -> conv2 (in-place)
I0401 12:27:22.743142 21213 net.cpp:122] Setting up relu2
I0401 12:27:22.743152 21213 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 12:27:22.743155 21213 net.cpp:137] Memory required for data: 188034816
I0401 12:27:22.743157 21213 layer_factory.hpp:77] Creating layer norm2
I0401 12:27:22.743165 21213 net.cpp:84] Creating Layer norm2
I0401 12:27:22.743168 21213 net.cpp:406] norm2 <- conv2
I0401 12:27:22.743172 21213 net.cpp:380] norm2 -> norm2
I0401 12:27:22.743686 21213 net.cpp:122] Setting up norm2
I0401 12:27:22.743695 21213 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 12:27:22.743697 21213 net.cpp:137] Memory required for data: 211922688
I0401 12:27:22.743700 21213 layer_factory.hpp:77] Creating layer pool2
I0401 12:27:22.743706 21213 net.cpp:84] Creating Layer pool2
I0401 12:27:22.743708 21213 net.cpp:406] pool2 <- norm2
I0401 12:27:22.743712 21213 net.cpp:380] pool2 -> pool2
I0401 12:27:22.743739 21213 net.cpp:122] Setting up pool2
I0401 12:27:22.743743 21213 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 12:27:22.743746 21213 net.cpp:137] Memory required for data: 217460480
I0401 12:27:22.743747 21213 layer_factory.hpp:77] Creating layer conv3
I0401 12:27:22.743757 21213 net.cpp:84] Creating Layer conv3
I0401 12:27:22.743758 21213 net.cpp:406] conv3 <- pool2
I0401 12:27:22.743763 21213 net.cpp:380] conv3 -> conv3
I0401 12:27:22.759737 21213 net.cpp:122] Setting up conv3
I0401 12:27:22.759765 21213 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 12:27:22.759770 21213 net.cpp:137] Memory required for data: 225767168
I0401 12:27:22.759783 21213 layer_factory.hpp:77] Creating layer relu3
I0401 12:27:22.759791 21213 net.cpp:84] Creating Layer relu3
I0401 12:27:22.759794 21213 net.cpp:406] relu3 <- conv3
I0401 12:27:22.759800 21213 net.cpp:367] relu3 -> conv3 (in-place)
I0401 12:27:22.760282 21213 net.cpp:122] Setting up relu3
I0401 12:27:22.760291 21213 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 12:27:22.760293 21213 net.cpp:137] Memory required for data: 234073856
I0401 12:27:22.760296 21213 layer_factory.hpp:77] Creating layer conv4
I0401 12:27:22.760306 21213 net.cpp:84] Creating Layer conv4
I0401 12:27:22.760309 21213 net.cpp:406] conv4 <- conv3
I0401 12:27:22.760314 21213 net.cpp:380] conv4 -> conv4
I0401 12:27:22.769160 21213 net.cpp:122] Setting up conv4
I0401 12:27:22.769179 21213 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 12:27:22.769181 21213 net.cpp:137] Memory required for data: 242380544
I0401 12:27:22.769189 21213 layer_factory.hpp:77] Creating layer relu4
I0401 12:27:22.769196 21213 net.cpp:84] Creating Layer relu4
I0401 12:27:22.769201 21213 net.cpp:406] relu4 <- conv4
I0401 12:27:22.769207 21213 net.cpp:367] relu4 -> conv4 (in-place)
I0401 12:27:22.769518 21213 net.cpp:122] Setting up relu4
I0401 12:27:22.769526 21213 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 12:27:22.769527 21213 net.cpp:137] Memory required for data: 250687232
I0401 12:27:22.769529 21213 layer_factory.hpp:77] Creating layer conv5
I0401 12:27:22.769539 21213 net.cpp:84] Creating Layer conv5
I0401 12:27:22.769542 21213 net.cpp:406] conv5 <- conv4
I0401 12:27:22.769547 21213 net.cpp:380] conv5 -> conv5
I0401 12:27:22.777218 21213 net.cpp:122] Setting up conv5
I0401 12:27:22.777237 21213 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 12:27:22.777240 21213 net.cpp:137] Memory required for data: 256225024
I0401 12:27:22.777251 21213 layer_factory.hpp:77] Creating layer relu5
I0401 12:27:22.777258 21213 net.cpp:84] Creating Layer relu5
I0401 12:27:22.777262 21213 net.cpp:406] relu5 <- conv5
I0401 12:27:22.777287 21213 net.cpp:367] relu5 -> conv5 (in-place)
I0401 12:27:22.777755 21213 net.cpp:122] Setting up relu5
I0401 12:27:22.777763 21213 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 12:27:22.777765 21213 net.cpp:137] Memory required for data: 261762816
I0401 12:27:22.777767 21213 layer_factory.hpp:77] Creating layer pool5
I0401 12:27:22.777776 21213 net.cpp:84] Creating Layer pool5
I0401 12:27:22.777779 21213 net.cpp:406] pool5 <- conv5
I0401 12:27:22.777783 21213 net.cpp:380] pool5 -> pool5
I0401 12:27:22.777818 21213 net.cpp:122] Setting up pool5
I0401 12:27:22.777822 21213 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0401 12:27:22.777825 21213 net.cpp:137] Memory required for data: 262942464
I0401 12:27:22.777827 21213 layer_factory.hpp:77] Creating layer fc6
I0401 12:27:22.777833 21213 net.cpp:84] Creating Layer fc6
I0401 12:27:22.777835 21213 net.cpp:406] fc6 <- pool5
I0401 12:27:22.777839 21213 net.cpp:380] fc6 -> fc6
I0401 12:27:23.115705 21213 net.cpp:122] Setting up fc6
I0401 12:27:23.115723 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.115726 21213 net.cpp:137] Memory required for data: 263466752
I0401 12:27:23.115734 21213 layer_factory.hpp:77] Creating layer relu6
I0401 12:27:23.115743 21213 net.cpp:84] Creating Layer relu6
I0401 12:27:23.115747 21213 net.cpp:406] relu6 <- fc6
I0401 12:27:23.115751 21213 net.cpp:367] relu6 -> fc6 (in-place)
I0401 12:27:23.118427 21213 net.cpp:122] Setting up relu6
I0401 12:27:23.118438 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.118441 21213 net.cpp:137] Memory required for data: 263991040
I0401 12:27:23.118445 21213 layer_factory.hpp:77] Creating layer drop6
I0401 12:27:23.118456 21213 net.cpp:84] Creating Layer drop6
I0401 12:27:23.118460 21213 net.cpp:406] drop6 <- fc6
I0401 12:27:23.118464 21213 net.cpp:367] drop6 -> fc6 (in-place)
I0401 12:27:23.118487 21213 net.cpp:122] Setting up drop6
I0401 12:27:23.118492 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.118494 21213 net.cpp:137] Memory required for data: 264515328
I0401 12:27:23.118496 21213 layer_factory.hpp:77] Creating layer fc7
I0401 12:27:23.118503 21213 net.cpp:84] Creating Layer fc7
I0401 12:27:23.118505 21213 net.cpp:406] fc7 <- fc6
I0401 12:27:23.118508 21213 net.cpp:380] fc7 -> fc7
I0401 12:27:23.265069 21213 net.cpp:122] Setting up fc7
I0401 12:27:23.265089 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.265092 21213 net.cpp:137] Memory required for data: 265039616
I0401 12:27:23.265100 21213 layer_factory.hpp:77] Creating layer relu7
I0401 12:27:23.265110 21213 net.cpp:84] Creating Layer relu7
I0401 12:27:23.265115 21213 net.cpp:406] relu7 <- fc7
I0401 12:27:23.265120 21213 net.cpp:367] relu7 -> fc7 (in-place)
I0401 12:27:23.265519 21213 net.cpp:122] Setting up relu7
I0401 12:27:23.265530 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.265532 21213 net.cpp:137] Memory required for data: 265563904
I0401 12:27:23.265534 21213 layer_factory.hpp:77] Creating layer drop7
I0401 12:27:23.265540 21213 net.cpp:84] Creating Layer drop7
I0401 12:27:23.265542 21213 net.cpp:406] drop7 <- fc7
I0401 12:27:23.265547 21213 net.cpp:367] drop7 -> fc7 (in-place)
I0401 12:27:23.265571 21213 net.cpp:122] Setting up drop7
I0401 12:27:23.265574 21213 net.cpp:129] Top shape: 32 4096 (131072)
I0401 12:27:23.265576 21213 net.cpp:137] Memory required for data: 266088192
I0401 12:27:23.265578 21213 layer_factory.hpp:77] Creating layer fc8
I0401 12:27:23.265586 21213 net.cpp:84] Creating Layer fc8
I0401 12:27:23.265588 21213 net.cpp:406] fc8 <- fc7
I0401 12:27:23.265591 21213 net.cpp:380] fc8 -> fc8
I0401 12:27:23.272809 21213 net.cpp:122] Setting up fc8
I0401 12:27:23.272820 21213 net.cpp:129] Top shape: 32 196 (6272)
I0401 12:27:23.272822 21213 net.cpp:137] Memory required for data: 266113280
I0401 12:27:23.272828 21213 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0401 12:27:23.272835 21213 net.cpp:84] Creating Layer fc8_fc8_0_split
I0401 12:27:23.272836 21213 net.cpp:406] fc8_fc8_0_split <- fc8
I0401 12:27:23.272859 21213 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0401 12:27:23.272866 21213 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0401 12:27:23.272904 21213 net.cpp:122] Setting up fc8_fc8_0_split
I0401 12:27:23.272909 21213 net.cpp:129] Top shape: 32 196 (6272)
I0401 12:27:23.272912 21213 net.cpp:129] Top shape: 32 196 (6272)
I0401 12:27:23.272913 21213 net.cpp:137] Memory required for data: 266163456
I0401 12:27:23.272915 21213 layer_factory.hpp:77] Creating layer accuracy
I0401 12:27:23.272922 21213 net.cpp:84] Creating Layer accuracy
I0401 12:27:23.272923 21213 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0401 12:27:23.272927 21213 net.cpp:406] accuracy <- label_val-data_1_split_0
I0401 12:27:23.272930 21213 net.cpp:380] accuracy -> accuracy
I0401 12:27:23.272936 21213 net.cpp:122] Setting up accuracy
I0401 12:27:23.272939 21213 net.cpp:129] Top shape: (1)
I0401 12:27:23.272941 21213 net.cpp:137] Memory required for data: 266163460
I0401 12:27:23.272943 21213 layer_factory.hpp:77] Creating layer loss
I0401 12:27:23.272948 21213 net.cpp:84] Creating Layer loss
I0401 12:27:23.272949 21213 net.cpp:406] loss <- fc8_fc8_0_split_1
I0401 12:27:23.272953 21213 net.cpp:406] loss <- label_val-data_1_split_1
I0401 12:27:23.272958 21213 net.cpp:380] loss -> loss
I0401 12:27:23.272962 21213 layer_factory.hpp:77] Creating layer loss
I0401 12:27:23.273552 21213 net.cpp:122] Setting up loss
I0401 12:27:23.273561 21213 net.cpp:129] Top shape: (1)
I0401 12:27:23.273561 21213 net.cpp:132]     with loss weight 1
I0401 12:27:23.273571 21213 net.cpp:137] Memory required for data: 266163464
I0401 12:27:23.273573 21213 net.cpp:198] loss needs backward computation.
I0401 12:27:23.273576 21213 net.cpp:200] accuracy does not need backward computation.
I0401 12:27:23.273579 21213 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0401 12:27:23.273581 21213 net.cpp:198] fc8 needs backward computation.
I0401 12:27:23.273584 21213 net.cpp:198] drop7 needs backward computation.
I0401 12:27:23.273586 21213 net.cpp:198] relu7 needs backward computation.
I0401 12:27:23.273588 21213 net.cpp:198] fc7 needs backward computation.
I0401 12:27:23.273591 21213 net.cpp:198] drop6 needs backward computation.
I0401 12:27:23.273592 21213 net.cpp:198] relu6 needs backward computation.
I0401 12:27:23.273594 21213 net.cpp:198] fc6 needs backward computation.
I0401 12:27:23.273597 21213 net.cpp:198] pool5 needs backward computation.
I0401 12:27:23.273599 21213 net.cpp:198] relu5 needs backward computation.
I0401 12:27:23.273602 21213 net.cpp:198] conv5 needs backward computation.
I0401 12:27:23.273604 21213 net.cpp:198] relu4 needs backward computation.
I0401 12:27:23.273607 21213 net.cpp:198] conv4 needs backward computation.
I0401 12:27:23.273608 21213 net.cpp:198] relu3 needs backward computation.
I0401 12:27:23.273612 21213 net.cpp:198] conv3 needs backward computation.
I0401 12:27:23.273613 21213 net.cpp:198] pool2 needs backward computation.
I0401 12:27:23.273615 21213 net.cpp:198] norm2 needs backward computation.
I0401 12:27:23.273617 21213 net.cpp:198] relu2 needs backward computation.
I0401 12:27:23.273619 21213 net.cpp:198] conv2 needs backward computation.
I0401 12:27:23.273622 21213 net.cpp:198] pool1 needs backward computation.
I0401 12:27:23.273624 21213 net.cpp:198] norm1 needs backward computation.
I0401 12:27:23.273627 21213 net.cpp:198] relu1 needs backward computation.
I0401 12:27:23.273628 21213 net.cpp:198] conv1 needs backward computation.
I0401 12:27:23.273631 21213 net.cpp:200] label_val-data_1_split does not need backward computation.
I0401 12:27:23.273634 21213 net.cpp:200] val-data does not need backward computation.
I0401 12:27:23.273636 21213 net.cpp:242] This network produces output accuracy
I0401 12:27:23.273638 21213 net.cpp:242] This network produces output loss
I0401 12:27:23.273653 21213 net.cpp:255] Network initialization done.
I0401 12:27:23.273733 21213 solver.cpp:56] Solver scaffolding done.
I0401 12:27:23.274130 21213 caffe.cpp:248] Starting Optimization
I0401 12:27:23.274137 21213 solver.cpp:272] Solving
I0401 12:27:23.274148 21213 solver.cpp:273] Learning Rate Policy: fixed
I0401 12:27:23.275750 21213 solver.cpp:330] Iteration 0, Testing net (#0)
I0401 12:27:23.275759 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:27:23.376487 21213 blocking_queue.cpp:49] Waiting for data
I0401 12:27:25.268182 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:27:25.336462 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00600962
I0401 12:27:25.336500 21213 solver.cpp:397]     Test net output #1: loss = 5.28512 (* 1 = 5.28512 loss)
I0401 12:27:25.476027 21213 solver.cpp:218] Iteration 0 (0 iter/s, 2.20181s/8 iters), loss = 5.27608
I0401 12:27:25.477560 21213 solver.cpp:237]     Train net output #0: loss = 5.27608 (* 1 = 5.27608 loss)
I0401 12:27:25.477571 21213 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0401 12:27:27.768056 21213 solver.cpp:218] Iteration 8 (3.49274 iter/s, 2.29047s/8 iters), loss = 5.2806
I0401 12:27:27.768091 21213 solver.cpp:237]     Train net output #0: loss = 5.2806 (* 1 = 5.2806 loss)
I0401 12:27:27.768097 21213 sgd_solver.cpp:105] Iteration 8, lr = 0.001
I0401 12:27:31.185889 21213 solver.cpp:218] Iteration 16 (2.34072 iter/s, 3.41776s/8 iters), loss = 5.28574
I0401 12:27:31.185933 21213 solver.cpp:237]     Train net output #0: loss = 5.28574 (* 1 = 5.28574 loss)
I0401 12:27:31.185940 21213 sgd_solver.cpp:105] Iteration 16, lr = 0.001
I0401 12:27:34.501044 21213 solver.cpp:218] Iteration 24 (2.41322 iter/s, 3.31507s/8 iters), loss = 5.28042
I0401 12:27:34.501078 21213 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0401 12:27:34.501085 21213 sgd_solver.cpp:105] Iteration 24, lr = 0.001
I0401 12:27:38.037799 21213 solver.cpp:218] Iteration 32 (2.26201 iter/s, 3.53668s/8 iters), loss = 5.28889
I0401 12:27:38.037838 21213 solver.cpp:237]     Train net output #0: loss = 5.28889 (* 1 = 5.28889 loss)
I0401 12:27:38.037843 21213 sgd_solver.cpp:105] Iteration 32, lr = 0.001
I0401 12:27:41.528733 21213 solver.cpp:218] Iteration 40 (2.29171 iter/s, 3.49084s/8 iters), loss = 5.27465
I0401 12:27:41.528789 21213 solver.cpp:237]     Train net output #0: loss = 5.27465 (* 1 = 5.27465 loss)
I0401 12:27:41.528797 21213 sgd_solver.cpp:105] Iteration 40, lr = 0.001
I0401 12:27:44.910491 21213 solver.cpp:218] Iteration 48 (2.36571 iter/s, 3.38165s/8 iters), loss = 5.28497
I0401 12:27:44.910533 21213 solver.cpp:237]     Train net output #0: loss = 5.28497 (* 1 = 5.28497 loss)
I0401 12:27:44.910539 21213 sgd_solver.cpp:105] Iteration 48, lr = 0.001
I0401 12:27:48.352802 21213 solver.cpp:218] Iteration 56 (2.32408 iter/s, 3.44222s/8 iters), loss = 5.28973
I0401 12:27:48.352835 21213 solver.cpp:237]     Train net output #0: loss = 5.28973 (* 1 = 5.28973 loss)
I0401 12:27:48.352840 21213 sgd_solver.cpp:105] Iteration 56, lr = 0.001
I0401 12:27:50.938823 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:27:51.218739 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_64.caffemodel
I0401 12:27:54.261943 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_64.solverstate
I0401 12:27:56.551277 21213 solver.cpp:330] Iteration 64, Testing net (#0)
I0401 12:27:56.551297 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:27:58.571405 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:27:58.708525 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00240385
I0401 12:27:58.708575 21213 solver.cpp:397]     Test net output #1: loss = 5.28255 (* 1 = 5.28255 loss)
I0401 12:27:58.849771 21213 solver.cpp:218] Iteration 64 (0.762138 iter/s, 10.4968s/8 iters), loss = 5.27622
I0401 12:27:58.849817 21213 solver.cpp:237]     Train net output #0: loss = 5.27622 (* 1 = 5.27622 loss)
I0401 12:27:58.849822 21213 sgd_solver.cpp:105] Iteration 64, lr = 0.001
I0401 12:28:01.468909 21213 solver.cpp:218] Iteration 72 (3.05456 iter/s, 2.61903s/8 iters), loss = 5.28712
I0401 12:28:01.468950 21213 solver.cpp:237]     Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
I0401 12:28:01.468955 21213 sgd_solver.cpp:105] Iteration 72, lr = 0.001
I0401 12:28:04.954444 21213 solver.cpp:218] Iteration 80 (2.29527 iter/s, 3.48543s/8 iters), loss = 5.26748
I0401 12:28:04.954488 21213 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0401 12:28:04.954494 21213 sgd_solver.cpp:105] Iteration 80, lr = 0.001
I0401 12:28:08.372404 21213 solver.cpp:218] Iteration 88 (2.34065 iter/s, 3.41785s/8 iters), loss = 5.2693
I0401 12:28:08.372447 21213 solver.cpp:237]     Train net output #0: loss = 5.2693 (* 1 = 5.2693 loss)
I0401 12:28:08.372453 21213 sgd_solver.cpp:105] Iteration 88, lr = 0.001
I0401 12:28:11.986400 21213 solver.cpp:218] Iteration 96 (2.21369 iter/s, 3.61388s/8 iters), loss = 5.27579
I0401 12:28:11.986451 21213 solver.cpp:237]     Train net output #0: loss = 5.27579 (* 1 = 5.27579 loss)
I0401 12:28:11.986459 21213 sgd_solver.cpp:105] Iteration 96, lr = 0.001
I0401 12:28:15.284150 21213 solver.cpp:218] Iteration 104 (2.42598 iter/s, 3.29763s/8 iters), loss = 5.27415
I0401 12:28:15.284193 21213 solver.cpp:237]     Train net output #0: loss = 5.27415 (* 1 = 5.27415 loss)
I0401 12:28:15.284198 21213 sgd_solver.cpp:105] Iteration 104, lr = 0.001
I0401 12:28:18.638535 21213 solver.cpp:218] Iteration 112 (2.38501 iter/s, 3.35428s/8 iters), loss = 5.26705
I0401 12:28:18.638586 21213 solver.cpp:237]     Train net output #0: loss = 5.26705 (* 1 = 5.26705 loss)
I0401 12:28:18.638594 21213 sgd_solver.cpp:105] Iteration 112, lr = 0.001
I0401 12:28:22.120788 21213 solver.cpp:218] Iteration 120 (2.29744 iter/s, 3.48214s/8 iters), loss = 5.26054
I0401 12:28:22.120905 21213 solver.cpp:237]     Train net output #0: loss = 5.26054 (* 1 = 5.26054 loss)
I0401 12:28:22.120913 21213 sgd_solver.cpp:105] Iteration 120, lr = 0.001
I0401 12:28:24.377517 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:28:24.963091 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_128.caffemodel
I0401 12:28:27.998407 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_128.solverstate
I0401 12:28:30.298678 21213 solver.cpp:330] Iteration 128, Testing net (#0)
I0401 12:28:30.298699 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:28:32.159179 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:28:32.361819 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0192308
I0401 12:28:32.361856 21213 solver.cpp:397]     Test net output #1: loss = 5.27913 (* 1 = 5.27913 loss)
I0401 12:28:32.488947 21213 solver.cpp:218] Iteration 128 (0.771607 iter/s, 10.368s/8 iters), loss = 5.2736
I0401 12:28:32.489008 21213 solver.cpp:237]     Train net output #0: loss = 5.2736 (* 1 = 5.2736 loss)
I0401 12:28:32.489019 21213 sgd_solver.cpp:105] Iteration 128, lr = 0.001
I0401 12:28:35.036929 21213 solver.cpp:218] Iteration 136 (3.13981 iter/s, 2.54793s/8 iters), loss = 5.27585
I0401 12:28:35.036969 21213 solver.cpp:237]     Train net output #0: loss = 5.27585 (* 1 = 5.27585 loss)
I0401 12:28:35.036974 21213 sgd_solver.cpp:105] Iteration 136, lr = 0.001
I0401 12:28:38.497295 21213 solver.cpp:218] Iteration 144 (2.31191 iter/s, 3.46034s/8 iters), loss = 5.27308
I0401 12:28:38.497336 21213 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0401 12:28:38.497341 21213 sgd_solver.cpp:105] Iteration 144, lr = 0.001
I0401 12:28:41.803328 21213 solver.cpp:218] Iteration 152 (2.41984 iter/s, 3.306s/8 iters), loss = 5.28457
I0401 12:28:41.803369 21213 solver.cpp:237]     Train net output #0: loss = 5.28457 (* 1 = 5.28457 loss)
I0401 12:28:41.803375 21213 sgd_solver.cpp:105] Iteration 152, lr = 0.001
I0401 12:28:45.321149 21213 solver.cpp:218] Iteration 160 (2.27415 iter/s, 3.51779s/8 iters), loss = 5.28647
I0401 12:28:45.321202 21213 solver.cpp:237]     Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
I0401 12:28:45.321213 21213 sgd_solver.cpp:105] Iteration 160, lr = 0.001
I0401 12:28:48.806425 21213 solver.cpp:218] Iteration 168 (2.29539 iter/s, 3.48524s/8 iters), loss = 5.28166
I0401 12:28:48.806476 21213 solver.cpp:237]     Train net output #0: loss = 5.28166 (* 1 = 5.28166 loss)
I0401 12:28:48.806484 21213 sgd_solver.cpp:105] Iteration 168, lr = 0.001
I0401 12:28:52.286312 21213 solver.cpp:218] Iteration 176 (2.29895 iter/s, 3.47985s/8 iters), loss = 5.29387
I0401 12:28:52.286455 21213 solver.cpp:237]     Train net output #0: loss = 5.29387 (* 1 = 5.29387 loss)
I0401 12:28:52.286463 21213 sgd_solver.cpp:105] Iteration 176, lr = 0.001
I0401 12:28:55.832908 21213 solver.cpp:218] Iteration 184 (2.25576 iter/s, 3.54647s/8 iters), loss = 5.29643
I0401 12:28:55.832953 21213 solver.cpp:237]     Train net output #0: loss = 5.29643 (* 1 = 5.29643 loss)
I0401 12:28:55.832960 21213 sgd_solver.cpp:105] Iteration 184, lr = 0.001
I0401 12:28:57.893313 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:28:58.991909 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_192.caffemodel
I0401 12:29:02.028002 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_192.solverstate
I0401 12:29:04.349362 21213 solver.cpp:330] Iteration 192, Testing net (#0)
I0401 12:29:04.349382 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:29:06.234620 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:29:06.494205 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0168269
I0401 12:29:06.494232 21213 solver.cpp:397]     Test net output #1: loss = 5.28065 (* 1 = 5.28065 loss)
I0401 12:29:06.631927 21213 solver.cpp:218] Iteration 192 (0.740806 iter/s, 10.799s/8 iters), loss = 5.27616
I0401 12:29:06.631973 21213 solver.cpp:237]     Train net output #0: loss = 5.27616 (* 1 = 5.27616 loss)
I0401 12:29:06.631978 21213 sgd_solver.cpp:105] Iteration 192, lr = 0.001
I0401 12:29:09.253008 21213 solver.cpp:218] Iteration 200 (3.05222 iter/s, 2.62104s/8 iters), loss = 5.29166
I0401 12:29:09.253049 21213 solver.cpp:237]     Train net output #0: loss = 5.29166 (* 1 = 5.29166 loss)
I0401 12:29:09.253055 21213 sgd_solver.cpp:105] Iteration 200, lr = 0.001
I0401 12:29:12.702667 21213 solver.cpp:218] Iteration 208 (2.31909 iter/s, 3.44963s/8 iters), loss = 5.26718
I0401 12:29:12.702709 21213 solver.cpp:237]     Train net output #0: loss = 5.26718 (* 1 = 5.26718 loss)
I0401 12:29:12.702714 21213 sgd_solver.cpp:105] Iteration 208, lr = 0.001
I0401 12:29:15.822729 21213 solver.cpp:218] Iteration 216 (2.56408 iter/s, 3.12003s/8 iters), loss = 5.30533
I0401 12:29:15.822770 21213 solver.cpp:237]     Train net output #0: loss = 5.30533 (* 1 = 5.30533 loss)
I0401 12:29:15.822778 21213 sgd_solver.cpp:105] Iteration 216, lr = 0.001
I0401 12:29:19.298044 21213 solver.cpp:218] Iteration 224 (2.30197 iter/s, 3.47528s/8 iters), loss = 5.28201
I0401 12:29:19.298090 21213 solver.cpp:237]     Train net output #0: loss = 5.28201 (* 1 = 5.28201 loss)
I0401 12:29:19.298096 21213 sgd_solver.cpp:105] Iteration 224, lr = 0.001
I0401 12:29:22.697268 21213 solver.cpp:218] Iteration 232 (2.35351 iter/s, 3.39918s/8 iters), loss = 5.28441
I0401 12:29:22.697388 21213 solver.cpp:237]     Train net output #0: loss = 5.28441 (* 1 = 5.28441 loss)
I0401 12:29:22.697396 21213 sgd_solver.cpp:105] Iteration 232, lr = 0.001
I0401 12:29:26.215299 21213 solver.cpp:218] Iteration 240 (2.27407 iter/s, 3.51791s/8 iters), loss = 5.27895
I0401 12:29:26.215354 21213 solver.cpp:237]     Train net output #0: loss = 5.27895 (* 1 = 5.27895 loss)
I0401 12:29:26.215363 21213 sgd_solver.cpp:105] Iteration 240, lr = 0.001
I0401 12:29:29.680433 21213 solver.cpp:218] Iteration 248 (2.30875 iter/s, 3.46508s/8 iters), loss = 5.26619
I0401 12:29:29.680486 21213 solver.cpp:237]     Train net output #0: loss = 5.26619 (* 1 = 5.26619 loss)
I0401 12:29:29.680495 21213 sgd_solver.cpp:105] Iteration 248, lr = 0.001
I0401 12:29:31.330983 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:29:32.742475 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_256.caffemodel
I0401 12:29:35.801684 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_256.solverstate
I0401 12:29:40.278615 21213 solver.cpp:330] Iteration 256, Testing net (#0)
I0401 12:29:40.278636 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:29:42.066797 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:29:42.413408 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00480769
I0401 12:29:42.413441 21213 solver.cpp:397]     Test net output #1: loss = 5.28057 (* 1 = 5.28057 loss)
I0401 12:29:42.548137 21213 solver.cpp:218] Iteration 256 (0.621711 iter/s, 12.8677s/8 iters), loss = 5.2759
I0401 12:29:42.549721 21213 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0401 12:29:42.549731 21213 sgd_solver.cpp:105] Iteration 256, lr = 0.001
I0401 12:29:45.103541 21213 solver.cpp:218] Iteration 264 (3.13257 iter/s, 2.55382s/8 iters), loss = 5.28488
I0401 12:29:45.103600 21213 solver.cpp:237]     Train net output #0: loss = 5.28488 (* 1 = 5.28488 loss)
I0401 12:29:45.103608 21213 sgd_solver.cpp:105] Iteration 264, lr = 0.001
I0401 12:29:48.543994 21213 solver.cpp:218] Iteration 272 (2.32531 iter/s, 3.4404s/8 iters), loss = 5.26163
I0401 12:29:48.544036 21213 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0401 12:29:48.544042 21213 sgd_solver.cpp:105] Iteration 272, lr = 0.001
I0401 12:29:51.877604 21213 solver.cpp:218] Iteration 280 (2.39983 iter/s, 3.33357s/8 iters), loss = 5.26926
I0401 12:29:51.877648 21213 solver.cpp:237]     Train net output #0: loss = 5.26926 (* 1 = 5.26926 loss)
I0401 12:29:51.877653 21213 sgd_solver.cpp:105] Iteration 280, lr = 0.001
I0401 12:29:55.331948 21213 solver.cpp:218] Iteration 288 (2.31596 iter/s, 3.45429s/8 iters), loss = 5.25599
I0401 12:29:55.332113 21213 solver.cpp:237]     Train net output #0: loss = 5.25599 (* 1 = 5.25599 loss)
I0401 12:29:55.332121 21213 sgd_solver.cpp:105] Iteration 288, lr = 0.001
I0401 12:29:58.862695 21213 solver.cpp:218] Iteration 296 (2.26591 iter/s, 3.53059s/8 iters), loss = 5.28545
I0401 12:29:58.862748 21213 solver.cpp:237]     Train net output #0: loss = 5.28545 (* 1 = 5.28545 loss)
I0401 12:29:58.862757 21213 sgd_solver.cpp:105] Iteration 296, lr = 0.001
I0401 12:30:02.369284 21213 solver.cpp:218] Iteration 304 (2.28145 iter/s, 3.50654s/8 iters), loss = 5.26678
I0401 12:30:02.369319 21213 solver.cpp:237]     Train net output #0: loss = 5.26678 (* 1 = 5.26678 loss)
I0401 12:30:02.369324 21213 sgd_solver.cpp:105] Iteration 304, lr = 0.001
I0401 12:30:05.875638 21213 solver.cpp:218] Iteration 312 (2.2816 iter/s, 3.50631s/8 iters), loss = 5.27357
I0401 12:30:05.875691 21213 solver.cpp:237]     Train net output #0: loss = 5.27357 (* 1 = 5.27357 loss)
I0401 12:30:05.875699 21213 sgd_solver.cpp:105] Iteration 312, lr = 0.001
I0401 12:30:07.228057 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:30:08.870586 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_320.caffemodel
I0401 12:30:11.889154 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_320.solverstate
I0401 12:30:14.190866 21213 solver.cpp:330] Iteration 320, Testing net (#0)
I0401 12:30:14.190886 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:30:16.059307 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:30:16.532694 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00480769
I0401 12:30:16.532724 21213 solver.cpp:397]     Test net output #1: loss = 5.28247 (* 1 = 5.28247 loss)
I0401 12:30:16.673763 21213 solver.cpp:218] Iteration 320 (0.740871 iter/s, 10.7981s/8 iters), loss = 5.27579
I0401 12:30:16.673810 21213 solver.cpp:237]     Train net output #0: loss = 5.27579 (* 1 = 5.27579 loss)
I0401 12:30:16.673815 21213 sgd_solver.cpp:105] Iteration 320, lr = 0.001
I0401 12:30:19.231972 21213 solver.cpp:218] Iteration 328 (3.12725 iter/s, 2.55816s/8 iters), loss = 5.26409
I0401 12:30:19.232007 21213 solver.cpp:237]     Train net output #0: loss = 5.26409 (* 1 = 5.26409 loss)
I0401 12:30:19.232012 21213 sgd_solver.cpp:105] Iteration 328, lr = 0.001
I0401 12:30:22.795316 21213 solver.cpp:218] Iteration 336 (2.24511 iter/s, 3.5633s/8 iters), loss = 5.29877
I0401 12:30:22.795367 21213 solver.cpp:237]     Train net output #0: loss = 5.29877 (* 1 = 5.29877 loss)
I0401 12:30:22.795377 21213 sgd_solver.cpp:105] Iteration 336, lr = 0.001
I0401 12:30:26.294752 21213 solver.cpp:218] Iteration 344 (2.28612 iter/s, 3.49938s/8 iters), loss = 5.26147
I0401 12:30:26.294904 21213 solver.cpp:237]     Train net output #0: loss = 5.26147 (* 1 = 5.26147 loss)
I0401 12:30:26.294910 21213 sgd_solver.cpp:105] Iteration 344, lr = 0.001
I0401 12:30:29.657090 21213 solver.cpp:218] Iteration 352 (2.37941 iter/s, 3.36218s/8 iters), loss = 5.25736
I0401 12:30:29.657130 21213 solver.cpp:237]     Train net output #0: loss = 5.25736 (* 1 = 5.25736 loss)
I0401 12:30:29.657135 21213 sgd_solver.cpp:105] Iteration 352, lr = 0.001
I0401 12:30:33.291473 21213 solver.cpp:218] Iteration 360 (2.20123 iter/s, 3.63433s/8 iters), loss = 5.29357
I0401 12:30:33.291519 21213 solver.cpp:237]     Train net output #0: loss = 5.29357 (* 1 = 5.29357 loss)
I0401 12:30:33.291527 21213 sgd_solver.cpp:105] Iteration 360, lr = 0.001
I0401 12:30:36.759315 21213 solver.cpp:218] Iteration 368 (2.30695 iter/s, 3.46778s/8 iters), loss = 5.25436
I0401 12:30:36.759368 21213 solver.cpp:237]     Train net output #0: loss = 5.25436 (* 1 = 5.25436 loss)
I0401 12:30:36.759375 21213 sgd_solver.cpp:105] Iteration 368, lr = 0.001
I0401 12:30:40.301719 21213 solver.cpp:218] Iteration 376 (2.25839 iter/s, 3.54235s/8 iters), loss = 5.26807
I0401 12:30:40.301771 21213 solver.cpp:237]     Train net output #0: loss = 5.26807 (* 1 = 5.26807 loss)
I0401 12:30:40.301780 21213 sgd_solver.cpp:105] Iteration 376, lr = 0.001
I0401 12:30:41.138736 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:30:43.053086 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_384.caffemodel
I0401 12:30:46.098848 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_384.solverstate
I0401 12:30:48.418058 21213 solver.cpp:330] Iteration 384, Testing net (#0)
I0401 12:30:48.418081 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:30:50.067850 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:30:50.562956 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0120192
I0401 12:30:50.562983 21213 solver.cpp:397]     Test net output #1: loss = 5.28055 (* 1 = 5.28055 loss)
I0401 12:30:50.701114 21213 solver.cpp:218] Iteration 384 (0.769278 iter/s, 10.3994s/8 iters), loss = 5.28291
I0401 12:30:50.701154 21213 solver.cpp:237]     Train net output #0: loss = 5.28291 (* 1 = 5.28291 loss)
I0401 12:30:50.701160 21213 sgd_solver.cpp:105] Iteration 384, lr = 0.001
I0401 12:30:53.403380 21213 solver.cpp:218] Iteration 392 (2.96053 iter/s, 2.70222s/8 iters), loss = 5.25811
I0401 12:30:53.403424 21213 solver.cpp:237]     Train net output #0: loss = 5.25811 (* 1 = 5.25811 loss)
I0401 12:30:53.403429 21213 sgd_solver.cpp:105] Iteration 392, lr = 0.001
I0401 12:30:56.768918 21213 solver.cpp:218] Iteration 400 (2.37707 iter/s, 3.36548s/8 iters), loss = 5.25653
I0401 12:30:56.769023 21213 solver.cpp:237]     Train net output #0: loss = 5.25653 (* 1 = 5.25653 loss)
I0401 12:30:56.769032 21213 sgd_solver.cpp:105] Iteration 400, lr = 0.001
I0401 12:31:00.063453 21213 solver.cpp:218] Iteration 408 (2.42834 iter/s, 3.29443s/8 iters), loss = 5.28779
I0401 12:31:00.063490 21213 solver.cpp:237]     Train net output #0: loss = 5.28779 (* 1 = 5.28779 loss)
I0401 12:31:00.063496 21213 sgd_solver.cpp:105] Iteration 408, lr = 0.001
I0401 12:31:03.534327 21213 solver.cpp:218] Iteration 416 (2.30493 iter/s, 3.47083s/8 iters), loss = 5.24975
I0401 12:31:03.534369 21213 solver.cpp:237]     Train net output #0: loss = 5.24975 (* 1 = 5.24975 loss)
I0401 12:31:03.534375 21213 sgd_solver.cpp:105] Iteration 416, lr = 0.001
I0401 12:31:06.944164 21213 solver.cpp:218] Iteration 424 (2.34619 iter/s, 3.40979s/8 iters), loss = 5.28587
I0401 12:31:06.944219 21213 solver.cpp:237]     Train net output #0: loss = 5.28587 (* 1 = 5.28587 loss)
I0401 12:31:06.944227 21213 sgd_solver.cpp:105] Iteration 424, lr = 0.001
I0401 12:31:10.499539 21213 solver.cpp:218] Iteration 432 (2.25016 iter/s, 3.55531s/8 iters), loss = 5.2639
I0401 12:31:10.499593 21213 solver.cpp:237]     Train net output #0: loss = 5.2639 (* 1 = 5.2639 loss)
I0401 12:31:10.499601 21213 sgd_solver.cpp:105] Iteration 432, lr = 0.001
I0401 12:31:13.981379 21213 solver.cpp:218] Iteration 440 (2.29768 iter/s, 3.48178s/8 iters), loss = 5.24853
I0401 12:31:13.981417 21213 solver.cpp:237]     Train net output #0: loss = 5.24853 (* 1 = 5.24853 loss)
I0401 12:31:13.981423 21213 sgd_solver.cpp:105] Iteration 440, lr = 0.001
I0401 12:31:14.601305 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:31:16.864267 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_448.caffemodel
I0401 12:31:19.925137 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_448.solverstate
I0401 12:31:22.218955 21213 solver.cpp:330] Iteration 448, Testing net (#0)
I0401 12:31:22.218976 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:31:23.824918 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:31:24.357154 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00961538
I0401 12:31:24.357192 21213 solver.cpp:397]     Test net output #1: loss = 5.27969 (* 1 = 5.27969 loss)
I0401 12:31:24.497741 21213 solver.cpp:218] Iteration 448 (0.760722 iter/s, 10.5163s/8 iters), loss = 5.2544
I0401 12:31:24.497795 21213 solver.cpp:237]     Train net output #0: loss = 5.2544 (* 1 = 5.2544 loss)
I0401 12:31:24.497803 21213 sgd_solver.cpp:105] Iteration 448, lr = 0.001
I0401 12:31:27.185680 21213 solver.cpp:218] Iteration 456 (2.97633 iter/s, 2.68788s/8 iters), loss = 5.27683
I0401 12:31:27.185820 21213 solver.cpp:237]     Train net output #0: loss = 5.27683 (* 1 = 5.27683 loss)
I0401 12:31:27.185829 21213 sgd_solver.cpp:105] Iteration 456, lr = 0.001
I0401 12:31:30.633760 21213 solver.cpp:218] Iteration 464 (2.32023 iter/s, 3.44793s/8 iters), loss = 5.23981
I0401 12:31:30.633816 21213 solver.cpp:237]     Train net output #0: loss = 5.23981 (* 1 = 5.23981 loss)
I0401 12:31:30.633824 21213 sgd_solver.cpp:105] Iteration 464, lr = 0.001
I0401 12:31:34.004742 21213 solver.cpp:218] Iteration 472 (2.37324 iter/s, 3.37092s/8 iters), loss = 5.26927
I0401 12:31:34.004779 21213 solver.cpp:237]     Train net output #0: loss = 5.26927 (* 1 = 5.26927 loss)
I0401 12:31:34.004786 21213 sgd_solver.cpp:105] Iteration 472, lr = 0.001
I0401 12:31:37.349607 21213 solver.cpp:218] Iteration 480 (2.39176 iter/s, 3.34481s/8 iters), loss = 5.28568
I0401 12:31:37.349650 21213 solver.cpp:237]     Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
I0401 12:31:37.349655 21213 sgd_solver.cpp:105] Iteration 480, lr = 0.001
I0401 12:31:40.813019 21213 solver.cpp:218] Iteration 488 (2.3099 iter/s, 3.46335s/8 iters), loss = 5.30198
I0401 12:31:40.813071 21213 solver.cpp:237]     Train net output #0: loss = 5.30198 (* 1 = 5.30198 loss)
I0401 12:31:40.813079 21213 sgd_solver.cpp:105] Iteration 488, lr = 0.001
I0401 12:31:44.270117 21213 solver.cpp:218] Iteration 496 (2.31412 iter/s, 3.45704s/8 iters), loss = 5.28928
I0401 12:31:44.270157 21213 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0401 12:31:44.270164 21213 sgd_solver.cpp:105] Iteration 496, lr = 0.001
I0401 12:31:47.732399 21213 solver.cpp:218] Iteration 504 (2.31065 iter/s, 3.46223s/8 iters), loss = 5.25689
I0401 12:31:47.732443 21213 solver.cpp:237]     Train net output #0: loss = 5.25689 (* 1 = 5.25689 loss)
I0401 12:31:47.732450 21213 sgd_solver.cpp:105] Iteration 504, lr = 0.001
I0401 12:31:47.887238 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:31:50.673316 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_512.caffemodel
I0401 12:31:53.691490 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_512.solverstate
I0401 12:31:56.031177 21213 solver.cpp:330] Iteration 512, Testing net (#0)
I0401 12:31:56.031198 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:31:57.673038 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:31:58.277386 21213 solver.cpp:397]     Test net output #0: accuracy = 0.00961538
I0401 12:31:58.277420 21213 solver.cpp:397]     Test net output #1: loss = 5.27865 (* 1 = 5.27865 loss)
I0401 12:31:58.411732 21213 solver.cpp:218] Iteration 512 (0.749114 iter/s, 10.6793s/8 iters), loss = 5.25711
I0401 12:31:58.411788 21213 solver.cpp:237]     Train net output #0: loss = 5.25711 (* 1 = 5.25711 loss)
I0401 12:31:58.411795 21213 sgd_solver.cpp:105] Iteration 512, lr = 0.001
I0401 12:32:01.141320 21213 solver.cpp:218] Iteration 520 (2.93092 iter/s, 2.72952s/8 iters), loss = 5.25489
I0401 12:32:01.141360 21213 solver.cpp:237]     Train net output #0: loss = 5.25489 (* 1 = 5.25489 loss)
I0401 12:32:01.141366 21213 sgd_solver.cpp:105] Iteration 520, lr = 0.001
I0401 12:32:04.631386 21213 solver.cpp:218] Iteration 528 (2.29225 iter/s, 3.49001s/8 iters), loss = 5.26788
I0401 12:32:04.631424 21213 solver.cpp:237]     Train net output #0: loss = 5.26788 (* 1 = 5.26788 loss)
I0401 12:32:04.631430 21213 sgd_solver.cpp:105] Iteration 528, lr = 0.001
I0401 12:32:07.805434 21213 solver.cpp:218] Iteration 536 (2.52048 iter/s, 3.174s/8 iters), loss = 5.25257
I0401 12:32:07.805500 21213 solver.cpp:237]     Train net output #0: loss = 5.25257 (* 1 = 5.25257 loss)
I0401 12:32:07.805510 21213 sgd_solver.cpp:105] Iteration 536, lr = 0.001
I0401 12:32:11.219609 21213 solver.cpp:218] Iteration 544 (2.34322 iter/s, 3.41411s/8 iters), loss = 5.27137
I0401 12:32:11.219642 21213 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0401 12:32:11.219647 21213 sgd_solver.cpp:105] Iteration 544, lr = 0.001
I0401 12:32:14.724642 21213 solver.cpp:218] Iteration 552 (2.28246 iter/s, 3.50499s/8 iters), loss = 5.2811
I0401 12:32:14.724694 21213 solver.cpp:237]     Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
I0401 12:32:14.724702 21213 sgd_solver.cpp:105] Iteration 552, lr = 0.001
I0401 12:32:18.095891 21213 solver.cpp:218] Iteration 560 (2.37305 iter/s, 3.37119s/8 iters), loss = 5.26826
I0401 12:32:18.095932 21213 solver.cpp:237]     Train net output #0: loss = 5.26826 (* 1 = 5.26826 loss)
I0401 12:32:18.095937 21213 sgd_solver.cpp:105] Iteration 560, lr = 0.001
I0401 12:32:21.298040 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:32:21.453687 21213 solver.cpp:218] Iteration 568 (2.38256 iter/s, 3.35773s/8 iters), loss = 5.27043
I0401 12:32:21.453752 21213 solver.cpp:237]     Train net output #0: loss = 5.27043 (* 1 = 5.27043 loss)
I0401 12:32:21.453760 21213 sgd_solver.cpp:105] Iteration 568, lr = 0.001
I0401 12:32:24.400229 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_576.caffemodel
I0401 12:32:27.448951 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_576.solverstate
I0401 12:32:29.747990 21213 solver.cpp:330] Iteration 576, Testing net (#0)
I0401 12:32:29.748100 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:32:31.158314 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:32:31.822742 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0168269
I0401 12:32:31.822774 21213 solver.cpp:397]     Test net output #1: loss = 5.27684 (* 1 = 5.27684 loss)
I0401 12:32:31.964315 21213 solver.cpp:218] Iteration 576 (0.761139 iter/s, 10.5106s/8 iters), loss = 5.27388
I0401 12:32:31.964368 21213 solver.cpp:237]     Train net output #0: loss = 5.27388 (* 1 = 5.27388 loss)
I0401 12:32:31.964375 21213 sgd_solver.cpp:105] Iteration 576, lr = 0.001
I0401 12:32:34.458359 21213 solver.cpp:218] Iteration 584 (3.20773 iter/s, 2.49398s/8 iters), loss = 5.26686
I0401 12:32:34.458405 21213 solver.cpp:237]     Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
I0401 12:32:34.458411 21213 sgd_solver.cpp:105] Iteration 584, lr = 0.001
I0401 12:32:37.965852 21213 solver.cpp:218] Iteration 592 (2.28087 iter/s, 3.50743s/8 iters), loss = 5.27711
I0401 12:32:37.965894 21213 solver.cpp:237]     Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
I0401 12:32:37.965900 21213 sgd_solver.cpp:105] Iteration 592, lr = 0.001
I0401 12:32:41.306321 21213 solver.cpp:218] Iteration 600 (2.39491 iter/s, 3.34041s/8 iters), loss = 5.24174
I0401 12:32:41.306365 21213 solver.cpp:237]     Train net output #0: loss = 5.24174 (* 1 = 5.24174 loss)
I0401 12:32:41.306370 21213 sgd_solver.cpp:105] Iteration 600, lr = 0.001
I0401 12:32:44.707561 21213 solver.cpp:218] Iteration 608 (2.35212 iter/s, 3.40118s/8 iters), loss = 5.28556
I0401 12:32:44.707615 21213 solver.cpp:237]     Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
I0401 12:32:44.707624 21213 sgd_solver.cpp:105] Iteration 608, lr = 0.001
I0401 12:32:48.345280 21213 solver.cpp:218] Iteration 616 (2.19922 iter/s, 3.63766s/8 iters), loss = 5.25795
I0401 12:32:48.345319 21213 solver.cpp:237]     Train net output #0: loss = 5.25795 (* 1 = 5.25795 loss)
I0401 12:32:48.345324 21213 sgd_solver.cpp:105] Iteration 616, lr = 0.001
I0401 12:32:51.821899 21213 solver.cpp:218] Iteration 624 (2.30112 iter/s, 3.47657s/8 iters), loss = 5.24277
I0401 12:32:51.821938 21213 solver.cpp:237]     Train net output #0: loss = 5.24277 (* 1 = 5.24277 loss)
I0401 12:32:51.821943 21213 sgd_solver.cpp:105] Iteration 624, lr = 0.001
I0401 12:32:54.883877 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:32:55.259804 21213 solver.cpp:218] Iteration 632 (2.32704 iter/s, 3.43785s/8 iters), loss = 5.26938
I0401 12:32:55.259857 21213 solver.cpp:237]     Train net output #0: loss = 5.26938 (* 1 = 5.26938 loss)
I0401 12:32:55.259865 21213 sgd_solver.cpp:105] Iteration 632, lr = 0.001
I0401 12:32:58.091917 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_640.caffemodel
I0401 12:33:01.158030 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_640.solverstate
I0401 12:33:03.470484 21213 solver.cpp:330] Iteration 640, Testing net (#0)
I0401 12:33:03.470499 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:33:04.840061 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:33:05.568293 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0192308
I0401 12:33:05.568333 21213 solver.cpp:397]     Test net output #1: loss = 5.27269 (* 1 = 5.27269 loss)
I0401 12:33:05.702850 21213 solver.cpp:218] Iteration 640 (0.766064 iter/s, 10.443s/8 iters), loss = 5.25553
I0401 12:33:05.702905 21213 solver.cpp:237]     Train net output #0: loss = 5.25553 (* 1 = 5.25553 loss)
I0401 12:33:05.702913 21213 sgd_solver.cpp:105] Iteration 640, lr = 0.001
I0401 12:33:08.216264 21213 solver.cpp:218] Iteration 648 (3.183 iter/s, 2.51335s/8 iters), loss = 5.25168
I0401 12:33:08.216300 21213 solver.cpp:237]     Train net output #0: loss = 5.25168 (* 1 = 5.25168 loss)
I0401 12:33:08.216305 21213 sgd_solver.cpp:105] Iteration 648, lr = 0.001
I0401 12:33:11.690635 21213 solver.cpp:218] Iteration 656 (2.30261 iter/s, 3.47432s/8 iters), loss = 5.25568
I0401 12:33:11.690678 21213 solver.cpp:237]     Train net output #0: loss = 5.25568 (* 1 = 5.25568 loss)
I0401 12:33:11.690683 21213 sgd_solver.cpp:105] Iteration 656, lr = 0.001
I0401 12:33:15.192515 21213 solver.cpp:218] Iteration 664 (2.28452 iter/s, 3.50183s/8 iters), loss = 5.23788
I0401 12:33:15.192553 21213 solver.cpp:237]     Train net output #0: loss = 5.23788 (* 1 = 5.23788 loss)
I0401 12:33:15.192557 21213 sgd_solver.cpp:105] Iteration 664, lr = 0.001
I0401 12:33:18.625571 21213 solver.cpp:218] Iteration 672 (2.33032 iter/s, 3.43301s/8 iters), loss = 5.25475
I0401 12:33:18.625627 21213 solver.cpp:237]     Train net output #0: loss = 5.25475 (* 1 = 5.25475 loss)
I0401 12:33:18.625636 21213 sgd_solver.cpp:105] Iteration 672, lr = 0.001
I0401 12:33:22.102344 21213 solver.cpp:218] Iteration 680 (2.30103 iter/s, 3.47671s/8 iters), loss = 5.26372
I0401 12:33:22.102392 21213 solver.cpp:237]     Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
I0401 12:33:22.102399 21213 sgd_solver.cpp:105] Iteration 680, lr = 0.001
I0401 12:33:25.453183 21213 solver.cpp:218] Iteration 688 (2.38751 iter/s, 3.35078s/8 iters), loss = 5.23785
I0401 12:33:25.453241 21213 solver.cpp:237]     Train net output #0: loss = 5.23785 (* 1 = 5.23785 loss)
I0401 12:33:25.453249 21213 sgd_solver.cpp:105] Iteration 688, lr = 0.001
I0401 12:33:28.029458 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:33:28.787459 21213 solver.cpp:218] Iteration 696 (2.39937 iter/s, 3.3342s/8 iters), loss = 5.25686
I0401 12:33:28.787518 21213 solver.cpp:237]     Train net output #0: loss = 5.25686 (* 1 = 5.25686 loss)
I0401 12:33:28.787525 21213 sgd_solver.cpp:105] Iteration 696, lr = 0.001
I0401 12:33:31.821380 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_704.caffemodel
I0401 12:33:36.359283 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_704.solverstate
I0401 12:33:39.593741 21213 solver.cpp:330] Iteration 704, Testing net (#0)
I0401 12:33:39.593763 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:33:40.908080 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:33:41.683913 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0180288
I0401 12:33:41.683945 21213 solver.cpp:397]     Test net output #1: loss = 5.26671 (* 1 = 5.26671 loss)
I0401 12:33:41.821246 21213 solver.cpp:218] Iteration 704 (0.613792 iter/s, 13.0337s/8 iters), loss = 5.27739
I0401 12:33:41.821298 21213 solver.cpp:237]     Train net output #0: loss = 5.27739 (* 1 = 5.27739 loss)
I0401 12:33:41.821305 21213 sgd_solver.cpp:105] Iteration 704, lr = 0.001
I0401 12:33:44.361377 21213 solver.cpp:218] Iteration 712 (3.14953 iter/s, 2.54006s/8 iters), loss = 5.24851
I0401 12:33:44.361416 21213 solver.cpp:237]     Train net output #0: loss = 5.24851 (* 1 = 5.24851 loss)
I0401 12:33:44.361423 21213 sgd_solver.cpp:105] Iteration 712, lr = 0.001
I0401 12:33:47.812059 21213 solver.cpp:218] Iteration 720 (2.31842 iter/s, 3.45063s/8 iters), loss = 5.24494
I0401 12:33:47.812104 21213 solver.cpp:237]     Train net output #0: loss = 5.24494 (* 1 = 5.24494 loss)
I0401 12:33:47.812110 21213 sgd_solver.cpp:105] Iteration 720, lr = 0.001
I0401 12:33:51.348701 21213 solver.cpp:218] Iteration 728 (2.26207 iter/s, 3.53658s/8 iters), loss = 5.24769
I0401 12:33:51.348757 21213 solver.cpp:237]     Train net output #0: loss = 5.24769 (* 1 = 5.24769 loss)
I0401 12:33:51.348765 21213 sgd_solver.cpp:105] Iteration 728, lr = 0.001
I0401 12:33:54.674384 21213 solver.cpp:218] Iteration 736 (2.40557 iter/s, 3.32561s/8 iters), loss = 5.23918
I0401 12:33:54.674427 21213 solver.cpp:237]     Train net output #0: loss = 5.23918 (* 1 = 5.23918 loss)
I0401 12:33:54.674432 21213 sgd_solver.cpp:105] Iteration 736, lr = 0.001
I0401 12:33:58.218806 21213 solver.cpp:218] Iteration 744 (2.2571 iter/s, 3.54437s/8 iters), loss = 5.26769
I0401 12:33:58.218860 21213 solver.cpp:237]     Train net output #0: loss = 5.26769 (* 1 = 5.26769 loss)
I0401 12:33:58.218869 21213 sgd_solver.cpp:105] Iteration 744, lr = 0.001
I0401 12:34:01.776262 21213 solver.cpp:218] Iteration 752 (2.24884 iter/s, 3.55739s/8 iters), loss = 5.2097
I0401 12:34:01.776316 21213 solver.cpp:237]     Train net output #0: loss = 5.2097 (* 1 = 5.2097 loss)
I0401 12:34:01.776324 21213 sgd_solver.cpp:105] Iteration 752, lr = 0.001
I0401 12:34:04.069931 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:34:05.220571 21213 solver.cpp:218] Iteration 760 (2.32272 iter/s, 3.44424s/8 iters), loss = 5.23465
I0401 12:34:05.220635 21213 solver.cpp:237]     Train net output #0: loss = 5.23465 (* 1 = 5.23465 loss)
I0401 12:34:05.220645 21213 sgd_solver.cpp:105] Iteration 760, lr = 0.001
I0401 12:34:05.574142 21213 blocking_queue.cpp:49] Waiting for data
I0401 12:34:08.267583 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_768.caffemodel
I0401 12:34:11.323429 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_768.solverstate
I0401 12:34:13.622747 21213 solver.cpp:330] Iteration 768, Testing net (#0)
I0401 12:34:13.622767 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:34:14.857849 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:34:15.711688 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0192308
I0401 12:34:15.711715 21213 solver.cpp:397]     Test net output #1: loss = 5.25355 (* 1 = 5.25355 loss)
I0401 12:34:15.847259 21213 solver.cpp:218] Iteration 768 (0.752826 iter/s, 10.6266s/8 iters), loss = 5.26183
I0401 12:34:15.847297 21213 solver.cpp:237]     Train net output #0: loss = 5.26183 (* 1 = 5.26183 loss)
I0401 12:34:15.847302 21213 sgd_solver.cpp:105] Iteration 768, lr = 0.001
I0401 12:34:18.244485 21213 solver.cpp:218] Iteration 776 (3.33727 iter/s, 2.39717s/8 iters), loss = 5.23368
I0401 12:34:18.244534 21213 solver.cpp:237]     Train net output #0: loss = 5.23368 (* 1 = 5.23368 loss)
I0401 12:34:18.244539 21213 sgd_solver.cpp:105] Iteration 776, lr = 0.001
I0401 12:34:21.681665 21213 solver.cpp:218] Iteration 784 (2.32753 iter/s, 3.43711s/8 iters), loss = 5.2575
I0401 12:34:21.681725 21213 solver.cpp:237]     Train net output #0: loss = 5.2575 (* 1 = 5.2575 loss)
I0401 12:34:21.681732 21213 sgd_solver.cpp:105] Iteration 784, lr = 0.001
I0401 12:34:25.219802 21213 solver.cpp:218] Iteration 792 (2.26112 iter/s, 3.53807s/8 iters), loss = 5.26152
I0401 12:34:25.219852 21213 solver.cpp:237]     Train net output #0: loss = 5.26152 (* 1 = 5.26152 loss)
I0401 12:34:25.219858 21213 sgd_solver.cpp:105] Iteration 792, lr = 0.001
I0401 12:34:28.686022 21213 solver.cpp:218] Iteration 800 (2.30803 iter/s, 3.46616s/8 iters), loss = 5.20657
I0401 12:34:28.686075 21213 solver.cpp:237]     Train net output #0: loss = 5.20657 (* 1 = 5.20657 loss)
I0401 12:34:28.686082 21213 sgd_solver.cpp:105] Iteration 800, lr = 0.001
I0401 12:34:32.190690 21213 solver.cpp:218] Iteration 808 (2.28272 iter/s, 3.5046s/8 iters), loss = 5.21431
I0401 12:34:32.190729 21213 solver.cpp:237]     Train net output #0: loss = 5.21431 (* 1 = 5.21431 loss)
I0401 12:34:32.190735 21213 sgd_solver.cpp:105] Iteration 808, lr = 0.001
I0401 12:34:35.601747 21213 solver.cpp:218] Iteration 816 (2.34535 iter/s, 3.41101s/8 iters), loss = 5.21945
I0401 12:34:35.601881 21213 solver.cpp:237]     Train net output #0: loss = 5.21945 (* 1 = 5.21945 loss)
I0401 12:34:35.601886 21213 sgd_solver.cpp:105] Iteration 816, lr = 0.001
I0401 12:34:37.534919 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:34:39.068477 21213 solver.cpp:218] Iteration 824 (2.30775 iter/s, 3.46659s/8 iters), loss = 5.17242
I0401 12:34:39.068526 21213 solver.cpp:237]     Train net output #0: loss = 5.17242 (* 1 = 5.17242 loss)
I0401 12:34:39.068532 21213 sgd_solver.cpp:105] Iteration 824, lr = 0.001
I0401 12:34:42.063658 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_832.caffemodel
I0401 12:34:45.112138 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_832.solverstate
I0401 12:34:47.426180 21213 solver.cpp:330] Iteration 832, Testing net (#0)
I0401 12:34:47.426198 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:34:48.604866 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:34:49.546048 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0252404
I0401 12:34:49.546083 21213 solver.cpp:397]     Test net output #1: loss = 5.21575 (* 1 = 5.21575 loss)
I0401 12:34:49.687431 21213 solver.cpp:218] Iteration 832 (0.753373 iter/s, 10.6189s/8 iters), loss = 5.26623
I0401 12:34:49.687489 21213 solver.cpp:237]     Train net output #0: loss = 5.26623 (* 1 = 5.26623 loss)
I0401 12:34:49.687497 21213 sgd_solver.cpp:105] Iteration 832, lr = 0.001
I0401 12:34:52.297518 21213 solver.cpp:218] Iteration 840 (3.06512 iter/s, 2.61001s/8 iters), loss = 5.19235
I0401 12:34:52.297577 21213 solver.cpp:237]     Train net output #0: loss = 5.19235 (* 1 = 5.19235 loss)
I0401 12:34:52.297586 21213 sgd_solver.cpp:105] Iteration 840, lr = 0.001
I0401 12:34:55.773931 21213 solver.cpp:218] Iteration 848 (2.30127 iter/s, 3.47634s/8 iters), loss = 5.22814
I0401 12:34:55.773972 21213 solver.cpp:237]     Train net output #0: loss = 5.22814 (* 1 = 5.22814 loss)
I0401 12:34:55.773977 21213 sgd_solver.cpp:105] Iteration 848, lr = 0.001
I0401 12:34:59.329854 21213 solver.cpp:218] Iteration 856 (2.2498 iter/s, 3.55587s/8 iters), loss = 5.15178
I0401 12:34:59.329892 21213 solver.cpp:237]     Train net output #0: loss = 5.15178 (* 1 = 5.15178 loss)
I0401 12:34:59.329898 21213 sgd_solver.cpp:105] Iteration 856, lr = 0.001
I0401 12:35:02.918747 21213 solver.cpp:218] Iteration 864 (2.22913 iter/s, 3.58884s/8 iters), loss = 5.20362
I0401 12:35:02.918784 21213 solver.cpp:237]     Train net output #0: loss = 5.20362 (* 1 = 5.20362 loss)
I0401 12:35:02.918790 21213 sgd_solver.cpp:105] Iteration 864, lr = 0.001
I0401 12:35:06.260855 21213 solver.cpp:218] Iteration 872 (2.39374 iter/s, 3.34205s/8 iters), loss = 5.17224
I0401 12:35:06.261423 21213 solver.cpp:237]     Train net output #0: loss = 5.17224 (* 1 = 5.17224 loss)
I0401 12:35:06.261432 21213 sgd_solver.cpp:105] Iteration 872, lr = 0.001
I0401 12:35:09.724355 21213 solver.cpp:218] Iteration 880 (2.31019 iter/s, 3.46292s/8 iters), loss = 5.17115
I0401 12:35:09.724404 21213 solver.cpp:237]     Train net output #0: loss = 5.17115 (* 1 = 5.17115 loss)
I0401 12:35:09.724409 21213 sgd_solver.cpp:105] Iteration 880, lr = 0.001
I0401 12:35:11.509912 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:35:13.305596 21213 solver.cpp:218] Iteration 888 (2.2339 iter/s, 3.58118s/8 iters), loss = 5.08263
I0401 12:35:13.305634 21213 solver.cpp:237]     Train net output #0: loss = 5.08263 (* 1 = 5.08263 loss)
I0401 12:35:13.305639 21213 sgd_solver.cpp:105] Iteration 888, lr = 0.001
I0401 12:35:16.272778 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_896.caffemodel
I0401 12:35:19.298600 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_896.solverstate
I0401 12:35:21.600971 21213 solver.cpp:330] Iteration 896, Testing net (#0)
I0401 12:35:21.600992 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:35:22.708858 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:35:23.689777 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0288462
I0401 12:35:23.689810 21213 solver.cpp:397]     Test net output #1: loss = 5.15616 (* 1 = 5.15616 loss)
I0401 12:35:23.826089 21213 solver.cpp:218] Iteration 896 (0.760423 iter/s, 10.5205s/8 iters), loss = 5.13768
I0401 12:35:23.827636 21213 solver.cpp:237]     Train net output #0: loss = 5.13768 (* 1 = 5.13768 loss)
I0401 12:35:23.827644 21213 sgd_solver.cpp:105] Iteration 896, lr = 0.001
I0401 12:35:26.423432 21213 solver.cpp:218] Iteration 904 (3.08192 iter/s, 2.59579s/8 iters), loss = 5.14738
I0401 12:35:26.423480 21213 solver.cpp:237]     Train net output #0: loss = 5.14738 (* 1 = 5.14738 loss)
I0401 12:35:26.423485 21213 sgd_solver.cpp:105] Iteration 904, lr = 0.001
I0401 12:35:29.854774 21213 solver.cpp:218] Iteration 912 (2.33149 iter/s, 3.43129s/8 iters), loss = 5.15438
I0401 12:35:29.854817 21213 solver.cpp:237]     Train net output #0: loss = 5.15438 (* 1 = 5.15438 loss)
I0401 12:35:29.854822 21213 sgd_solver.cpp:105] Iteration 912, lr = 0.001
I0401 12:35:33.263476 21213 solver.cpp:218] Iteration 920 (2.34697 iter/s, 3.40865s/8 iters), loss = 5.10241
I0401 12:35:33.263515 21213 solver.cpp:237]     Train net output #0: loss = 5.10241 (* 1 = 5.10241 loss)
I0401 12:35:33.263521 21213 sgd_solver.cpp:105] Iteration 920, lr = 0.001
I0401 12:35:36.777510 21213 solver.cpp:218] Iteration 928 (2.27662 iter/s, 3.51398s/8 iters), loss = 5.09461
I0401 12:35:36.777606 21213 solver.cpp:237]     Train net output #0: loss = 5.09461 (* 1 = 5.09461 loss)
I0401 12:35:36.777611 21213 sgd_solver.cpp:105] Iteration 928, lr = 0.001
I0401 12:35:40.256395 21213 solver.cpp:218] Iteration 936 (2.29966 iter/s, 3.47878s/8 iters), loss = 5.09991
I0401 12:35:40.256444 21213 solver.cpp:237]     Train net output #0: loss = 5.09991 (* 1 = 5.09991 loss)
I0401 12:35:40.256449 21213 sgd_solver.cpp:105] Iteration 936, lr = 0.001
I0401 12:35:43.844985 21213 solver.cpp:218] Iteration 944 (2.22933 iter/s, 3.58853s/8 iters), loss = 5.15934
I0401 12:35:43.845036 21213 solver.cpp:237]     Train net output #0: loss = 5.15934 (* 1 = 5.15934 loss)
I0401 12:35:43.845043 21213 sgd_solver.cpp:105] Iteration 944, lr = 0.001
I0401 12:35:45.282169 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:35:47.356627 21213 solver.cpp:218] Iteration 952 (2.27817 iter/s, 3.51158s/8 iters), loss = 5.08355
I0401 12:35:47.356664 21213 solver.cpp:237]     Train net output #0: loss = 5.08355 (* 1 = 5.08355 loss)
I0401 12:35:47.356670 21213 sgd_solver.cpp:105] Iteration 952, lr = 0.001
I0401 12:35:50.480082 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_960.caffemodel
I0401 12:35:53.957870 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_960.solverstate
I0401 12:35:57.598148 21213 solver.cpp:330] Iteration 960, Testing net (#0)
I0401 12:35:57.598167 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:35:58.726763 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:35:59.789028 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0240385
I0401 12:35:59.789067 21213 solver.cpp:397]     Test net output #1: loss = 5.12327 (* 1 = 5.12327 loss)
I0401 12:35:59.927263 21213 solver.cpp:218] Iteration 960 (0.636406 iter/s, 12.5706s/8 iters), loss = 5.06828
I0401 12:35:59.927337 21213 solver.cpp:237]     Train net output #0: loss = 5.06828 (* 1 = 5.06828 loss)
I0401 12:35:59.927347 21213 sgd_solver.cpp:105] Iteration 960, lr = 0.001
I0401 12:36:02.595852 21213 solver.cpp:218] Iteration 968 (2.99793 iter/s, 2.6685s/8 iters), loss = 5.10826
I0401 12:36:02.595903 21213 solver.cpp:237]     Train net output #0: loss = 5.10826 (* 1 = 5.10826 loss)
I0401 12:36:02.595911 21213 sgd_solver.cpp:105] Iteration 968, lr = 0.001
I0401 12:36:05.844058 21213 solver.cpp:218] Iteration 976 (2.46294 iter/s, 3.24815s/8 iters), loss = 5.04105
I0401 12:36:05.844099 21213 solver.cpp:237]     Train net output #0: loss = 5.04105 (* 1 = 5.04105 loss)
I0401 12:36:05.844103 21213 sgd_solver.cpp:105] Iteration 976, lr = 0.001
I0401 12:36:08.990821 21213 solver.cpp:218] Iteration 984 (2.54234 iter/s, 3.14671s/8 iters), loss = 5.11317
I0401 12:36:08.990984 21213 solver.cpp:237]     Train net output #0: loss = 5.11317 (* 1 = 5.11317 loss)
I0401 12:36:08.990993 21213 sgd_solver.cpp:105] Iteration 984, lr = 0.001
I0401 12:36:12.470499 21213 solver.cpp:218] Iteration 992 (2.29917 iter/s, 3.47951s/8 iters), loss = 5.07703
I0401 12:36:12.470539 21213 solver.cpp:237]     Train net output #0: loss = 5.07703 (* 1 = 5.07703 loss)
I0401 12:36:12.470546 21213 sgd_solver.cpp:105] Iteration 992, lr = 0.001
I0401 12:36:15.982625 21213 solver.cpp:218] Iteration 1000 (2.27786 iter/s, 3.51207s/8 iters), loss = 5.12108
I0401 12:36:15.982664 21213 solver.cpp:237]     Train net output #0: loss = 5.12108 (* 1 = 5.12108 loss)
I0401 12:36:15.982671 21213 sgd_solver.cpp:105] Iteration 1000, lr = 0.001
I0401 12:36:19.548511 21213 solver.cpp:218] Iteration 1008 (2.24351 iter/s, 3.56583s/8 iters), loss = 5.17885
I0401 12:36:19.548557 21213 solver.cpp:237]     Train net output #0: loss = 5.17885 (* 1 = 5.17885 loss)
I0401 12:36:19.548561 21213 sgd_solver.cpp:105] Iteration 1008, lr = 0.001
I0401 12:36:20.483150 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:36:22.826965 21213 solver.cpp:218] Iteration 1016 (2.44022 iter/s, 3.2784s/8 iters), loss = 5.06073
I0401 12:36:22.827011 21213 solver.cpp:237]     Train net output #0: loss = 5.06073 (* 1 = 5.06073 loss)
I0401 12:36:22.827018 21213 sgd_solver.cpp:105] Iteration 1016, lr = 0.001
I0401 12:36:25.853427 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1024.caffemodel
I0401 12:36:28.913849 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1024.solverstate
I0401 12:36:31.251030 21213 solver.cpp:330] Iteration 1024, Testing net (#0)
I0401 12:36:31.251052 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:36:32.288298 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:36:33.427351 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0300481
I0401 12:36:33.427376 21213 solver.cpp:397]     Test net output #1: loss = 5.09056 (* 1 = 5.09056 loss)
I0401 12:36:33.565351 21213 solver.cpp:218] Iteration 1024 (0.744994 iter/s, 10.7383s/8 iters), loss = 5.12567
I0401 12:36:33.565397 21213 solver.cpp:237]     Train net output #0: loss = 5.12567 (* 1 = 5.12567 loss)
I0401 12:36:33.565403 21213 sgd_solver.cpp:105] Iteration 1024, lr = 0.001
I0401 12:36:36.251370 21213 solver.cpp:218] Iteration 1032 (2.97845 iter/s, 2.68596s/8 iters), loss = 5.11916
I0401 12:36:36.251433 21213 solver.cpp:237]     Train net output #0: loss = 5.11916 (* 1 = 5.11916 loss)
I0401 12:36:36.251443 21213 sgd_solver.cpp:105] Iteration 1032, lr = 0.001
I0401 12:36:39.631994 21213 solver.cpp:218] Iteration 1040 (2.36648 iter/s, 3.38054s/8 iters), loss = 5.01709
I0401 12:36:39.632191 21213 solver.cpp:237]     Train net output #0: loss = 5.01709 (* 1 = 5.01709 loss)
I0401 12:36:39.632200 21213 sgd_solver.cpp:105] Iteration 1040, lr = 0.001
I0401 12:36:43.092816 21213 solver.cpp:218] Iteration 1048 (2.31172 iter/s, 3.46062s/8 iters), loss = 5.01018
I0401 12:36:43.092869 21213 solver.cpp:237]     Train net output #0: loss = 5.01018 (* 1 = 5.01018 loss)
I0401 12:36:43.092875 21213 sgd_solver.cpp:105] Iteration 1048, lr = 0.001
I0401 12:36:46.590128 21213 solver.cpp:218] Iteration 1056 (2.28751 iter/s, 3.49725s/8 iters), loss = 5.07463
I0401 12:36:46.590170 21213 solver.cpp:237]     Train net output #0: loss = 5.07463 (* 1 = 5.07463 loss)
I0401 12:36:46.590176 21213 sgd_solver.cpp:105] Iteration 1056, lr = 0.001
I0401 12:36:50.108165 21213 solver.cpp:218] Iteration 1064 (2.27403 iter/s, 3.51798s/8 iters), loss = 5.08289
I0401 12:36:50.108211 21213 solver.cpp:237]     Train net output #0: loss = 5.08289 (* 1 = 5.08289 loss)
I0401 12:36:50.108218 21213 sgd_solver.cpp:105] Iteration 1064, lr = 0.001
I0401 12:36:53.653689 21213 solver.cpp:218] Iteration 1072 (2.2564 iter/s, 3.54546s/8 iters), loss = 5.13064
I0401 12:36:53.653738 21213 solver.cpp:237]     Train net output #0: loss = 5.13064 (* 1 = 5.13064 loss)
I0401 12:36:53.653745 21213 sgd_solver.cpp:105] Iteration 1072, lr = 0.001
I0401 12:36:54.267676 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:36:57.099061 21213 solver.cpp:218] Iteration 1080 (2.322 iter/s, 3.44531s/8 iters), loss = 5.14855
I0401 12:36:57.099114 21213 solver.cpp:237]     Train net output #0: loss = 5.14855 (* 1 = 5.14855 loss)
I0401 12:36:57.099121 21213 sgd_solver.cpp:105] Iteration 1080, lr = 0.001
I0401 12:37:00.135614 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1088.caffemodel
I0401 12:37:03.138550 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1088.solverstate
I0401 12:37:05.453006 21213 solver.cpp:330] Iteration 1088, Testing net (#0)
I0401 12:37:05.453028 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:37:06.367988 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:37:07.523350 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0228365
I0401 12:37:07.523381 21213 solver.cpp:397]     Test net output #1: loss = 5.09049 (* 1 = 5.09049 loss)
I0401 12:37:07.664680 21213 solver.cpp:218] Iteration 1088 (0.757177 iter/s, 10.5656s/8 iters), loss = 4.95373
I0401 12:37:07.664731 21213 solver.cpp:237]     Train net output #0: loss = 4.95373 (* 1 = 4.95373 loss)
I0401 12:37:07.664741 21213 sgd_solver.cpp:105] Iteration 1088, lr = 0.001
I0401 12:37:10.175575 21213 solver.cpp:218] Iteration 1096 (3.18619 iter/s, 2.51083s/8 iters), loss = 5.00978
I0401 12:37:10.175709 21213 solver.cpp:237]     Train net output #0: loss = 5.00978 (* 1 = 5.00978 loss)
I0401 12:37:10.175716 21213 sgd_solver.cpp:105] Iteration 1096, lr = 0.001
I0401 12:37:13.640084 21213 solver.cpp:218] Iteration 1104 (2.30923 iter/s, 3.46436s/8 iters), loss = 5.02238
I0401 12:37:13.640142 21213 solver.cpp:237]     Train net output #0: loss = 5.02238 (* 1 = 5.02238 loss)
I0401 12:37:13.640149 21213 sgd_solver.cpp:105] Iteration 1104, lr = 0.001
I0401 12:37:17.112385 21213 solver.cpp:218] Iteration 1112 (2.30399 iter/s, 3.47223s/8 iters), loss = 5.05158
I0401 12:37:17.112426 21213 solver.cpp:237]     Train net output #0: loss = 5.05158 (* 1 = 5.05158 loss)
I0401 12:37:17.112432 21213 sgd_solver.cpp:105] Iteration 1112, lr = 0.001
I0401 12:37:20.569761 21213 solver.cpp:218] Iteration 1120 (2.31393 iter/s, 3.45732s/8 iters), loss = 5.15537
I0401 12:37:20.569802 21213 solver.cpp:237]     Train net output #0: loss = 5.15537 (* 1 = 5.15537 loss)
I0401 12:37:20.569806 21213 sgd_solver.cpp:105] Iteration 1120, lr = 0.001
I0401 12:37:23.936028 21213 solver.cpp:218] Iteration 1128 (2.37656 iter/s, 3.36621s/8 iters), loss = 4.98852
I0401 12:37:23.936079 21213 solver.cpp:237]     Train net output #0: loss = 4.98852 (* 1 = 4.98852 loss)
I0401 12:37:23.936086 21213 sgd_solver.cpp:105] Iteration 1128, lr = 0.001
I0401 12:37:27.297796 21213 solver.cpp:218] Iteration 1136 (2.37975 iter/s, 3.3617s/8 iters), loss = 5.00593
I0401 12:37:27.297834 21213 solver.cpp:237]     Train net output #0: loss = 5.00593 (* 1 = 5.00593 loss)
I0401 12:37:27.297840 21213 sgd_solver.cpp:105] Iteration 1136, lr = 0.001
I0401 12:37:27.566648 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:37:30.909106 21213 solver.cpp:218] Iteration 1144 (2.2153 iter/s, 3.61126s/8 iters), loss = 5.07983
I0401 12:37:30.909157 21213 solver.cpp:237]     Train net output #0: loss = 5.07983 (* 1 = 5.07983 loss)
I0401 12:37:30.909166 21213 sgd_solver.cpp:105] Iteration 1144, lr = 0.001
I0401 12:37:33.592247 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1152.caffemodel
I0401 12:37:36.689851 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1152.solverstate
I0401 12:37:39.053344 21213 solver.cpp:330] Iteration 1152, Testing net (#0)
I0401 12:37:39.053369 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:37:39.970527 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:37:41.187235 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0276442
I0401 12:37:41.187352 21213 solver.cpp:397]     Test net output #1: loss = 5.06568 (* 1 = 5.06568 loss)
I0401 12:37:41.329077 21213 solver.cpp:218] Iteration 1152 (0.76776 iter/s, 10.4199s/8 iters), loss = 5.10252
I0401 12:37:41.329128 21213 solver.cpp:237]     Train net output #0: loss = 5.10252 (* 1 = 5.10252 loss)
I0401 12:37:41.329135 21213 sgd_solver.cpp:105] Iteration 1152, lr = 0.001
I0401 12:37:43.938655 21213 solver.cpp:218] Iteration 1160 (3.06571 iter/s, 2.60951s/8 iters), loss = 5.0934
I0401 12:37:43.938704 21213 solver.cpp:237]     Train net output #0: loss = 5.0934 (* 1 = 5.0934 loss)
I0401 12:37:43.938711 21213 sgd_solver.cpp:105] Iteration 1160, lr = 0.001
I0401 12:37:47.536479 21213 solver.cpp:218] Iteration 1168 (2.2236 iter/s, 3.59777s/8 iters), loss = 4.941
I0401 12:37:47.536517 21213 solver.cpp:237]     Train net output #0: loss = 4.941 (* 1 = 4.941 loss)
I0401 12:37:47.536522 21213 sgd_solver.cpp:105] Iteration 1168, lr = 0.001
I0401 12:37:51.044872 21213 solver.cpp:218] Iteration 1176 (2.28028 iter/s, 3.50834s/8 iters), loss = 4.99043
I0401 12:37:51.044916 21213 solver.cpp:237]     Train net output #0: loss = 4.99043 (* 1 = 4.99043 loss)
I0401 12:37:51.044921 21213 sgd_solver.cpp:105] Iteration 1176, lr = 0.001
I0401 12:37:54.513154 21213 solver.cpp:218] Iteration 1184 (2.30666 iter/s, 3.46822s/8 iters), loss = 5.0374
I0401 12:37:54.513195 21213 solver.cpp:237]     Train net output #0: loss = 5.0374 (* 1 = 5.0374 loss)
I0401 12:37:54.513201 21213 sgd_solver.cpp:105] Iteration 1184, lr = 0.001
I0401 12:37:58.126997 21213 solver.cpp:218] Iteration 1192 (2.21375 iter/s, 3.61378s/8 iters), loss = 4.96177
I0401 12:37:58.127054 21213 solver.cpp:237]     Train net output #0: loss = 4.96177 (* 1 = 4.96177 loss)
I0401 12:37:58.127065 21213 sgd_solver.cpp:105] Iteration 1192, lr = 0.001
I0401 12:38:01.707065 21213 solver.cpp:218] Iteration 1200 (2.23463 iter/s, 3.58s/8 iters), loss = 5.0648
I0401 12:38:01.707104 21213 solver.cpp:237]     Train net output #0: loss = 5.0648 (* 1 = 5.0648 loss)
I0401 12:38:01.707110 21213 sgd_solver.cpp:105] Iteration 1200, lr = 0.001
I0401 12:38:01.752920 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:38:05.172403 21213 solver.cpp:218] Iteration 1208 (2.30862 iter/s, 3.46527s/8 iters), loss = 5.12431
I0401 12:38:05.172475 21213 solver.cpp:237]     Train net output #0: loss = 5.12431 (* 1 = 5.12431 loss)
I0401 12:38:05.172484 21213 sgd_solver.cpp:105] Iteration 1208, lr = 0.001
I0401 12:38:08.183473 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1216.caffemodel
I0401 12:38:11.357774 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1216.solverstate
I0401 12:38:13.772708 21213 solver.cpp:330] Iteration 1216, Testing net (#0)
I0401 12:38:13.772730 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:38:14.545217 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:38:15.861490 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0276442
I0401 12:38:15.861515 21213 solver.cpp:397]     Test net output #1: loss = 5.06561 (* 1 = 5.06561 loss)
I0401 12:38:16.002913 21213 solver.cpp:218] Iteration 1216 (0.738659 iter/s, 10.8304s/8 iters), loss = 5.12055
I0401 12:38:16.002950 21213 solver.cpp:237]     Train net output #0: loss = 5.12055 (* 1 = 5.12055 loss)
I0401 12:38:16.002955 21213 sgd_solver.cpp:105] Iteration 1216, lr = 0.001
I0401 12:38:18.620048 21213 solver.cpp:218] Iteration 1224 (3.05684 iter/s, 2.61708s/8 iters), loss = 5.05719
I0401 12:38:18.620106 21213 solver.cpp:237]     Train net output #0: loss = 5.05719 (* 1 = 5.05719 loss)
I0401 12:38:18.620115 21213 sgd_solver.cpp:105] Iteration 1224, lr = 0.001
I0401 12:38:22.086230 21213 solver.cpp:218] Iteration 1232 (2.30806 iter/s, 3.46611s/8 iters), loss = 5.00612
I0401 12:38:22.086272 21213 solver.cpp:237]     Train net output #0: loss = 5.00612 (* 1 = 5.00612 loss)
I0401 12:38:22.086277 21213 sgd_solver.cpp:105] Iteration 1232, lr = 0.001
I0401 12:38:25.591320 21213 solver.cpp:218] Iteration 1240 (2.28243 iter/s, 3.50504s/8 iters), loss = 5.09674
I0401 12:38:25.591363 21213 solver.cpp:237]     Train net output #0: loss = 5.09674 (* 1 = 5.09674 loss)
I0401 12:38:25.591368 21213 sgd_solver.cpp:105] Iteration 1240, lr = 0.001
I0401 12:38:29.149183 21213 solver.cpp:218] Iteration 1248 (2.24858 iter/s, 3.5578s/8 iters), loss = 5.0247
I0401 12:38:29.149230 21213 solver.cpp:237]     Train net output #0: loss = 5.0247 (* 1 = 5.0247 loss)
I0401 12:38:29.149238 21213 sgd_solver.cpp:105] Iteration 1248, lr = 0.001
I0401 12:38:32.633997 21213 solver.cpp:218] Iteration 1256 (2.29572 iter/s, 3.48475s/8 iters), loss = 4.9938
I0401 12:38:32.634050 21213 solver.cpp:237]     Train net output #0: loss = 4.9938 (* 1 = 4.9938 loss)
I0401 12:38:32.634058 21213 sgd_solver.cpp:105] Iteration 1256, lr = 0.001
I0401 12:38:35.933050 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:38:36.261771 21213 solver.cpp:218] Iteration 1264 (2.20525 iter/s, 3.6277s/8 iters), loss = 5.07704
I0401 12:38:36.261831 21213 solver.cpp:237]     Train net output #0: loss = 5.07704 (* 1 = 5.07704 loss)
I0401 12:38:36.261838 21213 sgd_solver.cpp:105] Iteration 1264, lr = 0.001
I0401 12:38:39.662182 21213 solver.cpp:218] Iteration 1272 (2.3527 iter/s, 3.40035s/8 iters), loss = 5.08312
I0401 12:38:39.662222 21213 solver.cpp:237]     Train net output #0: loss = 5.08312 (* 1 = 5.08312 loss)
I0401 12:38:39.662228 21213 sgd_solver.cpp:105] Iteration 1272, lr = 0.001
I0401 12:38:42.473102 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1280.caffemodel
I0401 12:38:45.505081 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1280.solverstate
I0401 12:38:47.890327 21213 solver.cpp:330] Iteration 1280, Testing net (#0)
I0401 12:38:47.890349 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:38:48.642349 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:38:50.033708 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0300481
I0401 12:38:50.033740 21213 solver.cpp:397]     Test net output #1: loss = 5.0524 (* 1 = 5.0524 loss)
I0401 12:38:50.171773 21213 solver.cpp:218] Iteration 1280 (0.761213 iter/s, 10.5095s/8 iters), loss = 5.13904
I0401 12:38:50.171828 21213 solver.cpp:237]     Train net output #0: loss = 5.13904 (* 1 = 5.13904 loss)
I0401 12:38:50.171835 21213 sgd_solver.cpp:105] Iteration 1280, lr = 0.001
I0401 12:38:52.529028 21213 solver.cpp:218] Iteration 1288 (3.39387 iter/s, 2.35719s/8 iters), loss = 5.07725
I0401 12:38:52.529078 21213 solver.cpp:237]     Train net output #0: loss = 5.07725 (* 1 = 5.07725 loss)
I0401 12:38:52.529085 21213 sgd_solver.cpp:105] Iteration 1288, lr = 0.001
I0401 12:38:55.977449 21213 solver.cpp:218] Iteration 1296 (2.31994 iter/s, 3.44836s/8 iters), loss = 5.00805
I0401 12:38:55.977494 21213 solver.cpp:237]     Train net output #0: loss = 5.00805 (* 1 = 5.00805 loss)
I0401 12:38:55.977499 21213 sgd_solver.cpp:105] Iteration 1296, lr = 0.001
I0401 12:38:59.450670 21213 solver.cpp:218] Iteration 1304 (2.30337 iter/s, 3.47316s/8 iters), loss = 4.93207
I0401 12:38:59.450709 21213 solver.cpp:237]     Train net output #0: loss = 4.93207 (* 1 = 4.93207 loss)
I0401 12:38:59.450714 21213 sgd_solver.cpp:105] Iteration 1304, lr = 0.001
I0401 12:39:02.956024 21213 solver.cpp:218] Iteration 1312 (2.28225 iter/s, 3.50531s/8 iters), loss = 4.93549
I0401 12:39:02.956058 21213 solver.cpp:237]     Train net output #0: loss = 4.93549 (* 1 = 4.93549 loss)
I0401 12:39:02.956063 21213 sgd_solver.cpp:105] Iteration 1312, lr = 0.001
I0401 12:39:06.411160 21213 solver.cpp:218] Iteration 1320 (2.31542 iter/s, 3.45509s/8 iters), loss = 4.93998
I0401 12:39:06.411195 21213 solver.cpp:237]     Train net output #0: loss = 4.93998 (* 1 = 4.93998 loss)
I0401 12:39:06.411201 21213 sgd_solver.cpp:105] Iteration 1320, lr = 0.001
I0401 12:39:09.301681 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:39:09.913105 21213 solver.cpp:218] Iteration 1328 (2.28448 iter/s, 3.50189s/8 iters), loss = 5.04022
I0401 12:39:09.913161 21213 solver.cpp:237]     Train net output #0: loss = 5.04022 (* 1 = 5.04022 loss)
I0401 12:39:09.913168 21213 sgd_solver.cpp:105] Iteration 1328, lr = 0.001
I0401 12:39:13.380353 21213 solver.cpp:218] Iteration 1336 (2.30735 iter/s, 3.46718s/8 iters), loss = 4.89616
I0401 12:39:13.380462 21213 solver.cpp:237]     Train net output #0: loss = 4.89616 (* 1 = 4.89616 loss)
I0401 12:39:13.380470 21213 sgd_solver.cpp:105] Iteration 1336, lr = 0.001
I0401 12:39:16.429971 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1344.caffemodel
I0401 12:39:19.575306 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1344.solverstate
I0401 12:39:21.883038 21213 solver.cpp:330] Iteration 1344, Testing net (#0)
I0401 12:39:21.883064 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:39:22.555740 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:39:24.002418 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0216346
I0401 12:39:24.002450 21213 solver.cpp:397]     Test net output #1: loss = 5.05689 (* 1 = 5.05689 loss)
I0401 12:39:24.143649 21213 solver.cpp:218] Iteration 1344 (0.743274 iter/s, 10.7632s/8 iters), loss = 5.04497
I0401 12:39:24.143710 21213 solver.cpp:237]     Train net output #0: loss = 5.04497 (* 1 = 5.04497 loss)
I0401 12:39:24.143719 21213 sgd_solver.cpp:105] Iteration 1344, lr = 0.001
I0401 12:39:26.658128 21213 solver.cpp:218] Iteration 1352 (3.18167 iter/s, 2.51441s/8 iters), loss = 5.064
I0401 12:39:26.658172 21213 solver.cpp:237]     Train net output #0: loss = 5.064 (* 1 = 5.064 loss)
I0401 12:39:26.658179 21213 sgd_solver.cpp:105] Iteration 1352, lr = 0.001
I0401 12:39:30.164582 21213 solver.cpp:218] Iteration 1360 (2.28154 iter/s, 3.5064s/8 iters), loss = 4.98334
I0401 12:39:30.164620 21213 solver.cpp:237]     Train net output #0: loss = 4.98334 (* 1 = 4.98334 loss)
I0401 12:39:30.164626 21213 sgd_solver.cpp:105] Iteration 1360, lr = 0.001
I0401 12:39:33.575253 21213 solver.cpp:218] Iteration 1368 (2.34561 iter/s, 3.41062s/8 iters), loss = 4.97133
I0401 12:39:33.575294 21213 solver.cpp:237]     Train net output #0: loss = 4.97133 (* 1 = 4.97133 loss)
I0401 12:39:33.575300 21213 sgd_solver.cpp:105] Iteration 1368, lr = 0.001
I0401 12:39:36.947531 21213 solver.cpp:218] Iteration 1376 (2.37232 iter/s, 3.37222s/8 iters), loss = 5.02273
I0401 12:39:36.947588 21213 solver.cpp:237]     Train net output #0: loss = 5.02273 (* 1 = 5.02273 loss)
I0401 12:39:36.947597 21213 sgd_solver.cpp:105] Iteration 1376, lr = 0.001
I0401 12:39:40.431530 21213 solver.cpp:218] Iteration 1384 (2.29625 iter/s, 3.48394s/8 iters), loss = 4.92038
I0401 12:39:40.431571 21213 solver.cpp:237]     Train net output #0: loss = 4.92038 (* 1 = 4.92038 loss)
I0401 12:39:40.431576 21213 sgd_solver.cpp:105] Iteration 1384, lr = 0.001
I0401 12:39:43.220332 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:39:44.772342 21213 solver.cpp:218] Iteration 1392 (1.85215 iter/s, 4.31931s/8 iters), loss = 5.01746
I0401 12:39:44.784977 21213 solver.cpp:237]     Train net output #0: loss = 5.01746 (* 1 = 5.01746 loss)
I0401 12:39:44.784993 21213 sgd_solver.cpp:105] Iteration 1392, lr = 0.001
I0401 12:39:53.025175 21213 solver.cpp:218] Iteration 1400 (0.97085 iter/s, 8.24021s/8 iters), loss = 4.99051
I0401 12:39:53.025229 21213 solver.cpp:237]     Train net output #0: loss = 4.99051 (* 1 = 4.99051 loss)
I0401 12:39:53.025238 21213 sgd_solver.cpp:105] Iteration 1400, lr = 0.001
I0401 12:40:00.395668 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1408.caffemodel
I0401 12:40:06.090782 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1408.solverstate
I0401 12:40:09.243230 21213 solver.cpp:330] Iteration 1408, Testing net (#0)
I0401 12:40:09.243258 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:40:11.233800 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:40:14.455621 21213 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0401 12:40:14.455658 21213 solver.cpp:397]     Test net output #1: loss = 5.03561 (* 1 = 5.03561 loss)
I0401 12:40:14.638216 21213 solver.cpp:218] Iteration 1408 (0.370148 iter/s, 21.613s/8 iters), loss = 5.0894
I0401 12:40:14.638273 21213 solver.cpp:237]     Train net output #0: loss = 5.0894 (* 1 = 5.0894 loss)
I0401 12:40:14.638280 21213 sgd_solver.cpp:105] Iteration 1408, lr = 0.001
I0401 12:40:18.768107 21213 solver.cpp:218] Iteration 1416 (1.93713 iter/s, 4.12982s/8 iters), loss = 4.96003
I0401 12:40:18.812966 21213 solver.cpp:237]     Train net output #0: loss = 4.96003 (* 1 = 4.96003 loss)
I0401 12:40:18.812981 21213 sgd_solver.cpp:105] Iteration 1416, lr = 0.001
I0401 12:40:24.060562 21213 solver.cpp:218] Iteration 1424 (1.52451 iter/s, 5.2476s/8 iters), loss = 4.9935
I0401 12:40:24.060614 21213 solver.cpp:237]     Train net output #0: loss = 4.9935 (* 1 = 4.9935 loss)
I0401 12:40:24.060622 21213 sgd_solver.cpp:105] Iteration 1424, lr = 0.001
I0401 12:40:28.466972 21213 solver.cpp:218] Iteration 1432 (1.81556 iter/s, 4.40635s/8 iters), loss = 4.91508
I0401 12:40:28.468027 21213 solver.cpp:237]     Train net output #0: loss = 4.91508 (* 1 = 4.91508 loss)
I0401 12:40:28.468039 21213 sgd_solver.cpp:105] Iteration 1432, lr = 0.001
I0401 12:40:33.021221 21213 solver.cpp:218] Iteration 1440 (1.75701 iter/s, 4.55319s/8 iters), loss = 4.98852
I0401 12:40:33.021279 21213 solver.cpp:237]     Train net output #0: loss = 4.98852 (* 1 = 4.98852 loss)
I0401 12:40:33.021287 21213 sgd_solver.cpp:105] Iteration 1440, lr = 0.001
I0401 12:40:37.129065 21213 solver.cpp:218] Iteration 1448 (1.94753 iter/s, 4.10777s/8 iters), loss = 4.97557
I0401 12:40:37.129123 21213 solver.cpp:237]     Train net output #0: loss = 4.97557 (* 1 = 4.97557 loss)
I0401 12:40:37.129132 21213 sgd_solver.cpp:105] Iteration 1448, lr = 0.001
I0401 12:40:39.702841 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:40:41.370023 21213 solver.cpp:218] Iteration 1456 (1.8864 iter/s, 4.24088s/8 iters), loss = 4.97225
I0401 12:40:41.370090 21213 solver.cpp:237]     Train net output #0: loss = 4.97225 (* 1 = 4.97225 loss)
I0401 12:40:41.370100 21213 sgd_solver.cpp:105] Iteration 1456, lr = 0.001
I0401 12:40:45.558156 21213 solver.cpp:218] Iteration 1464 (1.9102 iter/s, 4.18805s/8 iters), loss = 5.02638
I0401 12:40:45.558213 21213 solver.cpp:237]     Train net output #0: loss = 5.02638 (* 1 = 5.02638 loss)
I0401 12:40:45.558220 21213 sgd_solver.cpp:105] Iteration 1464, lr = 0.001
I0401 12:40:49.245932 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1472.caffemodel
I0401 12:40:52.853897 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1472.solverstate
I0401 12:40:55.624148 21213 solver.cpp:330] Iteration 1472, Testing net (#0)
I0401 12:40:55.624173 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:40:56.308511 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:40:58.712991 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0300481
I0401 12:40:58.713023 21213 solver.cpp:397]     Test net output #1: loss = 5.02629 (* 1 = 5.02629 loss)
I0401 12:40:58.865214 21213 solver.cpp:218] Iteration 1472 (0.601187 iter/s, 13.307s/8 iters), loss = 4.97679
I0401 12:40:58.866784 21213 solver.cpp:237]     Train net output #0: loss = 4.97679 (* 1 = 4.97679 loss)
I0401 12:40:58.866803 21213 sgd_solver.cpp:105] Iteration 1472, lr = 0.001
I0401 12:41:02.128005 21213 solver.cpp:218] Iteration 1480 (2.45307 iter/s, 3.26122s/8 iters), loss = 4.98901
I0401 12:41:02.128057 21213 solver.cpp:237]     Train net output #0: loss = 4.98901 (* 1 = 4.98901 loss)
I0401 12:41:02.128065 21213 sgd_solver.cpp:105] Iteration 1480, lr = 0.001
I0401 12:41:06.451579 21213 solver.cpp:218] Iteration 1488 (1.85044 iter/s, 4.3233s/8 iters), loss = 4.95577
I0401 12:41:06.451640 21213 solver.cpp:237]     Train net output #0: loss = 4.95577 (* 1 = 4.95577 loss)
I0401 12:41:06.451647 21213 sgd_solver.cpp:105] Iteration 1488, lr = 0.001
I0401 12:41:10.675747 21213 solver.cpp:218] Iteration 1496 (1.8939 iter/s, 4.2241s/8 iters), loss = 4.9613
I0401 12:41:10.675803 21213 solver.cpp:237]     Train net output #0: loss = 4.9613 (* 1 = 4.9613 loss)
I0401 12:41:10.675812 21213 sgd_solver.cpp:105] Iteration 1496, lr = 0.001
I0401 12:41:14.878196 21213 solver.cpp:218] Iteration 1504 (1.90368 iter/s, 4.20238s/8 iters), loss = 4.99549
I0401 12:41:14.878249 21213 solver.cpp:237]     Train net output #0: loss = 4.99549 (* 1 = 4.99549 loss)
I0401 12:41:14.878257 21213 sgd_solver.cpp:105] Iteration 1504, lr = 0.001
I0401 12:41:19.039441 21213 solver.cpp:218] Iteration 1512 (1.92253 iter/s, 4.16118s/8 iters), loss = 4.98992
I0401 12:41:19.039489 21213 solver.cpp:237]     Train net output #0: loss = 4.98992 (* 1 = 4.98992 loss)
I0401 12:41:19.039497 21213 sgd_solver.cpp:105] Iteration 1512, lr = 0.001
I0401 12:41:21.208557 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:41:22.798139 21213 solver.cpp:218] Iteration 1520 (2.12843 iter/s, 3.75864s/8 iters), loss = 4.99384
I0401 12:41:22.798178 21213 solver.cpp:237]     Train net output #0: loss = 4.99384 (* 1 = 4.99384 loss)
I0401 12:41:22.798184 21213 sgd_solver.cpp:105] Iteration 1520, lr = 0.001
I0401 12:41:23.148552 21213 blocking_queue.cpp:49] Waiting for data
I0401 12:41:26.385991 21213 solver.cpp:218] Iteration 1528 (2.22978 iter/s, 3.5878s/8 iters), loss = 5.02668
I0401 12:41:26.386051 21213 solver.cpp:237]     Train net output #0: loss = 5.02668 (* 1 = 5.02668 loss)
I0401 12:41:26.386061 21213 sgd_solver.cpp:105] Iteration 1528, lr = 0.001
I0401 12:41:29.449216 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1536.caffemodel
I0401 12:41:32.497557 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1536.solverstate
I0401 12:41:34.800161 21213 solver.cpp:330] Iteration 1536, Testing net (#0)
I0401 12:41:34.800177 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:41:35.285847 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:41:36.885318 21213 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0401 12:41:36.885358 21213 solver.cpp:397]     Test net output #1: loss = 5.00978 (* 1 = 5.00978 loss)
I0401 12:41:37.023200 21213 solver.cpp:218] Iteration 1536 (0.752081 iter/s, 10.6371s/8 iters), loss = 4.99471
I0401 12:41:37.023258 21213 solver.cpp:237]     Train net output #0: loss = 4.99471 (* 1 = 4.99471 loss)
I0401 12:41:37.023267 21213 sgd_solver.cpp:105] Iteration 1536, lr = 0.001
I0401 12:41:39.679690 21213 solver.cpp:218] Iteration 1544 (3.01157 iter/s, 2.65642s/8 iters), loss = 5.03638
I0401 12:41:39.679734 21213 solver.cpp:237]     Train net output #0: loss = 5.03638 (* 1 = 5.03638 loss)
I0401 12:41:39.679740 21213 sgd_solver.cpp:105] Iteration 1544, lr = 0.001
I0401 12:41:43.051566 21213 solver.cpp:218] Iteration 1552 (2.37261 iter/s, 3.37182s/8 iters), loss = 4.9382
I0401 12:41:43.051622 21213 solver.cpp:237]     Train net output #0: loss = 4.9382 (* 1 = 4.9382 loss)
I0401 12:41:43.051630 21213 sgd_solver.cpp:105] Iteration 1552, lr = 0.001
I0401 12:41:46.620065 21213 solver.cpp:218] Iteration 1560 (2.24188 iter/s, 3.56843s/8 iters), loss = 4.89434
I0401 12:41:46.620117 21213 solver.cpp:237]     Train net output #0: loss = 4.89434 (* 1 = 4.89434 loss)
I0401 12:41:46.620126 21213 sgd_solver.cpp:105] Iteration 1560, lr = 0.001
I0401 12:41:50.089690 21213 solver.cpp:218] Iteration 1568 (2.30577 iter/s, 3.46956s/8 iters), loss = 4.78172
I0401 12:41:50.089742 21213 solver.cpp:237]     Train net output #0: loss = 4.78172 (* 1 = 4.78172 loss)
I0401 12:41:50.089751 21213 sgd_solver.cpp:105] Iteration 1568, lr = 0.001
I0401 12:41:53.488413 21213 solver.cpp:218] Iteration 1576 (2.35387 iter/s, 3.39866s/8 iters), loss = 4.88997
I0401 12:41:53.488550 21213 solver.cpp:237]     Train net output #0: loss = 4.88997 (* 1 = 4.88997 loss)
I0401 12:41:53.488559 21213 sgd_solver.cpp:105] Iteration 1576, lr = 0.001
I0401 12:41:54.896431 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:41:56.998311 21213 solver.cpp:218] Iteration 1584 (2.27937 iter/s, 3.50974s/8 iters), loss = 4.96683
I0401 12:41:56.998368 21213 solver.cpp:237]     Train net output #0: loss = 4.96683 (* 1 = 4.96683 loss)
I0401 12:41:56.998376 21213 sgd_solver.cpp:105] Iteration 1584, lr = 0.001
I0401 12:42:00.383836 21213 solver.cpp:218] Iteration 1592 (2.36305 iter/s, 3.38545s/8 iters), loss = 4.94141
I0401 12:42:00.383893 21213 solver.cpp:237]     Train net output #0: loss = 4.94141 (* 1 = 4.94141 loss)
I0401 12:42:00.383899 21213 sgd_solver.cpp:105] Iteration 1592, lr = 0.001
I0401 12:42:03.385805 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1600.caffemodel
I0401 12:42:06.333967 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1600.solverstate
I0401 12:42:08.663434 21213 solver.cpp:330] Iteration 1600, Testing net (#0)
I0401 12:42:08.663462 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:42:09.131614 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:42:10.817392 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0324519
I0401 12:42:10.817421 21213 solver.cpp:397]     Test net output #1: loss = 4.99984 (* 1 = 4.99984 loss)
I0401 12:42:10.959154 21213 solver.cpp:218] Iteration 1600 (0.756482 iter/s, 10.5753s/8 iters), loss = 4.88
I0401 12:42:10.960741 21213 solver.cpp:237]     Train net output #0: loss = 4.88 (* 1 = 4.88 loss)
I0401 12:42:10.960752 21213 sgd_solver.cpp:105] Iteration 1600, lr = 0.001
I0401 12:42:13.379089 21213 solver.cpp:218] Iteration 1608 (3.30805 iter/s, 2.41834s/8 iters), loss = 4.96222
I0401 12:42:13.379133 21213 solver.cpp:237]     Train net output #0: loss = 4.96222 (* 1 = 4.96222 loss)
I0401 12:42:13.379138 21213 sgd_solver.cpp:105] Iteration 1608, lr = 0.001
I0401 12:42:16.799082 21213 solver.cpp:218] Iteration 1616 (2.33922 iter/s, 3.41994s/8 iters), loss = 5.04483
I0401 12:42:16.799124 21213 solver.cpp:237]     Train net output #0: loss = 5.04483 (* 1 = 5.04483 loss)
I0401 12:42:16.799129 21213 sgd_solver.cpp:105] Iteration 1616, lr = 0.001
I0401 12:42:20.344142 21213 solver.cpp:218] Iteration 1624 (2.25669 iter/s, 3.54501s/8 iters), loss = 4.85071
I0401 12:42:20.344182 21213 solver.cpp:237]     Train net output #0: loss = 4.85071 (* 1 = 4.85071 loss)
I0401 12:42:20.344187 21213 sgd_solver.cpp:105] Iteration 1624, lr = 0.001
I0401 12:42:23.822072 21213 solver.cpp:218] Iteration 1632 (2.30025 iter/s, 3.47788s/8 iters), loss = 4.90645
I0401 12:42:23.822230 21213 solver.cpp:237]     Train net output #0: loss = 4.90645 (* 1 = 4.90645 loss)
I0401 12:42:23.822239 21213 sgd_solver.cpp:105] Iteration 1632, lr = 0.001
I0401 12:42:27.307850 21213 solver.cpp:218] Iteration 1640 (2.29515 iter/s, 3.48561s/8 iters), loss = 4.96562
I0401 12:42:27.307904 21213 solver.cpp:237]     Train net output #0: loss = 4.96562 (* 1 = 4.96562 loss)
I0401 12:42:27.307911 21213 sgd_solver.cpp:105] Iteration 1640, lr = 0.001
I0401 12:42:28.244930 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:42:30.572502 21213 solver.cpp:218] Iteration 1648 (2.45054 iter/s, 3.26459s/8 iters), loss = 4.793
I0401 12:42:30.572556 21213 solver.cpp:237]     Train net output #0: loss = 4.793 (* 1 = 4.793 loss)
I0401 12:42:30.572563 21213 sgd_solver.cpp:105] Iteration 1648, lr = 0.001
I0401 12:42:34.069082 21213 solver.cpp:218] Iteration 1656 (2.28799 iter/s, 3.49652s/8 iters), loss = 4.94395
I0401 12:42:34.069123 21213 solver.cpp:237]     Train net output #0: loss = 4.94395 (* 1 = 4.94395 loss)
I0401 12:42:34.069128 21213 sgd_solver.cpp:105] Iteration 1656, lr = 0.001
I0401 12:42:37.005928 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1664.caffemodel
I0401 12:42:40.056227 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1664.solverstate
I0401 12:42:43.604048 21213 solver.cpp:330] Iteration 1664, Testing net (#0)
I0401 12:42:43.604066 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:42:44.004132 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:42:45.820152 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0300481
I0401 12:42:45.820205 21213 solver.cpp:397]     Test net output #1: loss = 4.97644 (* 1 = 4.97644 loss)
I0401 12:42:45.953915 21213 solver.cpp:218] Iteration 1664 (0.673129 iter/s, 11.8848s/8 iters), loss = 4.94557
I0401 12:42:45.953953 21213 solver.cpp:237]     Train net output #0: loss = 4.94557 (* 1 = 4.94557 loss)
I0401 12:42:45.953958 21213 sgd_solver.cpp:105] Iteration 1664, lr = 0.001
I0401 12:42:48.726240 21213 solver.cpp:218] Iteration 1672 (2.88572 iter/s, 2.77227s/8 iters), loss = 4.81228
I0401 12:42:48.726286 21213 solver.cpp:237]     Train net output #0: loss = 4.81228 (* 1 = 4.81228 loss)
I0401 12:42:48.726292 21213 sgd_solver.cpp:105] Iteration 1672, lr = 0.001
I0401 12:42:52.227530 21213 solver.cpp:218] Iteration 1680 (2.28491 iter/s, 3.50123s/8 iters), loss = 4.89127
I0401 12:42:52.227578 21213 solver.cpp:237]     Train net output #0: loss = 4.89127 (* 1 = 4.89127 loss)
I0401 12:42:52.227586 21213 sgd_solver.cpp:105] Iteration 1680, lr = 0.001
I0401 12:42:55.920291 21213 solver.cpp:218] Iteration 1688 (2.16644 iter/s, 3.6927s/8 iters), loss = 4.9138
I0401 12:42:55.920423 21213 solver.cpp:237]     Train net output #0: loss = 4.9138 (* 1 = 4.9138 loss)
I0401 12:42:55.920431 21213 sgd_solver.cpp:105] Iteration 1688, lr = 0.001
I0401 12:42:59.485077 21213 solver.cpp:218] Iteration 1696 (2.24426 iter/s, 3.56465s/8 iters), loss = 4.84966
I0401 12:42:59.485136 21213 solver.cpp:237]     Train net output #0: loss = 4.84966 (* 1 = 4.84966 loss)
I0401 12:42:59.485143 21213 sgd_solver.cpp:105] Iteration 1696, lr = 0.001
I0401 12:43:03.042243 21213 solver.cpp:218] Iteration 1704 (2.24902 iter/s, 3.5571s/8 iters), loss = 4.96272
I0401 12:43:03.042279 21213 solver.cpp:237]     Train net output #0: loss = 4.96272 (* 1 = 4.96272 loss)
I0401 12:43:03.042285 21213 sgd_solver.cpp:105] Iteration 1704, lr = 0.001
I0401 12:43:03.668256 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:43:06.710924 21213 solver.cpp:218] Iteration 1712 (2.18065 iter/s, 3.66864s/8 iters), loss = 4.94299
I0401 12:43:06.710961 21213 solver.cpp:237]     Train net output #0: loss = 4.94299 (* 1 = 4.94299 loss)
I0401 12:43:06.710966 21213 sgd_solver.cpp:105] Iteration 1712, lr = 0.001
I0401 12:43:10.334121 21213 solver.cpp:218] Iteration 1720 (2.20803 iter/s, 3.62314s/8 iters), loss = 4.83733
I0401 12:43:10.334180 21213 solver.cpp:237]     Train net output #0: loss = 4.83733 (* 1 = 4.83733 loss)
I0401 12:43:10.334189 21213 sgd_solver.cpp:105] Iteration 1720, lr = 0.001
I0401 12:43:13.391961 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1728.caffemodel
I0401 12:43:16.515897 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1728.solverstate
I0401 12:43:18.841404 21213 solver.cpp:330] Iteration 1728, Testing net (#0)
I0401 12:43:18.841424 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:43:19.116909 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:43:20.946008 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0360577
I0401 12:43:20.946048 21213 solver.cpp:397]     Test net output #1: loss = 4.96999 (* 1 = 4.96999 loss)
I0401 12:43:21.087497 21213 solver.cpp:218] Iteration 1728 (0.743956 iter/s, 10.7533s/8 iters), loss = 4.82739
I0401 12:43:21.087548 21213 solver.cpp:237]     Train net output #0: loss = 4.82739 (* 1 = 4.82739 loss)
I0401 12:43:21.087555 21213 sgd_solver.cpp:105] Iteration 1728, lr = 0.001
I0401 12:43:23.850028 21213 solver.cpp:218] Iteration 1736 (2.89596 iter/s, 2.76247s/8 iters), loss = 4.85147
I0401 12:43:23.850086 21213 solver.cpp:237]     Train net output #0: loss = 4.85147 (* 1 = 4.85147 loss)
I0401 12:43:23.850095 21213 sgd_solver.cpp:105] Iteration 1736, lr = 0.001
I0401 12:43:27.413519 21213 solver.cpp:218] Iteration 1744 (2.24503 iter/s, 3.56343s/8 iters), loss = 4.88023
I0401 12:43:27.413636 21213 solver.cpp:237]     Train net output #0: loss = 4.88023 (* 1 = 4.88023 loss)
I0401 12:43:27.413642 21213 sgd_solver.cpp:105] Iteration 1744, lr = 0.001
I0401 12:43:30.986202 21213 solver.cpp:218] Iteration 1752 (2.2393 iter/s, 3.57255s/8 iters), loss = 5.05975
I0401 12:43:30.986258 21213 solver.cpp:237]     Train net output #0: loss = 5.05975 (* 1 = 5.05975 loss)
I0401 12:43:30.986265 21213 sgd_solver.cpp:105] Iteration 1752, lr = 0.001
I0401 12:43:34.418452 21213 solver.cpp:218] Iteration 1760 (2.33088 iter/s, 3.43218s/8 iters), loss = 4.73373
I0401 12:43:34.418506 21213 solver.cpp:237]     Train net output #0: loss = 4.73373 (* 1 = 4.73373 loss)
I0401 12:43:34.418514 21213 sgd_solver.cpp:105] Iteration 1760, lr = 0.001
I0401 12:43:37.756690 21213 solver.cpp:218] Iteration 1768 (2.39689 iter/s, 3.33765s/8 iters), loss = 4.80011
I0401 12:43:37.756742 21213 solver.cpp:237]     Train net output #0: loss = 4.80011 (* 1 = 4.80011 loss)
I0401 12:43:37.756749 21213 sgd_solver.cpp:105] Iteration 1768, lr = 0.001
I0401 12:43:38.125248 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:43:41.057893 21213 solver.cpp:218] Iteration 1776 (2.42341 iter/s, 3.30114s/8 iters), loss = 4.86023
I0401 12:43:41.057950 21213 solver.cpp:237]     Train net output #0: loss = 4.86023 (* 1 = 4.86023 loss)
I0401 12:43:41.057957 21213 sgd_solver.cpp:105] Iteration 1776, lr = 0.001
I0401 12:43:44.614145 21213 solver.cpp:218] Iteration 1784 (2.2496 iter/s, 3.55619s/8 iters), loss = 4.8949
I0401 12:43:44.614183 21213 solver.cpp:237]     Train net output #0: loss = 4.8949 (* 1 = 4.8949 loss)
I0401 12:43:44.614188 21213 sgd_solver.cpp:105] Iteration 1784, lr = 0.001
I0401 12:43:47.661347 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1792.caffemodel
I0401 12:43:50.756876 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1792.solverstate
I0401 12:43:53.058897 21213 solver.cpp:330] Iteration 1792, Testing net (#0)
I0401 12:43:53.058921 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:43:53.321528 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:43:55.372555 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0336538
I0401 12:43:55.372591 21213 solver.cpp:397]     Test net output #1: loss = 4.95341 (* 1 = 4.95341 loss)
I0401 12:43:55.511353 21213 solver.cpp:218] Iteration 1792 (0.734136 iter/s, 10.8972s/8 iters), loss = 4.8595
I0401 12:43:55.511400 21213 solver.cpp:237]     Train net output #0: loss = 4.8595 (* 1 = 4.8595 loss)
I0401 12:43:55.511406 21213 sgd_solver.cpp:105] Iteration 1792, lr = 0.001
I0401 12:43:58.215688 21213 solver.cpp:218] Iteration 1800 (2.95828 iter/s, 2.70427s/8 iters), loss = 4.74621
I0401 12:43:58.215844 21213 solver.cpp:237]     Train net output #0: loss = 4.74621 (* 1 = 4.74621 loss)
I0401 12:43:58.215854 21213 sgd_solver.cpp:105] Iteration 1800, lr = 0.001
I0401 12:44:01.707569 21213 solver.cpp:218] Iteration 1808 (2.29114 iter/s, 3.49171s/8 iters), loss = 4.80248
I0401 12:44:01.707623 21213 solver.cpp:237]     Train net output #0: loss = 4.80248 (* 1 = 4.80248 loss)
I0401 12:44:01.707629 21213 sgd_solver.cpp:105] Iteration 1808, lr = 0.001
I0401 12:44:05.309790 21213 solver.cpp:218] Iteration 1816 (2.22089 iter/s, 3.60215s/8 iters), loss = 4.87537
I0401 12:44:05.309846 21213 solver.cpp:237]     Train net output #0: loss = 4.87537 (* 1 = 4.87537 loss)
I0401 12:44:05.309854 21213 sgd_solver.cpp:105] Iteration 1816, lr = 0.001
I0401 12:44:08.924327 21213 solver.cpp:218] Iteration 1824 (2.21333 iter/s, 3.61447s/8 iters), loss = 4.73989
I0401 12:44:08.924376 21213 solver.cpp:237]     Train net output #0: loss = 4.73989 (* 1 = 4.73989 loss)
I0401 12:44:08.924384 21213 sgd_solver.cpp:105] Iteration 1824, lr = 0.001
I0401 12:44:12.569938 21213 solver.cpp:218] Iteration 1832 (2.19445 iter/s, 3.64556s/8 iters), loss = 4.74819
I0401 12:44:12.569977 21213 solver.cpp:237]     Train net output #0: loss = 4.74819 (* 1 = 4.74819 loss)
I0401 12:44:12.569981 21213 sgd_solver.cpp:105] Iteration 1832, lr = 0.001
I0401 12:44:12.591797 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:44:15.969007 21213 solver.cpp:218] Iteration 1840 (2.35362 iter/s, 3.39901s/8 iters), loss = 4.9144
I0401 12:44:15.969046 21213 solver.cpp:237]     Train net output #0: loss = 4.9144 (* 1 = 4.9144 loss)
I0401 12:44:15.969053 21213 sgd_solver.cpp:105] Iteration 1840, lr = 0.001
I0401 12:44:19.638283 21213 solver.cpp:218] Iteration 1848 (2.1803 iter/s, 3.66923s/8 iters), loss = 4.96813
I0401 12:44:19.638321 21213 solver.cpp:237]     Train net output #0: loss = 4.96813 (* 1 = 4.96813 loss)
I0401 12:44:19.638326 21213 sgd_solver.cpp:105] Iteration 1848, lr = 0.001
I0401 12:44:22.725083 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1856.caffemodel
I0401 12:44:28.266337 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1856.solverstate
I0401 12:44:32.125932 21213 solver.cpp:330] Iteration 1856, Testing net (#0)
I0401 12:44:32.125953 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:44:32.292300 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:44:34.305192 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0444712
I0401 12:44:34.305233 21213 solver.cpp:397]     Test net output #1: loss = 4.92628 (* 1 = 4.92628 loss)
I0401 12:44:34.446537 21213 solver.cpp:218] Iteration 1856 (0.54024 iter/s, 14.8082s/8 iters), loss = 4.97754
I0401 12:44:34.446588 21213 solver.cpp:237]     Train net output #0: loss = 4.97754 (* 1 = 4.97754 loss)
I0401 12:44:34.446595 21213 sgd_solver.cpp:105] Iteration 1856, lr = 0.001
I0401 12:44:36.866528 21213 solver.cpp:218] Iteration 1864 (3.30588 iter/s, 2.41993s/8 iters), loss = 4.71467
I0401 12:44:36.866569 21213 solver.cpp:237]     Train net output #0: loss = 4.71467 (* 1 = 4.71467 loss)
I0401 12:44:36.866575 21213 sgd_solver.cpp:105] Iteration 1864, lr = 0.001
I0401 12:44:40.357333 21213 solver.cpp:218] Iteration 1872 (2.29177 iter/s, 3.49075s/8 iters), loss = 4.94515
I0401 12:44:40.357375 21213 solver.cpp:237]     Train net output #0: loss = 4.94515 (* 1 = 4.94515 loss)
I0401 12:44:40.357381 21213 sgd_solver.cpp:105] Iteration 1872, lr = 0.001
I0401 12:44:43.915469 21213 solver.cpp:218] Iteration 1880 (2.2484 iter/s, 3.55808s/8 iters), loss = 4.79352
I0401 12:44:43.915513 21213 solver.cpp:237]     Train net output #0: loss = 4.79352 (* 1 = 4.79352 loss)
I0401 12:44:43.915518 21213 sgd_solver.cpp:105] Iteration 1880, lr = 0.001
I0401 12:44:47.648734 21213 solver.cpp:218] Iteration 1888 (2.14293 iter/s, 3.73321s/8 iters), loss = 4.72614
I0401 12:44:47.648777 21213 solver.cpp:237]     Train net output #0: loss = 4.72614 (* 1 = 4.72614 loss)
I0401 12:44:47.648782 21213 sgd_solver.cpp:105] Iteration 1888, lr = 0.001
I0401 12:44:51.002454 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:44:51.278316 21213 solver.cpp:218] Iteration 1896 (2.20414 iter/s, 3.62953s/8 iters), loss = 4.8694
I0401 12:44:51.278362 21213 solver.cpp:237]     Train net output #0: loss = 4.8694 (* 1 = 4.8694 loss)
I0401 12:44:51.278367 21213 sgd_solver.cpp:105] Iteration 1896, lr = 0.001
I0401 12:44:54.877835 21213 solver.cpp:218] Iteration 1904 (2.22256 iter/s, 3.59946s/8 iters), loss = 4.76045
I0401 12:44:54.877882 21213 solver.cpp:237]     Train net output #0: loss = 4.76045 (* 1 = 4.76045 loss)
I0401 12:44:54.877888 21213 sgd_solver.cpp:105] Iteration 1904, lr = 0.001
I0401 12:44:58.176044 21213 solver.cpp:218] Iteration 1912 (2.4256 iter/s, 3.29815s/8 iters), loss = 4.98915
I0401 12:44:58.176095 21213 solver.cpp:237]     Train net output #0: loss = 4.98915 (* 1 = 4.98915 loss)
I0401 12:44:58.176103 21213 sgd_solver.cpp:105] Iteration 1912, lr = 0.001
I0401 12:45:01.182668 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1920.caffemodel
I0401 12:45:04.264250 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1920.solverstate
I0401 12:45:07.362012 21213 solver.cpp:330] Iteration 1920, Testing net (#0)
I0401 12:45:07.362036 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:45:07.447060 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:45:09.579752 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0324519
I0401 12:45:09.579782 21213 solver.cpp:397]     Test net output #1: loss = 4.926 (* 1 = 4.926 loss)
I0401 12:45:09.719316 21213 solver.cpp:218] Iteration 1920 (0.693047 iter/s, 11.5432s/8 iters), loss = 4.89287
I0401 12:45:09.719370 21213 solver.cpp:237]     Train net output #0: loss = 4.89287 (* 1 = 4.89287 loss)
I0401 12:45:09.719378 21213 sgd_solver.cpp:105] Iteration 1920, lr = 0.001
I0401 12:45:10.223652 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:45:12.431754 21213 solver.cpp:218] Iteration 1928 (2.94945 iter/s, 2.71237s/8 iters), loss = 4.74356
I0401 12:45:12.437942 21213 solver.cpp:237]     Train net output #0: loss = 4.74356 (* 1 = 4.74356 loss)
I0401 12:45:12.437961 21213 sgd_solver.cpp:105] Iteration 1928, lr = 0.001
I0401 12:45:15.974645 21213 solver.cpp:218] Iteration 1936 (2.26199 iter/s, 3.53671s/8 iters), loss = 4.81046
I0401 12:45:15.974687 21213 solver.cpp:237]     Train net output #0: loss = 4.81046 (* 1 = 4.81046 loss)
I0401 12:45:15.974694 21213 sgd_solver.cpp:105] Iteration 1936, lr = 0.001
I0401 12:45:19.736384 21213 solver.cpp:218] Iteration 1944 (2.12671 iter/s, 3.76169s/8 iters), loss = 4.90574
I0401 12:45:19.736421 21213 solver.cpp:237]     Train net output #0: loss = 4.90574 (* 1 = 4.90574 loss)
I0401 12:45:19.736428 21213 sgd_solver.cpp:105] Iteration 1944, lr = 0.001
I0401 12:45:23.319813 21213 solver.cpp:218] Iteration 1952 (2.23253 iter/s, 3.58338s/8 iters), loss = 4.74747
I0401 12:45:23.319862 21213 solver.cpp:237]     Train net output #0: loss = 4.74747 (* 1 = 4.74747 loss)
I0401 12:45:23.319869 21213 sgd_solver.cpp:105] Iteration 1952, lr = 0.001
I0401 12:45:26.294788 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:45:26.966531 21213 solver.cpp:218] Iteration 1960 (2.19379 iter/s, 3.64666s/8 iters), loss = 4.94808
I0401 12:45:26.966576 21213 solver.cpp:237]     Train net output #0: loss = 4.94808 (* 1 = 4.94808 loss)
I0401 12:45:26.966581 21213 sgd_solver.cpp:105] Iteration 1960, lr = 0.001
I0401 12:45:30.688736 21213 solver.cpp:218] Iteration 1968 (2.1493 iter/s, 3.72215s/8 iters), loss = 4.59418
I0401 12:45:30.688779 21213 solver.cpp:237]     Train net output #0: loss = 4.59418 (* 1 = 4.59418 loss)
I0401 12:45:30.688786 21213 sgd_solver.cpp:105] Iteration 1968, lr = 0.001
I0401 12:45:34.393416 21213 solver.cpp:218] Iteration 1976 (2.15946 iter/s, 3.70463s/8 iters), loss = 4.77363
I0401 12:45:34.393556 21213 solver.cpp:237]     Train net output #0: loss = 4.77363 (* 1 = 4.77363 loss)
I0401 12:45:34.393563 21213 sgd_solver.cpp:105] Iteration 1976, lr = 0.001
I0401 12:45:37.529423 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1984.caffemodel
I0401 12:45:40.520303 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1984.solverstate
I0401 12:45:42.897914 21213 solver.cpp:330] Iteration 1984, Testing net (#0)
I0401 12:45:42.897933 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:45:44.985653 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0348558
I0401 12:45:44.985694 21213 solver.cpp:397]     Test net output #1: loss = 4.91535 (* 1 = 4.91535 loss)
I0401 12:45:45.125036 21213 solver.cpp:218] Iteration 1984 (0.74547 iter/s, 10.7315s/8 iters), loss = 4.80162
I0401 12:45:45.125092 21213 solver.cpp:237]     Train net output #0: loss = 4.80162 (* 1 = 4.80162 loss)
I0401 12:45:45.125098 21213 sgd_solver.cpp:105] Iteration 1984, lr = 0.001
I0401 12:45:45.609659 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:45:47.828634 21213 solver.cpp:218] Iteration 1992 (2.9591 iter/s, 2.70353s/8 iters), loss = 4.6959
I0401 12:45:47.828680 21213 solver.cpp:237]     Train net output #0: loss = 4.6959 (* 1 = 4.6959 loss)
I0401 12:45:47.828685 21213 sgd_solver.cpp:105] Iteration 1992, lr = 0.001
I0401 12:45:51.297492 21213 solver.cpp:218] Iteration 2000 (2.30627 iter/s, 3.4688s/8 iters), loss = 4.63828
I0401 12:45:51.297535 21213 solver.cpp:237]     Train net output #0: loss = 4.63828 (* 1 = 4.63828 loss)
I0401 12:45:51.297540 21213 sgd_solver.cpp:105] Iteration 2000, lr = 0.001
I0401 12:45:55.020798 21213 solver.cpp:218] Iteration 2008 (2.14866 iter/s, 3.72325s/8 iters), loss = 4.72489
I0401 12:45:55.020840 21213 solver.cpp:237]     Train net output #0: loss = 4.72489 (* 1 = 4.72489 loss)
I0401 12:45:55.020845 21213 sgd_solver.cpp:105] Iteration 2008, lr = 0.001
I0401 12:45:58.599344 21213 solver.cpp:218] Iteration 2016 (2.23558 iter/s, 3.57849s/8 iters), loss = 4.69539
I0401 12:45:58.599381 21213 solver.cpp:237]     Train net output #0: loss = 4.69539 (* 1 = 4.69539 loss)
I0401 12:45:58.599386 21213 sgd_solver.cpp:105] Iteration 2016, lr = 0.001
I0401 12:46:00.900861 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:46:01.868427 21213 solver.cpp:218] Iteration 2024 (2.4472 iter/s, 3.26904s/8 iters), loss = 4.73313
I0401 12:46:01.868465 21213 solver.cpp:237]     Train net output #0: loss = 4.73313 (* 1 = 4.73313 loss)
I0401 12:46:01.868471 21213 sgd_solver.cpp:105] Iteration 2024, lr = 0.001
I0401 12:46:05.398082 21213 solver.cpp:218] Iteration 2032 (2.26654 iter/s, 3.5296s/8 iters), loss = 4.63287
I0401 12:46:05.398208 21213 solver.cpp:237]     Train net output #0: loss = 4.63287 (* 1 = 4.63287 loss)
I0401 12:46:05.398219 21213 sgd_solver.cpp:105] Iteration 2032, lr = 0.001
I0401 12:46:09.147877 21213 solver.cpp:218] Iteration 2040 (2.13353 iter/s, 3.74966s/8 iters), loss = 4.78133
I0401 12:46:09.147918 21213 solver.cpp:237]     Train net output #0: loss = 4.78133 (* 1 = 4.78133 loss)
I0401 12:46:09.147924 21213 sgd_solver.cpp:105] Iteration 2040, lr = 0.001
I0401 12:46:12.136904 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2048.caffemodel
I0401 12:46:15.377766 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2048.solverstate
I0401 12:46:17.764999 21213 solver.cpp:330] Iteration 2048, Testing net (#0)
I0401 12:46:17.765024 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:46:20.077656 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0408654
I0401 12:46:20.077684 21213 solver.cpp:397]     Test net output #1: loss = 4.902 (* 1 = 4.902 loss)
I0401 12:46:20.215328 21213 solver.cpp:218] Iteration 2048 (0.722843 iter/s, 11.0674s/8 iters), loss = 4.86682
I0401 12:46:20.215371 21213 solver.cpp:237]     Train net output #0: loss = 4.86682 (* 1 = 4.86682 loss)
I0401 12:46:20.215377 21213 sgd_solver.cpp:105] Iteration 2048, lr = 0.001
I0401 12:46:20.550855 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:46:22.896240 21213 solver.cpp:218] Iteration 2056 (2.98412 iter/s, 2.68085s/8 iters), loss = 4.77664
I0401 12:46:22.896281 21213 solver.cpp:237]     Train net output #0: loss = 4.77664 (* 1 = 4.77664 loss)
I0401 12:46:22.896286 21213 sgd_solver.cpp:105] Iteration 2056, lr = 0.001
I0401 12:46:26.453954 21213 solver.cpp:218] Iteration 2064 (2.24867 iter/s, 3.55766s/8 iters), loss = 4.63358
I0401 12:46:26.454000 21213 solver.cpp:237]     Train net output #0: loss = 4.63358 (* 1 = 4.63358 loss)
I0401 12:46:26.454008 21213 sgd_solver.cpp:105] Iteration 2064, lr = 0.001
I0401 12:46:30.015823 21213 solver.cpp:218] Iteration 2072 (2.24605 iter/s, 3.56181s/8 iters), loss = 4.85556
I0401 12:46:30.015872 21213 solver.cpp:237]     Train net output #0: loss = 4.85556 (* 1 = 4.85556 loss)
I0401 12:46:30.015877 21213 sgd_solver.cpp:105] Iteration 2072, lr = 0.001
I0401 12:46:33.659467 21213 solver.cpp:218] Iteration 2080 (2.19564 iter/s, 3.64358s/8 iters), loss = 4.77134
I0401 12:46:33.659521 21213 solver.cpp:237]     Train net output #0: loss = 4.77134 (* 1 = 4.77134 loss)
I0401 12:46:33.659529 21213 sgd_solver.cpp:105] Iteration 2080, lr = 0.001
I0401 12:46:35.993952 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:46:37.271555 21213 solver.cpp:218] Iteration 2088 (2.21483 iter/s, 3.61202s/8 iters), loss = 4.75993
I0401 12:46:37.271603 21213 solver.cpp:237]     Train net output #0: loss = 4.75993 (* 1 = 4.75993 loss)
I0401 12:46:37.271608 21213 sgd_solver.cpp:105] Iteration 2088, lr = 0.001
I0401 12:46:40.907439 21213 solver.cpp:218] Iteration 2096 (2.20033 iter/s, 3.63582s/8 iters), loss = 4.8407
I0401 12:46:40.907482 21213 solver.cpp:237]     Train net output #0: loss = 4.8407 (* 1 = 4.8407 loss)
I0401 12:46:40.907488 21213 sgd_solver.cpp:105] Iteration 2096, lr = 0.001
I0401 12:46:44.581284 21213 solver.cpp:218] Iteration 2104 (2.17759 iter/s, 3.67379s/8 iters), loss = 4.68586
I0401 12:46:44.581324 21213 solver.cpp:237]     Train net output #0: loss = 4.68586 (* 1 = 4.68586 loss)
I0401 12:46:44.581329 21213 sgd_solver.cpp:105] Iteration 2104, lr = 0.001
I0401 12:46:47.486799 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2112.caffemodel
I0401 12:46:51.205446 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2112.solverstate
I0401 12:46:53.522078 21213 solver.cpp:330] Iteration 2112, Testing net (#0)
I0401 12:46:53.522102 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:46:55.696238 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0288462
I0401 12:46:55.696275 21213 solver.cpp:397]     Test net output #1: loss = 4.87396 (* 1 = 4.87396 loss)
I0401 12:46:55.819667 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:46:55.828480 21213 solver.cpp:218] Iteration 2112 (0.711291 iter/s, 11.2472s/8 iters), loss = 4.87125
I0401 12:46:55.828524 21213 solver.cpp:237]     Train net output #0: loss = 4.87125 (* 1 = 4.87125 loss)
I0401 12:46:55.828529 21213 sgd_solver.cpp:105] Iteration 2112, lr = 0.001
I0401 12:46:58.592798 21213 solver.cpp:218] Iteration 2120 (2.89409 iter/s, 2.76426s/8 iters), loss = 4.76675
I0401 12:46:58.592845 21213 solver.cpp:237]     Train net output #0: loss = 4.76675 (* 1 = 4.76675 loss)
I0401 12:46:58.592851 21213 sgd_solver.cpp:105] Iteration 2120, lr = 0.001
I0401 12:47:02.071300 21213 solver.cpp:218] Iteration 2128 (2.29988 iter/s, 3.47844s/8 iters), loss = 4.74952
I0401 12:47:02.071336 21213 solver.cpp:237]     Train net output #0: loss = 4.74952 (* 1 = 4.74952 loss)
I0401 12:47:02.071341 21213 sgd_solver.cpp:105] Iteration 2128, lr = 0.001
I0401 12:47:05.466706 21213 solver.cpp:218] Iteration 2136 (2.35616 iter/s, 3.39535s/8 iters), loss = 4.81746
I0401 12:47:05.466753 21213 solver.cpp:237]     Train net output #0: loss = 4.81746 (* 1 = 4.81746 loss)
I0401 12:47:05.466759 21213 sgd_solver.cpp:105] Iteration 2136, lr = 0.001
I0401 12:47:09.124706 21213 solver.cpp:218] Iteration 2144 (2.18703 iter/s, 3.65794s/8 iters), loss = 4.70109
I0401 12:47:09.124861 21213 solver.cpp:237]     Train net output #0: loss = 4.70109 (* 1 = 4.70109 loss)
I0401 12:47:09.124871 21213 sgd_solver.cpp:105] Iteration 2144, lr = 0.001
I0401 12:47:11.020547 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:47:12.666579 21213 solver.cpp:218] Iteration 2152 (2.2588 iter/s, 3.54171s/8 iters), loss = 4.75044
I0401 12:47:12.666620 21213 solver.cpp:237]     Train net output #0: loss = 4.75044 (* 1 = 4.75044 loss)
I0401 12:47:12.666625 21213 sgd_solver.cpp:105] Iteration 2152, lr = 0.001
I0401 12:47:16.329953 21213 solver.cpp:218] Iteration 2160 (2.18381 iter/s, 3.66332s/8 iters), loss = 4.68338
I0401 12:47:16.330004 21213 solver.cpp:237]     Train net output #0: loss = 4.68338 (* 1 = 4.68338 loss)
I0401 12:47:16.330013 21213 sgd_solver.cpp:105] Iteration 2160, lr = 0.001
I0401 12:47:19.737361 21213 solver.cpp:218] Iteration 2168 (2.34787 iter/s, 3.40735s/8 iters), loss = 4.84397
I0401 12:47:19.737402 21213 solver.cpp:237]     Train net output #0: loss = 4.84397 (* 1 = 4.84397 loss)
I0401 12:47:19.737408 21213 sgd_solver.cpp:105] Iteration 2168, lr = 0.001
I0401 12:47:22.505527 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2176.caffemodel
I0401 12:47:25.522653 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2176.solverstate
I0401 12:47:27.838413 21213 solver.cpp:330] Iteration 2176, Testing net (#0)
I0401 12:47:27.838433 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:47:30.048239 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0420673
I0401 12:47:30.048280 21213 solver.cpp:397]     Test net output #1: loss = 4.84391 (* 1 = 4.84391 loss)
I0401 12:47:30.102691 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:47:30.186156 21213 solver.cpp:218] Iteration 2176 (0.765642 iter/s, 10.4487s/8 iters), loss = 4.82062
I0401 12:47:30.186205 21213 solver.cpp:237]     Train net output #0: loss = 4.82062 (* 1 = 4.82062 loss)
I0401 12:47:30.186211 21213 sgd_solver.cpp:105] Iteration 2176, lr = 0.001
I0401 12:47:32.812784 21213 solver.cpp:218] Iteration 2184 (3.0458 iter/s, 2.62657s/8 iters), loss = 4.65672
I0401 12:47:32.812841 21213 solver.cpp:237]     Train net output #0: loss = 4.65672 (* 1 = 4.65672 loss)
I0401 12:47:32.812852 21213 sgd_solver.cpp:105] Iteration 2184, lr = 0.001
I0401 12:47:36.371348 21213 solver.cpp:218] Iteration 2192 (2.24814 iter/s, 3.55849s/8 iters), loss = 4.81207
I0401 12:47:36.371394 21213 solver.cpp:237]     Train net output #0: loss = 4.81207 (* 1 = 4.81207 loss)
I0401 12:47:36.371400 21213 sgd_solver.cpp:105] Iteration 2192, lr = 0.001
I0401 12:47:39.801995 21213 solver.cpp:218] Iteration 2200 (2.33196 iter/s, 3.43059s/8 iters), loss = 4.58181
I0401 12:47:39.802109 21213 solver.cpp:237]     Train net output #0: loss = 4.58181 (* 1 = 4.58181 loss)
I0401 12:47:39.802119 21213 sgd_solver.cpp:105] Iteration 2200, lr = 0.001
I0401 12:47:43.387213 21213 solver.cpp:218] Iteration 2208 (2.23146 iter/s, 3.5851s/8 iters), loss = 4.78808
I0401 12:47:43.387249 21213 solver.cpp:237]     Train net output #0: loss = 4.78808 (* 1 = 4.78808 loss)
I0401 12:47:43.387255 21213 sgd_solver.cpp:105] Iteration 2208, lr = 0.001
I0401 12:47:44.926640 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:47:47.064834 21213 solver.cpp:218] Iteration 2216 (2.17535 iter/s, 3.67757s/8 iters), loss = 4.6798
I0401 12:47:47.064872 21213 solver.cpp:237]     Train net output #0: loss = 4.6798 (* 1 = 4.6798 loss)
I0401 12:47:47.064878 21213 sgd_solver.cpp:105] Iteration 2216, lr = 0.001
I0401 12:47:50.710705 21213 solver.cpp:218] Iteration 2224 (2.19429 iter/s, 3.64582s/8 iters), loss = 4.73131
I0401 12:47:50.710743 21213 solver.cpp:237]     Train net output #0: loss = 4.73131 (* 1 = 4.73131 loss)
I0401 12:47:50.710748 21213 sgd_solver.cpp:105] Iteration 2224, lr = 0.001
I0401 12:47:54.207211 21213 solver.cpp:218] Iteration 2232 (2.28804 iter/s, 3.49645s/8 iters), loss = 4.58202
I0401 12:47:54.207278 21213 solver.cpp:237]     Train net output #0: loss = 4.58202 (* 1 = 4.58202 loss)
I0401 12:47:54.207288 21213 sgd_solver.cpp:105] Iteration 2232, lr = 0.001
I0401 12:47:57.154661 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2240.caffemodel
I0401 12:48:01.018327 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2240.solverstate
I0401 12:48:03.390543 21213 solver.cpp:330] Iteration 2240, Testing net (#0)
I0401 12:48:03.390566 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:48:05.560873 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:48:05.624881 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0492788
I0401 12:48:05.624923 21213 solver.cpp:397]     Test net output #1: loss = 4.83701 (* 1 = 4.83701 loss)
I0401 12:48:05.768221 21213 solver.cpp:218] Iteration 2240 (0.691985 iter/s, 11.5609s/8 iters), loss = 4.76308
I0401 12:48:05.768270 21213 solver.cpp:237]     Train net output #0: loss = 4.76308 (* 1 = 4.76308 loss)
I0401 12:48:05.768278 21213 sgd_solver.cpp:105] Iteration 2240, lr = 0.001
I0401 12:48:08.367210 21213 solver.cpp:218] Iteration 2248 (3.07819 iter/s, 2.59893s/8 iters), loss = 4.76791
I0401 12:48:08.367247 21213 solver.cpp:237]     Train net output #0: loss = 4.76791 (* 1 = 4.76791 loss)
I0401 12:48:08.367252 21213 sgd_solver.cpp:105] Iteration 2248, lr = 0.001
I0401 12:48:11.839326 21213 solver.cpp:218] Iteration 2256 (2.30411 iter/s, 3.47206s/8 iters), loss = 4.53791
I0401 12:48:11.839458 21213 solver.cpp:237]     Train net output #0: loss = 4.53791 (* 1 = 4.53791 loss)
I0401 12:48:11.839466 21213 sgd_solver.cpp:105] Iteration 2256, lr = 0.001
I0401 12:48:15.411087 21213 solver.cpp:218] Iteration 2264 (2.23988 iter/s, 3.57162s/8 iters), loss = 4.50572
I0401 12:48:15.411124 21213 solver.cpp:237]     Train net output #0: loss = 4.50572 (* 1 = 4.50572 loss)
I0401 12:48:15.411130 21213 sgd_solver.cpp:105] Iteration 2264, lr = 0.001
I0401 12:48:18.996801 21213 solver.cpp:218] Iteration 2272 (2.23111 iter/s, 3.58566s/8 iters), loss = 4.6654
I0401 12:48:18.996852 21213 solver.cpp:237]     Train net output #0: loss = 4.6654 (* 1 = 4.6654 loss)
I0401 12:48:18.996861 21213 sgd_solver.cpp:105] Iteration 2272, lr = 0.001
I0401 12:48:19.994544 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:48:22.427724 21213 solver.cpp:218] Iteration 2280 (2.33177 iter/s, 3.43086s/8 iters), loss = 4.54982
I0401 12:48:22.427776 21213 solver.cpp:237]     Train net output #0: loss = 4.54982 (* 1 = 4.54982 loss)
I0401 12:48:22.427783 21213 sgd_solver.cpp:105] Iteration 2280, lr = 0.001
I0401 12:48:22.727768 21213 blocking_queue.cpp:49] Waiting for data
I0401 12:48:25.797713 21213 solver.cpp:218] Iteration 2288 (2.37394 iter/s, 3.36992s/8 iters), loss = 4.64933
I0401 12:48:25.797760 21213 solver.cpp:237]     Train net output #0: loss = 4.64933 (* 1 = 4.64933 loss)
I0401 12:48:25.797765 21213 sgd_solver.cpp:105] Iteration 2288, lr = 0.001
I0401 12:48:29.386796 21213 solver.cpp:218] Iteration 2296 (2.22902 iter/s, 3.58903s/8 iters), loss = 4.64163
I0401 12:48:29.386837 21213 solver.cpp:237]     Train net output #0: loss = 4.64163 (* 1 = 4.64163 loss)
I0401 12:48:29.386842 21213 sgd_solver.cpp:105] Iteration 2296, lr = 0.001
I0401 12:48:32.414979 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2304.caffemodel
I0401 12:48:35.488410 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2304.solverstate
I0401 12:48:37.788494 21213 solver.cpp:330] Iteration 2304, Testing net (#0)
I0401 12:48:37.788516 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:48:39.992141 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:48:40.119832 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0528846
I0401 12:48:40.119868 21213 solver.cpp:397]     Test net output #1: loss = 4.8023 (* 1 = 4.8023 loss)
I0401 12:48:40.261091 21213 solver.cpp:218] Iteration 2304 (0.735683 iter/s, 10.8743s/8 iters), loss = 4.5112
I0401 12:48:40.261132 21213 solver.cpp:237]     Train net output #0: loss = 4.5112 (* 1 = 4.5112 loss)
I0401 12:48:40.261137 21213 sgd_solver.cpp:105] Iteration 2304, lr = 0.001
I0401 12:48:42.762115 21213 solver.cpp:218] Iteration 2312 (3.19877 iter/s, 2.50096s/8 iters), loss = 4.62517
I0401 12:48:42.762259 21213 solver.cpp:237]     Train net output #0: loss = 4.62517 (* 1 = 4.62517 loss)
I0401 12:48:42.762266 21213 sgd_solver.cpp:105] Iteration 2312, lr = 0.001
I0401 12:48:46.204385 21213 solver.cpp:218] Iteration 2320 (2.32416 iter/s, 3.44211s/8 iters), loss = 4.5161
I0401 12:48:46.204430 21213 solver.cpp:237]     Train net output #0: loss = 4.5161 (* 1 = 4.5161 loss)
I0401 12:48:46.204437 21213 sgd_solver.cpp:105] Iteration 2320, lr = 0.001
I0401 12:48:49.832022 21213 solver.cpp:218] Iteration 2328 (2.20533 iter/s, 3.62758s/8 iters), loss = 4.62615
I0401 12:48:49.832077 21213 solver.cpp:237]     Train net output #0: loss = 4.62615 (* 1 = 4.62615 loss)
I0401 12:48:49.832085 21213 sgd_solver.cpp:105] Iteration 2328, lr = 0.001
I0401 12:48:53.523229 21213 solver.cpp:218] Iteration 2336 (2.16735 iter/s, 3.69114s/8 iters), loss = 4.69519
I0401 12:48:53.523289 21213 solver.cpp:237]     Train net output #0: loss = 4.69519 (* 1 = 4.69519 loss)
I0401 12:48:53.523300 21213 sgd_solver.cpp:105] Iteration 2336, lr = 0.001
I0401 12:48:54.438710 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:48:57.164512 21213 solver.cpp:218] Iteration 2344 (2.19707 iter/s, 3.64121s/8 iters), loss = 4.53091
I0401 12:48:57.164572 21213 solver.cpp:237]     Train net output #0: loss = 4.53091 (* 1 = 4.53091 loss)
I0401 12:48:57.164580 21213 sgd_solver.cpp:105] Iteration 2344, lr = 0.001
I0401 12:49:00.710345 21213 solver.cpp:218] Iteration 2352 (2.25622 iter/s, 3.54575s/8 iters), loss = 4.61624
I0401 12:49:00.710407 21213 solver.cpp:237]     Train net output #0: loss = 4.61624 (* 1 = 4.61624 loss)
I0401 12:49:00.710417 21213 sgd_solver.cpp:105] Iteration 2352, lr = 0.001
I0401 12:49:04.378211 21213 solver.cpp:218] Iteration 2360 (2.18115 iter/s, 3.66779s/8 iters), loss = 4.66013
I0401 12:49:04.378259 21213 solver.cpp:237]     Train net output #0: loss = 4.66013 (* 1 = 4.66013 loss)
I0401 12:49:04.378266 21213 sgd_solver.cpp:105] Iteration 2360, lr = 0.001
I0401 12:49:07.562176 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2368.caffemodel
I0401 12:49:12.036417 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2368.solverstate
I0401 12:49:15.438678 21213 solver.cpp:330] Iteration 2368, Testing net (#0)
I0401 12:49:15.438736 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:49:17.323058 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:49:17.543429 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0396635
I0401 12:49:17.543465 21213 solver.cpp:397]     Test net output #1: loss = 4.81999 (* 1 = 4.81999 loss)
I0401 12:49:17.686872 21213 solver.cpp:218] Iteration 2368 (0.601115 iter/s, 13.3086s/8 iters), loss = 4.39831
I0401 12:49:17.688469 21213 solver.cpp:237]     Train net output #0: loss = 4.39831 (* 1 = 4.39831 loss)
I0401 12:49:17.688484 21213 sgd_solver.cpp:105] Iteration 2368, lr = 0.001
I0401 12:49:20.213219 21213 solver.cpp:218] Iteration 2376 (3.16863 iter/s, 2.52475s/8 iters), loss = 4.49364
I0401 12:49:20.213261 21213 solver.cpp:237]     Train net output #0: loss = 4.49364 (* 1 = 4.49364 loss)
I0401 12:49:20.213266 21213 sgd_solver.cpp:105] Iteration 2376, lr = 0.001
I0401 12:49:23.685256 21213 solver.cpp:218] Iteration 2384 (2.30416 iter/s, 3.47198s/8 iters), loss = 4.65549
I0401 12:49:23.685292 21213 solver.cpp:237]     Train net output #0: loss = 4.65549 (* 1 = 4.65549 loss)
I0401 12:49:23.685297 21213 sgd_solver.cpp:105] Iteration 2384, lr = 0.001
I0401 12:49:27.111572 21213 solver.cpp:218] Iteration 2392 (2.3349 iter/s, 3.42627s/8 iters), loss = 4.48833
I0401 12:49:27.111613 21213 solver.cpp:237]     Train net output #0: loss = 4.48833 (* 1 = 4.48833 loss)
I0401 12:49:27.111618 21213 sgd_solver.cpp:105] Iteration 2392, lr = 0.001
I0401 12:49:30.860395 21213 solver.cpp:218] Iteration 2400 (2.13403 iter/s, 3.74877s/8 iters), loss = 4.46759
I0401 12:49:30.860445 21213 solver.cpp:237]     Train net output #0: loss = 4.46759 (* 1 = 4.46759 loss)
I0401 12:49:30.860455 21213 sgd_solver.cpp:105] Iteration 2400, lr = 0.001
I0401 12:49:31.393771 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:49:34.476392 21213 solver.cpp:218] Iteration 2408 (2.21243 iter/s, 3.61594s/8 iters), loss = 4.61718
I0401 12:49:34.476433 21213 solver.cpp:237]     Train net output #0: loss = 4.61718 (* 1 = 4.61718 loss)
I0401 12:49:34.476438 21213 sgd_solver.cpp:105] Iteration 2408, lr = 0.001
I0401 12:49:38.076205 21213 solver.cpp:218] Iteration 2416 (2.22237 iter/s, 3.59975s/8 iters), loss = 4.50305
I0401 12:49:38.076259 21213 solver.cpp:237]     Train net output #0: loss = 4.50305 (* 1 = 4.50305 loss)
I0401 12:49:38.076268 21213 sgd_solver.cpp:105] Iteration 2416, lr = 0.001
I0401 12:49:41.568300 21213 solver.cpp:218] Iteration 2424 (2.29093 iter/s, 3.49203s/8 iters), loss = 4.53712
I0401 12:49:41.568343 21213 solver.cpp:237]     Train net output #0: loss = 4.53712 (* 1 = 4.53712 loss)
I0401 12:49:41.568349 21213 sgd_solver.cpp:105] Iteration 2424, lr = 0.001
I0401 12:49:44.634845 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2432.caffemodel
I0401 12:49:47.674178 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2432.solverstate
I0401 12:49:49.983937 21213 solver.cpp:330] Iteration 2432, Testing net (#0)
I0401 12:49:49.983960 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:49:51.909075 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:49:52.172991 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0456731
I0401 12:49:52.173027 21213 solver.cpp:397]     Test net output #1: loss = 4.79615 (* 1 = 4.79615 loss)
I0401 12:49:52.311540 21213 solver.cpp:218] Iteration 2432 (0.744658 iter/s, 10.7432s/8 iters), loss = 4.41877
I0401 12:49:52.311595 21213 solver.cpp:237]     Train net output #0: loss = 4.41877 (* 1 = 4.41877 loss)
I0401 12:49:52.311605 21213 sgd_solver.cpp:105] Iteration 2432, lr = 0.001
I0401 12:49:54.792210 21213 solver.cpp:218] Iteration 2440 (3.22502 iter/s, 2.4806s/8 iters), loss = 4.53859
I0401 12:49:54.792255 21213 solver.cpp:237]     Train net output #0: loss = 4.53859 (* 1 = 4.53859 loss)
I0401 12:49:54.792261 21213 sgd_solver.cpp:105] Iteration 2440, lr = 0.001
I0401 12:49:58.454857 21213 solver.cpp:218] Iteration 2448 (2.18425 iter/s, 3.66259s/8 iters), loss = 4.41304
I0401 12:49:58.454903 21213 solver.cpp:237]     Train net output #0: loss = 4.41304 (* 1 = 4.41304 loss)
I0401 12:49:58.454910 21213 sgd_solver.cpp:105] Iteration 2448, lr = 0.001
I0401 12:50:01.969086 21213 solver.cpp:218] Iteration 2456 (2.2765 iter/s, 3.51417s/8 iters), loss = 4.55217
I0401 12:50:01.969133 21213 solver.cpp:237]     Train net output #0: loss = 4.55217 (* 1 = 4.55217 loss)
I0401 12:50:01.969141 21213 sgd_solver.cpp:105] Iteration 2456, lr = 0.001
I0401 12:50:05.513707 21213 solver.cpp:218] Iteration 2464 (2.25698 iter/s, 3.54456s/8 iters), loss = 4.55365
I0401 12:50:05.513756 21213 solver.cpp:237]     Train net output #0: loss = 4.55365 (* 1 = 4.55365 loss)
I0401 12:50:05.513762 21213 sgd_solver.cpp:105] Iteration 2464, lr = 0.001
I0401 12:50:05.681529 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:50:09.153375 21213 solver.cpp:218] Iteration 2472 (2.19804 iter/s, 3.6396s/8 iters), loss = 4.65139
I0401 12:50:09.153439 21213 solver.cpp:237]     Train net output #0: loss = 4.65139 (* 1 = 4.65139 loss)
I0401 12:50:09.153448 21213 sgd_solver.cpp:105] Iteration 2472, lr = 0.001
I0401 12:50:12.681763 21213 solver.cpp:218] Iteration 2480 (2.26737 iter/s, 3.52832s/8 iters), loss = 4.52908
I0401 12:50:12.681814 21213 solver.cpp:237]     Train net output #0: loss = 4.52908 (* 1 = 4.52908 loss)
I0401 12:50:12.681823 21213 sgd_solver.cpp:105] Iteration 2480, lr = 0.001
I0401 12:50:16.212322 21213 solver.cpp:218] Iteration 2488 (2.26597 iter/s, 3.5305s/8 iters), loss = 4.60331
I0401 12:50:16.212361 21213 solver.cpp:237]     Train net output #0: loss = 4.60331 (* 1 = 4.60331 loss)
I0401 12:50:16.212368 21213 sgd_solver.cpp:105] Iteration 2488, lr = 0.001
I0401 12:50:19.191241 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2496.caffemodel
I0401 12:50:22.806468 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2496.solverstate
I0401 12:50:26.482218 21213 solver.cpp:330] Iteration 2496, Testing net (#0)
I0401 12:50:26.482240 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:50:28.252216 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:50:28.572777 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0480769
I0401 12:50:28.572821 21213 solver.cpp:397]     Test net output #1: loss = 4.77393 (* 1 = 4.77393 loss)
I0401 12:50:28.712702 21213 solver.cpp:218] Iteration 2496 (0.639983 iter/s, 12.5003s/8 iters), loss = 4.51379
I0401 12:50:28.712762 21213 solver.cpp:237]     Train net output #0: loss = 4.51379 (* 1 = 4.51379 loss)
I0401 12:50:28.712770 21213 sgd_solver.cpp:105] Iteration 2496, lr = 0.001
I0401 12:50:31.329895 21213 solver.cpp:218] Iteration 2504 (3.05679 iter/s, 2.61713s/8 iters), loss = 4.43875
I0401 12:50:31.329927 21213 solver.cpp:237]     Train net output #0: loss = 4.43875 (* 1 = 4.43875 loss)
I0401 12:50:31.329933 21213 sgd_solver.cpp:105] Iteration 2504, lr = 0.001
I0401 12:50:34.811830 21213 solver.cpp:218] Iteration 2512 (2.29761 iter/s, 3.48188s/8 iters), loss = 4.31626
I0401 12:50:34.811888 21213 solver.cpp:237]     Train net output #0: loss = 4.31626 (* 1 = 4.31626 loss)
I0401 12:50:34.811897 21213 sgd_solver.cpp:105] Iteration 2512, lr = 0.001
I0401 12:50:38.437606 21213 solver.cpp:218] Iteration 2520 (2.20647 iter/s, 3.62571s/8 iters), loss = 4.47339
I0401 12:50:38.437649 21213 solver.cpp:237]     Train net output #0: loss = 4.47339 (* 1 = 4.47339 loss)
I0401 12:50:38.437654 21213 sgd_solver.cpp:105] Iteration 2520, lr = 0.001
I0401 12:50:41.877871 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:50:42.099659 21213 solver.cpp:218] Iteration 2528 (2.1846 iter/s, 3.66199s/8 iters), loss = 4.61292
I0401 12:50:42.099722 21213 solver.cpp:237]     Train net output #0: loss = 4.61292 (* 1 = 4.61292 loss)
I0401 12:50:42.099731 21213 sgd_solver.cpp:105] Iteration 2528, lr = 0.001
I0401 12:50:45.684123 21213 solver.cpp:218] Iteration 2536 (2.2319 iter/s, 3.58438s/8 iters), loss = 4.44545
I0401 12:50:45.684170 21213 solver.cpp:237]     Train net output #0: loss = 4.44545 (* 1 = 4.44545 loss)
I0401 12:50:45.684175 21213 sgd_solver.cpp:105] Iteration 2536, lr = 0.001
I0401 12:50:49.272393 21213 solver.cpp:218] Iteration 2544 (2.22952 iter/s, 3.58821s/8 iters), loss = 4.64376
I0401 12:50:49.272495 21213 solver.cpp:237]     Train net output #0: loss = 4.64376 (* 1 = 4.64376 loss)
I0401 12:50:49.272503 21213 sgd_solver.cpp:105] Iteration 2544, lr = 0.001
I0401 12:50:52.841708 21213 solver.cpp:218] Iteration 2552 (2.2414 iter/s, 3.5692s/8 iters), loss = 4.53254
I0401 12:50:52.841761 21213 solver.cpp:237]     Train net output #0: loss = 4.53254 (* 1 = 4.53254 loss)
I0401 12:50:52.841770 21213 sgd_solver.cpp:105] Iteration 2552, lr = 0.001
I0401 12:50:55.820742 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2560.caffemodel
I0401 12:50:58.828987 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2560.solverstate
I0401 12:51:01.175488 21213 solver.cpp:330] Iteration 2560, Testing net (#0)
I0401 12:51:01.175508 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:51:02.928500 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:51:03.335997 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0528846
I0401 12:51:03.336032 21213 solver.cpp:397]     Test net output #1: loss = 4.75214 (* 1 = 4.75214 loss)
I0401 12:51:03.473446 21213 solver.cpp:218] Iteration 2560 (0.752468 iter/s, 10.6317s/8 iters), loss = 4.27108
I0401 12:51:03.473501 21213 solver.cpp:237]     Train net output #0: loss = 4.27108 (* 1 = 4.27108 loss)
I0401 12:51:03.473510 21213 sgd_solver.cpp:105] Iteration 2560, lr = 0.001
I0401 12:51:05.972571 21213 solver.cpp:218] Iteration 2568 (3.20121 iter/s, 2.49905s/8 iters), loss = 4.50266
I0401 12:51:05.972616 21213 solver.cpp:237]     Train net output #0: loss = 4.50266 (* 1 = 4.50266 loss)
I0401 12:51:05.972622 21213 sgd_solver.cpp:105] Iteration 2568, lr = 0.001
I0401 12:51:09.352105 21213 solver.cpp:218] Iteration 2576 (2.36724 iter/s, 3.37947s/8 iters), loss = 4.56498
I0401 12:51:09.352164 21213 solver.cpp:237]     Train net output #0: loss = 4.56498 (* 1 = 4.56498 loss)
I0401 12:51:09.352172 21213 sgd_solver.cpp:105] Iteration 2576, lr = 0.001
I0401 12:51:12.930771 21213 solver.cpp:218] Iteration 2584 (2.23552 iter/s, 3.57859s/8 iters), loss = 4.58917
I0401 12:51:12.930824 21213 solver.cpp:237]     Train net output #0: loss = 4.58917 (* 1 = 4.58917 loss)
I0401 12:51:12.930832 21213 sgd_solver.cpp:105] Iteration 2584, lr = 0.001
I0401 12:51:15.784863 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:51:16.407295 21213 solver.cpp:218] Iteration 2592 (2.3012 iter/s, 3.47645s/8 iters), loss = 4.62599
I0401 12:51:16.407338 21213 solver.cpp:237]     Train net output #0: loss = 4.62599 (* 1 = 4.62599 loss)
I0401 12:51:16.407344 21213 sgd_solver.cpp:105] Iteration 2592, lr = 0.001
I0401 12:51:20.060933 21213 solver.cpp:218] Iteration 2600 (2.18963 iter/s, 3.65358s/8 iters), loss = 4.29969
I0401 12:51:20.061079 21213 solver.cpp:237]     Train net output #0: loss = 4.29969 (* 1 = 4.29969 loss)
I0401 12:51:20.061086 21213 sgd_solver.cpp:105] Iteration 2600, lr = 0.001
I0401 12:51:23.780591 21213 solver.cpp:218] Iteration 2608 (2.15083 iter/s, 3.7195s/8 iters), loss = 4.51456
I0401 12:51:23.780650 21213 solver.cpp:237]     Train net output #0: loss = 4.51456 (* 1 = 4.51456 loss)
I0401 12:51:23.780659 21213 sgd_solver.cpp:105] Iteration 2608, lr = 0.001
I0401 12:51:27.416316 21213 solver.cpp:218] Iteration 2616 (2.20043 iter/s, 3.63565s/8 iters), loss = 4.49978
I0401 12:51:27.416384 21213 solver.cpp:237]     Train net output #0: loss = 4.49978 (* 1 = 4.49978 loss)
I0401 12:51:27.416393 21213 sgd_solver.cpp:105] Iteration 2616, lr = 0.001
I0401 12:51:30.465752 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2624.caffemodel
I0401 12:51:33.842396 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2624.solverstate
I0401 12:51:37.573246 21213 solver.cpp:330] Iteration 2624, Testing net (#0)
I0401 12:51:37.573266 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:51:39.277899 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:51:39.719923 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0600962
I0401 12:51:39.719956 21213 solver.cpp:397]     Test net output #1: loss = 4.74715 (* 1 = 4.74715 loss)
I0401 12:51:39.872216 21213 solver.cpp:218] Iteration 2624 (0.642269 iter/s, 12.4558s/8 iters), loss = 4.37472
I0401 12:51:39.872275 21213 solver.cpp:237]     Train net output #0: loss = 4.37472 (* 1 = 4.37472 loss)
I0401 12:51:39.872283 21213 sgd_solver.cpp:105] Iteration 2624, lr = 0.001
I0401 12:51:42.549696 21213 solver.cpp:218] Iteration 2632 (2.98796 iter/s, 2.67741s/8 iters), loss = 4.15664
I0401 12:51:42.549746 21213 solver.cpp:237]     Train net output #0: loss = 4.15664 (* 1 = 4.15664 loss)
I0401 12:51:42.549752 21213 sgd_solver.cpp:105] Iteration 2632, lr = 0.001
I0401 12:51:45.999408 21213 solver.cpp:218] Iteration 2640 (2.31908 iter/s, 3.44965s/8 iters), loss = 4.33004
I0401 12:51:45.999462 21213 solver.cpp:237]     Train net output #0: loss = 4.33004 (* 1 = 4.33004 loss)
I0401 12:51:45.999471 21213 sgd_solver.cpp:105] Iteration 2640, lr = 0.001
I0401 12:51:49.229665 21213 solver.cpp:218] Iteration 2648 (2.47663 iter/s, 3.23019s/8 iters), loss = 4.48688
I0401 12:51:49.229715 21213 solver.cpp:237]     Train net output #0: loss = 4.48688 (* 1 = 4.48688 loss)
I0401 12:51:49.229724 21213 sgd_solver.cpp:105] Iteration 2648, lr = 0.001
I0401 12:51:52.042107 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:51:52.865336 21213 solver.cpp:218] Iteration 2656 (2.20046 iter/s, 3.63561s/8 iters), loss = 4.42677
I0401 12:51:52.865381 21213 solver.cpp:237]     Train net output #0: loss = 4.42677 (* 1 = 4.42677 loss)
I0401 12:51:52.865387 21213 sgd_solver.cpp:105] Iteration 2656, lr = 0.001
I0401 12:51:56.432298 21213 solver.cpp:218] Iteration 2664 (2.24284 iter/s, 3.5669s/8 iters), loss = 4.20441
I0401 12:51:56.432353 21213 solver.cpp:237]     Train net output #0: loss = 4.20441 (* 1 = 4.20441 loss)
I0401 12:51:56.432358 21213 sgd_solver.cpp:105] Iteration 2664, lr = 0.001
I0401 12:52:00.139952 21213 solver.cpp:218] Iteration 2672 (2.15774 iter/s, 3.70758s/8 iters), loss = 4.53057
I0401 12:52:00.140008 21213 solver.cpp:237]     Train net output #0: loss = 4.53057 (* 1 = 4.53057 loss)
I0401 12:52:00.140017 21213 sgd_solver.cpp:105] Iteration 2672, lr = 0.001
I0401 12:52:03.827711 21213 solver.cpp:218] Iteration 2680 (2.16938 iter/s, 3.68769s/8 iters), loss = 4.6498
I0401 12:52:03.827754 21213 solver.cpp:237]     Train net output #0: loss = 4.6498 (* 1 = 4.6498 loss)
I0401 12:52:03.827759 21213 sgd_solver.cpp:105] Iteration 2680, lr = 0.001
I0401 12:52:07.103422 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2688.caffemodel
I0401 12:52:11.635982 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2688.solverstate
I0401 12:52:13.942245 21213 solver.cpp:330] Iteration 2688, Testing net (#0)
I0401 12:52:13.942266 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:52:15.586627 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:52:16.116132 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0516827
I0401 12:52:16.116166 21213 solver.cpp:397]     Test net output #1: loss = 4.74132 (* 1 = 4.74132 loss)
I0401 12:52:16.256160 21213 solver.cpp:218] Iteration 2688 (0.643687 iter/s, 12.4284s/8 iters), loss = 4.48219
I0401 12:52:16.257711 21213 solver.cpp:237]     Train net output #0: loss = 4.48219 (* 1 = 4.48219 loss)
I0401 12:52:16.257723 21213 sgd_solver.cpp:105] Iteration 2688, lr = 0.001
I0401 12:52:18.906168 21213 solver.cpp:218] Iteration 2696 (3.02064 iter/s, 2.64845s/8 iters), loss = 4.34749
I0401 12:52:18.906221 21213 solver.cpp:237]     Train net output #0: loss = 4.34749 (* 1 = 4.34749 loss)
I0401 12:52:18.906229 21213 sgd_solver.cpp:105] Iteration 2696, lr = 0.001
I0401 12:52:22.339447 21213 solver.cpp:218] Iteration 2704 (2.33018 iter/s, 3.43321s/8 iters), loss = 4.71702
I0401 12:52:22.339568 21213 solver.cpp:237]     Train net output #0: loss = 4.71702 (* 1 = 4.71702 loss)
I0401 12:52:22.339577 21213 sgd_solver.cpp:105] Iteration 2704, lr = 0.001
I0401 12:52:25.891075 21213 solver.cpp:218] Iteration 2712 (2.25257 iter/s, 3.5515s/8 iters), loss = 4.33956
I0401 12:52:25.891119 21213 solver.cpp:237]     Train net output #0: loss = 4.33956 (* 1 = 4.33956 loss)
I0401 12:52:25.891124 21213 sgd_solver.cpp:105] Iteration 2712, lr = 0.001
I0401 12:52:28.187795 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:52:29.376279 21213 solver.cpp:218] Iteration 2720 (2.29547 iter/s, 3.48512s/8 iters), loss = 4.48422
I0401 12:52:29.376340 21213 solver.cpp:237]     Train net output #0: loss = 4.48422 (* 1 = 4.48422 loss)
I0401 12:52:29.376348 21213 sgd_solver.cpp:105] Iteration 2720, lr = 0.001
I0401 12:52:32.992431 21213 solver.cpp:218] Iteration 2728 (2.21234 iter/s, 3.61608s/8 iters), loss = 4.55655
I0401 12:52:32.992478 21213 solver.cpp:237]     Train net output #0: loss = 4.55655 (* 1 = 4.55655 loss)
I0401 12:52:32.992486 21213 sgd_solver.cpp:105] Iteration 2728, lr = 0.001
I0401 12:52:36.465140 21213 solver.cpp:218] Iteration 2736 (2.30372 iter/s, 3.47265s/8 iters), loss = 4.48028
I0401 12:52:36.465188 21213 solver.cpp:237]     Train net output #0: loss = 4.48028 (* 1 = 4.48028 loss)
I0401 12:52:36.465194 21213 sgd_solver.cpp:105] Iteration 2736, lr = 0.001
I0401 12:52:40.059242 21213 solver.cpp:218] Iteration 2744 (2.22591 iter/s, 3.59404s/8 iters), loss = 4.59651
I0401 12:52:40.059296 21213 solver.cpp:237]     Train net output #0: loss = 4.59651 (* 1 = 4.59651 loss)
I0401 12:52:40.059304 21213 sgd_solver.cpp:105] Iteration 2744, lr = 0.001
I0401 12:52:43.248102 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2752.caffemodel
I0401 12:52:46.261132 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2752.solverstate
I0401 12:52:48.636312 21213 solver.cpp:330] Iteration 2752, Testing net (#0)
I0401 12:52:48.636335 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:52:50.209934 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:52:50.862448 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0528846
I0401 12:52:50.862483 21213 solver.cpp:397]     Test net output #1: loss = 4.75753 (* 1 = 4.75753 loss)
I0401 12:52:51.002166 21213 solver.cpp:218] Iteration 2752 (0.73107 iter/s, 10.9429s/8 iters), loss = 4.39888
I0401 12:52:51.002218 21213 solver.cpp:237]     Train net output #0: loss = 4.39888 (* 1 = 4.39888 loss)
I0401 12:52:51.002226 21213 sgd_solver.cpp:105] Iteration 2752, lr = 0.001
I0401 12:52:53.533448 21213 solver.cpp:218] Iteration 2760 (3.16054 iter/s, 2.53121s/8 iters), loss = 4.37813
I0401 12:52:53.533598 21213 solver.cpp:237]     Train net output #0: loss = 4.37813 (* 1 = 4.37813 loss)
I0401 12:52:53.533607 21213 sgd_solver.cpp:105] Iteration 2760, lr = 0.001
I0401 12:52:57.105080 21213 solver.cpp:218] Iteration 2768 (2.23997 iter/s, 3.57147s/8 iters), loss = 4.40239
I0401 12:52:57.105134 21213 solver.cpp:237]     Train net output #0: loss = 4.40239 (* 1 = 4.40239 loss)
I0401 12:52:57.105144 21213 sgd_solver.cpp:105] Iteration 2768, lr = 0.001
I0401 12:53:00.626446 21213 solver.cpp:218] Iteration 2776 (2.27189 iter/s, 3.5213s/8 iters), loss = 4.19538
I0401 12:53:00.626493 21213 solver.cpp:237]     Train net output #0: loss = 4.19538 (* 1 = 4.19538 loss)
I0401 12:53:00.626498 21213 sgd_solver.cpp:105] Iteration 2776, lr = 0.001
I0401 12:53:02.643622 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:53:04.258564 21213 solver.cpp:218] Iteration 2784 (2.20261 iter/s, 3.63205s/8 iters), loss = 4.3316
I0401 12:53:04.258625 21213 solver.cpp:237]     Train net output #0: loss = 4.3316 (* 1 = 4.3316 loss)
I0401 12:53:04.258635 21213 sgd_solver.cpp:105] Iteration 2784, lr = 0.001
I0401 12:53:08.069504 21213 solver.cpp:218] Iteration 2792 (2.09926 iter/s, 3.81086s/8 iters), loss = 4.45781
I0401 12:53:08.069556 21213 solver.cpp:237]     Train net output #0: loss = 4.45781 (* 1 = 4.45781 loss)
I0401 12:53:08.069566 21213 sgd_solver.cpp:105] Iteration 2792, lr = 0.001
I0401 12:53:11.784159 21213 solver.cpp:218] Iteration 2800 (2.15367 iter/s, 3.71459s/8 iters), loss = 4.43374
I0401 12:53:11.784206 21213 solver.cpp:237]     Train net output #0: loss = 4.43374 (* 1 = 4.43374 loss)
I0401 12:53:11.784212 21213 sgd_solver.cpp:105] Iteration 2800, lr = 0.001
I0401 12:53:15.327533 21213 solver.cpp:218] Iteration 2808 (2.25777 iter/s, 3.54331s/8 iters), loss = 4.45067
I0401 12:53:15.327574 21213 solver.cpp:237]     Train net output #0: loss = 4.45067 (* 1 = 4.45067 loss)
I0401 12:53:15.327579 21213 sgd_solver.cpp:105] Iteration 2808, lr = 0.001
I0401 12:53:18.448632 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2816.caffemodel
I0401 12:53:21.583634 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2816.solverstate
I0401 12:53:23.914260 21213 solver.cpp:330] Iteration 2816, Testing net (#0)
I0401 12:53:23.914438 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:53:25.420349 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:53:26.144711 21213 solver.cpp:397]     Test net output #0: accuracy = 0.046875
I0401 12:53:26.144747 21213 solver.cpp:397]     Test net output #1: loss = 4.76095 (* 1 = 4.76095 loss)
I0401 12:53:26.280822 21213 solver.cpp:218] Iteration 2816 (0.730378 iter/s, 10.9532s/8 iters), loss = 4.35099
I0401 12:53:26.280875 21213 solver.cpp:237]     Train net output #0: loss = 4.35099 (* 1 = 4.35099 loss)
I0401 12:53:26.280891 21213 sgd_solver.cpp:105] Iteration 2816, lr = 0.001
I0401 12:53:28.867815 21213 solver.cpp:218] Iteration 2824 (3.09247 iter/s, 2.58693s/8 iters), loss = 4.31373
I0401 12:53:28.867858 21213 solver.cpp:237]     Train net output #0: loss = 4.31373 (* 1 = 4.31373 loss)
I0401 12:53:28.867864 21213 sgd_solver.cpp:105] Iteration 2824, lr = 0.001
I0401 12:53:32.503705 21213 solver.cpp:218] Iteration 2832 (2.20032 iter/s, 3.63583s/8 iters), loss = 4.12029
I0401 12:53:32.503751 21213 solver.cpp:237]     Train net output #0: loss = 4.12029 (* 1 = 4.12029 loss)
I0401 12:53:32.503756 21213 sgd_solver.cpp:105] Iteration 2832, lr = 0.001
I0401 12:53:36.180111 21213 solver.cpp:218] Iteration 2840 (2.17608 iter/s, 3.67634s/8 iters), loss = 4.39323
I0401 12:53:36.180171 21213 solver.cpp:237]     Train net output #0: loss = 4.39323 (* 1 = 4.39323 loss)
I0401 12:53:36.180178 21213 sgd_solver.cpp:105] Iteration 2840, lr = 0.001
I0401 12:53:37.769634 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:53:39.818684 21213 solver.cpp:218] Iteration 2848 (2.19871 iter/s, 3.6385s/8 iters), loss = 4.3748
I0401 12:53:39.818742 21213 solver.cpp:237]     Train net output #0: loss = 4.3748 (* 1 = 4.3748 loss)
I0401 12:53:39.818750 21213 sgd_solver.cpp:105] Iteration 2848, lr = 0.001
I0401 12:53:43.478969 21213 solver.cpp:218] Iteration 2856 (2.18566 iter/s, 3.66022s/8 iters), loss = 4.20675
I0401 12:53:43.479023 21213 solver.cpp:237]     Train net output #0: loss = 4.20675 (* 1 = 4.20675 loss)
I0401 12:53:43.479032 21213 sgd_solver.cpp:105] Iteration 2856, lr = 0.001
I0401 12:53:47.220681 21213 solver.cpp:218] Iteration 2864 (2.1381 iter/s, 3.74165s/8 iters), loss = 4.15817
I0401 12:53:47.220726 21213 solver.cpp:237]     Train net output #0: loss = 4.15817 (* 1 = 4.15817 loss)
I0401 12:53:47.220731 21213 sgd_solver.cpp:105] Iteration 2864, lr = 0.001
I0401 12:53:50.547535 21213 solver.cpp:218] Iteration 2872 (2.40472 iter/s, 3.3268s/8 iters), loss = 4.4577
I0401 12:53:50.547574 21213 solver.cpp:237]     Train net output #0: loss = 4.4577 (* 1 = 4.4577 loss)
I0401 12:53:50.547580 21213 sgd_solver.cpp:105] Iteration 2872, lr = 0.001
I0401 12:53:53.462611 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2880.caffemodel
I0401 12:53:56.467649 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2880.solverstate
I0401 12:54:00.721987 21213 solver.cpp:330] Iteration 2880, Testing net (#0)
I0401 12:54:00.722012 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:54:02.161918 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:54:02.937877 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0456731
I0401 12:54:02.937913 21213 solver.cpp:397]     Test net output #1: loss = 4.77899 (* 1 = 4.77899 loss)
I0401 12:54:03.079383 21213 solver.cpp:218] Iteration 2880 (0.638376 iter/s, 12.5318s/8 iters), loss = 4.32614
I0401 12:54:03.079437 21213 solver.cpp:237]     Train net output #0: loss = 4.32614 (* 1 = 4.32614 loss)
I0401 12:54:03.079444 21213 sgd_solver.cpp:105] Iteration 2880, lr = 0.001
I0401 12:54:05.802668 21213 solver.cpp:218] Iteration 2888 (2.93771 iter/s, 2.72321s/8 iters), loss = 4.18865
I0401 12:54:05.802728 21213 solver.cpp:237]     Train net output #0: loss = 4.18865 (* 1 = 4.18865 loss)
I0401 12:54:05.802738 21213 sgd_solver.cpp:105] Iteration 2888, lr = 0.001
I0401 12:54:09.186800 21213 solver.cpp:218] Iteration 2896 (2.36403 iter/s, 3.38405s/8 iters), loss = 4.07914
I0401 12:54:09.192991 21213 solver.cpp:237]     Train net output #0: loss = 4.07914 (* 1 = 4.07914 loss)
I0401 12:54:09.193014 21213 sgd_solver.cpp:105] Iteration 2896, lr = 0.001
I0401 12:54:12.748955 21213 solver.cpp:218] Iteration 2904 (2.24973 iter/s, 3.55598s/8 iters), loss = 4.11006
I0401 12:54:12.748996 21213 solver.cpp:237]     Train net output #0: loss = 4.11006 (* 1 = 4.11006 loss)
I0401 12:54:12.749001 21213 sgd_solver.cpp:105] Iteration 2904, lr = 0.001
I0401 12:54:14.169008 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:54:16.411082 21213 solver.cpp:218] Iteration 2912 (2.18456 iter/s, 3.66207s/8 iters), loss = 4.1332
I0401 12:54:16.411129 21213 solver.cpp:237]     Train net output #0: loss = 4.1332 (* 1 = 4.1332 loss)
I0401 12:54:16.411136 21213 sgd_solver.cpp:105] Iteration 2912, lr = 0.001
I0401 12:54:19.921504 21213 solver.cpp:218] Iteration 2920 (2.27897 iter/s, 3.51036s/8 iters), loss = 4.14499
I0401 12:54:19.921561 21213 solver.cpp:237]     Train net output #0: loss = 4.14499 (* 1 = 4.14499 loss)
I0401 12:54:19.921571 21213 sgd_solver.cpp:105] Iteration 2920, lr = 0.001
I0401 12:54:23.393780 21213 solver.cpp:218] Iteration 2928 (2.30402 iter/s, 3.4722s/8 iters), loss = 4.19013
I0401 12:54:23.393832 21213 solver.cpp:237]     Train net output #0: loss = 4.19013 (* 1 = 4.19013 loss)
I0401 12:54:23.393841 21213 sgd_solver.cpp:105] Iteration 2928, lr = 0.001
I0401 12:54:26.989272 21213 solver.cpp:218] Iteration 2936 (2.22505 iter/s, 3.59543s/8 iters), loss = 4.40477
I0401 12:54:26.989396 21213 solver.cpp:237]     Train net output #0: loss = 4.40477 (* 1 = 4.40477 loss)
I0401 12:54:26.989403 21213 sgd_solver.cpp:105] Iteration 2936, lr = 0.001
I0401 12:54:29.956539 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2944.caffemodel
I0401 12:54:33.079200 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2944.solverstate
I0401 12:54:35.381036 21213 solver.cpp:330] Iteration 2944, Testing net (#0)
I0401 12:54:35.381055 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:54:36.757628 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:54:37.721526 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0552885
I0401 12:54:37.721562 21213 solver.cpp:397]     Test net output #1: loss = 4.84495 (* 1 = 4.84495 loss)
I0401 12:54:37.862823 21213 solver.cpp:218] Iteration 2944 (0.735739 iter/s, 10.8734s/8 iters), loss = 4.51995
I0401 12:54:37.862865 21213 solver.cpp:237]     Train net output #0: loss = 4.51995 (* 1 = 4.51995 loss)
I0401 12:54:37.862871 21213 sgd_solver.cpp:105] Iteration 2944, lr = 0.001
I0401 12:54:40.374071 21213 solver.cpp:218] Iteration 2952 (3.18575 iter/s, 2.51119s/8 iters), loss = 4.33333
I0401 12:54:40.374122 21213 solver.cpp:237]     Train net output #0: loss = 4.33333 (* 1 = 4.33333 loss)
I0401 12:54:40.374128 21213 sgd_solver.cpp:105] Iteration 2952, lr = 0.001
I0401 12:54:43.868716 21213 solver.cpp:218] Iteration 2960 (2.28926 iter/s, 3.49458s/8 iters), loss = 4.3416
I0401 12:54:43.868754 21213 solver.cpp:237]     Train net output #0: loss = 4.3416 (* 1 = 4.3416 loss)
I0401 12:54:43.868759 21213 sgd_solver.cpp:105] Iteration 2960, lr = 0.001
I0401 12:54:47.592973 21213 solver.cpp:218] Iteration 2968 (2.14811 iter/s, 3.72421s/8 iters), loss = 4.26419
I0401 12:54:47.593011 21213 solver.cpp:237]     Train net output #0: loss = 4.26419 (* 1 = 4.26419 loss)
I0401 12:54:47.593016 21213 sgd_solver.cpp:105] Iteration 2968, lr = 0.001
I0401 12:54:48.604435 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:54:51.283378 21213 solver.cpp:218] Iteration 2976 (2.16781 iter/s, 3.69035s/8 iters), loss = 4.09151
I0401 12:54:51.283414 21213 solver.cpp:237]     Train net output #0: loss = 4.09151 (* 1 = 4.09151 loss)
I0401 12:54:51.283419 21213 sgd_solver.cpp:105] Iteration 2976, lr = 0.001
I0401 12:54:54.949836 21213 solver.cpp:218] Iteration 2984 (2.18197 iter/s, 3.66641s/8 iters), loss = 4.20994
I0401 12:54:54.949877 21213 solver.cpp:237]     Train net output #0: loss = 4.20994 (* 1 = 4.20994 loss)
I0401 12:54:54.949882 21213 sgd_solver.cpp:105] Iteration 2984, lr = 0.001
I0401 12:54:58.555577 21213 solver.cpp:218] Iteration 2992 (2.21872 iter/s, 3.60568s/8 iters), loss = 4.2646
I0401 12:54:58.555752 21213 solver.cpp:237]     Train net output #0: loss = 4.2646 (* 1 = 4.2646 loss)
I0401 12:54:58.555758 21213 sgd_solver.cpp:105] Iteration 2992, lr = 0.001
I0401 12:55:02.142345 21213 solver.cpp:218] Iteration 3000 (2.23054 iter/s, 3.58658s/8 iters), loss = 4.20922
I0401 12:55:02.142391 21213 solver.cpp:237]     Train net output #0: loss = 4.20922 (* 1 = 4.20922 loss)
I0401 12:55:02.142397 21213 sgd_solver.cpp:105] Iteration 3000, lr = 0.001
I0401 12:55:05.289655 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3008.caffemodel
I0401 12:55:08.396003 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3008.solverstate
I0401 12:55:10.731031 21213 solver.cpp:330] Iteration 3008, Testing net (#0)
I0401 12:55:10.731052 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:55:11.982929 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:55:12.841560 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0552885
I0401 12:55:12.841596 21213 solver.cpp:397]     Test net output #1: loss = 4.70821 (* 1 = 4.70821 loss)
I0401 12:55:12.982692 21213 solver.cpp:218] Iteration 3008 (0.737988 iter/s, 10.8403s/8 iters), loss = 4.18385
I0401 12:55:12.984251 21213 solver.cpp:237]     Train net output #0: loss = 4.18385 (* 1 = 4.18385 loss)
I0401 12:55:12.984261 21213 sgd_solver.cpp:105] Iteration 3008, lr = 0.001
I0401 12:55:15.726445 21213 solver.cpp:218] Iteration 3016 (2.91738 iter/s, 2.74219s/8 iters), loss = 4.2168
I0401 12:55:15.726485 21213 solver.cpp:237]     Train net output #0: loss = 4.2168 (* 1 = 4.2168 loss)
I0401 12:55:15.726490 21213 sgd_solver.cpp:105] Iteration 3016, lr = 0.001
I0401 12:55:19.326856 21213 solver.cpp:218] Iteration 3024 (2.222 iter/s, 3.60037s/8 iters), loss = 4.05979
I0401 12:55:19.326890 21213 solver.cpp:237]     Train net output #0: loss = 4.05979 (* 1 = 4.05979 loss)
I0401 12:55:19.326895 21213 sgd_solver.cpp:105] Iteration 3024, lr = 0.001
I0401 12:55:22.837266 21213 solver.cpp:218] Iteration 3032 (2.27897 iter/s, 3.51036s/8 iters), loss = 4.14366
I0401 12:55:22.837311 21213 solver.cpp:237]     Train net output #0: loss = 4.14366 (* 1 = 4.14366 loss)
I0401 12:55:22.837317 21213 sgd_solver.cpp:105] Iteration 3032, lr = 0.001
I0401 12:55:23.340548 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:55:26.360404 21213 solver.cpp:218] Iteration 3040 (2.27074 iter/s, 3.52308s/8 iters), loss = 4.21318
I0401 12:55:26.360455 21213 solver.cpp:237]     Train net output #0: loss = 4.21318 (* 1 = 4.21318 loss)
I0401 12:55:26.360463 21213 sgd_solver.cpp:105] Iteration 3040, lr = 0.001
I0401 12:55:26.749645 21213 blocking_queue.cpp:49] Waiting for data
I0401 12:55:30.072166 21213 solver.cpp:218] Iteration 3048 (2.15535 iter/s, 3.7117s/8 iters), loss = 4.16364
I0401 12:55:30.072286 21213 solver.cpp:237]     Train net output #0: loss = 4.16364 (* 1 = 4.16364 loss)
I0401 12:55:30.072294 21213 sgd_solver.cpp:105] Iteration 3048, lr = 0.001
I0401 12:55:33.375883 21213 solver.cpp:218] Iteration 3056 (2.42161 iter/s, 3.30358s/8 iters), loss = 4.27754
I0401 12:55:33.375948 21213 solver.cpp:237]     Train net output #0: loss = 4.27754 (* 1 = 4.27754 loss)
I0401 12:55:33.375957 21213 sgd_solver.cpp:105] Iteration 3056, lr = 0.001
I0401 12:55:36.851125 21213 solver.cpp:218] Iteration 3064 (2.30205 iter/s, 3.47517s/8 iters), loss = 4.10217
I0401 12:55:36.851167 21213 solver.cpp:237]     Train net output #0: loss = 4.10217 (* 1 = 4.10217 loss)
I0401 12:55:36.851172 21213 sgd_solver.cpp:105] Iteration 3064, lr = 0.001
I0401 12:55:39.840979 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3072.caffemodel
I0401 12:55:42.842224 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3072.solverstate
I0401 12:55:45.147451 21213 solver.cpp:330] Iteration 3072, Testing net (#0)
I0401 12:55:45.147475 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:55:46.364557 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:55:47.379933 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0564904
I0401 12:55:47.379963 21213 solver.cpp:397]     Test net output #1: loss = 4.68049 (* 1 = 4.68049 loss)
I0401 12:55:47.520946 21213 solver.cpp:218] Iteration 3072 (0.749782 iter/s, 10.6698s/8 iters), loss = 4.21398
I0401 12:55:47.520998 21213 solver.cpp:237]     Train net output #0: loss = 4.21398 (* 1 = 4.21398 loss)
I0401 12:55:47.521005 21213 sgd_solver.cpp:105] Iteration 3072, lr = 0.001
I0401 12:55:50.154721 21213 solver.cpp:218] Iteration 3080 (3.03756 iter/s, 2.6337s/8 iters), loss = 4.06126
I0401 12:55:50.154788 21213 solver.cpp:237]     Train net output #0: loss = 4.06126 (* 1 = 4.06126 loss)
I0401 12:55:50.154796 21213 sgd_solver.cpp:105] Iteration 3080, lr = 0.001
I0401 12:55:53.638898 21213 solver.cpp:218] Iteration 3088 (2.29615 iter/s, 3.4841s/8 iters), loss = 4.08435
I0401 12:55:53.638952 21213 solver.cpp:237]     Train net output #0: loss = 4.08435 (* 1 = 4.08435 loss)
I0401 12:55:53.638958 21213 sgd_solver.cpp:105] Iteration 3088, lr = 0.001
I0401 12:55:57.303715 21213 solver.cpp:218] Iteration 3096 (2.18296 iter/s, 3.66475s/8 iters), loss = 4.17601
I0401 12:55:57.303758 21213 solver.cpp:237]     Train net output #0: loss = 4.17601 (* 1 = 4.17601 loss)
I0401 12:55:57.303764 21213 sgd_solver.cpp:105] Iteration 3096, lr = 0.001
I0401 12:55:57.510471 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:56:00.779515 21213 solver.cpp:218] Iteration 3104 (2.30167 iter/s, 3.47574s/8 iters), loss = 4.37386
I0401 12:56:00.779670 21213 solver.cpp:237]     Train net output #0: loss = 4.37386 (* 1 = 4.37386 loss)
I0401 12:56:00.779680 21213 sgd_solver.cpp:105] Iteration 3104, lr = 0.001
I0401 12:56:04.436699 21213 solver.cpp:218] Iteration 3112 (2.18758 iter/s, 3.65701s/8 iters), loss = 4.10958
I0401 12:56:04.436753 21213 solver.cpp:237]     Train net output #0: loss = 4.10958 (* 1 = 4.10958 loss)
I0401 12:56:04.436759 21213 sgd_solver.cpp:105] Iteration 3112, lr = 0.001
I0401 12:56:08.154449 21213 solver.cpp:218] Iteration 3120 (2.15188 iter/s, 3.71768s/8 iters), loss = 4.13985
I0401 12:56:08.154508 21213 solver.cpp:237]     Train net output #0: loss = 4.13985 (* 1 = 4.13985 loss)
I0401 12:56:08.154517 21213 sgd_solver.cpp:105] Iteration 3120, lr = 0.001
I0401 12:56:11.678102 21213 solver.cpp:218] Iteration 3128 (2.27042 iter/s, 3.52358s/8 iters), loss = 4.00661
I0401 12:56:11.678143 21213 solver.cpp:237]     Train net output #0: loss = 4.00661 (* 1 = 4.00661 loss)
I0401 12:56:11.678148 21213 sgd_solver.cpp:105] Iteration 3128, lr = 0.001
I0401 12:56:14.608358 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3136.caffemodel
I0401 12:56:17.657418 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3136.solverstate
I0401 12:56:21.991772 21213 solver.cpp:330] Iteration 3136, Testing net (#0)
I0401 12:56:21.991791 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:56:23.164352 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:56:24.116497 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0661058
I0401 12:56:24.116539 21213 solver.cpp:397]     Test net output #1: loss = 4.67463 (* 1 = 4.67463 loss)
I0401 12:56:24.257966 21213 solver.cpp:218] Iteration 3136 (0.635939 iter/s, 12.5798s/8 iters), loss = 4.08016
I0401 12:56:24.259516 21213 solver.cpp:237]     Train net output #0: loss = 4.08016 (* 1 = 4.08016 loss)
I0401 12:56:24.259528 21213 sgd_solver.cpp:105] Iteration 3136, lr = 0.001
I0401 12:56:26.840643 21213 solver.cpp:218] Iteration 3144 (3.09942 iter/s, 2.58112s/8 iters), loss = 3.89798
I0401 12:56:26.840679 21213 solver.cpp:237]     Train net output #0: loss = 3.89798 (* 1 = 3.89798 loss)
I0401 12:56:26.840684 21213 sgd_solver.cpp:105] Iteration 3144, lr = 0.001
I0401 12:56:30.249281 21213 solver.cpp:218] Iteration 3152 (2.34701 iter/s, 3.40859s/8 iters), loss = 3.87944
I0401 12:56:30.249322 21213 solver.cpp:237]     Train net output #0: loss = 3.87944 (* 1 = 3.87944 loss)
I0401 12:56:30.249328 21213 sgd_solver.cpp:105] Iteration 3152, lr = 0.001
I0401 12:56:33.682649 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:56:33.858465 21213 solver.cpp:218] Iteration 3160 (2.2166 iter/s, 3.60913s/8 iters), loss = 3.98834
I0401 12:56:33.858508 21213 solver.cpp:237]     Train net output #0: loss = 3.98834 (* 1 = 3.98834 loss)
I0401 12:56:33.858515 21213 sgd_solver.cpp:105] Iteration 3160, lr = 0.001
I0401 12:56:37.450394 21213 solver.cpp:218] Iteration 3168 (2.22725 iter/s, 3.59187s/8 iters), loss = 4.23674
I0401 12:56:37.450445 21213 solver.cpp:237]     Train net output #0: loss = 4.23674 (* 1 = 4.23674 loss)
I0401 12:56:37.450453 21213 sgd_solver.cpp:105] Iteration 3168, lr = 0.001
I0401 12:56:40.821204 21213 solver.cpp:218] Iteration 3176 (2.37337 iter/s, 3.37074s/8 iters), loss = 4.20467
I0401 12:56:40.821262 21213 solver.cpp:237]     Train net output #0: loss = 4.20467 (* 1 = 4.20467 loss)
I0401 12:56:40.821270 21213 sgd_solver.cpp:105] Iteration 3176, lr = 0.001
I0401 12:56:44.385664 21213 solver.cpp:218] Iteration 3184 (2.24443 iter/s, 3.56438s/8 iters), loss = 3.96896
I0401 12:56:44.385720 21213 solver.cpp:237]     Train net output #0: loss = 3.96896 (* 1 = 3.96896 loss)
I0401 12:56:44.385727 21213 sgd_solver.cpp:105] Iteration 3184, lr = 0.001
I0401 12:56:48.113294 21213 solver.cpp:218] Iteration 3192 (2.14617 iter/s, 3.72756s/8 iters), loss = 3.89131
I0401 12:56:48.113337 21213 solver.cpp:237]     Train net output #0: loss = 3.89131 (* 1 = 3.89131 loss)
I0401 12:56:48.113343 21213 sgd_solver.cpp:105] Iteration 3192, lr = 0.001
I0401 12:56:51.289922 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3200.caffemodel
I0401 12:56:54.229288 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3200.solverstate
I0401 12:56:56.551936 21213 solver.cpp:330] Iteration 3200, Testing net (#0)
I0401 12:56:56.551959 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:56:57.598127 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:56:58.656895 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0588942
I0401 12:56:58.656929 21213 solver.cpp:397]     Test net output #1: loss = 4.72001 (* 1 = 4.72001 loss)
I0401 12:56:58.798610 21213 solver.cpp:218] Iteration 3200 (0.748695 iter/s, 10.6853s/8 iters), loss = 4.01274
I0401 12:56:58.798676 21213 solver.cpp:237]     Train net output #0: loss = 4.01274 (* 1 = 4.01274 loss)
I0401 12:56:58.798684 21213 sgd_solver.cpp:105] Iteration 3200, lr = 0.001
I0401 12:57:01.656841 21213 solver.cpp:218] Iteration 3208 (2.79901 iter/s, 2.85815s/8 iters), loss = 3.962
I0401 12:57:01.656903 21213 solver.cpp:237]     Train net output #0: loss = 3.962 (* 1 = 3.962 loss)
I0401 12:57:01.656913 21213 sgd_solver.cpp:105] Iteration 3208, lr = 0.001
I0401 12:57:05.251814 21213 solver.cpp:218] Iteration 3216 (2.22538 iter/s, 3.59489s/8 iters), loss = 4.08073
I0401 12:57:05.251935 21213 solver.cpp:237]     Train net output #0: loss = 4.08073 (* 1 = 4.08073 loss)
I0401 12:57:05.251945 21213 sgd_solver.cpp:105] Iteration 3216, lr = 0.001
I0401 12:57:08.548306 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:57:08.940291 21213 solver.cpp:218] Iteration 3224 (2.16899 iter/s, 3.68836s/8 iters), loss = 4.13297
I0401 12:57:08.940326 21213 solver.cpp:237]     Train net output #0: loss = 4.13297 (* 1 = 4.13297 loss)
I0401 12:57:08.940332 21213 sgd_solver.cpp:105] Iteration 3224, lr = 0.001
I0401 12:57:12.435003 21213 solver.cpp:218] Iteration 3232 (2.28921 iter/s, 3.49466s/8 iters), loss = 3.99983
I0401 12:57:12.435058 21213 solver.cpp:237]     Train net output #0: loss = 3.99983 (* 1 = 3.99983 loss)
I0401 12:57:12.435066 21213 sgd_solver.cpp:105] Iteration 3232, lr = 0.001
I0401 12:57:16.010926 21213 solver.cpp:218] Iteration 3240 (2.23722 iter/s, 3.57586s/8 iters), loss = 4.3826
I0401 12:57:16.010967 21213 solver.cpp:237]     Train net output #0: loss = 4.3826 (* 1 = 4.3826 loss)
I0401 12:57:16.010973 21213 sgd_solver.cpp:105] Iteration 3240, lr = 0.001
I0401 12:57:19.507449 21213 solver.cpp:218] Iteration 3248 (2.28802 iter/s, 3.49647s/8 iters), loss = 4.02484
I0401 12:57:19.507486 21213 solver.cpp:237]     Train net output #0: loss = 4.02484 (* 1 = 4.02484 loss)
I0401 12:57:19.507491 21213 sgd_solver.cpp:105] Iteration 3248, lr = 0.001
I0401 12:57:23.146704 21213 solver.cpp:218] Iteration 3256 (2.19828 iter/s, 3.6392s/8 iters), loss = 4.00419
I0401 12:57:23.146749 21213 solver.cpp:237]     Train net output #0: loss = 4.00419 (* 1 = 4.00419 loss)
I0401 12:57:23.146755 21213 sgd_solver.cpp:105] Iteration 3256, lr = 0.001
I0401 12:57:26.059300 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0401 12:57:29.220010 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0401 12:57:31.543263 21213 solver.cpp:330] Iteration 3264, Testing net (#0)
I0401 12:57:31.543282 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:57:32.538396 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:57:33.683940 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0721154
I0401 12:57:33.683979 21213 solver.cpp:397]     Test net output #1: loss = 4.68938 (* 1 = 4.68938 loss)
I0401 12:57:33.835515 21213 solver.cpp:218] Iteration 3264 (0.74845 iter/s, 10.6888s/8 iters), loss = 3.48653
I0401 12:57:33.837098 21213 solver.cpp:237]     Train net output #0: loss = 3.48653 (* 1 = 3.48653 loss)
I0401 12:57:33.837110 21213 sgd_solver.cpp:105] Iteration 3264, lr = 0.001
I0401 12:57:36.472659 21213 solver.cpp:218] Iteration 3272 (3.0354 iter/s, 2.63556s/8 iters), loss = 3.78207
I0401 12:57:36.472771 21213 solver.cpp:237]     Train net output #0: loss = 3.78207 (* 1 = 3.78207 loss)
I0401 12:57:36.472779 21213 sgd_solver.cpp:105] Iteration 3272, lr = 0.001
I0401 12:57:40.067641 21213 solver.cpp:218] Iteration 3280 (2.2254 iter/s, 3.59485s/8 iters), loss = 3.87467
I0401 12:57:40.067680 21213 solver.cpp:237]     Train net output #0: loss = 3.87467 (* 1 = 3.87467 loss)
I0401 12:57:40.067685 21213 sgd_solver.cpp:105] Iteration 3280, lr = 0.001
I0401 12:57:43.017843 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:57:43.716405 21213 solver.cpp:218] Iteration 3288 (2.19256 iter/s, 3.64871s/8 iters), loss = 3.92437
I0401 12:57:43.716456 21213 solver.cpp:237]     Train net output #0: loss = 3.92437 (* 1 = 3.92437 loss)
I0401 12:57:43.716464 21213 sgd_solver.cpp:105] Iteration 3288, lr = 0.001
I0401 12:57:47.220552 21213 solver.cpp:218] Iteration 3296 (2.28305 iter/s, 3.50408s/8 iters), loss = 3.80182
I0401 12:57:47.220607 21213 solver.cpp:237]     Train net output #0: loss = 3.80182 (* 1 = 3.80182 loss)
I0401 12:57:47.220616 21213 sgd_solver.cpp:105] Iteration 3296, lr = 0.001
I0401 12:57:50.645426 21213 solver.cpp:218] Iteration 3304 (2.3359 iter/s, 3.4248s/8 iters), loss = 3.92598
I0401 12:57:50.645483 21213 solver.cpp:237]     Train net output #0: loss = 3.92598 (* 1 = 3.92598 loss)
I0401 12:57:50.645491 21213 sgd_solver.cpp:105] Iteration 3304, lr = 0.001
I0401 12:57:54.140849 21213 solver.cpp:218] Iteration 3312 (2.28875 iter/s, 3.49535s/8 iters), loss = 4.04746
I0401 12:57:54.140904 21213 solver.cpp:237]     Train net output #0: loss = 4.04746 (* 1 = 4.04746 loss)
I0401 12:57:54.140913 21213 sgd_solver.cpp:105] Iteration 3312, lr = 0.001
I0401 12:57:57.849752 21213 solver.cpp:218] Iteration 3320 (2.15701 iter/s, 3.70884s/8 iters), loss = 3.7076
I0401 12:57:57.849793 21213 solver.cpp:237]     Train net output #0: loss = 3.7076 (* 1 = 3.7076 loss)
I0401 12:57:57.849799 21213 sgd_solver.cpp:105] Iteration 3320, lr = 0.001
I0401 12:58:01.117369 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3328.caffemodel
I0401 12:58:05.922578 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3328.solverstate
I0401 12:58:11.398703 21213 solver.cpp:330] Iteration 3328, Testing net (#0)
I0401 12:58:11.398813 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:58:12.432988 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:58:13.647429 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 12:58:13.647464 21213 solver.cpp:397]     Test net output #1: loss = 4.70013 (* 1 = 4.70013 loss)
I0401 12:58:13.788708 21213 solver.cpp:218] Iteration 3328 (0.501916 iter/s, 15.9389s/8 iters), loss = 3.91843
I0401 12:58:13.788772 21213 solver.cpp:237]     Train net output #0: loss = 3.91843 (* 1 = 3.91843 loss)
I0401 12:58:13.788784 21213 sgd_solver.cpp:105] Iteration 3328, lr = 0.001
I0401 12:58:16.532999 21213 solver.cpp:218] Iteration 3336 (2.91523 iter/s, 2.74421s/8 iters), loss = 3.79082
I0401 12:58:16.533082 21213 solver.cpp:237]     Train net output #0: loss = 3.79082 (* 1 = 3.79082 loss)
I0401 12:58:16.533097 21213 sgd_solver.cpp:105] Iteration 3336, lr = 0.001
I0401 12:58:20.153638 21213 solver.cpp:218] Iteration 3344 (2.20961 iter/s, 3.62055s/8 iters), loss = 3.79643
I0401 12:58:20.153697 21213 solver.cpp:237]     Train net output #0: loss = 3.79643 (* 1 = 3.79643 loss)
I0401 12:58:20.153707 21213 sgd_solver.cpp:105] Iteration 3344, lr = 0.001
I0401 12:58:22.639880 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:58:23.731096 21213 solver.cpp:218] Iteration 3352 (2.23627 iter/s, 3.57739s/8 iters), loss = 4.14206
I0401 12:58:23.731140 21213 solver.cpp:237]     Train net output #0: loss = 4.14206 (* 1 = 4.14206 loss)
I0401 12:58:23.731145 21213 sgd_solver.cpp:105] Iteration 3352, lr = 0.001
I0401 12:58:27.135737 21213 solver.cpp:218] Iteration 3360 (2.34978 iter/s, 3.40458s/8 iters), loss = 3.97599
I0401 12:58:27.135788 21213 solver.cpp:237]     Train net output #0: loss = 3.97599 (* 1 = 3.97599 loss)
I0401 12:58:27.135797 21213 sgd_solver.cpp:105] Iteration 3360, lr = 0.001
I0401 12:58:30.693466 21213 solver.cpp:218] Iteration 3368 (2.24866 iter/s, 3.55767s/8 iters), loss = 3.83575
I0401 12:58:30.693506 21213 solver.cpp:237]     Train net output #0: loss = 3.83575 (* 1 = 3.83575 loss)
I0401 12:58:30.693511 21213 sgd_solver.cpp:105] Iteration 3368, lr = 0.001
I0401 12:58:34.167426 21213 solver.cpp:218] Iteration 3376 (2.30289 iter/s, 3.4739s/8 iters), loss = 4.07835
I0401 12:58:34.167485 21213 solver.cpp:237]     Train net output #0: loss = 4.07835 (* 1 = 4.07835 loss)
I0401 12:58:34.167493 21213 sgd_solver.cpp:105] Iteration 3376, lr = 0.001
I0401 12:58:37.887260 21213 solver.cpp:218] Iteration 3384 (2.15068 iter/s, 3.71976s/8 iters), loss = 3.86084
I0401 12:58:37.887311 21213 solver.cpp:237]     Train net output #0: loss = 3.86084 (* 1 = 3.86084 loss)
I0401 12:58:37.887320 21213 sgd_solver.cpp:105] Iteration 3384, lr = 0.001
I0401 12:58:40.746953 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3392.caffemodel
I0401 12:58:44.264043 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3392.solverstate
I0401 12:58:46.666884 21213 solver.cpp:330] Iteration 3392, Testing net (#0)
I0401 12:58:46.666903 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:58:47.548466 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:58:48.765552 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0673077
I0401 12:58:48.765590 21213 solver.cpp:397]     Test net output #1: loss = 4.69221 (* 1 = 4.69221 loss)
I0401 12:58:48.907948 21213 solver.cpp:218] Iteration 3392 (0.725911 iter/s, 11.0206s/8 iters), loss = 3.89329
I0401 12:58:48.909519 21213 solver.cpp:237]     Train net output #0: loss = 3.89329 (* 1 = 3.89329 loss)
I0401 12:58:48.909538 21213 sgd_solver.cpp:105] Iteration 3392, lr = 0.001
I0401 12:58:51.443742 21213 solver.cpp:218] Iteration 3400 (3.15679 iter/s, 2.53422s/8 iters), loss = 3.87972
I0401 12:58:51.443784 21213 solver.cpp:237]     Train net output #0: loss = 3.87972 (* 1 = 3.87972 loss)
I0401 12:58:51.443789 21213 sgd_solver.cpp:105] Iteration 3400, lr = 0.001
I0401 12:58:55.161262 21213 solver.cpp:218] Iteration 3408 (2.152 iter/s, 3.71747s/8 iters), loss = 3.55036
I0401 12:58:55.161304 21213 solver.cpp:237]     Train net output #0: loss = 3.55036 (* 1 = 3.55036 loss)
I0401 12:58:55.161309 21213 sgd_solver.cpp:105] Iteration 3408, lr = 0.001
I0401 12:58:57.214022 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:58:58.742072 21213 solver.cpp:218] Iteration 3416 (2.23416 iter/s, 3.58076s/8 iters), loss = 3.78625
I0401 12:58:58.742115 21213 solver.cpp:237]     Train net output #0: loss = 3.78625 (* 1 = 3.78625 loss)
I0401 12:58:58.742120 21213 sgd_solver.cpp:105] Iteration 3416, lr = 0.001
I0401 12:59:02.433835 21213 solver.cpp:218] Iteration 3424 (2.16702 iter/s, 3.69171s/8 iters), loss = 4.04251
I0401 12:59:02.433878 21213 solver.cpp:237]     Train net output #0: loss = 4.04251 (* 1 = 4.04251 loss)
I0401 12:59:02.433887 21213 sgd_solver.cpp:105] Iteration 3424, lr = 0.001
I0401 12:59:05.991123 21213 solver.cpp:218] Iteration 3432 (2.24894 iter/s, 3.55723s/8 iters), loss = 3.85358
I0401 12:59:05.991171 21213 solver.cpp:237]     Train net output #0: loss = 3.85358 (* 1 = 3.85358 loss)
I0401 12:59:05.991179 21213 sgd_solver.cpp:105] Iteration 3432, lr = 0.001
I0401 12:59:09.649130 21213 solver.cpp:218] Iteration 3440 (2.18702 iter/s, 3.65794s/8 iters), loss = 3.91122
I0401 12:59:09.649190 21213 solver.cpp:237]     Train net output #0: loss = 3.91122 (* 1 = 3.91122 loss)
I0401 12:59:09.649199 21213 sgd_solver.cpp:105] Iteration 3440, lr = 0.001
I0401 12:59:13.112347 21213 solver.cpp:218] Iteration 3448 (2.31004 iter/s, 3.46314s/8 iters), loss = 3.78095
I0401 12:59:13.118611 21213 solver.cpp:237]     Train net output #0: loss = 3.78095 (* 1 = 3.78095 loss)
I0401 12:59:13.118633 21213 sgd_solver.cpp:105] Iteration 3448, lr = 0.001
I0401 12:59:17.770645 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3456.caffemodel
I0401 12:59:22.769131 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3456.solverstate
I0401 12:59:26.128021 21213 solver.cpp:330] Iteration 3456, Testing net (#0)
I0401 12:59:26.128049 21213 net.cpp:676] Ignoring source layer train-data
I0401 12:59:28.181259 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:59:32.505467 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0612981
I0401 12:59:32.505509 21213 solver.cpp:397]     Test net output #1: loss = 4.68414 (* 1 = 4.68414 loss)
I0401 12:59:32.677703 21213 solver.cpp:218] Iteration 3456 (0.409195 iter/s, 19.5506s/8 iters), loss = 3.85139
I0401 12:59:32.677755 21213 solver.cpp:237]     Train net output #0: loss = 3.85139 (* 1 = 3.85139 loss)
I0401 12:59:32.677763 21213 sgd_solver.cpp:105] Iteration 3456, lr = 0.001
I0401 12:59:37.590782 21213 solver.cpp:218] Iteration 3464 (1.63689 iter/s, 4.88733s/8 iters), loss = 3.56725
I0401 12:59:37.590847 21213 solver.cpp:237]     Train net output #0: loss = 3.56725 (* 1 = 3.56725 loss)
I0401 12:59:37.590857 21213 sgd_solver.cpp:105] Iteration 3464, lr = 0.001
I0401 12:59:44.417335 21213 solver.cpp:218] Iteration 3472 (1.17191 iter/s, 6.82647s/8 iters), loss = 3.85695
I0401 12:59:44.417400 21213 solver.cpp:237]     Train net output #0: loss = 3.85695 (* 1 = 3.85695 loss)
I0401 12:59:44.417410 21213 sgd_solver.cpp:105] Iteration 3472, lr = 0.001
I0401 12:59:46.550495 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 12:59:48.522389 21213 solver.cpp:218] Iteration 3480 (1.94885 iter/s, 4.10498s/8 iters), loss = 3.72929
I0401 12:59:48.522482 21213 solver.cpp:237]     Train net output #0: loss = 3.72929 (* 1 = 3.72929 loss)
I0401 12:59:48.522488 21213 sgd_solver.cpp:105] Iteration 3480, lr = 0.001
I0401 12:59:52.145846 21213 solver.cpp:218] Iteration 3488 (2.2079 iter/s, 3.62335s/8 iters), loss = 3.70706
I0401 12:59:52.145884 21213 solver.cpp:237]     Train net output #0: loss = 3.70706 (* 1 = 3.70706 loss)
I0401 12:59:52.145889 21213 sgd_solver.cpp:105] Iteration 3488, lr = 0.001
I0401 12:59:55.531759 21213 solver.cpp:218] Iteration 3496 (2.36277 iter/s, 3.38586s/8 iters), loss = 3.66681
I0401 12:59:55.531822 21213 solver.cpp:237]     Train net output #0: loss = 3.66681 (* 1 = 3.66681 loss)
I0401 12:59:55.531831 21213 sgd_solver.cpp:105] Iteration 3496, lr = 0.001
I0401 12:59:59.071101 21213 solver.cpp:218] Iteration 3504 (2.26036 iter/s, 3.53927s/8 iters), loss = 3.86988
I0401 12:59:59.071158 21213 solver.cpp:237]     Train net output #0: loss = 3.86988 (* 1 = 3.86988 loss)
I0401 12:59:59.071166 21213 sgd_solver.cpp:105] Iteration 3504, lr = 0.001
I0401 13:00:02.687222 21213 solver.cpp:218] Iteration 3512 (2.21236 iter/s, 3.61605s/8 iters), loss = 3.68047
I0401 13:00:02.687263 21213 solver.cpp:237]     Train net output #0: loss = 3.68047 (* 1 = 3.68047 loss)
I0401 13:00:02.687268 21213 sgd_solver.cpp:105] Iteration 3512, lr = 0.001
I0401 13:00:05.929448 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3520.caffemodel
I0401 13:00:10.469364 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3520.solverstate
I0401 13:00:13.000470 21213 solver.cpp:330] Iteration 3520, Testing net (#0)
I0401 13:00:13.000492 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:00:13.931457 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:00:15.434381 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0673077
I0401 13:00:15.434417 21213 solver.cpp:397]     Test net output #1: loss = 4.70556 (* 1 = 4.70556 loss)
I0401 13:00:15.575021 21213 solver.cpp:218] Iteration 3520 (0.620745 iter/s, 12.8877s/8 iters), loss = 3.57312
I0401 13:00:15.575090 21213 solver.cpp:237]     Train net output #0: loss = 3.57312 (* 1 = 3.57312 loss)
I0401 13:00:15.575098 21213 sgd_solver.cpp:105] Iteration 3520, lr = 0.001
I0401 13:00:18.224313 21213 solver.cpp:218] Iteration 3528 (3.01977 iter/s, 2.6492s/8 iters), loss = 3.39212
I0401 13:00:18.224372 21213 solver.cpp:237]     Train net output #0: loss = 3.39212 (* 1 = 3.39212 loss)
I0401 13:00:18.224380 21213 sgd_solver.cpp:105] Iteration 3528, lr = 0.001
I0401 13:00:21.763327 21213 solver.cpp:218] Iteration 3536 (2.26056 iter/s, 3.53894s/8 iters), loss = 3.62157
I0401 13:00:21.763485 21213 solver.cpp:237]     Train net output #0: loss = 3.62157 (* 1 = 3.62157 loss)
I0401 13:00:21.763494 21213 sgd_solver.cpp:105] Iteration 3536, lr = 0.001
I0401 13:00:23.185335 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:00:25.209481 21213 solver.cpp:218] Iteration 3544 (2.32154 iter/s, 3.44598s/8 iters), loss = 3.76017
I0401 13:00:25.209525 21213 solver.cpp:237]     Train net output #0: loss = 3.76017 (* 1 = 3.76017 loss)
I0401 13:00:25.209530 21213 sgd_solver.cpp:105] Iteration 3544, lr = 0.001
I0401 13:00:28.992870 21213 solver.cpp:218] Iteration 3552 (2.11454 iter/s, 3.78333s/8 iters), loss = 3.78682
I0401 13:00:28.992933 21213 solver.cpp:237]     Train net output #0: loss = 3.78682 (* 1 = 3.78682 loss)
I0401 13:00:28.992941 21213 sgd_solver.cpp:105] Iteration 3552, lr = 0.001
I0401 13:00:32.456876 21213 solver.cpp:218] Iteration 3560 (2.30951 iter/s, 3.46393s/8 iters), loss = 3.39239
I0401 13:00:32.456969 21213 solver.cpp:237]     Train net output #0: loss = 3.39239 (* 1 = 3.39239 loss)
I0401 13:00:32.456977 21213 sgd_solver.cpp:105] Iteration 3560, lr = 0.001
I0401 13:00:36.078498 21213 solver.cpp:218] Iteration 3568 (2.20902 iter/s, 3.62151s/8 iters), loss = 3.62515
I0401 13:00:36.078541 21213 solver.cpp:237]     Train net output #0: loss = 3.62515 (* 1 = 3.62515 loss)
I0401 13:00:36.078567 21213 sgd_solver.cpp:105] Iteration 3568, lr = 0.001
I0401 13:00:39.710173 21213 solver.cpp:218] Iteration 3576 (2.20288 iter/s, 3.63162s/8 iters), loss = 3.87673
I0401 13:00:39.710223 21213 solver.cpp:237]     Train net output #0: loss = 3.87673 (* 1 = 3.87673 loss)
I0401 13:00:39.710229 21213 sgd_solver.cpp:105] Iteration 3576, lr = 0.001
I0401 13:00:42.952725 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3584.caffemodel
I0401 13:00:46.048252 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3584.solverstate
I0401 13:00:49.541194 21213 solver.cpp:330] Iteration 3584, Testing net (#0)
I0401 13:00:49.541214 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:00:50.270051 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:00:51.709928 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0745192
I0401 13:00:51.709969 21213 solver.cpp:397]     Test net output #1: loss = 4.66654 (* 1 = 4.66654 loss)
I0401 13:00:51.847510 21213 solver.cpp:218] Iteration 3584 (0.659126 iter/s, 12.1373s/8 iters), loss = 3.56089
I0401 13:00:51.847681 21213 solver.cpp:237]     Train net output #0: loss = 3.56089 (* 1 = 3.56089 loss)
I0401 13:00:51.847690 21213 sgd_solver.cpp:105] Iteration 3584, lr = 0.001
I0401 13:00:54.448364 21213 solver.cpp:218] Iteration 3592 (3.07613 iter/s, 2.60067s/8 iters), loss = 3.79681
I0401 13:00:54.448412 21213 solver.cpp:237]     Train net output #0: loss = 3.79681 (* 1 = 3.79681 loss)
I0401 13:00:54.448421 21213 sgd_solver.cpp:105] Iteration 3592, lr = 0.001
I0401 13:00:58.001495 21213 solver.cpp:218] Iteration 3600 (2.25157 iter/s, 3.55308s/8 iters), loss = 3.57965
I0401 13:00:58.001533 21213 solver.cpp:237]     Train net output #0: loss = 3.57965 (* 1 = 3.57965 loss)
I0401 13:00:58.001538 21213 sgd_solver.cpp:105] Iteration 3600, lr = 0.001
I0401 13:00:58.853145 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:01:01.255450 21213 solver.cpp:218] Iteration 3608 (2.45859 iter/s, 3.2539s/8 iters), loss = 3.72747
I0401 13:01:01.255488 21213 solver.cpp:237]     Train net output #0: loss = 3.72747 (* 1 = 3.72747 loss)
I0401 13:01:01.255494 21213 sgd_solver.cpp:105] Iteration 3608, lr = 0.001
I0401 13:01:04.808975 21213 solver.cpp:218] Iteration 3616 (2.25132 iter/s, 3.55347s/8 iters), loss = 4.01284
I0401 13:01:04.809017 21213 solver.cpp:237]     Train net output #0: loss = 4.01284 (* 1 = 4.01284 loss)
I0401 13:01:04.809023 21213 sgd_solver.cpp:105] Iteration 3616, lr = 0.001
I0401 13:01:08.307247 21213 solver.cpp:218] Iteration 3624 (2.28688 iter/s, 3.49821s/8 iters), loss = 3.62425
I0401 13:01:08.307294 21213 solver.cpp:237]     Train net output #0: loss = 3.62425 (* 1 = 3.62425 loss)
I0401 13:01:08.307301 21213 sgd_solver.cpp:105] Iteration 3624, lr = 0.001
I0401 13:01:12.099350 21213 solver.cpp:218] Iteration 3632 (2.10968 iter/s, 3.79204s/8 iters), loss = 3.63423
I0401 13:01:12.099416 21213 solver.cpp:237]     Train net output #0: loss = 3.63423 (* 1 = 3.63423 loss)
I0401 13:01:12.099424 21213 sgd_solver.cpp:105] Iteration 3632, lr = 0.001
I0401 13:01:15.709530 21213 solver.cpp:218] Iteration 3640 (2.216 iter/s, 3.61011s/8 iters), loss = 3.5032
I0401 13:01:15.709569 21213 solver.cpp:237]     Train net output #0: loss = 3.5032 (* 1 = 3.5032 loss)
I0401 13:01:15.709574 21213 sgd_solver.cpp:105] Iteration 3640, lr = 0.001
I0401 13:01:18.843935 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3648.caffemodel
I0401 13:01:21.824862 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3648.solverstate
I0401 13:01:24.117512 21213 solver.cpp:330] Iteration 3648, Testing net (#0)
I0401 13:01:24.117583 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:01:24.749861 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:01:26.380751 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0612981
I0401 13:01:26.380791 21213 solver.cpp:397]     Test net output #1: loss = 4.68851 (* 1 = 4.68851 loss)
I0401 13:01:26.522168 21213 solver.cpp:218] Iteration 3648 (0.739879 iter/s, 10.8126s/8 iters), loss = 3.79139
I0401 13:01:26.522234 21213 solver.cpp:237]     Train net output #0: loss = 3.79139 (* 1 = 3.79139 loss)
I0401 13:01:26.522245 21213 sgd_solver.cpp:105] Iteration 3648, lr = 0.001
I0401 13:01:29.043454 21213 solver.cpp:218] Iteration 3656 (3.17308 iter/s, 2.52121s/8 iters), loss = 3.56606
I0401 13:01:29.043498 21213 solver.cpp:237]     Train net output #0: loss = 3.56606 (* 1 = 3.56606 loss)
I0401 13:01:29.043503 21213 sgd_solver.cpp:105] Iteration 3656, lr = 0.001
I0401 13:01:32.634542 21213 solver.cpp:218] Iteration 3664 (2.22777 iter/s, 3.59103s/8 iters), loss = 3.5524
I0401 13:01:32.634583 21213 solver.cpp:237]     Train net output #0: loss = 3.5524 (* 1 = 3.5524 loss)
I0401 13:01:32.634589 21213 sgd_solver.cpp:105] Iteration 3664, lr = 0.001
I0401 13:01:33.236260 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:01:36.281474 21213 solver.cpp:218] Iteration 3672 (2.19366 iter/s, 3.64687s/8 iters), loss = 3.79466
I0401 13:01:36.281533 21213 solver.cpp:237]     Train net output #0: loss = 3.79466 (* 1 = 3.79466 loss)
I0401 13:01:36.281543 21213 sgd_solver.cpp:105] Iteration 3672, lr = 0.001
I0401 13:01:39.918707 21213 solver.cpp:218] Iteration 3680 (2.19952 iter/s, 3.63716s/8 iters), loss = 3.91764
I0401 13:01:39.918772 21213 solver.cpp:237]     Train net output #0: loss = 3.91764 (* 1 = 3.91764 loss)
I0401 13:01:39.918781 21213 sgd_solver.cpp:105] Iteration 3680, lr = 0.001
I0401 13:01:43.542467 21213 solver.cpp:218] Iteration 3688 (2.2077 iter/s, 3.62369s/8 iters), loss = 3.78177
I0401 13:01:43.542526 21213 solver.cpp:237]     Train net output #0: loss = 3.78177 (* 1 = 3.78177 loss)
I0401 13:01:43.542534 21213 sgd_solver.cpp:105] Iteration 3688, lr = 0.001
I0401 13:01:47.151304 21213 solver.cpp:218] Iteration 3696 (2.21683 iter/s, 3.60876s/8 iters), loss = 4.01856
I0401 13:01:47.151358 21213 solver.cpp:237]     Train net output #0: loss = 4.01856 (* 1 = 4.01856 loss)
I0401 13:01:47.151366 21213 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0401 13:01:50.717881 21213 solver.cpp:218] Iteration 3704 (2.24309 iter/s, 3.56651s/8 iters), loss = 4.11615
I0401 13:01:50.717921 21213 solver.cpp:237]     Train net output #0: loss = 4.11615 (* 1 = 4.11615 loss)
I0401 13:01:50.717926 21213 sgd_solver.cpp:105] Iteration 3704, lr = 0.001
I0401 13:01:53.853521 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3712.caffemodel
I0401 13:01:57.110352 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3712.solverstate
I0401 13:01:59.492470 21213 solver.cpp:330] Iteration 3712, Testing net (#0)
I0401 13:01:59.492496 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:02:00.465304 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:02:04.751015 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0649038
I0401 13:02:04.751053 21213 solver.cpp:397]     Test net output #1: loss = 4.66065 (* 1 = 4.66065 loss)
I0401 13:02:04.973417 21213 solver.cpp:218] Iteration 3712 (0.562218 iter/s, 14.2294s/8 iters), loss = 3.54005
I0401 13:02:04.973475 21213 solver.cpp:237]     Train net output #0: loss = 3.54005 (* 1 = 3.54005 loss)
I0401 13:02:04.973484 21213 sgd_solver.cpp:105] Iteration 3712, lr = 0.001
I0401 13:02:09.597177 21213 solver.cpp:218] Iteration 3720 (1.73022 iter/s, 4.62369s/8 iters), loss = 3.67888
I0401 13:02:09.597229 21213 solver.cpp:237]     Train net output #0: loss = 3.67888 (* 1 = 3.67888 loss)
I0401 13:02:09.597235 21213 sgd_solver.cpp:105] Iteration 3720, lr = 0.001
I0401 13:02:13.987121 21213 solver.cpp:218] Iteration 3728 (1.82573 iter/s, 4.38181s/8 iters), loss = 3.48656
I0401 13:02:13.996942 21213 solver.cpp:237]     Train net output #0: loss = 3.48656 (* 1 = 3.48656 loss)
I0401 13:02:13.996966 21213 sgd_solver.cpp:105] Iteration 3728, lr = 0.001
I0401 13:02:14.417974 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:02:18.275293 21213 solver.cpp:218] Iteration 3736 (1.86988 iter/s, 4.27836s/8 iters), loss = 3.7365
I0401 13:02:18.275352 21213 solver.cpp:237]     Train net output #0: loss = 3.7365 (* 1 = 3.7365 loss)
I0401 13:02:18.275362 21213 sgd_solver.cpp:105] Iteration 3736, lr = 0.001
I0401 13:02:22.803983 21213 solver.cpp:218] Iteration 3744 (1.76654 iter/s, 4.52861s/8 iters), loss = 3.98752
I0401 13:02:22.810174 21213 solver.cpp:237]     Train net output #0: loss = 3.98752 (* 1 = 3.98752 loss)
I0401 13:02:22.810196 21213 sgd_solver.cpp:105] Iteration 3744, lr = 0.001
I0401 13:02:27.142758 21213 solver.cpp:218] Iteration 3752 (1.84736 iter/s, 4.33051s/8 iters), loss = 3.69075
I0401 13:02:27.143136 21213 solver.cpp:237]     Train net output #0: loss = 3.69075 (* 1 = 3.69075 loss)
I0401 13:02:27.143146 21213 sgd_solver.cpp:105] Iteration 3752, lr = 0.001
I0401 13:02:31.800933 21213 solver.cpp:218] Iteration 3760 (1.72149 iter/s, 4.64714s/8 iters), loss = 3.56841
I0401 13:02:31.800987 21213 solver.cpp:237]     Train net output #0: loss = 3.56841 (* 1 = 3.56841 loss)
I0401 13:02:31.800995 21213 sgd_solver.cpp:105] Iteration 3760, lr = 0.001
I0401 13:02:36.078971 21213 solver.cpp:218] Iteration 3768 (1.87005 iter/s, 4.27797s/8 iters), loss = 3.81836
I0401 13:02:36.079022 21213 solver.cpp:237]     Train net output #0: loss = 3.81836 (* 1 = 3.81836 loss)
I0401 13:02:36.079030 21213 sgd_solver.cpp:105] Iteration 3768, lr = 0.001
I0401 13:02:39.330374 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3776.caffemodel
I0401 13:02:42.363705 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3776.solverstate
I0401 13:02:44.710368 21213 solver.cpp:330] Iteration 3776, Testing net (#0)
I0401 13:02:44.710388 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:02:45.317926 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:02:47.117709 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 13:02:47.117743 21213 solver.cpp:397]     Test net output #1: loss = 4.63796 (* 1 = 4.63796 loss)
I0401 13:02:47.255334 21213 solver.cpp:218] Iteration 3776 (0.7158 iter/s, 11.1763s/8 iters), loss = 3.71625
I0401 13:02:47.255398 21213 solver.cpp:237]     Train net output #0: loss = 3.71625 (* 1 = 3.71625 loss)
I0401 13:02:47.255407 21213 sgd_solver.cpp:105] Iteration 3776, lr = 0.001
I0401 13:02:50.017414 21213 solver.cpp:218] Iteration 3784 (2.89645 iter/s, 2.762s/8 iters), loss = 3.55725
I0401 13:02:50.017472 21213 solver.cpp:237]     Train net output #0: loss = 3.55725 (* 1 = 3.55725 loss)
I0401 13:02:50.017482 21213 sgd_solver.cpp:105] Iteration 3784, lr = 0.001
I0401 13:02:53.447834 21213 solver.cpp:218] Iteration 3792 (2.33212 iter/s, 3.43035s/8 iters), loss = 3.35482
I0401 13:02:53.447897 21213 solver.cpp:237]     Train net output #0: loss = 3.35482 (* 1 = 3.35482 loss)
I0401 13:02:53.447906 21213 sgd_solver.cpp:105] Iteration 3792, lr = 0.001
I0401 13:02:53.481737 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:02:56.777442 21213 solver.cpp:218] Iteration 3800 (2.40274 iter/s, 3.32954s/8 iters), loss = 3.54561
I0401 13:02:56.777480 21213 solver.cpp:237]     Train net output #0: loss = 3.54561 (* 1 = 3.54561 loss)
I0401 13:02:56.777485 21213 sgd_solver.cpp:105] Iteration 3800, lr = 0.001
I0401 13:02:57.220708 21213 blocking_queue.cpp:49] Waiting for data
I0401 13:03:00.290376 21213 solver.cpp:218] Iteration 3808 (2.27733 iter/s, 3.51289s/8 iters), loss = 3.71301
I0401 13:03:00.290421 21213 solver.cpp:237]     Train net output #0: loss = 3.71301 (* 1 = 3.71301 loss)
I0401 13:03:00.290426 21213 sgd_solver.cpp:105] Iteration 3808, lr = 0.001
I0401 13:03:03.986865 21213 solver.cpp:218] Iteration 3816 (2.16425 iter/s, 3.69643s/8 iters), loss = 3.70335
I0401 13:03:03.986919 21213 solver.cpp:237]     Train net output #0: loss = 3.70335 (* 1 = 3.70335 loss)
I0401 13:03:03.986927 21213 sgd_solver.cpp:105] Iteration 3816, lr = 0.001
I0401 13:03:07.596387 21213 solver.cpp:218] Iteration 3824 (2.2164 iter/s, 3.60945s/8 iters), loss = 3.72102
I0401 13:03:07.596443 21213 solver.cpp:237]     Train net output #0: loss = 3.72102 (* 1 = 3.72102 loss)
I0401 13:03:07.596451 21213 sgd_solver.cpp:105] Iteration 3824, lr = 0.001
I0401 13:03:11.206908 21213 solver.cpp:218] Iteration 3832 (2.21579 iter/s, 3.61045s/8 iters), loss = 3.72273
I0401 13:03:11.206951 21213 solver.cpp:237]     Train net output #0: loss = 3.72273 (* 1 = 3.72273 loss)
I0401 13:03:11.206956 21213 sgd_solver.cpp:105] Iteration 3832, lr = 0.001
I0401 13:03:14.172412 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3840.caffemodel
I0401 13:03:17.284135 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3840.solverstate
I0401 13:03:19.611469 21213 solver.cpp:330] Iteration 3840, Testing net (#0)
I0401 13:03:19.611488 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:03:20.017166 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:03:21.943562 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0793269
I0401 13:03:21.943598 21213 solver.cpp:397]     Test net output #1: loss = 4.70315 (* 1 = 4.70315 loss)
I0401 13:03:22.085067 21213 solver.cpp:218] Iteration 3840 (0.735422 iter/s, 10.8781s/8 iters), loss = 3.64848
I0401 13:03:22.085122 21213 solver.cpp:237]     Train net output #0: loss = 3.64848 (* 1 = 3.64848 loss)
I0401 13:03:22.085131 21213 sgd_solver.cpp:105] Iteration 3840, lr = 0.001
I0401 13:03:24.738917 21213 solver.cpp:218] Iteration 3848 (3.01457 iter/s, 2.65378s/8 iters), loss = 3.56099
I0401 13:03:24.738960 21213 solver.cpp:237]     Train net output #0: loss = 3.56099 (* 1 = 3.56099 loss)
I0401 13:03:24.738965 21213 sgd_solver.cpp:105] Iteration 3848, lr = 0.001
I0401 13:03:27.924867 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:03:28.279320 21213 solver.cpp:218] Iteration 3856 (2.25966 iter/s, 3.54035s/8 iters), loss = 3.74963
I0401 13:03:28.279366 21213 solver.cpp:237]     Train net output #0: loss = 3.74963 (* 1 = 3.74963 loss)
I0401 13:03:28.279373 21213 sgd_solver.cpp:105] Iteration 3856, lr = 0.001
I0401 13:03:32.012269 21213 solver.cpp:218] Iteration 3864 (2.14311 iter/s, 3.73289s/8 iters), loss = 3.48841
I0401 13:03:32.012323 21213 solver.cpp:237]     Train net output #0: loss = 3.48841 (* 1 = 3.48841 loss)
I0401 13:03:32.012332 21213 sgd_solver.cpp:105] Iteration 3864, lr = 0.001
I0401 13:03:35.683758 21213 solver.cpp:218] Iteration 3872 (2.179 iter/s, 3.67142s/8 iters), loss = 3.89974
I0401 13:03:35.683820 21213 solver.cpp:237]     Train net output #0: loss = 3.89974 (* 1 = 3.89974 loss)
I0401 13:03:35.683827 21213 sgd_solver.cpp:105] Iteration 3872, lr = 0.001
I0401 13:03:39.150831 21213 solver.cpp:218] Iteration 3880 (2.30747 iter/s, 3.46699s/8 iters), loss = 3.44056
I0401 13:03:39.150897 21213 solver.cpp:237]     Train net output #0: loss = 3.44056 (* 1 = 3.44056 loss)
I0401 13:03:39.150905 21213 sgd_solver.cpp:105] Iteration 3880, lr = 0.001
I0401 13:03:42.732717 21213 solver.cpp:218] Iteration 3888 (2.23351 iter/s, 3.5818s/8 iters), loss = 3.41206
I0401 13:03:42.732792 21213 solver.cpp:237]     Train net output #0: loss = 3.41206 (* 1 = 3.41206 loss)
I0401 13:03:42.732802 21213 sgd_solver.cpp:105] Iteration 3888, lr = 0.001
I0401 13:03:46.056349 21213 solver.cpp:218] Iteration 3896 (2.40707 iter/s, 3.32354s/8 iters), loss = 3.14582
I0401 13:03:46.056406 21213 solver.cpp:237]     Train net output #0: loss = 3.14582 (* 1 = 3.14582 loss)
I0401 13:03:46.056412 21213 sgd_solver.cpp:105] Iteration 3896, lr = 0.001
I0401 13:03:49.184092 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3904.caffemodel
I0401 13:03:52.409876 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3904.solverstate
I0401 13:03:55.242246 21213 solver.cpp:330] Iteration 3904, Testing net (#0)
I0401 13:03:55.242265 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:03:55.580061 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:03:57.323429 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0745192
I0401 13:03:57.323465 21213 solver.cpp:397]     Test net output #1: loss = 4.7262 (* 1 = 4.7262 loss)
I0401 13:03:57.465095 21213 solver.cpp:218] Iteration 3904 (0.70122 iter/s, 11.4087s/8 iters), loss = 3.25778
I0401 13:03:57.465149 21213 solver.cpp:237]     Train net output #0: loss = 3.25778 (* 1 = 3.25778 loss)
I0401 13:03:57.465157 21213 sgd_solver.cpp:105] Iteration 3904, lr = 0.001
I0401 13:04:00.245576 21213 solver.cpp:218] Iteration 3912 (2.87727 iter/s, 2.78041s/8 iters), loss = 3.63
I0401 13:04:00.245746 21213 solver.cpp:237]     Train net output #0: loss = 3.63 (* 1 = 3.63 loss)
I0401 13:04:00.245756 21213 sgd_solver.cpp:105] Iteration 3912, lr = 0.001
I0401 13:04:03.130373 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:04:03.851977 21213 solver.cpp:218] Iteration 3920 (2.21839 iter/s, 3.60623s/8 iters), loss = 3.39228
I0401 13:04:03.852026 21213 solver.cpp:237]     Train net output #0: loss = 3.39228 (* 1 = 3.39228 loss)
I0401 13:04:03.852031 21213 sgd_solver.cpp:105] Iteration 3920, lr = 0.001
I0401 13:04:07.378638 21213 solver.cpp:218] Iteration 3928 (2.26848 iter/s, 3.52659s/8 iters), loss = 3.74791
I0401 13:04:07.378693 21213 solver.cpp:237]     Train net output #0: loss = 3.74791 (* 1 = 3.74791 loss)
I0401 13:04:07.378701 21213 sgd_solver.cpp:105] Iteration 3928, lr = 0.001
I0401 13:04:10.995913 21213 solver.cpp:218] Iteration 3936 (2.21165 iter/s, 3.6172s/8 iters), loss = 3.70217
I0401 13:04:10.995959 21213 solver.cpp:237]     Train net output #0: loss = 3.70217 (* 1 = 3.70217 loss)
I0401 13:04:10.995965 21213 sgd_solver.cpp:105] Iteration 3936, lr = 0.001
I0401 13:04:14.484505 21213 solver.cpp:218] Iteration 3944 (2.29323 iter/s, 3.48853s/8 iters), loss = 3.5459
I0401 13:04:14.484549 21213 solver.cpp:237]     Train net output #0: loss = 3.5459 (* 1 = 3.5459 loss)
I0401 13:04:14.484555 21213 sgd_solver.cpp:105] Iteration 3944, lr = 0.001
I0401 13:04:17.982537 21213 solver.cpp:218] Iteration 3952 (2.28704 iter/s, 3.49797s/8 iters), loss = 3.70746
I0401 13:04:17.982589 21213 solver.cpp:237]     Train net output #0: loss = 3.70746 (* 1 = 3.70746 loss)
I0401 13:04:17.982595 21213 sgd_solver.cpp:105] Iteration 3952, lr = 0.001
I0401 13:04:21.812377 21213 solver.cpp:218] Iteration 3960 (2.0889 iter/s, 3.82977s/8 iters), loss = 3.37634
I0401 13:04:21.812434 21213 solver.cpp:237]     Train net output #0: loss = 3.37634 (* 1 = 3.37634 loss)
I0401 13:04:21.812443 21213 sgd_solver.cpp:105] Iteration 3960, lr = 0.001
I0401 13:04:24.747678 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3968.caffemodel
I0401 13:04:27.836294 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3968.solverstate
I0401 13:04:31.141855 21213 solver.cpp:330] Iteration 3968, Testing net (#0)
I0401 13:04:31.141917 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:04:31.425716 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:04:33.287747 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:04:33.287783 21213 solver.cpp:397]     Test net output #1: loss = 4.70574 (* 1 = 4.70574 loss)
I0401 13:04:33.429184 21213 solver.cpp:218] Iteration 3968 (0.688661 iter/s, 11.6167s/8 iters), loss = 3.39363
I0401 13:04:33.429224 21213 solver.cpp:237]     Train net output #0: loss = 3.39363 (* 1 = 3.39363 loss)
I0401 13:04:33.429229 21213 sgd_solver.cpp:105] Iteration 3968, lr = 0.001
I0401 13:04:36.072753 21213 solver.cpp:218] Iteration 3976 (3.02628 iter/s, 2.64351s/8 iters), loss = 3.43822
I0401 13:04:36.072800 21213 solver.cpp:237]     Train net output #0: loss = 3.43822 (* 1 = 3.43822 loss)
I0401 13:04:36.072805 21213 sgd_solver.cpp:105] Iteration 3976, lr = 0.001
I0401 13:04:38.568990 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:04:39.642009 21213 solver.cpp:218] Iteration 3984 (2.2414 iter/s, 3.5692s/8 iters), loss = 3.56148
I0401 13:04:39.642042 21213 solver.cpp:237]     Train net output #0: loss = 3.56148 (* 1 = 3.56148 loss)
I0401 13:04:39.642048 21213 sgd_solver.cpp:105] Iteration 3984, lr = 0.001
I0401 13:04:43.211038 21213 solver.cpp:218] Iteration 3992 (2.24154 iter/s, 3.56898s/8 iters), loss = 3.42503
I0401 13:04:43.211087 21213 solver.cpp:237]     Train net output #0: loss = 3.42503 (* 1 = 3.42503 loss)
I0401 13:04:43.211095 21213 sgd_solver.cpp:105] Iteration 3992, lr = 0.001
I0401 13:04:46.765434 21213 solver.cpp:218] Iteration 4000 (2.25077 iter/s, 3.55433s/8 iters), loss = 3.65889
I0401 13:04:46.765477 21213 solver.cpp:237]     Train net output #0: loss = 3.65889 (* 1 = 3.65889 loss)
I0401 13:04:46.765482 21213 sgd_solver.cpp:105] Iteration 4000, lr = 0.001
I0401 13:04:50.316635 21213 solver.cpp:218] Iteration 4008 (2.25282 iter/s, 3.55111s/8 iters), loss = 3.38178
I0401 13:04:50.316725 21213 solver.cpp:237]     Train net output #0: loss = 3.38178 (* 1 = 3.38178 loss)
I0401 13:04:50.316743 21213 sgd_solver.cpp:105] Iteration 4008, lr = 0.001
I0401 13:04:53.900848 21213 solver.cpp:218] Iteration 4016 (2.23207 iter/s, 3.58411s/8 iters), loss = 3.40333
I0401 13:04:53.900905 21213 solver.cpp:237]     Train net output #0: loss = 3.40333 (* 1 = 3.40333 loss)
I0401 13:04:53.900914 21213 sgd_solver.cpp:105] Iteration 4016, lr = 0.001
I0401 13:04:57.552619 21213 solver.cpp:218] Iteration 4024 (2.19076 iter/s, 3.6517s/8 iters), loss = 3.17604
I0401 13:04:57.552661 21213 solver.cpp:237]     Train net output #0: loss = 3.17604 (* 1 = 3.17604 loss)
I0401 13:04:57.552666 21213 sgd_solver.cpp:105] Iteration 4024, lr = 0.001
I0401 13:05:00.451418 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4032.caffemodel
I0401 13:05:03.627961 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4032.solverstate
I0401 13:05:05.921824 21213 solver.cpp:330] Iteration 4032, Testing net (#0)
I0401 13:05:05.921844 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:05:06.198591 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:05:08.215391 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0745192
I0401 13:05:08.215427 21213 solver.cpp:397]     Test net output #1: loss = 4.79568 (* 1 = 4.79568 loss)
I0401 13:05:08.360225 21213 solver.cpp:218] Iteration 4032 (0.740223 iter/s, 10.8076s/8 iters), loss = 3.50158
I0401 13:05:08.360275 21213 solver.cpp:237]     Train net output #0: loss = 3.50158 (* 1 = 3.50158 loss)
I0401 13:05:08.360281 21213 sgd_solver.cpp:105] Iteration 4032, lr = 0.001
I0401 13:05:11.086283 21213 solver.cpp:218] Iteration 4040 (2.93471 iter/s, 2.72599s/8 iters), loss = 3.22773
I0401 13:05:11.086328 21213 solver.cpp:237]     Train net output #0: loss = 3.22773 (* 1 = 3.22773 loss)
I0401 13:05:11.086333 21213 sgd_solver.cpp:105] Iteration 4040, lr = 0.001
I0401 13:05:13.061271 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:05:14.574227 21213 solver.cpp:218] Iteration 4048 (2.29365 iter/s, 3.48789s/8 iters), loss = 3.13114
I0401 13:05:14.574272 21213 solver.cpp:237]     Train net output #0: loss = 3.13114 (* 1 = 3.13114 loss)
I0401 13:05:14.574278 21213 sgd_solver.cpp:105] Iteration 4048, lr = 0.001
I0401 13:05:18.113639 21213 solver.cpp:218] Iteration 4056 (2.2603 iter/s, 3.53935s/8 iters), loss = 3.40129
I0401 13:05:18.113687 21213 solver.cpp:237]     Train net output #0: loss = 3.40129 (* 1 = 3.40129 loss)
I0401 13:05:18.113693 21213 sgd_solver.cpp:105] Iteration 4056, lr = 0.001
I0401 13:05:21.732343 21213 solver.cpp:218] Iteration 4064 (2.21077 iter/s, 3.61864s/8 iters), loss = 3.06562
I0401 13:05:21.732388 21213 solver.cpp:237]     Train net output #0: loss = 3.06562 (* 1 = 3.06562 loss)
I0401 13:05:21.732395 21213 sgd_solver.cpp:105] Iteration 4064, lr = 0.001
I0401 13:05:25.279268 21213 solver.cpp:218] Iteration 4072 (2.25551 iter/s, 3.54687s/8 iters), loss = 3.53092
I0401 13:05:25.279323 21213 solver.cpp:237]     Train net output #0: loss = 3.53092 (* 1 = 3.53092 loss)
I0401 13:05:25.279332 21213 sgd_solver.cpp:105] Iteration 4072, lr = 0.001
I0401 13:05:28.707573 21213 solver.cpp:218] Iteration 4080 (2.33356 iter/s, 3.42824s/8 iters), loss = 3.28323
I0401 13:05:28.707624 21213 solver.cpp:237]     Train net output #0: loss = 3.28323 (* 1 = 3.28323 loss)
I0401 13:05:28.707633 21213 sgd_solver.cpp:105] Iteration 4080, lr = 0.001
I0401 13:05:32.426158 21213 solver.cpp:218] Iteration 4088 (2.15139 iter/s, 3.71852s/8 iters), loss = 3.31637
I0401 13:05:32.426198 21213 solver.cpp:237]     Train net output #0: loss = 3.31637 (* 1 = 3.31637 loss)
I0401 13:05:32.426204 21213 sgd_solver.cpp:105] Iteration 4088, lr = 0.001
I0401 13:05:35.548768 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4096.caffemodel
I0401 13:05:38.637892 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4096.solverstate
I0401 13:05:40.936007 21213 solver.cpp:330] Iteration 4096, Testing net (#0)
I0401 13:05:40.936029 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:05:41.105471 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:05:43.191550 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0817308
I0401 13:05:43.191584 21213 solver.cpp:397]     Test net output #1: loss = 4.79066 (* 1 = 4.79066 loss)
I0401 13:05:43.325878 21213 solver.cpp:218] Iteration 4096 (0.733966 iter/s, 10.8997s/8 iters), loss = 3.02782
I0401 13:05:43.325925 21213 solver.cpp:237]     Train net output #0: loss = 3.02782 (* 1 = 3.02782 loss)
I0401 13:05:43.325933 21213 sgd_solver.cpp:105] Iteration 4096, lr = 0.001
I0401 13:05:45.795629 21213 solver.cpp:218] Iteration 4104 (3.23928 iter/s, 2.46968s/8 iters), loss = 3.137
I0401 13:05:45.795683 21213 solver.cpp:237]     Train net output #0: loss = 3.137 (* 1 = 3.137 loss)
I0401 13:05:45.795691 21213 sgd_solver.cpp:105] Iteration 4104, lr = 0.001
I0401 13:05:47.606575 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:05:49.396549 21213 solver.cpp:218] Iteration 4112 (2.2217 iter/s, 3.60084s/8 iters), loss = 3.39545
I0401 13:05:49.396625 21213 solver.cpp:237]     Train net output #0: loss = 3.39545 (* 1 = 3.39545 loss)
I0401 13:05:49.396636 21213 sgd_solver.cpp:105] Iteration 4112, lr = 0.001
I0401 13:05:52.967458 21213 solver.cpp:218] Iteration 4120 (2.24038 iter/s, 3.57083s/8 iters), loss = 3.28702
I0401 13:05:52.967511 21213 solver.cpp:237]     Train net output #0: loss = 3.28702 (* 1 = 3.28702 loss)
I0401 13:05:52.967519 21213 sgd_solver.cpp:105] Iteration 4120, lr = 0.001
I0401 13:05:56.577052 21213 solver.cpp:218] Iteration 4128 (2.21636 iter/s, 3.60953s/8 iters), loss = 3.09994
I0401 13:05:56.577090 21213 solver.cpp:237]     Train net output #0: loss = 3.09994 (* 1 = 3.09994 loss)
I0401 13:05:56.577096 21213 sgd_solver.cpp:105] Iteration 4128, lr = 0.001
I0401 13:05:59.999500 21213 solver.cpp:218] Iteration 4136 (2.33754 iter/s, 3.42239s/8 iters), loss = 3.25705
I0401 13:05:59.999545 21213 solver.cpp:237]     Train net output #0: loss = 3.25705 (* 1 = 3.25705 loss)
I0401 13:05:59.999553 21213 sgd_solver.cpp:105] Iteration 4136, lr = 0.001
I0401 13:06:03.397461 21213 solver.cpp:218] Iteration 4144 (2.35439 iter/s, 3.3979s/8 iters), loss = 3.22115
I0401 13:06:03.397501 21213 solver.cpp:237]     Train net output #0: loss = 3.22115 (* 1 = 3.22115 loss)
I0401 13:06:03.397506 21213 sgd_solver.cpp:105] Iteration 4144, lr = 0.001
I0401 13:06:07.128893 21213 solver.cpp:218] Iteration 4152 (2.14399 iter/s, 3.73137s/8 iters), loss = 3.02038
I0401 13:06:07.128983 21213 solver.cpp:237]     Train net output #0: loss = 3.02038 (* 1 = 3.02038 loss)
I0401 13:06:07.128989 21213 sgd_solver.cpp:105] Iteration 4152, lr = 0.001
I0401 13:06:10.103490 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4160.caffemodel
I0401 13:06:14.748438 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4160.solverstate
I0401 13:06:18.724030 21213 solver.cpp:330] Iteration 4160, Testing net (#0)
I0401 13:06:18.724050 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:06:18.835883 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:06:20.941980 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:06:20.942018 21213 solver.cpp:397]     Test net output #1: loss = 4.81791 (* 1 = 4.81791 loss)
I0401 13:06:21.079718 21213 solver.cpp:218] Iteration 4160 (0.573447 iter/s, 13.9507s/8 iters), loss = 2.86033
I0401 13:06:21.079772 21213 solver.cpp:237]     Train net output #0: loss = 2.86033 (* 1 = 2.86033 loss)
I0401 13:06:21.079780 21213 sgd_solver.cpp:105] Iteration 4160, lr = 0.001
I0401 13:06:23.790169 21213 solver.cpp:218] Iteration 4168 (2.95162 iter/s, 2.71038s/8 iters), loss = 3.24097
I0401 13:06:23.790218 21213 solver.cpp:237]     Train net output #0: loss = 3.24097 (* 1 = 3.24097 loss)
I0401 13:06:23.790225 21213 sgd_solver.cpp:105] Iteration 4168, lr = 0.001
I0401 13:06:25.176265 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:06:27.324607 21213 solver.cpp:218] Iteration 4176 (2.26348 iter/s, 3.53438s/8 iters), loss = 3.67804
I0401 13:06:27.324645 21213 solver.cpp:237]     Train net output #0: loss = 3.67804 (* 1 = 3.67804 loss)
I0401 13:06:27.324651 21213 sgd_solver.cpp:105] Iteration 4176, lr = 0.001
I0401 13:06:30.935140 21213 solver.cpp:218] Iteration 4184 (2.21577 iter/s, 3.61048s/8 iters), loss = 3.30047
I0401 13:06:30.935192 21213 solver.cpp:237]     Train net output #0: loss = 3.30047 (* 1 = 3.30047 loss)
I0401 13:06:30.935199 21213 sgd_solver.cpp:105] Iteration 4184, lr = 0.001
I0401 13:06:34.456847 21213 solver.cpp:218] Iteration 4192 (2.27167 iter/s, 3.52164s/8 iters), loss = 2.8725
I0401 13:06:34.456943 21213 solver.cpp:237]     Train net output #0: loss = 2.8725 (* 1 = 2.8725 loss)
I0401 13:06:34.456954 21213 sgd_solver.cpp:105] Iteration 4192, lr = 0.001
I0401 13:06:37.994225 21213 solver.cpp:218] Iteration 4200 (2.26163 iter/s, 3.53727s/8 iters), loss = 2.74339
I0401 13:06:37.994371 21213 solver.cpp:237]     Train net output #0: loss = 2.74339 (* 1 = 2.74339 loss)
I0401 13:06:37.994379 21213 sgd_solver.cpp:105] Iteration 4200, lr = 0.001
I0401 13:06:41.652000 21213 solver.cpp:218] Iteration 4208 (2.18722 iter/s, 3.65762s/8 iters), loss = 2.96653
I0401 13:06:41.652050 21213 solver.cpp:237]     Train net output #0: loss = 2.96653 (* 1 = 2.96653 loss)
I0401 13:06:41.652056 21213 sgd_solver.cpp:105] Iteration 4208, lr = 0.001
I0401 13:06:45.110705 21213 solver.cpp:218] Iteration 4216 (2.31305 iter/s, 3.45864s/8 iters), loss = 2.97807
I0401 13:06:45.110760 21213 solver.cpp:237]     Train net output #0: loss = 2.97807 (* 1 = 2.97807 loss)
I0401 13:06:45.110769 21213 sgd_solver.cpp:105] Iteration 4216, lr = 0.001
I0401 13:06:48.143527 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4224.caffemodel
I0401 13:06:51.515947 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4224.solverstate
I0401 13:06:53.923593 21213 solver.cpp:330] Iteration 4224, Testing net (#0)
I0401 13:06:53.923616 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:06:53.958954 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:06:56.162125 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:06:56.162158 21213 solver.cpp:397]     Test net output #1: loss = 4.84478 (* 1 = 4.84478 loss)
I0401 13:06:56.296767 21213 solver.cpp:218] Iteration 4224 (0.715179 iter/s, 11.186s/8 iters), loss = 3.05488
I0401 13:06:56.296823 21213 solver.cpp:237]     Train net output #0: loss = 3.05488 (* 1 = 3.05488 loss)
I0401 13:06:56.296829 21213 sgd_solver.cpp:105] Iteration 4224, lr = 0.001
I0401 13:06:56.611168 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:06:59.010453 21213 solver.cpp:218] Iteration 4232 (2.9481 iter/s, 2.71361s/8 iters), loss = 2.90187
I0401 13:06:59.010497 21213 solver.cpp:237]     Train net output #0: loss = 2.90187 (* 1 = 2.90187 loss)
I0401 13:06:59.010504 21213 sgd_solver.cpp:105] Iteration 4232, lr = 0.001
I0401 13:07:00.151166 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:07:02.649886 21213 solver.cpp:218] Iteration 4240 (2.19818 iter/s, 3.63938s/8 iters), loss = 2.78588
I0401 13:07:02.649924 21213 solver.cpp:237]     Train net output #0: loss = 2.78588 (* 1 = 2.78588 loss)
I0401 13:07:02.649928 21213 sgd_solver.cpp:105] Iteration 4240, lr = 0.001
I0401 13:07:06.358140 21213 solver.cpp:218] Iteration 4248 (2.15738 iter/s, 3.7082s/8 iters), loss = 3.18101
I0401 13:07:06.358191 21213 solver.cpp:237]     Train net output #0: loss = 3.18101 (* 1 = 3.18101 loss)
I0401 13:07:06.358198 21213 sgd_solver.cpp:105] Iteration 4248, lr = 0.001
I0401 13:07:09.754935 21213 solver.cpp:218] Iteration 4256 (2.3552 iter/s, 3.39674s/8 iters), loss = 2.95785
I0401 13:07:09.755071 21213 solver.cpp:237]     Train net output #0: loss = 2.95785 (* 1 = 2.95785 loss)
I0401 13:07:09.755079 21213 sgd_solver.cpp:105] Iteration 4256, lr = 0.001
I0401 13:07:13.206483 21213 solver.cpp:218] Iteration 4264 (2.3179 iter/s, 3.4514s/8 iters), loss = 2.86549
I0401 13:07:13.206537 21213 solver.cpp:237]     Train net output #0: loss = 2.86549 (* 1 = 2.86549 loss)
I0401 13:07:13.206545 21213 sgd_solver.cpp:105] Iteration 4264, lr = 0.001
I0401 13:07:16.959713 21213 solver.cpp:218] Iteration 4272 (2.13154 iter/s, 3.75316s/8 iters), loss = 3.09253
I0401 13:07:16.959780 21213 solver.cpp:237]     Train net output #0: loss = 3.09253 (* 1 = 3.09253 loss)
I0401 13:07:16.959791 21213 sgd_solver.cpp:105] Iteration 4272, lr = 0.001
I0401 13:07:20.562018 21213 solver.cpp:218] Iteration 4280 (2.22085 iter/s, 3.60223s/8 iters), loss = 3.15606
I0401 13:07:20.562072 21213 solver.cpp:237]     Train net output #0: loss = 3.15606 (* 1 = 3.15606 loss)
I0401 13:07:20.562080 21213 sgd_solver.cpp:105] Iteration 4280, lr = 0.001
I0401 13:07:23.523109 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4288.caffemodel
I0401 13:07:26.598101 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4288.solverstate
I0401 13:07:28.888224 21213 solver.cpp:330] Iteration 4288, Testing net (#0)
I0401 13:07:28.888247 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:07:31.056371 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 13:07:31.056409 21213 solver.cpp:397]     Test net output #1: loss = 4.8988 (* 1 = 4.8988 loss)
I0401 13:07:31.192147 21213 solver.cpp:218] Iteration 4288 (0.752582 iter/s, 10.6301s/8 iters), loss = 2.9123
I0401 13:07:31.193717 21213 solver.cpp:237]     Train net output #0: loss = 2.9123 (* 1 = 2.9123 loss)
I0401 13:07:31.193727 21213 sgd_solver.cpp:105] Iteration 4288, lr = 0.001
I0401 13:07:31.316907 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:07:34.035581 21213 solver.cpp:218] Iteration 4296 (2.81506 iter/s, 2.84186s/8 iters), loss = 3.34418
I0401 13:07:34.035625 21213 solver.cpp:237]     Train net output #0: loss = 3.34418 (* 1 = 3.34418 loss)
I0401 13:07:34.035631 21213 sgd_solver.cpp:105] Iteration 4296, lr = 0.001
I0401 13:07:34.684679 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:07:37.615545 21213 solver.cpp:218] Iteration 4304 (2.2347 iter/s, 3.5799s/8 iters), loss = 2.94858
I0401 13:07:37.615597 21213 solver.cpp:237]     Train net output #0: loss = 2.94858 (* 1 = 2.94858 loss)
I0401 13:07:37.615604 21213 sgd_solver.cpp:105] Iteration 4304, lr = 0.001
I0401 13:07:41.272785 21213 solver.cpp:218] Iteration 4312 (2.18748 iter/s, 3.65717s/8 iters), loss = 3.38294
I0401 13:07:41.272922 21213 solver.cpp:237]     Train net output #0: loss = 3.38294 (* 1 = 3.38294 loss)
I0401 13:07:41.272933 21213 sgd_solver.cpp:105] Iteration 4312, lr = 0.001
I0401 13:07:44.920948 21213 solver.cpp:218] Iteration 4320 (2.19298 iter/s, 3.64801s/8 iters), loss = 2.79084
I0401 13:07:44.920991 21213 solver.cpp:237]     Train net output #0: loss = 2.79084 (* 1 = 2.79084 loss)
I0401 13:07:44.920997 21213 sgd_solver.cpp:105] Iteration 4320, lr = 0.001
I0401 13:07:48.253168 21213 solver.cpp:218] Iteration 4328 (2.40084 iter/s, 3.33217s/8 iters), loss = 2.79985
I0401 13:07:48.253206 21213 solver.cpp:237]     Train net output #0: loss = 2.79985 (* 1 = 2.79985 loss)
I0401 13:07:48.253211 21213 sgd_solver.cpp:105] Iteration 4328, lr = 0.001
I0401 13:07:51.948325 21213 solver.cpp:218] Iteration 4336 (2.16503 iter/s, 3.6951s/8 iters), loss = 3.13952
I0401 13:07:51.948367 21213 solver.cpp:237]     Train net output #0: loss = 3.13952 (* 1 = 3.13952 loss)
I0401 13:07:51.948374 21213 sgd_solver.cpp:105] Iteration 4336, lr = 0.001
I0401 13:07:55.570540 21213 solver.cpp:218] Iteration 4344 (2.20863 iter/s, 3.62215s/8 iters), loss = 3.12193
I0401 13:07:55.570595 21213 solver.cpp:237]     Train net output #0: loss = 3.12193 (* 1 = 3.12193 loss)
I0401 13:07:55.570603 21213 sgd_solver.cpp:105] Iteration 4344, lr = 0.001
I0401 13:07:58.533304 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4352.caffemodel
I0401 13:08:02.627774 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4352.solverstate
I0401 13:08:05.016599 21213 solver.cpp:330] Iteration 4352, Testing net (#0)
I0401 13:08:05.016626 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:08:07.348119 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0829327
I0401 13:08:07.348152 21213 solver.cpp:397]     Test net output #1: loss = 4.96056 (* 1 = 4.96056 loss)
I0401 13:08:07.490962 21213 solver.cpp:218] Iteration 4352 (0.671121 iter/s, 11.9204s/8 iters), loss = 3.02137
I0401 13:08:07.491014 21213 solver.cpp:237]     Train net output #0: loss = 3.02137 (* 1 = 3.02137 loss)
I0401 13:08:07.491021 21213 sgd_solver.cpp:105] Iteration 4352, lr = 0.001
I0401 13:08:07.495491 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:08:09.886976 21213 solver.cpp:218] Iteration 4360 (3.33897 iter/s, 2.39595s/8 iters), loss = 3.0293
I0401 13:08:09.887027 21213 solver.cpp:237]     Train net output #0: loss = 3.0293 (* 1 = 3.0293 loss)
I0401 13:08:09.887037 21213 sgd_solver.cpp:105] Iteration 4360, lr = 0.001
I0401 13:08:10.302183 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:08:13.443060 21213 solver.cpp:218] Iteration 4368 (2.24971 iter/s, 3.55602s/8 iters), loss = 3.22576
I0401 13:08:13.443238 21213 solver.cpp:237]     Train net output #0: loss = 3.22576 (* 1 = 3.22576 loss)
I0401 13:08:13.443246 21213 sgd_solver.cpp:105] Iteration 4368, lr = 0.001
I0401 13:08:17.052073 21213 solver.cpp:218] Iteration 4376 (2.21679 iter/s, 3.60882s/8 iters), loss = 3.17596
I0401 13:08:17.052129 21213 solver.cpp:237]     Train net output #0: loss = 3.17596 (* 1 = 3.17596 loss)
I0401 13:08:17.052137 21213 sgd_solver.cpp:105] Iteration 4376, lr = 0.001
I0401 13:08:20.725242 21213 solver.cpp:218] Iteration 4384 (2.178 iter/s, 3.6731s/8 iters), loss = 2.95764
I0401 13:08:20.725289 21213 solver.cpp:237]     Train net output #0: loss = 2.95764 (* 1 = 2.95764 loss)
I0401 13:08:20.725294 21213 sgd_solver.cpp:105] Iteration 4384, lr = 0.001
I0401 13:08:24.417657 21213 solver.cpp:218] Iteration 4392 (2.16664 iter/s, 3.69235s/8 iters), loss = 2.65817
I0401 13:08:24.417712 21213 solver.cpp:237]     Train net output #0: loss = 2.65817 (* 1 = 2.65817 loss)
I0401 13:08:24.417721 21213 sgd_solver.cpp:105] Iteration 4392, lr = 0.001
I0401 13:08:27.883817 21213 solver.cpp:218] Iteration 4400 (2.30807 iter/s, 3.4661s/8 iters), loss = 2.597
I0401 13:08:27.883858 21213 solver.cpp:237]     Train net output #0: loss = 2.597 (* 1 = 2.597 loss)
I0401 13:08:27.883863 21213 sgd_solver.cpp:105] Iteration 4400, lr = 0.001
I0401 13:08:31.509707 21213 solver.cpp:218] Iteration 4408 (2.20639 iter/s, 3.62583s/8 iters), loss = 2.71625
I0401 13:08:31.509748 21213 solver.cpp:237]     Train net output #0: loss = 2.71625 (* 1 = 2.71625 loss)
I0401 13:08:31.509753 21213 sgd_solver.cpp:105] Iteration 4408, lr = 0.001
I0401 13:08:34.419709 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4416.caffemodel
I0401 13:08:37.419162 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4416.solverstate
I0401 13:08:39.781292 21213 solver.cpp:330] Iteration 4416, Testing net (#0)
I0401 13:08:39.781313 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:08:42.075012 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0625
I0401 13:08:42.075052 21213 solver.cpp:397]     Test net output #1: loss = 5.08379 (* 1 = 5.08379 loss)
I0401 13:08:42.150538 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:08:42.226560 21213 solver.cpp:218] Iteration 4416 (0.746491 iter/s, 10.7168s/8 iters), loss = 2.85927
I0401 13:08:42.226620 21213 solver.cpp:237]     Train net output #0: loss = 2.85927 (* 1 = 2.85927 loss)
I0401 13:08:42.226629 21213 sgd_solver.cpp:105] Iteration 4416, lr = 0.001
I0401 13:08:44.857590 21213 solver.cpp:218] Iteration 4424 (3.04072 iter/s, 2.63096s/8 iters), loss = 2.96306
I0401 13:08:44.859612 21213 solver.cpp:237]     Train net output #0: loss = 2.96306 (* 1 = 2.96306 loss)
I0401 13:08:44.859625 21213 sgd_solver.cpp:105] Iteration 4424, lr = 0.001
I0401 13:08:44.946383 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:08:48.418129 21213 solver.cpp:218] Iteration 4432 (2.24813 iter/s, 3.55852s/8 iters), loss = 2.96999
I0401 13:08:48.418179 21213 solver.cpp:237]     Train net output #0: loss = 2.96999 (* 1 = 2.96999 loss)
I0401 13:08:48.418186 21213 sgd_solver.cpp:105] Iteration 4432, lr = 0.001
I0401 13:08:51.988616 21213 solver.cpp:218] Iteration 4440 (2.24063 iter/s, 3.57042s/8 iters), loss = 3.2136
I0401 13:08:51.988656 21213 solver.cpp:237]     Train net output #0: loss = 3.2136 (* 1 = 3.2136 loss)
I0401 13:08:51.988662 21213 sgd_solver.cpp:105] Iteration 4440, lr = 0.001
I0401 13:08:55.617811 21213 solver.cpp:218] Iteration 4448 (2.20438 iter/s, 3.62914s/8 iters), loss = 3.11013
I0401 13:08:55.617875 21213 solver.cpp:237]     Train net output #0: loss = 3.11013 (* 1 = 3.11013 loss)
I0401 13:08:55.617884 21213 sgd_solver.cpp:105] Iteration 4448, lr = 0.001
I0401 13:08:59.076426 21213 solver.cpp:218] Iteration 4456 (2.31312 iter/s, 3.45854s/8 iters), loss = 3.08289
I0401 13:08:59.076476 21213 solver.cpp:237]     Train net output #0: loss = 3.08289 (* 1 = 3.08289 loss)
I0401 13:08:59.076484 21213 sgd_solver.cpp:105] Iteration 4456, lr = 0.001
I0401 13:09:02.907197 21213 solver.cpp:218] Iteration 4464 (2.08839 iter/s, 3.83071s/8 iters), loss = 3.15192
I0401 13:09:02.907249 21213 solver.cpp:237]     Train net output #0: loss = 3.15192 (* 1 = 3.15192 loss)
I0401 13:09:02.907259 21213 sgd_solver.cpp:105] Iteration 4464, lr = 0.001
I0401 13:09:06.621129 21213 solver.cpp:218] Iteration 4472 (2.15409 iter/s, 3.71387s/8 iters), loss = 2.87989
I0401 13:09:06.621168 21213 solver.cpp:237]     Train net output #0: loss = 2.87989 (* 1 = 2.87989 loss)
I0401 13:09:06.621173 21213 sgd_solver.cpp:105] Iteration 4472, lr = 0.001
I0401 13:09:09.670184 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4480.caffemodel
I0401 13:09:12.697227 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4480.solverstate
I0401 13:09:15.018509 21213 solver.cpp:330] Iteration 4480, Testing net (#0)
I0401 13:09:15.018568 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:09:17.167732 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:09:17.221556 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0721154
I0401 13:09:17.221586 21213 solver.cpp:397]     Test net output #1: loss = 5.07169 (* 1 = 5.07169 loss)
I0401 13:09:17.361908 21213 solver.cpp:218] Iteration 4480 (0.744829 iter/s, 10.7407s/8 iters), loss = 2.77361
I0401 13:09:17.361982 21213 solver.cpp:237]     Train net output #0: loss = 2.77361 (* 1 = 2.77361 loss)
I0401 13:09:17.361991 21213 sgd_solver.cpp:105] Iteration 4480, lr = 0.001
I0401 13:09:19.671444 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:09:19.941330 21213 solver.cpp:218] Iteration 4488 (3.10157 iter/s, 2.57934s/8 iters), loss = 3.17177
I0401 13:09:19.941377 21213 solver.cpp:237]     Train net output #0: loss = 3.17177 (* 1 = 3.17177 loss)
I0401 13:09:19.941383 21213 sgd_solver.cpp:105] Iteration 4488, lr = 0.001
I0401 13:09:23.575799 21213 solver.cpp:218] Iteration 4496 (2.20119 iter/s, 3.6344s/8 iters), loss = 2.82979
I0401 13:09:23.575855 21213 solver.cpp:237]     Train net output #0: loss = 2.82979 (* 1 = 2.82979 loss)
I0401 13:09:23.575863 21213 sgd_solver.cpp:105] Iteration 4496, lr = 0.001
I0401 13:09:27.186925 21213 solver.cpp:218] Iteration 4504 (2.21542 iter/s, 3.61106s/8 iters), loss = 3.23902
I0401 13:09:27.186976 21213 solver.cpp:237]     Train net output #0: loss = 3.23902 (* 1 = 3.23902 loss)
I0401 13:09:27.186985 21213 sgd_solver.cpp:105] Iteration 4504, lr = 0.001
I0401 13:09:30.636689 21213 solver.cpp:218] Iteration 4512 (2.31905 iter/s, 3.4497s/8 iters), loss = 2.83902
I0401 13:09:30.636744 21213 solver.cpp:237]     Train net output #0: loss = 2.83902 (* 1 = 2.83902 loss)
I0401 13:09:30.636752 21213 sgd_solver.cpp:105] Iteration 4512, lr = 0.001
I0401 13:09:34.353962 21213 solver.cpp:218] Iteration 4520 (2.15215 iter/s, 3.71721s/8 iters), loss = 2.74695
I0401 13:09:34.354001 21213 solver.cpp:237]     Train net output #0: loss = 2.74695 (* 1 = 2.74695 loss)
I0401 13:09:34.354007 21213 sgd_solver.cpp:105] Iteration 4520, lr = 0.001
I0401 13:09:37.649214 21213 solver.cpp:218] Iteration 4528 (2.42777 iter/s, 3.2952s/8 iters), loss = 2.8421
I0401 13:09:37.649255 21213 solver.cpp:237]     Train net output #0: loss = 2.8421 (* 1 = 2.8421 loss)
I0401 13:09:37.649260 21213 sgd_solver.cpp:105] Iteration 4528, lr = 0.001
I0401 13:09:41.172569 21213 solver.cpp:218] Iteration 4536 (2.2706 iter/s, 3.52329s/8 iters), loss = 2.79334
I0401 13:09:41.172626 21213 solver.cpp:237]     Train net output #0: loss = 2.79334 (* 1 = 2.79334 loss)
I0401 13:09:41.172634 21213 sgd_solver.cpp:105] Iteration 4536, lr = 0.001
I0401 13:09:44.261495 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4544.caffemodel
I0401 13:09:49.152227 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4544.solverstate
I0401 13:09:52.865170 21213 solver.cpp:330] Iteration 4544, Testing net (#0)
I0401 13:09:52.865186 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:09:54.988695 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:09:55.111002 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0745192
I0401 13:09:55.111033 21213 solver.cpp:397]     Test net output #1: loss = 4.97079 (* 1 = 4.97079 loss)
I0401 13:09:55.251989 21213 solver.cpp:218] Iteration 4544 (0.568208 iter/s, 14.0794s/8 iters), loss = 3.00885
I0401 13:09:55.252046 21213 solver.cpp:237]     Train net output #0: loss = 3.00885 (* 1 = 3.00885 loss)
I0401 13:09:55.252051 21213 sgd_solver.cpp:105] Iteration 4544, lr = 0.001
I0401 13:09:57.245339 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:09:57.916540 21213 solver.cpp:218] Iteration 4552 (3.00246 iter/s, 2.66448s/8 iters), loss = 2.4626
I0401 13:09:57.916602 21213 solver.cpp:237]     Train net output #0: loss = 2.4626 (* 1 = 2.4626 loss)
I0401 13:09:57.916612 21213 sgd_solver.cpp:105] Iteration 4552, lr = 0.001
I0401 13:10:01.456195 21213 solver.cpp:218] Iteration 4560 (2.26015 iter/s, 3.53958s/8 iters), loss = 2.91109
I0401 13:10:01.456254 21213 solver.cpp:237]     Train net output #0: loss = 2.91109 (* 1 = 2.91109 loss)
I0401 13:10:01.456262 21213 sgd_solver.cpp:105] Iteration 4560, lr = 0.001
I0401 13:10:01.848592 21213 blocking_queue.cpp:49] Waiting for data
I0401 13:10:05.132354 21213 solver.cpp:218] Iteration 4568 (2.17622 iter/s, 3.67609s/8 iters), loss = 2.69709
I0401 13:10:05.132396 21213 solver.cpp:237]     Train net output #0: loss = 2.69709 (* 1 = 2.69709 loss)
I0401 13:10:05.132401 21213 sgd_solver.cpp:105] Iteration 4568, lr = 0.001
I0401 13:10:08.696696 21213 solver.cpp:218] Iteration 4576 (2.24449 iter/s, 3.56428s/8 iters), loss = 2.99822
I0401 13:10:08.696734 21213 solver.cpp:237]     Train net output #0: loss = 2.99822 (* 1 = 2.99822 loss)
I0401 13:10:08.696740 21213 sgd_solver.cpp:105] Iteration 4576, lr = 0.001
I0401 13:10:12.241932 21213 solver.cpp:218] Iteration 4584 (2.25659 iter/s, 3.54518s/8 iters), loss = 2.80938
I0401 13:10:12.241984 21213 solver.cpp:237]     Train net output #0: loss = 2.80938 (* 1 = 2.80938 loss)
I0401 13:10:12.241991 21213 sgd_solver.cpp:105] Iteration 4584, lr = 0.001
I0401 13:10:15.707746 21213 solver.cpp:218] Iteration 4592 (2.30831 iter/s, 3.46575s/8 iters), loss = 2.42381
I0401 13:10:15.707794 21213 solver.cpp:237]     Train net output #0: loss = 2.42381 (* 1 = 2.42381 loss)
I0401 13:10:15.707801 21213 sgd_solver.cpp:105] Iteration 4592, lr = 0.001
I0401 13:10:19.303544 21213 solver.cpp:218] Iteration 4600 (2.22486 iter/s, 3.59573s/8 iters), loss = 2.85471
I0401 13:10:19.303755 21213 solver.cpp:237]     Train net output #0: loss = 2.85471 (* 1 = 2.85471 loss)
I0401 13:10:19.303766 21213 sgd_solver.cpp:105] Iteration 4600, lr = 0.001
I0401 13:10:22.242532 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4608.caffemodel
I0401 13:10:25.282990 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4608.solverstate
I0401 13:10:27.594467 21213 solver.cpp:330] Iteration 4608, Testing net (#0)
I0401 13:10:27.594486 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:10:29.755010 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:10:29.973451 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:10:29.973491 21213 solver.cpp:397]     Test net output #1: loss = 4.89401 (* 1 = 4.89401 loss)
I0401 13:10:30.116138 21213 solver.cpp:218] Iteration 4608 (0.739892 iter/s, 10.8124s/8 iters), loss = 2.74684
I0401 13:10:30.116189 21213 solver.cpp:237]     Train net output #0: loss = 2.74684 (* 1 = 2.74684 loss)
I0401 13:10:30.116195 21213 sgd_solver.cpp:105] Iteration 4608, lr = 0.001
I0401 13:10:31.720702 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:10:32.701480 21213 solver.cpp:218] Iteration 4616 (3.09445 iter/s, 2.58528s/8 iters), loss = 2.48911
I0401 13:10:32.701524 21213 solver.cpp:237]     Train net output #0: loss = 2.48911 (* 1 = 2.48911 loss)
I0401 13:10:32.701529 21213 sgd_solver.cpp:105] Iteration 4616, lr = 0.001
I0401 13:10:36.094796 21213 solver.cpp:218] Iteration 4624 (2.35762 iter/s, 3.39326s/8 iters), loss = 2.48421
I0401 13:10:36.094851 21213 solver.cpp:237]     Train net output #0: loss = 2.48421 (* 1 = 2.48421 loss)
I0401 13:10:36.094859 21213 sgd_solver.cpp:105] Iteration 4624, lr = 0.001
I0401 13:10:39.719003 21213 solver.cpp:218] Iteration 4632 (2.20742 iter/s, 3.62414s/8 iters), loss = 2.5924
I0401 13:10:39.719053 21213 solver.cpp:237]     Train net output #0: loss = 2.5924 (* 1 = 2.5924 loss)
I0401 13:10:39.719060 21213 sgd_solver.cpp:105] Iteration 4632, lr = 0.001
I0401 13:10:43.375068 21213 solver.cpp:218] Iteration 4640 (2.18818 iter/s, 3.656s/8 iters), loss = 2.77225
I0401 13:10:43.375109 21213 solver.cpp:237]     Train net output #0: loss = 2.77225 (* 1 = 2.77225 loss)
I0401 13:10:43.375114 21213 sgd_solver.cpp:105] Iteration 4640, lr = 0.001
I0401 13:10:46.887766 21213 solver.cpp:218] Iteration 4648 (2.27748 iter/s, 3.51265s/8 iters), loss = 3.02669
I0401 13:10:46.887804 21213 solver.cpp:237]     Train net output #0: loss = 3.02669 (* 1 = 3.02669 loss)
I0401 13:10:46.887809 21213 sgd_solver.cpp:105] Iteration 4648, lr = 0.001
I0401 13:10:50.348543 21213 solver.cpp:218] Iteration 4656 (2.31166 iter/s, 3.46072s/8 iters), loss = 2.63312
I0401 13:10:50.348676 21213 solver.cpp:237]     Train net output #0: loss = 2.63312 (* 1 = 2.63312 loss)
I0401 13:10:50.348687 21213 sgd_solver.cpp:105] Iteration 4656, lr = 0.001
I0401 13:10:53.818887 21213 solver.cpp:218] Iteration 4664 (2.30535 iter/s, 3.47019s/8 iters), loss = 2.99644
I0401 13:10:53.818941 21213 solver.cpp:237]     Train net output #0: loss = 2.99644 (* 1 = 2.99644 loss)
I0401 13:10:53.818949 21213 sgd_solver.cpp:105] Iteration 4664, lr = 0.001
I0401 13:10:56.861415 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4672.caffemodel
I0401 13:10:59.975693 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4672.solverstate
I0401 13:11:02.277112 21213 solver.cpp:330] Iteration 4672, Testing net (#0)
I0401 13:11:02.277132 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:11:04.170110 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:11:04.429047 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0673077
I0401 13:11:04.429095 21213 solver.cpp:397]     Test net output #1: loss = 4.97507 (* 1 = 4.97507 loss)
I0401 13:11:04.570470 21213 solver.cpp:218] Iteration 4672 (0.74408 iter/s, 10.7515s/8 iters), loss = 2.78904
I0401 13:11:04.570529 21213 solver.cpp:237]     Train net output #0: loss = 2.78904 (* 1 = 2.78904 loss)
I0401 13:11:04.570538 21213 sgd_solver.cpp:105] Iteration 4672, lr = 0.001
I0401 13:11:05.967463 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:11:07.201619 21213 solver.cpp:218] Iteration 4680 (3.04058 iter/s, 2.63108s/8 iters), loss = 2.76506
I0401 13:11:07.201661 21213 solver.cpp:237]     Train net output #0: loss = 2.76506 (* 1 = 2.76506 loss)
I0401 13:11:07.201668 21213 sgd_solver.cpp:105] Iteration 4680, lr = 0.001
I0401 13:11:10.853973 21213 solver.cpp:218] Iteration 4688 (2.1904 iter/s, 3.6523s/8 iters), loss = 2.63918
I0401 13:11:10.854027 21213 solver.cpp:237]     Train net output #0: loss = 2.63918 (* 1 = 2.63918 loss)
I0401 13:11:10.854035 21213 sgd_solver.cpp:105] Iteration 4688, lr = 0.001
I0401 13:11:14.521569 21213 solver.cpp:218] Iteration 4696 (2.18131 iter/s, 3.66753s/8 iters), loss = 2.82729
I0401 13:11:14.521613 21213 solver.cpp:237]     Train net output #0: loss = 2.82729 (* 1 = 2.82729 loss)
I0401 13:11:14.521620 21213 sgd_solver.cpp:105] Iteration 4696, lr = 0.001
I0401 13:11:18.142679 21213 solver.cpp:218] Iteration 4704 (2.20931 iter/s, 3.62105s/8 iters), loss = 2.73656
I0401 13:11:18.142740 21213 solver.cpp:237]     Train net output #0: loss = 2.73656 (* 1 = 2.73656 loss)
I0401 13:11:18.142748 21213 sgd_solver.cpp:105] Iteration 4704, lr = 0.001
I0401 13:11:21.746369 21213 solver.cpp:218] Iteration 4712 (2.21999 iter/s, 3.60362s/8 iters), loss = 2.64051
I0401 13:11:21.746518 21213 solver.cpp:237]     Train net output #0: loss = 2.64051 (* 1 = 2.64051 loss)
I0401 13:11:21.746528 21213 sgd_solver.cpp:105] Iteration 4712, lr = 0.001
I0401 13:11:25.243173 21213 solver.cpp:218] Iteration 4720 (2.28791 iter/s, 3.49665s/8 iters), loss = 2.95886
I0401 13:11:25.243216 21213 solver.cpp:237]     Train net output #0: loss = 2.95886 (* 1 = 2.95886 loss)
I0401 13:11:25.243222 21213 sgd_solver.cpp:105] Iteration 4720, lr = 0.001
I0401 13:11:28.692401 21213 solver.cpp:218] Iteration 4728 (2.3194 iter/s, 3.44917s/8 iters), loss = 3.27971
I0401 13:11:28.692445 21213 solver.cpp:237]     Train net output #0: loss = 3.27971 (* 1 = 3.27971 loss)
I0401 13:11:28.692452 21213 sgd_solver.cpp:105] Iteration 4728, lr = 0.001
I0401 13:11:31.686645 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4736.caffemodel
I0401 13:11:36.084012 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4736.solverstate
I0401 13:11:39.193418 21213 solver.cpp:330] Iteration 4736, Testing net (#0)
I0401 13:11:39.193441 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:11:41.104496 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:11:41.446720 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0600962
I0401 13:11:41.446756 21213 solver.cpp:397]     Test net output #1: loss = 5.16513 (* 1 = 5.16513 loss)
I0401 13:11:41.589578 21213 solver.cpp:218] Iteration 4736 (0.620293 iter/s, 12.8971s/8 iters), loss = 3.37305
I0401 13:11:41.589624 21213 solver.cpp:237]     Train net output #0: loss = 3.37305 (* 1 = 3.37305 loss)
I0401 13:11:41.589629 21213 sgd_solver.cpp:105] Iteration 4736, lr = 0.001
I0401 13:11:42.658565 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:11:44.272792 21213 solver.cpp:218] Iteration 4744 (2.98157 iter/s, 2.68315s/8 iters), loss = 2.57649
I0401 13:11:44.272846 21213 solver.cpp:237]     Train net output #0: loss = 2.57649 (* 1 = 2.57649 loss)
I0401 13:11:44.272852 21213 sgd_solver.cpp:105] Iteration 4744, lr = 0.001
I0401 13:11:47.915179 21213 solver.cpp:218] Iteration 4752 (2.1964 iter/s, 3.64232s/8 iters), loss = 2.8093
I0401 13:11:47.915221 21213 solver.cpp:237]     Train net output #0: loss = 2.8093 (* 1 = 2.8093 loss)
I0401 13:11:47.915227 21213 sgd_solver.cpp:105] Iteration 4752, lr = 0.001
I0401 13:11:51.408668 21213 solver.cpp:218] Iteration 4760 (2.29001 iter/s, 3.49343s/8 iters), loss = 2.85378
I0401 13:11:51.408716 21213 solver.cpp:237]     Train net output #0: loss = 2.85378 (* 1 = 2.85378 loss)
I0401 13:11:51.408725 21213 sgd_solver.cpp:105] Iteration 4760, lr = 0.001
I0401 13:11:54.939626 21213 solver.cpp:218] Iteration 4768 (2.26571 iter/s, 3.5309s/8 iters), loss = 2.82918
I0401 13:11:54.939744 21213 solver.cpp:237]     Train net output #0: loss = 2.82918 (* 1 = 2.82918 loss)
I0401 13:11:54.939750 21213 sgd_solver.cpp:105] Iteration 4768, lr = 0.001
I0401 13:11:58.621505 21213 solver.cpp:218] Iteration 4776 (2.17288 iter/s, 3.68174s/8 iters), loss = 2.57115
I0401 13:11:58.621564 21213 solver.cpp:237]     Train net output #0: loss = 2.57115 (* 1 = 2.57115 loss)
I0401 13:11:58.621572 21213 sgd_solver.cpp:105] Iteration 4776, lr = 0.001
I0401 13:12:02.188704 21213 solver.cpp:218] Iteration 4784 (2.2427 iter/s, 3.56713s/8 iters), loss = 2.64302
I0401 13:12:02.188746 21213 solver.cpp:237]     Train net output #0: loss = 2.64302 (* 1 = 2.64302 loss)
I0401 13:12:02.188751 21213 sgd_solver.cpp:105] Iteration 4784, lr = 0.001
I0401 13:12:05.849578 21213 solver.cpp:218] Iteration 4792 (2.18531 iter/s, 3.66081s/8 iters), loss = 2.64513
I0401 13:12:05.855789 21213 solver.cpp:237]     Train net output #0: loss = 2.64513 (* 1 = 2.64513 loss)
I0401 13:12:05.855811 21213 sgd_solver.cpp:105] Iteration 4792, lr = 0.001
I0401 13:12:09.008366 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4800.caffemodel
I0401 13:12:12.666599 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4800.solverstate
I0401 13:12:15.024296 21213 solver.cpp:330] Iteration 4800, Testing net (#0)
I0401 13:12:15.024314 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:12:16.837720 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:12:17.235822 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0564904
I0401 13:12:17.235862 21213 solver.cpp:397]     Test net output #1: loss = 4.96608 (* 1 = 4.96608 loss)
I0401 13:12:17.377198 21213 solver.cpp:218] Iteration 4800 (0.694358 iter/s, 11.5214s/8 iters), loss = 3.17179
I0401 13:12:17.377250 21213 solver.cpp:237]     Train net output #0: loss = 3.17179 (* 1 = 3.17179 loss)
I0401 13:12:17.377257 21213 sgd_solver.cpp:105] Iteration 4800, lr = 0.001
I0401 13:12:17.993325 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:12:20.017935 21213 solver.cpp:218] Iteration 4808 (3.02953 iter/s, 2.64067s/8 iters), loss = 3.20058
I0401 13:12:20.017983 21213 solver.cpp:237]     Train net output #0: loss = 3.20058 (* 1 = 3.20058 loss)
I0401 13:12:20.017992 21213 sgd_solver.cpp:105] Iteration 4808, lr = 0.001
I0401 13:12:23.812079 21213 solver.cpp:218] Iteration 4816 (2.10855 iter/s, 3.79408s/8 iters), loss = 3.27008
I0401 13:12:23.812122 21213 solver.cpp:237]     Train net output #0: loss = 3.27008 (* 1 = 3.27008 loss)
I0401 13:12:23.812129 21213 sgd_solver.cpp:105] Iteration 4816, lr = 0.001
I0401 13:12:27.491565 21213 solver.cpp:218] Iteration 4824 (2.17425 iter/s, 3.67943s/8 iters), loss = 2.81882
I0401 13:12:27.491668 21213 solver.cpp:237]     Train net output #0: loss = 2.81882 (* 1 = 2.81882 loss)
I0401 13:12:27.491675 21213 sgd_solver.cpp:105] Iteration 4824, lr = 0.001
I0401 13:12:31.226061 21213 solver.cpp:218] Iteration 4832 (2.14226 iter/s, 3.73438s/8 iters), loss = 2.65648
I0401 13:12:31.226102 21213 solver.cpp:237]     Train net output #0: loss = 2.65648 (* 1 = 2.65648 loss)
I0401 13:12:31.226107 21213 sgd_solver.cpp:105] Iteration 4832, lr = 0.001
I0401 13:12:34.703896 21213 solver.cpp:218] Iteration 4840 (2.30032 iter/s, 3.47777s/8 iters), loss = 2.97048
I0401 13:12:34.703946 21213 solver.cpp:237]     Train net output #0: loss = 2.97048 (* 1 = 2.97048 loss)
I0401 13:12:34.703954 21213 sgd_solver.cpp:105] Iteration 4840, lr = 0.001
I0401 13:12:38.395103 21213 solver.cpp:218] Iteration 4848 (2.16735 iter/s, 3.69115s/8 iters), loss = 2.5497
I0401 13:12:38.395150 21213 solver.cpp:237]     Train net output #0: loss = 2.5497 (* 1 = 2.5497 loss)
I0401 13:12:38.395159 21213 sgd_solver.cpp:105] Iteration 4848, lr = 0.001
I0401 13:12:42.001679 21213 solver.cpp:218] Iteration 4856 (2.21821 iter/s, 3.60651s/8 iters), loss = 2.60702
I0401 13:12:42.001725 21213 solver.cpp:237]     Train net output #0: loss = 2.60702 (* 1 = 2.60702 loss)
I0401 13:12:42.001730 21213 sgd_solver.cpp:105] Iteration 4856, lr = 0.001
I0401 13:12:44.940054 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4864.caffemodel
I0401 13:12:47.960436 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4864.solverstate
I0401 13:12:50.299345 21213 solver.cpp:330] Iteration 4864, Testing net (#0)
I0401 13:12:50.299365 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:12:52.023568 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:12:52.468416 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0673077
I0401 13:12:52.468442 21213 solver.cpp:397]     Test net output #1: loss = 4.94687 (* 1 = 4.94687 loss)
I0401 13:12:52.609828 21213 solver.cpp:218] Iteration 4864 (0.754141 iter/s, 10.6081s/8 iters), loss = 2.77725
I0401 13:12:52.609881 21213 solver.cpp:237]     Train net output #0: loss = 2.77725 (* 1 = 2.77725 loss)
I0401 13:12:52.609891 21213 sgd_solver.cpp:105] Iteration 4864, lr = 0.001
I0401 13:12:52.868144 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:12:55.301112 21213 solver.cpp:218] Iteration 4872 (2.97263 iter/s, 2.69122s/8 iters), loss = 3.2619
I0401 13:12:55.301147 21213 solver.cpp:237]     Train net output #0: loss = 3.2619 (* 1 = 3.2619 loss)
I0401 13:12:55.301152 21213 sgd_solver.cpp:105] Iteration 4872, lr = 0.001
I0401 13:12:58.852993 21213 solver.cpp:218] Iteration 4880 (2.25236 iter/s, 3.55183s/8 iters), loss = 2.84803
I0401 13:12:58.853145 21213 solver.cpp:237]     Train net output #0: loss = 2.84803 (* 1 = 2.84803 loss)
I0401 13:12:58.853154 21213 sgd_solver.cpp:105] Iteration 4880, lr = 0.001
I0401 13:13:02.670327 21213 solver.cpp:218] Iteration 4888 (2.09579 iter/s, 3.81717s/8 iters), loss = 2.49691
I0401 13:13:02.670372 21213 solver.cpp:237]     Train net output #0: loss = 2.49691 (* 1 = 2.49691 loss)
I0401 13:13:02.670380 21213 sgd_solver.cpp:105] Iteration 4888, lr = 0.001
I0401 13:13:06.300940 21213 solver.cpp:218] Iteration 4896 (2.20352 iter/s, 3.63055s/8 iters), loss = 2.38756
I0401 13:13:06.300998 21213 solver.cpp:237]     Train net output #0: loss = 2.38756 (* 1 = 2.38756 loss)
I0401 13:13:06.301007 21213 sgd_solver.cpp:105] Iteration 4896, lr = 0.001
I0401 13:13:10.109277 21213 solver.cpp:218] Iteration 4904 (2.1007 iter/s, 3.80826s/8 iters), loss = 2.44052
I0401 13:13:10.109336 21213 solver.cpp:237]     Train net output #0: loss = 2.44052 (* 1 = 2.44052 loss)
I0401 13:13:10.109345 21213 sgd_solver.cpp:105] Iteration 4904, lr = 0.001
I0401 13:13:13.754449 21213 solver.cpp:218] Iteration 4912 (2.19473 iter/s, 3.6451s/8 iters), loss = 2.39756
I0401 13:13:13.754499 21213 solver.cpp:237]     Train net output #0: loss = 2.39756 (* 1 = 2.39756 loss)
I0401 13:13:13.754508 21213 sgd_solver.cpp:105] Iteration 4912, lr = 0.001
I0401 13:13:17.292969 21213 solver.cpp:218] Iteration 4920 (2.26087 iter/s, 3.53845s/8 iters), loss = 2.46743
I0401 13:13:17.293010 21213 solver.cpp:237]     Train net output #0: loss = 2.46743 (* 1 = 2.46743 loss)
I0401 13:13:17.293015 21213 sgd_solver.cpp:105] Iteration 4920, lr = 0.001
I0401 13:13:20.303194 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4928.caffemodel
I0401 13:13:23.324695 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4928.solverstate
I0401 13:13:25.619931 21213 solver.cpp:330] Iteration 4928, Testing net (#0)
I0401 13:13:25.619957 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:13:27.330579 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:13:27.845363 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0757212
I0401 13:13:27.845399 21213 solver.cpp:397]     Test net output #1: loss = 5.04458 (* 1 = 5.04458 loss)
I0401 13:13:27.986388 21213 solver.cpp:218] Iteration 4928 (0.748127 iter/s, 10.6934s/8 iters), loss = 2.67889
I0401 13:13:27.986438 21213 solver.cpp:237]     Train net output #0: loss = 2.67889 (* 1 = 2.67889 loss)
I0401 13:13:27.986444 21213 sgd_solver.cpp:105] Iteration 4928, lr = 0.001
I0401 13:13:28.007257 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:13:30.561991 21213 solver.cpp:218] Iteration 4936 (3.10614 iter/s, 2.57554s/8 iters), loss = 3.04721
I0401 13:13:30.568156 21213 solver.cpp:237]     Train net output #0: loss = 3.04721 (* 1 = 3.04721 loss)
I0401 13:13:30.568171 21213 sgd_solver.cpp:105] Iteration 4936, lr = 0.001
I0401 13:13:34.097313 21213 solver.cpp:218] Iteration 4944 (2.26683 iter/s, 3.52915s/8 iters), loss = 2.79672
I0401 13:13:34.097369 21213 solver.cpp:237]     Train net output #0: loss = 2.79672 (* 1 = 2.79672 loss)
I0401 13:13:34.097378 21213 sgd_solver.cpp:105] Iteration 4944, lr = 0.001
I0401 13:13:37.415792 21213 solver.cpp:218] Iteration 4952 (2.4108 iter/s, 3.31841s/8 iters), loss = 2.52738
I0401 13:13:37.415835 21213 solver.cpp:237]     Train net output #0: loss = 2.52738 (* 1 = 2.52738 loss)
I0401 13:13:37.415841 21213 sgd_solver.cpp:105] Iteration 4952, lr = 0.001
I0401 13:13:41.170248 21213 solver.cpp:218] Iteration 4960 (2.13084 iter/s, 3.7544s/8 iters), loss = 2.28471
I0401 13:13:41.170303 21213 solver.cpp:237]     Train net output #0: loss = 2.28471 (* 1 = 2.28471 loss)
I0401 13:13:41.170311 21213 sgd_solver.cpp:105] Iteration 4960, lr = 0.001
I0401 13:13:44.752427 21213 solver.cpp:218] Iteration 4968 (2.23332 iter/s, 3.58211s/8 iters), loss = 2.42606
I0401 13:13:44.752482 21213 solver.cpp:237]     Train net output #0: loss = 2.42606 (* 1 = 2.42606 loss)
I0401 13:13:44.752490 21213 sgd_solver.cpp:105] Iteration 4968, lr = 0.001
I0401 13:13:48.177316 21213 solver.cpp:218] Iteration 4976 (2.33589 iter/s, 3.42482s/8 iters), loss = 2.03339
I0401 13:13:48.177379 21213 solver.cpp:237]     Train net output #0: loss = 2.03339 (* 1 = 2.03339 loss)
I0401 13:13:48.177388 21213 sgd_solver.cpp:105] Iteration 4976, lr = 0.001
I0401 13:13:51.735384 21213 solver.cpp:218] Iteration 4984 (2.24846 iter/s, 3.55799s/8 iters), loss = 1.88577
I0401 13:13:51.735443 21213 solver.cpp:237]     Train net output #0: loss = 1.88577 (* 1 = 1.88577 loss)
I0401 13:13:51.735451 21213 sgd_solver.cpp:105] Iteration 4984, lr = 0.001
I0401 13:13:54.907933 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4992.caffemodel
I0401 13:13:55.611213 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:13:57.967078 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4992.solverstate
I0401 13:14:00.305467 21213 solver.cpp:330] Iteration 4992, Testing net (#0)
I0401 13:14:00.305491 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:14:01.934362 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:14:02.559160 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 13:14:02.559195 21213 solver.cpp:397]     Test net output #1: loss = 5.12786 (* 1 = 5.12786 loss)
I0401 13:14:02.696913 21213 solver.cpp:218] Iteration 4992 (0.72983 iter/s, 10.9615s/8 iters), loss = 2.35811
I0401 13:14:02.696969 21213 solver.cpp:237]     Train net output #0: loss = 2.35811 (* 1 = 2.35811 loss)
I0401 13:14:02.696977 21213 sgd_solver.cpp:105] Iteration 4992, lr = 0.001
I0401 13:14:05.342195 21213 solver.cpp:218] Iteration 5000 (3.02433 iter/s, 2.64521s/8 iters), loss = 2.43277
I0401 13:14:05.342240 21213 solver.cpp:237]     Train net output #0: loss = 2.43277 (* 1 = 2.43277 loss)
I0401 13:14:05.342245 21213 sgd_solver.cpp:105] Iteration 5000, lr = 0.001
I0401 13:14:08.869145 21213 solver.cpp:218] Iteration 5008 (2.26829 iter/s, 3.52689s/8 iters), loss = 2.16804
I0401 13:14:08.869205 21213 solver.cpp:237]     Train net output #0: loss = 2.16804 (* 1 = 2.16804 loss)
I0401 13:14:08.869215 21213 sgd_solver.cpp:105] Iteration 5008, lr = 0.001
I0401 13:14:12.299485 21213 solver.cpp:218] Iteration 5016 (2.33218 iter/s, 3.43027s/8 iters), loss = 2.36182
I0401 13:14:12.299543 21213 solver.cpp:237]     Train net output #0: loss = 2.36182 (* 1 = 2.36182 loss)
I0401 13:14:12.299551 21213 sgd_solver.cpp:105] Iteration 5016, lr = 0.001
I0401 13:14:15.966965 21213 solver.cpp:218] Iteration 5024 (2.18138 iter/s, 3.66741s/8 iters), loss = 2.06773
I0401 13:14:15.967015 21213 solver.cpp:237]     Train net output #0: loss = 2.06773 (* 1 = 2.06773 loss)
I0401 13:14:15.967022 21213 sgd_solver.cpp:105] Iteration 5024, lr = 0.001
I0401 13:14:19.437044 21213 solver.cpp:218] Iteration 5032 (2.30547 iter/s, 3.47001s/8 iters), loss = 2.31266
I0401 13:14:19.437103 21213 solver.cpp:237]     Train net output #0: loss = 2.31266 (* 1 = 2.31266 loss)
I0401 13:14:19.437110 21213 sgd_solver.cpp:105] Iteration 5032, lr = 0.001
I0401 13:14:23.024682 21213 solver.cpp:218] Iteration 5040 (2.22993 iter/s, 3.58756s/8 iters), loss = 2.02616
I0401 13:14:23.024731 21213 solver.cpp:237]     Train net output #0: loss = 2.02616 (* 1 = 2.02616 loss)
I0401 13:14:23.024737 21213 sgd_solver.cpp:105] Iteration 5040, lr = 0.001
I0401 13:14:26.720624 21213 solver.cpp:218] Iteration 5048 (2.16457 iter/s, 3.69588s/8 iters), loss = 2.1625
I0401 13:14:26.720669 21213 solver.cpp:237]     Train net output #0: loss = 2.1625 (* 1 = 2.1625 loss)
I0401 13:14:26.720675 21213 sgd_solver.cpp:105] Iteration 5048, lr = 0.001
I0401 13:14:29.564232 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5056.caffemodel
I0401 13:14:30.139809 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:14:34.085327 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5056.solverstate
I0401 13:14:37.405282 21213 solver.cpp:330] Iteration 5056, Testing net (#0)
I0401 13:14:37.405300 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:14:39.036368 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:14:39.697878 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0709135
I0401 13:14:39.697907 21213 solver.cpp:397]     Test net output #1: loss = 5.16457 (* 1 = 5.16457 loss)
I0401 13:14:39.838850 21213 solver.cpp:218] Iteration 5056 (0.609841 iter/s, 13.1182s/8 iters), loss = 2.08132
I0401 13:14:39.838903 21213 solver.cpp:237]     Train net output #0: loss = 2.08132 (* 1 = 2.08132 loss)
I0401 13:14:39.838909 21213 sgd_solver.cpp:105] Iteration 5056, lr = 0.001
I0401 13:14:42.473178 21213 solver.cpp:218] Iteration 5064 (3.03691 iter/s, 2.63426s/8 iters), loss = 2.54881
I0401 13:14:42.473232 21213 solver.cpp:237]     Train net output #0: loss = 2.54881 (* 1 = 2.54881 loss)
I0401 13:14:42.473239 21213 sgd_solver.cpp:105] Iteration 5064, lr = 0.001
I0401 13:14:45.987622 21213 solver.cpp:218] Iteration 5072 (2.27636 iter/s, 3.51438s/8 iters), loss = 2.10481
I0401 13:14:45.987670 21213 solver.cpp:237]     Train net output #0: loss = 2.10481 (* 1 = 2.10481 loss)
I0401 13:14:45.987679 21213 sgd_solver.cpp:105] Iteration 5072, lr = 0.001
I0401 13:14:49.664176 21213 solver.cpp:218] Iteration 5080 (2.17599 iter/s, 3.6765s/8 iters), loss = 2.25813
I0401 13:14:49.664216 21213 solver.cpp:237]     Train net output #0: loss = 2.25813 (* 1 = 2.25813 loss)
I0401 13:14:49.664222 21213 sgd_solver.cpp:105] Iteration 5080, lr = 0.001
I0401 13:14:53.314435 21213 solver.cpp:218] Iteration 5088 (2.19166 iter/s, 3.6502s/8 iters), loss = 2.10854
I0401 13:14:53.314479 21213 solver.cpp:237]     Train net output #0: loss = 2.10854 (* 1 = 2.10854 loss)
I0401 13:14:53.314486 21213 sgd_solver.cpp:105] Iteration 5088, lr = 0.001
I0401 13:14:56.934345 21213 solver.cpp:218] Iteration 5096 (2.21004 iter/s, 3.61985s/8 iters), loss = 2.12314
I0401 13:14:56.934397 21213 solver.cpp:237]     Train net output #0: loss = 2.12314 (* 1 = 2.12314 loss)
I0401 13:14:56.934406 21213 sgd_solver.cpp:105] Iteration 5096, lr = 0.001
I0401 13:15:00.696641 21213 solver.cpp:218] Iteration 5104 (2.1264 iter/s, 3.76223s/8 iters), loss = 1.93911
I0401 13:15:00.696694 21213 solver.cpp:237]     Train net output #0: loss = 1.93911 (* 1 = 1.93911 loss)
I0401 13:15:00.696704 21213 sgd_solver.cpp:105] Iteration 5104, lr = 0.001
I0401 13:15:04.438877 21213 solver.cpp:218] Iteration 5112 (2.138 iter/s, 3.74182s/8 iters), loss = 1.97474
I0401 13:15:04.439061 21213 solver.cpp:237]     Train net output #0: loss = 1.97474 (* 1 = 1.97474 loss)
I0401 13:15:04.439072 21213 sgd_solver.cpp:105] Iteration 5112, lr = 0.001
I0401 13:15:07.568049 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5120.caffemodel
I0401 13:15:07.792768 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:15:10.555207 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5120.solverstate
I0401 13:15:12.894105 21213 solver.cpp:330] Iteration 5120, Testing net (#0)
I0401 13:15:12.894124 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:15:14.567742 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:15:15.273304 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0805288
I0401 13:15:15.273339 21213 solver.cpp:397]     Test net output #1: loss = 5.18347 (* 1 = 5.18347 loss)
I0401 13:15:15.418327 21213 solver.cpp:218] Iteration 5120 (0.728646 iter/s, 10.9793s/8 iters), loss = 2.29585
I0401 13:15:15.419898 21213 solver.cpp:237]     Train net output #0: loss = 2.29585 (* 1 = 2.29585 loss)
I0401 13:15:15.419909 21213 sgd_solver.cpp:105] Iteration 5120, lr = 0.001
I0401 13:15:17.926530 21213 solver.cpp:218] Iteration 5128 (3.19154 iter/s, 2.50663s/8 iters), loss = 2.19538
I0401 13:15:17.926579 21213 solver.cpp:237]     Train net output #0: loss = 2.19538 (* 1 = 2.19538 loss)
I0401 13:15:17.926586 21213 sgd_solver.cpp:105] Iteration 5128, lr = 0.001
I0401 13:15:21.387861 21213 solver.cpp:218] Iteration 5136 (2.3113 iter/s, 3.46126s/8 iters), loss = 2.61939
I0401 13:15:21.387924 21213 solver.cpp:237]     Train net output #0: loss = 2.61939 (* 1 = 2.61939 loss)
I0401 13:15:21.387931 21213 sgd_solver.cpp:105] Iteration 5136, lr = 0.001
I0401 13:15:24.798593 21213 solver.cpp:218] Iteration 5144 (2.34559 iter/s, 3.41066s/8 iters), loss = 2.07141
I0401 13:15:24.798640 21213 solver.cpp:237]     Train net output #0: loss = 2.07141 (* 1 = 2.07141 loss)
I0401 13:15:24.798647 21213 sgd_solver.cpp:105] Iteration 5144, lr = 0.001
I0401 13:15:28.227958 21213 solver.cpp:218] Iteration 5152 (2.33283 iter/s, 3.4293s/8 iters), loss = 1.94214
I0401 13:15:28.228004 21213 solver.cpp:237]     Train net output #0: loss = 1.94214 (* 1 = 1.94214 loss)
I0401 13:15:28.228009 21213 sgd_solver.cpp:105] Iteration 5152, lr = 0.001
I0401 13:15:31.546815 21213 solver.cpp:218] Iteration 5160 (2.41051 iter/s, 3.31879s/8 iters), loss = 2.07747
I0401 13:15:31.546872 21213 solver.cpp:237]     Train net output #0: loss = 2.07747 (* 1 = 2.07747 loss)
I0401 13:15:31.546882 21213 sgd_solver.cpp:105] Iteration 5160, lr = 0.001
I0401 13:15:35.151875 21213 solver.cpp:218] Iteration 5168 (2.21915 iter/s, 3.60499s/8 iters), loss = 1.9048
I0401 13:15:35.151979 21213 solver.cpp:237]     Train net output #0: loss = 1.9048 (* 1 = 1.9048 loss)
I0401 13:15:35.151986 21213 sgd_solver.cpp:105] Iteration 5168, lr = 0.001
I0401 13:15:38.911626 21213 solver.cpp:218] Iteration 5176 (2.12787 iter/s, 3.75964s/8 iters), loss = 2.12623
I0401 13:15:38.911667 21213 solver.cpp:237]     Train net output #0: loss = 2.12623 (* 1 = 2.12623 loss)
I0401 13:15:38.911674 21213 sgd_solver.cpp:105] Iteration 5176, lr = 0.001
I0401 13:15:41.731451 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:15:41.809522 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5184.caffemodel
I0401 13:15:45.073223 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5184.solverstate
I0401 13:15:49.255618 21213 solver.cpp:330] Iteration 5184, Testing net (#0)
I0401 13:15:49.255647 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:15:50.688438 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:15:51.443120 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0865385
I0401 13:15:51.443148 21213 solver.cpp:397]     Test net output #1: loss = 5.23131 (* 1 = 5.23131 loss)
I0401 13:15:51.574026 21213 solver.cpp:218] Iteration 5184 (0.631794 iter/s, 12.6624s/8 iters), loss = 2.14636
I0401 13:15:51.574065 21213 solver.cpp:237]     Train net output #0: loss = 2.14636 (* 1 = 2.14636 loss)
I0401 13:15:51.574071 21213 sgd_solver.cpp:105] Iteration 5184, lr = 0.001
I0401 13:15:54.281565 21213 solver.cpp:218] Iteration 5192 (2.95478 iter/s, 2.70748s/8 iters), loss = 2.43516
I0401 13:15:54.281625 21213 solver.cpp:237]     Train net output #0: loss = 2.43516 (* 1 = 2.43516 loss)
I0401 13:15:54.281632 21213 sgd_solver.cpp:105] Iteration 5192, lr = 0.001
I0401 13:15:57.543987 21213 solver.cpp:218] Iteration 5200 (2.45222 iter/s, 3.26235s/8 iters), loss = 2.01137
I0401 13:15:57.544026 21213 solver.cpp:237]     Train net output #0: loss = 2.01137 (* 1 = 2.01137 loss)
I0401 13:15:57.544032 21213 sgd_solver.cpp:105] Iteration 5200, lr = 0.001
I0401 13:16:01.138145 21213 solver.cpp:218] Iteration 5208 (2.22587 iter/s, 3.5941s/8 iters), loss = 2.12724
I0401 13:16:01.138192 21213 solver.cpp:237]     Train net output #0: loss = 2.12724 (* 1 = 2.12724 loss)
I0401 13:16:01.138198 21213 sgd_solver.cpp:105] Iteration 5208, lr = 0.001
I0401 13:16:04.848623 21213 solver.cpp:218] Iteration 5216 (2.1561 iter/s, 3.71041s/8 iters), loss = 2.2909
I0401 13:16:04.848686 21213 solver.cpp:237]     Train net output #0: loss = 2.2909 (* 1 = 2.2909 loss)
I0401 13:16:04.848695 21213 sgd_solver.cpp:105] Iteration 5216, lr = 0.001
I0401 13:16:08.459398 21213 solver.cpp:218] Iteration 5224 (2.21564 iter/s, 3.6107s/8 iters), loss = 1.89584
I0401 13:16:08.459524 21213 solver.cpp:237]     Train net output #0: loss = 1.89584 (* 1 = 1.89584 loss)
I0401 13:16:08.459532 21213 sgd_solver.cpp:105] Iteration 5224, lr = 0.001
I0401 13:16:12.014163 21213 solver.cpp:218] Iteration 5232 (2.25059 iter/s, 3.55462s/8 iters), loss = 1.84781
I0401 13:16:12.014209 21213 solver.cpp:237]     Train net output #0: loss = 1.84781 (* 1 = 1.84781 loss)
I0401 13:16:12.014216 21213 sgd_solver.cpp:105] Iteration 5232, lr = 0.001
I0401 13:16:15.764997 21213 solver.cpp:218] Iteration 5240 (2.13289 iter/s, 3.75077s/8 iters), loss = 2.12932
I0401 13:16:15.765054 21213 solver.cpp:237]     Train net output #0: loss = 2.12932 (* 1 = 2.12932 loss)
I0401 13:16:15.765064 21213 sgd_solver.cpp:105] Iteration 5240, lr = 0.001
I0401 13:16:18.460157 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:16:18.770777 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5248.caffemodel
I0401 13:16:21.813958 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5248.solverstate
I0401 13:16:24.171653 21213 solver.cpp:330] Iteration 5248, Testing net (#0)
I0401 13:16:24.171671 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:16:25.471915 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:16:26.440999 21213 solver.cpp:397]     Test net output #0: accuracy = 0.102163
I0401 13:16:26.441040 21213 solver.cpp:397]     Test net output #1: loss = 5.16026 (* 1 = 5.16026 loss)
I0401 13:16:26.581076 21213 solver.cpp:218] Iteration 5248 (0.739644 iter/s, 10.816s/8 iters), loss = 2.04572
I0401 13:16:26.581135 21213 solver.cpp:237]     Train net output #0: loss = 2.04572 (* 1 = 2.04572 loss)
I0401 13:16:26.581141 21213 sgd_solver.cpp:105] Iteration 5248, lr = 0.001
I0401 13:16:29.135921 21213 solver.cpp:218] Iteration 5256 (3.1314 iter/s, 2.55477s/8 iters), loss = 1.95188
I0401 13:16:29.135967 21213 solver.cpp:237]     Train net output #0: loss = 1.95188 (* 1 = 1.95188 loss)
I0401 13:16:29.135972 21213 sgd_solver.cpp:105] Iteration 5256, lr = 0.001
I0401 13:16:32.784667 21213 solver.cpp:218] Iteration 5264 (2.19257 iter/s, 3.64868s/8 iters), loss = 1.49187
I0401 13:16:32.784723 21213 solver.cpp:237]     Train net output #0: loss = 1.49187 (* 1 = 1.49187 loss)
I0401 13:16:32.784730 21213 sgd_solver.cpp:105] Iteration 5264, lr = 0.001
I0401 13:16:36.447227 21213 solver.cpp:218] Iteration 5272 (2.18431 iter/s, 3.66249s/8 iters), loss = 1.94604
I0401 13:16:36.447279 21213 solver.cpp:237]     Train net output #0: loss = 1.94604 (* 1 = 1.94604 loss)
I0401 13:16:36.447288 21213 sgd_solver.cpp:105] Iteration 5272, lr = 0.001
I0401 13:16:40.287580 21213 solver.cpp:218] Iteration 5280 (2.0832 iter/s, 3.84025s/8 iters), loss = 2.25177
I0401 13:16:40.287722 21213 solver.cpp:237]     Train net output #0: loss = 2.25177 (* 1 = 2.25177 loss)
I0401 13:16:40.287730 21213 sgd_solver.cpp:105] Iteration 5280, lr = 0.001
I0401 13:16:43.638917 21213 solver.cpp:218] Iteration 5288 (2.38721 iter/s, 3.35119s/8 iters), loss = 1.69459
I0401 13:16:43.638955 21213 solver.cpp:237]     Train net output #0: loss = 1.69459 (* 1 = 1.69459 loss)
I0401 13:16:43.638962 21213 sgd_solver.cpp:105] Iteration 5288, lr = 0.001
I0401 13:16:47.268545 21213 solver.cpp:218] Iteration 5296 (2.20411 iter/s, 3.62958s/8 iters), loss = 2.00502
I0401 13:16:47.268590 21213 solver.cpp:237]     Train net output #0: loss = 2.00502 (* 1 = 2.00502 loss)
I0401 13:16:47.268599 21213 sgd_solver.cpp:105] Iteration 5296, lr = 0.001
I0401 13:16:50.840396 21213 solver.cpp:218] Iteration 5304 (2.23977 iter/s, 3.57179s/8 iters), loss = 1.93352
I0401 13:16:50.840443 21213 solver.cpp:237]     Train net output #0: loss = 1.93352 (* 1 = 1.93352 loss)
I0401 13:16:50.840451 21213 sgd_solver.cpp:105] Iteration 5304, lr = 0.001
I0401 13:16:53.202129 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:16:54.034729 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5312.caffemodel
I0401 13:16:58.581526 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5312.solverstate
I0401 13:17:02.263593 21213 solver.cpp:330] Iteration 5312, Testing net (#0)
I0401 13:17:02.263613 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:17:03.580682 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:17:04.476202 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0901442
I0401 13:17:04.476231 21213 solver.cpp:397]     Test net output #1: loss = 5.22957 (* 1 = 5.22957 loss)
I0401 13:17:04.617120 21213 solver.cpp:218] Iteration 5312 (0.580692 iter/s, 13.7767s/8 iters), loss = 1.65064
I0401 13:17:04.617187 21213 solver.cpp:237]     Train net output #0: loss = 1.65064 (* 1 = 1.65064 loss)
I0401 13:17:04.617197 21213 sgd_solver.cpp:105] Iteration 5312, lr = 0.001
I0401 13:17:07.312268 21213 solver.cpp:218] Iteration 5320 (2.96838 iter/s, 2.69507s/8 iters), loss = 1.87476
I0401 13:17:07.312309 21213 solver.cpp:237]     Train net output #0: loss = 1.87476 (* 1 = 1.87476 loss)
I0401 13:17:07.312314 21213 sgd_solver.cpp:105] Iteration 5320, lr = 0.001
I0401 13:17:07.668233 21213 blocking_queue.cpp:49] Waiting for data
I0401 13:17:10.810251 21213 solver.cpp:218] Iteration 5328 (2.28707 iter/s, 3.49793s/8 iters), loss = 1.60776
I0401 13:17:10.810344 21213 solver.cpp:237]     Train net output #0: loss = 1.60776 (* 1 = 1.60776 loss)
I0401 13:17:10.810351 21213 sgd_solver.cpp:105] Iteration 5328, lr = 0.001
I0401 13:17:14.527909 21213 solver.cpp:218] Iteration 5336 (2.15195 iter/s, 3.71756s/8 iters), loss = 1.80756
I0401 13:17:14.527951 21213 solver.cpp:237]     Train net output #0: loss = 1.80756 (* 1 = 1.80756 loss)
I0401 13:17:14.527957 21213 sgd_solver.cpp:105] Iteration 5336, lr = 0.001
I0401 13:17:18.060252 21213 solver.cpp:218] Iteration 5344 (2.26482 iter/s, 3.53228s/8 iters), loss = 1.92993
I0401 13:17:18.060308 21213 solver.cpp:237]     Train net output #0: loss = 1.92993 (* 1 = 1.92993 loss)
I0401 13:17:18.060315 21213 sgd_solver.cpp:105] Iteration 5344, lr = 0.001
I0401 13:17:21.569190 21213 solver.cpp:218] Iteration 5352 (2.27994 iter/s, 3.50887s/8 iters), loss = 2.0748
I0401 13:17:21.569243 21213 solver.cpp:237]     Train net output #0: loss = 2.0748 (* 1 = 2.0748 loss)
I0401 13:17:21.569252 21213 sgd_solver.cpp:105] Iteration 5352, lr = 0.001
I0401 13:17:25.127357 21213 solver.cpp:218] Iteration 5360 (2.24839 iter/s, 3.5581s/8 iters), loss = 1.80673
I0401 13:17:25.127404 21213 solver.cpp:237]     Train net output #0: loss = 1.80673 (* 1 = 1.80673 loss)
I0401 13:17:25.127413 21213 sgd_solver.cpp:105] Iteration 5360, lr = 0.001
I0401 13:17:28.881033 21213 solver.cpp:218] Iteration 5368 (2.13128 iter/s, 3.75361s/8 iters), loss = 2.11273
I0401 13:17:28.881089 21213 solver.cpp:237]     Train net output #0: loss = 2.11273 (* 1 = 2.11273 loss)
I0401 13:17:28.881098 21213 sgd_solver.cpp:105] Iteration 5368, lr = 0.001
I0401 13:17:30.992170 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:17:32.072019 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5376.caffemodel
I0401 13:17:35.382272 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5376.solverstate
I0401 13:17:37.672667 21213 solver.cpp:330] Iteration 5376, Testing net (#0)
I0401 13:17:37.672688 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:17:38.848539 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:17:39.857012 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0817308
I0401 13:17:39.857040 21213 solver.cpp:397]     Test net output #1: loss = 5.372 (* 1 = 5.372 loss)
I0401 13:17:39.998616 21213 solver.cpp:218] Iteration 5376 (0.719585 iter/s, 11.1175s/8 iters), loss = 1.66359
I0401 13:17:39.998675 21213 solver.cpp:237]     Train net output #0: loss = 1.66359 (* 1 = 1.66359 loss)
I0401 13:17:39.998685 21213 sgd_solver.cpp:105] Iteration 5376, lr = 0.001
I0401 13:17:42.644393 21213 solver.cpp:218] Iteration 5384 (3.02377 iter/s, 2.6457s/8 iters), loss = 1.71606
I0401 13:17:42.644585 21213 solver.cpp:237]     Train net output #0: loss = 1.71606 (* 1 = 1.71606 loss)
I0401 13:17:42.644594 21213 sgd_solver.cpp:105] Iteration 5384, lr = 0.001
I0401 13:17:46.345808 21213 solver.cpp:218] Iteration 5392 (2.16145 iter/s, 3.70121s/8 iters), loss = 1.47269
I0401 13:17:46.345844 21213 solver.cpp:237]     Train net output #0: loss = 1.47269 (* 1 = 1.47269 loss)
I0401 13:17:46.345850 21213 sgd_solver.cpp:105] Iteration 5392, lr = 0.001
I0401 13:17:50.008761 21213 solver.cpp:218] Iteration 5400 (2.18406 iter/s, 3.6629s/8 iters), loss = 1.66308
I0401 13:17:50.008812 21213 solver.cpp:237]     Train net output #0: loss = 1.66308 (* 1 = 1.66308 loss)
I0401 13:17:50.008821 21213 sgd_solver.cpp:105] Iteration 5400, lr = 0.001
I0401 13:17:53.489748 21213 solver.cpp:218] Iteration 5408 (2.29825 iter/s, 3.48092s/8 iters), loss = 1.7926
I0401 13:17:53.489801 21213 solver.cpp:237]     Train net output #0: loss = 1.7926 (* 1 = 1.7926 loss)
I0401 13:17:53.489810 21213 sgd_solver.cpp:105] Iteration 5408, lr = 0.001
I0401 13:17:57.127764 21213 solver.cpp:218] Iteration 5416 (2.19904 iter/s, 3.63795s/8 iters), loss = 1.68725
I0401 13:17:57.127827 21213 solver.cpp:237]     Train net output #0: loss = 1.68725 (* 1 = 1.68725 loss)
I0401 13:17:57.127836 21213 sgd_solver.cpp:105] Iteration 5416, lr = 0.001
I0401 13:18:00.619554 21213 solver.cpp:218] Iteration 5424 (2.29114 iter/s, 3.49171s/8 iters), loss = 2.01116
I0401 13:18:00.619608 21213 solver.cpp:237]     Train net output #0: loss = 2.01116 (* 1 = 2.01116 loss)
I0401 13:18:00.619614 21213 sgd_solver.cpp:105] Iteration 5424, lr = 0.001
I0401 13:18:04.219086 21213 solver.cpp:218] Iteration 5432 (2.22255 iter/s, 3.59947s/8 iters), loss = 1.79141
I0401 13:18:04.219122 21213 solver.cpp:237]     Train net output #0: loss = 1.79141 (* 1 = 1.79141 loss)
I0401 13:18:04.219127 21213 sgd_solver.cpp:105] Iteration 5432, lr = 0.001
I0401 13:18:05.757858 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:18:07.261862 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5440.caffemodel
I0401 13:18:10.349758 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5440.solverstate
I0401 13:18:12.731799 21213 solver.cpp:330] Iteration 5440, Testing net (#0)
I0401 13:18:12.731878 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:18:13.826836 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:18:14.947827 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0769231
I0401 13:18:14.947858 21213 solver.cpp:397]     Test net output #1: loss = 5.29606 (* 1 = 5.29606 loss)
I0401 13:18:15.089058 21213 solver.cpp:218] Iteration 5440 (0.735975 iter/s, 10.8699s/8 iters), loss = 2.08159
I0401 13:18:15.089100 21213 solver.cpp:237]     Train net output #0: loss = 2.08159 (* 1 = 2.08159 loss)
I0401 13:18:15.089105 21213 sgd_solver.cpp:105] Iteration 5440, lr = 0.001
I0401 13:18:17.620929 21213 solver.cpp:218] Iteration 5448 (3.15979 iter/s, 2.53181s/8 iters), loss = 1.86369
I0401 13:18:17.620973 21213 solver.cpp:237]     Train net output #0: loss = 1.86369 (* 1 = 1.86369 loss)
I0401 13:18:17.620978 21213 sgd_solver.cpp:105] Iteration 5448, lr = 0.001
I0401 13:18:21.286290 21213 solver.cpp:218] Iteration 5456 (2.18263 iter/s, 3.6653s/8 iters), loss = 1.46143
I0401 13:18:21.286345 21213 solver.cpp:237]     Train net output #0: loss = 1.46143 (* 1 = 1.46143 loss)
I0401 13:18:21.286352 21213 sgd_solver.cpp:105] Iteration 5456, lr = 0.001
I0401 13:18:24.785904 21213 solver.cpp:218] Iteration 5464 (2.28601 iter/s, 3.49954s/8 iters), loss = 1.61071
I0401 13:18:24.785957 21213 solver.cpp:237]     Train net output #0: loss = 1.61071 (* 1 = 1.61071 loss)
I0401 13:18:24.785965 21213 sgd_solver.cpp:105] Iteration 5464, lr = 0.001
I0401 13:18:28.341778 21213 solver.cpp:218] Iteration 5472 (2.24984 iter/s, 3.55581s/8 iters), loss = 1.9248
I0401 13:18:28.341825 21213 solver.cpp:237]     Train net output #0: loss = 1.9248 (* 1 = 1.9248 loss)
I0401 13:18:28.341830 21213 sgd_solver.cpp:105] Iteration 5472, lr = 0.001
I0401 13:18:32.122347 21213 solver.cpp:218] Iteration 5480 (2.11612 iter/s, 3.7805s/8 iters), loss = 1.77031
I0401 13:18:32.122397 21213 solver.cpp:237]     Train net output #0: loss = 1.77031 (* 1 = 1.77031 loss)
I0401 13:18:32.122406 21213 sgd_solver.cpp:105] Iteration 5480, lr = 0.001
I0401 13:18:35.693624 21213 solver.cpp:218] Iteration 5488 (2.24013 iter/s, 3.57121s/8 iters), loss = 2.24436
I0401 13:18:35.693666 21213 solver.cpp:237]     Train net output #0: loss = 2.24436 (* 1 = 2.24436 loss)
I0401 13:18:35.693673 21213 sgd_solver.cpp:105] Iteration 5488, lr = 0.001
I0401 13:18:39.280730 21213 solver.cpp:218] Iteration 5496 (2.23025 iter/s, 3.58705s/8 iters), loss = 2.06638
I0401 13:18:39.280779 21213 solver.cpp:237]     Train net output #0: loss = 2.06638 (* 1 = 2.06638 loss)
I0401 13:18:39.280787 21213 sgd_solver.cpp:105] Iteration 5496, lr = 0.001
I0401 13:18:40.470161 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:18:42.154520 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5504.caffemodel
I0401 13:18:45.150744 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5504.solverstate
I0401 13:18:47.485716 21213 solver.cpp:330] Iteration 5504, Testing net (#0)
I0401 13:18:47.485741 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:18:48.545847 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:18:49.671587 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0685096
I0401 13:18:49.671625 21213 solver.cpp:397]     Test net output #1: loss = 5.23075 (* 1 = 5.23075 loss)
I0401 13:18:49.826469 21213 solver.cpp:218] Iteration 5504 (0.758604 iter/s, 10.5457s/8 iters), loss = 2.13254
I0401 13:18:49.828047 21213 solver.cpp:237]     Train net output #0: loss = 2.13254 (* 1 = 2.13254 loss)
I0401 13:18:49.828063 21213 sgd_solver.cpp:105] Iteration 5504, lr = 0.001
I0401 13:18:52.383177 21213 solver.cpp:218] Iteration 5512 (3.13096 iter/s, 2.55513s/8 iters), loss = 1.60611
I0401 13:18:52.383225 21213 solver.cpp:237]     Train net output #0: loss = 1.60611 (* 1 = 1.60611 loss)
I0401 13:18:52.383230 21213 sgd_solver.cpp:105] Iteration 5512, lr = 0.001
I0401 13:18:56.139341 21213 solver.cpp:218] Iteration 5520 (2.12987 iter/s, 3.7561s/8 iters), loss = 1.83139
I0401 13:18:56.139402 21213 solver.cpp:237]     Train net output #0: loss = 1.83139 (* 1 = 1.83139 loss)
I0401 13:18:56.139410 21213 sgd_solver.cpp:105] Iteration 5520, lr = 0.001
I0401 13:18:59.834872 21213 solver.cpp:218] Iteration 5528 (2.16482 iter/s, 3.69546s/8 iters), loss = 1.63479
I0401 13:18:59.834914 21213 solver.cpp:237]     Train net output #0: loss = 1.63479 (* 1 = 1.63479 loss)
I0401 13:18:59.834920 21213 sgd_solver.cpp:105] Iteration 5528, lr = 0.001
I0401 13:19:03.408305 21213 solver.cpp:218] Iteration 5536 (2.23878 iter/s, 3.57337s/8 iters), loss = 1.98504
I0401 13:19:03.408350 21213 solver.cpp:237]     Train net output #0: loss = 1.98504 (* 1 = 1.98504 loss)
I0401 13:19:03.408355 21213 sgd_solver.cpp:105] Iteration 5536, lr = 0.001
I0401 13:19:07.051941 21213 solver.cpp:218] Iteration 5544 (2.19565 iter/s, 3.64357s/8 iters), loss = 1.96215
I0401 13:19:07.051997 21213 solver.cpp:237]     Train net output #0: loss = 1.96215 (* 1 = 1.96215 loss)
I0401 13:19:07.052006 21213 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0401 13:19:10.591239 21213 solver.cpp:218] Iteration 5552 (2.26038 iter/s, 3.53923s/8 iters), loss = 1.89961
I0401 13:19:10.591295 21213 solver.cpp:237]     Train net output #0: loss = 1.89961 (* 1 = 1.89961 loss)
I0401 13:19:10.591302 21213 sgd_solver.cpp:105] Iteration 5552, lr = 0.001
I0401 13:19:14.179438 21213 solver.cpp:218] Iteration 5560 (2.22958 iter/s, 3.58813s/8 iters), loss = 1.84681
I0401 13:19:14.179504 21213 solver.cpp:237]     Train net output #0: loss = 1.84681 (* 1 = 1.84681 loss)
I0401 13:19:14.179514 21213 sgd_solver.cpp:105] Iteration 5560, lr = 0.001
I0401 13:19:15.125517 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:19:17.073269 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5568.caffemodel
I0401 13:19:20.549226 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5568.solverstate
I0401 13:19:24.479570 21213 solver.cpp:330] Iteration 5568, Testing net (#0)
I0401 13:19:24.479588 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:19:25.475451 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:19:26.780719 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0757212
I0401 13:19:26.780755 21213 solver.cpp:397]     Test net output #1: loss = 5.23847 (* 1 = 5.23847 loss)
I0401 13:19:26.922135 21213 solver.cpp:218] Iteration 5568 (0.627814 iter/s, 12.7426s/8 iters), loss = 1.93324
I0401 13:19:26.922184 21213 solver.cpp:237]     Train net output #0: loss = 1.93324 (* 1 = 1.93324 loss)
I0401 13:19:26.922191 21213 sgd_solver.cpp:105] Iteration 5568, lr = 0.001
I0401 13:19:29.575178 21213 solver.cpp:218] Iteration 5576 (3.01548 iter/s, 2.65298s/8 iters), loss = 1.73819
I0401 13:19:29.575232 21213 solver.cpp:237]     Train net output #0: loss = 1.73819 (* 1 = 1.73819 loss)
I0401 13:19:29.575240 21213 sgd_solver.cpp:105] Iteration 5576, lr = 0.001
I0401 13:19:32.904366 21213 solver.cpp:218] Iteration 5584 (2.40304 iter/s, 3.32912s/8 iters), loss = 1.89002
I0401 13:19:32.904426 21213 solver.cpp:237]     Train net output #0: loss = 1.89002 (* 1 = 1.89002 loss)
I0401 13:19:32.904434 21213 sgd_solver.cpp:105] Iteration 5584, lr = 0.001
I0401 13:19:36.419416 21213 solver.cpp:218] Iteration 5592 (2.27598 iter/s, 3.51498s/8 iters), loss = 2.16631
I0401 13:19:36.419467 21213 solver.cpp:237]     Train net output #0: loss = 2.16631 (* 1 = 2.16631 loss)
I0401 13:19:36.419476 21213 sgd_solver.cpp:105] Iteration 5592, lr = 0.001
I0401 13:19:40.050534 21213 solver.cpp:218] Iteration 5600 (2.20322 iter/s, 3.63105s/8 iters), loss = 2.03457
I0401 13:19:40.050590 21213 solver.cpp:237]     Train net output #0: loss = 2.03457 (* 1 = 2.03457 loss)
I0401 13:19:40.050597 21213 sgd_solver.cpp:105] Iteration 5600, lr = 0.001
I0401 13:19:43.507010 21213 solver.cpp:218] Iteration 5608 (2.31454 iter/s, 3.45641s/8 iters), loss = 1.98804
I0401 13:19:43.507052 21213 solver.cpp:237]     Train net output #0: loss = 1.98804 (* 1 = 1.98804 loss)
I0401 13:19:43.507058 21213 sgd_solver.cpp:105] Iteration 5608, lr = 0.001
I0401 13:19:47.129778 21213 solver.cpp:218] Iteration 5616 (2.20829 iter/s, 3.62271s/8 iters), loss = 1.54656
I0401 13:19:47.129911 21213 solver.cpp:237]     Train net output #0: loss = 1.54656 (* 1 = 1.54656 loss)
I0401 13:19:47.129920 21213 sgd_solver.cpp:105] Iteration 5616, lr = 0.001
I0401 13:19:50.973448 21213 solver.cpp:218] Iteration 5624 (2.08142 iter/s, 3.84353s/8 iters), loss = 1.91249
I0401 13:19:50.973488 21213 solver.cpp:237]     Train net output #0: loss = 1.91249 (* 1 = 1.91249 loss)
I0401 13:19:50.973493 21213 sgd_solver.cpp:105] Iteration 5624, lr = 0.001
I0401 13:19:51.486868 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:19:53.861713 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5632.caffemodel
I0401 13:19:57.013840 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5632.solverstate
I0401 13:19:59.413514 21213 solver.cpp:330] Iteration 5632, Testing net (#0)
I0401 13:19:59.413534 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:20:00.278136 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:20:01.486344 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0877404
I0401 13:20:01.486377 21213 solver.cpp:397]     Test net output #1: loss = 5.12015 (* 1 = 5.12015 loss)
I0401 13:20:01.627869 21213 solver.cpp:218] Iteration 5632 (0.750867 iter/s, 10.6544s/8 iters), loss = 2.00602
I0401 13:20:01.627945 21213 solver.cpp:237]     Train net output #0: loss = 2.00602 (* 1 = 2.00602 loss)
I0401 13:20:01.627954 21213 sgd_solver.cpp:105] Iteration 5632, lr = 0.001
I0401 13:20:04.297152 21213 solver.cpp:218] Iteration 5640 (2.99716 iter/s, 2.66919s/8 iters), loss = 1.81321
I0401 13:20:04.297209 21213 solver.cpp:237]     Train net output #0: loss = 1.81321 (* 1 = 1.81321 loss)
I0401 13:20:04.297216 21213 sgd_solver.cpp:105] Iteration 5640, lr = 0.001
I0401 13:20:07.955543 21213 solver.cpp:218] Iteration 5648 (2.18679 iter/s, 3.65832s/8 iters), loss = 1.71348
I0401 13:20:07.955588 21213 solver.cpp:237]     Train net output #0: loss = 1.71348 (* 1 = 1.71348 loss)
I0401 13:20:07.955595 21213 sgd_solver.cpp:105] Iteration 5648, lr = 0.001
I0401 13:20:11.347033 21213 solver.cpp:218] Iteration 5656 (2.35889 iter/s, 3.39142s/8 iters), loss = 1.61192
I0401 13:20:11.347095 21213 solver.cpp:237]     Train net output #0: loss = 1.61192 (* 1 = 1.61192 loss)
I0401 13:20:11.347105 21213 sgd_solver.cpp:105] Iteration 5656, lr = 0.001
I0401 13:20:14.826956 21213 solver.cpp:218] Iteration 5664 (2.29895 iter/s, 3.47985s/8 iters), loss = 1.97202
I0401 13:20:14.827009 21213 solver.cpp:237]     Train net output #0: loss = 1.97202 (* 1 = 1.97202 loss)
I0401 13:20:14.827018 21213 sgd_solver.cpp:105] Iteration 5664, lr = 0.001
I0401 13:20:18.465308 21213 solver.cpp:218] Iteration 5672 (2.19884 iter/s, 3.63828s/8 iters), loss = 1.56598
I0401 13:20:18.465428 21213 solver.cpp:237]     Train net output #0: loss = 1.56598 (* 1 = 1.56598 loss)
I0401 13:20:18.465435 21213 sgd_solver.cpp:105] Iteration 5672, lr = 0.001
I0401 13:20:21.987025 21213 solver.cpp:218] Iteration 5680 (2.27171 iter/s, 3.52158s/8 iters), loss = 1.60845
I0401 13:20:21.987082 21213 solver.cpp:237]     Train net output #0: loss = 1.60845 (* 1 = 1.60845 loss)
I0401 13:20:21.987092 21213 sgd_solver.cpp:105] Iteration 5680, lr = 0.001
I0401 13:20:25.444109 21213 solver.cpp:218] Iteration 5688 (2.31414 iter/s, 3.45701s/8 iters), loss = 1.73478
I0401 13:20:25.444154 21213 solver.cpp:237]     Train net output #0: loss = 1.73478 (* 1 = 1.73478 loss)
I0401 13:20:25.444159 21213 sgd_solver.cpp:105] Iteration 5688, lr = 0.001
I0401 13:20:25.583436 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:20:28.258319 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5696.caffemodel
I0401 13:20:31.291944 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5696.solverstate
I0401 13:20:33.598832 21213 solver.cpp:330] Iteration 5696, Testing net (#0)
I0401 13:20:33.598858 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:20:34.429342 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:20:35.770874 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0829327
I0401 13:20:35.770925 21213 solver.cpp:397]     Test net output #1: loss = 5.20495 (* 1 = 5.20495 loss)
I0401 13:20:35.912463 21213 solver.cpp:218] Iteration 5696 (0.764212 iter/s, 10.4683s/8 iters), loss = 1.82979
I0401 13:20:35.912513 21213 solver.cpp:237]     Train net output #0: loss = 1.82979 (* 1 = 1.82979 loss)
I0401 13:20:35.912519 21213 sgd_solver.cpp:105] Iteration 5696, lr = 0.001
I0401 13:20:38.534533 21213 solver.cpp:218] Iteration 5704 (3.05109 iter/s, 2.62201s/8 iters), loss = 1.5139
I0401 13:20:38.534577 21213 solver.cpp:237]     Train net output #0: loss = 1.5139 (* 1 = 1.5139 loss)
I0401 13:20:38.534584 21213 sgd_solver.cpp:105] Iteration 5704, lr = 0.001
I0401 13:20:42.154155 21213 solver.cpp:218] Iteration 5712 (2.21021 iter/s, 3.61956s/8 iters), loss = 1.68129
I0401 13:20:42.154198 21213 solver.cpp:237]     Train net output #0: loss = 1.68129 (* 1 = 1.68129 loss)
I0401 13:20:42.154203 21213 sgd_solver.cpp:105] Iteration 5712, lr = 0.001
I0401 13:20:45.721854 21213 solver.cpp:218] Iteration 5720 (2.24238 iter/s, 3.56764s/8 iters), loss = 1.46195
I0401 13:20:45.721905 21213 solver.cpp:237]     Train net output #0: loss = 1.46195 (* 1 = 1.46195 loss)
I0401 13:20:45.721915 21213 sgd_solver.cpp:105] Iteration 5720, lr = 0.001
I0401 13:20:49.334105 21213 solver.cpp:218] Iteration 5728 (2.21473 iter/s, 3.61218s/8 iters), loss = 2.03434
I0401 13:20:49.334270 21213 solver.cpp:237]     Train net output #0: loss = 2.03434 (* 1 = 2.03434 loss)
I0401 13:20:49.334280 21213 sgd_solver.cpp:105] Iteration 5728, lr = 0.001
I0401 13:20:53.067008 21213 solver.cpp:218] Iteration 5736 (2.1432 iter/s, 3.73273s/8 iters), loss = 1.28453
I0401 13:20:53.067046 21213 solver.cpp:237]     Train net output #0: loss = 1.28453 (* 1 = 1.28453 loss)
I0401 13:20:53.067054 21213 sgd_solver.cpp:105] Iteration 5736, lr = 0.001
I0401 13:20:56.567387 21213 solver.cpp:218] Iteration 5744 (2.2855 iter/s, 3.50032s/8 iters), loss = 1.75387
I0401 13:20:56.567432 21213 solver.cpp:237]     Train net output #0: loss = 1.75387 (* 1 = 1.75387 loss)
I0401 13:20:56.567440 21213 sgd_solver.cpp:105] Iteration 5744, lr = 0.001
I0401 13:20:59.778676 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:20:59.953485 21213 solver.cpp:218] Iteration 5752 (2.36264 iter/s, 3.38604s/8 iters), loss = 1.87305
I0401 13:20:59.953524 21213 solver.cpp:237]     Train net output #0: loss = 1.87305 (* 1 = 1.87305 loss)
I0401 13:20:59.953531 21213 sgd_solver.cpp:105] Iteration 5752, lr = 0.001
I0401 13:21:03.173579 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5760.caffemodel
I0401 13:21:06.211766 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5760.solverstate
I0401 13:21:08.514705 21213 solver.cpp:330] Iteration 5760, Testing net (#0)
I0401 13:21:08.514724 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:21:09.358434 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:21:10.874541 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:21:10.874577 21213 solver.cpp:397]     Test net output #1: loss = 5.2829 (* 1 = 5.2829 loss)
I0401 13:21:11.013795 21213 solver.cpp:218] Iteration 5760 (0.72331 iter/s, 11.0603s/8 iters), loss = 2.00962
I0401 13:21:11.013845 21213 solver.cpp:237]     Train net output #0: loss = 2.00962 (* 1 = 2.00962 loss)
I0401 13:21:11.013850 21213 sgd_solver.cpp:105] Iteration 5760, lr = 0.001
I0401 13:21:13.695094 21213 solver.cpp:218] Iteration 5768 (2.9837 iter/s, 2.68123s/8 iters), loss = 1.72094
I0401 13:21:13.695143 21213 solver.cpp:237]     Train net output #0: loss = 1.72094 (* 1 = 1.72094 loss)
I0401 13:21:13.695150 21213 sgd_solver.cpp:105] Iteration 5768, lr = 0.001
I0401 13:21:16.971849 21213 solver.cpp:218] Iteration 5776 (2.44149 iter/s, 3.27669s/8 iters), loss = 1.33363
I0401 13:21:16.971912 21213 solver.cpp:237]     Train net output #0: loss = 1.33363 (* 1 = 1.33363 loss)
I0401 13:21:16.971921 21213 sgd_solver.cpp:105] Iteration 5776, lr = 0.001
I0401 13:21:20.704154 21213 solver.cpp:218] Iteration 5784 (2.14349 iter/s, 3.73223s/8 iters), loss = 1.79171
I0401 13:21:20.704267 21213 solver.cpp:237]     Train net output #0: loss = 1.79171 (* 1 = 1.79171 loss)
I0401 13:21:20.704275 21213 sgd_solver.cpp:105] Iteration 5784, lr = 0.001
I0401 13:21:24.277245 21213 solver.cpp:218] Iteration 5792 (2.23903 iter/s, 3.57297s/8 iters), loss = 1.62165
I0401 13:21:24.277282 21213 solver.cpp:237]     Train net output #0: loss = 1.62165 (* 1 = 1.62165 loss)
I0401 13:21:24.277287 21213 sgd_solver.cpp:105] Iteration 5792, lr = 0.001
I0401 13:21:27.915858 21213 solver.cpp:218] Iteration 5800 (2.19867 iter/s, 3.63856s/8 iters), loss = 1.64404
I0401 13:21:27.915900 21213 solver.cpp:237]     Train net output #0: loss = 1.64404 (* 1 = 1.64404 loss)
I0401 13:21:27.915905 21213 sgd_solver.cpp:105] Iteration 5800, lr = 0.001
I0401 13:21:31.454772 21213 solver.cpp:218] Iteration 5808 (2.26062 iter/s, 3.53886s/8 iters), loss = 1.59956
I0401 13:21:31.454823 21213 solver.cpp:237]     Train net output #0: loss = 1.59956 (* 1 = 1.59956 loss)
I0401 13:21:31.454828 21213 sgd_solver.cpp:105] Iteration 5808, lr = 0.001
I0401 13:21:34.441797 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:21:34.843298 21213 solver.cpp:218] Iteration 5816 (2.36095 iter/s, 3.38846s/8 iters), loss = 2.03197
I0401 13:21:34.843346 21213 solver.cpp:237]     Train net output #0: loss = 2.03197 (* 1 = 2.03197 loss)
I0401 13:21:34.843353 21213 sgd_solver.cpp:105] Iteration 5816, lr = 0.001
I0401 13:21:37.731724 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5824.caffemodel
I0401 13:21:41.912307 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5824.solverstate
I0401 13:21:46.592435 21213 solver.cpp:330] Iteration 5824, Testing net (#0)
I0401 13:21:46.592453 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:21:47.306576 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:21:48.699921 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0973558
I0401 13:21:48.699967 21213 solver.cpp:397]     Test net output #1: loss = 5.26418 (* 1 = 5.26418 loss)
I0401 13:21:48.841441 21213 solver.cpp:218] Iteration 5824 (0.571507 iter/s, 13.9981s/8 iters), loss = 1.6394
I0401 13:21:48.841511 21213 solver.cpp:237]     Train net output #0: loss = 1.6394 (* 1 = 1.6394 loss)
I0401 13:21:48.841519 21213 sgd_solver.cpp:105] Iteration 5824, lr = 0.001
I0401 13:21:51.441103 21213 solver.cpp:218] Iteration 5832 (3.07742 iter/s, 2.59958s/8 iters), loss = 1.60512
I0401 13:21:51.441270 21213 solver.cpp:237]     Train net output #0: loss = 1.60512 (* 1 = 1.60512 loss)
I0401 13:21:51.441279 21213 sgd_solver.cpp:105] Iteration 5832, lr = 0.001
I0401 13:21:54.932601 21213 solver.cpp:218] Iteration 5840 (2.2914 iter/s, 3.49132s/8 iters), loss = 1.6558
I0401 13:21:54.932644 21213 solver.cpp:237]     Train net output #0: loss = 1.6558 (* 1 = 1.6558 loss)
I0401 13:21:54.932651 21213 sgd_solver.cpp:105] Iteration 5840, lr = 0.001
I0401 13:21:58.543958 21213 solver.cpp:218] Iteration 5848 (2.21527 iter/s, 3.6113s/8 iters), loss = 1.43731
I0401 13:21:58.544005 21213 solver.cpp:237]     Train net output #0: loss = 1.43731 (* 1 = 1.43731 loss)
I0401 13:21:58.544010 21213 sgd_solver.cpp:105] Iteration 5848, lr = 0.001
I0401 13:22:02.041792 21213 solver.cpp:218] Iteration 5856 (2.28717 iter/s, 3.49777s/8 iters), loss = 1.47655
I0401 13:22:02.041833 21213 solver.cpp:237]     Train net output #0: loss = 1.47655 (* 1 = 1.47655 loss)
I0401 13:22:02.041838 21213 sgd_solver.cpp:105] Iteration 5856, lr = 0.001
I0401 13:22:05.511885 21213 solver.cpp:218] Iteration 5864 (2.30545 iter/s, 3.47004s/8 iters), loss = 1.40073
I0401 13:22:05.511934 21213 solver.cpp:237]     Train net output #0: loss = 1.40073 (* 1 = 1.40073 loss)
I0401 13:22:05.511941 21213 sgd_solver.cpp:105] Iteration 5864, lr = 0.001
I0401 13:22:08.743963 21213 solver.cpp:218] Iteration 5872 (2.47524 iter/s, 3.23201s/8 iters), loss = 1.53768
I0401 13:22:08.744021 21213 solver.cpp:237]     Train net output #0: loss = 1.53768 (* 1 = 1.53768 loss)
I0401 13:22:08.744029 21213 sgd_solver.cpp:105] Iteration 5872, lr = 0.001
I0401 13:22:11.542760 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:22:12.331506 21213 solver.cpp:218] Iteration 5880 (2.22998 iter/s, 3.58747s/8 iters), loss = 1.63982
I0401 13:22:12.331552 21213 solver.cpp:237]     Train net output #0: loss = 1.63982 (* 1 = 1.63982 loss)
I0401 13:22:12.331557 21213 sgd_solver.cpp:105] Iteration 5880, lr = 0.001
I0401 13:22:15.524595 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5888.caffemodel
I0401 13:22:18.563354 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5888.solverstate
I0401 13:22:20.882545 21213 solver.cpp:330] Iteration 5888, Testing net (#0)
I0401 13:22:20.882570 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:22:21.594859 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:22:23.170675 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0757212
I0401 13:22:23.170711 21213 solver.cpp:397]     Test net output #1: loss = 5.33818 (* 1 = 5.33818 loss)
I0401 13:22:23.318806 21213 solver.cpp:218] Iteration 5888 (0.728594 iter/s, 10.9801s/8 iters), loss = 1.50967
I0401 13:22:23.318852 21213 solver.cpp:237]     Train net output #0: loss = 1.50967 (* 1 = 1.50967 loss)
I0401 13:22:23.318858 21213 sgd_solver.cpp:105] Iteration 5888, lr = 0.001
I0401 13:22:25.912158 21213 solver.cpp:218] Iteration 5896 (3.08488 iter/s, 2.59329s/8 iters), loss = 1.28245
I0401 13:22:25.912199 21213 solver.cpp:237]     Train net output #0: loss = 1.28245 (* 1 = 1.28245 loss)
I0401 13:22:25.912204 21213 sgd_solver.cpp:105] Iteration 5896, lr = 0.001
I0401 13:22:29.119840 21213 solver.cpp:218] Iteration 5904 (2.4941 iter/s, 3.20757s/8 iters), loss = 1.12722
I0401 13:22:29.119889 21213 solver.cpp:237]     Train net output #0: loss = 1.12722 (* 1 = 1.12722 loss)
I0401 13:22:29.119896 21213 sgd_solver.cpp:105] Iteration 5904, lr = 0.001
I0401 13:22:32.641471 21213 solver.cpp:218] Iteration 5912 (2.27171 iter/s, 3.52157s/8 iters), loss = 1.19587
I0401 13:22:32.641571 21213 solver.cpp:237]     Train net output #0: loss = 1.19587 (* 1 = 1.19587 loss)
I0401 13:22:32.641578 21213 sgd_solver.cpp:105] Iteration 5912, lr = 0.001
I0401 13:22:36.068226 21213 solver.cpp:218] Iteration 5920 (2.33465 iter/s, 3.42664s/8 iters), loss = 1.36355
I0401 13:22:36.068282 21213 solver.cpp:237]     Train net output #0: loss = 1.36355 (* 1 = 1.36355 loss)
I0401 13:22:36.068291 21213 sgd_solver.cpp:105] Iteration 5920, lr = 0.001
I0401 13:22:39.652602 21213 solver.cpp:218] Iteration 5928 (2.23195 iter/s, 3.58431s/8 iters), loss = 1.49722
I0401 13:22:39.652652 21213 solver.cpp:237]     Train net output #0: loss = 1.49722 (* 1 = 1.49722 loss)
I0401 13:22:39.652662 21213 sgd_solver.cpp:105] Iteration 5928, lr = 0.001
I0401 13:22:43.139591 21213 solver.cpp:218] Iteration 5936 (2.29428 iter/s, 3.48693s/8 iters), loss = 1.27058
I0401 13:22:43.139629 21213 solver.cpp:237]     Train net output #0: loss = 1.27058 (* 1 = 1.27058 loss)
I0401 13:22:43.139634 21213 sgd_solver.cpp:105] Iteration 5936, lr = 0.001
I0401 13:22:45.467824 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:22:46.688992 21213 solver.cpp:218] Iteration 5944 (2.25394 iter/s, 3.54935s/8 iters), loss = 1.65903
I0401 13:22:46.689043 21213 solver.cpp:237]     Train net output #0: loss = 1.65903 (* 1 = 1.65903 loss)
I0401 13:22:46.689050 21213 sgd_solver.cpp:105] Iteration 5944, lr = 0.001
I0401 13:22:49.581717 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5952.caffemodel
I0401 13:22:52.763007 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5952.solverstate
I0401 13:22:55.132159 21213 solver.cpp:330] Iteration 5952, Testing net (#0)
I0401 13:22:55.132181 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:22:55.749205 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:22:57.365612 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0769231
I0401 13:22:57.365648 21213 solver.cpp:397]     Test net output #1: loss = 5.53962 (* 1 = 5.53962 loss)
I0401 13:22:57.512670 21213 solver.cpp:218] Iteration 5952 (0.739525 iter/s, 10.8178s/8 iters), loss = 1.54535
I0401 13:22:57.512724 21213 solver.cpp:237]     Train net output #0: loss = 1.54535 (* 1 = 1.54535 loss)
I0401 13:22:57.512732 21213 sgd_solver.cpp:105] Iteration 5952, lr = 0.001
I0401 13:23:00.211100 21213 solver.cpp:218] Iteration 5960 (2.96476 iter/s, 2.69836s/8 iters), loss = 1.39577
I0401 13:23:00.211144 21213 solver.cpp:237]     Train net output #0: loss = 1.39577 (* 1 = 1.39577 loss)
I0401 13:23:00.211149 21213 sgd_solver.cpp:105] Iteration 5960, lr = 0.001
I0401 13:23:03.846735 21213 solver.cpp:218] Iteration 5968 (2.20048 iter/s, 3.63558s/8 iters), loss = 1.35543
I0401 13:23:03.846774 21213 solver.cpp:237]     Train net output #0: loss = 1.35543 (* 1 = 1.35543 loss)
I0401 13:23:03.846779 21213 sgd_solver.cpp:105] Iteration 5968, lr = 0.001
I0401 13:23:07.315527 21213 solver.cpp:218] Iteration 5976 (2.30631 iter/s, 3.46874s/8 iters), loss = 1.01974
I0401 13:23:07.315567 21213 solver.cpp:237]     Train net output #0: loss = 1.01974 (* 1 = 1.01974 loss)
I0401 13:23:07.315572 21213 sgd_solver.cpp:105] Iteration 5976, lr = 0.001
I0401 13:23:10.849400 21213 solver.cpp:218] Iteration 5984 (2.26384 iter/s, 3.53381s/8 iters), loss = 1.25555
I0401 13:23:10.849452 21213 solver.cpp:237]     Train net output #0: loss = 1.25555 (* 1 = 1.25555 loss)
I0401 13:23:10.849462 21213 sgd_solver.cpp:105] Iteration 5984, lr = 0.001
I0401 13:23:14.479029 21213 solver.cpp:218] Iteration 5992 (2.20412 iter/s, 3.62956s/8 iters), loss = 1.67729
I0401 13:23:14.479071 21213 solver.cpp:237]     Train net output #0: loss = 1.67729 (* 1 = 1.67729 loss)
I0401 13:23:14.479076 21213 sgd_solver.cpp:105] Iteration 5992, lr = 0.001
I0401 13:23:18.179472 21213 solver.cpp:218] Iteration 6000 (2.16194 iter/s, 3.70038s/8 iters), loss = 1.11417
I0401 13:23:18.179529 21213 solver.cpp:237]     Train net output #0: loss = 1.11417 (* 1 = 1.11417 loss)
I0401 13:23:18.179538 21213 sgd_solver.cpp:105] Iteration 6000, lr = 0.001
I0401 13:23:20.197005 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:23:21.677032 21213 solver.cpp:218] Iteration 6008 (2.28735 iter/s, 3.49749s/8 iters), loss = 1.40402
I0401 13:23:21.681632 21213 solver.cpp:237]     Train net output #0: loss = 1.40402 (* 1 = 1.40402 loss)
I0401 13:23:21.681649 21213 sgd_solver.cpp:105] Iteration 6008, lr = 0.001
I0401 13:23:25.018937 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6016.caffemodel
I0401 13:23:30.702647 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6016.solverstate
I0401 13:23:37.298128 21213 solver.cpp:330] Iteration 6016, Testing net (#0)
I0401 13:23:37.298153 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:23:37.902779 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:23:39.773535 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0697115
I0401 13:23:39.773572 21213 solver.cpp:397]     Test net output #1: loss = 5.54781 (* 1 = 5.54781 loss)
I0401 13:23:39.915038 21213 solver.cpp:218] Iteration 6016 (0.438755 iter/s, 18.2334s/8 iters), loss = 1.66463
I0401 13:23:39.915081 21213 solver.cpp:237]     Train net output #0: loss = 1.66463 (* 1 = 1.66463 loss)
I0401 13:23:39.915087 21213 sgd_solver.cpp:105] Iteration 6016, lr = 0.001
I0401 13:23:42.723451 21213 solver.cpp:218] Iteration 6024 (2.84864 iter/s, 2.80835s/8 iters), loss = 1.1353
I0401 13:23:42.723505 21213 solver.cpp:237]     Train net output #0: loss = 1.1353 (* 1 = 1.1353 loss)
I0401 13:23:42.723513 21213 sgd_solver.cpp:105] Iteration 6024, lr = 0.001
I0401 13:23:46.099706 21213 solver.cpp:218] Iteration 6032 (2.36954 iter/s, 3.37619s/8 iters), loss = 1.41856
I0401 13:23:46.099759 21213 solver.cpp:237]     Train net output #0: loss = 1.41856 (* 1 = 1.41856 loss)
I0401 13:23:46.099767 21213 sgd_solver.cpp:105] Iteration 6032, lr = 0.001
I0401 13:23:49.869382 21213 solver.cpp:218] Iteration 6040 (2.12224 iter/s, 3.76961s/8 iters), loss = 0.887843
I0401 13:23:49.869434 21213 solver.cpp:237]     Train net output #0: loss = 0.887843 (* 1 = 0.887843 loss)
I0401 13:23:49.869442 21213 sgd_solver.cpp:105] Iteration 6040, lr = 0.001
I0401 13:23:53.605145 21213 solver.cpp:218] Iteration 6048 (2.14151 iter/s, 3.73569s/8 iters), loss = 1.34395
I0401 13:23:53.605204 21213 solver.cpp:237]     Train net output #0: loss = 1.34395 (* 1 = 1.34395 loss)
I0401 13:23:53.605212 21213 sgd_solver.cpp:105] Iteration 6048, lr = 0.001
I0401 13:23:57.549607 21213 solver.cpp:218] Iteration 6056 (2.0282 iter/s, 3.94439s/8 iters), loss = 1.29793
I0401 13:23:57.549780 21213 solver.cpp:237]     Train net output #0: loss = 1.29793 (* 1 = 1.29793 loss)
I0401 13:23:57.549790 21213 sgd_solver.cpp:105] Iteration 6056, lr = 0.001
I0401 13:24:01.379096 21213 solver.cpp:218] Iteration 6064 (2.08915 iter/s, 3.8293s/8 iters), loss = 1.3548
I0401 13:24:01.379148 21213 solver.cpp:237]     Train net output #0: loss = 1.3548 (* 1 = 1.3548 loss)
I0401 13:24:01.379155 21213 sgd_solver.cpp:105] Iteration 6064, lr = 0.001
I0401 13:24:02.990168 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:24:04.972556 21213 solver.cpp:218] Iteration 6072 (2.22631 iter/s, 3.5934s/8 iters), loss = 1.49307
I0401 13:24:04.972604 21213 solver.cpp:237]     Train net output #0: loss = 1.49307 (* 1 = 1.49307 loss)
I0401 13:24:04.972610 21213 sgd_solver.cpp:105] Iteration 6072, lr = 0.001
I0401 13:24:08.204623 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6080.caffemodel
I0401 13:24:11.202761 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6080.solverstate
I0401 13:24:13.517477 21213 solver.cpp:330] Iteration 6080, Testing net (#0)
I0401 13:24:13.517499 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:24:13.991832 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:24:15.596740 21213 blocking_queue.cpp:49] Waiting for data
I0401 13:24:15.685791 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:24:15.685830 21213 solver.cpp:397]     Test net output #1: loss = 5.65458 (* 1 = 5.65458 loss)
I0401 13:24:15.827040 21213 solver.cpp:218] Iteration 6080 (0.737027 iter/s, 10.8544s/8 iters), loss = 1.5647
I0401 13:24:15.827096 21213 solver.cpp:237]     Train net output #0: loss = 1.5647 (* 1 = 1.5647 loss)
I0401 13:24:15.827105 21213 sgd_solver.cpp:105] Iteration 6080, lr = 0.001
I0401 13:24:18.517693 21213 solver.cpp:218] Iteration 6088 (2.97333 iter/s, 2.69058s/8 iters), loss = 0.997285
I0401 13:24:18.517743 21213 solver.cpp:237]     Train net output #0: loss = 0.997285 (* 1 = 0.997285 loss)
I0401 13:24:18.517750 21213 sgd_solver.cpp:105] Iteration 6088, lr = 0.001
I0401 13:24:22.137346 21213 solver.cpp:218] Iteration 6096 (2.21019 iter/s, 3.6196s/8 iters), loss = 1.32793
I0401 13:24:22.137385 21213 solver.cpp:237]     Train net output #0: loss = 1.32793 (* 1 = 1.32793 loss)
I0401 13:24:22.137390 21213 sgd_solver.cpp:105] Iteration 6096, lr = 0.001
I0401 13:24:25.671639 21213 solver.cpp:218] Iteration 6104 (2.26357 iter/s, 3.53424s/8 iters), loss = 0.888341
I0401 13:24:25.671684 21213 solver.cpp:237]     Train net output #0: loss = 0.888341 (* 1 = 0.888341 loss)
I0401 13:24:25.671689 21213 sgd_solver.cpp:105] Iteration 6104, lr = 0.001
I0401 13:24:29.431841 21213 solver.cpp:218] Iteration 6112 (2.12758 iter/s, 3.76014s/8 iters), loss = 1.23708
I0401 13:24:29.431967 21213 solver.cpp:237]     Train net output #0: loss = 1.23708 (* 1 = 1.23708 loss)
I0401 13:24:29.431977 21213 sgd_solver.cpp:105] Iteration 6112, lr = 0.001
I0401 13:24:32.786352 21213 solver.cpp:218] Iteration 6120 (2.38495 iter/s, 3.35437s/8 iters), loss = 1.32274
I0401 13:24:32.786401 21213 solver.cpp:237]     Train net output #0: loss = 1.32274 (* 1 = 1.32274 loss)
I0401 13:24:32.786409 21213 sgd_solver.cpp:105] Iteration 6120, lr = 0.001
I0401 13:24:36.617498 21213 solver.cpp:218] Iteration 6128 (2.08818 iter/s, 3.83109s/8 iters), loss = 1.09601
I0401 13:24:36.617539 21213 solver.cpp:237]     Train net output #0: loss = 1.09601 (* 1 = 1.09601 loss)
I0401 13:24:36.617545 21213 sgd_solver.cpp:105] Iteration 6128, lr = 0.001
I0401 13:24:37.910778 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:24:40.099323 21213 solver.cpp:218] Iteration 6136 (2.29768 iter/s, 3.48177s/8 iters), loss = 1.5519
I0401 13:24:40.099362 21213 solver.cpp:237]     Train net output #0: loss = 1.5519 (* 1 = 1.5519 loss)
I0401 13:24:40.099368 21213 sgd_solver.cpp:105] Iteration 6136, lr = 0.001
I0401 13:24:43.098879 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6144.caffemodel
I0401 13:24:46.192806 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6144.solverstate
I0401 13:24:48.525789 21213 solver.cpp:330] Iteration 6144, Testing net (#0)
I0401 13:24:48.525813 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:24:48.936823 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:24:50.852957 21213 solver.cpp:397]     Test net output #0: accuracy = 0.078125
I0401 13:24:50.852990 21213 solver.cpp:397]     Test net output #1: loss = 5.66656 (* 1 = 5.66656 loss)
I0401 13:24:50.995199 21213 solver.cpp:218] Iteration 6144 (0.734226 iter/s, 10.8958s/8 iters), loss = 1.31304
I0401 13:24:50.995256 21213 solver.cpp:237]     Train net output #0: loss = 1.31304 (* 1 = 1.31304 loss)
I0401 13:24:50.995262 21213 sgd_solver.cpp:105] Iteration 6144, lr = 0.001
I0401 13:24:53.745028 21213 solver.cpp:218] Iteration 6152 (2.90935 iter/s, 2.74975s/8 iters), loss = 1.31431
I0401 13:24:53.751185 21213 solver.cpp:237]     Train net output #0: loss = 1.31431 (* 1 = 1.31431 loss)
I0401 13:24:53.751204 21213 sgd_solver.cpp:105] Iteration 6152, lr = 0.001
I0401 13:24:57.109148 21213 solver.cpp:218] Iteration 6160 (2.3824 iter/s, 3.35796s/8 iters), loss = 1.30098
I0401 13:24:57.109187 21213 solver.cpp:237]     Train net output #0: loss = 1.30098 (* 1 = 1.30098 loss)
I0401 13:24:57.109192 21213 sgd_solver.cpp:105] Iteration 6160, lr = 0.001
I0401 13:25:00.802795 21213 solver.cpp:218] Iteration 6168 (2.16591 iter/s, 3.69359s/8 iters), loss = 1.01689
I0401 13:25:00.802966 21213 solver.cpp:237]     Train net output #0: loss = 1.01689 (* 1 = 1.01689 loss)
I0401 13:25:00.802975 21213 sgd_solver.cpp:105] Iteration 6168, lr = 0.001
I0401 13:25:04.434686 21213 solver.cpp:218] Iteration 6176 (2.20282 iter/s, 3.63171s/8 iters), loss = 1.14361
I0401 13:25:04.434736 21213 solver.cpp:237]     Train net output #0: loss = 1.14361 (* 1 = 1.14361 loss)
I0401 13:25:04.434741 21213 sgd_solver.cpp:105] Iteration 6176, lr = 0.001
I0401 13:25:08.019891 21213 solver.cpp:218] Iteration 6184 (2.23143 iter/s, 3.58514s/8 iters), loss = 1.02588
I0401 13:25:08.019944 21213 solver.cpp:237]     Train net output #0: loss = 1.02588 (* 1 = 1.02588 loss)
I0401 13:25:08.019953 21213 sgd_solver.cpp:105] Iteration 6184, lr = 0.001
I0401 13:25:11.367586 21213 solver.cpp:218] Iteration 6192 (2.38975 iter/s, 3.34763s/8 iters), loss = 1.17968
I0401 13:25:11.367640 21213 solver.cpp:237]     Train net output #0: loss = 1.17968 (* 1 = 1.17968 loss)
I0401 13:25:11.367647 21213 sgd_solver.cpp:105] Iteration 6192, lr = 0.001
I0401 13:25:12.434689 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:25:15.007695 21213 solver.cpp:218] Iteration 6200 (2.19777 iter/s, 3.64005s/8 iters), loss = 1.38652
I0401 13:25:15.007735 21213 solver.cpp:237]     Train net output #0: loss = 1.38652 (* 1 = 1.38652 loss)
I0401 13:25:15.007740 21213 sgd_solver.cpp:105] Iteration 6200, lr = 0.001
I0401 13:25:18.004400 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6208.caffemodel
I0401 13:25:22.476189 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6208.solverstate
I0401 13:25:24.960323 21213 solver.cpp:330] Iteration 6208, Testing net (#0)
I0401 13:25:24.960347 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:25:25.254007 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:25:27.244688 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0745192
I0401 13:25:27.244726 21213 solver.cpp:397]     Test net output #1: loss = 5.63655 (* 1 = 5.63655 loss)
I0401 13:25:27.383301 21213 solver.cpp:218] Iteration 6208 (0.646435 iter/s, 12.3756s/8 iters), loss = 1.4443
I0401 13:25:27.383347 21213 solver.cpp:237]     Train net output #0: loss = 1.4443 (* 1 = 1.4443 loss)
I0401 13:25:27.383353 21213 sgd_solver.cpp:105] Iteration 6208, lr = 0.001
I0401 13:25:30.020615 21213 solver.cpp:218] Iteration 6216 (3.03346 iter/s, 2.63725s/8 iters), loss = 1.01349
I0401 13:25:30.020656 21213 solver.cpp:237]     Train net output #0: loss = 1.01349 (* 1 = 1.01349 loss)
I0401 13:25:30.020661 21213 sgd_solver.cpp:105] Iteration 6216, lr = 0.001
I0401 13:25:33.402585 21213 solver.cpp:218] Iteration 6224 (2.36553 iter/s, 3.38191s/8 iters), loss = 1.33614
I0401 13:25:33.403007 21213 solver.cpp:237]     Train net output #0: loss = 1.33614 (* 1 = 1.33614 loss)
I0401 13:25:33.403017 21213 sgd_solver.cpp:105] Iteration 6224, lr = 0.001
I0401 13:25:37.049216 21213 solver.cpp:218] Iteration 6232 (2.19407 iter/s, 3.6462s/8 iters), loss = 1.17772
I0401 13:25:37.049271 21213 solver.cpp:237]     Train net output #0: loss = 1.17772 (* 1 = 1.17772 loss)
I0401 13:25:37.049280 21213 sgd_solver.cpp:105] Iteration 6232, lr = 0.001
I0401 13:25:40.534289 21213 solver.cpp:218] Iteration 6240 (2.29555 iter/s, 3.48501s/8 iters), loss = 1.20379
I0401 13:25:40.534327 21213 solver.cpp:237]     Train net output #0: loss = 1.20379 (* 1 = 1.20379 loss)
I0401 13:25:40.534333 21213 sgd_solver.cpp:105] Iteration 6240, lr = 0.001
I0401 13:25:43.875501 21213 solver.cpp:218] Iteration 6248 (2.39438 iter/s, 3.34116s/8 iters), loss = 1.07538
I0401 13:25:43.875555 21213 solver.cpp:237]     Train net output #0: loss = 1.07538 (* 1 = 1.07538 loss)
I0401 13:25:43.875563 21213 sgd_solver.cpp:105] Iteration 6248, lr = 0.001
I0401 13:25:47.615954 21213 solver.cpp:218] Iteration 6256 (2.13882 iter/s, 3.74039s/8 iters), loss = 0.913809
I0401 13:25:47.616008 21213 solver.cpp:237]     Train net output #0: loss = 0.913809 (* 1 = 0.913809 loss)
I0401 13:25:47.616017 21213 sgd_solver.cpp:105] Iteration 6256, lr = 0.001
I0401 13:25:48.247423 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:25:51.169036 21213 solver.cpp:218] Iteration 6264 (2.25161 iter/s, 3.55302s/8 iters), loss = 1.10148
I0401 13:25:51.169092 21213 solver.cpp:237]     Train net output #0: loss = 1.10148 (* 1 = 1.10148 loss)
I0401 13:25:51.169101 21213 sgd_solver.cpp:105] Iteration 6264, lr = 0.001
I0401 13:25:54.306658 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6272.caffemodel
I0401 13:25:57.420347 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6272.solverstate
I0401 13:26:01.073848 21213 solver.cpp:330] Iteration 6272, Testing net (#0)
I0401 13:26:01.073874 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:26:01.305147 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:26:03.255442 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0829327
I0401 13:26:03.255476 21213 solver.cpp:397]     Test net output #1: loss = 5.68328 (* 1 = 5.68328 loss)
I0401 13:26:03.396279 21213 solver.cpp:218] Iteration 6272 (0.65428 iter/s, 12.2272s/8 iters), loss = 1.07077
I0401 13:26:03.396322 21213 solver.cpp:237]     Train net output #0: loss = 1.07077 (* 1 = 1.07077 loss)
I0401 13:26:03.396328 21213 sgd_solver.cpp:105] Iteration 6272, lr = 0.001
I0401 13:26:06.002214 21213 solver.cpp:218] Iteration 6280 (3.06999 iter/s, 2.60587s/8 iters), loss = 1.15314
I0401 13:26:06.008363 21213 solver.cpp:237]     Train net output #0: loss = 1.15314 (* 1 = 1.15314 loss)
I0401 13:26:06.008378 21213 sgd_solver.cpp:105] Iteration 6280, lr = 0.001
I0401 13:26:09.593066 21213 solver.cpp:218] Iteration 6288 (2.23171 iter/s, 3.5847s/8 iters), loss = 1.11287
I0401 13:26:09.593118 21213 solver.cpp:237]     Train net output #0: loss = 1.11287 (* 1 = 1.11287 loss)
I0401 13:26:09.593127 21213 sgd_solver.cpp:105] Iteration 6288, lr = 0.001
I0401 13:26:13.258955 21213 solver.cpp:218] Iteration 6296 (2.18232 iter/s, 3.66582s/8 iters), loss = 1.21022
I0401 13:26:13.258996 21213 solver.cpp:237]     Train net output #0: loss = 1.21022 (* 1 = 1.21022 loss)
I0401 13:26:13.259002 21213 sgd_solver.cpp:105] Iteration 6296, lr = 0.001
I0401 13:26:16.875324 21213 solver.cpp:218] Iteration 6304 (2.2122 iter/s, 3.61631s/8 iters), loss = 0.896351
I0401 13:26:16.875391 21213 solver.cpp:237]     Train net output #0: loss = 0.896351 (* 1 = 0.896351 loss)
I0401 13:26:16.875401 21213 sgd_solver.cpp:105] Iteration 6304, lr = 0.001
I0401 13:26:20.503887 21213 solver.cpp:218] Iteration 6312 (2.20478 iter/s, 3.62848s/8 iters), loss = 0.95429
I0401 13:26:20.503942 21213 solver.cpp:237]     Train net output #0: loss = 0.95429 (* 1 = 0.95429 loss)
I0401 13:26:20.503950 21213 sgd_solver.cpp:105] Iteration 6312, lr = 0.001
I0401 13:26:24.159708 21213 solver.cpp:218] Iteration 6320 (2.18833 iter/s, 3.65575s/8 iters), loss = 0.933159
I0401 13:26:24.159751 21213 solver.cpp:237]     Train net output #0: loss = 0.933159 (* 1 = 0.933159 loss)
I0401 13:26:24.159757 21213 sgd_solver.cpp:105] Iteration 6320, lr = 0.001
I0401 13:26:24.424214 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:26:27.703212 21213 solver.cpp:218] Iteration 6328 (2.25769 iter/s, 3.54345s/8 iters), loss = 0.973426
I0401 13:26:27.703259 21213 solver.cpp:237]     Train net output #0: loss = 0.973426 (* 1 = 0.973426 loss)
I0401 13:26:27.703267 21213 sgd_solver.cpp:105] Iteration 6328, lr = 0.001
I0401 13:26:30.757133 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6336.caffemodel
I0401 13:26:33.805389 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6336.solverstate
I0401 13:26:36.138049 21213 solver.cpp:330] Iteration 6336, Testing net (#0)
I0401 13:26:36.138160 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:26:36.326248 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:26:38.432276 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0865385
I0401 13:26:38.432314 21213 solver.cpp:397]     Test net output #1: loss = 5.68247 (* 1 = 5.68247 loss)
I0401 13:26:38.567076 21213 solver.cpp:218] Iteration 6336 (0.73639 iter/s, 10.8638s/8 iters), loss = 1.34543
I0401 13:26:38.567129 21213 solver.cpp:237]     Train net output #0: loss = 1.34543 (* 1 = 1.34543 loss)
I0401 13:26:38.567137 21213 sgd_solver.cpp:105] Iteration 6336, lr = 0.001
I0401 13:26:41.130646 21213 solver.cpp:218] Iteration 6344 (3.12073 iter/s, 2.5635s/8 iters), loss = 1.03963
I0401 13:26:41.130690 21213 solver.cpp:237]     Train net output #0: loss = 1.03963 (* 1 = 1.03963 loss)
I0401 13:26:41.130697 21213 sgd_solver.cpp:105] Iteration 6344, lr = 0.001
I0401 13:26:44.883013 21213 solver.cpp:218] Iteration 6352 (2.13202 iter/s, 3.7523s/8 iters), loss = 1.26777
I0401 13:26:44.883062 21213 solver.cpp:237]     Train net output #0: loss = 1.26777 (* 1 = 1.26777 loss)
I0401 13:26:44.883069 21213 sgd_solver.cpp:105] Iteration 6352, lr = 0.001
I0401 13:26:48.518610 21213 solver.cpp:218] Iteration 6360 (2.2005 iter/s, 3.63554s/8 iters), loss = 0.890187
I0401 13:26:48.518653 21213 solver.cpp:237]     Train net output #0: loss = 0.890187 (* 1 = 0.890187 loss)
I0401 13:26:48.518659 21213 sgd_solver.cpp:105] Iteration 6360, lr = 0.001
I0401 13:26:52.122143 21213 solver.cpp:218] Iteration 6368 (2.22008 iter/s, 3.60347s/8 iters), loss = 0.874026
I0401 13:26:52.122198 21213 solver.cpp:237]     Train net output #0: loss = 0.874026 (* 1 = 0.874026 loss)
I0401 13:26:52.122206 21213 sgd_solver.cpp:105] Iteration 6368, lr = 0.001
I0401 13:26:55.570351 21213 solver.cpp:218] Iteration 6376 (2.32009 iter/s, 3.44814s/8 iters), loss = 1.01363
I0401 13:26:55.570399 21213 solver.cpp:237]     Train net output #0: loss = 1.01363 (* 1 = 1.01363 loss)
I0401 13:26:55.570405 21213 sgd_solver.cpp:105] Iteration 6376, lr = 0.001
I0401 13:26:59.123484 21213 solver.cpp:218] Iteration 6384 (2.25157 iter/s, 3.55307s/8 iters), loss = 0.79616
I0401 13:26:59.123535 21213 solver.cpp:237]     Train net output #0: loss = 0.79616 (* 1 = 0.79616 loss)
I0401 13:26:59.123544 21213 sgd_solver.cpp:105] Iteration 6384, lr = 0.001
I0401 13:26:59.148540 21278 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:27:02.664139 21213 solver.cpp:218] Iteration 6392 (2.25951 iter/s, 3.54059s/8 iters), loss = 0.964876
I0401 13:27:02.664192 21213 solver.cpp:237]     Train net output #0: loss = 0.964876 (* 1 = 0.964876 loss)
I0401 13:27:02.664201 21213 sgd_solver.cpp:105] Iteration 6392, lr = 0.001
I0401 13:27:05.727663 21213 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6400.caffemodel
I0401 13:27:08.771731 21213 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6400.solverstate
I0401 13:27:11.138764 21213 solver.cpp:310] Iteration 6400, loss = 1.38654
I0401 13:27:11.138787 21213 solver.cpp:330] Iteration 6400, Testing net (#0)
I0401 13:27:11.138792 21213 net.cpp:676] Ignoring source layer train-data
I0401 13:27:11.252130 21306 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:27:13.403059 21213 solver.cpp:397]     Test net output #0: accuracy = 0.0829327
I0401 13:27:13.403096 21213 solver.cpp:397]     Test net output #1: loss = 5.76942 (* 1 = 5.76942 loss)
I0401 13:27:13.403101 21213 solver.cpp:315] Optimization Done.
I0401 13:27:13.403105 21213 caffe.cpp:259] Optimization Done.