I0407 08:22:57.765661 15775 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210407-082255-de1f/solver.prototxt
I0407 08:22:57.765802 15775 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 08:22:57.765806 15775 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 08:22:57.765862 15775 caffe.cpp:218] Using GPUs 0
I0407 08:22:57.785729 15775 caffe.cpp:223] GPU 0: GeForce GTX TITAN X
I0407 08:22:57.994544 15775 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0001
stepsize: 3366
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 08:22:57.995386 15775 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 08:22:57.996129 15775 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 08:22:57.996142 15775 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 08:22:57.996258 15775 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/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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"
}
I0407 08:22:57.996333 15775 layer_factory.hpp:77] Creating layer train-data
I0407 08:22:58.088107 15775 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0407 08:22:58.088389 15775 net.cpp:84] Creating Layer train-data
I0407 08:22:58.088419 15775 net.cpp:380] train-data -> data
I0407 08:22:58.088460 15775 net.cpp:380] train-data -> label
I0407 08:22:58.088483 15775 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:22:58.096284 15775 data_layer.cpp:45] output data size: 128,3,227,227
I0407 08:22:58.259054 15775 net.cpp:122] Setting up train-data
I0407 08:22:58.259081 15775 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 08:22:58.259086 15775 net.cpp:129] Top shape: 128 (128)
I0407 08:22:58.259088 15775 net.cpp:137] Memory required for data: 79149056
I0407 08:22:58.259097 15775 layer_factory.hpp:77] Creating layer conv1
I0407 08:22:58.259119 15775 net.cpp:84] Creating Layer conv1
I0407 08:22:58.259124 15775 net.cpp:406] conv1 <- data
I0407 08:22:58.259135 15775 net.cpp:380] conv1 -> conv1
I0407 08:22:58.706866 15775 net.cpp:122] Setting up conv1
I0407 08:22:58.706888 15775 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:22:58.706892 15775 net.cpp:137] Memory required for data: 227833856
I0407 08:22:58.706913 15775 layer_factory.hpp:77] Creating layer relu1
I0407 08:22:58.706923 15775 net.cpp:84] Creating Layer relu1
I0407 08:22:58.706928 15775 net.cpp:406] relu1 <- conv1
I0407 08:22:58.706933 15775 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:22:58.707221 15775 net.cpp:122] Setting up relu1
I0407 08:22:58.707231 15775 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:22:58.707232 15775 net.cpp:137] Memory required for data: 376518656
I0407 08:22:58.707235 15775 layer_factory.hpp:77] Creating layer norm1
I0407 08:22:58.707244 15775 net.cpp:84] Creating Layer norm1
I0407 08:22:58.707270 15775 net.cpp:406] norm1 <- conv1
I0407 08:22:58.707275 15775 net.cpp:380] norm1 -> norm1
I0407 08:22:58.707754 15775 net.cpp:122] Setting up norm1
I0407 08:22:58.707764 15775 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:22:58.707767 15775 net.cpp:137] Memory required for data: 525203456
I0407 08:22:58.707770 15775 layer_factory.hpp:77] Creating layer pool1
I0407 08:22:58.707777 15775 net.cpp:84] Creating Layer pool1
I0407 08:22:58.707779 15775 net.cpp:406] pool1 <- norm1
I0407 08:22:58.707783 15775 net.cpp:380] pool1 -> pool1
I0407 08:22:58.707818 15775 net.cpp:122] Setting up pool1
I0407 08:22:58.707823 15775 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 08:22:58.707825 15775 net.cpp:137] Memory required for data: 561035264
I0407 08:22:58.707828 15775 layer_factory.hpp:77] Creating layer conv2
I0407 08:22:58.707837 15775 net.cpp:84] Creating Layer conv2
I0407 08:22:58.707840 15775 net.cpp:406] conv2 <- pool1
I0407 08:22:58.707844 15775 net.cpp:380] conv2 -> conv2
I0407 08:22:58.713976 15775 net.cpp:122] Setting up conv2
I0407 08:22:58.713989 15775 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:22:58.713990 15775 net.cpp:137] Memory required for data: 656586752
I0407 08:22:58.713999 15775 layer_factory.hpp:77] Creating layer relu2
I0407 08:22:58.714004 15775 net.cpp:84] Creating Layer relu2
I0407 08:22:58.714007 15775 net.cpp:406] relu2 <- conv2
I0407 08:22:58.714011 15775 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:22:58.714457 15775 net.cpp:122] Setting up relu2
I0407 08:22:58.714468 15775 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:22:58.714469 15775 net.cpp:137] Memory required for data: 752138240
I0407 08:22:58.714473 15775 layer_factory.hpp:77] Creating layer norm2
I0407 08:22:58.714478 15775 net.cpp:84] Creating Layer norm2
I0407 08:22:58.714480 15775 net.cpp:406] norm2 <- conv2
I0407 08:22:58.714484 15775 net.cpp:380] norm2 -> norm2
I0407 08:22:58.714797 15775 net.cpp:122] Setting up norm2
I0407 08:22:58.714807 15775 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:22:58.714808 15775 net.cpp:137] Memory required for data: 847689728
I0407 08:22:58.714812 15775 layer_factory.hpp:77] Creating layer pool2
I0407 08:22:58.714820 15775 net.cpp:84] Creating Layer pool2
I0407 08:22:58.714823 15775 net.cpp:406] pool2 <- norm2
I0407 08:22:58.714828 15775 net.cpp:380] pool2 -> pool2
I0407 08:22:58.714856 15775 net.cpp:122] Setting up pool2
I0407 08:22:58.714861 15775 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:22:58.714864 15775 net.cpp:137] Memory required for data: 869840896
I0407 08:22:58.714865 15775 layer_factory.hpp:77] Creating layer conv3
I0407 08:22:58.714874 15775 net.cpp:84] Creating Layer conv3
I0407 08:22:58.714876 15775 net.cpp:406] conv3 <- pool2
I0407 08:22:58.714881 15775 net.cpp:380] conv3 -> conv3
I0407 08:22:58.725339 15775 net.cpp:122] Setting up conv3
I0407 08:22:58.725354 15775 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:22:58.725358 15775 net.cpp:137] Memory required for data: 903067648
I0407 08:22:58.725366 15775 layer_factory.hpp:77] Creating layer relu3
I0407 08:22:58.725373 15775 net.cpp:84] Creating Layer relu3
I0407 08:22:58.725375 15775 net.cpp:406] relu3 <- conv3
I0407 08:22:58.725381 15775 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:22:58.725899 15775 net.cpp:122] Setting up relu3
I0407 08:22:58.725909 15775 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:22:58.725912 15775 net.cpp:137] Memory required for data: 936294400
I0407 08:22:58.725915 15775 layer_factory.hpp:77] Creating layer conv4
I0407 08:22:58.725924 15775 net.cpp:84] Creating Layer conv4
I0407 08:22:58.725926 15775 net.cpp:406] conv4 <- conv3
I0407 08:22:58.725931 15775 net.cpp:380] conv4 -> conv4
I0407 08:22:58.737085 15775 net.cpp:122] Setting up conv4
I0407 08:22:58.737100 15775 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:22:58.737103 15775 net.cpp:137] Memory required for data: 969521152
I0407 08:22:58.737110 15775 layer_factory.hpp:77] Creating layer relu4
I0407 08:22:58.737118 15775 net.cpp:84] Creating Layer relu4
I0407 08:22:58.737139 15775 net.cpp:406] relu4 <- conv4
I0407 08:22:58.737146 15775 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:22:58.737488 15775 net.cpp:122] Setting up relu4
I0407 08:22:58.737496 15775 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:22:58.737498 15775 net.cpp:137] Memory required for data: 1002747904
I0407 08:22:58.737501 15775 layer_factory.hpp:77] Creating layer conv5
I0407 08:22:58.737512 15775 net.cpp:84] Creating Layer conv5
I0407 08:22:58.737515 15775 net.cpp:406] conv5 <- conv4
I0407 08:22:58.737520 15775 net.cpp:380] conv5 -> conv5
I0407 08:22:58.746017 15775 net.cpp:122] Setting up conv5
I0407 08:22:58.746031 15775 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:22:58.746033 15775 net.cpp:137] Memory required for data: 1024899072
I0407 08:22:58.746045 15775 layer_factory.hpp:77] Creating layer relu5
I0407 08:22:58.746052 15775 net.cpp:84] Creating Layer relu5
I0407 08:22:58.746054 15775 net.cpp:406] relu5 <- conv5
I0407 08:22:58.746060 15775 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:22:58.746572 15775 net.cpp:122] Setting up relu5
I0407 08:22:58.746582 15775 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:22:58.746584 15775 net.cpp:137] Memory required for data: 1047050240
I0407 08:22:58.746587 15775 layer_factory.hpp:77] Creating layer pool5
I0407 08:22:58.746593 15775 net.cpp:84] Creating Layer pool5
I0407 08:22:58.746596 15775 net.cpp:406] pool5 <- conv5
I0407 08:22:58.746600 15775 net.cpp:380] pool5 -> pool5
I0407 08:22:58.746637 15775 net.cpp:122] Setting up pool5
I0407 08:22:58.746642 15775 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 08:22:58.746644 15775 net.cpp:137] Memory required for data: 1051768832
I0407 08:22:58.746646 15775 layer_factory.hpp:77] Creating layer fc6
I0407 08:22:58.746657 15775 net.cpp:84] Creating Layer fc6
I0407 08:22:58.746659 15775 net.cpp:406] fc6 <- pool5
I0407 08:22:58.746663 15775 net.cpp:380] fc6 -> fc6
I0407 08:22:59.085840 15775 net.cpp:122] Setting up fc6
I0407 08:22:59.085858 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.085861 15775 net.cpp:137] Memory required for data: 1053865984
I0407 08:22:59.085870 15775 layer_factory.hpp:77] Creating layer relu6
I0407 08:22:59.085877 15775 net.cpp:84] Creating Layer relu6
I0407 08:22:59.085880 15775 net.cpp:406] relu6 <- fc6
I0407 08:22:59.085891 15775 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:22:59.086488 15775 net.cpp:122] Setting up relu6
I0407 08:22:59.086498 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.086499 15775 net.cpp:137] Memory required for data: 1055963136
I0407 08:22:59.086503 15775 layer_factory.hpp:77] Creating layer drop6
I0407 08:22:59.086508 15775 net.cpp:84] Creating Layer drop6
I0407 08:22:59.086510 15775 net.cpp:406] drop6 <- fc6
I0407 08:22:59.086515 15775 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:22:59.086539 15775 net.cpp:122] Setting up drop6
I0407 08:22:59.086544 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.086545 15775 net.cpp:137] Memory required for data: 1058060288
I0407 08:22:59.086547 15775 layer_factory.hpp:77] Creating layer fc7
I0407 08:22:59.086555 15775 net.cpp:84] Creating Layer fc7
I0407 08:22:59.086557 15775 net.cpp:406] fc7 <- fc6
I0407 08:22:59.086560 15775 net.cpp:380] fc7 -> fc7
I0407 08:22:59.232445 15775 net.cpp:122] Setting up fc7
I0407 08:22:59.232462 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.232465 15775 net.cpp:137] Memory required for data: 1060157440
I0407 08:22:59.232473 15775 layer_factory.hpp:77] Creating layer relu7
I0407 08:22:59.232481 15775 net.cpp:84] Creating Layer relu7
I0407 08:22:59.232483 15775 net.cpp:406] relu7 <- fc7
I0407 08:22:59.232491 15775 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:22:59.232858 15775 net.cpp:122] Setting up relu7
I0407 08:22:59.232865 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.232867 15775 net.cpp:137] Memory required for data: 1062254592
I0407 08:22:59.232870 15775 layer_factory.hpp:77] Creating layer drop7
I0407 08:22:59.232877 15775 net.cpp:84] Creating Layer drop7
I0407 08:22:59.232901 15775 net.cpp:406] drop7 <- fc7
I0407 08:22:59.232905 15775 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:22:59.232928 15775 net.cpp:122] Setting up drop7
I0407 08:22:59.232933 15775 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:22:59.232934 15775 net.cpp:137] Memory required for data: 1064351744
I0407 08:22:59.232936 15775 layer_factory.hpp:77] Creating layer fc8
I0407 08:22:59.232942 15775 net.cpp:84] Creating Layer fc8
I0407 08:22:59.232945 15775 net.cpp:406] fc8 <- fc7
I0407 08:22:59.232950 15775 net.cpp:380] fc8 -> fc8
I0407 08:22:59.241638 15775 net.cpp:122] Setting up fc8
I0407 08:22:59.241647 15775 net.cpp:129] Top shape: 128 196 (25088)
I0407 08:22:59.241650 15775 net.cpp:137] Memory required for data: 1064452096
I0407 08:22:59.241655 15775 layer_factory.hpp:77] Creating layer loss
I0407 08:22:59.241662 15775 net.cpp:84] Creating Layer loss
I0407 08:22:59.241664 15775 net.cpp:406] loss <- fc8
I0407 08:22:59.241667 15775 net.cpp:406] loss <- label
I0407 08:22:59.241672 15775 net.cpp:380] loss -> loss
I0407 08:22:59.241680 15775 layer_factory.hpp:77] Creating layer loss
I0407 08:22:59.243239 15775 net.cpp:122] Setting up loss
I0407 08:22:59.243247 15775 net.cpp:129] Top shape: (1)
I0407 08:22:59.243249 15775 net.cpp:132]     with loss weight 1
I0407 08:22:59.243264 15775 net.cpp:137] Memory required for data: 1064452100
I0407 08:22:59.243268 15775 net.cpp:198] loss needs backward computation.
I0407 08:22:59.243273 15775 net.cpp:198] fc8 needs backward computation.
I0407 08:22:59.243275 15775 net.cpp:198] drop7 needs backward computation.
I0407 08:22:59.243278 15775 net.cpp:198] relu7 needs backward computation.
I0407 08:22:59.243279 15775 net.cpp:198] fc7 needs backward computation.
I0407 08:22:59.243281 15775 net.cpp:198] drop6 needs backward computation.
I0407 08:22:59.243284 15775 net.cpp:198] relu6 needs backward computation.
I0407 08:22:59.243286 15775 net.cpp:198] fc6 needs backward computation.
I0407 08:22:59.243289 15775 net.cpp:198] pool5 needs backward computation.
I0407 08:22:59.243292 15775 net.cpp:198] relu5 needs backward computation.
I0407 08:22:59.243294 15775 net.cpp:198] conv5 needs backward computation.
I0407 08:22:59.243297 15775 net.cpp:198] relu4 needs backward computation.
I0407 08:22:59.243299 15775 net.cpp:198] conv4 needs backward computation.
I0407 08:22:59.243301 15775 net.cpp:198] relu3 needs backward computation.
I0407 08:22:59.243304 15775 net.cpp:198] conv3 needs backward computation.
I0407 08:22:59.243307 15775 net.cpp:198] pool2 needs backward computation.
I0407 08:22:59.243309 15775 net.cpp:198] norm2 needs backward computation.
I0407 08:22:59.243311 15775 net.cpp:198] relu2 needs backward computation.
I0407 08:22:59.243314 15775 net.cpp:198] conv2 needs backward computation.
I0407 08:22:59.243316 15775 net.cpp:198] pool1 needs backward computation.
I0407 08:22:59.243319 15775 net.cpp:198] norm1 needs backward computation.
I0407 08:22:59.243321 15775 net.cpp:198] relu1 needs backward computation.
I0407 08:22:59.243324 15775 net.cpp:198] conv1 needs backward computation.
I0407 08:22:59.243326 15775 net.cpp:200] train-data does not need backward computation.
I0407 08:22:59.243328 15775 net.cpp:242] This network produces output loss
I0407 08:22:59.243340 15775 net.cpp:255] Network initialization done.
I0407 08:22:59.243872 15775 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 08:22:59.243899 15775 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 08:22:59.244029 15775 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/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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"
}
I0407 08:22:59.244122 15775 layer_factory.hpp:77] Creating layer val-data
I0407 08:22:59.256011 15775 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0407 08:22:59.256232 15775 net.cpp:84] Creating Layer val-data
I0407 08:22:59.256242 15775 net.cpp:380] val-data -> data
I0407 08:22:59.256253 15775 net.cpp:380] val-data -> label
I0407 08:22:59.256259 15775 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:22:59.260248 15775 data_layer.cpp:45] output data size: 32,3,227,227
I0407 08:22:59.301146 15775 net.cpp:122] Setting up val-data
I0407 08:22:59.301164 15775 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 08:22:59.301167 15775 net.cpp:129] Top shape: 32 (32)
I0407 08:22:59.301169 15775 net.cpp:137] Memory required for data: 19787264
I0407 08:22:59.301174 15775 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 08:22:59.301185 15775 net.cpp:84] Creating Layer label_val-data_1_split
I0407 08:22:59.301188 15775 net.cpp:406] label_val-data_1_split <- label
I0407 08:22:59.301193 15775 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 08:22:59.301201 15775 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 08:22:59.301267 15775 net.cpp:122] Setting up label_val-data_1_split
I0407 08:22:59.301272 15775 net.cpp:129] Top shape: 32 (32)
I0407 08:22:59.301275 15775 net.cpp:129] Top shape: 32 (32)
I0407 08:22:59.301276 15775 net.cpp:137] Memory required for data: 19787520
I0407 08:22:59.301278 15775 layer_factory.hpp:77] Creating layer conv1
I0407 08:22:59.301288 15775 net.cpp:84] Creating Layer conv1
I0407 08:22:59.301291 15775 net.cpp:406] conv1 <- data
I0407 08:22:59.301295 15775 net.cpp:380] conv1 -> conv1
I0407 08:22:59.303838 15775 net.cpp:122] Setting up conv1
I0407 08:22:59.303846 15775 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:22:59.303849 15775 net.cpp:137] Memory required for data: 56958720
I0407 08:22:59.303858 15775 layer_factory.hpp:77] Creating layer relu1
I0407 08:22:59.303862 15775 net.cpp:84] Creating Layer relu1
I0407 08:22:59.303865 15775 net.cpp:406] relu1 <- conv1
I0407 08:22:59.303869 15775 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:22:59.304126 15775 net.cpp:122] Setting up relu1
I0407 08:22:59.304133 15775 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:22:59.304136 15775 net.cpp:137] Memory required for data: 94129920
I0407 08:22:59.304137 15775 layer_factory.hpp:77] Creating layer norm1
I0407 08:22:59.304144 15775 net.cpp:84] Creating Layer norm1
I0407 08:22:59.304147 15775 net.cpp:406] norm1 <- conv1
I0407 08:22:59.304152 15775 net.cpp:380] norm1 -> norm1
I0407 08:22:59.304596 15775 net.cpp:122] Setting up norm1
I0407 08:22:59.304605 15775 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:22:59.304607 15775 net.cpp:137] Memory required for data: 131301120
I0407 08:22:59.304610 15775 layer_factory.hpp:77] Creating layer pool1
I0407 08:22:59.304615 15775 net.cpp:84] Creating Layer pool1
I0407 08:22:59.304618 15775 net.cpp:406] pool1 <- norm1
I0407 08:22:59.304622 15775 net.cpp:380] pool1 -> pool1
I0407 08:22:59.304646 15775 net.cpp:122] Setting up pool1
I0407 08:22:59.304651 15775 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 08:22:59.304652 15775 net.cpp:137] Memory required for data: 140259072
I0407 08:22:59.304656 15775 layer_factory.hpp:77] Creating layer conv2
I0407 08:22:59.304661 15775 net.cpp:84] Creating Layer conv2
I0407 08:22:59.304663 15775 net.cpp:406] conv2 <- pool1
I0407 08:22:59.304685 15775 net.cpp:380] conv2 -> conv2
I0407 08:22:59.310736 15775 net.cpp:122] Setting up conv2
I0407 08:22:59.310750 15775 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:22:59.310751 15775 net.cpp:137] Memory required for data: 164146944
I0407 08:22:59.310760 15775 layer_factory.hpp:77] Creating layer relu2
I0407 08:22:59.310765 15775 net.cpp:84] Creating Layer relu2
I0407 08:22:59.310767 15775 net.cpp:406] relu2 <- conv2
I0407 08:22:59.310773 15775 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:22:59.311266 15775 net.cpp:122] Setting up relu2
I0407 08:22:59.311275 15775 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:22:59.311276 15775 net.cpp:137] Memory required for data: 188034816
I0407 08:22:59.311280 15775 layer_factory.hpp:77] Creating layer norm2
I0407 08:22:59.311287 15775 net.cpp:84] Creating Layer norm2
I0407 08:22:59.311290 15775 net.cpp:406] norm2 <- conv2
I0407 08:22:59.311295 15775 net.cpp:380] norm2 -> norm2
I0407 08:22:59.311802 15775 net.cpp:122] Setting up norm2
I0407 08:22:59.311812 15775 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:22:59.311815 15775 net.cpp:137] Memory required for data: 211922688
I0407 08:22:59.311817 15775 layer_factory.hpp:77] Creating layer pool2
I0407 08:22:59.311822 15775 net.cpp:84] Creating Layer pool2
I0407 08:22:59.311825 15775 net.cpp:406] pool2 <- norm2
I0407 08:22:59.311830 15775 net.cpp:380] pool2 -> pool2
I0407 08:22:59.311857 15775 net.cpp:122] Setting up pool2
I0407 08:22:59.311861 15775 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:22:59.311863 15775 net.cpp:137] Memory required for data: 217460480
I0407 08:22:59.311866 15775 layer_factory.hpp:77] Creating layer conv3
I0407 08:22:59.311873 15775 net.cpp:84] Creating Layer conv3
I0407 08:22:59.311875 15775 net.cpp:406] conv3 <- pool2
I0407 08:22:59.311880 15775 net.cpp:380] conv3 -> conv3
I0407 08:22:59.321751 15775 net.cpp:122] Setting up conv3
I0407 08:22:59.321766 15775 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:22:59.321769 15775 net.cpp:137] Memory required for data: 225767168
I0407 08:22:59.321779 15775 layer_factory.hpp:77] Creating layer relu3
I0407 08:22:59.321784 15775 net.cpp:84] Creating Layer relu3
I0407 08:22:59.321787 15775 net.cpp:406] relu3 <- conv3
I0407 08:22:59.321794 15775 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:22:59.322284 15775 net.cpp:122] Setting up relu3
I0407 08:22:59.322293 15775 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:22:59.322294 15775 net.cpp:137] Memory required for data: 234073856
I0407 08:22:59.322297 15775 layer_factory.hpp:77] Creating layer conv4
I0407 08:22:59.322306 15775 net.cpp:84] Creating Layer conv4
I0407 08:22:59.322309 15775 net.cpp:406] conv4 <- conv3
I0407 08:22:59.322315 15775 net.cpp:380] conv4 -> conv4
I0407 08:22:59.331331 15775 net.cpp:122] Setting up conv4
I0407 08:22:59.331346 15775 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:22:59.331347 15775 net.cpp:137] Memory required for data: 242380544
I0407 08:22:59.331353 15775 layer_factory.hpp:77] Creating layer relu4
I0407 08:22:59.331359 15775 net.cpp:84] Creating Layer relu4
I0407 08:22:59.331362 15775 net.cpp:406] relu4 <- conv4
I0407 08:22:59.331367 15775 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:22:59.331679 15775 net.cpp:122] Setting up relu4
I0407 08:22:59.331686 15775 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:22:59.331688 15775 net.cpp:137] Memory required for data: 250687232
I0407 08:22:59.331691 15775 layer_factory.hpp:77] Creating layer conv5
I0407 08:22:59.331699 15775 net.cpp:84] Creating Layer conv5
I0407 08:22:59.331702 15775 net.cpp:406] conv5 <- conv4
I0407 08:22:59.331708 15775 net.cpp:380] conv5 -> conv5
I0407 08:22:59.339571 15775 net.cpp:122] Setting up conv5
I0407 08:22:59.339586 15775 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:22:59.339589 15775 net.cpp:137] Memory required for data: 256225024
I0407 08:22:59.339599 15775 layer_factory.hpp:77] Creating layer relu5
I0407 08:22:59.339607 15775 net.cpp:84] Creating Layer relu5
I0407 08:22:59.339627 15775 net.cpp:406] relu5 <- conv5
I0407 08:22:59.339633 15775 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:22:59.340127 15775 net.cpp:122] Setting up relu5
I0407 08:22:59.340137 15775 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:22:59.340138 15775 net.cpp:137] Memory required for data: 261762816
I0407 08:22:59.340142 15775 layer_factory.hpp:77] Creating layer pool5
I0407 08:22:59.340149 15775 net.cpp:84] Creating Layer pool5
I0407 08:22:59.340152 15775 net.cpp:406] pool5 <- conv5
I0407 08:22:59.340157 15775 net.cpp:380] pool5 -> pool5
I0407 08:22:59.340189 15775 net.cpp:122] Setting up pool5
I0407 08:22:59.340193 15775 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 08:22:59.340196 15775 net.cpp:137] Memory required for data: 262942464
I0407 08:22:59.340198 15775 layer_factory.hpp:77] Creating layer fc6
I0407 08:22:59.340205 15775 net.cpp:84] Creating Layer fc6
I0407 08:22:59.340207 15775 net.cpp:406] fc6 <- pool5
I0407 08:22:59.340212 15775 net.cpp:380] fc6 -> fc6
I0407 08:22:59.704593 15775 net.cpp:122] Setting up fc6
I0407 08:22:59.704617 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.704620 15775 net.cpp:137] Memory required for data: 263466752
I0407 08:22:59.704632 15775 layer_factory.hpp:77] Creating layer relu6
I0407 08:22:59.704643 15775 net.cpp:84] Creating Layer relu6
I0407 08:22:59.704646 15775 net.cpp:406] relu6 <- fc6
I0407 08:22:59.704656 15775 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:22:59.705866 15775 net.cpp:122] Setting up relu6
I0407 08:22:59.705878 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.705881 15775 net.cpp:137] Memory required for data: 263991040
I0407 08:22:59.705886 15775 layer_factory.hpp:77] Creating layer drop6
I0407 08:22:59.705893 15775 net.cpp:84] Creating Layer drop6
I0407 08:22:59.705897 15775 net.cpp:406] drop6 <- fc6
I0407 08:22:59.705905 15775 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:22:59.705933 15775 net.cpp:122] Setting up drop6
I0407 08:22:59.705941 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.705945 15775 net.cpp:137] Memory required for data: 264515328
I0407 08:22:59.705948 15775 layer_factory.hpp:77] Creating layer fc7
I0407 08:22:59.705956 15775 net.cpp:84] Creating Layer fc7
I0407 08:22:59.705965 15775 net.cpp:406] fc7 <- fc6
I0407 08:22:59.705973 15775 net.cpp:380] fc7 -> fc7
I0407 08:22:59.865497 15775 net.cpp:122] Setting up fc7
I0407 08:22:59.865517 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.865520 15775 net.cpp:137] Memory required for data: 265039616
I0407 08:22:59.865527 15775 layer_factory.hpp:77] Creating layer relu7
I0407 08:22:59.865536 15775 net.cpp:84] Creating Layer relu7
I0407 08:22:59.865540 15775 net.cpp:406] relu7 <- fc7
I0407 08:22:59.865545 15775 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:22:59.865927 15775 net.cpp:122] Setting up relu7
I0407 08:22:59.865934 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.865936 15775 net.cpp:137] Memory required for data: 265563904
I0407 08:22:59.865939 15775 layer_factory.hpp:77] Creating layer drop7
I0407 08:22:59.865944 15775 net.cpp:84] Creating Layer drop7
I0407 08:22:59.865947 15775 net.cpp:406] drop7 <- fc7
I0407 08:22:59.865952 15775 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:22:59.865972 15775 net.cpp:122] Setting up drop7
I0407 08:22:59.865978 15775 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:22:59.865979 15775 net.cpp:137] Memory required for data: 266088192
I0407 08:22:59.865981 15775 layer_factory.hpp:77] Creating layer fc8
I0407 08:22:59.865988 15775 net.cpp:84] Creating Layer fc8
I0407 08:22:59.865989 15775 net.cpp:406] fc8 <- fc7
I0407 08:22:59.865994 15775 net.cpp:380] fc8 -> fc8
I0407 08:22:59.873194 15775 net.cpp:122] Setting up fc8
I0407 08:22:59.873206 15775 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:22:59.873208 15775 net.cpp:137] Memory required for data: 266113280
I0407 08:22:59.873214 15775 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 08:22:59.873219 15775 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 08:22:59.873220 15775 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 08:22:59.873243 15775 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 08:22:59.873250 15775 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 08:22:59.873281 15775 net.cpp:122] Setting up fc8_fc8_0_split
I0407 08:22:59.873286 15775 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:22:59.873288 15775 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:22:59.873291 15775 net.cpp:137] Memory required for data: 266163456
I0407 08:22:59.873292 15775 layer_factory.hpp:77] Creating layer accuracy
I0407 08:22:59.873297 15775 net.cpp:84] Creating Layer accuracy
I0407 08:22:59.873299 15775 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 08:22:59.873302 15775 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 08:22:59.873307 15775 net.cpp:380] accuracy -> accuracy
I0407 08:22:59.873313 15775 net.cpp:122] Setting up accuracy
I0407 08:22:59.873317 15775 net.cpp:129] Top shape: (1)
I0407 08:22:59.873318 15775 net.cpp:137] Memory required for data: 266163460
I0407 08:22:59.873320 15775 layer_factory.hpp:77] Creating layer loss
I0407 08:22:59.873324 15775 net.cpp:84] Creating Layer loss
I0407 08:22:59.873327 15775 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 08:22:59.873329 15775 net.cpp:406] loss <- label_val-data_1_split_1
I0407 08:22:59.873332 15775 net.cpp:380] loss -> loss
I0407 08:22:59.873338 15775 layer_factory.hpp:77] Creating layer loss
I0407 08:22:59.873934 15775 net.cpp:122] Setting up loss
I0407 08:22:59.873942 15775 net.cpp:129] Top shape: (1)
I0407 08:22:59.873944 15775 net.cpp:132]     with loss weight 1
I0407 08:22:59.873953 15775 net.cpp:137] Memory required for data: 266163464
I0407 08:22:59.873956 15775 net.cpp:198] loss needs backward computation.
I0407 08:22:59.873960 15775 net.cpp:200] accuracy does not need backward computation.
I0407 08:22:59.873962 15775 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 08:22:59.873965 15775 net.cpp:198] fc8 needs backward computation.
I0407 08:22:59.873967 15775 net.cpp:198] drop7 needs backward computation.
I0407 08:22:59.873970 15775 net.cpp:198] relu7 needs backward computation.
I0407 08:22:59.873971 15775 net.cpp:198] fc7 needs backward computation.
I0407 08:22:59.873973 15775 net.cpp:198] drop6 needs backward computation.
I0407 08:22:59.873975 15775 net.cpp:198] relu6 needs backward computation.
I0407 08:22:59.873977 15775 net.cpp:198] fc6 needs backward computation.
I0407 08:22:59.873980 15775 net.cpp:198] pool5 needs backward computation.
I0407 08:22:59.873982 15775 net.cpp:198] relu5 needs backward computation.
I0407 08:22:59.873984 15775 net.cpp:198] conv5 needs backward computation.
I0407 08:22:59.873986 15775 net.cpp:198] relu4 needs backward computation.
I0407 08:22:59.873989 15775 net.cpp:198] conv4 needs backward computation.
I0407 08:22:59.873991 15775 net.cpp:198] relu3 needs backward computation.
I0407 08:22:59.873993 15775 net.cpp:198] conv3 needs backward computation.
I0407 08:22:59.873996 15775 net.cpp:198] pool2 needs backward computation.
I0407 08:22:59.873998 15775 net.cpp:198] norm2 needs backward computation.
I0407 08:22:59.874001 15775 net.cpp:198] relu2 needs backward computation.
I0407 08:22:59.874002 15775 net.cpp:198] conv2 needs backward computation.
I0407 08:22:59.874004 15775 net.cpp:198] pool1 needs backward computation.
I0407 08:22:59.874006 15775 net.cpp:198] norm1 needs backward computation.
I0407 08:22:59.874009 15775 net.cpp:198] relu1 needs backward computation.
I0407 08:22:59.874011 15775 net.cpp:198] conv1 needs backward computation.
I0407 08:22:59.874013 15775 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 08:22:59.874017 15775 net.cpp:200] val-data does not need backward computation.
I0407 08:22:59.874018 15775 net.cpp:242] This network produces output accuracy
I0407 08:22:59.874022 15775 net.cpp:242] This network produces output loss
I0407 08:22:59.874035 15775 net.cpp:255] Network initialization done.
I0407 08:22:59.874099 15775 solver.cpp:56] Solver scaffolding done.
I0407 08:22:59.874476 15775 caffe.cpp:248] Starting Optimization
I0407 08:22:59.874485 15775 solver.cpp:272] Solving
I0407 08:22:59.874495 15775 solver.cpp:273] Learning Rate Policy: step
I0407 08:22:59.876243 15775 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 08:22:59.876251 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:22:59.977617 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:23:04.037012 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:23:04.084988 15775 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0407 08:23:04.085047 15775 solver.cpp:397]     Test net output #1: loss = 5.27741 (* 1 = 5.27741 loss)
I0407 08:23:04.234515 15775 solver.cpp:218] Iteration 0 (1.82953e+36 iter/s, 4.35995s/12 iters), loss = 5.29154
I0407 08:23:04.236081 15775 solver.cpp:237]     Train net output #0: loss = 5.29154 (* 1 = 5.29154 loss)
I0407 08:23:04.236100 15775 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 08:23:08.396513 15775 solver.cpp:218] Iteration 12 (2.88435 iter/s, 4.16038s/12 iters), loss = 5.28837
I0407 08:23:08.396554 15775 solver.cpp:237]     Train net output #0: loss = 5.28837 (* 1 = 5.28837 loss)
I0407 08:23:08.396564 15775 sgd_solver.cpp:105] Iteration 12, lr = 0.01
I0407 08:23:13.759481 15775 solver.cpp:218] Iteration 24 (2.23761 iter/s, 5.36287s/12 iters), loss = 5.28632
I0407 08:23:13.759516 15775 solver.cpp:237]     Train net output #0: loss = 5.28632 (* 1 = 5.28632 loss)
I0407 08:23:13.759521 15775 sgd_solver.cpp:105] Iteration 24, lr = 0.01
I0407 08:23:19.062652 15775 solver.cpp:218] Iteration 36 (2.26283 iter/s, 5.30308s/12 iters), loss = 5.28928
I0407 08:23:19.062690 15775 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0407 08:23:19.062697 15775 sgd_solver.cpp:105] Iteration 36, lr = 0.01
I0407 08:23:24.446909 15775 solver.cpp:218] Iteration 48 (2.22876 iter/s, 5.38417s/12 iters), loss = 5.31001
I0407 08:23:24.446952 15775 solver.cpp:237]     Train net output #0: loss = 5.31001 (* 1 = 5.31001 loss)
I0407 08:23:24.446961 15775 sgd_solver.cpp:105] Iteration 48, lr = 0.01
I0407 08:23:29.853219 15775 solver.cpp:218] Iteration 60 (2.21966 iter/s, 5.40622s/12 iters), loss = 5.26807
I0407 08:23:29.853291 15775 solver.cpp:237]     Train net output #0: loss = 5.26807 (* 1 = 5.26807 loss)
I0407 08:23:29.853298 15775 sgd_solver.cpp:105] Iteration 60, lr = 0.01
I0407 08:23:35.195150 15775 solver.cpp:218] Iteration 72 (2.24643 iter/s, 5.34181s/12 iters), loss = 5.31191
I0407 08:23:35.195190 15775 solver.cpp:237]     Train net output #0: loss = 5.31191 (* 1 = 5.31191 loss)
I0407 08:23:35.195199 15775 sgd_solver.cpp:105] Iteration 72, lr = 0.01
I0407 08:23:40.416543 15775 solver.cpp:218] Iteration 84 (2.29827 iter/s, 5.22131s/12 iters), loss = 5.29027
I0407 08:23:40.416579 15775 solver.cpp:237]     Train net output #0: loss = 5.29027 (* 1 = 5.29027 loss)
I0407 08:23:40.416586 15775 sgd_solver.cpp:105] Iteration 84, lr = 0.01
I0407 08:23:45.770115 15775 solver.cpp:218] Iteration 96 (2.24153 iter/s, 5.35349s/12 iters), loss = 5.26858
I0407 08:23:45.770150 15775 solver.cpp:237]     Train net output #0: loss = 5.26858 (* 1 = 5.26858 loss)
I0407 08:23:45.770159 15775 sgd_solver.cpp:105] Iteration 96, lr = 0.01
I0407 08:23:47.660236 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:23:47.974378 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 08:23:51.187666 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 08:23:53.521109 15775 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 08:23:53.521136 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:23:57.808598 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:23:57.888741 15775 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0407 08:23:57.888787 15775 solver.cpp:397]     Test net output #1: loss = 5.29457 (* 1 = 5.29457 loss)
I0407 08:23:59.959328 15775 solver.cpp:218] Iteration 108 (0.845721 iter/s, 14.1891s/12 iters), loss = 5.29516
I0407 08:23:59.959487 15775 solver.cpp:237]     Train net output #0: loss = 5.29516 (* 1 = 5.29516 loss)
I0407 08:23:59.959496 15775 sgd_solver.cpp:105] Iteration 108, lr = 0.01
I0407 08:24:05.191540 15775 solver.cpp:218] Iteration 120 (2.29358 iter/s, 5.23201s/12 iters), loss = 5.26871
I0407 08:24:05.191579 15775 solver.cpp:237]     Train net output #0: loss = 5.26871 (* 1 = 5.26871 loss)
I0407 08:24:05.191587 15775 sgd_solver.cpp:105] Iteration 120, lr = 0.01
I0407 08:24:10.469550 15775 solver.cpp:218] Iteration 132 (2.27362 iter/s, 5.27792s/12 iters), loss = 5.28539
I0407 08:24:10.469596 15775 solver.cpp:237]     Train net output #0: loss = 5.28539 (* 1 = 5.28539 loss)
I0407 08:24:10.469604 15775 sgd_solver.cpp:105] Iteration 132, lr = 0.01
I0407 08:24:15.761276 15775 solver.cpp:218] Iteration 144 (2.26773 iter/s, 5.29163s/12 iters), loss = 5.27269
I0407 08:24:15.761320 15775 solver.cpp:237]     Train net output #0: loss = 5.27269 (* 1 = 5.27269 loss)
I0407 08:24:15.761330 15775 sgd_solver.cpp:105] Iteration 144, lr = 0.01
I0407 08:24:21.013494 15775 solver.cpp:218] Iteration 156 (2.28479 iter/s, 5.25212s/12 iters), loss = 5.30626
I0407 08:24:21.013532 15775 solver.cpp:237]     Train net output #0: loss = 5.30626 (* 1 = 5.30626 loss)
I0407 08:24:21.013538 15775 sgd_solver.cpp:105] Iteration 156, lr = 0.01
I0407 08:24:26.354526 15775 solver.cpp:218] Iteration 168 (2.24679 iter/s, 5.34094s/12 iters), loss = 5.26604
I0407 08:24:26.354565 15775 solver.cpp:237]     Train net output #0: loss = 5.26604 (* 1 = 5.26604 loss)
I0407 08:24:26.354573 15775 sgd_solver.cpp:105] Iteration 168, lr = 0.01
I0407 08:24:31.869868 15775 solver.cpp:218] Iteration 180 (2.17579 iter/s, 5.51524s/12 iters), loss = 5.27425
I0407 08:24:31.869962 15775 solver.cpp:237]     Train net output #0: loss = 5.27425 (* 1 = 5.27425 loss)
I0407 08:24:31.869971 15775 sgd_solver.cpp:105] Iteration 180, lr = 0.01
I0407 08:24:37.194700 15775 solver.cpp:218] Iteration 192 (2.25365 iter/s, 5.32469s/12 iters), loss = 5.15259
I0407 08:24:37.194737 15775 solver.cpp:237]     Train net output #0: loss = 5.15259 (* 1 = 5.15259 loss)
I0407 08:24:37.194744 15775 sgd_solver.cpp:105] Iteration 192, lr = 0.01
I0407 08:24:41.366322 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:24:42.095335 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 08:24:45.160727 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 08:24:47.519670 15775 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 08:24:47.519695 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:24:51.856833 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:24:51.982986 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 08:24:51.983021 15775 solver.cpp:397]     Test net output #1: loss = 5.20035 (* 1 = 5.20035 loss)
I0407 08:24:52.124441 15775 solver.cpp:218] Iteration 204 (0.803773 iter/s, 14.9296s/12 iters), loss = 5.14736
I0407 08:24:52.124507 15775 solver.cpp:237]     Train net output #0: loss = 5.14736 (* 1 = 5.14736 loss)
I0407 08:24:52.124516 15775 sgd_solver.cpp:105] Iteration 204, lr = 0.01
I0407 08:24:56.551288 15775 solver.cpp:218] Iteration 216 (2.7108 iter/s, 4.42673s/12 iters), loss = 5.25309
I0407 08:24:56.551327 15775 solver.cpp:237]     Train net output #0: loss = 5.25309 (* 1 = 5.25309 loss)
I0407 08:24:56.551335 15775 sgd_solver.cpp:105] Iteration 216, lr = 0.01
I0407 08:25:01.930060 15775 solver.cpp:218] Iteration 228 (2.23103 iter/s, 5.37868s/12 iters), loss = 5.2283
I0407 08:25:01.930124 15775 solver.cpp:237]     Train net output #0: loss = 5.2283 (* 1 = 5.2283 loss)
I0407 08:25:01.930131 15775 sgd_solver.cpp:105] Iteration 228, lr = 0.01
I0407 08:25:07.294862 15775 solver.cpp:218] Iteration 240 (2.23685 iter/s, 5.36469s/12 iters), loss = 5.16883
I0407 08:25:07.294904 15775 solver.cpp:237]     Train net output #0: loss = 5.16883 (* 1 = 5.16883 loss)
I0407 08:25:07.294912 15775 sgd_solver.cpp:105] Iteration 240, lr = 0.01
I0407 08:25:12.630615 15775 solver.cpp:218] Iteration 252 (2.24902 iter/s, 5.33566s/12 iters), loss = 5.21816
I0407 08:25:12.630661 15775 solver.cpp:237]     Train net output #0: loss = 5.21816 (* 1 = 5.21816 loss)
I0407 08:25:12.630671 15775 sgd_solver.cpp:105] Iteration 252, lr = 0.01
I0407 08:25:17.375411 15775 solver.cpp:218] Iteration 264 (2.52914 iter/s, 4.7447s/12 iters), loss = 5.13257
I0407 08:25:17.375447 15775 solver.cpp:237]     Train net output #0: loss = 5.13257 (* 1 = 5.13257 loss)
I0407 08:25:17.375453 15775 sgd_solver.cpp:105] Iteration 264, lr = 0.01
I0407 08:25:22.544935 15775 solver.cpp:218] Iteration 276 (2.32134 iter/s, 5.16944s/12 iters), loss = 5.12379
I0407 08:25:22.544977 15775 solver.cpp:237]     Train net output #0: loss = 5.12379 (* 1 = 5.12379 loss)
I0407 08:25:22.544986 15775 sgd_solver.cpp:105] Iteration 276, lr = 0.01
I0407 08:25:27.702724 15775 solver.cpp:218] Iteration 288 (2.32662 iter/s, 5.1577s/12 iters), loss = 5.1731
I0407 08:25:27.702766 15775 solver.cpp:237]     Train net output #0: loss = 5.1731 (* 1 = 5.1731 loss)
I0407 08:25:27.702773 15775 sgd_solver.cpp:105] Iteration 288, lr = 0.01
I0407 08:25:33.156765 15775 solver.cpp:218] Iteration 300 (2.20024 iter/s, 5.45395s/12 iters), loss = 5.24344
I0407 08:25:33.156929 15775 solver.cpp:237]     Train net output #0: loss = 5.24344 (* 1 = 5.24344 loss)
I0407 08:25:33.156939 15775 sgd_solver.cpp:105] Iteration 300, lr = 0.01
I0407 08:25:34.143019 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:35.256261 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 08:25:38.288193 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 08:25:40.626431 15775 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 08:25:40.626456 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:25:45.079520 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:45.250808 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 08:25:45.250842 15775 solver.cpp:397]     Test net output #1: loss = 5.16357 (* 1 = 5.16357 loss)
I0407 08:25:47.161224 15775 solver.cpp:218] Iteration 312 (0.856886 iter/s, 14.0042s/12 iters), loss = 5.1284
I0407 08:25:47.161260 15775 solver.cpp:237]     Train net output #0: loss = 5.1284 (* 1 = 5.1284 loss)
I0407 08:25:47.161267 15775 sgd_solver.cpp:105] Iteration 312, lr = 0.01
I0407 08:25:52.552168 15775 solver.cpp:218] Iteration 324 (2.22599 iter/s, 5.39085s/12 iters), loss = 5.21169
I0407 08:25:52.552213 15775 solver.cpp:237]     Train net output #0: loss = 5.21169 (* 1 = 5.21169 loss)
I0407 08:25:52.552220 15775 sgd_solver.cpp:105] Iteration 324, lr = 0.01
I0407 08:25:57.892258 15775 solver.cpp:218] Iteration 336 (2.24719 iter/s, 5.33999s/12 iters), loss = 5.14278
I0407 08:25:57.892302 15775 solver.cpp:237]     Train net output #0: loss = 5.14278 (* 1 = 5.14278 loss)
I0407 08:25:57.892308 15775 sgd_solver.cpp:105] Iteration 336, lr = 0.01
I0407 08:26:02.917914 15775 solver.cpp:218] Iteration 348 (2.38779 iter/s, 5.02556s/12 iters), loss = 5.11892
I0407 08:26:02.917961 15775 solver.cpp:237]     Train net output #0: loss = 5.11892 (* 1 = 5.11892 loss)
I0407 08:26:02.917969 15775 sgd_solver.cpp:105] Iteration 348, lr = 0.01
I0407 08:26:08.170171 15775 solver.cpp:218] Iteration 360 (2.28477 iter/s, 5.25216s/12 iters), loss = 5.17903
I0407 08:26:08.170279 15775 solver.cpp:237]     Train net output #0: loss = 5.17903 (* 1 = 5.17903 loss)
I0407 08:26:08.170287 15775 sgd_solver.cpp:105] Iteration 360, lr = 0.01
I0407 08:26:13.408490 15775 solver.cpp:218] Iteration 372 (2.29088 iter/s, 5.23816s/12 iters), loss = 5.14746
I0407 08:26:13.408532 15775 solver.cpp:237]     Train net output #0: loss = 5.14746 (* 1 = 5.14746 loss)
I0407 08:26:13.408540 15775 sgd_solver.cpp:105] Iteration 372, lr = 0.01
I0407 08:26:18.629895 15775 solver.cpp:218] Iteration 384 (2.29827 iter/s, 5.22131s/12 iters), loss = 5.20477
I0407 08:26:18.629943 15775 solver.cpp:237]     Train net output #0: loss = 5.20477 (* 1 = 5.20477 loss)
I0407 08:26:18.629952 15775 sgd_solver.cpp:105] Iteration 384, lr = 0.01
I0407 08:26:23.961932 15775 solver.cpp:218] Iteration 396 (2.25059 iter/s, 5.33194s/12 iters), loss = 5.11741
I0407 08:26:23.961975 15775 solver.cpp:237]     Train net output #0: loss = 5.11741 (* 1 = 5.11741 loss)
I0407 08:26:23.961983 15775 sgd_solver.cpp:105] Iteration 396, lr = 0.01
I0407 08:26:27.211582 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:28.749639 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 08:26:31.804107 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 08:26:34.141804 15775 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 08:26:34.141829 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:26:38.548501 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:38.767446 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0407 08:26:38.767483 15775 solver.cpp:397]     Test net output #1: loss = 5.1263 (* 1 = 5.1263 loss)
I0407 08:26:38.907228 15775 solver.cpp:218] Iteration 408 (0.802936 iter/s, 14.9451s/12 iters), loss = 5.14457
I0407 08:26:38.907274 15775 solver.cpp:237]     Train net output #0: loss = 5.14457 (* 1 = 5.14457 loss)
I0407 08:26:38.907282 15775 sgd_solver.cpp:105] Iteration 408, lr = 0.01
I0407 08:26:42.996053 15775 solver.cpp:218] Iteration 420 (2.93489 iter/s, 4.08874s/12 iters), loss = 5.0738
I0407 08:26:42.996093 15775 solver.cpp:237]     Train net output #0: loss = 5.0738 (* 1 = 5.0738 loss)
I0407 08:26:42.996099 15775 sgd_solver.cpp:105] Iteration 420, lr = 0.01
I0407 08:26:48.359141 15775 solver.cpp:218] Iteration 432 (2.23755 iter/s, 5.363s/12 iters), loss = 5.04389
I0407 08:26:48.359189 15775 solver.cpp:237]     Train net output #0: loss = 5.04389 (* 1 = 5.04389 loss)
I0407 08:26:48.359197 15775 sgd_solver.cpp:105] Iteration 432, lr = 0.01
I0407 08:26:53.662815 15775 solver.cpp:218] Iteration 444 (2.26262 iter/s, 5.30358s/12 iters), loss = 5.04974
I0407 08:26:53.662855 15775 solver.cpp:237]     Train net output #0: loss = 5.04974 (* 1 = 5.04974 loss)
I0407 08:26:53.662863 15775 sgd_solver.cpp:105] Iteration 444, lr = 0.01
I0407 08:26:58.806208 15775 solver.cpp:218] Iteration 456 (2.33313 iter/s, 5.14331s/12 iters), loss = 5.14817
I0407 08:26:58.806246 15775 solver.cpp:237]     Train net output #0: loss = 5.14817 (* 1 = 5.14817 loss)
I0407 08:26:58.806254 15775 sgd_solver.cpp:105] Iteration 456, lr = 0.01
I0407 08:27:04.217275 15775 solver.cpp:218] Iteration 468 (2.21771 iter/s, 5.41098s/12 iters), loss = 5.0603
I0407 08:27:04.217325 15775 solver.cpp:237]     Train net output #0: loss = 5.0603 (* 1 = 5.0603 loss)
I0407 08:27:04.217334 15775 sgd_solver.cpp:105] Iteration 468, lr = 0.01
I0407 08:27:09.581252 15775 solver.cpp:218] Iteration 480 (2.23719 iter/s, 5.36388s/12 iters), loss = 4.95124
I0407 08:27:09.581357 15775 solver.cpp:237]     Train net output #0: loss = 4.95124 (* 1 = 4.95124 loss)
I0407 08:27:09.581364 15775 sgd_solver.cpp:105] Iteration 480, lr = 0.01
I0407 08:27:15.092428 15775 solver.cpp:218] Iteration 492 (2.17745 iter/s, 5.51102s/12 iters), loss = 5.07828
I0407 08:27:15.092475 15775 solver.cpp:237]     Train net output #0: loss = 5.07828 (* 1 = 5.07828 loss)
I0407 08:27:15.092483 15775 sgd_solver.cpp:105] Iteration 492, lr = 0.01
I0407 08:27:20.490804 15775 solver.cpp:218] Iteration 504 (2.22293 iter/s, 5.39828s/12 iters), loss = 5.10811
I0407 08:27:20.490850 15775 solver.cpp:237]     Train net output #0: loss = 5.10811 (* 1 = 5.10811 loss)
I0407 08:27:20.490859 15775 sgd_solver.cpp:105] Iteration 504, lr = 0.01
I0407 08:27:20.726436 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:22.607617 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 08:27:25.609333 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 08:27:29.165897 15775 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 08:27:29.165917 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:27:33.476212 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:33.714031 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0183824
I0407 08:27:33.714080 15775 solver.cpp:397]     Test net output #1: loss = 5.07407 (* 1 = 5.07407 loss)
I0407 08:27:35.552914 15775 solver.cpp:218] Iteration 516 (0.796709 iter/s, 15.062s/12 iters), loss = 5.09789
I0407 08:27:35.552954 15775 solver.cpp:237]     Train net output #0: loss = 5.09789 (* 1 = 5.09789 loss)
I0407 08:27:35.552963 15775 sgd_solver.cpp:105] Iteration 516, lr = 0.01
I0407 08:27:40.959307 15775 solver.cpp:218] Iteration 528 (2.21963 iter/s, 5.40631s/12 iters), loss = 5.13514
I0407 08:27:40.959435 15775 solver.cpp:237]     Train net output #0: loss = 5.13514 (* 1 = 5.13514 loss)
I0407 08:27:40.959443 15775 sgd_solver.cpp:105] Iteration 528, lr = 0.01
I0407 08:27:46.137123 15775 solver.cpp:218] Iteration 540 (2.31766 iter/s, 5.17764s/12 iters), loss = 5.00718
I0407 08:27:46.137162 15775 solver.cpp:237]     Train net output #0: loss = 5.00718 (* 1 = 5.00718 loss)
I0407 08:27:46.137169 15775 sgd_solver.cpp:105] Iteration 540, lr = 0.01
I0407 08:27:51.056362 15775 solver.cpp:218] Iteration 552 (2.43944 iter/s, 4.91915s/12 iters), loss = 5.1156
I0407 08:27:51.056402 15775 solver.cpp:237]     Train net output #0: loss = 5.1156 (* 1 = 5.1156 loss)
I0407 08:27:51.056411 15775 sgd_solver.cpp:105] Iteration 552, lr = 0.01
I0407 08:27:56.479290 15775 solver.cpp:218] Iteration 564 (2.21286 iter/s, 5.42284s/12 iters), loss = 5.02839
I0407 08:27:56.479331 15775 solver.cpp:237]     Train net output #0: loss = 5.02839 (* 1 = 5.02839 loss)
I0407 08:27:56.479338 15775 sgd_solver.cpp:105] Iteration 564, lr = 0.01
I0407 08:28:01.914595 15775 solver.cpp:218] Iteration 576 (2.20782 iter/s, 5.43521s/12 iters), loss = 5.00293
I0407 08:28:01.914645 15775 solver.cpp:237]     Train net output #0: loss = 5.00293 (* 1 = 5.00293 loss)
I0407 08:28:01.914654 15775 sgd_solver.cpp:105] Iteration 576, lr = 0.01
I0407 08:28:06.964022 15775 solver.cpp:218] Iteration 588 (2.37655 iter/s, 5.04933s/12 iters), loss = 5.01554
I0407 08:28:06.964071 15775 solver.cpp:237]     Train net output #0: loss = 5.01554 (* 1 = 5.01554 loss)
I0407 08:28:06.964079 15775 sgd_solver.cpp:105] Iteration 588, lr = 0.01
I0407 08:28:12.170583 15775 solver.cpp:218] Iteration 600 (2.30483 iter/s, 5.20646s/12 iters), loss = 5.0056
I0407 08:28:12.170686 15775 solver.cpp:237]     Train net output #0: loss = 5.0056 (* 1 = 5.0056 loss)
I0407 08:28:12.170694 15775 sgd_solver.cpp:105] Iteration 600, lr = 0.01
I0407 08:28:14.743404 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:17.089898 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 08:28:20.121791 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 08:28:24.343632 15775 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 08:28:24.343659 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:28:28.394991 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:28.678284 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0306373
I0407 08:28:28.678324 15775 solver.cpp:397]     Test net output #1: loss = 5.02228 (* 1 = 5.02228 loss)
I0407 08:28:28.816192 15775 solver.cpp:218] Iteration 612 (0.72092 iter/s, 16.6454s/12 iters), loss = 4.97881
I0407 08:28:28.816236 15775 solver.cpp:237]     Train net output #0: loss = 4.97881 (* 1 = 4.97881 loss)
I0407 08:28:28.816246 15775 sgd_solver.cpp:105] Iteration 612, lr = 0.01
I0407 08:28:33.203166 15775 solver.cpp:218] Iteration 624 (2.73543 iter/s, 4.38688s/12 iters), loss = 4.93163
I0407 08:28:33.203220 15775 solver.cpp:237]     Train net output #0: loss = 4.93163 (* 1 = 4.93163 loss)
I0407 08:28:33.203231 15775 sgd_solver.cpp:105] Iteration 624, lr = 0.01
I0407 08:28:38.440104 15775 solver.cpp:218] Iteration 636 (2.29146 iter/s, 5.23684s/12 iters), loss = 4.97323
I0407 08:28:38.440152 15775 solver.cpp:237]     Train net output #0: loss = 4.97323 (* 1 = 4.97323 loss)
I0407 08:28:38.440161 15775 sgd_solver.cpp:105] Iteration 636, lr = 0.01
I0407 08:28:43.580461 15775 solver.cpp:218] Iteration 648 (2.33451 iter/s, 5.14026s/12 iters), loss = 4.9759
I0407 08:28:43.580585 15775 solver.cpp:237]     Train net output #0: loss = 4.9759 (* 1 = 4.9759 loss)
I0407 08:28:43.580592 15775 sgd_solver.cpp:105] Iteration 648, lr = 0.01
I0407 08:28:49.083653 15775 solver.cpp:218] Iteration 660 (2.18062 iter/s, 5.50302s/12 iters), loss = 4.93585
I0407 08:28:49.083699 15775 solver.cpp:237]     Train net output #0: loss = 4.93585 (* 1 = 4.93585 loss)
I0407 08:28:49.083706 15775 sgd_solver.cpp:105] Iteration 660, lr = 0.01
I0407 08:28:54.565485 15775 solver.cpp:218] Iteration 672 (2.18909 iter/s, 5.48174s/12 iters), loss = 4.96601
I0407 08:28:54.565528 15775 solver.cpp:237]     Train net output #0: loss = 4.96601 (* 1 = 4.96601 loss)
I0407 08:28:54.565536 15775 sgd_solver.cpp:105] Iteration 672, lr = 0.01
I0407 08:28:59.951262 15775 solver.cpp:218] Iteration 684 (2.22813 iter/s, 5.38569s/12 iters), loss = 4.92038
I0407 08:28:59.951301 15775 solver.cpp:237]     Train net output #0: loss = 4.92038 (* 1 = 4.92038 loss)
I0407 08:28:59.951310 15775 sgd_solver.cpp:105] Iteration 684, lr = 0.01
I0407 08:29:00.763540 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:29:05.087476 15775 solver.cpp:218] Iteration 696 (2.33639 iter/s, 5.13613s/12 iters), loss = 4.83141
I0407 08:29:05.087519 15775 solver.cpp:237]     Train net output #0: loss = 4.83141 (* 1 = 4.83141 loss)
I0407 08:29:05.087527 15775 sgd_solver.cpp:105] Iteration 696, lr = 0.01
I0407 08:29:09.960564 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:10.375900 15775 solver.cpp:218] Iteration 708 (2.26915 iter/s, 5.28833s/12 iters), loss = 4.94156
I0407 08:29:10.375942 15775 solver.cpp:237]     Train net output #0: loss = 4.94156 (* 1 = 4.94156 loss)
I0407 08:29:10.375949 15775 sgd_solver.cpp:105] Iteration 708, lr = 0.01
I0407 08:29:12.502023 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 08:29:15.553210 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 08:29:19.330847 15775 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 08:29:19.330865 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:29:23.293486 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:23.606891 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0407 08:29:23.606920 15775 solver.cpp:397]     Test net output #1: loss = 4.95579 (* 1 = 4.95579 loss)
I0407 08:29:25.509990 15775 solver.cpp:218] Iteration 720 (0.79292 iter/s, 15.1339s/12 iters), loss = 4.94131
I0407 08:29:25.510035 15775 solver.cpp:237]     Train net output #0: loss = 4.94131 (* 1 = 4.94131 loss)
I0407 08:29:25.510042 15775 sgd_solver.cpp:105] Iteration 720, lr = 0.01
I0407 08:29:30.911836 15775 solver.cpp:218] Iteration 732 (2.2215 iter/s, 5.40176s/12 iters), loss = 4.78855
I0407 08:29:30.911873 15775 solver.cpp:237]     Train net output #0: loss = 4.78855 (* 1 = 4.78855 loss)
I0407 08:29:30.911880 15775 sgd_solver.cpp:105] Iteration 732, lr = 0.01
I0407 08:29:36.347568 15775 solver.cpp:218] Iteration 744 (2.20765 iter/s, 5.43564s/12 iters), loss = 4.82579
I0407 08:29:36.347620 15775 solver.cpp:237]     Train net output #0: loss = 4.82579 (* 1 = 4.82579 loss)
I0407 08:29:36.347630 15775 sgd_solver.cpp:105] Iteration 744, lr = 0.01
I0407 08:29:41.600585 15775 solver.cpp:218] Iteration 756 (2.28444 iter/s, 5.25292s/12 iters), loss = 4.78834
I0407 08:29:41.600626 15775 solver.cpp:237]     Train net output #0: loss = 4.78834 (* 1 = 4.78834 loss)
I0407 08:29:41.600633 15775 sgd_solver.cpp:105] Iteration 756, lr = 0.01
I0407 08:29:46.875607 15775 solver.cpp:218] Iteration 768 (2.27491 iter/s, 5.27494s/12 iters), loss = 4.88684
I0407 08:29:46.875746 15775 solver.cpp:237]     Train net output #0: loss = 4.88684 (* 1 = 4.88684 loss)
I0407 08:29:46.875753 15775 sgd_solver.cpp:105] Iteration 768, lr = 0.01
I0407 08:29:52.169348 15775 solver.cpp:218] Iteration 780 (2.26691 iter/s, 5.29356s/12 iters), loss = 4.85197
I0407 08:29:52.169409 15775 solver.cpp:237]     Train net output #0: loss = 4.85197 (* 1 = 4.85197 loss)
I0407 08:29:52.169423 15775 sgd_solver.cpp:105] Iteration 780, lr = 0.01
I0407 08:29:57.542423 15775 solver.cpp:218] Iteration 792 (2.2334 iter/s, 5.37297s/12 iters), loss = 4.96349
I0407 08:29:57.542464 15775 solver.cpp:237]     Train net output #0: loss = 4.96349 (* 1 = 4.96349 loss)
I0407 08:29:57.542471 15775 sgd_solver.cpp:105] Iteration 792, lr = 0.01
I0407 08:30:02.966966 15775 solver.cpp:218] Iteration 804 (2.21221 iter/s, 5.42445s/12 iters), loss = 4.87756
I0407 08:30:02.967015 15775 solver.cpp:237]     Train net output #0: loss = 4.87756 (* 1 = 4.87756 loss)
I0407 08:30:02.967023 15775 sgd_solver.cpp:105] Iteration 804, lr = 0.01
I0407 08:30:04.898061 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:07.848120 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 08:30:11.465319 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 08:30:15.285668 15775 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 08:30:15.285691 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:30:19.263736 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:19.613112 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0407 08:30:19.613143 15775 solver.cpp:397]     Test net output #1: loss = 4.91498 (* 1 = 4.91498 loss)
I0407 08:30:19.742429 15775 solver.cpp:218] Iteration 816 (0.715338 iter/s, 16.7753s/12 iters), loss = 4.84202
I0407 08:30:19.742477 15775 solver.cpp:237]     Train net output #0: loss = 4.84202 (* 1 = 4.84202 loss)
I0407 08:30:19.742486 15775 sgd_solver.cpp:105] Iteration 816, lr = 0.01
I0407 08:30:24.119755 15775 solver.cpp:218] Iteration 828 (2.74146 iter/s, 4.37723s/12 iters), loss = 4.84139
I0407 08:30:24.119803 15775 solver.cpp:237]     Train net output #0: loss = 4.84139 (* 1 = 4.84139 loss)
I0407 08:30:24.119812 15775 sgd_solver.cpp:105] Iteration 828, lr = 0.01
I0407 08:30:29.339309 15775 solver.cpp:218] Iteration 840 (2.29909 iter/s, 5.21946s/12 iters), loss = 4.6938
I0407 08:30:29.339360 15775 solver.cpp:237]     Train net output #0: loss = 4.6938 (* 1 = 4.6938 loss)
I0407 08:30:29.339370 15775 sgd_solver.cpp:105] Iteration 840, lr = 0.01
I0407 08:30:34.480453 15775 solver.cpp:218] Iteration 852 (2.33416 iter/s, 5.14104s/12 iters), loss = 4.65497
I0407 08:30:34.480492 15775 solver.cpp:237]     Train net output #0: loss = 4.65497 (* 1 = 4.65497 loss)
I0407 08:30:34.480499 15775 sgd_solver.cpp:105] Iteration 852, lr = 0.01
I0407 08:30:39.790216 15775 solver.cpp:218] Iteration 864 (2.26002 iter/s, 5.30968s/12 iters), loss = 4.74995
I0407 08:30:39.790261 15775 solver.cpp:237]     Train net output #0: loss = 4.74995 (* 1 = 4.74995 loss)
I0407 08:30:39.790271 15775 sgd_solver.cpp:105] Iteration 864, lr = 0.01
I0407 08:30:45.004026 15775 solver.cpp:218] Iteration 876 (2.30162 iter/s, 5.21372s/12 iters), loss = 4.75534
I0407 08:30:45.004067 15775 solver.cpp:237]     Train net output #0: loss = 4.75534 (* 1 = 4.75534 loss)
I0407 08:30:45.004074 15775 sgd_solver.cpp:105] Iteration 876, lr = 0.01
I0407 08:30:50.213013 15775 solver.cpp:218] Iteration 888 (2.30375 iter/s, 5.2089s/12 iters), loss = 4.76162
I0407 08:30:50.213105 15775 solver.cpp:237]     Train net output #0: loss = 4.76162 (* 1 = 4.76162 loss)
I0407 08:30:50.213114 15775 sgd_solver.cpp:105] Iteration 888, lr = 0.01
I0407 08:30:55.282603 15775 solver.cpp:218] Iteration 900 (2.36712 iter/s, 5.06945s/12 iters), loss = 4.75176
I0407 08:30:55.282645 15775 solver.cpp:237]     Train net output #0: loss = 4.75176 (* 1 = 4.75176 loss)
I0407 08:30:55.282652 15775 sgd_solver.cpp:105] Iteration 900, lr = 0.01
I0407 08:30:59.458592 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:00.636565 15775 solver.cpp:218] Iteration 912 (2.24137 iter/s, 5.35387s/12 iters), loss = 4.81443
I0407 08:31:00.636610 15775 solver.cpp:237]     Train net output #0: loss = 4.81443 (* 1 = 4.81443 loss)
I0407 08:31:00.636617 15775 sgd_solver.cpp:105] Iteration 912, lr = 0.01
I0407 08:31:02.716549 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 08:31:06.978204 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 08:31:10.733455 15775 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 08:31:10.733474 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:31:14.657872 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:15.050719 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0407 08:31:15.050768 15775 solver.cpp:397]     Test net output #1: loss = 4.76175 (* 1 = 4.76175 loss)
I0407 08:31:16.973907 15775 solver.cpp:218] Iteration 924 (0.734521 iter/s, 16.3372s/12 iters), loss = 4.66135
I0407 08:31:16.973966 15775 solver.cpp:237]     Train net output #0: loss = 4.66135 (* 1 = 4.66135 loss)
I0407 08:31:16.973976 15775 sgd_solver.cpp:105] Iteration 924, lr = 0.01
I0407 08:31:22.191005 15775 solver.cpp:218] Iteration 936 (2.30017 iter/s, 5.217s/12 iters), loss = 4.72755
I0407 08:31:22.191138 15775 solver.cpp:237]     Train net output #0: loss = 4.72755 (* 1 = 4.72755 loss)
I0407 08:31:22.191146 15775 sgd_solver.cpp:105] Iteration 936, lr = 0.01
I0407 08:31:27.294726 15775 solver.cpp:218] Iteration 948 (2.35131 iter/s, 5.10355s/12 iters), loss = 4.77171
I0407 08:31:27.294766 15775 solver.cpp:237]     Train net output #0: loss = 4.77171 (* 1 = 4.77171 loss)
I0407 08:31:27.294773 15775 sgd_solver.cpp:105] Iteration 948, lr = 0.01
I0407 08:31:32.630409 15775 solver.cpp:218] Iteration 960 (2.24905 iter/s, 5.33559s/12 iters), loss = 4.72132
I0407 08:31:32.630448 15775 solver.cpp:237]     Train net output #0: loss = 4.72132 (* 1 = 4.72132 loss)
I0407 08:31:32.630456 15775 sgd_solver.cpp:105] Iteration 960, lr = 0.01
I0407 08:31:38.050858 15775 solver.cpp:218] Iteration 972 (2.21388 iter/s, 5.42035s/12 iters), loss = 4.42711
I0407 08:31:38.050915 15775 solver.cpp:237]     Train net output #0: loss = 4.42711 (* 1 = 4.42711 loss)
I0407 08:31:38.050925 15775 sgd_solver.cpp:105] Iteration 972, lr = 0.01
I0407 08:31:43.243443 15775 solver.cpp:218] Iteration 984 (2.31103 iter/s, 5.19248s/12 iters), loss = 4.62405
I0407 08:31:43.243480 15775 solver.cpp:237]     Train net output #0: loss = 4.62405 (* 1 = 4.62405 loss)
I0407 08:31:43.243487 15775 sgd_solver.cpp:105] Iteration 984, lr = 0.01
I0407 08:31:48.478173 15775 solver.cpp:218] Iteration 996 (2.29242 iter/s, 5.23464s/12 iters), loss = 4.59153
I0407 08:31:48.478214 15775 solver.cpp:237]     Train net output #0: loss = 4.59153 (* 1 = 4.59153 loss)
I0407 08:31:48.478222 15775 sgd_solver.cpp:105] Iteration 996, lr = 0.01
I0407 08:31:53.773705 15775 solver.cpp:218] Iteration 1008 (2.2661 iter/s, 5.29544s/12 iters), loss = 4.6696
I0407 08:31:53.773835 15775 solver.cpp:237]     Train net output #0: loss = 4.6696 (* 1 = 4.6696 loss)
I0407 08:31:53.773846 15775 sgd_solver.cpp:105] Iteration 1008, lr = 0.01
I0407 08:31:54.839197 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:58.537333 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 08:32:02.062321 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 08:32:05.829254 15775 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 08:32:05.829277 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:32:09.683507 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:10.106658 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0407 08:32:10.106686 15775 solver.cpp:397]     Test net output #1: loss = 4.68216 (* 1 = 4.68216 loss)
I0407 08:32:10.246131 15775 solver.cpp:218] Iteration 1020 (0.728501 iter/s, 16.4722s/12 iters), loss = 4.61372
I0407 08:32:10.246173 15775 solver.cpp:237]     Train net output #0: loss = 4.61372 (* 1 = 4.61372 loss)
I0407 08:32:10.246181 15775 sgd_solver.cpp:105] Iteration 1020, lr = 0.01
I0407 08:32:14.536149 15775 solver.cpp:218] Iteration 1032 (2.79725 iter/s, 4.28993s/12 iters), loss = 4.90017
I0407 08:32:14.536195 15775 solver.cpp:237]     Train net output #0: loss = 4.90017 (* 1 = 4.90017 loss)
I0407 08:32:14.536201 15775 sgd_solver.cpp:105] Iteration 1032, lr = 0.01
I0407 08:32:19.903410 15775 solver.cpp:218] Iteration 1044 (2.23582 iter/s, 5.36717s/12 iters), loss = 4.44342
I0407 08:32:19.903451 15775 solver.cpp:237]     Train net output #0: loss = 4.44342 (* 1 = 4.44342 loss)
I0407 08:32:19.903460 15775 sgd_solver.cpp:105] Iteration 1044, lr = 0.01
I0407 08:32:25.103358 15775 solver.cpp:218] Iteration 1056 (2.30775 iter/s, 5.19986s/12 iters), loss = 4.51696
I0407 08:32:25.103472 15775 solver.cpp:237]     Train net output #0: loss = 4.51696 (* 1 = 4.51696 loss)
I0407 08:32:25.103482 15775 sgd_solver.cpp:105] Iteration 1056, lr = 0.01
I0407 08:32:30.601222 15775 solver.cpp:218] Iteration 1068 (2.18273 iter/s, 5.4977s/12 iters), loss = 4.38327
I0407 08:32:30.601262 15775 solver.cpp:237]     Train net output #0: loss = 4.38327 (* 1 = 4.38327 loss)
I0407 08:32:30.601270 15775 sgd_solver.cpp:105] Iteration 1068, lr = 0.01
I0407 08:32:35.824421 15775 solver.cpp:218] Iteration 1080 (2.29748 iter/s, 5.22311s/12 iters), loss = 4.29295
I0407 08:32:35.824472 15775 solver.cpp:237]     Train net output #0: loss = 4.29295 (* 1 = 4.29295 loss)
I0407 08:32:35.824483 15775 sgd_solver.cpp:105] Iteration 1080, lr = 0.01
I0407 08:32:41.148730 15775 solver.cpp:218] Iteration 1092 (2.25386 iter/s, 5.32421s/12 iters), loss = 4.49579
I0407 08:32:41.148777 15775 solver.cpp:237]     Train net output #0: loss = 4.49579 (* 1 = 4.49579 loss)
I0407 08:32:41.148785 15775 sgd_solver.cpp:105] Iteration 1092, lr = 0.01
I0407 08:32:46.541457 15775 solver.cpp:218] Iteration 1104 (2.22526 iter/s, 5.39263s/12 iters), loss = 4.36042
I0407 08:32:46.541498 15775 solver.cpp:237]     Train net output #0: loss = 4.36042 (* 1 = 4.36042 loss)
I0407 08:32:46.541505 15775 sgd_solver.cpp:105] Iteration 1104, lr = 0.01
I0407 08:32:49.839913 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:51.775679 15775 solver.cpp:218] Iteration 1116 (2.29264 iter/s, 5.23413s/12 iters), loss = 4.41614
I0407 08:32:51.775720 15775 solver.cpp:237]     Train net output #0: loss = 4.41614 (* 1 = 4.41614 loss)
I0407 08:32:51.775727 15775 sgd_solver.cpp:105] Iteration 1116, lr = 0.01
I0407 08:32:53.849905 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 08:32:56.871551 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 08:33:01.264562 15775 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 08:33:01.264580 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:33:05.135605 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:05.604480 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0759804
I0407 08:33:05.604527 15775 solver.cpp:397]     Test net output #1: loss = 4.43004 (* 1 = 4.43004 loss)
I0407 08:33:07.546481 15775 solver.cpp:218] Iteration 1128 (0.760907 iter/s, 15.7707s/12 iters), loss = 4.25096
I0407 08:33:07.546519 15775 solver.cpp:237]     Train net output #0: loss = 4.25096 (* 1 = 4.25096 loss)
I0407 08:33:07.546526 15775 sgd_solver.cpp:105] Iteration 1128, lr = 0.01
I0407 08:33:12.835587 15775 solver.cpp:218] Iteration 1140 (2.26885 iter/s, 5.28902s/12 iters), loss = 4.30326
I0407 08:33:12.835629 15775 solver.cpp:237]     Train net output #0: loss = 4.30326 (* 1 = 4.30326 loss)
I0407 08:33:12.835636 15775 sgd_solver.cpp:105] Iteration 1140, lr = 0.01
I0407 08:33:18.101820 15775 solver.cpp:218] Iteration 1152 (2.27871 iter/s, 5.26614s/12 iters), loss = 4.41068
I0407 08:33:18.101863 15775 solver.cpp:237]     Train net output #0: loss = 4.41068 (* 1 = 4.41068 loss)
I0407 08:33:18.101871 15775 sgd_solver.cpp:105] Iteration 1152, lr = 0.01
I0407 08:33:23.041621 15775 solver.cpp:218] Iteration 1164 (2.42929 iter/s, 4.93971s/12 iters), loss = 4.60593
I0407 08:33:23.041678 15775 solver.cpp:237]     Train net output #0: loss = 4.60593 (* 1 = 4.60593 loss)
I0407 08:33:23.041692 15775 sgd_solver.cpp:105] Iteration 1164, lr = 0.01
I0407 08:33:28.217504 15775 solver.cpp:218] Iteration 1176 (2.31849 iter/s, 5.17578s/12 iters), loss = 4.39359
I0407 08:33:28.217620 15775 solver.cpp:237]     Train net output #0: loss = 4.39359 (* 1 = 4.39359 loss)
I0407 08:33:28.217629 15775 sgd_solver.cpp:105] Iteration 1176, lr = 0.01
I0407 08:33:33.281422 15775 solver.cpp:218] Iteration 1188 (2.36978 iter/s, 5.06375s/12 iters), loss = 4.27655
I0407 08:33:33.281462 15775 solver.cpp:237]     Train net output #0: loss = 4.27655 (* 1 = 4.27655 loss)
I0407 08:33:33.281471 15775 sgd_solver.cpp:105] Iteration 1188, lr = 0.01
I0407 08:33:38.613977 15775 solver.cpp:218] Iteration 1200 (2.25037 iter/s, 5.33247s/12 iters), loss = 4.21949
I0407 08:33:38.614022 15775 solver.cpp:237]     Train net output #0: loss = 4.21949 (* 1 = 4.21949 loss)
I0407 08:33:38.614032 15775 sgd_solver.cpp:105] Iteration 1200, lr = 0.01
I0407 08:33:43.923430 15775 solver.cpp:218] Iteration 1212 (2.26016 iter/s, 5.30936s/12 iters), loss = 4.21263
I0407 08:33:43.923477 15775 solver.cpp:237]     Train net output #0: loss = 4.21263 (* 1 = 4.21263 loss)
I0407 08:33:43.923485 15775 sgd_solver.cpp:105] Iteration 1212, lr = 0.01
I0407 08:33:44.180979 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:48.801575 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 08:33:51.859386 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 08:33:54.202457 15775 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 08:33:54.202481 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:33:57.951740 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:58.451670 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0796569
I0407 08:33:58.451782 15775 solver.cpp:397]     Test net output #1: loss = 4.303 (* 1 = 4.303 loss)
I0407 08:33:58.593180 15775 solver.cpp:218] Iteration 1224 (0.818018 iter/s, 14.6696s/12 iters), loss = 4.26246
I0407 08:33:58.593230 15775 solver.cpp:237]     Train net output #0: loss = 4.26246 (* 1 = 4.26246 loss)
I0407 08:33:58.593240 15775 sgd_solver.cpp:105] Iteration 1224, lr = 0.01
I0407 08:34:03.042791 15775 solver.cpp:218] Iteration 1236 (2.69692 iter/s, 4.44952s/12 iters), loss = 4.21856
I0407 08:34:03.042834 15775 solver.cpp:237]     Train net output #0: loss = 4.21856 (* 1 = 4.21856 loss)
I0407 08:34:03.042842 15775 sgd_solver.cpp:105] Iteration 1236, lr = 0.01
I0407 08:34:08.235316 15775 solver.cpp:218] Iteration 1248 (2.31106 iter/s, 5.19243s/12 iters), loss = 4.148
I0407 08:34:08.235358 15775 solver.cpp:237]     Train net output #0: loss = 4.148 (* 1 = 4.148 loss)
I0407 08:34:08.235365 15775 sgd_solver.cpp:105] Iteration 1248, lr = 0.01
I0407 08:34:13.439555 15775 solver.cpp:218] Iteration 1260 (2.30585 iter/s, 5.20415s/12 iters), loss = 4.37202
I0407 08:34:13.439596 15775 solver.cpp:237]     Train net output #0: loss = 4.37202 (* 1 = 4.37202 loss)
I0407 08:34:13.439604 15775 sgd_solver.cpp:105] Iteration 1260, lr = 0.01
I0407 08:34:18.885025 15775 solver.cpp:218] Iteration 1272 (2.2037 iter/s, 5.44539s/12 iters), loss = 4.24994
I0407 08:34:18.885061 15775 solver.cpp:237]     Train net output #0: loss = 4.24994 (* 1 = 4.24994 loss)
I0407 08:34:18.885067 15775 sgd_solver.cpp:105] Iteration 1272, lr = 0.01
I0407 08:34:24.368433 15775 solver.cpp:218] Iteration 1284 (2.18845 iter/s, 5.48332s/12 iters), loss = 4.32786
I0407 08:34:24.368479 15775 solver.cpp:237]     Train net output #0: loss = 4.32786 (* 1 = 4.32786 loss)
I0407 08:34:24.368486 15775 sgd_solver.cpp:105] Iteration 1284, lr = 0.01
I0407 08:34:29.565053 15775 solver.cpp:218] Iteration 1296 (2.30923 iter/s, 5.19653s/12 iters), loss = 4.13921
I0407 08:34:29.565192 15775 solver.cpp:237]     Train net output #0: loss = 4.13921 (* 1 = 4.13921 loss)
I0407 08:34:29.565203 15775 sgd_solver.cpp:105] Iteration 1296, lr = 0.01
I0407 08:34:34.691632 15775 solver.cpp:218] Iteration 1308 (2.34083 iter/s, 5.1264s/12 iters), loss = 4.32062
I0407 08:34:34.691677 15775 solver.cpp:237]     Train net output #0: loss = 4.32062 (* 1 = 4.32062 loss)
I0407 08:34:34.691686 15775 sgd_solver.cpp:105] Iteration 1308, lr = 0.01
I0407 08:34:37.274950 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:39.885581 15775 solver.cpp:218] Iteration 1320 (2.31042 iter/s, 5.19386s/12 iters), loss = 4.27223
I0407 08:34:39.885624 15775 solver.cpp:237]     Train net output #0: loss = 4.27223 (* 1 = 4.27223 loss)
I0407 08:34:39.885634 15775 sgd_solver.cpp:105] Iteration 1320, lr = 0.01
I0407 08:34:42.034520 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 08:34:45.114773 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 08:34:47.418973 15775 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 08:34:47.418994 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:34:51.167732 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:51.754408 15775 solver.cpp:397]     Test net output #0: accuracy = 0.0998775
I0407 08:34:51.754464 15775 solver.cpp:397]     Test net output #1: loss = 4.15158 (* 1 = 4.15158 loss)
I0407 08:34:53.598948 15775 solver.cpp:218] Iteration 1332 (0.875068 iter/s, 13.7132s/12 iters), loss = 4.06243
I0407 08:34:53.599001 15775 solver.cpp:237]     Train net output #0: loss = 4.06243 (* 1 = 4.06243 loss)
I0407 08:34:53.599012 15775 sgd_solver.cpp:105] Iteration 1332, lr = 0.01
I0407 08:34:58.633435 15775 solver.cpp:218] Iteration 1344 (2.3836 iter/s, 5.03439s/12 iters), loss = 4.11304
I0407 08:34:58.633476 15775 solver.cpp:237]     Train net output #0: loss = 4.11304 (* 1 = 4.11304 loss)
I0407 08:34:58.633482 15775 sgd_solver.cpp:105] Iteration 1344, lr = 0.01
I0407 08:35:04.038491 15775 solver.cpp:218] Iteration 1356 (2.22018 iter/s, 5.40496s/12 iters), loss = 3.9623
I0407 08:35:04.038591 15775 solver.cpp:237]     Train net output #0: loss = 3.9623 (* 1 = 3.9623 loss)
I0407 08:35:04.038599 15775 sgd_solver.cpp:105] Iteration 1356, lr = 0.01
I0407 08:35:09.334935 15775 solver.cpp:218] Iteration 1368 (2.26573 iter/s, 5.2963s/12 iters), loss = 4.09085
I0407 08:35:09.334980 15775 solver.cpp:237]     Train net output #0: loss = 4.09085 (* 1 = 4.09085 loss)
I0407 08:35:09.334987 15775 sgd_solver.cpp:105] Iteration 1368, lr = 0.01
I0407 08:35:10.590122 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:35:14.574594 15775 solver.cpp:218] Iteration 1380 (2.29026 iter/s, 5.23957s/12 iters), loss = 4.1047
I0407 08:35:14.574635 15775 solver.cpp:237]     Train net output #0: loss = 4.1047 (* 1 = 4.1047 loss)
I0407 08:35:14.574642 15775 sgd_solver.cpp:105] Iteration 1380, lr = 0.01
I0407 08:35:19.845712 15775 solver.cpp:218] Iteration 1392 (2.27659 iter/s, 5.27103s/12 iters), loss = 3.99979
I0407 08:35:19.845755 15775 solver.cpp:237]     Train net output #0: loss = 3.99979 (* 1 = 3.99979 loss)
I0407 08:35:19.845762 15775 sgd_solver.cpp:105] Iteration 1392, lr = 0.01
I0407 08:35:25.237605 15775 solver.cpp:218] Iteration 1404 (2.2256 iter/s, 5.3918s/12 iters), loss = 3.89264
I0407 08:35:25.237648 15775 solver.cpp:237]     Train net output #0: loss = 3.89264 (* 1 = 3.89264 loss)
I0407 08:35:25.237655 15775 sgd_solver.cpp:105] Iteration 1404, lr = 0.01
I0407 08:35:29.970504 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:30.357975 15775 solver.cpp:218] Iteration 1416 (2.34362 iter/s, 5.12028s/12 iters), loss = 4.03186
I0407 08:35:30.358012 15775 solver.cpp:237]     Train net output #0: loss = 4.03186 (* 1 = 4.03186 loss)
I0407 08:35:30.358021 15775 sgd_solver.cpp:105] Iteration 1416, lr = 0.01
I0407 08:35:35.101398 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 08:35:38.131191 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 08:35:40.535537 15775 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 08:35:40.535563 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:35:44.268036 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:44.856251 15775 solver.cpp:397]     Test net output #0: accuracy = 0.126838
I0407 08:35:44.856287 15775 solver.cpp:397]     Test net output #1: loss = 4.03462 (* 1 = 4.03462 loss)
I0407 08:35:44.996933 15775 solver.cpp:218] Iteration 1428 (0.819738 iter/s, 14.6388s/12 iters), loss = 3.8426
I0407 08:35:44.996979 15775 solver.cpp:237]     Train net output #0: loss = 3.8426 (* 1 = 3.8426 loss)
I0407 08:35:44.996986 15775 sgd_solver.cpp:105] Iteration 1428, lr = 0.01
I0407 08:35:49.166721 15775 solver.cpp:218] Iteration 1440 (2.8779 iter/s, 4.1697s/12 iters), loss = 3.86114
I0407 08:35:49.166774 15775 solver.cpp:237]     Train net output #0: loss = 3.86114 (* 1 = 3.86114 loss)
I0407 08:35:49.166783 15775 sgd_solver.cpp:105] Iteration 1440, lr = 0.01
I0407 08:35:54.387774 15775 solver.cpp:218] Iteration 1452 (2.29843 iter/s, 5.22096s/12 iters), loss = 3.95506
I0407 08:35:54.387814 15775 solver.cpp:237]     Train net output #0: loss = 3.95506 (* 1 = 3.95506 loss)
I0407 08:35:54.387822 15775 sgd_solver.cpp:105] Iteration 1452, lr = 0.01
I0407 08:35:59.662045 15775 solver.cpp:218] Iteration 1464 (2.27524 iter/s, 5.27418s/12 iters), loss = 3.70884
I0407 08:35:59.662101 15775 solver.cpp:237]     Train net output #0: loss = 3.70884 (* 1 = 3.70884 loss)
I0407 08:35:59.662112 15775 sgd_solver.cpp:105] Iteration 1464, lr = 0.01
I0407 08:36:04.815834 15775 solver.cpp:218] Iteration 1476 (2.32843 iter/s, 5.15368s/12 iters), loss = 3.92217
I0407 08:36:04.815894 15775 solver.cpp:237]     Train net output #0: loss = 3.92217 (* 1 = 3.92217 loss)
I0407 08:36:04.815905 15775 sgd_solver.cpp:105] Iteration 1476, lr = 0.01
I0407 08:36:10.028079 15775 solver.cpp:218] Iteration 1488 (2.30232 iter/s, 5.21214s/12 iters), loss = 3.68318
I0407 08:36:10.028178 15775 solver.cpp:237]     Train net output #0: loss = 3.68318 (* 1 = 3.68318 loss)
I0407 08:36:10.028187 15775 sgd_solver.cpp:105] Iteration 1488, lr = 0.01
I0407 08:36:15.258334 15775 solver.cpp:218] Iteration 1500 (2.2944 iter/s, 5.23011s/12 iters), loss = 3.87105
I0407 08:36:15.258373 15775 solver.cpp:237]     Train net output #0: loss = 3.87105 (* 1 = 3.87105 loss)
I0407 08:36:15.258380 15775 sgd_solver.cpp:105] Iteration 1500, lr = 0.01
I0407 08:36:20.560636 15775 solver.cpp:218] Iteration 1512 (2.26321 iter/s, 5.30221s/12 iters), loss = 3.77096
I0407 08:36:20.560685 15775 solver.cpp:237]     Train net output #0: loss = 3.77096 (* 1 = 3.77096 loss)
I0407 08:36:20.560694 15775 sgd_solver.cpp:105] Iteration 1512, lr = 0.01
I0407 08:36:22.409528 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:25.816825 15775 solver.cpp:218] Iteration 1524 (2.28306 iter/s, 5.2561s/12 iters), loss = 3.92059
I0407 08:36:25.816861 15775 solver.cpp:237]     Train net output #0: loss = 3.92059 (* 1 = 3.92059 loss)
I0407 08:36:25.816869 15775 sgd_solver.cpp:105] Iteration 1524, lr = 0.01
I0407 08:36:27.784812 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 08:36:30.795264 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 08:36:33.093098 15775 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 08:36:33.093120 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:36:36.860965 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:37.489408 15775 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0407 08:36:37.489434 15775 solver.cpp:397]     Test net output #1: loss = 3.89671 (* 1 = 3.89671 loss)
I0407 08:36:39.620036 15775 solver.cpp:218] Iteration 1536 (0.869372 iter/s, 13.8031s/12 iters), loss = 3.47727
I0407 08:36:39.620086 15775 solver.cpp:237]     Train net output #0: loss = 3.47727 (* 1 = 3.47727 loss)
I0407 08:36:39.620095 15775 sgd_solver.cpp:105] Iteration 1536, lr = 0.01
I0407 08:36:45.055764 15775 solver.cpp:218] Iteration 1548 (2.20766 iter/s, 5.43563s/12 iters), loss = 3.73769
I0407 08:36:45.055924 15775 solver.cpp:237]     Train net output #0: loss = 3.73769 (* 1 = 3.73769 loss)
I0407 08:36:45.055941 15775 sgd_solver.cpp:105] Iteration 1548, lr = 0.01
I0407 08:36:50.390718 15775 solver.cpp:218] Iteration 1560 (2.2494 iter/s, 5.33476s/12 iters), loss = 3.61786
I0407 08:36:50.390755 15775 solver.cpp:237]     Train net output #0: loss = 3.61786 (* 1 = 3.61786 loss)
I0407 08:36:50.390763 15775 sgd_solver.cpp:105] Iteration 1560, lr = 0.01
I0407 08:36:55.740126 15775 solver.cpp:218] Iteration 1572 (2.24327 iter/s, 5.34933s/12 iters), loss = 3.66973
I0407 08:36:55.740166 15775 solver.cpp:237]     Train net output #0: loss = 3.66973 (* 1 = 3.66973 loss)
I0407 08:36:55.740173 15775 sgd_solver.cpp:105] Iteration 1572, lr = 0.01
I0407 08:37:00.816085 15775 solver.cpp:218] Iteration 1584 (2.36413 iter/s, 5.07587s/12 iters), loss = 3.89331
I0407 08:37:00.816143 15775 solver.cpp:237]     Train net output #0: loss = 3.89331 (* 1 = 3.89331 loss)
I0407 08:37:00.816155 15775 sgd_solver.cpp:105] Iteration 1584, lr = 0.01
I0407 08:37:06.042894 15775 solver.cpp:218] Iteration 1596 (2.2959 iter/s, 5.22671s/12 iters), loss = 3.61359
I0407 08:37:06.042935 15775 solver.cpp:237]     Train net output #0: loss = 3.61359 (* 1 = 3.61359 loss)
I0407 08:37:06.042943 15775 sgd_solver.cpp:105] Iteration 1596, lr = 0.01
I0407 08:37:11.395283 15775 solver.cpp:218] Iteration 1608 (2.24202 iter/s, 5.35231s/12 iters), loss = 3.5022
I0407 08:37:11.395319 15775 solver.cpp:237]     Train net output #0: loss = 3.5022 (* 1 = 3.5022 loss)
I0407 08:37:11.395325 15775 sgd_solver.cpp:105] Iteration 1608, lr = 0.01
I0407 08:37:15.479393 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:16.672647 15775 solver.cpp:218] Iteration 1620 (2.2739 iter/s, 5.27728s/12 iters), loss = 3.74052
I0407 08:37:16.672700 15775 solver.cpp:237]     Train net output #0: loss = 3.74052 (* 1 = 3.74052 loss)
I0407 08:37:16.672710 15775 sgd_solver.cpp:105] Iteration 1620, lr = 0.01
I0407 08:37:21.352339 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 08:37:24.394778 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 08:37:26.704370 15775 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 08:37:26.704388 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:37:30.336230 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:30.999420 15775 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0407 08:37:30.999454 15775 solver.cpp:397]     Test net output #1: loss = 3.78851 (* 1 = 3.78851 loss)
I0407 08:37:31.136819 15775 solver.cpp:218] Iteration 1632 (0.829645 iter/s, 14.464s/12 iters), loss = 3.4758
I0407 08:37:31.136869 15775 solver.cpp:237]     Train net output #0: loss = 3.4758 (* 1 = 3.4758 loss)
I0407 08:37:31.136878 15775 sgd_solver.cpp:105] Iteration 1632, lr = 0.01
I0407 08:37:35.496357 15775 solver.cpp:218] Iteration 1644 (2.75264 iter/s, 4.35945s/12 iters), loss = 3.74133
I0407 08:37:35.496392 15775 solver.cpp:237]     Train net output #0: loss = 3.74133 (* 1 = 3.74133 loss)
I0407 08:37:35.496399 15775 sgd_solver.cpp:105] Iteration 1644, lr = 0.01
I0407 08:37:40.779817 15775 solver.cpp:218] Iteration 1656 (2.27128 iter/s, 5.28337s/12 iters), loss = 3.54997
I0407 08:37:40.779865 15775 solver.cpp:237]     Train net output #0: loss = 3.54997 (* 1 = 3.54997 loss)
I0407 08:37:40.779872 15775 sgd_solver.cpp:105] Iteration 1656, lr = 0.01
I0407 08:37:46.150995 15775 solver.cpp:218] Iteration 1668 (2.23419 iter/s, 5.37108s/12 iters), loss = 3.772
I0407 08:37:46.151146 15775 solver.cpp:237]     Train net output #0: loss = 3.772 (* 1 = 3.772 loss)
I0407 08:37:46.151155 15775 sgd_solver.cpp:105] Iteration 1668, lr = 0.01
I0407 08:37:51.357041 15775 solver.cpp:218] Iteration 1680 (2.3051 iter/s, 5.20584s/12 iters), loss = 3.45336
I0407 08:37:51.357089 15775 solver.cpp:237]     Train net output #0: loss = 3.45336 (* 1 = 3.45336 loss)
I0407 08:37:51.357100 15775 sgd_solver.cpp:105] Iteration 1680, lr = 0.01
I0407 08:37:56.866508 15775 solver.cpp:218] Iteration 1692 (2.17811 iter/s, 5.50937s/12 iters), loss = 3.32264
I0407 08:37:56.866551 15775 solver.cpp:237]     Train net output #0: loss = 3.32264 (* 1 = 3.32264 loss)
I0407 08:37:56.866559 15775 sgd_solver.cpp:105] Iteration 1692, lr = 0.01
I0407 08:38:01.944259 15775 solver.cpp:218] Iteration 1704 (2.36329 iter/s, 5.07766s/12 iters), loss = 3.26995
I0407 08:38:01.944303 15775 solver.cpp:237]     Train net output #0: loss = 3.26995 (* 1 = 3.26995 loss)
I0407 08:38:01.944310 15775 sgd_solver.cpp:105] Iteration 1704, lr = 0.01
I0407 08:38:07.174748 15775 solver.cpp:218] Iteration 1716 (2.29428 iter/s, 5.2304s/12 iters), loss = 3.75603
I0407 08:38:07.174787 15775 solver.cpp:237]     Train net output #0: loss = 3.75603 (* 1 = 3.75603 loss)
I0407 08:38:07.174794 15775 sgd_solver.cpp:105] Iteration 1716, lr = 0.01
I0407 08:38:08.184592 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:12.492960 15775 solver.cpp:218] Iteration 1728 (2.25643 iter/s, 5.31813s/12 iters), loss = 3.51817
I0407 08:38:12.493001 15775 solver.cpp:237]     Train net output #0: loss = 3.51817 (* 1 = 3.51817 loss)
I0407 08:38:12.493008 15775 sgd_solver.cpp:105] Iteration 1728, lr = 0.01
I0407 08:38:14.565971 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 08:38:17.534461 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 08:38:19.853181 15775 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 08:38:19.853206 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:38:23.572122 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:24.317274 15775 solver.cpp:397]     Test net output #0: accuracy = 0.170956
I0407 08:38:24.317322 15775 solver.cpp:397]     Test net output #1: loss = 3.67011 (* 1 = 3.67011 loss)
I0407 08:38:26.185520 15775 solver.cpp:218] Iteration 1740 (0.876397 iter/s, 13.6924s/12 iters), loss = 3.48462
I0407 08:38:26.185583 15775 solver.cpp:237]     Train net output #0: loss = 3.48462 (* 1 = 3.48462 loss)
I0407 08:38:26.185595 15775 sgd_solver.cpp:105] Iteration 1740, lr = 0.01
I0407 08:38:31.301688 15775 solver.cpp:218] Iteration 1752 (2.34555 iter/s, 5.11607s/12 iters), loss = 3.47179
I0407 08:38:31.301726 15775 solver.cpp:237]     Train net output #0: loss = 3.47179 (* 1 = 3.47179 loss)
I0407 08:38:31.301734 15775 sgd_solver.cpp:105] Iteration 1752, lr = 0.01
I0407 08:38:36.711046 15775 solver.cpp:218] Iteration 1764 (2.21841 iter/s, 5.40927s/12 iters), loss = 3.67878
I0407 08:38:36.711087 15775 solver.cpp:237]     Train net output #0: loss = 3.67878 (* 1 = 3.67878 loss)
I0407 08:38:36.711095 15775 sgd_solver.cpp:105] Iteration 1764, lr = 0.01
I0407 08:38:41.700762 15775 solver.cpp:218] Iteration 1776 (2.40499 iter/s, 4.98963s/12 iters), loss = 3.18367
I0407 08:38:41.700806 15775 solver.cpp:237]     Train net output #0: loss = 3.18367 (* 1 = 3.18367 loss)
I0407 08:38:41.700814 15775 sgd_solver.cpp:105] Iteration 1776, lr = 0.01
I0407 08:38:46.638881 15775 solver.cpp:218] Iteration 1788 (2.43012 iter/s, 4.93803s/12 iters), loss = 3.14672
I0407 08:38:46.638926 15775 solver.cpp:237]     Train net output #0: loss = 3.14672 (* 1 = 3.14672 loss)
I0407 08:38:46.638932 15775 sgd_solver.cpp:105] Iteration 1788, lr = 0.01
I0407 08:38:52.126713 15775 solver.cpp:218] Iteration 1800 (2.18669 iter/s, 5.48774s/12 iters), loss = 3.53299
I0407 08:38:52.126816 15775 solver.cpp:237]     Train net output #0: loss = 3.53299 (* 1 = 3.53299 loss)
I0407 08:38:52.126824 15775 sgd_solver.cpp:105] Iteration 1800, lr = 0.01
I0407 08:38:57.427667 15775 solver.cpp:218] Iteration 1812 (2.26381 iter/s, 5.30081s/12 iters), loss = 3.10138
I0407 08:38:57.427709 15775 solver.cpp:237]     Train net output #0: loss = 3.10138 (* 1 = 3.10138 loss)
I0407 08:38:57.427716 15775 sgd_solver.cpp:105] Iteration 1812, lr = 0.01
I0407 08:39:00.659574 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:02.507285 15775 solver.cpp:218] Iteration 1824 (2.36242 iter/s, 5.07953s/12 iters), loss = 3.36283
I0407 08:39:02.507325 15775 solver.cpp:237]     Train net output #0: loss = 3.36283 (* 1 = 3.36283 loss)
I0407 08:39:02.507333 15775 sgd_solver.cpp:105] Iteration 1824, lr = 0.01
I0407 08:39:07.264809 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 08:39:10.370818 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 08:39:12.666615 15775 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 08:39:12.666635 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:39:16.271800 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:17.010017 15775 solver.cpp:397]     Test net output #0: accuracy = 0.174632
I0407 08:39:17.010049 15775 solver.cpp:397]     Test net output #1: loss = 3.72011 (* 1 = 3.72011 loss)
I0407 08:39:17.146898 15775 solver.cpp:218] Iteration 1836 (0.819701 iter/s, 14.6395s/12 iters), loss = 3.18155
I0407 08:39:17.146939 15775 solver.cpp:237]     Train net output #0: loss = 3.18155 (* 1 = 3.18155 loss)
I0407 08:39:17.146945 15775 sgd_solver.cpp:105] Iteration 1836, lr = 0.01
I0407 08:39:21.487082 15775 solver.cpp:218] Iteration 1848 (2.76491 iter/s, 4.3401s/12 iters), loss = 3.14836
I0407 08:39:21.487120 15775 solver.cpp:237]     Train net output #0: loss = 3.14836 (* 1 = 3.14836 loss)
I0407 08:39:21.487126 15775 sgd_solver.cpp:105] Iteration 1848, lr = 0.01
I0407 08:39:26.810957 15775 solver.cpp:218] Iteration 1860 (2.25403 iter/s, 5.32379s/12 iters), loss = 3.2645
I0407 08:39:26.811101 15775 solver.cpp:237]     Train net output #0: loss = 3.2645 (* 1 = 3.2645 loss)
I0407 08:39:26.811110 15775 sgd_solver.cpp:105] Iteration 1860, lr = 0.01
I0407 08:39:32.307967 15775 solver.cpp:218] Iteration 1872 (2.18308 iter/s, 5.49682s/12 iters), loss = 3.44138
I0407 08:39:32.308008 15775 solver.cpp:237]     Train net output #0: loss = 3.44138 (* 1 = 3.44138 loss)
I0407 08:39:32.308017 15775 sgd_solver.cpp:105] Iteration 1872, lr = 0.01
I0407 08:39:37.512984 15775 solver.cpp:218] Iteration 1884 (2.30551 iter/s, 5.20493s/12 iters), loss = 3.07741
I0407 08:39:37.513022 15775 solver.cpp:237]     Train net output #0: loss = 3.07741 (* 1 = 3.07741 loss)
I0407 08:39:37.513029 15775 sgd_solver.cpp:105] Iteration 1884, lr = 0.01
I0407 08:39:42.874243 15775 solver.cpp:218] Iteration 1896 (2.23832 iter/s, 5.36117s/12 iters), loss = 3.29923
I0407 08:39:42.874282 15775 solver.cpp:237]     Train net output #0: loss = 3.29923 (* 1 = 3.29923 loss)
I0407 08:39:42.874289 15775 sgd_solver.cpp:105] Iteration 1896, lr = 0.01
I0407 08:39:48.333189 15775 solver.cpp:218] Iteration 1908 (2.19826 iter/s, 5.45886s/12 iters), loss = 3.08818
I0407 08:39:48.333230 15775 solver.cpp:237]     Train net output #0: loss = 3.08818 (* 1 = 3.08818 loss)
I0407 08:39:48.333237 15775 sgd_solver.cpp:105] Iteration 1908, lr = 0.01
I0407 08:39:53.549932 15775 solver.cpp:218] Iteration 1920 (2.30033 iter/s, 5.21665s/12 iters), loss = 3.08402
I0407 08:39:53.549973 15775 solver.cpp:237]     Train net output #0: loss = 3.08402 (* 1 = 3.08402 loss)
I0407 08:39:53.549981 15775 sgd_solver.cpp:105] Iteration 1920, lr = 0.01
I0407 08:39:53.836078 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:58.776521 15775 solver.cpp:218] Iteration 1932 (2.29599 iter/s, 5.2265s/12 iters), loss = 3.26802
I0407 08:39:58.776640 15775 solver.cpp:237]     Train net output #0: loss = 3.26802 (* 1 = 3.26802 loss)
I0407 08:39:58.776648 15775 sgd_solver.cpp:105] Iteration 1932, lr = 0.01
I0407 08:40:00.805631 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 08:40:03.885833 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 08:40:06.191772 15775 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 08:40:06.191793 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:40:09.699748 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:10.472321 15775 solver.cpp:397]     Test net output #0: accuracy = 0.163603
I0407 08:40:10.472348 15775 solver.cpp:397]     Test net output #1: loss = 3.65245 (* 1 = 3.65245 loss)
I0407 08:40:12.286707 15775 solver.cpp:218] Iteration 1944 (0.888233 iter/s, 13.51s/12 iters), loss = 3.15742
I0407 08:40:12.286753 15775 solver.cpp:237]     Train net output #0: loss = 3.15742 (* 1 = 3.15742 loss)
I0407 08:40:12.286761 15775 sgd_solver.cpp:105] Iteration 1944, lr = 0.01
I0407 08:40:17.440735 15775 solver.cpp:218] Iteration 1956 (2.32832 iter/s, 5.15393s/12 iters), loss = 3.30476
I0407 08:40:17.440786 15775 solver.cpp:237]     Train net output #0: loss = 3.30476 (* 1 = 3.30476 loss)
I0407 08:40:17.440796 15775 sgd_solver.cpp:105] Iteration 1956, lr = 0.01
I0407 08:40:22.507212 15775 solver.cpp:218] Iteration 1968 (2.36855 iter/s, 5.06638s/12 iters), loss = 3.3591
I0407 08:40:22.507256 15775 solver.cpp:237]     Train net output #0: loss = 3.3591 (* 1 = 3.3591 loss)
I0407 08:40:22.507266 15775 sgd_solver.cpp:105] Iteration 1968, lr = 0.01
I0407 08:40:27.812465 15775 solver.cpp:218] Iteration 1980 (2.26195 iter/s, 5.30516s/12 iters), loss = 3.06774
I0407 08:40:27.812510 15775 solver.cpp:237]     Train net output #0: loss = 3.06774 (* 1 = 3.06774 loss)
I0407 08:40:27.812520 15775 sgd_solver.cpp:105] Iteration 1980, lr = 0.01
I0407 08:40:33.191015 15775 solver.cpp:218] Iteration 1992 (2.23112 iter/s, 5.37846s/12 iters), loss = 3.2123
I0407 08:40:33.191152 15775 solver.cpp:237]     Train net output #0: loss = 3.2123 (* 1 = 3.2123 loss)
I0407 08:40:33.191162 15775 sgd_solver.cpp:105] Iteration 1992, lr = 0.01
I0407 08:40:38.344240 15775 solver.cpp:218] Iteration 2004 (2.32872 iter/s, 5.15304s/12 iters), loss = 3.28494
I0407 08:40:38.344286 15775 solver.cpp:237]     Train net output #0: loss = 3.28494 (* 1 = 3.28494 loss)
I0407 08:40:38.344295 15775 sgd_solver.cpp:105] Iteration 2004, lr = 0.01
I0407 08:40:43.670608 15775 solver.cpp:218] Iteration 2016 (2.25298 iter/s, 5.32628s/12 iters), loss = 3.31581
I0407 08:40:43.670653 15775 solver.cpp:237]     Train net output #0: loss = 3.31581 (* 1 = 3.31581 loss)
I0407 08:40:43.670661 15775 sgd_solver.cpp:105] Iteration 2016, lr = 0.01
I0407 08:40:46.326436 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:48.892714 15775 solver.cpp:218] Iteration 2028 (2.29797 iter/s, 5.22201s/12 iters), loss = 3.2401
I0407 08:40:48.892756 15775 solver.cpp:237]     Train net output #0: loss = 3.2401 (* 1 = 3.2401 loss)
I0407 08:40:48.892765 15775 sgd_solver.cpp:105] Iteration 2028, lr = 0.01
I0407 08:40:53.575073 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 08:40:56.573895 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 08:40:58.876268 15775 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 08:40:58.876286 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:41:02.433679 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:03.253790 15775 solver.cpp:397]     Test net output #0: accuracy = 0.209559
I0407 08:41:03.253887 15775 solver.cpp:397]     Test net output #1: loss = 3.42351 (* 1 = 3.42351 loss)
I0407 08:41:03.383226 15775 solver.cpp:218] Iteration 2040 (0.828136 iter/s, 14.4904s/12 iters), loss = 3.13441
I0407 08:41:03.383270 15775 solver.cpp:237]     Train net output #0: loss = 3.13441 (* 1 = 3.13441 loss)
I0407 08:41:03.383278 15775 sgd_solver.cpp:105] Iteration 2040, lr = 0.01
I0407 08:41:07.775492 15775 solver.cpp:218] Iteration 2052 (2.73213 iter/s, 4.39218s/12 iters), loss = 3.30901
I0407 08:41:07.775535 15775 solver.cpp:237]     Train net output #0: loss = 3.30901 (* 1 = 3.30901 loss)
I0407 08:41:07.775543 15775 sgd_solver.cpp:105] Iteration 2052, lr = 0.01
I0407 08:41:09.514499 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:41:13.184605 15775 solver.cpp:218] Iteration 2064 (2.21852 iter/s, 5.40902s/12 iters), loss = 2.78771
I0407 08:41:13.184653 15775 solver.cpp:237]     Train net output #0: loss = 2.78771 (* 1 = 2.78771 loss)
I0407 08:41:13.184660 15775 sgd_solver.cpp:105] Iteration 2064, lr = 0.01
I0407 08:41:18.305011 15775 solver.cpp:218] Iteration 2076 (2.34361 iter/s, 5.12032s/12 iters), loss = 2.67524
I0407 08:41:18.305049 15775 solver.cpp:237]     Train net output #0: loss = 2.67524 (* 1 = 2.67524 loss)
I0407 08:41:18.305058 15775 sgd_solver.cpp:105] Iteration 2076, lr = 0.01
I0407 08:41:23.682736 15775 solver.cpp:218] Iteration 2088 (2.23146 iter/s, 5.37764s/12 iters), loss = 3.08047
I0407 08:41:23.682790 15775 solver.cpp:237]     Train net output #0: loss = 3.08047 (* 1 = 3.08047 loss)
I0407 08:41:23.682799 15775 sgd_solver.cpp:105] Iteration 2088, lr = 0.01
I0407 08:41:28.894572 15775 solver.cpp:218] Iteration 2100 (2.3025 iter/s, 5.21174s/12 iters), loss = 3.07009
I0407 08:41:28.894618 15775 solver.cpp:237]     Train net output #0: loss = 3.07009 (* 1 = 3.07009 loss)
I0407 08:41:28.894626 15775 sgd_solver.cpp:105] Iteration 2100, lr = 0.01
I0407 08:41:34.057488 15775 solver.cpp:218] Iteration 2112 (2.32431 iter/s, 5.16283s/12 iters), loss = 2.67676
I0407 08:41:34.057732 15775 solver.cpp:237]     Train net output #0: loss = 2.67676 (* 1 = 2.67676 loss)
I0407 08:41:34.057741 15775 sgd_solver.cpp:105] Iteration 2112, lr = 0.01
I0407 08:41:38.716406 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:39.071818 15775 solver.cpp:218] Iteration 2124 (2.39328 iter/s, 5.01404s/12 iters), loss = 3.00611
I0407 08:41:39.071861 15775 solver.cpp:237]     Train net output #0: loss = 3.00611 (* 1 = 3.00611 loss)
I0407 08:41:39.071868 15775 sgd_solver.cpp:105] Iteration 2124, lr = 0.01
I0407 08:41:44.339432 15775 solver.cpp:218] Iteration 2136 (2.27811 iter/s, 5.26753s/12 iters), loss = 3.01567
I0407 08:41:44.339471 15775 solver.cpp:237]     Train net output #0: loss = 3.01567 (* 1 = 3.01567 loss)
I0407 08:41:44.339480 15775 sgd_solver.cpp:105] Iteration 2136, lr = 0.01
I0407 08:41:46.344935 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 08:41:49.348057 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 08:41:51.661239 15775 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 08:41:51.661263 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:41:55.084777 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:55.934947 15775 solver.cpp:397]     Test net output #0: accuracy = 0.215686
I0407 08:41:55.934975 15775 solver.cpp:397]     Test net output #1: loss = 3.33824 (* 1 = 3.33824 loss)
I0407 08:41:57.903264 15775 solver.cpp:218] Iteration 2148 (0.884715 iter/s, 13.5637s/12 iters), loss = 2.61637
I0407 08:41:57.903308 15775 solver.cpp:237]     Train net output #0: loss = 2.61637 (* 1 = 2.61637 loss)
I0407 08:41:57.903316 15775 sgd_solver.cpp:105] Iteration 2148, lr = 0.01
I0407 08:42:03.245143 15775 solver.cpp:218] Iteration 2160 (2.24644 iter/s, 5.34179s/12 iters), loss = 2.60561
I0407 08:42:03.245185 15775 solver.cpp:237]     Train net output #0: loss = 2.60561 (* 1 = 2.60561 loss)
I0407 08:42:03.245193 15775 sgd_solver.cpp:105] Iteration 2160, lr = 0.01
I0407 08:42:08.370663 15775 solver.cpp:218] Iteration 2172 (2.34127 iter/s, 5.12543s/12 iters), loss = 2.88274
I0407 08:42:08.370757 15775 solver.cpp:237]     Train net output #0: loss = 2.88274 (* 1 = 2.88274 loss)
I0407 08:42:08.370766 15775 sgd_solver.cpp:105] Iteration 2172, lr = 0.01
I0407 08:42:13.575453 15775 solver.cpp:218] Iteration 2184 (2.30563 iter/s, 5.20465s/12 iters), loss = 2.70255
I0407 08:42:13.575503 15775 solver.cpp:237]     Train net output #0: loss = 2.70255 (* 1 = 2.70255 loss)
I0407 08:42:13.575512 15775 sgd_solver.cpp:105] Iteration 2184, lr = 0.01
I0407 08:42:18.865097 15775 solver.cpp:218] Iteration 2196 (2.26862 iter/s, 5.28955s/12 iters), loss = 2.74136
I0407 08:42:18.865149 15775 solver.cpp:237]     Train net output #0: loss = 2.74136 (* 1 = 2.74136 loss)
I0407 08:42:18.865156 15775 sgd_solver.cpp:105] Iteration 2196, lr = 0.01
I0407 08:42:23.972956 15775 solver.cpp:218] Iteration 2208 (2.34936 iter/s, 5.10776s/12 iters), loss = 2.90256
I0407 08:42:23.972998 15775 solver.cpp:237]     Train net output #0: loss = 2.90256 (* 1 = 2.90256 loss)
I0407 08:42:23.973006 15775 sgd_solver.cpp:105] Iteration 2208, lr = 0.01
I0407 08:42:29.229763 15775 solver.cpp:218] Iteration 2220 (2.2828 iter/s, 5.25671s/12 iters), loss = 2.7475
I0407 08:42:29.229816 15775 solver.cpp:237]     Train net output #0: loss = 2.7475 (* 1 = 2.7475 loss)
I0407 08:42:29.229826 15775 sgd_solver.cpp:105] Iteration 2220, lr = 0.01
I0407 08:42:30.894726 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:34.315956 15775 solver.cpp:218] Iteration 2232 (2.35937 iter/s, 5.08609s/12 iters), loss = 2.66962
I0407 08:42:34.315999 15775 solver.cpp:237]     Train net output #0: loss = 2.66962 (* 1 = 2.66962 loss)
I0407 08:42:34.316007 15775 sgd_solver.cpp:105] Iteration 2232, lr = 0.01
I0407 08:42:39.227684 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 08:42:42.283494 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 08:42:44.587110 15775 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 08:42:44.587129 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:42:48.062947 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:48.961423 15775 solver.cpp:397]     Test net output #0: accuracy = 0.230392
I0407 08:42:48.961457 15775 solver.cpp:397]     Test net output #1: loss = 3.3386 (* 1 = 3.3386 loss)
I0407 08:42:49.095934 15775 solver.cpp:218] Iteration 2244 (0.811917 iter/s, 14.7798s/12 iters), loss = 2.82255
I0407 08:42:49.095980 15775 solver.cpp:237]     Train net output #0: loss = 2.82255 (* 1 = 2.82255 loss)
I0407 08:42:49.095988 15775 sgd_solver.cpp:105] Iteration 2244, lr = 0.01
I0407 08:42:53.608052 15775 solver.cpp:218] Iteration 2256 (2.65956 iter/s, 4.51203s/12 iters), loss = 2.35484
I0407 08:42:53.608088 15775 solver.cpp:237]     Train net output #0: loss = 2.35484 (* 1 = 2.35484 loss)
I0407 08:42:53.608094 15775 sgd_solver.cpp:105] Iteration 2256, lr = 0.01
I0407 08:42:58.869834 15775 solver.cpp:218] Iteration 2268 (2.28063 iter/s, 5.2617s/12 iters), loss = 2.68089
I0407 08:42:58.869884 15775 solver.cpp:237]     Train net output #0: loss = 2.68089 (* 1 = 2.68089 loss)
I0407 08:42:58.869894 15775 sgd_solver.cpp:105] Iteration 2268, lr = 0.01
I0407 08:43:04.228698 15775 solver.cpp:218] Iteration 2280 (2.23932 iter/s, 5.35877s/12 iters), loss = 2.66856
I0407 08:43:04.228740 15775 solver.cpp:237]     Train net output #0: loss = 2.66856 (* 1 = 2.66856 loss)
I0407 08:43:04.228747 15775 sgd_solver.cpp:105] Iteration 2280, lr = 0.01
I0407 08:43:09.668064 15775 solver.cpp:218] Iteration 2292 (2.20617 iter/s, 5.43928s/12 iters), loss = 2.68126
I0407 08:43:09.668165 15775 solver.cpp:237]     Train net output #0: loss = 2.68126 (* 1 = 2.68126 loss)
I0407 08:43:09.668174 15775 sgd_solver.cpp:105] Iteration 2292, lr = 0.01
I0407 08:43:15.009407 15775 solver.cpp:218] Iteration 2304 (2.24669 iter/s, 5.3412s/12 iters), loss = 2.72245
I0407 08:43:15.009445 15775 solver.cpp:237]     Train net output #0: loss = 2.72245 (* 1 = 2.72245 loss)
I0407 08:43:15.009454 15775 sgd_solver.cpp:105] Iteration 2304, lr = 0.01
I0407 08:43:19.961683 15775 solver.cpp:218] Iteration 2316 (2.42317 iter/s, 4.95219s/12 iters), loss = 2.4755
I0407 08:43:19.961725 15775 solver.cpp:237]     Train net output #0: loss = 2.4755 (* 1 = 2.4755 loss)
I0407 08:43:19.961733 15775 sgd_solver.cpp:105] Iteration 2316, lr = 0.01
I0407 08:43:24.164489 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:25.345738 15775 solver.cpp:218] Iteration 2328 (2.22884 iter/s, 5.38397s/12 iters), loss = 2.68419
I0407 08:43:25.345782 15775 solver.cpp:237]     Train net output #0: loss = 2.68419 (* 1 = 2.68419 loss)
I0407 08:43:25.345789 15775 sgd_solver.cpp:105] Iteration 2328, lr = 0.01
I0407 08:43:30.537302 15775 solver.cpp:218] Iteration 2340 (2.31148 iter/s, 5.19147s/12 iters), loss = 2.69591
I0407 08:43:30.537345 15775 solver.cpp:237]     Train net output #0: loss = 2.69591 (* 1 = 2.69591 loss)
I0407 08:43:30.537353 15775 sgd_solver.cpp:105] Iteration 2340, lr = 0.01
I0407 08:43:32.571117 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 08:43:35.634306 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 08:43:38.162719 15775 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 08:43:38.162744 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:43:41.526260 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:42.452569 15775 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0407 08:43:42.452600 15775 solver.cpp:397]     Test net output #1: loss = 3.23424 (* 1 = 3.23424 loss)
I0407 08:43:44.241818 15775 solver.cpp:218] Iteration 2352 (0.875633 iter/s, 13.7044s/12 iters), loss = 2.36383
I0407 08:43:44.241861 15775 solver.cpp:237]     Train net output #0: loss = 2.36383 (* 1 = 2.36383 loss)
I0407 08:43:44.241868 15775 sgd_solver.cpp:105] Iteration 2352, lr = 0.01
I0407 08:43:49.230012 15775 solver.cpp:218] Iteration 2364 (2.40573 iter/s, 4.9881s/12 iters), loss = 2.3554
I0407 08:43:49.230054 15775 solver.cpp:237]     Train net output #0: loss = 2.3554 (* 1 = 2.3554 loss)
I0407 08:43:49.230062 15775 sgd_solver.cpp:105] Iteration 2364, lr = 0.01
I0407 08:43:54.308800 15775 solver.cpp:218] Iteration 2376 (2.36281 iter/s, 5.07871s/12 iters), loss = 2.93817
I0407 08:43:54.308838 15775 solver.cpp:237]     Train net output #0: loss = 2.93817 (* 1 = 2.93817 loss)
I0407 08:43:54.308845 15775 sgd_solver.cpp:105] Iteration 2376, lr = 0.01
I0407 08:43:59.548956 15775 solver.cpp:218] Iteration 2388 (2.29005 iter/s, 5.24007s/12 iters), loss = 2.62418
I0407 08:43:59.549000 15775 solver.cpp:237]     Train net output #0: loss = 2.62418 (* 1 = 2.62418 loss)
I0407 08:43:59.549008 15775 sgd_solver.cpp:105] Iteration 2388, lr = 0.01
I0407 08:44:04.962033 15775 solver.cpp:218] Iteration 2400 (2.21689 iter/s, 5.41299s/12 iters), loss = 2.52675
I0407 08:44:04.962074 15775 solver.cpp:237]     Train net output #0: loss = 2.52675 (* 1 = 2.52675 loss)
I0407 08:44:04.962082 15775 sgd_solver.cpp:105] Iteration 2400, lr = 0.01
I0407 08:44:10.202118 15775 solver.cpp:218] Iteration 2412 (2.29008 iter/s, 5.24s/12 iters), loss = 2.60249
I0407 08:44:10.202160 15775 solver.cpp:237]     Train net output #0: loss = 2.60249 (* 1 = 2.60249 loss)
I0407 08:44:10.202168 15775 sgd_solver.cpp:105] Iteration 2412, lr = 0.01
I0407 08:44:15.569610 15775 solver.cpp:218] Iteration 2424 (2.23572 iter/s, 5.3674s/12 iters), loss = 2.84266
I0407 08:44:15.569726 15775 solver.cpp:237]     Train net output #0: loss = 2.84266 (* 1 = 2.84266 loss)
I0407 08:44:15.569736 15775 sgd_solver.cpp:105] Iteration 2424, lr = 0.01
I0407 08:44:16.719781 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:21.065922 15775 solver.cpp:218] Iteration 2436 (2.18335 iter/s, 5.49615s/12 iters), loss = 2.92687
I0407 08:44:21.065965 15775 solver.cpp:237]     Train net output #0: loss = 2.92687 (* 1 = 2.92687 loss)
I0407 08:44:21.065973 15775 sgd_solver.cpp:105] Iteration 2436, lr = 0.01
I0407 08:44:25.639679 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 08:44:28.628180 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 08:44:31.442478 15775 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 08:44:31.442495 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:44:34.764479 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:35.722815 15775 solver.cpp:397]     Test net output #0: accuracy = 0.255515
I0407 08:44:35.722854 15775 solver.cpp:397]     Test net output #1: loss = 3.17496 (* 1 = 3.17496 loss)
I0407 08:44:35.864008 15775 solver.cpp:218] Iteration 2448 (0.810924 iter/s, 14.7979s/12 iters), loss = 2.64514
I0407 08:44:35.864069 15775 solver.cpp:237]     Train net output #0: loss = 2.64514 (* 1 = 2.64514 loss)
I0407 08:44:35.864080 15775 sgd_solver.cpp:105] Iteration 2448, lr = 0.01
I0407 08:44:40.218127 15775 solver.cpp:218] Iteration 2460 (2.75608 iter/s, 4.35402s/12 iters), loss = 2.47853
I0407 08:44:40.218169 15775 solver.cpp:237]     Train net output #0: loss = 2.47853 (* 1 = 2.47853 loss)
I0407 08:44:40.218178 15775 sgd_solver.cpp:105] Iteration 2460, lr = 0.01
I0407 08:44:45.643606 15775 solver.cpp:218] Iteration 2472 (2.21182 iter/s, 5.42539s/12 iters), loss = 2.45372
I0407 08:44:45.643769 15775 solver.cpp:237]     Train net output #0: loss = 2.45372 (* 1 = 2.45372 loss)
I0407 08:44:45.643780 15775 sgd_solver.cpp:105] Iteration 2472, lr = 0.01
I0407 08:44:50.727588 15775 solver.cpp:218] Iteration 2484 (2.36045 iter/s, 5.08377s/12 iters), loss = 2.37377
I0407 08:44:50.727633 15775 solver.cpp:237]     Train net output #0: loss = 2.37377 (* 1 = 2.37377 loss)
I0407 08:44:50.727643 15775 sgd_solver.cpp:105] Iteration 2484, lr = 0.01
I0407 08:44:55.837467 15775 solver.cpp:218] Iteration 2496 (2.34843 iter/s, 5.10979s/12 iters), loss = 2.15388
I0407 08:44:55.837517 15775 solver.cpp:237]     Train net output #0: loss = 2.15388 (* 1 = 2.15388 loss)
I0407 08:44:55.837527 15775 sgd_solver.cpp:105] Iteration 2496, lr = 0.01
I0407 08:45:01.232178 15775 solver.cpp:218] Iteration 2508 (2.22444 iter/s, 5.39462s/12 iters), loss = 2.5503
I0407 08:45:01.232221 15775 solver.cpp:237]     Train net output #0: loss = 2.5503 (* 1 = 2.5503 loss)
I0407 08:45:01.232229 15775 sgd_solver.cpp:105] Iteration 2508, lr = 0.01
I0407 08:45:06.497506 15775 solver.cpp:218] Iteration 2520 (2.2791 iter/s, 5.26524s/12 iters), loss = 2.12506
I0407 08:45:06.497546 15775 solver.cpp:237]     Train net output #0: loss = 2.12506 (* 1 = 2.12506 loss)
I0407 08:45:06.497555 15775 sgd_solver.cpp:105] Iteration 2520, lr = 0.01
I0407 08:45:09.695773 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:11.688683 15775 solver.cpp:218] Iteration 2532 (2.31166 iter/s, 5.19108s/12 iters), loss = 2.4356
I0407 08:45:11.688735 15775 solver.cpp:237]     Train net output #0: loss = 2.4356 (* 1 = 2.4356 loss)
I0407 08:45:11.688745 15775 sgd_solver.cpp:105] Iteration 2532, lr = 0.01
I0407 08:45:17.005384 15775 solver.cpp:218] Iteration 2544 (2.25708 iter/s, 5.31661s/12 iters), loss = 2.21797
I0407 08:45:17.005491 15775 solver.cpp:237]     Train net output #0: loss = 2.21797 (* 1 = 2.21797 loss)
I0407 08:45:17.005501 15775 sgd_solver.cpp:105] Iteration 2544, lr = 0.01
I0407 08:45:19.167285 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 08:45:22.071285 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 08:45:24.816597 15775 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 08:45:24.816617 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:45:28.265467 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:29.317524 15775 solver.cpp:397]     Test net output #0: accuracy = 0.278799
I0407 08:45:29.317553 15775 solver.cpp:397]     Test net output #1: loss = 3.15443 (* 1 = 3.15443 loss)
I0407 08:45:31.260614 15775 solver.cpp:218] Iteration 2556 (0.841808 iter/s, 14.255s/12 iters), loss = 2.23377
I0407 08:45:31.260658 15775 solver.cpp:237]     Train net output #0: loss = 2.23377 (* 1 = 2.23377 loss)
I0407 08:45:31.260664 15775 sgd_solver.cpp:105] Iteration 2556, lr = 0.01
I0407 08:45:36.584874 15775 solver.cpp:218] Iteration 2568 (2.25387 iter/s, 5.32416s/12 iters), loss = 2.38772
I0407 08:45:36.584929 15775 solver.cpp:237]     Train net output #0: loss = 2.38772 (* 1 = 2.38772 loss)
I0407 08:45:36.584937 15775 sgd_solver.cpp:105] Iteration 2568, lr = 0.01
I0407 08:45:41.872915 15775 solver.cpp:218] Iteration 2580 (2.26932 iter/s, 5.28793s/12 iters), loss = 2.5324
I0407 08:45:41.872961 15775 solver.cpp:237]     Train net output #0: loss = 2.5324 (* 1 = 2.5324 loss)
I0407 08:45:41.872969 15775 sgd_solver.cpp:105] Iteration 2580, lr = 0.01
I0407 08:45:47.075999 15775 solver.cpp:218] Iteration 2592 (2.30637 iter/s, 5.20299s/12 iters), loss = 2.18933
I0407 08:45:47.076138 15775 solver.cpp:237]     Train net output #0: loss = 2.18933 (* 1 = 2.18933 loss)
I0407 08:45:47.076145 15775 sgd_solver.cpp:105] Iteration 2592, lr = 0.01
I0407 08:45:52.156445 15775 solver.cpp:218] Iteration 2604 (2.36208 iter/s, 5.08026s/12 iters), loss = 2.34903
I0407 08:45:52.156492 15775 solver.cpp:237]     Train net output #0: loss = 2.34903 (* 1 = 2.34903 loss)
I0407 08:45:52.156500 15775 sgd_solver.cpp:105] Iteration 2604, lr = 0.01
I0407 08:45:57.368098 15775 solver.cpp:218] Iteration 2616 (2.30257 iter/s, 5.21156s/12 iters), loss = 2.27403
I0407 08:45:57.368142 15775 solver.cpp:237]     Train net output #0: loss = 2.27403 (* 1 = 2.27403 loss)
I0407 08:45:57.368151 15775 sgd_solver.cpp:105] Iteration 2616, lr = 0.01
I0407 08:46:02.594161 15775 solver.cpp:218] Iteration 2628 (2.29622 iter/s, 5.22597s/12 iters), loss = 2.403
I0407 08:46:02.594211 15775 solver.cpp:237]     Train net output #0: loss = 2.403 (* 1 = 2.403 loss)
I0407 08:46:02.594219 15775 sgd_solver.cpp:105] Iteration 2628, lr = 0.01
I0407 08:46:03.018738 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:08.034128 15775 solver.cpp:218] Iteration 2640 (2.20593 iter/s, 5.43987s/12 iters), loss = 2.21988
I0407 08:46:08.034171 15775 solver.cpp:237]     Train net output #0: loss = 2.21988 (* 1 = 2.21988 loss)
I0407 08:46:08.034179 15775 sgd_solver.cpp:105] Iteration 2640, lr = 0.01
I0407 08:46:12.811929 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 08:46:15.827594 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 08:46:18.859144 15775 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 08:46:18.859221 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:46:22.170205 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:23.203163 15775 solver.cpp:397]     Test net output #0: accuracy = 0.280637
I0407 08:46:23.203203 15775 solver.cpp:397]     Test net output #1: loss = 3.06205 (* 1 = 3.06205 loss)
I0407 08:46:23.340286 15775 solver.cpp:218] Iteration 2652 (0.784005 iter/s, 15.306s/12 iters), loss = 2.39285
I0407 08:46:23.340354 15775 solver.cpp:237]     Train net output #0: loss = 2.39285 (* 1 = 2.39285 loss)
I0407 08:46:23.340363 15775 sgd_solver.cpp:105] Iteration 2652, lr = 0.01
I0407 08:46:27.852102 15775 solver.cpp:218] Iteration 2664 (2.65975 iter/s, 4.5117s/12 iters), loss = 2.31775
I0407 08:46:27.852159 15775 solver.cpp:237]     Train net output #0: loss = 2.31775 (* 1 = 2.31775 loss)
I0407 08:46:27.852169 15775 sgd_solver.cpp:105] Iteration 2664, lr = 0.01
I0407 08:46:33.145995 15775 solver.cpp:218] Iteration 2676 (2.26681 iter/s, 5.29379s/12 iters), loss = 2.14525
I0407 08:46:33.146039 15775 solver.cpp:237]     Train net output #0: loss = 2.14525 (* 1 = 2.14525 loss)
I0407 08:46:33.146049 15775 sgd_solver.cpp:105] Iteration 2676, lr = 0.01
I0407 08:46:38.625077 15775 solver.cpp:218] Iteration 2688 (2.19019 iter/s, 5.47899s/12 iters), loss = 2.34791
I0407 08:46:38.625124 15775 solver.cpp:237]     Train net output #0: loss = 2.34791 (* 1 = 2.34791 loss)
I0407 08:46:38.625133 15775 sgd_solver.cpp:105] Iteration 2688, lr = 0.01
I0407 08:46:43.928725 15775 solver.cpp:218] Iteration 2700 (2.26263 iter/s, 5.30355s/12 iters), loss = 2.13853
I0407 08:46:43.928774 15775 solver.cpp:237]     Train net output #0: loss = 2.13853 (* 1 = 2.13853 loss)
I0407 08:46:43.928783 15775 sgd_solver.cpp:105] Iteration 2700, lr = 0.01
I0407 08:46:49.107836 15775 solver.cpp:218] Iteration 2712 (2.31704 iter/s, 5.17901s/12 iters), loss = 2.43644
I0407 08:46:49.107977 15775 solver.cpp:237]     Train net output #0: loss = 2.43644 (* 1 = 2.43644 loss)
I0407 08:46:49.107986 15775 sgd_solver.cpp:105] Iteration 2712, lr = 0.01
I0407 08:46:54.525905 15775 solver.cpp:218] Iteration 2724 (2.21489 iter/s, 5.41788s/12 iters), loss = 2.31064
I0407 08:46:54.525944 15775 solver.cpp:237]     Train net output #0: loss = 2.31064 (* 1 = 2.31064 loss)
I0407 08:46:54.525951 15775 sgd_solver.cpp:105] Iteration 2724, lr = 0.01
I0407 08:46:57.309909 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:59.982756 15775 solver.cpp:218] Iteration 2736 (2.19911 iter/s, 5.45676s/12 iters), loss = 2.23895
I0407 08:46:59.982807 15775 solver.cpp:237]     Train net output #0: loss = 2.23895 (* 1 = 2.23895 loss)
I0407 08:46:59.982816 15775 sgd_solver.cpp:105] Iteration 2736, lr = 0.01
I0407 08:47:05.412089 15775 solver.cpp:218] Iteration 2748 (2.21026 iter/s, 5.42923s/12 iters), loss = 2.29072
I0407 08:47:05.412127 15775 solver.cpp:237]     Train net output #0: loss = 2.29072 (* 1 = 2.29072 loss)
I0407 08:47:05.412132 15775 sgd_solver.cpp:105] Iteration 2748, lr = 0.01
I0407 08:47:07.615780 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 08:47:10.626410 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 08:47:14.490864 15775 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 08:47:14.490885 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:47:17.574738 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:47:17.823138 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:18.908849 15775 solver.cpp:397]     Test net output #0: accuracy = 0.271446
I0407 08:47:18.908891 15775 solver.cpp:397]     Test net output #1: loss = 3.17902 (* 1 = 3.17902 loss)
I0407 08:47:21.010478 15775 solver.cpp:218] Iteration 2760 (0.769317 iter/s, 15.5983s/12 iters), loss = 2.46001
I0407 08:47:21.010581 15775 solver.cpp:237]     Train net output #0: loss = 2.46001 (* 1 = 2.46001 loss)
I0407 08:47:21.010589 15775 sgd_solver.cpp:105] Iteration 2760, lr = 0.01
I0407 08:47:26.573964 15775 solver.cpp:218] Iteration 2772 (2.15698 iter/s, 5.56334s/12 iters), loss = 1.96896
I0407 08:47:26.574003 15775 solver.cpp:237]     Train net output #0: loss = 1.96896 (* 1 = 1.96896 loss)
I0407 08:47:26.574012 15775 sgd_solver.cpp:105] Iteration 2772, lr = 0.01
I0407 08:47:32.041783 15775 solver.cpp:218] Iteration 2784 (2.1947 iter/s, 5.46773s/12 iters), loss = 2.38937
I0407 08:47:32.041828 15775 solver.cpp:237]     Train net output #0: loss = 2.38937 (* 1 = 2.38937 loss)
I0407 08:47:32.041836 15775 sgd_solver.cpp:105] Iteration 2784, lr = 0.01
I0407 08:47:37.212491 15775 solver.cpp:218] Iteration 2796 (2.32081 iter/s, 5.17061s/12 iters), loss = 2.26564
I0407 08:47:37.212536 15775 solver.cpp:237]     Train net output #0: loss = 2.26564 (* 1 = 2.26564 loss)
I0407 08:47:37.212544 15775 sgd_solver.cpp:105] Iteration 2796, lr = 0.01
I0407 08:47:42.605640 15775 solver.cpp:218] Iteration 2808 (2.22508 iter/s, 5.39306s/12 iters), loss = 2.40756
I0407 08:47:42.605679 15775 solver.cpp:237]     Train net output #0: loss = 2.40756 (* 1 = 2.40756 loss)
I0407 08:47:42.605687 15775 sgd_solver.cpp:105] Iteration 2808, lr = 0.01
I0407 08:47:47.875121 15775 solver.cpp:218] Iteration 2820 (2.2773 iter/s, 5.26939s/12 iters), loss = 2.05311
I0407 08:47:47.875164 15775 solver.cpp:237]     Train net output #0: loss = 2.05311 (* 1 = 2.05311 loss)
I0407 08:47:47.875170 15775 sgd_solver.cpp:105] Iteration 2820, lr = 0.01
I0407 08:47:52.919366 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:53.248093 15775 solver.cpp:218] Iteration 2832 (2.23344 iter/s, 5.37288s/12 iters), loss = 2.06717
I0407 08:47:53.248136 15775 solver.cpp:237]     Train net output #0: loss = 2.06717 (* 1 = 2.06717 loss)
I0407 08:47:53.248142 15775 sgd_solver.cpp:105] Iteration 2832, lr = 0.01
I0407 08:47:58.187682 15775 solver.cpp:218] Iteration 2844 (2.42939 iter/s, 4.9395s/12 iters), loss = 2.06993
I0407 08:47:58.187727 15775 solver.cpp:237]     Train net output #0: loss = 2.06993 (* 1 = 2.06993 loss)
I0407 08:47:58.187736 15775 sgd_solver.cpp:105] Iteration 2844, lr = 0.01
I0407 08:48:02.563369 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 08:48:06.673924 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 08:48:10.512323 15775 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 08:48:10.512346 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:48:13.732141 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:14.847097 15775 solver.cpp:397]     Test net output #0: accuracy = 0.297794
I0407 08:48:14.847131 15775 solver.cpp:397]     Test net output #1: loss = 3.04849 (* 1 = 3.04849 loss)
I0407 08:48:14.988008 15775 solver.cpp:218] Iteration 2856 (0.714279 iter/s, 16.8002s/12 iters), loss = 2.10809
I0407 08:48:14.988054 15775 solver.cpp:237]     Train net output #0: loss = 2.10809 (* 1 = 2.10809 loss)
I0407 08:48:14.988065 15775 sgd_solver.cpp:105] Iteration 2856, lr = 0.01
I0407 08:48:19.217434 15775 solver.cpp:218] Iteration 2868 (2.83732 iter/s, 4.22934s/12 iters), loss = 1.96598
I0407 08:48:19.217483 15775 solver.cpp:237]     Train net output #0: loss = 1.96598 (* 1 = 1.96598 loss)
I0407 08:48:19.217494 15775 sgd_solver.cpp:105] Iteration 2868, lr = 0.01
I0407 08:48:24.393270 15775 solver.cpp:218] Iteration 2880 (2.31851 iter/s, 5.17574s/12 iters), loss = 2.45764
I0407 08:48:24.393385 15775 solver.cpp:237]     Train net output #0: loss = 2.45764 (* 1 = 2.45764 loss)
I0407 08:48:24.393393 15775 sgd_solver.cpp:105] Iteration 2880, lr = 0.01
I0407 08:48:29.327569 15775 solver.cpp:218] Iteration 2892 (2.43203 iter/s, 4.93415s/12 iters), loss = 2.20189
I0407 08:48:29.327605 15775 solver.cpp:237]     Train net output #0: loss = 2.20189 (* 1 = 2.20189 loss)
I0407 08:48:29.327613 15775 sgd_solver.cpp:105] Iteration 2892, lr = 0.01
I0407 08:48:34.698117 15775 solver.cpp:218] Iteration 2904 (2.23444 iter/s, 5.37047s/12 iters), loss = 1.92231
I0407 08:48:34.698155 15775 solver.cpp:237]     Train net output #0: loss = 1.92231 (* 1 = 1.92231 loss)
I0407 08:48:34.698163 15775 sgd_solver.cpp:105] Iteration 2904, lr = 0.01
I0407 08:48:39.725519 15775 solver.cpp:218] Iteration 2916 (2.38696 iter/s, 5.02732s/12 iters), loss = 1.84864
I0407 08:48:39.725567 15775 solver.cpp:237]     Train net output #0: loss = 1.84864 (* 1 = 1.84864 loss)
I0407 08:48:39.725577 15775 sgd_solver.cpp:105] Iteration 2916, lr = 0.01
I0407 08:48:45.011817 15775 solver.cpp:218] Iteration 2928 (2.27006 iter/s, 5.2862s/12 iters), loss = 2.27549
I0407 08:48:45.011860 15775 solver.cpp:237]     Train net output #0: loss = 2.27549 (* 1 = 2.27549 loss)
I0407 08:48:45.011868 15775 sgd_solver.cpp:105] Iteration 2928, lr = 0.01
I0407 08:48:46.995941 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:50.400983 15775 solver.cpp:218] Iteration 2940 (2.22673 iter/s, 5.38907s/12 iters), loss = 2.04105
I0407 08:48:50.401027 15775 solver.cpp:237]     Train net output #0: loss = 2.04105 (* 1 = 2.04105 loss)
I0407 08:48:50.401036 15775 sgd_solver.cpp:105] Iteration 2940, lr = 0.01
I0407 08:48:55.466970 15775 solver.cpp:218] Iteration 2952 (2.36878 iter/s, 5.06589s/12 iters), loss = 1.99327
I0407 08:48:55.467089 15775 solver.cpp:237]     Train net output #0: loss = 1.99327 (* 1 = 1.99327 loss)
I0407 08:48:55.467098 15775 sgd_solver.cpp:105] Iteration 2952, lr = 0.01
I0407 08:48:57.636574 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 08:49:00.977927 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 08:49:04.869506 15775 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 08:49:04.869525 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:49:08.095402 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:09.266093 15775 solver.cpp:397]     Test net output #0: accuracy = 0.306985
I0407 08:49:09.266124 15775 solver.cpp:397]     Test net output #1: loss = 3.059 (* 1 = 3.059 loss)
I0407 08:49:11.106552 15775 solver.cpp:218] Iteration 2964 (0.767295 iter/s, 15.6394s/12 iters), loss = 2.13145
I0407 08:49:11.106598 15775 solver.cpp:237]     Train net output #0: loss = 2.13145 (* 1 = 2.13145 loss)
I0407 08:49:11.106606 15775 sgd_solver.cpp:105] Iteration 2964, lr = 0.01
I0407 08:49:16.344687 15775 solver.cpp:218] Iteration 2976 (2.29093 iter/s, 5.23804s/12 iters), loss = 1.7773
I0407 08:49:16.344743 15775 solver.cpp:237]     Train net output #0: loss = 1.7773 (* 1 = 1.7773 loss)
I0407 08:49:16.344754 15775 sgd_solver.cpp:105] Iteration 2976, lr = 0.01
I0407 08:49:21.655889 15775 solver.cpp:218] Iteration 2988 (2.25942 iter/s, 5.3111s/12 iters), loss = 2.39647
I0407 08:49:21.655936 15775 solver.cpp:237]     Train net output #0: loss = 2.39647 (* 1 = 2.39647 loss)
I0407 08:49:21.655943 15775 sgd_solver.cpp:105] Iteration 2988, lr = 0.01
I0407 08:49:26.787262 15775 solver.cpp:218] Iteration 3000 (2.3386 iter/s, 5.13128s/12 iters), loss = 2.26435
I0407 08:49:26.787377 15775 solver.cpp:237]     Train net output #0: loss = 2.26435 (* 1 = 2.26435 loss)
I0407 08:49:26.787386 15775 sgd_solver.cpp:105] Iteration 3000, lr = 0.01
I0407 08:49:32.023277 15775 solver.cpp:218] Iteration 3012 (2.29189 iter/s, 5.23585s/12 iters), loss = 2.01897
I0407 08:49:32.023322 15775 solver.cpp:237]     Train net output #0: loss = 2.01897 (* 1 = 2.01897 loss)
I0407 08:49:32.023329 15775 sgd_solver.cpp:105] Iteration 3012, lr = 0.01
I0407 08:49:36.966404 15775 solver.cpp:218] Iteration 3024 (2.42766 iter/s, 4.94303s/12 iters), loss = 2.06167
I0407 08:49:36.966449 15775 solver.cpp:237]     Train net output #0: loss = 2.06167 (* 1 = 2.06167 loss)
I0407 08:49:36.966457 15775 sgd_solver.cpp:105] Iteration 3024, lr = 0.01
I0407 08:49:41.085357 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:42.223960 15775 solver.cpp:218] Iteration 3036 (2.28247 iter/s, 5.25746s/12 iters), loss = 2.01991
I0407 08:49:42.224007 15775 solver.cpp:237]     Train net output #0: loss = 2.01991 (* 1 = 2.01991 loss)
I0407 08:49:42.224014 15775 sgd_solver.cpp:105] Iteration 3036, lr = 0.01
I0407 08:49:47.590844 15775 solver.cpp:218] Iteration 3048 (2.23597 iter/s, 5.36679s/12 iters), loss = 1.74506
I0407 08:49:47.590885 15775 solver.cpp:237]     Train net output #0: loss = 1.74506 (* 1 = 1.74506 loss)
I0407 08:49:47.590893 15775 sgd_solver.cpp:105] Iteration 3048, lr = 0.01
I0407 08:49:52.276810 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 08:49:55.308320 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 08:49:58.754361 15775 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 08:49:58.754451 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:50:01.981299 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:03.175418 15775 solver.cpp:397]     Test net output #0: accuracy = 0.283701
I0407 08:50:03.175454 15775 solver.cpp:397]     Test net output #1: loss = 3.17074 (* 1 = 3.17074 loss)
I0407 08:50:03.309911 15775 solver.cpp:218] Iteration 3060 (0.763411 iter/s, 15.7189s/12 iters), loss = 2.27128
I0407 08:50:03.309955 15775 solver.cpp:237]     Train net output #0: loss = 2.27128 (* 1 = 2.27128 loss)
I0407 08:50:03.309963 15775 sgd_solver.cpp:105] Iteration 3060, lr = 0.01
I0407 08:50:07.720508 15775 solver.cpp:218] Iteration 3072 (2.72077 iter/s, 4.41051s/12 iters), loss = 1.67077
I0407 08:50:07.720556 15775 solver.cpp:237]     Train net output #0: loss = 1.67077 (* 1 = 1.67077 loss)
I0407 08:50:07.720563 15775 sgd_solver.cpp:105] Iteration 3072, lr = 0.01
I0407 08:50:12.786876 15775 solver.cpp:218] Iteration 3084 (2.36861 iter/s, 5.06627s/12 iters), loss = 2.08541
I0407 08:50:12.786917 15775 solver.cpp:237]     Train net output #0: loss = 2.08541 (* 1 = 2.08541 loss)
I0407 08:50:12.786924 15775 sgd_solver.cpp:105] Iteration 3084, lr = 0.01
I0407 08:50:18.071908 15775 solver.cpp:218] Iteration 3096 (2.2706 iter/s, 5.28495s/12 iters), loss = 2.16944
I0407 08:50:18.071945 15775 solver.cpp:237]     Train net output #0: loss = 2.16944 (* 1 = 2.16944 loss)
I0407 08:50:18.071954 15775 sgd_solver.cpp:105] Iteration 3096, lr = 0.01
I0407 08:50:23.357976 15775 solver.cpp:218] Iteration 3108 (2.27015 iter/s, 5.28599s/12 iters), loss = 1.92494
I0407 08:50:23.358019 15775 solver.cpp:237]     Train net output #0: loss = 1.92494 (* 1 = 1.92494 loss)
I0407 08:50:23.358027 15775 sgd_solver.cpp:105] Iteration 3108, lr = 0.01
I0407 08:50:28.576680 15775 solver.cpp:218] Iteration 3120 (2.29946 iter/s, 5.21861s/12 iters), loss = 2.15681
I0407 08:50:28.576723 15775 solver.cpp:237]     Train net output #0: loss = 2.15681 (* 1 = 2.15681 loss)
I0407 08:50:28.576731 15775 sgd_solver.cpp:105] Iteration 3120, lr = 0.01
I0407 08:50:34.022500 15775 solver.cpp:218] Iteration 3132 (2.20356 iter/s, 5.44573s/12 iters), loss = 1.8296
I0407 08:50:34.022634 15775 solver.cpp:237]     Train net output #0: loss = 1.8296 (* 1 = 1.8296 loss)
I0407 08:50:34.022644 15775 sgd_solver.cpp:105] Iteration 3132, lr = 0.01
I0407 08:50:35.083101 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:39.133489 15775 solver.cpp:218] Iteration 3144 (2.34796 iter/s, 5.11081s/12 iters), loss = 1.56837
I0407 08:50:39.133545 15775 solver.cpp:237]     Train net output #0: loss = 1.56837 (* 1 = 1.56837 loss)
I0407 08:50:39.133556 15775 sgd_solver.cpp:105] Iteration 3144, lr = 0.01
I0407 08:50:44.372432 15775 solver.cpp:218] Iteration 3156 (2.29058 iter/s, 5.23885s/12 iters), loss = 2.52365
I0407 08:50:44.372479 15775 solver.cpp:237]     Train net output #0: loss = 2.52365 (* 1 = 2.52365 loss)
I0407 08:50:44.372488 15775 sgd_solver.cpp:105] Iteration 3156, lr = 0.01
I0407 08:50:46.413349 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 08:50:49.460378 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 08:50:51.772269 15775 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 08:50:51.772292 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:50:54.839440 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:56.079210 15775 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0407 08:50:56.079246 15775 solver.cpp:397]     Test net output #1: loss = 3.15319 (* 1 = 3.15319 loss)
I0407 08:50:57.938694 15775 solver.cpp:218] Iteration 3168 (0.884556 iter/s, 13.5661s/12 iters), loss = 1.67624
I0407 08:50:57.938740 15775 solver.cpp:237]     Train net output #0: loss = 1.67624 (* 1 = 1.67624 loss)
I0407 08:50:57.938750 15775 sgd_solver.cpp:105] Iteration 3168, lr = 0.01
I0407 08:51:02.813184 15775 solver.cpp:218] Iteration 3180 (2.46184 iter/s, 4.8744s/12 iters), loss = 1.89246
I0407 08:51:02.813241 15775 solver.cpp:237]     Train net output #0: loss = 1.89246 (* 1 = 1.89246 loss)
I0407 08:51:02.813256 15775 sgd_solver.cpp:105] Iteration 3180, lr = 0.01
I0407 08:51:07.965162 15775 solver.cpp:218] Iteration 3192 (2.32925 iter/s, 5.15188s/12 iters), loss = 1.50983
I0407 08:51:07.965273 15775 solver.cpp:237]     Train net output #0: loss = 1.50983 (* 1 = 1.50983 loss)
I0407 08:51:07.965281 15775 sgd_solver.cpp:105] Iteration 3192, lr = 0.01
I0407 08:51:13.298365 15775 solver.cpp:218] Iteration 3204 (2.25012 iter/s, 5.33305s/12 iters), loss = 2.03875
I0407 08:51:13.298409 15775 solver.cpp:237]     Train net output #0: loss = 2.03875 (* 1 = 2.03875 loss)
I0407 08:51:13.298418 15775 sgd_solver.cpp:105] Iteration 3204, lr = 0.01
I0407 08:51:18.829036 15775 solver.cpp:218] Iteration 3216 (2.16975 iter/s, 5.53058s/12 iters), loss = 2.32128
I0407 08:51:18.829082 15775 solver.cpp:237]     Train net output #0: loss = 2.32128 (* 1 = 2.32128 loss)
I0407 08:51:18.829092 15775 sgd_solver.cpp:105] Iteration 3216, lr = 0.01
I0407 08:51:24.092744 15775 solver.cpp:218] Iteration 3228 (2.2798 iter/s, 5.26362s/12 iters), loss = 1.56408
I0407 08:51:24.092788 15775 solver.cpp:237]     Train net output #0: loss = 1.56408 (* 1 = 1.56408 loss)
I0407 08:51:24.092795 15775 sgd_solver.cpp:105] Iteration 3228, lr = 0.01
I0407 08:51:27.499612 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:29.443225 15775 solver.cpp:218] Iteration 3240 (2.24283 iter/s, 5.35039s/12 iters), loss = 2.03282
I0407 08:51:29.443266 15775 solver.cpp:237]     Train net output #0: loss = 2.03282 (* 1 = 2.03282 loss)
I0407 08:51:29.443274 15775 sgd_solver.cpp:105] Iteration 3240, lr = 0.01
I0407 08:51:34.702025 15775 solver.cpp:218] Iteration 3252 (2.28192 iter/s, 5.25872s/12 iters), loss = 1.76479
I0407 08:51:34.702064 15775 solver.cpp:237]     Train net output #0: loss = 1.76479 (* 1 = 1.76479 loss)
I0407 08:51:34.702070 15775 sgd_solver.cpp:105] Iteration 3252, lr = 0.01
I0407 08:51:39.461092 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 08:51:42.405830 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 08:51:44.706154 15775 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 08:51:44.706173 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:51:47.753690 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:49.029999 15775 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0407 08:51:49.030041 15775 solver.cpp:397]     Test net output #1: loss = 3.15602 (* 1 = 3.15602 loss)
I0407 08:51:49.170675 15775 solver.cpp:218] Iteration 3264 (0.829387 iter/s, 14.4685s/12 iters), loss = 2.01612
I0407 08:51:49.170739 15775 solver.cpp:237]     Train net output #0: loss = 2.01612 (* 1 = 2.01612 loss)
I0407 08:51:49.170751 15775 sgd_solver.cpp:105] Iteration 3264, lr = 0.01
I0407 08:51:53.625576 15775 solver.cpp:218] Iteration 3276 (2.69373 iter/s, 4.4548s/12 iters), loss = 1.40866
I0407 08:51:53.625628 15775 solver.cpp:237]     Train net output #0: loss = 1.40866 (* 1 = 1.40866 loss)
I0407 08:51:53.625636 15775 sgd_solver.cpp:105] Iteration 3276, lr = 0.01
I0407 08:51:58.888124 15775 solver.cpp:218] Iteration 3288 (2.28031 iter/s, 5.26245s/12 iters), loss = 1.91559
I0407 08:51:58.888165 15775 solver.cpp:237]     Train net output #0: loss = 1.91559 (* 1 = 1.91559 loss)
I0407 08:51:58.888172 15775 sgd_solver.cpp:105] Iteration 3288, lr = 0.01
I0407 08:52:04.265422 15775 solver.cpp:218] Iteration 3300 (2.23164 iter/s, 5.37721s/12 iters), loss = 2.03875
I0407 08:52:04.265465 15775 solver.cpp:237]     Train net output #0: loss = 2.03875 (* 1 = 2.03875 loss)
I0407 08:52:04.265470 15775 sgd_solver.cpp:105] Iteration 3300, lr = 0.01
I0407 08:52:09.517112 15775 solver.cpp:218] Iteration 3312 (2.28502 iter/s, 5.2516s/12 iters), loss = 2.19413
I0407 08:52:09.517221 15775 solver.cpp:237]     Train net output #0: loss = 2.19413 (* 1 = 2.19413 loss)
I0407 08:52:09.517230 15775 sgd_solver.cpp:105] Iteration 3312, lr = 0.01
I0407 08:52:14.513178 15775 solver.cpp:218] Iteration 3324 (2.40196 iter/s, 4.99591s/12 iters), loss = 2.12056
I0407 08:52:14.513228 15775 solver.cpp:237]     Train net output #0: loss = 2.12056 (* 1 = 2.12056 loss)
I0407 08:52:14.513238 15775 sgd_solver.cpp:105] Iteration 3324, lr = 0.01
I0407 08:52:19.636600 15775 solver.cpp:218] Iteration 3336 (2.34223 iter/s, 5.12333s/12 iters), loss = 1.90549
I0407 08:52:19.636664 15775 solver.cpp:237]     Train net output #0: loss = 1.90549 (* 1 = 1.90549 loss)
I0407 08:52:19.636678 15775 sgd_solver.cpp:105] Iteration 3336, lr = 0.01
I0407 08:52:20.130228 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:24.748136 15775 solver.cpp:218] Iteration 3348 (2.34768 iter/s, 5.11143s/12 iters), loss = 2.08286
I0407 08:52:24.748178 15775 solver.cpp:237]     Train net output #0: loss = 2.08286 (* 1 = 2.08286 loss)
I0407 08:52:24.748186 15775 sgd_solver.cpp:105] Iteration 3348, lr = 0.01
I0407 08:52:29.805456 15775 solver.cpp:218] Iteration 3360 (2.37284 iter/s, 5.05724s/12 iters), loss = 2.15313
I0407 08:52:29.805493 15775 solver.cpp:237]     Train net output #0: loss = 2.15313 (* 1 = 2.15313 loss)
I0407 08:52:29.805500 15775 sgd_solver.cpp:105] Iteration 3360, lr = 0.01
I0407 08:52:31.934234 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 08:52:34.932420 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 08:52:37.226672 15775 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 08:52:37.226696 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:52:40.230777 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:41.540014 15775 solver.cpp:397]     Test net output #0: accuracy = 0.283701
I0407 08:52:41.540050 15775 solver.cpp:397]     Test net output #1: loss = 3.13515 (* 1 = 3.13515 loss)
I0407 08:52:43.417243 15775 solver.cpp:218] Iteration 3372 (0.881597 iter/s, 13.6117s/12 iters), loss = 3.31395
I0407 08:52:43.417277 15775 solver.cpp:237]     Train net output #0: loss = 3.31395 (* 1 = 3.31395 loss)
I0407 08:52:43.417284 15775 sgd_solver.cpp:105] Iteration 3372, lr = 0.001
I0407 08:52:48.651856 15775 solver.cpp:218] Iteration 3384 (2.29247 iter/s, 5.23453s/12 iters), loss = 2.36321
I0407 08:52:48.651908 15775 solver.cpp:237]     Train net output #0: loss = 2.36321 (* 1 = 2.36321 loss)
I0407 08:52:48.651917 15775 sgd_solver.cpp:105] Iteration 3384, lr = 0.001
I0407 08:52:54.150837 15775 solver.cpp:218] Iteration 3396 (2.18226 iter/s, 5.49888s/12 iters), loss = 2.13261
I0407 08:52:54.150885 15775 solver.cpp:237]     Train net output #0: loss = 2.13261 (* 1 = 2.13261 loss)
I0407 08:52:54.150894 15775 sgd_solver.cpp:105] Iteration 3396, lr = 0.001
I0407 08:52:59.542361 15775 solver.cpp:218] Iteration 3408 (2.22575 iter/s, 5.39143s/12 iters), loss = 1.6675
I0407 08:52:59.542402 15775 solver.cpp:237]     Train net output #0: loss = 1.6675 (* 1 = 1.6675 loss)
I0407 08:52:59.542409 15775 sgd_solver.cpp:105] Iteration 3408, lr = 0.001
I0407 08:53:04.777921 15775 solver.cpp:218] Iteration 3420 (2.29206 iter/s, 5.23547s/12 iters), loss = 1.51478
I0407 08:53:04.777963 15775 solver.cpp:237]     Train net output #0: loss = 1.51478 (* 1 = 1.51478 loss)
I0407 08:53:04.777971 15775 sgd_solver.cpp:105] Iteration 3420, lr = 0.001
I0407 08:53:09.993438 15775 solver.cpp:218] Iteration 3432 (2.30087 iter/s, 5.21543s/12 iters), loss = 1.46058
I0407 08:53:09.993479 15775 solver.cpp:237]     Train net output #0: loss = 1.46058 (* 1 = 1.46058 loss)
I0407 08:53:09.993486 15775 sgd_solver.cpp:105] Iteration 3432, lr = 0.001
I0407 08:53:12.613310 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:15.169481 15775 solver.cpp:218] Iteration 3444 (2.31841 iter/s, 5.17596s/12 iters), loss = 1.45203
I0407 08:53:15.169523 15775 solver.cpp:237]     Train net output #0: loss = 1.45203 (* 1 = 1.45203 loss)
I0407 08:53:15.169531 15775 sgd_solver.cpp:105] Iteration 3444, lr = 0.001
I0407 08:53:20.538151 15775 solver.cpp:218] Iteration 3456 (2.23523 iter/s, 5.36858s/12 iters), loss = 1.22291
I0407 08:53:20.538189 15775 solver.cpp:237]     Train net output #0: loss = 1.22291 (* 1 = 1.22291 loss)
I0407 08:53:20.538197 15775 sgd_solver.cpp:105] Iteration 3456, lr = 0.001
I0407 08:53:24.983013 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 08:53:28.013375 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 08:53:30.319254 15775 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 08:53:30.319273 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:53:30.770978 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:53:33.406067 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:34.785773 15775 solver.cpp:397]     Test net output #0: accuracy = 0.380515
I0407 08:53:34.785815 15775 solver.cpp:397]     Test net output #1: loss = 2.70531 (* 1 = 2.70531 loss)
I0407 08:53:34.919700 15775 solver.cpp:218] Iteration 3468 (0.83441 iter/s, 14.3814s/12 iters), loss = 1.49288
I0407 08:53:34.919756 15775 solver.cpp:237]     Train net output #0: loss = 1.49288 (* 1 = 1.49288 loss)
I0407 08:53:34.919765 15775 sgd_solver.cpp:105] Iteration 3468, lr = 0.001
I0407 08:53:39.472527 15775 solver.cpp:218] Iteration 3480 (2.63578 iter/s, 4.55273s/12 iters), loss = 1.41311
I0407 08:53:39.472568 15775 solver.cpp:237]     Train net output #0: loss = 1.41311 (* 1 = 1.41311 loss)
I0407 08:53:39.472574 15775 sgd_solver.cpp:105] Iteration 3480, lr = 0.001
I0407 08:53:44.652391 15775 solver.cpp:218] Iteration 3492 (2.3167 iter/s, 5.17978s/12 iters), loss = 1.10911
I0407 08:53:44.652527 15775 solver.cpp:237]     Train net output #0: loss = 1.10911 (* 1 = 1.10911 loss)
I0407 08:53:44.652537 15775 sgd_solver.cpp:105] Iteration 3492, lr = 0.001
I0407 08:53:49.822867 15775 solver.cpp:218] Iteration 3504 (2.32095 iter/s, 5.1703s/12 iters), loss = 1.23731
I0407 08:53:49.822906 15775 solver.cpp:237]     Train net output #0: loss = 1.23731 (* 1 = 1.23731 loss)
I0407 08:53:49.822912 15775 sgd_solver.cpp:105] Iteration 3504, lr = 0.001
I0407 08:53:55.011029 15775 solver.cpp:218] Iteration 3516 (2.313 iter/s, 5.18808s/12 iters), loss = 1.2339
I0407 08:53:55.011070 15775 solver.cpp:237]     Train net output #0: loss = 1.2339 (* 1 = 1.2339 loss)
I0407 08:53:55.011077 15775 sgd_solver.cpp:105] Iteration 3516, lr = 0.001
I0407 08:54:00.135242 15775 solver.cpp:218] Iteration 3528 (2.34186 iter/s, 5.12412s/12 iters), loss = 1.26289
I0407 08:54:00.135279 15775 solver.cpp:237]     Train net output #0: loss = 1.26289 (* 1 = 1.26289 loss)
I0407 08:54:00.135288 15775 sgd_solver.cpp:105] Iteration 3528, lr = 0.001
I0407 08:54:05.283517 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:05.585927 15775 solver.cpp:218] Iteration 3540 (2.20159 iter/s, 5.4506s/12 iters), loss = 0.879847
I0407 08:54:05.585970 15775 solver.cpp:237]     Train net output #0: loss = 0.879847 (* 1 = 0.879847 loss)
I0407 08:54:05.585978 15775 sgd_solver.cpp:105] Iteration 3540, lr = 0.001
I0407 08:54:10.976099 15775 solver.cpp:218] Iteration 3552 (2.22631 iter/s, 5.39008s/12 iters), loss = 0.961726
I0407 08:54:10.976142 15775 solver.cpp:237]     Train net output #0: loss = 0.961726 (* 1 = 0.961726 loss)
I0407 08:54:10.976150 15775 sgd_solver.cpp:105] Iteration 3552, lr = 0.001
I0407 08:54:16.374081 15775 solver.cpp:218] Iteration 3564 (2.22309 iter/s, 5.39789s/12 iters), loss = 0.831375
I0407 08:54:16.374800 15775 solver.cpp:237]     Train net output #0: loss = 0.831375 (* 1 = 0.831375 loss)
I0407 08:54:16.374810 15775 sgd_solver.cpp:105] Iteration 3564, lr = 0.001
I0407 08:54:18.403503 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 08:54:21.342454 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 08:54:23.641278 15775 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 08:54:23.641295 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:54:26.621476 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:28.016038 15775 solver.cpp:397]     Test net output #0: accuracy = 0.385417
I0407 08:54:28.016083 15775 solver.cpp:397]     Test net output #1: loss = 2.65714 (* 1 = 2.65714 loss)
I0407 08:54:29.839123 15775 solver.cpp:218] Iteration 3576 (0.891251 iter/s, 13.4642s/12 iters), loss = 1.09358
I0407 08:54:29.839177 15775 solver.cpp:237]     Train net output #0: loss = 1.09358 (* 1 = 1.09358 loss)
I0407 08:54:29.839187 15775 sgd_solver.cpp:105] Iteration 3576, lr = 0.001
I0407 08:54:34.956238 15775 solver.cpp:218] Iteration 3588 (2.34512 iter/s, 5.11702s/12 iters), loss = 1.38278
I0407 08:54:34.956291 15775 solver.cpp:237]     Train net output #0: loss = 1.38278 (* 1 = 1.38278 loss)
I0407 08:54:34.956302 15775 sgd_solver.cpp:105] Iteration 3588, lr = 0.001
I0407 08:54:40.184594 15775 solver.cpp:218] Iteration 3600 (2.29522 iter/s, 5.22826s/12 iters), loss = 1.24442
I0407 08:54:40.184638 15775 solver.cpp:237]     Train net output #0: loss = 1.24442 (* 1 = 1.24442 loss)
I0407 08:54:40.184645 15775 sgd_solver.cpp:105] Iteration 3600, lr = 0.001
I0407 08:54:45.447793 15775 solver.cpp:218] Iteration 3612 (2.28002 iter/s, 5.26311s/12 iters), loss = 1.13586
I0407 08:54:45.447834 15775 solver.cpp:237]     Train net output #0: loss = 1.13586 (* 1 = 1.13586 loss)
I0407 08:54:45.447841 15775 sgd_solver.cpp:105] Iteration 3612, lr = 0.001
I0407 08:54:50.927696 15775 solver.cpp:218] Iteration 3624 (2.18985 iter/s, 5.47982s/12 iters), loss = 0.947231
I0407 08:54:50.927836 15775 solver.cpp:237]     Train net output #0: loss = 0.947231 (* 1 = 0.947231 loss)
I0407 08:54:50.927845 15775 sgd_solver.cpp:105] Iteration 3624, lr = 0.001
I0407 08:54:56.392719 15775 solver.cpp:218] Iteration 3636 (2.19586 iter/s, 5.46484s/12 iters), loss = 0.94565
I0407 08:54:56.392760 15775 solver.cpp:237]     Train net output #0: loss = 0.94565 (* 1 = 0.94565 loss)
I0407 08:54:56.392767 15775 sgd_solver.cpp:105] Iteration 3636, lr = 0.001
I0407 08:54:58.405166 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:01.669941 15775 solver.cpp:218] Iteration 3648 (2.27396 iter/s, 5.27713s/12 iters), loss = 0.669473
I0407 08:55:01.669988 15775 solver.cpp:237]     Train net output #0: loss = 0.669473 (* 1 = 0.669473 loss)
I0407 08:55:01.669996 15775 sgd_solver.cpp:105] Iteration 3648, lr = 0.001
I0407 08:55:06.632443 15775 solver.cpp:218] Iteration 3660 (2.41818 iter/s, 4.96241s/12 iters), loss = 0.917289
I0407 08:55:06.632485 15775 solver.cpp:237]     Train net output #0: loss = 0.917289 (* 1 = 0.917289 loss)
I0407 08:55:06.632493 15775 sgd_solver.cpp:105] Iteration 3660, lr = 0.001
I0407 08:55:11.180281 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 08:55:14.208577 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 08:55:16.519858 15775 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 08:55:16.519881 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:55:19.388890 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:20.941349 15775 solver.cpp:397]     Test net output #0: accuracy = 0.403186
I0407 08:55:20.941413 15775 solver.cpp:397]     Test net output #1: loss = 2.59891 (* 1 = 2.59891 loss)
I0407 08:55:21.079643 15775 solver.cpp:218] Iteration 3672 (0.830619 iter/s, 14.4471s/12 iters), loss = 1.16479
I0407 08:55:21.079712 15775 solver.cpp:237]     Train net output #0: loss = 1.16479 (* 1 = 1.16479 loss)
I0407 08:55:21.079722 15775 sgd_solver.cpp:105] Iteration 3672, lr = 0.001
I0407 08:55:25.407835 15775 solver.cpp:218] Iteration 3684 (2.77259 iter/s, 4.32808s/12 iters), loss = 0.82109
I0407 08:55:25.407881 15775 solver.cpp:237]     Train net output #0: loss = 0.82109 (* 1 = 0.82109 loss)
I0407 08:55:25.407889 15775 sgd_solver.cpp:105] Iteration 3684, lr = 0.001
I0407 08:55:30.804401 15775 solver.cpp:218] Iteration 3696 (2.22367 iter/s, 5.39648s/12 iters), loss = 1.15925
I0407 08:55:30.804445 15775 solver.cpp:237]     Train net output #0: loss = 1.15925 (* 1 = 1.15925 loss)
I0407 08:55:30.804452 15775 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0407 08:55:36.257359 15775 solver.cpp:218] Iteration 3708 (2.20068 iter/s, 5.45287s/12 iters), loss = 1.03501
I0407 08:55:36.257405 15775 solver.cpp:237]     Train net output #0: loss = 1.03501 (* 1 = 1.03501 loss)
I0407 08:55:36.257412 15775 sgd_solver.cpp:105] Iteration 3708, lr = 0.001
I0407 08:55:41.534466 15775 solver.cpp:218] Iteration 3720 (2.27401 iter/s, 5.27701s/12 iters), loss = 0.796326
I0407 08:55:41.534515 15775 solver.cpp:237]     Train net output #0: loss = 0.796326 (* 1 = 0.796326 loss)
I0407 08:55:41.534524 15775 sgd_solver.cpp:105] Iteration 3720, lr = 0.001
I0407 08:55:46.936774 15775 solver.cpp:218] Iteration 3732 (2.22131 iter/s, 5.40221s/12 iters), loss = 1.0039
I0407 08:55:46.936815 15775 solver.cpp:237]     Train net output #0: loss = 1.0039 (* 1 = 1.0039 loss)
I0407 08:55:46.936821 15775 sgd_solver.cpp:105] Iteration 3732, lr = 0.001
I0407 08:55:51.195952 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:52.324033 15775 solver.cpp:218] Iteration 3744 (2.22752 iter/s, 5.38716s/12 iters), loss = 0.903393
I0407 08:55:52.324084 15775 solver.cpp:237]     Train net output #0: loss = 0.903393 (* 1 = 0.903393 loss)
I0407 08:55:52.324093 15775 sgd_solver.cpp:105] Iteration 3744, lr = 0.001
I0407 08:55:57.715957 15775 solver.cpp:218] Iteration 3756 (2.22559 iter/s, 5.39183s/12 iters), loss = 0.913514
I0407 08:55:57.716001 15775 solver.cpp:237]     Train net output #0: loss = 0.913514 (* 1 = 0.913514 loss)
I0407 08:55:57.716008 15775 sgd_solver.cpp:105] Iteration 3756, lr = 0.001
I0407 08:56:02.742345 15775 solver.cpp:218] Iteration 3768 (2.38744 iter/s, 5.0263s/12 iters), loss = 0.909545
I0407 08:56:02.742390 15775 solver.cpp:237]     Train net output #0: loss = 0.909545 (* 1 = 0.909545 loss)
I0407 08:56:02.742398 15775 sgd_solver.cpp:105] Iteration 3768, lr = 0.001
I0407 08:56:04.850294 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 08:56:07.842041 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 08:56:10.156877 15775 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 08:56:10.156905 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:56:12.985185 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:14.454375 15775 solver.cpp:397]     Test net output #0: accuracy = 0.417279
I0407 08:56:14.454421 15775 solver.cpp:397]     Test net output #1: loss = 2.60302 (* 1 = 2.60302 loss)
I0407 08:56:16.500152 15775 solver.cpp:218] Iteration 3780 (0.872241 iter/s, 13.7577s/12 iters), loss = 0.673351
I0407 08:56:16.500195 15775 solver.cpp:237]     Train net output #0: loss = 0.673351 (* 1 = 0.673351 loss)
I0407 08:56:16.500202 15775 sgd_solver.cpp:105] Iteration 3780, lr = 0.001
I0407 08:56:21.806212 15775 solver.cpp:218] Iteration 3792 (2.2616 iter/s, 5.30597s/12 iters), loss = 0.874574
I0407 08:56:21.806349 15775 solver.cpp:237]     Train net output #0: loss = 0.874574 (* 1 = 0.874574 loss)
I0407 08:56:21.806360 15775 sgd_solver.cpp:105] Iteration 3792, lr = 0.001
I0407 08:56:27.225172 15775 solver.cpp:218] Iteration 3804 (2.21452 iter/s, 5.41878s/12 iters), loss = 0.984349
I0407 08:56:27.225215 15775 solver.cpp:237]     Train net output #0: loss = 0.984349 (* 1 = 0.984349 loss)
I0407 08:56:27.225224 15775 sgd_solver.cpp:105] Iteration 3804, lr = 0.001
I0407 08:56:32.555063 15775 solver.cpp:218] Iteration 3816 (2.25149 iter/s, 5.3298s/12 iters), loss = 0.843749
I0407 08:56:32.555107 15775 solver.cpp:237]     Train net output #0: loss = 0.843749 (* 1 = 0.843749 loss)
I0407 08:56:32.555114 15775 sgd_solver.cpp:105] Iteration 3816, lr = 0.001
I0407 08:56:37.855999 15775 solver.cpp:218] Iteration 3828 (2.26379 iter/s, 5.30085s/12 iters), loss = 0.87447
I0407 08:56:37.856038 15775 solver.cpp:237]     Train net output #0: loss = 0.87447 (* 1 = 0.87447 loss)
I0407 08:56:37.856046 15775 sgd_solver.cpp:105] Iteration 3828, lr = 0.001
I0407 08:56:43.140712 15775 solver.cpp:218] Iteration 3840 (2.27074 iter/s, 5.28463s/12 iters), loss = 0.857613
I0407 08:56:43.140759 15775 solver.cpp:237]     Train net output #0: loss = 0.857613 (* 1 = 0.857613 loss)
I0407 08:56:43.140770 15775 sgd_solver.cpp:105] Iteration 3840, lr = 0.001
I0407 08:56:44.320518 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:48.518702 15775 solver.cpp:218] Iteration 3852 (2.23136 iter/s, 5.3779s/12 iters), loss = 0.666989
I0407 08:56:48.518745 15775 solver.cpp:237]     Train net output #0: loss = 0.666989 (* 1 = 0.666989 loss)
I0407 08:56:48.518752 15775 sgd_solver.cpp:105] Iteration 3852, lr = 0.001
I0407 08:56:53.908623 15775 solver.cpp:218] Iteration 3864 (2.22641 iter/s, 5.38983s/12 iters), loss = 0.980955
I0407 08:56:53.908735 15775 solver.cpp:237]     Train net output #0: loss = 0.980955 (* 1 = 0.980955 loss)
I0407 08:56:53.908742 15775 sgd_solver.cpp:105] Iteration 3864, lr = 0.001
I0407 08:56:58.523592 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 08:57:01.473636 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 08:57:03.769556 15775 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 08:57:03.769577 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:57:06.561548 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:08.150823 15775 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0407 08:57:08.150852 15775 solver.cpp:397]     Test net output #1: loss = 2.60417 (* 1 = 2.60417 loss)
I0407 08:57:08.289907 15775 solver.cpp:218] Iteration 3876 (0.83443 iter/s, 14.3811s/12 iters), loss = 0.850122
I0407 08:57:08.291455 15775 solver.cpp:237]     Train net output #0: loss = 0.850122 (* 1 = 0.850122 loss)
I0407 08:57:08.291465 15775 sgd_solver.cpp:105] Iteration 3876, lr = 0.001
I0407 08:57:12.816489 15775 solver.cpp:218] Iteration 3888 (2.65194 iter/s, 4.525s/12 iters), loss = 0.821898
I0407 08:57:12.816531 15775 solver.cpp:237]     Train net output #0: loss = 0.821898 (* 1 = 0.821898 loss)
I0407 08:57:12.816540 15775 sgd_solver.cpp:105] Iteration 3888, lr = 0.001
I0407 08:57:18.173674 15775 solver.cpp:218] Iteration 3900 (2.24002 iter/s, 5.35709s/12 iters), loss = 0.690627
I0407 08:57:18.173720 15775 solver.cpp:237]     Train net output #0: loss = 0.690627 (* 1 = 0.690627 loss)
I0407 08:57:18.173729 15775 sgd_solver.cpp:105] Iteration 3900, lr = 0.001
I0407 08:57:23.481600 15775 solver.cpp:218] Iteration 3912 (2.26081 iter/s, 5.30783s/12 iters), loss = 0.634823
I0407 08:57:23.481643 15775 solver.cpp:237]     Train net output #0: loss = 0.634823 (* 1 = 0.634823 loss)
I0407 08:57:23.481650 15775 sgd_solver.cpp:105] Iteration 3912, lr = 0.001
I0407 08:57:28.777036 15775 solver.cpp:218] Iteration 3924 (2.26614 iter/s, 5.29534s/12 iters), loss = 0.850353
I0407 08:57:28.777164 15775 solver.cpp:237]     Train net output #0: loss = 0.850353 (* 1 = 0.850353 loss)
I0407 08:57:28.777173 15775 sgd_solver.cpp:105] Iteration 3924, lr = 0.001
I0407 08:57:33.709722 15775 solver.cpp:218] Iteration 3936 (2.43284 iter/s, 4.93252s/12 iters), loss = 0.679257
I0407 08:57:33.709766 15775 solver.cpp:237]     Train net output #0: loss = 0.679257 (* 1 = 0.679257 loss)
I0407 08:57:33.709774 15775 sgd_solver.cpp:105] Iteration 3936, lr = 0.001
I0407 08:57:37.113575 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:38.806370 15775 solver.cpp:218] Iteration 3948 (2.35453 iter/s, 5.09656s/12 iters), loss = 0.772704
I0407 08:57:38.806409 15775 solver.cpp:237]     Train net output #0: loss = 0.772704 (* 1 = 0.772704 loss)
I0407 08:57:38.806417 15775 sgd_solver.cpp:105] Iteration 3948, lr = 0.001
I0407 08:57:44.195750 15775 solver.cpp:218] Iteration 3960 (2.22664 iter/s, 5.38929s/12 iters), loss = 0.7935
I0407 08:57:44.195796 15775 solver.cpp:237]     Train net output #0: loss = 0.7935 (* 1 = 0.7935 loss)
I0407 08:57:44.195804 15775 sgd_solver.cpp:105] Iteration 3960, lr = 0.001
I0407 08:57:49.337458 15775 solver.cpp:218] Iteration 3972 (2.33389 iter/s, 5.14162s/12 iters), loss = 0.570102
I0407 08:57:49.337499 15775 solver.cpp:237]     Train net output #0: loss = 0.570102 (* 1 = 0.570102 loss)
I0407 08:57:49.337509 15775 sgd_solver.cpp:105] Iteration 3972, lr = 0.001
I0407 08:57:51.440269 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 08:57:54.426122 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 08:57:56.723868 15775 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 08:57:56.723888 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:57:59.589191 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:01.131821 15775 solver.cpp:397]     Test net output #0: accuracy = 0.422181
I0407 08:58:01.131873 15775 solver.cpp:397]     Test net output #1: loss = 2.61148 (* 1 = 2.61148 loss)
I0407 08:58:03.159271 15775 solver.cpp:218] Iteration 3984 (0.868202 iter/s, 13.8217s/12 iters), loss = 0.785575
I0407 08:58:03.159325 15775 solver.cpp:237]     Train net output #0: loss = 0.785575 (* 1 = 0.785575 loss)
I0407 08:58:03.159334 15775 sgd_solver.cpp:105] Iteration 3984, lr = 0.001
I0407 08:58:08.321905 15775 solver.cpp:218] Iteration 3996 (2.32444 iter/s, 5.16253s/12 iters), loss = 0.847422
I0407 08:58:08.321951 15775 solver.cpp:237]     Train net output #0: loss = 0.847422 (* 1 = 0.847422 loss)
I0407 08:58:08.321959 15775 sgd_solver.cpp:105] Iteration 3996, lr = 0.001
I0407 08:58:13.711078 15775 solver.cpp:218] Iteration 4008 (2.22672 iter/s, 5.38908s/12 iters), loss = 0.723073
I0407 08:58:13.711127 15775 solver.cpp:237]     Train net output #0: loss = 0.723073 (* 1 = 0.723073 loss)
I0407 08:58:13.711139 15775 sgd_solver.cpp:105] Iteration 4008, lr = 0.001
I0407 08:58:19.225090 15775 solver.cpp:218] Iteration 4020 (2.17631 iter/s, 5.51392s/12 iters), loss = 1.02231
I0407 08:58:19.225131 15775 solver.cpp:237]     Train net output #0: loss = 1.02231 (* 1 = 1.02231 loss)
I0407 08:58:19.225138 15775 sgd_solver.cpp:105] Iteration 4020, lr = 0.001
I0407 08:58:24.636449 15775 solver.cpp:218] Iteration 4032 (2.21759 iter/s, 5.41127s/12 iters), loss = 0.798995
I0407 08:58:24.636490 15775 solver.cpp:237]     Train net output #0: loss = 0.798995 (* 1 = 0.798995 loss)
I0407 08:58:24.636497 15775 sgd_solver.cpp:105] Iteration 4032, lr = 0.001
I0407 08:58:29.953487 15775 solver.cpp:218] Iteration 4044 (2.25693 iter/s, 5.31695s/12 iters), loss = 0.652966
I0407 08:58:29.953650 15775 solver.cpp:237]     Train net output #0: loss = 0.652966 (* 1 = 0.652966 loss)
I0407 08:58:29.953660 15775 sgd_solver.cpp:105] Iteration 4044, lr = 0.001
I0407 08:58:30.458114 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:35.184861 15775 solver.cpp:218] Iteration 4056 (2.29394 iter/s, 5.23117s/12 iters), loss = 0.745718
I0407 08:58:35.184913 15775 solver.cpp:237]     Train net output #0: loss = 0.745718 (* 1 = 0.745718 loss)
I0407 08:58:35.184923 15775 sgd_solver.cpp:105] Iteration 4056, lr = 0.001
I0407 08:58:40.296703 15775 solver.cpp:218] Iteration 4068 (2.34754 iter/s, 5.11174s/12 iters), loss = 0.73994
I0407 08:58:40.296753 15775 solver.cpp:237]     Train net output #0: loss = 0.73994 (* 1 = 0.73994 loss)
I0407 08:58:40.296763 15775 sgd_solver.cpp:105] Iteration 4068, lr = 0.001
I0407 08:58:44.997628 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 08:58:48.001574 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 08:58:51.946964 15775 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 08:58:51.946982 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:58:54.636379 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:56.206460 15775 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0407 08:58:56.206499 15775 solver.cpp:397]     Test net output #1: loss = 2.58107 (* 1 = 2.58107 loss)
I0407 08:58:56.345309 15775 solver.cpp:218] Iteration 4080 (0.747736 iter/s, 16.0484s/12 iters), loss = 0.943744
I0407 08:58:56.345368 15775 solver.cpp:237]     Train net output #0: loss = 0.943744 (* 1 = 0.943744 loss)
I0407 08:58:56.345378 15775 sgd_solver.cpp:105] Iteration 4080, lr = 0.001
I0407 08:59:00.801496 15775 solver.cpp:218] Iteration 4092 (2.69294 iter/s, 4.45609s/12 iters), loss = 0.690229
I0407 08:59:00.801591 15775 solver.cpp:237]     Train net output #0: loss = 0.690229 (* 1 = 0.690229 loss)
I0407 08:59:00.801599 15775 sgd_solver.cpp:105] Iteration 4092, lr = 0.001
I0407 08:59:06.159210 15775 solver.cpp:218] Iteration 4104 (2.23982 iter/s, 5.35758s/12 iters), loss = 0.653344
I0407 08:59:06.159253 15775 solver.cpp:237]     Train net output #0: loss = 0.653344 (* 1 = 0.653344 loss)
I0407 08:59:06.159260 15775 sgd_solver.cpp:105] Iteration 4104, lr = 0.001
I0407 08:59:11.599552 15775 solver.cpp:218] Iteration 4116 (2.20578 iter/s, 5.44025s/12 iters), loss = 0.547122
I0407 08:59:11.599596 15775 solver.cpp:237]     Train net output #0: loss = 0.547122 (* 1 = 0.547122 loss)
I0407 08:59:11.599604 15775 sgd_solver.cpp:105] Iteration 4116, lr = 0.001
I0407 08:59:17.086796 15775 solver.cpp:218] Iteration 4128 (2.18693 iter/s, 5.48715s/12 iters), loss = 0.869504
I0407 08:59:17.086846 15775 solver.cpp:237]     Train net output #0: loss = 0.869504 (* 1 = 0.869504 loss)
I0407 08:59:17.086854 15775 sgd_solver.cpp:105] Iteration 4128, lr = 0.001
I0407 08:59:22.535195 15775 solver.cpp:218] Iteration 4140 (2.20252 iter/s, 5.4483s/12 iters), loss = 0.682621
I0407 08:59:22.535235 15775 solver.cpp:237]     Train net output #0: loss = 0.682621 (* 1 = 0.682621 loss)
I0407 08:59:22.535243 15775 sgd_solver.cpp:105] Iteration 4140, lr = 0.001
I0407 08:59:25.434845 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:28.049787 15775 solver.cpp:218] Iteration 4152 (2.17608 iter/s, 5.5145s/12 iters), loss = 0.720171
I0407 08:59:28.049831 15775 solver.cpp:237]     Train net output #0: loss = 0.720171 (* 1 = 0.720171 loss)
I0407 08:59:28.049839 15775 sgd_solver.cpp:105] Iteration 4152, lr = 0.001
I0407 08:59:29.846045 15775 blocking_queue.cpp:49] Waiting for data
I0407 08:59:33.446485 15775 solver.cpp:218] Iteration 4164 (2.22362 iter/s, 5.39661s/12 iters), loss = 0.545155
I0407 08:59:33.446609 15775 solver.cpp:237]     Train net output #0: loss = 0.545155 (* 1 = 0.545155 loss)
I0407 08:59:33.446616 15775 sgd_solver.cpp:105] Iteration 4164, lr = 0.001
I0407 08:59:38.443625 15775 solver.cpp:218] Iteration 4176 (2.40145 iter/s, 4.99697s/12 iters), loss = 0.771099
I0407 08:59:38.443667 15775 solver.cpp:237]     Train net output #0: loss = 0.771099 (* 1 = 0.771099 loss)
I0407 08:59:38.443675 15775 sgd_solver.cpp:105] Iteration 4176, lr = 0.001
I0407 08:59:40.453539 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 08:59:43.988538 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 08:59:47.811012 15775 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 08:59:47.811038 15775 net.cpp:676] Ignoring source layer train-data
I0407 08:59:50.511700 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:52.132386 15775 solver.cpp:397]     Test net output #0: accuracy = 0.428309
I0407 08:59:52.132421 15775 solver.cpp:397]     Test net output #1: loss = 2.58512 (* 1 = 2.58512 loss)
I0407 08:59:54.144193 15775 solver.cpp:218] Iteration 4188 (0.764311 iter/s, 15.7004s/12 iters), loss = 0.678795
I0407 08:59:54.144233 15775 solver.cpp:237]     Train net output #0: loss = 0.678795 (* 1 = 0.678795 loss)
I0407 08:59:54.144241 15775 sgd_solver.cpp:105] Iteration 4188, lr = 0.001
I0407 08:59:59.582170 15775 solver.cpp:218] Iteration 4200 (2.20674 iter/s, 5.43789s/12 iters), loss = 0.736814
I0407 08:59:59.582217 15775 solver.cpp:237]     Train net output #0: loss = 0.736814 (* 1 = 0.736814 loss)
I0407 08:59:59.582226 15775 sgd_solver.cpp:105] Iteration 4200, lr = 0.001
I0407 09:00:04.881958 15775 solver.cpp:218] Iteration 4212 (2.26428 iter/s, 5.2997s/12 iters), loss = 0.849863
I0407 09:00:04.882067 15775 solver.cpp:237]     Train net output #0: loss = 0.849863 (* 1 = 0.849863 loss)
I0407 09:00:04.882076 15775 sgd_solver.cpp:105] Iteration 4212, lr = 0.001
I0407 09:00:10.157088 15775 solver.cpp:218] Iteration 4224 (2.2749 iter/s, 5.27496s/12 iters), loss = 0.70912
I0407 09:00:10.157136 15775 solver.cpp:237]     Train net output #0: loss = 0.70912 (* 1 = 0.70912 loss)
I0407 09:00:10.157145 15775 sgd_solver.cpp:105] Iteration 4224, lr = 0.001
I0407 09:00:15.416502 15775 solver.cpp:218] Iteration 4236 (2.28166 iter/s, 5.25932s/12 iters), loss = 0.714994
I0407 09:00:15.416544 15775 solver.cpp:237]     Train net output #0: loss = 0.714994 (* 1 = 0.714994 loss)
I0407 09:00:15.416553 15775 sgd_solver.cpp:105] Iteration 4236, lr = 0.001
I0407 09:00:20.456914 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:20.738605 15775 solver.cpp:218] Iteration 4248 (2.25479 iter/s, 5.32201s/12 iters), loss = 0.768594
I0407 09:00:20.738648 15775 solver.cpp:237]     Train net output #0: loss = 0.768594 (* 1 = 0.768594 loss)
I0407 09:00:20.738656 15775 sgd_solver.cpp:105] Iteration 4248, lr = 0.001
I0407 09:00:26.146859 15775 solver.cpp:218] Iteration 4260 (2.21887 iter/s, 5.40816s/12 iters), loss = 0.636721
I0407 09:00:26.146903 15775 solver.cpp:237]     Train net output #0: loss = 0.636721 (* 1 = 0.636721 loss)
I0407 09:00:26.146910 15775 sgd_solver.cpp:105] Iteration 4260, lr = 0.001
I0407 09:00:31.475441 15775 solver.cpp:218] Iteration 4272 (2.25204 iter/s, 5.32849s/12 iters), loss = 0.578221
I0407 09:00:31.475481 15775 solver.cpp:237]     Train net output #0: loss = 0.578221 (* 1 = 0.578221 loss)
I0407 09:00:31.475487 15775 sgd_solver.cpp:105] Iteration 4272, lr = 0.001
I0407 09:00:36.383621 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 09:00:41.247654 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 09:00:45.768959 15775 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 09:00:45.768986 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:00:48.408704 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:50.088755 15775 solver.cpp:397]     Test net output #0: accuracy = 0.414216
I0407 09:00:50.088801 15775 solver.cpp:397]     Test net output #1: loss = 2.61173 (* 1 = 2.61173 loss)
I0407 09:00:50.228775 15775 solver.cpp:218] Iteration 4284 (0.639892 iter/s, 18.7532s/12 iters), loss = 0.991352
I0407 09:00:50.228821 15775 solver.cpp:237]     Train net output #0: loss = 0.991352 (* 1 = 0.991352 loss)
I0407 09:00:50.228829 15775 sgd_solver.cpp:105] Iteration 4284, lr = 0.001
I0407 09:00:54.429756 15775 solver.cpp:218] Iteration 4296 (2.85653 iter/s, 4.2009s/12 iters), loss = 0.738346
I0407 09:00:54.429813 15775 solver.cpp:237]     Train net output #0: loss = 0.738346 (* 1 = 0.738346 loss)
I0407 09:00:54.429827 15775 sgd_solver.cpp:105] Iteration 4296, lr = 0.001
I0407 09:00:59.596274 15775 solver.cpp:218] Iteration 4308 (2.32269 iter/s, 5.16642s/12 iters), loss = 0.627069
I0407 09:00:59.596313 15775 solver.cpp:237]     Train net output #0: loss = 0.627069 (* 1 = 0.627069 loss)
I0407 09:00:59.596321 15775 sgd_solver.cpp:105] Iteration 4308, lr = 0.001
I0407 09:01:05.014364 15775 solver.cpp:218] Iteration 4320 (2.21484 iter/s, 5.418s/12 iters), loss = 0.663949
I0407 09:01:05.014407 15775 solver.cpp:237]     Train net output #0: loss = 0.663949 (* 1 = 0.663949 loss)
I0407 09:01:05.014415 15775 sgd_solver.cpp:105] Iteration 4320, lr = 0.001
I0407 09:01:10.288059 15775 solver.cpp:218] Iteration 4332 (2.27548 iter/s, 5.27361s/12 iters), loss = 0.638405
I0407 09:01:10.288151 15775 solver.cpp:237]     Train net output #0: loss = 0.638405 (* 1 = 0.638405 loss)
I0407 09:01:10.288161 15775 sgd_solver.cpp:105] Iteration 4332, lr = 0.001
I0407 09:01:15.700970 15775 solver.cpp:218] Iteration 4344 (2.21698 iter/s, 5.41277s/12 iters), loss = 0.463559
I0407 09:01:15.701014 15775 solver.cpp:237]     Train net output #0: loss = 0.463559 (* 1 = 0.463559 loss)
I0407 09:01:15.701021 15775 sgd_solver.cpp:105] Iteration 4344, lr = 0.001
I0407 09:01:17.658876 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:21.054658 15775 solver.cpp:218] Iteration 4356 (2.24148 iter/s, 5.3536s/12 iters), loss = 0.449648
I0407 09:01:21.054702 15775 solver.cpp:237]     Train net output #0: loss = 0.449648 (* 1 = 0.449648 loss)
I0407 09:01:21.054709 15775 sgd_solver.cpp:105] Iteration 4356, lr = 0.001
I0407 09:01:26.329532 15775 solver.cpp:218] Iteration 4368 (2.27497 iter/s, 5.27479s/12 iters), loss = 0.592103
I0407 09:01:26.329573 15775 solver.cpp:237]     Train net output #0: loss = 0.592103 (* 1 = 0.592103 loss)
I0407 09:01:26.329581 15775 sgd_solver.cpp:105] Iteration 4368, lr = 0.001
I0407 09:01:31.755270 15775 solver.cpp:218] Iteration 4380 (2.21172 iter/s, 5.42565s/12 iters), loss = 0.64046
I0407 09:01:31.755311 15775 solver.cpp:237]     Train net output #0: loss = 0.64046 (* 1 = 0.64046 loss)
I0407 09:01:31.755318 15775 sgd_solver.cpp:105] Iteration 4380, lr = 0.001
I0407 09:01:33.904748 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 09:01:38.428285 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 09:01:44.100368 15775 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 09:01:44.100476 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:01:46.688325 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:48.432011 15775 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0407 09:01:48.432044 15775 solver.cpp:397]     Test net output #1: loss = 2.64616 (* 1 = 2.64616 loss)
I0407 09:01:50.226718 15775 solver.cpp:218] Iteration 4392 (0.649657 iter/s, 18.4713s/12 iters), loss = 0.56154
I0407 09:01:50.226763 15775 solver.cpp:237]     Train net output #0: loss = 0.56154 (* 1 = 0.56154 loss)
I0407 09:01:50.226769 15775 sgd_solver.cpp:105] Iteration 4392, lr = 0.001
I0407 09:01:55.358860 15775 solver.cpp:218] Iteration 4404 (2.33824 iter/s, 5.13206s/12 iters), loss = 0.5867
I0407 09:01:55.358897 15775 solver.cpp:237]     Train net output #0: loss = 0.5867 (* 1 = 0.5867 loss)
I0407 09:01:55.358904 15775 sgd_solver.cpp:105] Iteration 4404, lr = 0.001
I0407 09:02:00.624145 15775 solver.cpp:218] Iteration 4416 (2.27912 iter/s, 5.2652s/12 iters), loss = 0.765055
I0407 09:02:00.624186 15775 solver.cpp:237]     Train net output #0: loss = 0.765055 (* 1 = 0.765055 loss)
I0407 09:02:00.624195 15775 sgd_solver.cpp:105] Iteration 4416, lr = 0.001
I0407 09:02:05.783916 15775 solver.cpp:218] Iteration 4428 (2.32572 iter/s, 5.15968s/12 iters), loss = 0.691121
I0407 09:02:05.783962 15775 solver.cpp:237]     Train net output #0: loss = 0.691121 (* 1 = 0.691121 loss)
I0407 09:02:05.783969 15775 sgd_solver.cpp:105] Iteration 4428, lr = 0.001
I0407 09:02:10.990370 15775 solver.cpp:218] Iteration 4440 (2.30487 iter/s, 5.20636s/12 iters), loss = 0.740848
I0407 09:02:10.990417 15775 solver.cpp:237]     Train net output #0: loss = 0.740848 (* 1 = 0.740848 loss)
I0407 09:02:10.990423 15775 sgd_solver.cpp:105] Iteration 4440, lr = 0.001
I0407 09:02:15.351835 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:16.524175 15775 solver.cpp:218] Iteration 4452 (2.16853 iter/s, 5.53371s/12 iters), loss = 0.576732
I0407 09:02:16.524217 15775 solver.cpp:237]     Train net output #0: loss = 0.576732 (* 1 = 0.576732 loss)
I0407 09:02:16.524224 15775 sgd_solver.cpp:105] Iteration 4452, lr = 0.001
I0407 09:02:21.928768 15775 solver.cpp:218] Iteration 4464 (2.22037 iter/s, 5.40451s/12 iters), loss = 0.487889
I0407 09:02:21.928810 15775 solver.cpp:237]     Train net output #0: loss = 0.487889 (* 1 = 0.487889 loss)
I0407 09:02:21.928818 15775 sgd_solver.cpp:105] Iteration 4464, lr = 0.001
I0407 09:02:27.243510 15775 solver.cpp:218] Iteration 4476 (2.25791 iter/s, 5.31465s/12 iters), loss = 0.613412
I0407 09:02:27.243552 15775 solver.cpp:237]     Train net output #0: loss = 0.613412 (* 1 = 0.613412 loss)
I0407 09:02:27.243559 15775 sgd_solver.cpp:105] Iteration 4476, lr = 0.001
I0407 09:02:31.977838 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 09:02:36.508180 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 09:02:40.866591 15775 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 09:02:40.866613 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:02:43.538364 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:45.278419 15775 solver.cpp:397]     Test net output #0: accuracy = 0.43076
I0407 09:02:45.278468 15775 solver.cpp:397]     Test net output #1: loss = 2.64601 (* 1 = 2.64601 loss)
I0407 09:02:45.419584 15775 solver.cpp:218] Iteration 4488 (0.660214 iter/s, 18.1759s/12 iters), loss = 0.483461
I0407 09:02:45.419665 15775 solver.cpp:237]     Train net output #0: loss = 0.483461 (* 1 = 0.483461 loss)
I0407 09:02:45.419673 15775 sgd_solver.cpp:105] Iteration 4488, lr = 0.001
I0407 09:02:49.643863 15775 solver.cpp:218] Iteration 4500 (2.84081 iter/s, 4.22415s/12 iters), loss = 0.511479
I0407 09:02:49.643910 15775 solver.cpp:237]     Train net output #0: loss = 0.511479 (* 1 = 0.511479 loss)
I0407 09:02:49.643918 15775 sgd_solver.cpp:105] Iteration 4500, lr = 0.001
I0407 09:02:54.884024 15775 solver.cpp:218] Iteration 4512 (2.29005 iter/s, 5.24007s/12 iters), loss = 0.743593
I0407 09:02:54.884065 15775 solver.cpp:237]     Train net output #0: loss = 0.743593 (* 1 = 0.743593 loss)
I0407 09:02:54.884073 15775 sgd_solver.cpp:105] Iteration 4512, lr = 0.001
I0407 09:03:00.235021 15775 solver.cpp:218] Iteration 4524 (2.24261 iter/s, 5.35091s/12 iters), loss = 0.621296
I0407 09:03:00.235078 15775 solver.cpp:237]     Train net output #0: loss = 0.621296 (* 1 = 0.621296 loss)
I0407 09:03:00.235090 15775 sgd_solver.cpp:105] Iteration 4524, lr = 0.001
I0407 09:03:05.343442 15775 solver.cpp:218] Iteration 4536 (2.34911 iter/s, 5.10833s/12 iters), loss = 0.526022
I0407 09:03:05.343480 15775 solver.cpp:237]     Train net output #0: loss = 0.526022 (* 1 = 0.526022 loss)
I0407 09:03:05.343487 15775 sgd_solver.cpp:105] Iteration 4536, lr = 0.001
I0407 09:03:10.442095 15775 solver.cpp:218] Iteration 4548 (2.3536 iter/s, 5.09857s/12 iters), loss = 0.519631
I0407 09:03:10.442138 15775 solver.cpp:237]     Train net output #0: loss = 0.519631 (* 1 = 0.519631 loss)
I0407 09:03:10.442147 15775 sgd_solver.cpp:105] Iteration 4548, lr = 0.001
I0407 09:03:11.714628 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:15.688242 15775 solver.cpp:218] Iteration 4560 (2.28743 iter/s, 5.24605s/12 iters), loss = 0.383181
I0407 09:03:15.688474 15775 solver.cpp:237]     Train net output #0: loss = 0.383181 (* 1 = 0.383181 loss)
I0407 09:03:15.688485 15775 sgd_solver.cpp:105] Iteration 4560, lr = 0.001
I0407 09:03:20.882964 15775 solver.cpp:218] Iteration 4572 (2.31016 iter/s, 5.19445s/12 iters), loss = 0.641114
I0407 09:03:20.883010 15775 solver.cpp:237]     Train net output #0: loss = 0.641114 (* 1 = 0.641114 loss)
I0407 09:03:20.883018 15775 sgd_solver.cpp:105] Iteration 4572, lr = 0.001
I0407 09:03:26.242559 15775 solver.cpp:218] Iteration 4584 (2.23901 iter/s, 5.3595s/12 iters), loss = 0.644148
I0407 09:03:26.242601 15775 solver.cpp:237]     Train net output #0: loss = 0.644148 (* 1 = 0.644148 loss)
I0407 09:03:26.242609 15775 sgd_solver.cpp:105] Iteration 4584, lr = 0.001
I0407 09:03:28.384541 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 09:03:34.414527 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 09:03:38.190659 15775 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 09:03:38.190685 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:03:40.725492 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:42.504555 15775 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0407 09:03:42.504588 15775 solver.cpp:397]     Test net output #1: loss = 2.62943 (* 1 = 2.62943 loss)
I0407 09:03:44.356492 15775 solver.cpp:218] Iteration 4596 (0.662479 iter/s, 18.1138s/12 iters), loss = 0.526942
I0407 09:03:44.356534 15775 solver.cpp:237]     Train net output #0: loss = 0.526942 (* 1 = 0.526942 loss)
I0407 09:03:44.356542 15775 sgd_solver.cpp:105] Iteration 4596, lr = 0.001
I0407 09:03:49.763506 15775 solver.cpp:218] Iteration 4608 (2.21938 iter/s, 5.40692s/12 iters), loss = 0.429211
I0407 09:03:49.763622 15775 solver.cpp:237]     Train net output #0: loss = 0.429211 (* 1 = 0.429211 loss)
I0407 09:03:49.763631 15775 sgd_solver.cpp:105] Iteration 4608, lr = 0.001
I0407 09:03:55.031616 15775 solver.cpp:218] Iteration 4620 (2.27793 iter/s, 5.26795s/12 iters), loss = 0.47323
I0407 09:03:55.031654 15775 solver.cpp:237]     Train net output #0: loss = 0.47323 (* 1 = 0.47323 loss)
I0407 09:03:55.031662 15775 sgd_solver.cpp:105] Iteration 4620, lr = 0.001
I0407 09:04:00.305220 15775 solver.cpp:218] Iteration 4632 (2.27552 iter/s, 5.27351s/12 iters), loss = 0.621251
I0407 09:04:00.305289 15775 solver.cpp:237]     Train net output #0: loss = 0.621251 (* 1 = 0.621251 loss)
I0407 09:04:00.305299 15775 sgd_solver.cpp:105] Iteration 4632, lr = 0.001
I0407 09:04:05.775151 15775 solver.cpp:218] Iteration 4644 (2.19386 iter/s, 5.46981s/12 iters), loss = 0.374965
I0407 09:04:05.775193 15775 solver.cpp:237]     Train net output #0: loss = 0.374965 (* 1 = 0.374965 loss)
I0407 09:04:05.775200 15775 sgd_solver.cpp:105] Iteration 4644, lr = 0.001
I0407 09:04:09.373417 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:11.160467 15775 solver.cpp:218] Iteration 4656 (2.22832 iter/s, 5.38523s/12 iters), loss = 0.402807
I0407 09:04:11.160511 15775 solver.cpp:237]     Train net output #0: loss = 0.402807 (* 1 = 0.402807 loss)
I0407 09:04:11.160521 15775 sgd_solver.cpp:105] Iteration 4656, lr = 0.001
I0407 09:04:16.586676 15775 solver.cpp:218] Iteration 4668 (2.21152 iter/s, 5.42612s/12 iters), loss = 0.50544
I0407 09:04:16.586717 15775 solver.cpp:237]     Train net output #0: loss = 0.50544 (* 1 = 0.50544 loss)
I0407 09:04:16.586725 15775 sgd_solver.cpp:105] Iteration 4668, lr = 0.001
I0407 09:04:21.884972 15775 solver.cpp:218] Iteration 4680 (2.26492 iter/s, 5.29821s/12 iters), loss = 0.448668
I0407 09:04:21.885141 15775 solver.cpp:237]     Train net output #0: loss = 0.448668 (* 1 = 0.448668 loss)
I0407 09:04:21.885154 15775 sgd_solver.cpp:105] Iteration 4680, lr = 0.001
I0407 09:04:26.790638 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 09:04:31.835988 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 09:04:36.196056 15775 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 09:04:36.196075 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:04:38.645422 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:40.480676 15775 solver.cpp:397]     Test net output #0: accuracy = 0.4375
I0407 09:04:40.480705 15775 solver.cpp:397]     Test net output #1: loss = 2.64873 (* 1 = 2.64873 loss)
I0407 09:04:40.621798 15775 solver.cpp:218] Iteration 4692 (0.64046 iter/s, 18.7365s/12 iters), loss = 0.483926
I0407 09:04:40.621856 15775 solver.cpp:237]     Train net output #0: loss = 0.483926 (* 1 = 0.483926 loss)
I0407 09:04:40.621867 15775 sgd_solver.cpp:105] Iteration 4692, lr = 0.001
I0407 09:04:44.862632 15775 solver.cpp:218] Iteration 4704 (2.8297 iter/s, 4.24074s/12 iters), loss = 0.639987
I0407 09:04:44.862674 15775 solver.cpp:237]     Train net output #0: loss = 0.639987 (* 1 = 0.639987 loss)
I0407 09:04:44.862682 15775 sgd_solver.cpp:105] Iteration 4704, lr = 0.001
I0407 09:04:50.048837 15775 solver.cpp:218] Iteration 4716 (2.31387 iter/s, 5.18611s/12 iters), loss = 0.450664
I0407 09:04:50.048879 15775 solver.cpp:237]     Train net output #0: loss = 0.450664 (* 1 = 0.450664 loss)
I0407 09:04:50.048892 15775 sgd_solver.cpp:105] Iteration 4716, lr = 0.001
I0407 09:04:55.187145 15775 solver.cpp:218] Iteration 4728 (2.33544 iter/s, 5.13822s/12 iters), loss = 0.57292
I0407 09:04:55.187256 15775 solver.cpp:237]     Train net output #0: loss = 0.57292 (* 1 = 0.57292 loss)
I0407 09:04:55.187265 15775 sgd_solver.cpp:105] Iteration 4728, lr = 0.001
I0407 09:05:00.335094 15775 solver.cpp:218] Iteration 4740 (2.3311 iter/s, 5.14779s/12 iters), loss = 0.405732
I0407 09:05:00.335140 15775 solver.cpp:237]     Train net output #0: loss = 0.405732 (* 1 = 0.405732 loss)
I0407 09:05:00.335148 15775 sgd_solver.cpp:105] Iteration 4740, lr = 0.001
I0407 09:05:05.698844 15775 solver.cpp:218] Iteration 4752 (2.23728 iter/s, 5.36366s/12 iters), loss = 0.496903
I0407 09:05:05.698889 15775 solver.cpp:237]     Train net output #0: loss = 0.496903 (* 1 = 0.496903 loss)
I0407 09:05:05.698897 15775 sgd_solver.cpp:105] Iteration 4752, lr = 0.001
I0407 09:05:06.271108 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:10.965286 15775 solver.cpp:218] Iteration 4764 (2.27862 iter/s, 5.26635s/12 iters), loss = 0.552822
I0407 09:05:10.965332 15775 solver.cpp:237]     Train net output #0: loss = 0.552822 (* 1 = 0.552822 loss)
I0407 09:05:10.965342 15775 sgd_solver.cpp:105] Iteration 4764, lr = 0.001
I0407 09:05:16.266214 15775 solver.cpp:218] Iteration 4776 (2.26379 iter/s, 5.30084s/12 iters), loss = 0.669901
I0407 09:05:16.266258 15775 solver.cpp:237]     Train net output #0: loss = 0.669901 (* 1 = 0.669901 loss)
I0407 09:05:16.266265 15775 sgd_solver.cpp:105] Iteration 4776, lr = 0.001
I0407 09:05:21.606946 15775 solver.cpp:218] Iteration 4788 (2.24692 iter/s, 5.34064s/12 iters), loss = 0.48355
I0407 09:05:21.606989 15775 solver.cpp:237]     Train net output #0: loss = 0.48355 (* 1 = 0.48355 loss)
I0407 09:05:21.606997 15775 sgd_solver.cpp:105] Iteration 4788, lr = 0.001
I0407 09:05:23.811668 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 09:05:28.344305 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 09:05:32.225402 15775 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 09:05:32.225427 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:05:34.633287 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:36.491451 15775 solver.cpp:397]     Test net output #0: accuracy = 0.438726
I0407 09:05:36.491487 15775 solver.cpp:397]     Test net output #1: loss = 2.66294 (* 1 = 2.66294 loss)
I0407 09:05:38.455987 15775 solver.cpp:218] Iteration 4800 (0.712213 iter/s, 16.8489s/12 iters), loss = 0.647122
I0407 09:05:38.456032 15775 solver.cpp:237]     Train net output #0: loss = 0.647122 (* 1 = 0.647122 loss)
I0407 09:05:38.456039 15775 sgd_solver.cpp:105] Iteration 4800, lr = 0.001
I0407 09:05:43.740249 15775 solver.cpp:218] Iteration 4812 (2.27093 iter/s, 5.28417s/12 iters), loss = 0.404581
I0407 09:05:43.740301 15775 solver.cpp:237]     Train net output #0: loss = 0.404581 (* 1 = 0.404581 loss)
I0407 09:05:43.740311 15775 sgd_solver.cpp:105] Iteration 4812, lr = 0.001
I0407 09:05:49.137498 15775 solver.cpp:218] Iteration 4824 (2.2234 iter/s, 5.39715s/12 iters), loss = 0.475673
I0407 09:05:49.137544 15775 solver.cpp:237]     Train net output #0: loss = 0.475673 (* 1 = 0.475673 loss)
I0407 09:05:49.137552 15775 sgd_solver.cpp:105] Iteration 4824, lr = 0.001
I0407 09:05:54.550299 15775 solver.cpp:218] Iteration 4836 (2.217 iter/s, 5.41271s/12 iters), loss = 0.497368
I0407 09:05:54.550335 15775 solver.cpp:237]     Train net output #0: loss = 0.497368 (* 1 = 0.497368 loss)
I0407 09:05:54.550341 15775 sgd_solver.cpp:105] Iteration 4836, lr = 0.001
I0407 09:05:56.737411 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:05:59.944346 15775 solver.cpp:218] Iteration 4848 (2.22471 iter/s, 5.39396s/12 iters), loss = 0.532847
I0407 09:05:59.944439 15775 solver.cpp:237]     Train net output #0: loss = 0.532847 (* 1 = 0.532847 loss)
I0407 09:05:59.944448 15775 sgd_solver.cpp:105] Iteration 4848, lr = 0.001
I0407 09:06:02.711995 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:05.087477 15775 solver.cpp:218] Iteration 4860 (2.33327 iter/s, 5.14299s/12 iters), loss = 0.45505
I0407 09:06:05.087532 15775 solver.cpp:237]     Train net output #0: loss = 0.45505 (* 1 = 0.45505 loss)
I0407 09:06:05.087543 15775 sgd_solver.cpp:105] Iteration 4860, lr = 0.001
I0407 09:06:10.225678 15775 solver.cpp:218] Iteration 4872 (2.33549 iter/s, 5.1381s/12 iters), loss = 0.486844
I0407 09:06:10.225736 15775 solver.cpp:237]     Train net output #0: loss = 0.486844 (* 1 = 0.486844 loss)
I0407 09:06:10.225749 15775 sgd_solver.cpp:105] Iteration 4872, lr = 0.001
I0407 09:06:15.503405 15775 solver.cpp:218] Iteration 4884 (2.27375 iter/s, 5.27763s/12 iters), loss = 0.521519
I0407 09:06:15.503443 15775 solver.cpp:237]     Train net output #0: loss = 0.521519 (* 1 = 0.521519 loss)
I0407 09:06:15.503451 15775 sgd_solver.cpp:105] Iteration 4884, lr = 0.001
I0407 09:06:20.315640 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 09:06:24.802644 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 09:06:28.628176 15775 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 09:06:28.628194 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:06:31.081130 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:32.974284 15775 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0407 09:06:32.974318 15775 solver.cpp:397]     Test net output #1: loss = 2.66151 (* 1 = 2.66151 loss)
I0407 09:06:33.112056 15775 solver.cpp:218] Iteration 4896 (0.681489 iter/s, 17.6085s/12 iters), loss = 0.453935
I0407 09:06:33.113636 15775 solver.cpp:237]     Train net output #0: loss = 0.453935 (* 1 = 0.453935 loss)
I0407 09:06:33.113651 15775 sgd_solver.cpp:105] Iteration 4896, lr = 0.001
I0407 09:06:37.316689 15775 solver.cpp:218] Iteration 4908 (2.85509 iter/s, 4.20302s/12 iters), loss = 0.404954
I0407 09:06:37.316725 15775 solver.cpp:237]     Train net output #0: loss = 0.404954 (* 1 = 0.404954 loss)
I0407 09:06:37.316731 15775 sgd_solver.cpp:105] Iteration 4908, lr = 0.001
I0407 09:06:42.537662 15775 solver.cpp:218] Iteration 4920 (2.29846 iter/s, 5.22089s/12 iters), loss = 0.48682
I0407 09:06:42.537703 15775 solver.cpp:237]     Train net output #0: loss = 0.48682 (* 1 = 0.48682 loss)
I0407 09:06:42.537711 15775 sgd_solver.cpp:105] Iteration 4920, lr = 0.001
I0407 09:06:47.857817 15775 solver.cpp:218] Iteration 4932 (2.25561 iter/s, 5.32006s/12 iters), loss = 0.422833
I0407 09:06:47.857861 15775 solver.cpp:237]     Train net output #0: loss = 0.422833 (* 1 = 0.422833 loss)
I0407 09:06:47.857869 15775 sgd_solver.cpp:105] Iteration 4932, lr = 0.001
I0407 09:06:53.024021 15775 solver.cpp:218] Iteration 4944 (2.32283 iter/s, 5.16612s/12 iters), loss = 0.736994
I0407 09:06:53.024061 15775 solver.cpp:237]     Train net output #0: loss = 0.736994 (* 1 = 0.736994 loss)
I0407 09:06:53.024070 15775 sgd_solver.cpp:105] Iteration 4944, lr = 0.001
I0407 09:06:58.113512 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:58.363479 15775 solver.cpp:218] Iteration 4956 (2.24745 iter/s, 5.33937s/12 iters), loss = 0.257559
I0407 09:06:58.363520 15775 solver.cpp:237]     Train net output #0: loss = 0.257559 (* 1 = 0.257559 loss)
I0407 09:06:58.363528 15775 sgd_solver.cpp:105] Iteration 4956, lr = 0.001
I0407 09:07:03.463129 15775 solver.cpp:218] Iteration 4968 (2.35314 iter/s, 5.09957s/12 iters), loss = 0.463503
I0407 09:07:03.463276 15775 solver.cpp:237]     Train net output #0: loss = 0.463503 (* 1 = 0.463503 loss)
I0407 09:07:03.463286 15775 sgd_solver.cpp:105] Iteration 4968, lr = 0.001
I0407 09:07:08.507920 15775 solver.cpp:218] Iteration 4980 (2.37878 iter/s, 5.0446s/12 iters), loss = 0.321038
I0407 09:07:08.507967 15775 solver.cpp:237]     Train net output #0: loss = 0.321038 (* 1 = 0.321038 loss)
I0407 09:07:08.507975 15775 sgd_solver.cpp:105] Iteration 4980, lr = 0.001
I0407 09:07:13.776091 15775 solver.cpp:218] Iteration 4992 (2.27787 iter/s, 5.26808s/12 iters), loss = 0.555252
I0407 09:07:13.776134 15775 solver.cpp:237]     Train net output #0: loss = 0.555252 (* 1 = 0.555252 loss)
I0407 09:07:13.776142 15775 sgd_solver.cpp:105] Iteration 4992, lr = 0.001
I0407 09:07:15.963956 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 09:07:20.109221 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 09:07:23.931646 15775 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 09:07:23.931671 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:07:26.386466 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:28.365290 15775 solver.cpp:397]     Test net output #0: accuracy = 0.438726
I0407 09:07:28.365325 15775 solver.cpp:397]     Test net output #1: loss = 2.68097 (* 1 = 2.68097 loss)
I0407 09:07:30.223515 15775 solver.cpp:218] Iteration 5004 (0.729604 iter/s, 16.4473s/12 iters), loss = 0.421536
I0407 09:07:30.223557 15775 solver.cpp:237]     Train net output #0: loss = 0.421536 (* 1 = 0.421536 loss)
I0407 09:07:30.223565 15775 sgd_solver.cpp:105] Iteration 5004, lr = 0.001
I0407 09:07:35.260826 15775 solver.cpp:218] Iteration 5016 (2.38226 iter/s, 5.03723s/12 iters), loss = 0.52511
I0407 09:07:35.260943 15775 solver.cpp:237]     Train net output #0: loss = 0.52511 (* 1 = 0.52511 loss)
I0407 09:07:35.260951 15775 sgd_solver.cpp:105] Iteration 5016, lr = 0.001
I0407 09:07:40.522143 15775 solver.cpp:218] Iteration 5028 (2.28087 iter/s, 5.26115s/12 iters), loss = 0.3936
I0407 09:07:40.522193 15775 solver.cpp:237]     Train net output #0: loss = 0.3936 (* 1 = 0.3936 loss)
I0407 09:07:40.522200 15775 sgd_solver.cpp:105] Iteration 5028, lr = 0.001
I0407 09:07:45.788420 15775 solver.cpp:218] Iteration 5040 (2.27869 iter/s, 5.26618s/12 iters), loss = 0.376767
I0407 09:07:45.788470 15775 solver.cpp:237]     Train net output #0: loss = 0.376767 (* 1 = 0.376767 loss)
I0407 09:07:45.788481 15775 sgd_solver.cpp:105] Iteration 5040, lr = 0.001
I0407 09:07:50.946496 15775 solver.cpp:218] Iteration 5052 (2.32649 iter/s, 5.15798s/12 iters), loss = 0.242453
I0407 09:07:50.946539 15775 solver.cpp:237]     Train net output #0: loss = 0.242453 (* 1 = 0.242453 loss)
I0407 09:07:50.946547 15775 sgd_solver.cpp:105] Iteration 5052, lr = 0.001
I0407 09:07:52.883976 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:56.111919 15775 solver.cpp:218] Iteration 5064 (2.32318 iter/s, 5.16534s/12 iters), loss = 0.244843
I0407 09:07:56.111960 15775 solver.cpp:237]     Train net output #0: loss = 0.244843 (* 1 = 0.244843 loss)
I0407 09:07:56.111968 15775 sgd_solver.cpp:105] Iteration 5064, lr = 0.001
I0407 09:08:00.923614 15775 solver.cpp:218] Iteration 5076 (2.49397 iter/s, 4.8116s/12 iters), loss = 0.500444
I0407 09:08:00.923666 15775 solver.cpp:237]     Train net output #0: loss = 0.500444 (* 1 = 0.500444 loss)
I0407 09:08:00.923676 15775 sgd_solver.cpp:105] Iteration 5076, lr = 0.001
I0407 09:08:06.061163 15775 solver.cpp:218] Iteration 5088 (2.33579 iter/s, 5.13746s/12 iters), loss = 0.595614
I0407 09:08:06.061321 15775 solver.cpp:237]     Train net output #0: loss = 0.595614 (* 1 = 0.595614 loss)
I0407 09:08:06.061331 15775 sgd_solver.cpp:105] Iteration 5088, lr = 0.001
I0407 09:08:10.765513 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 09:08:13.773679 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 09:08:17.540256 15775 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 09:08:17.540274 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:08:20.138463 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:22.162842 15775 solver.cpp:397]     Test net output #0: accuracy = 0.430147
I0407 09:08:22.162881 15775 solver.cpp:397]     Test net output #1: loss = 2.70793 (* 1 = 2.70793 loss)
I0407 09:08:22.305063 15775 solver.cpp:218] Iteration 5100 (0.738751 iter/s, 16.2436s/12 iters), loss = 0.275051
I0407 09:08:22.305115 15775 solver.cpp:237]     Train net output #0: loss = 0.275051 (* 1 = 0.275051 loss)
I0407 09:08:22.305126 15775 sgd_solver.cpp:105] Iteration 5100, lr = 0.001
I0407 09:08:26.450532 15775 solver.cpp:218] Iteration 5112 (2.89479 iter/s, 4.14538s/12 iters), loss = 0.508401
I0407 09:08:26.450579 15775 solver.cpp:237]     Train net output #0: loss = 0.508401 (* 1 = 0.508401 loss)
I0407 09:08:26.450588 15775 sgd_solver.cpp:105] Iteration 5112, lr = 0.001
I0407 09:08:31.640405 15775 solver.cpp:218] Iteration 5124 (2.31223 iter/s, 5.18979s/12 iters), loss = 0.426809
I0407 09:08:31.640448 15775 solver.cpp:237]     Train net output #0: loss = 0.426809 (* 1 = 0.426809 loss)
I0407 09:08:31.640458 15775 sgd_solver.cpp:105] Iteration 5124, lr = 0.001
I0407 09:08:36.769621 15775 solver.cpp:218] Iteration 5136 (2.33958 iter/s, 5.12913s/12 iters), loss = 0.451801
I0407 09:08:36.769721 15775 solver.cpp:237]     Train net output #0: loss = 0.451801 (* 1 = 0.451801 loss)
I0407 09:08:36.769731 15775 sgd_solver.cpp:105] Iteration 5136, lr = 0.001
I0407 09:08:41.974227 15775 solver.cpp:218] Iteration 5148 (2.30571 iter/s, 5.20446s/12 iters), loss = 0.386817
I0407 09:08:41.974273 15775 solver.cpp:237]     Train net output #0: loss = 0.386817 (* 1 = 0.386817 loss)
I0407 09:08:41.974280 15775 sgd_solver.cpp:105] Iteration 5148, lr = 0.001
I0407 09:08:46.054001 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:47.108743 15775 solver.cpp:218] Iteration 5160 (2.33717 iter/s, 5.13442s/12 iters), loss = 0.40317
I0407 09:08:47.108783 15775 solver.cpp:237]     Train net output #0: loss = 0.40317 (* 1 = 0.40317 loss)
I0407 09:08:47.108790 15775 sgd_solver.cpp:105] Iteration 5160, lr = 0.001
I0407 09:08:52.307695 15775 solver.cpp:218] Iteration 5172 (2.3082 iter/s, 5.19887s/12 iters), loss = 0.445614
I0407 09:08:52.307739 15775 solver.cpp:237]     Train net output #0: loss = 0.445614 (* 1 = 0.445614 loss)
I0407 09:08:52.307745 15775 sgd_solver.cpp:105] Iteration 5172, lr = 0.001
I0407 09:08:57.486552 15775 solver.cpp:218] Iteration 5184 (2.31715 iter/s, 5.17877s/12 iters), loss = 0.359337
I0407 09:08:57.486598 15775 solver.cpp:237]     Train net output #0: loss = 0.359337 (* 1 = 0.359337 loss)
I0407 09:08:57.486604 15775 sgd_solver.cpp:105] Iteration 5184, lr = 0.001
I0407 09:09:02.676528 15775 solver.cpp:218] Iteration 5196 (2.31219 iter/s, 5.18989s/12 iters), loss = 0.376534
I0407 09:09:02.676569 15775 solver.cpp:237]     Train net output #0: loss = 0.376534 (* 1 = 0.376534 loss)
I0407 09:09:02.676578 15775 sgd_solver.cpp:105] Iteration 5196, lr = 0.001
I0407 09:09:04.783414 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 09:09:07.699741 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 09:09:11.718523 15775 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 09:09:11.718555 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:09:13.977886 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:15.982782 15775 solver.cpp:397]     Test net output #0: accuracy = 0.446691
I0407 09:09:15.982821 15775 solver.cpp:397]     Test net output #1: loss = 2.71229 (* 1 = 2.71229 loss)
I0407 09:09:17.789985 15775 solver.cpp:218] Iteration 5208 (0.794002 iter/s, 15.1133s/12 iters), loss = 0.394378
I0407 09:09:17.790036 15775 solver.cpp:237]     Train net output #0: loss = 0.394378 (* 1 = 0.394378 loss)
I0407 09:09:17.790046 15775 sgd_solver.cpp:105] Iteration 5208, lr = 0.001
I0407 09:09:23.011348 15775 solver.cpp:218] Iteration 5220 (2.29829 iter/s, 5.22127s/12 iters), loss = 0.513069
I0407 09:09:23.011391 15775 solver.cpp:237]     Train net output #0: loss = 0.513069 (* 1 = 0.513069 loss)
I0407 09:09:23.011399 15775 sgd_solver.cpp:105] Iteration 5220, lr = 0.001
I0407 09:09:28.416115 15775 solver.cpp:218] Iteration 5232 (2.2203 iter/s, 5.40468s/12 iters), loss = 0.43497
I0407 09:09:28.416157 15775 solver.cpp:237]     Train net output #0: loss = 0.43497 (* 1 = 0.43497 loss)
I0407 09:09:28.416163 15775 sgd_solver.cpp:105] Iteration 5232, lr = 0.001
I0407 09:09:33.760748 15775 solver.cpp:218] Iteration 5244 (2.24528 iter/s, 5.34454s/12 iters), loss = 0.369734
I0407 09:09:33.760797 15775 solver.cpp:237]     Train net output #0: loss = 0.369734 (* 1 = 0.369734 loss)
I0407 09:09:33.760804 15775 sgd_solver.cpp:105] Iteration 5244, lr = 0.001
I0407 09:09:39.070124 15775 solver.cpp:218] Iteration 5256 (2.26019 iter/s, 5.30929s/12 iters), loss = 0.387922
I0407 09:09:39.070449 15775 solver.cpp:237]     Train net output #0: loss = 0.387922 (* 1 = 0.387922 loss)
I0407 09:09:39.070458 15775 sgd_solver.cpp:105] Iteration 5256, lr = 0.001
I0407 09:09:40.345707 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:44.093158 15775 solver.cpp:218] Iteration 5268 (2.38917 iter/s, 5.02267s/12 iters), loss = 0.33954
I0407 09:09:44.093209 15775 solver.cpp:237]     Train net output #0: loss = 0.33954 (* 1 = 0.33954 loss)
I0407 09:09:44.093219 15775 sgd_solver.cpp:105] Iteration 5268, lr = 0.001
I0407 09:09:49.110445 15775 solver.cpp:218] Iteration 5280 (2.39178 iter/s, 5.01719s/12 iters), loss = 0.340514
I0407 09:09:49.110496 15775 solver.cpp:237]     Train net output #0: loss = 0.340514 (* 1 = 0.340514 loss)
I0407 09:09:49.110502 15775 sgd_solver.cpp:105] Iteration 5280, lr = 0.001
I0407 09:09:54.434190 15775 solver.cpp:218] Iteration 5292 (2.25409 iter/s, 5.32365s/12 iters), loss = 0.330031
I0407 09:09:54.434231 15775 solver.cpp:237]     Train net output #0: loss = 0.330031 (* 1 = 0.330031 loss)
I0407 09:09:54.434237 15775 sgd_solver.cpp:105] Iteration 5292, lr = 0.001
I0407 09:09:59.276857 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 09:10:02.296375 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 09:10:06.266897 15775 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 09:10:06.266921 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:10:08.473767 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:10.533097 15775 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0407 09:10:10.533537 15775 solver.cpp:397]     Test net output #1: loss = 2.73422 (* 1 = 2.73422 loss)
I0407 09:10:10.674780 15775 solver.cpp:218] Iteration 5304 (0.738896 iter/s, 16.2405s/12 iters), loss = 0.381343
I0407 09:10:10.674821 15775 solver.cpp:237]     Train net output #0: loss = 0.381343 (* 1 = 0.381343 loss)
I0407 09:10:10.674829 15775 sgd_solver.cpp:105] Iteration 5304, lr = 0.001
I0407 09:10:15.053171 15775 solver.cpp:218] Iteration 5316 (2.74079 iter/s, 4.3783s/12 iters), loss = 0.398986
I0407 09:10:15.053221 15775 solver.cpp:237]     Train net output #0: loss = 0.398986 (* 1 = 0.398986 loss)
I0407 09:10:15.053231 15775 sgd_solver.cpp:105] Iteration 5316, lr = 0.001
I0407 09:10:20.345736 15775 solver.cpp:218] Iteration 5328 (2.26737 iter/s, 5.29247s/12 iters), loss = 0.446163
I0407 09:10:20.345777 15775 solver.cpp:237]     Train net output #0: loss = 0.446163 (* 1 = 0.446163 loss)
I0407 09:10:20.345784 15775 sgd_solver.cpp:105] Iteration 5328, lr = 0.001
I0407 09:10:25.492197 15775 solver.cpp:218] Iteration 5340 (2.33174 iter/s, 5.14638s/12 iters), loss = 0.401424
I0407 09:10:25.492236 15775 solver.cpp:237]     Train net output #0: loss = 0.401424 (* 1 = 0.401424 loss)
I0407 09:10:25.492244 15775 sgd_solver.cpp:105] Iteration 5340, lr = 0.001
I0407 09:10:30.641826 15775 solver.cpp:218] Iteration 5352 (2.3303 iter/s, 5.14955s/12 iters), loss = 0.328184
I0407 09:10:30.641867 15775 solver.cpp:237]     Train net output #0: loss = 0.328184 (* 1 = 0.328184 loss)
I0407 09:10:30.641875 15775 sgd_solver.cpp:105] Iteration 5352, lr = 0.001
I0407 09:10:34.273046 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:35.878615 15775 solver.cpp:218] Iteration 5364 (2.29152 iter/s, 5.2367s/12 iters), loss = 0.582523
I0407 09:10:35.878671 15775 solver.cpp:237]     Train net output #0: loss = 0.582523 (* 1 = 0.582523 loss)
I0407 09:10:35.878681 15775 sgd_solver.cpp:105] Iteration 5364, lr = 0.001
I0407 09:10:41.270638 15775 solver.cpp:218] Iteration 5376 (2.22555 iter/s, 5.39192s/12 iters), loss = 0.437618
I0407 09:10:41.270761 15775 solver.cpp:237]     Train net output #0: loss = 0.437618 (* 1 = 0.437618 loss)
I0407 09:10:41.270771 15775 sgd_solver.cpp:105] Iteration 5376, lr = 0.001
I0407 09:10:46.527185 15775 solver.cpp:218] Iteration 5388 (2.28294 iter/s, 5.25638s/12 iters), loss = 0.401983
I0407 09:10:46.527235 15775 solver.cpp:237]     Train net output #0: loss = 0.401983 (* 1 = 0.401983 loss)
I0407 09:10:46.527242 15775 sgd_solver.cpp:105] Iteration 5388, lr = 0.001
I0407 09:10:51.739082 15775 solver.cpp:218] Iteration 5400 (2.30246 iter/s, 5.21181s/12 iters), loss = 0.359214
I0407 09:10:51.739128 15775 solver.cpp:237]     Train net output #0: loss = 0.359214 (* 1 = 0.359214 loss)
I0407 09:10:51.739137 15775 sgd_solver.cpp:105] Iteration 5400, lr = 0.001
I0407 09:10:53.870453 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 09:10:56.895673 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 09:11:00.669884 15775 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 09:11:00.669904 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:11:02.970017 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:05.204980 15775 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0407 09:11:05.205009 15775 solver.cpp:397]     Test net output #1: loss = 2.70527 (* 1 = 2.70527 loss)
I0407 09:11:06.988399 15775 solver.cpp:218] Iteration 5412 (0.786928 iter/s, 15.2492s/12 iters), loss = 0.361524
I0407 09:11:06.988435 15775 solver.cpp:237]     Train net output #0: loss = 0.361524 (* 1 = 0.361524 loss)
I0407 09:11:06.988441 15775 sgd_solver.cpp:105] Iteration 5412, lr = 0.001
I0407 09:11:12.302650 15775 solver.cpp:218] Iteration 5424 (2.25812 iter/s, 5.31417s/12 iters), loss = 0.390478
I0407 09:11:12.302772 15775 solver.cpp:237]     Train net output #0: loss = 0.390478 (* 1 = 0.390478 loss)
I0407 09:11:12.302779 15775 sgd_solver.cpp:105] Iteration 5424, lr = 0.001
I0407 09:11:17.503516 15775 solver.cpp:218] Iteration 5436 (2.30738 iter/s, 5.2007s/12 iters), loss = 0.416625
I0407 09:11:17.503562 15775 solver.cpp:237]     Train net output #0: loss = 0.416625 (* 1 = 0.416625 loss)
I0407 09:11:17.503571 15775 sgd_solver.cpp:105] Iteration 5436, lr = 0.001
I0407 09:11:22.718865 15775 solver.cpp:218] Iteration 5448 (2.30094 iter/s, 5.21526s/12 iters), loss = 0.555078
I0407 09:11:22.718909 15775 solver.cpp:237]     Train net output #0: loss = 0.555078 (* 1 = 0.555078 loss)
I0407 09:11:22.718917 15775 sgd_solver.cpp:105] Iteration 5448, lr = 0.001
I0407 09:11:27.998435 15775 solver.cpp:218] Iteration 5460 (2.27295 iter/s, 5.27948s/12 iters), loss = 0.347554
I0407 09:11:27.998481 15775 solver.cpp:237]     Train net output #0: loss = 0.347554 (* 1 = 0.347554 loss)
I0407 09:11:27.998488 15775 sgd_solver.cpp:105] Iteration 5460, lr = 0.001
I0407 09:11:28.480329 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:33.266258 15775 solver.cpp:218] Iteration 5472 (2.27802 iter/s, 5.26773s/12 iters), loss = 0.325262
I0407 09:11:33.266304 15775 solver.cpp:237]     Train net output #0: loss = 0.325262 (* 1 = 0.325262 loss)
I0407 09:11:33.266312 15775 sgd_solver.cpp:105] Iteration 5472, lr = 0.001
I0407 09:11:38.589689 15775 solver.cpp:218] Iteration 5484 (2.25422 iter/s, 5.32334s/12 iters), loss = 0.239996
I0407 09:11:38.589730 15775 solver.cpp:237]     Train net output #0: loss = 0.239996 (* 1 = 0.239996 loss)
I0407 09:11:38.589737 15775 sgd_solver.cpp:105] Iteration 5484, lr = 0.001
I0407 09:11:43.694125 15775 solver.cpp:218] Iteration 5496 (2.35093 iter/s, 5.10436s/12 iters), loss = 0.49047
I0407 09:11:43.694240 15775 solver.cpp:237]     Train net output #0: loss = 0.49047 (* 1 = 0.49047 loss)
I0407 09:11:43.694249 15775 sgd_solver.cpp:105] Iteration 5496, lr = 0.001
I0407 09:11:48.541862 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 09:11:51.581852 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 09:11:55.519129 15775 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 09:11:55.519152 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:11:57.669137 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:59.957319 15775 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0407 09:11:59.957348 15775 solver.cpp:397]     Test net output #1: loss = 2.70827 (* 1 = 2.70827 loss)
I0407 09:12:00.096560 15775 solver.cpp:218] Iteration 5508 (0.731608 iter/s, 16.4022s/12 iters), loss = 0.435671
I0407 09:12:00.096606 15775 solver.cpp:237]     Train net output #0: loss = 0.435671 (* 1 = 0.435671 loss)
I0407 09:12:00.096614 15775 sgd_solver.cpp:105] Iteration 5508, lr = 0.001
I0407 09:12:04.584694 15775 solver.cpp:218] Iteration 5520 (2.67377 iter/s, 4.48805s/12 iters), loss = 0.486474
I0407 09:12:04.584736 15775 solver.cpp:237]     Train net output #0: loss = 0.486474 (* 1 = 0.486474 loss)
I0407 09:12:04.584744 15775 sgd_solver.cpp:105] Iteration 5520, lr = 0.001
I0407 09:12:07.221652 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:12:09.839532 15775 solver.cpp:218] Iteration 5532 (2.28365 iter/s, 5.25475s/12 iters), loss = 0.388243
I0407 09:12:09.839577 15775 solver.cpp:237]     Train net output #0: loss = 0.388243 (* 1 = 0.388243 loss)
I0407 09:12:09.839586 15775 sgd_solver.cpp:105] Iteration 5532, lr = 0.001
I0407 09:12:15.199622 15775 solver.cpp:218] Iteration 5544 (2.23881 iter/s, 5.35999s/12 iters), loss = 0.4577
I0407 09:12:15.199759 15775 solver.cpp:237]     Train net output #0: loss = 0.4577 (* 1 = 0.4577 loss)
I0407 09:12:15.199769 15775 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0407 09:12:20.509028 15775 solver.cpp:218] Iteration 5556 (2.26022 iter/s, 5.30923s/12 iters), loss = 0.371845
I0407 09:12:20.509074 15775 solver.cpp:237]     Train net output #0: loss = 0.371845 (* 1 = 0.371845 loss)
I0407 09:12:20.509083 15775 sgd_solver.cpp:105] Iteration 5556, lr = 0.001
I0407 09:12:23.336166 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:25.852424 15775 solver.cpp:218] Iteration 5568 (2.2458 iter/s, 5.34331s/12 iters), loss = 0.368562
I0407 09:12:25.852464 15775 solver.cpp:237]     Train net output #0: loss = 0.368562 (* 1 = 0.368562 loss)
I0407 09:12:25.852473 15775 sgd_solver.cpp:105] Iteration 5568, lr = 0.001
I0407 09:12:31.186988 15775 solver.cpp:218] Iteration 5580 (2.24952 iter/s, 5.33447s/12 iters), loss = 0.233938
I0407 09:12:31.187036 15775 solver.cpp:237]     Train net output #0: loss = 0.233938 (* 1 = 0.233938 loss)
I0407 09:12:31.187042 15775 sgd_solver.cpp:105] Iteration 5580, lr = 0.001
I0407 09:12:36.420760 15775 solver.cpp:218] Iteration 5592 (2.29284 iter/s, 5.23368s/12 iters), loss = 0.297688
I0407 09:12:36.420804 15775 solver.cpp:237]     Train net output #0: loss = 0.297688 (* 1 = 0.297688 loss)
I0407 09:12:36.420812 15775 sgd_solver.cpp:105] Iteration 5592, lr = 0.001
I0407 09:12:41.802987 15775 solver.cpp:218] Iteration 5604 (2.2296 iter/s, 5.38214s/12 iters), loss = 0.285659
I0407 09:12:41.803033 15775 solver.cpp:237]     Train net output #0: loss = 0.285659 (* 1 = 0.285659 loss)
I0407 09:12:41.803043 15775 sgd_solver.cpp:105] Iteration 5604, lr = 0.001
I0407 09:12:43.969553 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 09:12:46.998581 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 09:12:51.282214 15775 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 09:12:51.282236 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:12:53.396136 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:55.557647 15775 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0407 09:12:55.557674 15775 solver.cpp:397]     Test net output #1: loss = 2.75079 (* 1 = 2.75079 loss)
I0407 09:12:57.400132 15775 solver.cpp:218] Iteration 5616 (0.769379 iter/s, 15.597s/12 iters), loss = 0.415805
I0407 09:12:57.400177 15775 solver.cpp:237]     Train net output #0: loss = 0.415805 (* 1 = 0.415805 loss)
I0407 09:12:57.400183 15775 sgd_solver.cpp:105] Iteration 5616, lr = 0.001
I0407 09:13:02.559154 15775 solver.cpp:218] Iteration 5628 (2.32606 iter/s, 5.15893s/12 iters), loss = 0.301313
I0407 09:13:02.559199 15775 solver.cpp:237]     Train net output #0: loss = 0.301313 (* 1 = 0.301313 loss)
I0407 09:13:02.559207 15775 sgd_solver.cpp:105] Iteration 5628, lr = 0.001
I0407 09:13:07.882171 15775 solver.cpp:218] Iteration 5640 (2.2544 iter/s, 5.32292s/12 iters), loss = 0.290008
I0407 09:13:07.882236 15775 solver.cpp:237]     Train net output #0: loss = 0.290008 (* 1 = 0.290008 loss)
I0407 09:13:07.882248 15775 sgd_solver.cpp:105] Iteration 5640, lr = 0.001
I0407 09:13:13.187527 15775 solver.cpp:218] Iteration 5652 (2.26191 iter/s, 5.30525s/12 iters), loss = 0.377742
I0407 09:13:13.187566 15775 solver.cpp:237]     Train net output #0: loss = 0.377742 (* 1 = 0.377742 loss)
I0407 09:13:13.187573 15775 sgd_solver.cpp:105] Iteration 5652, lr = 0.001
I0407 09:13:18.404917 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:18.624631 15775 solver.cpp:218] Iteration 5664 (2.20709 iter/s, 5.43702s/12 iters), loss = 0.386929
I0407 09:13:18.624680 15775 solver.cpp:237]     Train net output #0: loss = 0.386929 (* 1 = 0.386929 loss)
I0407 09:13:18.624689 15775 sgd_solver.cpp:105] Iteration 5664, lr = 0.001
I0407 09:13:23.963570 15775 solver.cpp:218] Iteration 5676 (2.24768 iter/s, 5.33885s/12 iters), loss = 0.354295
I0407 09:13:23.963613 15775 solver.cpp:237]     Train net output #0: loss = 0.354295 (* 1 = 0.354295 loss)
I0407 09:13:23.963619 15775 sgd_solver.cpp:105] Iteration 5676, lr = 0.001
I0407 09:13:28.979180 15775 solver.cpp:218] Iteration 5688 (2.39257 iter/s, 5.01553s/12 iters), loss = 0.39756
I0407 09:13:28.979219 15775 solver.cpp:237]     Train net output #0: loss = 0.39756 (* 1 = 0.39756 loss)
I0407 09:13:28.979226 15775 sgd_solver.cpp:105] Iteration 5688, lr = 0.001
I0407 09:13:34.222409 15775 solver.cpp:218] Iteration 5700 (2.28871 iter/s, 5.24314s/12 iters), loss = 0.285653
I0407 09:13:34.222468 15775 solver.cpp:237]     Train net output #0: loss = 0.285653 (* 1 = 0.285653 loss)
I0407 09:13:34.222479 15775 sgd_solver.cpp:105] Iteration 5700, lr = 0.001
I0407 09:13:38.929769 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 09:13:41.925818 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 09:13:46.126065 15775 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 09:13:46.126091 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:13:48.209436 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:50.452641 15775 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0407 09:13:50.452783 15775 solver.cpp:397]     Test net output #1: loss = 2.77747 (* 1 = 2.77747 loss)
I0407 09:13:50.593832 15775 solver.cpp:218] Iteration 5712 (0.732991 iter/s, 16.3713s/12 iters), loss = 0.40072
I0407 09:13:50.593897 15775 solver.cpp:237]     Train net output #0: loss = 0.40072 (* 1 = 0.40072 loss)
I0407 09:13:50.593906 15775 sgd_solver.cpp:105] Iteration 5712, lr = 0.001
I0407 09:13:55.123366 15775 solver.cpp:218] Iteration 5724 (2.64934 iter/s, 4.52943s/12 iters), loss = 0.362709
I0407 09:13:55.123405 15775 solver.cpp:237]     Train net output #0: loss = 0.362709 (* 1 = 0.362709 loss)
I0407 09:13:55.123414 15775 sgd_solver.cpp:105] Iteration 5724, lr = 0.001
I0407 09:14:00.300897 15775 solver.cpp:218] Iteration 5736 (2.31775 iter/s, 5.17745s/12 iters), loss = 0.297038
I0407 09:14:00.300938 15775 solver.cpp:237]     Train net output #0: loss = 0.297038 (* 1 = 0.297038 loss)
I0407 09:14:00.300947 15775 sgd_solver.cpp:105] Iteration 5736, lr = 0.001
I0407 09:14:05.339259 15775 solver.cpp:218] Iteration 5748 (2.38176 iter/s, 5.03828s/12 iters), loss = 0.331039
I0407 09:14:05.339301 15775 solver.cpp:237]     Train net output #0: loss = 0.331039 (* 1 = 0.331039 loss)
I0407 09:14:05.339308 15775 sgd_solver.cpp:105] Iteration 5748, lr = 0.001
I0407 09:14:10.492377 15775 solver.cpp:218] Iteration 5760 (2.32872 iter/s, 5.15304s/12 iters), loss = 0.3633
I0407 09:14:10.492411 15775 solver.cpp:237]     Train net output #0: loss = 0.3633 (* 1 = 0.3633 loss)
I0407 09:14:10.492419 15775 sgd_solver.cpp:105] Iteration 5760, lr = 0.001
I0407 09:14:12.466830 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:15.822343 15775 solver.cpp:218] Iteration 5772 (2.25145 iter/s, 5.32989s/12 iters), loss = 0.373164
I0407 09:14:15.822381 15775 solver.cpp:237]     Train net output #0: loss = 0.373164 (* 1 = 0.373164 loss)
I0407 09:14:15.822387 15775 sgd_solver.cpp:105] Iteration 5772, lr = 0.001
I0407 09:14:21.227795 15775 solver.cpp:218] Iteration 5784 (2.22002 iter/s, 5.40536s/12 iters), loss = 0.406163
I0407 09:14:21.227943 15775 solver.cpp:237]     Train net output #0: loss = 0.406163 (* 1 = 0.406163 loss)
I0407 09:14:21.227954 15775 sgd_solver.cpp:105] Iteration 5784, lr = 0.001
I0407 09:14:26.360090 15775 solver.cpp:218] Iteration 5796 (2.33822 iter/s, 5.1321s/12 iters), loss = 0.349382
I0407 09:14:26.360133 15775 solver.cpp:237]     Train net output #0: loss = 0.349382 (* 1 = 0.349382 loss)
I0407 09:14:26.360141 15775 sgd_solver.cpp:105] Iteration 5796, lr = 0.001
I0407 09:14:31.725678 15775 solver.cpp:218] Iteration 5808 (2.23651 iter/s, 5.3655s/12 iters), loss = 0.55251
I0407 09:14:31.725731 15775 solver.cpp:237]     Train net output #0: loss = 0.55251 (* 1 = 0.55251 loss)
I0407 09:14:31.725740 15775 sgd_solver.cpp:105] Iteration 5808, lr = 0.001
I0407 09:14:33.720890 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 09:14:36.724138 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 09:14:40.965654 15775 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 09:14:40.965677 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:14:43.029147 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:45.287406 15775 solver.cpp:397]     Test net output #0: accuracy = 0.438726
I0407 09:14:45.287444 15775 solver.cpp:397]     Test net output #1: loss = 2.80501 (* 1 = 2.80501 loss)
I0407 09:14:47.197544 15775 solver.cpp:218] Iteration 5820 (0.775609 iter/s, 15.4717s/12 iters), loss = 0.235191
I0407 09:14:47.197587 15775 solver.cpp:237]     Train net output #0: loss = 0.235191 (* 1 = 0.235191 loss)
I0407 09:14:47.197594 15775 sgd_solver.cpp:105] Iteration 5820, lr = 0.001
I0407 09:14:52.306190 15775 solver.cpp:218] Iteration 5832 (2.349 iter/s, 5.10856s/12 iters), loss = 0.188562
I0407 09:14:52.306310 15775 solver.cpp:237]     Train net output #0: loss = 0.188562 (* 1 = 0.188562 loss)
I0407 09:14:52.306319 15775 sgd_solver.cpp:105] Iteration 5832, lr = 0.001
I0407 09:14:57.541687 15775 solver.cpp:218] Iteration 5844 (2.29212 iter/s, 5.23533s/12 iters), loss = 0.300547
I0407 09:14:57.541730 15775 solver.cpp:237]     Train net output #0: loss = 0.300547 (* 1 = 0.300547 loss)
I0407 09:14:57.541738 15775 sgd_solver.cpp:105] Iteration 5844, lr = 0.001
I0407 09:15:02.769807 15775 solver.cpp:218] Iteration 5856 (2.29532 iter/s, 5.22803s/12 iters), loss = 0.409564
I0407 09:15:02.769847 15775 solver.cpp:237]     Train net output #0: loss = 0.409564 (* 1 = 0.409564 loss)
I0407 09:15:02.769856 15775 sgd_solver.cpp:105] Iteration 5856, lr = 0.001
I0407 09:15:07.276247 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:08.176786 15775 solver.cpp:218] Iteration 5868 (2.21939 iter/s, 5.40689s/12 iters), loss = 0.411655
I0407 09:15:08.176836 15775 solver.cpp:237]     Train net output #0: loss = 0.411655 (* 1 = 0.411655 loss)
I0407 09:15:08.176846 15775 sgd_solver.cpp:105] Iteration 5868, lr = 0.001
I0407 09:15:13.331545 15775 solver.cpp:218] Iteration 5880 (2.32799 iter/s, 5.15467s/12 iters), loss = 0.278927
I0407 09:15:13.331588 15775 solver.cpp:237]     Train net output #0: loss = 0.278927 (* 1 = 0.278927 loss)
I0407 09:15:13.331595 15775 sgd_solver.cpp:105] Iteration 5880, lr = 0.001
I0407 09:15:18.611021 15775 solver.cpp:218] Iteration 5892 (2.27299 iter/s, 5.27939s/12 iters), loss = 0.193341
I0407 09:15:18.611060 15775 solver.cpp:237]     Train net output #0: loss = 0.193341 (* 1 = 0.193341 loss)
I0407 09:15:18.611068 15775 sgd_solver.cpp:105] Iteration 5892, lr = 0.001
I0407 09:15:23.971733 15775 solver.cpp:218] Iteration 5904 (2.23855 iter/s, 5.36062s/12 iters), loss = 0.268411
I0407 09:15:23.971854 15775 solver.cpp:237]     Train net output #0: loss = 0.268411 (* 1 = 0.268411 loss)
I0407 09:15:23.971864 15775 sgd_solver.cpp:105] Iteration 5904, lr = 0.001
I0407 09:15:28.706732 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 09:15:31.721904 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 09:15:35.503019 15775 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 09:15:35.503044 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:15:37.546725 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:39.819389 15775 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 09:15:39.819424 15775 solver.cpp:397]     Test net output #1: loss = 2.74277 (* 1 = 2.74277 loss)
I0407 09:15:39.960482 15775 solver.cpp:218] Iteration 5916 (0.750538 iter/s, 15.9885s/12 iters), loss = 0.282202
I0407 09:15:39.960532 15775 solver.cpp:237]     Train net output #0: loss = 0.282202 (* 1 = 0.282202 loss)
I0407 09:15:39.960542 15775 sgd_solver.cpp:105] Iteration 5916, lr = 0.001
I0407 09:15:44.283449 15775 solver.cpp:218] Iteration 5928 (2.77593 iter/s, 4.32288s/12 iters), loss = 0.281583
I0407 09:15:44.283493 15775 solver.cpp:237]     Train net output #0: loss = 0.281583 (* 1 = 0.281583 loss)
I0407 09:15:44.283500 15775 sgd_solver.cpp:105] Iteration 5928, lr = 0.001
I0407 09:15:49.482844 15775 solver.cpp:218] Iteration 5940 (2.308 iter/s, 5.1993s/12 iters), loss = 0.366568
I0407 09:15:49.482892 15775 solver.cpp:237]     Train net output #0: loss = 0.366568 (* 1 = 0.366568 loss)
I0407 09:15:49.482900 15775 sgd_solver.cpp:105] Iteration 5940, lr = 0.001
I0407 09:15:54.713876 15775 solver.cpp:218] Iteration 5952 (2.29404 iter/s, 5.23094s/12 iters), loss = 0.307737
I0407 09:15:54.714022 15775 solver.cpp:237]     Train net output #0: loss = 0.307737 (* 1 = 0.307737 loss)
I0407 09:15:54.714033 15775 sgd_solver.cpp:105] Iteration 5952, lr = 0.001
I0407 09:15:59.984127 15775 solver.cpp:218] Iteration 5964 (2.27701 iter/s, 5.27006s/12 iters), loss = 0.224172
I0407 09:15:59.984169 15775 solver.cpp:237]     Train net output #0: loss = 0.224172 (* 1 = 0.224172 loss)
I0407 09:15:59.984176 15775 sgd_solver.cpp:105] Iteration 5964, lr = 0.001
I0407 09:16:01.414135 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:05.183826 15775 solver.cpp:218] Iteration 5976 (2.30787 iter/s, 5.19961s/12 iters), loss = 0.306742
I0407 09:16:05.183872 15775 solver.cpp:237]     Train net output #0: loss = 0.306742 (* 1 = 0.306742 loss)
I0407 09:16:05.183881 15775 sgd_solver.cpp:105] Iteration 5976, lr = 0.001
I0407 09:16:10.427817 15775 solver.cpp:218] Iteration 5988 (2.28837 iter/s, 5.24391s/12 iters), loss = 0.294626
I0407 09:16:10.427857 15775 solver.cpp:237]     Train net output #0: loss = 0.294626 (* 1 = 0.294626 loss)
I0407 09:16:10.427865 15775 sgd_solver.cpp:105] Iteration 5988, lr = 0.001
I0407 09:16:15.786296 15775 solver.cpp:218] Iteration 6000 (2.23948 iter/s, 5.35839s/12 iters), loss = 0.432341
I0407 09:16:15.786342 15775 solver.cpp:237]     Train net output #0: loss = 0.432341 (* 1 = 0.432341 loss)
I0407 09:16:15.786350 15775 sgd_solver.cpp:105] Iteration 6000, lr = 0.001
I0407 09:16:21.180904 15775 solver.cpp:218] Iteration 6012 (2.22448 iter/s, 5.39451s/12 iters), loss = 0.444475
I0407 09:16:21.180950 15775 solver.cpp:237]     Train net output #0: loss = 0.444475 (* 1 = 0.444475 loss)
I0407 09:16:21.180958 15775 sgd_solver.cpp:105] Iteration 6012, lr = 0.001
I0407 09:16:23.351966 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 09:16:26.439656 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 09:16:30.663496 15775 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 09:16:30.663518 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:16:32.606894 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:34.931211 15775 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0407 09:16:34.931246 15775 solver.cpp:397]     Test net output #1: loss = 2.84365 (* 1 = 2.84365 loss)
I0407 09:16:36.872709 15775 solver.cpp:218] Iteration 6024 (0.764738 iter/s, 15.6917s/12 iters), loss = 0.320497
I0407 09:16:36.872752 15775 solver.cpp:237]     Train net output #0: loss = 0.320497 (* 1 = 0.320497 loss)
I0407 09:16:36.872761 15775 sgd_solver.cpp:105] Iteration 6024, lr = 0.001
I0407 09:16:42.046986 15775 solver.cpp:218] Iteration 6036 (2.3192 iter/s, 5.17419s/12 iters), loss = 0.21396
I0407 09:16:42.047031 15775 solver.cpp:237]     Train net output #0: loss = 0.21396 (* 1 = 0.21396 loss)
I0407 09:16:42.047039 15775 sgd_solver.cpp:105] Iteration 6036, lr = 0.001
I0407 09:16:47.413012 15775 solver.cpp:218] Iteration 6048 (2.23633 iter/s, 5.36594s/12 iters), loss = 0.374125
I0407 09:16:47.413053 15775 solver.cpp:237]     Train net output #0: loss = 0.374125 (* 1 = 0.374125 loss)
I0407 09:16:47.413061 15775 sgd_solver.cpp:105] Iteration 6048, lr = 0.001
I0407 09:16:52.508862 15775 solver.cpp:218] Iteration 6060 (2.3549 iter/s, 5.09575s/12 iters), loss = 0.281376
I0407 09:16:52.508934 15775 solver.cpp:237]     Train net output #0: loss = 0.281376 (* 1 = 0.281376 loss)
I0407 09:16:52.508945 15775 sgd_solver.cpp:105] Iteration 6060, lr = 0.001
I0407 09:16:56.124518 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:57.702108 15775 solver.cpp:218] Iteration 6072 (2.31074 iter/s, 5.19314s/12 iters), loss = 0.343484
I0407 09:16:57.702239 15775 solver.cpp:237]     Train net output #0: loss = 0.343484 (* 1 = 0.343484 loss)
I0407 09:16:57.702246 15775 sgd_solver.cpp:105] Iteration 6072, lr = 0.001
I0407 09:17:02.436062 15775 solver.cpp:218] Iteration 6084 (2.53497 iter/s, 4.73378s/12 iters), loss = 0.307909
I0407 09:17:02.436110 15775 solver.cpp:237]     Train net output #0: loss = 0.307909 (* 1 = 0.307909 loss)
I0407 09:17:02.436118 15775 sgd_solver.cpp:105] Iteration 6084, lr = 0.001
I0407 09:17:07.730564 15775 solver.cpp:218] Iteration 6096 (2.26654 iter/s, 5.29441s/12 iters), loss = 0.280985
I0407 09:17:07.730618 15775 solver.cpp:237]     Train net output #0: loss = 0.280985 (* 1 = 0.280985 loss)
I0407 09:17:07.730626 15775 sgd_solver.cpp:105] Iteration 6096, lr = 0.001
I0407 09:17:12.858603 15775 solver.cpp:218] Iteration 6108 (2.34012 iter/s, 5.12794s/12 iters), loss = 0.336839
I0407 09:17:12.858659 15775 solver.cpp:237]     Train net output #0: loss = 0.336839 (* 1 = 0.336839 loss)
I0407 09:17:12.858673 15775 sgd_solver.cpp:105] Iteration 6108, lr = 0.001
I0407 09:17:17.658614 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 09:17:20.696300 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 09:17:23.435640 15775 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 09:17:23.435668 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:17:25.386765 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:27.735812 15775 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0407 09:17:27.735918 15775 solver.cpp:397]     Test net output #1: loss = 2.75512 (* 1 = 2.75512 loss)
I0407 09:17:27.877238 15775 solver.cpp:218] Iteration 6120 (0.799016 iter/s, 15.0185s/12 iters), loss = 0.243711
I0407 09:17:27.878811 15775 solver.cpp:237]     Train net output #0: loss = 0.243711 (* 1 = 0.243711 loss)
I0407 09:17:27.878830 15775 sgd_solver.cpp:105] Iteration 6120, lr = 0.001
I0407 09:17:32.278841 15775 solver.cpp:218] Iteration 6132 (2.72727 iter/s, 4.4s/12 iters), loss = 0.187085
I0407 09:17:32.278885 15775 solver.cpp:237]     Train net output #0: loss = 0.187085 (* 1 = 0.187085 loss)
I0407 09:17:32.278893 15775 sgd_solver.cpp:105] Iteration 6132, lr = 0.001
I0407 09:17:37.721616 15775 solver.cpp:218] Iteration 6144 (2.20479 iter/s, 5.44268s/12 iters), loss = 0.51436
I0407 09:17:37.721657 15775 solver.cpp:237]     Train net output #0: loss = 0.51436 (* 1 = 0.51436 loss)
I0407 09:17:37.721664 15775 sgd_solver.cpp:105] Iteration 6144, lr = 0.001
I0407 09:17:43.127449 15775 solver.cpp:218] Iteration 6156 (2.21986 iter/s, 5.40574s/12 iters), loss = 0.308169
I0407 09:17:43.127494 15775 solver.cpp:237]     Train net output #0: loss = 0.308169 (* 1 = 0.308169 loss)
I0407 09:17:43.127503 15775 sgd_solver.cpp:105] Iteration 6156, lr = 0.001
I0407 09:17:48.549486 15775 solver.cpp:218] Iteration 6168 (2.21323 iter/s, 5.42195s/12 iters), loss = 0.325754
I0407 09:17:48.549531 15775 solver.cpp:237]     Train net output #0: loss = 0.325754 (* 1 = 0.325754 loss)
I0407 09:17:48.549540 15775 sgd_solver.cpp:105] Iteration 6168, lr = 0.001
I0407 09:17:49.174183 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:53.665202 15775 solver.cpp:218] Iteration 6180 (2.34575 iter/s, 5.11563s/12 iters), loss = 0.41277
I0407 09:17:53.665244 15775 solver.cpp:237]     Train net output #0: loss = 0.41277 (* 1 = 0.41277 loss)
I0407 09:17:53.665253 15775 sgd_solver.cpp:105] Iteration 6180, lr = 0.001
I0407 09:17:59.011173 15775 solver.cpp:218] Iteration 6192 (2.24472 iter/s, 5.34588s/12 iters), loss = 0.26064
I0407 09:17:59.011330 15775 solver.cpp:237]     Train net output #0: loss = 0.26064 (* 1 = 0.26064 loss)
I0407 09:17:59.011339 15775 sgd_solver.cpp:105] Iteration 6192, lr = 0.001
I0407 09:18:04.298141 15775 solver.cpp:218] Iteration 6204 (2.26982 iter/s, 5.28677s/12 iters), loss = 0.236829
I0407 09:18:04.298192 15775 solver.cpp:237]     Train net output #0: loss = 0.236829 (* 1 = 0.236829 loss)
I0407 09:18:04.298200 15775 sgd_solver.cpp:105] Iteration 6204, lr = 0.001
I0407 09:18:09.635004 15775 solver.cpp:218] Iteration 6216 (2.24855 iter/s, 5.33677s/12 iters), loss = 0.313196
I0407 09:18:09.635049 15775 solver.cpp:237]     Train net output #0: loss = 0.313196 (* 1 = 0.313196 loss)
I0407 09:18:09.635057 15775 sgd_solver.cpp:105] Iteration 6216, lr = 0.001
I0407 09:18:11.869948 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 09:18:14.862150 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 09:18:18.957327 15775 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 09:18:18.957360 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:18:20.966668 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:22.371922 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:18:23.609469 15775 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0407 09:18:23.609496 15775 solver.cpp:397]     Test net output #1: loss = 2.78276 (* 1 = 2.78276 loss)
I0407 09:18:25.605654 15775 solver.cpp:218] Iteration 6228 (0.751385 iter/s, 15.9705s/12 iters), loss = 0.307971
I0407 09:18:25.605693 15775 solver.cpp:237]     Train net output #0: loss = 0.307971 (* 1 = 0.307971 loss)
I0407 09:18:25.605700 15775 sgd_solver.cpp:105] Iteration 6228, lr = 0.001
I0407 09:18:30.167915 15775 solver.cpp:218] Iteration 6240 (2.63035 iter/s, 4.56213s/12 iters), loss = 0.279685
I0407 09:18:30.168007 15775 solver.cpp:237]     Train net output #0: loss = 0.279685 (* 1 = 0.279685 loss)
I0407 09:18:30.168015 15775 sgd_solver.cpp:105] Iteration 6240, lr = 0.001
I0407 09:18:35.064991 15775 solver.cpp:218] Iteration 6252 (2.45051 iter/s, 4.89694s/12 iters), loss = 0.302416
I0407 09:18:35.065027 15775 solver.cpp:237]     Train net output #0: loss = 0.302416 (* 1 = 0.302416 loss)
I0407 09:18:35.065034 15775 sgd_solver.cpp:105] Iteration 6252, lr = 0.001
I0407 09:18:40.255036 15775 solver.cpp:218] Iteration 6264 (2.3123 iter/s, 5.18965s/12 iters), loss = 0.339954
I0407 09:18:40.255072 15775 solver.cpp:237]     Train net output #0: loss = 0.339954 (* 1 = 0.339954 loss)
I0407 09:18:40.255079 15775 sgd_solver.cpp:105] Iteration 6264, lr = 0.001
I0407 09:18:43.065871 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:45.531163 15775 solver.cpp:218] Iteration 6276 (2.27443 iter/s, 5.27604s/12 iters), loss = 0.159408
I0407 09:18:45.531199 15775 solver.cpp:237]     Train net output #0: loss = 0.159408 (* 1 = 0.159408 loss)
I0407 09:18:45.531208 15775 sgd_solver.cpp:105] Iteration 6276, lr = 0.001
I0407 09:18:50.613139 15775 solver.cpp:218] Iteration 6288 (2.36132 iter/s, 5.08189s/12 iters), loss = 0.308655
I0407 09:18:50.613179 15775 solver.cpp:237]     Train net output #0: loss = 0.308655 (* 1 = 0.308655 loss)
I0407 09:18:50.613188 15775 sgd_solver.cpp:105] Iteration 6288, lr = 0.001
I0407 09:18:55.756340 15775 solver.cpp:218] Iteration 6300 (2.33322 iter/s, 5.14311s/12 iters), loss = 0.353439
I0407 09:18:55.756395 15775 solver.cpp:237]     Train net output #0: loss = 0.353439 (* 1 = 0.353439 loss)
I0407 09:18:55.756405 15775 sgd_solver.cpp:105] Iteration 6300, lr = 0.001
I0407 09:19:01.324177 15775 solver.cpp:218] Iteration 6312 (2.15527 iter/s, 5.56774s/12 iters), loss = 0.276341
I0407 09:19:01.324265 15775 solver.cpp:237]     Train net output #0: loss = 0.276341 (* 1 = 0.276341 loss)
I0407 09:19:01.324273 15775 sgd_solver.cpp:105] Iteration 6312, lr = 0.001
I0407 09:19:05.819185 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 09:19:08.942359 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 09:19:11.253186 15775 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 09:19:11.253204 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:19:13.290565 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:15.856968 15775 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0407 09:19:15.857000 15775 solver.cpp:397]     Test net output #1: loss = 2.76631 (* 1 = 2.76631 loss)
I0407 09:19:15.998006 15775 solver.cpp:218] Iteration 6324 (0.817792 iter/s, 14.6736s/12 iters), loss = 0.327632
I0407 09:19:15.998046 15775 solver.cpp:237]     Train net output #0: loss = 0.327632 (* 1 = 0.327632 loss)
I0407 09:19:15.998054 15775 sgd_solver.cpp:105] Iteration 6324, lr = 0.001
I0407 09:19:20.390873 15775 solver.cpp:218] Iteration 6336 (2.73175 iter/s, 4.39278s/12 iters), loss = 0.208512
I0407 09:19:20.390918 15775 solver.cpp:237]     Train net output #0: loss = 0.208512 (* 1 = 0.208512 loss)
I0407 09:19:20.390928 15775 sgd_solver.cpp:105] Iteration 6336, lr = 0.001
I0407 09:19:25.615737 15775 solver.cpp:218] Iteration 6348 (2.29675 iter/s, 5.22477s/12 iters), loss = 0.356951
I0407 09:19:25.615780 15775 solver.cpp:237]     Train net output #0: loss = 0.356951 (* 1 = 0.356951 loss)
I0407 09:19:25.615787 15775 sgd_solver.cpp:105] Iteration 6348, lr = 0.001
I0407 09:19:30.982074 15775 solver.cpp:218] Iteration 6360 (2.2362 iter/s, 5.36625s/12 iters), loss = 0.222055
I0407 09:19:30.982111 15775 solver.cpp:237]     Train net output #0: loss = 0.222055 (* 1 = 0.222055 loss)
I0407 09:19:30.982118 15775 sgd_solver.cpp:105] Iteration 6360, lr = 0.001
I0407 09:19:35.955412 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:36.148711 15775 solver.cpp:218] Iteration 6372 (2.32263 iter/s, 5.16655s/12 iters), loss = 0.204389
I0407 09:19:36.148758 15775 solver.cpp:237]     Train net output #0: loss = 0.204389 (* 1 = 0.204389 loss)
I0407 09:19:36.148766 15775 sgd_solver.cpp:105] Iteration 6372, lr = 0.001
I0407 09:19:41.624541 15775 solver.cpp:218] Iteration 6384 (2.19148 iter/s, 5.47574s/12 iters), loss = 0.363674
I0407 09:19:41.624579 15775 solver.cpp:237]     Train net output #0: loss = 0.363674 (* 1 = 0.363674 loss)
I0407 09:19:41.624585 15775 sgd_solver.cpp:105] Iteration 6384, lr = 0.001
I0407 09:19:46.631577 15775 solver.cpp:218] Iteration 6396 (2.39667 iter/s, 5.00696s/12 iters), loss = 0.230405
I0407 09:19:46.631625 15775 solver.cpp:237]     Train net output #0: loss = 0.230405 (* 1 = 0.230405 loss)
I0407 09:19:46.631633 15775 sgd_solver.cpp:105] Iteration 6396, lr = 0.001
I0407 09:19:51.834936 15775 solver.cpp:218] Iteration 6408 (2.30624 iter/s, 5.20326s/12 iters), loss = 0.377213
I0407 09:19:51.834981 15775 solver.cpp:237]     Train net output #0: loss = 0.377213 (* 1 = 0.377213 loss)
I0407 09:19:51.834990 15775 sgd_solver.cpp:105] Iteration 6408, lr = 0.001
I0407 09:19:57.186767 15775 solver.cpp:218] Iteration 6420 (2.24226 iter/s, 5.35174s/12 iters), loss = 0.416711
I0407 09:19:57.186813 15775 solver.cpp:237]     Train net output #0: loss = 0.416711 (* 1 = 0.416711 loss)
I0407 09:19:57.186821 15775 sgd_solver.cpp:105] Iteration 6420, lr = 0.001
I0407 09:19:59.282276 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 09:20:02.298434 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 09:20:04.604593 15775 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 09:20:04.604614 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:20:06.465548 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:08.987555 15775 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0407 09:20:08.987587 15775 solver.cpp:397]     Test net output #1: loss = 2.76495 (* 1 = 2.76495 loss)
I0407 09:20:10.967314 15775 solver.cpp:218] Iteration 6432 (0.870802 iter/s, 13.7804s/12 iters), loss = 0.26002
I0407 09:20:10.967360 15775 solver.cpp:237]     Train net output #0: loss = 0.26002 (* 1 = 0.26002 loss)
I0407 09:20:10.967368 15775 sgd_solver.cpp:105] Iteration 6432, lr = 0.001
I0407 09:20:16.314647 15775 solver.cpp:218] Iteration 6444 (2.24415 iter/s, 5.34725s/12 iters), loss = 0.222394
I0407 09:20:16.314685 15775 solver.cpp:237]     Train net output #0: loss = 0.222394 (* 1 = 0.222394 loss)
I0407 09:20:16.314692 15775 sgd_solver.cpp:105] Iteration 6444, lr = 0.001
I0407 09:20:21.741307 15775 solver.cpp:218] Iteration 6456 (2.21134 iter/s, 5.42658s/12 iters), loss = 0.298698
I0407 09:20:21.741348 15775 solver.cpp:237]     Train net output #0: loss = 0.298698 (* 1 = 0.298698 loss)
I0407 09:20:21.741355 15775 sgd_solver.cpp:105] Iteration 6456, lr = 0.001
I0407 09:20:27.020097 15775 solver.cpp:218] Iteration 6468 (2.27328 iter/s, 5.27871s/12 iters), loss = 0.199493
I0407 09:20:27.020136 15775 solver.cpp:237]     Train net output #0: loss = 0.199493 (* 1 = 0.199493 loss)
I0407 09:20:27.020143 15775 sgd_solver.cpp:105] Iteration 6468, lr = 0.001
I0407 09:20:29.174273 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:32.464991 15775 solver.cpp:218] Iteration 6480 (2.20394 iter/s, 5.44481s/12 iters), loss = 0.129308
I0407 09:20:32.465040 15775 solver.cpp:237]     Train net output #0: loss = 0.129308 (* 1 = 0.129308 loss)
I0407 09:20:32.465049 15775 sgd_solver.cpp:105] Iteration 6480, lr = 0.001
I0407 09:20:37.806224 15775 solver.cpp:218] Iteration 6492 (2.24671 iter/s, 5.34114s/12 iters), loss = 0.295348
I0407 09:20:37.806362 15775 solver.cpp:237]     Train net output #0: loss = 0.295348 (* 1 = 0.295348 loss)
I0407 09:20:37.806371 15775 sgd_solver.cpp:105] Iteration 6492, lr = 0.001
I0407 09:20:43.027145 15775 solver.cpp:218] Iteration 6504 (2.29852 iter/s, 5.22075s/12 iters), loss = 0.18256
I0407 09:20:43.027181 15775 solver.cpp:237]     Train net output #0: loss = 0.18256 (* 1 = 0.18256 loss)
I0407 09:20:43.027189 15775 sgd_solver.cpp:105] Iteration 6504, lr = 0.001
I0407 09:20:48.236896 15775 solver.cpp:218] Iteration 6516 (2.30341 iter/s, 5.20967s/12 iters), loss = 0.239277
I0407 09:20:48.236940 15775 solver.cpp:237]     Train net output #0: loss = 0.239277 (* 1 = 0.239277 loss)
I0407 09:20:48.236948 15775 sgd_solver.cpp:105] Iteration 6516, lr = 0.001
I0407 09:20:53.081871 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 09:20:56.083829 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 09:20:58.413501 15775 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 09:20:58.413524 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:21:00.256673 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:02.882701 15775 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 09:21:02.882750 15775 solver.cpp:397]     Test net output #1: loss = 2.76706 (* 1 = 2.76706 loss)
I0407 09:21:03.023540 15775 solver.cpp:218] Iteration 6528 (0.811551 iter/s, 14.7865s/12 iters), loss = 0.208014
I0407 09:21:03.023588 15775 solver.cpp:237]     Train net output #0: loss = 0.208014 (* 1 = 0.208014 loss)
I0407 09:21:03.023598 15775 sgd_solver.cpp:105] Iteration 6528, lr = 0.001
I0407 09:21:07.165570 15775 solver.cpp:218] Iteration 6540 (2.89719 iter/s, 4.14195s/12 iters), loss = 0.218569
I0407 09:21:07.165611 15775 solver.cpp:237]     Train net output #0: loss = 0.218569 (* 1 = 0.218569 loss)
I0407 09:21:07.165617 15775 sgd_solver.cpp:105] Iteration 6540, lr = 0.001
I0407 09:21:12.717924 15775 solver.cpp:218] Iteration 6552 (2.16128 iter/s, 5.55227s/12 iters), loss = 0.338364
I0407 09:21:12.718024 15775 solver.cpp:237]     Train net output #0: loss = 0.338364 (* 1 = 0.338364 loss)
I0407 09:21:12.718034 15775 sgd_solver.cpp:105] Iteration 6552, lr = 0.001
I0407 09:21:18.031579 15775 solver.cpp:218] Iteration 6564 (2.25839 iter/s, 5.31351s/12 iters), loss = 0.364762
I0407 09:21:18.031622 15775 solver.cpp:237]     Train net output #0: loss = 0.364762 (* 1 = 0.364762 loss)
I0407 09:21:18.031630 15775 sgd_solver.cpp:105] Iteration 6564, lr = 0.001
I0407 09:21:22.526904 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:23.370098 15775 solver.cpp:218] Iteration 6576 (2.24785 iter/s, 5.33844s/12 iters), loss = 0.253794
I0407 09:21:23.370134 15775 solver.cpp:237]     Train net output #0: loss = 0.253794 (* 1 = 0.253794 loss)
I0407 09:21:23.370141 15775 sgd_solver.cpp:105] Iteration 6576, lr = 0.001
I0407 09:21:28.653169 15775 solver.cpp:218] Iteration 6588 (2.27144 iter/s, 5.28299s/12 iters), loss = 0.203354
I0407 09:21:28.653213 15775 solver.cpp:237]     Train net output #0: loss = 0.203354 (* 1 = 0.203354 loss)
I0407 09:21:28.653221 15775 sgd_solver.cpp:105] Iteration 6588, lr = 0.001
I0407 09:21:33.767733 15775 solver.cpp:218] Iteration 6600 (2.34628 iter/s, 5.11447s/12 iters), loss = 0.167006
I0407 09:21:33.767787 15775 solver.cpp:237]     Train net output #0: loss = 0.167006 (* 1 = 0.167006 loss)
I0407 09:21:33.767800 15775 sgd_solver.cpp:105] Iteration 6600, lr = 0.001
I0407 09:21:38.879070 15775 solver.cpp:218] Iteration 6612 (2.34777 iter/s, 5.11124s/12 iters), loss = 0.309655
I0407 09:21:38.879113 15775 solver.cpp:237]     Train net output #0: loss = 0.309655 (* 1 = 0.309655 loss)
I0407 09:21:38.879122 15775 sgd_solver.cpp:105] Iteration 6612, lr = 0.001
I0407 09:21:44.053776 15775 solver.cpp:218] Iteration 6624 (2.31901 iter/s, 5.17462s/12 iters), loss = 0.144556
I0407 09:21:44.053963 15775 solver.cpp:237]     Train net output #0: loss = 0.144556 (* 1 = 0.144556 loss)
I0407 09:21:44.053975 15775 sgd_solver.cpp:105] Iteration 6624, lr = 0.001
I0407 09:21:45.894239 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 09:21:48.901021 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 09:21:51.221213 15775 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 09:21:51.221230 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:21:52.961287 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:55.500083 15775 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0407 09:21:55.500128 15775 solver.cpp:397]     Test net output #1: loss = 2.81438 (* 1 = 2.81438 loss)
I0407 09:21:57.357625 15775 solver.cpp:218] Iteration 6636 (0.902013 iter/s, 13.3036s/12 iters), loss = 0.213364
I0407 09:21:57.357669 15775 solver.cpp:237]     Train net output #0: loss = 0.213364 (* 1 = 0.213364 loss)
I0407 09:21:57.357677 15775 sgd_solver.cpp:105] Iteration 6636, lr = 0.001
I0407 09:22:02.556937 15775 solver.cpp:218] Iteration 6648 (2.30804 iter/s, 5.19923s/12 iters), loss = 0.236651
I0407 09:22:02.556975 15775 solver.cpp:237]     Train net output #0: loss = 0.236651 (* 1 = 0.236651 loss)
I0407 09:22:02.556982 15775 sgd_solver.cpp:105] Iteration 6648, lr = 0.001
I0407 09:22:07.908561 15775 solver.cpp:218] Iteration 6660 (2.24235 iter/s, 5.35153s/12 iters), loss = 0.316051
I0407 09:22:07.908608 15775 solver.cpp:237]     Train net output #0: loss = 0.316051 (* 1 = 0.316051 loss)
I0407 09:22:07.908617 15775 sgd_solver.cpp:105] Iteration 6660, lr = 0.001
I0407 09:22:13.164198 15775 solver.cpp:218] Iteration 6672 (2.2833 iter/s, 5.25555s/12 iters), loss = 0.186583
I0407 09:22:13.164240 15775 solver.cpp:237]     Train net output #0: loss = 0.186583 (* 1 = 0.186583 loss)
I0407 09:22:13.164248 15775 sgd_solver.cpp:105] Iteration 6672, lr = 0.001
I0407 09:22:14.649796 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:18.665446 15775 solver.cpp:218] Iteration 6684 (2.18136 iter/s, 5.50116s/12 iters), loss = 0.175787
I0407 09:22:18.665483 15775 solver.cpp:237]     Train net output #0: loss = 0.175787 (* 1 = 0.175787 loss)
I0407 09:22:18.665490 15775 sgd_solver.cpp:105] Iteration 6684, lr = 0.001
I0407 09:22:24.072660 15775 solver.cpp:218] Iteration 6696 (2.21929 iter/s, 5.40713s/12 iters), loss = 0.215263
I0407 09:22:24.072705 15775 solver.cpp:237]     Train net output #0: loss = 0.215263 (* 1 = 0.215263 loss)
I0407 09:22:24.072715 15775 sgd_solver.cpp:105] Iteration 6696, lr = 0.001
I0407 09:22:29.545043 15775 solver.cpp:218] Iteration 6708 (2.19287 iter/s, 5.47229s/12 iters), loss = 0.301671
I0407 09:22:29.545086 15775 solver.cpp:237]     Train net output #0: loss = 0.301671 (* 1 = 0.301671 loss)
I0407 09:22:29.545094 15775 sgd_solver.cpp:105] Iteration 6708, lr = 0.001
I0407 09:22:34.901809 15775 solver.cpp:218] Iteration 6720 (2.2402 iter/s, 5.35668s/12 iters), loss = 0.313318
I0407 09:22:34.901854 15775 solver.cpp:237]     Train net output #0: loss = 0.313318 (* 1 = 0.313318 loss)
I0407 09:22:34.901861 15775 sgd_solver.cpp:105] Iteration 6720, lr = 0.001
I0407 09:22:39.680181 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 09:22:42.700763 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 09:22:45.048593 15775 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 09:22:45.048733 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:22:46.791031 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:49.488770 15775 solver.cpp:397]     Test net output #0: accuracy = 0.453431
I0407 09:22:49.488797 15775 solver.cpp:397]     Test net output #1: loss = 2.83856 (* 1 = 2.83856 loss)
I0407 09:22:49.623991 15775 solver.cpp:218] Iteration 6732 (0.815104 iter/s, 14.722s/12 iters), loss = 0.183038
I0407 09:22:49.625552 15775 solver.cpp:237]     Train net output #0: loss = 0.183038 (* 1 = 0.183038 loss)
I0407 09:22:49.625566 15775 sgd_solver.cpp:105] Iteration 6732, lr = 0.0001
I0407 09:22:54.010404 15775 solver.cpp:218] Iteration 6744 (2.73671 iter/s, 4.38482s/12 iters), loss = 0.190767
I0407 09:22:54.010447 15775 solver.cpp:237]     Train net output #0: loss = 0.190767 (* 1 = 0.190767 loss)
I0407 09:22:54.010457 15775 sgd_solver.cpp:105] Iteration 6744, lr = 0.0001
I0407 09:22:59.217561 15775 solver.cpp:218] Iteration 6756 (2.30456 iter/s, 5.20707s/12 iters), loss = 0.307047
I0407 09:22:59.217607 15775 solver.cpp:237]     Train net output #0: loss = 0.307047 (* 1 = 0.307047 loss)
I0407 09:22:59.217614 15775 sgd_solver.cpp:105] Iteration 6756, lr = 0.0001
I0407 09:23:04.586104 15775 solver.cpp:218] Iteration 6768 (2.23528 iter/s, 5.36845s/12 iters), loss = 0.363447
I0407 09:23:04.586158 15775 solver.cpp:237]     Train net output #0: loss = 0.363447 (* 1 = 0.363447 loss)
I0407 09:23:04.586169 15775 sgd_solver.cpp:105] Iteration 6768, lr = 0.0001
I0407 09:23:08.283061 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:09.968047 15775 solver.cpp:218] Iteration 6780 (2.22972 iter/s, 5.38185s/12 iters), loss = 0.260021
I0407 09:23:09.968086 15775 solver.cpp:237]     Train net output #0: loss = 0.260021 (* 1 = 0.260021 loss)
I0407 09:23:09.968094 15775 sgd_solver.cpp:105] Iteration 6780, lr = 0.0001
I0407 09:23:15.302354 15775 solver.cpp:218] Iteration 6792 (2.24963 iter/s, 5.33422s/12 iters), loss = 0.122612
I0407 09:23:15.302444 15775 solver.cpp:237]     Train net output #0: loss = 0.122612 (* 1 = 0.122612 loss)
I0407 09:23:15.302453 15775 sgd_solver.cpp:105] Iteration 6792, lr = 0.0001
I0407 09:23:20.669766 15775 solver.cpp:218] Iteration 6804 (2.23577 iter/s, 5.36727s/12 iters), loss = 0.293254
I0407 09:23:20.669811 15775 solver.cpp:237]     Train net output #0: loss = 0.293254 (* 1 = 0.293254 loss)
I0407 09:23:20.669819 15775 sgd_solver.cpp:105] Iteration 6804, lr = 0.0001
I0407 09:23:25.931326 15775 solver.cpp:218] Iteration 6816 (2.28073 iter/s, 5.26147s/12 iters), loss = 0.180989
I0407 09:23:25.931370 15775 solver.cpp:237]     Train net output #0: loss = 0.180989 (* 1 = 0.180989 loss)
I0407 09:23:25.931377 15775 sgd_solver.cpp:105] Iteration 6816, lr = 0.0001
I0407 09:23:31.344229 15775 solver.cpp:218] Iteration 6828 (2.21696 iter/s, 5.41282s/12 iters), loss = 0.174703
I0407 09:23:31.344270 15775 solver.cpp:237]     Train net output #0: loss = 0.174703 (* 1 = 0.174703 loss)
I0407 09:23:31.344278 15775 sgd_solver.cpp:105] Iteration 6828, lr = 0.0001
I0407 09:23:33.469743 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 09:23:36.559012 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 09:23:39.338321 15775 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 09:23:39.338340 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:23:41.064318 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:43.873749 15775 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0407 09:23:43.873791 15775 solver.cpp:397]     Test net output #1: loss = 2.79776 (* 1 = 2.79776 loss)
I0407 09:23:45.886940 15775 solver.cpp:218] Iteration 6840 (0.825163 iter/s, 14.5426s/12 iters), loss = 0.10646
I0407 09:23:45.887046 15775 solver.cpp:237]     Train net output #0: loss = 0.10646 (* 1 = 0.10646 loss)
I0407 09:23:45.887055 15775 sgd_solver.cpp:105] Iteration 6840, lr = 0.0001
I0407 09:23:51.093634 15775 solver.cpp:218] Iteration 6852 (2.30479 iter/s, 5.20655s/12 iters), loss = 0.239638
I0407 09:23:51.093672 15775 solver.cpp:237]     Train net output #0: loss = 0.239638 (* 1 = 0.239638 loss)
I0407 09:23:51.093679 15775 sgd_solver.cpp:105] Iteration 6852, lr = 0.0001
I0407 09:23:56.519966 15775 solver.cpp:218] Iteration 6864 (2.21147 iter/s, 5.42624s/12 iters), loss = 0.206385
I0407 09:23:56.520020 15775 solver.cpp:237]     Train net output #0: loss = 0.206385 (* 1 = 0.206385 loss)
I0407 09:23:56.520030 15775 sgd_solver.cpp:105] Iteration 6864, lr = 0.0001
I0407 09:24:01.895184 15775 solver.cpp:218] Iteration 6876 (2.23251 iter/s, 5.37512s/12 iters), loss = 0.236434
I0407 09:24:01.895224 15775 solver.cpp:237]     Train net output #0: loss = 0.236434 (* 1 = 0.236434 loss)
I0407 09:24:01.895232 15775 sgd_solver.cpp:105] Iteration 6876, lr = 0.0001
I0407 09:24:02.547231 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:07.089821 15775 solver.cpp:218] Iteration 6888 (2.31011 iter/s, 5.19455s/12 iters), loss = 0.197228
I0407 09:24:07.089864 15775 solver.cpp:237]     Train net output #0: loss = 0.197228 (* 1 = 0.197228 loss)
I0407 09:24:07.089871 15775 sgd_solver.cpp:105] Iteration 6888, lr = 0.0001
I0407 09:24:12.294006 15775 solver.cpp:218] Iteration 6900 (2.30588 iter/s, 5.20409s/12 iters), loss = 0.226616
I0407 09:24:12.294055 15775 solver.cpp:237]     Train net output #0: loss = 0.226616 (* 1 = 0.226616 loss)
I0407 09:24:12.294064 15775 sgd_solver.cpp:105] Iteration 6900, lr = 0.0001
I0407 09:24:17.753196 15775 solver.cpp:218] Iteration 6912 (2.19817 iter/s, 5.4591s/12 iters), loss = 0.195279
I0407 09:24:17.753319 15775 solver.cpp:237]     Train net output #0: loss = 0.195279 (* 1 = 0.195279 loss)
I0407 09:24:17.753326 15775 sgd_solver.cpp:105] Iteration 6912, lr = 0.0001
I0407 09:24:22.912312 15775 solver.cpp:218] Iteration 6924 (2.32606 iter/s, 5.15895s/12 iters), loss = 0.131771
I0407 09:24:22.912361 15775 solver.cpp:237]     Train net output #0: loss = 0.131771 (* 1 = 0.131771 loss)
I0407 09:24:22.912370 15775 sgd_solver.cpp:105] Iteration 6924, lr = 0.0001
I0407 09:24:27.696444 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 09:24:30.739472 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 09:24:33.038204 15775 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 09:24:33.038224 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:24:33.610102 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:24:34.650631 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:37.420328 15775 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0407 09:24:37.420361 15775 solver.cpp:397]     Test net output #1: loss = 2.78671 (* 1 = 2.78671 loss)
I0407 09:24:37.561210 15775 solver.cpp:218] Iteration 6936 (0.819182 iter/s, 14.6488s/12 iters), loss = 0.192647
I0407 09:24:37.561255 15775 solver.cpp:237]     Train net output #0: loss = 0.192647 (* 1 = 0.192647 loss)
I0407 09:24:37.561264 15775 sgd_solver.cpp:105] Iteration 6936, lr = 0.0001
I0407 09:24:41.959784 15775 solver.cpp:218] Iteration 6948 (2.72821 iter/s, 4.39849s/12 iters), loss = 0.156775
I0407 09:24:41.959831 15775 solver.cpp:237]     Train net output #0: loss = 0.156775 (* 1 = 0.156775 loss)
I0407 09:24:41.959841 15775 sgd_solver.cpp:105] Iteration 6948, lr = 0.0001
I0407 09:24:47.393568 15775 solver.cpp:218] Iteration 6960 (2.20844 iter/s, 5.43369s/12 iters), loss = 0.201708
I0407 09:24:47.393611 15775 solver.cpp:237]     Train net output #0: loss = 0.201708 (* 1 = 0.201708 loss)
I0407 09:24:47.393620 15775 sgd_solver.cpp:105] Iteration 6960, lr = 0.0001
I0407 09:24:52.742414 15775 solver.cpp:218] Iteration 6972 (2.24351 iter/s, 5.34876s/12 iters), loss = 0.168353
I0407 09:24:52.742547 15775 solver.cpp:237]     Train net output #0: loss = 0.168353 (* 1 = 0.168353 loss)
I0407 09:24:52.742558 15775 sgd_solver.cpp:105] Iteration 6972, lr = 0.0001
I0407 09:24:55.496652 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:58.018937 15775 solver.cpp:218] Iteration 6984 (2.2743 iter/s, 5.27635s/12 iters), loss = 0.258954
I0407 09:24:58.018981 15775 solver.cpp:237]     Train net output #0: loss = 0.258954 (* 1 = 0.258954 loss)
I0407 09:24:58.018990 15775 sgd_solver.cpp:105] Iteration 6984, lr = 0.0001
I0407 09:25:03.296555 15775 solver.cpp:218] Iteration 6996 (2.27379 iter/s, 5.27753s/12 iters), loss = 0.203726
I0407 09:25:03.296595 15775 solver.cpp:237]     Train net output #0: loss = 0.203726 (* 1 = 0.203726 loss)
I0407 09:25:03.296603 15775 sgd_solver.cpp:105] Iteration 6996, lr = 0.0001
I0407 09:25:08.577530 15775 solver.cpp:218] Iteration 7008 (2.27234 iter/s, 5.28089s/12 iters), loss = 0.196518
I0407 09:25:08.577577 15775 solver.cpp:237]     Train net output #0: loss = 0.196518 (* 1 = 0.196518 loss)
I0407 09:25:08.577585 15775 sgd_solver.cpp:105] Iteration 7008, lr = 0.0001
I0407 09:25:13.769346 15775 solver.cpp:218] Iteration 7020 (2.31137 iter/s, 5.19173s/12 iters), loss = 0.244629
I0407 09:25:13.769388 15775 solver.cpp:237]     Train net output #0: loss = 0.244629 (* 1 = 0.244629 loss)
I0407 09:25:13.769397 15775 sgd_solver.cpp:105] Iteration 7020, lr = 0.0001
I0407 09:25:19.079790 15775 solver.cpp:218] Iteration 7032 (2.25974 iter/s, 5.31036s/12 iters), loss = 0.209226
I0407 09:25:19.079833 15775 solver.cpp:237]     Train net output #0: loss = 0.209226 (* 1 = 0.209226 loss)
I0407 09:25:19.079839 15775 sgd_solver.cpp:105] Iteration 7032, lr = 0.0001
I0407 09:25:21.278338 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 09:25:24.289050 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 09:25:26.591805 15775 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 09:25:26.591825 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:25:28.216323 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:30.915727 15775 solver.cpp:397]     Test net output #0: accuracy = 0.46201
I0407 09:25:30.915762 15775 solver.cpp:397]     Test net output #1: loss = 2.7773 (* 1 = 2.7773 loss)
I0407 09:25:32.772975 15775 solver.cpp:218] Iteration 7044 (0.876356 iter/s, 13.6931s/12 iters), loss = 0.212891
I0407 09:25:32.773013 15775 solver.cpp:237]     Train net output #0: loss = 0.212891 (* 1 = 0.212891 loss)
I0407 09:25:32.773021 15775 sgd_solver.cpp:105] Iteration 7044, lr = 0.0001
I0407 09:25:37.987907 15775 solver.cpp:218] Iteration 7056 (2.30112 iter/s, 5.21485s/12 iters), loss = 0.214166
I0407 09:25:37.987958 15775 solver.cpp:237]     Train net output #0: loss = 0.214166 (* 1 = 0.214166 loss)
I0407 09:25:37.987968 15775 sgd_solver.cpp:105] Iteration 7056, lr = 0.0001
I0407 09:25:43.364807 15775 solver.cpp:218] Iteration 7068 (2.23181 iter/s, 5.3768s/12 iters), loss = 0.177785
I0407 09:25:43.364852 15775 solver.cpp:237]     Train net output #0: loss = 0.177785 (* 1 = 0.177785 loss)
I0407 09:25:43.364859 15775 sgd_solver.cpp:105] Iteration 7068, lr = 0.0001
I0407 09:25:48.615677 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:48.781451 15775 solver.cpp:218] Iteration 7080 (2.21543 iter/s, 5.41655s/12 iters), loss = 0.121524
I0407 09:25:48.781493 15775 solver.cpp:237]     Train net output #0: loss = 0.121524 (* 1 = 0.121524 loss)
I0407 09:25:48.781502 15775 sgd_solver.cpp:105] Iteration 7080, lr = 0.0001
I0407 09:25:54.202718 15775 solver.cpp:218] Iteration 7092 (2.21354 iter/s, 5.42118s/12 iters), loss = 0.168077
I0407 09:25:54.202757 15775 solver.cpp:237]     Train net output #0: loss = 0.168077 (* 1 = 0.168077 loss)
I0407 09:25:54.202765 15775 sgd_solver.cpp:105] Iteration 7092, lr = 0.0001
I0407 09:25:59.596101 15775 solver.cpp:218] Iteration 7104 (2.22498 iter/s, 5.3933s/12 iters), loss = 0.217399
I0407 09:25:59.596268 15775 solver.cpp:237]     Train net output #0: loss = 0.217399 (* 1 = 0.217399 loss)
I0407 09:25:59.596279 15775 sgd_solver.cpp:105] Iteration 7104, lr = 0.0001
I0407 09:26:05.042435 15775 solver.cpp:218] Iteration 7116 (2.2034 iter/s, 5.44613s/12 iters), loss = 0.307791
I0407 09:26:05.042479 15775 solver.cpp:237]     Train net output #0: loss = 0.307791 (* 1 = 0.307791 loss)
I0407 09:26:05.042486 15775 sgd_solver.cpp:105] Iteration 7116, lr = 0.0001
I0407 09:26:10.358634 15775 solver.cpp:218] Iteration 7128 (2.25729 iter/s, 5.31611s/12 iters), loss = 0.206883
I0407 09:26:10.358675 15775 solver.cpp:237]     Train net output #0: loss = 0.206883 (* 1 = 0.206883 loss)
I0407 09:26:10.358681 15775 sgd_solver.cpp:105] Iteration 7128, lr = 0.0001
I0407 09:26:14.988117 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 09:26:18.013643 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 09:26:20.323123 15775 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 09:26:20.323144 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:26:21.842883 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:24.604226 15775 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0407 09:26:24.604257 15775 solver.cpp:397]     Test net output #1: loss = 2.77018 (* 1 = 2.77018 loss)
I0407 09:26:24.739362 15775 solver.cpp:218] Iteration 7140 (0.834458 iter/s, 14.3806s/12 iters), loss = 0.159387
I0407 09:26:24.739421 15775 solver.cpp:237]     Train net output #0: loss = 0.159387 (* 1 = 0.159387 loss)
I0407 09:26:24.739431 15775 sgd_solver.cpp:105] Iteration 7140, lr = 0.0001
I0407 09:26:29.023591 15775 solver.cpp:218] Iteration 7152 (2.80103 iter/s, 4.28414s/12 iters), loss = 0.228182
I0407 09:26:29.023630 15775 solver.cpp:237]     Train net output #0: loss = 0.228182 (* 1 = 0.228182 loss)
I0407 09:26:29.023638 15775 sgd_solver.cpp:105] Iteration 7152, lr = 0.0001
I0407 09:26:34.230108 15775 solver.cpp:218] Iteration 7164 (2.30484 iter/s, 5.20643s/12 iters), loss = 0.152388
I0407 09:26:34.230206 15775 solver.cpp:237]     Train net output #0: loss = 0.152388 (* 1 = 0.152388 loss)
I0407 09:26:34.230216 15775 sgd_solver.cpp:105] Iteration 7164, lr = 0.0001
I0407 09:26:39.422395 15775 solver.cpp:218] Iteration 7176 (2.31118 iter/s, 5.19215s/12 iters), loss = 0.231802
I0407 09:26:39.422437 15775 solver.cpp:237]     Train net output #0: loss = 0.231802 (* 1 = 0.231802 loss)
I0407 09:26:39.422444 15775 sgd_solver.cpp:105] Iteration 7176, lr = 0.0001
I0407 09:26:41.674469 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:44.828414 15775 solver.cpp:218] Iteration 7188 (2.21978 iter/s, 5.40593s/12 iters), loss = 0.213253
I0407 09:26:44.828459 15775 solver.cpp:237]     Train net output #0: loss = 0.213253 (* 1 = 0.213253 loss)
I0407 09:26:44.828467 15775 sgd_solver.cpp:105] Iteration 7188, lr = 0.0001
I0407 09:26:49.981614 15775 solver.cpp:218] Iteration 7200 (2.32869 iter/s, 5.15311s/12 iters), loss = 0.250984
I0407 09:26:49.981654 15775 solver.cpp:237]     Train net output #0: loss = 0.250984 (* 1 = 0.250984 loss)
I0407 09:26:49.981662 15775 sgd_solver.cpp:105] Iteration 7200, lr = 0.0001
I0407 09:26:55.358340 15775 solver.cpp:218] Iteration 7212 (2.23188 iter/s, 5.37664s/12 iters), loss = 0.288394
I0407 09:26:55.358381 15775 solver.cpp:237]     Train net output #0: loss = 0.288394 (* 1 = 0.288394 loss)
I0407 09:26:55.358389 15775 sgd_solver.cpp:105] Iteration 7212, lr = 0.0001
I0407 09:27:00.563890 15775 solver.cpp:218] Iteration 7224 (2.30527 iter/s, 5.20546s/12 iters), loss = 0.300839
I0407 09:27:00.563942 15775 solver.cpp:237]     Train net output #0: loss = 0.300839 (* 1 = 0.300839 loss)
I0407 09:27:00.563952 15775 sgd_solver.cpp:105] Iteration 7224, lr = 0.0001
I0407 09:27:05.793032 15775 solver.cpp:218] Iteration 7236 (2.29487 iter/s, 5.22904s/12 iters), loss = 0.200409
I0407 09:27:05.793154 15775 solver.cpp:237]     Train net output #0: loss = 0.200409 (* 1 = 0.200409 loss)
I0407 09:27:05.793161 15775 sgd_solver.cpp:105] Iteration 7236, lr = 0.0001
I0407 09:27:07.928663 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 09:27:10.926934 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 09:27:13.238765 15775 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 09:27:13.238790 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:27:14.718250 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:17.490237 15775 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0407 09:27:17.490288 15775 solver.cpp:397]     Test net output #1: loss = 2.78559 (* 1 = 2.78559 loss)
I0407 09:27:19.468350 15775 solver.cpp:218] Iteration 7248 (0.877507 iter/s, 13.6751s/12 iters), loss = 0.34004
I0407 09:27:19.468400 15775 solver.cpp:237]     Train net output #0: loss = 0.34004 (* 1 = 0.34004 loss)
I0407 09:27:19.468407 15775 sgd_solver.cpp:105] Iteration 7248, lr = 0.0001
I0407 09:27:24.873795 15775 solver.cpp:218] Iteration 7260 (2.22002 iter/s, 5.40535s/12 iters), loss = 0.156104
I0407 09:27:24.873839 15775 solver.cpp:237]     Train net output #0: loss = 0.156104 (* 1 = 0.156104 loss)
I0407 09:27:24.873847 15775 sgd_solver.cpp:105] Iteration 7260, lr = 0.0001
I0407 09:27:30.255923 15775 solver.cpp:218] Iteration 7272 (2.22964 iter/s, 5.38204s/12 iters), loss = 0.190749
I0407 09:27:30.255970 15775 solver.cpp:237]     Train net output #0: loss = 0.190749 (* 1 = 0.190749 loss)
I0407 09:27:30.255978 15775 sgd_solver.cpp:105] Iteration 7272, lr = 0.0001
I0407 09:27:34.838030 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:35.669123 15775 solver.cpp:218] Iteration 7284 (2.21684 iter/s, 5.41311s/12 iters), loss = 0.145423
I0407 09:27:35.669170 15775 solver.cpp:237]     Train net output #0: loss = 0.145423 (* 1 = 0.145423 loss)
I0407 09:27:35.669178 15775 sgd_solver.cpp:105] Iteration 7284, lr = 0.0001
I0407 09:27:40.753870 15775 solver.cpp:218] Iteration 7296 (2.36004 iter/s, 5.08465s/12 iters), loss = 0.206602
I0407 09:27:40.753979 15775 solver.cpp:237]     Train net output #0: loss = 0.206602 (* 1 = 0.206602 loss)
I0407 09:27:40.753988 15775 sgd_solver.cpp:105] Iteration 7296, lr = 0.0001
I0407 09:27:45.683147 15775 solver.cpp:218] Iteration 7308 (2.43451 iter/s, 4.92913s/12 iters), loss = 0.219412
I0407 09:27:45.683192 15775 solver.cpp:237]     Train net output #0: loss = 0.219412 (* 1 = 0.219412 loss)
I0407 09:27:45.683199 15775 sgd_solver.cpp:105] Iteration 7308, lr = 0.0001
I0407 09:27:50.881042 15775 solver.cpp:218] Iteration 7320 (2.30866 iter/s, 5.19781s/12 iters), loss = 0.180196
I0407 09:27:50.881074 15775 solver.cpp:237]     Train net output #0: loss = 0.180196 (* 1 = 0.180196 loss)
I0407 09:27:50.881081 15775 sgd_solver.cpp:105] Iteration 7320, lr = 0.0001
I0407 09:27:56.054044 15775 solver.cpp:218] Iteration 7332 (2.31977 iter/s, 5.17293s/12 iters), loss = 0.151155
I0407 09:27:56.054086 15775 solver.cpp:237]     Train net output #0: loss = 0.151155 (* 1 = 0.151155 loss)
I0407 09:27:56.054095 15775 sgd_solver.cpp:105] Iteration 7332, lr = 0.0001
I0407 09:28:00.701388 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 09:28:03.728799 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 09:28:06.156183 15775 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 09:28:06.156201 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:28:07.685220 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:10.496357 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 09:28:10.496397 15775 solver.cpp:397]     Test net output #1: loss = 2.77412 (* 1 = 2.77412 loss)
I0407 09:28:10.630676 15775 solver.cpp:218] Iteration 7344 (0.823243 iter/s, 14.5765s/12 iters), loss = 0.190714
I0407 09:28:10.630723 15775 solver.cpp:237]     Train net output #0: loss = 0.190714 (* 1 = 0.190714 loss)
I0407 09:28:10.630731 15775 sgd_solver.cpp:105] Iteration 7344, lr = 0.0001
I0407 09:28:14.940886 15775 solver.cpp:218] Iteration 7356 (2.78414 iter/s, 4.31012s/12 iters), loss = 0.234899
I0407 09:28:14.941037 15775 solver.cpp:237]     Train net output #0: loss = 0.234899 (* 1 = 0.234899 loss)
I0407 09:28:14.941048 15775 sgd_solver.cpp:105] Iteration 7356, lr = 0.0001
I0407 09:28:20.372488 15775 solver.cpp:218] Iteration 7368 (2.20937 iter/s, 5.43141s/12 iters), loss = 0.171428
I0407 09:28:20.372529 15775 solver.cpp:237]     Train net output #0: loss = 0.171428 (* 1 = 0.171428 loss)
I0407 09:28:20.372536 15775 sgd_solver.cpp:105] Iteration 7368, lr = 0.0001
I0407 09:28:25.475080 15775 solver.cpp:218] Iteration 7380 (2.35178 iter/s, 5.10251s/12 iters), loss = 0.195193
I0407 09:28:25.475121 15775 solver.cpp:237]     Train net output #0: loss = 0.195193 (* 1 = 0.195193 loss)
I0407 09:28:25.475129 15775 sgd_solver.cpp:105] Iteration 7380, lr = 0.0001
I0407 09:28:26.977147 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:30.917415 15775 solver.cpp:218] Iteration 7392 (2.20497 iter/s, 5.44225s/12 iters), loss = 0.164932
I0407 09:28:30.917455 15775 solver.cpp:237]     Train net output #0: loss = 0.164932 (* 1 = 0.164932 loss)
I0407 09:28:30.917464 15775 sgd_solver.cpp:105] Iteration 7392, lr = 0.0001
I0407 09:28:36.138320 15775 solver.cpp:218] Iteration 7404 (2.29849 iter/s, 5.22082s/12 iters), loss = 0.191292
I0407 09:28:36.138358 15775 solver.cpp:237]     Train net output #0: loss = 0.191292 (* 1 = 0.191292 loss)
I0407 09:28:36.138366 15775 sgd_solver.cpp:105] Iteration 7404, lr = 0.0001
I0407 09:28:41.380628 15775 solver.cpp:218] Iteration 7416 (2.28911 iter/s, 5.24222s/12 iters), loss = 0.199219
I0407 09:28:41.380672 15775 solver.cpp:237]     Train net output #0: loss = 0.199219 (* 1 = 0.199219 loss)
I0407 09:28:41.380681 15775 sgd_solver.cpp:105] Iteration 7416, lr = 0.0001
I0407 09:28:46.368392 15775 solver.cpp:218] Iteration 7428 (2.40593 iter/s, 4.98768s/12 iters), loss = 0.126367
I0407 09:28:46.368525 15775 solver.cpp:237]     Train net output #0: loss = 0.126367 (* 1 = 0.126367 loss)
I0407 09:28:46.368535 15775 sgd_solver.cpp:105] Iteration 7428, lr = 0.0001
I0407 09:28:51.816973 15775 solver.cpp:218] Iteration 7440 (2.20248 iter/s, 5.44841s/12 iters), loss = 0.0946572
I0407 09:28:51.817020 15775 solver.cpp:237]     Train net output #0: loss = 0.0946572 (* 1 = 0.0946572 loss)
I0407 09:28:51.817028 15775 sgd_solver.cpp:105] Iteration 7440, lr = 0.0001
I0407 09:28:53.897375 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 09:28:56.908746 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 09:28:59.208917 15775 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 09:28:59.208935 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:29:00.610591 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:03.513818 15775 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0407 09:29:03.513850 15775 solver.cpp:397]     Test net output #1: loss = 2.79619 (* 1 = 2.79619 loss)
I0407 09:29:05.471350 15775 solver.cpp:218] Iteration 7452 (0.878848 iter/s, 13.6542s/12 iters), loss = 0.205252
I0407 09:29:05.471392 15775 solver.cpp:237]     Train net output #0: loss = 0.205252 (* 1 = 0.205252 loss)
I0407 09:29:05.471400 15775 sgd_solver.cpp:105] Iteration 7452, lr = 0.0001
I0407 09:29:10.511732 15775 solver.cpp:218] Iteration 7464 (2.38081 iter/s, 5.04029s/12 iters), loss = 0.267691
I0407 09:29:10.511777 15775 solver.cpp:237]     Train net output #0: loss = 0.267691 (* 1 = 0.267691 loss)
I0407 09:29:10.511785 15775 sgd_solver.cpp:105] Iteration 7464, lr = 0.0001
I0407 09:29:15.780800 15775 solver.cpp:218] Iteration 7476 (2.27748 iter/s, 5.26898s/12 iters), loss = 0.220136
I0407 09:29:15.780848 15775 solver.cpp:237]     Train net output #0: loss = 0.220136 (* 1 = 0.220136 loss)
I0407 09:29:15.780858 15775 sgd_solver.cpp:105] Iteration 7476, lr = 0.0001
I0407 09:29:19.253975 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:20.875221 15775 solver.cpp:218] Iteration 7488 (2.35556 iter/s, 5.09433s/12 iters), loss = 0.185967
I0407 09:29:20.875259 15775 solver.cpp:237]     Train net output #0: loss = 0.185967 (* 1 = 0.185967 loss)
I0407 09:29:20.875267 15775 sgd_solver.cpp:105] Iteration 7488, lr = 0.0001
I0407 09:29:26.021740 15775 solver.cpp:218] Iteration 7500 (2.33171 iter/s, 5.14643s/12 iters), loss = 0.185146
I0407 09:29:26.021785 15775 solver.cpp:237]     Train net output #0: loss = 0.185146 (* 1 = 0.185146 loss)
I0407 09:29:26.021792 15775 sgd_solver.cpp:105] Iteration 7500, lr = 0.0001
I0407 09:29:31.173168 15775 solver.cpp:218] Iteration 7512 (2.32949 iter/s, 5.15134s/12 iters), loss = 0.230294
I0407 09:29:31.173214 15775 solver.cpp:237]     Train net output #0: loss = 0.230294 (* 1 = 0.230294 loss)
I0407 09:29:31.173223 15775 sgd_solver.cpp:105] Iteration 7512, lr = 0.0001
I0407 09:29:36.464839 15775 solver.cpp:218] Iteration 7524 (2.26775 iter/s, 5.29158s/12 iters), loss = 0.215981
I0407 09:29:36.464901 15775 solver.cpp:237]     Train net output #0: loss = 0.215981 (* 1 = 0.215981 loss)
I0407 09:29:36.464910 15775 sgd_solver.cpp:105] Iteration 7524, lr = 0.0001
I0407 09:29:41.739938 15775 solver.cpp:218] Iteration 7536 (2.27488 iter/s, 5.27501s/12 iters), loss = 0.277953
I0407 09:29:41.739979 15775 solver.cpp:237]     Train net output #0: loss = 0.277953 (* 1 = 0.277953 loss)
I0407 09:29:41.739986 15775 sgd_solver.cpp:105] Iteration 7536, lr = 0.0001
I0407 09:29:46.525513 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 09:29:49.484652 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 09:29:51.807904 15775 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 09:29:51.807926 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:29:53.229388 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:56.200847 15775 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0407 09:29:56.200892 15775 solver.cpp:397]     Test net output #1: loss = 2.77075 (* 1 = 2.77075 loss)
I0407 09:29:56.335637 15775 solver.cpp:218] Iteration 7548 (0.822167 iter/s, 14.5956s/12 iters), loss = 0.0995509
I0407 09:29:56.335683 15775 solver.cpp:237]     Train net output #0: loss = 0.0995509 (* 1 = 0.0995509 loss)
I0407 09:29:56.335691 15775 sgd_solver.cpp:105] Iteration 7548, lr = 0.0001
I0407 09:30:00.815331 15775 solver.cpp:218] Iteration 7560 (2.6788 iter/s, 4.47961s/12 iters), loss = 0.234615
I0407 09:30:00.815373 15775 solver.cpp:237]     Train net output #0: loss = 0.234615 (* 1 = 0.234615 loss)
I0407 09:30:00.815380 15775 sgd_solver.cpp:105] Iteration 7560, lr = 0.0001
I0407 09:30:06.105780 15775 solver.cpp:218] Iteration 7572 (2.26828 iter/s, 5.29036s/12 iters), loss = 0.156556
I0407 09:30:06.105823 15775 solver.cpp:237]     Train net output #0: loss = 0.156556 (* 1 = 0.156556 loss)
I0407 09:30:06.105831 15775 sgd_solver.cpp:105] Iteration 7572, lr = 0.0001
I0407 09:30:11.272186 15775 solver.cpp:218] Iteration 7584 (2.32273 iter/s, 5.16632s/12 iters), loss = 0.236838
I0407 09:30:11.272222 15775 solver.cpp:237]     Train net output #0: loss = 0.236838 (* 1 = 0.236838 loss)
I0407 09:30:11.272229 15775 sgd_solver.cpp:105] Iteration 7584, lr = 0.0001
I0407 09:30:11.958397 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:16.659869 15775 solver.cpp:218] Iteration 7596 (2.22734 iter/s, 5.3876s/12 iters), loss = 0.306201
I0407 09:30:16.659909 15775 solver.cpp:237]     Train net output #0: loss = 0.306201 (* 1 = 0.306201 loss)
I0407 09:30:16.659915 15775 sgd_solver.cpp:105] Iteration 7596, lr = 0.0001
I0407 09:30:21.949218 15775 solver.cpp:218] Iteration 7608 (2.26875 iter/s, 5.28926s/12 iters), loss = 0.243947
I0407 09:30:21.949354 15775 solver.cpp:237]     Train net output #0: loss = 0.243947 (* 1 = 0.243947 loss)
I0407 09:30:21.949363 15775 sgd_solver.cpp:105] Iteration 7608, lr = 0.0001
I0407 09:30:27.243690 15775 solver.cpp:218] Iteration 7620 (2.26659 iter/s, 5.29429s/12 iters), loss = 0.200246
I0407 09:30:27.243731 15775 solver.cpp:237]     Train net output #0: loss = 0.200246 (* 1 = 0.200246 loss)
I0407 09:30:27.243739 15775 sgd_solver.cpp:105] Iteration 7620, lr = 0.0001
I0407 09:30:29.757453 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:30:32.264744 15775 solver.cpp:218] Iteration 7632 (2.38998 iter/s, 5.02096s/12 iters), loss = 0.180509
I0407 09:30:32.264808 15775 solver.cpp:237]     Train net output #0: loss = 0.180509 (* 1 = 0.180509 loss)
I0407 09:30:32.264822 15775 sgd_solver.cpp:105] Iteration 7632, lr = 0.0001
I0407 09:30:37.617739 15775 solver.cpp:218] Iteration 7644 (2.24178 iter/s, 5.35289s/12 iters), loss = 0.084686
I0407 09:30:37.617789 15775 solver.cpp:237]     Train net output #0: loss = 0.084686 (* 1 = 0.084686 loss)
I0407 09:30:37.617796 15775 sgd_solver.cpp:105] Iteration 7644, lr = 0.0001
I0407 09:30:39.686095 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 09:30:42.681820 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 09:30:44.994465 15775 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 09:30:44.994490 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:30:46.349534 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:49.373080 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0407 09:30:49.373113 15775 solver.cpp:397]     Test net output #1: loss = 2.78887 (* 1 = 2.78887 loss)
I0407 09:30:51.198356 15775 solver.cpp:218] Iteration 7656 (0.883621 iter/s, 13.5805s/12 iters), loss = 0.114475
I0407 09:30:51.198405 15775 solver.cpp:237]     Train net output #0: loss = 0.114475 (* 1 = 0.114475 loss)
I0407 09:30:51.198415 15775 sgd_solver.cpp:105] Iteration 7656, lr = 0.0001
I0407 09:30:56.661972 15775 solver.cpp:218] Iteration 7668 (2.19638 iter/s, 5.46352s/12 iters), loss = 0.254494
I0407 09:30:56.662071 15775 solver.cpp:237]     Train net output #0: loss = 0.254494 (* 1 = 0.254494 loss)
I0407 09:30:56.662079 15775 sgd_solver.cpp:105] Iteration 7668, lr = 0.0001
I0407 09:31:01.815399 15775 solver.cpp:218] Iteration 7680 (2.32861 iter/s, 5.15329s/12 iters), loss = 0.208128
I0407 09:31:01.815439 15775 solver.cpp:237]     Train net output #0: loss = 0.208128 (* 1 = 0.208128 loss)
I0407 09:31:01.815448 15775 sgd_solver.cpp:105] Iteration 7680, lr = 0.0001
I0407 09:31:04.814925 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:07.312110 15775 solver.cpp:218] Iteration 7692 (2.18316 iter/s, 5.49662s/12 iters), loss = 0.170597
I0407 09:31:07.312152 15775 solver.cpp:237]     Train net output #0: loss = 0.170597 (* 1 = 0.170597 loss)
I0407 09:31:07.312160 15775 sgd_solver.cpp:105] Iteration 7692, lr = 0.0001
I0407 09:31:12.555634 15775 solver.cpp:218] Iteration 7704 (2.28858 iter/s, 5.24343s/12 iters), loss = 0.104252
I0407 09:31:12.555681 15775 solver.cpp:237]     Train net output #0: loss = 0.104252 (* 1 = 0.104252 loss)
I0407 09:31:12.555689 15775 sgd_solver.cpp:105] Iteration 7704, lr = 0.0001
I0407 09:31:17.805547 15775 solver.cpp:218] Iteration 7716 (2.28579 iter/s, 5.24982s/12 iters), loss = 0.364449
I0407 09:31:17.805595 15775 solver.cpp:237]     Train net output #0: loss = 0.364449 (* 1 = 0.364449 loss)
I0407 09:31:17.805603 15775 sgd_solver.cpp:105] Iteration 7716, lr = 0.0001
I0407 09:31:23.088852 15775 solver.cpp:218] Iteration 7728 (2.27135 iter/s, 5.28321s/12 iters), loss = 0.219849
I0407 09:31:23.088907 15775 solver.cpp:237]     Train net output #0: loss = 0.219849 (* 1 = 0.219849 loss)
I0407 09:31:23.088915 15775 sgd_solver.cpp:105] Iteration 7728, lr = 0.0001
I0407 09:31:28.620333 15775 solver.cpp:218] Iteration 7740 (2.16944 iter/s, 5.53139s/12 iters), loss = 0.267337
I0407 09:31:28.620479 15775 solver.cpp:237]     Train net output #0: loss = 0.267337 (* 1 = 0.267337 loss)
I0407 09:31:28.620488 15775 sgd_solver.cpp:105] Iteration 7740, lr = 0.0001
I0407 09:31:33.513622 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 09:31:36.443346 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 09:31:38.751618 15775 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 09:31:38.751638 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:31:40.113066 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:43.126926 15775 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0407 09:31:43.126966 15775 solver.cpp:397]     Test net output #1: loss = 2.77631 (* 1 = 2.77631 loss)
I0407 09:31:43.268137 15775 solver.cpp:218] Iteration 7752 (0.819249 iter/s, 14.6476s/12 iters), loss = 0.202699
I0407 09:31:43.268186 15775 solver.cpp:237]     Train net output #0: loss = 0.202699 (* 1 = 0.202699 loss)
I0407 09:31:43.268194 15775 sgd_solver.cpp:105] Iteration 7752, lr = 0.0001
I0407 09:31:47.736685 15775 solver.cpp:218] Iteration 7764 (2.68548 iter/s, 4.46847s/12 iters), loss = 0.0838488
I0407 09:31:47.736726 15775 solver.cpp:237]     Train net output #0: loss = 0.0838488 (* 1 = 0.0838488 loss)
I0407 09:31:47.736734 15775 sgd_solver.cpp:105] Iteration 7764, lr = 0.0001
I0407 09:31:52.777436 15775 solver.cpp:218] Iteration 7776 (2.38064 iter/s, 5.04066s/12 iters), loss = 0.17133
I0407 09:31:52.777482 15775 solver.cpp:237]     Train net output #0: loss = 0.17133 (* 1 = 0.17133 loss)
I0407 09:31:52.777490 15775 sgd_solver.cpp:105] Iteration 7776, lr = 0.0001
I0407 09:31:58.026767 15775 solver.cpp:218] Iteration 7788 (2.28605 iter/s, 5.24924s/12 iters), loss = 0.28539
I0407 09:31:58.026810 15775 solver.cpp:237]     Train net output #0: loss = 0.28539 (* 1 = 0.28539 loss)
I0407 09:31:58.026818 15775 sgd_solver.cpp:105] Iteration 7788, lr = 0.0001
I0407 09:31:58.033911 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:03.212095 15775 solver.cpp:218] Iteration 7800 (2.31426 iter/s, 5.18524s/12 iters), loss = 0.198718
I0407 09:32:03.212209 15775 solver.cpp:237]     Train net output #0: loss = 0.198718 (* 1 = 0.198718 loss)
I0407 09:32:03.212218 15775 sgd_solver.cpp:105] Iteration 7800, lr = 0.0001
I0407 09:32:08.353811 15775 solver.cpp:218] Iteration 7812 (2.33392 iter/s, 5.14156s/12 iters), loss = 0.224926
I0407 09:32:08.353853 15775 solver.cpp:237]     Train net output #0: loss = 0.224926 (* 1 = 0.224926 loss)
I0407 09:32:08.353861 15775 sgd_solver.cpp:105] Iteration 7812, lr = 0.0001
I0407 09:32:13.542713 15775 solver.cpp:218] Iteration 7824 (2.31267 iter/s, 5.18881s/12 iters), loss = 0.174964
I0407 09:32:13.542757 15775 solver.cpp:237]     Train net output #0: loss = 0.174964 (* 1 = 0.174964 loss)
I0407 09:32:13.542763 15775 sgd_solver.cpp:105] Iteration 7824, lr = 0.0001
I0407 09:32:18.845803 15775 solver.cpp:218] Iteration 7836 (2.26287 iter/s, 5.303s/12 iters), loss = 0.267359
I0407 09:32:18.845846 15775 solver.cpp:237]     Train net output #0: loss = 0.267359 (* 1 = 0.267359 loss)
I0407 09:32:18.845854 15775 sgd_solver.cpp:105] Iteration 7836, lr = 0.0001
I0407 09:32:24.334246 15775 solver.cpp:218] Iteration 7848 (2.18645 iter/s, 5.48836s/12 iters), loss = 0.12231
I0407 09:32:24.334293 15775 solver.cpp:237]     Train net output #0: loss = 0.12231 (* 1 = 0.12231 loss)
I0407 09:32:24.334301 15775 sgd_solver.cpp:105] Iteration 7848, lr = 0.0001
I0407 09:32:26.513742 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 09:32:29.548357 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 09:32:31.855183 15775 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 09:32:31.855203 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:32:33.154407 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:36.190276 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 09:32:36.190407 15775 solver.cpp:397]     Test net output #1: loss = 2.77592 (* 1 = 2.77592 loss)
I0407 09:32:38.121388 15775 solver.cpp:218] Iteration 7860 (0.870385 iter/s, 13.787s/12 iters), loss = 0.18764
I0407 09:32:38.121426 15775 solver.cpp:237]     Train net output #0: loss = 0.18764 (* 1 = 0.18764 loss)
I0407 09:32:38.121433 15775 sgd_solver.cpp:105] Iteration 7860, lr = 0.0001
I0407 09:32:43.179543 15775 solver.cpp:218] Iteration 7872 (2.37245 iter/s, 5.05807s/12 iters), loss = 0.0927208
I0407 09:32:43.179598 15775 solver.cpp:237]     Train net output #0: loss = 0.0927208 (* 1 = 0.0927208 loss)
I0407 09:32:43.179610 15775 sgd_solver.cpp:105] Iteration 7872, lr = 0.0001
I0407 09:32:48.617997 15775 solver.cpp:218] Iteration 7884 (2.20655 iter/s, 5.43835s/12 iters), loss = 0.244021
I0407 09:32:48.618049 15775 solver.cpp:237]     Train net output #0: loss = 0.244021 (* 1 = 0.244021 loss)
I0407 09:32:48.618059 15775 sgd_solver.cpp:105] Iteration 7884, lr = 0.0001
I0407 09:32:50.888128 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:53.902029 15775 solver.cpp:218] Iteration 7896 (2.27104 iter/s, 5.28393s/12 iters), loss = 0.235531
I0407 09:32:53.902081 15775 solver.cpp:237]     Train net output #0: loss = 0.235531 (* 1 = 0.235531 loss)
I0407 09:32:53.902091 15775 sgd_solver.cpp:105] Iteration 7896, lr = 0.0001
I0407 09:32:59.195375 15775 solver.cpp:218] Iteration 7908 (2.26704 iter/s, 5.29325s/12 iters), loss = 0.224285
I0407 09:32:59.195413 15775 solver.cpp:237]     Train net output #0: loss = 0.224285 (* 1 = 0.224285 loss)
I0407 09:32:59.195420 15775 sgd_solver.cpp:105] Iteration 7908, lr = 0.0001
I0407 09:33:04.448689 15775 solver.cpp:218] Iteration 7920 (2.28431 iter/s, 5.25323s/12 iters), loss = 0.227626
I0407 09:33:04.448729 15775 solver.cpp:237]     Train net output #0: loss = 0.227627 (* 1 = 0.227627 loss)
I0407 09:33:04.448735 15775 sgd_solver.cpp:105] Iteration 7920, lr = 0.0001
I0407 09:33:09.808367 15775 solver.cpp:218] Iteration 7932 (2.23898 iter/s, 5.35959s/12 iters), loss = 0.177788
I0407 09:33:09.808473 15775 solver.cpp:237]     Train net output #0: loss = 0.177788 (* 1 = 0.177788 loss)
I0407 09:33:09.808482 15775 sgd_solver.cpp:105] Iteration 7932, lr = 0.0001
I0407 09:33:15.145196 15775 solver.cpp:218] Iteration 7944 (2.24859 iter/s, 5.33668s/12 iters), loss = 0.158173
I0407 09:33:15.145242 15775 solver.cpp:237]     Train net output #0: loss = 0.158173 (* 1 = 0.158173 loss)
I0407 09:33:15.145251 15775 sgd_solver.cpp:105] Iteration 7944, lr = 0.0001
I0407 09:33:19.686439 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 09:33:22.700050 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 09:33:24.995015 15775 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 09:33:24.995034 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:33:26.243005 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:29.355198 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0407 09:33:29.355229 15775 solver.cpp:397]     Test net output #1: loss = 2.78432 (* 1 = 2.78432 loss)
I0407 09:33:29.495965 15775 solver.cpp:218] Iteration 7956 (0.8362 iter/s, 14.3506s/12 iters), loss = 0.131539
I0407 09:33:29.496014 15775 solver.cpp:237]     Train net output #0: loss = 0.131539 (* 1 = 0.131539 loss)
I0407 09:33:29.496021 15775 sgd_solver.cpp:105] Iteration 7956, lr = 0.0001
I0407 09:33:33.901183 15775 solver.cpp:218] Iteration 7968 (2.72409 iter/s, 4.40513s/12 iters), loss = 0.230298
I0407 09:33:33.901227 15775 solver.cpp:237]     Train net output #0: loss = 0.230298 (* 1 = 0.230298 loss)
I0407 09:33:33.901235 15775 sgd_solver.cpp:105] Iteration 7968, lr = 0.0001
I0407 09:33:39.102317 15775 solver.cpp:218] Iteration 7980 (2.30723 iter/s, 5.20105s/12 iters), loss = 0.322107
I0407 09:33:39.102353 15775 solver.cpp:237]     Train net output #0: loss = 0.322107 (* 1 = 0.322107 loss)
I0407 09:33:39.102360 15775 sgd_solver.cpp:105] Iteration 7980, lr = 0.0001
I0407 09:33:43.404958 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:44.193581 15775 solver.cpp:218] Iteration 7992 (2.35702 iter/s, 5.09118s/12 iters), loss = 0.132409
I0407 09:33:44.193629 15775 solver.cpp:237]     Train net output #0: loss = 0.132409 (* 1 = 0.132409 loss)
I0407 09:33:44.193637 15775 sgd_solver.cpp:105] Iteration 7992, lr = 0.0001
I0407 09:33:49.525353 15775 solver.cpp:218] Iteration 8004 (2.2507 iter/s, 5.33168s/12 iters), loss = 0.18291
I0407 09:33:49.525393 15775 solver.cpp:237]     Train net output #0: loss = 0.18291 (* 1 = 0.18291 loss)
I0407 09:33:49.525400 15775 sgd_solver.cpp:105] Iteration 8004, lr = 0.0001
I0407 09:33:54.855419 15775 solver.cpp:218] Iteration 8016 (2.25142 iter/s, 5.32998s/12 iters), loss = 0.277694
I0407 09:33:54.855459 15775 solver.cpp:237]     Train net output #0: loss = 0.277694 (* 1 = 0.277694 loss)
I0407 09:33:54.855468 15775 sgd_solver.cpp:105] Iteration 8016, lr = 0.0001
I0407 09:34:00.212157 15775 solver.cpp:218] Iteration 8028 (2.24021 iter/s, 5.35665s/12 iters), loss = 0.12535
I0407 09:34:00.212206 15775 solver.cpp:237]     Train net output #0: loss = 0.12535 (* 1 = 0.12535 loss)
I0407 09:34:00.212214 15775 sgd_solver.cpp:105] Iteration 8028, lr = 0.0001
I0407 09:34:05.326721 15775 solver.cpp:218] Iteration 8040 (2.34628 iter/s, 5.11447s/12 iters), loss = 0.16967
I0407 09:34:05.326761 15775 solver.cpp:237]     Train net output #0: loss = 0.16967 (* 1 = 0.16967 loss)
I0407 09:34:05.326768 15775 sgd_solver.cpp:105] Iteration 8040, lr = 0.0001
I0407 09:34:10.762732 15775 solver.cpp:218] Iteration 8052 (2.20754 iter/s, 5.43593s/12 iters), loss = 0.197566
I0407 09:34:10.762773 15775 solver.cpp:237]     Train net output #0: loss = 0.197566 (* 1 = 0.197566 loss)
I0407 09:34:10.762780 15775 sgd_solver.cpp:105] Iteration 8052, lr = 0.0001
I0407 09:34:13.030786 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 09:34:15.985896 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 09:34:18.288851 15775 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 09:34:18.288870 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:34:19.489835 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:22.671602 15775 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 09:34:22.671629 15775 solver.cpp:397]     Test net output #1: loss = 2.78979 (* 1 = 2.78979 loss)
I0407 09:34:24.708302 15775 solver.cpp:218] Iteration 8064 (0.860497 iter/s, 13.9454s/12 iters), loss = 0.0869159
I0407 09:34:24.708346 15775 solver.cpp:237]     Train net output #0: loss = 0.0869159 (* 1 = 0.0869159 loss)
I0407 09:34:24.708353 15775 sgd_solver.cpp:105] Iteration 8064, lr = 0.0001
I0407 09:34:29.967427 15775 solver.cpp:218] Iteration 8076 (2.28179 iter/s, 5.25904s/12 iters), loss = 0.179315
I0407 09:34:29.967471 15775 solver.cpp:237]     Train net output #0: loss = 0.179315 (* 1 = 0.179315 loss)
I0407 09:34:29.967478 15775 sgd_solver.cpp:105] Iteration 8076, lr = 0.0001
I0407 09:34:35.410851 15775 solver.cpp:218] Iteration 8088 (2.20453 iter/s, 5.44333s/12 iters), loss = 0.109006
I0407 09:34:35.410889 15775 solver.cpp:237]     Train net output #0: loss = 0.109006 (* 1 = 0.109006 loss)
I0407 09:34:35.410898 15775 sgd_solver.cpp:105] Iteration 8088, lr = 0.0001
I0407 09:34:36.838083 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:40.440376 15775 solver.cpp:218] Iteration 8100 (2.38595 iter/s, 5.02944s/12 iters), loss = 0.225336
I0407 09:34:40.440418 15775 solver.cpp:237]     Train net output #0: loss = 0.225336 (* 1 = 0.225336 loss)
I0407 09:34:40.440424 15775 sgd_solver.cpp:105] Iteration 8100, lr = 0.0001
I0407 09:34:45.698197 15775 solver.cpp:218] Iteration 8112 (2.28235 iter/s, 5.25773s/12 iters), loss = 0.261921
I0407 09:34:45.698244 15775 solver.cpp:237]     Train net output #0: loss = 0.261921 (* 1 = 0.261921 loss)
I0407 09:34:45.698256 15775 sgd_solver.cpp:105] Iteration 8112, lr = 0.0001
I0407 09:34:51.177444 15775 solver.cpp:218] Iteration 8124 (2.19012 iter/s, 5.47916s/12 iters), loss = 0.264464
I0407 09:34:51.177610 15775 solver.cpp:237]     Train net output #0: loss = 0.264464 (* 1 = 0.264464 loss)
I0407 09:34:51.177623 15775 sgd_solver.cpp:105] Iteration 8124, lr = 0.0001
I0407 09:34:56.323855 15775 solver.cpp:218] Iteration 8136 (2.33182 iter/s, 5.1462s/12 iters), loss = 0.0979184
I0407 09:34:56.323899 15775 solver.cpp:237]     Train net output #0: loss = 0.0979184 (* 1 = 0.0979184 loss)
I0407 09:34:56.323906 15775 sgd_solver.cpp:105] Iteration 8136, lr = 0.0001
I0407 09:35:01.822054 15775 solver.cpp:218] Iteration 8148 (2.18257 iter/s, 5.49811s/12 iters), loss = 0.193246
I0407 09:35:01.822094 15775 solver.cpp:237]     Train net output #0: loss = 0.193246 (* 1 = 0.193246 loss)
I0407 09:35:01.822103 15775 sgd_solver.cpp:105] Iteration 8148, lr = 0.0001
I0407 09:35:06.583465 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 09:35:09.581786 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 09:35:11.886507 15775 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 09:35:11.886523 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:35:13.083827 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:16.222033 15775 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0407 09:35:16.222065 15775 solver.cpp:397]     Test net output #1: loss = 2.77553 (* 1 = 2.77553 loss)
I0407 09:35:16.363323 15775 solver.cpp:218] Iteration 8160 (0.825245 iter/s, 14.5411s/12 iters), loss = 0.244302
I0407 09:35:16.363384 15775 solver.cpp:237]     Train net output #0: loss = 0.244302 (* 1 = 0.244302 loss)
I0407 09:35:16.363394 15775 sgd_solver.cpp:105] Iteration 8160, lr = 0.0001
I0407 09:35:20.696568 15775 solver.cpp:218] Iteration 8172 (2.76935 iter/s, 4.33314s/12 iters), loss = 0.232459
I0407 09:35:20.696611 15775 solver.cpp:237]     Train net output #0: loss = 0.232458 (* 1 = 0.232458 loss)
I0407 09:35:20.696619 15775 sgd_solver.cpp:105] Iteration 8172, lr = 0.0001
I0407 09:35:26.104297 15775 solver.cpp:218] Iteration 8184 (2.21908 iter/s, 5.40764s/12 iters), loss = 0.142146
I0407 09:35:26.104422 15775 solver.cpp:237]     Train net output #0: loss = 0.142146 (* 1 = 0.142146 loss)
I0407 09:35:26.104431 15775 sgd_solver.cpp:105] Iteration 8184, lr = 0.0001
I0407 09:35:29.941336 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:31.476348 15775 solver.cpp:218] Iteration 8196 (2.23385 iter/s, 5.37188s/12 iters), loss = 0.159702
I0407 09:35:31.476390 15775 solver.cpp:237]     Train net output #0: loss = 0.159702 (* 1 = 0.159702 loss)
I0407 09:35:31.476397 15775 sgd_solver.cpp:105] Iteration 8196, lr = 0.0001
I0407 09:35:36.827898 15775 solver.cpp:218] Iteration 8208 (2.24238 iter/s, 5.35146s/12 iters), loss = 0.203403
I0407 09:35:36.827952 15775 solver.cpp:237]     Train net output #0: loss = 0.203403 (* 1 = 0.203403 loss)
I0407 09:35:36.827965 15775 sgd_solver.cpp:105] Iteration 8208, lr = 0.0001
I0407 09:35:41.890682 15775 solver.cpp:218] Iteration 8220 (2.37028 iter/s, 5.06269s/12 iters), loss = 0.2243
I0407 09:35:41.890719 15775 solver.cpp:237]     Train net output #0: loss = 0.2243 (* 1 = 0.2243 loss)
I0407 09:35:41.890727 15775 sgd_solver.cpp:105] Iteration 8220, lr = 0.0001
I0407 09:35:47.380877 15775 solver.cpp:218] Iteration 8232 (2.18575 iter/s, 5.49011s/12 iters), loss = 0.25165
I0407 09:35:47.380940 15775 solver.cpp:237]     Train net output #0: loss = 0.25165 (* 1 = 0.25165 loss)
I0407 09:35:47.380952 15775 sgd_solver.cpp:105] Iteration 8232, lr = 0.0001
I0407 09:35:52.532946 15775 solver.cpp:218] Iteration 8244 (2.32921 iter/s, 5.15196s/12 iters), loss = 0.296633
I0407 09:35:52.532987 15775 solver.cpp:237]     Train net output #0: loss = 0.296633 (* 1 = 0.296633 loss)
I0407 09:35:52.532995 15775 sgd_solver.cpp:105] Iteration 8244, lr = 0.0001
I0407 09:35:57.660349 15775 solver.cpp:218] Iteration 8256 (2.34041 iter/s, 5.12732s/12 iters), loss = 0.117871
I0407 09:35:57.660538 15775 solver.cpp:237]     Train net output #0: loss = 0.117871 (* 1 = 0.117871 loss)
I0407 09:35:57.660549 15775 sgd_solver.cpp:105] Iteration 8256, lr = 0.0001
I0407 09:35:59.755928 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 09:36:02.777660 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 09:36:05.081199 15775 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 09:36:05.081220 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:36:06.195776 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:09.356230 15775 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0407 09:36:09.356258 15775 solver.cpp:397]     Test net output #1: loss = 2.77471 (* 1 = 2.77471 loss)
I0407 09:36:11.299086 15775 solver.cpp:218] Iteration 8268 (0.879865 iter/s, 13.6385s/12 iters), loss = 0.200328
I0407 09:36:11.299130 15775 solver.cpp:237]     Train net output #0: loss = 0.200328 (* 1 = 0.200328 loss)
I0407 09:36:11.299137 15775 sgd_solver.cpp:105] Iteration 8268, lr = 0.0001
I0407 09:36:16.615181 15775 solver.cpp:218] Iteration 8280 (2.25733 iter/s, 5.316s/12 iters), loss = 0.178023
I0407 09:36:16.615224 15775 solver.cpp:237]     Train net output #0: loss = 0.178023 (* 1 = 0.178023 loss)
I0407 09:36:16.615233 15775 sgd_solver.cpp:105] Iteration 8280, lr = 0.0001
I0407 09:36:21.986915 15775 solver.cpp:218] Iteration 8292 (2.23395 iter/s, 5.37164s/12 iters), loss = 0.110845
I0407 09:36:21.986958 15775 solver.cpp:237]     Train net output #0: loss = 0.110845 (* 1 = 0.110845 loss)
I0407 09:36:21.986968 15775 sgd_solver.cpp:105] Iteration 8292, lr = 0.0001
I0407 09:36:22.592711 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:27.103220 15775 solver.cpp:218] Iteration 8304 (2.34548 iter/s, 5.11622s/12 iters), loss = 0.276164
I0407 09:36:27.103262 15775 solver.cpp:237]     Train net output #0: loss = 0.276164 (* 1 = 0.276164 loss)
I0407 09:36:27.103268 15775 sgd_solver.cpp:105] Iteration 8304, lr = 0.0001
I0407 09:36:29.874419 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:36:32.148851 15775 solver.cpp:218] Iteration 8316 (2.37834 iter/s, 5.04554s/12 iters), loss = 0.0950775
I0407 09:36:32.148906 15775 solver.cpp:237]     Train net output #0: loss = 0.0950775 (* 1 = 0.0950775 loss)
I0407 09:36:32.148916 15775 sgd_solver.cpp:105] Iteration 8316, lr = 0.0001
I0407 09:36:37.492367 15775 solver.cpp:218] Iteration 8328 (2.24575 iter/s, 5.34342s/12 iters), loss = 0.203504
I0407 09:36:37.492408 15775 solver.cpp:237]     Train net output #0: loss = 0.203504 (* 1 = 0.203504 loss)
I0407 09:36:37.492415 15775 sgd_solver.cpp:105] Iteration 8328, lr = 0.0001
I0407 09:36:42.973763 15775 solver.cpp:218] Iteration 8340 (2.18926 iter/s, 5.48131s/12 iters), loss = 0.111682
I0407 09:36:42.973811 15775 solver.cpp:237]     Train net output #0: loss = 0.111682 (* 1 = 0.111682 loss)
I0407 09:36:42.973819 15775 sgd_solver.cpp:105] Iteration 8340, lr = 0.0001
I0407 09:36:48.083160 15775 solver.cpp:218] Iteration 8352 (2.34866 iter/s, 5.1093s/12 iters), loss = 0.218374
I0407 09:36:48.083217 15775 solver.cpp:237]     Train net output #0: loss = 0.218374 (* 1 = 0.218374 loss)
I0407 09:36:48.083227 15775 sgd_solver.cpp:105] Iteration 8352, lr = 0.0001
I0407 09:36:52.636117 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 09:36:55.608235 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 09:36:57.903527 15775 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 09:36:57.903544 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:36:58.967002 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:02.185660 15775 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0407 09:37:02.185806 15775 solver.cpp:397]     Test net output #1: loss = 2.78232 (* 1 = 2.78232 loss)
I0407 09:37:02.324307 15775 solver.cpp:218] Iteration 8364 (0.842638 iter/s, 14.241s/12 iters), loss = 0.136067
I0407 09:37:02.324363 15775 solver.cpp:237]     Train net output #0: loss = 0.136067 (* 1 = 0.136067 loss)
I0407 09:37:02.324375 15775 sgd_solver.cpp:105] Iteration 8364, lr = 0.0001
I0407 09:37:06.711711 15775 solver.cpp:218] Iteration 8376 (2.73516 iter/s, 4.38731s/12 iters), loss = 0.17559
I0407 09:37:06.711755 15775 solver.cpp:237]     Train net output #0: loss = 0.17559 (* 1 = 0.17559 loss)
I0407 09:37:06.711763 15775 sgd_solver.cpp:105] Iteration 8376, lr = 0.0001
I0407 09:37:12.039923 15775 solver.cpp:218] Iteration 8388 (2.2522 iter/s, 5.32812s/12 iters), loss = 0.148543
I0407 09:37:12.039970 15775 solver.cpp:237]     Train net output #0: loss = 0.148543 (* 1 = 0.148543 loss)
I0407 09:37:12.039978 15775 sgd_solver.cpp:105] Iteration 8388, lr = 0.0001
I0407 09:37:14.840026 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:17.268148 15775 solver.cpp:218] Iteration 8400 (2.29527 iter/s, 5.22813s/12 iters), loss = 0.167037
I0407 09:37:17.268188 15775 solver.cpp:237]     Train net output #0: loss = 0.167037 (* 1 = 0.167037 loss)
I0407 09:37:17.268195 15775 sgd_solver.cpp:105] Iteration 8400, lr = 0.0001
I0407 09:37:22.547247 15775 solver.cpp:218] Iteration 8412 (2.27315 iter/s, 5.27901s/12 iters), loss = 0.173118
I0407 09:37:22.547292 15775 solver.cpp:237]     Train net output #0: loss = 0.173118 (* 1 = 0.173118 loss)
I0407 09:37:22.547300 15775 sgd_solver.cpp:105] Iteration 8412, lr = 0.0001
I0407 09:37:28.019259 15775 solver.cpp:218] Iteration 8424 (2.19301 iter/s, 5.47192s/12 iters), loss = 0.302702
I0407 09:37:28.019300 15775 solver.cpp:237]     Train net output #0: loss = 0.302702 (* 1 = 0.302702 loss)
I0407 09:37:28.019309 15775 sgd_solver.cpp:105] Iteration 8424, lr = 0.0001
I0407 09:37:33.312759 15775 solver.cpp:218] Iteration 8436 (2.26697 iter/s, 5.29341s/12 iters), loss = 0.274028
I0407 09:37:33.312865 15775 solver.cpp:237]     Train net output #0: loss = 0.274028 (* 1 = 0.274028 loss)
I0407 09:37:33.312873 15775 sgd_solver.cpp:105] Iteration 8436, lr = 0.0001
I0407 09:37:38.789935 15775 solver.cpp:218] Iteration 8448 (2.19097 iter/s, 5.47703s/12 iters), loss = 0.152959
I0407 09:37:38.789978 15775 solver.cpp:237]     Train net output #0: loss = 0.152959 (* 1 = 0.152959 loss)
I0407 09:37:38.789988 15775 sgd_solver.cpp:105] Iteration 8448, lr = 0.0001
I0407 09:37:44.222939 15775 solver.cpp:218] Iteration 8460 (2.20876 iter/s, 5.43292s/12 iters), loss = 0.160577
I0407 09:37:44.222975 15775 solver.cpp:237]     Train net output #0: loss = 0.160577 (* 1 = 0.160577 loss)
I0407 09:37:44.222983 15775 sgd_solver.cpp:105] Iteration 8460, lr = 0.0001
I0407 09:37:46.361547 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 09:37:49.386245 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 09:37:51.698274 15775 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 09:37:51.698294 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:37:52.734156 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:56.016774 15775 solver.cpp:397]     Test net output #0: accuracy = 0.462623
I0407 09:37:56.016804 15775 solver.cpp:397]     Test net output #1: loss = 2.77601 (* 1 = 2.77601 loss)
I0407 09:37:57.883035 15775 solver.cpp:218] Iteration 8472 (0.878479 iter/s, 13.66s/12 iters), loss = 0.155588
I0407 09:37:57.883074 15775 solver.cpp:237]     Train net output #0: loss = 0.155588 (* 1 = 0.155588 loss)
I0407 09:37:57.883080 15775 sgd_solver.cpp:105] Iteration 8472, lr = 0.0001
I0407 09:38:03.204202 15775 solver.cpp:218] Iteration 8484 (2.25518 iter/s, 5.32108s/12 iters), loss = 0.239091
I0407 09:38:03.204239 15775 solver.cpp:237]     Train net output #0: loss = 0.239091 (* 1 = 0.239091 loss)
I0407 09:38:03.204246 15775 sgd_solver.cpp:105] Iteration 8484, lr = 0.0001
I0407 09:38:08.534260 15775 solver.cpp:218] Iteration 8496 (2.25142 iter/s, 5.32997s/12 iters), loss = 0.133987
I0407 09:38:08.534401 15775 solver.cpp:237]     Train net output #0: loss = 0.133987 (* 1 = 0.133987 loss)
I0407 09:38:08.534411 15775 sgd_solver.cpp:105] Iteration 8496, lr = 0.0001
I0407 09:38:08.569203 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:13.946738 15775 solver.cpp:218] Iteration 8508 (2.21718 iter/s, 5.41229s/12 iters), loss = 0.186882
I0407 09:38:13.946779 15775 solver.cpp:237]     Train net output #0: loss = 0.186882 (* 1 = 0.186882 loss)
I0407 09:38:13.946785 15775 sgd_solver.cpp:105] Iteration 8508, lr = 0.0001
I0407 09:38:19.231798 15775 solver.cpp:218] Iteration 8520 (2.27059 iter/s, 5.28497s/12 iters), loss = 0.189981
I0407 09:38:19.231858 15775 solver.cpp:237]     Train net output #0: loss = 0.189981 (* 1 = 0.189981 loss)
I0407 09:38:19.231868 15775 sgd_solver.cpp:105] Iteration 8520, lr = 0.0001
I0407 09:38:24.606546 15775 solver.cpp:218] Iteration 8532 (2.23271 iter/s, 5.37464s/12 iters), loss = 0.126007
I0407 09:38:24.606600 15775 solver.cpp:237]     Train net output #0: loss = 0.126007 (* 1 = 0.126007 loss)
I0407 09:38:24.606609 15775 sgd_solver.cpp:105] Iteration 8532, lr = 0.0001
I0407 09:38:29.668452 15775 solver.cpp:218] Iteration 8544 (2.37069 iter/s, 5.06181s/12 iters), loss = 0.270667
I0407 09:38:29.668491 15775 solver.cpp:237]     Train net output #0: loss = 0.270667 (* 1 = 0.270667 loss)
I0407 09:38:29.668499 15775 sgd_solver.cpp:105] Iteration 8544, lr = 0.0001
I0407 09:38:34.973691 15775 solver.cpp:218] Iteration 8556 (2.26195 iter/s, 5.30515s/12 iters), loss = 0.165452
I0407 09:38:34.973734 15775 solver.cpp:237]     Train net output #0: loss = 0.165452 (* 1 = 0.165452 loss)
I0407 09:38:34.973742 15775 sgd_solver.cpp:105] Iteration 8556, lr = 0.0001
I0407 09:38:39.627403 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 09:38:42.650408 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 09:38:44.964018 15775 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 09:38:44.964038 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:38:45.979720 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:49.286901 15775 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0407 09:38:49.286936 15775 solver.cpp:397]     Test net output #1: loss = 2.7795 (* 1 = 2.7795 loss)
I0407 09:38:49.422243 15775 solver.cpp:218] Iteration 8568 (0.830541 iter/s, 14.4484s/12 iters), loss = 0.219798
I0407 09:38:49.423843 15775 solver.cpp:237]     Train net output #0: loss = 0.219798 (* 1 = 0.219798 loss)
I0407 09:38:49.423856 15775 sgd_solver.cpp:105] Iteration 8568, lr = 0.0001
I0407 09:38:53.848757 15775 solver.cpp:218] Iteration 8580 (2.71194 iter/s, 4.42488s/12 iters), loss = 0.165831
I0407 09:38:53.848796 15775 solver.cpp:237]     Train net output #0: loss = 0.165831 (* 1 = 0.165831 loss)
I0407 09:38:53.848804 15775 sgd_solver.cpp:105] Iteration 8580, lr = 0.0001
I0407 09:38:59.100354 15775 solver.cpp:218] Iteration 8592 (2.28506 iter/s, 5.25151s/12 iters), loss = 0.10014
I0407 09:38:59.100402 15775 solver.cpp:237]     Train net output #0: loss = 0.10014 (* 1 = 0.10014 loss)
I0407 09:38:59.100411 15775 sgd_solver.cpp:105] Iteration 8592, lr = 0.0001
I0407 09:39:01.342001 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:04.408264 15775 solver.cpp:218] Iteration 8604 (2.26082 iter/s, 5.30782s/12 iters), loss = 0.184438
I0407 09:39:04.408309 15775 solver.cpp:237]     Train net output #0: loss = 0.184438 (* 1 = 0.184438 loss)
I0407 09:39:04.408316 15775 sgd_solver.cpp:105] Iteration 8604, lr = 0.0001
I0407 09:39:09.760710 15775 solver.cpp:218] Iteration 8616 (2.242 iter/s, 5.35236s/12 iters), loss = 0.214161
I0407 09:39:09.760840 15775 solver.cpp:237]     Train net output #0: loss = 0.214161 (* 1 = 0.214161 loss)
I0407 09:39:09.760849 15775 sgd_solver.cpp:105] Iteration 8616, lr = 0.0001
I0407 09:39:15.234361 15775 solver.cpp:218] Iteration 8628 (2.19239 iter/s, 5.47347s/12 iters), loss = 0.267955
I0407 09:39:15.234405 15775 solver.cpp:237]     Train net output #0: loss = 0.267955 (* 1 = 0.267955 loss)
I0407 09:39:15.234412 15775 sgd_solver.cpp:105] Iteration 8628, lr = 0.0001
I0407 09:39:20.574261 15775 solver.cpp:218] Iteration 8640 (2.24727 iter/s, 5.33981s/12 iters), loss = 0.135936
I0407 09:39:20.574319 15775 solver.cpp:237]     Train net output #0: loss = 0.135936 (* 1 = 0.135936 loss)
I0407 09:39:20.574333 15775 sgd_solver.cpp:105] Iteration 8640, lr = 0.0001
I0407 09:39:25.811745 15775 solver.cpp:218] Iteration 8652 (2.29122 iter/s, 5.23739s/12 iters), loss = 0.151961
I0407 09:39:25.811786 15775 solver.cpp:237]     Train net output #0: loss = 0.151961 (* 1 = 0.151961 loss)
I0407 09:39:25.811794 15775 sgd_solver.cpp:105] Iteration 8652, lr = 0.0001
I0407 09:39:30.932004 15775 solver.cpp:218] Iteration 8664 (2.34367 iter/s, 5.12017s/12 iters), loss = 0.208429
I0407 09:39:30.932054 15775 solver.cpp:237]     Train net output #0: loss = 0.208429 (* 1 = 0.208429 loss)
I0407 09:39:30.932062 15775 sgd_solver.cpp:105] Iteration 8664, lr = 0.0001
I0407 09:39:33.045516 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 09:39:36.033013 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 09:39:38.359109 15775 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 09:39:38.359133 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:39:39.340095 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:42.653741 15775 solver.cpp:397]     Test net output #0: accuracy = 0.46201
I0407 09:39:42.653813 15775 solver.cpp:397]     Test net output #1: loss = 2.78384 (* 1 = 2.78384 loss)
I0407 09:39:44.607852 15775 solver.cpp:218] Iteration 8676 (0.877469 iter/s, 13.6757s/12 iters), loss = 0.20332
I0407 09:39:44.607916 15775 solver.cpp:237]     Train net output #0: loss = 0.20332 (* 1 = 0.20332 loss)
I0407 09:39:44.607926 15775 sgd_solver.cpp:105] Iteration 8676, lr = 0.0001
I0407 09:39:49.977928 15775 solver.cpp:218] Iteration 8688 (2.23465 iter/s, 5.36997s/12 iters), loss = 0.226908
I0407 09:39:49.977972 15775 solver.cpp:237]     Train net output #0: loss = 0.226908 (* 1 = 0.226908 loss)
I0407 09:39:49.977978 15775 sgd_solver.cpp:105] Iteration 8688, lr = 0.0001
I0407 09:39:54.417440 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:55.178180 15775 solver.cpp:218] Iteration 8700 (2.30762 iter/s, 5.20016s/12 iters), loss = 0.293073
I0407 09:39:55.178225 15775 solver.cpp:237]     Train net output #0: loss = 0.293073 (* 1 = 0.293073 loss)
I0407 09:39:55.178231 15775 sgd_solver.cpp:105] Iteration 8700, lr = 0.0001
I0407 09:40:00.242110 15775 solver.cpp:218] Iteration 8712 (2.36974 iter/s, 5.06384s/12 iters), loss = 0.0823797
I0407 09:40:00.242156 15775 solver.cpp:237]     Train net output #0: loss = 0.0823796 (* 1 = 0.0823796 loss)
I0407 09:40:00.242163 15775 sgd_solver.cpp:105] Iteration 8712, lr = 0.0001
I0407 09:40:05.632783 15775 solver.cpp:218] Iteration 8724 (2.2261 iter/s, 5.39058s/12 iters), loss = 0.225253
I0407 09:40:05.632828 15775 solver.cpp:237]     Train net output #0: loss = 0.225253 (* 1 = 0.225253 loss)
I0407 09:40:05.632835 15775 sgd_solver.cpp:105] Iteration 8724, lr = 0.0001
I0407 09:40:10.884124 15775 solver.cpp:218] Iteration 8736 (2.28517 iter/s, 5.25125s/12 iters), loss = 0.178038
I0407 09:40:10.884163 15775 solver.cpp:237]     Train net output #0: loss = 0.178038 (* 1 = 0.178038 loss)
I0407 09:40:10.884172 15775 sgd_solver.cpp:105] Iteration 8736, lr = 0.0001
I0407 09:40:16.539064 15775 solver.cpp:218] Iteration 8748 (2.12207 iter/s, 5.65485s/12 iters), loss = 0.200482
I0407 09:40:16.539222 15775 solver.cpp:237]     Train net output #0: loss = 0.200482 (* 1 = 0.200482 loss)
I0407 09:40:16.539233 15775 sgd_solver.cpp:105] Iteration 8748, lr = 0.0001
I0407 09:40:21.816598 15775 solver.cpp:218] Iteration 8760 (2.27387 iter/s, 5.27734s/12 iters), loss = 0.114772
I0407 09:40:21.816642 15775 solver.cpp:237]     Train net output #0: loss = 0.114772 (* 1 = 0.114772 loss)
I0407 09:40:21.816651 15775 sgd_solver.cpp:105] Iteration 8760, lr = 0.0001
I0407 09:40:26.542656 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 09:40:29.547874 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 09:40:31.881594 15775 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 09:40:31.881615 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:40:32.807691 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:36.167990 15775 solver.cpp:397]     Test net output #0: accuracy = 0.465074
I0407 09:40:36.168025 15775 solver.cpp:397]     Test net output #1: loss = 2.79614 (* 1 = 2.79614 loss)
I0407 09:40:36.302038 15775 solver.cpp:218] Iteration 8772 (0.828426 iter/s, 14.4853s/12 iters), loss = 0.296499
I0407 09:40:36.302081 15775 solver.cpp:237]     Train net output #0: loss = 0.296499 (* 1 = 0.296499 loss)
I0407 09:40:36.302088 15775 sgd_solver.cpp:105] Iteration 8772, lr = 0.0001
I0407 09:40:40.587915 15775 solver.cpp:218] Iteration 8784 (2.79995 iter/s, 4.28579s/12 iters), loss = 0.278127
I0407 09:40:40.587961 15775 solver.cpp:237]     Train net output #0: loss = 0.278127 (* 1 = 0.278127 loss)
I0407 09:40:40.587970 15775 sgd_solver.cpp:105] Iteration 8784, lr = 0.0001
I0407 09:40:45.737562 15775 solver.cpp:218] Iteration 8796 (2.3303 iter/s, 5.14956s/12 iters), loss = 0.320462
I0407 09:40:45.737607 15775 solver.cpp:237]     Train net output #0: loss = 0.320462 (* 1 = 0.320462 loss)
I0407 09:40:45.737614 15775 sgd_solver.cpp:105] Iteration 8796, lr = 0.0001
I0407 09:40:47.272972 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:51.114642 15775 solver.cpp:218] Iteration 8808 (2.23173 iter/s, 5.37699s/12 iters), loss = 0.0962093
I0407 09:40:51.114683 15775 solver.cpp:237]     Train net output #0: loss = 0.0962093 (* 1 = 0.0962093 loss)
I0407 09:40:51.114691 15775 sgd_solver.cpp:105] Iteration 8808, lr = 0.0001
I0407 09:40:56.526345 15775 solver.cpp:218] Iteration 8820 (2.21745 iter/s, 5.41161s/12 iters), loss = 0.182791
I0407 09:40:56.526387 15775 solver.cpp:237]     Train net output #0: loss = 0.182791 (* 1 = 0.182791 loss)
I0407 09:40:56.526394 15775 sgd_solver.cpp:105] Iteration 8820, lr = 0.0001
I0407 09:41:02.015588 15775 solver.cpp:218] Iteration 8832 (2.18613 iter/s, 5.48915s/12 iters), loss = 0.178365
I0407 09:41:02.015630 15775 solver.cpp:237]     Train net output #0: loss = 0.178365 (* 1 = 0.178365 loss)
I0407 09:41:02.015637 15775 sgd_solver.cpp:105] Iteration 8832, lr = 0.0001
I0407 09:41:07.385399 15775 solver.cpp:218] Iteration 8844 (2.23475 iter/s, 5.36972s/12 iters), loss = 0.170143
I0407 09:41:07.385444 15775 solver.cpp:237]     Train net output #0: loss = 0.170143 (* 1 = 0.170143 loss)
I0407 09:41:07.385452 15775 sgd_solver.cpp:105] Iteration 8844, lr = 0.0001
I0407 09:41:12.412004 15775 solver.cpp:218] Iteration 8856 (2.38734 iter/s, 5.02651s/12 iters), loss = 0.119312
I0407 09:41:12.412048 15775 solver.cpp:237]     Train net output #0: loss = 0.119312 (* 1 = 0.119312 loss)
I0407 09:41:12.412056 15775 sgd_solver.cpp:105] Iteration 8856, lr = 0.0001
I0407 09:41:17.869287 15775 solver.cpp:218] Iteration 8868 (2.19893 iter/s, 5.45719s/12 iters), loss = 0.216871
I0407 09:41:17.869431 15775 solver.cpp:237]     Train net output #0: loss = 0.216871 (* 1 = 0.216871 loss)
I0407 09:41:17.869446 15775 sgd_solver.cpp:105] Iteration 8868, lr = 0.0001
I0407 09:41:19.872058 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 09:41:22.890132 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 09:41:25.216226 15775 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 09:41:25.216248 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:41:26.124527 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:29.581593 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 09:41:29.581624 15775 solver.cpp:397]     Test net output #1: loss = 2.7977 (* 1 = 2.7977 loss)
I0407 09:41:31.367637 15775 solver.cpp:218] Iteration 8880 (0.889013 iter/s, 13.4981s/12 iters), loss = 0.195215
I0407 09:41:31.367681 15775 solver.cpp:237]     Train net output #0: loss = 0.195215 (* 1 = 0.195215 loss)
I0407 09:41:31.367689 15775 sgd_solver.cpp:105] Iteration 8880, lr = 0.0001
I0407 09:41:36.383443 15775 solver.cpp:218] Iteration 8892 (2.39248 iter/s, 5.01571s/12 iters), loss = 0.10408
I0407 09:41:36.383500 15775 solver.cpp:237]     Train net output #0: loss = 0.10408 (* 1 = 0.10408 loss)
I0407 09:41:36.383512 15775 sgd_solver.cpp:105] Iteration 8892, lr = 0.0001
I0407 09:41:40.124331 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:41.707063 15775 solver.cpp:218] Iteration 8904 (2.25415 iter/s, 5.32352s/12 iters), loss = 0.142892
I0407 09:41:41.707121 15775 solver.cpp:237]     Train net output #0: loss = 0.142892 (* 1 = 0.142892 loss)
I0407 09:41:41.707131 15775 sgd_solver.cpp:105] Iteration 8904, lr = 0.0001
I0407 09:41:47.012498 15775 solver.cpp:218] Iteration 8916 (2.26188 iter/s, 5.30533s/12 iters), loss = 0.189756
I0407 09:41:47.012547 15775 solver.cpp:237]     Train net output #0: loss = 0.189756 (* 1 = 0.189756 loss)
I0407 09:41:47.012557 15775 sgd_solver.cpp:105] Iteration 8916, lr = 0.0001
I0407 09:41:52.345100 15775 solver.cpp:218] Iteration 8928 (2.25035 iter/s, 5.33251s/12 iters), loss = 0.106127
I0407 09:41:52.345259 15775 solver.cpp:237]     Train net output #0: loss = 0.106127 (* 1 = 0.106127 loss)
I0407 09:41:52.345270 15775 sgd_solver.cpp:105] Iteration 8928, lr = 0.0001
I0407 09:41:57.675164 15775 solver.cpp:218] Iteration 8940 (2.25147 iter/s, 5.32986s/12 iters), loss = 0.287958
I0407 09:41:57.675211 15775 solver.cpp:237]     Train net output #0: loss = 0.287958 (* 1 = 0.287958 loss)
I0407 09:41:57.675221 15775 sgd_solver.cpp:105] Iteration 8940, lr = 0.0001
I0407 09:42:02.947769 15775 solver.cpp:218] Iteration 8952 (2.27596 iter/s, 5.27251s/12 iters), loss = 0.167046
I0407 09:42:02.947819 15775 solver.cpp:237]     Train net output #0: loss = 0.167046 (* 1 = 0.167046 loss)
I0407 09:42:02.947829 15775 sgd_solver.cpp:105] Iteration 8952, lr = 0.0001
I0407 09:42:08.149744 15775 solver.cpp:218] Iteration 8964 (2.30686 iter/s, 5.20188s/12 iters), loss = 0.229616
I0407 09:42:08.149789 15775 solver.cpp:237]     Train net output #0: loss = 0.229616 (* 1 = 0.229616 loss)
I0407 09:42:08.149797 15775 sgd_solver.cpp:105] Iteration 8964, lr = 0.0001
I0407 09:42:12.584575 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 09:42:16.202208 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 09:42:18.509975 15775 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 09:42:18.509994 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:42:19.385262 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:22.873937 15775 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0407 09:42:22.874017 15775 solver.cpp:397]     Test net output #1: loss = 2.79118 (* 1 = 2.79118 loss)
I0407 09:42:23.003568 15775 solver.cpp:218] Iteration 8976 (0.80788 iter/s, 14.8537s/12 iters), loss = 0.170598
I0407 09:42:23.003618 15775 solver.cpp:237]     Train net output #0: loss = 0.170598 (* 1 = 0.170598 loss)
I0407 09:42:23.003625 15775 sgd_solver.cpp:105] Iteration 8976, lr = 0.0001
I0407 09:42:27.289806 15775 solver.cpp:218] Iteration 8988 (2.79972 iter/s, 4.28615s/12 iters), loss = 0.0769355
I0407 09:42:27.289861 15775 solver.cpp:237]     Train net output #0: loss = 0.0769355 (* 1 = 0.0769355 loss)
I0407 09:42:27.289871 15775 sgd_solver.cpp:105] Iteration 8988, lr = 0.0001
I0407 09:42:30.526003 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:42:32.165915 15775 solver.cpp:218] Iteration 9000 (2.46103 iter/s, 4.87601s/12 iters), loss = 0.137046
I0407 09:42:32.165961 15775 solver.cpp:237]     Train net output #0: loss = 0.137046 (* 1 = 0.137046 loss)
I0407 09:42:32.165969 15775 sgd_solver.cpp:105] Iteration 9000, lr = 0.0001
I0407 09:42:32.889675 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:37.394378 15775 solver.cpp:218] Iteration 9012 (2.29517 iter/s, 5.22837s/12 iters), loss = 0.257716
I0407 09:42:37.394426 15775 solver.cpp:237]     Train net output #0: loss = 0.257716 (* 1 = 0.257716 loss)
I0407 09:42:37.394435 15775 sgd_solver.cpp:105] Iteration 9012, lr = 0.0001
I0407 09:42:42.581104 15775 solver.cpp:218] Iteration 9024 (2.31364 iter/s, 5.18663s/12 iters), loss = 0.267739
I0407 09:42:42.581143 15775 solver.cpp:237]     Train net output #0: loss = 0.267739 (* 1 = 0.267739 loss)
I0407 09:42:42.581151 15775 sgd_solver.cpp:105] Iteration 9024, lr = 0.0001
I0407 09:42:48.024983 15775 solver.cpp:218] Iteration 9036 (2.20435 iter/s, 5.44379s/12 iters), loss = 0.148599
I0407 09:42:48.025032 15775 solver.cpp:237]     Train net output #0: loss = 0.148599 (* 1 = 0.148599 loss)
I0407 09:42:48.025040 15775 sgd_solver.cpp:105] Iteration 9036, lr = 0.0001
I0407 09:42:53.298626 15775 solver.cpp:218] Iteration 9048 (2.27551 iter/s, 5.27355s/12 iters), loss = 0.190266
I0407 09:42:53.298763 15775 solver.cpp:237]     Train net output #0: loss = 0.190266 (* 1 = 0.190266 loss)
I0407 09:42:53.298772 15775 sgd_solver.cpp:105] Iteration 9048, lr = 0.0001
I0407 09:42:58.636189 15775 solver.cpp:218] Iteration 9060 (2.24829 iter/s, 5.33739s/12 iters), loss = 0.119327
I0407 09:42:58.636235 15775 solver.cpp:237]     Train net output #0: loss = 0.119327 (* 1 = 0.119327 loss)
I0407 09:42:58.636245 15775 sgd_solver.cpp:105] Iteration 9060, lr = 0.0001
I0407 09:43:04.041849 15775 solver.cpp:218] Iteration 9072 (2.21993 iter/s, 5.40557s/12 iters), loss = 0.0824048
I0407 09:43:04.041893 15775 solver.cpp:237]     Train net output #0: loss = 0.0824047 (* 1 = 0.0824047 loss)
I0407 09:43:04.041899 15775 sgd_solver.cpp:105] Iteration 9072, lr = 0.0001
I0407 09:43:06.008786 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 09:43:11.514572 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 09:43:13.827811 15775 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 09:43:13.827828 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:43:14.617607 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:18.136560 15775 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0407 09:43:18.136596 15775 solver.cpp:397]     Test net output #1: loss = 2.78248 (* 1 = 2.78248 loss)
I0407 09:43:19.979810 15775 solver.cpp:218] Iteration 9084 (0.752926 iter/s, 15.9378s/12 iters), loss = 0.233106
I0407 09:43:19.979857 15775 solver.cpp:237]     Train net output #0: loss = 0.233106 (* 1 = 0.233106 loss)
I0407 09:43:19.979866 15775 sgd_solver.cpp:105] Iteration 9084, lr = 0.0001
I0407 09:43:25.266616 15775 solver.cpp:218] Iteration 9096 (2.26984 iter/s, 5.28671s/12 iters), loss = 0.198415
I0407 09:43:25.266722 15775 solver.cpp:237]     Train net output #0: loss = 0.198415 (* 1 = 0.198415 loss)
I0407 09:43:25.266731 15775 sgd_solver.cpp:105] Iteration 9096, lr = 0.0001
I0407 09:43:28.361714 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:30.573132 15775 solver.cpp:218] Iteration 9108 (2.26143 iter/s, 5.30637s/12 iters), loss = 0.302202
I0407 09:43:30.573177 15775 solver.cpp:237]     Train net output #0: loss = 0.302202 (* 1 = 0.302202 loss)
I0407 09:43:30.573186 15775 sgd_solver.cpp:105] Iteration 9108, lr = 0.0001
I0407 09:43:35.755198 15775 solver.cpp:218] Iteration 9120 (2.31572 iter/s, 5.18198s/12 iters), loss = 0.230548
I0407 09:43:35.755255 15775 solver.cpp:237]     Train net output #0: loss = 0.230548 (* 1 = 0.230548 loss)
I0407 09:43:35.755265 15775 sgd_solver.cpp:105] Iteration 9120, lr = 0.0001
I0407 09:43:41.111403 15775 solver.cpp:218] Iteration 9132 (2.24044 iter/s, 5.3561s/12 iters), loss = 0.201977
I0407 09:43:41.111449 15775 solver.cpp:237]     Train net output #0: loss = 0.201977 (* 1 = 0.201977 loss)
I0407 09:43:41.111457 15775 sgd_solver.cpp:105] Iteration 9132, lr = 0.0001
I0407 09:43:46.324040 15775 solver.cpp:218] Iteration 9144 (2.30214 iter/s, 5.21255s/12 iters), loss = 0.126563
I0407 09:43:46.324085 15775 solver.cpp:237]     Train net output #0: loss = 0.126563 (* 1 = 0.126563 loss)
I0407 09:43:46.324093 15775 sgd_solver.cpp:105] Iteration 9144, lr = 0.0001
I0407 09:43:51.637163 15775 solver.cpp:218] Iteration 9156 (2.2586 iter/s, 5.31303s/12 iters), loss = 0.171573
I0407 09:43:51.637204 15775 solver.cpp:237]     Train net output #0: loss = 0.171573 (* 1 = 0.171573 loss)
I0407 09:43:51.637212 15775 sgd_solver.cpp:105] Iteration 9156, lr = 0.0001
I0407 09:43:56.839985 15775 solver.cpp:218] Iteration 9168 (2.30648 iter/s, 5.20274s/12 iters), loss = 0.223802
I0407 09:43:56.840114 15775 solver.cpp:237]     Train net output #0: loss = 0.223802 (* 1 = 0.223802 loss)
I0407 09:43:56.840124 15775 sgd_solver.cpp:105] Iteration 9168, lr = 0.0001
I0407 09:44:01.557219 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 09:44:05.973708 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 09:44:09.090745 15775 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 09:44:09.090766 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:44:09.850093 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:13.401523 15775 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 09:44:13.401554 15775 solver.cpp:397]     Test net output #1: loss = 2.78557 (* 1 = 2.78557 loss)
I0407 09:44:13.538446 15775 solver.cpp:218] Iteration 9180 (0.718639 iter/s, 16.6982s/12 iters), loss = 0.126074
I0407 09:44:13.538508 15775 solver.cpp:237]     Train net output #0: loss = 0.126074 (* 1 = 0.126074 loss)
I0407 09:44:13.538517 15775 sgd_solver.cpp:105] Iteration 9180, lr = 0.0001
I0407 09:44:17.874361 15775 solver.cpp:218] Iteration 9192 (2.76765 iter/s, 4.33581s/12 iters), loss = 0.148098
I0407 09:44:17.874404 15775 solver.cpp:237]     Train net output #0: loss = 0.148098 (* 1 = 0.148098 loss)
I0407 09:44:17.874413 15775 sgd_solver.cpp:105] Iteration 9192, lr = 0.0001
I0407 09:44:23.122661 15775 solver.cpp:218] Iteration 9204 (2.28649 iter/s, 5.24821s/12 iters), loss = 0.128494
I0407 09:44:23.122704 15775 solver.cpp:237]     Train net output #0: loss = 0.128494 (* 1 = 0.128494 loss)
I0407 09:44:23.122710 15775 sgd_solver.cpp:105] Iteration 9204, lr = 0.0001
I0407 09:44:23.184644 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:28.634991 15775 solver.cpp:218] Iteration 9216 (2.17697 iter/s, 5.51224s/12 iters), loss = 0.2307
I0407 09:44:28.635118 15775 solver.cpp:237]     Train net output #0: loss = 0.2307 (* 1 = 0.2307 loss)
I0407 09:44:28.635133 15775 sgd_solver.cpp:105] Iteration 9216, lr = 0.0001
I0407 09:44:33.964706 15775 solver.cpp:218] Iteration 9228 (2.2516 iter/s, 5.32955s/12 iters), loss = 0.256333
I0407 09:44:33.964756 15775 solver.cpp:237]     Train net output #0: loss = 0.256333 (* 1 = 0.256333 loss)
I0407 09:44:33.964766 15775 sgd_solver.cpp:105] Iteration 9228, lr = 0.0001
I0407 09:44:39.221791 15775 solver.cpp:218] Iteration 9240 (2.28267 iter/s, 5.257s/12 iters), loss = 0.201656
I0407 09:44:39.221829 15775 solver.cpp:237]     Train net output #0: loss = 0.201656 (* 1 = 0.201656 loss)
I0407 09:44:39.221837 15775 sgd_solver.cpp:105] Iteration 9240, lr = 0.0001
I0407 09:44:44.628408 15775 solver.cpp:218] Iteration 9252 (2.21954 iter/s, 5.40654s/12 iters), loss = 0.157593
I0407 09:44:44.628455 15775 solver.cpp:237]     Train net output #0: loss = 0.157593 (* 1 = 0.157593 loss)
I0407 09:44:44.628464 15775 sgd_solver.cpp:105] Iteration 9252, lr = 0.0001
I0407 09:44:49.931193 15775 solver.cpp:218] Iteration 9264 (2.263 iter/s, 5.30269s/12 iters), loss = 0.181314
I0407 09:44:49.931241 15775 solver.cpp:237]     Train net output #0: loss = 0.181314 (* 1 = 0.181314 loss)
I0407 09:44:49.931250 15775 sgd_solver.cpp:105] Iteration 9264, lr = 0.0001
I0407 09:44:55.035255 15775 solver.cpp:218] Iteration 9276 (2.35111 iter/s, 5.10397s/12 iters), loss = 0.183511
I0407 09:44:55.035301 15775 solver.cpp:237]     Train net output #0: loss = 0.183511 (* 1 = 0.183511 loss)
I0407 09:44:55.035310 15775 sgd_solver.cpp:105] Iteration 9276, lr = 0.0001
I0407 09:44:57.215515 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 09:45:01.690359 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 09:45:04.126464 15775 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 09:45:04.126482 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:45:04.834314 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:08.405364 15775 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0407 09:45:08.405393 15775 solver.cpp:397]     Test net output #1: loss = 2.7946 (* 1 = 2.7946 loss)
I0407 09:45:10.366865 15775 solver.cpp:218] Iteration 9288 (0.782704 iter/s, 15.3315s/12 iters), loss = 0.227677
I0407 09:45:10.366905 15775 solver.cpp:237]     Train net output #0: loss = 0.227677 (* 1 = 0.227677 loss)
I0407 09:45:10.366914 15775 sgd_solver.cpp:105] Iteration 9288, lr = 0.0001
I0407 09:45:15.578668 15775 solver.cpp:218] Iteration 9300 (2.3025 iter/s, 5.21172s/12 iters), loss = 0.0991249
I0407 09:45:15.578714 15775 solver.cpp:237]     Train net output #0: loss = 0.0991249 (* 1 = 0.0991249 loss)
I0407 09:45:15.578722 15775 sgd_solver.cpp:105] Iteration 9300, lr = 0.0001
I0407 09:45:17.954671 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:20.740993 15775 solver.cpp:218] Iteration 9312 (2.32458 iter/s, 5.16223s/12 iters), loss = 0.182747
I0407 09:45:20.741039 15775 solver.cpp:237]     Train net output #0: loss = 0.182747 (* 1 = 0.182747 loss)
I0407 09:45:20.741047 15775 sgd_solver.cpp:105] Iteration 9312, lr = 0.0001
I0407 09:45:26.135376 15775 solver.cpp:218] Iteration 9324 (2.22458 iter/s, 5.39429s/12 iters), loss = 0.331795
I0407 09:45:26.135434 15775 solver.cpp:237]     Train net output #0: loss = 0.331795 (* 1 = 0.331795 loss)
I0407 09:45:26.135444 15775 sgd_solver.cpp:105] Iteration 9324, lr = 0.0001
I0407 09:45:31.352756 15775 solver.cpp:218] Iteration 9336 (2.30005 iter/s, 5.21728s/12 iters), loss = 0.215172
I0407 09:45:31.352799 15775 solver.cpp:237]     Train net output #0: loss = 0.215172 (* 1 = 0.215172 loss)
I0407 09:45:31.352807 15775 sgd_solver.cpp:105] Iteration 9336, lr = 0.0001
I0407 09:45:36.607095 15775 solver.cpp:218] Iteration 9348 (2.28387 iter/s, 5.25425s/12 iters), loss = 0.109224
I0407 09:45:36.607223 15775 solver.cpp:237]     Train net output #0: loss = 0.109224 (* 1 = 0.109224 loss)
I0407 09:45:36.607234 15775 sgd_solver.cpp:105] Iteration 9348, lr = 0.0001
I0407 09:45:41.941922 15775 solver.cpp:218] Iteration 9360 (2.24944 iter/s, 5.33466s/12 iters), loss = 0.195852
I0407 09:45:41.941980 15775 solver.cpp:237]     Train net output #0: loss = 0.195852 (* 1 = 0.195852 loss)
I0407 09:45:41.941992 15775 sgd_solver.cpp:105] Iteration 9360, lr = 0.0001
I0407 09:45:47.231703 15775 solver.cpp:218] Iteration 9372 (2.26857 iter/s, 5.28968s/12 iters), loss = 0.0763284
I0407 09:45:47.231748 15775 solver.cpp:237]     Train net output #0: loss = 0.0763284 (* 1 = 0.0763284 loss)
I0407 09:45:47.231756 15775 sgd_solver.cpp:105] Iteration 9372, lr = 0.0001
I0407 09:45:52.096810 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 09:45:56.706606 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 09:45:59.096077 15775 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 09:45:59.096097 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:45:59.768874 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:03.493252 15775 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0407 09:46:03.493278 15775 solver.cpp:397]     Test net output #1: loss = 2.78917 (* 1 = 2.78917 loss)
I0407 09:46:03.623515 15775 solver.cpp:218] Iteration 9384 (0.732079 iter/s, 16.3917s/12 iters), loss = 0.17757
I0407 09:46:03.623558 15775 solver.cpp:237]     Train net output #0: loss = 0.17757 (* 1 = 0.17757 loss)
I0407 09:46:03.623566 15775 sgd_solver.cpp:105] Iteration 9384, lr = 0.0001
I0407 09:46:07.717604 15775 solver.cpp:218] Iteration 9396 (2.93111 iter/s, 4.09401s/12 iters), loss = 0.118849
I0407 09:46:07.717764 15775 solver.cpp:237]     Train net output #0: loss = 0.118849 (* 1 = 0.118849 loss)
I0407 09:46:07.717773 15775 sgd_solver.cpp:105] Iteration 9396, lr = 0.0001
I0407 09:46:12.311385 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:13.044143 15775 solver.cpp:218] Iteration 9408 (2.25296 iter/s, 5.32634s/12 iters), loss = 0.239455
I0407 09:46:13.044193 15775 solver.cpp:237]     Train net output #0: loss = 0.239455 (* 1 = 0.239455 loss)
I0407 09:46:13.044199 15775 sgd_solver.cpp:105] Iteration 9408, lr = 0.0001
I0407 09:46:18.141952 15775 solver.cpp:218] Iteration 9420 (2.354 iter/s, 5.09772s/12 iters), loss = 0.148894
I0407 09:46:18.141999 15775 solver.cpp:237]     Train net output #0: loss = 0.148894 (* 1 = 0.148894 loss)
I0407 09:46:18.142006 15775 sgd_solver.cpp:105] Iteration 9420, lr = 0.0001
I0407 09:46:23.439549 15775 solver.cpp:218] Iteration 9432 (2.26522 iter/s, 5.29751s/12 iters), loss = 0.136226
I0407 09:46:23.439600 15775 solver.cpp:237]     Train net output #0: loss = 0.136226 (* 1 = 0.136226 loss)
I0407 09:46:23.439610 15775 sgd_solver.cpp:105] Iteration 9432, lr = 0.0001
I0407 09:46:28.559461 15775 solver.cpp:218] Iteration 9444 (2.34383 iter/s, 5.11982s/12 iters), loss = 0.141263
I0407 09:46:28.559500 15775 solver.cpp:237]     Train net output #0: loss = 0.141263 (* 1 = 0.141263 loss)
I0407 09:46:28.559507 15775 sgd_solver.cpp:105] Iteration 9444, lr = 0.0001
I0407 09:46:33.813613 15775 solver.cpp:218] Iteration 9456 (2.28394 iter/s, 5.25407s/12 iters), loss = 0.155403
I0407 09:46:33.813654 15775 solver.cpp:237]     Train net output #0: loss = 0.155403 (* 1 = 0.155403 loss)
I0407 09:46:33.813663 15775 sgd_solver.cpp:105] Iteration 9456, lr = 0.0001
I0407 09:46:38.866866 15775 solver.cpp:218] Iteration 9468 (2.37475 iter/s, 5.05316s/12 iters), loss = 0.202033
I0407 09:46:38.866972 15775 solver.cpp:237]     Train net output #0: loss = 0.202033 (* 1 = 0.202033 loss)
I0407 09:46:38.866981 15775 sgd_solver.cpp:105] Iteration 9468, lr = 0.0001
I0407 09:46:44.039175 15775 solver.cpp:218] Iteration 9480 (2.32012 iter/s, 5.17215s/12 iters), loss = 0.128295
I0407 09:46:44.039232 15775 solver.cpp:237]     Train net output #0: loss = 0.128295 (* 1 = 0.128295 loss)
I0407 09:46:44.039243 15775 sgd_solver.cpp:105] Iteration 9480, lr = 0.0001
I0407 09:46:46.137526 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 09:46:50.502921 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 09:46:53.479776 15775 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 09:46:53.479795 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:46:54.105449 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:57.925103 15775 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0407 09:46:57.925132 15775 solver.cpp:397]     Test net output #1: loss = 2.80709 (* 1 = 2.80709 loss)
I0407 09:46:59.767215 15775 solver.cpp:218] Iteration 9492 (0.762976 iter/s, 15.7279s/12 iters), loss = 0.170891
I0407 09:46:59.767262 15775 solver.cpp:237]     Train net output #0: loss = 0.170891 (* 1 = 0.170891 loss)
I0407 09:46:59.767271 15775 sgd_solver.cpp:105] Iteration 9492, lr = 0.0001
I0407 09:47:05.078788 15775 solver.cpp:218] Iteration 9504 (2.25926 iter/s, 5.31147s/12 iters), loss = 0.176479
I0407 09:47:05.078845 15775 solver.cpp:237]     Train net output #0: loss = 0.176479 (* 1 = 0.176479 loss)
I0407 09:47:05.078856 15775 sgd_solver.cpp:105] Iteration 9504, lr = 0.0001
I0407 09:47:06.494701 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:10.326475 15775 solver.cpp:218] Iteration 9516 (2.28676 iter/s, 5.24759s/12 iters), loss = 0.0869653
I0407 09:47:10.326597 15775 solver.cpp:237]     Train net output #0: loss = 0.0869653 (* 1 = 0.0869653 loss)
I0407 09:47:10.326606 15775 sgd_solver.cpp:105] Iteration 9516, lr = 0.0001
I0407 09:47:15.548610 15775 solver.cpp:218] Iteration 9528 (2.29798 iter/s, 5.22197s/12 iters), loss = 0.168655
I0407 09:47:15.548658 15775 solver.cpp:237]     Train net output #0: loss = 0.168655 (* 1 = 0.168655 loss)
I0407 09:47:15.548667 15775 sgd_solver.cpp:105] Iteration 9528, lr = 0.0001
I0407 09:47:20.625870 15775 solver.cpp:218] Iteration 9540 (2.36352 iter/s, 5.07717s/12 iters), loss = 0.14013
I0407 09:47:20.625916 15775 solver.cpp:237]     Train net output #0: loss = 0.14013 (* 1 = 0.14013 loss)
I0407 09:47:20.625924 15775 sgd_solver.cpp:105] Iteration 9540, lr = 0.0001
I0407 09:47:26.004985 15775 solver.cpp:218] Iteration 9552 (2.23089 iter/s, 5.37903s/12 iters), loss = 0.196021
I0407 09:47:26.005041 15775 solver.cpp:237]     Train net output #0: loss = 0.196021 (* 1 = 0.196021 loss)
I0407 09:47:26.005053 15775 sgd_solver.cpp:105] Iteration 9552, lr = 0.0001
I0407 09:47:31.296083 15775 solver.cpp:218] Iteration 9564 (2.268 iter/s, 5.291s/12 iters), loss = 0.177279
I0407 09:47:31.296129 15775 solver.cpp:237]     Train net output #0: loss = 0.177279 (* 1 = 0.177279 loss)
I0407 09:47:31.296137 15775 sgd_solver.cpp:105] Iteration 9564, lr = 0.0001
I0407 09:47:36.548911 15775 solver.cpp:218] Iteration 9576 (2.28453 iter/s, 5.25273s/12 iters), loss = 0.164927
I0407 09:47:36.548957 15775 solver.cpp:237]     Train net output #0: loss = 0.164927 (* 1 = 0.164927 loss)
I0407 09:47:36.548966 15775 sgd_solver.cpp:105] Iteration 9576, lr = 0.0001
I0407 09:47:41.392846 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 09:47:45.961107 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 09:47:50.326215 15775 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 09:47:50.326234 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:47:50.986127 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:54.670445 15775 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0407 09:47:54.670480 15775 solver.cpp:397]     Test net output #1: loss = 2.80319 (* 1 = 2.80319 loss)
I0407 09:47:54.808917 15775 solver.cpp:218] Iteration 9588 (0.65718 iter/s, 18.2598s/12 iters), loss = 0.28031
I0407 09:47:54.810477 15775 solver.cpp:237]     Train net output #0: loss = 0.28031 (* 1 = 0.28031 loss)
I0407 09:47:54.810492 15775 sgd_solver.cpp:105] Iteration 9588, lr = 0.0001
I0407 09:47:59.178256 15775 solver.cpp:218] Iteration 9600 (2.74741 iter/s, 4.36775s/12 iters), loss = 0.151852
I0407 09:47:59.178303 15775 solver.cpp:237]     Train net output #0: loss = 0.151852 (* 1 = 0.151852 loss)
I0407 09:47:59.178311 15775 sgd_solver.cpp:105] Iteration 9600, lr = 0.0001
I0407 09:48:02.962478 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:04.420794 15775 solver.cpp:218] Iteration 9612 (2.28901 iter/s, 5.24245s/12 iters), loss = 0.12765
I0407 09:48:04.420835 15775 solver.cpp:237]     Train net output #0: loss = 0.12765 (* 1 = 0.12765 loss)
I0407 09:48:04.420842 15775 sgd_solver.cpp:105] Iteration 9612, lr = 0.0001
I0407 09:48:09.849131 15775 solver.cpp:218] Iteration 9624 (2.21066 iter/s, 5.42824s/12 iters), loss = 0.135463
I0407 09:48:09.849176 15775 solver.cpp:237]     Train net output #0: loss = 0.135463 (* 1 = 0.135463 loss)
I0407 09:48:09.849184 15775 sgd_solver.cpp:105] Iteration 9624, lr = 0.0001
I0407 09:48:15.019057 15775 solver.cpp:218] Iteration 9636 (2.32116 iter/s, 5.16984s/12 iters), loss = 0.15202
I0407 09:48:15.019201 15775 solver.cpp:237]     Train net output #0: loss = 0.15202 (* 1 = 0.15202 loss)
I0407 09:48:15.019212 15775 sgd_solver.cpp:105] Iteration 9636, lr = 0.0001
I0407 09:48:20.202482 15775 solver.cpp:218] Iteration 9648 (2.31515 iter/s, 5.18324s/12 iters), loss = 0.141664
I0407 09:48:20.202531 15775 solver.cpp:237]     Train net output #0: loss = 0.141664 (* 1 = 0.141664 loss)
I0407 09:48:20.202538 15775 sgd_solver.cpp:105] Iteration 9648, lr = 0.0001
I0407 09:48:25.683687 15775 solver.cpp:218] Iteration 9660 (2.18934 iter/s, 5.48111s/12 iters), loss = 0.122585
I0407 09:48:25.683733 15775 solver.cpp:237]     Train net output #0: loss = 0.122585 (* 1 = 0.122585 loss)
I0407 09:48:25.683743 15775 sgd_solver.cpp:105] Iteration 9660, lr = 0.0001
I0407 09:48:30.831939 15775 solver.cpp:218] Iteration 9672 (2.33093 iter/s, 5.14816s/12 iters), loss = 0.194214
I0407 09:48:30.831991 15775 solver.cpp:237]     Train net output #0: loss = 0.194214 (* 1 = 0.194214 loss)
I0407 09:48:30.832001 15775 sgd_solver.cpp:105] Iteration 9672, lr = 0.0001
I0407 09:48:36.125952 15775 solver.cpp:218] Iteration 9684 (2.26675 iter/s, 5.29392s/12 iters), loss = 0.154156
I0407 09:48:36.125996 15775 solver.cpp:237]     Train net output #0: loss = 0.154156 (* 1 = 0.154156 loss)
I0407 09:48:36.126003 15775 sgd_solver.cpp:105] Iteration 9684, lr = 0.0001
I0407 09:48:38.315619 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 09:48:43.488997 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 09:48:47.372617 15775 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 09:48:47.372692 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:48:47.914767 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:50.659564 15775 blocking_queue.cpp:49] Waiting for data
I0407 09:48:51.632952 15775 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0407 09:48:51.632984 15775 solver.cpp:397]     Test net output #1: loss = 2.79821 (* 1 = 2.79821 loss)
I0407 09:48:53.435176 15775 solver.cpp:218] Iteration 9696 (0.693278 iter/s, 17.3091s/12 iters), loss = 0.213284
I0407 09:48:53.435228 15775 solver.cpp:237]     Train net output #0: loss = 0.213284 (* 1 = 0.213284 loss)
I0407 09:48:53.435237 15775 sgd_solver.cpp:105] Iteration 9696, lr = 0.0001
I0407 09:48:58.604811 15775 solver.cpp:218] Iteration 9708 (2.32129 iter/s, 5.16954s/12 iters), loss = 0.164927
I0407 09:48:58.604848 15775 solver.cpp:237]     Train net output #0: loss = 0.164927 (* 1 = 0.164927 loss)
I0407 09:48:58.604854 15775 sgd_solver.cpp:105] Iteration 9708, lr = 0.0001
I0407 09:48:59.351450 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:03.794610 15775 solver.cpp:218] Iteration 9720 (2.31227 iter/s, 5.18971s/12 iters), loss = 0.18559
I0407 09:49:03.794661 15775 solver.cpp:237]     Train net output #0: loss = 0.18559 (* 1 = 0.18559 loss)
I0407 09:49:03.794672 15775 sgd_solver.cpp:105] Iteration 9720, lr = 0.0001
I0407 09:49:09.124004 15775 solver.cpp:218] Iteration 9732 (2.2517 iter/s, 5.3293s/12 iters), loss = 0.124581
I0407 09:49:09.124051 15775 solver.cpp:237]     Train net output #0: loss = 0.124581 (* 1 = 0.124581 loss)
I0407 09:49:09.124058 15775 sgd_solver.cpp:105] Iteration 9732, lr = 0.0001
I0407 09:49:14.429980 15775 solver.cpp:218] Iteration 9744 (2.26164 iter/s, 5.30589s/12 iters), loss = 0.182487
I0407 09:49:14.430027 15775 solver.cpp:237]     Train net output #0: loss = 0.182487 (* 1 = 0.182487 loss)
I0407 09:49:14.430033 15775 sgd_solver.cpp:105] Iteration 9744, lr = 0.0001
I0407 09:49:19.782027 15775 solver.cpp:218] Iteration 9756 (2.24217 iter/s, 5.35195s/12 iters), loss = 0.102209
I0407 09:49:19.782157 15775 solver.cpp:237]     Train net output #0: loss = 0.102209 (* 1 = 0.102209 loss)
I0407 09:49:19.782169 15775 sgd_solver.cpp:105] Iteration 9756, lr = 0.0001
I0407 09:49:25.162781 15775 solver.cpp:218] Iteration 9768 (2.23024 iter/s, 5.38059s/12 iters), loss = 0.131875
I0407 09:49:25.162827 15775 solver.cpp:237]     Train net output #0: loss = 0.131875 (* 1 = 0.131875 loss)
I0407 09:49:25.162835 15775 sgd_solver.cpp:105] Iteration 9768, lr = 0.0001
I0407 09:49:30.081877 15775 solver.cpp:218] Iteration 9780 (2.43952 iter/s, 4.91901s/12 iters), loss = 0.144339
I0407 09:49:30.081916 15775 solver.cpp:237]     Train net output #0: loss = 0.144339 (* 1 = 0.144339 loss)
I0407 09:49:30.081924 15775 sgd_solver.cpp:105] Iteration 9780, lr = 0.0001
I0407 09:49:34.834777 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 09:49:39.550606 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 09:49:43.509400 15775 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 09:49:43.509420 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:49:44.033205 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:47.782961 15775 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0407 09:49:47.782996 15775 solver.cpp:397]     Test net output #1: loss = 2.80099 (* 1 = 2.80099 loss)
I0407 09:49:47.913172 15775 solver.cpp:218] Iteration 9792 (0.67298 iter/s, 17.8311s/12 iters), loss = 0.284332
I0407 09:49:47.913224 15775 solver.cpp:237]     Train net output #0: loss = 0.284332 (* 1 = 0.284332 loss)
I0407 09:49:47.913233 15775 sgd_solver.cpp:105] Iteration 9792, lr = 0.0001
I0407 09:49:52.284960 15775 solver.cpp:218] Iteration 9804 (2.74493 iter/s, 4.3717s/12 iters), loss = 0.184522
I0407 09:49:52.285100 15775 solver.cpp:237]     Train net output #0: loss = 0.184522 (* 1 = 0.184522 loss)
I0407 09:49:52.285109 15775 sgd_solver.cpp:105] Iteration 9804, lr = 0.0001
I0407 09:49:55.484755 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:57.713879 15775 solver.cpp:218] Iteration 9816 (2.21046 iter/s, 5.42874s/12 iters), loss = 0.106026
I0407 09:49:57.713925 15775 solver.cpp:237]     Train net output #0: loss = 0.106026 (* 1 = 0.106026 loss)
I0407 09:49:57.713933 15775 sgd_solver.cpp:105] Iteration 9816, lr = 0.0001
I0407 09:50:02.938012 15775 solver.cpp:218] Iteration 9828 (2.29707 iter/s, 5.22404s/12 iters), loss = 0.213115
I0407 09:50:02.938060 15775 solver.cpp:237]     Train net output #0: loss = 0.213115 (* 1 = 0.213115 loss)
I0407 09:50:02.938068 15775 sgd_solver.cpp:105] Iteration 9828, lr = 0.0001
I0407 09:50:08.325975 15775 solver.cpp:218] Iteration 9840 (2.22723 iter/s, 5.38787s/12 iters), loss = 0.246496
I0407 09:50:08.326033 15775 solver.cpp:237]     Train net output #0: loss = 0.246497 (* 1 = 0.246497 loss)
I0407 09:50:08.326045 15775 sgd_solver.cpp:105] Iteration 9840, lr = 0.0001
I0407 09:50:13.658087 15775 solver.cpp:218] Iteration 9852 (2.25056 iter/s, 5.33201s/12 iters), loss = 0.205428
I0407 09:50:13.658145 15775 solver.cpp:237]     Train net output #0: loss = 0.205428 (* 1 = 0.205428 loss)
I0407 09:50:13.658155 15775 sgd_solver.cpp:105] Iteration 9852, lr = 0.0001
I0407 09:50:18.940282 15775 solver.cpp:218] Iteration 9864 (2.27183 iter/s, 5.2821s/12 iters), loss = 0.21726
I0407 09:50:18.940327 15775 solver.cpp:237]     Train net output #0: loss = 0.21726 (* 1 = 0.21726 loss)
I0407 09:50:18.940335 15775 sgd_solver.cpp:105] Iteration 9864, lr = 0.0001
I0407 09:50:24.215428 15775 solver.cpp:218] Iteration 9876 (2.27486 iter/s, 5.27506s/12 iters), loss = 0.249407
I0407 09:50:24.215517 15775 solver.cpp:237]     Train net output #0: loss = 0.249407 (* 1 = 0.249407 loss)
I0407 09:50:24.215526 15775 sgd_solver.cpp:105] Iteration 9876, lr = 0.0001
I0407 09:50:29.449712 15775 solver.cpp:218] Iteration 9888 (2.29263 iter/s, 5.23415s/12 iters), loss = 0.193869
I0407 09:50:29.449757 15775 solver.cpp:237]     Train net output #0: loss = 0.193869 (* 1 = 0.193869 loss)
I0407 09:50:29.449764 15775 sgd_solver.cpp:105] Iteration 9888, lr = 0.0001
I0407 09:50:31.627467 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 09:50:36.024458 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 09:50:38.845136 15775 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 09:50:38.845153 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:50:39.348192 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:43.203734 15775 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0407 09:50:43.203769 15775 solver.cpp:397]     Test net output #1: loss = 2.79862 (* 1 = 2.79862 loss)
I0407 09:50:45.118007 15775 solver.cpp:218] Iteration 9900 (0.765885 iter/s, 15.6681s/12 iters), loss = 0.202655
I0407 09:50:45.118067 15775 solver.cpp:237]     Train net output #0: loss = 0.202655 (* 1 = 0.202655 loss)
I0407 09:50:45.118078 15775 sgd_solver.cpp:105] Iteration 9900, lr = 0.0001
I0407 09:50:50.334579 15775 solver.cpp:218] Iteration 9912 (2.30041 iter/s, 5.21647s/12 iters), loss = 0.308391
I0407 09:50:50.334619 15775 solver.cpp:237]     Train net output #0: loss = 0.308391 (* 1 = 0.308391 loss)
I0407 09:50:50.334625 15775 sgd_solver.cpp:105] Iteration 9912, lr = 0.0001
I0407 09:50:50.430362 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:55.653986 15775 solver.cpp:218] Iteration 9924 (2.25593 iter/s, 5.31932s/12 iters), loss = 0.197434
I0407 09:50:55.654148 15775 solver.cpp:237]     Train net output #0: loss = 0.197434 (* 1 = 0.197434 loss)
I0407 09:50:55.654157 15775 sgd_solver.cpp:105] Iteration 9924, lr = 0.0001
I0407 09:51:00.980057 15775 solver.cpp:218] Iteration 9936 (2.25316 iter/s, 5.32586s/12 iters), loss = 0.157159
I0407 09:51:00.980110 15775 solver.cpp:237]     Train net output #0: loss = 0.15716 (* 1 = 0.15716 loss)
I0407 09:51:00.980121 15775 sgd_solver.cpp:105] Iteration 9936, lr = 0.0001
I0407 09:51:06.244501 15775 solver.cpp:218] Iteration 9948 (2.27949 iter/s, 5.26435s/12 iters), loss = 0.203385
I0407 09:51:06.244542 15775 solver.cpp:237]     Train net output #0: loss = 0.203385 (* 1 = 0.203385 loss)
I0407 09:51:06.244550 15775 sgd_solver.cpp:105] Iteration 9948, lr = 0.0001
I0407 09:51:11.576861 15775 solver.cpp:218] Iteration 9960 (2.25045 iter/s, 5.33227s/12 iters), loss = 0.143722
I0407 09:51:11.576927 15775 solver.cpp:237]     Train net output #0: loss = 0.143722 (* 1 = 0.143722 loss)
I0407 09:51:11.576942 15775 sgd_solver.cpp:105] Iteration 9960, lr = 0.0001
I0407 09:51:16.474040 15775 solver.cpp:218] Iteration 9972 (2.45044 iter/s, 4.89707s/12 iters), loss = 0.246464
I0407 09:51:16.474095 15775 solver.cpp:237]     Train net output #0: loss = 0.246464 (* 1 = 0.246464 loss)
I0407 09:51:16.474105 15775 sgd_solver.cpp:105] Iteration 9972, lr = 0.0001
I0407 09:51:21.599442 15775 solver.cpp:218] Iteration 9984 (2.34133 iter/s, 5.1253s/12 iters), loss = 0.151494
I0407 09:51:21.599503 15775 solver.cpp:237]     Train net output #0: loss = 0.151494 (* 1 = 0.151494 loss)
I0407 09:51:21.599514 15775 sgd_solver.cpp:105] Iteration 9984, lr = 0.0001
I0407 09:51:26.454092 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 09:51:30.959329 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 09:51:33.332249 15775 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 09:51:33.332275 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:51:33.758469 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:37.745553 15775 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0407 09:51:37.745586 15775 solver.cpp:397]     Test net output #1: loss = 2.80023 (* 1 = 2.80023 loss)
I0407 09:51:37.886790 15775 solver.cpp:218] Iteration 9996 (0.736775 iter/s, 16.2872s/12 iters), loss = 0.146576
I0407 09:51:37.886854 15775 solver.cpp:237]     Train net output #0: loss = 0.146576 (* 1 = 0.146576 loss)
I0407 09:51:37.886863 15775 sgd_solver.cpp:105] Iteration 9996, lr = 0.0001
I0407 09:51:42.201056 15775 solver.cpp:218] Iteration 10008 (2.78154 iter/s, 4.31416s/12 iters), loss = 0.150558
I0407 09:51:42.201109 15775 solver.cpp:237]     Train net output #0: loss = 0.150558 (* 1 = 0.150558 loss)
I0407 09:51:42.201120 15775 sgd_solver.cpp:105] Iteration 10008, lr = 0.0001
I0407 09:51:44.437494 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:47.426571 15775 solver.cpp:218] Iteration 10020 (2.29647 iter/s, 5.22542s/12 iters), loss = 0.143988
I0407 09:51:47.426615 15775 solver.cpp:237]     Train net output #0: loss = 0.143988 (* 1 = 0.143988 loss)
I0407 09:51:47.426623 15775 sgd_solver.cpp:105] Iteration 10020, lr = 0.0001
I0407 09:51:52.690600 15775 solver.cpp:218] Iteration 10032 (2.27966 iter/s, 5.26394s/12 iters), loss = 0.20282
I0407 09:51:52.690655 15775 solver.cpp:237]     Train net output #0: loss = 0.20282 (* 1 = 0.20282 loss)
I0407 09:51:52.690666 15775 sgd_solver.cpp:105] Iteration 10032, lr = 0.0001
I0407 09:51:57.800948 15775 solver.cpp:218] Iteration 10044 (2.34822 iter/s, 5.11026s/12 iters), loss = 0.240436
I0407 09:51:57.801079 15775 solver.cpp:237]     Train net output #0: loss = 0.240436 (* 1 = 0.240436 loss)
I0407 09:51:57.801088 15775 sgd_solver.cpp:105] Iteration 10044, lr = 0.0001
I0407 09:52:02.963201 15775 solver.cpp:218] Iteration 10056 (2.32465 iter/s, 5.16208s/12 iters), loss = 0.212926
I0407 09:52:02.963248 15775 solver.cpp:237]     Train net output #0: loss = 0.212926 (* 1 = 0.212926 loss)
I0407 09:52:02.963256 15775 sgd_solver.cpp:105] Iteration 10056, lr = 0.0001
I0407 09:52:08.271837 15775 solver.cpp:218] Iteration 10068 (2.26051 iter/s, 5.30855s/12 iters), loss = 0.155927
I0407 09:52:08.271883 15775 solver.cpp:237]     Train net output #0: loss = 0.155927 (* 1 = 0.155927 loss)
I0407 09:52:08.271890 15775 sgd_solver.cpp:105] Iteration 10068, lr = 0.0001
I0407 09:52:13.636561 15775 solver.cpp:218] Iteration 10080 (2.23687 iter/s, 5.36464s/12 iters), loss = 0.257291
I0407 09:52:13.636601 15775 solver.cpp:237]     Train net output #0: loss = 0.257291 (* 1 = 0.257291 loss)
I0407 09:52:13.636610 15775 sgd_solver.cpp:105] Iteration 10080, lr = 0.0001
I0407 09:52:18.869587 15775 solver.cpp:218] Iteration 10092 (2.29317 iter/s, 5.23294s/12 iters), loss = 0.140405
I0407 09:52:18.869642 15775 solver.cpp:237]     Train net output #0: loss = 0.140405 (* 1 = 0.140405 loss)
I0407 09:52:18.869653 15775 sgd_solver.cpp:105] Iteration 10092, lr = 0.0001
I0407 09:52:21.125428 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 09:52:25.895536 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 09:52:28.249903 15775 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 09:52:28.249963 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:52:28.695365 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:32.608776 15775 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0407 09:52:32.608812 15775 solver.cpp:397]     Test net output #1: loss = 2.79313 (* 1 = 2.79313 loss)
I0407 09:52:34.497972 15775 solver.cpp:218] Iteration 10104 (0.767841 iter/s, 15.6282s/12 iters), loss = 0.110996
I0407 09:52:34.498014 15775 solver.cpp:237]     Train net output #0: loss = 0.110996 (* 1 = 0.110996 loss)
I0407 09:52:34.498023 15775 sgd_solver.cpp:105] Iteration 10104, lr = 1e-05
I0407 09:52:39.116322 15806 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:39.838129 15775 solver.cpp:218] Iteration 10116 (2.24716 iter/s, 5.34007s/12 iters), loss = 0.140319
I0407 09:52:39.838176 15775 solver.cpp:237]     Train net output #0: loss = 0.140319 (* 1 = 0.140319 loss)
I0407 09:52:39.838183 15775 sgd_solver.cpp:105] Iteration 10116, lr = 1e-05
I0407 09:52:45.071777 15775 solver.cpp:218] Iteration 10128 (2.2929 iter/s, 5.23355s/12 iters), loss = 0.219552
I0407 09:52:45.071835 15775 solver.cpp:237]     Train net output #0: loss = 0.219552 (* 1 = 0.219552 loss)
I0407 09:52:45.071846 15775 sgd_solver.cpp:105] Iteration 10128, lr = 1e-05
I0407 09:52:50.329653 15775 solver.cpp:218] Iteration 10140 (2.28233 iter/s, 5.25777s/12 iters), loss = 0.156479
I0407 09:52:50.329694 15775 solver.cpp:237]     Train net output #0: loss = 0.156479 (* 1 = 0.156479 loss)
I0407 09:52:50.329702 15775 sgd_solver.cpp:105] Iteration 10140, lr = 1e-05
I0407 09:52:55.608155 15775 solver.cpp:218] Iteration 10152 (2.27341 iter/s, 5.27841s/12 iters), loss = 0.142809
I0407 09:52:55.608196 15775 solver.cpp:237]     Train net output #0: loss = 0.142809 (* 1 = 0.142809 loss)
I0407 09:52:55.608204 15775 sgd_solver.cpp:105] Iteration 10152, lr = 1e-05
I0407 09:53:00.843679 15775 solver.cpp:218] Iteration 10164 (2.29207 iter/s, 5.23543s/12 iters), loss = 0.178734
I0407 09:53:00.843825 15775 solver.cpp:237]     Train net output #0: loss = 0.178734 (* 1 = 0.178734 loss)
I0407 09:53:00.843837 15775 sgd_solver.cpp:105] Iteration 10164, lr = 1e-05
I0407 09:53:06.041625 15775 solver.cpp:218] Iteration 10176 (2.30869 iter/s, 5.19776s/12 iters), loss = 0.193976
I0407 09:53:06.041671 15775 solver.cpp:237]     Train net output #0: loss = 0.193976 (* 1 = 0.193976 loss)
I0407 09:53:06.041680 15775 sgd_solver.cpp:105] Iteration 10176, lr = 1e-05
I0407 09:53:11.194289 15775 solver.cpp:218] Iteration 10188 (2.32893 iter/s, 5.15258s/12 iters), loss = 0.188027
I0407 09:53:11.194345 15775 solver.cpp:237]     Train net output #0: loss = 0.188027 (* 1 = 0.188027 loss)
I0407 09:53:11.194355 15775 sgd_solver.cpp:105] Iteration 10188, lr = 1e-05
I0407 09:53:15.945991 15775 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 09:53:19.074204 15775 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 09:53:21.453879 15775 solver.cpp:310] Iteration 10200, loss = 0.185753
I0407 09:53:21.453908 15775 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 09:53:21.453912 15775 net.cpp:676] Ignoring source layer train-data
I0407 09:53:21.833256 15837 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:25.804838 15775 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 09:53:25.804872 15775 solver.cpp:397]     Test net output #1: loss = 2.78563 (* 1 = 2.78563 loss)
I0407 09:53:25.804878 15775 solver.cpp:315] Optimization Done.
I0407 09:53:25.804893 15775 caffe.cpp:259] Optimization Done.