DIGITS-CNN/cars/architecture-investigations/fc/3-layers/256/caffe_output.log

I0410 13:29:53.663193 18534 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210410-132952-4f7e/solver.prototxt
I0410 13:29:53.663447 18534 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 13:29:53.663457 18534 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 13:29:53.663565 18534 caffe.cpp:218] Using GPUs 2
I0410 13:29:53.690752 18534 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0410 13:29:53.985544 18534 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 13:29:53.989303 18534 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 13:29:53.989905 18534 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 13:29:53.989920 18534 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 13:29:53.990103 18534 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
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: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:29:53.990195 18534 layer_factory.hpp:77] Creating layer train-data
I0410 13:29:53.992305 18534 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 13:29:53.992511 18534 net.cpp:84] Creating Layer train-data
I0410 13:29:53.992522 18534 net.cpp:380] train-data -> data
I0410 13:29:53.992542 18534 net.cpp:380] train-data -> label
I0410 13:29:53.992552 18534 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:29:53.997439 18534 data_layer.cpp:45] output data size: 128,3,227,227
I0410 13:29:54.124639 18534 net.cpp:122] Setting up train-data
I0410 13:29:54.124661 18534 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 13:29:54.124666 18534 net.cpp:129] Top shape: 128 (128)
I0410 13:29:54.124671 18534 net.cpp:137] Memory required for data: 79149056
I0410 13:29:54.124681 18534 layer_factory.hpp:77] Creating layer conv1
I0410 13:29:54.124701 18534 net.cpp:84] Creating Layer conv1
I0410 13:29:54.124707 18534 net.cpp:406] conv1 <- data
I0410 13:29:54.124719 18534 net.cpp:380] conv1 -> conv1
I0410 13:29:54.706710 18534 net.cpp:122] Setting up conv1
I0410 13:29:54.706732 18534 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:54.706737 18534 net.cpp:137] Memory required for data: 227833856
I0410 13:29:54.706758 18534 layer_factory.hpp:77] Creating layer relu1
I0410 13:29:54.706787 18534 net.cpp:84] Creating Layer relu1
I0410 13:29:54.706791 18534 net.cpp:406] relu1 <- conv1
I0410 13:29:54.706797 18534 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:29:54.707087 18534 net.cpp:122] Setting up relu1
I0410 13:29:54.707094 18534 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:54.707098 18534 net.cpp:137] Memory required for data: 376518656
I0410 13:29:54.707103 18534 layer_factory.hpp:77] Creating layer norm1
I0410 13:29:54.707111 18534 net.cpp:84] Creating Layer norm1
I0410 13:29:54.707114 18534 net.cpp:406] norm1 <- conv1
I0410 13:29:54.707119 18534 net.cpp:380] norm1 -> norm1
I0410 13:29:54.707556 18534 net.cpp:122] Setting up norm1
I0410 13:29:54.707566 18534 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:54.707569 18534 net.cpp:137] Memory required for data: 525203456
I0410 13:29:54.707573 18534 layer_factory.hpp:77] Creating layer pool1
I0410 13:29:54.707581 18534 net.cpp:84] Creating Layer pool1
I0410 13:29:54.707584 18534 net.cpp:406] pool1 <- norm1
I0410 13:29:54.707589 18534 net.cpp:380] pool1 -> pool1
I0410 13:29:54.707625 18534 net.cpp:122] Setting up pool1
I0410 13:29:54.707633 18534 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 13:29:54.707635 18534 net.cpp:137] Memory required for data: 561035264
I0410 13:29:54.707639 18534 layer_factory.hpp:77] Creating layer conv2
I0410 13:29:54.707649 18534 net.cpp:84] Creating Layer conv2
I0410 13:29:54.707653 18534 net.cpp:406] conv2 <- pool1
I0410 13:29:54.707657 18534 net.cpp:380] conv2 -> conv2
I0410 13:29:54.716485 18534 net.cpp:122] Setting up conv2
I0410 13:29:54.716496 18534 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:54.716500 18534 net.cpp:137] Memory required for data: 656586752
I0410 13:29:54.716508 18534 layer_factory.hpp:77] Creating layer relu2
I0410 13:29:54.716516 18534 net.cpp:84] Creating Layer relu2
I0410 13:29:54.716519 18534 net.cpp:406] relu2 <- conv2
I0410 13:29:54.716526 18534 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:29:54.717005 18534 net.cpp:122] Setting up relu2
I0410 13:29:54.717015 18534 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:54.717020 18534 net.cpp:137] Memory required for data: 752138240
I0410 13:29:54.717022 18534 layer_factory.hpp:77] Creating layer norm2
I0410 13:29:54.717031 18534 net.cpp:84] Creating Layer norm2
I0410 13:29:54.717034 18534 net.cpp:406] norm2 <- conv2
I0410 13:29:54.717041 18534 net.cpp:380] norm2 -> norm2
I0410 13:29:54.717384 18534 net.cpp:122] Setting up norm2
I0410 13:29:54.717396 18534 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:54.717401 18534 net.cpp:137] Memory required for data: 847689728
I0410 13:29:54.717406 18534 layer_factory.hpp:77] Creating layer pool2
I0410 13:29:54.717414 18534 net.cpp:84] Creating Layer pool2
I0410 13:29:54.717419 18534 net.cpp:406] pool2 <- norm2
I0410 13:29:54.717428 18534 net.cpp:380] pool2 -> pool2
I0410 13:29:54.717469 18534 net.cpp:122] Setting up pool2
I0410 13:29:54.717476 18534 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:54.717481 18534 net.cpp:137] Memory required for data: 869840896
I0410 13:29:54.717485 18534 layer_factory.hpp:77] Creating layer conv3
I0410 13:29:54.717501 18534 net.cpp:84] Creating Layer conv3
I0410 13:29:54.717506 18534 net.cpp:406] conv3 <- pool2
I0410 13:29:54.717514 18534 net.cpp:380] conv3 -> conv3
I0410 13:29:54.732287 18534 net.cpp:122] Setting up conv3
I0410 13:29:54.732301 18534 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:54.732306 18534 net.cpp:137] Memory required for data: 903067648
I0410 13:29:54.732316 18534 layer_factory.hpp:77] Creating layer relu3
I0410 13:29:54.732326 18534 net.cpp:84] Creating Layer relu3
I0410 13:29:54.732329 18534 net.cpp:406] relu3 <- conv3
I0410 13:29:54.732334 18534 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:29:54.732810 18534 net.cpp:122] Setting up relu3
I0410 13:29:54.732820 18534 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:54.732822 18534 net.cpp:137] Memory required for data: 936294400
I0410 13:29:54.732826 18534 layer_factory.hpp:77] Creating layer conv4
I0410 13:29:54.732853 18534 net.cpp:84] Creating Layer conv4
I0410 13:29:54.732858 18534 net.cpp:406] conv4 <- conv3
I0410 13:29:54.732864 18534 net.cpp:380] conv4 -> conv4
I0410 13:29:54.743155 18534 net.cpp:122] Setting up conv4
I0410 13:29:54.743170 18534 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:54.743175 18534 net.cpp:137] Memory required for data: 969521152
I0410 13:29:54.743182 18534 layer_factory.hpp:77] Creating layer relu4
I0410 13:29:54.743191 18534 net.cpp:84] Creating Layer relu4
I0410 13:29:54.743194 18534 net.cpp:406] relu4 <- conv4
I0410 13:29:54.743199 18534 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:29:54.743535 18534 net.cpp:122] Setting up relu4
I0410 13:29:54.743543 18534 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:54.743546 18534 net.cpp:137] Memory required for data: 1002747904
I0410 13:29:54.743549 18534 layer_factory.hpp:77] Creating layer conv5
I0410 13:29:54.743561 18534 net.cpp:84] Creating Layer conv5
I0410 13:29:54.743563 18534 net.cpp:406] conv5 <- conv4
I0410 13:29:54.743571 18534 net.cpp:380] conv5 -> conv5
I0410 13:29:54.752957 18534 net.cpp:122] Setting up conv5
I0410 13:29:54.752971 18534 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:54.752975 18534 net.cpp:137] Memory required for data: 1024899072
I0410 13:29:54.752987 18534 layer_factory.hpp:77] Creating layer relu5
I0410 13:29:54.752995 18534 net.cpp:84] Creating Layer relu5
I0410 13:29:54.752998 18534 net.cpp:406] relu5 <- conv5
I0410 13:29:54.753006 18534 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:29:54.753476 18534 net.cpp:122] Setting up relu5
I0410 13:29:54.753485 18534 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:54.753489 18534 net.cpp:137] Memory required for data: 1047050240
I0410 13:29:54.753492 18534 layer_factory.hpp:77] Creating layer pool5
I0410 13:29:54.753500 18534 net.cpp:84] Creating Layer pool5
I0410 13:29:54.753504 18534 net.cpp:406] pool5 <- conv5
I0410 13:29:54.753509 18534 net.cpp:380] pool5 -> pool5
I0410 13:29:54.753547 18534 net.cpp:122] Setting up pool5
I0410 13:29:54.753553 18534 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 13:29:54.753556 18534 net.cpp:137] Memory required for data: 1051768832
I0410 13:29:54.753559 18534 layer_factory.hpp:77] Creating layer fc6
I0410 13:29:54.753568 18534 net.cpp:84] Creating Layer fc6
I0410 13:29:54.753571 18534 net.cpp:406] fc6 <- pool5
I0410 13:29:54.753577 18534 net.cpp:380] fc6 -> fc6
I0410 13:29:54.776508 18534 net.cpp:122] Setting up fc6
I0410 13:29:54.776526 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.776530 18534 net.cpp:137] Memory required for data: 1051899904
I0410 13:29:54.776540 18534 layer_factory.hpp:77] Creating layer relu6
I0410 13:29:54.776547 18534 net.cpp:84] Creating Layer relu6
I0410 13:29:54.776551 18534 net.cpp:406] relu6 <- fc6
I0410 13:29:54.776559 18534 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:29:54.777164 18534 net.cpp:122] Setting up relu6
I0410 13:29:54.777173 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.777176 18534 net.cpp:137] Memory required for data: 1052030976
I0410 13:29:54.777180 18534 layer_factory.hpp:77] Creating layer drop6
I0410 13:29:54.777189 18534 net.cpp:84] Creating Layer drop6
I0410 13:29:54.777192 18534 net.cpp:406] drop6 <- fc6
I0410 13:29:54.777199 18534 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:29:54.777225 18534 net.cpp:122] Setting up drop6
I0410 13:29:54.777230 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.777233 18534 net.cpp:137] Memory required for data: 1052162048
I0410 13:29:54.777236 18534 layer_factory.hpp:77] Creating layer fc7
I0410 13:29:54.777245 18534 net.cpp:84] Creating Layer fc7
I0410 13:29:54.777247 18534 net.cpp:406] fc7 <- fc6
I0410 13:29:54.777253 18534 net.cpp:380] fc7 -> fc7
I0410 13:29:54.777886 18534 net.cpp:122] Setting up fc7
I0410 13:29:54.777892 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.777895 18534 net.cpp:137] Memory required for data: 1052293120
I0410 13:29:54.777900 18534 layer_factory.hpp:77] Creating layer relu7
I0410 13:29:54.777923 18534 net.cpp:84] Creating Layer relu7
I0410 13:29:54.777927 18534 net.cpp:406] relu7 <- fc7
I0410 13:29:54.777932 18534 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:29:54.778425 18534 net.cpp:122] Setting up relu7
I0410 13:29:54.778434 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.778437 18534 net.cpp:137] Memory required for data: 1052424192
I0410 13:29:54.778441 18534 layer_factory.hpp:77] Creating layer drop7
I0410 13:29:54.778448 18534 net.cpp:84] Creating Layer drop7
I0410 13:29:54.778451 18534 net.cpp:406] drop7 <- fc7
I0410 13:29:54.778456 18534 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:29:54.778481 18534 net.cpp:122] Setting up drop7
I0410 13:29:54.778486 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.778487 18534 net.cpp:137] Memory required for data: 1052555264
I0410 13:29:54.778491 18534 layer_factory.hpp:77] Creating layer fc7.5
I0410 13:29:54.778496 18534 net.cpp:84] Creating Layer fc7.5
I0410 13:29:54.778499 18534 net.cpp:406] fc7.5 <- fc7
I0410 13:29:54.778506 18534 net.cpp:380] fc7.5 -> fc7.5
I0410 13:29:54.779145 18534 net.cpp:122] Setting up fc7.5
I0410 13:29:54.779151 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.779155 18534 net.cpp:137] Memory required for data: 1052686336
I0410 13:29:54.779160 18534 layer_factory.hpp:77] Creating layer relu7.5
I0410 13:29:54.779165 18534 net.cpp:84] Creating Layer relu7.5
I0410 13:29:54.779170 18534 net.cpp:406] relu7.5 <- fc7.5
I0410 13:29:54.779173 18534 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 13:29:54.779654 18534 net.cpp:122] Setting up relu7.5
I0410 13:29:54.779662 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.779665 18534 net.cpp:137] Memory required for data: 1052817408
I0410 13:29:54.779668 18534 layer_factory.hpp:77] Creating layer drop7.5
I0410 13:29:54.779675 18534 net.cpp:84] Creating Layer drop7.5
I0410 13:29:54.779678 18534 net.cpp:406] drop7.5 <- fc7.5
I0410 13:29:54.779685 18534 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 13:29:54.779707 18534 net.cpp:122] Setting up drop7.5
I0410 13:29:54.779711 18534 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:54.779714 18534 net.cpp:137] Memory required for data: 1052948480
I0410 13:29:54.779718 18534 layer_factory.hpp:77] Creating layer fc8
I0410 13:29:54.779723 18534 net.cpp:84] Creating Layer fc8
I0410 13:29:54.779727 18534 net.cpp:406] fc8 <- fc7.5
I0410 13:29:54.779732 18534 net.cpp:380] fc8 -> fc8
I0410 13:29:54.780239 18534 net.cpp:122] Setting up fc8
I0410 13:29:54.780246 18534 net.cpp:129] Top shape: 128 196 (25088)
I0410 13:29:54.780248 18534 net.cpp:137] Memory required for data: 1053048832
I0410 13:29:54.780258 18534 layer_factory.hpp:77] Creating layer loss
I0410 13:29:54.780264 18534 net.cpp:84] Creating Layer loss
I0410 13:29:54.780268 18534 net.cpp:406] loss <- fc8
I0410 13:29:54.780272 18534 net.cpp:406] loss <- label
I0410 13:29:54.780277 18534 net.cpp:380] loss -> loss
I0410 13:29:54.780290 18534 layer_factory.hpp:77] Creating layer loss
I0410 13:29:54.780858 18534 net.cpp:122] Setting up loss
I0410 13:29:54.780866 18534 net.cpp:129] Top shape: (1)
I0410 13:29:54.780869 18534 net.cpp:132]     with loss weight 1
I0410 13:29:54.780885 18534 net.cpp:137] Memory required for data: 1053048836
I0410 13:29:54.780889 18534 net.cpp:198] loss needs backward computation.
I0410 13:29:54.780896 18534 net.cpp:198] fc8 needs backward computation.
I0410 13:29:54.780900 18534 net.cpp:198] drop7.5 needs backward computation.
I0410 13:29:54.780902 18534 net.cpp:198] relu7.5 needs backward computation.
I0410 13:29:54.780905 18534 net.cpp:198] fc7.5 needs backward computation.
I0410 13:29:54.780910 18534 net.cpp:198] drop7 needs backward computation.
I0410 13:29:54.780912 18534 net.cpp:198] relu7 needs backward computation.
I0410 13:29:54.780915 18534 net.cpp:198] fc7 needs backward computation.
I0410 13:29:54.780918 18534 net.cpp:198] drop6 needs backward computation.
I0410 13:29:54.780921 18534 net.cpp:198] relu6 needs backward computation.
I0410 13:29:54.780925 18534 net.cpp:198] fc6 needs backward computation.
I0410 13:29:54.780938 18534 net.cpp:198] pool5 needs backward computation.
I0410 13:29:54.780941 18534 net.cpp:198] relu5 needs backward computation.
I0410 13:29:54.780944 18534 net.cpp:198] conv5 needs backward computation.
I0410 13:29:54.780948 18534 net.cpp:198] relu4 needs backward computation.
I0410 13:29:54.780953 18534 net.cpp:198] conv4 needs backward computation.
I0410 13:29:54.780956 18534 net.cpp:198] relu3 needs backward computation.
I0410 13:29:54.780961 18534 net.cpp:198] conv3 needs backward computation.
I0410 13:29:54.780964 18534 net.cpp:198] pool2 needs backward computation.
I0410 13:29:54.780967 18534 net.cpp:198] norm2 needs backward computation.
I0410 13:29:54.780972 18534 net.cpp:198] relu2 needs backward computation.
I0410 13:29:54.780974 18534 net.cpp:198] conv2 needs backward computation.
I0410 13:29:54.780977 18534 net.cpp:198] pool1 needs backward computation.
I0410 13:29:54.780982 18534 net.cpp:198] norm1 needs backward computation.
I0410 13:29:54.780984 18534 net.cpp:198] relu1 needs backward computation.
I0410 13:29:54.780988 18534 net.cpp:198] conv1 needs backward computation.
I0410 13:29:54.780992 18534 net.cpp:200] train-data does not need backward computation.
I0410 13:29:54.780995 18534 net.cpp:242] This network produces output loss
I0410 13:29:54.781011 18534 net.cpp:255] Network initialization done.
I0410 13:29:54.781498 18534 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 13:29:54.781529 18534 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 13:29:54.781684 18534 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
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: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:29:54.781777 18534 layer_factory.hpp:77] Creating layer val-data
I0410 13:29:54.783335 18534 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 13:29:54.783538 18534 net.cpp:84] Creating Layer val-data
I0410 13:29:54.783548 18534 net.cpp:380] val-data -> data
I0410 13:29:54.783556 18534 net.cpp:380] val-data -> label
I0410 13:29:54.783562 18534 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:29:54.787456 18534 data_layer.cpp:45] output data size: 32,3,227,227
I0410 13:29:54.819896 18534 net.cpp:122] Setting up val-data
I0410 13:29:54.819916 18534 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 13:29:54.819921 18534 net.cpp:129] Top shape: 32 (32)
I0410 13:29:54.819924 18534 net.cpp:137] Memory required for data: 19787264
I0410 13:29:54.819948 18534 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 13:29:54.819960 18534 net.cpp:84] Creating Layer label_val-data_1_split
I0410 13:29:54.819964 18534 net.cpp:406] label_val-data_1_split <- label
I0410 13:29:54.819972 18534 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 13:29:54.819979 18534 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 13:29:54.820088 18534 net.cpp:122] Setting up label_val-data_1_split
I0410 13:29:54.820094 18534 net.cpp:129] Top shape: 32 (32)
I0410 13:29:54.820097 18534 net.cpp:129] Top shape: 32 (32)
I0410 13:29:54.820101 18534 net.cpp:137] Memory required for data: 19787520
I0410 13:29:54.820104 18534 layer_factory.hpp:77] Creating layer conv1
I0410 13:29:54.820116 18534 net.cpp:84] Creating Layer conv1
I0410 13:29:54.820118 18534 net.cpp:406] conv1 <- data
I0410 13:29:54.820124 18534 net.cpp:380] conv1 -> conv1
I0410 13:29:54.822129 18534 net.cpp:122] Setting up conv1
I0410 13:29:54.822139 18534 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:54.822142 18534 net.cpp:137] Memory required for data: 56958720
I0410 13:29:54.822152 18534 layer_factory.hpp:77] Creating layer relu1
I0410 13:29:54.822158 18534 net.cpp:84] Creating Layer relu1
I0410 13:29:54.822162 18534 net.cpp:406] relu1 <- conv1
I0410 13:29:54.822167 18534 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:29:54.822643 18534 net.cpp:122] Setting up relu1
I0410 13:29:54.822654 18534 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:54.822656 18534 net.cpp:137] Memory required for data: 94129920
I0410 13:29:54.822660 18534 layer_factory.hpp:77] Creating layer norm1
I0410 13:29:54.822669 18534 net.cpp:84] Creating Layer norm1
I0410 13:29:54.822672 18534 net.cpp:406] norm1 <- conv1
I0410 13:29:54.822677 18534 net.cpp:380] norm1 -> norm1
I0410 13:29:54.824062 18534 net.cpp:122] Setting up norm1
I0410 13:29:54.824071 18534 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:54.824075 18534 net.cpp:137] Memory required for data: 131301120
I0410 13:29:54.824079 18534 layer_factory.hpp:77] Creating layer pool1
I0410 13:29:54.824086 18534 net.cpp:84] Creating Layer pool1
I0410 13:29:54.824090 18534 net.cpp:406] pool1 <- norm1
I0410 13:29:54.824095 18534 net.cpp:380] pool1 -> pool1
I0410 13:29:54.824124 18534 net.cpp:122] Setting up pool1
I0410 13:29:54.824129 18534 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 13:29:54.824132 18534 net.cpp:137] Memory required for data: 140259072
I0410 13:29:54.824136 18534 layer_factory.hpp:77] Creating layer conv2
I0410 13:29:54.824144 18534 net.cpp:84] Creating Layer conv2
I0410 13:29:54.824147 18534 net.cpp:406] conv2 <- pool1
I0410 13:29:54.824152 18534 net.cpp:380] conv2 -> conv2
I0410 13:29:54.833151 18534 net.cpp:122] Setting up conv2
I0410 13:29:54.833166 18534 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:54.833169 18534 net.cpp:137] Memory required for data: 164146944
I0410 13:29:54.833179 18534 layer_factory.hpp:77] Creating layer relu2
I0410 13:29:54.833187 18534 net.cpp:84] Creating Layer relu2
I0410 13:29:54.833190 18534 net.cpp:406] relu2 <- conv2
I0410 13:29:54.833197 18534 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:29:54.833691 18534 net.cpp:122] Setting up relu2
I0410 13:29:54.833700 18534 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:54.833703 18534 net.cpp:137] Memory required for data: 188034816
I0410 13:29:54.833707 18534 layer_factory.hpp:77] Creating layer norm2
I0410 13:29:54.833717 18534 net.cpp:84] Creating Layer norm2
I0410 13:29:54.833720 18534 net.cpp:406] norm2 <- conv2
I0410 13:29:54.833727 18534 net.cpp:380] norm2 -> norm2
I0410 13:29:54.834112 18534 net.cpp:122] Setting up norm2
I0410 13:29:54.834121 18534 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:54.834125 18534 net.cpp:137] Memory required for data: 211922688
I0410 13:29:54.834128 18534 layer_factory.hpp:77] Creating layer pool2
I0410 13:29:54.834136 18534 net.cpp:84] Creating Layer pool2
I0410 13:29:54.834139 18534 net.cpp:406] pool2 <- norm2
I0410 13:29:54.834164 18534 net.cpp:380] pool2 -> pool2
I0410 13:29:54.834195 18534 net.cpp:122] Setting up pool2
I0410 13:29:54.834201 18534 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:54.834204 18534 net.cpp:137] Memory required for data: 217460480
I0410 13:29:54.834208 18534 layer_factory.hpp:77] Creating layer conv3
I0410 13:29:54.834216 18534 net.cpp:84] Creating Layer conv3
I0410 13:29:54.834220 18534 net.cpp:406] conv3 <- pool2
I0410 13:29:54.834226 18534 net.cpp:380] conv3 -> conv3
I0410 13:29:54.845543 18534 net.cpp:122] Setting up conv3
I0410 13:29:54.845563 18534 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:54.845566 18534 net.cpp:137] Memory required for data: 225767168
I0410 13:29:54.845579 18534 layer_factory.hpp:77] Creating layer relu3
I0410 13:29:54.845589 18534 net.cpp:84] Creating Layer relu3
I0410 13:29:54.845593 18534 net.cpp:406] relu3 <- conv3
I0410 13:29:54.845599 18534 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:29:54.845949 18534 net.cpp:122] Setting up relu3
I0410 13:29:54.845980 18534 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:54.845984 18534 net.cpp:137] Memory required for data: 234073856
I0410 13:29:54.845988 18534 layer_factory.hpp:77] Creating layer conv4
I0410 13:29:54.846004 18534 net.cpp:84] Creating Layer conv4
I0410 13:29:54.846007 18534 net.cpp:406] conv4 <- conv3
I0410 13:29:54.846014 18534 net.cpp:380] conv4 -> conv4
I0410 13:29:54.856132 18534 net.cpp:122] Setting up conv4
I0410 13:29:54.856148 18534 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:54.856151 18534 net.cpp:137] Memory required for data: 242380544
I0410 13:29:54.856159 18534 layer_factory.hpp:77] Creating layer relu4
I0410 13:29:54.856168 18534 net.cpp:84] Creating Layer relu4
I0410 13:29:54.856173 18534 net.cpp:406] relu4 <- conv4
I0410 13:29:54.856178 18534 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:29:54.856786 18534 net.cpp:122] Setting up relu4
I0410 13:29:54.856796 18534 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:54.856798 18534 net.cpp:137] Memory required for data: 250687232
I0410 13:29:54.856802 18534 layer_factory.hpp:77] Creating layer conv5
I0410 13:29:54.856813 18534 net.cpp:84] Creating Layer conv5
I0410 13:29:54.856817 18534 net.cpp:406] conv5 <- conv4
I0410 13:29:54.856823 18534 net.cpp:380] conv5 -> conv5
I0410 13:29:54.865550 18534 net.cpp:122] Setting up conv5
I0410 13:29:54.865566 18534 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:54.865569 18534 net.cpp:137] Memory required for data: 256225024
I0410 13:29:54.865581 18534 layer_factory.hpp:77] Creating layer relu5
I0410 13:29:54.865589 18534 net.cpp:84] Creating Layer relu5
I0410 13:29:54.865593 18534 net.cpp:406] relu5 <- conv5
I0410 13:29:54.865602 18534 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:29:54.866328 18534 net.cpp:122] Setting up relu5
I0410 13:29:54.866338 18534 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:54.866343 18534 net.cpp:137] Memory required for data: 261762816
I0410 13:29:54.866345 18534 layer_factory.hpp:77] Creating layer pool5
I0410 13:29:54.866356 18534 net.cpp:84] Creating Layer pool5
I0410 13:29:54.866359 18534 net.cpp:406] pool5 <- conv5
I0410 13:29:54.866365 18534 net.cpp:380] pool5 -> pool5
I0410 13:29:54.866405 18534 net.cpp:122] Setting up pool5
I0410 13:29:54.866411 18534 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 13:29:54.866415 18534 net.cpp:137] Memory required for data: 262942464
I0410 13:29:54.866417 18534 layer_factory.hpp:77] Creating layer fc6
I0410 13:29:54.866425 18534 net.cpp:84] Creating Layer fc6
I0410 13:29:54.866427 18534 net.cpp:406] fc6 <- pool5
I0410 13:29:54.866433 18534 net.cpp:380] fc6 -> fc6
I0410 13:29:54.892019 18534 net.cpp:122] Setting up fc6
I0410 13:29:54.892038 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.892042 18534 net.cpp:137] Memory required for data: 262975232
I0410 13:29:54.892051 18534 layer_factory.hpp:77] Creating layer relu6
I0410 13:29:54.892060 18534 net.cpp:84] Creating Layer relu6
I0410 13:29:54.892064 18534 net.cpp:406] relu6 <- fc6
I0410 13:29:54.892089 18534 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:29:54.892510 18534 net.cpp:122] Setting up relu6
I0410 13:29:54.892518 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.892521 18534 net.cpp:137] Memory required for data: 263008000
I0410 13:29:54.892525 18534 layer_factory.hpp:77] Creating layer drop6
I0410 13:29:54.892531 18534 net.cpp:84] Creating Layer drop6
I0410 13:29:54.892535 18534 net.cpp:406] drop6 <- fc6
I0410 13:29:54.892541 18534 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:29:54.892566 18534 net.cpp:122] Setting up drop6
I0410 13:29:54.892571 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.892575 18534 net.cpp:137] Memory required for data: 263040768
I0410 13:29:54.892577 18534 layer_factory.hpp:77] Creating layer fc7
I0410 13:29:54.892583 18534 net.cpp:84] Creating Layer fc7
I0410 13:29:54.892586 18534 net.cpp:406] fc7 <- fc6
I0410 13:29:54.892592 18534 net.cpp:380] fc7 -> fc7
I0410 13:29:54.893234 18534 net.cpp:122] Setting up fc7
I0410 13:29:54.893240 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.893244 18534 net.cpp:137] Memory required for data: 263073536
I0410 13:29:54.893249 18534 layer_factory.hpp:77] Creating layer relu7
I0410 13:29:54.893255 18534 net.cpp:84] Creating Layer relu7
I0410 13:29:54.893260 18534 net.cpp:406] relu7 <- fc7
I0410 13:29:54.893263 18534 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:29:54.893846 18534 net.cpp:122] Setting up relu7
I0410 13:29:54.893853 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.893857 18534 net.cpp:137] Memory required for data: 263106304
I0410 13:29:54.893860 18534 layer_factory.hpp:77] Creating layer drop7
I0410 13:29:54.893867 18534 net.cpp:84] Creating Layer drop7
I0410 13:29:54.893870 18534 net.cpp:406] drop7 <- fc7
I0410 13:29:54.893877 18534 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:29:54.893899 18534 net.cpp:122] Setting up drop7
I0410 13:29:54.893904 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.893908 18534 net.cpp:137] Memory required for data: 263139072
I0410 13:29:54.893910 18534 layer_factory.hpp:77] Creating layer fc7.5
I0410 13:29:54.893918 18534 net.cpp:84] Creating Layer fc7.5
I0410 13:29:54.893920 18534 net.cpp:406] fc7.5 <- fc7
I0410 13:29:54.893926 18534 net.cpp:380] fc7.5 -> fc7.5
I0410 13:29:54.894587 18534 net.cpp:122] Setting up fc7.5
I0410 13:29:54.894594 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.894598 18534 net.cpp:137] Memory required for data: 263171840
I0410 13:29:54.894603 18534 layer_factory.hpp:77] Creating layer relu7.5
I0410 13:29:54.894609 18534 net.cpp:84] Creating Layer relu7.5
I0410 13:29:54.894613 18534 net.cpp:406] relu7.5 <- fc7.5
I0410 13:29:54.894618 18534 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 13:29:54.895108 18534 net.cpp:122] Setting up relu7.5
I0410 13:29:54.895117 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.895119 18534 net.cpp:137] Memory required for data: 263204608
I0410 13:29:54.895123 18534 layer_factory.hpp:77] Creating layer drop7.5
I0410 13:29:54.895128 18534 net.cpp:84] Creating Layer drop7.5
I0410 13:29:54.895133 18534 net.cpp:406] drop7.5 <- fc7.5
I0410 13:29:54.895138 18534 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 13:29:54.895161 18534 net.cpp:122] Setting up drop7.5
I0410 13:29:54.895166 18534 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:54.895169 18534 net.cpp:137] Memory required for data: 263237376
I0410 13:29:54.895172 18534 layer_factory.hpp:77] Creating layer fc8
I0410 13:29:54.895179 18534 net.cpp:84] Creating Layer fc8
I0410 13:29:54.895184 18534 net.cpp:406] fc8 <- fc7.5
I0410 13:29:54.895190 18534 net.cpp:380] fc8 -> fc8
I0410 13:29:54.895700 18534 net.cpp:122] Setting up fc8
I0410 13:29:54.895707 18534 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:54.895710 18534 net.cpp:137] Memory required for data: 263262464
I0410 13:29:54.895720 18534 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 13:29:54.895725 18534 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 13:29:54.895730 18534 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 13:29:54.895735 18534 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 13:29:54.895750 18534 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 13:29:54.895783 18534 net.cpp:122] Setting up fc8_fc8_0_split
I0410 13:29:54.895788 18534 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:54.895792 18534 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:54.895794 18534 net.cpp:137] Memory required for data: 263312640
I0410 13:29:54.895798 18534 layer_factory.hpp:77] Creating layer accuracy
I0410 13:29:54.895805 18534 net.cpp:84] Creating Layer accuracy
I0410 13:29:54.895809 18534 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 13:29:54.895814 18534 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 13:29:54.895820 18534 net.cpp:380] accuracy -> accuracy
I0410 13:29:54.895828 18534 net.cpp:122] Setting up accuracy
I0410 13:29:54.895833 18534 net.cpp:129] Top shape: (1)
I0410 13:29:54.895834 18534 net.cpp:137] Memory required for data: 263312644
I0410 13:29:54.895838 18534 layer_factory.hpp:77] Creating layer loss
I0410 13:29:54.895843 18534 net.cpp:84] Creating Layer loss
I0410 13:29:54.895846 18534 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 13:29:54.895850 18534 net.cpp:406] loss <- label_val-data_1_split_1
I0410 13:29:54.895856 18534 net.cpp:380] loss -> loss
I0410 13:29:54.895864 18534 layer_factory.hpp:77] Creating layer loss
I0410 13:29:54.897456 18534 net.cpp:122] Setting up loss
I0410 13:29:54.897465 18534 net.cpp:129] Top shape: (1)
I0410 13:29:54.897469 18534 net.cpp:132]     with loss weight 1
I0410 13:29:54.897478 18534 net.cpp:137] Memory required for data: 263312648
I0410 13:29:54.897482 18534 net.cpp:198] loss needs backward computation.
I0410 13:29:54.897487 18534 net.cpp:200] accuracy does not need backward computation.
I0410 13:29:54.897491 18534 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 13:29:54.897495 18534 net.cpp:198] fc8 needs backward computation.
I0410 13:29:54.897498 18534 net.cpp:198] drop7.5 needs backward computation.
I0410 13:29:54.897501 18534 net.cpp:198] relu7.5 needs backward computation.
I0410 13:29:54.897505 18534 net.cpp:198] fc7.5 needs backward computation.
I0410 13:29:54.897508 18534 net.cpp:198] drop7 needs backward computation.
I0410 13:29:54.897511 18534 net.cpp:198] relu7 needs backward computation.
I0410 13:29:54.897514 18534 net.cpp:198] fc7 needs backward computation.
I0410 13:29:54.897517 18534 net.cpp:198] drop6 needs backward computation.
I0410 13:29:54.897521 18534 net.cpp:198] relu6 needs backward computation.
I0410 13:29:54.897523 18534 net.cpp:198] fc6 needs backward computation.
I0410 13:29:54.897527 18534 net.cpp:198] pool5 needs backward computation.
I0410 13:29:54.897531 18534 net.cpp:198] relu5 needs backward computation.
I0410 13:29:54.897534 18534 net.cpp:198] conv5 needs backward computation.
I0410 13:29:54.897538 18534 net.cpp:198] relu4 needs backward computation.
I0410 13:29:54.897542 18534 net.cpp:198] conv4 needs backward computation.
I0410 13:29:54.897545 18534 net.cpp:198] relu3 needs backward computation.
I0410 13:29:54.897549 18534 net.cpp:198] conv3 needs backward computation.
I0410 13:29:54.897553 18534 net.cpp:198] pool2 needs backward computation.
I0410 13:29:54.897557 18534 net.cpp:198] norm2 needs backward computation.
I0410 13:29:54.897560 18534 net.cpp:198] relu2 needs backward computation.
I0410 13:29:54.897563 18534 net.cpp:198] conv2 needs backward computation.
I0410 13:29:54.897567 18534 net.cpp:198] pool1 needs backward computation.
I0410 13:29:54.897572 18534 net.cpp:198] norm1 needs backward computation.
I0410 13:29:54.897575 18534 net.cpp:198] relu1 needs backward computation.
I0410 13:29:54.897580 18534 net.cpp:198] conv1 needs backward computation.
I0410 13:29:54.897584 18534 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 13:29:54.897588 18534 net.cpp:200] val-data does not need backward computation.
I0410 13:29:54.897591 18534 net.cpp:242] This network produces output accuracy
I0410 13:29:54.897595 18534 net.cpp:242] This network produces output loss
I0410 13:29:54.897612 18534 net.cpp:255] Network initialization done.
I0410 13:29:54.897693 18534 solver.cpp:56] Solver scaffolding done.
I0410 13:29:54.898193 18534 caffe.cpp:248] Starting Optimization
I0410 13:29:54.898202 18534 solver.cpp:272] Solving
I0410 13:29:54.898206 18534 solver.cpp:273] Learning Rate Policy: exp
I0410 13:29:54.898998 18534 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 13:29:54.899008 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:29:54.901469 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:29:59.473608 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:29:59.517617 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:29:59.517664 18534 solver.cpp:397]     Test net output #1: loss = 5.27848 (* 1 = 5.27848 loss)
I0410 13:29:59.608510 18534 solver.cpp:218] Iteration 0 (0 iter/s, 4.7101s/12 iters), loss = 5.28161
I0410 13:29:59.610020 18534 solver.cpp:237]     Train net output #0: loss = 5.28161 (* 1 = 5.28161 loss)
I0410 13:29:59.610054 18534 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 13:30:03.371474 18534 solver.cpp:218] Iteration 12 (3.19039 iter/s, 3.7613s/12 iters), loss = 5.27475
I0410 13:30:03.371522 18534 solver.cpp:237]     Train net output #0: loss = 5.27475 (* 1 = 5.27475 loss)
I0410 13:30:03.371534 18534 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 13:30:08.161870 18534 solver.cpp:218] Iteration 24 (2.50513 iter/s, 4.79016s/12 iters), loss = 5.27852
I0410 13:30:08.161918 18534 solver.cpp:237]     Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
I0410 13:30:08.161931 18534 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 13:30:12.911378 18534 solver.cpp:218] Iteration 36 (2.5267 iter/s, 4.74928s/12 iters), loss = 5.27605
I0410 13:30:12.911429 18534 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0410 13:30:12.911442 18534 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 13:30:17.739624 18534 solver.cpp:218] Iteration 48 (2.4855 iter/s, 4.828s/12 iters), loss = 5.28252
I0410 13:30:17.739675 18534 solver.cpp:237]     Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
I0410 13:30:17.739687 18534 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 13:30:22.616101 18534 solver.cpp:218] Iteration 60 (2.46091 iter/s, 4.87624s/12 iters), loss = 5.27403
I0410 13:30:22.616142 18534 solver.cpp:237]     Train net output #0: loss = 5.27403 (* 1 = 5.27403 loss)
I0410 13:30:22.616151 18534 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 13:30:27.412252 18534 solver.cpp:218] Iteration 72 (2.50213 iter/s, 4.79592s/12 iters), loss = 5.27676
I0410 13:30:27.412359 18534 solver.cpp:237]     Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
I0410 13:30:27.412372 18534 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 13:30:32.304652 18534 solver.cpp:218] Iteration 84 (2.45293 iter/s, 4.8921s/12 iters), loss = 5.28257
I0410 13:30:32.304710 18534 solver.cpp:237]     Train net output #0: loss = 5.28257 (* 1 = 5.28257 loss)
I0410 13:30:32.304723 18534 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 13:30:37.105619 18534 solver.cpp:218] Iteration 96 (2.49963 iter/s, 4.80071s/12 iters), loss = 5.28458
I0410 13:30:37.105676 18534 solver.cpp:237]     Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
I0410 13:30:37.105687 18534 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 13:30:38.739358 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:39.043084 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 13:30:39.369868 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 13:30:39.611589 18534 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 13:30:39.611620 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:30:43.968668 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:44.051288 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:30:44.051337 18534 solver.cpp:397]     Test net output #1: loss = 5.27909 (* 1 = 5.27909 loss)
I0410 13:30:45.924911 18534 solver.cpp:218] Iteration 108 (1.36071 iter/s, 8.8189s/12 iters), loss = 5.2773
I0410 13:30:45.924962 18534 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0410 13:30:45.924973 18534 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 13:30:50.852160 18534 solver.cpp:218] Iteration 120 (2.43556 iter/s, 4.927s/12 iters), loss = 5.27674
I0410 13:30:50.852216 18534 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0410 13:30:50.852227 18534 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 13:30:55.723656 18534 solver.cpp:218] Iteration 132 (2.46344 iter/s, 4.87124s/12 iters), loss = 5.26321
I0410 13:30:55.723709 18534 solver.cpp:237]     Train net output #0: loss = 5.26321 (* 1 = 5.26321 loss)
I0410 13:30:55.723722 18534 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 13:31:00.533746 18534 solver.cpp:218] Iteration 144 (2.49488 iter/s, 4.80984s/12 iters), loss = 5.28312
I0410 13:31:00.533900 18534 solver.cpp:237]     Train net output #0: loss = 5.28312 (* 1 = 5.28312 loss)
I0410 13:31:00.533913 18534 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 13:31:05.323438 18534 solver.cpp:218] Iteration 156 (2.50556 iter/s, 4.78935s/12 iters), loss = 5.26666
I0410 13:31:05.323482 18534 solver.cpp:237]     Train net output #0: loss = 5.26666 (* 1 = 5.26666 loss)
I0410 13:31:05.323491 18534 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 13:31:10.105538 18534 solver.cpp:218] Iteration 168 (2.50948 iter/s, 4.78186s/12 iters), loss = 5.27579
I0410 13:31:10.105583 18534 solver.cpp:237]     Train net output #0: loss = 5.27579 (* 1 = 5.27579 loss)
I0410 13:31:10.105592 18534 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 13:31:14.895036 18534 solver.cpp:218] Iteration 180 (2.50561 iter/s, 4.78926s/12 iters), loss = 5.27202
I0410 13:31:14.895079 18534 solver.cpp:237]     Train net output #0: loss = 5.27202 (* 1 = 5.27202 loss)
I0410 13:31:14.895088 18534 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 13:31:19.685078 18534 solver.cpp:218] Iteration 192 (2.50532 iter/s, 4.78981s/12 iters), loss = 5.27785
I0410 13:31:19.685119 18534 solver.cpp:237]     Train net output #0: loss = 5.27785 (* 1 = 5.27785 loss)
I0410 13:31:19.685127 18534 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 13:31:23.368081 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:24.020737 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 13:31:24.321892 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 13:31:24.533603 18534 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 13:31:24.533622 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:31:28.893201 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:29.017606 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:31:29.017653 18534 solver.cpp:397]     Test net output #1: loss = 5.28011 (* 1 = 5.28011 loss)
I0410 13:31:29.098984 18534 solver.cpp:218] Iteration 204 (1.27477 iter/s, 9.4135s/12 iters), loss = 5.27588
I0410 13:31:29.099030 18534 solver.cpp:237]     Train net output #0: loss = 5.27588 (* 1 = 5.27588 loss)
I0410 13:31:29.099040 18534 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 13:31:33.218127 18534 solver.cpp:218] Iteration 216 (2.91338 iter/s, 4.11892s/12 iters), loss = 5.27831
I0410 13:31:33.218215 18534 solver.cpp:237]     Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
I0410 13:31:33.218228 18534 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 13:31:38.180310 18534 solver.cpp:218] Iteration 228 (2.41843 iter/s, 4.96189s/12 iters), loss = 5.26715
I0410 13:31:38.180366 18534 solver.cpp:237]     Train net output #0: loss = 5.26715 (* 1 = 5.26715 loss)
I0410 13:31:38.180380 18534 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 13:31:43.090833 18534 solver.cpp:218] Iteration 240 (2.44386 iter/s, 4.91027s/12 iters), loss = 5.28166
I0410 13:31:43.090879 18534 solver.cpp:237]     Train net output #0: loss = 5.28166 (* 1 = 5.28166 loss)
I0410 13:31:43.090888 18534 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 13:31:48.063540 18534 solver.cpp:218] Iteration 252 (2.41329 iter/s, 4.97246s/12 iters), loss = 5.26818
I0410 13:31:48.063583 18534 solver.cpp:237]     Train net output #0: loss = 5.26818 (* 1 = 5.26818 loss)
I0410 13:31:48.063593 18534 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 13:31:52.918509 18534 solver.cpp:218] Iteration 264 (2.47182 iter/s, 4.85473s/12 iters), loss = 5.27261
I0410 13:31:52.918552 18534 solver.cpp:237]     Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
I0410 13:31:52.918560 18534 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 13:31:57.742635 18534 solver.cpp:218] Iteration 276 (2.48762 iter/s, 4.82388s/12 iters), loss = 5.28407
I0410 13:31:57.742691 18534 solver.cpp:237]     Train net output #0: loss = 5.28407 (* 1 = 5.28407 loss)
I0410 13:31:57.742703 18534 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 13:32:02.541939 18534 solver.cpp:218] Iteration 288 (2.50049 iter/s, 4.79905s/12 iters), loss = 5.2788
I0410 13:32:02.541996 18534 solver.cpp:237]     Train net output #0: loss = 5.2788 (* 1 = 5.2788 loss)
I0410 13:32:02.542006 18534 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 13:32:07.318933 18534 solver.cpp:218] Iteration 300 (2.51217 iter/s, 4.77674s/12 iters), loss = 5.27725
I0410 13:32:07.319036 18534 solver.cpp:237]     Train net output #0: loss = 5.27725 (* 1 = 5.27725 loss)
I0410 13:32:07.319046 18534 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 13:32:08.272953 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:09.289129 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 13:32:09.589613 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 13:32:09.786068 18534 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 13:32:09.786087 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:32:14.020292 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:14.176368 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:32:14.176398 18534 solver.cpp:397]     Test net output #1: loss = 5.28117 (* 1 = 5.28117 loss)
I0410 13:32:16.078338 18534 solver.cpp:218] Iteration 312 (1.37003 iter/s, 8.75896s/12 iters), loss = 5.28115
I0410 13:32:16.078388 18534 solver.cpp:237]     Train net output #0: loss = 5.28115 (* 1 = 5.28115 loss)
I0410 13:32:16.078398 18534 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 13:32:21.071646 18534 solver.cpp:218] Iteration 324 (2.40334 iter/s, 4.99306s/12 iters), loss = 5.25676
I0410 13:32:21.071693 18534 solver.cpp:237]     Train net output #0: loss = 5.25676 (* 1 = 5.25676 loss)
I0410 13:32:21.071704 18534 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 13:32:26.272891 18534 solver.cpp:218] Iteration 336 (2.30726 iter/s, 5.20098s/12 iters), loss = 5.26438
I0410 13:32:26.272941 18534 solver.cpp:237]     Train net output #0: loss = 5.26438 (* 1 = 5.26438 loss)
I0410 13:32:26.272953 18534 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 13:32:31.064895 18534 solver.cpp:218] Iteration 348 (2.5043 iter/s, 4.79175s/12 iters), loss = 5.26767
I0410 13:32:31.064952 18534 solver.cpp:237]     Train net output #0: loss = 5.26767 (* 1 = 5.26767 loss)
I0410 13:32:31.064965 18534 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 13:32:35.903517 18534 solver.cpp:218] Iteration 360 (2.48017 iter/s, 4.83837s/12 iters), loss = 5.28826
I0410 13:32:35.903569 18534 solver.cpp:237]     Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
I0410 13:32:35.903581 18534 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 13:32:40.753170 18534 solver.cpp:218] Iteration 372 (2.47453 iter/s, 4.8494s/12 iters), loss = 5.27248
I0410 13:32:40.753332 18534 solver.cpp:237]     Train net output #0: loss = 5.27248 (* 1 = 5.27248 loss)
I0410 13:32:40.753347 18534 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 13:32:45.603082 18534 solver.cpp:218] Iteration 384 (2.47445 iter/s, 4.84955s/12 iters), loss = 5.27624
I0410 13:32:45.603139 18534 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0410 13:32:45.603152 18534 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 13:32:50.537994 18534 solver.cpp:218] Iteration 396 (2.43178 iter/s, 4.93465s/12 iters), loss = 5.27089
I0410 13:32:50.538051 18534 solver.cpp:237]     Train net output #0: loss = 5.27089 (* 1 = 5.27089 loss)
I0410 13:32:50.538062 18534 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 13:32:53.611829 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:54.984066 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 13:32:55.322871 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 13:32:55.538416 18534 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 13:32:55.538445 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:32:59.773816 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:59.975656 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:32:59.975703 18534 solver.cpp:397]     Test net output #1: loss = 5.2828 (* 1 = 5.2828 loss)
I0410 13:33:00.057114 18534 solver.cpp:218] Iteration 408 (1.26068 iter/s, 9.51868s/12 iters), loss = 5.27624
I0410 13:33:00.057193 18534 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0410 13:33:00.057210 18534 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 13:33:04.165495 18534 solver.cpp:218] Iteration 420 (2.92103 iter/s, 4.10814s/12 iters), loss = 5.27614
I0410 13:33:04.165549 18534 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0410 13:33:04.165560 18534 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 13:33:08.953805 18534 solver.cpp:218] Iteration 432 (2.50623 iter/s, 4.78806s/12 iters), loss = 5.27
I0410 13:33:08.953864 18534 solver.cpp:237]     Train net output #0: loss = 5.27 (* 1 = 5.27 loss)
I0410 13:33:08.953876 18534 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 13:33:13.735204 18534 solver.cpp:218] Iteration 444 (2.50986 iter/s, 4.78114s/12 iters), loss = 5.28157
I0410 13:33:13.735319 18534 solver.cpp:237]     Train net output #0: loss = 5.28157 (* 1 = 5.28157 loss)
I0410 13:33:13.735332 18534 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 13:33:18.538532 18534 solver.cpp:218] Iteration 456 (2.49843 iter/s, 4.80302s/12 iters), loss = 5.2802
I0410 13:33:18.538580 18534 solver.cpp:237]     Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
I0410 13:33:18.538590 18534 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 13:33:23.335702 18534 solver.cpp:218] Iteration 468 (2.5016 iter/s, 4.79692s/12 iters), loss = 5.27886
I0410 13:33:23.335747 18534 solver.cpp:237]     Train net output #0: loss = 5.27886 (* 1 = 5.27886 loss)
I0410 13:33:23.335757 18534 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 13:33:28.157419 18534 solver.cpp:218] Iteration 480 (2.48887 iter/s, 4.82147s/12 iters), loss = 5.26992
I0410 13:33:28.157472 18534 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0410 13:33:28.157485 18534 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 13:33:33.001309 18534 solver.cpp:218] Iteration 492 (2.47748 iter/s, 4.84363s/12 iters), loss = 5.28213
I0410 13:33:33.001370 18534 solver.cpp:237]     Train net output #0: loss = 5.28213 (* 1 = 5.28213 loss)
I0410 13:33:33.001382 18534 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 13:33:37.867831 18534 solver.cpp:218] Iteration 504 (2.46596 iter/s, 4.86626s/12 iters), loss = 5.2691
I0410 13:33:37.867884 18534 solver.cpp:237]     Train net output #0: loss = 5.2691 (* 1 = 5.2691 loss)
I0410 13:33:37.867895 18534 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 13:33:38.115304 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:39.810492 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 13:33:41.329054 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 13:33:41.536455 18534 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 13:33:41.536479 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:33:45.747658 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:45.983698 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:33:45.983745 18534 solver.cpp:397]     Test net output #1: loss = 5.28307 (* 1 = 5.28307 loss)
I0410 13:33:47.740131 18534 solver.cpp:218] Iteration 516 (1.21558 iter/s, 9.87185s/12 iters), loss = 5.27985
I0410 13:33:47.740190 18534 solver.cpp:237]     Train net output #0: loss = 5.27985 (* 1 = 5.27985 loss)
I0410 13:33:47.740202 18534 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 13:33:52.547663 18534 solver.cpp:218] Iteration 528 (2.49622 iter/s, 4.80727s/12 iters), loss = 5.26805
I0410 13:33:52.547725 18534 solver.cpp:237]     Train net output #0: loss = 5.26805 (* 1 = 5.26805 loss)
I0410 13:33:52.547737 18534 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 13:33:57.413000 18534 solver.cpp:218] Iteration 540 (2.46656 iter/s, 4.86507s/12 iters), loss = 5.27231
I0410 13:33:57.413060 18534 solver.cpp:237]     Train net output #0: loss = 5.27231 (* 1 = 5.27231 loss)
I0410 13:33:57.413072 18534 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 13:34:02.264195 18534 solver.cpp:218] Iteration 552 (2.47375 iter/s, 4.85093s/12 iters), loss = 5.26956
I0410 13:34:02.264250 18534 solver.cpp:237]     Train net output #0: loss = 5.26956 (* 1 = 5.26956 loss)
I0410 13:34:02.264259 18534 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 13:34:07.129266 18534 solver.cpp:218] Iteration 564 (2.46669 iter/s, 4.86481s/12 iters), loss = 5.26224
I0410 13:34:07.129317 18534 solver.cpp:237]     Train net output #0: loss = 5.26224 (* 1 = 5.26224 loss)
I0410 13:34:07.129329 18534 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 13:34:11.950103 18534 solver.cpp:218] Iteration 576 (2.48933 iter/s, 4.82058s/12 iters), loss = 5.27795
I0410 13:34:11.950165 18534 solver.cpp:237]     Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
I0410 13:34:11.950176 18534 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 13:34:16.942466 18534 solver.cpp:218] Iteration 588 (2.4038 iter/s, 4.9921s/12 iters), loss = 5.26649
I0410 13:34:16.942584 18534 solver.cpp:237]     Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
I0410 13:34:16.942597 18534 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 13:34:21.777030 18534 solver.cpp:218] Iteration 600 (2.48229 iter/s, 4.83425s/12 iters), loss = 5.26037
I0410 13:34:21.777086 18534 solver.cpp:237]     Train net output #0: loss = 5.26037 (* 1 = 5.26037 loss)
I0410 13:34:21.777098 18534 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 13:34:24.098258 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:26.158953 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 13:34:26.652654 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 13:34:26.875074 18534 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 13:34:26.875097 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:34:31.177489 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:31.460793 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:34:31.460839 18534 solver.cpp:397]     Test net output #1: loss = 5.28377 (* 1 = 5.28377 loss)
I0410 13:34:31.543380 18534 solver.cpp:218] Iteration 612 (1.22876 iter/s, 9.7659s/12 iters), loss = 5.27557
I0410 13:34:31.543452 18534 solver.cpp:237]     Train net output #0: loss = 5.27557 (* 1 = 5.27557 loss)
I0410 13:34:31.543468 18534 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 13:34:35.732069 18534 solver.cpp:218] Iteration 624 (2.86502 iter/s, 4.18845s/12 iters), loss = 5.28132
I0410 13:34:35.732106 18534 solver.cpp:237]     Train net output #0: loss = 5.28132 (* 1 = 5.28132 loss)
I0410 13:34:35.732113 18534 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 13:34:40.541985 18534 solver.cpp:218] Iteration 636 (2.49498 iter/s, 4.80966s/12 iters), loss = 5.28425
I0410 13:34:40.542032 18534 solver.cpp:237]     Train net output #0: loss = 5.28425 (* 1 = 5.28425 loss)
I0410 13:34:40.542042 18534 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 13:34:45.397914 18534 solver.cpp:218] Iteration 648 (2.47133 iter/s, 4.85568s/12 iters), loss = 5.2738
I0410 13:34:45.397980 18534 solver.cpp:237]     Train net output #0: loss = 5.2738 (* 1 = 5.2738 loss)
I0410 13:34:45.397991 18534 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 13:34:50.259383 18534 solver.cpp:218] Iteration 660 (2.46852 iter/s, 4.8612s/12 iters), loss = 5.26752
I0410 13:34:50.259496 18534 solver.cpp:237]     Train net output #0: loss = 5.26752 (* 1 = 5.26752 loss)
I0410 13:34:50.259505 18534 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 13:34:55.138547 18534 solver.cpp:218] Iteration 672 (2.45959 iter/s, 4.87885s/12 iters), loss = 5.27453
I0410 13:34:55.138593 18534 solver.cpp:237]     Train net output #0: loss = 5.27453 (* 1 = 5.27453 loss)
I0410 13:34:55.138602 18534 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 13:34:59.112092 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:34:59.968622 18534 solver.cpp:218] Iteration 684 (2.48456 iter/s, 4.82983s/12 iters), loss = 5.27293
I0410 13:34:59.968675 18534 solver.cpp:237]     Train net output #0: loss = 5.27293 (* 1 = 5.27293 loss)
I0410 13:34:59.968686 18534 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 13:35:04.809859 18534 solver.cpp:218] Iteration 696 (2.47883 iter/s, 4.84099s/12 iters), loss = 5.269
I0410 13:35:04.809916 18534 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0410 13:35:04.809927 18534 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 13:35:09.483995 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:09.851850 18534 solver.cpp:218] Iteration 708 (2.38014 iter/s, 5.04173s/12 iters), loss = 5.26117
I0410 13:35:09.851902 18534 solver.cpp:237]     Train net output #0: loss = 5.26117 (* 1 = 5.26117 loss)
I0410 13:35:09.851913 18534 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 13:35:11.810812 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 13:35:12.119690 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 13:35:12.335443 18534 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 13:35:12.335470 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:35:16.369786 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:16.691017 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:35:16.691066 18534 solver.cpp:397]     Test net output #1: loss = 5.28506 (* 1 = 5.28506 loss)
I0410 13:35:18.424029 18534 solver.cpp:218] Iteration 720 (1.39994 iter/s, 8.57179s/12 iters), loss = 5.27203
I0410 13:35:18.424075 18534 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0410 13:35:18.424083 18534 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 13:35:23.410380 18534 solver.cpp:218] Iteration 732 (2.40669 iter/s, 4.9861s/12 iters), loss = 5.27484
I0410 13:35:23.412896 18534 solver.cpp:237]     Train net output #0: loss = 5.27484 (* 1 = 5.27484 loss)
I0410 13:35:23.412905 18534 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 13:35:28.255098 18534 solver.cpp:218] Iteration 744 (2.47831 iter/s, 4.842s/12 iters), loss = 5.2771
I0410 13:35:28.255141 18534 solver.cpp:237]     Train net output #0: loss = 5.2771 (* 1 = 5.2771 loss)
I0410 13:35:28.255149 18534 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 13:35:33.182710 18534 solver.cpp:218] Iteration 756 (2.43538 iter/s, 4.92737s/12 iters), loss = 5.27571
I0410 13:35:33.182757 18534 solver.cpp:237]     Train net output #0: loss = 5.27571 (* 1 = 5.27571 loss)
I0410 13:35:33.182766 18534 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 13:35:38.007187 18534 solver.cpp:218] Iteration 768 (2.48744 iter/s, 4.82423s/12 iters), loss = 5.27662
I0410 13:35:38.007248 18534 solver.cpp:237]     Train net output #0: loss = 5.27662 (* 1 = 5.27662 loss)
I0410 13:35:38.007261 18534 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 13:35:42.848177 18534 solver.cpp:218] Iteration 780 (2.47896 iter/s, 4.84073s/12 iters), loss = 5.26435
I0410 13:35:42.848228 18534 solver.cpp:237]     Train net output #0: loss = 5.26435 (* 1 = 5.26435 loss)
I0410 13:35:42.848239 18534 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 13:35:47.659857 18534 solver.cpp:218] Iteration 792 (2.49406 iter/s, 4.81143s/12 iters), loss = 5.2665
I0410 13:35:47.659904 18534 solver.cpp:237]     Train net output #0: loss = 5.2665 (* 1 = 5.2665 loss)
I0410 13:35:47.659914 18534 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 13:35:52.504144 18534 solver.cpp:218] Iteration 804 (2.47727 iter/s, 4.84404s/12 iters), loss = 5.2861
I0410 13:35:52.504195 18534 solver.cpp:237]     Train net output #0: loss = 5.2861 (* 1 = 5.2861 loss)
I0410 13:35:52.504206 18534 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 13:35:54.190066 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:56.885318 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 13:35:57.185425 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 13:35:57.394968 18534 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 13:35:57.394994 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:36:01.453764 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:01.815716 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:36:01.815764 18534 solver.cpp:397]     Test net output #1: loss = 5.28532 (* 1 = 5.28532 loss)
I0410 13:36:01.897536 18534 solver.cpp:218] Iteration 816 (1.27755 iter/s, 9.39297s/12 iters), loss = 5.27614
I0410 13:36:01.897585 18534 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0410 13:36:01.897598 18534 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 13:36:06.074225 18534 solver.cpp:218] Iteration 828 (2.87324 iter/s, 4.17647s/12 iters), loss = 5.28136
I0410 13:36:06.074278 18534 solver.cpp:237]     Train net output #0: loss = 5.28136 (* 1 = 5.28136 loss)
I0410 13:36:06.074293 18534 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 13:36:10.890159 18534 solver.cpp:218] Iteration 840 (2.49186 iter/s, 4.81568s/12 iters), loss = 5.2318
I0410 13:36:10.890215 18534 solver.cpp:237]     Train net output #0: loss = 5.2318 (* 1 = 5.2318 loss)
I0410 13:36:10.890226 18534 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 13:36:15.718786 18534 solver.cpp:218] Iteration 852 (2.48531 iter/s, 4.82836s/12 iters), loss = 5.29634
I0410 13:36:15.718843 18534 solver.cpp:237]     Train net output #0: loss = 5.29634 (* 1 = 5.29634 loss)
I0410 13:36:15.718856 18534 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 13:36:20.583461 18534 solver.cpp:218] Iteration 864 (2.4669 iter/s, 4.86441s/12 iters), loss = 5.26108
I0410 13:36:20.583513 18534 solver.cpp:237]     Train net output #0: loss = 5.26108 (* 1 = 5.26108 loss)
I0410 13:36:20.583525 18534 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 13:36:25.402231 18534 solver.cpp:218] Iteration 876 (2.49039 iter/s, 4.81852s/12 iters), loss = 5.26896
I0410 13:36:25.402349 18534 solver.cpp:237]     Train net output #0: loss = 5.26896 (* 1 = 5.26896 loss)
I0410 13:36:25.402360 18534 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 13:36:30.314301 18534 solver.cpp:218] Iteration 888 (2.44312 iter/s, 4.91175s/12 iters), loss = 5.26325
I0410 13:36:30.314363 18534 solver.cpp:237]     Train net output #0: loss = 5.26325 (* 1 = 5.26325 loss)
I0410 13:36:30.314375 18534 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 13:36:35.156154 18534 solver.cpp:218] Iteration 900 (2.47852 iter/s, 4.84159s/12 iters), loss = 5.2733
I0410 13:36:35.156204 18534 solver.cpp:237]     Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
I0410 13:36:35.156214 18534 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 13:36:38.875677 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:39.947882 18534 solver.cpp:218] Iteration 912 (2.50444 iter/s, 4.79148s/12 iters), loss = 5.26026
I0410 13:36:39.947937 18534 solver.cpp:237]     Train net output #0: loss = 5.26026 (* 1 = 5.26026 loss)
I0410 13:36:39.947948 18534 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 13:36:41.937578 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 13:36:42.258810 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 13:36:42.472784 18534 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 13:36:42.472815 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:36:46.452205 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:46.875205 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:36:46.875252 18534 solver.cpp:397]     Test net output #1: loss = 5.28555 (* 1 = 5.28555 loss)
I0410 13:36:48.630923 18534 solver.cpp:218] Iteration 924 (1.38207 iter/s, 8.68264s/12 iters), loss = 5.28608
I0410 13:36:48.630977 18534 solver.cpp:237]     Train net output #0: loss = 5.28608 (* 1 = 5.28608 loss)
I0410 13:36:48.630988 18534 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 13:36:54.103402 18534 solver.cpp:218] Iteration 936 (2.1929 iter/s, 5.4722s/12 iters), loss = 5.25781
I0410 13:36:54.103446 18534 solver.cpp:237]     Train net output #0: loss = 5.25781 (* 1 = 5.25781 loss)
I0410 13:36:54.103454 18534 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 13:36:58.902216 18534 solver.cpp:218] Iteration 948 (2.50074 iter/s, 4.79857s/12 iters), loss = 5.2872
I0410 13:36:58.902284 18534 solver.cpp:237]     Train net output #0: loss = 5.2872 (* 1 = 5.2872 loss)
I0410 13:36:58.902293 18534 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 13:37:03.757905 18534 solver.cpp:218] Iteration 960 (2.47147 iter/s, 4.85542s/12 iters), loss = 5.2595
I0410 13:37:03.757951 18534 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0410 13:37:03.757977 18534 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 13:37:08.758894 18534 solver.cpp:218] Iteration 972 (2.39965 iter/s, 5.00074s/12 iters), loss = 5.27295
I0410 13:37:08.758944 18534 solver.cpp:237]     Train net output #0: loss = 5.27295 (* 1 = 5.27295 loss)
I0410 13:37:08.758955 18534 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 13:37:13.776257 18534 solver.cpp:218] Iteration 984 (2.39182 iter/s, 5.01711s/12 iters), loss = 5.28891
I0410 13:37:13.776311 18534 solver.cpp:237]     Train net output #0: loss = 5.28891 (* 1 = 5.28891 loss)
I0410 13:37:13.776322 18534 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 13:37:18.628089 18534 solver.cpp:218] Iteration 996 (2.47342 iter/s, 4.85158s/12 iters), loss = 5.27786
I0410 13:37:18.628135 18534 solver.cpp:237]     Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
I0410 13:37:18.628145 18534 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 13:37:23.492348 18534 solver.cpp:218] Iteration 1008 (2.4671 iter/s, 4.86401s/12 iters), loss = 5.2849
I0410 13:37:23.492403 18534 solver.cpp:237]     Train net output #0: loss = 5.2849 (* 1 = 5.2849 loss)
I0410 13:37:23.492414 18534 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 13:37:24.488366 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:27.916412 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 13:37:28.244669 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 13:37:28.463104 18534 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 13:37:28.463127 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:37:32.504130 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:32.935128 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:37:32.935178 18534 solver.cpp:397]     Test net output #1: loss = 5.28564 (* 1 = 5.28564 loss)
I0410 13:37:33.017513 18534 solver.cpp:218] Iteration 1020 (1.25988 iter/s, 9.52473s/12 iters), loss = 5.28479
I0410 13:37:33.017567 18534 solver.cpp:237]     Train net output #0: loss = 5.28479 (* 1 = 5.28479 loss)
I0410 13:37:33.017580 18534 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 13:37:37.065877 18534 solver.cpp:218] Iteration 1032 (2.96432 iter/s, 4.04814s/12 iters), loss = 5.24847
I0410 13:37:37.065927 18534 solver.cpp:237]     Train net output #0: loss = 5.24847 (* 1 = 5.24847 loss)
I0410 13:37:37.065935 18534 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 13:37:42.016116 18534 solver.cpp:218] Iteration 1044 (2.42425 iter/s, 4.94998s/12 iters), loss = 5.25597
I0410 13:37:42.016176 18534 solver.cpp:237]     Train net output #0: loss = 5.25597 (* 1 = 5.25597 loss)
I0410 13:37:42.016188 18534 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 13:37:46.838300 18534 solver.cpp:218] Iteration 1056 (2.48863 iter/s, 4.82193s/12 iters), loss = 5.26657
I0410 13:37:46.838348 18534 solver.cpp:237]     Train net output #0: loss = 5.26657 (* 1 = 5.26657 loss)
I0410 13:37:46.838356 18534 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 13:37:51.683388 18534 solver.cpp:218] Iteration 1068 (2.47686 iter/s, 4.84484s/12 iters), loss = 5.28865
I0410 13:37:51.683444 18534 solver.cpp:237]     Train net output #0: loss = 5.28865 (* 1 = 5.28865 loss)
I0410 13:37:51.683456 18534 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 13:37:56.530668 18534 solver.cpp:218] Iteration 1080 (2.47574 iter/s, 4.84703s/12 iters), loss = 5.27137
I0410 13:37:56.530712 18534 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0410 13:37:56.530720 18534 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 13:38:01.514587 18534 solver.cpp:218] Iteration 1092 (2.40786 iter/s, 4.98367s/12 iters), loss = 5.28335
I0410 13:38:01.514636 18534 solver.cpp:237]     Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
I0410 13:38:01.514647 18534 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 13:38:06.429992 18534 solver.cpp:218] Iteration 1104 (2.44143 iter/s, 4.91515s/12 iters), loss = 5.27348
I0410 13:38:06.430083 18534 solver.cpp:237]     Train net output #0: loss = 5.27348 (* 1 = 5.27348 loss)
I0410 13:38:06.430092 18534 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 13:38:09.500939 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:11.283672 18534 solver.cpp:218] Iteration 1116 (2.4725 iter/s, 4.85339s/12 iters), loss = 5.27346
I0410 13:38:11.283720 18534 solver.cpp:237]     Train net output #0: loss = 5.27346 (* 1 = 5.27346 loss)
I0410 13:38:11.283730 18534 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 13:38:13.238874 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 13:38:13.528414 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 13:38:13.727325 18534 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 13:38:13.727353 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:38:17.685722 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:18.161461 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:38:18.161505 18534 solver.cpp:397]     Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
I0410 13:38:20.030804 18534 solver.cpp:218] Iteration 1128 (1.37194 iter/s, 8.74673s/12 iters), loss = 5.2726
I0410 13:38:20.030861 18534 solver.cpp:237]     Train net output #0: loss = 5.2726 (* 1 = 5.2726 loss)
I0410 13:38:20.030875 18534 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 13:38:24.868975 18534 solver.cpp:218] Iteration 1140 (2.48041 iter/s, 4.83791s/12 iters), loss = 5.26804
I0410 13:38:24.869033 18534 solver.cpp:237]     Train net output #0: loss = 5.26804 (* 1 = 5.26804 loss)
I0410 13:38:24.869045 18534 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 13:38:29.718784 18534 solver.cpp:218] Iteration 1152 (2.47446 iter/s, 4.84955s/12 iters), loss = 5.2789
I0410 13:38:29.718845 18534 solver.cpp:237]     Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
I0410 13:38:29.718858 18534 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 13:38:34.570839 18534 solver.cpp:218] Iteration 1164 (2.47331 iter/s, 4.8518s/12 iters), loss = 5.27529
I0410 13:38:34.570888 18534 solver.cpp:237]     Train net output #0: loss = 5.27529 (* 1 = 5.27529 loss)
I0410 13:38:34.570896 18534 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 13:38:39.382244 18534 solver.cpp:218] Iteration 1176 (2.4942 iter/s, 4.81116s/12 iters), loss = 5.28766
I0410 13:38:39.382339 18534 solver.cpp:237]     Train net output #0: loss = 5.28766 (* 1 = 5.28766 loss)
I0410 13:38:39.382349 18534 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 13:38:44.229672 18534 solver.cpp:218] Iteration 1188 (2.47569 iter/s, 4.84713s/12 iters), loss = 5.27054
I0410 13:38:44.229725 18534 solver.cpp:237]     Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
I0410 13:38:44.229737 18534 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 13:38:49.118588 18534 solver.cpp:218] Iteration 1200 (2.45466 iter/s, 4.88867s/12 iters), loss = 5.28757
I0410 13:38:49.118639 18534 solver.cpp:237]     Train net output #0: loss = 5.28757 (* 1 = 5.28757 loss)
I0410 13:38:49.118650 18534 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 13:38:53.992424 18534 solver.cpp:218] Iteration 1212 (2.46226 iter/s, 4.87358s/12 iters), loss = 5.26516
I0410 13:38:53.992483 18534 solver.cpp:237]     Train net output #0: loss = 5.26516 (* 1 = 5.26516 loss)
I0410 13:38:53.992497 18534 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 13:38:54.270185 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:58.375242 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 13:38:59.705274 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 13:38:59.923827 18534 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 13:38:59.923856 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:39:03.884922 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:04.394629 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:39:04.394670 18534 solver.cpp:397]     Test net output #1: loss = 5.28598 (* 1 = 5.28598 loss)
I0410 13:39:04.477064 18534 solver.cpp:218] Iteration 1224 (1.14458 iter/s, 10.4842s/12 iters), loss = 5.28163
I0410 13:39:04.477113 18534 solver.cpp:237]     Train net output #0: loss = 5.28163 (* 1 = 5.28163 loss)
I0410 13:39:04.477121 18534 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 13:39:08.597750 18534 solver.cpp:218] Iteration 1236 (2.91229 iter/s, 4.12046s/12 iters), loss = 5.26979
I0410 13:39:08.597801 18534 solver.cpp:237]     Train net output #0: loss = 5.26979 (* 1 = 5.26979 loss)
I0410 13:39:08.597813 18534 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 13:39:13.477164 18534 solver.cpp:218] Iteration 1248 (2.45944 iter/s, 4.87916s/12 iters), loss = 5.27968
I0410 13:39:13.477263 18534 solver.cpp:237]     Train net output #0: loss = 5.27968 (* 1 = 5.27968 loss)
I0410 13:39:13.477272 18534 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 13:39:18.346927 18534 solver.cpp:218] Iteration 1260 (2.46434 iter/s, 4.86947s/12 iters), loss = 5.27254
I0410 13:39:18.346974 18534 solver.cpp:237]     Train net output #0: loss = 5.27254 (* 1 = 5.27254 loss)
I0410 13:39:18.346984 18534 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 13:39:23.253991 18534 solver.cpp:218] Iteration 1272 (2.44558 iter/s, 4.9068s/12 iters), loss = 5.24685
I0410 13:39:23.254040 18534 solver.cpp:237]     Train net output #0: loss = 5.24685 (* 1 = 5.24685 loss)
I0410 13:39:23.254052 18534 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 13:39:28.134604 18534 solver.cpp:218] Iteration 1284 (2.45883 iter/s, 4.88036s/12 iters), loss = 5.28184
I0410 13:39:28.134662 18534 solver.cpp:237]     Train net output #0: loss = 5.28184 (* 1 = 5.28184 loss)
I0410 13:39:28.134673 18534 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 13:39:33.047551 18534 solver.cpp:218] Iteration 1296 (2.44265 iter/s, 4.91269s/12 iters), loss = 5.26901
I0410 13:39:33.047600 18534 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0410 13:39:33.047611 18534 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 13:39:38.055063 18534 solver.cpp:218] Iteration 1308 (2.39652 iter/s, 5.00726s/12 iters), loss = 5.25573
I0410 13:39:38.055102 18534 solver.cpp:237]     Train net output #0: loss = 5.25573 (* 1 = 5.25573 loss)
I0410 13:39:38.055111 18534 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 13:39:40.509371 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:42.919997 18534 solver.cpp:218] Iteration 1320 (2.46676 iter/s, 4.86469s/12 iters), loss = 5.27207
I0410 13:39:42.920054 18534 solver.cpp:237]     Train net output #0: loss = 5.27207 (* 1 = 5.27207 loss)
I0410 13:39:42.920068 18534 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 13:39:44.893997 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 13:39:45.216480 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 13:39:45.427958 18534 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 13:39:45.427979 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:39:49.309180 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:49.863237 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:39:49.863286 18534 solver.cpp:397]     Test net output #1: loss = 5.28663 (* 1 = 5.28663 loss)
I0410 13:39:51.627775 18534 solver.cpp:218] Iteration 1332 (1.37814 iter/s, 8.70737s/12 iters), loss = 5.28842
I0410 13:39:51.627837 18534 solver.cpp:237]     Train net output #0: loss = 5.28842 (* 1 = 5.28842 loss)
I0410 13:39:51.627849 18534 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 13:39:56.412345 18534 solver.cpp:218] Iteration 1344 (2.5082 iter/s, 4.78431s/12 iters), loss = 5.28654
I0410 13:39:56.412405 18534 solver.cpp:237]     Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
I0410 13:39:56.412417 18534 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 13:40:01.174154 18534 solver.cpp:218] Iteration 1356 (2.52019 iter/s, 4.76155s/12 iters), loss = 5.27615
I0410 13:40:01.174214 18534 solver.cpp:237]     Train net output #0: loss = 5.27615 (* 1 = 5.27615 loss)
I0410 13:40:01.174226 18534 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 13:40:05.521457 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:40:06.050148 18534 solver.cpp:218] Iteration 1368 (2.46117 iter/s, 4.87573s/12 iters), loss = 5.2694
I0410 13:40:06.050191 18534 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0410 13:40:06.050199 18534 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 13:40:11.108291 18534 solver.cpp:218] Iteration 1380 (2.37253 iter/s, 5.05789s/12 iters), loss = 5.27038
I0410 13:40:11.108337 18534 solver.cpp:237]     Train net output #0: loss = 5.27038 (* 1 = 5.27038 loss)
I0410 13:40:11.108348 18534 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 13:40:16.028270 18534 solver.cpp:218] Iteration 1392 (2.43916 iter/s, 4.91973s/12 iters), loss = 5.2717
I0410 13:40:16.028395 18534 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0410 13:40:16.028407 18534 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 13:40:20.996186 18534 solver.cpp:218] Iteration 1404 (2.41566 iter/s, 4.96759s/12 iters), loss = 5.2763
I0410 13:40:20.996235 18534 solver.cpp:237]     Train net output #0: loss = 5.2763 (* 1 = 5.2763 loss)
I0410 13:40:20.996244 18534 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 13:40:25.579975 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:25.929078 18534 solver.cpp:218] Iteration 1416 (2.43277 iter/s, 4.93264s/12 iters), loss = 5.2601
I0410 13:40:25.929126 18534 solver.cpp:237]     Train net output #0: loss = 5.2601 (* 1 = 5.2601 loss)
I0410 13:40:25.929134 18534 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 13:40:30.472674 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 13:40:30.771234 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 13:40:30.968163 18534 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 13:40:30.968183 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:40:34.693394 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:35.281253 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:40:35.281287 18534 solver.cpp:397]     Test net output #1: loss = 5.28625 (* 1 = 5.28625 loss)
I0410 13:40:35.362166 18534 solver.cpp:218] Iteration 1428 (1.27217 iter/s, 9.43266s/12 iters), loss = 5.27638
I0410 13:40:35.362213 18534 solver.cpp:237]     Train net output #0: loss = 5.27638 (* 1 = 5.27638 loss)
I0410 13:40:35.362222 18534 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 13:40:39.442085 18534 solver.cpp:218] Iteration 1440 (2.94139 iter/s, 4.0797s/12 iters), loss = 5.28258
I0410 13:40:39.442131 18534 solver.cpp:237]     Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
I0410 13:40:39.442139 18534 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 13:40:44.290338 18534 solver.cpp:218] Iteration 1452 (2.47524 iter/s, 4.848s/12 iters), loss = 5.27833
I0410 13:40:44.290382 18534 solver.cpp:237]     Train net output #0: loss = 5.27833 (* 1 = 5.27833 loss)
I0410 13:40:44.290391 18534 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 13:40:49.079349 18534 solver.cpp:218] Iteration 1464 (2.50586 iter/s, 4.78877s/12 iters), loss = 5.27424
I0410 13:40:49.079460 18534 solver.cpp:237]     Train net output #0: loss = 5.27424 (* 1 = 5.27424 loss)
I0410 13:40:49.079473 18534 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 13:40:53.977725 18534 solver.cpp:218] Iteration 1476 (2.44995 iter/s, 4.89807s/12 iters), loss = 5.27581
I0410 13:40:53.977771 18534 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0410 13:40:53.977780 18534 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 13:40:58.930330 18534 solver.cpp:218] Iteration 1488 (2.42309 iter/s, 4.95235s/12 iters), loss = 5.25515
I0410 13:40:58.930370 18534 solver.cpp:237]     Train net output #0: loss = 5.25515 (* 1 = 5.25515 loss)
I0410 13:40:58.930378 18534 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 13:41:03.732648 18534 solver.cpp:218] Iteration 1500 (2.49892 iter/s, 4.80208s/12 iters), loss = 5.26916
I0410 13:41:03.732694 18534 solver.cpp:237]     Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
I0410 13:41:03.732703 18534 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 13:41:08.573686 18534 solver.cpp:218] Iteration 1512 (2.47893 iter/s, 4.84079s/12 iters), loss = 5.28331
I0410 13:41:08.573735 18534 solver.cpp:237]     Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
I0410 13:41:08.573745 18534 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 13:41:10.323379 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:13.459836 18534 solver.cpp:218] Iteration 1524 (2.45605 iter/s, 4.8859s/12 iters), loss = 5.27187
I0410 13:41:13.459880 18534 solver.cpp:237]     Train net output #0: loss = 5.27187 (* 1 = 5.27187 loss)
I0410 13:41:13.459888 18534 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 13:41:15.493710 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 13:41:15.782516 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 13:41:15.982985 18534 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 13:41:15.983011 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:41:19.817205 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:20.450618 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:41:20.450656 18534 solver.cpp:397]     Test net output #1: loss = 5.28617 (* 1 = 5.28617 loss)
I0410 13:41:22.262660 18534 solver.cpp:218] Iteration 1536 (1.36326 iter/s, 8.80243s/12 iters), loss = 5.27731
I0410 13:41:22.262717 18534 solver.cpp:237]     Train net output #0: loss = 5.27731 (* 1 = 5.27731 loss)
I0410 13:41:22.262728 18534 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 13:41:27.169088 18534 solver.cpp:218] Iteration 1548 (2.4459 iter/s, 4.90617s/12 iters), loss = 5.2331
I0410 13:41:27.169131 18534 solver.cpp:237]     Train net output #0: loss = 5.2331 (* 1 = 5.2331 loss)
I0410 13:41:27.169139 18534 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 13:41:32.074345 18534 solver.cpp:218] Iteration 1560 (2.44648 iter/s, 4.90501s/12 iters), loss = 5.28892
I0410 13:41:32.074388 18534 solver.cpp:237]     Train net output #0: loss = 5.28892 (* 1 = 5.28892 loss)
I0410 13:41:32.074398 18534 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 13:41:37.045097 18534 solver.cpp:218] Iteration 1572 (2.41424 iter/s, 4.97051s/12 iters), loss = 5.26062
I0410 13:41:37.045140 18534 solver.cpp:237]     Train net output #0: loss = 5.26062 (* 1 = 5.26062 loss)
I0410 13:41:37.045150 18534 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 13:41:41.968989 18534 solver.cpp:218] Iteration 1584 (2.43722 iter/s, 4.92364s/12 iters), loss = 5.26571
I0410 13:41:41.969043 18534 solver.cpp:237]     Train net output #0: loss = 5.26571 (* 1 = 5.26571 loss)
I0410 13:41:41.969055 18534 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 13:41:46.947049 18534 solver.cpp:218] Iteration 1596 (2.4107 iter/s, 4.9778s/12 iters), loss = 5.26948
I0410 13:41:46.947096 18534 solver.cpp:237]     Train net output #0: loss = 5.26948 (* 1 = 5.26948 loss)
I0410 13:41:46.947105 18534 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 13:41:51.816301 18534 solver.cpp:218] Iteration 1608 (2.46457 iter/s, 4.869s/12 iters), loss = 5.26525
I0410 13:41:51.816375 18534 solver.cpp:237]     Train net output #0: loss = 5.26525 (* 1 = 5.26525 loss)
I0410 13:41:51.816386 18534 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 13:41:55.776684 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:56.849591 18534 solver.cpp:218] Iteration 1620 (2.38426 iter/s, 5.03301s/12 iters), loss = 5.2584
I0410 13:41:56.849624 18534 solver.cpp:237]     Train net output #0: loss = 5.2584 (* 1 = 5.2584 loss)
I0410 13:41:56.849633 18534 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 13:42:01.283069 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 13:42:02.530117 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 13:42:03.373245 18534 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 13:42:03.373277 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:42:07.239606 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:07.908511 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:42:07.908546 18534 solver.cpp:397]     Test net output #1: loss = 5.28635 (* 1 = 5.28635 loss)
I0410 13:42:07.990150 18534 solver.cpp:218] Iteration 1632 (1.07719 iter/s, 11.1401s/12 iters), loss = 5.28953
I0410 13:42:07.990195 18534 solver.cpp:237]     Train net output #0: loss = 5.28953 (* 1 = 5.28953 loss)
I0410 13:42:07.990204 18534 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 13:42:12.118046 18534 solver.cpp:218] Iteration 1644 (2.90721 iter/s, 4.12767s/12 iters), loss = 5.25694
I0410 13:42:12.118099 18534 solver.cpp:237]     Train net output #0: loss = 5.25694 (* 1 = 5.25694 loss)
I0410 13:42:12.118110 18534 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 13:42:16.962599 18534 solver.cpp:218] Iteration 1656 (2.47714 iter/s, 4.8443s/12 iters), loss = 5.29322
I0410 13:42:16.962652 18534 solver.cpp:237]     Train net output #0: loss = 5.29322 (* 1 = 5.29322 loss)
I0410 13:42:16.962662 18534 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 13:42:21.919958 18534 solver.cpp:218] Iteration 1668 (2.42077 iter/s, 4.9571s/12 iters), loss = 5.25956
I0410 13:42:21.920083 18534 solver.cpp:237]     Train net output #0: loss = 5.25956 (* 1 = 5.25956 loss)
I0410 13:42:21.920095 18534 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 13:42:26.855760 18534 solver.cpp:218] Iteration 1680 (2.43138 iter/s, 4.93548s/12 iters), loss = 5.27437
I0410 13:42:26.855803 18534 solver.cpp:237]     Train net output #0: loss = 5.27437 (* 1 = 5.27437 loss)
I0410 13:42:26.855811 18534 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 13:42:31.695783 18534 solver.cpp:218] Iteration 1692 (2.47945 iter/s, 4.83978s/12 iters), loss = 5.28934
I0410 13:42:31.695829 18534 solver.cpp:237]     Train net output #0: loss = 5.28934 (* 1 = 5.28934 loss)
I0410 13:42:31.695838 18534 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 13:42:36.633426 18534 solver.cpp:218] Iteration 1704 (2.43043 iter/s, 4.93739s/12 iters), loss = 5.27153
I0410 13:42:36.633479 18534 solver.cpp:237]     Train net output #0: loss = 5.27153 (* 1 = 5.27153 loss)
I0410 13:42:36.633491 18534 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 13:42:41.541481 18534 solver.cpp:218] Iteration 1716 (2.44509 iter/s, 4.9078s/12 iters), loss = 5.2827
I0410 13:42:41.541534 18534 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0410 13:42:41.541546 18534 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 13:42:42.556828 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:46.404891 18534 solver.cpp:218] Iteration 1728 (2.46753 iter/s, 4.86316s/12 iters), loss = 5.2833
I0410 13:42:46.404942 18534 solver.cpp:237]     Train net output #0: loss = 5.2833 (* 1 = 5.2833 loss)
I0410 13:42:46.404954 18534 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 13:42:48.374109 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 13:42:48.712298 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 13:42:48.986196 18534 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 13:42:48.986224 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:42:52.870054 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:53.653672 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:42:53.653712 18534 solver.cpp:397]     Test net output #1: loss = 5.28667 (* 1 = 5.28667 loss)
I0410 13:42:55.439415 18534 solver.cpp:218] Iteration 1740 (1.3283 iter/s, 9.03411s/12 iters), loss = 5.25524
I0410 13:42:55.439476 18534 solver.cpp:237]     Train net output #0: loss = 5.25524 (* 1 = 5.25524 loss)
I0410 13:42:55.439487 18534 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 13:43:00.253151 18534 solver.cpp:218] Iteration 1752 (2.493 iter/s, 4.81348s/12 iters), loss = 5.26547
I0410 13:43:00.253204 18534 solver.cpp:237]     Train net output #0: loss = 5.26547 (* 1 = 5.26547 loss)
I0410 13:43:00.253216 18534 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 13:43:05.288933 18534 solver.cpp:218] Iteration 1764 (2.38307 iter/s, 5.03552s/12 iters), loss = 5.26562
I0410 13:43:05.288978 18534 solver.cpp:237]     Train net output #0: loss = 5.26562 (* 1 = 5.26562 loss)
I0410 13:43:05.288987 18534 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 13:43:10.443536 18534 solver.cpp:218] Iteration 1776 (2.32813 iter/s, 5.15434s/12 iters), loss = 5.28033
I0410 13:43:10.443589 18534 solver.cpp:237]     Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
I0410 13:43:10.443603 18534 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 13:43:15.497172 18534 solver.cpp:218] Iteration 1788 (2.37465 iter/s, 5.05337s/12 iters), loss = 5.26145
I0410 13:43:15.497231 18534 solver.cpp:237]     Train net output #0: loss = 5.26145 (* 1 = 5.26145 loss)
I0410 13:43:15.497241 18534 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 13:43:20.376101 18534 solver.cpp:218] Iteration 1800 (2.45968 iter/s, 4.87867s/12 iters), loss = 5.2771
I0410 13:43:20.376145 18534 solver.cpp:237]     Train net output #0: loss = 5.2771 (* 1 = 5.2771 loss)
I0410 13:43:20.376154 18534 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 13:43:25.361191 18534 solver.cpp:218] Iteration 1812 (2.4073 iter/s, 4.98484s/12 iters), loss = 5.26824
I0410 13:43:25.361346 18534 solver.cpp:237]     Train net output #0: loss = 5.26824 (* 1 = 5.26824 loss)
I0410 13:43:25.361361 18534 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 13:43:28.470266 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:30.230392 18534 solver.cpp:218] Iteration 1824 (2.46465 iter/s, 4.86885s/12 iters), loss = 5.27174
I0410 13:43:30.230449 18534 solver.cpp:237]     Train net output #0: loss = 5.27174 (* 1 = 5.27174 loss)
I0410 13:43:30.230461 18534 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 13:43:34.622071 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 13:43:34.914342 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 13:43:35.112812 18534 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 13:43:35.112833 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:43:38.733146 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:39.483863 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:43:39.483897 18534 solver.cpp:397]     Test net output #1: loss = 5.28617 (* 1 = 5.28617 loss)
I0410 13:43:39.559962 18534 solver.cpp:218] Iteration 1836 (1.28629 iter/s, 9.32915s/12 iters), loss = 5.27303
I0410 13:43:39.560007 18534 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0410 13:43:39.560016 18534 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 13:43:43.825582 18534 solver.cpp:218] Iteration 1848 (2.81334 iter/s, 4.2654s/12 iters), loss = 5.27249
I0410 13:43:43.825628 18534 solver.cpp:237]     Train net output #0: loss = 5.27249 (* 1 = 5.27249 loss)
I0410 13:43:43.825639 18534 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 13:43:48.736793 18534 solver.cpp:218] Iteration 1860 (2.44351 iter/s, 4.91097s/12 iters), loss = 5.28343
I0410 13:43:48.736835 18534 solver.cpp:237]     Train net output #0: loss = 5.28343 (* 1 = 5.28343 loss)
I0410 13:43:48.736846 18534 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 13:43:53.675109 18534 solver.cpp:218] Iteration 1872 (2.4301 iter/s, 4.93807s/12 iters), loss = 5.26734
I0410 13:43:53.675163 18534 solver.cpp:237]     Train net output #0: loss = 5.26734 (* 1 = 5.26734 loss)
I0410 13:43:53.675176 18534 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 13:43:58.709173 18534 solver.cpp:218] Iteration 1884 (2.38388 iter/s, 5.0338s/12 iters), loss = 5.28415
I0410 13:43:58.713330 18534 solver.cpp:237]     Train net output #0: loss = 5.28415 (* 1 = 5.28415 loss)
I0410 13:43:58.713343 18534 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 13:44:03.644413 18534 solver.cpp:218] Iteration 1896 (2.43364 iter/s, 4.93088s/12 iters), loss = 5.26649
I0410 13:44:03.644474 18534 solver.cpp:237]     Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
I0410 13:44:03.644485 18534 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 13:44:08.561659 18534 solver.cpp:218] Iteration 1908 (2.44052 iter/s, 4.91698s/12 iters), loss = 5.28576
I0410 13:44:08.561714 18534 solver.cpp:237]     Train net output #0: loss = 5.28576 (* 1 = 5.28576 loss)
I0410 13:44:08.561726 18534 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 13:44:13.511379 18534 solver.cpp:218] Iteration 1920 (2.42451 iter/s, 4.94946s/12 iters), loss = 5.27432
I0410 13:44:13.511425 18534 solver.cpp:237]     Train net output #0: loss = 5.27432 (* 1 = 5.27432 loss)
I0410 13:44:13.511436 18534 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 13:44:13.822059 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:18.407060 18534 solver.cpp:218] Iteration 1932 (2.45126 iter/s, 4.89543s/12 iters), loss = 5.28252
I0410 13:44:18.407111 18534 solver.cpp:237]     Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
I0410 13:44:18.407121 18534 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 13:44:20.382221 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 13:44:20.692525 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 13:44:20.894696 18534 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 13:44:20.894716 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:44:24.497129 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:25.280117 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:44:25.280162 18534 solver.cpp:397]     Test net output #1: loss = 5.28662 (* 1 = 5.28662 loss)
I0410 13:44:27.112540 18534 solver.cpp:218] Iteration 1944 (1.3785 iter/s, 8.70508s/12 iters), loss = 5.27493
I0410 13:44:27.112581 18534 solver.cpp:237]     Train net output #0: loss = 5.27493 (* 1 = 5.27493 loss)
I0410 13:44:27.112591 18534 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 13:44:31.949651 18534 solver.cpp:218] Iteration 1956 (2.48094 iter/s, 4.83687s/12 iters), loss = 5.27989
I0410 13:44:31.949754 18534 solver.cpp:237]     Train net output #0: loss = 5.27989 (* 1 = 5.27989 loss)
I0410 13:44:31.949764 18534 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 13:44:36.836545 18534 solver.cpp:218] Iteration 1968 (2.4557 iter/s, 4.88659s/12 iters), loss = 5.27226
I0410 13:44:36.836597 18534 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0410 13:44:36.836609 18534 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 13:44:41.755842 18534 solver.cpp:218] Iteration 1980 (2.4395 iter/s, 4.91904s/12 iters), loss = 5.257
I0410 13:44:41.755899 18534 solver.cpp:237]     Train net output #0: loss = 5.257 (* 1 = 5.257 loss)
I0410 13:44:41.755911 18534 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 13:44:46.767843 18534 solver.cpp:218] Iteration 1992 (2.39438 iter/s, 5.01173s/12 iters), loss = 5.28134
I0410 13:44:46.767900 18534 solver.cpp:237]     Train net output #0: loss = 5.28134 (* 1 = 5.28134 loss)
I0410 13:44:46.767913 18534 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 13:44:51.608670 18534 solver.cpp:218] Iteration 2004 (2.47905 iter/s, 4.84057s/12 iters), loss = 5.2775
I0410 13:44:51.608721 18534 solver.cpp:237]     Train net output #0: loss = 5.2775 (* 1 = 5.2775 loss)
I0410 13:44:51.608732 18534 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 13:44:56.517414 18534 solver.cpp:218] Iteration 2016 (2.44474 iter/s, 4.90849s/12 iters), loss = 5.25399
I0410 13:44:56.517462 18534 solver.cpp:237]     Train net output #0: loss = 5.25399 (* 1 = 5.25399 loss)
I0410 13:44:56.517472 18534 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 13:44:58.970155 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:01.373481 18534 solver.cpp:218] Iteration 2028 (2.47126 iter/s, 4.85582s/12 iters), loss = 5.27698
I0410 13:45:01.373528 18534 solver.cpp:237]     Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
I0410 13:45:01.373536 18534 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 13:45:05.813743 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 13:45:06.122370 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 13:45:06.332306 18534 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 13:45:06.332336 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:45:10.320626 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:11.155006 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:45:11.155045 18534 solver.cpp:397]     Test net output #1: loss = 5.28634 (* 1 = 5.28634 loss)
I0410 13:45:11.236682 18534 solver.cpp:218] Iteration 2040 (1.2167 iter/s, 9.86276s/12 iters), loss = 5.28346
I0410 13:45:11.236727 18534 solver.cpp:237]     Train net output #0: loss = 5.28346 (* 1 = 5.28346 loss)
I0410 13:45:11.236735 18534 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 13:45:15.395424 18534 solver.cpp:218] Iteration 2052 (2.88564 iter/s, 4.15852s/12 iters), loss = 5.28439
I0410 13:45:15.395474 18534 solver.cpp:237]     Train net output #0: loss = 5.28439 (* 1 = 5.28439 loss)
I0410 13:45:15.395486 18534 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 13:45:15.395787 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:45:20.260733 18534 solver.cpp:218] Iteration 2064 (2.46657 iter/s, 4.86505s/12 iters), loss = 5.27439
I0410 13:45:20.260798 18534 solver.cpp:237]     Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
I0410 13:45:20.260812 18534 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 13:45:25.184263 18534 solver.cpp:218] Iteration 2076 (2.4374 iter/s, 4.92327s/12 iters), loss = 5.27902
I0410 13:45:25.184309 18534 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0410 13:45:25.184319 18534 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 13:45:30.090042 18534 solver.cpp:218] Iteration 2088 (2.44622 iter/s, 4.90552s/12 iters), loss = 5.27517
I0410 13:45:30.090106 18534 solver.cpp:237]     Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
I0410 13:45:30.090119 18534 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 13:45:35.027304 18534 solver.cpp:218] Iteration 2100 (2.43063 iter/s, 4.93699s/12 iters), loss = 5.26904
I0410 13:45:35.027366 18534 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0410 13:45:35.027379 18534 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 13:45:40.035570 18534 solver.cpp:218] Iteration 2112 (2.39617 iter/s, 5.008s/12 iters), loss = 5.28467
I0410 13:45:40.035674 18534 solver.cpp:237]     Train net output #0: loss = 5.28467 (* 1 = 5.28467 loss)
I0410 13:45:40.035684 18534 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 13:45:44.590283 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:44.901757 18534 solver.cpp:218] Iteration 2124 (2.46615 iter/s, 4.86589s/12 iters), loss = 5.25463
I0410 13:45:44.901810 18534 solver.cpp:237]     Train net output #0: loss = 5.25463 (* 1 = 5.25463 loss)
I0410 13:45:44.901821 18534 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 13:45:49.751251 18534 solver.cpp:218] Iteration 2136 (2.47461 iter/s, 4.84924s/12 iters), loss = 5.2764
I0410 13:45:49.751308 18534 solver.cpp:237]     Train net output #0: loss = 5.2764 (* 1 = 5.2764 loss)
I0410 13:45:49.751320 18534 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 13:45:51.770931 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 13:45:52.062979 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 13:45:52.260489 18534 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 13:45:52.260512 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:45:55.822860 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:56.682685 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:45:56.682734 18534 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0410 13:45:58.560966 18534 solver.cpp:218] Iteration 2148 (1.3622 iter/s, 8.8093s/12 iters), loss = 5.28078
I0410 13:45:58.561023 18534 solver.cpp:237]     Train net output #0: loss = 5.28078 (* 1 = 5.28078 loss)
I0410 13:45:58.561036 18534 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 13:46:03.478660 18534 solver.cpp:218] Iteration 2160 (2.4403 iter/s, 4.91744s/12 iters), loss = 5.2848
I0410 13:46:03.478705 18534 solver.cpp:237]     Train net output #0: loss = 5.2848 (* 1 = 5.2848 loss)
I0410 13:46:03.478714 18534 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 13:46:08.418728 18534 solver.cpp:218] Iteration 2172 (2.42924 iter/s, 4.93981s/12 iters), loss = 5.27221
I0410 13:46:08.418787 18534 solver.cpp:237]     Train net output #0: loss = 5.27221 (* 1 = 5.27221 loss)
I0410 13:46:08.418798 18534 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 13:46:13.468091 18534 solver.cpp:218] Iteration 2184 (2.37666 iter/s, 5.0491s/12 iters), loss = 5.27447
I0410 13:46:13.468217 18534 solver.cpp:237]     Train net output #0: loss = 5.27447 (* 1 = 5.27447 loss)
I0410 13:46:13.468228 18534 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 13:46:18.346036 18534 solver.cpp:218] Iteration 2196 (2.46021 iter/s, 4.87762s/12 iters), loss = 5.25488
I0410 13:46:18.346081 18534 solver.cpp:237]     Train net output #0: loss = 5.25488 (* 1 = 5.25488 loss)
I0410 13:46:18.346091 18534 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 13:46:23.377676 18534 solver.cpp:218] Iteration 2208 (2.38503 iter/s, 5.03138s/12 iters), loss = 5.26895
I0410 13:46:23.377737 18534 solver.cpp:237]     Train net output #0: loss = 5.26895 (* 1 = 5.26895 loss)
I0410 13:46:23.377749 18534 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 13:46:28.342695 18534 solver.cpp:218] Iteration 2220 (2.41704 iter/s, 4.96476s/12 iters), loss = 5.28312
I0410 13:46:28.342739 18534 solver.cpp:237]     Train net output #0: loss = 5.28312 (* 1 = 5.28312 loss)
I0410 13:46:28.342747 18534 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 13:46:30.066275 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:33.157220 18534 solver.cpp:218] Iteration 2232 (2.49258 iter/s, 4.81428s/12 iters), loss = 5.28436
I0410 13:46:33.157269 18534 solver.cpp:237]     Train net output #0: loss = 5.28436 (* 1 = 5.28436 loss)
I0410 13:46:33.157277 18534 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 13:46:37.573470 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 13:46:37.885687 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 13:46:38.097815 18534 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 13:46:38.097836 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:46:41.822669 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:42.728652 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:46:42.728691 18534 solver.cpp:397]     Test net output #1: loss = 5.28652 (* 1 = 5.28652 loss)
I0410 13:46:42.810940 18534 solver.cpp:218] Iteration 2244 (1.2431 iter/s, 9.65329s/12 iters), loss = 5.27566
I0410 13:46:42.810993 18534 solver.cpp:237]     Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
I0410 13:46:42.811002 18534 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 13:46:46.972930 18534 solver.cpp:218] Iteration 2256 (2.8834 iter/s, 4.16176s/12 iters), loss = 5.24245
I0410 13:46:46.973021 18534 solver.cpp:237]     Train net output #0: loss = 5.24245 (* 1 = 5.24245 loss)
I0410 13:46:46.973033 18534 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 13:46:51.905190 18534 solver.cpp:218] Iteration 2268 (2.43311 iter/s, 4.93197s/12 iters), loss = 5.28528
I0410 13:46:51.905246 18534 solver.cpp:237]     Train net output #0: loss = 5.28528 (* 1 = 5.28528 loss)
I0410 13:46:51.905257 18534 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 13:46:56.732141 18534 solver.cpp:218] Iteration 2280 (2.48617 iter/s, 4.8267s/12 iters), loss = 5.25228
I0410 13:46:56.732197 18534 solver.cpp:237]     Train net output #0: loss = 5.25228 (* 1 = 5.25228 loss)
I0410 13:46:56.732209 18534 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 13:47:01.681000 18534 solver.cpp:218] Iteration 2292 (2.42493 iter/s, 4.9486s/12 iters), loss = 5.27268
I0410 13:47:01.681066 18534 solver.cpp:237]     Train net output #0: loss = 5.27268 (* 1 = 5.27268 loss)
I0410 13:47:01.681079 18534 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 13:47:06.642146 18534 solver.cpp:218] Iteration 2304 (2.41892 iter/s, 4.96088s/12 iters), loss = 5.26897
I0410 13:47:06.642189 18534 solver.cpp:237]     Train net output #0: loss = 5.26897 (* 1 = 5.26897 loss)
I0410 13:47:06.642200 18534 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 13:47:11.588977 18534 solver.cpp:218] Iteration 2316 (2.42591 iter/s, 4.94659s/12 iters), loss = 5.26201
I0410 13:47:11.589020 18534 solver.cpp:237]     Train net output #0: loss = 5.26201 (* 1 = 5.26201 loss)
I0410 13:47:11.589027 18534 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 13:47:15.462493 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:16.492455 18534 solver.cpp:218] Iteration 2328 (2.44736 iter/s, 4.90323s/12 iters), loss = 5.25837
I0410 13:47:16.492501 18534 solver.cpp:237]     Train net output #0: loss = 5.25837 (* 1 = 5.25837 loss)
I0410 13:47:16.492511 18534 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 13:47:21.359845 18534 solver.cpp:218] Iteration 2340 (2.46551 iter/s, 4.86714s/12 iters), loss = 5.29248
I0410 13:47:21.359975 18534 solver.cpp:237]     Train net output #0: loss = 5.29248 (* 1 = 5.29248 loss)
I0410 13:47:21.359984 18534 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 13:47:23.344362 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 13:47:23.651834 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 13:47:23.861552 18534 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 13:47:23.861573 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:47:27.476670 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:28.416282 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:47:28.416312 18534 solver.cpp:397]     Test net output #1: loss = 5.28685 (* 1 = 5.28685 loss)
I0410 13:47:30.286339 18534 solver.cpp:218] Iteration 2352 (1.34439 iter/s, 8.92601s/12 iters), loss = 5.25741
I0410 13:47:30.286387 18534 solver.cpp:237]     Train net output #0: loss = 5.25741 (* 1 = 5.25741 loss)
I0410 13:47:30.286397 18534 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 13:47:35.157807 18534 solver.cpp:218] Iteration 2364 (2.46345 iter/s, 4.87122s/12 iters), loss = 5.30509
I0410 13:47:35.157853 18534 solver.cpp:237]     Train net output #0: loss = 5.30509 (* 1 = 5.30509 loss)
I0410 13:47:35.157862 18534 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 13:47:40.007349 18534 solver.cpp:218] Iteration 2376 (2.47459 iter/s, 4.8493s/12 iters), loss = 5.2617
I0410 13:47:40.007398 18534 solver.cpp:237]     Train net output #0: loss = 5.2617 (* 1 = 5.2617 loss)
I0410 13:47:40.007411 18534 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 13:47:44.850766 18534 solver.cpp:218] Iteration 2388 (2.47772 iter/s, 4.84317s/12 iters), loss = 5.27417
I0410 13:47:44.850816 18534 solver.cpp:237]     Train net output #0: loss = 5.27417 (* 1 = 5.27417 loss)
I0410 13:47:44.850827 18534 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 13:47:49.808379 18534 solver.cpp:218] Iteration 2400 (2.42064 iter/s, 4.95736s/12 iters), loss = 5.28426
I0410 13:47:49.808434 18534 solver.cpp:237]     Train net output #0: loss = 5.28426 (* 1 = 5.28426 loss)
I0410 13:47:49.808445 18534 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 13:47:54.645371 18534 solver.cpp:218] Iteration 2412 (2.48101 iter/s, 4.83674s/12 iters), loss = 5.27203
I0410 13:47:54.645522 18534 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0410 13:47:54.645534 18534 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 13:47:59.484714 18534 solver.cpp:218] Iteration 2424 (2.47986 iter/s, 4.83899s/12 iters), loss = 5.27404
I0410 13:47:59.484766 18534 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0410 13:47:59.484778 18534 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 13:48:00.523298 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:04.370589 18534 solver.cpp:218] Iteration 2436 (2.45619 iter/s, 4.88562s/12 iters), loss = 5.27835
I0410 13:48:04.370635 18534 solver.cpp:237]     Train net output #0: loss = 5.27835 (* 1 = 5.27835 loss)
I0410 13:48:04.370644 18534 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 13:48:08.786798 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 13:48:09.092365 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 13:48:09.289813 18534 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 13:48:09.289832 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:48:12.764746 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:13.737673 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:48:13.737711 18534 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0410 13:48:13.820058 18534 solver.cpp:218] Iteration 2448 (1.26997 iter/s, 9.44905s/12 iters), loss = 5.25639
I0410 13:48:13.820104 18534 solver.cpp:237]     Train net output #0: loss = 5.25639 (* 1 = 5.25639 loss)
I0410 13:48:13.820113 18534 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 13:48:17.818037 18534 solver.cpp:218] Iteration 2460 (3.00168 iter/s, 3.99777s/12 iters), loss = 5.26547
I0410 13:48:17.818079 18534 solver.cpp:237]     Train net output #0: loss = 5.26547 (* 1 = 5.26547 loss)
I0410 13:48:17.818089 18534 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 13:48:22.726689 18534 solver.cpp:218] Iteration 2472 (2.44479 iter/s, 4.9084s/12 iters), loss = 5.27031
I0410 13:48:22.726747 18534 solver.cpp:237]     Train net output #0: loss = 5.27031 (* 1 = 5.27031 loss)
I0410 13:48:22.726758 18534 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 13:48:27.657912 18534 solver.cpp:218] Iteration 2484 (2.4336 iter/s, 4.93096s/12 iters), loss = 5.2742
I0410 13:48:27.658041 18534 solver.cpp:237]     Train net output #0: loss = 5.2742 (* 1 = 5.2742 loss)
I0410 13:48:27.658056 18534 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 13:48:32.566123 18534 solver.cpp:218] Iteration 2496 (2.44505 iter/s, 4.90788s/12 iters), loss = 5.27137
I0410 13:48:32.566164 18534 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0410 13:48:32.566174 18534 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 13:48:37.438905 18534 solver.cpp:218] Iteration 2508 (2.46278 iter/s, 4.87254s/12 iters), loss = 5.28655
I0410 13:48:37.438951 18534 solver.cpp:237]     Train net output #0: loss = 5.28655 (* 1 = 5.28655 loss)
I0410 13:48:37.438961 18534 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 13:48:42.270000 18534 solver.cpp:218] Iteration 2520 (2.48404 iter/s, 4.83085s/12 iters), loss = 5.27688
I0410 13:48:42.270042 18534 solver.cpp:237]     Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
I0410 13:48:42.270051 18534 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 13:48:45.393015 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:47.151089 18534 solver.cpp:218] Iteration 2532 (2.45859 iter/s, 4.88085s/12 iters), loss = 5.28173
I0410 13:48:47.151134 18534 solver.cpp:237]     Train net output #0: loss = 5.28173 (* 1 = 5.28173 loss)
I0410 13:48:47.151142 18534 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 13:48:52.059047 18534 solver.cpp:218] Iteration 2544 (2.44513 iter/s, 4.90771s/12 iters), loss = 5.27289
I0410 13:48:52.059095 18534 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0410 13:48:52.059104 18534 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 13:48:54.030054 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 13:48:55.469806 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 13:48:55.920276 18534 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 13:48:55.920305 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:48:59.407665 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:00.428134 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:49:00.428184 18534 solver.cpp:397]     Test net output #1: loss = 5.2865 (* 1 = 5.2865 loss)
I0410 13:49:02.315357 18534 solver.cpp:218] Iteration 2556 (1.17006 iter/s, 10.2558s/12 iters), loss = 5.28037
I0410 13:49:02.315410 18534 solver.cpp:237]     Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
I0410 13:49:02.315421 18534 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 13:49:07.199365 18534 solver.cpp:218] Iteration 2568 (2.45712 iter/s, 4.88376s/12 iters), loss = 5.28386
I0410 13:49:07.199412 18534 solver.cpp:237]     Train net output #0: loss = 5.28386 (* 1 = 5.28386 loss)
I0410 13:49:07.199424 18534 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 13:49:12.251444 18534 solver.cpp:218] Iteration 2580 (2.37538 iter/s, 5.05182s/12 iters), loss = 5.27017
I0410 13:49:12.251495 18534 solver.cpp:237]     Train net output #0: loss = 5.27017 (* 1 = 5.27017 loss)
I0410 13:49:12.251507 18534 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 13:49:17.176956 18534 solver.cpp:218] Iteration 2592 (2.43642 iter/s, 4.92526s/12 iters), loss = 5.28825
I0410 13:49:17.177002 18534 solver.cpp:237]     Train net output #0: loss = 5.28825 (* 1 = 5.28825 loss)
I0410 13:49:17.177012 18534 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 13:49:22.086643 18534 solver.cpp:218] Iteration 2604 (2.44427 iter/s, 4.90944s/12 iters), loss = 5.25854
I0410 13:49:22.086697 18534 solver.cpp:237]     Train net output #0: loss = 5.25854 (* 1 = 5.25854 loss)
I0410 13:49:22.086710 18534 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 13:49:27.006229 18534 solver.cpp:218] Iteration 2616 (2.43936 iter/s, 4.91933s/12 iters), loss = 5.28425
I0410 13:49:27.006273 18534 solver.cpp:237]     Train net output #0: loss = 5.28425 (* 1 = 5.28425 loss)
I0410 13:49:27.006281 18534 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 13:49:31.911867 18534 solver.cpp:218] Iteration 2628 (2.44629 iter/s, 4.90539s/12 iters), loss = 5.27786
I0410 13:49:31.911993 18534 solver.cpp:237]     Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
I0410 13:49:31.912004 18534 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 13:49:32.349289 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:36.903359 18534 solver.cpp:218] Iteration 2640 (2.40425 iter/s, 4.99116s/12 iters), loss = 5.27955
I0410 13:49:36.903414 18534 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0410 13:49:36.903426 18534 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 13:49:41.390265 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 13:49:41.717594 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 13:49:41.932211 18534 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 13:49:41.932231 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:49:45.235688 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:46.288867 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:49:46.288918 18534 solver.cpp:397]     Test net output #1: loss = 5.28672 (* 1 = 5.28672 loss)
I0410 13:49:46.371248 18534 solver.cpp:218] Iteration 2652 (1.2675 iter/s, 9.46746s/12 iters), loss = 5.2716
I0410 13:49:46.371294 18534 solver.cpp:237]     Train net output #0: loss = 5.2716 (* 1 = 5.2716 loss)
I0410 13:49:46.371305 18534 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 13:49:50.519181 18534 solver.cpp:218] Iteration 2664 (2.89316 iter/s, 4.14772s/12 iters), loss = 5.28109
I0410 13:49:50.519234 18534 solver.cpp:237]     Train net output #0: loss = 5.28109 (* 1 = 5.28109 loss)
I0410 13:49:50.519246 18534 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 13:49:55.353940 18534 solver.cpp:218] Iteration 2676 (2.48216 iter/s, 4.8345s/12 iters), loss = 5.26909
I0410 13:49:55.354005 18534 solver.cpp:237]     Train net output #0: loss = 5.26909 (* 1 = 5.26909 loss)
I0410 13:49:55.354014 18534 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 13:50:00.256740 18534 solver.cpp:218] Iteration 2688 (2.44771 iter/s, 4.90253s/12 iters), loss = 5.25793
I0410 13:50:00.256788 18534 solver.cpp:237]     Train net output #0: loss = 5.25793 (* 1 = 5.25793 loss)
I0410 13:50:00.256799 18534 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 13:50:05.194787 18534 solver.cpp:218] Iteration 2700 (2.43023 iter/s, 4.9378s/12 iters), loss = 5.27926
I0410 13:50:05.194943 18534 solver.cpp:237]     Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
I0410 13:50:05.194954 18534 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 13:50:10.079912 18534 solver.cpp:218] Iteration 2712 (2.45661 iter/s, 4.88477s/12 iters), loss = 5.28214
I0410 13:50:10.079954 18534 solver.cpp:237]     Train net output #0: loss = 5.28214 (* 1 = 5.28214 loss)
I0410 13:50:10.079962 18534 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 13:50:14.998870 18534 solver.cpp:218] Iteration 2724 (2.43966 iter/s, 4.91871s/12 iters), loss = 5.25766
I0410 13:50:14.998917 18534 solver.cpp:237]     Train net output #0: loss = 5.25766 (* 1 = 5.25766 loss)
I0410 13:50:14.998926 18534 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 13:50:17.532485 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:19.906102 18534 solver.cpp:218] Iteration 2736 (2.4455 iter/s, 4.90698s/12 iters), loss = 5.28042
I0410 13:50:19.906160 18534 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0410 13:50:19.906172 18534 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 13:50:24.858215 18534 solver.cpp:218] Iteration 2748 (2.42334 iter/s, 4.95185s/12 iters), loss = 5.2766
I0410 13:50:24.858274 18534 solver.cpp:237]     Train net output #0: loss = 5.2766 (* 1 = 5.2766 loss)
I0410 13:50:24.858287 18534 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 13:50:26.820363 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 13:50:27.944939 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 13:50:28.333932 18534 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 13:50:28.333981 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:50:31.131075 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:50:31.730337 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:33.049293 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:50:33.049343 18534 solver.cpp:397]     Test net output #1: loss = 5.28656 (* 1 = 5.28656 loss)
I0410 13:50:34.845121 18534 solver.cpp:218] Iteration 2760 (1.20163 iter/s, 9.98645s/12 iters), loss = 5.27607
I0410 13:50:34.845176 18534 solver.cpp:237]     Train net output #0: loss = 5.27607 (* 1 = 5.27607 loss)
I0410 13:50:34.845188 18534 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 13:50:39.731323 18534 solver.cpp:218] Iteration 2772 (2.45601 iter/s, 4.88597s/12 iters), loss = 5.27817
I0410 13:50:39.734297 18534 solver.cpp:237]     Train net output #0: loss = 5.27817 (* 1 = 5.27817 loss)
I0410 13:50:39.734310 18534 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 13:50:44.596092 18534 solver.cpp:218] Iteration 2784 (2.4683 iter/s, 4.86165s/12 iters), loss = 5.27476
I0410 13:50:44.596148 18534 solver.cpp:237]     Train net output #0: loss = 5.27476 (* 1 = 5.27476 loss)
I0410 13:50:44.596158 18534 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 13:50:49.477886 18534 solver.cpp:218] Iteration 2796 (2.45822 iter/s, 4.88158s/12 iters), loss = 5.27196
I0410 13:50:49.477937 18534 solver.cpp:237]     Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
I0410 13:50:49.477947 18534 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 13:50:54.403802 18534 solver.cpp:218] Iteration 2808 (2.4362 iter/s, 4.92571s/12 iters), loss = 5.26155
I0410 13:50:54.403842 18534 solver.cpp:237]     Train net output #0: loss = 5.26155 (* 1 = 5.26155 loss)
I0410 13:50:54.403851 18534 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 13:50:59.278251 18534 solver.cpp:218] Iteration 2820 (2.46192 iter/s, 4.87425s/12 iters), loss = 5.27682
I0410 13:50:59.278295 18534 solver.cpp:237]     Train net output #0: loss = 5.27682 (* 1 = 5.27682 loss)
I0410 13:50:59.278303 18534 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 13:51:03.935477 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:04.222401 18534 solver.cpp:218] Iteration 2832 (2.42737 iter/s, 4.94362s/12 iters), loss = 5.26175
I0410 13:51:04.222458 18534 solver.cpp:237]     Train net output #0: loss = 5.26175 (* 1 = 5.26175 loss)
I0410 13:51:04.222470 18534 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 13:51:09.158187 18534 solver.cpp:218] Iteration 2844 (2.43133 iter/s, 4.93557s/12 iters), loss = 5.26725
I0410 13:51:09.158246 18534 solver.cpp:237]     Train net output #0: loss = 5.26725 (* 1 = 5.26725 loss)
I0410 13:51:09.158258 18534 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 13:51:13.581038 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 13:51:13.995906 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 13:51:14.208585 18534 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 13:51:14.208613 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:51:17.513069 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:18.649106 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:51:18.649155 18534 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 13:51:18.731928 18534 solver.cpp:218] Iteration 2856 (1.25347 iter/s, 9.57339s/12 iters), loss = 5.28775
I0410 13:51:18.731983 18534 solver.cpp:237]     Train net output #0: loss = 5.28775 (* 1 = 5.28775 loss)
I0410 13:51:18.731994 18534 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 13:51:22.875000 18534 solver.cpp:218] Iteration 2868 (2.89653 iter/s, 4.14289s/12 iters), loss = 5.28143
I0410 13:51:22.875041 18534 solver.cpp:237]     Train net output #0: loss = 5.28143 (* 1 = 5.28143 loss)
I0410 13:51:22.875048 18534 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 13:51:27.755273 18534 solver.cpp:218] Iteration 2880 (2.45898 iter/s, 4.88008s/12 iters), loss = 5.27903
I0410 13:51:27.755321 18534 solver.cpp:237]     Train net output #0: loss = 5.27903 (* 1 = 5.27903 loss)
I0410 13:51:27.755328 18534 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 13:51:32.787595 18534 solver.cpp:218] Iteration 2892 (2.38468 iter/s, 5.03211s/12 iters), loss = 5.27356
I0410 13:51:32.787643 18534 solver.cpp:237]     Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
I0410 13:51:32.787653 18534 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 13:51:37.711936 18534 solver.cpp:218] Iteration 2904 (2.43698 iter/s, 4.92413s/12 iters), loss = 5.25599
I0410 13:51:37.711987 18534 solver.cpp:237]     Train net output #0: loss = 5.25599 (* 1 = 5.25599 loss)
I0410 13:51:37.711998 18534 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 13:51:42.824540 18534 solver.cpp:218] Iteration 2916 (2.34724 iter/s, 5.11239s/12 iters), loss = 5.27277
I0410 13:51:42.824592 18534 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0410 13:51:42.824604 18534 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 13:51:47.748306 18534 solver.cpp:218] Iteration 2928 (2.43726 iter/s, 4.92355s/12 iters), loss = 5.27804
I0410 13:51:47.748435 18534 solver.cpp:237]     Train net output #0: loss = 5.27804 (* 1 = 5.27804 loss)
I0410 13:51:47.748445 18534 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 13:51:49.553267 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:52.669625 18534 solver.cpp:218] Iteration 2940 (2.43851 iter/s, 4.92103s/12 iters), loss = 5.28208
I0410 13:51:52.669672 18534 solver.cpp:237]     Train net output #0: loss = 5.28208 (* 1 = 5.28208 loss)
I0410 13:51:52.669682 18534 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 13:51:57.598073 18534 solver.cpp:218] Iteration 2952 (2.43495 iter/s, 4.92824s/12 iters), loss = 5.28108
I0410 13:51:57.598116 18534 solver.cpp:237]     Train net output #0: loss = 5.28108 (* 1 = 5.28108 loss)
I0410 13:51:57.598125 18534 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 13:51:59.564602 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 13:51:59.854683 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 13:52:00.051337 18534 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 13:52:00.051355 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:52:03.535028 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:04.711851 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:52:04.711900 18534 solver.cpp:397]     Test net output #1: loss = 5.28655 (* 1 = 5.28655 loss)
I0410 13:52:06.444269 18534 solver.cpp:218] Iteration 2964 (1.35656 iter/s, 8.84588s/12 iters), loss = 5.24114
I0410 13:52:06.444325 18534 solver.cpp:237]     Train net output #0: loss = 5.24114 (* 1 = 5.24114 loss)
I0410 13:52:06.444336 18534 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 13:52:11.304877 18534 solver.cpp:218] Iteration 2976 (2.46894 iter/s, 4.8604s/12 iters), loss = 5.28295
I0410 13:52:11.304929 18534 solver.cpp:237]     Train net output #0: loss = 5.28295 (* 1 = 5.28295 loss)
I0410 13:52:11.304940 18534 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 13:52:16.217334 18534 solver.cpp:218] Iteration 2988 (2.44287 iter/s, 4.91225s/12 iters), loss = 5.26275
I0410 13:52:16.217375 18534 solver.cpp:237]     Train net output #0: loss = 5.26275 (* 1 = 5.26275 loss)
I0410 13:52:16.217384 18534 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 13:52:21.063481 18534 solver.cpp:218] Iteration 3000 (2.4763 iter/s, 4.84594s/12 iters), loss = 5.26714
I0410 13:52:21.063587 18534 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0410 13:52:21.063598 18534 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 13:52:25.877792 18534 solver.cpp:218] Iteration 3012 (2.4927 iter/s, 4.81405s/12 iters), loss = 5.27472
I0410 13:52:25.877837 18534 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0410 13:52:25.877847 18534 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 13:52:30.778385 18534 solver.cpp:218] Iteration 3024 (2.44879 iter/s, 4.90039s/12 iters), loss = 5.25925
I0410 13:52:30.778434 18534 solver.cpp:237]     Train net output #0: loss = 5.25925 (* 1 = 5.25925 loss)
I0410 13:52:30.778443 18534 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 13:52:34.676970 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:35.671218 18534 solver.cpp:218] Iteration 3036 (2.45267 iter/s, 4.89262s/12 iters), loss = 5.25829
I0410 13:52:35.671270 18534 solver.cpp:237]     Train net output #0: loss = 5.25829 (* 1 = 5.25829 loss)
I0410 13:52:35.671281 18534 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 13:52:40.526640 18534 solver.cpp:218] Iteration 3048 (2.47157 iter/s, 4.85521s/12 iters), loss = 5.29219
I0410 13:52:40.526695 18534 solver.cpp:237]     Train net output #0: loss = 5.29219 (* 1 = 5.29219 loss)
I0410 13:52:40.526706 18534 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 13:52:44.937613 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 13:52:46.369163 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 13:52:47.555792 18534 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 13:52:47.555819 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:52:50.815780 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:52.042230 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:52:52.042393 18534 solver.cpp:397]     Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
I0410 13:52:52.128418 18534 solver.cpp:218] Iteration 3060 (1.03436 iter/s, 11.6014s/12 iters), loss = 5.2571
I0410 13:52:52.128473 18534 solver.cpp:237]     Train net output #0: loss = 5.2571 (* 1 = 5.2571 loss)
I0410 13:52:52.128484 18534 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 13:52:56.365445 18534 solver.cpp:218] Iteration 3072 (2.83231 iter/s, 4.23683s/12 iters), loss = 5.30645
I0410 13:52:56.365504 18534 solver.cpp:237]     Train net output #0: loss = 5.30645 (* 1 = 5.30645 loss)
I0410 13:52:56.365516 18534 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 13:53:01.333005 18534 solver.cpp:218] Iteration 3084 (2.41578 iter/s, 4.96734s/12 iters), loss = 5.27315
I0410 13:53:01.333053 18534 solver.cpp:237]     Train net output #0: loss = 5.27315 (* 1 = 5.27315 loss)
I0410 13:53:01.333062 18534 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 13:53:06.195055 18534 solver.cpp:218] Iteration 3096 (2.4682 iter/s, 4.86184s/12 iters), loss = 5.27319
I0410 13:53:06.195108 18534 solver.cpp:237]     Train net output #0: loss = 5.27319 (* 1 = 5.27319 loss)
I0410 13:53:06.195119 18534 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 13:53:11.138078 18534 solver.cpp:218] Iteration 3108 (2.42777 iter/s, 4.9428s/12 iters), loss = 5.27974
I0410 13:53:11.138130 18534 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0410 13:53:11.138140 18534 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 13:53:16.007977 18534 solver.cpp:218] Iteration 3120 (2.46422 iter/s, 4.86969s/12 iters), loss = 5.26781
I0410 13:53:16.008021 18534 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0410 13:53:16.008031 18534 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 13:53:20.886519 18534 solver.cpp:218] Iteration 3132 (2.45986 iter/s, 4.87833s/12 iters), loss = 5.27547
I0410 13:53:20.886574 18534 solver.cpp:237]     Train net output #0: loss = 5.27547 (* 1 = 5.27547 loss)
I0410 13:53:20.886586 18534 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 13:53:21.961688 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:25.735515 18534 solver.cpp:218] Iteration 3144 (2.47485 iter/s, 4.84878s/12 iters), loss = 5.28116
I0410 13:53:25.735637 18534 solver.cpp:237]     Train net output #0: loss = 5.28116 (* 1 = 5.28116 loss)
I0410 13:53:25.735652 18534 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 13:53:30.601389 18534 solver.cpp:218] Iteration 3156 (2.4663 iter/s, 4.86559s/12 iters), loss = 5.25061
I0410 13:53:30.601438 18534 solver.cpp:237]     Train net output #0: loss = 5.25061 (* 1 = 5.25061 loss)
I0410 13:53:30.601446 18534 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 13:53:32.575497 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 13:53:32.903156 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 13:53:33.115986 18534 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 13:53:33.116004 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:53:36.406147 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:37.667325 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:53:37.667374 18534 solver.cpp:397]     Test net output #1: loss = 5.28654 (* 1 = 5.28654 loss)
I0410 13:53:39.474241 18534 solver.cpp:218] Iteration 3168 (1.35249 iter/s, 8.87251s/12 iters), loss = 5.26818
I0410 13:53:39.474298 18534 solver.cpp:237]     Train net output #0: loss = 5.26818 (* 1 = 5.26818 loss)
I0410 13:53:39.474310 18534 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 13:53:44.561215 18534 solver.cpp:218] Iteration 3180 (2.35907 iter/s, 5.08675s/12 iters), loss = 5.27377
I0410 13:53:44.561259 18534 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0410 13:53:44.561269 18534 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 13:53:49.435550 18534 solver.cpp:218] Iteration 3192 (2.46198 iter/s, 4.87412s/12 iters), loss = 5.27766
I0410 13:53:49.435604 18534 solver.cpp:237]     Train net output #0: loss = 5.27766 (* 1 = 5.27766 loss)
I0410 13:53:49.435616 18534 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 13:53:54.327687 18534 solver.cpp:218] Iteration 3204 (2.45303 iter/s, 4.89192s/12 iters), loss = 5.26376
I0410 13:53:54.327736 18534 solver.cpp:237]     Train net output #0: loss = 5.26376 (* 1 = 5.26376 loss)
I0410 13:53:54.327747 18534 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 13:53:59.210074 18534 solver.cpp:218] Iteration 3216 (2.45792 iter/s, 4.88217s/12 iters), loss = 5.29043
I0410 13:53:59.210201 18534 solver.cpp:237]     Train net output #0: loss = 5.29043 (* 1 = 5.29043 loss)
I0410 13:53:59.210209 18534 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 13:54:04.109467 18534 solver.cpp:218] Iteration 3228 (2.44943 iter/s, 4.8991s/12 iters), loss = 5.27741
I0410 13:54:04.109516 18534 solver.cpp:237]     Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
I0410 13:54:04.109527 18534 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 13:54:07.289515 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:08.996781 18534 solver.cpp:218] Iteration 3240 (2.45544 iter/s, 4.8871s/12 iters), loss = 5.27958
I0410 13:54:08.996834 18534 solver.cpp:237]     Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
I0410 13:54:08.996846 18534 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 13:54:13.854673 18534 solver.cpp:218] Iteration 3252 (2.47032 iter/s, 4.85767s/12 iters), loss = 5.27332
I0410 13:54:13.854722 18534 solver.cpp:237]     Train net output #0: loss = 5.27332 (* 1 = 5.27332 loss)
I0410 13:54:13.854730 18534 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 13:54:18.331359 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 13:54:18.648033 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 13:54:18.852330 18534 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 13:54:18.852360 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:54:22.073942 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:23.464251 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:54:23.464301 18534 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0410 13:54:23.545702 18534 solver.cpp:218] Iteration 3264 (1.23831 iter/s, 9.69066s/12 iters), loss = 5.27891
I0410 13:54:23.545753 18534 solver.cpp:237]     Train net output #0: loss = 5.27891 (* 1 = 5.27891 loss)
I0410 13:54:23.545764 18534 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 13:54:27.519101 18534 solver.cpp:218] Iteration 3276 (3.02023 iter/s, 3.97321s/12 iters), loss = 5.29063
I0410 13:54:27.519140 18534 solver.cpp:237]     Train net output #0: loss = 5.29063 (* 1 = 5.29063 loss)
I0410 13:54:27.519148 18534 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 13:54:32.681118 18534 solver.cpp:218] Iteration 3288 (2.32477 iter/s, 5.1618s/12 iters), loss = 5.26022
I0410 13:54:32.681232 18534 solver.cpp:237]     Train net output #0: loss = 5.26022 (* 1 = 5.26022 loss)
I0410 13:54:32.681244 18534 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 13:54:37.611120 18534 solver.cpp:218] Iteration 3300 (2.43421 iter/s, 4.92972s/12 iters), loss = 5.28152
I0410 13:54:37.611160 18534 solver.cpp:237]     Train net output #0: loss = 5.28152 (* 1 = 5.28152 loss)
I0410 13:54:37.611171 18534 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 13:54:42.682767 18534 solver.cpp:218] Iteration 3312 (2.3662 iter/s, 5.07143s/12 iters), loss = 5.25691
I0410 13:54:42.682818 18534 solver.cpp:237]     Train net output #0: loss = 5.25691 (* 1 = 5.25691 loss)
I0410 13:54:42.682830 18534 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 13:54:47.633766 18534 solver.cpp:218] Iteration 3324 (2.42386 iter/s, 4.95078s/12 iters), loss = 5.28168
I0410 13:54:47.633822 18534 solver.cpp:237]     Train net output #0: loss = 5.28168 (* 1 = 5.28168 loss)
I0410 13:54:47.633834 18534 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 13:54:52.568141 18534 solver.cpp:218] Iteration 3336 (2.43203 iter/s, 4.93415s/12 iters), loss = 5.27565
I0410 13:54:52.568182 18534 solver.cpp:237]     Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
I0410 13:54:52.568192 18534 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 13:54:53.042114 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:57.530052 18534 solver.cpp:218] Iteration 3348 (2.41853 iter/s, 4.9617s/12 iters), loss = 5.27632
I0410 13:54:57.530107 18534 solver.cpp:237]     Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
I0410 13:54:57.530118 18534 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 13:55:02.408170 18534 solver.cpp:218] Iteration 3360 (2.46008 iter/s, 4.87789s/12 iters), loss = 5.26746
I0410 13:55:02.408226 18534 solver.cpp:237]     Train net output #0: loss = 5.26746 (* 1 = 5.26746 loss)
I0410 13:55:02.408237 18534 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 13:55:04.385883 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 13:55:04.713044 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 13:55:04.924924 18534 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 13:55:04.924954 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:55:08.093315 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:09.423844 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:55:09.423889 18534 solver.cpp:397]     Test net output #1: loss = 5.28673 (* 1 = 5.28673 loss)
I0410 13:55:11.288753 18534 solver.cpp:218] Iteration 3372 (1.35132 iter/s, 8.88023s/12 iters), loss = 5.28676
I0410 13:55:11.288801 18534 solver.cpp:237]     Train net output #0: loss = 5.28676 (* 1 = 5.28676 loss)
I0410 13:55:11.288810 18534 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 13:55:16.247691 18534 solver.cpp:218] Iteration 3384 (2.41998 iter/s, 4.95872s/12 iters), loss = 5.26803
I0410 13:55:16.247742 18534 solver.cpp:237]     Train net output #0: loss = 5.26803 (* 1 = 5.26803 loss)
I0410 13:55:16.247753 18534 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 13:55:21.186200 18534 solver.cpp:218] Iteration 3396 (2.43 iter/s, 4.93828s/12 iters), loss = 5.26322
I0410 13:55:21.186257 18534 solver.cpp:237]     Train net output #0: loss = 5.26322 (* 1 = 5.26322 loss)
I0410 13:55:21.186269 18534 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 13:55:26.064620 18534 solver.cpp:218] Iteration 3408 (2.45993 iter/s, 4.87818s/12 iters), loss = 5.28842
I0410 13:55:26.064680 18534 solver.cpp:237]     Train net output #0: loss = 5.28842 (* 1 = 5.28842 loss)
I0410 13:55:26.064692 18534 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 13:55:31.097153 18534 solver.cpp:218] Iteration 3420 (2.3846 iter/s, 5.0323s/12 iters), loss = 5.27726
I0410 13:55:31.097204 18534 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0410 13:55:31.097215 18534 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 13:55:35.930235 18534 solver.cpp:218] Iteration 3432 (2.483 iter/s, 4.83286s/12 iters), loss = 5.25867
I0410 13:55:35.936615 18534 solver.cpp:237]     Train net output #0: loss = 5.25867 (* 1 = 5.25867 loss)
I0410 13:55:35.936628 18534 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 13:55:38.459511 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:40.840502 18534 solver.cpp:218] Iteration 3444 (2.44712 iter/s, 4.90372s/12 iters), loss = 5.27741
I0410 13:55:40.840554 18534 solver.cpp:237]     Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
I0410 13:55:40.840567 18534 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 13:55:45.750393 18534 solver.cpp:218] Iteration 3456 (2.44416 iter/s, 4.90967s/12 iters), loss = 5.27324
I0410 13:55:45.750447 18534 solver.cpp:237]     Train net output #0: loss = 5.27324 (* 1 = 5.27324 loss)
I0410 13:55:45.750459 18534 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 13:55:50.177436 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 13:55:51.559549 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 13:55:52.828866 18534 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 13:55:52.828888 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:55:52.852106 18534 blocking_queue.cpp:49] Waiting for data
I0410 13:55:55.897608 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:57.273559 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:55:57.273602 18534 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0410 13:55:57.356009 18534 solver.cpp:218] Iteration 3468 (1.03402 iter/s, 11.6052s/12 iters), loss = 5.27228
I0410 13:55:57.356058 18534 solver.cpp:237]     Train net output #0: loss = 5.27228 (* 1 = 5.27228 loss)
I0410 13:55:57.356068 18534 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 13:56:01.548080 18534 solver.cpp:218] Iteration 3480 (2.86268 iter/s, 4.19187s/12 iters), loss = 5.27969
I0410 13:56:01.548125 18534 solver.cpp:237]     Train net output #0: loss = 5.27969 (* 1 = 5.27969 loss)
I0410 13:56:01.548135 18534 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 13:56:06.452253 18534 solver.cpp:218] Iteration 3492 (2.447 iter/s, 4.90396s/12 iters), loss = 5.29013
I0410 13:56:06.452379 18534 solver.cpp:237]     Train net output #0: loss = 5.29013 (* 1 = 5.29013 loss)
I0410 13:56:06.452391 18534 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 13:56:11.352530 18534 solver.cpp:218] Iteration 3504 (2.44899 iter/s, 4.89998s/12 iters), loss = 5.27107
I0410 13:56:11.352581 18534 solver.cpp:237]     Train net output #0: loss = 5.27107 (* 1 = 5.27107 loss)
I0410 13:56:11.352592 18534 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 13:56:16.285550 18534 solver.cpp:218] Iteration 3516 (2.4327 iter/s, 4.9328s/12 iters), loss = 5.26419
I0410 13:56:16.285595 18534 solver.cpp:237]     Train net output #0: loss = 5.26419 (* 1 = 5.26419 loss)
I0410 13:56:16.285604 18534 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 13:56:21.198030 18534 solver.cpp:218] Iteration 3528 (2.44287 iter/s, 4.91226s/12 iters), loss = 5.27082
I0410 13:56:21.198086 18534 solver.cpp:237]     Train net output #0: loss = 5.27082 (* 1 = 5.27082 loss)
I0410 13:56:21.198099 18534 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 13:56:25.854604 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:26.112296 18534 solver.cpp:218] Iteration 3540 (2.44198 iter/s, 4.91404s/12 iters), loss = 5.25907
I0410 13:56:26.112346 18534 solver.cpp:237]     Train net output #0: loss = 5.25907 (* 1 = 5.25907 loss)
I0410 13:56:26.112358 18534 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 13:56:31.059653 18534 solver.cpp:218] Iteration 3552 (2.42565 iter/s, 4.94713s/12 iters), loss = 5.26499
I0410 13:56:31.059710 18534 solver.cpp:237]     Train net output #0: loss = 5.26499 (* 1 = 5.26499 loss)
I0410 13:56:31.059720 18534 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 13:56:35.981331 18534 solver.cpp:218] Iteration 3564 (2.4383 iter/s, 4.92145s/12 iters), loss = 5.29169
I0410 13:56:35.981375 18534 solver.cpp:237]     Train net output #0: loss = 5.29169 (* 1 = 5.29169 loss)
I0410 13:56:35.981384 18534 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 13:56:37.971051 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 13:56:38.267185 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 13:56:38.475437 18534 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 13:56:38.475455 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:56:41.554607 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:42.985621 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:56:42.985672 18534 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 13:56:44.710381 18534 solver.cpp:218] Iteration 3576 (1.37478 iter/s, 8.7287s/12 iters), loss = 5.28262
I0410 13:56:44.710433 18534 solver.cpp:237]     Train net output #0: loss = 5.28262 (* 1 = 5.28262 loss)
I0410 13:56:44.710443 18534 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 13:56:49.617306 18534 solver.cpp:218] Iteration 3588 (2.44564 iter/s, 4.90669s/12 iters), loss = 5.27672
I0410 13:56:49.617362 18534 solver.cpp:237]     Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
I0410 13:56:49.617374 18534 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 13:56:54.739559 18534 solver.cpp:218] Iteration 3600 (2.34283 iter/s, 5.12201s/12 iters), loss = 5.263
I0410 13:56:54.739610 18534 solver.cpp:237]     Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
I0410 13:56:54.739622 18534 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 13:56:59.749919 18534 solver.cpp:218] Iteration 3612 (2.39515 iter/s, 5.01013s/12 iters), loss = 5.24175
I0410 13:56:59.749997 18534 solver.cpp:237]     Train net output #0: loss = 5.24175 (* 1 = 5.24175 loss)
I0410 13:56:59.750008 18534 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 13:57:04.594909 18534 solver.cpp:218] Iteration 3624 (2.47691 iter/s, 4.84474s/12 iters), loss = 5.27466
I0410 13:57:04.594947 18534 solver.cpp:237]     Train net output #0: loss = 5.27466 (* 1 = 5.27466 loss)
I0410 13:57:04.594955 18534 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 13:57:09.507524 18534 solver.cpp:218] Iteration 3636 (2.4428 iter/s, 4.91239s/12 iters), loss = 5.2805
I0410 13:57:09.507616 18534 solver.cpp:237]     Train net output #0: loss = 5.2805 (* 1 = 5.2805 loss)
I0410 13:57:09.507627 18534 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 13:57:11.356223 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:14.405850 18534 solver.cpp:218] Iteration 3648 (2.44995 iter/s, 4.89806s/12 iters), loss = 5.28628
I0410 13:57:14.405897 18534 solver.cpp:237]     Train net output #0: loss = 5.28628 (* 1 = 5.28628 loss)
I0410 13:57:14.405906 18534 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 13:57:19.265648 18534 solver.cpp:218] Iteration 3660 (2.46935 iter/s, 4.85957s/12 iters), loss = 5.27537
I0410 13:57:19.265695 18534 solver.cpp:237]     Train net output #0: loss = 5.27537 (* 1 = 5.27537 loss)
I0410 13:57:19.265704 18534 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 13:57:23.642071 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 13:57:23.966810 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 13:57:24.181293 18534 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 13:57:24.181314 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:57:27.102458 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:28.578889 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:57:28.578936 18534 solver.cpp:397]     Test net output #1: loss = 5.28629 (* 1 = 5.28629 loss)
I0410 13:57:28.657272 18534 solver.cpp:218] Iteration 3672 (1.27779 iter/s, 9.39125s/12 iters), loss = 5.25118
I0410 13:57:28.657330 18534 solver.cpp:237]     Train net output #0: loss = 5.25118 (* 1 = 5.25118 loss)
I0410 13:57:28.657344 18534 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 13:57:32.776001 18534 solver.cpp:218] Iteration 3684 (2.91367 iter/s, 4.11852s/12 iters), loss = 5.26885
I0410 13:57:32.776058 18534 solver.cpp:237]     Train net output #0: loss = 5.26885 (* 1 = 5.26885 loss)
I0410 13:57:32.776068 18534 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 13:57:37.583217 18534 solver.cpp:218] Iteration 3696 (2.49637 iter/s, 4.80698s/12 iters), loss = 5.26058
I0410 13:57:37.583276 18534 solver.cpp:237]     Train net output #0: loss = 5.26058 (* 1 = 5.26058 loss)
I0410 13:57:37.583287 18534 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 13:57:42.419710 18534 solver.cpp:218] Iteration 3708 (2.48126 iter/s, 4.83625s/12 iters), loss = 5.26908
I0410 13:57:42.419848 18534 solver.cpp:237]     Train net output #0: loss = 5.26908 (* 1 = 5.26908 loss)
I0410 13:57:42.419860 18534 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 13:57:47.227048 18534 solver.cpp:218] Iteration 3720 (2.49635 iter/s, 4.80703s/12 iters), loss = 5.27118
I0410 13:57:47.227111 18534 solver.cpp:237]     Train net output #0: loss = 5.27118 (* 1 = 5.27118 loss)
I0410 13:57:47.227124 18534 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 13:57:52.165319 18534 solver.cpp:218] Iteration 3732 (2.43012 iter/s, 4.93803s/12 iters), loss = 5.25511
I0410 13:57:52.165366 18534 solver.cpp:237]     Train net output #0: loss = 5.25511 (* 1 = 5.25511 loss)
I0410 13:57:52.165377 18534 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 13:57:56.073307 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:57.065570 18534 solver.cpp:218] Iteration 3744 (2.44897 iter/s, 4.90002s/12 iters), loss = 5.25759
I0410 13:57:57.065625 18534 solver.cpp:237]     Train net output #0: loss = 5.25759 (* 1 = 5.25759 loss)
I0410 13:57:57.065637 18534 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 13:58:01.977952 18534 solver.cpp:218] Iteration 3756 (2.44292 iter/s, 4.91215s/12 iters), loss = 5.2806
I0410 13:58:01.978009 18534 solver.cpp:237]     Train net output #0: loss = 5.2806 (* 1 = 5.2806 loss)
I0410 13:58:01.978018 18534 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 13:58:06.889197 18534 solver.cpp:218] Iteration 3768 (2.44349 iter/s, 4.91101s/12 iters), loss = 5.26327
I0410 13:58:06.889256 18534 solver.cpp:237]     Train net output #0: loss = 5.26327 (* 1 = 5.26327 loss)
I0410 13:58:06.889268 18534 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 13:58:08.876515 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 13:58:09.202071 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 13:58:09.416577 18534 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 13:58:09.416600 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:58:12.360644 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:13.849270 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:58:13.849368 18534 solver.cpp:397]     Test net output #1: loss = 5.28716 (* 1 = 5.28716 loss)
I0410 13:58:15.687021 18534 solver.cpp:218] Iteration 3780 (1.36403 iter/s, 8.79746s/12 iters), loss = 5.30948
I0410 13:58:15.687062 18534 solver.cpp:237]     Train net output #0: loss = 5.30948 (* 1 = 5.30948 loss)
I0410 13:58:15.687072 18534 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 13:58:20.624857 18534 solver.cpp:218] Iteration 3792 (2.43032 iter/s, 4.93761s/12 iters), loss = 5.27702
I0410 13:58:20.624898 18534 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0410 13:58:20.624907 18534 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 13:58:25.608675 18534 solver.cpp:218] Iteration 3804 (2.4079 iter/s, 4.98359s/12 iters), loss = 5.26806
I0410 13:58:25.608732 18534 solver.cpp:237]     Train net output #0: loss = 5.26806 (* 1 = 5.26806 loss)
I0410 13:58:25.608745 18534 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 13:58:30.409615 18534 solver.cpp:218] Iteration 3816 (2.49963 iter/s, 4.8007s/12 iters), loss = 5.27194
I0410 13:58:30.409675 18534 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0410 13:58:30.409687 18534 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 13:58:35.417536 18534 solver.cpp:218] Iteration 3828 (2.39632 iter/s, 5.00767s/12 iters), loss = 5.26481
I0410 13:58:35.417590 18534 solver.cpp:237]     Train net output #0: loss = 5.26481 (* 1 = 5.26481 loss)
I0410 13:58:35.417603 18534 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 13:58:40.321696 18534 solver.cpp:218] Iteration 3840 (2.44702 iter/s, 4.90393s/12 iters), loss = 5.26797
I0410 13:58:40.321755 18534 solver.cpp:237]     Train net output #0: loss = 5.26797 (* 1 = 5.26797 loss)
I0410 13:58:40.321768 18534 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 13:58:41.450762 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:45.258486 18534 solver.cpp:218] Iteration 3852 (2.43085 iter/s, 4.93655s/12 iters), loss = 5.27317
I0410 13:58:45.258611 18534 solver.cpp:237]     Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
I0410 13:58:45.258625 18534 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 13:58:50.129521 18534 solver.cpp:218] Iteration 3864 (2.46369 iter/s, 4.87074s/12 iters), loss = 5.25396
I0410 13:58:50.129565 18534 solver.cpp:237]     Train net output #0: loss = 5.25396 (* 1 = 5.25396 loss)
I0410 13:58:50.129575 18534 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 13:58:54.586144 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 13:58:54.878114 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 13:58:55.074946 18534 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 13:58:55.074968 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:58:57.928241 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:59.465678 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:58:59.465708 18534 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 13:58:59.547889 18534 solver.cpp:218] Iteration 3876 (1.27416 iter/s, 9.41799s/12 iters), loss = 5.27159
I0410 13:58:59.547931 18534 solver.cpp:237]     Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
I0410 13:58:59.547940 18534 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 13:59:03.599046 18534 solver.cpp:218] Iteration 3888 (2.96225 iter/s, 4.05097s/12 iters), loss = 5.26901
I0410 13:59:03.599089 18534 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0410 13:59:03.599097 18534 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 13:59:08.544872 18534 solver.cpp:218] Iteration 3900 (2.4264 iter/s, 4.9456s/12 iters), loss = 5.27367
I0410 13:59:08.544915 18534 solver.cpp:237]     Train net output #0: loss = 5.27367 (* 1 = 5.27367 loss)
I0410 13:59:08.544924 18534 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 13:59:13.625157 18534 solver.cpp:218] Iteration 3912 (2.36218 iter/s, 5.08005s/12 iters), loss = 5.26214
I0410 13:59:13.625212 18534 solver.cpp:237]     Train net output #0: loss = 5.26214 (* 1 = 5.26214 loss)
I0410 13:59:13.625224 18534 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 13:59:18.470074 18534 solver.cpp:218] Iteration 3924 (2.47694 iter/s, 4.84468s/12 iters), loss = 5.29213
I0410 13:59:18.470201 18534 solver.cpp:237]     Train net output #0: loss = 5.29213 (* 1 = 5.29213 loss)
I0410 13:59:18.470214 18534 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 13:59:23.263891 18534 solver.cpp:218] Iteration 3936 (2.50338 iter/s, 4.79351s/12 iters), loss = 5.27368
I0410 13:59:23.263948 18534 solver.cpp:237]     Train net output #0: loss = 5.27368 (* 1 = 5.27368 loss)
I0410 13:59:23.263960 18534 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 13:59:26.513597 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:28.096388 18534 solver.cpp:218] Iteration 3948 (2.48331 iter/s, 4.83226s/12 iters), loss = 5.28531
I0410 13:59:28.096451 18534 solver.cpp:237]     Train net output #0: loss = 5.28531 (* 1 = 5.28531 loss)
I0410 13:59:28.096462 18534 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 13:59:32.900781 18534 solver.cpp:218] Iteration 3960 (2.49784 iter/s, 4.80415s/12 iters), loss = 5.27059
I0410 13:59:32.900840 18534 solver.cpp:237]     Train net output #0: loss = 5.27059 (* 1 = 5.27059 loss)
I0410 13:59:32.900851 18534 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 13:59:37.722442 18534 solver.cpp:218] Iteration 3972 (2.48889 iter/s, 4.82143s/12 iters), loss = 5.27986
I0410 13:59:37.722501 18534 solver.cpp:237]     Train net output #0: loss = 5.27986 (* 1 = 5.27986 loss)
I0410 13:59:37.722512 18534 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 13:59:39.683574 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 13:59:39.992170 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 13:59:40.202196 18534 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 13:59:40.202219 18534 net.cpp:676] Ignoring source layer train-data
I0410 13:59:43.038408 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:44.612443 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:59:44.612475 18534 solver.cpp:397]     Test net output #1: loss = 5.28652 (* 1 = 5.28652 loss)
I0410 13:59:46.374650 18534 solver.cpp:218] Iteration 3984 (1.38699 iter/s, 8.65184s/12 iters), loss = 5.2827
I0410 13:59:46.374707 18534 solver.cpp:237]     Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
I0410 13:59:46.374718 18534 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 13:59:51.280862 18534 solver.cpp:218] Iteration 3996 (2.446 iter/s, 4.90597s/12 iters), loss = 5.26268
I0410 13:59:51.280973 18534 solver.cpp:237]     Train net output #0: loss = 5.26268 (* 1 = 5.26268 loss)
I0410 13:59:51.280985 18534 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 13:59:56.186911 18534 solver.cpp:218] Iteration 4008 (2.44611 iter/s, 4.90576s/12 iters), loss = 5.28665
I0410 13:59:56.186964 18534 solver.cpp:237]     Train net output #0: loss = 5.28665 (* 1 = 5.28665 loss)
I0410 13:59:56.186976 18534 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 14:00:01.065727 18534 solver.cpp:218] Iteration 4020 (2.45973 iter/s, 4.87859s/12 iters), loss = 5.25567
I0410 14:00:01.065768 18534 solver.cpp:237]     Train net output #0: loss = 5.25567 (* 1 = 5.25567 loss)
I0410 14:00:01.065778 18534 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 14:00:05.957449 18534 solver.cpp:218] Iteration 4032 (2.45324 iter/s, 4.8915s/12 iters), loss = 5.27469
I0410 14:00:05.957502 18534 solver.cpp:237]     Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
I0410 14:00:05.957515 18534 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 14:00:10.826404 18534 solver.cpp:218] Iteration 4044 (2.46471 iter/s, 4.86872s/12 iters), loss = 5.27338
I0410 14:00:10.826459 18534 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0410 14:00:10.826470 18534 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 14:00:11.326936 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:15.706691 18534 solver.cpp:218] Iteration 4056 (2.45899 iter/s, 4.88005s/12 iters), loss = 5.2729
I0410 14:00:15.706738 18534 solver.cpp:237]     Train net output #0: loss = 5.2729 (* 1 = 5.2729 loss)
I0410 14:00:15.706749 18534 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 14:00:20.579270 18534 solver.cpp:218] Iteration 4068 (2.46288 iter/s, 4.87235s/12 iters), loss = 5.27384
I0410 14:00:20.579319 18534 solver.cpp:237]     Train net output #0: loss = 5.27384 (* 1 = 5.27384 loss)
I0410 14:00:20.579329 18534 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 14:00:25.114763 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 14:00:25.443506 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 14:00:25.658491 18534 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 14:00:25.658515 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:00:28.433228 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:30.037674 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:00:30.037724 18534 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0410 14:00:30.120396 18534 solver.cpp:218] Iteration 4080 (1.25777 iter/s, 9.54073s/12 iters), loss = 5.28422
I0410 14:00:30.120474 18534 solver.cpp:237]     Train net output #0: loss = 5.28422 (* 1 = 5.28422 loss)
I0410 14:00:30.120491 18534 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 14:00:34.250809 18534 solver.cpp:218] Iteration 4092 (2.90544 iter/s, 4.13019s/12 iters), loss = 5.26395
I0410 14:00:34.250862 18534 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0410 14:00:34.250874 18534 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 14:00:39.044597 18534 solver.cpp:218] Iteration 4104 (2.50336 iter/s, 4.79355s/12 iters), loss = 5.26388
I0410 14:00:39.044652 18534 solver.cpp:237]     Train net output #0: loss = 5.26388 (* 1 = 5.26388 loss)
I0410 14:00:39.044665 18534 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 14:00:44.008086 18534 solver.cpp:218] Iteration 4116 (2.41777 iter/s, 4.96325s/12 iters), loss = 5.29101
I0410 14:00:44.008133 18534 solver.cpp:237]     Train net output #0: loss = 5.29101 (* 1 = 5.29101 loss)
I0410 14:00:44.008144 18534 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 14:00:48.892021 18534 solver.cpp:218] Iteration 4128 (2.45715 iter/s, 4.88371s/12 iters), loss = 5.26641
I0410 14:00:48.892063 18534 solver.cpp:237]     Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
I0410 14:00:48.892071 18534 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 14:00:53.826246 18534 solver.cpp:218] Iteration 4140 (2.43211 iter/s, 4.934s/12 iters), loss = 5.26064
I0410 14:00:53.826300 18534 solver.cpp:237]     Train net output #0: loss = 5.26064 (* 1 = 5.26064 loss)
I0410 14:00:53.826313 18534 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 14:00:56.418938 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:58.769977 18534 solver.cpp:218] Iteration 4152 (2.42744 iter/s, 4.94347s/12 iters), loss = 5.27218
I0410 14:00:58.770038 18534 solver.cpp:237]     Train net output #0: loss = 5.27218 (* 1 = 5.27218 loss)
I0410 14:00:58.770051 18534 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 14:00:58.770494 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:01:03.679359 18534 solver.cpp:218] Iteration 4164 (2.44442 iter/s, 4.90914s/12 iters), loss = 5.26368
I0410 14:01:03.679402 18534 solver.cpp:237]     Train net output #0: loss = 5.26368 (* 1 = 5.26368 loss)
I0410 14:01:03.679411 18534 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 14:01:08.550801 18534 solver.cpp:218] Iteration 4176 (2.46345 iter/s, 4.87122s/12 iters), loss = 5.2665
I0410 14:01:08.550849 18534 solver.cpp:237]     Train net output #0: loss = 5.2665 (* 1 = 5.2665 loss)
I0410 14:01:08.550859 18534 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 14:01:10.534106 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 14:01:11.054617 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 14:01:11.974507 18534 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 14:01:11.974535 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:01:14.745904 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:16.399490 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:01:16.399523 18534 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 14:01:18.126052 18534 solver.cpp:218] Iteration 4188 (1.25328 iter/s, 9.57485s/12 iters), loss = 5.26747
I0410 14:01:18.126106 18534 solver.cpp:237]     Train net output #0: loss = 5.26747 (* 1 = 5.26747 loss)
I0410 14:01:18.126116 18534 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 14:01:23.035342 18534 solver.cpp:218] Iteration 4200 (2.44447 iter/s, 4.90905s/12 iters), loss = 5.28457
I0410 14:01:23.035392 18534 solver.cpp:237]     Train net output #0: loss = 5.28457 (* 1 = 5.28457 loss)
I0410 14:01:23.035400 18534 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 14:01:27.942524 18534 solver.cpp:218] Iteration 4212 (2.44551 iter/s, 4.90695s/12 iters), loss = 5.27002
I0410 14:01:27.942639 18534 solver.cpp:237]     Train net output #0: loss = 5.27002 (* 1 = 5.27002 loss)
I0410 14:01:27.942651 18534 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 14:01:33.049587 18534 solver.cpp:218] Iteration 4224 (2.34983 iter/s, 5.10675s/12 iters), loss = 5.26431
I0410 14:01:33.049634 18534 solver.cpp:237]     Train net output #0: loss = 5.26431 (* 1 = 5.26431 loss)
I0410 14:01:33.049643 18534 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 14:01:38.062141 18534 solver.cpp:218] Iteration 4236 (2.3941 iter/s, 5.01231s/12 iters), loss = 5.26836
I0410 14:01:38.062188 18534 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0410 14:01:38.062198 18534 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 14:01:42.710424 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:42.935158 18534 solver.cpp:218] Iteration 4248 (2.46266 iter/s, 4.87278s/12 iters), loss = 5.24199
I0410 14:01:42.935207 18534 solver.cpp:237]     Train net output #0: loss = 5.24199 (* 1 = 5.24199 loss)
I0410 14:01:42.935220 18534 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 14:01:47.822834 18534 solver.cpp:218] Iteration 4260 (2.45527 iter/s, 4.88744s/12 iters), loss = 5.2661
I0410 14:01:47.822890 18534 solver.cpp:237]     Train net output #0: loss = 5.2661 (* 1 = 5.2661 loss)
I0410 14:01:47.822902 18534 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 14:01:53.055656 18534 solver.cpp:218] Iteration 4272 (2.29333 iter/s, 5.23257s/12 iters), loss = 5.29128
I0410 14:01:53.055703 18534 solver.cpp:237]     Train net output #0: loss = 5.29128 (* 1 = 5.29128 loss)
I0410 14:01:53.055712 18534 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 14:01:57.678666 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 14:01:57.981794 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 14:01:58.179260 18534 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 14:01:58.179280 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:02:01.022521 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:02.714507 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:02:02.714558 18534 solver.cpp:397]     Test net output #1: loss = 5.28679 (* 1 = 5.28679 loss)
I0410 14:02:02.797042 18534 solver.cpp:218] Iteration 4284 (1.23191 iter/s, 9.74098s/12 iters), loss = 5.27796
I0410 14:02:02.797094 18534 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0410 14:02:02.797106 18534 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 14:02:06.928781 18534 solver.cpp:218] Iteration 4296 (2.90449 iter/s, 4.13153s/12 iters), loss = 5.27674
I0410 14:02:06.928828 18534 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0410 14:02:06.928838 18534 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 14:02:11.796927 18534 solver.cpp:218] Iteration 4308 (2.46512 iter/s, 4.86791s/12 iters), loss = 5.26318
I0410 14:02:11.796978 18534 solver.cpp:237]     Train net output #0: loss = 5.26318 (* 1 = 5.26318 loss)
I0410 14:02:11.796990 18534 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 14:02:16.721012 18534 solver.cpp:218] Iteration 4320 (2.43712 iter/s, 4.92384s/12 iters), loss = 5.24947
I0410 14:02:16.721079 18534 solver.cpp:237]     Train net output #0: loss = 5.24947 (* 1 = 5.24947 loss)
I0410 14:02:16.721099 18534 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 14:02:21.630014 18534 solver.cpp:218] Iteration 4332 (2.44461 iter/s, 4.90876s/12 iters), loss = 5.278
I0410 14:02:21.630060 18534 solver.cpp:237]     Train net output #0: loss = 5.278 (* 1 = 5.278 loss)
I0410 14:02:21.630069 18534 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 14:02:26.701479 18534 solver.cpp:218] Iteration 4344 (2.36629 iter/s, 5.07122s/12 iters), loss = 5.28056
I0410 14:02:26.701532 18534 solver.cpp:237]     Train net output #0: loss = 5.28056 (* 1 = 5.28056 loss)
I0410 14:02:26.701543 18534 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 14:02:28.582222 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:31.638788 18534 solver.cpp:218] Iteration 4356 (2.43059 iter/s, 4.93707s/12 iters), loss = 5.29065
I0410 14:02:31.638837 18534 solver.cpp:237]     Train net output #0: loss = 5.29065 (* 1 = 5.29065 loss)
I0410 14:02:31.638849 18534 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 14:02:36.499662 18534 solver.cpp:218] Iteration 4368 (2.46881 iter/s, 4.86064s/12 iters), loss = 5.2746
I0410 14:02:36.499711 18534 solver.cpp:237]     Train net output #0: loss = 5.2746 (* 1 = 5.2746 loss)
I0410 14:02:36.499719 18534 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 14:02:41.381331 18534 solver.cpp:218] Iteration 4380 (2.45829 iter/s, 4.88143s/12 iters), loss = 5.26128
I0410 14:02:41.381374 18534 solver.cpp:237]     Train net output #0: loss = 5.26128 (* 1 = 5.26128 loss)
I0410 14:02:41.381383 18534 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 14:02:43.338568 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 14:02:43.717167 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 14:02:43.926362 18534 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 14:02:43.926380 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:02:46.586336 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:48.343533 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:02:48.343582 18534 solver.cpp:397]     Test net output #1: loss = 5.28674 (* 1 = 5.28674 loss)
I0410 14:02:50.202044 18534 solver.cpp:218] Iteration 4392 (1.36049 iter/s, 8.82034s/12 iters), loss = 5.26825
I0410 14:02:50.202101 18534 solver.cpp:237]     Train net output #0: loss = 5.26825 (* 1 = 5.26825 loss)
I0410 14:02:50.202112 18534 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 14:02:55.031904 18534 solver.cpp:218] Iteration 4404 (2.48467 iter/s, 4.82962s/12 iters), loss = 5.26546
I0410 14:02:55.031961 18534 solver.cpp:237]     Train net output #0: loss = 5.26546 (* 1 = 5.26546 loss)
I0410 14:02:55.031972 18534 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 14:02:59.963778 18534 solver.cpp:218] Iteration 4416 (2.43327 iter/s, 4.93162s/12 iters), loss = 5.26398
I0410 14:02:59.963855 18534 solver.cpp:237]     Train net output #0: loss = 5.26398 (* 1 = 5.26398 loss)
I0410 14:02:59.963865 18534 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 14:03:04.874770 18534 solver.cpp:218] Iteration 4428 (2.44363 iter/s, 4.91072s/12 iters), loss = 5.26908
I0410 14:03:04.874830 18534 solver.cpp:237]     Train net output #0: loss = 5.26908 (* 1 = 5.26908 loss)
I0410 14:03:04.874842 18534 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 14:03:09.781232 18534 solver.cpp:218] Iteration 4440 (2.44587 iter/s, 4.90622s/12 iters), loss = 5.26285
I0410 14:03:09.781280 18534 solver.cpp:237]     Train net output #0: loss = 5.26285 (* 1 = 5.26285 loss)
I0410 14:03:09.781288 18534 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 14:03:13.756917 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:14.709981 18534 solver.cpp:218] Iteration 4452 (2.43482 iter/s, 4.9285s/12 iters), loss = 5.25755
I0410 14:03:14.710029 18534 solver.cpp:237]     Train net output #0: loss = 5.25755 (* 1 = 5.25755 loss)
I0410 14:03:14.710037 18534 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 14:03:19.630417 18534 solver.cpp:218] Iteration 4464 (2.43893 iter/s, 4.92019s/12 iters), loss = 5.27974
I0410 14:03:19.630465 18534 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0410 14:03:19.630472 18534 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 14:03:24.550278 18534 solver.cpp:218] Iteration 4476 (2.43921 iter/s, 4.91963s/12 iters), loss = 5.25846
I0410 14:03:24.550333 18534 solver.cpp:237]     Train net output #0: loss = 5.25846 (* 1 = 5.25846 loss)
I0410 14:03:24.550344 18534 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 14:03:29.001111 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 14:03:29.701809 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 14:03:29.917263 18534 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 14:03:29.917285 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:03:32.550026 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:34.429863 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:03:34.429908 18534 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0410 14:03:34.512379 18534 solver.cpp:218] Iteration 4488 (1.20462 iter/s, 9.96168s/12 iters), loss = 5.30794
I0410 14:03:34.512430 18534 solver.cpp:237]     Train net output #0: loss = 5.30794 (* 1 = 5.30794 loss)
I0410 14:03:34.512441 18534 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 14:03:38.738265 18534 solver.cpp:218] Iteration 4500 (2.83979 iter/s, 4.22567s/12 iters), loss = 5.27322
I0410 14:03:38.738320 18534 solver.cpp:237]     Train net output #0: loss = 5.27322 (* 1 = 5.27322 loss)
I0410 14:03:38.738332 18534 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 14:03:43.542407 18534 solver.cpp:218] Iteration 4512 (2.49797 iter/s, 4.8039s/12 iters), loss = 5.26936
I0410 14:03:43.542472 18534 solver.cpp:237]     Train net output #0: loss = 5.26936 (* 1 = 5.26936 loss)
I0410 14:03:43.542484 18534 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 14:03:48.359696 18534 solver.cpp:218] Iteration 4524 (2.49116 iter/s, 4.81704s/12 iters), loss = 5.27562
I0410 14:03:48.359758 18534 solver.cpp:237]     Train net output #0: loss = 5.27562 (* 1 = 5.27562 loss)
I0410 14:03:48.359769 18534 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 14:03:53.167125 18534 solver.cpp:218] Iteration 4536 (2.49626 iter/s, 4.80718s/12 iters), loss = 5.26714
I0410 14:03:53.167179 18534 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0410 14:03:53.167191 18534 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 14:03:58.006799 18534 solver.cpp:218] Iteration 4548 (2.47963 iter/s, 4.83943s/12 iters), loss = 5.26371
I0410 14:03:58.006853 18534 solver.cpp:237]     Train net output #0: loss = 5.26371 (* 1 = 5.26371 loss)
I0410 14:03:58.006865 18534 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 14:03:59.214498 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:02.820570 18534 solver.cpp:218] Iteration 4560 (2.49297 iter/s, 4.81353s/12 iters), loss = 5.27468
I0410 14:04:02.820688 18534 solver.cpp:237]     Train net output #0: loss = 5.27468 (* 1 = 5.27468 loss)
I0410 14:04:02.820699 18534 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 14:04:07.655377 18534 solver.cpp:218] Iteration 4572 (2.48216 iter/s, 4.83451s/12 iters), loss = 5.26442
I0410 14:04:07.655432 18534 solver.cpp:237]     Train net output #0: loss = 5.26442 (* 1 = 5.26442 loss)
I0410 14:04:07.655445 18534 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 14:04:12.451408 18534 solver.cpp:218] Iteration 4584 (2.5022 iter/s, 4.79579s/12 iters), loss = 5.27686
I0410 14:04:12.451472 18534 solver.cpp:237]     Train net output #0: loss = 5.27686 (* 1 = 5.27686 loss)
I0410 14:04:12.451484 18534 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 14:04:14.405803 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 14:04:14.845055 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 14:04:15.723942 18534 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 14:04:15.723974 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:04:18.305682 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:20.223507 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:04:20.223556 18534 solver.cpp:397]     Test net output #1: loss = 5.28653 (* 1 = 5.28653 loss)
I0410 14:04:22.136904 18534 solver.cpp:218] Iteration 4596 (1.23902 iter/s, 9.68508s/12 iters), loss = 5.27441
I0410 14:04:22.136946 18534 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0410 14:04:22.136955 18534 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 14:04:27.064935 18534 solver.cpp:218] Iteration 4608 (2.43517 iter/s, 4.9278s/12 iters), loss = 5.27198
I0410 14:04:27.064990 18534 solver.cpp:237]     Train net output #0: loss = 5.27198 (* 1 = 5.27198 loss)
I0410 14:04:27.065001 18534 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 14:04:31.990592 18534 solver.cpp:218] Iteration 4620 (2.43634 iter/s, 4.92541s/12 iters), loss = 5.26378
I0410 14:04:31.990644 18534 solver.cpp:237]     Train net output #0: loss = 5.26378 (* 1 = 5.26378 loss)
I0410 14:04:31.990656 18534 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 14:04:36.989692 18534 solver.cpp:218] Iteration 4632 (2.40055 iter/s, 4.99885s/12 iters), loss = 5.29385
I0410 14:04:36.989861 18534 solver.cpp:237]     Train net output #0: loss = 5.29385 (* 1 = 5.29385 loss)
I0410 14:04:36.989873 18534 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 14:04:41.968421 18534 solver.cpp:218] Iteration 4644 (2.41043 iter/s, 4.97837s/12 iters), loss = 5.26678
I0410 14:04:41.968477 18534 solver.cpp:237]     Train net output #0: loss = 5.26678 (* 1 = 5.26678 loss)
I0410 14:04:41.968489 18534 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 14:04:45.368018 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:46.944702 18534 solver.cpp:218] Iteration 4656 (2.41156 iter/s, 4.97604s/12 iters), loss = 5.2796
I0410 14:04:46.944742 18534 solver.cpp:237]     Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
I0410 14:04:46.944751 18534 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 14:04:51.915709 18534 solver.cpp:218] Iteration 4668 (2.41411 iter/s, 4.97078s/12 iters), loss = 5.26708
I0410 14:04:51.915745 18534 solver.cpp:237]     Train net output #0: loss = 5.26708 (* 1 = 5.26708 loss)
I0410 14:04:51.915753 18534 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 14:04:56.803104 18534 solver.cpp:218] Iteration 4680 (2.45541 iter/s, 4.88717s/12 iters), loss = 5.27805
I0410 14:04:56.803158 18534 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0410 14:04:56.803170 18534 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 14:05:01.239760 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 14:05:01.586781 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 14:05:02.369922 18534 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 14:05:02.369951 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:05:04.914810 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:06.878151 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:05:06.878193 18534 solver.cpp:397]     Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 14:05:06.960556 18534 solver.cpp:218] Iteration 4692 (1.18145 iter/s, 10.157s/12 iters), loss = 5.27109
I0410 14:05:06.960626 18534 solver.cpp:237]     Train net output #0: loss = 5.27109 (* 1 = 5.27109 loss)
I0410 14:05:06.960639 18534 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 14:05:11.026480 18534 solver.cpp:218] Iteration 4704 (2.95152 iter/s, 4.0657s/12 iters), loss = 5.26382
I0410 14:05:11.026623 18534 solver.cpp:237]     Train net output #0: loss = 5.26382 (* 1 = 5.26382 loss)
I0410 14:05:11.026633 18534 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 14:05:15.861261 18534 solver.cpp:218] Iteration 4716 (2.48219 iter/s, 4.83445s/12 iters), loss = 5.2789
I0410 14:05:15.861320 18534 solver.cpp:237]     Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
I0410 14:05:15.861331 18534 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 14:05:20.666013 18534 solver.cpp:218] Iteration 4728 (2.49766 iter/s, 4.80451s/12 iters), loss = 5.26211
I0410 14:05:20.666069 18534 solver.cpp:237]     Train net output #0: loss = 5.26211 (* 1 = 5.26211 loss)
I0410 14:05:20.666080 18534 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 14:05:25.507814 18534 solver.cpp:218] Iteration 4740 (2.47854 iter/s, 4.84156s/12 iters), loss = 5.27805
I0410 14:05:25.507877 18534 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0410 14:05:25.507889 18534 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 14:05:30.450191 18534 solver.cpp:218] Iteration 4752 (2.42811 iter/s, 4.94212s/12 iters), loss = 5.28027
I0410 14:05:30.450249 18534 solver.cpp:237]     Train net output #0: loss = 5.28027 (* 1 = 5.28027 loss)
I0410 14:05:30.450271 18534 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 14:05:30.968282 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:35.384714 18534 solver.cpp:218] Iteration 4764 (2.43197 iter/s, 4.93427s/12 iters), loss = 5.28063
I0410 14:05:35.384766 18534 solver.cpp:237]     Train net output #0: loss = 5.28063 (* 1 = 5.28063 loss)
I0410 14:05:35.384779 18534 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 14:05:40.299165 18534 solver.cpp:218] Iteration 4776 (2.4419 iter/s, 4.91421s/12 iters), loss = 5.26558
I0410 14:05:40.299211 18534 solver.cpp:237]     Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
I0410 14:05:40.299221 18534 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 14:05:45.228041 18534 solver.cpp:218] Iteration 4788 (2.43475 iter/s, 4.92863s/12 iters), loss = 5.29316
I0410 14:05:45.228137 18534 solver.cpp:237]     Train net output #0: loss = 5.29316 (* 1 = 5.29316 loss)
I0410 14:05:45.228150 18534 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 14:05:47.206262 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 14:05:47.507973 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 14:05:48.368285 18534 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 14:05:48.368311 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:05:51.039597 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:52.963642 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:05:52.963680 18534 solver.cpp:397]     Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
I0410 14:05:54.833989 18534 solver.cpp:218] Iteration 4800 (1.24928 iter/s, 9.6055s/12 iters), loss = 5.27253
I0410 14:05:54.834036 18534 solver.cpp:237]     Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
I0410 14:05:54.834045 18534 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 14:05:59.776341 18534 solver.cpp:218] Iteration 4812 (2.42811 iter/s, 4.94212s/12 iters), loss = 5.26268
I0410 14:05:59.776386 18534 solver.cpp:237]     Train net output #0: loss = 5.26268 (* 1 = 5.26268 loss)
I0410 14:05:59.776394 18534 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 14:06:04.676218 18534 solver.cpp:218] Iteration 4824 (2.44916 iter/s, 4.89964s/12 iters), loss = 5.29238
I0410 14:06:04.676260 18534 solver.cpp:237]     Train net output #0: loss = 5.29238 (* 1 = 5.29238 loss)
I0410 14:06:04.676268 18534 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 14:06:09.669991 18534 solver.cpp:218] Iteration 4836 (2.40311 iter/s, 4.99352s/12 iters), loss = 5.2619
I0410 14:06:09.670049 18534 solver.cpp:237]     Train net output #0: loss = 5.2619 (* 1 = 5.2619 loss)
I0410 14:06:09.670061 18534 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 14:06:10.030827 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:06:14.563694 18534 solver.cpp:218] Iteration 4848 (2.45226 iter/s, 4.89345s/12 iters), loss = 5.26811
I0410 14:06:14.563737 18534 solver.cpp:237]     Train net output #0: loss = 5.26811 (* 1 = 5.26811 loss)
I0410 14:06:14.563746 18534 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 14:06:17.137907 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:19.457038 18534 solver.cpp:218] Iteration 4860 (2.45243 iter/s, 4.89311s/12 iters), loss = 5.26794
I0410 14:06:19.457087 18534 solver.cpp:237]     Train net output #0: loss = 5.26794 (* 1 = 5.26794 loss)
I0410 14:06:19.457096 18534 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 14:06:24.390699 18534 solver.cpp:218] Iteration 4872 (2.43239 iter/s, 4.93341s/12 iters), loss = 5.26412
I0410 14:06:24.390745 18534 solver.cpp:237]     Train net output #0: loss = 5.26412 (* 1 = 5.26412 loss)
I0410 14:06:24.390754 18534 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 14:06:29.305686 18534 solver.cpp:218] Iteration 4884 (2.44163 iter/s, 4.91475s/12 iters), loss = 5.26737
I0410 14:06:29.305747 18534 solver.cpp:237]     Train net output #0: loss = 5.26737 (* 1 = 5.26737 loss)
I0410 14:06:29.305759 18534 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 14:06:33.879088 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 14:06:34.609182 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 14:06:34.822939 18534 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 14:06:34.822962 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:06:37.238111 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:39.171339 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:06:39.171383 18534 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 14:06:39.252513 18534 solver.cpp:218] Iteration 4896 (1.20647 iter/s, 9.9464s/12 iters), loss = 5.26946
I0410 14:06:39.252562 18534 solver.cpp:237]     Train net output #0: loss = 5.26946 (* 1 = 5.26946 loss)
I0410 14:06:39.252573 18534 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 14:06:43.415050 18534 solver.cpp:218] Iteration 4908 (2.88301 iter/s, 4.16232s/12 iters), loss = 5.29033
I0410 14:06:43.415093 18534 solver.cpp:237]     Train net output #0: loss = 5.29033 (* 1 = 5.29033 loss)
I0410 14:06:43.415102 18534 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 14:06:48.418200 18534 solver.cpp:218] Iteration 4920 (2.3986 iter/s, 5.00291s/12 iters), loss = 5.27149
I0410 14:06:48.418323 18534 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0410 14:06:48.418334 18534 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 14:06:53.282511 18534 solver.cpp:218] Iteration 4932 (2.4671 iter/s, 4.86401s/12 iters), loss = 5.26398
I0410 14:06:53.282557 18534 solver.cpp:237]     Train net output #0: loss = 5.26398 (* 1 = 5.26398 loss)
I0410 14:06:53.282565 18534 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 14:06:58.140475 18534 solver.cpp:218] Iteration 4944 (2.47029 iter/s, 4.85773s/12 iters), loss = 5.2659
I0410 14:06:58.140520 18534 solver.cpp:237]     Train net output #0: loss = 5.2659 (* 1 = 5.2659 loss)
I0410 14:06:58.140529 18534 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 14:07:02.888422 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:03.079031 18534 solver.cpp:218] Iteration 4956 (2.42998 iter/s, 4.93832s/12 iters), loss = 5.25053
I0410 14:07:03.079073 18534 solver.cpp:237]     Train net output #0: loss = 5.25053 (* 1 = 5.25053 loss)
I0410 14:07:03.079082 18534 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 14:07:07.959661 18534 solver.cpp:218] Iteration 4968 (2.45882 iter/s, 4.8804s/12 iters), loss = 5.26377
I0410 14:07:07.959714 18534 solver.cpp:237]     Train net output #0: loss = 5.26377 (* 1 = 5.26377 loss)
I0410 14:07:07.959725 18534 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 14:07:12.890306 18534 solver.cpp:218] Iteration 4980 (2.43388 iter/s, 4.9304s/12 iters), loss = 5.29271
I0410 14:07:12.890348 18534 solver.cpp:237]     Train net output #0: loss = 5.29271 (* 1 = 5.29271 loss)
I0410 14:07:12.890359 18534 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 14:07:17.798513 18534 solver.cpp:218] Iteration 4992 (2.445 iter/s, 4.90798s/12 iters), loss = 5.2835
I0410 14:07:17.798554 18534 solver.cpp:237]     Train net output #0: loss = 5.2835 (* 1 = 5.2835 loss)
I0410 14:07:17.798564 18534 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 14:07:19.798645 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 14:07:20.347246 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 14:07:20.914685 18534 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 14:07:20.914716 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:07:23.291375 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:25.277034 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:07:25.277083 18534 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 14:07:27.196889 18534 solver.cpp:218] Iteration 5004 (1.27687 iter/s, 9.39798s/12 iters), loss = 5.27917
I0410 14:07:27.196936 18534 solver.cpp:237]     Train net output #0: loss = 5.27917 (* 1 = 5.27917 loss)
I0410 14:07:27.196946 18534 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 14:07:32.089864 18534 solver.cpp:218] Iteration 5016 (2.45262 iter/s, 4.89274s/12 iters), loss = 5.26752
I0410 14:07:32.089912 18534 solver.cpp:237]     Train net output #0: loss = 5.26752 (* 1 = 5.26752 loss)
I0410 14:07:32.089920 18534 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 14:07:36.924360 18534 solver.cpp:218] Iteration 5028 (2.48228 iter/s, 4.83426s/12 iters), loss = 5.25019
I0410 14:07:36.924404 18534 solver.cpp:237]     Train net output #0: loss = 5.25019 (* 1 = 5.25019 loss)
I0410 14:07:36.924413 18534 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 14:07:41.779165 18534 solver.cpp:218] Iteration 5040 (2.4719 iter/s, 4.85457s/12 iters), loss = 5.28451
I0410 14:07:41.779212 18534 solver.cpp:237]     Train net output #0: loss = 5.28451 (* 1 = 5.28451 loss)
I0410 14:07:41.779220 18534 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 14:07:46.690609 18534 solver.cpp:218] Iteration 5052 (2.4434 iter/s, 4.9112s/12 iters), loss = 5.27084
I0410 14:07:46.690667 18534 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0410 14:07:46.690680 18534 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 14:07:48.630232 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:51.655778 18534 solver.cpp:218] Iteration 5064 (2.41696 iter/s, 4.96491s/12 iters), loss = 5.29019
I0410 14:07:51.655907 18534 solver.cpp:237]     Train net output #0: loss = 5.29019 (* 1 = 5.29019 loss)
I0410 14:07:51.655920 18534 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 14:07:56.544926 18534 solver.cpp:218] Iteration 5076 (2.45457 iter/s, 4.88883s/12 iters), loss = 5.27279
I0410 14:07:56.544976 18534 solver.cpp:237]     Train net output #0: loss = 5.27279 (* 1 = 5.27279 loss)
I0410 14:07:56.544988 18534 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 14:08:01.517330 18534 solver.cpp:218] Iteration 5088 (2.41344 iter/s, 4.97216s/12 iters), loss = 5.2646
I0410 14:08:01.517382 18534 solver.cpp:237]     Train net output #0: loss = 5.2646 (* 1 = 5.2646 loss)
I0410 14:08:01.517393 18534 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 14:08:05.981436 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 14:08:07.316174 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 14:08:07.529420 18534 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 14:08:07.529448 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:08:09.899732 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:11.914623 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:08:11.914657 18534 solver.cpp:397]     Test net output #1: loss = 5.28653 (* 1 = 5.28653 loss)
I0410 14:08:11.997216 18534 solver.cpp:218] Iteration 5100 (1.1451 iter/s, 10.4794s/12 iters), loss = 5.26652
I0410 14:08:11.997272 18534 solver.cpp:237]     Train net output #0: loss = 5.26652 (* 1 = 5.26652 loss)
I0410 14:08:11.997283 18534 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 14:08:16.163930 18534 solver.cpp:218] Iteration 5112 (2.88012 iter/s, 4.16649s/12 iters), loss = 5.264
I0410 14:08:16.163981 18534 solver.cpp:237]     Train net output #0: loss = 5.264 (* 1 = 5.264 loss)
I0410 14:08:16.163992 18534 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 14:08:21.083678 18534 solver.cpp:218] Iteration 5124 (2.43927 iter/s, 4.91951s/12 iters), loss = 5.27266
I0410 14:08:21.083724 18534 solver.cpp:237]     Train net output #0: loss = 5.27266 (* 1 = 5.27266 loss)
I0410 14:08:21.083734 18534 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 14:08:26.067891 18534 solver.cpp:218] Iteration 5136 (2.40772 iter/s, 4.98397s/12 iters), loss = 5.27026
I0410 14:08:26.068042 18534 solver.cpp:237]     Train net output #0: loss = 5.27026 (* 1 = 5.27026 loss)
I0410 14:08:26.068056 18534 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 14:08:30.917809 18534 solver.cpp:218] Iteration 5148 (2.47444 iter/s, 4.84958s/12 iters), loss = 5.26115
I0410 14:08:30.917860 18534 solver.cpp:237]     Train net output #0: loss = 5.26115 (* 1 = 5.26115 loss)
I0410 14:08:30.917871 18534 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 14:08:35.033205 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:35.938796 18534 solver.cpp:218] Iteration 5160 (2.39009 iter/s, 5.02074s/12 iters), loss = 5.25612
I0410 14:08:35.938835 18534 solver.cpp:237]     Train net output #0: loss = 5.25612 (* 1 = 5.25612 loss)
I0410 14:08:35.938844 18534 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 14:08:40.798403 18534 solver.cpp:218] Iteration 5172 (2.46945 iter/s, 4.85937s/12 iters), loss = 5.27784
I0410 14:08:40.798460 18534 solver.cpp:237]     Train net output #0: loss = 5.27784 (* 1 = 5.27784 loss)
I0410 14:08:40.798472 18534 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 14:08:45.768537 18534 solver.cpp:218] Iteration 5184 (2.41454 iter/s, 4.96988s/12 iters), loss = 5.27205
I0410 14:08:45.768589 18534 solver.cpp:237]     Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
I0410 14:08:45.768601 18534 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 14:08:50.582741 18534 solver.cpp:218] Iteration 5196 (2.49275 iter/s, 4.81396s/12 iters), loss = 5.30738
I0410 14:08:50.582793 18534 solver.cpp:237]     Train net output #0: loss = 5.30738 (* 1 = 5.30738 loss)
I0410 14:08:50.582806 18534 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 14:08:52.614343 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 14:08:53.100692 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 14:08:53.718741 18534 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 14:08:53.718770 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:08:56.119937 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:58.165135 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:08:58.165180 18534 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0410 14:09:00.003630 18534 solver.cpp:218] Iteration 5208 (1.27382 iter/s, 9.42048s/12 iters), loss = 5.27279
I0410 14:09:00.003686 18534 solver.cpp:237]     Train net output #0: loss = 5.27279 (* 1 = 5.27279 loss)
I0410 14:09:00.003700 18534 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 14:09:04.930722 18534 solver.cpp:218] Iteration 5220 (2.43563 iter/s, 4.92685s/12 iters), loss = 5.27384
I0410 14:09:04.930770 18534 solver.cpp:237]     Train net output #0: loss = 5.27384 (* 1 = 5.27384 loss)
I0410 14:09:04.930781 18534 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 14:09:09.917428 18534 solver.cpp:218] Iteration 5232 (2.40652 iter/s, 4.98646s/12 iters), loss = 5.2804
I0410 14:09:09.917470 18534 solver.cpp:237]     Train net output #0: loss = 5.2804 (* 1 = 5.2804 loss)
I0410 14:09:09.917479 18534 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 14:09:14.979001 18534 solver.cpp:218] Iteration 5244 (2.37092 iter/s, 5.06133s/12 iters), loss = 5.27066
I0410 14:09:14.979043 18534 solver.cpp:237]     Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
I0410 14:09:14.979050 18534 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 14:09:19.863624 18534 solver.cpp:218] Iteration 5256 (2.45681 iter/s, 4.88439s/12 iters), loss = 5.25703
I0410 14:09:19.863684 18534 solver.cpp:237]     Train net output #0: loss = 5.25703 (* 1 = 5.25703 loss)
I0410 14:09:19.863698 18534 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 14:09:21.117172 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:24.742910 18534 solver.cpp:218] Iteration 5268 (2.4595 iter/s, 4.87904s/12 iters), loss = 5.27696
I0410 14:09:24.742959 18534 solver.cpp:237]     Train net output #0: loss = 5.27696 (* 1 = 5.27696 loss)
I0410 14:09:24.742967 18534 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 14:09:29.620543 18534 solver.cpp:218] Iteration 5280 (2.46033 iter/s, 4.87739s/12 iters), loss = 5.2674
I0410 14:09:29.620692 18534 solver.cpp:237]     Train net output #0: loss = 5.2674 (* 1 = 5.2674 loss)
I0410 14:09:29.620707 18534 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 14:09:34.456787 18534 solver.cpp:218] Iteration 5292 (2.48144 iter/s, 4.83591s/12 iters), loss = 5.27698
I0410 14:09:34.456840 18534 solver.cpp:237]     Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
I0410 14:09:34.456851 18534 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 14:09:38.940742 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 14:09:39.479388 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 14:09:40.085136 18534 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 14:09:40.085165 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:09:42.333761 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:44.423508 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:09:44.423547 18534 solver.cpp:397]     Test net output #1: loss = 5.28647 (* 1 = 5.28647 loss)
I0410 14:09:44.504168 18534 solver.cpp:218] Iteration 5304 (1.19439 iter/s, 10.0469s/12 iters), loss = 5.27081
I0410 14:09:44.504227 18534 solver.cpp:237]     Train net output #0: loss = 5.27081 (* 1 = 5.27081 loss)
I0410 14:09:44.504238 18534 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 14:09:48.658756 18534 solver.cpp:218] Iteration 5316 (2.88853 iter/s, 4.15437s/12 iters), loss = 5.26819
I0410 14:09:48.658794 18534 solver.cpp:237]     Train net output #0: loss = 5.26819 (* 1 = 5.26819 loss)
I0410 14:09:48.658803 18534 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 14:09:53.622570 18534 solver.cpp:218] Iteration 5328 (2.41761 iter/s, 4.96357s/12 iters), loss = 5.25997
I0410 14:09:53.622625 18534 solver.cpp:237]     Train net output #0: loss = 5.25997 (* 1 = 5.25997 loss)
I0410 14:09:53.622637 18534 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 14:09:58.543323 18534 solver.cpp:218] Iteration 5340 (2.43878 iter/s, 4.9205s/12 iters), loss = 5.30224
I0410 14:09:58.543376 18534 solver.cpp:237]     Train net output #0: loss = 5.30224 (* 1 = 5.30224 loss)
I0410 14:09:58.543388 18534 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 14:10:03.391250 18534 solver.cpp:218] Iteration 5352 (2.47541 iter/s, 4.84768s/12 iters), loss = 5.27602
I0410 14:10:03.391397 18534 solver.cpp:237]     Train net output #0: loss = 5.27602 (* 1 = 5.27602 loss)
I0410 14:10:03.391409 18534 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 14:10:06.666225 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:08.186883 18534 solver.cpp:218] Iteration 5364 (2.50245 iter/s, 4.7953s/12 iters), loss = 5.27655
I0410 14:10:08.186928 18534 solver.cpp:237]     Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
I0410 14:10:08.186936 18534 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 14:10:13.081660 18534 solver.cpp:218] Iteration 5376 (2.45171 iter/s, 4.89453s/12 iters), loss = 5.26539
I0410 14:10:13.081719 18534 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0410 14:10:13.081732 18534 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 14:10:17.892290 18534 solver.cpp:218] Iteration 5388 (2.4946 iter/s, 4.81039s/12 iters), loss = 5.26667
I0410 14:10:17.892340 18534 solver.cpp:237]     Train net output #0: loss = 5.26667 (* 1 = 5.26667 loss)
I0410 14:10:17.892351 18534 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 14:10:22.677450 18534 solver.cpp:218] Iteration 5400 (2.50788 iter/s, 4.78492s/12 iters), loss = 5.2698
I0410 14:10:22.677500 18534 solver.cpp:237]     Train net output #0: loss = 5.2698 (* 1 = 5.2698 loss)
I0410 14:10:22.677510 18534 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 14:10:24.646152 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 14:10:24.969197 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 14:10:25.180004 18534 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 14:10:25.180037 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:10:27.587338 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:29.699530 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:10:29.699568 18534 solver.cpp:397]     Test net output #1: loss = 5.28658 (* 1 = 5.28658 loss)
I0410 14:10:31.553289 18534 solver.cpp:218] Iteration 5412 (1.35204 iter/s, 8.87545s/12 iters), loss = 5.26411
I0410 14:10:31.553351 18534 solver.cpp:237]     Train net output #0: loss = 5.26411 (* 1 = 5.26411 loss)
I0410 14:10:31.553364 18534 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 14:10:36.500613 18534 solver.cpp:218] Iteration 5424 (2.42568 iter/s, 4.94707s/12 iters), loss = 5.27958
I0410 14:10:36.500710 18534 solver.cpp:237]     Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
I0410 14:10:36.500720 18534 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 14:10:41.379899 18534 solver.cpp:218] Iteration 5436 (2.45952 iter/s, 4.87899s/12 iters), loss = 5.26717
I0410 14:10:41.379947 18534 solver.cpp:237]     Train net output #0: loss = 5.26717 (* 1 = 5.26717 loss)
I0410 14:10:41.379956 18534 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 14:10:46.351294 18534 solver.cpp:218] Iteration 5448 (2.41393 iter/s, 4.97115s/12 iters), loss = 5.27909
I0410 14:10:46.351351 18534 solver.cpp:237]     Train net output #0: loss = 5.27909 (* 1 = 5.27909 loss)
I0410 14:10:46.351361 18534 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 14:10:51.219871 18534 solver.cpp:218] Iteration 5460 (2.46491 iter/s, 4.86833s/12 iters), loss = 5.27997
I0410 14:10:51.219928 18534 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0410 14:10:51.219940 18534 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 14:10:51.761358 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:56.115741 18534 solver.cpp:218] Iteration 5472 (2.45117 iter/s, 4.89562s/12 iters), loss = 5.27826
I0410 14:10:56.115789 18534 solver.cpp:237]     Train net output #0: loss = 5.27826 (* 1 = 5.27826 loss)
I0410 14:10:56.115801 18534 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 14:11:01.050582 18534 solver.cpp:218] Iteration 5484 (2.43181 iter/s, 4.9346s/12 iters), loss = 5.27448
I0410 14:11:01.050637 18534 solver.cpp:237]     Train net output #0: loss = 5.27448 (* 1 = 5.27448 loss)
I0410 14:11:01.050648 18534 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 14:11:06.001469 18534 solver.cpp:218] Iteration 5496 (2.42393 iter/s, 4.95064s/12 iters), loss = 5.29228
I0410 14:11:06.001515 18534 solver.cpp:237]     Train net output #0: loss = 5.29228 (* 1 = 5.29228 loss)
I0410 14:11:06.001524 18534 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 14:11:10.415592 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 14:11:10.731891 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 14:11:10.935155 18534 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 14:11:10.935182 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:11:13.217383 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:15.388444 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:11:15.388485 18534 solver.cpp:397]     Test net output #1: loss = 5.2875 (* 1 = 5.2875 loss)
I0410 14:11:15.470726 18534 solver.cpp:218] Iteration 5508 (1.26731 iter/s, 9.46885s/12 iters), loss = 5.27688
I0410 14:11:15.470774 18534 solver.cpp:237]     Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
I0410 14:11:15.470784 18534 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 14:11:19.605052 18534 solver.cpp:218] Iteration 5520 (2.90268 iter/s, 4.13411s/12 iters), loss = 5.27598
I0410 14:11:19.605098 18534 solver.cpp:237]     Train net output #0: loss = 5.27598 (* 1 = 5.27598 loss)
I0410 14:11:19.605106 18534 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 14:11:20.361567 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:11:24.443722 18534 solver.cpp:218] Iteration 5532 (2.48014 iter/s, 4.83843s/12 iters), loss = 5.28887
I0410 14:11:24.443768 18534 solver.cpp:237]     Train net output #0: loss = 5.28887 (* 1 = 5.28887 loss)
I0410 14:11:24.443778 18534 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 14:11:29.311197 18534 solver.cpp:218] Iteration 5544 (2.46547 iter/s, 4.86723s/12 iters), loss = 5.25797
I0410 14:11:29.311249 18534 solver.cpp:237]     Train net output #0: loss = 5.25797 (* 1 = 5.25797 loss)
I0410 14:11:29.311261 18534 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 14:11:34.217281 18534 solver.cpp:218] Iteration 5556 (2.44607 iter/s, 4.90583s/12 iters), loss = 5.26917
I0410 14:11:34.217339 18534 solver.cpp:237]     Train net output #0: loss = 5.26917 (* 1 = 5.26917 loss)
I0410 14:11:34.217350 18534 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 14:11:36.858814 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:39.120335 18534 solver.cpp:218] Iteration 5568 (2.44758 iter/s, 4.90281s/12 iters), loss = 5.27776
I0410 14:11:39.120388 18534 solver.cpp:237]     Train net output #0: loss = 5.27776 (* 1 = 5.27776 loss)
I0410 14:11:39.120400 18534 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 14:11:44.096726 18534 solver.cpp:218] Iteration 5580 (2.41151 iter/s, 4.97614s/12 iters), loss = 5.25968
I0410 14:11:44.096805 18534 solver.cpp:237]     Train net output #0: loss = 5.25968 (* 1 = 5.25968 loss)
I0410 14:11:44.096817 18534 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 14:11:49.036504 18534 solver.cpp:218] Iteration 5592 (2.42939 iter/s, 4.93951s/12 iters), loss = 5.26885
I0410 14:11:49.036545 18534 solver.cpp:237]     Train net output #0: loss = 5.26885 (* 1 = 5.26885 loss)
I0410 14:11:49.036554 18534 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 14:11:53.932282 18534 solver.cpp:218] Iteration 5604 (2.45121 iter/s, 4.89555s/12 iters), loss = 5.26367
I0410 14:11:53.932320 18534 solver.cpp:237]     Train net output #0: loss = 5.26367 (* 1 = 5.26367 loss)
I0410 14:11:53.932329 18534 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 14:11:55.933638 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 14:11:56.236804 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 14:11:56.464804 18534 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 14:11:56.464828 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:11:58.798918 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:01.104566 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:12:01.104615 18534 solver.cpp:397]     Test net output #1: loss = 5.28684 (* 1 = 5.28684 loss)
I0410 14:12:02.902381 18534 solver.cpp:218] Iteration 5616 (1.33783 iter/s, 8.96972s/12 iters), loss = 5.29348
I0410 14:12:02.902428 18534 solver.cpp:237]     Train net output #0: loss = 5.29348 (* 1 = 5.29348 loss)
I0410 14:12:02.902439 18534 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 14:12:07.811568 18534 solver.cpp:218] Iteration 5628 (2.44452 iter/s, 4.90894s/12 iters), loss = 5.27392
I0410 14:12:07.811612 18534 solver.cpp:237]     Train net output #0: loss = 5.27392 (* 1 = 5.27392 loss)
I0410 14:12:07.811621 18534 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 14:12:12.704025 18534 solver.cpp:218] Iteration 5640 (2.45287 iter/s, 4.89222s/12 iters), loss = 5.26338
I0410 14:12:12.704068 18534 solver.cpp:237]     Train net output #0: loss = 5.26338 (* 1 = 5.26338 loss)
I0410 14:12:12.704079 18534 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 14:12:17.632879 18534 solver.cpp:218] Iteration 5652 (2.43476 iter/s, 4.92861s/12 iters), loss = 5.26535
I0410 14:12:17.633042 18534 solver.cpp:237]     Train net output #0: loss = 5.26535 (* 1 = 5.26535 loss)
I0410 14:12:17.633057 18534 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 14:12:22.465842 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:22.623638 18534 solver.cpp:218] Iteration 5664 (2.40462 iter/s, 4.9904s/12 iters), loss = 5.25089
I0410 14:12:22.623692 18534 solver.cpp:237]     Train net output #0: loss = 5.25089 (* 1 = 5.25089 loss)
I0410 14:12:22.623704 18534 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 14:12:27.511411 18534 solver.cpp:218] Iteration 5676 (2.45523 iter/s, 4.88752s/12 iters), loss = 5.265
I0410 14:12:27.511485 18534 solver.cpp:237]     Train net output #0: loss = 5.265 (* 1 = 5.265 loss)
I0410 14:12:27.511503 18534 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 14:12:32.376303 18534 solver.cpp:218] Iteration 5688 (2.46679 iter/s, 4.86463s/12 iters), loss = 5.29405
I0410 14:12:32.376353 18534 solver.cpp:237]     Train net output #0: loss = 5.29405 (* 1 = 5.29405 loss)
I0410 14:12:32.376363 18534 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 14:12:37.282078 18534 solver.cpp:218] Iteration 5700 (2.44622 iter/s, 4.90553s/12 iters), loss = 5.28705
I0410 14:12:37.282137 18534 solver.cpp:237]     Train net output #0: loss = 5.28705 (* 1 = 5.28705 loss)
I0410 14:12:37.282148 18534 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 14:12:41.711927 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 14:12:42.029397 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 14:12:42.241655 18534 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 14:12:42.241674 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:12:44.447324 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:46.681407 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:12:46.681458 18534 solver.cpp:397]     Test net output #1: loss = 5.28681 (* 1 = 5.28681 loss)
I0410 14:12:46.763864 18534 solver.cpp:218] Iteration 5712 (1.26564 iter/s, 9.48137s/12 iters), loss = 5.27858
I0410 14:12:46.763907 18534 solver.cpp:237]     Train net output #0: loss = 5.27858 (* 1 = 5.27858 loss)
I0410 14:12:46.763916 18534 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 14:12:50.975705 18534 solver.cpp:218] Iteration 5724 (2.84926 iter/s, 4.21162s/12 iters), loss = 5.26452
I0410 14:12:50.975821 18534 solver.cpp:237]     Train net output #0: loss = 5.26452 (* 1 = 5.26452 loss)
I0410 14:12:50.975831 18534 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 14:12:55.918490 18534 solver.cpp:218] Iteration 5736 (2.42793 iter/s, 4.94247s/12 iters), loss = 5.24267
I0410 14:12:55.918540 18534 solver.cpp:237]     Train net output #0: loss = 5.24267 (* 1 = 5.24267 loss)
I0410 14:12:55.918550 18534 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 14:13:00.803385 18534 solver.cpp:218] Iteration 5748 (2.45667 iter/s, 4.88465s/12 iters), loss = 5.27843
I0410 14:13:00.803428 18534 solver.cpp:237]     Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
I0410 14:13:00.803437 18534 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 14:13:05.659471 18534 solver.cpp:218] Iteration 5760 (2.47125 iter/s, 4.85585s/12 iters), loss = 5.26456
I0410 14:13:05.659525 18534 solver.cpp:237]     Train net output #0: loss = 5.26456 (* 1 = 5.26456 loss)
I0410 14:13:05.659535 18534 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 14:13:07.680017 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:10.666508 18534 solver.cpp:218] Iteration 5772 (2.39675 iter/s, 5.00678s/12 iters), loss = 5.29057
I0410 14:13:10.666554 18534 solver.cpp:237]     Train net output #0: loss = 5.29057 (* 1 = 5.29057 loss)
I0410 14:13:10.666564 18534 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 14:13:15.556952 18534 solver.cpp:218] Iteration 5784 (2.45389 iter/s, 4.8902s/12 iters), loss = 5.26989
I0410 14:13:15.557005 18534 solver.cpp:237]     Train net output #0: loss = 5.26989 (* 1 = 5.26989 loss)
I0410 14:13:15.557016 18534 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 14:13:20.450242 18534 solver.cpp:218] Iteration 5796 (2.45246 iter/s, 4.89305s/12 iters), loss = 5.27271
I0410 14:13:20.450284 18534 solver.cpp:237]     Train net output #0: loss = 5.27271 (* 1 = 5.27271 loss)
I0410 14:13:20.450291 18534 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 14:13:25.267078 18534 solver.cpp:218] Iteration 5808 (2.49138 iter/s, 4.8166s/12 iters), loss = 5.26507
I0410 14:13:25.267163 18534 solver.cpp:237]     Train net output #0: loss = 5.26507 (* 1 = 5.26507 loss)
I0410 14:13:25.267171 18534 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 14:13:27.223489 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 14:13:28.074947 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 14:13:28.435148 18534 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 14:13:28.435178 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:13:30.574834 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:32.857719 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:13:32.857767 18534 solver.cpp:397]     Test net output #1: loss = 5.28673 (* 1 = 5.28673 loss)
I0410 14:13:34.757620 18534 solver.cpp:218] Iteration 5820 (1.26448 iter/s, 9.4901s/12 iters), loss = 5.27288
I0410 14:13:34.757675 18534 solver.cpp:237]     Train net output #0: loss = 5.27288 (* 1 = 5.27288 loss)
I0410 14:13:34.757688 18534 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 14:13:39.655989 18534 solver.cpp:218] Iteration 5832 (2.44992 iter/s, 4.89812s/12 iters), loss = 5.27203
I0410 14:13:39.656039 18534 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0410 14:13:39.656051 18534 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 14:13:44.486552 18534 solver.cpp:218] Iteration 5844 (2.48431 iter/s, 4.83032s/12 iters), loss = 5.26297
I0410 14:13:44.486605 18534 solver.cpp:237]     Train net output #0: loss = 5.26297 (* 1 = 5.26297 loss)
I0410 14:13:44.486616 18534 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 14:13:49.401065 18534 solver.cpp:218] Iteration 5856 (2.44187 iter/s, 4.91427s/12 iters), loss = 5.2605
I0410 14:13:49.401108 18534 solver.cpp:237]     Train net output #0: loss = 5.2605 (* 1 = 5.2605 loss)
I0410 14:13:49.401116 18534 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 14:13:53.519115 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:54.305675 18534 solver.cpp:218] Iteration 5868 (2.4468 iter/s, 4.90437s/12 iters), loss = 5.25517
I0410 14:13:54.305744 18534 solver.cpp:237]     Train net output #0: loss = 5.25517 (* 1 = 5.25517 loss)
I0410 14:13:54.305760 18534 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 14:13:59.237139 18534 solver.cpp:218] Iteration 5880 (2.43348 iter/s, 4.9312s/12 iters), loss = 5.27875
I0410 14:13:59.237288 18534 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0410 14:13:59.237301 18534 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 14:14:04.101250 18534 solver.cpp:218] Iteration 5892 (2.46722 iter/s, 4.86377s/12 iters), loss = 5.26982
I0410 14:14:04.101295 18534 solver.cpp:237]     Train net output #0: loss = 5.26982 (* 1 = 5.26982 loss)
I0410 14:14:04.101305 18534 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 14:14:08.951406 18534 solver.cpp:218] Iteration 5904 (2.47427 iter/s, 4.84991s/12 iters), loss = 5.30357
I0410 14:14:08.951462 18534 solver.cpp:237]     Train net output #0: loss = 5.30357 (* 1 = 5.30357 loss)
I0410 14:14:08.951473 18534 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 14:14:13.369752 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 14:14:13.682626 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 14:14:13.883208 18534 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 14:14:13.883235 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:14:15.932950 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:18.250443 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:14:18.250505 18534 solver.cpp:397]     Test net output #1: loss = 5.28677 (* 1 = 5.28677 loss)
I0410 14:14:18.332823 18534 solver.cpp:218] Iteration 5916 (1.27918 iter/s, 9.381s/12 iters), loss = 5.26682
I0410 14:14:18.332877 18534 solver.cpp:237]     Train net output #0: loss = 5.26682 (* 1 = 5.26682 loss)
I0410 14:14:18.332888 18534 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 14:14:22.387260 18534 solver.cpp:218] Iteration 5928 (2.95988 iter/s, 4.05422s/12 iters), loss = 5.2718
I0410 14:14:22.387315 18534 solver.cpp:237]     Train net output #0: loss = 5.2718 (* 1 = 5.2718 loss)
I0410 14:14:22.387326 18534 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 14:14:27.266561 18534 solver.cpp:218] Iteration 5940 (2.45949 iter/s, 4.87905s/12 iters), loss = 5.28019
I0410 14:14:27.266618 18534 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0410 14:14:27.266631 18534 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 14:14:32.192191 18534 solver.cpp:218] Iteration 5952 (2.43636 iter/s, 4.92538s/12 iters), loss = 5.27425
I0410 14:14:32.192293 18534 solver.cpp:237]     Train net output #0: loss = 5.27425 (* 1 = 5.27425 loss)
I0410 14:14:32.192304 18534 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 14:14:37.120944 18534 solver.cpp:218] Iteration 5964 (2.43484 iter/s, 4.92846s/12 iters), loss = 5.25872
I0410 14:14:37.120998 18534 solver.cpp:237]     Train net output #0: loss = 5.25872 (* 1 = 5.25872 loss)
I0410 14:14:37.121009 18534 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 14:14:38.400094 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:42.002702 18534 solver.cpp:218] Iteration 5976 (2.45825 iter/s, 4.88151s/12 iters), loss = 5.27573
I0410 14:14:42.002756 18534 solver.cpp:237]     Train net output #0: loss = 5.27573 (* 1 = 5.27573 loss)
I0410 14:14:42.002768 18534 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 14:14:46.934674 18534 solver.cpp:218] Iteration 5988 (2.43323 iter/s, 4.93172s/12 iters), loss = 5.26303
I0410 14:14:46.934722 18534 solver.cpp:237]     Train net output #0: loss = 5.26303 (* 1 = 5.26303 loss)
I0410 14:14:46.934731 18534 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 14:14:51.825636 18534 solver.cpp:218] Iteration 6000 (2.45363 iter/s, 4.89072s/12 iters), loss = 5.28248
I0410 14:14:51.825683 18534 solver.cpp:237]     Train net output #0: loss = 5.28248 (* 1 = 5.28248 loss)
I0410 14:14:51.825692 18534 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 14:14:56.696138 18534 solver.cpp:218] Iteration 6012 (2.46393 iter/s, 4.87026s/12 iters), loss = 5.26995
I0410 14:14:56.696187 18534 solver.cpp:237]     Train net output #0: loss = 5.26995 (* 1 = 5.26995 loss)
I0410 14:14:56.696197 18534 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 14:14:58.689178 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 14:14:59.095253 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 14:14:59.295331 18534 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 14:14:59.295359 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:15:01.343220 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:03.743261 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:15:03.743422 18534 solver.cpp:397]     Test net output #1: loss = 5.28679 (* 1 = 5.28679 loss)
I0410 14:15:05.538898 18534 solver.cpp:218] Iteration 6024 (1.3571 iter/s, 8.84237s/12 iters), loss = 5.26611
I0410 14:15:05.538947 18534 solver.cpp:237]     Train net output #0: loss = 5.26611 (* 1 = 5.26611 loss)
I0410 14:15:05.538959 18534 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 14:15:10.385726 18534 solver.cpp:218] Iteration 6036 (2.47597 iter/s, 4.84658s/12 iters), loss = 5.26077
I0410 14:15:10.385782 18534 solver.cpp:237]     Train net output #0: loss = 5.26077 (* 1 = 5.26077 loss)
I0410 14:15:10.385794 18534 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 14:15:15.249887 18534 solver.cpp:218] Iteration 6048 (2.46715 iter/s, 4.86391s/12 iters), loss = 5.30352
I0410 14:15:15.249939 18534 solver.cpp:237]     Train net output #0: loss = 5.30352 (* 1 = 5.30352 loss)
I0410 14:15:15.249950 18534 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 14:15:20.165072 18534 solver.cpp:218] Iteration 6060 (2.44154 iter/s, 4.91494s/12 iters), loss = 5.27576
I0410 14:15:20.165127 18534 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0410 14:15:20.165139 18534 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 14:15:23.578868 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:25.104750 18534 solver.cpp:218] Iteration 6072 (2.42944 iter/s, 4.93942s/12 iters), loss = 5.27482
I0410 14:15:25.104805 18534 solver.cpp:237]     Train net output #0: loss = 5.27482 (* 1 = 5.27482 loss)
I0410 14:15:25.104816 18534 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 14:15:30.024454 18534 solver.cpp:218] Iteration 6084 (2.4393 iter/s, 4.91945s/12 iters), loss = 5.2581
I0410 14:15:30.024513 18534 solver.cpp:237]     Train net output #0: loss = 5.2581 (* 1 = 5.2581 loss)
I0410 14:15:30.024524 18534 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 14:15:34.947295 18534 solver.cpp:218] Iteration 6096 (2.43774 iter/s, 4.92259s/12 iters), loss = 5.26012
I0410 14:15:34.947391 18534 solver.cpp:237]     Train net output #0: loss = 5.26012 (* 1 = 5.26012 loss)
I0410 14:15:34.947402 18534 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 14:15:39.914614 18534 solver.cpp:218] Iteration 6108 (2.41593 iter/s, 4.96703s/12 iters), loss = 5.27179
I0410 14:15:39.914670 18534 solver.cpp:237]     Train net output #0: loss = 5.27179 (* 1 = 5.27179 loss)
I0410 14:15:39.914681 18534 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 14:15:44.463094 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 14:15:44.775282 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 14:15:44.979826 18534 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 14:15:44.979856 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:15:47.131232 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:49.544443 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:15:49.544488 18534 solver.cpp:397]     Test net output #1: loss = 5.28674 (* 1 = 5.28674 loss)
I0410 14:15:49.627120 18534 solver.cpp:218] Iteration 6120 (1.23557 iter/s, 9.71208s/12 iters), loss = 5.26559
I0410 14:15:49.627166 18534 solver.cpp:237]     Train net output #0: loss = 5.26559 (* 1 = 5.26559 loss)
I0410 14:15:49.627177 18534 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 14:15:54.011569 18534 solver.cpp:218] Iteration 6132 (2.73708 iter/s, 4.38423s/12 iters), loss = 5.27311
I0410 14:15:54.011618 18534 solver.cpp:237]     Train net output #0: loss = 5.27311 (* 1 = 5.27311 loss)
I0410 14:15:54.011629 18534 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 14:15:58.850450 18534 solver.cpp:218] Iteration 6144 (2.48004 iter/s, 4.83864s/12 iters), loss = 5.27149
I0410 14:15:58.850503 18534 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0410 14:15:58.850515 18534 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 14:16:03.763655 18534 solver.cpp:218] Iteration 6156 (2.44252 iter/s, 4.91296s/12 iters), loss = 5.27921
I0410 14:16:03.763703 18534 solver.cpp:237]     Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
I0410 14:16:03.763712 18534 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 14:16:08.625376 18534 solver.cpp:218] Iteration 6168 (2.46839 iter/s, 4.86147s/12 iters), loss = 5.28973
I0410 14:16:08.626041 18534 solver.cpp:237]     Train net output #0: loss = 5.28973 (* 1 = 5.28973 loss)
I0410 14:16:08.626052 18534 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 14:16:09.211099 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:13.579851 18534 solver.cpp:218] Iteration 6180 (2.42247 iter/s, 4.95362s/12 iters), loss = 5.28083
I0410 14:16:13.579900 18534 solver.cpp:237]     Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
I0410 14:16:13.579910 18534 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 14:16:18.451295 18534 solver.cpp:218] Iteration 6192 (2.46346 iter/s, 4.8712s/12 iters), loss = 5.26592
I0410 14:16:18.451349 18534 solver.cpp:237]     Train net output #0: loss = 5.26592 (* 1 = 5.26592 loss)
I0410 14:16:18.451360 18534 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 14:16:23.323843 18534 solver.cpp:218] Iteration 6204 (2.4629 iter/s, 4.8723s/12 iters), loss = 5.28763
I0410 14:16:23.323889 18534 solver.cpp:237]     Train net output #0: loss = 5.28763 (* 1 = 5.28763 loss)
I0410 14:16:23.323897 18534 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 14:16:28.123170 18534 solver.cpp:218] Iteration 6216 (2.50047 iter/s, 4.79909s/12 iters), loss = 5.2784
I0410 14:16:28.123216 18534 solver.cpp:237]     Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
I0410 14:16:28.123229 18534 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 14:16:30.109663 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 14:16:30.938685 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 14:16:31.178357 18534 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 14:16:31.178381 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:16:33.075348 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:33.997843 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:16:35.561115 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:16:35.561161 18534 solver.cpp:397]     Test net output #1: loss = 5.28656 (* 1 = 5.28656 loss)
I0410 14:16:37.373445 18534 solver.cpp:218] Iteration 6228 (1.29732 iter/s, 9.24987s/12 iters), loss = 5.27612
I0410 14:16:37.373497 18534 solver.cpp:237]     Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
I0410 14:16:37.373508 18534 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 14:16:42.305296 18534 solver.cpp:218] Iteration 6240 (2.43329 iter/s, 4.9316s/12 iters), loss = 5.27893
I0410 14:16:42.305416 18534 solver.cpp:237]     Train net output #0: loss = 5.27893 (* 1 = 5.27893 loss)
I0410 14:16:42.305428 18534 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 14:16:47.260113 18534 solver.cpp:218] Iteration 6252 (2.42204 iter/s, 4.9545s/12 iters), loss = 5.25924
I0410 14:16:47.260157 18534 solver.cpp:237]     Train net output #0: loss = 5.25924 (* 1 = 5.25924 loss)
I0410 14:16:47.260166 18534 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 14:16:52.184348 18534 solver.cpp:218] Iteration 6264 (2.43705 iter/s, 4.92399s/12 iters), loss = 5.26705
I0410 14:16:52.184403 18534 solver.cpp:237]     Train net output #0: loss = 5.26705 (* 1 = 5.26705 loss)
I0410 14:16:52.184415 18534 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 14:16:54.814631 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:56.998263 18534 solver.cpp:218] Iteration 6276 (2.4929 iter/s, 4.81367s/12 iters), loss = 5.27804
I0410 14:16:56.998318 18534 solver.cpp:237]     Train net output #0: loss = 5.27804 (* 1 = 5.27804 loss)
I0410 14:16:56.998330 18534 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 14:17:01.948585 18534 solver.cpp:218] Iteration 6288 (2.42421 iter/s, 4.95007s/12 iters), loss = 5.26047
I0410 14:17:01.948635 18534 solver.cpp:237]     Train net output #0: loss = 5.26047 (* 1 = 5.26047 loss)
I0410 14:17:01.948647 18534 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 14:17:06.860680 18534 solver.cpp:218] Iteration 6300 (2.44307 iter/s, 4.91185s/12 iters), loss = 5.26993
I0410 14:17:06.860735 18534 solver.cpp:237]     Train net output #0: loss = 5.26993 (* 1 = 5.26993 loss)
I0410 14:17:06.860747 18534 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 14:17:11.787189 18534 solver.cpp:218] Iteration 6312 (2.43592 iter/s, 4.92626s/12 iters), loss = 5.26255
I0410 14:17:11.787235 18534 solver.cpp:237]     Train net output #0: loss = 5.26255 (* 1 = 5.26255 loss)
I0410 14:17:11.787246 18534 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 14:17:16.266422 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 14:17:16.574748 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 14:17:16.789392 18534 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 14:17:16.789420 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:17:18.686586 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:21.172159 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:17:21.172207 18534 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 14:17:21.253196 18534 solver.cpp:218] Iteration 6324 (1.26775 iter/s, 9.4656s/12 iters), loss = 5.29864
I0410 14:17:21.253248 18534 solver.cpp:237]     Train net output #0: loss = 5.29864 (* 1 = 5.29864 loss)
I0410 14:17:21.253260 18534 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 14:17:25.507558 18534 solver.cpp:218] Iteration 6336 (2.82079 iter/s, 4.25413s/12 iters), loss = 5.27084
I0410 14:17:25.507611 18534 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0410 14:17:25.507622 18534 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 14:17:30.427506 18534 solver.cpp:218] Iteration 6348 (2.43917 iter/s, 4.9197s/12 iters), loss = 5.25941
I0410 14:17:30.427567 18534 solver.cpp:237]     Train net output #0: loss = 5.25941 (* 1 = 5.25941 loss)
I0410 14:17:30.427578 18534 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 14:17:35.392849 18534 solver.cpp:218] Iteration 6360 (2.41688 iter/s, 4.96508s/12 iters), loss = 5.26696
I0410 14:17:35.392910 18534 solver.cpp:237]     Train net output #0: loss = 5.26696 (* 1 = 5.26696 loss)
I0410 14:17:35.392922 18534 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 14:17:40.213997 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:40.353595 18534 solver.cpp:218] Iteration 6372 (2.41912 iter/s, 4.96049s/12 iters), loss = 5.25305
I0410 14:17:40.353653 18534 solver.cpp:237]     Train net output #0: loss = 5.25305 (* 1 = 5.25305 loss)
I0410 14:17:40.353664 18534 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 14:17:45.376929 18534 solver.cpp:218] Iteration 6384 (2.38897 iter/s, 5.02308s/12 iters), loss = 5.26748
I0410 14:17:45.376982 18534 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0410 14:17:45.376994 18534 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 14:17:50.302618 18534 solver.cpp:218] Iteration 6396 (2.43633 iter/s, 4.92544s/12 iters), loss = 5.29587
I0410 14:17:50.302724 18534 solver.cpp:237]     Train net output #0: loss = 5.29587 (* 1 = 5.29587 loss)
I0410 14:17:50.302734 18534 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 14:17:55.244793 18534 solver.cpp:218] Iteration 6408 (2.42823 iter/s, 4.94188s/12 iters), loss = 5.28409
I0410 14:17:55.244843 18534 solver.cpp:237]     Train net output #0: loss = 5.28409 (* 1 = 5.28409 loss)
I0410 14:17:55.244854 18534 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 14:18:00.226231 18534 solver.cpp:218] Iteration 6420 (2.40907 iter/s, 4.98118s/12 iters), loss = 5.27931
I0410 14:18:00.226289 18534 solver.cpp:237]     Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
I0410 14:18:00.226300 18534 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 14:18:02.257938 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 14:18:02.566195 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 14:18:02.780706 18534 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 14:18:02.780736 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:18:04.743849 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:07.471700 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:18:07.471750 18534 solver.cpp:397]     Test net output #1: loss = 5.28664 (* 1 = 5.28664 loss)
I0410 14:18:09.246771 18534 solver.cpp:218] Iteration 6432 (1.33036 iter/s, 9.02013s/12 iters), loss = 5.27047
I0410 14:18:09.246836 18534 solver.cpp:237]     Train net output #0: loss = 5.27047 (* 1 = 5.27047 loss)
I0410 14:18:09.246848 18534 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 14:18:14.218901 18534 solver.cpp:218] Iteration 6444 (2.41358 iter/s, 4.97187s/12 iters), loss = 5.24393
I0410 14:18:14.218950 18534 solver.cpp:237]     Train net output #0: loss = 5.24393 (* 1 = 5.24393 loss)
I0410 14:18:14.218958 18534 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 14:18:19.127144 18534 solver.cpp:218] Iteration 6456 (2.44499 iter/s, 4.908s/12 iters), loss = 5.27463
I0410 14:18:19.127202 18534 solver.cpp:237]     Train net output #0: loss = 5.27463 (* 1 = 5.27463 loss)
I0410 14:18:19.127213 18534 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 14:18:24.149919 18534 solver.cpp:218] Iteration 6468 (2.38924 iter/s, 5.02252s/12 iters), loss = 5.26401
I0410 14:18:24.150032 18534 solver.cpp:237]     Train net output #0: loss = 5.26401 (* 1 = 5.26401 loss)
I0410 14:18:24.150041 18534 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 14:18:26.205685 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:29.156422 18534 solver.cpp:218] Iteration 6480 (2.39703 iter/s, 5.0062s/12 iters), loss = 5.28845
I0410 14:18:29.156463 18534 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0410 14:18:29.156471 18534 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 14:18:34.118367 18534 solver.cpp:218] Iteration 6492 (2.41853 iter/s, 4.9617s/12 iters), loss = 5.26915
I0410 14:18:34.118422 18534 solver.cpp:237]     Train net output #0: loss = 5.26915 (* 1 = 5.26915 loss)
I0410 14:18:34.118432 18534 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 14:18:39.148717 18534 solver.cpp:218] Iteration 6504 (2.38564 iter/s, 5.0301s/12 iters), loss = 5.2716
I0410 14:18:39.148762 18534 solver.cpp:237]     Train net output #0: loss = 5.2716 (* 1 = 5.2716 loss)
I0410 14:18:39.148772 18534 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 14:18:43.991930 18534 solver.cpp:218] Iteration 6516 (2.47782 iter/s, 4.84297s/12 iters), loss = 5.26337
I0410 14:18:43.991981 18534 solver.cpp:237]     Train net output #0: loss = 5.26337 (* 1 = 5.26337 loss)
I0410 14:18:43.991992 18534 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 14:18:48.419986 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 14:18:48.761438 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 14:18:48.968361 18534 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 14:18:48.968389 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:18:50.914991 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:53.468370 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:18:53.468418 18534 solver.cpp:397]     Test net output #1: loss = 5.28671 (* 1 = 5.28671 loss)
I0410 14:18:53.551291 18534 solver.cpp:218] Iteration 6528 (1.25537 iter/s, 9.55894s/12 iters), loss = 5.2707
I0410 14:18:53.551360 18534 solver.cpp:237]     Train net output #0: loss = 5.2707 (* 1 = 5.2707 loss)
I0410 14:18:53.551376 18534 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 14:18:57.725013 18534 solver.cpp:218] Iteration 6540 (2.87529 iter/s, 4.17349s/12 iters), loss = 5.27021
I0410 14:18:57.726384 18534 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0410 14:18:57.726397 18534 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 14:19:02.563851 18534 solver.cpp:218] Iteration 6552 (2.48073 iter/s, 4.83728s/12 iters), loss = 5.26443
I0410 14:19:02.563901 18534 solver.cpp:237]     Train net output #0: loss = 5.26443 (* 1 = 5.26443 loss)
I0410 14:19:02.563912 18534 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 14:19:07.465669 18534 solver.cpp:218] Iteration 6564 (2.44819 iter/s, 4.90157s/12 iters), loss = 5.25746
I0410 14:19:07.465718 18534 solver.cpp:237]     Train net output #0: loss = 5.25746 (* 1 = 5.25746 loss)
I0410 14:19:07.465730 18534 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 14:19:11.616585 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:12.379623 18534 solver.cpp:218] Iteration 6576 (2.44215 iter/s, 4.91371s/12 iters), loss = 5.25794
I0410 14:19:12.379669 18534 solver.cpp:237]     Train net output #0: loss = 5.25794 (* 1 = 5.25794 loss)
I0410 14:19:12.379679 18534 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 14:19:17.333271 18534 solver.cpp:218] Iteration 6588 (2.42258 iter/s, 4.9534s/12 iters), loss = 5.27871
I0410 14:19:17.333324 18534 solver.cpp:237]     Train net output #0: loss = 5.27871 (* 1 = 5.27871 loss)
I0410 14:19:17.333336 18534 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 14:19:22.232640 18534 solver.cpp:218] Iteration 6600 (2.44942 iter/s, 4.89912s/12 iters), loss = 5.27518
I0410 14:19:22.232681 18534 solver.cpp:237]     Train net output #0: loss = 5.27518 (* 1 = 5.27518 loss)
I0410 14:19:22.232689 18534 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 14:19:27.192431 18534 solver.cpp:218] Iteration 6612 (2.41957 iter/s, 4.95955s/12 iters), loss = 5.30447
I0410 14:19:27.192489 18534 solver.cpp:237]     Train net output #0: loss = 5.30447 (* 1 = 5.30447 loss)
I0410 14:19:27.192502 18534 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 14:19:32.166489 18534 solver.cpp:218] Iteration 6624 (2.41264 iter/s, 4.9738s/12 iters), loss = 5.2699
I0410 14:19:32.166697 18534 solver.cpp:237]     Train net output #0: loss = 5.2699 (* 1 = 5.2699 loss)
I0410 14:19:32.166709 18534 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 14:19:34.194006 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 14:19:34.483778 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 14:19:34.701807 18534 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 14:19:34.701830 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:19:36.521003 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:39.107044 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:19:39.107093 18534 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0410 14:19:40.880911 18534 solver.cpp:218] Iteration 6636 (1.37711 iter/s, 8.71388s/12 iters), loss = 5.27434
I0410 14:19:40.880956 18534 solver.cpp:237]     Train net output #0: loss = 5.27434 (* 1 = 5.27434 loss)
I0410 14:19:40.880965 18534 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 14:19:45.823406 18534 solver.cpp:218] Iteration 6648 (2.42804 iter/s, 4.94225s/12 iters), loss = 5.27723
I0410 14:19:45.823462 18534 solver.cpp:237]     Train net output #0: loss = 5.27723 (* 1 = 5.27723 loss)
I0410 14:19:45.823473 18534 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 14:19:50.698124 18534 solver.cpp:218] Iteration 6660 (2.46181 iter/s, 4.87447s/12 iters), loss = 5.28245
I0410 14:19:50.698174 18534 solver.cpp:237]     Train net output #0: loss = 5.28245 (* 1 = 5.28245 loss)
I0410 14:19:50.698185 18534 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 14:19:55.679049 18534 solver.cpp:218] Iteration 6672 (2.40931 iter/s, 4.98067s/12 iters), loss = 5.2623
I0410 14:19:55.679096 18534 solver.cpp:237]     Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
I0410 14:19:55.679106 18534 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 14:19:56.990444 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:00.661365 18534 solver.cpp:218] Iteration 6684 (2.40864 iter/s, 4.98207s/12 iters), loss = 5.27493
I0410 14:20:00.661408 18534 solver.cpp:237]     Train net output #0: loss = 5.27493 (* 1 = 5.27493 loss)
I0410 14:20:00.661418 18534 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 14:20:05.649133 18534 solver.cpp:218] Iteration 6696 (2.406 iter/s, 4.98752s/12 iters), loss = 5.26815
I0410 14:20:05.649289 18534 solver.cpp:237]     Train net output #0: loss = 5.26815 (* 1 = 5.26815 loss)
I0410 14:20:05.649303 18534 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 14:20:10.568164 18534 solver.cpp:218] Iteration 6708 (2.43968 iter/s, 4.91869s/12 iters), loss = 5.27316
I0410 14:20:10.568212 18534 solver.cpp:237]     Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
I0410 14:20:10.568220 18534 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 14:20:15.478951 18534 solver.cpp:218] Iteration 6720 (2.44372 iter/s, 4.91054s/12 iters), loss = 5.26959
I0410 14:20:15.478996 18534 solver.cpp:237]     Train net output #0: loss = 5.26959 (* 1 = 5.26959 loss)
I0410 14:20:15.479007 18534 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 14:20:19.977720 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 14:20:20.276520 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 14:20:20.477159 18534 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 14:20:20.477190 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:20:22.299719 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:24.938423 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:20:24.938472 18534 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0410 14:20:25.020817 18534 solver.cpp:218] Iteration 6732 (1.25767 iter/s, 9.54145s/12 iters), loss = 5.26895
I0410 14:20:25.020866 18534 solver.cpp:237]     Train net output #0: loss = 5.26895 (* 1 = 5.26895 loss)
I0410 14:20:25.020879 18534 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 14:20:29.193075 18534 solver.cpp:218] Iteration 6744 (2.87629 iter/s, 4.17204s/12 iters), loss = 5.25947
I0410 14:20:29.193128 18534 solver.cpp:237]     Train net output #0: loss = 5.25947 (* 1 = 5.25947 loss)
I0410 14:20:29.193140 18534 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 14:20:34.104079 18534 solver.cpp:218] Iteration 6756 (2.44362 iter/s, 4.91075s/12 iters), loss = 5.29319
I0410 14:20:34.104135 18534 solver.cpp:237]     Train net output #0: loss = 5.29319 (* 1 = 5.29319 loss)
I0410 14:20:34.104146 18534 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 14:20:38.984037 18534 solver.cpp:218] Iteration 6768 (2.45916 iter/s, 4.87971s/12 iters), loss = 5.27287
I0410 14:20:38.984181 18534 solver.cpp:237]     Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
I0410 14:20:38.984194 18534 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 14:20:42.385710 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:43.866673 18534 solver.cpp:218] Iteration 6780 (2.45786 iter/s, 4.8823s/12 iters), loss = 5.27693
I0410 14:20:43.866734 18534 solver.cpp:237]     Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
I0410 14:20:43.866745 18534 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 14:20:48.842367 18534 solver.cpp:218] Iteration 6792 (2.41185 iter/s, 4.97544s/12 iters), loss = 5.25698
I0410 14:20:48.842406 18534 solver.cpp:237]     Train net output #0: loss = 5.25698 (* 1 = 5.25698 loss)
I0410 14:20:48.842414 18534 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 14:20:53.789712 18534 solver.cpp:218] Iteration 6804 (2.42566 iter/s, 4.9471s/12 iters), loss = 5.26502
I0410 14:20:53.789763 18534 solver.cpp:237]     Train net output #0: loss = 5.26502 (* 1 = 5.26502 loss)
I0410 14:20:53.789774 18534 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 14:20:58.720852 18534 solver.cpp:218] Iteration 6816 (2.43364 iter/s, 4.93089s/12 iters), loss = 5.27941
I0410 14:20:58.720911 18534 solver.cpp:237]     Train net output #0: loss = 5.27941 (* 1 = 5.27941 loss)
I0410 14:20:58.720923 18534 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 14:21:03.672516 18534 solver.cpp:218] Iteration 6828 (2.42355 iter/s, 4.95141s/12 iters), loss = 5.2658
I0410 14:21:03.672564 18534 solver.cpp:237]     Train net output #0: loss = 5.2658 (* 1 = 5.2658 loss)
I0410 14:21:03.672572 18534 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 14:21:05.686369 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 14:21:05.977036 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 14:21:06.185125 18534 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 14:21:06.185149 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:21:07.962358 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:10.631592 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:21:10.631707 18534 solver.cpp:397]     Test net output #1: loss = 5.28732 (* 1 = 5.28732 loss)
I0410 14:21:12.577600 18534 solver.cpp:218] Iteration 6840 (1.3476 iter/s, 8.90469s/12 iters), loss = 5.27623
I0410 14:21:12.577644 18534 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0410 14:21:12.577653 18534 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 14:21:17.557493 18534 solver.cpp:218] Iteration 6852 (2.40981 iter/s, 4.97965s/12 iters), loss = 5.27322
I0410 14:21:17.557538 18534 solver.cpp:237]     Train net output #0: loss = 5.27322 (* 1 = 5.27322 loss)
I0410 14:21:17.557546 18534 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 14:21:22.413193 18534 solver.cpp:218] Iteration 6864 (2.47145 iter/s, 4.85546s/12 iters), loss = 5.2786
I0410 14:21:22.413245 18534 solver.cpp:237]     Train net output #0: loss = 5.2786 (* 1 = 5.2786 loss)
I0410 14:21:22.413257 18534 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 14:21:27.320550 18534 solver.cpp:218] Iteration 6876 (2.44543 iter/s, 4.90711s/12 iters), loss = 5.2829
I0410 14:21:27.320598 18534 solver.cpp:237]     Train net output #0: loss = 5.2829 (* 1 = 5.2829 loss)
I0410 14:21:27.320608 18534 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 14:21:27.935735 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:32.519451 18534 solver.cpp:218] Iteration 6888 (2.3083 iter/s, 5.19864s/12 iters), loss = 5.28253
I0410 14:21:32.519511 18534 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0410 14:21:32.519526 18534 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 14:21:37.365483 18534 solver.cpp:218] Iteration 6900 (2.47638 iter/s, 4.84578s/12 iters), loss = 5.26713
I0410 14:21:37.365530 18534 solver.cpp:237]     Train net output #0: loss = 5.26713 (* 1 = 5.26713 loss)
I0410 14:21:37.365537 18534 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 14:21:42.274930 18534 solver.cpp:218] Iteration 6912 (2.44439 iter/s, 4.90919s/12 iters), loss = 5.28754
I0410 14:21:42.275091 18534 solver.cpp:237]     Train net output #0: loss = 5.28754 (* 1 = 5.28754 loss)
I0410 14:21:42.275105 18534 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 14:21:47.128556 18534 solver.cpp:218] Iteration 6924 (2.47256 iter/s, 4.85327s/12 iters), loss = 5.28134
I0410 14:21:47.128608 18534 solver.cpp:237]     Train net output #0: loss = 5.28134 (* 1 = 5.28134 loss)
I0410 14:21:47.128618 18534 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 14:21:51.493674 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 14:21:51.909672 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 14:21:52.127871 18534 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 14:21:52.127898 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:21:52.422577 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:21:54.026145 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:56.798872 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:21:56.798903 18534 solver.cpp:397]     Test net output #1: loss = 5.28704 (* 1 = 5.28704 loss)
I0410 14:21:56.880982 18534 solver.cpp:218] Iteration 6936 (1.23052 iter/s, 9.752s/12 iters), loss = 5.28341
I0410 14:21:56.881026 18534 solver.cpp:237]     Train net output #0: loss = 5.28341 (* 1 = 5.28341 loss)
I0410 14:21:56.881036 18534 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 14:22:01.068248 18534 solver.cpp:218] Iteration 6948 (2.86598 iter/s, 4.18705s/12 iters), loss = 5.277
I0410 14:22:01.068305 18534 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0410 14:22:01.068318 18534 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 14:22:06.097451 18534 solver.cpp:218] Iteration 6960 (2.38619 iter/s, 5.02895s/12 iters), loss = 5.26397
I0410 14:22:06.097501 18534 solver.cpp:237]     Train net output #0: loss = 5.26397 (* 1 = 5.26397 loss)
I0410 14:22:06.097513 18534 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 14:22:11.049865 18534 solver.cpp:218] Iteration 6972 (2.42318 iter/s, 4.95217s/12 iters), loss = 5.26613
I0410 14:22:11.049919 18534 solver.cpp:237]     Train net output #0: loss = 5.26613 (* 1 = 5.26613 loss)
I0410 14:22:11.049930 18534 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 14:22:13.779012 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:15.956043 18534 solver.cpp:218] Iteration 6984 (2.44602 iter/s, 4.90593s/12 iters), loss = 5.27623
I0410 14:22:15.956094 18534 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0410 14:22:15.956104 18534 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 14:22:20.861727 18534 solver.cpp:218] Iteration 6996 (2.44627 iter/s, 4.90543s/12 iters), loss = 5.25854
I0410 14:22:20.861780 18534 solver.cpp:237]     Train net output #0: loss = 5.25854 (* 1 = 5.25854 loss)
I0410 14:22:20.861794 18534 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 14:22:25.849655 18534 solver.cpp:218] Iteration 7008 (2.40593 iter/s, 4.98768s/12 iters), loss = 5.25967
I0410 14:22:25.849704 18534 solver.cpp:237]     Train net output #0: loss = 5.25967 (* 1 = 5.25967 loss)
I0410 14:22:25.849712 18534 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 14:22:30.759609 18534 solver.cpp:218] Iteration 7020 (2.44414 iter/s, 4.9097s/12 iters), loss = 5.25895
I0410 14:22:30.759667 18534 solver.cpp:237]     Train net output #0: loss = 5.25895 (* 1 = 5.25895 loss)
I0410 14:22:30.759680 18534 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 14:22:35.688169 18534 solver.cpp:218] Iteration 7032 (2.43491 iter/s, 4.92831s/12 iters), loss = 5.30403
I0410 14:22:35.688220 18534 solver.cpp:237]     Train net output #0: loss = 5.30403 (* 1 = 5.30403 loss)
I0410 14:22:35.688231 18534 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 14:22:37.667681 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 14:22:37.970818 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 14:22:38.175235 18534 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 14:22:38.175266 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:22:39.731312 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:42.474036 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:22:42.474084 18534 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 14:22:44.164357 18534 solver.cpp:218] Iteration 7044 (1.41579 iter/s, 8.47581s/12 iters), loss = 5.27153
I0410 14:22:44.164491 18534 solver.cpp:237]     Train net output #0: loss = 5.27153 (* 1 = 5.27153 loss)
I0410 14:22:44.164502 18534 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 14:22:49.228929 18534 solver.cpp:218] Iteration 7056 (2.36955 iter/s, 5.06424s/12 iters), loss = 5.27028
I0410 14:22:49.228972 18534 solver.cpp:237]     Train net output #0: loss = 5.27028 (* 1 = 5.27028 loss)
I0410 14:22:49.228981 18534 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 14:22:54.202136 18534 solver.cpp:218] Iteration 7068 (2.41305 iter/s, 4.97296s/12 iters), loss = 5.26722
I0410 14:22:54.202194 18534 solver.cpp:237]     Train net output #0: loss = 5.26722 (* 1 = 5.26722 loss)
I0410 14:22:54.202206 18534 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 14:22:58.976999 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:59.085641 18534 solver.cpp:218] Iteration 7080 (2.45738 iter/s, 4.88325s/12 iters), loss = 5.24365
I0410 14:22:59.085700 18534 solver.cpp:237]     Train net output #0: loss = 5.24365 (* 1 = 5.24365 loss)
I0410 14:22:59.085711 18534 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 14:23:03.990491 18534 solver.cpp:218] Iteration 7092 (2.44668 iter/s, 4.9046s/12 iters), loss = 5.26754
I0410 14:23:03.990543 18534 solver.cpp:237]     Train net output #0: loss = 5.26754 (* 1 = 5.26754 loss)
I0410 14:23:03.990556 18534 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 14:23:08.898495 18534 solver.cpp:218] Iteration 7104 (2.44511 iter/s, 4.90776s/12 iters), loss = 5.29487
I0410 14:23:08.898541 18534 solver.cpp:237]     Train net output #0: loss = 5.29487 (* 1 = 5.29487 loss)
I0410 14:23:08.898550 18534 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 14:23:13.774649 18534 solver.cpp:218] Iteration 7116 (2.46108 iter/s, 4.87591s/12 iters), loss = 5.27444
I0410 14:23:13.774699 18534 solver.cpp:237]     Train net output #0: loss = 5.27444 (* 1 = 5.27444 loss)
I0410 14:23:13.774711 18534 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 14:23:18.662684 18534 solver.cpp:218] Iteration 7128 (2.4551 iter/s, 4.88778s/12 iters), loss = 5.275
I0410 14:23:18.662806 18534 solver.cpp:237]     Train net output #0: loss = 5.275 (* 1 = 5.275 loss)
I0410 14:23:18.662819 18534 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 14:23:23.047569 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 14:23:23.344727 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 14:23:23.543838 18534 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 14:23:23.543866 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:23:25.136636 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:27.923447 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:23:27.923487 18534 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0410 14:23:28.005707 18534 solver.cpp:218] Iteration 7140 (1.28445 iter/s, 9.34254s/12 iters), loss = 5.26389
I0410 14:23:28.005766 18534 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0410 14:23:28.005777 18534 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 14:23:32.351891 18534 solver.cpp:218] Iteration 7152 (2.76119 iter/s, 4.34594s/12 iters), loss = 5.24745
I0410 14:23:32.351948 18534 solver.cpp:237]     Train net output #0: loss = 5.24745 (* 1 = 5.24745 loss)
I0410 14:23:32.351960 18534 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 14:23:37.178519 18534 solver.cpp:218] Iteration 7164 (2.48634 iter/s, 4.82638s/12 iters), loss = 5.27238
I0410 14:23:37.178572 18534 solver.cpp:237]     Train net output #0: loss = 5.27238 (* 1 = 5.27238 loss)
I0410 14:23:37.178584 18534 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 14:23:42.079854 18534 solver.cpp:218] Iteration 7176 (2.44844 iter/s, 4.90109s/12 iters), loss = 5.25712
I0410 14:23:42.079903 18534 solver.cpp:237]     Train net output #0: loss = 5.25712 (* 1 = 5.25712 loss)
I0410 14:23:42.079914 18534 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 14:23:44.132684 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:46.937322 18534 solver.cpp:218] Iteration 7188 (2.47055 iter/s, 4.85723s/12 iters), loss = 5.27437
I0410 14:23:46.937361 18534 solver.cpp:237]     Train net output #0: loss = 5.27437 (* 1 = 5.27437 loss)
I0410 14:23:46.937369 18534 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 14:23:51.828631 18534 solver.cpp:218] Iteration 7200 (2.45345 iter/s, 4.89107s/12 iters), loss = 5.27128
I0410 14:23:51.830272 18534 solver.cpp:237]     Train net output #0: loss = 5.27128 (* 1 = 5.27128 loss)
I0410 14:23:51.830283 18534 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 14:23:56.857633 18534 solver.cpp:218] Iteration 7212 (2.38703 iter/s, 5.02716s/12 iters), loss = 5.27979
I0410 14:23:56.857683 18534 solver.cpp:237]     Train net output #0: loss = 5.27979 (* 1 = 5.27979 loss)
I0410 14:23:56.857695 18534 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 14:24:01.743615 18534 solver.cpp:218] Iteration 7224 (2.45613 iter/s, 4.88574s/12 iters), loss = 5.26562
I0410 14:24:01.743674 18534 solver.cpp:237]     Train net output #0: loss = 5.26562 (* 1 = 5.26562 loss)
I0410 14:24:01.743685 18534 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 14:24:06.723536 18534 solver.cpp:218] Iteration 7236 (2.4098 iter/s, 4.97966s/12 iters), loss = 5.2737
I0410 14:24:06.723594 18534 solver.cpp:237]     Train net output #0: loss = 5.2737 (* 1 = 5.2737 loss)
I0410 14:24:06.723606 18534 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 14:24:08.709991 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 14:24:09.033625 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 14:24:09.246851 18534 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 14:24:09.246870 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:24:10.814785 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:13.787343 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:24:13.787380 18534 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0410 14:24:15.603013 18534 solver.cpp:218] Iteration 7248 (1.35149 iter/s, 8.87907s/12 iters), loss = 5.27323
I0410 14:24:15.603073 18534 solver.cpp:237]     Train net output #0: loss = 5.27323 (* 1 = 5.27323 loss)
I0410 14:24:15.603085 18534 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 14:24:20.460618 18534 solver.cpp:218] Iteration 7260 (2.47048 iter/s, 4.85735s/12 iters), loss = 5.27021
I0410 14:24:20.460667 18534 solver.cpp:237]     Train net output #0: loss = 5.27021 (* 1 = 5.27021 loss)
I0410 14:24:20.460676 18534 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 14:24:25.342530 18534 solver.cpp:218] Iteration 7272 (2.45817 iter/s, 4.88167s/12 iters), loss = 5.25394
I0410 14:24:25.342640 18534 solver.cpp:237]     Train net output #0: loss = 5.25394 (* 1 = 5.25394 loss)
I0410 14:24:25.342649 18534 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 14:24:29.512686 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:30.237151 18534 solver.cpp:218] Iteration 7284 (2.45183 iter/s, 4.89431s/12 iters), loss = 5.2574
I0410 14:24:30.237210 18534 solver.cpp:237]     Train net output #0: loss = 5.2574 (* 1 = 5.2574 loss)
I0410 14:24:30.237221 18534 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 14:24:35.257568 18534 solver.cpp:218] Iteration 7296 (2.39036 iter/s, 5.02016s/12 iters), loss = 5.27963
I0410 14:24:35.257616 18534 solver.cpp:237]     Train net output #0: loss = 5.27963 (* 1 = 5.27963 loss)
I0410 14:24:35.257624 18534 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 14:24:40.086499 18534 solver.cpp:218] Iteration 7308 (2.48515 iter/s, 4.82869s/12 iters), loss = 5.2834
I0410 14:24:40.086556 18534 solver.cpp:237]     Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
I0410 14:24:40.086568 18534 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 14:24:45.206450 18534 solver.cpp:218] Iteration 7320 (2.34389 iter/s, 5.1197s/12 iters), loss = 5.29403
I0410 14:24:45.206502 18534 solver.cpp:237]     Train net output #0: loss = 5.29403 (* 1 = 5.29403 loss)
I0410 14:24:45.206512 18534 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 14:24:50.198258 18534 solver.cpp:218] Iteration 7332 (2.40403 iter/s, 4.99162s/12 iters), loss = 5.26801
I0410 14:24:50.198305 18534 solver.cpp:237]     Train net output #0: loss = 5.26801 (* 1 = 5.26801 loss)
I0410 14:24:50.198314 18534 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 14:24:54.549685 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 14:24:54.839073 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 14:24:55.052605 18534 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 14:24:55.052628 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:24:56.586037 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:59.506505 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:24:59.506553 18534 solver.cpp:397]     Test net output #1: loss = 5.28745 (* 1 = 5.28745 loss)
I0410 14:24:59.589107 18534 solver.cpp:218] Iteration 7344 (1.27788 iter/s, 9.39054s/12 iters), loss = 5.27724
I0410 14:24:59.589156 18534 solver.cpp:237]     Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
I0410 14:24:59.589166 18534 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 14:25:03.745592 18534 solver.cpp:218] Iteration 7356 (2.88717 iter/s, 4.15631s/12 iters), loss = 5.28251
I0410 14:25:03.745640 18534 solver.cpp:237]     Train net output #0: loss = 5.28251 (* 1 = 5.28251 loss)
I0410 14:25:03.745651 18534 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 14:25:08.708521 18534 solver.cpp:218] Iteration 7368 (2.41802 iter/s, 4.96274s/12 iters), loss = 5.27584
I0410 14:25:08.708580 18534 solver.cpp:237]     Train net output #0: loss = 5.27584 (* 1 = 5.27584 loss)
I0410 14:25:08.708591 18534 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 14:25:13.599236 18534 solver.cpp:218] Iteration 7380 (2.45373 iter/s, 4.89052s/12 iters), loss = 5.26325
I0410 14:25:13.599279 18534 solver.cpp:237]     Train net output #0: loss = 5.26325 (* 1 = 5.26325 loss)
I0410 14:25:13.599288 18534 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 14:25:14.947996 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:18.434053 18534 solver.cpp:218] Iteration 7392 (2.48209 iter/s, 4.83464s/12 iters), loss = 5.27412
I0410 14:25:18.434101 18534 solver.cpp:237]     Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
I0410 14:25:18.434113 18534 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 14:25:23.289120 18534 solver.cpp:218] Iteration 7404 (2.47174 iter/s, 4.85488s/12 iters), loss = 5.27016
I0410 14:25:23.289180 18534 solver.cpp:237]     Train net output #0: loss = 5.27016 (* 1 = 5.27016 loss)
I0410 14:25:23.289192 18534 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 14:25:28.342814 18534 solver.cpp:218] Iteration 7416 (2.3746 iter/s, 5.05349s/12 iters), loss = 5.26757
I0410 14:25:28.342965 18534 solver.cpp:237]     Train net output #0: loss = 5.26757 (* 1 = 5.26757 loss)
I0410 14:25:28.342978 18534 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 14:25:33.350728 18534 solver.cpp:218] Iteration 7428 (2.39635 iter/s, 5.00762s/12 iters), loss = 5.27857
I0410 14:25:33.350780 18534 solver.cpp:237]     Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
I0410 14:25:33.350791 18534 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 14:25:38.226027 18534 solver.cpp:218] Iteration 7440 (2.46149 iter/s, 4.8751s/12 iters), loss = 5.25873
I0410 14:25:38.226086 18534 solver.cpp:237]     Train net output #0: loss = 5.25873 (* 1 = 5.25873 loss)
I0410 14:25:38.226099 18534 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 14:25:40.202126 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 14:25:40.928267 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 14:25:41.139859 18534 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 14:25:41.139878 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:25:42.663367 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:45.569358 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:25:45.569407 18534 solver.cpp:397]     Test net output #1: loss = 5.2868 (* 1 = 5.2868 loss)
I0410 14:25:47.462594 18534 solver.cpp:218] Iteration 7452 (1.29923 iter/s, 9.23625s/12 iters), loss = 5.26637
I0410 14:25:47.462649 18534 solver.cpp:237]     Train net output #0: loss = 5.26637 (* 1 = 5.26637 loss)
I0410 14:25:47.462661 18534 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 14:25:52.437141 18534 solver.cpp:218] Iteration 7464 (2.41238 iter/s, 4.97435s/12 iters), loss = 5.2873
I0410 14:25:52.437197 18534 solver.cpp:237]     Train net output #0: loss = 5.2873 (* 1 = 5.2873 loss)
I0410 14:25:52.437209 18534 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 14:25:57.443336 18534 solver.cpp:218] Iteration 7476 (2.39713 iter/s, 5.00599s/12 iters), loss = 5.27512
I0410 14:25:57.443394 18534 solver.cpp:237]     Train net output #0: loss = 5.27512 (* 1 = 5.27512 loss)
I0410 14:25:57.443406 18534 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 14:26:00.857740 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:02.274735 18534 solver.cpp:218] Iteration 7488 (2.48386 iter/s, 4.83119s/12 iters), loss = 5.26971
I0410 14:26:02.274796 18534 solver.cpp:237]     Train net output #0: loss = 5.26971 (* 1 = 5.26971 loss)
I0410 14:26:02.274807 18534 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 14:26:07.155196 18534 solver.cpp:218] Iteration 7500 (2.45888 iter/s, 4.88026s/12 iters), loss = 5.25974
I0410 14:26:07.155244 18534 solver.cpp:237]     Train net output #0: loss = 5.25974 (* 1 = 5.25974 loss)
I0410 14:26:07.155256 18534 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 14:26:12.068393 18534 solver.cpp:218] Iteration 7512 (2.4425 iter/s, 4.91301s/12 iters), loss = 5.26086
I0410 14:26:12.068437 18534 solver.cpp:237]     Train net output #0: loss = 5.26086 (* 1 = 5.26086 loss)
I0410 14:26:12.068445 18534 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 14:26:16.969256 18534 solver.cpp:218] Iteration 7524 (2.44864 iter/s, 4.90067s/12 iters), loss = 5.26759
I0410 14:26:16.969300 18534 solver.cpp:237]     Train net output #0: loss = 5.26759 (* 1 = 5.26759 loss)
I0410 14:26:16.969310 18534 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 14:26:21.929252 18534 solver.cpp:218] Iteration 7536 (2.41945 iter/s, 4.9598s/12 iters), loss = 5.26247
I0410 14:26:21.929296 18534 solver.cpp:237]     Train net output #0: loss = 5.26247 (* 1 = 5.26247 loss)
I0410 14:26:21.929303 18534 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 14:26:26.390494 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 14:26:26.711891 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 14:26:27.364814 18534 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 14:26:27.364846 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:26:28.883224 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:31.849004 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:26:31.849153 18534 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0410 14:26:31.931658 18534 solver.cpp:218] Iteration 7548 (1.19975 iter/s, 10.0021s/12 iters), loss = 5.28221
I0410 14:26:31.931705 18534 solver.cpp:237]     Train net output #0: loss = 5.28221 (* 1 = 5.28221 loss)
I0410 14:26:31.931716 18534 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 14:26:36.158416 18534 solver.cpp:218] Iteration 7560 (2.83917 iter/s, 4.22658s/12 iters), loss = 5.26955
I0410 14:26:36.158460 18534 solver.cpp:237]     Train net output #0: loss = 5.26955 (* 1 = 5.26955 loss)
I0410 14:26:36.158470 18534 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 14:26:41.046540 18534 solver.cpp:218] Iteration 7572 (2.45503 iter/s, 4.88792s/12 iters), loss = 5.27978
I0410 14:26:41.046586 18534 solver.cpp:237]     Train net output #0: loss = 5.27978 (* 1 = 5.27978 loss)
I0410 14:26:41.046595 18534 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 14:26:45.853057 18534 solver.cpp:218] Iteration 7584 (2.49671 iter/s, 4.80633s/12 iters), loss = 5.28675
I0410 14:26:45.853103 18534 solver.cpp:237]     Train net output #0: loss = 5.28675 (* 1 = 5.28675 loss)
I0410 14:26:45.853113 18534 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 14:26:46.480399 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:50.868544 18534 solver.cpp:218] Iteration 7596 (2.39268 iter/s, 5.01529s/12 iters), loss = 5.27826
I0410 14:26:50.868599 18534 solver.cpp:237]     Train net output #0: loss = 5.27826 (* 1 = 5.27826 loss)
I0410 14:26:50.868611 18534 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 14:26:55.853731 18534 solver.cpp:218] Iteration 7608 (2.40723 iter/s, 4.98498s/12 iters), loss = 5.26342
I0410 14:26:55.853775 18534 solver.cpp:237]     Train net output #0: loss = 5.26342 (* 1 = 5.26342 loss)
I0410 14:26:55.853782 18534 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 14:27:00.771147 18534 solver.cpp:218] Iteration 7620 (2.4404 iter/s, 4.91722s/12 iters), loss = 5.27991
I0410 14:27:00.771201 18534 solver.cpp:237]     Train net output #0: loss = 5.27991 (* 1 = 5.27991 loss)
I0410 14:27:00.771214 18534 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 14:27:01.546363 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:27:05.668231 18534 solver.cpp:218] Iteration 7632 (2.45054 iter/s, 4.89688s/12 iters), loss = 5.2809
I0410 14:27:05.668342 18534 solver.cpp:237]     Train net output #0: loss = 5.2809 (* 1 = 5.2809 loss)
I0410 14:27:05.668355 18534 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 14:27:10.552171 18534 solver.cpp:218] Iteration 7644 (2.45716 iter/s, 4.88368s/12 iters), loss = 5.28322
I0410 14:27:10.552222 18534 solver.cpp:237]     Train net output #0: loss = 5.28322 (* 1 = 5.28322 loss)
I0410 14:27:10.552232 18534 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 14:27:12.541599 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 14:27:12.849092 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 14:27:13.047585 18534 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 14:27:13.047608 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:27:14.481367 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:17.701433 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:27:17.701476 18534 solver.cpp:397]     Test net output #1: loss = 5.2871 (* 1 = 5.2871 loss)
I0410 14:27:19.483928 18534 solver.cpp:218] Iteration 7656 (1.34357 iter/s, 8.93144s/12 iters), loss = 5.27372
I0410 14:27:19.483978 18534 solver.cpp:237]     Train net output #0: loss = 5.27372 (* 1 = 5.27372 loss)
I0410 14:27:19.483985 18534 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 14:27:24.354461 18534 solver.cpp:218] Iteration 7668 (2.4639 iter/s, 4.87033s/12 iters), loss = 5.26632
I0410 14:27:24.354511 18534 solver.cpp:237]     Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
I0410 14:27:24.354521 18534 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 14:27:29.302521 18534 solver.cpp:218] Iteration 7680 (2.42529 iter/s, 4.94786s/12 iters), loss = 5.26284
I0410 14:27:29.302567 18534 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0410 14:27:29.302577 18534 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 14:27:31.984057 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:34.162211 18534 solver.cpp:218] Iteration 7692 (2.4694 iter/s, 4.85949s/12 iters), loss = 5.27152
I0410 14:27:34.162257 18534 solver.cpp:237]     Train net output #0: loss = 5.27152 (* 1 = 5.27152 loss)
I0410 14:27:34.162266 18534 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 14:27:39.185878 18534 solver.cpp:218] Iteration 7704 (2.38879 iter/s, 5.02345s/12 iters), loss = 5.25265
I0410 14:27:39.186061 18534 solver.cpp:237]     Train net output #0: loss = 5.25265 (* 1 = 5.25265 loss)
I0410 14:27:39.186074 18534 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 14:27:44.042750 18534 solver.cpp:218] Iteration 7716 (2.47089 iter/s, 4.85654s/12 iters), loss = 5.25494
I0410 14:27:44.042800 18534 solver.cpp:237]     Train net output #0: loss = 5.25494 (* 1 = 5.25494 loss)
I0410 14:27:44.042811 18534 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 14:27:49.004338 18534 solver.cpp:218] Iteration 7728 (2.41868 iter/s, 4.96138s/12 iters), loss = 5.25944
I0410 14:27:49.004392 18534 solver.cpp:237]     Train net output #0: loss = 5.25944 (* 1 = 5.25944 loss)
I0410 14:27:49.004403 18534 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 14:27:54.041695 18534 solver.cpp:218] Iteration 7740 (2.3823 iter/s, 5.03715s/12 iters), loss = 5.30004
I0410 14:27:54.041744 18534 solver.cpp:237]     Train net output #0: loss = 5.30004 (* 1 = 5.30004 loss)
I0410 14:27:54.041755 18534 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 14:27:58.473068 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 14:27:58.808727 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 14:27:59.021808 18534 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 14:27:59.021828 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:28:00.346575 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:03.376361 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:28:03.376426 18534 solver.cpp:397]     Test net output #1: loss = 5.28673 (* 1 = 5.28673 loss)
I0410 14:28:03.458766 18534 solver.cpp:218] Iteration 7752 (1.27433 iter/s, 9.41673s/12 iters), loss = 5.26782
I0410 14:28:03.458834 18534 solver.cpp:237]     Train net output #0: loss = 5.26782 (* 1 = 5.26782 loss)
I0410 14:28:03.458853 18534 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 14:28:07.615664 18534 solver.cpp:218] Iteration 7764 (2.88691 iter/s, 4.1567s/12 iters), loss = 5.27586
I0410 14:28:07.615715 18534 solver.cpp:237]     Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
I0410 14:28:07.615725 18534 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 14:28:12.482583 18534 solver.cpp:218] Iteration 7776 (2.46573 iter/s, 4.86672s/12 iters), loss = 5.2716
I0410 14:28:12.482719 18534 solver.cpp:237]     Train net output #0: loss = 5.2716 (* 1 = 5.2716 loss)
I0410 14:28:12.482728 18534 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 14:28:17.373790 18534 solver.cpp:218] Iteration 7788 (2.45353 iter/s, 4.89092s/12 iters), loss = 5.24465
I0410 14:28:17.373834 18534 solver.cpp:237]     Train net output #0: loss = 5.24465 (* 1 = 5.24465 loss)
I0410 14:28:17.373843 18534 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 14:28:17.381892 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:22.286185 18534 solver.cpp:218] Iteration 7800 (2.4429 iter/s, 4.91219s/12 iters), loss = 5.26847
I0410 14:28:22.286232 18534 solver.cpp:237]     Train net output #0: loss = 5.26847 (* 1 = 5.26847 loss)
I0410 14:28:22.286242 18534 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 14:28:27.258455 18534 solver.cpp:218] Iteration 7812 (2.41349 iter/s, 4.97206s/12 iters), loss = 5.29476
I0410 14:28:27.258512 18534 solver.cpp:237]     Train net output #0: loss = 5.29476 (* 1 = 5.29476 loss)
I0410 14:28:27.258527 18534 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 14:28:32.098582 18534 solver.cpp:218] Iteration 7824 (2.47938 iter/s, 4.83991s/12 iters), loss = 5.27495
I0410 14:28:32.098636 18534 solver.cpp:237]     Train net output #0: loss = 5.27495 (* 1 = 5.27495 loss)
I0410 14:28:32.098649 18534 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 14:28:37.005991 18534 solver.cpp:218] Iteration 7836 (2.44539 iter/s, 4.9072s/12 iters), loss = 5.27511
I0410 14:28:37.006048 18534 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0410 14:28:37.006060 18534 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 14:28:41.944347 18534 solver.cpp:218] Iteration 7848 (2.43006 iter/s, 4.93814s/12 iters), loss = 5.25846
I0410 14:28:41.944393 18534 solver.cpp:237]     Train net output #0: loss = 5.25846 (* 1 = 5.25846 loss)
I0410 14:28:41.944402 18534 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 14:28:43.939393 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 14:28:44.243006 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 14:28:44.451164 18534 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 14:28:44.451187 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:28:45.802464 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:48.918627 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:28:48.918678 18534 solver.cpp:397]     Test net output #1: loss = 5.2866 (* 1 = 5.2866 loss)
I0410 14:28:50.725531 18534 solver.cpp:218] Iteration 7860 (1.36661 iter/s, 8.78086s/12 iters), loss = 5.24451
I0410 14:28:50.725587 18534 solver.cpp:237]     Train net output #0: loss = 5.24451 (* 1 = 5.24451 loss)
I0410 14:28:50.725598 18534 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 14:28:55.635769 18534 solver.cpp:218] Iteration 7872 (2.44398 iter/s, 4.91002s/12 iters), loss = 5.26781
I0410 14:28:55.635828 18534 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0410 14:28:55.635838 18534 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 14:29:00.522688 18534 solver.cpp:218] Iteration 7884 (2.45564 iter/s, 4.8867s/12 iters), loss = 5.25864
I0410 14:29:00.522747 18534 solver.cpp:237]     Train net output #0: loss = 5.25864 (* 1 = 5.25864 loss)
I0410 14:29:00.522758 18534 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 14:29:02.634622 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:05.458137 18534 solver.cpp:218] Iteration 7896 (2.4315 iter/s, 4.93523s/12 iters), loss = 5.27728
I0410 14:29:05.458194 18534 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0410 14:29:05.458204 18534 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 14:29:10.482496 18534 solver.cpp:218] Iteration 7908 (2.38847 iter/s, 5.02414s/12 iters), loss = 5.27079
I0410 14:29:10.482547 18534 solver.cpp:237]     Train net output #0: loss = 5.27079 (* 1 = 5.27079 loss)
I0410 14:29:10.482559 18534 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 14:29:15.325157 18534 solver.cpp:218] Iteration 7920 (2.47808 iter/s, 4.84245s/12 iters), loss = 5.28563
I0410 14:29:15.325316 18534 solver.cpp:237]     Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
I0410 14:29:15.325330 18534 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 14:29:20.205035 18534 solver.cpp:218] Iteration 7932 (2.45924 iter/s, 4.87956s/12 iters), loss = 5.26264
I0410 14:29:20.205090 18534 solver.cpp:237]     Train net output #0: loss = 5.26264 (* 1 = 5.26264 loss)
I0410 14:29:20.205101 18534 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 14:29:25.116825 18534 solver.cpp:218] Iteration 7944 (2.44321 iter/s, 4.91158s/12 iters), loss = 5.26603
I0410 14:29:25.116875 18534 solver.cpp:237]     Train net output #0: loss = 5.26603 (* 1 = 5.26603 loss)
I0410 14:29:25.116887 18534 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 14:29:29.526546 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 14:29:30.176432 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 14:29:31.270143 18534 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 14:29:31.270176 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:29:32.678119 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:35.780568 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:29:35.780619 18534 solver.cpp:397]     Test net output #1: loss = 5.28709 (* 1 = 5.28709 loss)
I0410 14:29:35.863178 18534 solver.cpp:218] Iteration 7956 (1.1167 iter/s, 10.746s/12 iters), loss = 5.27569
I0410 14:29:35.863229 18534 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0410 14:29:35.863241 18534 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 14:29:40.110378 18534 solver.cpp:218] Iteration 7968 (2.82552 iter/s, 4.24701s/12 iters), loss = 5.27367
I0410 14:29:40.110422 18534 solver.cpp:237]     Train net output #0: loss = 5.27367 (* 1 = 5.27367 loss)
I0410 14:29:40.110430 18534 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 14:29:45.038086 18534 solver.cpp:218] Iteration 7980 (2.43531 iter/s, 4.9275s/12 iters), loss = 5.25594
I0410 14:29:45.038142 18534 solver.cpp:237]     Train net output #0: loss = 5.25594 (* 1 = 5.25594 loss)
I0410 14:29:45.038154 18534 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 14:29:49.243217 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:49.951041 18534 solver.cpp:218] Iteration 7992 (2.44263 iter/s, 4.91273s/12 iters), loss = 5.25546
I0410 14:29:49.951100 18534 solver.cpp:237]     Train net output #0: loss = 5.25546 (* 1 = 5.25546 loss)
I0410 14:29:49.951112 18534 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 14:29:54.838815 18534 solver.cpp:218] Iteration 8004 (2.45521 iter/s, 4.88756s/12 iters), loss = 5.27815
I0410 14:29:54.838856 18534 solver.cpp:237]     Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
I0410 14:29:54.838865 18534 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 14:30:00.045169 18534 solver.cpp:218] Iteration 8016 (2.30497 iter/s, 5.20614s/12 iters), loss = 5.27619
I0410 14:30:00.045228 18534 solver.cpp:237]     Train net output #0: loss = 5.27619 (* 1 = 5.27619 loss)
I0410 14:30:00.045243 18534 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 14:30:04.928294 18534 solver.cpp:218] Iteration 8028 (2.45755 iter/s, 4.88291s/12 iters), loss = 5.29255
I0410 14:30:04.928342 18534 solver.cpp:237]     Train net output #0: loss = 5.29255 (* 1 = 5.29255 loss)
I0410 14:30:04.928354 18534 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 14:30:09.817688 18534 solver.cpp:218] Iteration 8040 (2.4544 iter/s, 4.88918s/12 iters), loss = 5.26388
I0410 14:30:09.817745 18534 solver.cpp:237]     Train net output #0: loss = 5.26388 (* 1 = 5.26388 loss)
I0410 14:30:09.817757 18534 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 14:30:14.753882 18534 solver.cpp:218] Iteration 8052 (2.43113 iter/s, 4.93598s/12 iters), loss = 5.28111
I0410 14:30:14.753934 18534 solver.cpp:237]     Train net output #0: loss = 5.28111 (* 1 = 5.28111 loss)
I0410 14:30:14.753945 18534 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 14:30:16.763394 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 14:30:17.098662 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 14:30:17.313076 18534 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 14:30:17.313095 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:30:18.549439 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:21.691536 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:30:21.691689 18534 solver.cpp:397]     Test net output #1: loss = 5.28702 (* 1 = 5.28702 loss)
I0410 14:30:23.554507 18534 solver.cpp:218] Iteration 8064 (1.36359 iter/s, 8.80029s/12 iters), loss = 5.27813
I0410 14:30:23.554549 18534 solver.cpp:237]     Train net output #0: loss = 5.27813 (* 1 = 5.27813 loss)
I0410 14:30:23.554558 18534 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 14:30:28.472692 18534 solver.cpp:218] Iteration 8076 (2.44003 iter/s, 4.91798s/12 iters), loss = 5.27839
I0410 14:30:28.472745 18534 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0410 14:30:28.472759 18534 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 14:30:33.349077 18534 solver.cpp:218] Iteration 8088 (2.46095 iter/s, 4.87617s/12 iters), loss = 5.26207
I0410 14:30:33.349123 18534 solver.cpp:237]     Train net output #0: loss = 5.26207 (* 1 = 5.26207 loss)
I0410 14:30:33.349134 18534 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 14:30:34.761649 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:38.282990 18534 solver.cpp:218] Iteration 8100 (2.43225 iter/s, 4.9337s/12 iters), loss = 5.26187
I0410 14:30:38.283048 18534 solver.cpp:237]     Train net output #0: loss = 5.26187 (* 1 = 5.26187 loss)
I0410 14:30:38.283059 18534 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 14:30:43.200706 18534 solver.cpp:218] Iteration 8112 (2.44027 iter/s, 4.91749s/12 iters), loss = 5.2672
I0410 14:30:43.200753 18534 solver.cpp:237]     Train net output #0: loss = 5.2672 (* 1 = 5.2672 loss)
I0410 14:30:43.200762 18534 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 14:30:48.115396 18534 solver.cpp:218] Iteration 8124 (2.44177 iter/s, 4.91448s/12 iters), loss = 5.27198
I0410 14:30:48.115448 18534 solver.cpp:237]     Train net output #0: loss = 5.27198 (* 1 = 5.27198 loss)
I0410 14:30:48.115459 18534 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 14:30:52.957980 18534 solver.cpp:218] Iteration 8136 (2.47813 iter/s, 4.84236s/12 iters), loss = 5.28417
I0410 14:30:52.958062 18534 solver.cpp:237]     Train net output #0: loss = 5.28417 (* 1 = 5.28417 loss)
I0410 14:30:52.958074 18534 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 14:30:57.796039 18534 solver.cpp:218] Iteration 8148 (2.48046 iter/s, 4.83781s/12 iters), loss = 5.25062
I0410 14:30:57.796097 18534 solver.cpp:237]     Train net output #0: loss = 5.25062 (* 1 = 5.25062 loss)
I0410 14:30:57.796110 18534 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 14:31:02.197263 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 14:31:02.564682 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 14:31:02.773102 18534 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 14:31:02.773129 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:31:03.927732 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:07.190523 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:31:07.190574 18534 solver.cpp:397]     Test net output #1: loss = 5.28678 (* 1 = 5.28678 loss)
I0410 14:31:07.272962 18534 solver.cpp:218] Iteration 8160 (1.26628 iter/s, 9.47656s/12 iters), loss = 5.26228
I0410 14:31:07.273013 18534 solver.cpp:237]     Train net output #0: loss = 5.26228 (* 1 = 5.26228 loss)
I0410 14:31:07.273025 18534 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 14:31:11.461845 18534 solver.cpp:218] Iteration 8172 (2.86486 iter/s, 4.18868s/12 iters), loss = 5.28501
I0410 14:31:11.461903 18534 solver.cpp:237]     Train net output #0: loss = 5.28501 (* 1 = 5.28501 loss)
I0410 14:31:11.461915 18534 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 14:31:16.401306 18534 solver.cpp:218] Iteration 8184 (2.42952 iter/s, 4.93924s/12 iters), loss = 5.27118
I0410 14:31:16.401350 18534 solver.cpp:237]     Train net output #0: loss = 5.27118 (* 1 = 5.27118 loss)
I0410 14:31:16.401358 18534 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 14:31:19.900761 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:21.323877 18534 solver.cpp:218] Iteration 8196 (2.43785 iter/s, 4.92236s/12 iters), loss = 5.27555
I0410 14:31:21.323927 18534 solver.cpp:237]     Train net output #0: loss = 5.27555 (* 1 = 5.27555 loss)
I0410 14:31:21.323938 18534 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 14:31:26.250902 18534 solver.cpp:218] Iteration 8208 (2.43565 iter/s, 4.92681s/12 iters), loss = 5.25857
I0410 14:31:26.251039 18534 solver.cpp:237]     Train net output #0: loss = 5.25857 (* 1 = 5.25857 loss)
I0410 14:31:26.251050 18534 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 14:31:31.144044 18534 solver.cpp:218] Iteration 8220 (2.45256 iter/s, 4.89284s/12 iters), loss = 5.26462
I0410 14:31:31.144091 18534 solver.cpp:237]     Train net output #0: loss = 5.26462 (* 1 = 5.26462 loss)
I0410 14:31:31.144100 18534 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 14:31:36.008179 18534 solver.cpp:218] Iteration 8232 (2.46715 iter/s, 4.86392s/12 iters), loss = 5.2691
I0410 14:31:36.008234 18534 solver.cpp:237]     Train net output #0: loss = 5.2691 (* 1 = 5.2691 loss)
I0410 14:31:36.008245 18534 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 14:31:40.932679 18534 solver.cpp:218] Iteration 8244 (2.43691 iter/s, 4.92428s/12 iters), loss = 5.25197
I0410 14:31:40.932729 18534 solver.cpp:237]     Train net output #0: loss = 5.25197 (* 1 = 5.25197 loss)
I0410 14:31:40.932739 18534 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 14:31:45.795751 18534 solver.cpp:218] Iteration 8256 (2.46768 iter/s, 4.86286s/12 iters), loss = 5.27264
I0410 14:31:45.795804 18534 solver.cpp:237]     Train net output #0: loss = 5.27264 (* 1 = 5.27264 loss)
I0410 14:31:45.795815 18534 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 14:31:47.824065 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 14:31:48.269388 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 14:31:48.742169 18534 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 14:31:48.742197 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:31:49.918627 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:53.199921 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 14:31:53.199966 18534 solver.cpp:397]     Test net output #1: loss = 5.28688 (* 1 = 5.28688 loss)
I0410 14:31:55.035481 18534 solver.cpp:218] Iteration 8268 (1.29879 iter/s, 9.23937s/12 iters), loss = 5.27955
I0410 14:31:55.035529 18534 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0410 14:31:55.035537 18534 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 14:31:59.975734 18534 solver.cpp:218] Iteration 8280 (2.42914 iter/s, 4.94003s/12 iters), loss = 5.28477
I0410 14:31:59.975879 18534 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0410 14:31:59.975891 18534 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 14:32:04.912088 18534 solver.cpp:218] Iteration 8292 (2.4311 iter/s, 4.93604s/12 iters), loss = 5.29
I0410 14:32:04.912145 18534 solver.cpp:237]     Train net output #0: loss = 5.29 (* 1 = 5.29 loss)
I0410 14:32:04.912158 18534 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 14:32:05.584585 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:09.847795 18534 solver.cpp:218] Iteration 8304 (2.43137 iter/s, 4.93548s/12 iters), loss = 5.27918
I0410 14:32:09.847849 18534 solver.cpp:237]     Train net output #0: loss = 5.27918 (* 1 = 5.27918 loss)
I0410 14:32:09.847859 18534 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 14:32:11.025053 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:32:14.733578 18534 solver.cpp:218] Iteration 8316 (2.45621 iter/s, 4.88557s/12 iters), loss = 5.2722
I0410 14:32:14.733621 18534 solver.cpp:237]     Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
I0410 14:32:14.733630 18534 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 14:32:19.652295 18534 solver.cpp:218] Iteration 8328 (2.43976 iter/s, 4.91851s/12 iters), loss = 5.28281
I0410 14:32:19.652333 18534 solver.cpp:237]     Train net output #0: loss = 5.28281 (* 1 = 5.28281 loss)
I0410 14:32:19.652343 18534 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 14:32:24.511675 18534 solver.cpp:218] Iteration 8340 (2.46956 iter/s, 4.85917s/12 iters), loss = 5.27261
I0410 14:32:24.511732 18534 solver.cpp:237]     Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
I0410 14:32:24.511744 18534 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 14:32:29.405973 18534 solver.cpp:218] Iteration 8352 (2.45195 iter/s, 4.89406s/12 iters), loss = 5.28843
I0410 14:32:29.406023 18534 solver.cpp:237]     Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
I0410 14:32:29.406034 18534 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 14:32:33.793655 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 14:32:34.408493 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 14:32:36.739197 18534 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 14:32:36.739233 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:32:37.924805 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:41.328150 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:32:41.328198 18534 solver.cpp:397]     Test net output #1: loss = 5.28658 (* 1 = 5.28658 loss)
I0410 14:32:41.409932 18534 solver.cpp:218] Iteration 8364 (0.999707 iter/s, 12.0035s/12 iters), loss = 5.26614
I0410 14:32:41.410020 18534 solver.cpp:237]     Train net output #0: loss = 5.26614 (* 1 = 5.26614 loss)
I0410 14:32:41.410032 18534 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 14:32:45.565613 18534 solver.cpp:218] Iteration 8376 (2.88778 iter/s, 4.15545s/12 iters), loss = 5.26389
I0410 14:32:45.565665 18534 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0410 14:32:45.565673 18534 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 14:32:50.450071 18534 solver.cpp:218] Iteration 8388 (2.45688 iter/s, 4.88423s/12 iters), loss = 5.26132
I0410 14:32:50.450125 18534 solver.cpp:237]     Train net output #0: loss = 5.26132 (* 1 = 5.26132 loss)
I0410 14:32:50.450137 18534 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 14:32:53.177510 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:55.282930 18534 solver.cpp:218] Iteration 8400 (2.48312 iter/s, 4.83264s/12 iters), loss = 5.26521
I0410 14:32:55.282987 18534 solver.cpp:237]     Train net output #0: loss = 5.26521 (* 1 = 5.26521 loss)
I0410 14:32:55.282999 18534 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 14:33:00.192636 18534 solver.cpp:218] Iteration 8412 (2.44425 iter/s, 4.90948s/12 iters), loss = 5.24965
I0410 14:33:00.192677 18534 solver.cpp:237]     Train net output #0: loss = 5.24965 (* 1 = 5.24965 loss)
I0410 14:33:00.192684 18534 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 14:33:05.099927 18534 solver.cpp:218] Iteration 8424 (2.44545 iter/s, 4.90707s/12 iters), loss = 5.2533
I0410 14:33:05.100076 18534 solver.cpp:237]     Train net output #0: loss = 5.2533 (* 1 = 5.2533 loss)
I0410 14:33:05.100090 18534 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 14:33:09.986829 18534 solver.cpp:218] Iteration 8436 (2.4557 iter/s, 4.88659s/12 iters), loss = 5.25579
I0410 14:33:09.986878 18534 solver.cpp:237]     Train net output #0: loss = 5.25579 (* 1 = 5.25579 loss)
I0410 14:33:09.986889 18534 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 14:33:14.838001 18534 solver.cpp:218] Iteration 8448 (2.47374 iter/s, 4.85096s/12 iters), loss = 5.29365
I0410 14:33:14.838049 18534 solver.cpp:237]     Train net output #0: loss = 5.29365 (* 1 = 5.29365 loss)
I0410 14:33:14.838058 18534 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 14:33:19.652624 18534 solver.cpp:218] Iteration 8460 (2.49252 iter/s, 4.8144s/12 iters), loss = 5.27324
I0410 14:33:19.652680 18534 solver.cpp:237]     Train net output #0: loss = 5.27324 (* 1 = 5.27324 loss)
I0410 14:33:19.652690 18534 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 14:33:21.652381 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 14:33:21.971352 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 14:33:22.184382 18534 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 14:33:22.184410 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:33:23.370661 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:26.720651 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:33:26.720698 18534 solver.cpp:397]     Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
I0410 14:33:28.596499 18534 solver.cpp:218] Iteration 8472 (1.34175 iter/s, 8.94352s/12 iters), loss = 5.27191
I0410 14:33:28.596546 18534 solver.cpp:237]     Train net output #0: loss = 5.27191 (* 1 = 5.27191 loss)
I0410 14:33:28.596555 18534 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 14:33:33.412909 18534 solver.cpp:218] Iteration 8484 (2.4916 iter/s, 4.81619s/12 iters), loss = 5.27183
I0410 14:33:33.412961 18534 solver.cpp:237]     Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
I0410 14:33:33.412973 18534 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 14:33:38.300338 18534 solver.cpp:218] Iteration 8496 (2.45539 iter/s, 4.8872s/12 iters), loss = 5.25561
I0410 14:33:38.300426 18534 solver.cpp:237]     Train net output #0: loss = 5.25561 (* 1 = 5.25561 loss)
I0410 14:33:38.300439 18534 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 14:33:38.347656 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:43.193272 18534 solver.cpp:218] Iteration 8508 (2.45265 iter/s, 4.89267s/12 iters), loss = 5.27599
I0410 14:33:43.193320 18534 solver.cpp:237]     Train net output #0: loss = 5.27599 (* 1 = 5.27599 loss)
I0410 14:33:43.193328 18534 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 14:33:48.275388 18534 solver.cpp:218] Iteration 8520 (2.36133 iter/s, 5.08189s/12 iters), loss = 5.29549
I0410 14:33:48.275439 18534 solver.cpp:237]     Train net output #0: loss = 5.29549 (* 1 = 5.29549 loss)
I0410 14:33:48.275449 18534 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 14:33:53.385335 18534 solver.cpp:218] Iteration 8532 (2.34847 iter/s, 5.10972s/12 iters), loss = 5.2702
I0410 14:33:53.385381 18534 solver.cpp:237]     Train net output #0: loss = 5.2702 (* 1 = 5.2702 loss)
I0410 14:33:53.385392 18534 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 14:33:58.321379 18534 solver.cpp:218] Iteration 8544 (2.43121 iter/s, 4.93582s/12 iters), loss = 5.27097
I0410 14:33:58.321434 18534 solver.cpp:237]     Train net output #0: loss = 5.27097 (* 1 = 5.27097 loss)
I0410 14:33:58.321444 18534 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 14:34:03.246930 18534 solver.cpp:218] Iteration 8556 (2.43639 iter/s, 4.92532s/12 iters), loss = 5.2573
I0410 14:34:03.246978 18534 solver.cpp:237]     Train net output #0: loss = 5.2573 (* 1 = 5.2573 loss)
I0410 14:34:03.246990 18534 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 14:34:07.789397 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 14:34:08.207758 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 14:34:08.497849 18534 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 14:34:08.497992 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:34:09.644883 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:13.132200 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:34:13.132241 18534 solver.cpp:397]     Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
I0410 14:34:13.214177 18534 solver.cpp:218] Iteration 8568 (1.20399 iter/s, 9.96686s/12 iters), loss = 5.24694
I0410 14:34:13.214227 18534 solver.cpp:237]     Train net output #0: loss = 5.24694 (* 1 = 5.24694 loss)
I0410 14:34:13.214236 18534 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 14:34:17.439345 18534 solver.cpp:218] Iteration 8580 (2.84026 iter/s, 4.22497s/12 iters), loss = 5.26408
I0410 14:34:17.439390 18534 solver.cpp:237]     Train net output #0: loss = 5.26408 (* 1 = 5.26408 loss)
I0410 14:34:17.439399 18534 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 14:34:22.318568 18534 solver.cpp:218] Iteration 8592 (2.45952 iter/s, 4.87901s/12 iters), loss = 5.25017
I0410 14:34:22.318614 18534 solver.cpp:237]     Train net output #0: loss = 5.25017 (* 1 = 5.25017 loss)
I0410 14:34:22.318624 18534 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 14:34:24.439246 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:27.213641 18534 solver.cpp:218] Iteration 8604 (2.45155 iter/s, 4.89486s/12 iters), loss = 5.26897
I0410 14:34:27.213686 18534 solver.cpp:237]     Train net output #0: loss = 5.26897 (* 1 = 5.26897 loss)
I0410 14:34:27.213696 18534 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 14:34:32.136782 18534 solver.cpp:218] Iteration 8616 (2.43758 iter/s, 4.92292s/12 iters), loss = 5.26494
I0410 14:34:32.136827 18534 solver.cpp:237]     Train net output #0: loss = 5.26494 (* 1 = 5.26494 loss)
I0410 14:34:32.136835 18534 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 14:34:37.202816 18534 solver.cpp:218] Iteration 8628 (2.36882 iter/s, 5.06581s/12 iters), loss = 5.28461
I0410 14:34:37.202862 18534 solver.cpp:237]     Train net output #0: loss = 5.28461 (* 1 = 5.28461 loss)
I0410 14:34:37.202870 18534 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 14:34:42.052062 18534 solver.cpp:218] Iteration 8640 (2.47472 iter/s, 4.84903s/12 iters), loss = 5.26333
I0410 14:34:42.052142 18534 solver.cpp:237]     Train net output #0: loss = 5.26333 (* 1 = 5.26333 loss)
I0410 14:34:42.052152 18534 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 14:34:46.927572 18534 solver.cpp:218] Iteration 8652 (2.46141 iter/s, 4.87525s/12 iters), loss = 5.26694
I0410 14:34:46.927628 18534 solver.cpp:237]     Train net output #0: loss = 5.26694 (* 1 = 5.26694 loss)
I0410 14:34:46.927641 18534 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 14:34:51.879143 18534 solver.cpp:218] Iteration 8664 (2.42359 iter/s, 4.95134s/12 iters), loss = 5.27219
I0410 14:34:51.879197 18534 solver.cpp:237]     Train net output #0: loss = 5.27219 (* 1 = 5.27219 loss)
I0410 14:34:51.879209 18534 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 14:34:53.860750 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 14:34:54.180966 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 14:34:54.398124 18534 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 14:34:54.398149 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:34:55.352093 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:58.774746 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:34:58.774793 18534 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 14:35:00.706579 18534 solver.cpp:218] Iteration 8676 (1.35945 iter/s, 8.82708s/12 iters), loss = 5.27619
I0410 14:35:00.706626 18534 solver.cpp:237]     Train net output #0: loss = 5.27619 (* 1 = 5.27619 loss)
I0410 14:35:00.706637 18534 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 14:35:05.807847 18534 solver.cpp:218] Iteration 8688 (2.35246 iter/s, 5.10104s/12 iters), loss = 5.26013
I0410 14:35:05.807893 18534 solver.cpp:237]     Train net output #0: loss = 5.26013 (* 1 = 5.26013 loss)
I0410 14:35:05.807901 18534 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 14:35:09.976940 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:10.661283 18534 solver.cpp:218] Iteration 8700 (2.47259 iter/s, 4.85321s/12 iters), loss = 5.26497
I0410 14:35:10.661336 18534 solver.cpp:237]     Train net output #0: loss = 5.26497 (* 1 = 5.26497 loss)
I0410 14:35:10.661347 18534 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 14:35:15.575369 18534 solver.cpp:218] Iteration 8712 (2.44207 iter/s, 4.91386s/12 iters), loss = 5.27875
I0410 14:35:15.575502 18534 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0410 14:35:15.575512 18534 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 14:35:20.510854 18534 solver.cpp:218] Iteration 8724 (2.43152 iter/s, 4.93518s/12 iters), loss = 5.28232
I0410 14:35:20.510905 18534 solver.cpp:237]     Train net output #0: loss = 5.28232 (* 1 = 5.28232 loss)
I0410 14:35:20.510916 18534 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 14:35:25.451347 18534 solver.cpp:218] Iteration 8736 (2.42902 iter/s, 4.94026s/12 iters), loss = 5.29446
I0410 14:35:25.451400 18534 solver.cpp:237]     Train net output #0: loss = 5.29446 (* 1 = 5.29446 loss)
I0410 14:35:25.451409 18534 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 14:35:30.366479 18534 solver.cpp:218] Iteration 8748 (2.44155 iter/s, 4.91491s/12 iters), loss = 5.26925
I0410 14:35:30.366523 18534 solver.cpp:237]     Train net output #0: loss = 5.26925 (* 1 = 5.26925 loss)
I0410 14:35:30.366534 18534 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 14:35:35.263356 18534 solver.cpp:218] Iteration 8760 (2.45065 iter/s, 4.89666s/12 iters), loss = 5.27815
I0410 14:35:35.263402 18534 solver.cpp:237]     Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
I0410 14:35:35.263411 18534 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 14:35:39.754894 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 14:35:40.050676 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 14:35:40.251843 18534 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 14:35:40.251873 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:35:41.220921 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:44.683475 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:35:44.683503 18534 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0410 14:35:44.765581 18534 solver.cpp:218] Iteration 8772 (1.26291 iter/s, 9.50185s/12 iters), loss = 5.28196
I0410 14:35:44.765625 18534 solver.cpp:237]     Train net output #0: loss = 5.28196 (* 1 = 5.28196 loss)
I0410 14:35:44.765631 18534 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 14:35:48.980731 18534 solver.cpp:218] Iteration 8784 (2.84701 iter/s, 4.21495s/12 iters), loss = 5.27435
I0410 14:35:48.980849 18534 solver.cpp:237]     Train net output #0: loss = 5.27435 (* 1 = 5.27435 loss)
I0410 14:35:48.980859 18534 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 14:35:53.881867 18534 solver.cpp:218] Iteration 8796 (2.44856 iter/s, 4.90085s/12 iters), loss = 5.25869
I0410 14:35:53.881912 18534 solver.cpp:237]     Train net output #0: loss = 5.25869 (* 1 = 5.25869 loss)
I0410 14:35:53.881920 18534 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 14:35:55.279943 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:58.749042 18534 solver.cpp:218] Iteration 8808 (2.46561 iter/s, 4.86696s/12 iters), loss = 5.26374
I0410 14:35:58.749089 18534 solver.cpp:237]     Train net output #0: loss = 5.26374 (* 1 = 5.26374 loss)
I0410 14:35:58.749099 18534 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 14:36:03.671788 18534 solver.cpp:218] Iteration 8820 (2.43777 iter/s, 4.92252s/12 iters), loss = 5.26572
I0410 14:36:03.671833 18534 solver.cpp:237]     Train net output #0: loss = 5.26572 (* 1 = 5.26572 loss)
I0410 14:36:03.671841 18534 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 14:36:08.609969 18534 solver.cpp:218] Iteration 8832 (2.43016 iter/s, 4.93794s/12 iters), loss = 5.26413
I0410 14:36:08.610018 18534 solver.cpp:237]     Train net output #0: loss = 5.26413 (* 1 = 5.26413 loss)
I0410 14:36:08.610029 18534 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 14:36:13.578114 18534 solver.cpp:218] Iteration 8844 (2.4155 iter/s, 4.96791s/12 iters), loss = 5.29702
I0410 14:36:13.578172 18534 solver.cpp:237]     Train net output #0: loss = 5.29702 (* 1 = 5.29702 loss)
I0410 14:36:13.578184 18534 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 14:36:18.675695 18534 solver.cpp:218] Iteration 8856 (2.35417 iter/s, 5.09734s/12 iters), loss = 5.2592
I0410 14:36:18.675747 18534 solver.cpp:237]     Train net output #0: loss = 5.2592 (* 1 = 5.2592 loss)
I0410 14:36:18.675758 18534 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 14:36:23.605824 18534 solver.cpp:218] Iteration 8868 (2.43413 iter/s, 4.9299s/12 iters), loss = 5.25776
I0410 14:36:23.605947 18534 solver.cpp:237]     Train net output #0: loss = 5.25776 (* 1 = 5.25776 loss)
I0410 14:36:23.605986 18534 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 14:36:25.783789 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 14:36:26.102437 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 14:36:26.313583 18534 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 14:36:26.313608 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:36:27.238596 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:30.692951 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:36:30.693001 18534 solver.cpp:397]     Test net output #1: loss = 5.28685 (* 1 = 5.28685 loss)
I0410 14:36:32.644193 18534 solver.cpp:218] Iteration 8880 (1.32774 iter/s, 9.03793s/12 iters), loss = 5.28048
I0410 14:36:32.644258 18534 solver.cpp:237]     Train net output #0: loss = 5.28048 (* 1 = 5.28048 loss)
I0410 14:36:32.644271 18534 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 14:36:37.552856 18534 solver.cpp:218] Iteration 8892 (2.44478 iter/s, 4.90842s/12 iters), loss = 5.27877
I0410 14:36:37.552915 18534 solver.cpp:237]     Train net output #0: loss = 5.27877 (* 1 = 5.27877 loss)
I0410 14:36:37.552927 18534 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 14:36:41.028270 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:42.428454 18534 solver.cpp:218] Iteration 8904 (2.46135 iter/s, 4.87536s/12 iters), loss = 5.27727
I0410 14:36:42.428505 18534 solver.cpp:237]     Train net output #0: loss = 5.27727 (* 1 = 5.27727 loss)
I0410 14:36:42.428514 18534 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 14:36:47.345211 18534 solver.cpp:218] Iteration 8916 (2.44075 iter/s, 4.91652s/12 iters), loss = 5.26288
I0410 14:36:47.345284 18534 solver.cpp:237]     Train net output #0: loss = 5.26288 (* 1 = 5.26288 loss)
I0410 14:36:47.345300 18534 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 14:36:52.245093 18534 solver.cpp:218] Iteration 8928 (2.44916 iter/s, 4.89964s/12 iters), loss = 5.26071
I0410 14:36:52.245141 18534 solver.cpp:237]     Train net output #0: loss = 5.26071 (* 1 = 5.26071 loss)
I0410 14:36:52.245152 18534 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 14:36:57.344802 18534 solver.cpp:218] Iteration 8940 (2.35318 iter/s, 5.09948s/12 iters), loss = 5.26749
I0410 14:36:57.344972 18534 solver.cpp:237]     Train net output #0: loss = 5.26749 (* 1 = 5.26749 loss)
I0410 14:36:57.344985 18534 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 14:37:02.217079 18534 solver.cpp:218] Iteration 8952 (2.46309 iter/s, 4.87194s/12 iters), loss = 5.25679
I0410 14:37:02.217123 18534 solver.cpp:237]     Train net output #0: loss = 5.25679 (* 1 = 5.25679 loss)
I0410 14:37:02.217133 18534 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 14:37:07.061348 18534 solver.cpp:218] Iteration 8964 (2.47726 iter/s, 4.84405s/12 iters), loss = 5.27621
I0410 14:37:07.061391 18534 solver.cpp:237]     Train net output #0: loss = 5.27621 (* 1 = 5.27621 loss)
I0410 14:37:07.061400 18534 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 14:37:11.544476 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 14:37:11.844229 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 14:37:12.050913 18534 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 14:37:12.050935 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:37:12.936185 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:16.449309 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:37:16.449359 18534 solver.cpp:397]     Test net output #1: loss = 5.28692 (* 1 = 5.28692 loss)
I0410 14:37:16.531803 18534 solver.cpp:218] Iteration 8976 (1.26715 iter/s, 9.47008s/12 iters), loss = 5.27752
I0410 14:37:16.531855 18534 solver.cpp:237]     Train net output #0: loss = 5.27752 (* 1 = 5.27752 loss)
I0410 14:37:16.531867 18534 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 14:37:20.644439 18534 solver.cpp:218] Iteration 8988 (2.91798 iter/s, 4.11243s/12 iters), loss = 5.2843
I0410 14:37:20.644493 18534 solver.cpp:237]     Train net output #0: loss = 5.2843 (* 1 = 5.2843 loss)
I0410 14:37:20.644503 18534 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 14:37:22.240063 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:37:25.568576 18534 solver.cpp:218] Iteration 9000 (2.43709 iter/s, 4.92391s/12 iters), loss = 5.28755
I0410 14:37:25.568622 18534 solver.cpp:237]     Train net output #0: loss = 5.28755 (* 1 = 5.28755 loss)
I0410 14:37:25.568631 18534 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 14:37:26.281314 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:30.447643 18534 solver.cpp:218] Iteration 9012 (2.4596 iter/s, 4.87884s/12 iters), loss = 5.28455
I0410 14:37:30.447731 18534 solver.cpp:237]     Train net output #0: loss = 5.28455 (* 1 = 5.28455 loss)
I0410 14:37:30.447741 18534 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 14:37:35.353680 18534 solver.cpp:218] Iteration 9024 (2.44609 iter/s, 4.90578s/12 iters), loss = 5.26485
I0410 14:37:35.353727 18534 solver.cpp:237]     Train net output #0: loss = 5.26485 (* 1 = 5.26485 loss)
I0410 14:37:35.353736 18534 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 14:37:40.499500 18534 solver.cpp:218] Iteration 9036 (2.3321 iter/s, 5.14558s/12 iters), loss = 5.27227
I0410 14:37:40.499547 18534 solver.cpp:237]     Train net output #0: loss = 5.27227 (* 1 = 5.27227 loss)
I0410 14:37:40.499557 18534 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 14:37:45.372189 18534 solver.cpp:218] Iteration 9048 (2.46282 iter/s, 4.87246s/12 iters), loss = 5.27458
I0410 14:37:45.372241 18534 solver.cpp:237]     Train net output #0: loss = 5.27458 (* 1 = 5.27458 loss)
I0410 14:37:45.372251 18534 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 14:37:50.208413 18534 solver.cpp:218] Iteration 9060 (2.48139 iter/s, 4.836s/12 iters), loss = 5.29085
I0410 14:37:50.208456 18534 solver.cpp:237]     Train net output #0: loss = 5.29085 (* 1 = 5.29085 loss)
I0410 14:37:50.208462 18534 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 14:37:55.138998 18534 solver.cpp:218] Iteration 9072 (2.4339 iter/s, 4.93036s/12 iters), loss = 5.26972
I0410 14:37:55.139041 18534 solver.cpp:237]     Train net output #0: loss = 5.26972 (* 1 = 5.26972 loss)
I0410 14:37:55.139050 18534 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 14:37:57.140014 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 14:37:57.471968 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 14:37:57.687340 18534 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 14:37:57.687366 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:37:58.563740 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:02.099165 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:38:02.099296 18534 solver.cpp:397]     Test net output #1: loss = 5.28711 (* 1 = 5.28711 loss)
I0410 14:38:04.004760 18534 solver.cpp:218] Iteration 9084 (1.35358 iter/s, 8.86541s/12 iters), loss = 5.25947
I0410 14:38:04.004802 18534 solver.cpp:237]     Train net output #0: loss = 5.25947 (* 1 = 5.25947 loss)
I0410 14:38:04.004812 18534 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 14:38:08.990329 18534 solver.cpp:218] Iteration 9096 (2.40706 iter/s, 4.98534s/12 iters), loss = 5.26602
I0410 14:38:08.990381 18534 solver.cpp:237]     Train net output #0: loss = 5.26602 (* 1 = 5.26602 loss)
I0410 14:38:08.990391 18534 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 14:38:11.881014 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:13.898654 18534 solver.cpp:218] Iteration 9108 (2.44494 iter/s, 4.9081s/12 iters), loss = 5.26136
I0410 14:38:13.898711 18534 solver.cpp:237]     Train net output #0: loss = 5.26136 (* 1 = 5.26136 loss)
I0410 14:38:13.898722 18534 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 14:38:18.711611 18534 solver.cpp:218] Iteration 9120 (2.49339 iter/s, 4.81272s/12 iters), loss = 5.25239
I0410 14:38:18.711668 18534 solver.cpp:237]     Train net output #0: loss = 5.25239 (* 1 = 5.25239 loss)
I0410 14:38:18.711679 18534 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 14:38:23.556483 18534 solver.cpp:218] Iteration 9132 (2.47697 iter/s, 4.84464s/12 iters), loss = 5.25114
I0410 14:38:23.556538 18534 solver.cpp:237]     Train net output #0: loss = 5.25114 (* 1 = 5.25114 loss)
I0410 14:38:23.556550 18534 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 14:38:28.533200 18534 solver.cpp:218] Iteration 9144 (2.41134 iter/s, 4.97648s/12 iters), loss = 5.25909
I0410 14:38:28.533247 18534 solver.cpp:237]     Train net output #0: loss = 5.25909 (* 1 = 5.25909 loss)
I0410 14:38:28.533257 18534 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 14:38:33.661669 18534 solver.cpp:218] Iteration 9156 (2.33999 iter/s, 5.12824s/12 iters), loss = 5.29006
I0410 14:38:33.661782 18534 solver.cpp:237]     Train net output #0: loss = 5.29006 (* 1 = 5.29006 loss)
I0410 14:38:33.661792 18534 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 14:38:38.554606 18534 solver.cpp:218] Iteration 9168 (2.45266 iter/s, 4.89265s/12 iters), loss = 5.27295
I0410 14:38:38.554651 18534 solver.cpp:237]     Train net output #0: loss = 5.27295 (* 1 = 5.27295 loss)
I0410 14:38:38.554661 18534 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 14:38:43.035560 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 14:38:43.376971 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 14:38:43.635232 18534 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 14:38:43.635257 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:38:44.374014 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:47.928078 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:38:47.928107 18534 solver.cpp:397]     Test net output #1: loss = 5.28737 (* 1 = 5.28737 loss)
I0410 14:38:48.010412 18534 solver.cpp:218] Iteration 9180 (1.26911 iter/s, 9.45543s/12 iters), loss = 5.27327
I0410 14:38:48.010452 18534 solver.cpp:237]     Train net output #0: loss = 5.27327 (* 1 = 5.27327 loss)
I0410 14:38:48.010460 18534 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 14:38:52.375545 18534 solver.cpp:218] Iteration 9192 (2.74919 iter/s, 4.36493s/12 iters), loss = 5.27415
I0410 14:38:52.375600 18534 solver.cpp:237]     Train net output #0: loss = 5.27415 (* 1 = 5.27415 loss)
I0410 14:38:52.375613 18534 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 14:38:57.221160 18534 solver.cpp:218] Iteration 9204 (2.47659 iter/s, 4.84538s/12 iters), loss = 5.26523
I0410 14:38:57.221213 18534 solver.cpp:237]     Train net output #0: loss = 5.26523 (* 1 = 5.26523 loss)
I0410 14:38:57.221225 18534 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 14:38:57.289160 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:02.126996 18534 solver.cpp:218] Iteration 9216 (2.44618 iter/s, 4.9056s/12 iters), loss = 5.27712
I0410 14:39:02.127038 18534 solver.cpp:237]     Train net output #0: loss = 5.27712 (* 1 = 5.27712 loss)
I0410 14:39:02.127048 18534 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 14:39:06.995559 18534 solver.cpp:218] Iteration 9228 (2.46491 iter/s, 4.86834s/12 iters), loss = 5.28449
I0410 14:39:06.995708 18534 solver.cpp:237]     Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
I0410 14:39:06.995720 18534 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 14:39:11.925366 18534 solver.cpp:218] Iteration 9240 (2.43433 iter/s, 4.92948s/12 iters), loss = 5.26139
I0410 14:39:11.925406 18534 solver.cpp:237]     Train net output #0: loss = 5.26139 (* 1 = 5.26139 loss)
I0410 14:39:11.925415 18534 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 14:39:16.820528 18534 solver.cpp:218] Iteration 9252 (2.45151 iter/s, 4.89494s/12 iters), loss = 5.27384
I0410 14:39:16.820580 18534 solver.cpp:237]     Train net output #0: loss = 5.27384 (* 1 = 5.27384 loss)
I0410 14:39:16.820590 18534 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 14:39:21.742328 18534 solver.cpp:218] Iteration 9264 (2.43825 iter/s, 4.92157s/12 iters), loss = 5.26196
I0410 14:39:21.742377 18534 solver.cpp:237]     Train net output #0: loss = 5.26196 (* 1 = 5.26196 loss)
I0410 14:39:21.742386 18534 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 14:39:26.683415 18534 solver.cpp:218] Iteration 9276 (2.42873 iter/s, 4.94085s/12 iters), loss = 5.2485
I0410 14:39:26.683476 18534 solver.cpp:237]     Train net output #0: loss = 5.2485 (* 1 = 5.2485 loss)
I0410 14:39:26.683488 18534 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 14:39:28.706398 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 14:39:29.365659 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 14:39:31.298689 18534 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 14:39:31.298719 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:39:32.012977 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:35.737516 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:39:35.737562 18534 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 14:39:37.716691 18534 solver.cpp:218] Iteration 9288 (1.08766 iter/s, 11.0328s/12 iters), loss = 5.26793
I0410 14:39:37.716821 18534 solver.cpp:237]     Train net output #0: loss = 5.26793 (* 1 = 5.26793 loss)
I0410 14:39:37.716835 18534 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 14:39:42.810187 18534 solver.cpp:218] Iteration 9300 (2.35609 iter/s, 5.09318s/12 iters), loss = 5.2495
I0410 14:39:42.810237 18534 solver.cpp:237]     Train net output #0: loss = 5.2495 (* 1 = 5.2495 loss)
I0410 14:39:42.810248 18534 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 14:39:44.974876 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:47.707216 18534 solver.cpp:218] Iteration 9312 (2.45058 iter/s, 4.8968s/12 iters), loss = 5.27182
I0410 14:39:47.707262 18534 solver.cpp:237]     Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
I0410 14:39:47.707271 18534 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 14:39:52.576191 18534 solver.cpp:218] Iteration 9324 (2.4647 iter/s, 4.86874s/12 iters), loss = 5.27793
I0410 14:39:52.576253 18534 solver.cpp:237]     Train net output #0: loss = 5.27793 (* 1 = 5.27793 loss)
I0410 14:39:52.576265 18534 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 14:39:57.411928 18534 solver.cpp:218] Iteration 9336 (2.48165 iter/s, 4.8355s/12 iters), loss = 5.2858
I0410 14:39:57.411980 18534 solver.cpp:237]     Train net output #0: loss = 5.2858 (* 1 = 5.2858 loss)
I0410 14:39:57.411993 18534 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 14:40:02.374550 18534 solver.cpp:218] Iteration 9348 (2.41819 iter/s, 4.96239s/12 iters), loss = 5.27055
I0410 14:40:02.374591 18534 solver.cpp:237]     Train net output #0: loss = 5.27055 (* 1 = 5.27055 loss)
I0410 14:40:02.374603 18534 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 14:40:07.243772 18534 solver.cpp:218] Iteration 9360 (2.46457 iter/s, 4.869s/12 iters), loss = 5.27083
I0410 14:40:07.243820 18534 solver.cpp:237]     Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
I0410 14:40:07.243831 18534 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 14:40:12.131211 18534 solver.cpp:218] Iteration 9372 (2.45539 iter/s, 4.8872s/12 iters), loss = 5.27133
I0410 14:40:12.133354 18534 solver.cpp:237]     Train net output #0: loss = 5.27133 (* 1 = 5.27133 loss)
I0410 14:40:12.133374 18534 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 14:40:16.637193 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 14:40:16.921741 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 14:40:17.119387 18534 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 14:40:17.119415 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:40:17.854401 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:21.496146 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:40:21.496196 18534 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0410 14:40:21.580355 18534 solver.cpp:218] Iteration 9384 (1.27029 iter/s, 9.44668s/12 iters), loss = 5.2768
I0410 14:40:21.580395 18534 solver.cpp:237]     Train net output #0: loss = 5.2768 (* 1 = 5.2768 loss)
I0410 14:40:21.580406 18534 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 14:40:25.754703 18534 solver.cpp:218] Iteration 9396 (2.87484 iter/s, 4.17415s/12 iters), loss = 5.26557
I0410 14:40:25.754757 18534 solver.cpp:237]     Train net output #0: loss = 5.26557 (* 1 = 5.26557 loss)
I0410 14:40:25.754770 18534 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 14:40:29.964058 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:30.597025 18534 solver.cpp:218] Iteration 9408 (2.47827 iter/s, 4.84209s/12 iters), loss = 5.26946
I0410 14:40:30.597079 18534 solver.cpp:237]     Train net output #0: loss = 5.26946 (* 1 = 5.26946 loss)
I0410 14:40:30.597091 18534 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 14:40:35.495460 18534 solver.cpp:218] Iteration 9420 (2.44988 iter/s, 4.89819s/12 iters), loss = 5.27759
I0410 14:40:35.495518 18534 solver.cpp:237]     Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
I0410 14:40:35.495532 18534 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 14:40:40.416676 18534 solver.cpp:218] Iteration 9432 (2.43854 iter/s, 4.92097s/12 iters), loss = 5.28384
I0410 14:40:40.416733 18534 solver.cpp:237]     Train net output #0: loss = 5.28384 (* 1 = 5.28384 loss)
I0410 14:40:40.416744 18534 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 14:40:45.365904 18534 solver.cpp:218] Iteration 9444 (2.42474 iter/s, 4.94899s/12 iters), loss = 5.28561
I0410 14:40:45.366076 18534 solver.cpp:237]     Train net output #0: loss = 5.28561 (* 1 = 5.28561 loss)
I0410 14:40:45.366091 18534 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 14:40:50.317328 18534 solver.cpp:218] Iteration 9456 (2.42372 iter/s, 4.95107s/12 iters), loss = 5.26621
I0410 14:40:50.317380 18534 solver.cpp:237]     Train net output #0: loss = 5.26621 (* 1 = 5.26621 loss)
I0410 14:40:50.317390 18534 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 14:40:55.138219 18534 solver.cpp:218] Iteration 9468 (2.48929 iter/s, 4.82066s/12 iters), loss = 5.2801
I0410 14:40:55.138278 18534 solver.cpp:237]     Train net output #0: loss = 5.2801 (* 1 = 5.2801 loss)
I0410 14:40:55.138288 18534 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 14:41:00.117871 18534 solver.cpp:218] Iteration 9480 (2.40992 iter/s, 4.97941s/12 iters), loss = 5.27566
I0410 14:41:00.117918 18534 solver.cpp:237]     Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
I0410 14:41:00.117928 18534 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 14:41:02.268798 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 14:41:02.597779 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 14:41:02.827877 18534 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 14:41:02.827898 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:41:03.544096 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:07.259699 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:41:07.259761 18534 solver.cpp:397]     Test net output #1: loss = 5.28641 (* 1 = 5.28641 loss)
I0410 14:41:09.116180 18534 solver.cpp:218] Iteration 9492 (1.33364 iter/s, 8.99793s/12 iters), loss = 5.26913
I0410 14:41:09.116240 18534 solver.cpp:237]     Train net output #0: loss = 5.26913 (* 1 = 5.26913 loss)
I0410 14:41:09.116252 18534 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 14:41:13.990206 18534 solver.cpp:218] Iteration 9504 (2.46216 iter/s, 4.87378s/12 iters), loss = 5.25857
I0410 14:41:13.990267 18534 solver.cpp:237]     Train net output #0: loss = 5.25857 (* 1 = 5.25857 loss)
I0410 14:41:13.990284 18534 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 14:41:15.444636 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:18.912691 18534 solver.cpp:218] Iteration 9516 (2.43791 iter/s, 4.92225s/12 iters), loss = 5.2647
I0410 14:41:18.912737 18534 solver.cpp:237]     Train net output #0: loss = 5.2647 (* 1 = 5.2647 loss)
I0410 14:41:18.912748 18534 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 14:41:23.839149 18534 solver.cpp:218] Iteration 9528 (2.43594 iter/s, 4.92623s/12 iters), loss = 5.26302
I0410 14:41:23.839190 18534 solver.cpp:237]     Train net output #0: loss = 5.26302 (* 1 = 5.26302 loss)
I0410 14:41:23.839197 18534 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 14:41:28.708808 18534 solver.cpp:218] Iteration 9540 (2.46435 iter/s, 4.86943s/12 iters), loss = 5.24907
I0410 14:41:28.708861 18534 solver.cpp:237]     Train net output #0: loss = 5.24907 (* 1 = 5.24907 loss)
I0410 14:41:28.708874 18534 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 14:41:33.575366 18534 solver.cpp:218] Iteration 9552 (2.46593 iter/s, 4.86632s/12 iters), loss = 5.29919
I0410 14:41:33.575423 18534 solver.cpp:237]     Train net output #0: loss = 5.29919 (* 1 = 5.29919 loss)
I0410 14:41:33.575434 18534 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 14:41:38.440847 18534 solver.cpp:218] Iteration 9564 (2.46648 iter/s, 4.86524s/12 iters), loss = 5.25451
I0410 14:41:38.440907 18534 solver.cpp:237]     Train net output #0: loss = 5.25451 (* 1 = 5.25451 loss)
I0410 14:41:38.440919 18534 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 14:41:43.329890 18534 solver.cpp:218] Iteration 9576 (2.45459 iter/s, 4.8888s/12 iters), loss = 5.2613
I0410 14:41:43.329936 18534 solver.cpp:237]     Train net output #0: loss = 5.2613 (* 1 = 5.2613 loss)
I0410 14:41:43.329946 18534 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 14:41:47.750336 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 14:41:48.062877 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 14:41:48.273653 18534 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 14:41:48.273685 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:41:48.944903 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:52.791396 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:41:52.791431 18534 solver.cpp:397]     Test net output #1: loss = 5.28671 (* 1 = 5.28671 loss)
I0410 14:41:52.871284 18534 solver.cpp:218] Iteration 9588 (1.25773 iter/s, 9.54101s/12 iters), loss = 5.27474
I0410 14:41:52.871327 18534 solver.cpp:237]     Train net output #0: loss = 5.27474 (* 1 = 5.27474 loss)
I0410 14:41:52.871335 18534 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 14:41:56.976614 18534 solver.cpp:218] Iteration 9600 (2.92317 iter/s, 4.10513s/12 iters), loss = 5.27428
I0410 14:41:56.976671 18534 solver.cpp:237]     Train net output #0: loss = 5.27428 (* 1 = 5.27428 loss)
I0410 14:41:56.976682 18534 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 14:42:00.583621 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:01.909068 18534 solver.cpp:218] Iteration 9612 (2.43299 iter/s, 4.93221s/12 iters), loss = 5.271
I0410 14:42:01.909123 18534 solver.cpp:237]     Train net output #0: loss = 5.271 (* 1 = 5.271 loss)
I0410 14:42:01.909134 18534 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 14:42:06.824822 18534 solver.cpp:218] Iteration 9624 (2.44125 iter/s, 4.91551s/12 iters), loss = 5.26716
I0410 14:42:06.824877 18534 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0410 14:42:06.824888 18534 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 14:42:11.694464 18534 solver.cpp:218] Iteration 9636 (2.46437 iter/s, 4.86941s/12 iters), loss = 5.25756
I0410 14:42:11.694511 18534 solver.cpp:237]     Train net output #0: loss = 5.25756 (* 1 = 5.25756 loss)
I0410 14:42:11.694520 18534 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 14:42:16.512610 18534 solver.cpp:218] Iteration 9648 (2.49071 iter/s, 4.81791s/12 iters), loss = 5.26307
I0410 14:42:16.512668 18534 solver.cpp:237]     Train net output #0: loss = 5.26307 (* 1 = 5.26307 loss)
I0410 14:42:16.512679 18534 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 14:42:21.373912 18534 solver.cpp:218] Iteration 9660 (2.46859 iter/s, 4.86107s/12 iters), loss = 5.25538
I0410 14:42:21.374008 18534 solver.cpp:237]     Train net output #0: loss = 5.25538 (* 1 = 5.25538 loss)
I0410 14:42:21.374022 18534 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 14:42:26.291224 18534 solver.cpp:218] Iteration 9672 (2.4405 iter/s, 4.91703s/12 iters), loss = 5.27257
I0410 14:42:26.291280 18534 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0410 14:42:26.291291 18534 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 14:42:31.300559 18534 solver.cpp:218] Iteration 9684 (2.39564 iter/s, 5.00909s/12 iters), loss = 5.28967
I0410 14:42:31.300611 18534 solver.cpp:237]     Train net output #0: loss = 5.28967 (* 1 = 5.28967 loss)
I0410 14:42:31.300623 18534 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 14:42:33.330945 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 14:42:33.640813 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 14:42:33.854280 18534 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 14:42:33.854308 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:42:34.537315 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:37.010867 18534 blocking_queue.cpp:49] Waiting for data
I0410 14:42:38.350757 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:42:38.350805 18534 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0410 14:42:40.124269 18534 solver.cpp:218] Iteration 9696 (1.36003 iter/s, 8.82334s/12 iters), loss = 5.28561
I0410 14:42:40.124328 18534 solver.cpp:237]     Train net output #0: loss = 5.28561 (* 1 = 5.28561 loss)
I0410 14:42:40.124341 18534 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 14:42:44.944679 18534 solver.cpp:218] Iteration 9708 (2.48954 iter/s, 4.82017s/12 iters), loss = 5.2865
I0410 14:42:44.944731 18534 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0410 14:42:44.944743 18534 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 14:42:45.659914 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:49.792291 18534 solver.cpp:218] Iteration 9720 (2.47557 iter/s, 4.84738s/12 iters), loss = 5.28686
I0410 14:42:49.792337 18534 solver.cpp:237]     Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 14:42:49.792347 18534 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 14:42:54.662698 18534 solver.cpp:218] Iteration 9732 (2.46397 iter/s, 4.87018s/12 iters), loss = 5.264
I0410 14:42:54.662822 18534 solver.cpp:237]     Train net output #0: loss = 5.264 (* 1 = 5.264 loss)
I0410 14:42:54.662830 18534 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 14:42:59.474611 18534 solver.cpp:218] Iteration 9744 (2.49397 iter/s, 4.81161s/12 iters), loss = 5.26706
I0410 14:42:59.474669 18534 solver.cpp:237]     Train net output #0: loss = 5.26706 (* 1 = 5.26706 loss)
I0410 14:42:59.474680 18534 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 14:43:04.561570 18534 solver.cpp:218] Iteration 9756 (2.35909 iter/s, 5.08671s/12 iters), loss = 5.27334
I0410 14:43:04.561626 18534 solver.cpp:237]     Train net output #0: loss = 5.27334 (* 1 = 5.27334 loss)
I0410 14:43:04.561638 18534 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 14:43:09.601624 18534 solver.cpp:218] Iteration 9768 (2.38105 iter/s, 5.0398s/12 iters), loss = 5.28596
I0410 14:43:09.601676 18534 solver.cpp:237]     Train net output #0: loss = 5.28596 (* 1 = 5.28596 loss)
I0410 14:43:09.601687 18534 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 14:43:14.424707 18534 solver.cpp:218] Iteration 9780 (2.48815 iter/s, 4.82286s/12 iters), loss = 5.27236
I0410 14:43:14.424753 18534 solver.cpp:237]     Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
I0410 14:43:14.424764 18534 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 14:43:18.907091 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 14:43:19.339547 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 14:43:19.628669 18534 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 14:43:19.628695 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:43:20.270030 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:24.110319 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:43:24.110356 18534 solver.cpp:397]     Test net output #1: loss = 5.28667 (* 1 = 5.28667 loss)
I0410 14:43:24.192762 18534 solver.cpp:218] Iteration 9792 (1.22854 iter/s, 9.76765s/12 iters), loss = 5.25243
I0410 14:43:24.192821 18534 solver.cpp:237]     Train net output #0: loss = 5.25243 (* 1 = 5.25243 loss)
I0410 14:43:24.192831 18534 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 14:43:28.321949 18534 solver.cpp:218] Iteration 9804 (2.90629 iter/s, 4.12897s/12 iters), loss = 5.27066
I0410 14:43:28.322127 18534 solver.cpp:237]     Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
I0410 14:43:28.322139 18534 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 14:43:31.228132 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:33.202606 18534 solver.cpp:218] Iteration 9816 (2.45887 iter/s, 4.8803s/12 iters), loss = 5.2628
I0410 14:43:33.202661 18534 solver.cpp:237]     Train net output #0: loss = 5.2628 (* 1 = 5.2628 loss)
I0410 14:43:33.202672 18534 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 14:43:38.024194 18534 solver.cpp:218] Iteration 9828 (2.48893 iter/s, 4.82135s/12 iters), loss = 5.25514
I0410 14:43:38.024245 18534 solver.cpp:237]     Train net output #0: loss = 5.25514 (* 1 = 5.25514 loss)
I0410 14:43:38.024255 18534 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 14:43:43.038004 18534 solver.cpp:218] Iteration 9840 (2.3935 iter/s, 5.01357s/12 iters), loss = 5.2511
I0410 14:43:43.038055 18534 solver.cpp:237]     Train net output #0: loss = 5.2511 (* 1 = 5.2511 loss)
I0410 14:43:43.038067 18534 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 14:43:47.973176 18534 solver.cpp:218] Iteration 9852 (2.43164 iter/s, 4.93493s/12 iters), loss = 5.27009
I0410 14:43:47.973232 18534 solver.cpp:237]     Train net output #0: loss = 5.27009 (* 1 = 5.27009 loss)
I0410 14:43:47.973244 18534 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 14:43:52.985687 18534 solver.cpp:218] Iteration 9864 (2.39413 iter/s, 5.01226s/12 iters), loss = 5.28889
I0410 14:43:52.985747 18534 solver.cpp:237]     Train net output #0: loss = 5.28889 (* 1 = 5.28889 loss)
I0410 14:43:52.985759 18534 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 14:43:57.830960 18534 solver.cpp:218] Iteration 9876 (2.47677 iter/s, 4.84503s/12 iters), loss = 5.2701
I0410 14:43:57.831022 18534 solver.cpp:237]     Train net output #0: loss = 5.2701 (* 1 = 5.2701 loss)
I0410 14:43:57.831033 18534 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 14:44:02.712857 18534 solver.cpp:218] Iteration 9888 (2.45818 iter/s, 4.88165s/12 iters), loss = 5.27584
I0410 14:44:02.712934 18534 solver.cpp:237]     Train net output #0: loss = 5.27584 (* 1 = 5.27584 loss)
I0410 14:44:02.712946 18534 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 14:44:04.679339 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 14:44:04.999131 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 14:44:05.208710 18534 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 14:44:05.208730 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:44:05.731173 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:09.725765 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:44:09.725801 18534 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0410 14:44:11.665619 18534 solver.cpp:218] Iteration 9900 (1.34043 iter/s, 8.95236s/12 iters), loss = 5.27479
I0410 14:44:11.665668 18534 solver.cpp:237]     Train net output #0: loss = 5.27479 (* 1 = 5.27479 loss)
I0410 14:44:11.665678 18534 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 14:44:16.565385 18534 solver.cpp:218] Iteration 9912 (2.44921 iter/s, 4.89953s/12 iters), loss = 5.25696
I0410 14:44:16.565438 18534 solver.cpp:237]     Train net output #0: loss = 5.25696 (* 1 = 5.25696 loss)
I0410 14:44:16.565448 18534 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 14:44:16.667649 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:21.463059 18534 solver.cpp:218] Iteration 9924 (2.45026 iter/s, 4.89744s/12 iters), loss = 5.27099
I0410 14:44:21.463115 18534 solver.cpp:237]     Train net output #0: loss = 5.27099 (* 1 = 5.27099 loss)
I0410 14:44:21.463125 18534 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 14:44:26.342335 18534 solver.cpp:218] Iteration 9936 (2.4595 iter/s, 4.87904s/12 iters), loss = 5.2885
I0410 14:44:26.342379 18534 solver.cpp:237]     Train net output #0: loss = 5.2885 (* 1 = 5.2885 loss)
I0410 14:44:26.342388 18534 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 14:44:31.262145 18534 solver.cpp:218] Iteration 9948 (2.43923 iter/s, 4.91958s/12 iters), loss = 5.25951
I0410 14:44:31.262193 18534 solver.cpp:237]     Train net output #0: loss = 5.25951 (* 1 = 5.25951 loss)
I0410 14:44:31.262204 18534 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 14:44:36.158255 18534 solver.cpp:218] Iteration 9960 (2.45104 iter/s, 4.89588s/12 iters), loss = 5.26968
I0410 14:44:36.158423 18534 solver.cpp:237]     Train net output #0: loss = 5.26968 (* 1 = 5.26968 loss)
I0410 14:44:36.158437 18534 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 14:44:41.086592 18534 solver.cpp:218] Iteration 9972 (2.43507 iter/s, 4.92799s/12 iters), loss = 5.26316
I0410 14:44:41.086642 18534 solver.cpp:237]     Train net output #0: loss = 5.26316 (* 1 = 5.26316 loss)
I0410 14:44:41.086654 18534 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 14:44:45.993822 18534 solver.cpp:218] Iteration 9984 (2.44549 iter/s, 4.90699s/12 iters), loss = 5.24719
I0410 14:44:45.993872 18534 solver.cpp:237]     Train net output #0: loss = 5.24719 (* 1 = 5.24719 loss)
I0410 14:44:45.993881 18534 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 14:44:50.393821 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 14:44:51.212801 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 14:44:51.434640 18534 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 14:44:51.434669 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:44:51.953509 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:55.839910 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:44:55.839960 18534 solver.cpp:397]     Test net output #1: loss = 5.28746 (* 1 = 5.28746 loss)
I0410 14:44:55.922309 18534 solver.cpp:218] Iteration 9996 (1.20869 iter/s, 9.92808s/12 iters), loss = 5.27029
I0410 14:44:55.922356 18534 solver.cpp:237]     Train net output #0: loss = 5.27029 (* 1 = 5.27029 loss)
I0410 14:44:55.922367 18534 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 14:45:00.100855 18534 solver.cpp:218] Iteration 10008 (2.87195 iter/s, 4.17834s/12 iters), loss = 5.24436
I0410 14:45:00.100899 18534 solver.cpp:237]     Train net output #0: loss = 5.24436 (* 1 = 5.24436 loss)
I0410 14:45:00.100909 18534 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 14:45:02.283413 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:04.961716 18534 solver.cpp:218] Iteration 10020 (2.46881 iter/s, 4.86063s/12 iters), loss = 5.26846
I0410 14:45:04.961762 18534 solver.cpp:237]     Train net output #0: loss = 5.26846 (* 1 = 5.26846 loss)
I0410 14:45:04.961771 18534 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 14:45:09.897856 18534 solver.cpp:218] Iteration 10032 (2.43117 iter/s, 4.9359s/12 iters), loss = 5.27802
I0410 14:45:09.897987 18534 solver.cpp:237]     Train net output #0: loss = 5.27802 (* 1 = 5.27802 loss)
I0410 14:45:09.898000 18534 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 14:45:14.793913 18534 solver.cpp:218] Iteration 10044 (2.45111 iter/s, 4.89574s/12 iters), loss = 5.28471
I0410 14:45:14.793985 18534 solver.cpp:237]     Train net output #0: loss = 5.28471 (* 1 = 5.28471 loss)
I0410 14:45:14.793998 18534 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 14:45:19.651749 18534 solver.cpp:218] Iteration 10056 (2.47037 iter/s, 4.85758s/12 iters), loss = 5.2768
I0410 14:45:19.651798 18534 solver.cpp:237]     Train net output #0: loss = 5.2768 (* 1 = 5.2768 loss)
I0410 14:45:19.651808 18534 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 14:45:24.804180 18534 solver.cpp:218] Iteration 10068 (2.32911 iter/s, 5.15218s/12 iters), loss = 5.274
I0410 14:45:24.804237 18534 solver.cpp:237]     Train net output #0: loss = 5.274 (* 1 = 5.274 loss)
I0410 14:45:24.804250 18534 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 14:45:29.649468 18534 solver.cpp:218] Iteration 10080 (2.47675 iter/s, 4.84505s/12 iters), loss = 5.26335
I0410 14:45:29.649518 18534 solver.cpp:237]     Train net output #0: loss = 5.26335 (* 1 = 5.26335 loss)
I0410 14:45:29.649528 18534 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 14:45:34.484151 18534 solver.cpp:218] Iteration 10092 (2.48219 iter/s, 4.83444s/12 iters), loss = 5.27394
I0410 14:45:34.484195 18534 solver.cpp:237]     Train net output #0: loss = 5.27394 (* 1 = 5.27394 loss)
I0410 14:45:34.484205 18534 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 14:45:36.463091 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 14:45:36.789631 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 14:45:37.006826 18534 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 14:45:37.006855 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:45:37.477396 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:41.597610 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:45:41.597715 18534 solver.cpp:397]     Test net output #1: loss = 5.28708 (* 1 = 5.28708 loss)
I0410 14:45:43.541239 18534 solver.cpp:218] Iteration 10104 (1.32498 iter/s, 9.05671s/12 iters), loss = 5.27303
I0410 14:45:43.541290 18534 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0410 14:45:43.541301 18534 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 14:45:47.807780 18538 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:48.419661 18534 solver.cpp:218] Iteration 10116 (2.45993 iter/s, 4.87819s/12 iters), loss = 5.26127
I0410 14:45:48.419701 18534 solver.cpp:237]     Train net output #0: loss = 5.26127 (* 1 = 5.26127 loss)
I0410 14:45:48.419710 18534 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 14:45:53.336786 18534 solver.cpp:218] Iteration 10128 (2.44056 iter/s, 4.9169s/12 iters), loss = 5.27442
I0410 14:45:53.336838 18534 solver.cpp:237]     Train net output #0: loss = 5.27442 (* 1 = 5.27442 loss)
I0410 14:45:53.336849 18534 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 14:45:58.254907 18534 solver.cpp:218] Iteration 10140 (2.44007 iter/s, 4.91788s/12 iters), loss = 5.28133
I0410 14:45:58.254961 18534 solver.cpp:237]     Train net output #0: loss = 5.28133 (* 1 = 5.28133 loss)
I0410 14:45:58.254971 18534 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 14:46:03.121620 18534 solver.cpp:218] Iteration 10152 (2.46585 iter/s, 4.86647s/12 iters), loss = 5.27674
I0410 14:46:03.121675 18534 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0410 14:46:03.121686 18534 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 14:46:08.065168 18534 solver.cpp:218] Iteration 10164 (2.42752 iter/s, 4.94331s/12 iters), loss = 5.26675
I0410 14:46:08.065207 18534 solver.cpp:237]     Train net output #0: loss = 5.26675 (* 1 = 5.26675 loss)
I0410 14:46:08.065214 18534 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 14:46:12.953562 18534 solver.cpp:218] Iteration 10176 (2.4549 iter/s, 4.88817s/12 iters), loss = 5.2773
I0410 14:46:12.953626 18534 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0410 14:46:12.953634 18534 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 14:46:17.822516 18534 solver.cpp:218] Iteration 10188 (2.46473 iter/s, 4.86869s/12 iters), loss = 5.27726
I0410 14:46:17.822579 18534 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0410 14:46:17.822592 18534 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 14:46:22.383455 18534 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 14:46:22.689483 18534 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 14:46:22.910619 18534 solver.cpp:310] Iteration 10200, loss = 5.26505
I0410 14:46:22.910642 18534 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 14:46:22.910647 18534 net.cpp:676] Ignoring source layer train-data
I0410 14:46:23.250124 18546 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:46:27.188465 18534 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 14:46:27.188511 18534 solver.cpp:397]     Test net output #1: loss = 5.28643 (* 1 = 5.28643 loss)
I0410 14:46:27.188522 18534 solver.cpp:315] Optimization Done.
I0410 14:46:27.188529 18534 caffe.cpp:259] Optimization Done.