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

I0407 22:24:57.000056   359 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-222455-6e13/solver.prototxt
I0407 22:24:57.000228   359 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 22:24:57.000236   359 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 22:24:57.002756   359 caffe.cpp:218] Using GPUs 2
I0407 22:24:57.045344   359 caffe.cpp:223] GPU 2: GeForce RTX 2080
I0407 22:24:57.335467   359 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "sigmoid"
gamma: -0.0019607844
momentum: 0.9
weight_decay: 0.0001
stepsize: 5100
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 22:24:57.336266   359 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 22:24:57.336860   359 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 22:24:57.336874   359 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 22:24:57.337000   359 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0407 22:24:57.337081   359 layer_factory.hpp:77] Creating layer train-data
I0407 22:24:57.339022   359 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db
I0407 22:24:57.339668   359 net.cpp:84] Creating Layer train-data
I0407 22:24:57.339680   359 net.cpp:380] train-data -> data
I0407 22:24:57.339700   359 net.cpp:380] train-data -> label
I0407 22:24:57.339711   359 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0407 22:24:57.344272   359 data_layer.cpp:45] output data size: 128,3,227,227
I0407 22:24:57.471490   359 net.cpp:122] Setting up train-data
I0407 22:24:57.471513   359 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 22:24:57.471518   359 net.cpp:129] Top shape: 128 (128)
I0407 22:24:57.471519   359 net.cpp:137] Memory required for data: 79149056
I0407 22:24:57.471529   359 layer_factory.hpp:77] Creating layer conv1
I0407 22:24:57.471549   359 net.cpp:84] Creating Layer conv1
I0407 22:24:57.471554   359 net.cpp:406] conv1 <- data
I0407 22:24:57.471566   359 net.cpp:380] conv1 -> conv1
I0407 22:24:58.376600   359 net.cpp:122] Setting up conv1
I0407 22:24:58.376621   359 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:24:58.376624   359 net.cpp:137] Memory required for data: 227833856
I0407 22:24:58.376643   359 layer_factory.hpp:77] Creating layer relu1
I0407 22:24:58.376653   359 net.cpp:84] Creating Layer relu1
I0407 22:24:58.376657   359 net.cpp:406] relu1 <- conv1
I0407 22:24:58.376662   359 net.cpp:367] relu1 -> conv1 (in-place)
I0407 22:24:58.376996   359 net.cpp:122] Setting up relu1
I0407 22:24:58.377004   359 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:24:58.377007   359 net.cpp:137] Memory required for data: 376518656
I0407 22:24:58.377010   359 layer_factory.hpp:77] Creating layer norm1
I0407 22:24:58.377019   359 net.cpp:84] Creating Layer norm1
I0407 22:24:58.377038   359 net.cpp:406] norm1 <- conv1
I0407 22:24:58.377043   359 net.cpp:380] norm1 -> norm1
I0407 22:24:58.377588   359 net.cpp:122] Setting up norm1
I0407 22:24:58.377597   359 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:24:58.377600   359 net.cpp:137] Memory required for data: 525203456
I0407 22:24:58.377604   359 layer_factory.hpp:77] Creating layer pool1
I0407 22:24:58.377610   359 net.cpp:84] Creating Layer pool1
I0407 22:24:58.377614   359 net.cpp:406] pool1 <- norm1
I0407 22:24:58.377619   359 net.cpp:380] pool1 -> pool1
I0407 22:24:58.377650   359 net.cpp:122] Setting up pool1
I0407 22:24:58.377655   359 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 22:24:58.377657   359 net.cpp:137] Memory required for data: 561035264
I0407 22:24:58.377660   359 layer_factory.hpp:77] Creating layer conv2
I0407 22:24:58.377669   359 net.cpp:84] Creating Layer conv2
I0407 22:24:58.377672   359 net.cpp:406] conv2 <- pool1
I0407 22:24:58.377676   359 net.cpp:380] conv2 -> conv2
I0407 22:24:58.388175   359 net.cpp:122] Setting up conv2
I0407 22:24:58.388195   359 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:24:58.388200   359 net.cpp:137] Memory required for data: 656586752
I0407 22:24:58.388213   359 layer_factory.hpp:77] Creating layer relu2
I0407 22:24:58.388221   359 net.cpp:84] Creating Layer relu2
I0407 22:24:58.388226   359 net.cpp:406] relu2 <- conv2
I0407 22:24:58.388233   359 net.cpp:367] relu2 -> conv2 (in-place)
I0407 22:24:58.388893   359 net.cpp:122] Setting up relu2
I0407 22:24:58.388906   359 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:24:58.388911   359 net.cpp:137] Memory required for data: 752138240
I0407 22:24:58.388916   359 layer_factory.hpp:77] Creating layer norm2
I0407 22:24:58.388924   359 net.cpp:84] Creating Layer norm2
I0407 22:24:58.388929   359 net.cpp:406] norm2 <- conv2
I0407 22:24:58.388936   359 net.cpp:380] norm2 -> norm2
I0407 22:24:58.389379   359 net.cpp:122] Setting up norm2
I0407 22:24:58.389391   359 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:24:58.389395   359 net.cpp:137] Memory required for data: 847689728
I0407 22:24:58.389400   359 layer_factory.hpp:77] Creating layer pool2
I0407 22:24:58.389408   359 net.cpp:84] Creating Layer pool2
I0407 22:24:58.389413   359 net.cpp:406] pool2 <- norm2
I0407 22:24:58.389420   359 net.cpp:380] pool2 -> pool2
I0407 22:24:58.389453   359 net.cpp:122] Setting up pool2
I0407 22:24:58.389459   359 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:24:58.389463   359 net.cpp:137] Memory required for data: 869840896
I0407 22:24:58.389467   359 layer_factory.hpp:77] Creating layer conv3
I0407 22:24:58.389477   359 net.cpp:84] Creating Layer conv3
I0407 22:24:58.389482   359 net.cpp:406] conv3 <- pool2
I0407 22:24:58.389487   359 net.cpp:380] conv3 -> conv3
I0407 22:24:58.400347   359 net.cpp:122] Setting up conv3
I0407 22:24:58.400363   359 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:24:58.400367   359 net.cpp:137] Memory required for data: 903067648
I0407 22:24:58.400375   359 layer_factory.hpp:77] Creating layer relu3
I0407 22:24:58.400382   359 net.cpp:84] Creating Layer relu3
I0407 22:24:58.400386   359 net.cpp:406] relu3 <- conv3
I0407 22:24:58.400391   359 net.cpp:367] relu3 -> conv3 (in-place)
I0407 22:24:58.400890   359 net.cpp:122] Setting up relu3
I0407 22:24:58.400900   359 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:24:58.400903   359 net.cpp:137] Memory required for data: 936294400
I0407 22:24:58.400907   359 layer_factory.hpp:77] Creating layer conv4
I0407 22:24:58.400915   359 net.cpp:84] Creating Layer conv4
I0407 22:24:58.400918   359 net.cpp:406] conv4 <- conv3
I0407 22:24:58.400923   359 net.cpp:380] conv4 -> conv4
I0407 22:24:58.411839   359 net.cpp:122] Setting up conv4
I0407 22:24:58.411855   359 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:24:58.411859   359 net.cpp:137] Memory required for data: 969521152
I0407 22:24:58.411867   359 layer_factory.hpp:77] Creating layer relu4
I0407 22:24:58.411876   359 net.cpp:84] Creating Layer relu4
I0407 22:24:58.411896   359 net.cpp:406] relu4 <- conv4
I0407 22:24:58.411902   359 net.cpp:367] relu4 -> conv4 (in-place)
I0407 22:24:58.412392   359 net.cpp:122] Setting up relu4
I0407 22:24:58.412402   359 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:24:58.412405   359 net.cpp:137] Memory required for data: 1002747904
I0407 22:24:58.412408   359 layer_factory.hpp:77] Creating layer conv5
I0407 22:24:58.412417   359 net.cpp:84] Creating Layer conv5
I0407 22:24:58.412420   359 net.cpp:406] conv5 <- conv4
I0407 22:24:58.412426   359 net.cpp:380] conv5 -> conv5
I0407 22:24:58.421608   359 net.cpp:122] Setting up conv5
I0407 22:24:58.421627   359 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:24:58.421629   359 net.cpp:137] Memory required for data: 1024899072
I0407 22:24:58.421643   359 layer_factory.hpp:77] Creating layer relu5
I0407 22:24:58.421653   359 net.cpp:84] Creating Layer relu5
I0407 22:24:58.421655   359 net.cpp:406] relu5 <- conv5
I0407 22:24:58.421661   359 net.cpp:367] relu5 -> conv5 (in-place)
I0407 22:24:58.422227   359 net.cpp:122] Setting up relu5
I0407 22:24:58.422238   359 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:24:58.422241   359 net.cpp:137] Memory required for data: 1047050240
I0407 22:24:58.422245   359 layer_factory.hpp:77] Creating layer pool5
I0407 22:24:58.422250   359 net.cpp:84] Creating Layer pool5
I0407 22:24:58.422253   359 net.cpp:406] pool5 <- conv5
I0407 22:24:58.422259   359 net.cpp:380] pool5 -> pool5
I0407 22:24:58.422294   359 net.cpp:122] Setting up pool5
I0407 22:24:58.422300   359 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 22:24:58.422303   359 net.cpp:137] Memory required for data: 1051768832
I0407 22:24:58.422307   359 layer_factory.hpp:77] Creating layer fc6
I0407 22:24:58.422315   359 net.cpp:84] Creating Layer fc6
I0407 22:24:58.422318   359 net.cpp:406] fc6 <- pool5
I0407 22:24:58.422323   359 net.cpp:380] fc6 -> fc6
I0407 22:24:58.781308   359 net.cpp:122] Setting up fc6
I0407 22:24:58.781327   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.781330   359 net.cpp:137] Memory required for data: 1053865984
I0407 22:24:58.781339   359 layer_factory.hpp:77] Creating layer relu6
I0407 22:24:58.781348   359 net.cpp:84] Creating Layer relu6
I0407 22:24:58.781352   359 net.cpp:406] relu6 <- fc6
I0407 22:24:58.781359   359 net.cpp:367] relu6 -> fc6 (in-place)
I0407 22:24:58.782140   359 net.cpp:122] Setting up relu6
I0407 22:24:58.782151   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.782155   359 net.cpp:137] Memory required for data: 1055963136
I0407 22:24:58.782158   359 layer_factory.hpp:77] Creating layer drop6
I0407 22:24:58.782164   359 net.cpp:84] Creating Layer drop6
I0407 22:24:58.782167   359 net.cpp:406] drop6 <- fc6
I0407 22:24:58.782172   359 net.cpp:367] drop6 -> fc6 (in-place)
I0407 22:24:58.782200   359 net.cpp:122] Setting up drop6
I0407 22:24:58.782205   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.782207   359 net.cpp:137] Memory required for data: 1058060288
I0407 22:24:58.782210   359 layer_factory.hpp:77] Creating layer fc7
I0407 22:24:58.782218   359 net.cpp:84] Creating Layer fc7
I0407 22:24:58.782222   359 net.cpp:406] fc7 <- fc6
I0407 22:24:58.782225   359 net.cpp:380] fc7 -> fc7
I0407 22:24:58.941969   359 net.cpp:122] Setting up fc7
I0407 22:24:58.941987   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.941990   359 net.cpp:137] Memory required for data: 1060157440
I0407 22:24:58.941999   359 layer_factory.hpp:77] Creating layer relu7
I0407 22:24:58.942008   359 net.cpp:84] Creating Layer relu7
I0407 22:24:58.942011   359 net.cpp:406] relu7 <- fc7
I0407 22:24:58.942018   359 net.cpp:367] relu7 -> fc7 (in-place)
I0407 22:24:58.942502   359 net.cpp:122] Setting up relu7
I0407 22:24:58.942517   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.942519   359 net.cpp:137] Memory required for data: 1062254592
I0407 22:24:58.942523   359 layer_factory.hpp:77] Creating layer drop7
I0407 22:24:58.942529   359 net.cpp:84] Creating Layer drop7
I0407 22:24:58.942548   359 net.cpp:406] drop7 <- fc7
I0407 22:24:58.942554   359 net.cpp:367] drop7 -> fc7 (in-place)
I0407 22:24:58.942576   359 net.cpp:122] Setting up drop7
I0407 22:24:58.942581   359 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:24:58.942584   359 net.cpp:137] Memory required for data: 1064351744
I0407 22:24:58.942586   359 layer_factory.hpp:77] Creating layer fc8
I0407 22:24:58.942595   359 net.cpp:84] Creating Layer fc8
I0407 22:24:58.942597   359 net.cpp:406] fc8 <- fc7
I0407 22:24:58.942602   359 net.cpp:380] fc8 -> fc8
I0407 22:24:58.950403   359 net.cpp:122] Setting up fc8
I0407 22:24:58.950412   359 net.cpp:129] Top shape: 128 196 (25088)
I0407 22:24:58.950415   359 net.cpp:137] Memory required for data: 1064452096
I0407 22:24:58.950421   359 layer_factory.hpp:77] Creating layer loss
I0407 22:24:58.950428   359 net.cpp:84] Creating Layer loss
I0407 22:24:58.950431   359 net.cpp:406] loss <- fc8
I0407 22:24:58.950435   359 net.cpp:406] loss <- label
I0407 22:24:58.950441   359 net.cpp:380] loss -> loss
I0407 22:24:58.950449   359 layer_factory.hpp:77] Creating layer loss
I0407 22:24:58.951120   359 net.cpp:122] Setting up loss
I0407 22:24:58.951129   359 net.cpp:129] Top shape: (1)
I0407 22:24:58.951133   359 net.cpp:132]     with loss weight 1
I0407 22:24:58.951148   359 net.cpp:137] Memory required for data: 1064452100
I0407 22:24:58.951151   359 net.cpp:198] loss needs backward computation.
I0407 22:24:58.951157   359 net.cpp:198] fc8 needs backward computation.
I0407 22:24:58.951160   359 net.cpp:198] drop7 needs backward computation.
I0407 22:24:58.951164   359 net.cpp:198] relu7 needs backward computation.
I0407 22:24:58.951165   359 net.cpp:198] fc7 needs backward computation.
I0407 22:24:58.951169   359 net.cpp:198] drop6 needs backward computation.
I0407 22:24:58.951170   359 net.cpp:198] relu6 needs backward computation.
I0407 22:24:58.951174   359 net.cpp:198] fc6 needs backward computation.
I0407 22:24:58.951176   359 net.cpp:198] pool5 needs backward computation.
I0407 22:24:58.951179   359 net.cpp:198] relu5 needs backward computation.
I0407 22:24:58.951182   359 net.cpp:198] conv5 needs backward computation.
I0407 22:24:58.951185   359 net.cpp:198] relu4 needs backward computation.
I0407 22:24:58.951189   359 net.cpp:198] conv4 needs backward computation.
I0407 22:24:58.951191   359 net.cpp:198] relu3 needs backward computation.
I0407 22:24:58.951193   359 net.cpp:198] conv3 needs backward computation.
I0407 22:24:58.951203   359 net.cpp:198] pool2 needs backward computation.
I0407 22:24:58.951207   359 net.cpp:198] norm2 needs backward computation.
I0407 22:24:58.951210   359 net.cpp:198] relu2 needs backward computation.
I0407 22:24:58.951212   359 net.cpp:198] conv2 needs backward computation.
I0407 22:24:58.951215   359 net.cpp:198] pool1 needs backward computation.
I0407 22:24:58.951218   359 net.cpp:198] norm1 needs backward computation.
I0407 22:24:58.951221   359 net.cpp:198] relu1 needs backward computation.
I0407 22:24:58.951223   359 net.cpp:198] conv1 needs backward computation.
I0407 22:24:58.951226   359 net.cpp:200] train-data does not need backward computation.
I0407 22:24:58.951229   359 net.cpp:242] This network produces output loss
I0407 22:24:58.951246   359 net.cpp:255] Network initialization done.
I0407 22:24:58.951777   359 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 22:24:58.951807   359 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 22:24:58.951942   359 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0407 22:24:58.952044   359 layer_factory.hpp:77] Creating layer val-data
I0407 22:24:58.953537   359 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db
I0407 22:24:58.954196   359 net.cpp:84] Creating Layer val-data
I0407 22:24:58.954207   359 net.cpp:380] val-data -> data
I0407 22:24:58.954216   359 net.cpp:380] val-data -> label
I0407 22:24:58.954221   359 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0407 22:24:58.957778   359 data_layer.cpp:45] output data size: 32,3,227,227
I0407 22:24:58.991655   359 net.cpp:122] Setting up val-data
I0407 22:24:58.991675   359 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 22:24:58.991679   359 net.cpp:129] Top shape: 32 (32)
I0407 22:24:58.991683   359 net.cpp:137] Memory required for data: 19787264
I0407 22:24:58.991688   359 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 22:24:58.991699   359 net.cpp:84] Creating Layer label_val-data_1_split
I0407 22:24:58.991703   359 net.cpp:406] label_val-data_1_split <- label
I0407 22:24:58.991708   359 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 22:24:58.991717   359 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 22:24:58.991762   359 net.cpp:122] Setting up label_val-data_1_split
I0407 22:24:58.991768   359 net.cpp:129] Top shape: 32 (32)
I0407 22:24:58.991771   359 net.cpp:129] Top shape: 32 (32)
I0407 22:24:58.991773   359 net.cpp:137] Memory required for data: 19787520
I0407 22:24:58.991776   359 layer_factory.hpp:77] Creating layer conv1
I0407 22:24:58.991787   359 net.cpp:84] Creating Layer conv1
I0407 22:24:58.991789   359 net.cpp:406] conv1 <- data
I0407 22:24:58.991794   359 net.cpp:380] conv1 -> conv1
I0407 22:24:58.994642   359 net.cpp:122] Setting up conv1
I0407 22:24:58.994653   359 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:24:58.994657   359 net.cpp:137] Memory required for data: 56958720
I0407 22:24:58.994665   359 layer_factory.hpp:77] Creating layer relu1
I0407 22:24:58.994671   359 net.cpp:84] Creating Layer relu1
I0407 22:24:58.994674   359 net.cpp:406] relu1 <- conv1
I0407 22:24:58.994679   359 net.cpp:367] relu1 -> conv1 (in-place)
I0407 22:24:58.995002   359 net.cpp:122] Setting up relu1
I0407 22:24:58.995012   359 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:24:58.995014   359 net.cpp:137] Memory required for data: 94129920
I0407 22:24:58.995018   359 layer_factory.hpp:77] Creating layer norm1
I0407 22:24:58.995024   359 net.cpp:84] Creating Layer norm1
I0407 22:24:58.995028   359 net.cpp:406] norm1 <- conv1
I0407 22:24:58.995033   359 net.cpp:380] norm1 -> norm1
I0407 22:24:58.995978   359 net.cpp:122] Setting up norm1
I0407 22:24:58.995988   359 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:24:58.995990   359 net.cpp:137] Memory required for data: 131301120
I0407 22:24:58.995995   359 layer_factory.hpp:77] Creating layer pool1
I0407 22:24:58.996001   359 net.cpp:84] Creating Layer pool1
I0407 22:24:58.996003   359 net.cpp:406] pool1 <- norm1
I0407 22:24:58.996008   359 net.cpp:380] pool1 -> pool1
I0407 22:24:58.996034   359 net.cpp:122] Setting up pool1
I0407 22:24:58.996039   359 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 22:24:58.996042   359 net.cpp:137] Memory required for data: 140259072
I0407 22:24:58.996044   359 layer_factory.hpp:77] Creating layer conv2
I0407 22:24:58.996052   359 net.cpp:84] Creating Layer conv2
I0407 22:24:58.996054   359 net.cpp:406] conv2 <- pool1
I0407 22:24:58.996076   359 net.cpp:380] conv2 -> conv2
I0407 22:24:59.004604   359 net.cpp:122] Setting up conv2
I0407 22:24:59.004626   359 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:24:59.004629   359 net.cpp:137] Memory required for data: 164146944
I0407 22:24:59.004640   359 layer_factory.hpp:77] Creating layer relu2
I0407 22:24:59.004648   359 net.cpp:84] Creating Layer relu2
I0407 22:24:59.004652   359 net.cpp:406] relu2 <- conv2
I0407 22:24:59.004657   359 net.cpp:367] relu2 -> conv2 (in-place)
I0407 22:24:59.005244   359 net.cpp:122] Setting up relu2
I0407 22:24:59.005254   359 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:24:59.005256   359 net.cpp:137] Memory required for data: 188034816
I0407 22:24:59.005259   359 layer_factory.hpp:77] Creating layer norm2
I0407 22:24:59.005270   359 net.cpp:84] Creating Layer norm2
I0407 22:24:59.005272   359 net.cpp:406] norm2 <- conv2
I0407 22:24:59.005277   359 net.cpp:380] norm2 -> norm2
I0407 22:24:59.006044   359 net.cpp:122] Setting up norm2
I0407 22:24:59.006053   359 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:24:59.006057   359 net.cpp:137] Memory required for data: 211922688
I0407 22:24:59.006060   359 layer_factory.hpp:77] Creating layer pool2
I0407 22:24:59.006067   359 net.cpp:84] Creating Layer pool2
I0407 22:24:59.006070   359 net.cpp:406] pool2 <- norm2
I0407 22:24:59.006078   359 net.cpp:380] pool2 -> pool2
I0407 22:24:59.006105   359 net.cpp:122] Setting up pool2
I0407 22:24:59.006111   359 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:24:59.006114   359 net.cpp:137] Memory required for data: 217460480
I0407 22:24:59.006116   359 layer_factory.hpp:77] Creating layer conv3
I0407 22:24:59.006126   359 net.cpp:84] Creating Layer conv3
I0407 22:24:59.006129   359 net.cpp:406] conv3 <- pool2
I0407 22:24:59.006135   359 net.cpp:380] conv3 -> conv3
I0407 22:24:59.018026   359 net.cpp:122] Setting up conv3
I0407 22:24:59.018043   359 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:24:59.018045   359 net.cpp:137] Memory required for data: 225767168
I0407 22:24:59.018056   359 layer_factory.hpp:77] Creating layer relu3
I0407 22:24:59.018066   359 net.cpp:84] Creating Layer relu3
I0407 22:24:59.018069   359 net.cpp:406] relu3 <- conv3
I0407 22:24:59.018075   359 net.cpp:367] relu3 -> conv3 (in-place)
I0407 22:24:59.018676   359 net.cpp:122] Setting up relu3
I0407 22:24:59.018685   359 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:24:59.018688   359 net.cpp:137] Memory required for data: 234073856
I0407 22:24:59.018692   359 layer_factory.hpp:77] Creating layer conv4
I0407 22:24:59.018702   359 net.cpp:84] Creating Layer conv4
I0407 22:24:59.018705   359 net.cpp:406] conv4 <- conv3
I0407 22:24:59.018712   359 net.cpp:380] conv4 -> conv4
I0407 22:24:59.029397   359 net.cpp:122] Setting up conv4
I0407 22:24:59.029410   359 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:24:59.029414   359 net.cpp:137] Memory required for data: 242380544
I0407 22:24:59.029422   359 layer_factory.hpp:77] Creating layer relu4
I0407 22:24:59.029428   359 net.cpp:84] Creating Layer relu4
I0407 22:24:59.029431   359 net.cpp:406] relu4 <- conv4
I0407 22:24:59.029438   359 net.cpp:367] relu4 -> conv4 (in-place)
I0407 22:24:59.029821   359 net.cpp:122] Setting up relu4
I0407 22:24:59.029830   359 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:24:59.029832   359 net.cpp:137] Memory required for data: 250687232
I0407 22:24:59.029835   359 layer_factory.hpp:77] Creating layer conv5
I0407 22:24:59.029846   359 net.cpp:84] Creating Layer conv5
I0407 22:24:59.029850   359 net.cpp:406] conv5 <- conv4
I0407 22:24:59.029856   359 net.cpp:380] conv5 -> conv5
I0407 22:24:59.039891   359 net.cpp:122] Setting up conv5
I0407 22:24:59.039907   359 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:24:59.039911   359 net.cpp:137] Memory required for data: 256225024
I0407 22:24:59.039922   359 layer_factory.hpp:77] Creating layer relu5
I0407 22:24:59.039929   359 net.cpp:84] Creating Layer relu5
I0407 22:24:59.039948   359 net.cpp:406] relu5 <- conv5
I0407 22:24:59.039954   359 net.cpp:367] relu5 -> conv5 (in-place)
I0407 22:24:59.040582   359 net.cpp:122] Setting up relu5
I0407 22:24:59.040592   359 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:24:59.040596   359 net.cpp:137] Memory required for data: 261762816
I0407 22:24:59.040598   359 layer_factory.hpp:77] Creating layer pool5
I0407 22:24:59.040608   359 net.cpp:84] Creating Layer pool5
I0407 22:24:59.040611   359 net.cpp:406] pool5 <- conv5
I0407 22:24:59.040616   359 net.cpp:380] pool5 -> pool5
I0407 22:24:59.040652   359 net.cpp:122] Setting up pool5
I0407 22:24:59.040657   359 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 22:24:59.040659   359 net.cpp:137] Memory required for data: 262942464
I0407 22:24:59.040663   359 layer_factory.hpp:77] Creating layer fc6
I0407 22:24:59.040670   359 net.cpp:84] Creating Layer fc6
I0407 22:24:59.040673   359 net.cpp:406] fc6 <- pool5
I0407 22:24:59.040678   359 net.cpp:380] fc6 -> fc6
I0407 22:24:59.399106   359 net.cpp:122] Setting up fc6
I0407 22:24:59.399128   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.399132   359 net.cpp:137] Memory required for data: 263466752
I0407 22:24:59.399140   359 layer_factory.hpp:77] Creating layer relu6
I0407 22:24:59.399152   359 net.cpp:84] Creating Layer relu6
I0407 22:24:59.399154   359 net.cpp:406] relu6 <- fc6
I0407 22:24:59.399160   359 net.cpp:367] relu6 -> fc6 (in-place)
I0407 22:24:59.399969   359 net.cpp:122] Setting up relu6
I0407 22:24:59.399978   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.399981   359 net.cpp:137] Memory required for data: 263991040
I0407 22:24:59.399984   359 layer_factory.hpp:77] Creating layer drop6
I0407 22:24:59.399991   359 net.cpp:84] Creating Layer drop6
I0407 22:24:59.399994   359 net.cpp:406] drop6 <- fc6
I0407 22:24:59.400000   359 net.cpp:367] drop6 -> fc6 (in-place)
I0407 22:24:59.400023   359 net.cpp:122] Setting up drop6
I0407 22:24:59.400028   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.400032   359 net.cpp:137] Memory required for data: 264515328
I0407 22:24:59.400033   359 layer_factory.hpp:77] Creating layer fc7
I0407 22:24:59.400040   359 net.cpp:84] Creating Layer fc7
I0407 22:24:59.400043   359 net.cpp:406] fc7 <- fc6
I0407 22:24:59.400048   359 net.cpp:380] fc7 -> fc7
I0407 22:24:59.559554   359 net.cpp:122] Setting up fc7
I0407 22:24:59.559576   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.559579   359 net.cpp:137] Memory required for data: 265039616
I0407 22:24:59.559588   359 layer_factory.hpp:77] Creating layer relu7
I0407 22:24:59.559597   359 net.cpp:84] Creating Layer relu7
I0407 22:24:59.559599   359 net.cpp:406] relu7 <- fc7
I0407 22:24:59.559605   359 net.cpp:367] relu7 -> fc7 (in-place)
I0407 22:24:59.560138   359 net.cpp:122] Setting up relu7
I0407 22:24:59.560149   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.560153   359 net.cpp:137] Memory required for data: 265563904
I0407 22:24:59.560155   359 layer_factory.hpp:77] Creating layer drop7
I0407 22:24:59.560161   359 net.cpp:84] Creating Layer drop7
I0407 22:24:59.560164   359 net.cpp:406] drop7 <- fc7
I0407 22:24:59.560169   359 net.cpp:367] drop7 -> fc7 (in-place)
I0407 22:24:59.560194   359 net.cpp:122] Setting up drop7
I0407 22:24:59.560199   359 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:24:59.560202   359 net.cpp:137] Memory required for data: 266088192
I0407 22:24:59.560204   359 layer_factory.hpp:77] Creating layer fc8
I0407 22:24:59.560211   359 net.cpp:84] Creating Layer fc8
I0407 22:24:59.560214   359 net.cpp:406] fc8 <- fc7
I0407 22:24:59.560218   359 net.cpp:380] fc8 -> fc8
I0407 22:24:59.568007   359 net.cpp:122] Setting up fc8
I0407 22:24:59.568018   359 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:24:59.568020   359 net.cpp:137] Memory required for data: 266113280
I0407 22:24:59.568027   359 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 22:24:59.568032   359 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 22:24:59.568035   359 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 22:24:59.568056   359 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 22:24:59.568063   359 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 22:24:59.568091   359 net.cpp:122] Setting up fc8_fc8_0_split
I0407 22:24:59.568095   359 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:24:59.568099   359 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:24:59.568101   359 net.cpp:137] Memory required for data: 266163456
I0407 22:24:59.568104   359 layer_factory.hpp:77] Creating layer accuracy
I0407 22:24:59.568111   359 net.cpp:84] Creating Layer accuracy
I0407 22:24:59.568114   359 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 22:24:59.568117   359 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 22:24:59.568122   359 net.cpp:380] accuracy -> accuracy
I0407 22:24:59.568128   359 net.cpp:122] Setting up accuracy
I0407 22:24:59.568132   359 net.cpp:129] Top shape: (1)
I0407 22:24:59.568135   359 net.cpp:137] Memory required for data: 266163460
I0407 22:24:59.568137   359 layer_factory.hpp:77] Creating layer loss
I0407 22:24:59.568141   359 net.cpp:84] Creating Layer loss
I0407 22:24:59.568145   359 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 22:24:59.568147   359 net.cpp:406] loss <- label_val-data_1_split_1
I0407 22:24:59.568151   359 net.cpp:380] loss -> loss
I0407 22:24:59.568158   359 layer_factory.hpp:77] Creating layer loss
I0407 22:24:59.568816   359 net.cpp:122] Setting up loss
I0407 22:24:59.568826   359 net.cpp:129] Top shape: (1)
I0407 22:24:59.568830   359 net.cpp:132]     with loss weight 1
I0407 22:24:59.568838   359 net.cpp:137] Memory required for data: 266163464
I0407 22:24:59.568842   359 net.cpp:198] loss needs backward computation.
I0407 22:24:59.568846   359 net.cpp:200] accuracy does not need backward computation.
I0407 22:24:59.568850   359 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 22:24:59.568852   359 net.cpp:198] fc8 needs backward computation.
I0407 22:24:59.568856   359 net.cpp:198] drop7 needs backward computation.
I0407 22:24:59.568858   359 net.cpp:198] relu7 needs backward computation.
I0407 22:24:59.568861   359 net.cpp:198] fc7 needs backward computation.
I0407 22:24:59.568863   359 net.cpp:198] drop6 needs backward computation.
I0407 22:24:59.568866   359 net.cpp:198] relu6 needs backward computation.
I0407 22:24:59.568868   359 net.cpp:198] fc6 needs backward computation.
I0407 22:24:59.568872   359 net.cpp:198] pool5 needs backward computation.
I0407 22:24:59.568876   359 net.cpp:198] relu5 needs backward computation.
I0407 22:24:59.568877   359 net.cpp:198] conv5 needs backward computation.
I0407 22:24:59.568881   359 net.cpp:198] relu4 needs backward computation.
I0407 22:24:59.568883   359 net.cpp:198] conv4 needs backward computation.
I0407 22:24:59.568886   359 net.cpp:198] relu3 needs backward computation.
I0407 22:24:59.568889   359 net.cpp:198] conv3 needs backward computation.
I0407 22:24:59.568892   359 net.cpp:198] pool2 needs backward computation.
I0407 22:24:59.568894   359 net.cpp:198] norm2 needs backward computation.
I0407 22:24:59.568897   359 net.cpp:198] relu2 needs backward computation.
I0407 22:24:59.568900   359 net.cpp:198] conv2 needs backward computation.
I0407 22:24:59.568902   359 net.cpp:198] pool1 needs backward computation.
I0407 22:24:59.568905   359 net.cpp:198] norm1 needs backward computation.
I0407 22:24:59.568909   359 net.cpp:198] relu1 needs backward computation.
I0407 22:24:59.568912   359 net.cpp:198] conv1 needs backward computation.
I0407 22:24:59.568914   359 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 22:24:59.568918   359 net.cpp:200] val-data does not need backward computation.
I0407 22:24:59.568920   359 net.cpp:242] This network produces output accuracy
I0407 22:24:59.568923   359 net.cpp:242] This network produces output loss
I0407 22:24:59.568939   359 net.cpp:255] Network initialization done.
I0407 22:24:59.569005   359 solver.cpp:56] Solver scaffolding done.
I0407 22:24:59.569339   359 caffe.cpp:248] Starting Optimization
I0407 22:24:59.569347   359 solver.cpp:272] Solving
I0407 22:24:59.569358   359 solver.cpp:273] Learning Rate Policy: sigmoid
I0407 22:24:59.571000   359 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 22:24:59.571009   359 net.cpp:676] Ignoring source layer train-data
I0407 22:24:59.658704   359 blocking_queue.cpp:49] Waiting for data
I0407 22:25:03.936782   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:03.982277   359 solver.cpp:397]     Test net output #0: accuracy = 0.00245098
I0407 22:25:03.982334   359 solver.cpp:397]     Test net output #1: loss = 5.28174 (* 1 = 5.28174 loss)
I0407 22:25:04.081135   359 solver.cpp:218] Iteration 0 (0 iter/s, 4.51172s/12 iters), loss = 5.28806
I0407 22:25:04.082665   359 solver.cpp:237]     Train net output #0: loss = 5.28806 (* 1 = 5.28806 loss)
I0407 22:25:04.082700   359 sgd_solver.cpp:105] Iteration 0, lr = 0.00999955
I0407 22:25:07.896353   359 solver.cpp:218] Iteration 12 (3.14657 iter/s, 3.81367s/12 iters), loss = 5.28562
I0407 22:25:07.896394   359 solver.cpp:237]     Train net output #0: loss = 5.28562 (* 1 = 5.28562 loss)
I0407 22:25:07.896401   359 sgd_solver.cpp:105] Iteration 12, lr = 0.00999954
I0407 22:25:12.851897   359 solver.cpp:218] Iteration 24 (2.42156 iter/s, 4.95549s/12 iters), loss = 5.29167
I0407 22:25:12.851936   359 solver.cpp:237]     Train net output #0: loss = 5.29167 (* 1 = 5.29167 loss)
I0407 22:25:12.851944   359 sgd_solver.cpp:105] Iteration 24, lr = 0.00999952
I0407 22:25:17.794610   359 solver.cpp:218] Iteration 36 (2.42785 iter/s, 4.94265s/12 iters), loss = 5.28802
I0407 22:25:17.794651   359 solver.cpp:237]     Train net output #0: loss = 5.28802 (* 1 = 5.28802 loss)
I0407 22:25:17.794661   359 sgd_solver.cpp:105] Iteration 36, lr = 0.00999951
I0407 22:25:22.766485   359 solver.cpp:218] Iteration 48 (2.41361 iter/s, 4.97181s/12 iters), loss = 5.2704
I0407 22:25:22.766522   359 solver.cpp:237]     Train net output #0: loss = 5.2704 (* 1 = 5.2704 loss)
I0407 22:25:22.766530   359 sgd_solver.cpp:105] Iteration 48, lr = 0.0099995
I0407 22:25:27.680014   359 solver.cpp:218] Iteration 60 (2.44226 iter/s, 4.91347s/12 iters), loss = 5.26748
I0407 22:25:27.680188   359 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0407 22:25:27.680197   359 sgd_solver.cpp:105] Iteration 60, lr = 0.00999949
I0407 22:25:32.628957   359 solver.cpp:218] Iteration 72 (2.42485 iter/s, 4.94875s/12 iters), loss = 5.29883
I0407 22:25:32.628998   359 solver.cpp:237]     Train net output #0: loss = 5.29883 (* 1 = 5.29883 loss)
I0407 22:25:32.629009   359 sgd_solver.cpp:105] Iteration 72, lr = 0.00999948
I0407 22:25:37.554020   359 solver.cpp:218] Iteration 84 (2.43655 iter/s, 4.92501s/12 iters), loss = 5.30654
I0407 22:25:37.554062   359 solver.cpp:237]     Train net output #0: loss = 5.30654 (* 1 = 5.30654 loss)
I0407 22:25:37.554071   359 sgd_solver.cpp:105] Iteration 84, lr = 0.00999946
I0407 22:25:42.509307   359 solver.cpp:218] Iteration 96 (2.42169 iter/s, 4.95522s/12 iters), loss = 5.29047
I0407 22:25:42.509352   359 solver.cpp:237]     Train net output #0: loss = 5.29047 (* 1 = 5.29047 loss)
I0407 22:25:42.509361   359 sgd_solver.cpp:105] Iteration 96, lr = 0.00999945
I0407 22:25:44.187778   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:44.489156   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 22:25:50.035602   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 22:25:53.812662   359 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 22:25:53.812680   359 net.cpp:676] Ignoring source layer train-data
I0407 22:25:58.538599   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:58.625958   359 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0407 22:25:58.626006   359 solver.cpp:397]     Test net output #1: loss = 5.28653 (* 1 = 5.28653 loss)
I0407 22:26:00.399950   359 solver.cpp:218] Iteration 108 (0.670745 iter/s, 17.8906s/12 iters), loss = 5.29295
I0407 22:26:00.399988   359 solver.cpp:237]     Train net output #0: loss = 5.29295 (* 1 = 5.29295 loss)
I0407 22:26:00.399996   359 sgd_solver.cpp:105] Iteration 108, lr = 0.00999944
I0407 22:26:05.338814   359 solver.cpp:218] Iteration 120 (2.42974 iter/s, 4.9388s/12 iters), loss = 5.26551
I0407 22:26:05.338848   359 solver.cpp:237]     Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
I0407 22:26:05.338856   359 sgd_solver.cpp:105] Iteration 120, lr = 0.00999943
I0407 22:26:10.315505   359 solver.cpp:218] Iteration 132 (2.41127 iter/s, 4.97663s/12 iters), loss = 5.30808
I0407 22:26:10.315548   359 solver.cpp:237]     Train net output #0: loss = 5.30808 (* 1 = 5.30808 loss)
I0407 22:26:10.315557   359 sgd_solver.cpp:105] Iteration 132, lr = 0.00999941
I0407 22:26:15.271690   359 solver.cpp:218] Iteration 144 (2.42125 iter/s, 4.95612s/12 iters), loss = 5.29402
I0407 22:26:15.271726   359 solver.cpp:237]     Train net output #0: loss = 5.29402 (* 1 = 5.29402 loss)
I0407 22:26:15.271734   359 sgd_solver.cpp:105] Iteration 144, lr = 0.0099994
I0407 22:26:20.201300   359 solver.cpp:218] Iteration 156 (2.4343 iter/s, 4.92955s/12 iters), loss = 5.26807
I0407 22:26:20.201336   359 solver.cpp:237]     Train net output #0: loss = 5.26807 (* 1 = 5.26807 loss)
I0407 22:26:20.201344   359 sgd_solver.cpp:105] Iteration 156, lr = 0.00999938
I0407 22:26:25.184865   359 solver.cpp:218] Iteration 168 (2.40794 iter/s, 4.98351s/12 iters), loss = 5.28113
I0407 22:26:25.184901   359 solver.cpp:237]     Train net output #0: loss = 5.28113 (* 1 = 5.28113 loss)
I0407 22:26:25.184909   359 sgd_solver.cpp:105] Iteration 168, lr = 0.00999937
I0407 22:26:30.109724   359 solver.cpp:218] Iteration 180 (2.43665 iter/s, 4.9248s/12 iters), loss = 5.29121
I0407 22:26:30.109831   359 solver.cpp:237]     Train net output #0: loss = 5.29121 (* 1 = 5.29121 loss)
I0407 22:26:30.109840   359 sgd_solver.cpp:105] Iteration 180, lr = 0.00999935
I0407 22:26:35.060994   359 solver.cpp:218] Iteration 192 (2.42368 iter/s, 4.95114s/12 iters), loss = 5.27884
I0407 22:26:35.061033   359 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0407 22:26:35.061039   359 sgd_solver.cpp:105] Iteration 192, lr = 0.00999934
I0407 22:26:38.851106   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:39.524173   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 22:26:42.585925   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 22:26:44.957439   359 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 22:26:44.957466   359 net.cpp:676] Ignoring source layer train-data
I0407 22:26:49.607137   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:49.742899   359 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0407 22:26:49.742942   359 solver.cpp:397]     Test net output #1: loss = 5.19495 (* 1 = 5.19495 loss)
I0407 22:26:49.839781   359 solver.cpp:218] Iteration 204 (0.811979 iter/s, 14.7787s/12 iters), loss = 5.24342
I0407 22:26:49.839821   359 solver.cpp:237]     Train net output #0: loss = 5.24342 (* 1 = 5.24342 loss)
I0407 22:26:49.839828   359 sgd_solver.cpp:105] Iteration 204, lr = 0.00999932
I0407 22:26:53.951974   359 solver.cpp:218] Iteration 216 (2.91819 iter/s, 4.11213s/12 iters), loss = 5.2751
I0407 22:26:53.952008   359 solver.cpp:237]     Train net output #0: loss = 5.2751 (* 1 = 5.2751 loss)
I0407 22:26:53.952015   359 sgd_solver.cpp:105] Iteration 216, lr = 0.00999931
I0407 22:26:58.894639   359 solver.cpp:218] Iteration 228 (2.42787 iter/s, 4.94261s/12 iters), loss = 5.2056
I0407 22:26:58.894675   359 solver.cpp:237]     Train net output #0: loss = 5.2056 (* 1 = 5.2056 loss)
I0407 22:26:58.894683   359 sgd_solver.cpp:105] Iteration 228, lr = 0.00999929
I0407 22:27:03.779997   359 solver.cpp:218] Iteration 240 (2.45635 iter/s, 4.8853s/12 iters), loss = 5.21841
I0407 22:27:03.780162   359 solver.cpp:237]     Train net output #0: loss = 5.21841 (* 1 = 5.21841 loss)
I0407 22:27:03.780172   359 sgd_solver.cpp:105] Iteration 240, lr = 0.00999927
I0407 22:27:08.759843   359 solver.cpp:218] Iteration 252 (2.4098 iter/s, 4.97966s/12 iters), loss = 5.21303
I0407 22:27:08.759881   359 solver.cpp:237]     Train net output #0: loss = 5.21303 (* 1 = 5.21303 loss)
I0407 22:27:08.759888   359 sgd_solver.cpp:105] Iteration 252, lr = 0.00999926
I0407 22:27:13.693011   359 solver.cpp:218] Iteration 264 (2.43255 iter/s, 4.9331s/12 iters), loss = 5.17119
I0407 22:27:13.693055   359 solver.cpp:237]     Train net output #0: loss = 5.17119 (* 1 = 5.17119 loss)
I0407 22:27:13.693063   359 sgd_solver.cpp:105] Iteration 264, lr = 0.00999924
I0407 22:27:18.646687   359 solver.cpp:218] Iteration 276 (2.42248 iter/s, 4.95361s/12 iters), loss = 5.10664
I0407 22:27:18.646729   359 solver.cpp:237]     Train net output #0: loss = 5.10664 (* 1 = 5.10664 loss)
I0407 22:27:18.646737   359 sgd_solver.cpp:105] Iteration 276, lr = 0.00999922
I0407 22:27:23.590082   359 solver.cpp:218] Iteration 288 (2.42751 iter/s, 4.94333s/12 iters), loss = 5.15481
I0407 22:27:23.590126   359 solver.cpp:237]     Train net output #0: loss = 5.15481 (* 1 = 5.15481 loss)
I0407 22:27:23.590133   359 sgd_solver.cpp:105] Iteration 288, lr = 0.0099992
I0407 22:27:28.508064   359 solver.cpp:218] Iteration 300 (2.44006 iter/s, 4.91792s/12 iters), loss = 5.1391
I0407 22:27:28.508106   359 solver.cpp:237]     Train net output #0: loss = 5.1391 (* 1 = 5.1391 loss)
I0407 22:27:28.508114   359 sgd_solver.cpp:105] Iteration 300, lr = 0.00999918
I0407 22:27:29.468757   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:30.625247   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 22:27:33.680151   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 22:27:36.049736   359 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 22:27:36.049819   359 net.cpp:676] Ignoring source layer train-data
I0407 22:27:40.323453   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:40.484102   359 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0407 22:27:40.484138   359 solver.cpp:397]     Test net output #1: loss = 5.14301 (* 1 = 5.14301 loss)
I0407 22:27:42.278084   359 solver.cpp:218] Iteration 312 (0.871464 iter/s, 13.7699s/12 iters), loss = 5.16477
I0407 22:27:42.278126   359 solver.cpp:237]     Train net output #0: loss = 5.16477 (* 1 = 5.16477 loss)
I0407 22:27:42.278133   359 sgd_solver.cpp:105] Iteration 312, lr = 0.00999916
I0407 22:27:47.238617   359 solver.cpp:218] Iteration 324 (2.41913 iter/s, 4.96046s/12 iters), loss = 5.10394
I0407 22:27:47.238658   359 solver.cpp:237]     Train net output #0: loss = 5.10394 (* 1 = 5.10394 loss)
I0407 22:27:47.238667   359 sgd_solver.cpp:105] Iteration 324, lr = 0.00999914
I0407 22:27:52.168494   359 solver.cpp:218] Iteration 336 (2.43417 iter/s, 4.92981s/12 iters), loss = 5.16258
I0407 22:27:52.168537   359 solver.cpp:237]     Train net output #0: loss = 5.16258 (* 1 = 5.16258 loss)
I0407 22:27:52.168546   359 sgd_solver.cpp:105] Iteration 336, lr = 0.00999912
I0407 22:27:57.110576   359 solver.cpp:218] Iteration 348 (2.42816 iter/s, 4.94201s/12 iters), loss = 5.16971
I0407 22:27:57.110622   359 solver.cpp:237]     Train net output #0: loss = 5.16971 (* 1 = 5.16971 loss)
I0407 22:27:57.110631   359 sgd_solver.cpp:105] Iteration 348, lr = 0.0099991
I0407 22:28:02.080013   359 solver.cpp:218] Iteration 360 (2.4148 iter/s, 4.96936s/12 iters), loss = 5.08358
I0407 22:28:02.080055   359 solver.cpp:237]     Train net output #0: loss = 5.08358 (* 1 = 5.08358 loss)
I0407 22:28:02.080065   359 sgd_solver.cpp:105] Iteration 360, lr = 0.00999908
I0407 22:28:07.004658   359 solver.cpp:218] Iteration 372 (2.43676 iter/s, 4.92458s/12 iters), loss = 5.17792
I0407 22:28:07.004829   359 solver.cpp:237]     Train net output #0: loss = 5.17792 (* 1 = 5.17792 loss)
I0407 22:28:07.004838   359 sgd_solver.cpp:105] Iteration 372, lr = 0.00999906
I0407 22:28:11.959836   359 solver.cpp:218] Iteration 384 (2.4218 iter/s, 4.95499s/12 iters), loss = 5.14355
I0407 22:28:11.959878   359 solver.cpp:237]     Train net output #0: loss = 5.14355 (* 1 = 5.14355 loss)
I0407 22:28:11.959887   359 sgd_solver.cpp:105] Iteration 384, lr = 0.00999904
I0407 22:28:16.881438   359 solver.cpp:218] Iteration 396 (2.43826 iter/s, 4.92154s/12 iters), loss = 5.17333
I0407 22:28:16.881474   359 solver.cpp:237]     Train net output #0: loss = 5.17333 (* 1 = 5.17333 loss)
I0407 22:28:16.881481   359 sgd_solver.cpp:105] Iteration 396, lr = 0.00999901
I0407 22:28:19.977671   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:21.349093   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 22:28:25.832041   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 22:28:29.169131   359 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 22:28:29.169149   359 net.cpp:676] Ignoring source layer train-data
I0407 22:28:33.587616   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:33.794279   359 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0407 22:28:33.794325   359 solver.cpp:397]     Test net output #1: loss = 5.09501 (* 1 = 5.09501 loss)
I0407 22:28:33.890836   359 solver.cpp:218] Iteration 408 (0.705496 iter/s, 17.0093s/12 iters), loss = 5.08627
I0407 22:28:33.890900   359 solver.cpp:237]     Train net output #0: loss = 5.08627 (* 1 = 5.08627 loss)
I0407 22:28:33.890913   359 sgd_solver.cpp:105] Iteration 408, lr = 0.00999899
I0407 22:28:38.042706   359 solver.cpp:218] Iteration 420 (2.89032 iter/s, 4.15179s/12 iters), loss = 5.02218
I0407 22:28:38.042827   359 solver.cpp:237]     Train net output #0: loss = 5.02218 (* 1 = 5.02218 loss)
I0407 22:28:38.042836   359 sgd_solver.cpp:105] Iteration 420, lr = 0.00999897
I0407 22:28:42.962224   359 solver.cpp:218] Iteration 432 (2.43933 iter/s, 4.91938s/12 iters), loss = 5.16687
I0407 22:28:42.962265   359 solver.cpp:237]     Train net output #0: loss = 5.16687 (* 1 = 5.16687 loss)
I0407 22:28:42.962272   359 sgd_solver.cpp:105] Iteration 432, lr = 0.00999894
I0407 22:28:47.891464   359 solver.cpp:218] Iteration 444 (2.43448 iter/s, 4.92917s/12 iters), loss = 5.01469
I0407 22:28:47.891505   359 solver.cpp:237]     Train net output #0: loss = 5.01469 (* 1 = 5.01469 loss)
I0407 22:28:47.891512   359 sgd_solver.cpp:105] Iteration 444, lr = 0.00999892
I0407 22:28:52.833674   359 solver.cpp:218] Iteration 456 (2.4281 iter/s, 4.94215s/12 iters), loss = 5.04734
I0407 22:28:52.833719   359 solver.cpp:237]     Train net output #0: loss = 5.04734 (* 1 = 5.04734 loss)
I0407 22:28:52.833726   359 sgd_solver.cpp:105] Iteration 456, lr = 0.00999889
I0407 22:28:57.785079   359 solver.cpp:218] Iteration 468 (2.42359 iter/s, 4.95134s/12 iters), loss = 5.1518
I0407 22:28:57.785120   359 solver.cpp:237]     Train net output #0: loss = 5.1518 (* 1 = 5.1518 loss)
I0407 22:28:57.785128   359 sgd_solver.cpp:105] Iteration 468, lr = 0.00999886
I0407 22:29:02.684864   359 solver.cpp:218] Iteration 480 (2.44912 iter/s, 4.89972s/12 iters), loss = 5.13715
I0407 22:29:02.684908   359 solver.cpp:237]     Train net output #0: loss = 5.13715 (* 1 = 5.13715 loss)
I0407 22:29:02.684917   359 sgd_solver.cpp:105] Iteration 480, lr = 0.00999884
I0407 22:29:07.649720   359 solver.cpp:218] Iteration 492 (2.41702 iter/s, 4.96479s/12 iters), loss = 5.02726
I0407 22:29:07.649762   359 solver.cpp:237]     Train net output #0: loss = 5.02726 (* 1 = 5.02726 loss)
I0407 22:29:07.649771   359 sgd_solver.cpp:105] Iteration 492, lr = 0.00999881
I0407 22:29:12.556727   359 solver.cpp:218] Iteration 504 (2.44552 iter/s, 4.90694s/12 iters), loss = 5.06168
I0407 22:29:12.556864   359 solver.cpp:237]     Train net output #0: loss = 5.06168 (* 1 = 5.06168 loss)
I0407 22:29:12.556874   359 sgd_solver.cpp:105] Iteration 504, lr = 0.00999878
I0407 22:29:12.793963   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:14.535104   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 22:29:17.622326   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 22:29:19.984289   359 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 22:29:19.984308   359 net.cpp:676] Ignoring source layer train-data
I0407 22:29:24.211604   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:24.449582   359 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0407 22:29:24.449625   359 solver.cpp:397]     Test net output #1: loss = 5.03868 (* 1 = 5.03868 loss)
I0407 22:29:26.382103   359 solver.cpp:218] Iteration 516 (0.86798 iter/s, 13.8252s/12 iters), loss = 5.04089
I0407 22:29:26.382153   359 solver.cpp:237]     Train net output #0: loss = 5.04089 (* 1 = 5.04089 loss)
I0407 22:29:26.382162   359 sgd_solver.cpp:105] Iteration 516, lr = 0.00999875
I0407 22:29:31.556311   359 solver.cpp:218] Iteration 528 (2.31923 iter/s, 5.17413s/12 iters), loss = 5.11859
I0407 22:29:31.556351   359 solver.cpp:237]     Train net output #0: loss = 5.11859 (* 1 = 5.11859 loss)
I0407 22:29:31.556360   359 sgd_solver.cpp:105] Iteration 528, lr = 0.00999872
I0407 22:29:36.519137   359 solver.cpp:218] Iteration 540 (2.41801 iter/s, 4.96276s/12 iters), loss = 4.98663
I0407 22:29:36.519182   359 solver.cpp:237]     Train net output #0: loss = 4.98663 (* 1 = 4.98663 loss)
I0407 22:29:36.519191   359 sgd_solver.cpp:105] Iteration 540, lr = 0.00999869
I0407 22:29:41.679147   359 solver.cpp:218] Iteration 552 (2.32561 iter/s, 5.15994s/12 iters), loss = 4.89262
I0407 22:29:41.679185   359 solver.cpp:237]     Train net output #0: loss = 4.89262 (* 1 = 4.89262 loss)
I0407 22:29:41.679193   359 sgd_solver.cpp:105] Iteration 552, lr = 0.00999866
I0407 22:29:46.902901   359 solver.cpp:218] Iteration 564 (2.29723 iter/s, 5.22369s/12 iters), loss = 5.01488
I0407 22:29:46.903031   359 solver.cpp:237]     Train net output #0: loss = 5.01488 (* 1 = 5.01488 loss)
I0407 22:29:46.903041   359 sgd_solver.cpp:105] Iteration 564, lr = 0.00999863
I0407 22:29:51.820363   359 solver.cpp:218] Iteration 576 (2.44036 iter/s, 4.91731s/12 iters), loss = 5.11047
I0407 22:29:51.820405   359 solver.cpp:237]     Train net output #0: loss = 5.11047 (* 1 = 5.11047 loss)
I0407 22:29:51.820415   359 sgd_solver.cpp:105] Iteration 576, lr = 0.0099986
I0407 22:29:56.786377   359 solver.cpp:218] Iteration 588 (2.41646 iter/s, 4.96595s/12 iters), loss = 5.0596
I0407 22:29:56.786419   359 solver.cpp:237]     Train net output #0: loss = 5.0596 (* 1 = 5.0596 loss)
I0407 22:29:56.786428   359 sgd_solver.cpp:105] Iteration 588, lr = 0.00999856
I0407 22:30:01.696429   359 solver.cpp:218] Iteration 600 (2.444 iter/s, 4.90998s/12 iters), loss = 5.00307
I0407 22:30:01.696473   359 solver.cpp:237]     Train net output #0: loss = 5.00307 (* 1 = 5.00307 loss)
I0407 22:30:01.696481   359 sgd_solver.cpp:105] Iteration 600, lr = 0.00999853
I0407 22:30:04.082912   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:06.179540   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 22:30:09.268191   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 22:30:11.650826   359 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 22:30:11.650844   359 net.cpp:676] Ignoring source layer train-data
I0407 22:30:16.100869   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:16.416594   359 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0407 22:30:16.416641   359 solver.cpp:397]     Test net output #1: loss = 4.99111 (* 1 = 4.99111 loss)
I0407 22:30:16.513608   359 solver.cpp:218] Iteration 612 (0.809875 iter/s, 14.8171s/12 iters), loss = 4.94482
I0407 22:30:16.513654   359 solver.cpp:237]     Train net output #0: loss = 4.94482 (* 1 = 4.94482 loss)
I0407 22:30:16.513662   359 sgd_solver.cpp:105] Iteration 612, lr = 0.00999849
I0407 22:30:20.595207   359 solver.cpp:218] Iteration 624 (2.94008 iter/s, 4.08153s/12 iters), loss = 5.06131
I0407 22:30:20.595358   359 solver.cpp:237]     Train net output #0: loss = 5.06131 (* 1 = 5.06131 loss)
I0407 22:30:20.595368   359 sgd_solver.cpp:105] Iteration 624, lr = 0.00999846
I0407 22:30:25.532660   359 solver.cpp:218] Iteration 636 (2.43049 iter/s, 4.93728s/12 iters), loss = 4.99104
I0407 22:30:25.532699   359 solver.cpp:237]     Train net output #0: loss = 4.99104 (* 1 = 4.99104 loss)
I0407 22:30:25.532707   359 sgd_solver.cpp:105] Iteration 636, lr = 0.00999842
I0407 22:30:30.427517   359 solver.cpp:218] Iteration 648 (2.45158 iter/s, 4.8948s/12 iters), loss = 4.97093
I0407 22:30:30.427562   359 solver.cpp:237]     Train net output #0: loss = 4.97093 (* 1 = 4.97093 loss)
I0407 22:30:30.427570   359 sgd_solver.cpp:105] Iteration 648, lr = 0.00999838
I0407 22:30:35.438525   359 solver.cpp:218] Iteration 660 (2.39476 iter/s, 5.01094s/12 iters), loss = 4.91551
I0407 22:30:35.438557   359 solver.cpp:237]     Train net output #0: loss = 4.91551 (* 1 = 4.91551 loss)
I0407 22:30:35.438565   359 sgd_solver.cpp:105] Iteration 660, lr = 0.00999834
I0407 22:30:40.433089   359 solver.cpp:218] Iteration 672 (2.40264 iter/s, 4.9945s/12 iters), loss = 4.99966
I0407 22:30:40.433125   359 solver.cpp:237]     Train net output #0: loss = 4.99966 (* 1 = 4.99966 loss)
I0407 22:30:40.433135   359 sgd_solver.cpp:105] Iteration 672, lr = 0.0099983
I0407 22:30:45.276988   359 solver.cpp:218] Iteration 684 (2.47737 iter/s, 4.84384s/12 iters), loss = 4.84708
I0407 22:30:45.277024   359 solver.cpp:237]     Train net output #0: loss = 4.84708 (* 1 = 4.84708 loss)
I0407 22:30:45.277032   359 sgd_solver.cpp:105] Iteration 684, lr = 0.00999826
I0407 22:30:46.048619   359 blocking_queue.cpp:49] Waiting for data
I0407 22:30:50.219533   359 solver.cpp:218] Iteration 696 (2.42793 iter/s, 4.94248s/12 iters), loss = 4.90099
I0407 22:30:50.219574   359 solver.cpp:237]     Train net output #0: loss = 4.90099 (* 1 = 4.90099 loss)
I0407 22:30:50.219583   359 sgd_solver.cpp:105] Iteration 696, lr = 0.00999822
I0407 22:30:54.781718   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:55.152725   359 solver.cpp:218] Iteration 708 (2.43253 iter/s, 4.93313s/12 iters), loss = 5.01674
I0407 22:30:55.152765   359 solver.cpp:237]     Train net output #0: loss = 5.01674 (* 1 = 5.01674 loss)
I0407 22:30:55.152773   359 sgd_solver.cpp:105] Iteration 708, lr = 0.00999818
I0407 22:30:57.139627   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 22:31:02.480870   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 22:31:04.856963   359 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 22:31:04.856981   359 net.cpp:676] Ignoring source layer train-data
I0407 22:31:09.474417   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:09.809592   359 solver.cpp:397]     Test net output #0: accuracy = 0.0349265
I0407 22:31:09.809638   359 solver.cpp:397]     Test net output #1: loss = 4.88016 (* 1 = 4.88016 loss)
I0407 22:31:11.613674   359 solver.cpp:218] Iteration 720 (0.729002 iter/s, 16.4609s/12 iters), loss = 4.89186
I0407 22:31:11.613708   359 solver.cpp:237]     Train net output #0: loss = 4.89186 (* 1 = 4.89186 loss)
I0407 22:31:11.613714   359 sgd_solver.cpp:105] Iteration 720, lr = 0.00999814
I0407 22:31:16.581562   359 solver.cpp:218] Iteration 732 (2.41554 iter/s, 4.96783s/12 iters), loss = 4.8736
I0407 22:31:16.581598   359 solver.cpp:237]     Train net output #0: loss = 4.8736 (* 1 = 4.8736 loss)
I0407 22:31:16.581605   359 sgd_solver.cpp:105] Iteration 732, lr = 0.00999809
I0407 22:31:21.489038   359 solver.cpp:218] Iteration 744 (2.44528 iter/s, 4.90742s/12 iters), loss = 4.85927
I0407 22:31:21.489078   359 solver.cpp:237]     Train net output #0: loss = 4.85927 (* 1 = 4.85927 loss)
I0407 22:31:21.489084   359 sgd_solver.cpp:105] Iteration 744, lr = 0.00999805
I0407 22:31:26.414517   359 solver.cpp:218] Iteration 756 (2.43634 iter/s, 4.92542s/12 iters), loss = 4.65793
I0407 22:31:26.414664   359 solver.cpp:237]     Train net output #0: loss = 4.65793 (* 1 = 4.65793 loss)
I0407 22:31:26.414674   359 sgd_solver.cpp:105] Iteration 756, lr = 0.009998
I0407 22:31:31.361675   359 solver.cpp:218] Iteration 768 (2.42572 iter/s, 4.94699s/12 iters), loss = 4.72027
I0407 22:31:31.361716   359 solver.cpp:237]     Train net output #0: loss = 4.72027 (* 1 = 4.72027 loss)
I0407 22:31:31.361726   359 sgd_solver.cpp:105] Iteration 768, lr = 0.00999795
I0407 22:31:36.293408   359 solver.cpp:218] Iteration 780 (2.43326 iter/s, 4.93166s/12 iters), loss = 4.8095
I0407 22:31:36.293455   359 solver.cpp:237]     Train net output #0: loss = 4.8095 (* 1 = 4.8095 loss)
I0407 22:31:36.293463   359 sgd_solver.cpp:105] Iteration 780, lr = 0.00999791
I0407 22:31:41.203377   359 solver.cpp:218] Iteration 792 (2.44404 iter/s, 4.9099s/12 iters), loss = 4.91927
I0407 22:31:41.203415   359 solver.cpp:237]     Train net output #0: loss = 4.91927 (* 1 = 4.91927 loss)
I0407 22:31:41.203423   359 sgd_solver.cpp:105] Iteration 792, lr = 0.00999785
I0407 22:31:46.121747   359 solver.cpp:218] Iteration 804 (2.43986 iter/s, 4.91831s/12 iters), loss = 4.78404
I0407 22:31:46.121783   359 solver.cpp:237]     Train net output #0: loss = 4.78404 (* 1 = 4.78404 loss)
I0407 22:31:46.121789   359 sgd_solver.cpp:105] Iteration 804, lr = 0.0099978
I0407 22:31:47.830135   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:50.604972   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 22:31:55.502758   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 22:31:57.982527   359 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 22:31:57.982635   359 net.cpp:676] Ignoring source layer train-data
I0407 22:32:02.365247   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:02.721709   359 solver.cpp:397]     Test net output #0: accuracy = 0.0465686
I0407 22:32:02.721740   359 solver.cpp:397]     Test net output #1: loss = 4.76026 (* 1 = 4.76026 loss)
I0407 22:32:02.817966   359 solver.cpp:218] Iteration 816 (0.71873 iter/s, 16.6961s/12 iters), loss = 4.67944
I0407 22:32:02.818039   359 solver.cpp:237]     Train net output #0: loss = 4.67944 (* 1 = 4.67944 loss)
I0407 22:32:02.818055   359 sgd_solver.cpp:105] Iteration 816, lr = 0.00999775
I0407 22:32:06.937144   359 solver.cpp:218] Iteration 828 (2.91327 iter/s, 4.11909s/12 iters), loss = 4.74997
I0407 22:32:06.937181   359 solver.cpp:237]     Train net output #0: loss = 4.74997 (* 1 = 4.74997 loss)
I0407 22:32:06.937191   359 sgd_solver.cpp:105] Iteration 828, lr = 0.0099977
I0407 22:32:11.848239   359 solver.cpp:218] Iteration 840 (2.44348 iter/s, 4.91104s/12 iters), loss = 4.78239
I0407 22:32:11.848280   359 solver.cpp:237]     Train net output #0: loss = 4.78239 (* 1 = 4.78239 loss)
I0407 22:32:11.848289   359 sgd_solver.cpp:105] Iteration 840, lr = 0.00999764
I0407 22:32:16.808430   359 solver.cpp:218] Iteration 852 (2.4193 iter/s, 4.96012s/12 iters), loss = 4.65832
I0407 22:32:16.808476   359 solver.cpp:237]     Train net output #0: loss = 4.65832 (* 1 = 4.65832 loss)
I0407 22:32:16.808485   359 sgd_solver.cpp:105] Iteration 852, lr = 0.00999759
I0407 22:32:21.736987   359 solver.cpp:218] Iteration 864 (2.43482 iter/s, 4.92849s/12 iters), loss = 4.8104
I0407 22:32:21.737030   359 solver.cpp:237]     Train net output #0: loss = 4.8104 (* 1 = 4.8104 loss)
I0407 22:32:21.737040   359 sgd_solver.cpp:105] Iteration 864, lr = 0.00999753
I0407 22:32:26.718418   359 solver.cpp:218] Iteration 876 (2.40898 iter/s, 4.98136s/12 iters), loss = 4.80148
I0407 22:32:26.718461   359 solver.cpp:237]     Train net output #0: loss = 4.80148 (* 1 = 4.80148 loss)
I0407 22:32:26.718469   359 sgd_solver.cpp:105] Iteration 876, lr = 0.00999747
I0407 22:32:31.612859   359 solver.cpp:218] Iteration 888 (2.45179 iter/s, 4.89438s/12 iters), loss = 4.79278
I0407 22:32:31.613009   359 solver.cpp:237]     Train net output #0: loss = 4.79278 (* 1 = 4.79278 loss)
I0407 22:32:31.613018   359 sgd_solver.cpp:105] Iteration 888, lr = 0.00999741
I0407 22:32:36.590762   359 solver.cpp:218] Iteration 900 (2.41074 iter/s, 4.97773s/12 iters), loss = 4.67766
I0407 22:32:36.590807   359 solver.cpp:237]     Train net output #0: loss = 4.67766 (* 1 = 4.67766 loss)
I0407 22:32:36.590816   359 sgd_solver.cpp:105] Iteration 900, lr = 0.00999735
I0407 22:32:40.446219   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:41.531286   359 solver.cpp:218] Iteration 912 (2.42893 iter/s, 4.94045s/12 iters), loss = 4.83716
I0407 22:32:41.531327   359 solver.cpp:237]     Train net output #0: loss = 4.83716 (* 1 = 4.83716 loss)
I0407 22:32:41.531335   359 sgd_solver.cpp:105] Iteration 912, lr = 0.00999729
I0407 22:32:43.534842   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 22:32:48.348703   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 22:32:53.865550   359 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 22:32:53.865566   359 net.cpp:676] Ignoring source layer train-data
I0407 22:32:57.934777   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:58.338527   359 solver.cpp:397]     Test net output #0: accuracy = 0.0526961
I0407 22:32:58.338574   359 solver.cpp:397]     Test net output #1: loss = 4.64697 (* 1 = 4.64697 loss)
I0407 22:33:00.147841   359 solver.cpp:218] Iteration 924 (0.644591 iter/s, 18.6165s/12 iters), loss = 4.59192
I0407 22:33:00.147883   359 solver.cpp:237]     Train net output #0: loss = 4.59192 (* 1 = 4.59192 loss)
I0407 22:33:00.147892   359 sgd_solver.cpp:105] Iteration 924, lr = 0.00999722
I0407 22:33:05.110913   359 solver.cpp:218] Iteration 936 (2.41789 iter/s, 4.963s/12 iters), loss = 4.47134
I0407 22:33:05.111032   359 solver.cpp:237]     Train net output #0: loss = 4.47134 (* 1 = 4.47134 loss)
I0407 22:33:05.111042   359 sgd_solver.cpp:105] Iteration 936, lr = 0.00999716
I0407 22:33:10.071822   359 solver.cpp:218] Iteration 948 (2.41898 iter/s, 4.96077s/12 iters), loss = 4.64827
I0407 22:33:10.071862   359 solver.cpp:237]     Train net output #0: loss = 4.64827 (* 1 = 4.64827 loss)
I0407 22:33:10.071871   359 sgd_solver.cpp:105] Iteration 948, lr = 0.00999709
I0407 22:33:14.996212   359 solver.cpp:218] Iteration 960 (2.43688 iter/s, 4.92432s/12 iters), loss = 4.65115
I0407 22:33:14.996250   359 solver.cpp:237]     Train net output #0: loss = 4.65115 (* 1 = 4.65115 loss)
I0407 22:33:14.996259   359 sgd_solver.cpp:105] Iteration 960, lr = 0.00999702
I0407 22:33:19.943707   359 solver.cpp:218] Iteration 972 (2.4255 iter/s, 4.94743s/12 iters), loss = 4.75913
I0407 22:33:19.943754   359 solver.cpp:237]     Train net output #0: loss = 4.75913 (* 1 = 4.75913 loss)
I0407 22:33:19.943763   359 sgd_solver.cpp:105] Iteration 972, lr = 0.00999695
I0407 22:33:24.741403   359 solver.cpp:218] Iteration 984 (2.50124 iter/s, 4.79763s/12 iters), loss = 4.52512
I0407 22:33:24.741446   359 solver.cpp:237]     Train net output #0: loss = 4.52512 (* 1 = 4.52512 loss)
I0407 22:33:24.741453   359 sgd_solver.cpp:105] Iteration 984, lr = 0.00999688
I0407 22:33:29.687659   359 solver.cpp:218] Iteration 996 (2.42611 iter/s, 4.94619s/12 iters), loss = 4.73446
I0407 22:33:29.687703   359 solver.cpp:237]     Train net output #0: loss = 4.73446 (* 1 = 4.73446 loss)
I0407 22:33:29.687711   359 sgd_solver.cpp:105] Iteration 996, lr = 0.0099968
I0407 22:33:34.659121   359 solver.cpp:218] Iteration 1008 (2.41381 iter/s, 4.97139s/12 iters), loss = 4.23608
I0407 22:33:34.659162   359 solver.cpp:237]     Train net output #0: loss = 4.23608 (* 1 = 4.23608 loss)
I0407 22:33:34.659169   359 sgd_solver.cpp:105] Iteration 1008, lr = 0.00999672
I0407 22:33:35.640033   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:39.085248   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 22:33:41.908701   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 22:33:44.267123   359 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 22:33:44.267141   359 net.cpp:676] Ignoring source layer train-data
I0407 22:33:48.382591   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:48.822115   359 solver.cpp:397]     Test net output #0: accuracy = 0.0612745
I0407 22:33:48.822146   359 solver.cpp:397]     Test net output #1: loss = 4.49162 (* 1 = 4.49162 loss)
I0407 22:33:48.918799   359 solver.cpp:218] Iteration 1020 (0.841539 iter/s, 14.2596s/12 iters), loss = 4.52069
I0407 22:33:48.918857   359 solver.cpp:237]     Train net output #0: loss = 4.52069 (* 1 = 4.52069 loss)
I0407 22:33:48.918869   359 sgd_solver.cpp:105] Iteration 1020, lr = 0.00999665
I0407 22:33:53.033442   359 solver.cpp:218] Iteration 1032 (2.91647 iter/s, 4.11456s/12 iters), loss = 4.36611
I0407 22:33:53.033484   359 solver.cpp:237]     Train net output #0: loss = 4.36611 (* 1 = 4.36611 loss)
I0407 22:33:53.033493   359 sgd_solver.cpp:105] Iteration 1032, lr = 0.00999657
I0407 22:33:57.986956   359 solver.cpp:218] Iteration 1044 (2.42255 iter/s, 4.95345s/12 iters), loss = 4.5116
I0407 22:33:57.986992   359 solver.cpp:237]     Train net output #0: loss = 4.5116 (* 1 = 4.5116 loss)
I0407 22:33:57.987000   359 sgd_solver.cpp:105] Iteration 1044, lr = 0.00999648
I0407 22:34:02.841928   359 solver.cpp:218] Iteration 1056 (2.47172 iter/s, 4.85491s/12 iters), loss = 4.39033
I0407 22:34:02.841967   359 solver.cpp:237]     Train net output #0: loss = 4.39033 (* 1 = 4.39033 loss)
I0407 22:34:02.841975   359 sgd_solver.cpp:105] Iteration 1056, lr = 0.0099964
I0407 22:34:07.756639   359 solver.cpp:218] Iteration 1068 (2.44168 iter/s, 4.91465s/12 iters), loss = 4.31935
I0407 22:34:07.756770   359 solver.cpp:237]     Train net output #0: loss = 4.31935 (* 1 = 4.31935 loss)
I0407 22:34:07.756779   359 sgd_solver.cpp:105] Iteration 1068, lr = 0.00999632
I0407 22:34:12.726732   359 solver.cpp:218] Iteration 1080 (2.41452 iter/s, 4.96994s/12 iters), loss = 4.47299
I0407 22:34:12.726776   359 solver.cpp:237]     Train net output #0: loss = 4.47299 (* 1 = 4.47299 loss)
I0407 22:34:12.726785   359 sgd_solver.cpp:105] Iteration 1080, lr = 0.00999623
I0407 22:34:17.582882   359 solver.cpp:218] Iteration 1092 (2.47113 iter/s, 4.85609s/12 iters), loss = 4.21391
I0407 22:34:17.582921   359 solver.cpp:237]     Train net output #0: loss = 4.21391 (* 1 = 4.21391 loss)
I0407 22:34:17.582929   359 sgd_solver.cpp:105] Iteration 1092, lr = 0.00999614
I0407 22:34:22.533805   359 solver.cpp:218] Iteration 1104 (2.42382 iter/s, 4.95086s/12 iters), loss = 4.69628
I0407 22:34:22.533843   359 solver.cpp:237]     Train net output #0: loss = 4.69628 (* 1 = 4.69628 loss)
I0407 22:34:22.533850   359 sgd_solver.cpp:105] Iteration 1104, lr = 0.00999605
I0407 22:34:25.619745   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:27.427727   359 solver.cpp:218] Iteration 1116 (2.45205 iter/s, 4.89386s/12 iters), loss = 4.34636
I0407 22:34:27.427764   359 solver.cpp:237]     Train net output #0: loss = 4.34636 (* 1 = 4.34636 loss)
I0407 22:34:27.427772   359 sgd_solver.cpp:105] Iteration 1116, lr = 0.00999595
I0407 22:34:29.422978   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 22:34:32.512549   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 22:34:36.219897   359 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 22:34:36.219918   359 net.cpp:676] Ignoring source layer train-data
I0407 22:34:40.466763   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:40.992725   359 solver.cpp:397]     Test net output #0: accuracy = 0.0778186
I0407 22:34:40.992771   359 solver.cpp:397]     Test net output #1: loss = 4.36458 (* 1 = 4.36458 loss)
I0407 22:34:42.793181   359 solver.cpp:218] Iteration 1128 (0.780977 iter/s, 15.3654s/12 iters), loss = 4.1891
I0407 22:34:42.793221   359 solver.cpp:237]     Train net output #0: loss = 4.1891 (* 1 = 4.1891 loss)
I0407 22:34:42.793229   359 sgd_solver.cpp:105] Iteration 1128, lr = 0.00999586
I0407 22:34:47.728471   359 solver.cpp:218] Iteration 1140 (2.4315 iter/s, 4.93522s/12 iters), loss = 4.35371
I0407 22:34:47.728518   359 solver.cpp:237]     Train net output #0: loss = 4.35371 (* 1 = 4.35371 loss)
I0407 22:34:47.728528   359 sgd_solver.cpp:105] Iteration 1140, lr = 0.00999576
I0407 22:34:52.632295   359 solver.cpp:218] Iteration 1152 (2.4471 iter/s, 4.90376s/12 iters), loss = 3.96796
I0407 22:34:52.632331   359 solver.cpp:237]     Train net output #0: loss = 3.96796 (* 1 = 3.96796 loss)
I0407 22:34:52.632339   359 sgd_solver.cpp:105] Iteration 1152, lr = 0.00999566
I0407 22:34:57.603988   359 solver.cpp:218] Iteration 1164 (2.41369 iter/s, 4.97163s/12 iters), loss = 4.15408
I0407 22:34:57.604030   359 solver.cpp:237]     Train net output #0: loss = 4.15408 (* 1 = 4.15408 loss)
I0407 22:34:57.604039   359 sgd_solver.cpp:105] Iteration 1164, lr = 0.00999555
I0407 22:35:02.549484   359 solver.cpp:218] Iteration 1176 (2.42648 iter/s, 4.94543s/12 iters), loss = 4.15201
I0407 22:35:02.549527   359 solver.cpp:237]     Train net output #0: loss = 4.15201 (* 1 = 4.15201 loss)
I0407 22:35:02.549535   359 sgd_solver.cpp:105] Iteration 1176, lr = 0.00999545
I0407 22:35:07.461488   359 solver.cpp:218] Iteration 1188 (2.44303 iter/s, 4.91194s/12 iters), loss = 3.98278
I0407 22:35:07.461529   359 solver.cpp:237]     Train net output #0: loss = 3.98278 (* 1 = 3.98278 loss)
I0407 22:35:07.461537   359 sgd_solver.cpp:105] Iteration 1188, lr = 0.00999534
I0407 22:35:12.443728   359 solver.cpp:218] Iteration 1200 (2.40859 iter/s, 4.98218s/12 iters), loss = 4.23676
I0407 22:35:12.443876   359 solver.cpp:237]     Train net output #0: loss = 4.23676 (* 1 = 4.23676 loss)
I0407 22:35:12.443886   359 sgd_solver.cpp:105] Iteration 1200, lr = 0.00999523
I0407 22:35:17.360339   359 solver.cpp:218] Iteration 1212 (2.44079 iter/s, 4.91644s/12 iters), loss = 4.04756
I0407 22:35:17.360383   359 solver.cpp:237]     Train net output #0: loss = 4.04756 (* 1 = 4.04756 loss)
I0407 22:35:17.360391   359 sgd_solver.cpp:105] Iteration 1212, lr = 0.00999511
I0407 22:35:17.625289   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:21.844188   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 22:35:24.919265   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 22:35:27.275514   359 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 22:35:27.275533   359 net.cpp:676] Ignoring source layer train-data
I0407 22:35:31.362200   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:31.937912   359 solver.cpp:397]     Test net output #0: accuracy = 0.0808824
I0407 22:35:31.937959   359 solver.cpp:397]     Test net output #1: loss = 4.26455 (* 1 = 4.26455 loss)
I0407 22:35:32.033993   359 solver.cpp:218] Iteration 1224 (0.817797 iter/s, 14.6736s/12 iters), loss = 4.16022
I0407 22:35:32.034037   359 solver.cpp:237]     Train net output #0: loss = 4.16022 (* 1 = 4.16022 loss)
I0407 22:35:32.034045   359 sgd_solver.cpp:105] Iteration 1224, lr = 0.009995
I0407 22:35:36.215714   359 solver.cpp:218] Iteration 1236 (2.86968 iter/s, 4.18166s/12 iters), loss = 4.14807
I0407 22:35:36.215751   359 solver.cpp:237]     Train net output #0: loss = 4.14807 (* 1 = 4.14807 loss)
I0407 22:35:36.215762   359 sgd_solver.cpp:105] Iteration 1236, lr = 0.00999488
I0407 22:35:41.153381   359 solver.cpp:218] Iteration 1248 (2.43033 iter/s, 4.93761s/12 iters), loss = 4.01501
I0407 22:35:41.153419   359 solver.cpp:237]     Train net output #0: loss = 4.01501 (* 1 = 4.01501 loss)
I0407 22:35:41.153427   359 sgd_solver.cpp:105] Iteration 1248, lr = 0.00999476
I0407 22:35:46.095556   359 solver.cpp:218] Iteration 1260 (2.42811 iter/s, 4.94211s/12 iters), loss = 3.90919
I0407 22:35:46.095719   359 solver.cpp:237]     Train net output #0: loss = 3.90919 (* 1 = 3.90919 loss)
I0407 22:35:46.095729   359 sgd_solver.cpp:105] Iteration 1260, lr = 0.00999463
I0407 22:35:50.956984   359 solver.cpp:218] Iteration 1272 (2.4685 iter/s, 4.86125s/12 iters), loss = 3.89158
I0407 22:35:50.957023   359 solver.cpp:237]     Train net output #0: loss = 3.89158 (* 1 = 3.89158 loss)
I0407 22:35:50.957031   359 sgd_solver.cpp:105] Iteration 1272, lr = 0.0099945
I0407 22:35:55.918812   359 solver.cpp:218] Iteration 1284 (2.41849 iter/s, 4.96177s/12 iters), loss = 3.97335
I0407 22:35:55.918848   359 solver.cpp:237]     Train net output #0: loss = 3.97335 (* 1 = 3.97335 loss)
I0407 22:35:55.918855   359 sgd_solver.cpp:105] Iteration 1284, lr = 0.00999437
I0407 22:36:00.840694   359 solver.cpp:218] Iteration 1296 (2.43812 iter/s, 4.92182s/12 iters), loss = 4.00292
I0407 22:36:00.840731   359 solver.cpp:237]     Train net output #0: loss = 4.00292 (* 1 = 4.00292 loss)
I0407 22:36:00.840739   359 sgd_solver.cpp:105] Iteration 1296, lr = 0.00999424
I0407 22:36:05.786229   359 solver.cpp:218] Iteration 1308 (2.42646 iter/s, 4.94548s/12 iters), loss = 4.08033
I0407 22:36:05.786267   359 solver.cpp:237]     Train net output #0: loss = 4.08033 (* 1 = 4.08033 loss)
I0407 22:36:05.786275   359 sgd_solver.cpp:105] Iteration 1308, lr = 0.0099941
I0407 22:36:08.250079   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:10.687924   359 solver.cpp:218] Iteration 1320 (2.44816 iter/s, 4.90163s/12 iters), loss = 4.1993
I0407 22:36:10.687971   359 solver.cpp:237]     Train net output #0: loss = 4.1993 (* 1 = 4.1993 loss)
I0407 22:36:10.687980   359 sgd_solver.cpp:105] Iteration 1320, lr = 0.00999396
I0407 22:36:12.724865   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 22:36:16.704424   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 22:36:19.068617   359 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 22:36:19.068635   359 net.cpp:676] Ignoring source layer train-data
I0407 22:36:23.350137   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:24.033901   359 solver.cpp:397]     Test net output #0: accuracy = 0.115196
I0407 22:36:24.033941   359 solver.cpp:397]     Test net output #1: loss = 4.0472 (* 1 = 4.0472 loss)
I0407 22:36:25.826490   359 solver.cpp:218] Iteration 1332 (0.792682 iter/s, 15.1385s/12 iters), loss = 3.8674
I0407 22:36:25.826531   359 solver.cpp:237]     Train net output #0: loss = 3.8674 (* 1 = 3.8674 loss)
I0407 22:36:25.826539   359 sgd_solver.cpp:105] Iteration 1332, lr = 0.00999382
I0407 22:36:30.738754   359 solver.cpp:218] Iteration 1344 (2.4429 iter/s, 4.9122s/12 iters), loss = 3.83042
I0407 22:36:30.738801   359 solver.cpp:237]     Train net output #0: loss = 3.83042 (* 1 = 3.83042 loss)
I0407 22:36:30.738811   359 sgd_solver.cpp:105] Iteration 1344, lr = 0.00999367
I0407 22:36:35.691824   359 solver.cpp:218] Iteration 1356 (2.42277 iter/s, 4.953s/12 iters), loss = 4.03057
I0407 22:36:35.691864   359 solver.cpp:237]     Train net output #0: loss = 4.03057 (* 1 = 4.03057 loss)
I0407 22:36:35.691872   359 sgd_solver.cpp:105] Iteration 1356, lr = 0.00999352
I0407 22:36:40.628046   359 solver.cpp:218] Iteration 1368 (2.43104 iter/s, 4.93616s/12 iters), loss = 3.99038
I0407 22:36:40.628091   359 solver.cpp:237]     Train net output #0: loss = 3.99038 (* 1 = 3.99038 loss)
I0407 22:36:40.628099   359 sgd_solver.cpp:105] Iteration 1368, lr = 0.00999337
I0407 22:36:41.805462   359 blocking_queue.cpp:49] Waiting for data
I0407 22:36:45.591382   359 solver.cpp:218] Iteration 1380 (2.41776 iter/s, 4.96326s/12 iters), loss = 3.90185
I0407 22:36:45.591426   359 solver.cpp:237]     Train net output #0: loss = 3.90185 (* 1 = 3.90185 loss)
I0407 22:36:45.591434   359 sgd_solver.cpp:105] Iteration 1380, lr = 0.00999321
I0407 22:36:50.489408   359 solver.cpp:218] Iteration 1392 (2.45 iter/s, 4.89796s/12 iters), loss = 4.01423
I0407 22:36:50.489535   359 solver.cpp:237]     Train net output #0: loss = 4.01423 (* 1 = 4.01423 loss)
I0407 22:36:50.489545   359 sgd_solver.cpp:105] Iteration 1392, lr = 0.00999305
I0407 22:36:55.458361   359 solver.cpp:218] Iteration 1404 (2.41507 iter/s, 4.9688s/12 iters), loss = 3.78515
I0407 22:36:55.458403   359 solver.cpp:237]     Train net output #0: loss = 3.78515 (* 1 = 3.78515 loss)
I0407 22:36:55.458412   359 sgd_solver.cpp:105] Iteration 1404, lr = 0.00999288
I0407 22:37:00.012928   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:00.354723   359 solver.cpp:218] Iteration 1416 (2.45083 iter/s, 4.8963s/12 iters), loss = 3.86286
I0407 22:37:00.354764   359 solver.cpp:237]     Train net output #0: loss = 3.86286 (* 1 = 3.86286 loss)
I0407 22:37:00.354773   359 sgd_solver.cpp:105] Iteration 1416, lr = 0.00999271
I0407 22:37:04.854851   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 22:37:08.827879   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 22:37:11.759481   359 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 22:37:11.759498   359 net.cpp:676] Ignoring source layer train-data
I0407 22:37:15.826653   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:16.482923   359 solver.cpp:397]     Test net output #0: accuracy = 0.110294
I0407 22:37:16.482971   359 solver.cpp:397]     Test net output #1: loss = 4.01474 (* 1 = 4.01474 loss)
I0407 22:37:16.579562   359 solver.cpp:218] Iteration 1428 (0.739611 iter/s, 16.2248s/12 iters), loss = 3.76934
I0407 22:37:16.579612   359 solver.cpp:237]     Train net output #0: loss = 3.76934 (* 1 = 3.76934 loss)
I0407 22:37:16.579619   359 sgd_solver.cpp:105] Iteration 1428, lr = 0.00999254
I0407 22:37:20.879366   359 solver.cpp:218] Iteration 1440 (2.79087 iter/s, 4.29974s/12 iters), loss = 3.79479
I0407 22:37:20.879532   359 solver.cpp:237]     Train net output #0: loss = 3.79479 (* 1 = 3.79479 loss)
I0407 22:37:20.879541   359 sgd_solver.cpp:105] Iteration 1440, lr = 0.00999236
I0407 22:37:25.823222   359 solver.cpp:218] Iteration 1452 (2.42735 iter/s, 4.94367s/12 iters), loss = 3.83387
I0407 22:37:25.823263   359 solver.cpp:237]     Train net output #0: loss = 3.83387 (* 1 = 3.83387 loss)
I0407 22:37:25.823272   359 sgd_solver.cpp:105] Iteration 1452, lr = 0.00999218
I0407 22:37:30.762645   359 solver.cpp:218] Iteration 1464 (2.42946 iter/s, 4.93936s/12 iters), loss = 3.49704
I0407 22:37:30.762682   359 solver.cpp:237]     Train net output #0: loss = 3.49704 (* 1 = 3.49704 loss)
I0407 22:37:30.762691   359 sgd_solver.cpp:105] Iteration 1464, lr = 0.00999199
I0407 22:37:35.785454   359 solver.cpp:218] Iteration 1476 (2.38913 iter/s, 5.02275s/12 iters), loss = 3.77816
I0407 22:37:35.785496   359 solver.cpp:237]     Train net output #0: loss = 3.77816 (* 1 = 3.77816 loss)
I0407 22:37:35.785504   359 sgd_solver.cpp:105] Iteration 1476, lr = 0.0099918
I0407 22:37:40.759732   359 solver.cpp:218] Iteration 1488 (2.41244 iter/s, 4.97421s/12 iters), loss = 3.68157
I0407 22:37:40.759775   359 solver.cpp:237]     Train net output #0: loss = 3.68157 (* 1 = 3.68157 loss)
I0407 22:37:40.759783   359 sgd_solver.cpp:105] Iteration 1488, lr = 0.00999161
I0407 22:37:45.792701   359 solver.cpp:218] Iteration 1500 (2.38431 iter/s, 5.0329s/12 iters), loss = 3.85981
I0407 22:37:45.792744   359 solver.cpp:237]     Train net output #0: loss = 3.85981 (* 1 = 3.85981 loss)
I0407 22:37:45.792752   359 sgd_solver.cpp:105] Iteration 1500, lr = 0.00999141
I0407 22:37:50.967263   359 solver.cpp:218] Iteration 1512 (2.31907 iter/s, 5.1745s/12 iters), loss = 3.58055
I0407 22:37:50.967398   359 solver.cpp:237]     Train net output #0: loss = 3.58055 (* 1 = 3.58055 loss)
I0407 22:37:50.967407   359 sgd_solver.cpp:105] Iteration 1512, lr = 0.00999121
I0407 22:37:52.703716   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:55.821451   359 solver.cpp:218] Iteration 1524 (2.47217 iter/s, 4.85404s/12 iters), loss = 3.73534
I0407 22:37:55.821487   359 solver.cpp:237]     Train net output #0: loss = 3.73534 (* 1 = 3.73534 loss)
I0407 22:37:55.821494   359 sgd_solver.cpp:105] Iteration 1524, lr = 0.009991
I0407 22:37:57.910384   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 22:38:01.042002   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 22:38:03.403066   359 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 22:38:03.403086   359 net.cpp:676] Ignoring source layer train-data
I0407 22:38:07.249096   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:07.957537   359 solver.cpp:397]     Test net output #0: accuracy = 0.131127
I0407 22:38:07.957581   359 solver.cpp:397]     Test net output #1: loss = 3.87971 (* 1 = 3.87971 loss)
I0407 22:38:09.773028   359 solver.cpp:218] Iteration 1536 (0.860122 iter/s, 13.9515s/12 iters), loss = 3.37834
I0407 22:38:09.773062   359 solver.cpp:237]     Train net output #0: loss = 3.37834 (* 1 = 3.37834 loss)
I0407 22:38:09.773070   359 sgd_solver.cpp:105] Iteration 1536, lr = 0.00999078
I0407 22:38:14.798009   359 solver.cpp:218] Iteration 1548 (2.3881 iter/s, 5.02493s/12 iters), loss = 3.56426
I0407 22:38:14.798050   359 solver.cpp:237]     Train net output #0: loss = 3.56426 (* 1 = 3.56426 loss)
I0407 22:38:14.798058   359 sgd_solver.cpp:105] Iteration 1548, lr = 0.00999056
I0407 22:38:19.762490   359 solver.cpp:218] Iteration 1560 (2.4172 iter/s, 4.96441s/12 iters), loss = 3.63487
I0407 22:38:19.762537   359 solver.cpp:237]     Train net output #0: loss = 3.63487 (* 1 = 3.63487 loss)
I0407 22:38:19.762545   359 sgd_solver.cpp:105] Iteration 1560, lr = 0.00999034
I0407 22:38:24.742230   359 solver.cpp:218] Iteration 1572 (2.4098 iter/s, 4.97967s/12 iters), loss = 3.66285
I0407 22:38:24.742388   359 solver.cpp:237]     Train net output #0: loss = 3.66285 (* 1 = 3.66285 loss)
I0407 22:38:24.742404   359 sgd_solver.cpp:105] Iteration 1572, lr = 0.00999011
I0407 22:38:29.863627   359 solver.cpp:218] Iteration 1584 (2.34319 iter/s, 5.12122s/12 iters), loss = 3.51581
I0407 22:38:29.863664   359 solver.cpp:237]     Train net output #0: loss = 3.51581 (* 1 = 3.51581 loss)
I0407 22:38:29.863672   359 sgd_solver.cpp:105] Iteration 1584, lr = 0.00998987
I0407 22:38:34.847849   359 solver.cpp:218] Iteration 1596 (2.40763 iter/s, 4.98416s/12 iters), loss = 3.26739
I0407 22:38:34.847888   359 solver.cpp:237]     Train net output #0: loss = 3.26739 (* 1 = 3.26739 loss)
I0407 22:38:34.847895   359 sgd_solver.cpp:105] Iteration 1596, lr = 0.00998963
I0407 22:38:40.061777   359 solver.cpp:218] Iteration 1608 (2.30155 iter/s, 5.21387s/12 iters), loss = 3.56462
I0407 22:38:40.061815   359 solver.cpp:237]     Train net output #0: loss = 3.56462 (* 1 = 3.56462 loss)
I0407 22:38:40.061822   359 sgd_solver.cpp:105] Iteration 1608, lr = 0.00998939
I0407 22:38:43.895316   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:44.947158   359 solver.cpp:218] Iteration 1620 (2.45634 iter/s, 4.88532s/12 iters), loss = 3.76939
I0407 22:38:44.947208   359 solver.cpp:237]     Train net output #0: loss = 3.76939 (* 1 = 3.76939 loss)
I0407 22:38:44.947218   359 sgd_solver.cpp:105] Iteration 1620, lr = 0.00998913
I0407 22:38:49.420012   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 22:38:52.522264   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 22:38:54.877012   359 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 22:38:54.877141   359 net.cpp:676] Ignoring source layer train-data
I0407 22:38:58.904289   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:59.651060   359 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0407 22:38:59.651093   359 solver.cpp:397]     Test net output #1: loss = 3.72563 (* 1 = 3.72563 loss)
I0407 22:38:59.747587   359 solver.cpp:218] Iteration 1632 (0.810792 iter/s, 14.8003s/12 iters), loss = 3.5402
I0407 22:38:59.747635   359 solver.cpp:237]     Train net output #0: loss = 3.5402 (* 1 = 3.5402 loss)
I0407 22:38:59.747643   359 sgd_solver.cpp:105] Iteration 1632, lr = 0.00998887
I0407 22:39:03.867409   359 solver.cpp:218] Iteration 1644 (2.91279 iter/s, 4.11976s/12 iters), loss = 3.39617
I0407 22:39:03.867441   359 solver.cpp:237]     Train net output #0: loss = 3.39617 (* 1 = 3.39617 loss)
I0407 22:39:03.867449   359 sgd_solver.cpp:105] Iteration 1644, lr = 0.00998861
I0407 22:39:08.843333   359 solver.cpp:218] Iteration 1656 (2.41164 iter/s, 4.97587s/12 iters), loss = 3.47303
I0407 22:39:08.843375   359 solver.cpp:237]     Train net output #0: loss = 3.47303 (* 1 = 3.47303 loss)
I0407 22:39:08.843384   359 sgd_solver.cpp:105] Iteration 1656, lr = 0.00998834
I0407 22:39:13.711283   359 solver.cpp:218] Iteration 1668 (2.46514 iter/s, 4.86789s/12 iters), loss = 3.42902
I0407 22:39:13.711319   359 solver.cpp:237]     Train net output #0: loss = 3.42902 (* 1 = 3.42902 loss)
I0407 22:39:13.711328   359 sgd_solver.cpp:105] Iteration 1668, lr = 0.00998806
I0407 22:39:18.650694   359 solver.cpp:218] Iteration 1680 (2.42947 iter/s, 4.93935s/12 iters), loss = 3.12802
I0407 22:39:18.650739   359 solver.cpp:237]     Train net output #0: loss = 3.12802 (* 1 = 3.12802 loss)
I0407 22:39:18.650748   359 sgd_solver.cpp:105] Iteration 1680, lr = 0.00998778
I0407 22:39:23.563797   359 solver.cpp:218] Iteration 1692 (2.44248 iter/s, 4.91303s/12 iters), loss = 3.12681
I0407 22:39:23.563850   359 solver.cpp:237]     Train net output #0: loss = 3.12681 (* 1 = 3.12681 loss)
I0407 22:39:23.563860   359 sgd_solver.cpp:105] Iteration 1692, lr = 0.00998749
I0407 22:39:28.526150   359 solver.cpp:218] Iteration 1704 (2.41824 iter/s, 4.96228s/12 iters), loss = 3.151
I0407 22:39:28.526324   359 solver.cpp:237]     Train net output #0: loss = 3.151 (* 1 = 3.151 loss)
I0407 22:39:28.526332   359 sgd_solver.cpp:105] Iteration 1704, lr = 0.00998719
I0407 22:39:33.444808   359 solver.cpp:218] Iteration 1716 (2.43978 iter/s, 4.91847s/12 iters), loss = 3.32572
I0407 22:39:33.444845   359 solver.cpp:237]     Train net output #0: loss = 3.32572 (* 1 = 3.32572 loss)
I0407 22:39:33.444852   359 sgd_solver.cpp:105] Iteration 1716, lr = 0.00998688
I0407 22:39:34.456984   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:38.346740   359 solver.cpp:218] Iteration 1728 (2.44804 iter/s, 4.90187s/12 iters), loss = 3.33658
I0407 22:39:38.346781   359 solver.cpp:237]     Train net output #0: loss = 3.33658 (* 1 = 3.33658 loss)
I0407 22:39:38.346788   359 sgd_solver.cpp:105] Iteration 1728, lr = 0.00998657
I0407 22:39:40.381992   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 22:39:43.566239   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 22:39:45.939190   359 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 22:39:45.939219   359 net.cpp:676] Ignoring source layer train-data
I0407 22:39:49.854434   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:50.574000   359 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0407 22:39:50.574049   359 solver.cpp:397]     Test net output #1: loss = 3.626 (* 1 = 3.626 loss)
I0407 22:39:52.444792   359 solver.cpp:218] Iteration 1740 (0.851186 iter/s, 14.098s/12 iters), loss = 3.19325
I0407 22:39:52.444828   359 solver.cpp:237]     Train net output #0: loss = 3.19325 (* 1 = 3.19325 loss)
I0407 22:39:52.444835   359 sgd_solver.cpp:105] Iteration 1740, lr = 0.00998625
I0407 22:39:57.374161   359 solver.cpp:218] Iteration 1752 (2.43441 iter/s, 4.92932s/12 iters), loss = 3.13466
I0407 22:39:57.374197   359 solver.cpp:237]     Train net output #0: loss = 3.13466 (* 1 = 3.13466 loss)
I0407 22:39:57.374204   359 sgd_solver.cpp:105] Iteration 1752, lr = 0.00998593
I0407 22:40:02.352735   359 solver.cpp:218] Iteration 1764 (2.41036 iter/s, 4.97852s/12 iters), loss = 3.22794
I0407 22:40:02.352860   359 solver.cpp:237]     Train net output #0: loss = 3.22794 (* 1 = 3.22794 loss)
I0407 22:40:02.352869   359 sgd_solver.cpp:105] Iteration 1764, lr = 0.00998559
I0407 22:40:07.304322   359 solver.cpp:218] Iteration 1776 (2.42354 iter/s, 4.95144s/12 iters), loss = 3.18758
I0407 22:40:07.304366   359 solver.cpp:237]     Train net output #0: loss = 3.18758 (* 1 = 3.18758 loss)
I0407 22:40:07.304374   359 sgd_solver.cpp:105] Iteration 1776, lr = 0.00998525
I0407 22:40:12.302299   359 solver.cpp:218] Iteration 1788 (2.40101 iter/s, 4.9979s/12 iters), loss = 3.0682
I0407 22:40:12.302345   359 solver.cpp:237]     Train net output #0: loss = 3.0682 (* 1 = 3.0682 loss)
I0407 22:40:12.302353   359 sgd_solver.cpp:105] Iteration 1788, lr = 0.0099849
I0407 22:40:17.184746   359 solver.cpp:218] Iteration 1800 (2.45782 iter/s, 4.88238s/12 iters), loss = 2.91501
I0407 22:40:17.184787   359 solver.cpp:237]     Train net output #0: loss = 2.91501 (* 1 = 2.91501 loss)
I0407 22:40:17.184796   359 sgd_solver.cpp:105] Iteration 1800, lr = 0.00998454
I0407 22:40:22.162169   359 solver.cpp:218] Iteration 1812 (2.41092 iter/s, 4.97735s/12 iters), loss = 3.29513
I0407 22:40:22.162211   359 solver.cpp:237]     Train net output #0: loss = 3.29513 (* 1 = 3.29513 loss)
I0407 22:40:22.162220   359 sgd_solver.cpp:105] Iteration 1812, lr = 0.00998417
I0407 22:40:25.314308   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:27.094609   359 solver.cpp:218] Iteration 1824 (2.43291 iter/s, 4.93237s/12 iters), loss = 3.4254
I0407 22:40:27.094653   359 solver.cpp:237]     Train net output #0: loss = 3.4254 (* 1 = 3.4254 loss)
I0407 22:40:27.094661   359 sgd_solver.cpp:105] Iteration 1824, lr = 0.0099838
I0407 22:40:31.569416   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 22:40:34.641258   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 22:40:37.078544   359 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 22:40:37.078564   359 net.cpp:676] Ignoring source layer train-data
I0407 22:40:41.028375   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:41.862079   359 solver.cpp:397]     Test net output #0: accuracy = 0.181373
I0407 22:40:41.862124   359 solver.cpp:397]     Test net output #1: loss = 3.53646 (* 1 = 3.53646 loss)
I0407 22:40:41.959156   359 solver.cpp:218] Iteration 1836 (0.807295 iter/s, 14.8645s/12 iters), loss = 3.25231
I0407 22:40:41.959223   359 solver.cpp:237]     Train net output #0: loss = 3.25231 (* 1 = 3.25231 loss)
I0407 22:40:41.959233   359 sgd_solver.cpp:105] Iteration 1836, lr = 0.00998341
I0407 22:40:46.102376   359 solver.cpp:218] Iteration 1848 (2.89636 iter/s, 4.14314s/12 iters), loss = 3.28534
I0407 22:40:46.102421   359 solver.cpp:237]     Train net output #0: loss = 3.28534 (* 1 = 3.28534 loss)
I0407 22:40:46.102429   359 sgd_solver.cpp:105] Iteration 1848, lr = 0.00998302
I0407 22:40:51.040313   359 solver.cpp:218] Iteration 1860 (2.4302 iter/s, 4.93787s/12 iters), loss = 2.97721
I0407 22:40:51.040352   359 solver.cpp:237]     Train net output #0: loss = 2.97721 (* 1 = 2.97721 loss)
I0407 22:40:51.040360   359 sgd_solver.cpp:105] Iteration 1860, lr = 0.00998261
I0407 22:40:55.966456   359 solver.cpp:218] Iteration 1872 (2.43601 iter/s, 4.92609s/12 iters), loss = 2.8339
I0407 22:40:55.966493   359 solver.cpp:237]     Train net output #0: loss = 2.8339 (* 1 = 2.8339 loss)
I0407 22:40:55.966501   359 sgd_solver.cpp:105] Iteration 1872, lr = 0.0099822
I0407 22:41:00.919643   359 solver.cpp:218] Iteration 1884 (2.42271 iter/s, 4.95313s/12 iters), loss = 3.01308
I0407 22:41:00.919684   359 solver.cpp:237]     Train net output #0: loss = 3.01308 (* 1 = 3.01308 loss)
I0407 22:41:00.919692   359 sgd_solver.cpp:105] Iteration 1884, lr = 0.00998178
I0407 22:41:05.817286   359 solver.cpp:218] Iteration 1896 (2.45019 iter/s, 4.89758s/12 iters), loss = 3.05372
I0407 22:41:05.817401   359 solver.cpp:237]     Train net output #0: loss = 3.05372 (* 1 = 3.05372 loss)
I0407 22:41:05.817411   359 sgd_solver.cpp:105] Iteration 1896, lr = 0.00998134
I0407 22:41:10.788357   359 solver.cpp:218] Iteration 1908 (2.41403 iter/s, 4.97094s/12 iters), loss = 2.96224
I0407 22:41:10.788396   359 solver.cpp:237]     Train net output #0: loss = 2.96224 (* 1 = 2.96224 loss)
I0407 22:41:10.788403   359 sgd_solver.cpp:105] Iteration 1908, lr = 0.0099809
I0407 22:41:15.696400   359 solver.cpp:218] Iteration 1920 (2.445 iter/s, 4.90798s/12 iters), loss = 2.92034
I0407 22:41:15.696446   359 solver.cpp:237]     Train net output #0: loss = 2.92034 (* 1 = 2.92034 loss)
I0407 22:41:15.696455   359 sgd_solver.cpp:105] Iteration 1920, lr = 0.00998045
I0407 22:41:15.991915   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:20.653021   359 solver.cpp:218] Iteration 1932 (2.42104 iter/s, 4.95655s/12 iters), loss = 3.14069
I0407 22:41:20.653064   359 solver.cpp:237]     Train net output #0: loss = 3.14069 (* 1 = 3.14069 loss)
I0407 22:41:20.653072   359 sgd_solver.cpp:105] Iteration 1932, lr = 0.00997998
I0407 22:41:22.773319   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 22:41:25.864236   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 22:41:28.227759   359 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 22:41:28.227777   359 net.cpp:676] Ignoring source layer train-data
I0407 22:41:32.138288   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:32.936493   359 solver.cpp:397]     Test net output #0: accuracy = 0.193015
I0407 22:41:32.936539   359 solver.cpp:397]     Test net output #1: loss = 3.47011 (* 1 = 3.47011 loss)
I0407 22:41:34.749590   359 solver.cpp:218] Iteration 1944 (0.851276 iter/s, 14.0965s/12 iters), loss = 3.1482
I0407 22:41:34.749629   359 solver.cpp:237]     Train net output #0: loss = 3.1482 (* 1 = 3.1482 loss)
I0407 22:41:34.749636   359 sgd_solver.cpp:105] Iteration 1944, lr = 0.00997951
I0407 22:41:39.694242   359 solver.cpp:218] Iteration 1956 (2.42689 iter/s, 4.9446s/12 iters), loss = 2.96406
I0407 22:41:39.694414   359 solver.cpp:237]     Train net output #0: loss = 2.96406 (* 1 = 2.96406 loss)
I0407 22:41:39.694424   359 sgd_solver.cpp:105] Iteration 1956, lr = 0.00997902
I0407 22:41:44.628644   359 solver.cpp:218] Iteration 1968 (2.432 iter/s, 4.93421s/12 iters), loss = 2.81329
I0407 22:41:44.628688   359 solver.cpp:237]     Train net output #0: loss = 2.81329 (* 1 = 2.81329 loss)
I0407 22:41:44.628696   359 sgd_solver.cpp:105] Iteration 1968, lr = 0.00997852
I0407 22:41:49.602478   359 solver.cpp:218] Iteration 1980 (2.41266 iter/s, 4.97377s/12 iters), loss = 2.97557
I0407 22:41:49.602519   359 solver.cpp:237]     Train net output #0: loss = 2.97557 (* 1 = 2.97557 loss)
I0407 22:41:49.602526   359 sgd_solver.cpp:105] Iteration 1980, lr = 0.00997801
I0407 22:41:54.548319   359 solver.cpp:218] Iteration 1992 (2.42631 iter/s, 4.94578s/12 iters), loss = 2.91323
I0407 22:41:54.548359   359 solver.cpp:237]     Train net output #0: loss = 2.91323 (* 1 = 2.91323 loss)
I0407 22:41:54.548368   359 sgd_solver.cpp:105] Iteration 1992, lr = 0.00997749
I0407 22:41:59.498108   359 solver.cpp:218] Iteration 2004 (2.42438 iter/s, 4.94973s/12 iters), loss = 2.86267
I0407 22:41:59.498152   359 solver.cpp:237]     Train net output #0: loss = 2.86267 (* 1 = 2.86267 loss)
I0407 22:41:59.498162   359 sgd_solver.cpp:105] Iteration 2004, lr = 0.00997696
I0407 22:42:04.465466   359 solver.cpp:218] Iteration 2016 (2.41581 iter/s, 4.96729s/12 iters), loss = 2.82093
I0407 22:42:04.465524   359 solver.cpp:237]     Train net output #0: loss = 2.82093 (* 1 = 2.82093 loss)
I0407 22:42:04.465538   359 sgd_solver.cpp:105] Iteration 2016, lr = 0.00997641
I0407 22:42:06.967725   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:09.377447   359 solver.cpp:218] Iteration 2028 (2.44304 iter/s, 4.91191s/12 iters), loss = 3.11125
I0407 22:42:09.377490   359 solver.cpp:237]     Train net output #0: loss = 3.11125 (* 1 = 3.11125 loss)
I0407 22:42:09.377499   359 sgd_solver.cpp:105] Iteration 2028, lr = 0.00997585
I0407 22:42:13.875124   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 22:42:17.015957   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 22:42:19.374635   359 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 22:42:19.374653   359 net.cpp:676] Ignoring source layer train-data
I0407 22:42:23.261555   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:24.114142   359 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0407 22:42:24.114192   359 solver.cpp:397]     Test net output #1: loss = 3.24905 (* 1 = 3.24905 loss)
I0407 22:42:24.210613   359 solver.cpp:218] Iteration 2040 (0.809002 iter/s, 14.8331s/12 iters), loss = 2.63953
I0407 22:42:24.210654   359 solver.cpp:237]     Train net output #0: loss = 2.63953 (* 1 = 2.63953 loss)
I0407 22:42:24.210661   359 sgd_solver.cpp:105] Iteration 2040, lr = 0.00997527
I0407 22:42:28.373304   359 solver.cpp:218] Iteration 2052 (2.88279 iter/s, 4.16263s/12 iters), loss = 2.96616
I0407 22:42:28.373347   359 solver.cpp:237]     Train net output #0: loss = 2.96616 (* 1 = 2.96616 loss)
I0407 22:42:28.373355   359 sgd_solver.cpp:105] Iteration 2052, lr = 0.00997469
I0407 22:42:29.953039   359 blocking_queue.cpp:49] Waiting for data
I0407 22:42:33.262485   359 solver.cpp:218] Iteration 2064 (2.45443 iter/s, 4.88912s/12 iters), loss = 2.97788
I0407 22:42:33.262521   359 solver.cpp:237]     Train net output #0: loss = 2.97788 (* 1 = 2.97788 loss)
I0407 22:42:33.262529   359 sgd_solver.cpp:105] Iteration 2064, lr = 0.00997408
I0407 22:42:38.240365   359 solver.cpp:218] Iteration 2076 (2.41069 iter/s, 4.97782s/12 iters), loss = 3.04856
I0407 22:42:38.240401   359 solver.cpp:237]     Train net output #0: loss = 3.04856 (* 1 = 3.04856 loss)
I0407 22:42:38.240408   359 sgd_solver.cpp:105] Iteration 2076, lr = 0.00997347
I0407 22:42:43.194154   359 solver.cpp:218] Iteration 2088 (2.42241 iter/s, 4.95374s/12 iters), loss = 2.89056
I0407 22:42:43.194188   359 solver.cpp:237]     Train net output #0: loss = 2.89056 (* 1 = 2.89056 loss)
I0407 22:42:43.194196   359 sgd_solver.cpp:105] Iteration 2088, lr = 0.00997284
I0407 22:42:48.136507   359 solver.cpp:218] Iteration 2100 (2.42802 iter/s, 4.9423s/12 iters), loss = 3.05797
I0407 22:42:48.136633   359 solver.cpp:237]     Train net output #0: loss = 3.05797 (* 1 = 3.05797 loss)
I0407 22:42:48.136642   359 sgd_solver.cpp:105] Iteration 2100, lr = 0.0099722
I0407 22:42:53.081180   359 solver.cpp:218] Iteration 2112 (2.42692 iter/s, 4.94453s/12 iters), loss = 2.60516
I0407 22:42:53.081218   359 solver.cpp:237]     Train net output #0: loss = 2.60516 (* 1 = 2.60516 loss)
I0407 22:42:53.081224   359 sgd_solver.cpp:105] Iteration 2112, lr = 0.00997153
I0407 22:42:57.717440   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:58.030218   359 solver.cpp:218] Iteration 2124 (2.42474 iter/s, 4.94898s/12 iters), loss = 2.90187
I0407 22:42:58.030259   359 solver.cpp:237]     Train net output #0: loss = 2.90187 (* 1 = 2.90187 loss)
I0407 22:42:58.030268   359 sgd_solver.cpp:105] Iteration 2124, lr = 0.00997086
I0407 22:43:02.951489   359 solver.cpp:218] Iteration 2136 (2.43843 iter/s, 4.92121s/12 iters), loss = 3.1684
I0407 22:43:02.951524   359 solver.cpp:237]     Train net output #0: loss = 3.1684 (* 1 = 3.1684 loss)
I0407 22:43:02.951532   359 sgd_solver.cpp:105] Iteration 2136, lr = 0.00997017
I0407 22:43:04.986749   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 22:43:08.056052   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 22:43:10.412533   359 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 22:43:10.412554   359 net.cpp:676] Ignoring source layer train-data
I0407 22:43:14.105819   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:14.981710   359 solver.cpp:397]     Test net output #0: accuracy = 0.262255
I0407 22:43:14.981756   359 solver.cpp:397]     Test net output #1: loss = 3.15991 (* 1 = 3.15991 loss)
I0407 22:43:16.770299   359 solver.cpp:218] Iteration 2148 (0.868386 iter/s, 13.8187s/12 iters), loss = 2.6207
I0407 22:43:16.770344   359 solver.cpp:237]     Train net output #0: loss = 2.6207 (* 1 = 2.6207 loss)
I0407 22:43:16.770351   359 sgd_solver.cpp:105] Iteration 2148, lr = 0.00996946
I0407 22:43:21.637567   359 solver.cpp:218] Iteration 2160 (2.46548 iter/s, 4.86721s/12 iters), loss = 3.12555
I0407 22:43:21.637723   359 solver.cpp:237]     Train net output #0: loss = 3.12555 (* 1 = 3.12555 loss)
I0407 22:43:21.637732   359 sgd_solver.cpp:105] Iteration 2160, lr = 0.00996873
I0407 22:43:26.586370   359 solver.cpp:218] Iteration 2172 (2.42491 iter/s, 4.94863s/12 iters), loss = 2.9212
I0407 22:43:26.586406   359 solver.cpp:237]     Train net output #0: loss = 2.9212 (* 1 = 2.9212 loss)
I0407 22:43:26.586413   359 sgd_solver.cpp:105] Iteration 2172, lr = 0.00996799
I0407 22:43:31.510375   359 solver.cpp:218] Iteration 2184 (2.43707 iter/s, 4.92395s/12 iters), loss = 2.61246
I0407 22:43:31.510416   359 solver.cpp:237]     Train net output #0: loss = 2.61246 (* 1 = 2.61246 loss)
I0407 22:43:31.510423   359 sgd_solver.cpp:105] Iteration 2184, lr = 0.00996723
I0407 22:43:36.463024   359 solver.cpp:218] Iteration 2196 (2.42298 iter/s, 4.95259s/12 iters), loss = 2.73664
I0407 22:43:36.463070   359 solver.cpp:237]     Train net output #0: loss = 2.73664 (* 1 = 2.73664 loss)
I0407 22:43:36.463078   359 sgd_solver.cpp:105] Iteration 2196, lr = 0.00996646
I0407 22:43:41.413550   359 solver.cpp:218] Iteration 2208 (2.42402 iter/s, 4.95046s/12 iters), loss = 2.63206
I0407 22:43:41.413590   359 solver.cpp:237]     Train net output #0: loss = 2.63206 (* 1 = 2.63206 loss)
I0407 22:43:41.413599   359 sgd_solver.cpp:105] Iteration 2208, lr = 0.00996566
I0407 22:43:46.349711   359 solver.cpp:218] Iteration 2220 (2.43107 iter/s, 4.9361s/12 iters), loss = 2.47727
I0407 22:43:46.349750   359 solver.cpp:237]     Train net output #0: loss = 2.47727 (* 1 = 2.47727 loss)
I0407 22:43:46.349757   359 sgd_solver.cpp:105] Iteration 2220, lr = 0.00996485
I0407 22:43:48.122831   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:51.218158   359 solver.cpp:218] Iteration 2232 (2.46488 iter/s, 4.86839s/12 iters), loss = 2.48663
I0407 22:43:51.218200   359 solver.cpp:237]     Train net output #0: loss = 2.48663 (* 1 = 2.48663 loss)
I0407 22:43:51.218209   359 sgd_solver.cpp:105] Iteration 2232, lr = 0.00996401
I0407 22:43:55.724633   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 22:43:58.784642   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 22:44:02.889842   359 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 22:44:02.889859   359 net.cpp:676] Ignoring source layer train-data
I0407 22:44:06.451486   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:07.376595   359 solver.cpp:397]     Test net output #0: accuracy = 0.273897
I0407 22:44:07.376646   359 solver.cpp:397]     Test net output #1: loss = 3.17993 (* 1 = 3.17993 loss)
I0407 22:44:07.473145   359 solver.cpp:218] Iteration 2244 (0.738239 iter/s, 16.2549s/12 iters), loss = 2.46823
I0407 22:44:07.473194   359 solver.cpp:237]     Train net output #0: loss = 2.46823 (* 1 = 2.46823 loss)
I0407 22:44:07.473203   359 sgd_solver.cpp:105] Iteration 2244, lr = 0.00996316
I0407 22:44:11.575385   359 solver.cpp:218] Iteration 2256 (2.92528 iter/s, 4.10217s/12 iters), loss = 2.4896
I0407 22:44:11.575423   359 solver.cpp:237]     Train net output #0: loss = 2.4896 (* 1 = 2.4896 loss)
I0407 22:44:11.575433   359 sgd_solver.cpp:105] Iteration 2256, lr = 0.00996228
I0407 22:44:16.564204   359 solver.cpp:218] Iteration 2268 (2.40541 iter/s, 4.98876s/12 iters), loss = 2.43589
I0407 22:44:16.564240   359 solver.cpp:237]     Train net output #0: loss = 2.43589 (* 1 = 2.43589 loss)
I0407 22:44:16.564249   359 sgd_solver.cpp:105] Iteration 2268, lr = 0.00996139
I0407 22:44:21.590222   359 solver.cpp:218] Iteration 2280 (2.38761 iter/s, 5.02595s/12 iters), loss = 2.43739
I0407 22:44:21.590266   359 solver.cpp:237]     Train net output #0: loss = 2.43739 (* 1 = 2.43739 loss)
I0407 22:44:21.590276   359 sgd_solver.cpp:105] Iteration 2280, lr = 0.00996047
I0407 22:44:26.538518   359 solver.cpp:218] Iteration 2292 (2.42511 iter/s, 4.94823s/12 iters), loss = 2.44551
I0407 22:44:26.538664   359 solver.cpp:237]     Train net output #0: loss = 2.44551 (* 1 = 2.44551 loss)
I0407 22:44:26.538674   359 sgd_solver.cpp:105] Iteration 2292, lr = 0.00995954
I0407 22:44:31.483511   359 solver.cpp:218] Iteration 2304 (2.42678 iter/s, 4.94483s/12 iters), loss = 2.43083
I0407 22:44:31.483551   359 solver.cpp:237]     Train net output #0: loss = 2.43083 (* 1 = 2.43083 loss)
I0407 22:44:31.483559   359 sgd_solver.cpp:105] Iteration 2304, lr = 0.00995858
I0407 22:44:36.507511   359 solver.cpp:218] Iteration 2316 (2.38857 iter/s, 5.02394s/12 iters), loss = 2.68947
I0407 22:44:36.507551   359 solver.cpp:237]     Train net output #0: loss = 2.68947 (* 1 = 2.68947 loss)
I0407 22:44:36.507560   359 sgd_solver.cpp:105] Iteration 2316, lr = 0.00995759
I0407 22:44:40.379441   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:41.403112   359 solver.cpp:218] Iteration 2328 (2.45121 iter/s, 4.89554s/12 iters), loss = 2.6636
I0407 22:44:41.403156   359 solver.cpp:237]     Train net output #0: loss = 2.6636 (* 1 = 2.6636 loss)
I0407 22:44:41.403163   359 sgd_solver.cpp:105] Iteration 2328, lr = 0.00995659
I0407 22:44:46.347402   359 solver.cpp:218] Iteration 2340 (2.42708 iter/s, 4.94422s/12 iters), loss = 2.40418
I0407 22:44:46.347448   359 solver.cpp:237]     Train net output #0: loss = 2.40418 (* 1 = 2.40418 loss)
I0407 22:44:46.347457   359 sgd_solver.cpp:105] Iteration 2340, lr = 0.00995556
I0407 22:44:48.363399   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 22:44:51.524742   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 22:44:53.978636   359 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 22:44:53.978653   359 net.cpp:676] Ignoring source layer train-data
I0407 22:44:57.708062   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:58.705417   359 solver.cpp:397]     Test net output #0: accuracy = 0.252451
I0407 22:44:58.705457   359 solver.cpp:397]     Test net output #1: loss = 3.26199 (* 1 = 3.26199 loss)
I0407 22:45:00.496237   359 solver.cpp:218] Iteration 2352 (0.848131 iter/s, 14.1488s/12 iters), loss = 2.34774
I0407 22:45:00.496279   359 solver.cpp:237]     Train net output #0: loss = 2.34774 (* 1 = 2.34774 loss)
I0407 22:45:00.496289   359 sgd_solver.cpp:105] Iteration 2352, lr = 0.00995451
I0407 22:45:05.401335   359 solver.cpp:218] Iteration 2364 (2.44647 iter/s, 4.90503s/12 iters), loss = 2.51786
I0407 22:45:05.401373   359 solver.cpp:237]     Train net output #0: loss = 2.51786 (* 1 = 2.51786 loss)
I0407 22:45:05.401381   359 sgd_solver.cpp:105] Iteration 2364, lr = 0.00995343
I0407 22:45:10.361184   359 solver.cpp:218] Iteration 2376 (2.41946 iter/s, 4.95979s/12 iters), loss = 2.59695
I0407 22:45:10.361224   359 solver.cpp:237]     Train net output #0: loss = 2.59695 (* 1 = 2.59695 loss)
I0407 22:45:10.361232   359 sgd_solver.cpp:105] Iteration 2376, lr = 0.00995233
I0407 22:45:15.275897   359 solver.cpp:218] Iteration 2388 (2.44168 iter/s, 4.91465s/12 iters), loss = 2.55361
I0407 22:45:15.275935   359 solver.cpp:237]     Train net output #0: loss = 2.55361 (* 1 = 2.55361 loss)
I0407 22:45:15.275943   359 sgd_solver.cpp:105] Iteration 2388, lr = 0.0099512
I0407 22:45:20.219977   359 solver.cpp:218] Iteration 2400 (2.42717 iter/s, 4.94402s/12 iters), loss = 2.29065
I0407 22:45:20.220019   359 solver.cpp:237]     Train net output #0: loss = 2.29065 (* 1 = 2.29065 loss)
I0407 22:45:20.220027   359 sgd_solver.cpp:105] Iteration 2400, lr = 0.00995004
I0407 22:45:25.143514   359 solver.cpp:218] Iteration 2412 (2.43731 iter/s, 4.92347s/12 iters), loss = 2.76306
I0407 22:45:25.143559   359 solver.cpp:237]     Train net output #0: loss = 2.76306 (* 1 = 2.76306 loss)
I0407 22:45:25.143568   359 sgd_solver.cpp:105] Iteration 2412, lr = 0.00994886
I0407 22:45:30.110707   359 solver.cpp:218] Iteration 2424 (2.41588 iter/s, 4.96713s/12 iters), loss = 2.06413
I0407 22:45:30.110862   359 solver.cpp:237]     Train net output #0: loss = 2.06413 (* 1 = 2.06413 loss)
I0407 22:45:30.110872   359 sgd_solver.cpp:105] Iteration 2424, lr = 0.00994765
I0407 22:45:31.152237   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:35.006736   359 solver.cpp:218] Iteration 2436 (2.45105 iter/s, 4.89586s/12 iters), loss = 2.38329
I0407 22:45:35.006778   359 solver.cpp:237]     Train net output #0: loss = 2.38329 (* 1 = 2.38329 loss)
I0407 22:45:35.006785   359 sgd_solver.cpp:105] Iteration 2436, lr = 0.00994641
I0407 22:45:39.507761   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 22:45:43.694573   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 22:45:46.619050   359 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 22:45:46.619069   359 net.cpp:676] Ignoring source layer train-data
I0407 22:45:50.383879   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:51.464020   359 solver.cpp:397]     Test net output #0: accuracy = 0.239583
I0407 22:45:51.464053   359 solver.cpp:397]     Test net output #1: loss = 3.32404 (* 1 = 3.32404 loss)
I0407 22:45:51.560308   359 solver.cpp:218] Iteration 2448 (0.724923 iter/s, 16.5535s/12 iters), loss = 2.68792
I0407 22:45:51.560364   359 solver.cpp:237]     Train net output #0: loss = 2.68792 (* 1 = 2.68792 loss)
I0407 22:45:51.560374   359 sgd_solver.cpp:105] Iteration 2448, lr = 0.00994514
I0407 22:45:55.595785   359 solver.cpp:218] Iteration 2460 (2.97368 iter/s, 4.0354s/12 iters), loss = 2.49981
I0407 22:45:55.595826   359 solver.cpp:237]     Train net output #0: loss = 2.49981 (* 1 = 2.49981 loss)
I0407 22:45:55.595835   359 sgd_solver.cpp:105] Iteration 2460, lr = 0.00994384
I0407 22:46:00.445024   359 solver.cpp:218] Iteration 2472 (2.47464 iter/s, 4.84918s/12 iters), loss = 2.17352
I0407 22:46:00.445158   359 solver.cpp:237]     Train net output #0: loss = 2.17352 (* 1 = 2.17352 loss)
I0407 22:46:00.445165   359 sgd_solver.cpp:105] Iteration 2472, lr = 0.00994251
I0407 22:46:05.404966   359 solver.cpp:218] Iteration 2484 (2.41946 iter/s, 4.95979s/12 iters), loss = 2.29499
I0407 22:46:05.405000   359 solver.cpp:237]     Train net output #0: loss = 2.29499 (* 1 = 2.29499 loss)
I0407 22:46:05.405007   359 sgd_solver.cpp:105] Iteration 2484, lr = 0.00994115
I0407 22:46:10.333974   359 solver.cpp:218] Iteration 2496 (2.43459 iter/s, 4.92896s/12 iters), loss = 2.5319
I0407 22:46:10.334015   359 solver.cpp:237]     Train net output #0: loss = 2.5319 (* 1 = 2.5319 loss)
I0407 22:46:10.334023   359 sgd_solver.cpp:105] Iteration 2496, lr = 0.00993976
I0407 22:46:15.308440   359 solver.cpp:218] Iteration 2508 (2.41235 iter/s, 4.97441s/12 iters), loss = 2.37591
I0407 22:46:15.308475   359 solver.cpp:237]     Train net output #0: loss = 2.37591 (* 1 = 2.37591 loss)
I0407 22:46:15.308482   359 sgd_solver.cpp:105] Iteration 2508, lr = 0.00993833
I0407 22:46:20.222555   359 solver.cpp:218] Iteration 2520 (2.44197 iter/s, 4.91406s/12 iters), loss = 2.29151
I0407 22:46:20.222589   359 solver.cpp:237]     Train net output #0: loss = 2.29151 (* 1 = 2.29151 loss)
I0407 22:46:20.222596   359 sgd_solver.cpp:105] Iteration 2520, lr = 0.00993687
I0407 22:46:23.421361   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:25.167135   359 solver.cpp:218] Iteration 2532 (2.42693 iter/s, 4.94453s/12 iters), loss = 2.35582
I0407 22:46:25.167168   359 solver.cpp:237]     Train net output #0: loss = 2.35582 (* 1 = 2.35582 loss)
I0407 22:46:25.167176   359 sgd_solver.cpp:105] Iteration 2532, lr = 0.00993538
I0407 22:46:30.112251   359 solver.cpp:218] Iteration 2544 (2.42666 iter/s, 4.94506s/12 iters), loss = 2.42731
I0407 22:46:30.112291   359 solver.cpp:237]     Train net output #0: loss = 2.42731 (* 1 = 2.42731 loss)
I0407 22:46:30.112300   359 sgd_solver.cpp:105] Iteration 2544, lr = 0.00993385
I0407 22:46:32.149363   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 22:46:35.288813   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 22:46:38.053391   359 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 22:46:38.053411   359 net.cpp:676] Ignoring source layer train-data
I0407 22:46:41.696125   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:42.827939   359 solver.cpp:397]     Test net output #0: accuracy = 0.284314
I0407 22:46:42.827984   359 solver.cpp:397]     Test net output #1: loss = 3.15071 (* 1 = 3.15071 loss)
I0407 22:46:44.611464   359 solver.cpp:218] Iteration 2556 (0.827636 iter/s, 14.4991s/12 iters), loss = 2.2578
I0407 22:46:44.611512   359 solver.cpp:237]     Train net output #0: loss = 2.2578 (* 1 = 2.2578 loss)
I0407 22:46:44.611521   359 sgd_solver.cpp:105] Iteration 2556, lr = 0.00993228
I0407 22:46:49.662487   359 solver.cpp:218] Iteration 2568 (2.3758 iter/s, 5.05094s/12 iters), loss = 2.19678
I0407 22:46:49.662552   359 solver.cpp:237]     Train net output #0: loss = 2.19678 (* 1 = 2.19678 loss)
I0407 22:46:49.662567   359 sgd_solver.cpp:105] Iteration 2568, lr = 0.00993068
I0407 22:46:54.587882   359 solver.cpp:218] Iteration 2580 (2.4364 iter/s, 4.92531s/12 iters), loss = 1.96284
I0407 22:46:54.587924   359 solver.cpp:237]     Train net output #0: loss = 1.96284 (* 1 = 1.96284 loss)
I0407 22:46:54.587931   359 sgd_solver.cpp:105] Iteration 2580, lr = 0.00992905
I0407 22:46:59.540197   359 solver.cpp:218] Iteration 2592 (2.42314 iter/s, 4.95226s/12 iters), loss = 2.35947
I0407 22:46:59.540233   359 solver.cpp:237]     Train net output #0: loss = 2.35947 (* 1 = 2.35947 loss)
I0407 22:46:59.540241   359 sgd_solver.cpp:105] Iteration 2592, lr = 0.00992737
I0407 22:47:04.440917   359 solver.cpp:218] Iteration 2604 (2.44865 iter/s, 4.90066s/12 iters), loss = 2.09648
I0407 22:47:04.441054   359 solver.cpp:237]     Train net output #0: loss = 2.09648 (* 1 = 2.09648 loss)
I0407 22:47:04.441063   359 sgd_solver.cpp:105] Iteration 2604, lr = 0.00992565
I0407 22:47:09.410863   359 solver.cpp:218] Iteration 2616 (2.41459 iter/s, 4.96979s/12 iters), loss = 2.16389
I0407 22:47:09.410907   359 solver.cpp:237]     Train net output #0: loss = 2.16389 (* 1 = 2.16389 loss)
I0407 22:47:09.410914   359 sgd_solver.cpp:105] Iteration 2616, lr = 0.00992389
I0407 22:47:14.319983   359 solver.cpp:218] Iteration 2628 (2.44446 iter/s, 4.90905s/12 iters), loss = 1.97931
I0407 22:47:14.320024   359 solver.cpp:237]     Train net output #0: loss = 1.97931 (* 1 = 1.97931 loss)
I0407 22:47:14.320032   359 sgd_solver.cpp:105] Iteration 2628, lr = 0.0099221
I0407 22:47:14.738675   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:19.256965   359 solver.cpp:218] Iteration 2640 (2.43067 iter/s, 4.93692s/12 iters), loss = 2.25741
I0407 22:47:19.257009   359 solver.cpp:237]     Train net output #0: loss = 2.25741 (* 1 = 2.25741 loss)
I0407 22:47:19.257016   359 sgd_solver.cpp:105] Iteration 2640, lr = 0.00992026
I0407 22:47:23.661909   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 22:47:26.747251   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 22:47:29.112129   359 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 22:47:29.112147   359 net.cpp:676] Ignoring source layer train-data
I0407 22:47:32.740947   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:33.908448   359 solver.cpp:397]     Test net output #0: accuracy = 0.272059
I0407 22:47:33.908484   359 solver.cpp:397]     Test net output #1: loss = 3.15613 (* 1 = 3.15613 loss)
I0407 22:47:34.004735   359 solver.cpp:218] Iteration 2652 (0.813687 iter/s, 14.7477s/12 iters), loss = 2.32786
I0407 22:47:34.004778   359 solver.cpp:237]     Train net output #0: loss = 2.32786 (* 1 = 2.32786 loss)
I0407 22:47:34.004787   359 sgd_solver.cpp:105] Iteration 2652, lr = 0.00991837
I0407 22:47:38.125959   359 solver.cpp:218] Iteration 2664 (2.91181 iter/s, 4.12115s/12 iters), loss = 2.42141
I0407 22:47:38.126102   359 solver.cpp:237]     Train net output #0: loss = 2.42141 (* 1 = 2.42141 loss)
I0407 22:47:38.126111   359 sgd_solver.cpp:105] Iteration 2664, lr = 0.00991645
I0407 22:47:42.995462   359 solver.cpp:218] Iteration 2676 (2.4644 iter/s, 4.86934s/12 iters), loss = 1.88912
I0407 22:47:42.995507   359 solver.cpp:237]     Train net output #0: loss = 1.88912 (* 1 = 1.88912 loss)
I0407 22:47:42.995515   359 sgd_solver.cpp:105] Iteration 2676, lr = 0.00991447
I0407 22:47:47.908500   359 solver.cpp:218] Iteration 2688 (2.44251 iter/s, 4.91298s/12 iters), loss = 2.26359
I0407 22:47:47.908532   359 solver.cpp:237]     Train net output #0: loss = 2.26359 (* 1 = 2.26359 loss)
I0407 22:47:47.908540   359 sgd_solver.cpp:105] Iteration 2688, lr = 0.00991246
I0407 22:47:52.857479   359 solver.cpp:218] Iteration 2700 (2.42477 iter/s, 4.94892s/12 iters), loss = 2.33243
I0407 22:47:52.857511   359 solver.cpp:237]     Train net output #0: loss = 2.33243 (* 1 = 2.33243 loss)
I0407 22:47:52.857518   359 sgd_solver.cpp:105] Iteration 2700, lr = 0.00991039
I0407 22:47:57.767093   359 solver.cpp:218] Iteration 2712 (2.44421 iter/s, 4.90956s/12 iters), loss = 2.30132
I0407 22:47:57.767129   359 solver.cpp:237]     Train net output #0: loss = 2.30132 (* 1 = 2.30132 loss)
I0407 22:47:57.767138   359 sgd_solver.cpp:105] Iteration 2712, lr = 0.00990828
I0407 22:48:02.710635   359 solver.cpp:218] Iteration 2724 (2.42744 iter/s, 4.94349s/12 iters), loss = 2.0974
I0407 22:48:02.710675   359 solver.cpp:237]     Train net output #0: loss = 2.0974 (* 1 = 2.0974 loss)
I0407 22:48:02.710681   359 sgd_solver.cpp:105] Iteration 2724, lr = 0.00990611
I0407 22:48:05.232297   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:07.613812   359 solver.cpp:218] Iteration 2736 (2.44742 iter/s, 4.90311s/12 iters), loss = 2.26774
I0407 22:48:07.613849   359 solver.cpp:237]     Train net output #0: loss = 2.26774 (* 1 = 2.26774 loss)
I0407 22:48:07.613857   359 sgd_solver.cpp:105] Iteration 2736, lr = 0.0099039
I0407 22:48:12.586241   359 solver.cpp:218] Iteration 2748 (2.41334 iter/s, 4.97237s/12 iters), loss = 2.08512
I0407 22:48:12.586366   359 solver.cpp:237]     Train net output #0: loss = 2.08512 (* 1 = 2.08512 loss)
I0407 22:48:12.586375   359 sgd_solver.cpp:105] Iteration 2748, lr = 0.00990163
I0407 22:48:14.588271   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 22:48:17.679343   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 22:48:20.043385   359 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 22:48:20.043403   359 net.cpp:676] Ignoring source layer train-data
I0407 22:48:23.333578   359 blocking_queue.cpp:49] Waiting for data
I0407 22:48:23.601482   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:24.816747   359 solver.cpp:397]     Test net output #0: accuracy = 0.283088
I0407 22:48:24.816795   359 solver.cpp:397]     Test net output #1: loss = 3.17317 (* 1 = 3.17317 loss)
I0407 22:48:26.637207   359 solver.cpp:218] Iteration 2760 (0.854044 iter/s, 14.0508s/12 iters), loss = 2.25691
I0407 22:48:26.637259   359 solver.cpp:237]     Train net output #0: loss = 2.25691 (* 1 = 2.25691 loss)
I0407 22:48:26.637269   359 sgd_solver.cpp:105] Iteration 2760, lr = 0.00989932
I0407 22:48:31.531042   359 solver.cpp:218] Iteration 2772 (2.4521 iter/s, 4.89376s/12 iters), loss = 2.27579
I0407 22:48:31.531082   359 solver.cpp:237]     Train net output #0: loss = 2.27579 (* 1 = 2.27579 loss)
I0407 22:48:31.531090   359 sgd_solver.cpp:105] Iteration 2772, lr = 0.00989694
I0407 22:48:36.490770   359 solver.cpp:218] Iteration 2784 (2.41952 iter/s, 4.95966s/12 iters), loss = 1.85031
I0407 22:48:36.490814   359 solver.cpp:237]     Train net output #0: loss = 1.85031 (* 1 = 1.85031 loss)
I0407 22:48:36.490823   359 sgd_solver.cpp:105] Iteration 2784, lr = 0.00989452
I0407 22:48:41.406931   359 solver.cpp:218] Iteration 2796 (2.44096 iter/s, 4.9161s/12 iters), loss = 2.35421
I0407 22:48:41.406965   359 solver.cpp:237]     Train net output #0: loss = 2.35421 (* 1 = 2.35421 loss)
I0407 22:48:41.406973   359 sgd_solver.cpp:105] Iteration 2796, lr = 0.00989203
I0407 22:48:46.345438   359 solver.cpp:218] Iteration 2808 (2.42991 iter/s, 4.93846s/12 iters), loss = 2.1644
I0407 22:48:46.345599   359 solver.cpp:237]     Train net output #0: loss = 2.1644 (* 1 = 2.1644 loss)
I0407 22:48:46.345609   359 sgd_solver.cpp:105] Iteration 2808, lr = 0.00988949
I0407 22:48:51.282537   359 solver.cpp:218] Iteration 2820 (2.43066 iter/s, 4.93693s/12 iters), loss = 1.87829
I0407 22:48:51.282572   359 solver.cpp:237]     Train net output #0: loss = 1.87829 (* 1 = 1.87829 loss)
I0407 22:48:51.282579   359 sgd_solver.cpp:105] Iteration 2820, lr = 0.00988689
I0407 22:48:55.939746   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:56.224982   359 solver.cpp:218] Iteration 2832 (2.42797 iter/s, 4.94239s/12 iters), loss = 2.01649
I0407 22:48:56.225016   359 solver.cpp:237]     Train net output #0: loss = 2.01649 (* 1 = 2.01649 loss)
I0407 22:48:56.225023   359 sgd_solver.cpp:105] Iteration 2832, lr = 0.00988423
I0407 22:49:01.161550   359 solver.cpp:218] Iteration 2844 (2.43087 iter/s, 4.93651s/12 iters), loss = 1.9606
I0407 22:49:01.161585   359 solver.cpp:237]     Train net output #0: loss = 1.9606 (* 1 = 1.9606 loss)
I0407 22:49:01.161593   359 sgd_solver.cpp:105] Iteration 2844, lr = 0.0098815
I0407 22:49:05.659430   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 22:49:08.729086   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 22:49:11.087358   359 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 22:49:11.087375   359 net.cpp:676] Ignoring source layer train-data
I0407 22:49:14.623360   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:15.875644   359 solver.cpp:397]     Test net output #0: accuracy = 0.304534
I0407 22:49:15.875692   359 solver.cpp:397]     Test net output #1: loss = 3.03383 (* 1 = 3.03383 loss)
I0407 22:49:15.972419   359 solver.cpp:218] Iteration 2856 (0.81022 iter/s, 14.8108s/12 iters), loss = 1.85983
I0407 22:49:15.972462   359 solver.cpp:237]     Train net output #0: loss = 1.85983 (* 1 = 1.85983 loss)
I0407 22:49:15.972471   359 sgd_solver.cpp:105] Iteration 2856, lr = 0.00987872
I0407 22:49:20.129029   359 solver.cpp:218] Iteration 2868 (2.88701 iter/s, 4.15655s/12 iters), loss = 2.07138
I0407 22:49:20.129158   359 solver.cpp:237]     Train net output #0: loss = 2.07138 (* 1 = 2.07138 loss)
I0407 22:49:20.129168   359 sgd_solver.cpp:105] Iteration 2868, lr = 0.00987586
I0407 22:49:25.049413   359 solver.cpp:218] Iteration 2880 (2.43891 iter/s, 4.92024s/12 iters), loss = 2.13079
I0407 22:49:25.049450   359 solver.cpp:237]     Train net output #0: loss = 2.13079 (* 1 = 2.13079 loss)
I0407 22:49:25.049458   359 sgd_solver.cpp:105] Iteration 2880, lr = 0.00987295
I0407 22:49:30.000919   359 solver.cpp:218] Iteration 2892 (2.42353 iter/s, 4.95145s/12 iters), loss = 2.06035
I0407 22:49:30.000954   359 solver.cpp:237]     Train net output #0: loss = 2.06035 (* 1 = 2.06035 loss)
I0407 22:49:30.000962   359 sgd_solver.cpp:105] Iteration 2892, lr = 0.00986996
I0407 22:49:34.916677   359 solver.cpp:218] Iteration 2904 (2.44116 iter/s, 4.9157s/12 iters), loss = 2.31742
I0407 22:49:34.916713   359 solver.cpp:237]     Train net output #0: loss = 2.31742 (* 1 = 2.31742 loss)
I0407 22:49:34.916721   359 sgd_solver.cpp:105] Iteration 2904, lr = 0.00986691
I0407 22:49:39.877827   359 solver.cpp:218] Iteration 2916 (2.41882 iter/s, 4.96109s/12 iters), loss = 1.92919
I0407 22:49:39.877864   359 solver.cpp:237]     Train net output #0: loss = 1.92919 (* 1 = 1.92919 loss)
I0407 22:49:39.877873   359 sgd_solver.cpp:105] Iteration 2916, lr = 0.00986378
I0407 22:49:44.795217   359 solver.cpp:218] Iteration 2928 (2.44035 iter/s, 4.91733s/12 iters), loss = 1.81033
I0407 22:49:44.795258   359 solver.cpp:237]     Train net output #0: loss = 1.81033 (* 1 = 1.81033 loss)
I0407 22:49:44.795266   359 sgd_solver.cpp:105] Iteration 2928, lr = 0.00986058
I0407 22:49:46.627804   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:49.728142   359 solver.cpp:218] Iteration 2940 (2.43267 iter/s, 4.93285s/12 iters), loss = 2.03943
I0407 22:49:49.728201   359 solver.cpp:237]     Train net output #0: loss = 2.03943 (* 1 = 2.03943 loss)
I0407 22:49:49.728214   359 sgd_solver.cpp:105] Iteration 2940, lr = 0.00985731
I0407 22:49:54.657171   359 solver.cpp:218] Iteration 2952 (2.43459 iter/s, 4.92895s/12 iters), loss = 2.02725
I0407 22:49:54.657321   359 solver.cpp:237]     Train net output #0: loss = 2.02725 (* 1 = 2.02725 loss)
I0407 22:49:54.657332   359 sgd_solver.cpp:105] Iteration 2952, lr = 0.00985396
I0407 22:49:56.686234   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 22:49:59.871939   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 22:50:02.232760   359 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 22:50:02.232780   359 net.cpp:676] Ignoring source layer train-data
I0407 22:50:05.661442   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:06.950773   359 solver.cpp:397]     Test net output #0: accuracy = 0.299632
I0407 22:50:06.950816   359 solver.cpp:397]     Test net output #1: loss = 3.07793 (* 1 = 3.07793 loss)
I0407 22:50:08.736313   359 solver.cpp:218] Iteration 2964 (0.852336 iter/s, 14.079s/12 iters), loss = 2.05118
I0407 22:50:08.736361   359 solver.cpp:237]     Train net output #0: loss = 2.05118 (* 1 = 2.05118 loss)
I0407 22:50:08.736371   359 sgd_solver.cpp:105] Iteration 2964, lr = 0.00985054
I0407 22:50:13.663187   359 solver.cpp:218] Iteration 2976 (2.43565 iter/s, 4.92681s/12 iters), loss = 1.90589
I0407 22:50:13.663225   359 solver.cpp:237]     Train net output #0: loss = 1.90589 (* 1 = 1.90589 loss)
I0407 22:50:13.663233   359 sgd_solver.cpp:105] Iteration 2976, lr = 0.00984703
I0407 22:50:18.610044   359 solver.cpp:218] Iteration 2988 (2.42581 iter/s, 4.9468s/12 iters), loss = 2.09538
I0407 22:50:18.610085   359 solver.cpp:237]     Train net output #0: loss = 2.09538 (* 1 = 2.09538 loss)
I0407 22:50:18.610093   359 sgd_solver.cpp:105] Iteration 2988, lr = 0.00984345
I0407 22:50:23.547987   359 solver.cpp:218] Iteration 3000 (2.43019 iter/s, 4.93788s/12 iters), loss = 1.83884
I0407 22:50:23.548025   359 solver.cpp:237]     Train net output #0: loss = 1.83884 (* 1 = 1.83884 loss)
I0407 22:50:23.548033   359 sgd_solver.cpp:105] Iteration 3000, lr = 0.00983978
I0407 22:50:28.594018   359 solver.cpp:218] Iteration 3012 (2.37813 iter/s, 5.04598s/12 iters), loss = 1.87208
I0407 22:50:28.594077   359 solver.cpp:237]     Train net output #0: loss = 1.87208 (* 1 = 1.87208 loss)
I0407 22:50:28.594085   359 sgd_solver.cpp:105] Iteration 3012, lr = 0.00983603
I0407 22:50:33.816968   359 solver.cpp:218] Iteration 3024 (2.29759 iter/s, 5.22287s/12 iters), loss = 1.93008
I0407 22:50:33.817006   359 solver.cpp:237]     Train net output #0: loss = 1.93008 (* 1 = 1.93008 loss)
I0407 22:50:33.817014   359 sgd_solver.cpp:105] Iteration 3024, lr = 0.00983219
I0407 22:50:37.810873   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:38.804452   359 solver.cpp:218] Iteration 3036 (2.40605 iter/s, 4.98743s/12 iters), loss = 1.70603
I0407 22:50:38.804488   359 solver.cpp:237]     Train net output #0: loss = 1.70603 (* 1 = 1.70603 loss)
I0407 22:50:38.804495   359 sgd_solver.cpp:105] Iteration 3036, lr = 0.00982826
I0407 22:50:43.883749   359 solver.cpp:218] Iteration 3048 (2.36256 iter/s, 5.07924s/12 iters), loss = 1.91323
I0407 22:50:43.883790   359 solver.cpp:237]     Train net output #0: loss = 1.91323 (* 1 = 1.91323 loss)
I0407 22:50:43.883797   359 sgd_solver.cpp:105] Iteration 3048, lr = 0.00982425
I0407 22:50:48.389130   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 22:50:52.510715   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 22:50:54.960774   359 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 22:50:54.960794   359 net.cpp:676] Ignoring source layer train-data
I0407 22:50:58.439750   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:59.740667   359 solver.cpp:397]     Test net output #0: accuracy = 0.313113
I0407 22:50:59.740809   359 solver.cpp:397]     Test net output #1: loss = 2.98991 (* 1 = 2.98991 loss)
I0407 22:50:59.837545   359 solver.cpp:218] Iteration 3060 (0.752176 iter/s, 15.9537s/12 iters), loss = 1.75386
I0407 22:50:59.837592   359 solver.cpp:237]     Train net output #0: loss = 1.75386 (* 1 = 1.75386 loss)
I0407 22:50:59.837601   359 sgd_solver.cpp:105] Iteration 3060, lr = 0.00982014
I0407 22:51:04.122287   359 solver.cpp:218] Iteration 3072 (2.80068 iter/s, 4.28468s/12 iters), loss = 1.54107
I0407 22:51:04.122321   359 solver.cpp:237]     Train net output #0: loss = 1.54107 (* 1 = 1.54107 loss)
I0407 22:51:04.122328   359 sgd_solver.cpp:105] Iteration 3072, lr = 0.00981593
I0407 22:51:09.134703   359 solver.cpp:218] Iteration 3084 (2.39408 iter/s, 5.01236s/12 iters), loss = 1.87213
I0407 22:51:09.134748   359 solver.cpp:237]     Train net output #0: loss = 1.87213 (* 1 = 1.87213 loss)
I0407 22:51:09.134757   359 sgd_solver.cpp:105] Iteration 3084, lr = 0.00981163
I0407 22:51:14.128625   359 solver.cpp:218] Iteration 3096 (2.40295 iter/s, 4.99385s/12 iters), loss = 1.77313
I0407 22:51:14.128671   359 solver.cpp:237]     Train net output #0: loss = 1.77313 (* 1 = 1.77313 loss)
I0407 22:51:14.128680   359 sgd_solver.cpp:105] Iteration 3096, lr = 0.00980724
I0407 22:51:18.986593   359 solver.cpp:218] Iteration 3108 (2.47021 iter/s, 4.8579s/12 iters), loss = 1.94607
I0407 22:51:18.986634   359 solver.cpp:237]     Train net output #0: loss = 1.94607 (* 1 = 1.94607 loss)
I0407 22:51:18.986642   359 sgd_solver.cpp:105] Iteration 3108, lr = 0.00980274
I0407 22:51:23.914330   359 solver.cpp:218] Iteration 3120 (2.43522 iter/s, 4.92768s/12 iters), loss = 2.19232
I0407 22:51:23.914369   359 solver.cpp:237]     Train net output #0: loss = 2.19232 (* 1 = 2.19232 loss)
I0407 22:51:23.914376   359 sgd_solver.cpp:105] Iteration 3120, lr = 0.00979814
I0407 22:51:28.876279   359 solver.cpp:218] Iteration 3132 (2.41843 iter/s, 4.96189s/12 iters), loss = 1.86277
I0407 22:51:28.876312   359 solver.cpp:237]     Train net output #0: loss = 1.86277 (* 1 = 1.86277 loss)
I0407 22:51:28.876319   359 sgd_solver.cpp:105] Iteration 3132, lr = 0.00979343
I0407 22:51:29.947422   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:33.763401   359 solver.cpp:218] Iteration 3144 (2.45546 iter/s, 4.88706s/12 iters), loss = 1.78341
I0407 22:51:33.763442   359 solver.cpp:237]     Train net output #0: loss = 1.78341 (* 1 = 1.78341 loss)
I0407 22:51:33.763450   359 sgd_solver.cpp:105] Iteration 3144, lr = 0.00978861
I0407 22:51:38.727494   359 solver.cpp:218] Iteration 3156 (2.41739 iter/s, 4.96403s/12 iters), loss = 1.60958
I0407 22:51:38.727533   359 solver.cpp:237]     Train net output #0: loss = 1.60958 (* 1 = 1.60958 loss)
I0407 22:51:38.727540   359 sgd_solver.cpp:105] Iteration 3156, lr = 0.00978369
I0407 22:51:40.710310   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 22:51:45.026276   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 22:51:48.847363   359 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 22:51:48.847381   359 net.cpp:676] Ignoring source layer train-data
I0407 22:51:52.225072   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:53.618080   359 solver.cpp:397]     Test net output #0: accuracy = 0.319853
I0407 22:51:53.618124   359 solver.cpp:397]     Test net output #1: loss = 2.96404 (* 1 = 2.96404 loss)
I0407 22:51:55.414750   359 solver.cpp:218] Iteration 3168 (0.719115 iter/s, 16.6872s/12 iters), loss = 1.72166
I0407 22:51:55.414785   359 solver.cpp:237]     Train net output #0: loss = 1.72166 (* 1 = 1.72166 loss)
I0407 22:51:55.414794   359 sgd_solver.cpp:105] Iteration 3168, lr = 0.00977866
I0407 22:52:00.319183   359 solver.cpp:218] Iteration 3180 (2.4468 iter/s, 4.90437s/12 iters), loss = 1.62192
I0407 22:52:00.319308   359 solver.cpp:237]     Train net output #0: loss = 1.62192 (* 1 = 1.62192 loss)
I0407 22:52:00.319317   359 sgd_solver.cpp:105] Iteration 3180, lr = 0.0097735
I0407 22:52:05.216598   359 solver.cpp:218] Iteration 3192 (2.45034 iter/s, 4.89727s/12 iters), loss = 1.81731
I0407 22:52:05.216640   359 solver.cpp:237]     Train net output #0: loss = 1.81731 (* 1 = 1.81731 loss)
I0407 22:52:05.216648   359 sgd_solver.cpp:105] Iteration 3192, lr = 0.00976824
I0407 22:52:10.143244   359 solver.cpp:218] Iteration 3204 (2.43577 iter/s, 4.92657s/12 iters), loss = 1.89343
I0407 22:52:10.143287   359 solver.cpp:237]     Train net output #0: loss = 1.89343 (* 1 = 1.89343 loss)
I0407 22:52:10.143296   359 sgd_solver.cpp:105] Iteration 3204, lr = 0.00976285
I0407 22:52:15.104357   359 solver.cpp:218] Iteration 3216 (2.41884 iter/s, 4.96105s/12 iters), loss = 1.50783
I0407 22:52:15.104393   359 solver.cpp:237]     Train net output #0: loss = 1.50783 (* 1 = 1.50783 loss)
I0407 22:52:15.104399   359 sgd_solver.cpp:105] Iteration 3216, lr = 0.00975734
I0407 22:52:20.017047   359 solver.cpp:218] Iteration 3228 (2.44268 iter/s, 4.91264s/12 iters), loss = 1.82193
I0407 22:52:20.017084   359 solver.cpp:237]     Train net output #0: loss = 1.82193 (* 1 = 1.82193 loss)
I0407 22:52:20.017092   359 sgd_solver.cpp:105] Iteration 3228, lr = 0.00975171
I0407 22:52:23.233575   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:24.955689   359 solver.cpp:218] Iteration 3240 (2.42985 iter/s, 4.93858s/12 iters), loss = 2.01245
I0407 22:52:24.955731   359 solver.cpp:237]     Train net output #0: loss = 2.01245 (* 1 = 2.01245 loss)
I0407 22:52:24.955740   359 sgd_solver.cpp:105] Iteration 3240, lr = 0.00974595
I0407 22:52:29.877923   359 solver.cpp:218] Iteration 3252 (2.43795 iter/s, 4.92217s/12 iters), loss = 1.8181
I0407 22:52:29.877959   359 solver.cpp:237]     Train net output #0: loss = 1.8181 (* 1 = 1.8181 loss)
I0407 22:52:29.877965   359 sgd_solver.cpp:105] Iteration 3252, lr = 0.00974005
I0407 22:52:34.374315   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 22:52:37.939534   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 22:52:42.060667   359 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 22:52:42.060688   359 net.cpp:676] Ignoring source layer train-data
I0407 22:52:45.365583   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:46.692616   359 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0407 22:52:46.692664   359 solver.cpp:397]     Test net output #1: loss = 3.02124 (* 1 = 3.02124 loss)
I0407 22:52:46.789216   359 solver.cpp:218] Iteration 3264 (0.709588 iter/s, 16.9112s/12 iters), loss = 1.81548
I0407 22:52:46.789254   359 solver.cpp:237]     Train net output #0: loss = 1.81548 (* 1 = 1.81548 loss)
I0407 22:52:46.789263   359 sgd_solver.cpp:105] Iteration 3264, lr = 0.00973403
I0407 22:52:50.907032   359 solver.cpp:218] Iteration 3276 (2.9142 iter/s, 4.11776s/12 iters), loss = 1.84454
I0407 22:52:50.907068   359 solver.cpp:237]     Train net output #0: loss = 1.84454 (* 1 = 1.84454 loss)
I0407 22:52:50.907074   359 sgd_solver.cpp:105] Iteration 3276, lr = 0.00972787
I0407 22:52:55.844164   359 solver.cpp:218] Iteration 3288 (2.43059 iter/s, 4.93707s/12 iters), loss = 1.45229
I0407 22:52:55.844206   359 solver.cpp:237]     Train net output #0: loss = 1.45229 (* 1 = 1.45229 loss)
I0407 22:52:55.844214   359 sgd_solver.cpp:105] Iteration 3288, lr = 0.00972157
I0407 22:53:00.785689   359 solver.cpp:218] Iteration 3300 (2.42843 iter/s, 4.94146s/12 iters), loss = 1.59583
I0407 22:53:00.785729   359 solver.cpp:237]     Train net output #0: loss = 1.59583 (* 1 = 1.59583 loss)
I0407 22:53:00.785737   359 sgd_solver.cpp:105] Iteration 3300, lr = 0.00971513
I0407 22:53:05.713490   359 solver.cpp:218] Iteration 3312 (2.43519 iter/s, 4.92774s/12 iters), loss = 1.94547
I0407 22:53:05.713624   359 solver.cpp:237]     Train net output #0: loss = 1.94547 (* 1 = 1.94547 loss)
I0407 22:53:05.713632   359 sgd_solver.cpp:105] Iteration 3312, lr = 0.00970855
I0407 22:53:10.669137   359 solver.cpp:218] Iteration 3324 (2.42156 iter/s, 4.95549s/12 iters), loss = 1.7866
I0407 22:53:10.669180   359 solver.cpp:237]     Train net output #0: loss = 1.7866 (* 1 = 1.7866 loss)
I0407 22:53:10.669188   359 sgd_solver.cpp:105] Iteration 3324, lr = 0.00970181
I0407 22:53:15.620919   359 solver.cpp:218] Iteration 3336 (2.4234 iter/s, 4.95171s/12 iters), loss = 1.43149
I0407 22:53:15.620962   359 solver.cpp:237]     Train net output #0: loss = 1.43149 (* 1 = 1.43149 loss)
I0407 22:53:15.620971   359 sgd_solver.cpp:105] Iteration 3336, lr = 0.00969493
I0407 22:53:16.067467   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:20.512449   359 solver.cpp:218] Iteration 3348 (2.45325 iter/s, 4.89146s/12 iters), loss = 1.57974
I0407 22:53:20.512490   359 solver.cpp:237]     Train net output #0: loss = 1.57974 (* 1 = 1.57974 loss)
I0407 22:53:20.512499   359 sgd_solver.cpp:105] Iteration 3348, lr = 0.00968789
I0407 22:53:25.477869   359 solver.cpp:218] Iteration 3360 (2.41675 iter/s, 4.96535s/12 iters), loss = 1.89577
I0407 22:53:25.477910   359 solver.cpp:237]     Train net output #0: loss = 1.89577 (* 1 = 1.89577 loss)
I0407 22:53:25.477918   359 sgd_solver.cpp:105] Iteration 3360, lr = 0.0096807
I0407 22:53:27.475548   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 22:53:30.577740   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 22:53:34.989799   359 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 22:53:34.989815   359 net.cpp:676] Ignoring source layer train-data
I0407 22:53:38.015815   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:39.354274   359 solver.cpp:397]     Test net output #0: accuracy = 0.303922
I0407 22:53:39.354315   359 solver.cpp:397]     Test net output #1: loss = 2.98053 (* 1 = 2.98053 loss)
I0407 22:53:41.153334   359 solver.cpp:218] Iteration 3372 (0.765531 iter/s, 15.6754s/12 iters), loss = 1.74271
I0407 22:53:41.153375   359 solver.cpp:237]     Train net output #0: loss = 1.74271 (* 1 = 1.74271 loss)
I0407 22:53:41.153383   359 sgd_solver.cpp:105] Iteration 3372, lr = 0.00967335
I0407 22:53:46.088308   359 solver.cpp:218] Iteration 3384 (2.43165 iter/s, 4.93491s/12 iters), loss = 1.61365
I0407 22:53:46.088351   359 solver.cpp:237]     Train net output #0: loss = 1.61365 (* 1 = 1.61365 loss)
I0407 22:53:46.088357   359 sgd_solver.cpp:105] Iteration 3384, lr = 0.00966583
I0407 22:53:51.047209   359 solver.cpp:218] Iteration 3396 (2.41992 iter/s, 4.95884s/12 iters), loss = 1.52177
I0407 22:53:51.047253   359 solver.cpp:237]     Train net output #0: loss = 1.52177 (* 1 = 1.52177 loss)
I0407 22:53:51.047261   359 sgd_solver.cpp:105] Iteration 3396, lr = 0.00965815
I0407 22:53:55.980113   359 solver.cpp:218] Iteration 3408 (2.43268 iter/s, 4.93283s/12 iters), loss = 1.84539
I0407 22:53:55.980157   359 solver.cpp:237]     Train net output #0: loss = 1.84539 (* 1 = 1.84539 loss)
I0407 22:53:55.980165   359 sgd_solver.cpp:105] Iteration 3408, lr = 0.00965029
I0407 22:54:00.915119   359 solver.cpp:218] Iteration 3420 (2.43164 iter/s, 4.93494s/12 iters), loss = 1.69592
I0407 22:54:00.915166   359 solver.cpp:237]     Train net output #0: loss = 1.69592 (* 1 = 1.69592 loss)
I0407 22:54:00.915174   359 sgd_solver.cpp:105] Iteration 3420, lr = 0.00964226
I0407 22:54:05.856339   359 solver.cpp:218] Iteration 3432 (2.42858 iter/s, 4.94115s/12 iters), loss = 1.71626
I0407 22:54:05.856387   359 solver.cpp:237]     Train net output #0: loss = 1.71626 (* 1 = 1.71626 loss)
I0407 22:54:05.856395   359 sgd_solver.cpp:105] Iteration 3432, lr = 0.00963406
I0407 22:54:08.425891   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:10.768893   359 solver.cpp:218] Iteration 3444 (2.44276 iter/s, 4.91248s/12 iters), loss = 1.78531
I0407 22:54:10.768937   359 solver.cpp:237]     Train net output #0: loss = 1.78531 (* 1 = 1.78531 loss)
I0407 22:54:10.768946   359 sgd_solver.cpp:105] Iteration 3444, lr = 0.00962567
I0407 22:54:15.631819   359 solver.cpp:218] Iteration 3456 (2.46768 iter/s, 4.86286s/12 iters), loss = 1.8038
I0407 22:54:15.631856   359 solver.cpp:237]     Train net output #0: loss = 1.8038 (* 1 = 1.8038 loss)
I0407 22:54:15.631863   359 sgd_solver.cpp:105] Iteration 3456, lr = 0.0096171
I0407 22:54:20.116326   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 22:54:23.230362   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 22:54:25.599941   359 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 22:54:25.599961   359 net.cpp:676] Ignoring source layer train-data
I0407 22:54:25.979009   359 blocking_queue.cpp:49] Waiting for data
I0407 22:54:28.577630   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:29.964434   359 solver.cpp:397]     Test net output #0: accuracy = 0.297181
I0407 22:54:29.964470   359 solver.cpp:397]     Test net output #1: loss = 3.10223 (* 1 = 3.10223 loss)
I0407 22:54:30.060896   359 solver.cpp:218] Iteration 3468 (0.831658 iter/s, 14.429s/12 iters), loss = 1.71592
I0407 22:54:30.060936   359 solver.cpp:237]     Train net output #0: loss = 1.71592 (* 1 = 1.71592 loss)
I0407 22:54:30.060943   359 sgd_solver.cpp:105] Iteration 3468, lr = 0.00960834
I0407 22:54:34.042717   359 solver.cpp:218] Iteration 3480 (3.01374 iter/s, 3.98176s/12 iters), loss = 1.78015
I0407 22:54:34.042757   359 solver.cpp:237]     Train net output #0: loss = 1.78015 (* 1 = 1.78015 loss)
I0407 22:54:34.042765   359 sgd_solver.cpp:105] Iteration 3480, lr = 0.00959939
I0407 22:54:38.969643   359 solver.cpp:218] Iteration 3492 (2.43563 iter/s, 4.92686s/12 iters), loss = 1.64672
I0407 22:54:38.969753   359 solver.cpp:237]     Train net output #0: loss = 1.64672 (* 1 = 1.64672 loss)
I0407 22:54:38.969760   359 sgd_solver.cpp:105] Iteration 3492, lr = 0.00959024
I0407 22:54:43.925062   359 solver.cpp:218] Iteration 3504 (2.42166 iter/s, 4.95528s/12 iters), loss = 1.89541
I0407 22:54:43.925109   359 solver.cpp:237]     Train net output #0: loss = 1.89541 (* 1 = 1.89541 loss)
I0407 22:54:43.925118   359 sgd_solver.cpp:105] Iteration 3504, lr = 0.0095809
I0407 22:54:48.824512   359 solver.cpp:218] Iteration 3516 (2.44929 iter/s, 4.89939s/12 iters), loss = 1.91145
I0407 22:54:48.824544   359 solver.cpp:237]     Train net output #0: loss = 1.91145 (* 1 = 1.91145 loss)
I0407 22:54:48.824553   359 sgd_solver.cpp:105] Iteration 3516, lr = 0.00957135
I0407 22:54:53.741083   359 solver.cpp:218] Iteration 3528 (2.44075 iter/s, 4.91651s/12 iters), loss = 1.44124
I0407 22:54:53.741124   359 solver.cpp:237]     Train net output #0: loss = 1.44124 (* 1 = 1.44124 loss)
I0407 22:54:53.741133   359 sgd_solver.cpp:105] Iteration 3528, lr = 0.00956159
I0407 22:54:58.331110   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:58.587404   359 solver.cpp:218] Iteration 3540 (2.47614 iter/s, 4.84626s/12 iters), loss = 1.56631
I0407 22:54:58.587445   359 solver.cpp:237]     Train net output #0: loss = 1.56631 (* 1 = 1.56631 loss)
I0407 22:54:58.587453   359 sgd_solver.cpp:105] Iteration 3540, lr = 0.00955162
I0407 22:55:03.489917   359 solver.cpp:218] Iteration 3552 (2.44775 iter/s, 4.90246s/12 iters), loss = 1.99541
I0407 22:55:03.489951   359 solver.cpp:237]     Train net output #0: loss = 1.99541 (* 1 = 1.99541 loss)
I0407 22:55:03.489959   359 sgd_solver.cpp:105] Iteration 3552, lr = 0.00954143
I0407 22:55:08.438408   359 solver.cpp:218] Iteration 3564 (2.42501 iter/s, 4.94844s/12 iters), loss = 1.75829
I0407 22:55:08.438442   359 solver.cpp:237]     Train net output #0: loss = 1.75829 (* 1 = 1.75829 loss)
I0407 22:55:08.438449   359 sgd_solver.cpp:105] Iteration 3564, lr = 0.00953103
I0407 22:55:10.426291   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 22:55:13.532604   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 22:55:15.900574   359 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 22:55:15.900593   359 net.cpp:676] Ignoring source layer train-data
I0407 22:55:18.917930   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:20.338215   359 solver.cpp:397]     Test net output #0: accuracy = 0.317402
I0407 22:55:20.338258   359 solver.cpp:397]     Test net output #1: loss = 3.03856 (* 1 = 3.03856 loss)
I0407 22:55:22.125113   359 solver.cpp:218] Iteration 3576 (0.876768 iter/s, 13.6866s/12 iters), loss = 1.82348
I0407 22:55:22.125156   359 solver.cpp:237]     Train net output #0: loss = 1.82348 (* 1 = 1.82348 loss)
I0407 22:55:22.125164   359 sgd_solver.cpp:105] Iteration 3576, lr = 0.0095204
I0407 22:55:27.052601   359 solver.cpp:218] Iteration 3588 (2.43535 iter/s, 4.92742s/12 iters), loss = 1.5205
I0407 22:55:27.052639   359 solver.cpp:237]     Train net output #0: loss = 1.5205 (* 1 = 1.5205 loss)
I0407 22:55:27.052646   359 sgd_solver.cpp:105] Iteration 3588, lr = 0.00950954
I0407 22:55:32.014633   359 solver.cpp:218] Iteration 3600 (2.41839 iter/s, 4.96197s/12 iters), loss = 1.56863
I0407 22:55:32.014675   359 solver.cpp:237]     Train net output #0: loss = 1.56863 (* 1 = 1.56863 loss)
I0407 22:55:32.014683   359 sgd_solver.cpp:105] Iteration 3600, lr = 0.00949845
I0407 22:55:36.968760   359 solver.cpp:218] Iteration 3612 (2.42225 iter/s, 4.95406s/12 iters), loss = 1.74721
I0407 22:55:36.968801   359 solver.cpp:237]     Train net output #0: loss = 1.74721 (* 1 = 1.74721 loss)
I0407 22:55:36.968809   359 sgd_solver.cpp:105] Iteration 3612, lr = 0.00948712
I0407 22:55:41.915572   359 solver.cpp:218] Iteration 3624 (2.42583 iter/s, 4.94675s/12 iters), loss = 1.32363
I0407 22:55:41.915701   359 solver.cpp:237]     Train net output #0: loss = 1.32363 (* 1 = 1.32363 loss)
I0407 22:55:41.915710   359 sgd_solver.cpp:105] Iteration 3624, lr = 0.00947555
I0407 22:55:46.881790   359 solver.cpp:218] Iteration 3636 (2.4164 iter/s, 4.96607s/12 iters), loss = 1.42952
I0407 22:55:46.881827   359 solver.cpp:237]     Train net output #0: loss = 1.42952 (* 1 = 1.42952 loss)
I0407 22:55:46.881835   359 sgd_solver.cpp:105] Iteration 3636, lr = 0.00946373
I0407 22:55:48.705510   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:51.770571   359 solver.cpp:218] Iteration 3648 (2.45463 iter/s, 4.88872s/12 iters), loss = 1.4643
I0407 22:55:51.770614   359 solver.cpp:237]     Train net output #0: loss = 1.4643 (* 1 = 1.4643 loss)
I0407 22:55:51.770622   359 sgd_solver.cpp:105] Iteration 3648, lr = 0.00945166
I0407 22:55:56.723450   359 solver.cpp:218] Iteration 3660 (2.42286 iter/s, 4.95282s/12 iters), loss = 1.42625
I0407 22:55:56.723487   359 solver.cpp:237]     Train net output #0: loss = 1.42625 (* 1 = 1.42625 loss)
I0407 22:55:56.723495   359 sgd_solver.cpp:105] Iteration 3660, lr = 0.00943934
I0407 22:56:01.163729   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 22:56:04.252982   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 22:56:06.616099   359 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 22:56:06.616117   359 net.cpp:676] Ignoring source layer train-data
I0407 22:56:09.556977   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:11.049046   359 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0407 22:56:11.049093   359 solver.cpp:397]     Test net output #1: loss = 3.03693 (* 1 = 3.03693 loss)
I0407 22:56:11.145462   359 solver.cpp:218] Iteration 3672 (0.832065 iter/s, 14.4219s/12 iters), loss = 1.59599
I0407 22:56:11.145499   359 solver.cpp:237]     Train net output #0: loss = 1.59599 (* 1 = 1.59599 loss)
I0407 22:56:11.145506   359 sgd_solver.cpp:105] Iteration 3672, lr = 0.00942676
I0407 22:56:15.304809   359 solver.cpp:218] Iteration 3684 (2.88511 iter/s, 4.15929s/12 iters), loss = 1.48452
I0407 22:56:15.304971   359 solver.cpp:237]     Train net output #0: loss = 1.48452 (* 1 = 1.48452 loss)
I0407 22:56:15.304980   359 sgd_solver.cpp:105] Iteration 3684, lr = 0.00941391
I0407 22:56:20.271034   359 solver.cpp:218] Iteration 3696 (2.41641 iter/s, 4.96604s/12 iters), loss = 1.43322
I0407 22:56:20.271077   359 solver.cpp:237]     Train net output #0: loss = 1.43322 (* 1 = 1.43322 loss)
I0407 22:56:20.271086   359 sgd_solver.cpp:105] Iteration 3696, lr = 0.00940079
I0407 22:56:25.196681   359 solver.cpp:218] Iteration 3708 (2.43626 iter/s, 4.92558s/12 iters), loss = 1.16543
I0407 22:56:25.196725   359 solver.cpp:237]     Train net output #0: loss = 1.16543 (* 1 = 1.16543 loss)
I0407 22:56:25.196735   359 sgd_solver.cpp:105] Iteration 3708, lr = 0.0093874
I0407 22:56:30.150964   359 solver.cpp:218] Iteration 3720 (2.42218 iter/s, 4.95422s/12 iters), loss = 1.78309
I0407 22:56:30.151001   359 solver.cpp:237]     Train net output #0: loss = 1.78309 (* 1 = 1.78309 loss)
I0407 22:56:30.151010   359 sgd_solver.cpp:105] Iteration 3720, lr = 0.00937373
I0407 22:56:35.094007   359 solver.cpp:218] Iteration 3732 (2.42768 iter/s, 4.94298s/12 iters), loss = 1.28626
I0407 22:56:35.094048   359 solver.cpp:237]     Train net output #0: loss = 1.28626 (* 1 = 1.28626 loss)
I0407 22:56:35.094056   359 sgd_solver.cpp:105] Iteration 3732, lr = 0.00935977
I0407 22:56:39.051785   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:40.023315   359 solver.cpp:218] Iteration 3744 (2.43445 iter/s, 4.92925s/12 iters), loss = 1.57416
I0407 22:56:40.023353   359 solver.cpp:237]     Train net output #0: loss = 1.57416 (* 1 = 1.57416 loss)
I0407 22:56:40.023362   359 sgd_solver.cpp:105] Iteration 3744, lr = 0.00934553
I0407 22:56:45.009591   359 solver.cpp:218] Iteration 3756 (2.40663 iter/s, 4.98622s/12 iters), loss = 1.47913
I0407 22:56:45.009625   359 solver.cpp:237]     Train net output #0: loss = 1.47913 (* 1 = 1.47913 loss)
I0407 22:56:45.009634   359 sgd_solver.cpp:105] Iteration 3756, lr = 0.00933099
I0407 22:56:49.940285   359 solver.cpp:218] Iteration 3768 (2.43376 iter/s, 4.93064s/12 iters), loss = 1.3796
I0407 22:56:49.940412   359 solver.cpp:237]     Train net output #0: loss = 1.3796 (* 1 = 1.3796 loss)
I0407 22:56:49.940421   359 sgd_solver.cpp:105] Iteration 3768, lr = 0.00931615
I0407 22:56:51.956657   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 22:56:55.111392   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 22:56:57.535313   359 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 22:56:57.535332   359 net.cpp:676] Ignoring source layer train-data
I0407 22:57:00.533215   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:02.193050   359 solver.cpp:397]     Test net output #0: accuracy = 0.308211
I0407 22:57:02.193097   359 solver.cpp:397]     Test net output #1: loss = 3.07509 (* 1 = 3.07509 loss)
I0407 22:57:03.967428   359 solver.cpp:218] Iteration 3780 (0.855493 iter/s, 14.027s/12 iters), loss = 1.33665
I0407 22:57:03.967471   359 solver.cpp:237]     Train net output #0: loss = 1.33665 (* 1 = 1.33665 loss)
I0407 22:57:03.967479   359 sgd_solver.cpp:105] Iteration 3780, lr = 0.00930101
I0407 22:57:08.915087   359 solver.cpp:218] Iteration 3792 (2.42542 iter/s, 4.94759s/12 iters), loss = 1.36408
I0407 22:57:08.915132   359 solver.cpp:237]     Train net output #0: loss = 1.36408 (* 1 = 1.36408 loss)
I0407 22:57:08.915140   359 sgd_solver.cpp:105] Iteration 3792, lr = 0.00928555
I0407 22:57:13.822329   359 solver.cpp:218] Iteration 3804 (2.4454 iter/s, 4.90717s/12 iters), loss = 1.28694
I0407 22:57:13.822369   359 solver.cpp:237]     Train net output #0: loss = 1.28694 (* 1 = 1.28694 loss)
I0407 22:57:13.822377   359 sgd_solver.cpp:105] Iteration 3804, lr = 0.00926979
I0407 22:57:18.775259   359 solver.cpp:218] Iteration 3816 (2.42284 iter/s, 4.95287s/12 iters), loss = 1.4879
I0407 22:57:18.775296   359 solver.cpp:237]     Train net output #0: loss = 1.4879 (* 1 = 1.4879 loss)
I0407 22:57:18.775305   359 sgd_solver.cpp:105] Iteration 3816, lr = 0.0092537
I0407 22:57:23.694917   359 solver.cpp:218] Iteration 3828 (2.43922 iter/s, 4.9196s/12 iters), loss = 1.40069
I0407 22:57:23.695066   359 solver.cpp:237]     Train net output #0: loss = 1.40069 (* 1 = 1.40069 loss)
I0407 22:57:23.695076   359 sgd_solver.cpp:105] Iteration 3828, lr = 0.00923728
I0407 22:57:28.654698   359 solver.cpp:218] Iteration 3840 (2.41955 iter/s, 4.95961s/12 iters), loss = 1.30807
I0407 22:57:28.654744   359 solver.cpp:237]     Train net output #0: loss = 1.30807 (* 1 = 1.30807 loss)
I0407 22:57:28.654753   359 sgd_solver.cpp:105] Iteration 3840, lr = 0.00922054
I0407 22:57:29.756131   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:33.486899   359 solver.cpp:218] Iteration 3852 (2.48337 iter/s, 4.83214s/12 iters), loss = 1.5054
I0407 22:57:33.486941   359 solver.cpp:237]     Train net output #0: loss = 1.5054 (* 1 = 1.5054 loss)
I0407 22:57:33.486949   359 sgd_solver.cpp:105] Iteration 3852, lr = 0.00920346
I0407 22:57:38.419373   359 solver.cpp:218] Iteration 3864 (2.43289 iter/s, 4.93241s/12 iters), loss = 1.32235
I0407 22:57:38.419415   359 solver.cpp:237]     Train net output #0: loss = 1.32235 (* 1 = 1.32235 loss)
I0407 22:57:38.419423   359 sgd_solver.cpp:105] Iteration 3864, lr = 0.00918604
I0407 22:57:42.897219   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 22:57:46.039988   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 22:57:48.402369   359 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 22:57:48.402385   359 net.cpp:676] Ignoring source layer train-data
I0407 22:57:51.336009   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:53.038373   359 solver.cpp:397]     Test net output #0: accuracy = 0.322304
I0407 22:57:53.038420   359 solver.cpp:397]     Test net output #1: loss = 3.15085 (* 1 = 3.15085 loss)
I0407 22:57:53.135298   359 solver.cpp:218] Iteration 3876 (0.815447 iter/s, 14.7159s/12 iters), loss = 1.29692
I0407 22:57:53.135342   359 solver.cpp:237]     Train net output #0: loss = 1.29692 (* 1 = 1.29692 loss)
I0407 22:57:53.135350   359 sgd_solver.cpp:105] Iteration 3876, lr = 0.00916827
I0407 22:57:57.240638   359 solver.cpp:218] Iteration 3888 (2.92307 iter/s, 4.10527s/12 iters), loss = 1.1866
I0407 22:57:57.240765   359 solver.cpp:237]     Train net output #0: loss = 1.1866 (* 1 = 1.1866 loss)
I0407 22:57:57.240774   359 sgd_solver.cpp:105] Iteration 3888, lr = 0.00915015
I0407 22:58:02.198922   359 solver.cpp:218] Iteration 3900 (2.42027 iter/s, 4.95813s/12 iters), loss = 1.35107
I0407 22:58:02.198976   359 solver.cpp:237]     Train net output #0: loss = 1.35107 (* 1 = 1.35107 loss)
I0407 22:58:02.198987   359 sgd_solver.cpp:105] Iteration 3900, lr = 0.00913168
I0407 22:58:07.118937   359 solver.cpp:218] Iteration 3912 (2.43905 iter/s, 4.91994s/12 iters), loss = 1.32408
I0407 22:58:07.118981   359 solver.cpp:237]     Train net output #0: loss = 1.32408 (* 1 = 1.32408 loss)
I0407 22:58:07.118989   359 sgd_solver.cpp:105] Iteration 3912, lr = 0.00911284
I0407 22:58:12.036504   359 solver.cpp:218] Iteration 3924 (2.44026 iter/s, 4.9175s/12 iters), loss = 1.04679
I0407 22:58:12.036551   359 solver.cpp:237]     Train net output #0: loss = 1.04679 (* 1 = 1.04679 loss)
I0407 22:58:12.036559   359 sgd_solver.cpp:105] Iteration 3924, lr = 0.00909363
I0407 22:58:16.966168   359 solver.cpp:218] Iteration 3936 (2.43428 iter/s, 4.9296s/12 iters), loss = 1.03738
I0407 22:58:16.966207   359 solver.cpp:237]     Train net output #0: loss = 1.03738 (* 1 = 1.03738 loss)
I0407 22:58:16.966214   359 sgd_solver.cpp:105] Iteration 3936, lr = 0.00907405
I0407 22:58:20.266736   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:21.915828   359 solver.cpp:218] Iteration 3948 (2.42444 iter/s, 4.9496s/12 iters), loss = 1.55883
I0407 22:58:21.915870   359 solver.cpp:237]     Train net output #0: loss = 1.55883 (* 1 = 1.55883 loss)
I0407 22:58:21.915879   359 sgd_solver.cpp:105] Iteration 3948, lr = 0.00905409
I0407 22:58:26.839010   359 solver.cpp:218] Iteration 3960 (2.43748 iter/s, 4.92312s/12 iters), loss = 1.16935
I0407 22:58:26.839046   359 solver.cpp:237]     Train net output #0: loss = 1.16935 (* 1 = 1.16935 loss)
I0407 22:58:26.839053   359 sgd_solver.cpp:105] Iteration 3960, lr = 0.00903374
I0407 22:58:31.700322   359 solver.cpp:218] Iteration 3972 (2.4685 iter/s, 4.86125s/12 iters), loss = 1.00229
I0407 22:58:31.700532   359 solver.cpp:237]     Train net output #0: loss = 1.00229 (* 1 = 1.00229 loss)
I0407 22:58:31.700547   359 sgd_solver.cpp:105] Iteration 3972, lr = 0.00901301
I0407 22:58:33.714874   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 22:58:36.835970   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 22:58:39.232069   359 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 22:58:39.232086   359 net.cpp:676] Ignoring source layer train-data
I0407 22:58:42.364213   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:44.073710   359 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0407 22:58:44.073755   359 solver.cpp:397]     Test net output #1: loss = 3.19912 (* 1 = 3.19912 loss)
I0407 22:58:45.872015   359 solver.cpp:218] Iteration 3984 (0.846772 iter/s, 14.1715s/12 iters), loss = 1.46864
I0407 22:58:45.872053   359 solver.cpp:237]     Train net output #0: loss = 1.46864 (* 1 = 1.46864 loss)
I0407 22:58:45.872061   359 sgd_solver.cpp:105] Iteration 3984, lr = 0.00899188
I0407 22:58:50.795529   359 solver.cpp:218] Iteration 3996 (2.43731 iter/s, 4.92346s/12 iters), loss = 1.06736
I0407 22:58:50.795572   359 solver.cpp:237]     Train net output #0: loss = 1.06736 (* 1 = 1.06736 loss)
I0407 22:58:50.795580   359 sgd_solver.cpp:105] Iteration 3996, lr = 0.00897035
I0407 22:58:55.753439   359 solver.cpp:218] Iteration 4008 (2.42041 iter/s, 4.95784s/12 iters), loss = 1.24715
I0407 22:58:55.753484   359 solver.cpp:237]     Train net output #0: loss = 1.24715 (* 1 = 1.24715 loss)
I0407 22:58:55.753494   359 sgd_solver.cpp:105] Iteration 4008, lr = 0.00894841
I0407 22:59:00.688710   359 solver.cpp:218] Iteration 4020 (2.43151 iter/s, 4.93521s/12 iters), loss = 1.62328
I0407 22:59:00.688757   359 solver.cpp:237]     Train net output #0: loss = 1.62328 (* 1 = 1.62328 loss)
I0407 22:59:00.688766   359 sgd_solver.cpp:105] Iteration 4020, lr = 0.00892607
I0407 22:59:05.590694   359 solver.cpp:218] Iteration 4032 (2.44802 iter/s, 4.90192s/12 iters), loss = 1.28528
I0407 22:59:05.590814   359 solver.cpp:237]     Train net output #0: loss = 1.28528 (* 1 = 1.28528 loss)
I0407 22:59:05.590822   359 sgd_solver.cpp:105] Iteration 4032, lr = 0.0089033
I0407 22:59:10.494958   359 solver.cpp:218] Iteration 4044 (2.44692 iter/s, 4.90412s/12 iters), loss = 1.08686
I0407 22:59:10.495004   359 solver.cpp:237]     Train net output #0: loss = 1.08686 (* 1 = 1.08686 loss)
I0407 22:59:10.495012   359 sgd_solver.cpp:105] Iteration 4044, lr = 0.00888011
I0407 22:59:10.971974   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:15.421900   359 solver.cpp:218] Iteration 4056 (2.43562 iter/s, 4.92688s/12 iters), loss = 1.43536
I0407 22:59:15.421945   359 solver.cpp:237]     Train net output #0: loss = 1.43536 (* 1 = 1.43536 loss)
I0407 22:59:15.421953   359 sgd_solver.cpp:105] Iteration 4056, lr = 0.0088565
I0407 22:59:20.351946   359 solver.cpp:218] Iteration 4068 (2.43409 iter/s, 4.92998s/12 iters), loss = 1.2736
I0407 22:59:20.351989   359 solver.cpp:237]     Train net output #0: loss = 1.2736 (* 1 = 1.2736 loss)
I0407 22:59:20.351996   359 sgd_solver.cpp:105] Iteration 4068, lr = 0.00883245
I0407 22:59:24.777037   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 22:59:29.177081   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 22:59:31.699450   359 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 22:59:31.699465   359 net.cpp:676] Ignoring source layer train-data
I0407 22:59:34.641240   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:36.284500   359 solver.cpp:397]     Test net output #0: accuracy = 0.323529
I0407 22:59:36.284646   359 solver.cpp:397]     Test net output #1: loss = 3.14619 (* 1 = 3.14619 loss)
I0407 22:59:36.381498   359 solver.cpp:218] Iteration 4080 (0.748621 iter/s, 16.0295s/12 iters), loss = 1.19587
I0407 22:59:36.381542   359 solver.cpp:237]     Train net output #0: loss = 1.19587 (* 1 = 1.19587 loss)
I0407 22:59:36.381549   359 sgd_solver.cpp:105] Iteration 4080, lr = 0.00880797
I0407 22:59:40.510213   359 solver.cpp:218] Iteration 4092 (2.90652 iter/s, 4.12865s/12 iters), loss = 1.26657
I0407 22:59:40.510258   359 solver.cpp:237]     Train net output #0: loss = 1.26657 (* 1 = 1.26657 loss)
I0407 22:59:40.510267   359 sgd_solver.cpp:105] Iteration 4092, lr = 0.00878304
I0407 22:59:45.452402   359 solver.cpp:218] Iteration 4104 (2.42811 iter/s, 4.94212s/12 iters), loss = 1.40396
I0407 22:59:45.452447   359 solver.cpp:237]     Train net output #0: loss = 1.40396 (* 1 = 1.40396 loss)
I0407 22:59:45.452455   359 sgd_solver.cpp:105] Iteration 4104, lr = 0.00875767
I0407 22:59:50.409837   359 solver.cpp:218] Iteration 4116 (2.42064 iter/s, 4.95736s/12 iters), loss = 1.27666
I0407 22:59:50.409880   359 solver.cpp:237]     Train net output #0: loss = 1.27666 (* 1 = 1.27666 loss)
I0407 22:59:50.409889   359 sgd_solver.cpp:105] Iteration 4116, lr = 0.00873184
I0407 22:59:55.328012   359 solver.cpp:218] Iteration 4128 (2.43996 iter/s, 4.91811s/12 iters), loss = 1.10314
I0407 22:59:55.328045   359 solver.cpp:237]     Train net output #0: loss = 1.10314 (* 1 = 1.10314 loss)
I0407 22:59:55.328052   359 sgd_solver.cpp:105] Iteration 4128, lr = 0.00870556
I0407 23:00:00.289180   359 solver.cpp:218] Iteration 4140 (2.41881 iter/s, 4.96111s/12 iters), loss = 1.34791
I0407 23:00:00.289219   359 solver.cpp:237]     Train net output #0: loss = 1.34791 (* 1 = 1.34791 loss)
I0407 23:00:00.289227   359 sgd_solver.cpp:105] Iteration 4140, lr = 0.00867881
I0407 23:00:02.866626   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:05.180112   359 solver.cpp:218] Iteration 4152 (2.45355 iter/s, 4.89087s/12 iters), loss = 1.45668
I0407 23:00:05.180158   359 solver.cpp:237]     Train net output #0: loss = 1.45668 (* 1 = 1.45668 loss)
I0407 23:00:05.180166   359 sgd_solver.cpp:105] Iteration 4152, lr = 0.0086516
I0407 23:00:06.758963   359 blocking_queue.cpp:49] Waiting for data
I0407 23:00:10.133764   359 solver.cpp:218] Iteration 4164 (2.42249 iter/s, 4.95358s/12 iters), loss = 0.80139
I0407 23:00:10.133810   359 solver.cpp:237]     Train net output #0: loss = 0.80139 (* 1 = 0.80139 loss)
I0407 23:00:10.133818   359 sgd_solver.cpp:105] Iteration 4164, lr = 0.00862391
I0407 23:00:15.034997   359 solver.cpp:218] Iteration 4176 (2.4484 iter/s, 4.90117s/12 iters), loss = 1.35264
I0407 23:00:15.035044   359 solver.cpp:237]     Train net output #0: loss = 1.35264 (* 1 = 1.35264 loss)
I0407 23:00:15.035053   359 sgd_solver.cpp:105] Iteration 4176, lr = 0.00859575
I0407 23:00:17.019186   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 23:00:21.290120   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 23:00:24.105581   359 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 23:00:24.105600   359 net.cpp:676] Ignoring source layer train-data
I0407 23:00:27.069430   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:28.889843   359 solver.cpp:397]     Test net output #0: accuracy = 0.316176
I0407 23:00:28.889892   359 solver.cpp:397]     Test net output #1: loss = 3.2268 (* 1 = 3.2268 loss)
I0407 23:00:30.651959   359 solver.cpp:218] Iteration 4188 (0.768399 iter/s, 15.6169s/12 iters), loss = 1.26156
I0407 23:00:30.651999   359 solver.cpp:237]     Train net output #0: loss = 1.26156 (* 1 = 1.26156 loss)
I0407 23:00:30.652007   359 sgd_solver.cpp:105] Iteration 4188, lr = 0.00856711
I0407 23:00:35.575906   359 solver.cpp:218] Iteration 4200 (2.4371 iter/s, 4.92388s/12 iters), loss = 1.15826
I0407 23:00:35.575951   359 solver.cpp:237]     Train net output #0: loss = 1.15826 (* 1 = 1.15826 loss)
I0407 23:00:35.575959   359 sgd_solver.cpp:105] Iteration 4200, lr = 0.00853798
I0407 23:00:40.534448   359 solver.cpp:218] Iteration 4212 (2.4201 iter/s, 4.95848s/12 iters), loss = 1.24572
I0407 23:00:40.534613   359 solver.cpp:237]     Train net output #0: loss = 1.24572 (* 1 = 1.24572 loss)
I0407 23:00:40.534622   359 sgd_solver.cpp:105] Iteration 4212, lr = 0.00850836
I0407 23:00:45.426296   359 solver.cpp:218] Iteration 4224 (2.45315 iter/s, 4.89167s/12 iters), loss = 0.929474
I0407 23:00:45.426337   359 solver.cpp:237]     Train net output #0: loss = 0.929474 (* 1 = 0.929474 loss)
I0407 23:00:45.426344   359 sgd_solver.cpp:105] Iteration 4224, lr = 0.00847826
I0407 23:00:50.392489   359 solver.cpp:218] Iteration 4236 (2.41637 iter/s, 4.96614s/12 iters), loss = 1.17604
I0407 23:00:50.392526   359 solver.cpp:237]     Train net output #0: loss = 1.17604 (* 1 = 1.17604 loss)
I0407 23:00:50.392534   359 sgd_solver.cpp:105] Iteration 4236, lr = 0.00844765
I0407 23:00:55.056447   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:55.281594   359 solver.cpp:218] Iteration 4248 (2.45447 iter/s, 4.88904s/12 iters), loss = 1.20396
I0407 23:00:55.281641   359 solver.cpp:237]     Train net output #0: loss = 1.20396 (* 1 = 1.20396 loss)
I0407 23:00:55.281648   359 sgd_solver.cpp:105] Iteration 4248, lr = 0.00841654
I0407 23:01:00.224630   359 solver.cpp:218] Iteration 4260 (2.42769 iter/s, 4.94297s/12 iters), loss = 1.30071
I0407 23:01:00.224678   359 solver.cpp:237]     Train net output #0: loss = 1.30071 (* 1 = 1.30071 loss)
I0407 23:01:00.224687   359 sgd_solver.cpp:105] Iteration 4260, lr = 0.00838493
I0407 23:01:05.163117   359 solver.cpp:218] Iteration 4272 (2.42993 iter/s, 4.93842s/12 iters), loss = 0.907478
I0407 23:01:05.163162   359 solver.cpp:237]     Train net output #0: loss = 0.907478 (* 1 = 0.907478 loss)
I0407 23:01:05.163172   359 sgd_solver.cpp:105] Iteration 4272, lr = 0.00835281
I0407 23:01:09.643810   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 23:01:12.732884   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 23:01:15.492810   359 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 23:01:15.492828   359 net.cpp:676] Ignoring source layer train-data
I0407 23:01:18.268864   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:19.993580   359 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0407 23:01:19.993623   359 solver.cpp:397]     Test net output #1: loss = 3.17454 (* 1 = 3.17454 loss)
I0407 23:01:20.090034   359 solver.cpp:218] Iteration 4284 (0.803921 iter/s, 14.9268s/12 iters), loss = 1.1332
I0407 23:01:20.090072   359 solver.cpp:237]     Train net output #0: loss = 1.1332 (* 1 = 1.1332 loss)
I0407 23:01:20.090080   359 sgd_solver.cpp:105] Iteration 4284, lr = 0.00832018
I0407 23:01:24.208346   359 solver.cpp:218] Iteration 4296 (2.91386 iter/s, 4.11825s/12 iters), loss = 1.09216
I0407 23:01:24.208396   359 solver.cpp:237]     Train net output #0: loss = 1.09216 (* 1 = 1.09216 loss)
I0407 23:01:24.208410   359 sgd_solver.cpp:105] Iteration 4296, lr = 0.00828704
I0407 23:01:29.155835   359 solver.cpp:218] Iteration 4308 (2.42551 iter/s, 4.94742s/12 iters), loss = 1.20225
I0407 23:01:29.155875   359 solver.cpp:237]     Train net output #0: loss = 1.20225 (* 1 = 1.20225 loss)
I0407 23:01:29.155884   359 sgd_solver.cpp:105] Iteration 4308, lr = 0.00825338
I0407 23:01:34.067395   359 solver.cpp:218] Iteration 4320 (2.44324 iter/s, 4.9115s/12 iters), loss = 1.19316
I0407 23:01:34.067433   359 solver.cpp:237]     Train net output #0: loss = 1.19316 (* 1 = 1.19316 loss)
I0407 23:01:34.067441   359 sgd_solver.cpp:105] Iteration 4320, lr = 0.0082192
I0407 23:01:39.042028   359 solver.cpp:218] Iteration 4332 (2.41227 iter/s, 4.97458s/12 iters), loss = 1.08762
I0407 23:01:39.042062   359 solver.cpp:237]     Train net output #0: loss = 1.08762 (* 1 = 1.08762 loss)
I0407 23:01:39.042069   359 sgd_solver.cpp:105] Iteration 4332, lr = 0.0081845
I0407 23:01:43.957597   359 solver.cpp:218] Iteration 4344 (2.44125 iter/s, 4.91551s/12 iters), loss = 0.923437
I0407 23:01:43.957777   359 solver.cpp:237]     Train net output #0: loss = 0.923437 (* 1 = 0.923437 loss)
I0407 23:01:43.957798   359 sgd_solver.cpp:105] Iteration 4344, lr = 0.00814928
I0407 23:01:45.825765   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:48.904299   359 solver.cpp:218] Iteration 4356 (2.42595 iter/s, 4.94651s/12 iters), loss = 1.15211
I0407 23:01:48.904340   359 solver.cpp:237]     Train net output #0: loss = 1.15211 (* 1 = 1.15211 loss)
I0407 23:01:48.904348   359 sgd_solver.cpp:105] Iteration 4356, lr = 0.00811353
I0407 23:01:53.825569   359 solver.cpp:218] Iteration 4368 (2.43843 iter/s, 4.9212s/12 iters), loss = 1.17825
I0407 23:01:53.825613   359 solver.cpp:237]     Train net output #0: loss = 1.17825 (* 1 = 1.17825 loss)
I0407 23:01:53.825620   359 sgd_solver.cpp:105] Iteration 4368, lr = 0.00807725
I0407 23:01:58.777667   359 solver.cpp:218] Iteration 4380 (2.42325 iter/s, 4.95203s/12 iters), loss = 1.07236
I0407 23:01:58.777707   359 solver.cpp:237]     Train net output #0: loss = 1.07236 (* 1 = 1.07236 loss)
I0407 23:01:58.777716   359 sgd_solver.cpp:105] Iteration 4380, lr = 0.00804044
I0407 23:02:00.772776   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 23:02:03.842010   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 23:02:06.212311   359 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 23:02:06.212329   359 net.cpp:676] Ignoring source layer train-data
I0407 23:02:08.871178   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:10.609735   359 solver.cpp:397]     Test net output #0: accuracy = 0.352328
I0407 23:02:10.609783   359 solver.cpp:397]     Test net output #1: loss = 3.0698 (* 1 = 3.0698 loss)
I0407 23:02:12.402021   359 solver.cpp:218] Iteration 4392 (0.88078 iter/s, 13.6243s/12 iters), loss = 1.30571
I0407 23:02:12.402076   359 solver.cpp:237]     Train net output #0: loss = 1.30571 (* 1 = 1.30571 loss)
I0407 23:02:12.402084   359 sgd_solver.cpp:105] Iteration 4392, lr = 0.0080031
I0407 23:02:17.323110   359 solver.cpp:218] Iteration 4404 (2.43852 iter/s, 4.92101s/12 iters), loss = 1.05286
I0407 23:02:17.323215   359 solver.cpp:237]     Train net output #0: loss = 1.05286 (* 1 = 1.05286 loss)
I0407 23:02:17.323225   359 sgd_solver.cpp:105] Iteration 4404, lr = 0.00796523
I0407 23:02:22.288081   359 solver.cpp:218] Iteration 4416 (2.41699 iter/s, 4.96485s/12 iters), loss = 1.1881
I0407 23:02:22.288121   359 solver.cpp:237]     Train net output #0: loss = 1.1881 (* 1 = 1.1881 loss)
I0407 23:02:22.288130   359 sgd_solver.cpp:105] Iteration 4416, lr = 0.00792683
I0407 23:02:27.180815   359 solver.cpp:218] Iteration 4428 (2.45265 iter/s, 4.89267s/12 iters), loss = 1.18597
I0407 23:02:27.180855   359 solver.cpp:237]     Train net output #0: loss = 1.18597 (* 1 = 1.18597 loss)
I0407 23:02:27.180863   359 sgd_solver.cpp:105] Iteration 4428, lr = 0.0078879
I0407 23:02:32.143658   359 solver.cpp:218] Iteration 4440 (2.418 iter/s, 4.96278s/12 iters), loss = 0.850651
I0407 23:02:32.143699   359 solver.cpp:237]     Train net output #0: loss = 0.850651 (* 1 = 0.850651 loss)
I0407 23:02:32.143707   359 sgd_solver.cpp:105] Iteration 4440, lr = 0.00784843
I0407 23:02:36.092711   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:37.025038   359 solver.cpp:218] Iteration 4452 (2.45835 iter/s, 4.88131s/12 iters), loss = 1.04663
I0407 23:02:37.025085   359 solver.cpp:237]     Train net output #0: loss = 1.04663 (* 1 = 1.04663 loss)
I0407 23:02:37.025094   359 sgd_solver.cpp:105] Iteration 4452, lr = 0.00780843
I0407 23:02:41.990219   359 solver.cpp:218] Iteration 4464 (2.41686 iter/s, 4.96511s/12 iters), loss = 1.07428
I0407 23:02:41.990262   359 solver.cpp:237]     Train net output #0: loss = 1.07428 (* 1 = 1.07428 loss)
I0407 23:02:41.990269   359 sgd_solver.cpp:105] Iteration 4464, lr = 0.0077679
I0407 23:02:46.901567   359 solver.cpp:218] Iteration 4476 (2.44335 iter/s, 4.91128s/12 iters), loss = 0.912665
I0407 23:02:46.901614   359 solver.cpp:237]     Train net output #0: loss = 0.912665 (* 1 = 0.912665 loss)
I0407 23:02:46.901624   359 sgd_solver.cpp:105] Iteration 4476, lr = 0.00772684
I0407 23:02:51.394461   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 23:02:55.574654   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 23:02:59.708824   359 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 23:02:59.708842   359 net.cpp:676] Ignoring source layer train-data
I0407 23:03:02.337529   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:04.114943   359 solver.cpp:397]     Test net output #0: accuracy = 0.344363
I0407 23:03:04.114993   359 solver.cpp:397]     Test net output #1: loss = 3.10434 (* 1 = 3.10434 loss)
I0407 23:03:04.211478   359 solver.cpp:218] Iteration 4488 (0.693248 iter/s, 17.3098s/12 iters), loss = 0.779825
I0407 23:03:04.211525   359 solver.cpp:237]     Train net output #0: loss = 0.779825 (* 1 = 0.779825 loss)
I0407 23:03:04.211534   359 sgd_solver.cpp:105] Iteration 4488, lr = 0.00768525
I0407 23:03:08.291023   359 solver.cpp:218] Iteration 4500 (2.94155 iter/s, 4.07948s/12 iters), loss = 0.777058
I0407 23:03:08.291064   359 solver.cpp:237]     Train net output #0: loss = 0.777058 (* 1 = 0.777058 loss)
I0407 23:03:08.291071   359 sgd_solver.cpp:105] Iteration 4500, lr = 0.00764313
I0407 23:03:13.197472   359 solver.cpp:218] Iteration 4512 (2.44579 iter/s, 4.90639s/12 iters), loss = 0.890965
I0407 23:03:13.197516   359 solver.cpp:237]     Train net output #0: loss = 0.890965 (* 1 = 0.890965 loss)
I0407 23:03:13.197525   359 sgd_solver.cpp:105] Iteration 4512, lr = 0.00760048
I0407 23:03:18.064895   359 solver.cpp:218] Iteration 4524 (2.4654 iter/s, 4.86736s/12 iters), loss = 1.10968
I0407 23:03:18.064934   359 solver.cpp:237]     Train net output #0: loss = 1.10968 (* 1 = 1.10968 loss)
I0407 23:03:18.064944   359 sgd_solver.cpp:105] Iteration 4524, lr = 0.0075573
I0407 23:03:23.037331   359 solver.cpp:218] Iteration 4536 (2.41333 iter/s, 4.97237s/12 iters), loss = 0.852607
I0407 23:03:23.037443   359 solver.cpp:237]     Train net output #0: loss = 0.852607 (* 1 = 0.852607 loss)
I0407 23:03:23.037451   359 sgd_solver.cpp:105] Iteration 4536, lr = 0.00751361
I0407 23:03:28.023815   359 solver.cpp:218] Iteration 4548 (2.40657 iter/s, 4.98635s/12 iters), loss = 1.0859
I0407 23:03:28.023852   359 solver.cpp:237]     Train net output #0: loss = 1.0859 (* 1 = 1.0859 loss)
I0407 23:03:28.023860   359 sgd_solver.cpp:105] Iteration 4548, lr = 0.00746939
I0407 23:03:29.331871   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:33.036319   359 solver.cpp:218] Iteration 4560 (2.39404 iter/s, 5.01245s/12 iters), loss = 1.12001
I0407 23:03:33.036357   359 solver.cpp:237]     Train net output #0: loss = 1.12001 (* 1 = 1.12001 loss)
I0407 23:03:33.036365   359 sgd_solver.cpp:105] Iteration 4560, lr = 0.00742466
I0407 23:03:37.971357   359 solver.cpp:218] Iteration 4572 (2.43162 iter/s, 4.93498s/12 iters), loss = 1.08174
I0407 23:03:37.971395   359 solver.cpp:237]     Train net output #0: loss = 1.08174 (* 1 = 1.08174 loss)
I0407 23:03:37.971402   359 sgd_solver.cpp:105] Iteration 4572, lr = 0.00737941
I0407 23:03:42.927739   359 solver.cpp:218] Iteration 4584 (2.42115 iter/s, 4.95633s/12 iters), loss = 0.919457
I0407 23:03:42.927776   359 solver.cpp:237]     Train net output #0: loss = 0.919457 (* 1 = 0.919457 loss)
I0407 23:03:42.927783   359 sgd_solver.cpp:105] Iteration 4584, lr = 0.00733365
I0407 23:03:45.004302   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 23:03:48.106365   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 23:03:50.478353   359 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 23:03:50.478371   359 net.cpp:676] Ignoring source layer train-data
I0407 23:03:53.257681   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:55.208550   359 solver.cpp:397]     Test net output #0: accuracy = 0.355392
I0407 23:03:55.208596   359 solver.cpp:397]     Test net output #1: loss = 3.0353 (* 1 = 3.0353 loss)
I0407 23:03:57.061884   359 solver.cpp:218] Iteration 4596 (0.849012 iter/s, 14.1341s/12 iters), loss = 1.01907
I0407 23:03:57.061933   359 solver.cpp:237]     Train net output #0: loss = 1.01907 (* 1 = 1.01907 loss)
I0407 23:03:57.061941   359 sgd_solver.cpp:105] Iteration 4596, lr = 0.00728739
I0407 23:04:02.015368   359 solver.cpp:218] Iteration 4608 (2.42257 iter/s, 4.95342s/12 iters), loss = 1.03239
I0407 23:04:02.015406   359 solver.cpp:237]     Train net output #0: loss = 1.03239 (* 1 = 1.03239 loss)
I0407 23:04:02.015414   359 sgd_solver.cpp:105] Iteration 4608, lr = 0.00724063
I0407 23:04:07.018111   359 solver.cpp:218] Iteration 4620 (2.39871 iter/s, 5.00269s/12 iters), loss = 0.820322
I0407 23:04:07.018154   359 solver.cpp:237]     Train net output #0: loss = 0.820322 (* 1 = 0.820322 loss)
I0407 23:04:07.018162   359 sgd_solver.cpp:105] Iteration 4620, lr = 0.00719337
I0407 23:04:11.869942   359 solver.cpp:218] Iteration 4632 (2.47332 iter/s, 4.85177s/12 iters), loss = 1.01248
I0407 23:04:11.869976   359 solver.cpp:237]     Train net output #0: loss = 1.01248 (* 1 = 1.01248 loss)
I0407 23:04:11.869984   359 sgd_solver.cpp:105] Iteration 4632, lr = 0.00714562
I0407 23:04:16.785553   359 solver.cpp:218] Iteration 4644 (2.44123 iter/s, 4.91555s/12 iters), loss = 0.868681
I0407 23:04:16.785595   359 solver.cpp:237]     Train net output #0: loss = 0.868681 (* 1 = 0.868681 loss)
I0407 23:04:16.785604   359 sgd_solver.cpp:105] Iteration 4644, lr = 0.00709739
I0407 23:04:20.158550   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:21.725051   359 solver.cpp:218] Iteration 4656 (2.42943 iter/s, 4.93943s/12 iters), loss = 0.895408
I0407 23:04:21.725095   359 solver.cpp:237]     Train net output #0: loss = 0.895408 (* 1 = 0.895408 loss)
I0407 23:04:21.725102   359 sgd_solver.cpp:105] Iteration 4656, lr = 0.00704868
I0407 23:04:26.661913   359 solver.cpp:218] Iteration 4668 (2.43072 iter/s, 4.9368s/12 iters), loss = 1.19164
I0407 23:04:26.662040   359 solver.cpp:237]     Train net output #0: loss = 1.19164 (* 1 = 1.19164 loss)
I0407 23:04:26.662048   359 sgd_solver.cpp:105] Iteration 4668, lr = 0.0069995
I0407 23:04:31.571571   359 solver.cpp:218] Iteration 4680 (2.44423 iter/s, 4.90951s/12 iters), loss = 0.866611
I0407 23:04:31.571610   359 solver.cpp:237]     Train net output #0: loss = 0.866611 (* 1 = 0.866611 loss)
I0407 23:04:31.571619   359 sgd_solver.cpp:105] Iteration 4680, lr = 0.00694985
I0407 23:04:36.015635   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 23:04:40.339160   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 23:04:43.731345   359 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 23:04:43.731362   359 net.cpp:676] Ignoring source layer train-data
I0407 23:04:46.538678   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:48.667326   359 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0407 23:04:48.667382   359 solver.cpp:397]     Test net output #1: loss = 3.18518 (* 1 = 3.18518 loss)
I0407 23:04:48.764021   359 solver.cpp:218] Iteration 4692 (0.697984 iter/s, 17.1924s/12 iters), loss = 1.11231
I0407 23:04:48.764063   359 solver.cpp:237]     Train net output #0: loss = 1.11231 (* 1 = 1.11231 loss)
I0407 23:04:48.764072   359 sgd_solver.cpp:105] Iteration 4692, lr = 0.00689974
I0407 23:04:52.885627   359 solver.cpp:218] Iteration 4704 (2.91153 iter/s, 4.12154s/12 iters), loss = 0.821108
I0407 23:04:52.885666   359 solver.cpp:237]     Train net output #0: loss = 0.821108 (* 1 = 0.821108 loss)
I0407 23:04:52.885673   359 sgd_solver.cpp:105] Iteration 4704, lr = 0.00684919
I0407 23:04:57.789563   359 solver.cpp:218] Iteration 4716 (2.44704 iter/s, 4.90388s/12 iters), loss = 0.731509
I0407 23:04:57.789701   359 solver.cpp:237]     Train net output #0: loss = 0.731509 (* 1 = 0.731509 loss)
I0407 23:04:57.789710   359 sgd_solver.cpp:105] Iteration 4716, lr = 0.00679819
I0407 23:05:02.699545   359 solver.cpp:218] Iteration 4728 (2.44408 iter/s, 4.90982s/12 iters), loss = 1.09188
I0407 23:05:02.699590   359 solver.cpp:237]     Train net output #0: loss = 1.09188 (* 1 = 1.09188 loss)
I0407 23:05:02.699599   359 sgd_solver.cpp:105] Iteration 4728, lr = 0.00674676
I0407 23:05:07.689182   359 solver.cpp:218] Iteration 4740 (2.40501 iter/s, 4.98957s/12 iters), loss = 0.68834
I0407 23:05:07.689220   359 solver.cpp:237]     Train net output #0: loss = 0.68834 (* 1 = 0.68834 loss)
I0407 23:05:07.689229   359 sgd_solver.cpp:105] Iteration 4740, lr = 0.00669491
I0407 23:05:12.675359   359 solver.cpp:218] Iteration 4752 (2.40668 iter/s, 4.98612s/12 iters), loss = 0.783096
I0407 23:05:12.675407   359 solver.cpp:237]     Train net output #0: loss = 0.783096 (* 1 = 0.783096 loss)
I0407 23:05:12.675416   359 sgd_solver.cpp:105] Iteration 4752, lr = 0.00664264
I0407 23:05:13.182657   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:17.643110   359 solver.cpp:218] Iteration 4764 (2.41561 iter/s, 4.96769s/12 iters), loss = 0.976755
I0407 23:05:17.643149   359 solver.cpp:237]     Train net output #0: loss = 0.976755 (* 1 = 0.976755 loss)
I0407 23:05:17.643157   359 sgd_solver.cpp:105] Iteration 4764, lr = 0.00658996
I0407 23:05:22.654903   359 solver.cpp:218] Iteration 4776 (2.39438 iter/s, 5.01173s/12 iters), loss = 0.813546
I0407 23:05:22.654948   359 solver.cpp:237]     Train net output #0: loss = 0.813546 (* 1 = 0.813546 loss)
I0407 23:05:22.654956   359 sgd_solver.cpp:105] Iteration 4776, lr = 0.00653689
I0407 23:05:27.560628   359 solver.cpp:218] Iteration 4788 (2.44615 iter/s, 4.90566s/12 iters), loss = 0.932335
I0407 23:05:27.560667   359 solver.cpp:237]     Train net output #0: loss = 0.932335 (* 1 = 0.932335 loss)
I0407 23:05:27.560675   359 sgd_solver.cpp:105] Iteration 4788, lr = 0.00648343
I0407 23:05:29.552392   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 23:05:33.405416   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 23:05:36.520601   359 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 23:05:36.520617   359 net.cpp:676] Ignoring source layer train-data
I0407 23:05:39.249437   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:41.456301   359 solver.cpp:397]     Test net output #0: accuracy = 0.364583
I0407 23:05:41.456336   359 solver.cpp:397]     Test net output #1: loss = 3.15568 (* 1 = 3.15568 loss)
I0407 23:05:43.324231   359 solver.cpp:218] Iteration 4800 (0.761251 iter/s, 15.7635s/12 iters), loss = 0.874731
I0407 23:05:43.324280   359 solver.cpp:237]     Train net output #0: loss = 0.874731 (* 1 = 0.874731 loss)
I0407 23:05:43.324287   359 sgd_solver.cpp:105] Iteration 4800, lr = 0.0064296
I0407 23:05:48.240773   359 solver.cpp:218] Iteration 4812 (2.44078 iter/s, 4.91647s/12 iters), loss = 0.70028
I0407 23:05:48.240818   359 solver.cpp:237]     Train net output #0: loss = 0.70028 (* 1 = 0.70028 loss)
I0407 23:05:48.240828   359 sgd_solver.cpp:105] Iteration 4812, lr = 0.00637541
I0407 23:05:53.208781   359 solver.cpp:218] Iteration 4824 (2.41549 iter/s, 4.96793s/12 iters), loss = 0.785002
I0407 23:05:53.208829   359 solver.cpp:237]     Train net output #0: loss = 0.785002 (* 1 = 0.785002 loss)
I0407 23:05:53.208838   359 sgd_solver.cpp:105] Iteration 4824, lr = 0.00632086
I0407 23:05:58.198913   359 solver.cpp:218] Iteration 4836 (2.40478 iter/s, 4.99007s/12 iters), loss = 0.711259
I0407 23:05:58.198953   359 solver.cpp:237]     Train net output #0: loss = 0.711259 (* 1 = 0.711259 loss)
I0407 23:05:58.198962   359 sgd_solver.cpp:105] Iteration 4836, lr = 0.00626597
I0407 23:06:00.234081   359 blocking_queue.cpp:49] Waiting for data
I0407 23:06:03.153045   359 solver.cpp:218] Iteration 4848 (2.42225 iter/s, 4.95407s/12 iters), loss = 0.77453
I0407 23:06:03.153090   359 solver.cpp:237]     Train net output #0: loss = 0.77453 (* 1 = 0.77453 loss)
I0407 23:06:03.153097   359 sgd_solver.cpp:105] Iteration 4848, lr = 0.00621076
I0407 23:06:05.812907   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:08.101795   359 solver.cpp:218] Iteration 4860 (2.42489 iter/s, 4.94868s/12 iters), loss = 0.931621
I0407 23:06:08.101838   359 solver.cpp:237]     Train net output #0: loss = 0.931621 (* 1 = 0.931621 loss)
I0407 23:06:08.101847   359 sgd_solver.cpp:105] Iteration 4860, lr = 0.00615523
I0407 23:06:13.024924   359 solver.cpp:218] Iteration 4872 (2.43751 iter/s, 4.92306s/12 iters), loss = 0.982474
I0407 23:06:13.024971   359 solver.cpp:237]     Train net output #0: loss = 0.982474 (* 1 = 0.982474 loss)
I0407 23:06:13.024979   359 sgd_solver.cpp:105] Iteration 4872, lr = 0.0060994
I0407 23:06:17.970460   359 solver.cpp:218] Iteration 4884 (2.42646 iter/s, 4.94547s/12 iters), loss = 0.737415
I0407 23:06:17.970504   359 solver.cpp:237]     Train net output #0: loss = 0.737415 (* 1 = 0.737415 loss)
I0407 23:06:17.970512   359 sgd_solver.cpp:105] Iteration 4884, lr = 0.00604327
I0407 23:06:22.354007   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 23:06:25.483212   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 23:06:28.072077   359 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 23:06:28.072094   359 net.cpp:676] Ignoring source layer train-data
I0407 23:06:30.701858   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:32.736217   359 solver.cpp:397]     Test net output #0: accuracy = 0.367647
I0407 23:06:32.736244   359 solver.cpp:397]     Test net output #1: loss = 3.02149 (* 1 = 3.02149 loss)
I0407 23:06:32.832558   359 solver.cpp:218] Iteration 4896 (0.807427 iter/s, 14.862s/12 iters), loss = 0.760391
I0407 23:06:32.832603   359 solver.cpp:237]     Train net output #0: loss = 0.760391 (* 1 = 0.760391 loss)
I0407 23:06:32.832612   359 sgd_solver.cpp:105] Iteration 4896, lr = 0.00598688
I0407 23:06:36.929620   359 solver.cpp:218] Iteration 4908 (2.92897 iter/s, 4.097s/12 iters), loss = 0.764885
I0407 23:06:36.929656   359 solver.cpp:237]     Train net output #0: loss = 0.764885 (* 1 = 0.764885 loss)
I0407 23:06:36.929664   359 sgd_solver.cpp:105] Iteration 4908, lr = 0.00593022
I0407 23:06:41.883965   359 solver.cpp:218] Iteration 4920 (2.42215 iter/s, 4.95428s/12 iters), loss = 0.940427
I0407 23:06:41.884011   359 solver.cpp:237]     Train net output #0: loss = 0.940427 (* 1 = 0.940427 loss)
I0407 23:06:41.884019   359 sgd_solver.cpp:105] Iteration 4920, lr = 0.00587331
I0407 23:06:46.776434   359 solver.cpp:218] Iteration 4932 (2.45279 iter/s, 4.8924s/12 iters), loss = 0.897402
I0407 23:06:46.776484   359 solver.cpp:237]     Train net output #0: loss = 0.897402 (* 1 = 0.897402 loss)
I0407 23:06:46.776494   359 sgd_solver.cpp:105] Iteration 4932, lr = 0.00581616
I0407 23:06:51.752323   359 solver.cpp:218] Iteration 4944 (2.41166 iter/s, 4.97582s/12 iters), loss = 0.832853
I0407 23:06:51.752368   359 solver.cpp:237]     Train net output #0: loss = 0.832853 (* 1 = 0.832853 loss)
I0407 23:06:51.752377   359 sgd_solver.cpp:105] Iteration 4944, lr = 0.0057588
I0407 23:06:56.452111   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:56.648890   359 solver.cpp:218] Iteration 4956 (2.45073 iter/s, 4.8965s/12 iters), loss = 0.689222
I0407 23:06:56.648924   359 solver.cpp:237]     Train net output #0: loss = 0.689222 (* 1 = 0.689222 loss)
I0407 23:06:56.648932   359 sgd_solver.cpp:105] Iteration 4956, lr = 0.00570123
I0407 23:07:01.592567   359 solver.cpp:218] Iteration 4968 (2.42737 iter/s, 4.94362s/12 iters), loss = 0.366809
I0407 23:07:01.592710   359 solver.cpp:237]     Train net output #0: loss = 0.366809 (* 1 = 0.366809 loss)
I0407 23:07:01.592718   359 sgd_solver.cpp:105] Iteration 4968, lr = 0.00564347
I0407 23:07:06.515650   359 solver.cpp:218] Iteration 4980 (2.43757 iter/s, 4.92293s/12 iters), loss = 0.479785
I0407 23:07:06.515688   359 solver.cpp:237]     Train net output #0: loss = 0.479785 (* 1 = 0.479785 loss)
I0407 23:07:06.515695   359 sgd_solver.cpp:105] Iteration 4980, lr = 0.00558554
I0407 23:07:11.458390   359 solver.cpp:218] Iteration 4992 (2.42783 iter/s, 4.94268s/12 iters), loss = 0.729526
I0407 23:07:11.458427   359 solver.cpp:237]     Train net output #0: loss = 0.729526 (* 1 = 0.729526 loss)
I0407 23:07:11.458434   359 sgd_solver.cpp:105] Iteration 4992, lr = 0.00552744
I0407 23:07:13.449579   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 23:07:16.561566   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 23:07:19.019897   359 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 23:07:19.019917   359 net.cpp:676] Ignoring source layer train-data
I0407 23:07:21.678143   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:23.734504   359 solver.cpp:397]     Test net output #0: accuracy = 0.379902
I0407 23:07:23.734550   359 solver.cpp:397]     Test net output #1: loss = 3.08703 (* 1 = 3.08703 loss)
I0407 23:07:25.583346   359 solver.cpp:218] Iteration 5004 (0.849564 iter/s, 14.1249s/12 iters), loss = 0.401737
I0407 23:07:25.583397   359 solver.cpp:237]     Train net output #0: loss = 0.401737 (* 1 = 0.401737 loss)
I0407 23:07:25.583405   359 sgd_solver.cpp:105] Iteration 5004, lr = 0.0054692
I0407 23:07:30.571161   359 solver.cpp:218] Iteration 5016 (2.4059 iter/s, 4.98775s/12 iters), loss = 0.620913
I0407 23:07:30.571208   359 solver.cpp:237]     Train net output #0: loss = 0.620913 (* 1 = 0.620913 loss)
I0407 23:07:30.571216   359 sgd_solver.cpp:105] Iteration 5016, lr = 0.00541084
I0407 23:07:35.521492   359 solver.cpp:218] Iteration 5028 (2.42411 iter/s, 4.95026s/12 iters), loss = 0.647608
I0407 23:07:35.521632   359 solver.cpp:237]     Train net output #0: loss = 0.647608 (* 1 = 0.647608 loss)
I0407 23:07:35.521642   359 sgd_solver.cpp:105] Iteration 5028, lr = 0.00535236
I0407 23:07:40.458485   359 solver.cpp:218] Iteration 5040 (2.43071 iter/s, 4.93683s/12 iters), loss = 0.65534
I0407 23:07:40.458532   359 solver.cpp:237]     Train net output #0: loss = 0.65534 (* 1 = 0.65534 loss)
I0407 23:07:40.458540   359 sgd_solver.cpp:105] Iteration 5040, lr = 0.00529378
I0407 23:07:45.444732   359 solver.cpp:218] Iteration 5052 (2.40665 iter/s, 4.98618s/12 iters), loss = 0.575499
I0407 23:07:45.444774   359 solver.cpp:237]     Train net output #0: loss = 0.575499 (* 1 = 0.575499 loss)
I0407 23:07:45.444782   359 sgd_solver.cpp:105] Iteration 5052, lr = 0.00523512
I0407 23:07:47.281433   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:50.295415   359 solver.cpp:218] Iteration 5064 (2.47391 iter/s, 4.85062s/12 iters), loss = 0.906331
I0407 23:07:50.295460   359 solver.cpp:237]     Train net output #0: loss = 0.906331 (* 1 = 0.906331 loss)
I0407 23:07:50.295469   359 sgd_solver.cpp:105] Iteration 5064, lr = 0.0051764
I0407 23:07:55.257282   359 solver.cpp:218] Iteration 5076 (2.41848 iter/s, 4.9618s/12 iters), loss = 0.596021
I0407 23:07:55.257321   359 solver.cpp:237]     Train net output #0: loss = 0.596021 (* 1 = 0.596021 loss)
I0407 23:07:55.257329   359 sgd_solver.cpp:105] Iteration 5076, lr = 0.00511763
I0407 23:08:00.217500   359 solver.cpp:218] Iteration 5088 (2.41928 iter/s, 4.96016s/12 iters), loss = 0.823575
I0407 23:08:00.217536   359 solver.cpp:237]     Train net output #0: loss = 0.823575 (* 1 = 0.823575 loss)
I0407 23:08:00.217545   359 sgd_solver.cpp:105] Iteration 5088, lr = 0.00505882
I0407 23:08:04.707762   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 23:08:07.831751   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 23:08:10.195988   359 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 23:08:10.196007   359 net.cpp:676] Ignoring source layer train-data
I0407 23:08:12.549998   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:14.568877   359 solver.cpp:397]     Test net output #0: accuracy = 0.370098
I0407 23:08:14.568924   359 solver.cpp:397]     Test net output #1: loss = 3.10449 (* 1 = 3.10449 loss)
I0407 23:08:14.665768   359 solver.cpp:218] Iteration 5100 (0.830553 iter/s, 14.4482s/12 iters), loss = 0.744585
I0407 23:08:14.665807   359 solver.cpp:237]     Train net output #0: loss = 0.744585 (* 1 = 0.744585 loss)
I0407 23:08:14.665817   359 sgd_solver.cpp:105] Iteration 5100, lr = 0.005
I0407 23:08:18.806478   359 solver.cpp:218] Iteration 5112 (2.89809 iter/s, 4.14065s/12 iters), loss = 0.670824
I0407 23:08:18.806510   359 solver.cpp:237]     Train net output #0: loss = 0.670824 (* 1 = 0.670824 loss)
I0407 23:08:18.806517   359 sgd_solver.cpp:105] Iteration 5112, lr = 0.00494118
I0407 23:08:23.739210   359 solver.cpp:218] Iteration 5124 (2.43275 iter/s, 4.93268s/12 iters), loss = 0.651989
I0407 23:08:23.739246   359 solver.cpp:237]     Train net output #0: loss = 0.651989 (* 1 = 0.651989 loss)
I0407 23:08:23.739253   359 sgd_solver.cpp:105] Iteration 5124, lr = 0.00488237
I0407 23:08:28.681856   359 solver.cpp:218] Iteration 5136 (2.42787 iter/s, 4.94259s/12 iters), loss = 0.458284
I0407 23:08:28.681891   359 solver.cpp:237]     Train net output #0: loss = 0.458284 (* 1 = 0.458284 loss)
I0407 23:08:28.681900   359 sgd_solver.cpp:105] Iteration 5136, lr = 0.0048236
I0407 23:08:33.633774   359 solver.cpp:218] Iteration 5148 (2.42333 iter/s, 4.95186s/12 iters), loss = 0.590291
I0407 23:08:33.633818   359 solver.cpp:237]     Train net output #0: loss = 0.590291 (* 1 = 0.590291 loss)
I0407 23:08:33.633827   359 sgd_solver.cpp:105] Iteration 5148, lr = 0.00476488
I0407 23:08:37.657568   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:38.561758   359 solver.cpp:218] Iteration 5160 (2.43511 iter/s, 4.92792s/12 iters), loss = 0.577291
I0407 23:08:38.561887   359 solver.cpp:237]     Train net output #0: loss = 0.577291 (* 1 = 0.577291 loss)
I0407 23:08:38.561895   359 sgd_solver.cpp:105] Iteration 5160, lr = 0.00470622
I0407 23:08:43.500888   359 solver.cpp:218] Iteration 5172 (2.42965 iter/s, 4.93898s/12 iters), loss = 0.661538
I0407 23:08:43.500932   359 solver.cpp:237]     Train net output #0: loss = 0.661538 (* 1 = 0.661538 loss)
I0407 23:08:43.500941   359 sgd_solver.cpp:105] Iteration 5172, lr = 0.00464764
I0407 23:08:48.400684   359 solver.cpp:218] Iteration 5184 (2.44911 iter/s, 4.89973s/12 iters), loss = 0.358573
I0407 23:08:48.400730   359 solver.cpp:237]     Train net output #0: loss = 0.358573 (* 1 = 0.358573 loss)
I0407 23:08:48.400739   359 sgd_solver.cpp:105] Iteration 5184, lr = 0.00458916
I0407 23:08:53.309351   359 solver.cpp:218] Iteration 5196 (2.44469 iter/s, 4.9086s/12 iters), loss = 0.27501
I0407 23:08:53.309396   359 solver.cpp:237]     Train net output #0: loss = 0.27501 (* 1 = 0.27501 loss)
I0407 23:08:53.309404   359 sgd_solver.cpp:105] Iteration 5196, lr = 0.0045308
I0407 23:08:55.294013   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 23:08:58.374043   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 23:09:00.744858   359 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 23:09:00.744876   359 net.cpp:676] Ignoring source layer train-data
I0407 23:09:03.268515   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:05.517493   359 solver.cpp:397]     Test net output #0: accuracy = 0.381127
I0407 23:09:05.517540   359 solver.cpp:397]     Test net output #1: loss = 3.10979 (* 1 = 3.10979 loss)
I0407 23:09:07.335219   359 solver.cpp:218] Iteration 5208 (0.855567 iter/s, 14.0258s/12 iters), loss = 0.510135
I0407 23:09:07.335266   359 solver.cpp:237]     Train net output #0: loss = 0.510135 (* 1 = 0.510135 loss)
I0407 23:09:07.335274   359 sgd_solver.cpp:105] Iteration 5208, lr = 0.00447256
I0407 23:09:12.320524   359 solver.cpp:218] Iteration 5220 (2.40711 iter/s, 4.98524s/12 iters), loss = 0.437447
I0407 23:09:12.320667   359 solver.cpp:237]     Train net output #0: loss = 0.437447 (* 1 = 0.437447 loss)
I0407 23:09:12.320677   359 sgd_solver.cpp:105] Iteration 5220, lr = 0.00441446
I0407 23:09:17.229663   359 solver.cpp:218] Iteration 5232 (2.4445 iter/s, 4.90898s/12 iters), loss = 0.348768
I0407 23:09:17.229701   359 solver.cpp:237]     Train net output #0: loss = 0.348768 (* 1 = 0.348768 loss)
I0407 23:09:17.229709   359 sgd_solver.cpp:105] Iteration 5232, lr = 0.00435653
I0407 23:09:22.266436   359 solver.cpp:218] Iteration 5244 (2.3825 iter/s, 5.03672s/12 iters), loss = 0.47217
I0407 23:09:22.266476   359 solver.cpp:237]     Train net output #0: loss = 0.47217 (* 1 = 0.47217 loss)
I0407 23:09:22.266484   359 sgd_solver.cpp:105] Iteration 5244, lr = 0.00429877
I0407 23:09:27.221897   359 solver.cpp:218] Iteration 5256 (2.4216 iter/s, 4.9554s/12 iters), loss = 0.531433
I0407 23:09:27.221944   359 solver.cpp:237]     Train net output #0: loss = 0.531433 (* 1 = 0.531433 loss)
I0407 23:09:27.221952   359 sgd_solver.cpp:105] Iteration 5256, lr = 0.0042412
I0407 23:09:28.476162   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:32.108680   359 solver.cpp:218] Iteration 5268 (2.45564 iter/s, 4.88672s/12 iters), loss = 0.521499
I0407 23:09:32.108714   359 solver.cpp:237]     Train net output #0: loss = 0.521499 (* 1 = 0.521499 loss)
I0407 23:09:32.108722   359 sgd_solver.cpp:105] Iteration 5268, lr = 0.00418384
I0407 23:09:37.075927   359 solver.cpp:218] Iteration 5280 (2.41585 iter/s, 4.9672s/12 iters), loss = 0.657227
I0407 23:09:37.075966   359 solver.cpp:237]     Train net output #0: loss = 0.657227 (* 1 = 0.657227 loss)
I0407 23:09:37.075973   359 sgd_solver.cpp:105] Iteration 5280, lr = 0.00412669
I0407 23:09:41.969507   359 solver.cpp:218] Iteration 5292 (2.45222 iter/s, 4.89352s/12 iters), loss = 0.364044
I0407 23:09:41.969550   359 solver.cpp:237]     Train net output #0: loss = 0.364044 (* 1 = 0.364044 loss)
I0407 23:09:41.969558   359 sgd_solver.cpp:105] Iteration 5292, lr = 0.00406978
I0407 23:09:46.410845   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 23:09:49.721287   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 23:09:52.087182   359 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 23:09:52.087206   359 net.cpp:676] Ignoring source layer train-data
I0407 23:09:54.421185   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:56.533413   359 solver.cpp:397]     Test net output #0: accuracy = 0.390319
I0407 23:09:56.533447   359 solver.cpp:397]     Test net output #1: loss = 3.06499 (* 1 = 3.06499 loss)
I0407 23:09:56.629779   359 solver.cpp:218] Iteration 5304 (0.818544 iter/s, 14.6602s/12 iters), loss = 0.290723
I0407 23:09:56.629854   359 solver.cpp:237]     Train net output #0: loss = 0.290723 (* 1 = 0.290723 loss)
I0407 23:09:56.629870   359 sgd_solver.cpp:105] Iteration 5304, lr = 0.00401312
I0407 23:10:00.773187   359 solver.cpp:218] Iteration 5316 (2.89623 iter/s, 4.14332s/12 iters), loss = 0.678481
I0407 23:10:00.773232   359 solver.cpp:237]     Train net output #0: loss = 0.678481 (* 1 = 0.678481 loss)
I0407 23:10:00.773241   359 sgd_solver.cpp:105] Iteration 5316, lr = 0.00395672
I0407 23:10:05.697723   359 solver.cpp:218] Iteration 5328 (2.43681 iter/s, 4.92448s/12 iters), loss = 0.40485
I0407 23:10:05.697759   359 solver.cpp:237]     Train net output #0: loss = 0.40485 (* 1 = 0.40485 loss)
I0407 23:10:05.697767   359 sgd_solver.cpp:105] Iteration 5328, lr = 0.0039006
I0407 23:10:10.650027   359 solver.cpp:218] Iteration 5340 (2.42314 iter/s, 4.95225s/12 iters), loss = 0.476173
I0407 23:10:10.650063   359 solver.cpp:237]     Train net output #0: loss = 0.476173 (* 1 = 0.476173 loss)
I0407 23:10:10.650071   359 sgd_solver.cpp:105] Iteration 5340, lr = 0.00384477
I0407 23:10:15.558791   359 solver.cpp:218] Iteration 5352 (2.44463 iter/s, 4.90871s/12 iters), loss = 0.45152
I0407 23:10:15.558828   359 solver.cpp:237]     Train net output #0: loss = 0.45152 (* 1 = 0.45152 loss)
I0407 23:10:15.558835   359 sgd_solver.cpp:105] Iteration 5352, lr = 0.00378924
I0407 23:10:18.931526   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:20.503518   359 solver.cpp:218] Iteration 5364 (2.42685 iter/s, 4.94467s/12 iters), loss = 0.472535
I0407 23:10:20.503556   359 solver.cpp:237]     Train net output #0: loss = 0.472535 (* 1 = 0.472535 loss)
I0407 23:10:20.503563   359 sgd_solver.cpp:105] Iteration 5364, lr = 0.00373403
I0407 23:10:25.455345   359 solver.cpp:218] Iteration 5376 (2.42338 iter/s, 4.95177s/12 iters), loss = 0.339578
I0407 23:10:25.455384   359 solver.cpp:237]     Train net output #0: loss = 0.339578 (* 1 = 0.339578 loss)
I0407 23:10:25.455391   359 sgd_solver.cpp:105] Iteration 5376, lr = 0.00367914
I0407 23:10:30.403879   359 solver.cpp:218] Iteration 5388 (2.42499 iter/s, 4.94848s/12 iters), loss = 0.329
I0407 23:10:30.403918   359 solver.cpp:237]     Train net output #0: loss = 0.329 (* 1 = 0.329 loss)
I0407 23:10:30.403925   359 sgd_solver.cpp:105] Iteration 5388, lr = 0.00362459
I0407 23:10:35.242074   359 solver.cpp:218] Iteration 5400 (2.48029 iter/s, 4.83814s/12 iters), loss = 0.300492
I0407 23:10:35.242113   359 solver.cpp:237]     Train net output #0: loss = 0.300492 (* 1 = 0.300492 loss)
I0407 23:10:35.242121   359 sgd_solver.cpp:105] Iteration 5400, lr = 0.0035704
I0407 23:10:37.239481   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 23:10:41.514129   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 23:10:43.912750   359 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 23:10:43.912768   359 net.cpp:676] Ignoring source layer train-data
I0407 23:10:46.360626   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:48.694677   359 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0407 23:10:48.694725   359 solver.cpp:397]     Test net output #1: loss = 2.93582 (* 1 = 2.93582 loss)
I0407 23:10:50.504892   359 solver.cpp:218] Iteration 5412 (0.786228 iter/s, 15.2627s/12 iters), loss = 0.316812
I0407 23:10:50.505004   359 solver.cpp:237]     Train net output #0: loss = 0.316812 (* 1 = 0.316812 loss)
I0407 23:10:50.505012   359 sgd_solver.cpp:105] Iteration 5412, lr = 0.00351657
I0407 23:10:55.414255   359 solver.cpp:218] Iteration 5424 (2.44438 iter/s, 4.90923s/12 iters), loss = 0.392943
I0407 23:10:55.414299   359 solver.cpp:237]     Train net output #0: loss = 0.392943 (* 1 = 0.392943 loss)
I0407 23:10:55.414307   359 sgd_solver.cpp:105] Iteration 5424, lr = 0.00346311
I0407 23:11:00.315161   359 solver.cpp:218] Iteration 5436 (2.44856 iter/s, 4.90084s/12 iters), loss = 0.486893
I0407 23:11:00.315220   359 solver.cpp:237]     Train net output #0: loss = 0.486893 (* 1 = 0.486893 loss)
I0407 23:11:00.315233   359 sgd_solver.cpp:105] Iteration 5436, lr = 0.00341004
I0407 23:11:05.259418   359 solver.cpp:218] Iteration 5448 (2.4271 iter/s, 4.94418s/12 iters), loss = 0.304622
I0407 23:11:05.259452   359 solver.cpp:237]     Train net output #0: loss = 0.304622 (* 1 = 0.304622 loss)
I0407 23:11:05.259459   359 sgd_solver.cpp:105] Iteration 5448, lr = 0.00335736
I0407 23:11:10.148643   359 solver.cpp:218] Iteration 5460 (2.4544 iter/s, 4.88917s/12 iters), loss = 0.433443
I0407 23:11:10.148679   359 solver.cpp:237]     Train net output #0: loss = 0.433443 (* 1 = 0.433443 loss)
I0407 23:11:10.148687   359 sgd_solver.cpp:105] Iteration 5460, lr = 0.00330509
I0407 23:11:10.683794   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:14.939079   359 solver.cpp:218] Iteration 5472 (2.50502 iter/s, 4.79038s/12 iters), loss = 0.227015
I0407 23:11:14.939128   359 solver.cpp:237]     Train net output #0: loss = 0.227015 (* 1 = 0.227015 loss)
I0407 23:11:14.939137   359 sgd_solver.cpp:105] Iteration 5472, lr = 0.00325324
I0407 23:11:19.948105   359 solver.cpp:218] Iteration 5484 (2.39571 iter/s, 5.00895s/12 iters), loss = 0.63865
I0407 23:11:19.948150   359 solver.cpp:237]     Train net output #0: loss = 0.63865 (* 1 = 0.63865 loss)
I0407 23:11:19.948159   359 sgd_solver.cpp:105] Iteration 5484, lr = 0.00320181
I0407 23:11:24.890550   359 solver.cpp:218] Iteration 5496 (2.42798 iter/s, 4.94237s/12 iters), loss = 0.474697
I0407 23:11:24.890722   359 solver.cpp:237]     Train net output #0: loss = 0.474697 (* 1 = 0.474697 loss)
I0407 23:11:24.890731   359 sgd_solver.cpp:105] Iteration 5496, lr = 0.00315081
I0407 23:11:29.351128   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 23:11:33.410147   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 23:11:36.455431   359 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 23:11:36.455449   359 net.cpp:676] Ignoring source layer train-data
I0407 23:11:38.838516   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:41.222564   359 solver.cpp:397]     Test net output #0: accuracy = 0.427696
I0407 23:11:41.222604   359 solver.cpp:397]     Test net output #1: loss = 2.84702 (* 1 = 2.84702 loss)
I0407 23:11:41.318938   359 solver.cpp:218] Iteration 5508 (0.730452 iter/s, 16.4282s/12 iters), loss = 0.303231
I0407 23:11:41.318982   359 solver.cpp:237]     Train net output #0: loss = 0.303231 (* 1 = 0.303231 loss)
I0407 23:11:41.318991   359 sgd_solver.cpp:105] Iteration 5508, lr = 0.00310026
I0407 23:11:45.424505   359 solver.cpp:218] Iteration 5520 (2.9229 iter/s, 4.10551s/12 iters), loss = 0.404447
I0407 23:11:45.424542   359 solver.cpp:237]     Train net output #0: loss = 0.404447 (* 1 = 0.404447 loss)
I0407 23:11:45.424551   359 sgd_solver.cpp:105] Iteration 5520, lr = 0.00305015
I0407 23:11:47.838690   359 blocking_queue.cpp:49] Waiting for data
I0407 23:11:50.383118   359 solver.cpp:218] Iteration 5532 (2.42006 iter/s, 4.95855s/12 iters), loss = 0.238364
I0407 23:11:50.383157   359 solver.cpp:237]     Train net output #0: loss = 0.238364 (* 1 = 0.238364 loss)
I0407 23:11:50.383165   359 sgd_solver.cpp:105] Iteration 5532, lr = 0.0030005
I0407 23:11:55.311910   359 solver.cpp:218] Iteration 5544 (2.4347 iter/s, 4.92873s/12 iters), loss = 0.375532
I0407 23:11:55.312031   359 solver.cpp:237]     Train net output #0: loss = 0.375532 (* 1 = 0.375532 loss)
I0407 23:11:55.312041   359 sgd_solver.cpp:105] Iteration 5544, lr = 0.00295132
I0407 23:12:00.365229   359 solver.cpp:218] Iteration 5556 (2.37474 iter/s, 5.05318s/12 iters), loss = 0.372204
I0407 23:12:00.365263   359 solver.cpp:237]     Train net output #0: loss = 0.372204 (* 1 = 0.372204 loss)
I0407 23:12:00.365270   359 sgd_solver.cpp:105] Iteration 5556, lr = 0.00290261
I0407 23:12:03.050983   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:05.316218   359 solver.cpp:218] Iteration 5568 (2.42378 iter/s, 4.95094s/12 iters), loss = 0.242734
I0407 23:12:05.316255   359 solver.cpp:237]     Train net output #0: loss = 0.242734 (* 1 = 0.242734 loss)
I0407 23:12:05.316263   359 sgd_solver.cpp:105] Iteration 5568, lr = 0.00285438
I0407 23:12:10.244213   359 solver.cpp:218] Iteration 5580 (2.4351 iter/s, 4.92794s/12 iters), loss = 0.20582
I0407 23:12:10.244254   359 solver.cpp:237]     Train net output #0: loss = 0.20582 (* 1 = 0.20582 loss)
I0407 23:12:10.244263   359 sgd_solver.cpp:105] Iteration 5580, lr = 0.00280663
I0407 23:12:15.203958   359 solver.cpp:218] Iteration 5592 (2.41951 iter/s, 4.95969s/12 iters), loss = 0.297734
I0407 23:12:15.203994   359 solver.cpp:237]     Train net output #0: loss = 0.297734 (* 1 = 0.297734 loss)
I0407 23:12:15.204002   359 sgd_solver.cpp:105] Iteration 5592, lr = 0.00275937
I0407 23:12:20.126761   359 solver.cpp:218] Iteration 5604 (2.43766 iter/s, 4.92275s/12 iters), loss = 0.439124
I0407 23:12:20.126802   359 solver.cpp:237]     Train net output #0: loss = 0.439124 (* 1 = 0.439124 loss)
I0407 23:12:20.126811   359 sgd_solver.cpp:105] Iteration 5604, lr = 0.00271261
I0407 23:12:22.170949   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 23:12:26.229061   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 23:12:30.063935   359 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 23:12:30.063956   359 net.cpp:676] Ignoring source layer train-data
I0407 23:12:32.436645   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:34.856134   359 solver.cpp:397]     Test net output #0: accuracy = 0.435662
I0407 23:12:34.856169   359 solver.cpp:397]     Test net output #1: loss = 2.89615 (* 1 = 2.89615 loss)
I0407 23:12:36.632707   359 solver.cpp:218] Iteration 5616 (0.727014 iter/s, 16.5059s/12 iters), loss = 0.331044
I0407 23:12:36.632753   359 solver.cpp:237]     Train net output #0: loss = 0.331044 (* 1 = 0.331044 loss)
I0407 23:12:36.632761   359 sgd_solver.cpp:105] Iteration 5616, lr = 0.00266635
I0407 23:12:41.692314   359 solver.cpp:218] Iteration 5628 (2.37175 iter/s, 5.05955s/12 iters), loss = 0.389382
I0407 23:12:41.692351   359 solver.cpp:237]     Train net output #0: loss = 0.389382 (* 1 = 0.389382 loss)
I0407 23:12:41.692358   359 sgd_solver.cpp:105] Iteration 5628, lr = 0.00262059
I0407 23:12:46.717353   359 solver.cpp:218] Iteration 5640 (2.38807 iter/s, 5.02498s/12 iters), loss = 0.374075
I0407 23:12:46.717396   359 solver.cpp:237]     Train net output #0: loss = 0.374075 (* 1 = 0.374075 loss)
I0407 23:12:46.717402   359 sgd_solver.cpp:105] Iteration 5640, lr = 0.00257534
I0407 23:12:51.776077   359 solver.cpp:218] Iteration 5652 (2.37217 iter/s, 5.05867s/12 iters), loss = 0.245748
I0407 23:12:51.776108   359 solver.cpp:237]     Train net output #0: loss = 0.245748 (* 1 = 0.245748 loss)
I0407 23:12:51.776116   359 sgd_solver.cpp:105] Iteration 5652, lr = 0.00253061
I0407 23:12:56.510118   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:56.678118   359 solver.cpp:218] Iteration 5664 (2.44798 iter/s, 4.902s/12 iters), loss = 0.179748
I0407 23:12:56.678148   359 solver.cpp:237]     Train net output #0: loss = 0.179748 (* 1 = 0.179748 loss)
I0407 23:12:56.678155   359 sgd_solver.cpp:105] Iteration 5664, lr = 0.00248639
I0407 23:13:01.638792   359 solver.cpp:218] Iteration 5676 (2.41905 iter/s, 4.96063s/12 iters), loss = 0.356952
I0407 23:13:01.638820   359 solver.cpp:237]     Train net output #0: loss = 0.356952 (* 1 = 0.356952 loss)
I0407 23:13:01.638828   359 sgd_solver.cpp:105] Iteration 5676, lr = 0.0024427
I0407 23:13:06.605708   359 solver.cpp:218] Iteration 5688 (2.41601 iter/s, 4.96687s/12 iters), loss = 0.308913
I0407 23:13:06.605746   359 solver.cpp:237]     Train net output #0: loss = 0.308913 (* 1 = 0.308913 loss)
I0407 23:13:06.605753   359 sgd_solver.cpp:105] Iteration 5688, lr = 0.00239952
I0407 23:13:11.461937   359 solver.cpp:218] Iteration 5700 (2.47108 iter/s, 4.85617s/12 iters), loss = 0.389904
I0407 23:13:11.461978   359 solver.cpp:237]     Train net output #0: loss = 0.389904 (* 1 = 0.389904 loss)
I0407 23:13:11.461987   359 sgd_solver.cpp:105] Iteration 5700, lr = 0.00235687
I0407 23:13:15.926808   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 23:13:20.141396   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 23:13:23.874502   359 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 23:13:23.874519   359 net.cpp:676] Ignoring source layer train-data
I0407 23:13:26.197837   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:28.663111   359 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0407 23:13:28.663331   359 solver.cpp:397]     Test net output #1: loss = 2.88431 (* 1 = 2.88431 loss)
I0407 23:13:28.759913   359 solver.cpp:218] Iteration 5712 (0.693726 iter/s, 17.2979s/12 iters), loss = 0.310276
I0407 23:13:28.759960   359 solver.cpp:237]     Train net output #0: loss = 0.310276 (* 1 = 0.310276 loss)
I0407 23:13:28.759968   359 sgd_solver.cpp:105] Iteration 5712, lr = 0.00231475
I0407 23:13:32.957828   359 solver.cpp:218] Iteration 5724 (2.85861 iter/s, 4.19785s/12 iters), loss = 0.385243
I0407 23:13:32.957868   359 solver.cpp:237]     Train net output #0: loss = 0.385243 (* 1 = 0.385243 loss)
I0407 23:13:32.957876   359 sgd_solver.cpp:105] Iteration 5724, lr = 0.00227316
I0407 23:13:37.826519   359 solver.cpp:218] Iteration 5736 (2.46476 iter/s, 4.86863s/12 iters), loss = 0.323
I0407 23:13:37.826558   359 solver.cpp:237]     Train net output #0: loss = 0.323 (* 1 = 0.323 loss)
I0407 23:13:37.826567   359 sgd_solver.cpp:105] Iteration 5736, lr = 0.0022321
I0407 23:13:42.735463   359 solver.cpp:218] Iteration 5748 (2.44455 iter/s, 4.90888s/12 iters), loss = 0.269936
I0407 23:13:42.735502   359 solver.cpp:237]     Train net output #0: loss = 0.269936 (* 1 = 0.269936 loss)
I0407 23:13:42.735509   359 sgd_solver.cpp:105] Iteration 5748, lr = 0.00219157
I0407 23:13:47.683583   359 solver.cpp:218] Iteration 5760 (2.42519 iter/s, 4.94806s/12 iters), loss = 0.227433
I0407 23:13:47.683625   359 solver.cpp:237]     Train net output #0: loss = 0.227433 (* 1 = 0.227433 loss)
I0407 23:13:47.683634   359 sgd_solver.cpp:105] Iteration 5760, lr = 0.00215157
I0407 23:13:49.597967   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:52.574467   359 solver.cpp:218] Iteration 5772 (2.45357 iter/s, 4.89083s/12 iters), loss = 0.158779
I0407 23:13:52.574502   359 solver.cpp:237]     Train net output #0: loss = 0.158779 (* 1 = 0.158779 loss)
I0407 23:13:52.574509   359 sgd_solver.cpp:105] Iteration 5772, lr = 0.0021121
I0407 23:13:57.531615   359 solver.cpp:218] Iteration 5784 (2.42077 iter/s, 4.95709s/12 iters), loss = 0.225406
I0407 23:13:57.531652   359 solver.cpp:237]     Train net output #0: loss = 0.225406 (* 1 = 0.225406 loss)
I0407 23:13:57.531661   359 sgd_solver.cpp:105] Iteration 5784, lr = 0.00207317
I0407 23:14:02.442128   359 solver.cpp:218] Iteration 5796 (2.44376 iter/s, 4.91046s/12 iters), loss = 0.260227
I0407 23:14:02.442282   359 solver.cpp:237]     Train net output #0: loss = 0.260227 (* 1 = 0.260227 loss)
I0407 23:14:02.442293   359 sgd_solver.cpp:105] Iteration 5796, lr = 0.00203477
I0407 23:14:07.388200   359 solver.cpp:218] Iteration 5808 (2.42625 iter/s, 4.9459s/12 iters), loss = 0.212148
I0407 23:14:07.388238   359 solver.cpp:237]     Train net output #0: loss = 0.212148 (* 1 = 0.212148 loss)
I0407 23:14:07.388247   359 sgd_solver.cpp:105] Iteration 5808, lr = 0.0019969
I0407 23:14:09.397231   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 23:14:12.773272   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 23:14:16.778278   359 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 23:14:16.778295   359 net.cpp:676] Ignoring source layer train-data
I0407 23:14:19.008105   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:21.349109   359 solver.cpp:397]     Test net output #0: accuracy = 0.443015
I0407 23:14:21.349156   359 solver.cpp:397]     Test net output #1: loss = 2.83557 (* 1 = 2.83557 loss)
I0407 23:14:23.160087   359 solver.cpp:218] Iteration 5820 (0.760851 iter/s, 15.7718s/12 iters), loss = 0.406367
I0407 23:14:23.160125   359 solver.cpp:237]     Train net output #0: loss = 0.406367 (* 1 = 0.406367 loss)
I0407 23:14:23.160133   359 sgd_solver.cpp:105] Iteration 5820, lr = 0.00195956
I0407 23:14:28.109808   359 solver.cpp:218] Iteration 5832 (2.42441 iter/s, 4.94966s/12 iters), loss = 0.310057
I0407 23:14:28.109850   359 solver.cpp:237]     Train net output #0: loss = 0.310057 (* 1 = 0.310057 loss)
I0407 23:14:28.109858   359 sgd_solver.cpp:105] Iteration 5832, lr = 0.00192275
I0407 23:14:33.014621   359 solver.cpp:218] Iteration 5844 (2.44661 iter/s, 4.90475s/12 iters), loss = 0.263185
I0407 23:14:33.014744   359 solver.cpp:237]     Train net output #0: loss = 0.263185 (* 1 = 0.263185 loss)
I0407 23:14:33.014755   359 sgd_solver.cpp:105] Iteration 5844, lr = 0.00188647
I0407 23:14:37.971206   359 solver.cpp:218] Iteration 5856 (2.42109 iter/s, 4.95645s/12 iters), loss = 0.19987
I0407 23:14:37.971242   359 solver.cpp:237]     Train net output #0: loss = 0.19987 (* 1 = 0.19987 loss)
I0407 23:14:37.971251   359 sgd_solver.cpp:105] Iteration 5856, lr = 0.00185072
I0407 23:14:42.130307   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:42.918393   359 solver.cpp:218] Iteration 5868 (2.42565 iter/s, 4.94713s/12 iters), loss = 0.199371
I0407 23:14:42.918435   359 solver.cpp:237]     Train net output #0: loss = 0.199371 (* 1 = 0.199371 loss)
I0407 23:14:42.918444   359 sgd_solver.cpp:105] Iteration 5868, lr = 0.0018155
I0407 23:14:47.815058   359 solver.cpp:218] Iteration 5880 (2.45068 iter/s, 4.8966s/12 iters), loss = 0.184291
I0407 23:14:47.815104   359 solver.cpp:237]     Train net output #0: loss = 0.184291 (* 1 = 0.184291 loss)
I0407 23:14:47.815114   359 sgd_solver.cpp:105] Iteration 5880, lr = 0.0017808
I0407 23:14:52.780167   359 solver.cpp:218] Iteration 5892 (2.4169 iter/s, 4.96504s/12 iters), loss = 0.264258
I0407 23:14:52.780211   359 solver.cpp:237]     Train net output #0: loss = 0.264258 (* 1 = 0.264258 loss)
I0407 23:14:52.780220   359 sgd_solver.cpp:105] Iteration 5892, lr = 0.00174662
I0407 23:14:57.756559   359 solver.cpp:218] Iteration 5904 (2.41142 iter/s, 4.97632s/12 iters), loss = 0.284302
I0407 23:14:57.756620   359 solver.cpp:237]     Train net output #0: loss = 0.284302 (* 1 = 0.284302 loss)
I0407 23:14:57.756631   359 sgd_solver.cpp:105] Iteration 5904, lr = 0.00171296
I0407 23:15:02.260032   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 23:15:05.350765   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 23:15:08.007269   359 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 23:15:08.007289   359 net.cpp:676] Ignoring source layer train-data
I0407 23:15:10.244650   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:12.810101   359 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0407 23:15:12.810149   359 solver.cpp:397]     Test net output #1: loss = 2.83536 (* 1 = 2.83536 loss)
I0407 23:15:12.906216   359 solver.cpp:218] Iteration 5916 (0.792102 iter/s, 15.1496s/12 iters), loss = 0.143237
I0407 23:15:12.906260   359 solver.cpp:237]     Train net output #0: loss = 0.143237 (* 1 = 0.143237 loss)
I0407 23:15:12.906268   359 sgd_solver.cpp:105] Iteration 5916, lr = 0.00167982
I0407 23:15:16.993103   359 solver.cpp:218] Iteration 5928 (2.93626 iter/s, 4.08682s/12 iters), loss = 0.145722
I0407 23:15:16.993140   359 solver.cpp:237]     Train net output #0: loss = 0.145722 (* 1 = 0.145722 loss)
I0407 23:15:16.993149   359 sgd_solver.cpp:105] Iteration 5928, lr = 0.00164719
I0407 23:15:21.842190   359 solver.cpp:218] Iteration 5940 (2.47472 iter/s, 4.84903s/12 iters), loss = 0.133358
I0407 23:15:21.842231   359 solver.cpp:237]     Train net output #0: loss = 0.133358 (* 1 = 0.133358 loss)
I0407 23:15:21.842239   359 sgd_solver.cpp:105] Iteration 5940, lr = 0.00161507
I0407 23:15:26.799206   359 solver.cpp:218] Iteration 5952 (2.42085 iter/s, 4.95693s/12 iters), loss = 0.313479
I0407 23:15:26.799253   359 solver.cpp:237]     Train net output #0: loss = 0.313479 (* 1 = 0.313479 loss)
I0407 23:15:26.799263   359 sgd_solver.cpp:105] Iteration 5952, lr = 0.00158346
I0407 23:15:31.779289   359 solver.cpp:218] Iteration 5964 (2.40964 iter/s, 4.98001s/12 iters), loss = 0.124743
I0407 23:15:31.779335   359 solver.cpp:237]     Train net output #0: loss = 0.124743 (* 1 = 0.124743 loss)
I0407 23:15:31.779345   359 sgd_solver.cpp:105] Iteration 5964, lr = 0.00155235
I0407 23:15:33.063752   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:36.666793   359 solver.cpp:218] Iteration 5976 (2.45528 iter/s, 4.88743s/12 iters), loss = 0.146947
I0407 23:15:36.666956   359 solver.cpp:237]     Train net output #0: loss = 0.146947 (* 1 = 0.146947 loss)
I0407 23:15:36.666966   359 sgd_solver.cpp:105] Iteration 5976, lr = 0.00152174
I0407 23:15:41.544632   359 solver.cpp:218] Iteration 5988 (2.4602 iter/s, 4.87766s/12 iters), loss = 0.148596
I0407 23:15:41.544668   359 solver.cpp:237]     Train net output #0: loss = 0.148596 (* 1 = 0.148596 loss)
I0407 23:15:41.544677   359 sgd_solver.cpp:105] Iteration 5988, lr = 0.00149164
I0407 23:15:46.482853   359 solver.cpp:218] Iteration 6000 (2.43006 iter/s, 4.93815s/12 iters), loss = 0.233873
I0407 23:15:46.482905   359 solver.cpp:237]     Train net output #0: loss = 0.233873 (* 1 = 0.233873 loss)
I0407 23:15:46.482916   359 sgd_solver.cpp:105] Iteration 6000, lr = 0.00146202
I0407 23:15:51.396286   359 solver.cpp:218] Iteration 6012 (2.44232 iter/s, 4.91336s/12 iters), loss = 0.278592
I0407 23:15:51.396332   359 solver.cpp:237]     Train net output #0: loss = 0.278592 (* 1 = 0.278592 loss)
I0407 23:15:51.396342   359 sgd_solver.cpp:105] Iteration 6012, lr = 0.00143289
I0407 23:15:53.396051   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 23:15:57.885305   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 23:16:00.275216   359 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 23:16:00.275236   359 net.cpp:676] Ignoring source layer train-data
I0407 23:16:02.468118   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:05.065683   359 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0407 23:16:05.065723   359 solver.cpp:397]     Test net output #1: loss = 2.83763 (* 1 = 2.83763 loss)
I0407 23:16:06.846371   359 solver.cpp:218] Iteration 6024 (0.776699 iter/s, 15.45s/12 iters), loss = 0.245023
I0407 23:16:06.846490   359 solver.cpp:237]     Train net output #0: loss = 0.245023 (* 1 = 0.245023 loss)
I0407 23:16:06.846499   359 sgd_solver.cpp:105] Iteration 6024, lr = 0.00140425
I0407 23:16:11.799628   359 solver.cpp:218] Iteration 6036 (2.42272 iter/s, 4.95312s/12 iters), loss = 0.170038
I0407 23:16:11.799665   359 solver.cpp:237]     Train net output #0: loss = 0.170038 (* 1 = 0.170038 loss)
I0407 23:16:11.799674   359 sgd_solver.cpp:105] Iteration 6036, lr = 0.00137609
I0407 23:16:16.704784   359 solver.cpp:218] Iteration 6048 (2.44643 iter/s, 4.9051s/12 iters), loss = 0.217919
I0407 23:16:16.704824   359 solver.cpp:237]     Train net output #0: loss = 0.217919 (* 1 = 0.217919 loss)
I0407 23:16:16.704833   359 sgd_solver.cpp:105] Iteration 6048, lr = 0.0013484
I0407 23:16:21.646584   359 solver.cpp:218] Iteration 6060 (2.42829 iter/s, 4.94174s/12 iters), loss = 0.134132
I0407 23:16:21.646625   359 solver.cpp:237]     Train net output #0: loss = 0.134132 (* 1 = 0.134132 loss)
I0407 23:16:21.646634   359 sgd_solver.cpp:105] Iteration 6060, lr = 0.00132119
I0407 23:16:25.035243   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:26.537616   359 solver.cpp:218] Iteration 6072 (2.4535 iter/s, 4.89097s/12 iters), loss = 0.237463
I0407 23:16:26.537659   359 solver.cpp:237]     Train net output #0: loss = 0.237463 (* 1 = 0.237463 loss)
I0407 23:16:26.537668   359 sgd_solver.cpp:105] Iteration 6072, lr = 0.00129444
I0407 23:16:31.477509   359 solver.cpp:218] Iteration 6084 (2.42924 iter/s, 4.93982s/12 iters), loss = 0.241037
I0407 23:16:31.477552   359 solver.cpp:237]     Train net output #0: loss = 0.241037 (* 1 = 0.241037 loss)
I0407 23:16:31.477560   359 sgd_solver.cpp:105] Iteration 6084, lr = 0.00126816
I0407 23:16:36.413036   359 solver.cpp:218] Iteration 6096 (2.43138 iter/s, 4.93546s/12 iters), loss = 0.269924
I0407 23:16:36.413079   359 solver.cpp:237]     Train net output #0: loss = 0.269924 (* 1 = 0.269924 loss)
I0407 23:16:36.413089   359 sgd_solver.cpp:105] Iteration 6096, lr = 0.00124233
I0407 23:16:41.308813   359 solver.cpp:218] Iteration 6108 (2.45113 iter/s, 4.89571s/12 iters), loss = 0.185467
I0407 23:16:41.308938   359 solver.cpp:237]     Train net output #0: loss = 0.185467 (* 1 = 0.185467 loss)
I0407 23:16:41.308948   359 sgd_solver.cpp:105] Iteration 6108, lr = 0.00121696
I0407 23:16:45.815301   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 23:16:48.882534   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 23:16:51.347414   359 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 23:16:51.347432   359 net.cpp:676] Ignoring source layer train-data
I0407 23:16:53.483459   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:56.131894   359 solver.cpp:397]     Test net output #0: accuracy = 0.445466
I0407 23:16:56.131943   359 solver.cpp:397]     Test net output #1: loss = 2.83952 (* 1 = 2.83952 loss)
I0407 23:16:56.228446   359 solver.cpp:218] Iteration 6120 (0.804318 iter/s, 14.9195s/12 iters), loss = 0.145287
I0407 23:16:56.228493   359 solver.cpp:237]     Train net output #0: loss = 0.145287 (* 1 = 0.145287 loss)
I0407 23:16:56.228502   359 sgd_solver.cpp:105] Iteration 6120, lr = 0.00119203
I0407 23:17:00.337146   359 solver.cpp:218] Iteration 6132 (2.92068 iter/s, 4.10864s/12 iters), loss = 0.132761
I0407 23:17:00.337185   359 solver.cpp:237]     Train net output #0: loss = 0.132761 (* 1 = 0.132761 loss)
I0407 23:17:00.337193   359 sgd_solver.cpp:105] Iteration 6132, lr = 0.00116755
I0407 23:17:05.298271   359 solver.cpp:218] Iteration 6144 (2.41883 iter/s, 4.96107s/12 iters), loss = 0.162121
I0407 23:17:05.298308   359 solver.cpp:237]     Train net output #0: loss = 0.162121 (* 1 = 0.162121 loss)
I0407 23:17:05.298317   359 sgd_solver.cpp:105] Iteration 6144, lr = 0.0011435
I0407 23:17:10.255321   359 solver.cpp:218] Iteration 6156 (2.42082 iter/s, 4.95699s/12 iters), loss = 0.186738
I0407 23:17:10.255363   359 solver.cpp:237]     Train net output #0: loss = 0.186738 (* 1 = 0.186738 loss)
I0407 23:17:10.255371   359 sgd_solver.cpp:105] Iteration 6156, lr = 0.00111989
I0407 23:17:15.203159   359 solver.cpp:218] Iteration 6168 (2.42533 iter/s, 4.94778s/12 iters), loss = 0.137813
I0407 23:17:15.203291   359 solver.cpp:237]     Train net output #0: loss = 0.137812 (* 1 = 0.137812 loss)
I0407 23:17:15.203300   359 sgd_solver.cpp:105] Iteration 6168, lr = 0.0010967
I0407 23:17:15.768159   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:20.098423   359 solver.cpp:218] Iteration 6180 (2.45142 iter/s, 4.89512s/12 iters), loss = 0.253244
I0407 23:17:20.098469   359 solver.cpp:237]     Train net output #0: loss = 0.253244 (* 1 = 0.253244 loss)
I0407 23:17:20.098477   359 sgd_solver.cpp:105] Iteration 6180, lr = 0.00107393
I0407 23:17:25.054626   359 solver.cpp:218] Iteration 6192 (2.42124 iter/s, 4.95613s/12 iters), loss = 0.189062
I0407 23:17:25.054667   359 solver.cpp:237]     Train net output #0: loss = 0.189062 (* 1 = 0.189062 loss)
I0407 23:17:25.054675   359 sgd_solver.cpp:105] Iteration 6192, lr = 0.00105159
I0407 23:17:29.953361   359 solver.cpp:218] Iteration 6204 (2.44964 iter/s, 4.89868s/12 iters), loss = 0.222306
I0407 23:17:29.953399   359 solver.cpp:237]     Train net output #0: loss = 0.222306 (* 1 = 0.222306 loss)
I0407 23:17:29.953406   359 sgd_solver.cpp:105] Iteration 6204, lr = 0.00102965
I0407 23:17:34.915452   359 solver.cpp:218] Iteration 6216 (2.41836 iter/s, 4.96204s/12 iters), loss = 0.200733
I0407 23:17:34.915488   359 solver.cpp:237]     Train net output #0: loss = 0.200733 (* 1 = 0.200733 loss)
I0407 23:17:34.915498   359 sgd_solver.cpp:105] Iteration 6216, lr = 0.00100812
I0407 23:17:36.912931   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 23:17:40.000715   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 23:17:42.434772   359 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 23:17:42.434790   359 net.cpp:676] Ignoring source layer train-data
I0407 23:17:44.584822   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:46.061904   359 blocking_queue.cpp:49] Waiting for data
I0407 23:17:47.348780   359 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0407 23:17:47.348807   359 solver.cpp:397]     Test net output #1: loss = 2.85965 (* 1 = 2.85965 loss)
I0407 23:17:49.122150   359 solver.cpp:218] Iteration 6228 (0.844676 iter/s, 14.2066s/12 iters), loss = 0.129902
I0407 23:17:49.122196   359 solver.cpp:237]     Train net output #0: loss = 0.129902 (* 1 = 0.129902 loss)
I0407 23:17:49.122205   359 sgd_solver.cpp:105] Iteration 6228, lr = 0.00098699
I0407 23:17:54.039830   359 solver.cpp:218] Iteration 6240 (2.44021 iter/s, 4.91762s/12 iters), loss = 0.153368
I0407 23:17:54.039870   359 solver.cpp:237]     Train net output #0: loss = 0.153368 (* 1 = 0.153368 loss)
I0407 23:17:54.039878   359 sgd_solver.cpp:105] Iteration 6240, lr = 0.000966255
I0407 23:17:58.976161   359 solver.cpp:218] Iteration 6252 (2.43099 iter/s, 4.93627s/12 iters), loss = 0.0896474
I0407 23:17:58.976199   359 solver.cpp:237]     Train net output #0: loss = 0.0896474 (* 1 = 0.0896474 loss)
I0407 23:17:58.976208   359 sgd_solver.cpp:105] Iteration 6252, lr = 0.000945911
I0407 23:18:03.978098   359 solver.cpp:218] Iteration 6264 (2.3991 iter/s, 5.00188s/12 iters), loss = 0.259995
I0407 23:18:03.978142   359 solver.cpp:237]     Train net output #0: loss = 0.259995 (* 1 = 0.259995 loss)
I0407 23:18:03.978149   359 sgd_solver.cpp:105] Iteration 6264, lr = 0.00092595
I0407 23:18:06.676151   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:08.906256   359 solver.cpp:218] Iteration 6276 (2.43502 iter/s, 4.92809s/12 iters), loss = 0.273486
I0407 23:18:08.906299   359 solver.cpp:237]     Train net output #0: loss = 0.273486 (* 1 = 0.273486 loss)
I0407 23:18:08.906307   359 sgd_solver.cpp:105] Iteration 6276, lr = 0.000906369
I0407 23:18:13.853348   359 solver.cpp:218] Iteration 6288 (2.4257 iter/s, 4.94703s/12 iters), loss = 0.139224
I0407 23:18:13.853389   359 solver.cpp:237]     Train net output #0: loss = 0.139224 (* 1 = 0.139224 loss)
I0407 23:18:13.853399   359 sgd_solver.cpp:105] Iteration 6288, lr = 0.000887162
I0407 23:18:18.791798   359 solver.cpp:218] Iteration 6300 (2.42994 iter/s, 4.93839s/12 iters), loss = 0.134716
I0407 23:18:18.791942   359 solver.cpp:237]     Train net output #0: loss = 0.134716 (* 1 = 0.134716 loss)
I0407 23:18:18.791951   359 sgd_solver.cpp:105] Iteration 6300, lr = 0.000868323
I0407 23:18:23.745375   359 solver.cpp:218] Iteration 6312 (2.42257 iter/s, 4.95341s/12 iters), loss = 0.139055
I0407 23:18:23.745416   359 solver.cpp:237]     Train net output #0: loss = 0.139055 (* 1 = 0.139055 loss)
I0407 23:18:23.745424   359 sgd_solver.cpp:105] Iteration 6312, lr = 0.000849846
I0407 23:18:28.225522   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 23:18:31.307723   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 23:18:33.675303   359 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 23:18:33.675323   359 net.cpp:676] Ignoring source layer train-data
I0407 23:18:35.731346   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:38.459969   359 solver.cpp:397]     Test net output #0: accuracy = 0.457721
I0407 23:18:38.460003   359 solver.cpp:397]     Test net output #1: loss = 2.81101 (* 1 = 2.81101 loss)
I0407 23:18:38.556648   359 solver.cpp:218] Iteration 6324 (0.810198 iter/s, 14.8112s/12 iters), loss = 0.153668
I0407 23:18:38.556690   359 solver.cpp:237]     Train net output #0: loss = 0.153668 (* 1 = 0.153668 loss)
I0407 23:18:38.556699   359 sgd_solver.cpp:105] Iteration 6324, lr = 0.000831727
I0407 23:18:42.664073   359 solver.cpp:218] Iteration 6336 (2.92159 iter/s, 4.10736s/12 iters), loss = 0.163364
I0407 23:18:42.664115   359 solver.cpp:237]     Train net output #0: loss = 0.163364 (* 1 = 0.163364 loss)
I0407 23:18:42.664122   359 sgd_solver.cpp:105] Iteration 6336, lr = 0.00081396
I0407 23:18:47.559902   359 solver.cpp:218] Iteration 6348 (2.4511 iter/s, 4.89576s/12 iters), loss = 0.231072
I0407 23:18:47.559952   359 solver.cpp:237]     Train net output #0: loss = 0.231071 (* 1 = 0.231071 loss)
I0407 23:18:47.559959   359 sgd_solver.cpp:105] Iteration 6348, lr = 0.000796539
I0407 23:18:52.520370   359 solver.cpp:218] Iteration 6360 (2.41916 iter/s, 4.9604s/12 iters), loss = 0.120645
I0407 23:18:52.520499   359 solver.cpp:237]     Train net output #0: loss = 0.120645 (* 1 = 0.120645 loss)
I0407 23:18:52.520507   359 sgd_solver.cpp:105] Iteration 6360, lr = 0.000779459
I0407 23:18:57.274106   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:57.413798   359 solver.cpp:218] Iteration 6372 (2.45234 iter/s, 4.89328s/12 iters), loss = 0.153222
I0407 23:18:57.413831   359 solver.cpp:237]     Train net output #0: loss = 0.153222 (* 1 = 0.153222 loss)
I0407 23:18:57.413839   359 sgd_solver.cpp:105] Iteration 6372, lr = 0.000762716
I0407 23:19:02.350708   359 solver.cpp:218] Iteration 6384 (2.4307 iter/s, 4.93686s/12 iters), loss = 0.0881359
I0407 23:19:02.350745   359 solver.cpp:237]     Train net output #0: loss = 0.0881359 (* 1 = 0.0881359 loss)
I0407 23:19:02.350752   359 sgd_solver.cpp:105] Iteration 6384, lr = 0.000746303
I0407 23:19:07.270803   359 solver.cpp:218] Iteration 6396 (2.439 iter/s, 4.92004s/12 iters), loss = 0.135171
I0407 23:19:07.270850   359 solver.cpp:237]     Train net output #0: loss = 0.135171 (* 1 = 0.135171 loss)
I0407 23:19:07.270859   359 sgd_solver.cpp:105] Iteration 6396, lr = 0.000730215
I0407 23:19:12.212148   359 solver.cpp:218] Iteration 6408 (2.42852 iter/s, 4.94128s/12 iters), loss = 0.274553
I0407 23:19:12.212185   359 solver.cpp:237]     Train net output #0: loss = 0.274553 (* 1 = 0.274553 loss)
I0407 23:19:12.212193   359 sgd_solver.cpp:105] Iteration 6408, lr = 0.000714447
I0407 23:19:17.134399   359 solver.cpp:218] Iteration 6420 (2.43794 iter/s, 4.9222s/12 iters), loss = 0.138166
I0407 23:19:17.134439   359 solver.cpp:237]     Train net output #0: loss = 0.138166 (* 1 = 0.138166 loss)
I0407 23:19:17.134447   359 sgd_solver.cpp:105] Iteration 6420, lr = 0.000698994
I0407 23:19:19.124998   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 23:19:22.204881   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 23:19:24.673915   359 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 23:19:24.674012   359 net.cpp:676] Ignoring source layer train-data
I0407 23:19:26.581594   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:29.139668   359 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 23:19:29.139700   359 solver.cpp:397]     Test net output #1: loss = 2.84339 (* 1 = 2.84339 loss)
I0407 23:19:30.940819   359 solver.cpp:218] Iteration 6432 (0.869165 iter/s, 13.8063s/12 iters), loss = 0.150292
I0407 23:19:30.940857   359 solver.cpp:237]     Train net output #0: loss = 0.150292 (* 1 = 0.150292 loss)
I0407 23:19:30.940865   359 sgd_solver.cpp:105] Iteration 6432, lr = 0.000683851
I0407 23:19:35.942068   359 solver.cpp:218] Iteration 6444 (2.39943 iter/s, 5.00119s/12 iters), loss = 0.246275
I0407 23:19:35.942111   359 solver.cpp:237]     Train net output #0: loss = 0.246274 (* 1 = 0.246274 loss)
I0407 23:19:35.942119   359 sgd_solver.cpp:105] Iteration 6444, lr = 0.000669012
I0407 23:19:40.886096   359 solver.cpp:218] Iteration 6456 (2.4272 iter/s, 4.94396s/12 iters), loss = 0.215359
I0407 23:19:40.886143   359 solver.cpp:237]     Train net output #0: loss = 0.215359 (* 1 = 0.215359 loss)
I0407 23:19:40.886152   359 sgd_solver.cpp:105] Iteration 6456, lr = 0.000654472
I0407 23:19:45.861039   359 solver.cpp:218] Iteration 6468 (2.41212 iter/s, 4.97487s/12 iters), loss = 0.126235
I0407 23:19:45.861086   359 solver.cpp:237]     Train net output #0: loss = 0.126235 (* 1 = 0.126235 loss)
I0407 23:19:45.861094   359 sgd_solver.cpp:105] Iteration 6468, lr = 0.000640227
I0407 23:19:47.810267   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:50.822371   359 solver.cpp:218] Iteration 6480 (2.41874 iter/s, 4.96127s/12 iters), loss = 0.105952
I0407 23:19:50.822408   359 solver.cpp:237]     Train net output #0: loss = 0.105952 (* 1 = 0.105952 loss)
I0407 23:19:50.822417   359 sgd_solver.cpp:105] Iteration 6480, lr = 0.000626271
I0407 23:19:55.739878   359 solver.cpp:218] Iteration 6492 (2.44029 iter/s, 4.91745s/12 iters), loss = 0.238412
I0407 23:19:55.740053   359 solver.cpp:237]     Train net output #0: loss = 0.238412 (* 1 = 0.238412 loss)
I0407 23:19:55.740069   359 sgd_solver.cpp:105] Iteration 6492, lr = 0.0006126
I0407 23:20:00.695417   359 solver.cpp:218] Iteration 6504 (2.42162 iter/s, 4.95535s/12 iters), loss = 0.251793
I0407 23:20:00.695458   359 solver.cpp:237]     Train net output #0: loss = 0.251793 (* 1 = 0.251793 loss)
I0407 23:20:00.695467   359 sgd_solver.cpp:105] Iteration 6504, lr = 0.000599207
I0407 23:20:05.594112   359 solver.cpp:218] Iteration 6516 (2.44966 iter/s, 4.89864s/12 iters), loss = 0.196607
I0407 23:20:05.594148   359 solver.cpp:237]     Train net output #0: loss = 0.196607 (* 1 = 0.196607 loss)
I0407 23:20:05.594156   359 sgd_solver.cpp:105] Iteration 6516, lr = 0.00058609
I0407 23:20:10.109151   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 23:20:13.216063   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 23:20:15.603492   359 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 23:20:15.603508   359 net.cpp:676] Ignoring source layer train-data
I0407 23:20:17.508338   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:20.119869   359 solver.cpp:397]     Test net output #0: accuracy = 0.457721
I0407 23:20:20.119915   359 solver.cpp:397]     Test net output #1: loss = 2.87348 (* 1 = 2.87348 loss)
I0407 23:20:20.216583   359 solver.cpp:218] Iteration 6528 (0.820659 iter/s, 14.6224s/12 iters), loss = 0.0990301
I0407 23:20:20.216629   359 solver.cpp:237]     Train net output #0: loss = 0.0990301 (* 1 = 0.0990301 loss)
I0407 23:20:20.216637   359 sgd_solver.cpp:105] Iteration 6528, lr = 0.000573242
I0407 23:20:24.354866   359 solver.cpp:218] Iteration 6540 (2.8998 iter/s, 4.13822s/12 iters), loss = 0.175362
I0407 23:20:24.354910   359 solver.cpp:237]     Train net output #0: loss = 0.175362 (* 1 = 0.175362 loss)
I0407 23:20:24.354919   359 sgd_solver.cpp:105] Iteration 6540, lr = 0.000560659
I0407 23:20:29.286823   359 solver.cpp:218] Iteration 6552 (2.43315 iter/s, 4.93189s/12 iters), loss = 0.13445
I0407 23:20:29.286944   359 solver.cpp:237]     Train net output #0: loss = 0.13445 (* 1 = 0.13445 loss)
I0407 23:20:29.286954   359 sgd_solver.cpp:105] Iteration 6552, lr = 0.000548335
I0407 23:20:34.175601   359 solver.cpp:218] Iteration 6564 (2.45467 iter/s, 4.88864s/12 iters), loss = 0.184569
I0407 23:20:34.175639   359 solver.cpp:237]     Train net output #0: loss = 0.184569 (* 1 = 0.184569 loss)
I0407 23:20:34.175648   359 sgd_solver.cpp:105] Iteration 6564, lr = 0.000536268
I0407 23:20:38.373523   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:39.128399   359 solver.cpp:218] Iteration 6576 (2.4229 iter/s, 4.95274s/12 iters), loss = 0.22993
I0407 23:20:39.128435   359 solver.cpp:237]     Train net output #0: loss = 0.22993 (* 1 = 0.22993 loss)
I0407 23:20:39.128443   359 sgd_solver.cpp:105] Iteration 6576, lr = 0.000524451
I0407 23:20:44.056602   359 solver.cpp:218] Iteration 6588 (2.43499 iter/s, 4.92815s/12 iters), loss = 0.13458
I0407 23:20:44.056636   359 solver.cpp:237]     Train net output #0: loss = 0.13458 (* 1 = 0.13458 loss)
I0407 23:20:44.056643   359 sgd_solver.cpp:105] Iteration 6588, lr = 0.000512881
I0407 23:20:49.049531   359 solver.cpp:218] Iteration 6600 (2.40343 iter/s, 4.99287s/12 iters), loss = 0.151401
I0407 23:20:49.049571   359 solver.cpp:237]     Train net output #0: loss = 0.151401 (* 1 = 0.151401 loss)
I0407 23:20:49.049580   359 sgd_solver.cpp:105] Iteration 6600, lr = 0.000501552
I0407 23:20:53.991566   359 solver.cpp:218] Iteration 6612 (2.42818 iter/s, 4.94197s/12 iters), loss = 0.177412
I0407 23:20:53.991613   359 solver.cpp:237]     Train net output #0: loss = 0.177412 (* 1 = 0.177412 loss)
I0407 23:20:53.991621   359 sgd_solver.cpp:105] Iteration 6612, lr = 0.00049046
I0407 23:20:58.907235   359 solver.cpp:218] Iteration 6624 (2.44121 iter/s, 4.9156s/12 iters), loss = 0.160151
I0407 23:20:58.907280   359 solver.cpp:237]     Train net output #0: loss = 0.160151 (* 1 = 0.160151 loss)
I0407 23:20:58.907289   359 sgd_solver.cpp:105] Iteration 6624, lr = 0.000479602
I0407 23:21:00.898057   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 23:21:03.982336   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 23:21:06.351004   359 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 23:21:06.351023   359 net.cpp:676] Ignoring source layer train-data
I0407 23:21:08.286592   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:11.145256   359 solver.cpp:397]     Test net output #0: accuracy = 0.454044
I0407 23:21:11.145303   359 solver.cpp:397]     Test net output #1: loss = 2.86725 (* 1 = 2.86725 loss)
I0407 23:21:12.957787   359 solver.cpp:218] Iteration 6636 (0.854064 iter/s, 14.0505s/12 iters), loss = 0.115546
I0407 23:21:12.957829   359 solver.cpp:237]     Train net output #0: loss = 0.115546 (* 1 = 0.115546 loss)
I0407 23:21:12.957837   359 sgd_solver.cpp:105] Iteration 6636, lr = 0.000468972
I0407 23:21:17.907053   359 solver.cpp:218] Iteration 6648 (2.42463 iter/s, 4.94921s/12 iters), loss = 0.131054
I0407 23:21:17.907088   359 solver.cpp:237]     Train net output #0: loss = 0.131054 (* 1 = 0.131054 loss)
I0407 23:21:17.907095   359 sgd_solver.cpp:105] Iteration 6648, lr = 0.000458566
I0407 23:21:22.831107   359 solver.cpp:218] Iteration 6660 (2.43704 iter/s, 4.924s/12 iters), loss = 0.0919114
I0407 23:21:22.831142   359 solver.cpp:237]     Train net output #0: loss = 0.0919114 (* 1 = 0.0919114 loss)
I0407 23:21:22.831151   359 sgd_solver.cpp:105] Iteration 6660, lr = 0.00044838
I0407 23:21:27.782884   359 solver.cpp:218] Iteration 6672 (2.4234 iter/s, 4.95172s/12 iters), loss = 0.109363
I0407 23:21:27.782924   359 solver.cpp:237]     Train net output #0: loss = 0.109363 (* 1 = 0.109363 loss)
I0407 23:21:27.782932   359 sgd_solver.cpp:105] Iteration 6672, lr = 0.000438411
I0407 23:21:29.098017   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:32.668825   359 solver.cpp:218] Iteration 6684 (2.45606 iter/s, 4.88588s/12 iters), loss = 0.0794411
I0407 23:21:32.668939   359 solver.cpp:237]     Train net output #0: loss = 0.079441 (* 1 = 0.079441 loss)
I0407 23:21:32.668947   359 sgd_solver.cpp:105] Iteration 6684, lr = 0.000428653
I0407 23:21:37.626327   359 solver.cpp:218] Iteration 6696 (2.42064 iter/s, 4.95737s/12 iters), loss = 0.192344
I0407 23:21:37.626370   359 solver.cpp:237]     Train net output #0: loss = 0.192344 (* 1 = 0.192344 loss)
I0407 23:21:37.626377   359 sgd_solver.cpp:105] Iteration 6696, lr = 0.000419102
I0407 23:21:42.536317   359 solver.cpp:218] Iteration 6708 (2.44403 iter/s, 4.90992s/12 iters), loss = 0.189281
I0407 23:21:42.536360   359 solver.cpp:237]     Train net output #0: loss = 0.189281 (* 1 = 0.189281 loss)
I0407 23:21:42.536370   359 sgd_solver.cpp:105] Iteration 6708, lr = 0.000409755
I0407 23:21:47.443796   359 solver.cpp:218] Iteration 6720 (2.44528 iter/s, 4.90741s/12 iters), loss = 0.0837439
I0407 23:21:47.443840   359 solver.cpp:237]     Train net output #0: loss = 0.0837439 (* 1 = 0.0837439 loss)
I0407 23:21:47.443847   359 sgd_solver.cpp:105] Iteration 6720, lr = 0.000400608
I0407 23:21:51.929875   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 23:21:55.912889   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 23:21:58.291445   359 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 23:21:58.291465   359 net.cpp:676] Ignoring source layer train-data
I0407 23:22:00.027452   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:02.689129   359 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0407 23:22:02.689332   359 solver.cpp:397]     Test net output #1: loss = 2.84666 (* 1 = 2.84666 loss)
I0407 23:22:02.785645   359 solver.cpp:218] Iteration 6732 (0.782178 iter/s, 15.3418s/12 iters), loss = 0.0341394
I0407 23:22:02.785689   359 solver.cpp:237]     Train net output #0: loss = 0.0341394 (* 1 = 0.0341394 loss)
I0407 23:22:02.785698   359 sgd_solver.cpp:105] Iteration 6732, lr = 0.000391657
I0407 23:22:06.918531   359 solver.cpp:218] Iteration 6744 (2.90358 iter/s, 4.13283s/12 iters), loss = 0.172641
I0407 23:22:06.918565   359 solver.cpp:237]     Train net output #0: loss = 0.172641 (* 1 = 0.172641 loss)
I0407 23:22:06.918573   359 sgd_solver.cpp:105] Iteration 6744, lr = 0.000382898
I0407 23:22:11.868175   359 solver.cpp:218] Iteration 6756 (2.42444 iter/s, 4.94959s/12 iters), loss = 0.241404
I0407 23:22:11.868216   359 solver.cpp:237]     Train net output #0: loss = 0.241404 (* 1 = 0.241404 loss)
I0407 23:22:11.868223   359 sgd_solver.cpp:105] Iteration 6756, lr = 0.000374327
I0407 23:22:16.728605   359 solver.cpp:218] Iteration 6768 (2.46895 iter/s, 4.86037s/12 iters), loss = 0.279273
I0407 23:22:16.728642   359 solver.cpp:237]     Train net output #0: loss = 0.279273 (* 1 = 0.279273 loss)
I0407 23:22:16.728650   359 sgd_solver.cpp:105] Iteration 6768, lr = 0.000365941
I0407 23:22:20.148819   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:21.670934   359 solver.cpp:218] Iteration 6780 (2.42803 iter/s, 4.94227s/12 iters), loss = 0.128633
I0407 23:22:21.670977   359 solver.cpp:237]     Train net output #0: loss = 0.128633 (* 1 = 0.128633 loss)
I0407 23:22:21.670986   359 sgd_solver.cpp:105] Iteration 6780, lr = 0.000357735
I0407 23:22:26.599380   359 solver.cpp:218] Iteration 6792 (2.43487 iter/s, 4.92839s/12 iters), loss = 0.183782
I0407 23:22:26.599417   359 solver.cpp:237]     Train net output #0: loss = 0.183782 (* 1 = 0.183782 loss)
I0407 23:22:26.599426   359 sgd_solver.cpp:105] Iteration 6792, lr = 0.000349707
I0407 23:22:31.549271   359 solver.cpp:218] Iteration 6804 (2.42432 iter/s, 4.94984s/12 iters), loss = 0.152019
I0407 23:22:31.549310   359 solver.cpp:237]     Train net output #0: loss = 0.152019 (* 1 = 0.152019 loss)
I0407 23:22:31.549319   359 sgd_solver.cpp:105] Iteration 6804, lr = 0.000341853
I0407 23:22:36.446985   359 solver.cpp:218] Iteration 6816 (2.45015 iter/s, 4.89766s/12 iters), loss = 0.167886
I0407 23:22:36.447109   359 solver.cpp:237]     Train net output #0: loss = 0.167886 (* 1 = 0.167886 loss)
I0407 23:22:36.447118   359 sgd_solver.cpp:105] Iteration 6816, lr = 0.000334169
I0407 23:22:41.410583   359 solver.cpp:218] Iteration 6828 (2.41767 iter/s, 4.96346s/12 iters), loss = 0.105224
I0407 23:22:41.410617   359 solver.cpp:237]     Train net output #0: loss = 0.105224 (* 1 = 0.105224 loss)
I0407 23:22:41.410625   359 sgd_solver.cpp:105] Iteration 6828, lr = 0.000326652
I0407 23:22:43.398420   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 23:22:47.631776   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 23:22:51.158975   359 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 23:22:51.158994   359 net.cpp:676] Ignoring source layer train-data
I0407 23:22:52.835144   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:55.525240   359 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0407 23:22:55.525287   359 solver.cpp:397]     Test net output #1: loss = 2.84198 (* 1 = 2.84198 loss)
I0407 23:22:57.320017   359 solver.cpp:218] Iteration 6840 (0.754273 iter/s, 15.9094s/12 iters), loss = 0.213791
I0407 23:22:57.320061   359 solver.cpp:237]     Train net output #0: loss = 0.213791 (* 1 = 0.213791 loss)
I0407 23:22:57.320070   359 sgd_solver.cpp:105] Iteration 6840, lr = 0.000319298
I0407 23:23:02.217681   359 solver.cpp:218] Iteration 6852 (2.45018 iter/s, 4.8976s/12 iters), loss = 0.144369
I0407 23:23:02.217728   359 solver.cpp:237]     Train net output #0: loss = 0.144369 (* 1 = 0.144369 loss)
I0407 23:23:02.217736   359 sgd_solver.cpp:105] Iteration 6852, lr = 0.000312105
I0407 23:23:07.196313   359 solver.cpp:218] Iteration 6864 (2.41033 iter/s, 4.97857s/12 iters), loss = 0.196857
I0407 23:23:07.197065   359 solver.cpp:237]     Train net output #0: loss = 0.196857 (* 1 = 0.196857 loss)
I0407 23:23:07.197077   359 sgd_solver.cpp:105] Iteration 6864, lr = 0.000305068
I0407 23:23:12.125314   359 solver.cpp:218] Iteration 6876 (2.43495 iter/s, 4.92823s/12 iters), loss = 0.215531
I0407 23:23:12.125355   359 solver.cpp:237]     Train net output #0: loss = 0.215531 (* 1 = 0.215531 loss)
I0407 23:23:12.125361   359 sgd_solver.cpp:105] Iteration 6876, lr = 0.000298185
I0407 23:23:12.732437   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:17.067513   359 solver.cpp:218] Iteration 6888 (2.4281 iter/s, 4.94213s/12 iters), loss = 0.170668
I0407 23:23:17.067559   359 solver.cpp:237]     Train net output #0: loss = 0.170668 (* 1 = 0.170668 loss)
I0407 23:23:17.067569   359 sgd_solver.cpp:105] Iteration 6888, lr = 0.000291453
I0407 23:23:22.020543   359 solver.cpp:218] Iteration 6900 (2.42279 iter/s, 4.95296s/12 iters), loss = 0.184094
I0407 23:23:22.020581   359 solver.cpp:237]     Train net output #0: loss = 0.184094 (* 1 = 0.184094 loss)
I0407 23:23:22.020588   359 sgd_solver.cpp:105] Iteration 6900, lr = 0.000284869
I0407 23:23:26.911700   359 solver.cpp:218] Iteration 6912 (2.45344 iter/s, 4.8911s/12 iters), loss = 0.305297
I0407 23:23:26.911737   359 solver.cpp:237]     Train net output #0: loss = 0.305297 (* 1 = 0.305297 loss)
I0407 23:23:26.911747   359 sgd_solver.cpp:105] Iteration 6912, lr = 0.000278428
I0407 23:23:31.828889   359 solver.cpp:218] Iteration 6924 (2.44045 iter/s, 4.91713s/12 iters), loss = 0.262044
I0407 23:23:31.828924   359 solver.cpp:237]     Train net output #0: loss = 0.262044 (* 1 = 0.262044 loss)
I0407 23:23:31.828933   359 sgd_solver.cpp:105] Iteration 6924, lr = 0.00027213
I0407 23:23:36.315490   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 23:23:40.034473   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 23:23:43.089866   359 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 23:23:43.089884   359 net.cpp:676] Ignoring source layer train-data
I0407 23:23:43.654500   359 blocking_queue.cpp:49] Waiting for data
I0407 23:23:44.722661   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:47.463395   359 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0407 23:23:47.463443   359 solver.cpp:397]     Test net output #1: loss = 2.82497 (* 1 = 2.82497 loss)
I0407 23:23:47.560518   359 solver.cpp:218] Iteration 6936 (0.762799 iter/s, 15.7315s/12 iters), loss = 0.118046
I0407 23:23:47.560585   359 solver.cpp:237]     Train net output #0: loss = 0.118046 (* 1 = 0.118046 loss)
I0407 23:23:47.560600   359 sgd_solver.cpp:105] Iteration 6936, lr = 0.00026597
I0407 23:23:51.671131   359 solver.cpp:218] Iteration 6948 (2.91933 iter/s, 4.11054s/12 iters), loss = 0.097086
I0407 23:23:51.671175   359 solver.cpp:237]     Train net output #0: loss = 0.097086 (* 1 = 0.097086 loss)
I0407 23:23:51.671185   359 sgd_solver.cpp:105] Iteration 6948, lr = 0.000259946
I0407 23:23:56.623329   359 solver.cpp:218] Iteration 6960 (2.4232 iter/s, 4.95213s/12 iters), loss = 0.112395
I0407 23:23:56.623373   359 solver.cpp:237]     Train net output #0: loss = 0.112395 (* 1 = 0.112395 loss)
I0407 23:23:56.623380   359 sgd_solver.cpp:105] Iteration 6960, lr = 0.000254054
I0407 23:24:01.587177   359 solver.cpp:218] Iteration 6972 (2.41751 iter/s, 4.96379s/12 iters), loss = 0.14027
I0407 23:24:01.587222   359 solver.cpp:237]     Train net output #0: loss = 0.14027 (* 1 = 0.14027 loss)
I0407 23:24:01.587231   359 sgd_solver.cpp:105] Iteration 6972, lr = 0.000248293
I0407 23:24:04.314172   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:06.516044   359 solver.cpp:218] Iteration 6984 (2.43467 iter/s, 4.92881s/12 iters), loss = 0.167093
I0407 23:24:06.516083   359 solver.cpp:237]     Train net output #0: loss = 0.167093 (* 1 = 0.167093 loss)
I0407 23:24:06.516090   359 sgd_solver.cpp:105] Iteration 6984, lr = 0.000242659
I0407 23:24:11.514892   359 solver.cpp:218] Iteration 6996 (2.40058 iter/s, 4.99878s/12 iters), loss = 0.201238
I0407 23:24:11.515044   359 solver.cpp:237]     Train net output #0: loss = 0.201238 (* 1 = 0.201238 loss)
I0407 23:24:11.515054   359 sgd_solver.cpp:105] Iteration 6996, lr = 0.00023715
I0407 23:24:16.443599   359 solver.cpp:218] Iteration 7008 (2.4348 iter/s, 4.92853s/12 iters), loss = 0.0924055
I0407 23:24:16.443646   359 solver.cpp:237]     Train net output #0: loss = 0.0924055 (* 1 = 0.0924055 loss)
I0407 23:24:16.443655   359 sgd_solver.cpp:105] Iteration 7008, lr = 0.000231763
I0407 23:24:21.383993   359 solver.cpp:218] Iteration 7020 (2.42899 iter/s, 4.94032s/12 iters), loss = 0.153556
I0407 23:24:21.384040   359 solver.cpp:237]     Train net output #0: loss = 0.153556 (* 1 = 0.153556 loss)
I0407 23:24:21.384048   359 sgd_solver.cpp:105] Iteration 7020, lr = 0.000226495
I0407 23:24:26.370350   359 solver.cpp:218] Iteration 7032 (2.4066 iter/s, 4.98628s/12 iters), loss = 0.136955
I0407 23:24:26.370398   359 solver.cpp:237]     Train net output #0: loss = 0.136955 (* 1 = 0.136955 loss)
I0407 23:24:26.370407   359 sgd_solver.cpp:105] Iteration 7032, lr = 0.000221345
I0407 23:24:28.369777   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 23:24:31.476897   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 23:24:33.903177   359 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 23:24:33.903194   359 net.cpp:676] Ignoring source layer train-data
I0407 23:24:35.673597   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:38.711179   359 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0407 23:24:38.711243   359 solver.cpp:397]     Test net output #1: loss = 2.83419 (* 1 = 2.83419 loss)
I0407 23:24:40.521961   359 solver.cpp:218] Iteration 7044 (0.847965 iter/s, 14.1515s/12 iters), loss = 0.114693
I0407 23:24:40.522001   359 solver.cpp:237]     Train net output #0: loss = 0.114693 (* 1 = 0.114693 loss)
I0407 23:24:40.522007   359 sgd_solver.cpp:105] Iteration 7044, lr = 0.000216309
I0407 23:24:45.452225   359 solver.cpp:218] Iteration 7056 (2.43398 iter/s, 4.9302s/12 iters), loss = 0.170558
I0407 23:24:45.452363   359 solver.cpp:237]     Train net output #0: loss = 0.170558 (* 1 = 0.170558 loss)
I0407 23:24:45.452373   359 sgd_solver.cpp:105] Iteration 7056, lr = 0.000211385
I0407 23:24:50.392465   359 solver.cpp:218] Iteration 7068 (2.42911 iter/s, 4.94009s/12 iters), loss = 0.188994
I0407 23:24:50.392504   359 solver.cpp:237]     Train net output #0: loss = 0.188994 (* 1 = 0.188994 loss)
I0407 23:24:50.392513   359 sgd_solver.cpp:105] Iteration 7068, lr = 0.000206571
I0407 23:24:55.213446   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:55.323170   359 solver.cpp:218] Iteration 7080 (2.43376 iter/s, 4.93065s/12 iters), loss = 0.216889
I0407 23:24:55.323213   359 solver.cpp:237]     Train net output #0: loss = 0.216889 (* 1 = 0.216889 loss)
I0407 23:24:55.323221   359 sgd_solver.cpp:105] Iteration 7080, lr = 0.000201864
I0407 23:25:00.184571   359 solver.cpp:218] Iteration 7092 (2.46846 iter/s, 4.86134s/12 iters), loss = 0.159934
I0407 23:25:00.184615   359 solver.cpp:237]     Train net output #0: loss = 0.159934 (* 1 = 0.159934 loss)
I0407 23:25:00.184623   359 sgd_solver.cpp:105] Iteration 7092, lr = 0.000197262
I0407 23:25:05.111347   359 solver.cpp:218] Iteration 7104 (2.4357 iter/s, 4.92671s/12 iters), loss = 0.0967507
I0407 23:25:05.111384   359 solver.cpp:237]     Train net output #0: loss = 0.0967507 (* 1 = 0.0967507 loss)
I0407 23:25:05.111393   359 sgd_solver.cpp:105] Iteration 7104, lr = 0.000192763
I0407 23:25:10.049810   359 solver.cpp:218] Iteration 7116 (2.42993 iter/s, 4.93841s/12 iters), loss = 0.212849
I0407 23:25:10.049844   359 solver.cpp:237]     Train net output #0: loss = 0.212849 (* 1 = 0.212849 loss)
I0407 23:25:10.049851   359 sgd_solver.cpp:105] Iteration 7116, lr = 0.000188365
I0407 23:25:14.970294   359 solver.cpp:218] Iteration 7128 (2.43881 iter/s, 4.92043s/12 iters), loss = 0.216186
I0407 23:25:14.970332   359 solver.cpp:237]     Train net output #0: loss = 0.216186 (* 1 = 0.216186 loss)
I0407 23:25:14.970340   359 sgd_solver.cpp:105] Iteration 7128, lr = 0.000184065
I0407 23:25:19.462628   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 23:25:22.590390   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 23:25:24.958521   359 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 23:25:24.958539   359 net.cpp:676] Ignoring source layer train-data
I0407 23:25:26.613893   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:29.475989   359 solver.cpp:397]     Test net output #0: accuracy = 0.45527
I0407 23:25:29.476022   359 solver.cpp:397]     Test net output #1: loss = 2.85037 (* 1 = 2.85037 loss)
I0407 23:25:29.572391   359 solver.cpp:218] Iteration 7140 (0.821804 iter/s, 14.602s/12 iters), loss = 0.174071
I0407 23:25:29.572436   359 solver.cpp:237]     Train net output #0: loss = 0.174071 (* 1 = 0.174071 loss)
I0407 23:25:29.572444   359 sgd_solver.cpp:105] Iteration 7140, lr = 0.000179862
I0407 23:25:33.689715   359 solver.cpp:218] Iteration 7152 (2.91456 iter/s, 4.11726s/12 iters), loss = 0.145695
I0407 23:25:33.689760   359 solver.cpp:237]     Train net output #0: loss = 0.145695 (* 1 = 0.145695 loss)
I0407 23:25:33.689769   359 sgd_solver.cpp:105] Iteration 7152, lr = 0.000175753
I0407 23:25:38.629235   359 solver.cpp:218] Iteration 7164 (2.42942 iter/s, 4.93945s/12 iters), loss = 0.0993061
I0407 23:25:38.629281   359 solver.cpp:237]     Train net output #0: loss = 0.0993061 (* 1 = 0.0993061 loss)
I0407 23:25:38.629288   359 sgd_solver.cpp:105] Iteration 7164, lr = 0.000171736
I0407 23:25:43.583881   359 solver.cpp:218] Iteration 7176 (2.422 iter/s, 4.95458s/12 iters), loss = 0.0892541
I0407 23:25:43.583933   359 solver.cpp:237]     Train net output #0: loss = 0.0892541 (* 1 = 0.0892541 loss)
I0407 23:25:43.583942   359 sgd_solver.cpp:105] Iteration 7176, lr = 0.000167809
I0407 23:25:45.655647   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:48.480268   359 solver.cpp:218] Iteration 7188 (2.45083 iter/s, 4.89631s/12 iters), loss = 0.188101
I0407 23:25:48.480309   359 solver.cpp:237]     Train net output #0: loss = 0.188101 (* 1 = 0.188101 loss)
I0407 23:25:48.480319   359 sgd_solver.cpp:105] Iteration 7188, lr = 0.000163971
I0407 23:25:53.413872   359 solver.cpp:218] Iteration 7200 (2.43233 iter/s, 4.93354s/12 iters), loss = 0.188401
I0407 23:25:53.413934   359 solver.cpp:237]     Train net output #0: loss = 0.188401 (* 1 = 0.188401 loss)
I0407 23:25:53.413942   359 sgd_solver.cpp:105] Iteration 7200, lr = 0.000160219
I0407 23:25:58.344467   359 solver.cpp:218] Iteration 7212 (2.43382 iter/s, 4.93052s/12 iters), loss = 0.127247
I0407 23:25:58.344501   359 solver.cpp:237]     Train net output #0: loss = 0.127247 (* 1 = 0.127247 loss)
I0407 23:25:58.344509   359 sgd_solver.cpp:105] Iteration 7212, lr = 0.000156551
I0407 23:26:03.242328   359 solver.cpp:218] Iteration 7224 (2.45008 iter/s, 4.89781s/12 iters), loss = 0.152345
I0407 23:26:03.242363   359 solver.cpp:237]     Train net output #0: loss = 0.152345 (* 1 = 0.152345 loss)
I0407 23:26:03.242372   359 sgd_solver.cpp:105] Iteration 7224, lr = 0.000152967
I0407 23:26:08.191819   359 solver.cpp:218] Iteration 7236 (2.42452 iter/s, 4.94944s/12 iters), loss = 0.101342
I0407 23:26:08.191854   359 solver.cpp:237]     Train net output #0: loss = 0.101342 (* 1 = 0.101342 loss)
I0407 23:26:08.191862   359 sgd_solver.cpp:105] Iteration 7236, lr = 0.000149463
I0407 23:26:10.191319   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 23:26:13.329421   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 23:26:15.743396   359 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 23:26:15.743415   359 net.cpp:676] Ignoring source layer train-data
I0407 23:26:17.394312   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:20.311830   359 solver.cpp:397]     Test net output #0: accuracy = 0.457721
I0407 23:26:20.311866   359 solver.cpp:397]     Test net output #1: loss = 2.8281 (* 1 = 2.8281 loss)
I0407 23:26:22.118371   359 solver.cpp:218] Iteration 7248 (0.861668 iter/s, 13.9265s/12 iters), loss = 0.161142
I0407 23:26:22.118412   359 solver.cpp:237]     Train net output #0: loss = 0.161142 (* 1 = 0.161142 loss)
I0407 23:26:22.118422   359 sgd_solver.cpp:105] Iteration 7248, lr = 0.000146038
I0407 23:26:27.061333   359 solver.cpp:218] Iteration 7260 (2.42772 iter/s, 4.9429s/12 iters), loss = 0.0641212
I0407 23:26:27.061465   359 solver.cpp:237]     Train net output #0: loss = 0.0641212 (* 1 = 0.0641212 loss)
I0407 23:26:27.061473   359 sgd_solver.cpp:105] Iteration 7260, lr = 0.00014269
I0407 23:26:31.940481   359 solver.cpp:218] Iteration 7272 (2.45952 iter/s, 4.879s/12 iters), loss = 0.097481
I0407 23:26:31.940527   359 solver.cpp:237]     Train net output #0: loss = 0.0974811 (* 1 = 0.0974811 loss)
I0407 23:26:31.940536   359 sgd_solver.cpp:105] Iteration 7272, lr = 0.000139418
I0407 23:26:36.102092   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:36.841253   359 solver.cpp:218] Iteration 7284 (2.44863 iter/s, 4.90069s/12 iters), loss = 0.119114
I0407 23:26:36.841300   359 solver.cpp:237]     Train net output #0: loss = 0.119114 (* 1 = 0.119114 loss)
I0407 23:26:36.841307   359 sgd_solver.cpp:105] Iteration 7284, lr = 0.00013622
I0407 23:26:41.810151   359 solver.cpp:218] Iteration 7296 (2.41506 iter/s, 4.96883s/12 iters), loss = 0.0925093
I0407 23:26:41.810197   359 solver.cpp:237]     Train net output #0: loss = 0.0925093 (* 1 = 0.0925093 loss)
I0407 23:26:41.810206   359 sgd_solver.cpp:105] Iteration 7296, lr = 0.000133094
I0407 23:26:46.759048   359 solver.cpp:218] Iteration 7308 (2.42482 iter/s, 4.94883s/12 iters), loss = 0.153806
I0407 23:26:46.759092   359 solver.cpp:237]     Train net output #0: loss = 0.153806 (* 1 = 0.153806 loss)
I0407 23:26:46.759101   359 sgd_solver.cpp:105] Iteration 7308, lr = 0.00013004
I0407 23:26:51.659354   359 solver.cpp:218] Iteration 7320 (2.44886 iter/s, 4.90024s/12 iters), loss = 0.263687
I0407 23:26:51.659399   359 solver.cpp:237]     Train net output #0: loss = 0.263687 (* 1 = 0.263687 loss)
I0407 23:26:51.659407   359 sgd_solver.cpp:105] Iteration 7320, lr = 0.000127054
I0407 23:26:56.606472   359 solver.cpp:218] Iteration 7332 (2.42569 iter/s, 4.94705s/12 iters), loss = 0.123806
I0407 23:26:56.606519   359 solver.cpp:237]     Train net output #0: loss = 0.123806 (* 1 = 0.123806 loss)
I0407 23:26:56.606531   359 sgd_solver.cpp:105] Iteration 7332, lr = 0.000124136
I0407 23:27:01.061975   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 23:27:04.517289   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 23:27:07.873240   359 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 23:27:07.873258   359 net.cpp:676] Ignoring source layer train-data
I0407 23:27:09.503477   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:12.685493   359 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0407 23:27:12.685520   359 solver.cpp:397]     Test net output #1: loss = 2.81908 (* 1 = 2.81908 loss)
I0407 23:27:12.782277   359 solver.cpp:218] Iteration 7344 (0.741852 iter/s, 16.1757s/12 iters), loss = 0.103389
I0407 23:27:12.782321   359 solver.cpp:237]     Train net output #0: loss = 0.10339 (* 1 = 0.10339 loss)
I0407 23:27:12.782330   359 sgd_solver.cpp:105] Iteration 7344, lr = 0.000121284
I0407 23:27:16.921480   359 solver.cpp:218] Iteration 7356 (2.89915 iter/s, 4.13914s/12 iters), loss = 0.128865
I0407 23:27:16.921522   359 solver.cpp:237]     Train net output #0: loss = 0.128865 (* 1 = 0.128865 loss)
I0407 23:27:16.921531   359 sgd_solver.cpp:105] Iteration 7356, lr = 0.000118497
I0407 23:27:21.841703   359 solver.cpp:218] Iteration 7368 (2.43895 iter/s, 4.92016s/12 iters), loss = 0.146078
I0407 23:27:21.841749   359 solver.cpp:237]     Train net output #0: loss = 0.146078 (* 1 = 0.146078 loss)
I0407 23:27:21.841758   359 sgd_solver.cpp:105] Iteration 7368, lr = 0.000115774
I0407 23:27:26.791333   359 solver.cpp:218] Iteration 7380 (2.42446 iter/s, 4.94957s/12 iters), loss = 0.0587999
I0407 23:27:26.791374   359 solver.cpp:237]     Train net output #0: loss = 0.0587999 (* 1 = 0.0587999 loss)
I0407 23:27:26.791383   359 sgd_solver.cpp:105] Iteration 7380, lr = 0.000113112
I0407 23:27:28.137576   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:31.679008   359 solver.cpp:218] Iteration 7392 (2.45518 iter/s, 4.88762s/12 iters), loss = 0.196448
I0407 23:27:31.679145   359 solver.cpp:237]     Train net output #0: loss = 0.196448 (* 1 = 0.196448 loss)
I0407 23:27:31.679155   359 sgd_solver.cpp:105] Iteration 7392, lr = 0.00011051
I0407 23:27:36.649873   359 solver.cpp:218] Iteration 7404 (2.41414 iter/s, 4.97072s/12 iters), loss = 0.104589
I0407 23:27:36.649904   359 solver.cpp:237]     Train net output #0: loss = 0.104589 (* 1 = 0.104589 loss)
I0407 23:27:36.649912   359 sgd_solver.cpp:105] Iteration 7404, lr = 0.000107968
I0407 23:27:41.546430   359 solver.cpp:218] Iteration 7416 (2.45073 iter/s, 4.89651s/12 iters), loss = 0.0983313
I0407 23:27:41.546465   359 solver.cpp:237]     Train net output #0: loss = 0.0983313 (* 1 = 0.0983313 loss)
I0407 23:27:41.546473   359 sgd_solver.cpp:105] Iteration 7416, lr = 0.000105484
I0407 23:27:46.514627   359 solver.cpp:218] Iteration 7428 (2.41539 iter/s, 4.96814s/12 iters), loss = 0.15201
I0407 23:27:46.514665   359 solver.cpp:237]     Train net output #0: loss = 0.15201 (* 1 = 0.15201 loss)
I0407 23:27:46.514673   359 sgd_solver.cpp:105] Iteration 7428, lr = 0.000103056
I0407 23:27:51.431454   359 solver.cpp:218] Iteration 7440 (2.44063 iter/s, 4.91677s/12 iters), loss = 0.167093
I0407 23:27:51.431491   359 solver.cpp:237]     Train net output #0: loss = 0.167093 (* 1 = 0.167093 loss)
I0407 23:27:51.431499   359 sgd_solver.cpp:105] Iteration 7440, lr = 0.000100684
I0407 23:27:53.427947   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 23:27:56.502161   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 23:27:58.871912   359 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 23:27:58.871929   359 net.cpp:676] Ignoring source layer train-data
I0407 23:28:00.456387   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:03.699553   359 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0407 23:28:03.699710   359 solver.cpp:397]     Test net output #1: loss = 2.84002 (* 1 = 2.84002 loss)
I0407 23:28:05.497198   359 solver.cpp:218] Iteration 7452 (0.853141 iter/s, 14.0657s/12 iters), loss = 0.0823287
I0407 23:28:05.497236   359 solver.cpp:237]     Train net output #0: loss = 0.0823287 (* 1 = 0.0823287 loss)
I0407 23:28:05.497244   359 sgd_solver.cpp:105] Iteration 7452, lr = 9.83655e-05
I0407 23:28:10.405025   359 solver.cpp:218] Iteration 7464 (2.4451 iter/s, 4.90777s/12 iters), loss = 0.0612384
I0407 23:28:10.405064   359 solver.cpp:237]     Train net output #0: loss = 0.0612384 (* 1 = 0.0612384 loss)
I0407 23:28:10.405071   359 sgd_solver.cpp:105] Iteration 7464, lr = 9.61e-05
I0407 23:28:15.322244   359 solver.cpp:218] Iteration 7476 (2.44043 iter/s, 4.91716s/12 iters), loss = 0.10629
I0407 23:28:15.322284   359 solver.cpp:237]     Train net output #0: loss = 0.10629 (* 1 = 0.10629 loss)
I0407 23:28:15.322293   359 sgd_solver.cpp:105] Iteration 7476, lr = 9.38862e-05
I0407 23:28:18.794477   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:20.268381   359 solver.cpp:218] Iteration 7488 (2.42617 iter/s, 4.94608s/12 iters), loss = 0.157365
I0407 23:28:20.268424   359 solver.cpp:237]     Train net output #0: loss = 0.157365 (* 1 = 0.157365 loss)
I0407 23:28:20.268432   359 sgd_solver.cpp:105] Iteration 7488, lr = 9.1723e-05
I0407 23:28:25.195538   359 solver.cpp:218] Iteration 7500 (2.43552 iter/s, 4.92709s/12 iters), loss = 0.0981837
I0407 23:28:25.195590   359 solver.cpp:237]     Train net output #0: loss = 0.0981837 (* 1 = 0.0981837 loss)
I0407 23:28:25.195603   359 sgd_solver.cpp:105] Iteration 7500, lr = 8.96091e-05
I0407 23:28:30.147296   359 solver.cpp:218] Iteration 7512 (2.42342 iter/s, 4.95169s/12 iters), loss = 0.100816
I0407 23:28:30.147337   359 solver.cpp:237]     Train net output #0: loss = 0.100816 (* 1 = 0.100816 loss)
I0407 23:28:30.147346   359 sgd_solver.cpp:105] Iteration 7512, lr = 8.75435e-05
I0407 23:28:35.043851   359 solver.cpp:218] Iteration 7524 (2.45073 iter/s, 4.89649s/12 iters), loss = 0.128969
I0407 23:28:35.044023   359 solver.cpp:237]     Train net output #0: loss = 0.128969 (* 1 = 0.128969 loss)
I0407 23:28:35.044035   359 sgd_solver.cpp:105] Iteration 7524, lr = 8.55251e-05
I0407 23:28:39.916781   359 solver.cpp:218] Iteration 7536 (2.46268 iter/s, 4.87274s/12 iters), loss = 0.0964068
I0407 23:28:39.916826   359 solver.cpp:237]     Train net output #0: loss = 0.0964068 (* 1 = 0.0964068 loss)
I0407 23:28:39.916834   359 sgd_solver.cpp:105] Iteration 7536, lr = 8.35528e-05
I0407 23:28:44.411808   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 23:28:47.514835   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 23:28:49.876381   359 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 23:28:49.876400   359 net.cpp:676] Ignoring source layer train-data
I0407 23:28:51.413739   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:54.714722   359 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 23:28:54.714771   359 solver.cpp:397]     Test net output #1: loss = 2.82414 (* 1 = 2.82414 loss)
I0407 23:28:54.811138   359 solver.cpp:218] Iteration 7548 (0.805679 iter/s, 14.8943s/12 iters), loss = 0.215424
I0407 23:28:54.811184   359 solver.cpp:237]     Train net output #0: loss = 0.215424 (* 1 = 0.215424 loss)
I0407 23:28:54.811192   359 sgd_solver.cpp:105] Iteration 7548, lr = 8.16257e-05
I0407 23:28:58.913132   359 solver.cpp:218] Iteration 7560 (2.92545 iter/s, 4.10193s/12 iters), loss = 0.160461
I0407 23:28:58.913178   359 solver.cpp:237]     Train net output #0: loss = 0.160461 (* 1 = 0.160461 loss)
I0407 23:28:58.913187   359 sgd_solver.cpp:105] Iteration 7560, lr = 7.97426e-05
I0407 23:29:03.854527   359 solver.cpp:218] Iteration 7572 (2.4285 iter/s, 4.94133s/12 iters), loss = 0.0862786
I0407 23:29:03.854568   359 solver.cpp:237]     Train net output #0: loss = 0.0862786 (* 1 = 0.0862786 loss)
I0407 23:29:03.854578   359 sgd_solver.cpp:105] Iteration 7572, lr = 7.79027e-05
I0407 23:29:08.836550   359 solver.cpp:218] Iteration 7584 (2.40869 iter/s, 4.98196s/12 iters), loss = 0.0929231
I0407 23:29:08.836692   359 solver.cpp:237]     Train net output #0: loss = 0.0929231 (* 1 = 0.0929231 loss)
I0407 23:29:08.836701   359 sgd_solver.cpp:105] Iteration 7584, lr = 7.61049e-05
I0407 23:29:09.457921   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:29:13.764643   359 solver.cpp:218] Iteration 7596 (2.4351 iter/s, 4.92793s/12 iters), loss = 0.130177
I0407 23:29:13.764689   359 solver.cpp:237]     Train net output #0: loss = 0.130177 (* 1 = 0.130177 loss)
I0407 23:29:13.764698   359 sgd_solver.cpp:105] Iteration 7596, lr = 7.43482e-05
I0407 23:29:18.683015   359 solver.cpp:218] Iteration 7608 (2.43986 iter/s, 4.91831s/12 iters), loss = 0.124939
I0407 23:29:18.683053   359 solver.cpp:237]     Train net output #0: loss = 0.124939 (* 1 = 0.124939 loss)
I0407 23:29:18.683063   359 sgd_solver.cpp:105] Iteration 7608, lr = 7.26318e-05
I0407 23:29:23.533573   359 solver.cpp:218] Iteration 7620 (2.47397 iter/s, 4.8505s/12 iters), loss = 0.0977765
I0407 23:29:23.533617   359 solver.cpp:237]     Train net output #0: loss = 0.0977765 (* 1 = 0.0977765 loss)
I0407 23:29:23.533625   359 sgd_solver.cpp:105] Iteration 7620, lr = 7.09548e-05
I0407 23:29:25.874089   359 blocking_queue.cpp:49] Waiting for data
I0407 23:29:28.392765   359 solver.cpp:218] Iteration 7632 (2.46958 iter/s, 4.85913s/12 iters), loss = 0.0783972
I0407 23:29:28.392810   359 solver.cpp:237]     Train net output #0: loss = 0.0783972 (* 1 = 0.0783972 loss)
I0407 23:29:28.392818   359 sgd_solver.cpp:105] Iteration 7632, lr = 6.93162e-05
I0407 23:29:33.337855   359 solver.cpp:218] Iteration 7644 (2.42668 iter/s, 4.94502s/12 iters), loss = 0.186941
I0407 23:29:33.337898   359 solver.cpp:237]     Train net output #0: loss = 0.186941 (* 1 = 0.186941 loss)
I0407 23:29:33.337905   359 sgd_solver.cpp:105] Iteration 7644, lr = 6.77152e-05
I0407 23:29:35.343619   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 23:29:38.440865   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 23:29:40.827283   359 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 23:29:40.827399   359 net.cpp:676] Ignoring source layer train-data
I0407 23:29:42.337857   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:29:45.688679   359 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0407 23:29:45.688707   359 solver.cpp:397]     Test net output #1: loss = 2.83726 (* 1 = 2.83726 loss)
I0407 23:29:47.478560   359 solver.cpp:218] Iteration 7656 (0.848618 iter/s, 14.1406s/12 iters), loss = 0.100135
I0407 23:29:47.478597   359 solver.cpp:237]     Train net output #0: loss = 0.100135 (* 1 = 0.100135 loss)
I0407 23:29:47.478605   359 sgd_solver.cpp:105] Iteration 7656, lr = 6.61509e-05
I0407 23:29:52.431851   359 solver.cpp:218] Iteration 7668 (2.42266 iter/s, 4.95324s/12 iters), loss = 0.133091
I0407 23:29:52.431883   359 solver.cpp:237]     Train net output #0: loss = 0.133091 (* 1 = 0.133091 loss)
I0407 23:29:52.431890   359 sgd_solver.cpp:105] Iteration 7668, lr = 6.46225e-05
I0407 23:29:57.359477   359 solver.cpp:218] Iteration 7680 (2.43528 iter/s, 4.92757s/12 iters), loss = 0.138438
I0407 23:29:57.359513   359 solver.cpp:237]     Train net output #0: loss = 0.138438 (* 1 = 0.138438 loss)
I0407 23:29:57.359520   359 sgd_solver.cpp:105] Iteration 7680, lr = 6.31292e-05
I0407 23:30:00.120311   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:30:02.288190   359 solver.cpp:218] Iteration 7692 (2.43474 iter/s, 4.92866s/12 iters), loss = 0.171719
I0407 23:30:02.288228   359 solver.cpp:237]     Train net output #0: loss = 0.171719 (* 1 = 0.171719 loss)
I0407 23:30:02.288235   359 sgd_solver.cpp:105] Iteration 7692, lr = 6.16702e-05
I0407 23:30:07.204720   359 solver.cpp:218] Iteration 7704 (2.44078 iter/s, 4.91647s/12 iters), loss = 0.0526268
I0407 23:30:07.204764   359 solver.cpp:237]     Train net output #0: loss = 0.0526269 (* 1 = 0.0526269 loss)
I0407 23:30:07.204773   359 sgd_solver.cpp:105] Iteration 7704, lr = 6.02447e-05
I0407 23:30:12.156980   359 solver.cpp:218] Iteration 7716 (2.42317 iter/s, 4.95219s/12 iters), loss = 0.0985625
I0407 23:30:12.157104   359 solver.cpp:237]     Train net output #0: loss = 0.0985625 (* 1 = 0.0985625 loss)
I0407 23:30:12.157112   359 sgd_solver.cpp:105] Iteration 7716, lr = 5.8852e-05
I0407 23:30:17.069025   359 solver.cpp:218] Iteration 7728 (2.44305 iter/s, 4.9119s/12 iters), loss = 0.136825
I0407 23:30:17.069072   359 solver.cpp:237]     Train net output #0: loss = 0.136825 (* 1 = 0.136825 loss)
I0407 23:30:17.069079   359 sgd_solver.cpp:105] Iteration 7728, lr = 5.74913e-05
I0407 23:30:22.034132   359 solver.cpp:218] Iteration 7740 (2.4169 iter/s, 4.96505s/12 iters), loss = 0.132538
I0407 23:30:22.034163   359 solver.cpp:237]     Train net output #0: loss = 0.132538 (* 1 = 0.132538 loss)
I0407 23:30:22.034171   359 sgd_solver.cpp:105] Iteration 7740, lr = 5.61618e-05
I0407 23:30:26.498157   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 23:30:29.626694   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 23:30:31.996178   359 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 23:30:31.996196   359 net.cpp:676] Ignoring source layer train-data
I0407 23:30:33.442696   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:30:36.751013   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:30:36.751061   359 solver.cpp:397]     Test net output #1: loss = 2.82652 (* 1 = 2.82652 loss)
I0407 23:30:36.847776   359 solver.cpp:218] Iteration 7752 (0.810067 iter/s, 14.8136s/12 iters), loss = 0.161823
I0407 23:30:36.847820   359 solver.cpp:237]     Train net output #0: loss = 0.161824 (* 1 = 0.161824 loss)
I0407 23:30:36.847829   359 sgd_solver.cpp:105] Iteration 7752, lr = 5.4863e-05
I0407 23:30:40.917726   359 solver.cpp:218] Iteration 7764 (2.94848 iter/s, 4.06989s/12 iters), loss = 0.180322
I0407 23:30:40.917771   359 solver.cpp:237]     Train net output #0: loss = 0.180322 (* 1 = 0.180322 loss)
I0407 23:30:40.917780   359 sgd_solver.cpp:105] Iteration 7764, lr = 5.3594e-05
I0407 23:30:45.918315   359 solver.cpp:218] Iteration 7776 (2.39975 iter/s, 5.00052s/12 iters), loss = 0.166381
I0407 23:30:45.918469   359 solver.cpp:237]     Train net output #0: loss = 0.166381 (* 1 = 0.166381 loss)
I0407 23:30:45.918479   359 sgd_solver.cpp:105] Iteration 7776, lr = 5.23542e-05
I0407 23:30:50.836014   359 solver.cpp:218] Iteration 7788 (2.44025 iter/s, 4.91752s/12 iters), loss = 0.13637
I0407 23:30:50.836061   359 solver.cpp:237]     Train net output #0: loss = 0.13637 (* 1 = 0.13637 loss)
I0407 23:30:50.836068   359 sgd_solver.cpp:105] Iteration 7788, lr = 5.11429e-05
I0407 23:30:50.842371   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:30:55.787715   359 solver.cpp:218] Iteration 7800 (2.42344 iter/s, 4.95163s/12 iters), loss = 0.115531
I0407 23:30:55.787762   359 solver.cpp:237]     Train net output #0: loss = 0.115531 (* 1 = 0.115531 loss)
I0407 23:30:55.787771   359 sgd_solver.cpp:105] Iteration 7800, lr = 4.99596e-05
I0407 23:31:00.731448   359 solver.cpp:218] Iteration 7812 (2.42735 iter/s, 4.94367s/12 iters), loss = 0.159319
I0407 23:31:00.731490   359 solver.cpp:237]     Train net output #0: loss = 0.159319 (* 1 = 0.159319 loss)
I0407 23:31:00.731498   359 sgd_solver.cpp:105] Iteration 7812, lr = 4.88034e-05
I0407 23:31:05.690143   359 solver.cpp:218] Iteration 7824 (2.42003 iter/s, 4.95862s/12 iters), loss = 0.190898
I0407 23:31:05.690202   359 solver.cpp:237]     Train net output #0: loss = 0.190898 (* 1 = 0.190898 loss)
I0407 23:31:05.690213   359 sgd_solver.cpp:105] Iteration 7824, lr = 4.76739e-05
I0407 23:31:10.649706   359 solver.cpp:218] Iteration 7836 (2.4196 iter/s, 4.95949s/12 iters), loss = 0.131701
I0407 23:31:10.649742   359 solver.cpp:237]     Train net output #0: loss = 0.131701 (* 1 = 0.131701 loss)
I0407 23:31:10.649749   359 sgd_solver.cpp:105] Iteration 7836, lr = 4.65705e-05
I0407 23:31:15.593343   359 solver.cpp:218] Iteration 7848 (2.42739 iter/s, 4.94358s/12 iters), loss = 0.100684
I0407 23:31:15.593381   359 solver.cpp:237]     Train net output #0: loss = 0.100684 (* 1 = 0.100684 loss)
I0407 23:31:15.593389   359 sgd_solver.cpp:105] Iteration 7848, lr = 4.54924e-05
I0407 23:31:17.607589   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 23:31:20.820639   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 23:31:23.295486   359 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 23:31:23.295502   359 net.cpp:676] Ignoring source layer train-data
I0407 23:31:24.710500   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:31:28.186986   359 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 23:31:28.187026   359 solver.cpp:397]     Test net output #1: loss = 2.83505 (* 1 = 2.83505 loss)
I0407 23:31:29.999689   359 solver.cpp:218] Iteration 7860 (0.83297 iter/s, 14.4063s/12 iters), loss = 0.110066
I0407 23:31:29.999734   359 solver.cpp:237]     Train net output #0: loss = 0.110066 (* 1 = 0.110066 loss)
I0407 23:31:29.999742   359 sgd_solver.cpp:105] Iteration 7860, lr = 4.44392e-05
I0407 23:31:34.907042   359 solver.cpp:218] Iteration 7872 (2.44534 iter/s, 4.90729s/12 iters), loss = 0.135548
I0407 23:31:34.907086   359 solver.cpp:237]     Train net output #0: loss = 0.135548 (* 1 = 0.135548 loss)
I0407 23:31:34.907094   359 sgd_solver.cpp:105] Iteration 7872, lr = 4.34102e-05
I0407 23:31:39.873056   359 solver.cpp:218] Iteration 7884 (2.41646 iter/s, 4.96595s/12 iters), loss = 0.054767
I0407 23:31:39.873098   359 solver.cpp:237]     Train net output #0: loss = 0.054767 (* 1 = 0.054767 loss)
I0407 23:31:39.873106   359 sgd_solver.cpp:105] Iteration 7884, lr = 4.2405e-05
I0407 23:31:41.984211   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:31:44.824985   359 solver.cpp:218] Iteration 7896 (2.42333 iter/s, 4.95186s/12 iters), loss = 0.199566
I0407 23:31:44.825026   359 solver.cpp:237]     Train net output #0: loss = 0.199566 (* 1 = 0.199566 loss)
I0407 23:31:44.825033   359 sgd_solver.cpp:105] Iteration 7896, lr = 4.1423e-05
I0407 23:31:49.743180   359 solver.cpp:218] Iteration 7908 (2.43995 iter/s, 4.91813s/12 iters), loss = 0.127736
I0407 23:31:49.743315   359 solver.cpp:237]     Train net output #0: loss = 0.127736 (* 1 = 0.127736 loss)
I0407 23:31:49.743324   359 sgd_solver.cpp:105] Iteration 7908, lr = 4.04636e-05
I0407 23:31:54.692584   359 solver.cpp:218] Iteration 7920 (2.42461 iter/s, 4.94925s/12 iters), loss = 0.0679647
I0407 23:31:54.692621   359 solver.cpp:237]     Train net output #0: loss = 0.0679648 (* 1 = 0.0679648 loss)
I0407 23:31:54.692629   359 sgd_solver.cpp:105] Iteration 7920, lr = 3.95264e-05
I0407 23:31:59.591023   359 solver.cpp:218] Iteration 7932 (2.44979 iter/s, 4.89839s/12 iters), loss = 0.146791
I0407 23:31:59.591060   359 solver.cpp:237]     Train net output #0: loss = 0.146791 (* 1 = 0.146791 loss)
I0407 23:31:59.591068   359 sgd_solver.cpp:105] Iteration 7932, lr = 3.86107e-05
I0407 23:32:04.563835   359 solver.cpp:218] Iteration 7944 (2.41315 iter/s, 4.97275s/12 iters), loss = 0.112108
I0407 23:32:04.563874   359 solver.cpp:237]     Train net output #0: loss = 0.112108 (* 1 = 0.112108 loss)
I0407 23:32:04.563882   359 sgd_solver.cpp:105] Iteration 7944, lr = 3.77162e-05
I0407 23:32:09.028057   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 23:32:12.097879   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 23:32:14.462730   359 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 23:32:14.462759   359 net.cpp:676] Ignoring source layer train-data
I0407 23:32:15.847432   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:32:19.101797   359 solver.cpp:397]     Test net output #0: accuracy = 0.45527
I0407 23:32:19.101836   359 solver.cpp:397]     Test net output #1: loss = 2.82293 (* 1 = 2.82293 loss)
I0407 23:32:19.198400   359 solver.cpp:218] Iteration 7956 (0.819981 iter/s, 14.6345s/12 iters), loss = 0.0440542
I0407 23:32:19.198443   359 solver.cpp:237]     Train net output #0: loss = 0.0440543 (* 1 = 0.0440543 loss)
I0407 23:32:19.198451   359 sgd_solver.cpp:105] Iteration 7956, lr = 3.68424e-05
I0407 23:32:23.230445   359 solver.cpp:218] Iteration 7968 (2.9762 iter/s, 4.03199s/12 iters), loss = 0.182227
I0407 23:32:23.230568   359 solver.cpp:237]     Train net output #0: loss = 0.182227 (* 1 = 0.182227 loss)
I0407 23:32:23.230577   359 sgd_solver.cpp:105] Iteration 7968, lr = 3.59887e-05
I0407 23:32:28.163815   359 solver.cpp:218] Iteration 7980 (2.43248 iter/s, 4.93323s/12 iters), loss = 0.199141
I0407 23:32:28.163852   359 solver.cpp:237]     Train net output #0: loss = 0.199141 (* 1 = 0.199141 loss)
I0407 23:32:28.163861   359 sgd_solver.cpp:105] Iteration 7980, lr = 3.51547e-05
I0407 23:32:32.363152   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:32:33.021293   359 solver.cpp:218] Iteration 7992 (2.47045 iter/s, 4.85742s/12 iters), loss = 0.152924
I0407 23:32:33.021327   359 solver.cpp:237]     Train net output #0: loss = 0.152924 (* 1 = 0.152924 loss)
I0407 23:32:33.021335   359 sgd_solver.cpp:105] Iteration 7992, lr = 3.434e-05
I0407 23:32:37.985229   359 solver.cpp:218] Iteration 8004 (2.41746 iter/s, 4.96389s/12 iters), loss = 0.0793058
I0407 23:32:37.985263   359 solver.cpp:237]     Train net output #0: loss = 0.0793058 (* 1 = 0.0793058 loss)
I0407 23:32:37.985271   359 sgd_solver.cpp:105] Iteration 8004, lr = 3.35441e-05
I0407 23:32:42.897962   359 solver.cpp:218] Iteration 8016 (2.44266 iter/s, 4.91268s/12 iters), loss = 0.14935
I0407 23:32:42.897997   359 solver.cpp:237]     Train net output #0: loss = 0.14935 (* 1 = 0.14935 loss)
I0407 23:32:42.898005   359 sgd_solver.cpp:105] Iteration 8016, lr = 3.27666e-05
I0407 23:32:47.788993   359 solver.cpp:218] Iteration 8028 (2.4535 iter/s, 4.89098s/12 iters), loss = 0.115184
I0407 23:32:47.789032   359 solver.cpp:237]     Train net output #0: loss = 0.115184 (* 1 = 0.115184 loss)
I0407 23:32:47.789041   359 sgd_solver.cpp:105] Iteration 8028, lr = 3.20071e-05
I0407 23:32:52.683718   359 solver.cpp:218] Iteration 8040 (2.45165 iter/s, 4.89466s/12 iters), loss = 0.169018
I0407 23:32:52.683758   359 solver.cpp:237]     Train net output #0: loss = 0.169018 (* 1 = 0.169018 loss)
I0407 23:32:52.683766   359 sgd_solver.cpp:105] Iteration 8040, lr = 3.12651e-05
I0407 23:32:57.533496   359 solver.cpp:218] Iteration 8052 (2.47437 iter/s, 4.84972s/12 iters), loss = 0.0941235
I0407 23:32:57.533654   359 solver.cpp:237]     Train net output #0: loss = 0.0941235 (* 1 = 0.0941235 loss)
I0407 23:32:57.533664   359 sgd_solver.cpp:105] Iteration 8052, lr = 3.05403e-05
I0407 23:32:59.543113   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 23:33:02.643108   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 23:33:05.006484   359 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 23:33:05.006502   359 net.cpp:676] Ignoring source layer train-data
I0407 23:33:06.332792   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:33:09.763608   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:33:09.763656   359 solver.cpp:397]     Test net output #1: loss = 2.83435 (* 1 = 2.83435 loss)
I0407 23:33:11.501518   359 solver.cpp:218] Iteration 8064 (0.859117 iter/s, 13.9678s/12 iters), loss = 0.109753
I0407 23:33:11.501559   359 solver.cpp:237]     Train net output #0: loss = 0.109753 (* 1 = 0.109753 loss)
I0407 23:33:11.501567   359 sgd_solver.cpp:105] Iteration 8064, lr = 2.98322e-05
I0407 23:33:16.439133   359 solver.cpp:218] Iteration 8076 (2.43035 iter/s, 4.93756s/12 iters), loss = 0.0864122
I0407 23:33:16.439169   359 solver.cpp:237]     Train net output #0: loss = 0.0864122 (* 1 = 0.0864122 loss)
I0407 23:33:16.439177   359 sgd_solver.cpp:105] Iteration 8076, lr = 2.91405e-05
I0407 23:33:21.394717   359 solver.cpp:218] Iteration 8088 (2.42154 iter/s, 4.95553s/12 iters), loss = 0.139236
I0407 23:33:21.394753   359 solver.cpp:237]     Train net output #0: loss = 0.139236 (* 1 = 0.139236 loss)
I0407 23:33:21.394760   359 sgd_solver.cpp:105] Iteration 8088, lr = 2.84647e-05
I0407 23:33:22.773025   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:33:26.297230   359 solver.cpp:218] Iteration 8100 (2.44775 iter/s, 4.90245s/12 iters), loss = 0.171817
I0407 23:33:26.297267   359 solver.cpp:237]     Train net output #0: loss = 0.171817 (* 1 = 0.171817 loss)
I0407 23:33:26.297276   359 sgd_solver.cpp:105] Iteration 8100, lr = 2.78046e-05
I0407 23:33:31.270700   359 solver.cpp:218] Iteration 8112 (2.41283 iter/s, 4.97342s/12 iters), loss = 0.177519
I0407 23:33:31.270818   359 solver.cpp:237]     Train net output #0: loss = 0.177519 (* 1 = 0.177519 loss)
I0407 23:33:31.270828   359 sgd_solver.cpp:105] Iteration 8112, lr = 2.71598e-05
I0407 23:33:36.160349   359 solver.cpp:218] Iteration 8124 (2.45423 iter/s, 4.88952s/12 iters), loss = 0.138745
I0407 23:33:36.160383   359 solver.cpp:237]     Train net output #0: loss = 0.138745 (* 1 = 0.138745 loss)
I0407 23:33:36.160392   359 sgd_solver.cpp:105] Iteration 8124, lr = 2.65299e-05
I0407 23:33:41.049350   359 solver.cpp:218] Iteration 8136 (2.45452 iter/s, 4.88895s/12 iters), loss = 0.144014
I0407 23:33:41.049391   359 solver.cpp:237]     Train net output #0: loss = 0.144014 (* 1 = 0.144014 loss)
I0407 23:33:41.049398   359 sgd_solver.cpp:105] Iteration 8136, lr = 2.59145e-05
I0407 23:33:46.007997   359 solver.cpp:218] Iteration 8148 (2.42004 iter/s, 4.95859s/12 iters), loss = 0.115102
I0407 23:33:46.008036   359 solver.cpp:237]     Train net output #0: loss = 0.115102 (* 1 = 0.115102 loss)
I0407 23:33:46.008044   359 sgd_solver.cpp:105] Iteration 8148, lr = 2.53134e-05
I0407 23:33:50.515622   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 23:33:53.589617   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 23:33:55.999799   359 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 23:33:55.999816   359 net.cpp:676] Ignoring source layer train-data
I0407 23:33:57.269635   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:00.778331   359 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 23:34:00.778373   359 solver.cpp:397]     Test net output #1: loss = 2.84238 (* 1 = 2.84238 loss)
I0407 23:34:00.874678   359 solver.cpp:218] Iteration 8160 (0.807178 iter/s, 14.8666s/12 iters), loss = 0.199744
I0407 23:34:00.874722   359 solver.cpp:237]     Train net output #0: loss = 0.199744 (* 1 = 0.199744 loss)
I0407 23:34:00.874732   359 sgd_solver.cpp:105] Iteration 8160, lr = 2.47262e-05
I0407 23:34:04.965214   359 solver.cpp:218] Iteration 8172 (2.93365 iter/s, 4.09047s/12 iters), loss = 0.231639
I0407 23:34:04.965356   359 solver.cpp:237]     Train net output #0: loss = 0.231639 (* 1 = 0.231639 loss)
I0407 23:34:04.965365   359 sgd_solver.cpp:105] Iteration 8172, lr = 2.41526e-05
I0407 23:34:09.918386   359 solver.cpp:218] Iteration 8184 (2.42277 iter/s, 4.95301s/12 iters), loss = 0.143868
I0407 23:34:09.918423   359 solver.cpp:237]     Train net output #0: loss = 0.143868 (* 1 = 0.143868 loss)
I0407 23:34:09.918431   359 sgd_solver.cpp:105] Iteration 8184, lr = 2.35923e-05
I0407 23:34:13.396580   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:14.833173   359 solver.cpp:218] Iteration 8196 (2.44164 iter/s, 4.91473s/12 iters), loss = 0.0685172
I0407 23:34:14.833211   359 solver.cpp:237]     Train net output #0: loss = 0.0685173 (* 1 = 0.0685173 loss)
I0407 23:34:14.833218   359 sgd_solver.cpp:105] Iteration 8196, lr = 2.30449e-05
I0407 23:34:19.807173   359 solver.cpp:218] Iteration 8208 (2.41258 iter/s, 4.97394s/12 iters), loss = 0.14359
I0407 23:34:19.807224   359 solver.cpp:237]     Train net output #0: loss = 0.14359 (* 1 = 0.14359 loss)
I0407 23:34:19.807233   359 sgd_solver.cpp:105] Iteration 8208, lr = 2.25102e-05
I0407 23:34:24.754192   359 solver.cpp:218] Iteration 8220 (2.42574 iter/s, 4.94695s/12 iters), loss = 0.0174887
I0407 23:34:24.754232   359 solver.cpp:237]     Train net output #0: loss = 0.0174888 (* 1 = 0.0174888 loss)
I0407 23:34:24.754240   359 sgd_solver.cpp:105] Iteration 8220, lr = 2.19879e-05
I0407 23:34:29.654958   359 solver.cpp:218] Iteration 8232 (2.44862 iter/s, 4.90071s/12 iters), loss = 0.133079
I0407 23:34:29.654996   359 solver.cpp:237]     Train net output #0: loss = 0.133079 (* 1 = 0.133079 loss)
I0407 23:34:29.655007   359 sgd_solver.cpp:105] Iteration 8232, lr = 2.14777e-05
I0407 23:34:34.613402   359 solver.cpp:218] Iteration 8244 (2.42014 iter/s, 4.95839s/12 iters), loss = 0.0642758
I0407 23:34:34.613440   359 solver.cpp:237]     Train net output #0: loss = 0.0642759 (* 1 = 0.0642759 loss)
I0407 23:34:34.613447   359 sgd_solver.cpp:105] Iteration 8244, lr = 2.09793e-05
I0407 23:34:39.521891   359 solver.cpp:218] Iteration 8256 (2.44477 iter/s, 4.90843s/12 iters), loss = 0.0774939
I0407 23:34:39.522049   359 solver.cpp:237]     Train net output #0: loss = 0.077494 (* 1 = 0.077494 loss)
I0407 23:34:39.522058   359 sgd_solver.cpp:105] Iteration 8256, lr = 2.04924e-05
I0407 23:34:41.525800   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 23:34:44.588554   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 23:34:46.951015   359 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 23:34:46.951032   359 net.cpp:676] Ignoring source layer train-data
I0407 23:34:48.098776   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:51.387286   359 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0407 23:34:51.387331   359 solver.cpp:397]     Test net output #1: loss = 2.83274 (* 1 = 2.83274 loss)
I0407 23:34:53.118649   359 solver.cpp:218] Iteration 8268 (0.882576 iter/s, 13.5966s/12 iters), loss = 0.091885
I0407 23:34:53.118693   359 solver.cpp:237]     Train net output #0: loss = 0.0918851 (* 1 = 0.0918851 loss)
I0407 23:34:53.118702   359 sgd_solver.cpp:105] Iteration 8268, lr = 2.00168e-05
I0407 23:34:58.066449   359 solver.cpp:218] Iteration 8280 (2.42535 iter/s, 4.94773s/12 iters), loss = 0.147981
I0407 23:34:58.066495   359 solver.cpp:237]     Train net output #0: loss = 0.147981 (* 1 = 0.147981 loss)
I0407 23:34:58.066504   359 sgd_solver.cpp:105] Iteration 8280, lr = 1.95522e-05
I0407 23:35:02.989535   359 solver.cpp:218] Iteration 8292 (2.43753 iter/s, 4.92302s/12 iters), loss = 0.144756
I0407 23:35:02.989579   359 solver.cpp:237]     Train net output #0: loss = 0.144756 (* 1 = 0.144756 loss)
I0407 23:35:02.989588   359 sgd_solver.cpp:105] Iteration 8292, lr = 1.90984e-05
I0407 23:35:03.587188   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:35:07.839907   359 solver.cpp:218] Iteration 8304 (2.47407 iter/s, 4.85031s/12 iters), loss = 0.122598
I0407 23:35:07.839943   359 solver.cpp:237]     Train net output #0: loss = 0.122599 (* 1 = 0.122599 loss)
I0407 23:35:07.839951   359 sgd_solver.cpp:105] Iteration 8304, lr = 1.86551e-05
I0407 23:35:10.672946   359 blocking_queue.cpp:49] Waiting for data
I0407 23:35:12.806915   359 solver.cpp:218] Iteration 8316 (2.41597 iter/s, 4.96695s/12 iters), loss = 0.089899
I0407 23:35:12.806954   359 solver.cpp:237]     Train net output #0: loss = 0.0898991 (* 1 = 0.0898991 loss)
I0407 23:35:12.806962   359 sgd_solver.cpp:105] Iteration 8316, lr = 1.82221e-05
I0407 23:35:17.669307   359 solver.cpp:218] Iteration 8328 (2.46795 iter/s, 4.86234s/12 iters), loss = 0.124047
I0407 23:35:17.669342   359 solver.cpp:237]     Train net output #0: loss = 0.124047 (* 1 = 0.124047 loss)
I0407 23:35:17.669350   359 sgd_solver.cpp:105] Iteration 8328, lr = 1.77991e-05
I0407 23:35:22.583503   359 solver.cpp:218] Iteration 8340 (2.44193 iter/s, 4.91414s/12 iters), loss = 0.0786122
I0407 23:35:22.583547   359 solver.cpp:237]     Train net output #0: loss = 0.0786123 (* 1 = 0.0786123 loss)
I0407 23:35:22.583555   359 sgd_solver.cpp:105] Iteration 8340, lr = 1.73859e-05
I0407 23:35:27.570639   359 solver.cpp:218] Iteration 8352 (2.40622 iter/s, 4.98707s/12 iters), loss = 0.123852
I0407 23:35:27.570683   359 solver.cpp:237]     Train net output #0: loss = 0.123852 (* 1 = 0.123852 loss)
I0407 23:35:27.570693   359 sgd_solver.cpp:105] Iteration 8352, lr = 1.69823e-05
I0407 23:35:32.068830   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 23:35:35.198611   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 23:35:37.552906   359 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 23:35:37.552925   359 net.cpp:676] Ignoring source layer train-data
I0407 23:35:38.627948   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:35:41.923678   359 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0407 23:35:41.923846   359 solver.cpp:397]     Test net output #1: loss = 2.83897 (* 1 = 2.83897 loss)
I0407 23:35:42.020275   359 solver.cpp:218] Iteration 8364 (0.830475 iter/s, 14.4496s/12 iters), loss = 0.0775367
I0407 23:35:42.020320   359 solver.cpp:237]     Train net output #0: loss = 0.0775368 (* 1 = 0.0775368 loss)
I0407 23:35:42.020330   359 sgd_solver.cpp:105] Iteration 8364, lr = 1.6588e-05
I0407 23:35:46.119009   359 solver.cpp:218] Iteration 8376 (2.92778 iter/s, 4.09866s/12 iters), loss = 0.118774
I0407 23:35:46.119052   359 solver.cpp:237]     Train net output #0: loss = 0.118774 (* 1 = 0.118774 loss)
I0407 23:35:46.119060   359 sgd_solver.cpp:105] Iteration 8376, lr = 1.62029e-05
I0407 23:35:51.069802   359 solver.cpp:218] Iteration 8388 (2.42389 iter/s, 4.95073s/12 iters), loss = 0.0871738
I0407 23:35:51.069844   359 solver.cpp:237]     Train net output #0: loss = 0.0871739 (* 1 = 0.0871739 loss)
I0407 23:35:51.069852   359 sgd_solver.cpp:105] Iteration 8388, lr = 1.58267e-05
I0407 23:35:53.821846   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:35:55.958904   359 solver.cpp:218] Iteration 8400 (2.45447 iter/s, 4.88905s/12 iters), loss = 0.149992
I0407 23:35:55.958937   359 solver.cpp:237]     Train net output #0: loss = 0.149992 (* 1 = 0.149992 loss)
I0407 23:35:55.958945   359 sgd_solver.cpp:105] Iteration 8400, lr = 1.54592e-05
I0407 23:36:00.911273   359 solver.cpp:218] Iteration 8412 (2.42311 iter/s, 4.95232s/12 iters), loss = 0.0321403
I0407 23:36:00.911309   359 solver.cpp:237]     Train net output #0: loss = 0.0321404 (* 1 = 0.0321404 loss)
I0407 23:36:00.911315   359 sgd_solver.cpp:105] Iteration 8412, lr = 1.51002e-05
I0407 23:36:05.826252   359 solver.cpp:218] Iteration 8424 (2.44155 iter/s, 4.91492s/12 iters), loss = 0.0848399
I0407 23:36:05.826297   359 solver.cpp:237]     Train net output #0: loss = 0.08484 (* 1 = 0.08484 loss)
I0407 23:36:05.826306   359 sgd_solver.cpp:105] Iteration 8424, lr = 1.47496e-05
I0407 23:36:10.771190   359 solver.cpp:218] Iteration 8436 (2.42675 iter/s, 4.94488s/12 iters), loss = 0.118401
I0407 23:36:10.771235   359 solver.cpp:237]     Train net output #0: loss = 0.118401 (* 1 = 0.118401 loss)
I0407 23:36:10.771245   359 sgd_solver.cpp:105] Iteration 8436, lr = 1.44071e-05
I0407 23:36:15.706318   359 solver.cpp:218] Iteration 8448 (2.43158 iter/s, 4.93506s/12 iters), loss = 0.199423
I0407 23:36:15.706467   359 solver.cpp:237]     Train net output #0: loss = 0.199423 (* 1 = 0.199423 loss)
I0407 23:36:15.706477   359 sgd_solver.cpp:105] Iteration 8448, lr = 1.40725e-05
I0407 23:36:20.670230   359 solver.cpp:218] Iteration 8460 (2.41753 iter/s, 4.96375s/12 iters), loss = 0.115787
I0407 23:36:20.670269   359 solver.cpp:237]     Train net output #0: loss = 0.115787 (* 1 = 0.115787 loss)
I0407 23:36:20.670276   359 sgd_solver.cpp:105] Iteration 8460, lr = 1.37457e-05
I0407 23:36:22.669723   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 23:36:25.751446   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 23:36:28.113206   359 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 23:36:28.113232   359 net.cpp:676] Ignoring source layer train-data
I0407 23:36:29.256511   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:36:32.916054   359 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0407 23:36:32.916097   359 solver.cpp:397]     Test net output #1: loss = 2.82356 (* 1 = 2.82356 loss)
I0407 23:36:34.718864   359 solver.cpp:218] Iteration 8472 (0.85418 iter/s, 14.0486s/12 iters), loss = 0.125319
I0407 23:36:34.718904   359 solver.cpp:237]     Train net output #0: loss = 0.125319 (* 1 = 0.125319 loss)
I0407 23:36:34.718911   359 sgd_solver.cpp:105] Iteration 8472, lr = 1.34265e-05
I0407 23:36:39.697643   359 solver.cpp:218] Iteration 8484 (2.41026 iter/s, 4.97872s/12 iters), loss = 0.144618
I0407 23:36:39.697682   359 solver.cpp:237]     Train net output #0: loss = 0.144618 (* 1 = 0.144618 loss)
I0407 23:36:39.697690   359 sgd_solver.cpp:105] Iteration 8484, lr = 1.31147e-05
I0407 23:36:44.599232   359 solver.cpp:218] Iteration 8496 (2.44822 iter/s, 4.90153s/12 iters), loss = 0.069995
I0407 23:36:44.599278   359 solver.cpp:237]     Train net output #0: loss = 0.0699951 (* 1 = 0.0699951 loss)
I0407 23:36:44.599287   359 sgd_solver.cpp:105] Iteration 8496, lr = 1.28101e-05
I0407 23:36:44.635639   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:36:49.501340   359 solver.cpp:218] Iteration 8508 (2.44796 iter/s, 4.90204s/12 iters), loss = 0.0924
I0407 23:36:49.501523   359 solver.cpp:237]     Train net output #0: loss = 0.0924001 (* 1 = 0.0924001 loss)
I0407 23:36:49.501533   359 sgd_solver.cpp:105] Iteration 8508, lr = 1.25126e-05
I0407 23:36:54.462152   359 solver.cpp:218] Iteration 8520 (2.41906 iter/s, 4.96061s/12 iters), loss = 0.105946
I0407 23:36:54.462196   359 solver.cpp:237]     Train net output #0: loss = 0.105946 (* 1 = 0.105946 loss)
I0407 23:36:54.462204   359 sgd_solver.cpp:105] Iteration 8520, lr = 1.2222e-05
I0407 23:36:59.358388   359 solver.cpp:218] Iteration 8532 (2.45089 iter/s, 4.89617s/12 iters), loss = 0.191108
I0407 23:36:59.358426   359 solver.cpp:237]     Train net output #0: loss = 0.191108 (* 1 = 0.191108 loss)
I0407 23:36:59.358434   359 sgd_solver.cpp:105] Iteration 8532, lr = 1.19381e-05
I0407 23:37:04.319348   359 solver.cpp:218] Iteration 8544 (2.41892 iter/s, 4.9609s/12 iters), loss = 0.0866535
I0407 23:37:04.319396   359 solver.cpp:237]     Train net output #0: loss = 0.0866536 (* 1 = 0.0866536 loss)
I0407 23:37:04.319403   359 sgd_solver.cpp:105] Iteration 8544, lr = 1.16608e-05
I0407 23:37:09.266324   359 solver.cpp:218] Iteration 8556 (2.42576 iter/s, 4.9469s/12 iters), loss = 0.0468395
I0407 23:37:09.266379   359 solver.cpp:237]     Train net output #0: loss = 0.0468396 (* 1 = 0.0468396 loss)
I0407 23:37:09.266391   359 sgd_solver.cpp:105] Iteration 8556, lr = 1.13899e-05
I0407 23:37:13.731248   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 23:37:16.826028   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 23:37:19.188450   359 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 23:37:19.188469   359 net.cpp:676] Ignoring source layer train-data
I0407 23:37:20.282374   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:37:23.907529   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:37:23.907574   359 solver.cpp:397]     Test net output #1: loss = 2.83594 (* 1 = 2.83594 loss)
I0407 23:37:24.004022   359 solver.cpp:218] Iteration 8568 (0.814243 iter/s, 14.7376s/12 iters), loss = 0.0781373
I0407 23:37:24.004073   359 solver.cpp:237]     Train net output #0: loss = 0.0781374 (* 1 = 0.0781374 loss)
I0407 23:37:24.004081   359 sgd_solver.cpp:105] Iteration 8568, lr = 1.11254e-05
I0407 23:37:28.215729   359 solver.cpp:218] Iteration 8580 (2.84925 iter/s, 4.21164s/12 iters), loss = 0.0775844
I0407 23:37:28.215766   359 solver.cpp:237]     Train net output #0: loss = 0.0775845 (* 1 = 0.0775845 loss)
I0407 23:37:28.215773   359 sgd_solver.cpp:105] Iteration 8580, lr = 1.08669e-05
I0407 23:37:33.258900   359 solver.cpp:218] Iteration 8592 (2.37948 iter/s, 5.04311s/12 iters), loss = 0.110503
I0407 23:37:33.258944   359 solver.cpp:237]     Train net output #0: loss = 0.110503 (* 1 = 0.110503 loss)
I0407 23:37:33.258953   359 sgd_solver.cpp:105] Iteration 8592, lr = 1.06145e-05
I0407 23:37:35.387615   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:37:38.152248   359 solver.cpp:218] Iteration 8604 (2.45234 iter/s, 4.89328s/12 iters), loss = 0.165899
I0407 23:37:38.152295   359 solver.cpp:237]     Train net output #0: loss = 0.165899 (* 1 = 0.165899 loss)
I0407 23:37:38.152303   359 sgd_solver.cpp:105] Iteration 8604, lr = 1.03679e-05
I0407 23:37:43.119252   359 solver.cpp:218] Iteration 8616 (2.41598 iter/s, 4.96693s/12 iters), loss = 0.0955551
I0407 23:37:43.119297   359 solver.cpp:237]     Train net output #0: loss = 0.0955551 (* 1 = 0.0955551 loss)
I0407 23:37:43.119304   359 sgd_solver.cpp:105] Iteration 8616, lr = 1.0127e-05
I0407 23:37:48.027545   359 solver.cpp:218] Iteration 8628 (2.44487 iter/s, 4.90823s/12 iters), loss = 0.0759271
I0407 23:37:48.027586   359 solver.cpp:237]     Train net output #0: loss = 0.0759272 (* 1 = 0.0759272 loss)
I0407 23:37:48.027595   359 sgd_solver.cpp:105] Iteration 8628, lr = 9.89177e-06
I0407 23:37:52.936188   359 solver.cpp:218] Iteration 8640 (2.4447 iter/s, 4.90858s/12 iters), loss = 0.0806167
I0407 23:37:52.936350   359 solver.cpp:237]     Train net output #0: loss = 0.0806167 (* 1 = 0.0806167 loss)
I0407 23:37:52.936360   359 sgd_solver.cpp:105] Iteration 8640, lr = 9.66196e-06
I0407 23:37:57.858758   359 solver.cpp:218] Iteration 8652 (2.43784 iter/s, 4.92238s/12 iters), loss = 0.154482
I0407 23:37:57.858803   359 solver.cpp:237]     Train net output #0: loss = 0.154482 (* 1 = 0.154482 loss)
I0407 23:37:57.858810   359 sgd_solver.cpp:105] Iteration 8652, lr = 9.43749e-06
I0407 23:38:02.821100   359 solver.cpp:218] Iteration 8664 (2.41824 iter/s, 4.96228s/12 iters), loss = 0.0870906
I0407 23:38:02.821141   359 solver.cpp:237]     Train net output #0: loss = 0.0870906 (* 1 = 0.0870906 loss)
I0407 23:38:02.821148   359 sgd_solver.cpp:105] Iteration 8664, lr = 9.21823e-06
I0407 23:38:04.811540   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 23:38:07.887387   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 23:38:10.309214   359 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 23:38:10.309235   359 net.cpp:676] Ignoring source layer train-data
I0407 23:38:11.371522   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:38:15.141376   359 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0407 23:38:15.141422   359 solver.cpp:397]     Test net output #1: loss = 2.82661 (* 1 = 2.82661 loss)
I0407 23:38:16.930375   359 solver.cpp:218] Iteration 8676 (0.850509 iter/s, 14.1092s/12 iters), loss = 0.0449423
I0407 23:38:16.930423   359 solver.cpp:237]     Train net output #0: loss = 0.0449423 (* 1 = 0.0449423 loss)
I0407 23:38:16.930431   359 sgd_solver.cpp:105] Iteration 8676, lr = 9.00405e-06
I0407 23:38:21.901751   359 solver.cpp:218] Iteration 8688 (2.41385 iter/s, 4.97131s/12 iters), loss = 0.0630664
I0407 23:38:21.901794   359 solver.cpp:237]     Train net output #0: loss = 0.0630664 (* 1 = 0.0630664 loss)
I0407 23:38:21.901803   359 sgd_solver.cpp:105] Iteration 8688, lr = 8.79485e-06
I0407 23:38:26.116315   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:38:26.788120   359 solver.cpp:218] Iteration 8700 (2.45584 iter/s, 4.88631s/12 iters), loss = 0.0818737
I0407 23:38:26.788162   359 solver.cpp:237]     Train net output #0: loss = 0.0818738 (* 1 = 0.0818738 loss)
I0407 23:38:26.788172   359 sgd_solver.cpp:105] Iteration 8700, lr = 8.5905e-06
I0407 23:38:31.746891   359 solver.cpp:218] Iteration 8712 (2.41999 iter/s, 4.9587s/12 iters), loss = 0.0961585
I0407 23:38:31.746935   359 solver.cpp:237]     Train net output #0: loss = 0.0961586 (* 1 = 0.0961586 loss)
I0407 23:38:31.746944   359 sgd_solver.cpp:105] Iteration 8712, lr = 8.3909e-06
I0407 23:38:36.654266   359 solver.cpp:218] Iteration 8724 (2.44533 iter/s, 4.90731s/12 iters), loss = 0.188736
I0407 23:38:36.654305   359 solver.cpp:237]     Train net output #0: loss = 0.188736 (* 1 = 0.188736 loss)
I0407 23:38:36.654314   359 sgd_solver.cpp:105] Iteration 8724, lr = 8.19593e-06
I0407 23:38:41.599942   359 solver.cpp:218] Iteration 8736 (2.42639 iter/s, 4.94562s/12 iters), loss = 0.175551
I0407 23:38:41.599982   359 solver.cpp:237]     Train net output #0: loss = 0.175551 (* 1 = 0.175551 loss)
I0407 23:38:41.599989   359 sgd_solver.cpp:105] Iteration 8736, lr = 8.00549e-06
I0407 23:38:46.522909   359 solver.cpp:218] Iteration 8748 (2.43758 iter/s, 4.92291s/12 iters), loss = 0.283982
I0407 23:38:46.522948   359 solver.cpp:237]     Train net output #0: loss = 0.283982 (* 1 = 0.283982 loss)
I0407 23:38:46.522954   359 sgd_solver.cpp:105] Iteration 8748, lr = 7.81947e-06
I0407 23:38:51.479146   359 solver.cpp:218] Iteration 8760 (2.42122 iter/s, 4.95618s/12 iters), loss = 0.11002
I0407 23:38:51.479183   359 solver.cpp:237]     Train net output #0: loss = 0.11002 (* 1 = 0.11002 loss)
I0407 23:38:51.479192   359 sgd_solver.cpp:105] Iteration 8760, lr = 7.63777e-06
I0407 23:38:55.937175   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 23:38:59.037871   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 23:39:01.429013   359 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 23:39:01.429029   359 net.cpp:676] Ignoring source layer train-data
I0407 23:39:02.386000   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:39:05.864818   359 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 23:39:05.864850   359 solver.cpp:397]     Test net output #1: loss = 2.83201 (* 1 = 2.83201 loss)
I0407 23:39:05.961374   359 solver.cpp:218] Iteration 8772 (0.828607 iter/s, 14.4821s/12 iters), loss = 0.0881024
I0407 23:39:05.961450   359 solver.cpp:237]     Train net output #0: loss = 0.0881025 (* 1 = 0.0881025 loss)
I0407 23:39:05.961467   359 sgd_solver.cpp:105] Iteration 8772, lr = 7.46029e-06
I0407 23:39:10.080976   359 solver.cpp:218] Iteration 8784 (2.91297 iter/s, 4.11951s/12 iters), loss = 0.162071
I0407 23:39:10.081019   359 solver.cpp:237]     Train net output #0: loss = 0.162071 (* 1 = 0.162071 loss)
I0407 23:39:10.081027   359 sgd_solver.cpp:105] Iteration 8784, lr = 7.28692e-06
I0407 23:39:15.019028   359 solver.cpp:218] Iteration 8796 (2.43014 iter/s, 4.93799s/12 iters), loss = 0.136573
I0407 23:39:15.019073   359 solver.cpp:237]     Train net output #0: loss = 0.136573 (* 1 = 0.136573 loss)
I0407 23:39:15.019083   359 sgd_solver.cpp:105] Iteration 8796, lr = 7.11759e-06
I0407 23:39:16.425887   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:39:19.914045   359 solver.cpp:218] Iteration 8808 (2.45151 iter/s, 4.89495s/12 iters), loss = 0.0656361
I0407 23:39:19.914089   359 solver.cpp:237]     Train net output #0: loss = 0.0656361 (* 1 = 0.0656361 loss)
I0407 23:39:19.914098   359 sgd_solver.cpp:105] Iteration 8808, lr = 6.95219e-06
I0407 23:39:24.690186   359 solver.cpp:218] Iteration 8820 (2.51252 iter/s, 4.77608s/12 iters), loss = 0.0844655
I0407 23:39:24.690224   359 solver.cpp:237]     Train net output #0: loss = 0.0844655 (* 1 = 0.0844655 loss)
I0407 23:39:24.690232   359 sgd_solver.cpp:105] Iteration 8820, lr = 6.79063e-06
I0407 23:39:29.608309   359 solver.cpp:218] Iteration 8832 (2.43998 iter/s, 4.91807s/12 iters), loss = 0.190377
I0407 23:39:29.608433   359 solver.cpp:237]     Train net output #0: loss = 0.190377 (* 1 = 0.190377 loss)
I0407 23:39:29.608441   359 sgd_solver.cpp:105] Iteration 8832, lr = 6.63282e-06
I0407 23:39:34.551301   359 solver.cpp:218] Iteration 8844 (2.42775 iter/s, 4.94284s/12 iters), loss = 0.121329
I0407 23:39:34.551345   359 solver.cpp:237]     Train net output #0: loss = 0.121329 (* 1 = 0.121329 loss)
I0407 23:39:34.551354   359 sgd_solver.cpp:105] Iteration 8844, lr = 6.47867e-06
I0407 23:39:39.516156   359 solver.cpp:218] Iteration 8856 (2.41702 iter/s, 4.96479s/12 iters), loss = 0.157651
I0407 23:39:39.516202   359 solver.cpp:237]     Train net output #0: loss = 0.157651 (* 1 = 0.157651 loss)
I0407 23:39:39.516211   359 sgd_solver.cpp:105] Iteration 8856, lr = 6.3281e-06
I0407 23:39:44.451517   359 solver.cpp:218] Iteration 8868 (2.43147 iter/s, 4.93529s/12 iters), loss = 0.149847
I0407 23:39:44.451558   359 solver.cpp:237]     Train net output #0: loss = 0.149847 (* 1 = 0.149847 loss)
I0407 23:39:44.451567   359 sgd_solver.cpp:105] Iteration 8868, lr = 6.18104e-06
I0407 23:39:46.433789   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 23:39:50.187085   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 23:39:52.551925   359 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 23:39:52.551944   359 net.cpp:676] Ignoring source layer train-data
I0407 23:39:53.509279   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:39:57.332002   359 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0407 23:39:57.332049   359 solver.cpp:397]     Test net output #1: loss = 2.8289 (* 1 = 2.8289 loss)
I0407 23:39:59.136139   359 solver.cpp:218] Iteration 8880 (0.817186 iter/s, 14.6845s/12 iters), loss = 0.102348
I0407 23:39:59.136188   359 solver.cpp:237]     Train net output #0: loss = 0.102348 (* 1 = 0.102348 loss)
I0407 23:39:59.136198   359 sgd_solver.cpp:105] Iteration 8880, lr = 6.03739e-06
I0407 23:40:04.090639   359 solver.cpp:218] Iteration 8892 (2.42207 iter/s, 4.95443s/12 iters), loss = 0.0871479
I0407 23:40:04.090806   359 solver.cpp:237]     Train net output #0: loss = 0.087148 (* 1 = 0.087148 loss)
I0407 23:40:04.090816   359 sgd_solver.cpp:105] Iteration 8892, lr = 5.89707e-06
I0407 23:40:07.591856   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:40:08.971700   359 solver.cpp:218] Iteration 8904 (2.45857 iter/s, 4.88088s/12 iters), loss = 0.13032
I0407 23:40:08.971735   359 solver.cpp:237]     Train net output #0: loss = 0.13032 (* 1 = 0.13032 loss)
I0407 23:40:08.971743   359 sgd_solver.cpp:105] Iteration 8904, lr = 5.76001e-06
I0407 23:40:13.937816   359 solver.cpp:218] Iteration 8916 (2.4164 iter/s, 4.96606s/12 iters), loss = 0.0716483
I0407 23:40:13.937858   359 solver.cpp:237]     Train net output #0: loss = 0.0716483 (* 1 = 0.0716483 loss)
I0407 23:40:13.937867   359 sgd_solver.cpp:105] Iteration 8916, lr = 5.62614e-06
I0407 23:40:18.847456   359 solver.cpp:218] Iteration 8928 (2.4442 iter/s, 4.90958s/12 iters), loss = 0.119038
I0407 23:40:18.847499   359 solver.cpp:237]     Train net output #0: loss = 0.119038 (* 1 = 0.119038 loss)
I0407 23:40:18.847508   359 sgd_solver.cpp:105] Iteration 8928, lr = 5.49538e-06
I0407 23:40:23.762256   359 solver.cpp:218] Iteration 8940 (2.44164 iter/s, 4.91474s/12 iters), loss = 0.0533999
I0407 23:40:23.762291   359 solver.cpp:237]     Train net output #0: loss = 0.0533999 (* 1 = 0.0533999 loss)
I0407 23:40:23.762298   359 sgd_solver.cpp:105] Iteration 8940, lr = 5.36766e-06
I0407 23:40:28.687304   359 solver.cpp:218] Iteration 8952 (2.43655 iter/s, 4.92499s/12 iters), loss = 0.0766114
I0407 23:40:28.687340   359 solver.cpp:237]     Train net output #0: loss = 0.0766115 (* 1 = 0.0766115 loss)
I0407 23:40:28.687348   359 sgd_solver.cpp:105] Iteration 8952, lr = 5.2429e-06
I0407 23:40:33.637977   359 solver.cpp:218] Iteration 8964 (2.42394 iter/s, 4.95061s/12 iters), loss = 0.153665
I0407 23:40:33.638021   359 solver.cpp:237]     Train net output #0: loss = 0.153665 (* 1 = 0.153665 loss)
I0407 23:40:33.638029   359 sgd_solver.cpp:105] Iteration 8964, lr = 5.12104e-06
I0407 23:40:38.039327   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 23:40:41.170154   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 23:40:43.534703   359 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 23:40:43.534723   359 net.cpp:676] Ignoring source layer train-data
I0407 23:40:44.420064   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:40:47.966501   359 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0407 23:40:47.966533   359 solver.cpp:397]     Test net output #1: loss = 2.84442 (* 1 = 2.84442 loss)
I0407 23:40:48.063611   359 solver.cpp:218] Iteration 8976 (0.831857 iter/s, 14.4255s/12 iters), loss = 0.0681887
I0407 23:40:48.063649   359 solver.cpp:237]     Train net output #0: loss = 0.0681888 (* 1 = 0.0681888 loss)
I0407 23:40:48.063657   359 sgd_solver.cpp:105] Iteration 8976, lr = 5.00201e-06
I0407 23:40:52.189750   359 solver.cpp:218] Iteration 8988 (2.90833 iter/s, 4.12608s/12 iters), loss = 0.0454001
I0407 23:40:52.189787   359 solver.cpp:237]     Train net output #0: loss = 0.0454001 (* 1 = 0.0454001 loss)
I0407 23:40:52.189795   359 sgd_solver.cpp:105] Iteration 8988, lr = 4.88574e-06
I0407 23:40:55.404886   359 blocking_queue.cpp:49] Waiting for data
I0407 23:40:57.128886   359 solver.cpp:218] Iteration 9000 (2.4296 iter/s, 4.93908s/12 iters), loss = 0.0909112
I0407 23:40:57.128926   359 solver.cpp:237]     Train net output #0: loss = 0.0909113 (* 1 = 0.0909113 loss)
I0407 23:40:57.128933   359 sgd_solver.cpp:105] Iteration 9000, lr = 4.77218e-06
I0407 23:40:57.822428   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:41:02.033378   359 solver.cpp:218] Iteration 9012 (2.44676 iter/s, 4.90444s/12 iters), loss = 0.107862
I0407 23:41:02.033414   359 solver.cpp:237]     Train net output #0: loss = 0.107862 (* 1 = 0.107862 loss)
I0407 23:41:02.033421   359 sgd_solver.cpp:105] Iteration 9012, lr = 4.66126e-06
I0407 23:41:06.955420   359 solver.cpp:218] Iteration 9024 (2.43804 iter/s, 4.92198s/12 iters), loss = 0.208127
I0407 23:41:06.955462   359 solver.cpp:237]     Train net output #0: loss = 0.208127 (* 1 = 0.208127 loss)
I0407 23:41:06.955471   359 sgd_solver.cpp:105] Iteration 9024, lr = 4.55291e-06
I0407 23:41:11.937781   359 solver.cpp:218] Iteration 9036 (2.40853 iter/s, 4.9823s/12 iters), loss = 0.114744
I0407 23:41:11.937922   359 solver.cpp:237]     Train net output #0: loss = 0.114745 (* 1 = 0.114745 loss)
I0407 23:41:11.937930   359 sgd_solver.cpp:105] Iteration 9036, lr = 4.44708e-06
I0407 23:41:16.900611   359 solver.cpp:218] Iteration 9048 (2.41805 iter/s, 4.96268s/12 iters), loss = 0.113315
I0407 23:41:16.900647   359 solver.cpp:237]     Train net output #0: loss = 0.113315 (* 1 = 0.113315 loss)
I0407 23:41:16.900655   359 sgd_solver.cpp:105] Iteration 9048, lr = 4.34371e-06
I0407 23:41:21.853181   359 solver.cpp:218] Iteration 9060 (2.42301 iter/s, 4.95251s/12 iters), loss = 0.139511
I0407 23:41:21.853236   359 solver.cpp:237]     Train net output #0: loss = 0.139511 (* 1 = 0.139511 loss)
I0407 23:41:21.853250   359 sgd_solver.cpp:105] Iteration 9060, lr = 4.24274e-06
I0407 23:41:26.844935   359 solver.cpp:218] Iteration 9072 (2.404 iter/s, 4.99168s/12 iters), loss = 0.0976321
I0407 23:41:26.844978   359 solver.cpp:237]     Train net output #0: loss = 0.0976322 (* 1 = 0.0976322 loss)
I0407 23:41:26.844986   359 sgd_solver.cpp:105] Iteration 9072, lr = 4.14412e-06
I0407 23:41:28.838894   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 23:41:31.946161   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 23:41:34.308960   359 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 23:41:34.308980   359 net.cpp:676] Ignoring source layer train-data
I0407 23:41:35.104416   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:41:38.670513   359 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 23:41:38.670555   359 solver.cpp:397]     Test net output #1: loss = 2.83304 (* 1 = 2.83304 loss)
I0407 23:41:40.500010   359 solver.cpp:218] Iteration 9084 (0.878799 iter/s, 13.655s/12 iters), loss = 0.118667
I0407 23:41:40.500056   359 solver.cpp:237]     Train net output #0: loss = 0.118667 (* 1 = 0.118667 loss)
I0407 23:41:40.500064   359 sgd_solver.cpp:105] Iteration 9084, lr = 4.04779e-06
I0407 23:41:45.462385   359 solver.cpp:218] Iteration 9096 (2.41823 iter/s, 4.96231s/12 iters), loss = 0.12539
I0407 23:41:45.462509   359 solver.cpp:237]     Train net output #0: loss = 0.12539 (* 1 = 0.12539 loss)
I0407 23:41:45.462517   359 sgd_solver.cpp:105] Iteration 9096, lr = 3.95369e-06
I0407 23:41:48.343343   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:41:50.357234   359 solver.cpp:218] Iteration 9108 (2.45163 iter/s, 4.89471s/12 iters), loss = 0.0646588
I0407 23:41:50.357275   359 solver.cpp:237]     Train net output #0: loss = 0.0646588 (* 1 = 0.0646588 loss)
I0407 23:41:50.357282   359 sgd_solver.cpp:105] Iteration 9108, lr = 3.86179e-06
I0407 23:41:55.320041   359 solver.cpp:218] Iteration 9120 (2.41802 iter/s, 4.96275s/12 iters), loss = 0.17825
I0407 23:41:55.320080   359 solver.cpp:237]     Train net output #0: loss = 0.17825 (* 1 = 0.17825 loss)
I0407 23:41:55.320088   359 sgd_solver.cpp:105] Iteration 9120, lr = 3.77202e-06
I0407 23:42:00.233377   359 solver.cpp:218] Iteration 9132 (2.44236 iter/s, 4.91328s/12 iters), loss = 0.0906506
I0407 23:42:00.233415   359 solver.cpp:237]     Train net output #0: loss = 0.0906506 (* 1 = 0.0906506 loss)
I0407 23:42:00.233423   359 sgd_solver.cpp:105] Iteration 9132, lr = 3.68433e-06
I0407 23:42:05.177191   359 solver.cpp:218] Iteration 9144 (2.4273 iter/s, 4.94376s/12 iters), loss = 0.0635827
I0407 23:42:05.177232   359 solver.cpp:237]     Train net output #0: loss = 0.0635828 (* 1 = 0.0635828 loss)
I0407 23:42:05.177240   359 sgd_solver.cpp:105] Iteration 9144, lr = 3.59868e-06
I0407 23:42:10.095171   359 solver.cpp:218] Iteration 9156 (2.44006 iter/s, 4.91791s/12 iters), loss = 0.122193
I0407 23:42:10.095223   359 solver.cpp:237]     Train net output #0: loss = 0.122193 (* 1 = 0.122193 loss)
I0407 23:42:10.095233   359 sgd_solver.cpp:105] Iteration 9156, lr = 3.51503e-06
I0407 23:42:15.059562   359 solver.cpp:218] Iteration 9168 (2.41725 iter/s, 4.96432s/12 iters), loss = 0.117753
I0407 23:42:15.059609   359 solver.cpp:237]     Train net output #0: loss = 0.117753 (* 1 = 0.117753 loss)
I0407 23:42:15.059623   359 sgd_solver.cpp:105] Iteration 9168, lr = 3.43331e-06
I0407 23:42:19.603961   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 23:42:22.757802   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 23:42:25.131599   359 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 23:42:25.131618   359 net.cpp:676] Ignoring source layer train-data
I0407 23:42:25.961233   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:42:29.925479   359 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0407 23:42:29.925540   359 solver.cpp:397]     Test net output #1: loss = 2.83207 (* 1 = 2.83207 loss)
I0407 23:42:30.021684   359 solver.cpp:218] Iteration 9180 (0.802029 iter/s, 14.962s/12 iters), loss = 0.0381248
I0407 23:42:30.021733   359 solver.cpp:237]     Train net output #0: loss = 0.0381248 (* 1 = 0.0381248 loss)
I0407 23:42:30.021741   359 sgd_solver.cpp:105] Iteration 9180, lr = 3.3535e-06
I0407 23:42:34.232491   359 solver.cpp:218] Iteration 9192 (2.84986 iter/s, 4.21074s/12 iters), loss = 0.0942718
I0407 23:42:34.232535   359 solver.cpp:237]     Train net output #0: loss = 0.0942719 (* 1 = 0.0942719 loss)
I0407 23:42:34.232543   359 sgd_solver.cpp:105] Iteration 9192, lr = 3.27554e-06
I0407 23:42:39.242657   359 solver.cpp:218] Iteration 9204 (2.39516 iter/s, 5.0101s/12 iters), loss = 0.169785
I0407 23:42:39.242695   359 solver.cpp:237]     Train net output #0: loss = 0.169785 (* 1 = 0.169785 loss)
I0407 23:42:39.242703   359 sgd_solver.cpp:105] Iteration 9204, lr = 3.19939e-06
I0407 23:42:39.316300   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:42:44.217150   359 solver.cpp:218] Iteration 9216 (2.41233 iter/s, 4.97444s/12 iters), loss = 0.100025
I0407 23:42:44.217187   359 solver.cpp:237]     Train net output #0: loss = 0.100025 (* 1 = 0.100025 loss)
I0407 23:42:44.217195   359 sgd_solver.cpp:105] Iteration 9216, lr = 3.12501e-06
I0407 23:42:49.171494   359 solver.cpp:218] Iteration 9228 (2.42215 iter/s, 4.95429s/12 iters), loss = 0.11473
I0407 23:42:49.171535   359 solver.cpp:237]     Train net output #0: loss = 0.11473 (* 1 = 0.11473 loss)
I0407 23:42:49.171541   359 sgd_solver.cpp:105] Iteration 9228, lr = 3.05237e-06
I0407 23:42:54.106451   359 solver.cpp:218] Iteration 9240 (2.43166 iter/s, 4.9349s/12 iters), loss = 0.0410729
I0407 23:42:54.106573   359 solver.cpp:237]     Train net output #0: loss = 0.0410729 (* 1 = 0.0410729 loss)
I0407 23:42:54.106582   359 sgd_solver.cpp:105] Iteration 9240, lr = 2.98141e-06
I0407 23:42:59.078850   359 solver.cpp:218] Iteration 9252 (2.41339 iter/s, 4.97226s/12 iters), loss = 0.0620218
I0407 23:42:59.078891   359 solver.cpp:237]     Train net output #0: loss = 0.0620219 (* 1 = 0.0620219 loss)
I0407 23:42:59.078899   359 sgd_solver.cpp:105] Iteration 9252, lr = 2.91209e-06
I0407 23:43:04.040300   359 solver.cpp:218] Iteration 9264 (2.41868 iter/s, 4.96139s/12 iters), loss = 0.0994477
I0407 23:43:04.040338   359 solver.cpp:237]     Train net output #0: loss = 0.0994478 (* 1 = 0.0994478 loss)
I0407 23:43:04.040345   359 sgd_solver.cpp:105] Iteration 9264, lr = 2.84439e-06
I0407 23:43:09.029474   359 solver.cpp:218] Iteration 9276 (2.40524 iter/s, 4.98911s/12 iters), loss = 0.273761
I0407 23:43:09.029520   359 solver.cpp:237]     Train net output #0: loss = 0.273761 (* 1 = 0.273761 loss)
I0407 23:43:09.029527   359 sgd_solver.cpp:105] Iteration 9276, lr = 2.77826e-06
I0407 23:43:11.017052   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 23:43:14.122756   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 23:43:16.488492   359 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 23:43:16.488510   359 net.cpp:676] Ignoring source layer train-data
I0407 23:43:17.266289   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:43:21.331053   359 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0407 23:43:21.331094   359 solver.cpp:397]     Test net output #1: loss = 2.83806 (* 1 = 2.83806 loss)
I0407 23:43:23.131387   359 solver.cpp:218] Iteration 9288 (0.850953 iter/s, 14.1018s/12 iters), loss = 0.116045
I0407 23:43:23.131428   359 solver.cpp:237]     Train net output #0: loss = 0.116045 (* 1 = 0.116045 loss)
I0407 23:43:23.131435   359 sgd_solver.cpp:105] Iteration 9288, lr = 2.71367e-06
I0407 23:43:28.078056   359 solver.cpp:218] Iteration 9300 (2.42591 iter/s, 4.94661s/12 iters), loss = 0.1753
I0407 23:43:28.078198   359 solver.cpp:237]     Train net output #0: loss = 0.1753 (* 1 = 0.1753 loss)
I0407 23:43:28.078207   359 sgd_solver.cpp:105] Iteration 9300, lr = 2.65059e-06
I0407 23:43:30.237421   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:43:32.972517   359 solver.cpp:218] Iteration 9312 (2.45183 iter/s, 4.8943s/12 iters), loss = 0.166904
I0407 23:43:32.972554   359 solver.cpp:237]     Train net output #0: loss = 0.166904 (* 1 = 0.166904 loss)
I0407 23:43:32.972563   359 sgd_solver.cpp:105] Iteration 9312, lr = 2.58896e-06
I0407 23:43:37.932895   359 solver.cpp:218] Iteration 9324 (2.4192 iter/s, 4.96032s/12 iters), loss = 0.124534
I0407 23:43:37.932934   359 solver.cpp:237]     Train net output #0: loss = 0.124534 (* 1 = 0.124534 loss)
I0407 23:43:37.932942   359 sgd_solver.cpp:105] Iteration 9324, lr = 2.52877e-06
I0407 23:43:42.845811   359 solver.cpp:218] Iteration 9336 (2.44257 iter/s, 4.91286s/12 iters), loss = 0.141922
I0407 23:43:42.845849   359 solver.cpp:237]     Train net output #0: loss = 0.141922 (* 1 = 0.141922 loss)
I0407 23:43:42.845856   359 sgd_solver.cpp:105] Iteration 9336, lr = 2.46998e-06
I0407 23:43:47.794771   359 solver.cpp:218] Iteration 9348 (2.42478 iter/s, 4.9489s/12 iters), loss = 0.112526
I0407 23:43:47.794811   359 solver.cpp:237]     Train net output #0: loss = 0.112526 (* 1 = 0.112526 loss)
I0407 23:43:47.794818   359 sgd_solver.cpp:105] Iteration 9348, lr = 2.41256e-06
I0407 23:43:52.770589   359 solver.cpp:218] Iteration 9360 (2.41169 iter/s, 4.97576s/12 iters), loss = 0.206143
I0407 23:43:52.770627   359 solver.cpp:237]     Train net output #0: loss = 0.206143 (* 1 = 0.206143 loss)
I0407 23:43:52.770634   359 sgd_solver.cpp:105] Iteration 9360, lr = 2.35647e-06
I0407 23:43:57.705350   359 solver.cpp:218] Iteration 9372 (2.43176 iter/s, 4.93471s/12 iters), loss = 0.0886094
I0407 23:43:57.705386   359 solver.cpp:237]     Train net output #0: loss = 0.0886095 (* 1 = 0.0886095 loss)
I0407 23:43:57.705394   359 sgd_solver.cpp:105] Iteration 9372, lr = 2.30168e-06
I0407 23:44:02.185900   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 23:44:05.282016   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 23:44:07.648779   359 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 23:44:07.648798   359 net.cpp:676] Ignoring source layer train-data
I0407 23:44:08.388518   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:44:12.408736   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:44:12.408782   359 solver.cpp:397]     Test net output #1: loss = 2.82807 (* 1 = 2.82807 loss)
I0407 23:44:12.505198   359 solver.cpp:218] Iteration 9384 (0.810823 iter/s, 14.7998s/12 iters), loss = 0.14763
I0407 23:44:12.505244   359 solver.cpp:237]     Train net output #0: loss = 0.14763 (* 1 = 0.14763 loss)
I0407 23:44:12.505251   359 sgd_solver.cpp:105] Iteration 9384, lr = 2.24817e-06
I0407 23:44:16.570714   359 solver.cpp:218] Iteration 9396 (2.9517 iter/s, 4.06545s/12 iters), loss = 0.164532
I0407 23:44:16.570756   359 solver.cpp:237]     Train net output #0: loss = 0.164532 (* 1 = 0.164532 loss)
I0407 23:44:16.570765   359 sgd_solver.cpp:105] Iteration 9396, lr = 2.1959e-06
I0407 23:44:20.831952   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:44:21.499477   359 solver.cpp:218] Iteration 9408 (2.43472 iter/s, 4.9287s/12 iters), loss = 0.0997483
I0407 23:44:21.499524   359 solver.cpp:237]     Train net output #0: loss = 0.0997483 (* 1 = 0.0997483 loss)
I0407 23:44:21.499533   359 sgd_solver.cpp:105] Iteration 9408, lr = 2.14484e-06
I0407 23:44:26.424047   359 solver.cpp:218] Iteration 9420 (2.43679 iter/s, 4.9245s/12 iters), loss = 0.164456
I0407 23:44:26.424093   359 solver.cpp:237]     Train net output #0: loss = 0.164456 (* 1 = 0.164456 loss)
I0407 23:44:26.424103   359 sgd_solver.cpp:105] Iteration 9420, lr = 2.09498e-06
I0407 23:44:31.365551   359 solver.cpp:218] Iteration 9432 (2.42844 iter/s, 4.94144s/12 iters), loss = 0.0614223
I0407 23:44:31.365597   359 solver.cpp:237]     Train net output #0: loss = 0.0614224 (* 1 = 0.0614224 loss)
I0407 23:44:31.365605   359 sgd_solver.cpp:105] Iteration 9432, lr = 2.04627e-06
I0407 23:44:36.270550   359 solver.cpp:218] Iteration 9444 (2.44652 iter/s, 4.90493s/12 iters), loss = 0.179541
I0407 23:44:36.270721   359 solver.cpp:237]     Train net output #0: loss = 0.179541 (* 1 = 0.179541 loss)
I0407 23:44:36.270731   359 sgd_solver.cpp:105] Iteration 9444, lr = 1.99869e-06
I0407 23:44:41.219415   359 solver.cpp:218] Iteration 9456 (2.4249 iter/s, 4.94867s/12 iters), loss = 0.168537
I0407 23:44:41.219463   359 solver.cpp:237]     Train net output #0: loss = 0.168537 (* 1 = 0.168537 loss)
I0407 23:44:41.219471   359 sgd_solver.cpp:105] Iteration 9456, lr = 1.95222e-06
I0407 23:44:46.137250   359 solver.cpp:218] Iteration 9468 (2.44013 iter/s, 4.91777s/12 iters), loss = 0.0856009
I0407 23:44:46.137285   359 solver.cpp:237]     Train net output #0: loss = 0.085601 (* 1 = 0.085601 loss)
I0407 23:44:46.137293   359 sgd_solver.cpp:105] Iteration 9468, lr = 1.90683e-06
I0407 23:44:51.092250   359 solver.cpp:218] Iteration 9480 (2.42182 iter/s, 4.95495s/12 iters), loss = 0.163074
I0407 23:44:51.092288   359 solver.cpp:237]     Train net output #0: loss = 0.163074 (* 1 = 0.163074 loss)
I0407 23:44:51.092295   359 sgd_solver.cpp:105] Iteration 9480, lr = 1.8625e-06
I0407 23:44:53.079025   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 23:44:56.168238   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 23:44:58.536355   359 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 23:44:58.536373   359 net.cpp:676] Ignoring source layer train-data
I0407 23:44:59.243846   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:03.317701   359 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 23:45:03.317749   359 solver.cpp:397]     Test net output #1: loss = 2.83584 (* 1 = 2.83584 loss)
I0407 23:45:05.141343   359 solver.cpp:218] Iteration 9492 (0.854152 iter/s, 14.049s/12 iters), loss = 0.10268
I0407 23:45:05.141388   359 solver.cpp:237]     Train net output #0: loss = 0.10268 (* 1 = 0.10268 loss)
I0407 23:45:05.141396   359 sgd_solver.cpp:105] Iteration 9492, lr = 1.81919e-06
I0407 23:45:10.049109   359 solver.cpp:218] Iteration 9504 (2.44514 iter/s, 4.9077s/12 iters), loss = 0.069278
I0407 23:45:10.049254   359 solver.cpp:237]     Train net output #0: loss = 0.0692781 (* 1 = 0.0692781 loss)
I0407 23:45:10.049263   359 sgd_solver.cpp:105] Iteration 9504, lr = 1.7769e-06
I0407 23:45:11.483283   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:14.952170   359 solver.cpp:218] Iteration 9516 (2.44753 iter/s, 4.9029s/12 iters), loss = 0.0581919
I0407 23:45:14.952208   359 solver.cpp:237]     Train net output #0: loss = 0.0581919 (* 1 = 0.0581919 loss)
I0407 23:45:14.952216   359 sgd_solver.cpp:105] Iteration 9516, lr = 1.73558e-06
I0407 23:45:19.905041   359 solver.cpp:218] Iteration 9528 (2.42286 iter/s, 4.95282s/12 iters), loss = 0.0740821
I0407 23:45:19.905079   359 solver.cpp:237]     Train net output #0: loss = 0.0740821 (* 1 = 0.0740821 loss)
I0407 23:45:19.905087   359 sgd_solver.cpp:105] Iteration 9528, lr = 1.69523e-06
I0407 23:45:24.838248   359 solver.cpp:218] Iteration 9540 (2.43253 iter/s, 4.93314s/12 iters), loss = 0.13533
I0407 23:45:24.838294   359 solver.cpp:237]     Train net output #0: loss = 0.13533 (* 1 = 0.13533 loss)
I0407 23:45:24.838302   359 sgd_solver.cpp:105] Iteration 9540, lr = 1.65581e-06
I0407 23:45:29.755347   359 solver.cpp:218] Iteration 9552 (2.4405 iter/s, 4.91703s/12 iters), loss = 0.111031
I0407 23:45:29.755390   359 solver.cpp:237]     Train net output #0: loss = 0.111031 (* 1 = 0.111031 loss)
I0407 23:45:29.755398   359 sgd_solver.cpp:105] Iteration 9552, lr = 1.61731e-06
I0407 23:45:34.718720   359 solver.cpp:218] Iteration 9564 (2.41774 iter/s, 4.96331s/12 iters), loss = 0.140934
I0407 23:45:34.718760   359 solver.cpp:237]     Train net output #0: loss = 0.140934 (* 1 = 0.140934 loss)
I0407 23:45:34.718768   359 sgd_solver.cpp:105] Iteration 9564, lr = 1.57971e-06
I0407 23:45:39.594220   359 solver.cpp:218] Iteration 9576 (2.46132 iter/s, 4.87544s/12 iters), loss = 0.130819
I0407 23:45:39.594259   359 solver.cpp:237]     Train net output #0: loss = 0.130819 (* 1 = 0.130819 loss)
I0407 23:45:39.594266   359 sgd_solver.cpp:105] Iteration 9576, lr = 1.54298e-06
I0407 23:45:43.980114   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 23:45:47.073791   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 23:45:49.440476   359 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 23:45:49.440495   359 net.cpp:676] Ignoring source layer train-data
I0407 23:45:50.103266   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:53.966145   359 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 23:45:53.966192   359 solver.cpp:397]     Test net output #1: loss = 2.83786 (* 1 = 2.83786 loss)
I0407 23:45:54.062616   359 solver.cpp:218] Iteration 9588 (0.829398 iter/s, 14.4683s/12 iters), loss = 0.175383
I0407 23:45:54.062656   359 solver.cpp:237]     Train net output #0: loss = 0.175384 (* 1 = 0.175384 loss)
I0407 23:45:54.062665   359 sgd_solver.cpp:105] Iteration 9588, lr = 1.5071e-06
I0407 23:45:58.184581   359 solver.cpp:218] Iteration 9600 (2.91127 iter/s, 4.12191s/12 iters), loss = 0.142131
I0407 23:45:58.184618   359 solver.cpp:237]     Train net output #0: loss = 0.142132 (* 1 = 0.142132 loss)
I0407 23:45:58.184628   359 sgd_solver.cpp:105] Iteration 9600, lr = 1.47206e-06
I0407 23:46:01.747241   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:46:03.097952   359 solver.cpp:218] Iteration 9612 (2.44234 iter/s, 4.91332s/12 iters), loss = 0.1103
I0407 23:46:03.097991   359 solver.cpp:237]     Train net output #0: loss = 0.1103 (* 1 = 0.1103 loss)
I0407 23:46:03.097999   359 sgd_solver.cpp:105] Iteration 9612, lr = 1.43783e-06
I0407 23:46:08.020946   359 solver.cpp:218] Iteration 9624 (2.43757 iter/s, 4.92293s/12 iters), loss = 0.0665706
I0407 23:46:08.020983   359 solver.cpp:237]     Train net output #0: loss = 0.0665706 (* 1 = 0.0665706 loss)
I0407 23:46:08.020992   359 sgd_solver.cpp:105] Iteration 9624, lr = 1.4044e-06
I0407 23:46:12.976524   359 solver.cpp:218] Iteration 9636 (2.42154 iter/s, 4.95552s/12 iters), loss = 0.188979
I0407 23:46:12.976560   359 solver.cpp:237]     Train net output #0: loss = 0.188979 (* 1 = 0.188979 loss)
I0407 23:46:12.976567   359 sgd_solver.cpp:105] Iteration 9636, lr = 1.37175e-06
I0407 23:46:17.875429   359 solver.cpp:218] Iteration 9648 (2.44956 iter/s, 4.89884s/12 iters), loss = 0.0693474
I0407 23:46:17.875550   359 solver.cpp:237]     Train net output #0: loss = 0.0693475 (* 1 = 0.0693475 loss)
I0407 23:46:17.875560   359 sgd_solver.cpp:105] Iteration 9648, lr = 1.33985e-06
I0407 23:46:22.846493   359 solver.cpp:218] Iteration 9660 (2.41404 iter/s, 4.97092s/12 iters), loss = 0.142862
I0407 23:46:22.846540   359 solver.cpp:237]     Train net output #0: loss = 0.142862 (* 1 = 0.142862 loss)
I0407 23:46:22.846549   359 sgd_solver.cpp:105] Iteration 9660, lr = 1.3087e-06
I0407 23:46:27.756002   359 solver.cpp:218] Iteration 9672 (2.44427 iter/s, 4.90944s/12 iters), loss = 0.159316
I0407 23:46:27.756042   359 solver.cpp:237]     Train net output #0: loss = 0.159316 (* 1 = 0.159316 loss)
I0407 23:46:27.756050   359 sgd_solver.cpp:105] Iteration 9672, lr = 1.27827e-06
I0407 23:46:32.700523   359 solver.cpp:218] Iteration 9684 (2.42696 iter/s, 4.94446s/12 iters), loss = 0.182466
I0407 23:46:32.700558   359 solver.cpp:237]     Train net output #0: loss = 0.182466 (* 1 = 0.182466 loss)
I0407 23:46:32.700567   359 sgd_solver.cpp:105] Iteration 9684, lr = 1.24855e-06
I0407 23:46:34.698504   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 23:46:37.793181   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 23:46:40.155972   359 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 23:46:40.155987   359 net.cpp:676] Ignoring source layer train-data
I0407 23:46:40.768965   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:46:43.878679   359 blocking_queue.cpp:49] Waiting for data
I0407 23:46:44.983029   359 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 23:46:44.983075   359 solver.cpp:397]     Test net output #1: loss = 2.83867 (* 1 = 2.83867 loss)
I0407 23:46:46.763945   359 solver.cpp:218] Iteration 9696 (0.853281 iter/s, 14.0634s/12 iters), loss = 0.142083
I0407 23:46:46.763983   359 solver.cpp:237]     Train net output #0: loss = 0.142083 (* 1 = 0.142083 loss)
I0407 23:46:46.763990   359 sgd_solver.cpp:105] Iteration 9696, lr = 1.21951e-06
I0407 23:46:51.716920   359 solver.cpp:218] Iteration 9708 (2.42281 iter/s, 4.95292s/12 iters), loss = 0.114182
I0407 23:46:51.717041   359 solver.cpp:237]     Train net output #0: loss = 0.114182 (* 1 = 0.114182 loss)
I0407 23:46:51.717048   359 sgd_solver.cpp:105] Iteration 9708, lr = 1.19116e-06
I0407 23:46:52.436004   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:46:56.626152   359 solver.cpp:218] Iteration 9720 (2.44444 iter/s, 4.9091s/12 iters), loss = 0.179163
I0407 23:46:56.626191   359 solver.cpp:237]     Train net output #0: loss = 0.179163 (* 1 = 0.179163 loss)
I0407 23:46:56.626199   359 sgd_solver.cpp:105] Iteration 9720, lr = 1.16346e-06
I0407 23:47:01.593101   359 solver.cpp:218] Iteration 9732 (2.416 iter/s, 4.96689s/12 iters), loss = 0.178441
I0407 23:47:01.593139   359 solver.cpp:237]     Train net output #0: loss = 0.178441 (* 1 = 0.178441 loss)
I0407 23:47:01.593147   359 sgd_solver.cpp:105] Iteration 9732, lr = 1.13641e-06
I0407 23:47:06.506975   359 solver.cpp:218] Iteration 9744 (2.44209 iter/s, 4.91382s/12 iters), loss = 0.122313
I0407 23:47:06.507016   359 solver.cpp:237]     Train net output #0: loss = 0.122313 (* 1 = 0.122313 loss)
I0407 23:47:06.507023   359 sgd_solver.cpp:105] Iteration 9744, lr = 1.10999e-06
I0407 23:47:11.483729   359 solver.cpp:218] Iteration 9756 (2.41124 iter/s, 4.97669s/12 iters), loss = 0.135807
I0407 23:47:11.483770   359 solver.cpp:237]     Train net output #0: loss = 0.135807 (* 1 = 0.135807 loss)
I0407 23:47:11.483779   359 sgd_solver.cpp:105] Iteration 9756, lr = 1.08417e-06
I0407 23:47:16.410970   359 solver.cpp:218] Iteration 9768 (2.43547 iter/s, 4.92718s/12 iters), loss = 0.09179
I0407 23:47:16.411010   359 solver.cpp:237]     Train net output #0: loss = 0.0917901 (* 1 = 0.0917901 loss)
I0407 23:47:16.411018   359 sgd_solver.cpp:105] Iteration 9768, lr = 1.05897e-06
I0407 23:47:21.338914   359 solver.cpp:218] Iteration 9780 (2.43512 iter/s, 4.92789s/12 iters), loss = 0.155029
I0407 23:47:21.338954   359 solver.cpp:237]     Train net output #0: loss = 0.155029 (* 1 = 0.155029 loss)
I0407 23:47:21.338963   359 sgd_solver.cpp:105] Iteration 9780, lr = 1.03434e-06
I0407 23:47:25.818817   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 23:47:28.913245   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 23:47:31.423696   359 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 23:47:31.423713   359 net.cpp:676] Ignoring source layer train-data
I0407 23:47:31.988075   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:47:36.285781   359 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 23:47:36.285807   359 solver.cpp:397]     Test net output #1: loss = 2.8326 (* 1 = 2.8326 loss)
I0407 23:47:36.381963   359 solver.cpp:218] Iteration 9792 (0.797715 iter/s, 15.043s/12 iters), loss = 0.180558
I0407 23:47:36.382001   359 solver.cpp:237]     Train net output #0: loss = 0.180558 (* 1 = 0.180558 loss)
I0407 23:47:36.382009   359 sgd_solver.cpp:105] Iteration 9792, lr = 1.01029e-06
I0407 23:47:40.488464   359 solver.cpp:218] Iteration 9804 (2.92224 iter/s, 4.10644s/12 iters), loss = 0.0752499
I0407 23:47:40.488519   359 solver.cpp:237]     Train net output #0: loss = 0.07525 (* 1 = 0.07525 loss)
I0407 23:47:40.488530   359 sgd_solver.cpp:105] Iteration 9804, lr = 9.868e-07
I0407 23:47:43.394654   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:47:45.380812   359 solver.cpp:218] Iteration 9816 (2.45284 iter/s, 4.89228s/12 iters), loss = 0.155441
I0407 23:47:45.380853   359 solver.cpp:237]     Train net output #0: loss = 0.155441 (* 1 = 0.155441 loss)
I0407 23:47:45.380861   359 sgd_solver.cpp:105] Iteration 9816, lr = 9.63854e-07
I0407 23:47:50.344197   359 solver.cpp:218] Iteration 9828 (2.41773 iter/s, 4.96333s/12 iters), loss = 0.171004
I0407 23:47:50.344231   359 solver.cpp:237]     Train net output #0: loss = 0.171004 (* 1 = 0.171004 loss)
I0407 23:47:50.344238   359 sgd_solver.cpp:105] Iteration 9828, lr = 9.41442e-07
I0407 23:47:55.281509   359 solver.cpp:218] Iteration 9840 (2.4305 iter/s, 4.93726s/12 iters), loss = 0.134581
I0407 23:47:55.281543   359 solver.cpp:237]     Train net output #0: loss = 0.134581 (* 1 = 0.134581 loss)
I0407 23:47:55.281551   359 sgd_solver.cpp:105] Iteration 9840, lr = 9.19551e-07
I0407 23:48:00.208547   359 solver.cpp:218] Iteration 9852 (2.43557 iter/s, 4.92698s/12 iters), loss = 0.217192
I0407 23:48:00.208673   359 solver.cpp:237]     Train net output #0: loss = 0.217192 (* 1 = 0.217192 loss)
I0407 23:48:00.208683   359 sgd_solver.cpp:105] Iteration 9852, lr = 8.98169e-07
I0407 23:48:05.156338   359 solver.cpp:218] Iteration 9864 (2.4254 iter/s, 4.94764s/12 iters), loss = 0.0965633
I0407 23:48:05.156380   359 solver.cpp:237]     Train net output #0: loss = 0.0965633 (* 1 = 0.0965633 loss)
I0407 23:48:05.156388   359 sgd_solver.cpp:105] Iteration 9864, lr = 8.77284e-07
I0407 23:48:10.085152   359 solver.cpp:218] Iteration 9876 (2.43469 iter/s, 4.92875s/12 iters), loss = 0.215828
I0407 23:48:10.085189   359 solver.cpp:237]     Train net output #0: loss = 0.215828 (* 1 = 0.215828 loss)
I0407 23:48:10.085197   359 sgd_solver.cpp:105] Iteration 9876, lr = 8.56885e-07
I0407 23:48:15.033823   359 solver.cpp:218] Iteration 9888 (2.42492 iter/s, 4.94862s/12 iters), loss = 0.126415
I0407 23:48:15.033859   359 solver.cpp:237]     Train net output #0: loss = 0.126415 (* 1 = 0.126415 loss)
I0407 23:48:15.033869   359 sgd_solver.cpp:105] Iteration 9888, lr = 8.3696e-07
I0407 23:48:17.039280   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 23:48:20.152248   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 23:48:22.518414   359 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 23:48:22.518432   359 net.cpp:676] Ignoring source layer train-data
I0407 23:48:23.000597   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:48:26.889662   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:48:26.889706   359 solver.cpp:397]     Test net output #1: loss = 2.82148 (* 1 = 2.82148 loss)
I0407 23:48:28.719249   359 solver.cpp:218] Iteration 9900 (0.87685 iter/s, 13.6854s/12 iters), loss = 0.0737331
I0407 23:48:28.719292   359 solver.cpp:237]     Train net output #0: loss = 0.0737331 (* 1 = 0.0737331 loss)
I0407 23:48:28.719300   359 sgd_solver.cpp:105] Iteration 9900, lr = 8.17498e-07
I0407 23:48:33.615123   359 solver.cpp:218] Iteration 9912 (2.45108 iter/s, 4.89581s/12 iters), loss = 0.100437
I0407 23:48:33.615269   359 solver.cpp:237]     Train net output #0: loss = 0.100437 (* 1 = 0.100437 loss)
I0407 23:48:33.615278   359 sgd_solver.cpp:105] Iteration 9912, lr = 7.98489e-07
I0407 23:48:33.708283   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:48:38.555037   359 solver.cpp:218] Iteration 9924 (2.42927 iter/s, 4.93975s/12 iters), loss = 0.120905
I0407 23:48:38.555073   359 solver.cpp:237]     Train net output #0: loss = 0.120905 (* 1 = 0.120905 loss)
I0407 23:48:38.555080   359 sgd_solver.cpp:105] Iteration 9924, lr = 7.79922e-07
I0407 23:48:43.471093   359 solver.cpp:218] Iteration 9936 (2.44101 iter/s, 4.916s/12 iters), loss = 0.104876
I0407 23:48:43.471132   359 solver.cpp:237]     Train net output #0: loss = 0.104876 (* 1 = 0.104876 loss)
I0407 23:48:43.471140   359 sgd_solver.cpp:105] Iteration 9936, lr = 7.61786e-07
I0407 23:48:48.405019   359 solver.cpp:218] Iteration 9948 (2.43217 iter/s, 4.93387s/12 iters), loss = 0.121452
I0407 23:48:48.405061   359 solver.cpp:237]     Train net output #0: loss = 0.121452 (* 1 = 0.121452 loss)
I0407 23:48:48.405068   359 sgd_solver.cpp:105] Iteration 9948, lr = 7.44072e-07
I0407 23:48:53.327111   359 solver.cpp:218] Iteration 9960 (2.43802 iter/s, 4.92203s/12 iters), loss = 0.0796441
I0407 23:48:53.327152   359 solver.cpp:237]     Train net output #0: loss = 0.0796441 (* 1 = 0.0796441 loss)
I0407 23:48:53.327160   359 sgd_solver.cpp:105] Iteration 9960, lr = 7.2677e-07
I0407 23:48:58.290238   359 solver.cpp:218] Iteration 9972 (2.41786 iter/s, 4.96307s/12 iters), loss = 0.141926
I0407 23:48:58.290271   359 solver.cpp:237]     Train net output #0: loss = 0.141926 (* 1 = 0.141926 loss)
I0407 23:48:58.290278   359 sgd_solver.cpp:105] Iteration 9972, lr = 7.09871e-07
I0407 23:49:03.209168   359 solver.cpp:218] Iteration 9984 (2.43958 iter/s, 4.91888s/12 iters), loss = 0.152807
I0407 23:49:03.209204   359 solver.cpp:237]     Train net output #0: loss = 0.152807 (* 1 = 0.152807 loss)
I0407 23:49:03.209211   359 sgd_solver.cpp:105] Iteration 9984, lr = 6.93364e-07
I0407 23:49:07.649179   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 23:49:10.758925   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 23:49:13.121933   359 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 23:49:13.121951   359 net.cpp:676] Ignoring source layer train-data
I0407 23:49:13.596901   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:49:17.666776   359 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 23:49:17.666821   359 solver.cpp:397]     Test net output #1: loss = 2.84798 (* 1 = 2.84798 loss)
I0407 23:49:17.763316   359 solver.cpp:218] Iteration 9996 (0.824512 iter/s, 14.5541s/12 iters), loss = 0.174449
I0407 23:49:17.763365   359 solver.cpp:237]     Train net output #0: loss = 0.174449 (* 1 = 0.174449 loss)
I0407 23:49:17.763375   359 sgd_solver.cpp:105] Iteration 9996, lr = 6.77241e-07
I0407 23:49:21.880429   359 solver.cpp:218] Iteration 10008 (2.91471 iter/s, 4.11704s/12 iters), loss = 0.158894
I0407 23:49:21.880471   359 solver.cpp:237]     Train net output #0: loss = 0.158895 (* 1 = 0.158895 loss)
I0407 23:49:21.880479   359 sgd_solver.cpp:105] Iteration 10008, lr = 6.61493e-07
I0407 23:49:24.086469   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:49:26.825052   359 solver.cpp:218] Iteration 10020 (2.42691 iter/s, 4.94456s/12 iters), loss = 0.101659
I0407 23:49:26.825096   359 solver.cpp:237]     Train net output #0: loss = 0.101659 (* 1 = 0.101659 loss)
I0407 23:49:26.825104   359 sgd_solver.cpp:105] Iteration 10020, lr = 6.46111e-07
I0407 23:49:31.748116   359 solver.cpp:218] Iteration 10032 (2.43754 iter/s, 4.923s/12 iters), loss = 0.165941
I0407 23:49:31.748163   359 solver.cpp:237]     Train net output #0: loss = 0.165941 (* 1 = 0.165941 loss)
I0407 23:49:31.748172   359 sgd_solver.cpp:105] Iteration 10032, lr = 6.31087e-07
I0407 23:49:36.691609   359 solver.cpp:218] Iteration 10044 (2.42747 iter/s, 4.94343s/12 iters), loss = 0.108188
I0407 23:49:36.691653   359 solver.cpp:237]     Train net output #0: loss = 0.108188 (* 1 = 0.108188 loss)
I0407 23:49:36.691661   359 sgd_solver.cpp:105] Iteration 10044, lr = 6.16412e-07
I0407 23:49:41.592237   359 solver.cpp:218] Iteration 10056 (2.4487 iter/s, 4.90057s/12 iters), loss = 0.18827
I0407 23:49:41.592394   359 solver.cpp:237]     Train net output #0: loss = 0.18827 (* 1 = 0.18827 loss)
I0407 23:49:41.592404   359 sgd_solver.cpp:105] Iteration 10056, lr = 6.02079e-07
I0407 23:49:46.561509   359 solver.cpp:218] Iteration 10068 (2.41493 iter/s, 4.9691s/12 iters), loss = 0.131724
I0407 23:49:46.561554   359 solver.cpp:237]     Train net output #0: loss = 0.131724 (* 1 = 0.131724 loss)
I0407 23:49:46.561563   359 sgd_solver.cpp:105] Iteration 10068, lr = 5.88078e-07
I0407 23:49:51.573681   359 solver.cpp:218] Iteration 10080 (2.3942 iter/s, 5.01211s/12 iters), loss = 0.157332
I0407 23:49:51.573726   359 solver.cpp:237]     Train net output #0: loss = 0.157332 (* 1 = 0.157332 loss)
I0407 23:49:51.573735   359 sgd_solver.cpp:105] Iteration 10080, lr = 5.74403e-07
I0407 23:49:56.477515   359 solver.cpp:218] Iteration 10092 (2.4471 iter/s, 4.90377s/12 iters), loss = 0.0513291
I0407 23:49:56.477560   359 solver.cpp:237]     Train net output #0: loss = 0.0513291 (* 1 = 0.0513291 loss)
I0407 23:49:56.477567   359 sgd_solver.cpp:105] Iteration 10092, lr = 5.61047e-07
I0407 23:49:58.425443   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 23:50:01.488281   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 23:50:03.855386   359 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 23:50:03.855412   359 net.cpp:676] Ignoring source layer train-data
I0407 23:50:04.295897   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:50:08.660059   359 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 23:50:08.660099   359 solver.cpp:397]     Test net output #1: loss = 2.8317 (* 1 = 2.8317 loss)
I0407 23:50:10.435355   359 solver.cpp:218] Iteration 10104 (0.859736 iter/s, 13.9578s/12 iters), loss = 0.0973125
I0407 23:50:10.435393   359 solver.cpp:237]     Train net output #0: loss = 0.0973126 (* 1 = 0.0973126 loss)
I0407 23:50:10.435401   359 sgd_solver.cpp:105] Iteration 10104, lr = 5.48e-07
I0407 23:50:14.570070   364 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:50:15.170166   359 solver.cpp:218] Iteration 10116 (2.53445 iter/s, 4.73476s/12 iters), loss = 0.0722294
I0407 23:50:15.170202   359 solver.cpp:237]     Train net output #0: loss = 0.0722295 (* 1 = 0.0722295 loss)
I0407 23:50:15.170210   359 sgd_solver.cpp:105] Iteration 10116, lr = 5.35257e-07
I0407 23:50:19.991789   359 solver.cpp:218] Iteration 10128 (2.48882 iter/s, 4.82157s/12 iters), loss = 0.0625775
I0407 23:50:19.991825   359 solver.cpp:237]     Train net output #0: loss = 0.0625775 (* 1 = 0.0625775 loss)
I0407 23:50:19.991832   359 sgd_solver.cpp:105] Iteration 10128, lr = 5.22811e-07
I0407 23:50:24.814267   359 solver.cpp:218] Iteration 10140 (2.48838 iter/s, 4.82242s/12 iters), loss = 0.035246
I0407 23:50:24.814301   359 solver.cpp:237]     Train net output #0: loss = 0.035246 (* 1 = 0.035246 loss)
I0407 23:50:24.814309   359 sgd_solver.cpp:105] Iteration 10140, lr = 5.10653e-07
I0407 23:50:29.618716   359 solver.cpp:218] Iteration 10152 (2.49771 iter/s, 4.80439s/12 iters), loss = 0.0725636
I0407 23:50:29.618753   359 solver.cpp:237]     Train net output #0: loss = 0.0725636 (* 1 = 0.0725636 loss)
I0407 23:50:29.618760   359 sgd_solver.cpp:105] Iteration 10152, lr = 4.98779e-07
I0407 23:50:34.473207   359 solver.cpp:218] Iteration 10164 (2.47196 iter/s, 4.85444s/12 iters), loss = 0.131913
I0407 23:50:34.473242   359 solver.cpp:237]     Train net output #0: loss = 0.131913 (* 1 = 0.131913 loss)
I0407 23:50:34.473250   359 sgd_solver.cpp:105] Iteration 10164, lr = 4.87181e-07
I0407 23:50:39.397006   359 solver.cpp:218] Iteration 10176 (2.43717 iter/s, 4.92375s/12 iters), loss = 0.14953
I0407 23:50:39.397039   359 solver.cpp:237]     Train net output #0: loss = 0.14953 (* 1 = 0.14953 loss)
I0407 23:50:39.397047   359 sgd_solver.cpp:105] Iteration 10176, lr = 4.75852e-07
I0407 23:50:44.281131   359 solver.cpp:218] Iteration 10188 (2.45697 iter/s, 4.88407s/12 iters), loss = 0.211933
I0407 23:50:44.281167   359 solver.cpp:237]     Train net output #0: loss = 0.211933 (* 1 = 0.211933 loss)
I0407 23:50:44.281175   359 sgd_solver.cpp:105] Iteration 10188, lr = 4.64787e-07
I0407 23:50:48.643961   359 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 23:50:51.757793   359 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 23:50:55.223758   359 solver.cpp:310] Iteration 10200, loss = 0.0844708
I0407 23:50:55.223788   359 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 23:50:55.223794   359 net.cpp:676] Ignoring source layer train-data
I0407 23:50:55.608233   369 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:50:59.650789   359 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 23:50:59.650828   359 solver.cpp:397]     Test net output #1: loss = 2.82001 (* 1 = 2.82001 loss)
I0407 23:50:59.650838   359 solver.cpp:315] Optimization Done.
I0407 23:50:59.650846   359 caffe.cpp:259] Optimization Done.