DIGITS-CNN/cars/split-investigations/50.25.25/base/caffe_output.log

I0401 13:34:42.789883  8859 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-124727-241c/solver.prototxt
I0401 13:34:42.790042  8859 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0401 13:34:42.790047  8859 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0401 13:34:42.790105  8859 caffe.cpp:218] Using GPUs 3
I0401 13:34:42.804970  8859 caffe.cpp:223] GPU 3: GeForce GTX TITAN X
I0401 13:34:43.023434  8859 solver.cpp:44] Initializing solver from parameters:
test_iter: 127
test_interval: 64
base_lr: 0.001
display: 8
max_iter: 6400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1.0000001e-05
snapshot: 64
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0401 13:34:43.024375  8859 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0401 13:34:43.025027  8859 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0401 13:34:43.025039  8859 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0401 13:34:43.025162  8859 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 13:34:43.025240  8859 layer_factory.hpp:77] Creating layer train-data
I0401 13:34:43.036691  8859 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/train_db
I0401 13:34:43.036931  8859 net.cpp:84] Creating Layer train-data
I0401 13:34:43.036947  8859 net.cpp:380] train-data -> data
I0401 13:34:43.036970  8859 net.cpp:380] train-data -> label
I0401 13:34:43.036981  8859 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/mean.binaryproto
I0401 13:34:43.041013  8859 data_layer.cpp:45] output data size: 128,3,227,227
I0401 13:34:43.178112  8859 net.cpp:122] Setting up train-data
I0401 13:34:43.178133  8859 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0401 13:34:43.178138  8859 net.cpp:129] Top shape: 128 (128)
I0401 13:34:43.178141  8859 net.cpp:137] Memory required for data: 79149056
I0401 13:34:43.178151  8859 layer_factory.hpp:77] Creating layer conv1
I0401 13:34:43.178174  8859 net.cpp:84] Creating Layer conv1
I0401 13:34:43.178180  8859 net.cpp:406] conv1 <- data
I0401 13:34:43.178194  8859 net.cpp:380] conv1 -> conv1
I0401 13:34:43.641196  8859 net.cpp:122] Setting up conv1
I0401 13:34:43.641216  8859 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:34:43.641218  8859 net.cpp:137] Memory required for data: 227833856
I0401 13:34:43.641237  8859 layer_factory.hpp:77] Creating layer relu1
I0401 13:34:43.641247  8859 net.cpp:84] Creating Layer relu1
I0401 13:34:43.641249  8859 net.cpp:406] relu1 <- conv1
I0401 13:34:43.641254  8859 net.cpp:367] relu1 -> conv1 (in-place)
I0401 13:34:43.641511  8859 net.cpp:122] Setting up relu1
I0401 13:34:43.641520  8859 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:34:43.641521  8859 net.cpp:137] Memory required for data: 376518656
I0401 13:34:43.641525  8859 layer_factory.hpp:77] Creating layer norm1
I0401 13:34:43.641532  8859 net.cpp:84] Creating Layer norm1
I0401 13:34:43.641535  8859 net.cpp:406] norm1 <- conv1
I0401 13:34:43.641561  8859 net.cpp:380] norm1 -> norm1
I0401 13:34:43.643884  8859 net.cpp:122] Setting up norm1
I0401 13:34:43.643893  8859 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:34:43.643895  8859 net.cpp:137] Memory required for data: 525203456
I0401 13:34:43.643898  8859 layer_factory.hpp:77] Creating layer pool1
I0401 13:34:43.643906  8859 net.cpp:84] Creating Layer pool1
I0401 13:34:43.643908  8859 net.cpp:406] pool1 <- norm1
I0401 13:34:43.643913  8859 net.cpp:380] pool1 -> pool1
I0401 13:34:43.643945  8859 net.cpp:122] Setting up pool1
I0401 13:34:43.643950  8859 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0401 13:34:43.643952  8859 net.cpp:137] Memory required for data: 561035264
I0401 13:34:43.643954  8859 layer_factory.hpp:77] Creating layer conv2
I0401 13:34:43.643963  8859 net.cpp:84] Creating Layer conv2
I0401 13:34:43.643965  8859 net.cpp:406] conv2 <- pool1
I0401 13:34:43.643970  8859 net.cpp:380] conv2 -> conv2
I0401 13:34:43.649616  8859 net.cpp:122] Setting up conv2
I0401 13:34:43.649632  8859 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:34:43.649636  8859 net.cpp:137] Memory required for data: 656586752
I0401 13:34:43.649646  8859 layer_factory.hpp:77] Creating layer relu2
I0401 13:34:43.649652  8859 net.cpp:84] Creating Layer relu2
I0401 13:34:43.649654  8859 net.cpp:406] relu2 <- conv2
I0401 13:34:43.649659  8859 net.cpp:367] relu2 -> conv2 (in-place)
I0401 13:34:43.650050  8859 net.cpp:122] Setting up relu2
I0401 13:34:43.650058  8859 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:34:43.650060  8859 net.cpp:137] Memory required for data: 752138240
I0401 13:34:43.650063  8859 layer_factory.hpp:77] Creating layer norm2
I0401 13:34:43.650069  8859 net.cpp:84] Creating Layer norm2
I0401 13:34:43.650072  8859 net.cpp:406] norm2 <- conv2
I0401 13:34:43.650076  8859 net.cpp:380] norm2 -> norm2
I0401 13:34:43.650331  8859 net.cpp:122] Setting up norm2
I0401 13:34:43.650338  8859 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:34:43.650341  8859 net.cpp:137] Memory required for data: 847689728
I0401 13:34:43.650342  8859 layer_factory.hpp:77] Creating layer pool2
I0401 13:34:43.650349  8859 net.cpp:84] Creating Layer pool2
I0401 13:34:43.650352  8859 net.cpp:406] pool2 <- norm2
I0401 13:34:43.650355  8859 net.cpp:380] pool2 -> pool2
I0401 13:34:43.650379  8859 net.cpp:122] Setting up pool2
I0401 13:34:43.650383  8859 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:34:43.650385  8859 net.cpp:137] Memory required for data: 869840896
I0401 13:34:43.650388  8859 layer_factory.hpp:77] Creating layer conv3
I0401 13:34:43.650394  8859 net.cpp:84] Creating Layer conv3
I0401 13:34:43.650398  8859 net.cpp:406] conv3 <- pool2
I0401 13:34:43.650400  8859 net.cpp:380] conv3 -> conv3
I0401 13:34:43.660058  8859 net.cpp:122] Setting up conv3
I0401 13:34:43.660079  8859 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:34:43.660082  8859 net.cpp:137] Memory required for data: 903067648
I0401 13:34:43.660092  8859 layer_factory.hpp:77] Creating layer relu3
I0401 13:34:43.660100  8859 net.cpp:84] Creating Layer relu3
I0401 13:34:43.660104  8859 net.cpp:406] relu3 <- conv3
I0401 13:34:43.660109  8859 net.cpp:367] relu3 -> conv3 (in-place)
I0401 13:34:43.660495  8859 net.cpp:122] Setting up relu3
I0401 13:34:43.660504  8859 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:34:43.660506  8859 net.cpp:137] Memory required for data: 936294400
I0401 13:34:43.660508  8859 layer_factory.hpp:77] Creating layer conv4
I0401 13:34:43.660517  8859 net.cpp:84] Creating Layer conv4
I0401 13:34:43.660519  8859 net.cpp:406] conv4 <- conv3
I0401 13:34:43.660524  8859 net.cpp:380] conv4 -> conv4
I0401 13:34:43.669760  8859 net.cpp:122] Setting up conv4
I0401 13:34:43.669780  8859 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:34:43.669782  8859 net.cpp:137] Memory required for data: 969521152
I0401 13:34:43.669790  8859 layer_factory.hpp:77] Creating layer relu4
I0401 13:34:43.669797  8859 net.cpp:84] Creating Layer relu4
I0401 13:34:43.669818  8859 net.cpp:406] relu4 <- conv4
I0401 13:34:43.669826  8859 net.cpp:367] relu4 -> conv4 (in-place)
I0401 13:34:43.670140  8859 net.cpp:122] Setting up relu4
I0401 13:34:43.670146  8859 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:34:43.670148  8859 net.cpp:137] Memory required for data: 1002747904
I0401 13:34:43.670151  8859 layer_factory.hpp:77] Creating layer conv5
I0401 13:34:43.670161  8859 net.cpp:84] Creating Layer conv5
I0401 13:34:43.670163  8859 net.cpp:406] conv5 <- conv4
I0401 13:34:43.670169  8859 net.cpp:380] conv5 -> conv5
I0401 13:34:43.678172  8859 net.cpp:122] Setting up conv5
I0401 13:34:43.678195  8859 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:34:43.678200  8859 net.cpp:137] Memory required for data: 1024899072
I0401 13:34:43.678215  8859 layer_factory.hpp:77] Creating layer relu5
I0401 13:34:43.678225  8859 net.cpp:84] Creating Layer relu5
I0401 13:34:43.678229  8859 net.cpp:406] relu5 <- conv5
I0401 13:34:43.678238  8859 net.cpp:367] relu5 -> conv5 (in-place)
I0401 13:34:43.678871  8859 net.cpp:122] Setting up relu5
I0401 13:34:43.678882  8859 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:34:43.678886  8859 net.cpp:137] Memory required for data: 1047050240
I0401 13:34:43.678889  8859 layer_factory.hpp:77] Creating layer pool5
I0401 13:34:43.678897  8859 net.cpp:84] Creating Layer pool5
I0401 13:34:43.678905  8859 net.cpp:406] pool5 <- conv5
I0401 13:34:43.678910  8859 net.cpp:380] pool5 -> pool5
I0401 13:34:43.678954  8859 net.cpp:122] Setting up pool5
I0401 13:34:43.678961  8859 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0401 13:34:43.678964  8859 net.cpp:137] Memory required for data: 1051768832
I0401 13:34:43.678967  8859 layer_factory.hpp:77] Creating layer fc6
I0401 13:34:43.678978  8859 net.cpp:84] Creating Layer fc6
I0401 13:34:43.678982  8859 net.cpp:406] fc6 <- pool5
I0401 13:34:43.678988  8859 net.cpp:380] fc6 -> fc6
I0401 13:34:44.072808  8859 net.cpp:122] Setting up fc6
I0401 13:34:44.072827  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.072830  8859 net.cpp:137] Memory required for data: 1053865984
I0401 13:34:44.072839  8859 layer_factory.hpp:77] Creating layer relu6
I0401 13:34:44.072846  8859 net.cpp:84] Creating Layer relu6
I0401 13:34:44.072849  8859 net.cpp:406] relu6 <- fc6
I0401 13:34:44.072855  8859 net.cpp:367] relu6 -> fc6 (in-place)
I0401 13:34:44.073478  8859 net.cpp:122] Setting up relu6
I0401 13:34:44.073487  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.073488  8859 net.cpp:137] Memory required for data: 1055963136
I0401 13:34:44.073491  8859 layer_factory.hpp:77] Creating layer drop6
I0401 13:34:44.073498  8859 net.cpp:84] Creating Layer drop6
I0401 13:34:44.073499  8859 net.cpp:406] drop6 <- fc6
I0401 13:34:44.073504  8859 net.cpp:367] drop6 -> fc6 (in-place)
I0401 13:34:44.073529  8859 net.cpp:122] Setting up drop6
I0401 13:34:44.073534  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.073536  8859 net.cpp:137] Memory required for data: 1058060288
I0401 13:34:44.073539  8859 layer_factory.hpp:77] Creating layer fc7
I0401 13:34:44.073544  8859 net.cpp:84] Creating Layer fc7
I0401 13:34:44.073546  8859 net.cpp:406] fc7 <- fc6
I0401 13:34:44.073551  8859 net.cpp:380] fc7 -> fc7
I0401 13:34:44.261099  8859 net.cpp:122] Setting up fc7
I0401 13:34:44.261121  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.261123  8859 net.cpp:137] Memory required for data: 1060157440
I0401 13:34:44.261132  8859 layer_factory.hpp:77] Creating layer relu7
I0401 13:34:44.261138  8859 net.cpp:84] Creating Layer relu7
I0401 13:34:44.261142  8859 net.cpp:406] relu7 <- fc7
I0401 13:34:44.261147  8859 net.cpp:367] relu7 -> fc7 (in-place)
I0401 13:34:44.261518  8859 net.cpp:122] Setting up relu7
I0401 13:34:44.261526  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.261528  8859 net.cpp:137] Memory required for data: 1062254592
I0401 13:34:44.261531  8859 layer_factory.hpp:77] Creating layer drop7
I0401 13:34:44.261535  8859 net.cpp:84] Creating Layer drop7
I0401 13:34:44.261538  8859 net.cpp:406] drop7 <- fc7
I0401 13:34:44.261564  8859 net.cpp:367] drop7 -> fc7 (in-place)
I0401 13:34:44.261585  8859 net.cpp:122] Setting up drop7
I0401 13:34:44.261590  8859 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:34:44.261591  8859 net.cpp:137] Memory required for data: 1064351744
I0401 13:34:44.261593  8859 layer_factory.hpp:77] Creating layer fc8
I0401 13:34:44.261601  8859 net.cpp:84] Creating Layer fc8
I0401 13:34:44.261603  8859 net.cpp:406] fc8 <- fc7
I0401 13:34:44.261606  8859 net.cpp:380] fc8 -> fc8
I0401 13:34:44.268805  8859 net.cpp:122] Setting up fc8
I0401 13:34:44.268824  8859 net.cpp:129] Top shape: 128 196 (25088)
I0401 13:34:44.268826  8859 net.cpp:137] Memory required for data: 1064452096
I0401 13:34:44.268834  8859 layer_factory.hpp:77] Creating layer loss
I0401 13:34:44.268841  8859 net.cpp:84] Creating Layer loss
I0401 13:34:44.268844  8859 net.cpp:406] loss <- fc8
I0401 13:34:44.268848  8859 net.cpp:406] loss <- label
I0401 13:34:44.268855  8859 net.cpp:380] loss -> loss
I0401 13:34:44.268889  8859 layer_factory.hpp:77] Creating layer loss
I0401 13:34:44.269552  8859 net.cpp:122] Setting up loss
I0401 13:34:44.269562  8859 net.cpp:129] Top shape: (1)
I0401 13:34:44.269564  8859 net.cpp:132]     with loss weight 1
I0401 13:34:44.269587  8859 net.cpp:137] Memory required for data: 1064452100
I0401 13:34:44.269589  8859 net.cpp:198] loss needs backward computation.
I0401 13:34:44.269595  8859 net.cpp:198] fc8 needs backward computation.
I0401 13:34:44.269598  8859 net.cpp:198] drop7 needs backward computation.
I0401 13:34:44.269600  8859 net.cpp:198] relu7 needs backward computation.
I0401 13:34:44.269603  8859 net.cpp:198] fc7 needs backward computation.
I0401 13:34:44.269604  8859 net.cpp:198] drop6 needs backward computation.
I0401 13:34:44.269606  8859 net.cpp:198] relu6 needs backward computation.
I0401 13:34:44.269609  8859 net.cpp:198] fc6 needs backward computation.
I0401 13:34:44.269611  8859 net.cpp:198] pool5 needs backward computation.
I0401 13:34:44.269613  8859 net.cpp:198] relu5 needs backward computation.
I0401 13:34:44.269616  8859 net.cpp:198] conv5 needs backward computation.
I0401 13:34:44.269618  8859 net.cpp:198] relu4 needs backward computation.
I0401 13:34:44.269620  8859 net.cpp:198] conv4 needs backward computation.
I0401 13:34:44.269623  8859 net.cpp:198] relu3 needs backward computation.
I0401 13:34:44.269625  8859 net.cpp:198] conv3 needs backward computation.
I0401 13:34:44.269627  8859 net.cpp:198] pool2 needs backward computation.
I0401 13:34:44.269630  8859 net.cpp:198] norm2 needs backward computation.
I0401 13:34:44.269632  8859 net.cpp:198] relu2 needs backward computation.
I0401 13:34:44.269634  8859 net.cpp:198] conv2 needs backward computation.
I0401 13:34:44.269636  8859 net.cpp:198] pool1 needs backward computation.
I0401 13:34:44.269639  8859 net.cpp:198] norm1 needs backward computation.
I0401 13:34:44.269641  8859 net.cpp:198] relu1 needs backward computation.
I0401 13:34:44.269644  8859 net.cpp:198] conv1 needs backward computation.
I0401 13:34:44.269646  8859 net.cpp:200] train-data does not need backward computation.
I0401 13:34:44.269649  8859 net.cpp:242] This network produces output loss
I0401 13:34:44.269660  8859 net.cpp:255] Network initialization done.
I0401 13:34:44.270200  8859 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0401 13:34:44.270228  8859 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0401 13:34:44.270357  8859 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 13:34:44.270462  8859 layer_factory.hpp:77] Creating layer val-data
I0401 13:34:44.284107  8859 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/val_db
I0401 13:34:44.288101  8859 net.cpp:84] Creating Layer val-data
I0401 13:34:44.288125  8859 net.cpp:380] val-data -> data
I0401 13:34:44.288137  8859 net.cpp:380] val-data -> label
I0401 13:34:44.288144  8859 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115855-7678/mean.binaryproto
I0401 13:34:44.291903  8859 data_layer.cpp:45] output data size: 32,3,227,227
I0401 13:34:44.332834  8859 net.cpp:122] Setting up val-data
I0401 13:34:44.332876  8859 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0401 13:34:44.332890  8859 net.cpp:129] Top shape: 32 (32)
I0401 13:34:44.332893  8859 net.cpp:137] Memory required for data: 19787264
I0401 13:34:44.332901  8859 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0401 13:34:44.332914  8859 net.cpp:84] Creating Layer label_val-data_1_split
I0401 13:34:44.332919  8859 net.cpp:406] label_val-data_1_split <- label
I0401 13:34:44.332928  8859 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0401 13:34:44.332939  8859 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0401 13:34:44.333030  8859 net.cpp:122] Setting up label_val-data_1_split
I0401 13:34:44.333039  8859 net.cpp:129] Top shape: 32 (32)
I0401 13:34:44.333043  8859 net.cpp:129] Top shape: 32 (32)
I0401 13:34:44.333045  8859 net.cpp:137] Memory required for data: 19787520
I0401 13:34:44.333050  8859 layer_factory.hpp:77] Creating layer conv1
I0401 13:34:44.333065  8859 net.cpp:84] Creating Layer conv1
I0401 13:34:44.333068  8859 net.cpp:406] conv1 <- data
I0401 13:34:44.333076  8859 net.cpp:380] conv1 -> conv1
I0401 13:34:44.347378  8859 net.cpp:122] Setting up conv1
I0401 13:34:44.347396  8859 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:34:44.347398  8859 net.cpp:137] Memory required for data: 56958720
I0401 13:34:44.347409  8859 layer_factory.hpp:77] Creating layer relu1
I0401 13:34:44.347417  8859 net.cpp:84] Creating Layer relu1
I0401 13:34:44.347420  8859 net.cpp:406] relu1 <- conv1
I0401 13:34:44.347424  8859 net.cpp:367] relu1 -> conv1 (in-place)
I0401 13:34:44.347704  8859 net.cpp:122] Setting up relu1
I0401 13:34:44.347712  8859 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:34:44.347715  8859 net.cpp:137] Memory required for data: 94129920
I0401 13:34:44.347718  8859 layer_factory.hpp:77] Creating layer norm1
I0401 13:34:44.347725  8859 net.cpp:84] Creating Layer norm1
I0401 13:34:44.347728  8859 net.cpp:406] norm1 <- conv1
I0401 13:34:44.347733  8859 net.cpp:380] norm1 -> norm1
I0401 13:34:44.348426  8859 net.cpp:122] Setting up norm1
I0401 13:34:44.348435  8859 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:34:44.348438  8859 net.cpp:137] Memory required for data: 131301120
I0401 13:34:44.348439  8859 layer_factory.hpp:77] Creating layer pool1
I0401 13:34:44.348445  8859 net.cpp:84] Creating Layer pool1
I0401 13:34:44.348448  8859 net.cpp:406] pool1 <- norm1
I0401 13:34:44.348451  8859 net.cpp:380] pool1 -> pool1
I0401 13:34:44.348476  8859 net.cpp:122] Setting up pool1
I0401 13:34:44.348480  8859 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0401 13:34:44.348482  8859 net.cpp:137] Memory required for data: 140259072
I0401 13:34:44.348484  8859 layer_factory.hpp:77] Creating layer conv2
I0401 13:34:44.348492  8859 net.cpp:84] Creating Layer conv2
I0401 13:34:44.348495  8859 net.cpp:406] conv2 <- pool1
I0401 13:34:44.348520  8859 net.cpp:380] conv2 -> conv2
I0401 13:34:44.357295  8859 net.cpp:122] Setting up conv2
I0401 13:34:44.357313  8859 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:34:44.357316  8859 net.cpp:137] Memory required for data: 164146944
I0401 13:34:44.357326  8859 layer_factory.hpp:77] Creating layer relu2
I0401 13:34:44.357334  8859 net.cpp:84] Creating Layer relu2
I0401 13:34:44.357338  8859 net.cpp:406] relu2 <- conv2
I0401 13:34:44.357343  8859 net.cpp:367] relu2 -> conv2 (in-place)
I0401 13:34:44.357812  8859 net.cpp:122] Setting up relu2
I0401 13:34:44.357821  8859 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:34:44.357825  8859 net.cpp:137] Memory required for data: 188034816
I0401 13:34:44.357826  8859 layer_factory.hpp:77] Creating layer norm2
I0401 13:34:44.357836  8859 net.cpp:84] Creating Layer norm2
I0401 13:34:44.357838  8859 net.cpp:406] norm2 <- conv2
I0401 13:34:44.357842  8859 net.cpp:380] norm2 -> norm2
I0401 13:34:44.358336  8859 net.cpp:122] Setting up norm2
I0401 13:34:44.358345  8859 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:34:44.358347  8859 net.cpp:137] Memory required for data: 211922688
I0401 13:34:44.358350  8859 layer_factory.hpp:77] Creating layer pool2
I0401 13:34:44.358357  8859 net.cpp:84] Creating Layer pool2
I0401 13:34:44.358359  8859 net.cpp:406] pool2 <- norm2
I0401 13:34:44.358363  8859 net.cpp:380] pool2 -> pool2
I0401 13:34:44.358392  8859 net.cpp:122] Setting up pool2
I0401 13:34:44.358395  8859 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:34:44.358397  8859 net.cpp:137] Memory required for data: 217460480
I0401 13:34:44.358399  8859 layer_factory.hpp:77] Creating layer conv3
I0401 13:34:44.358408  8859 net.cpp:84] Creating Layer conv3
I0401 13:34:44.358410  8859 net.cpp:406] conv3 <- pool2
I0401 13:34:44.358414  8859 net.cpp:380] conv3 -> conv3
I0401 13:34:44.368432  8859 net.cpp:122] Setting up conv3
I0401 13:34:44.368453  8859 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:34:44.368455  8859 net.cpp:137] Memory required for data: 225767168
I0401 13:34:44.368465  8859 layer_factory.hpp:77] Creating layer relu3
I0401 13:34:44.368472  8859 net.cpp:84] Creating Layer relu3
I0401 13:34:44.368475  8859 net.cpp:406] relu3 <- conv3
I0401 13:34:44.368480  8859 net.cpp:367] relu3 -> conv3 (in-place)
I0401 13:34:44.368963  8859 net.cpp:122] Setting up relu3
I0401 13:34:44.368973  8859 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:34:44.368974  8859 net.cpp:137] Memory required for data: 234073856
I0401 13:34:44.368978  8859 layer_factory.hpp:77] Creating layer conv4
I0401 13:34:44.368986  8859 net.cpp:84] Creating Layer conv4
I0401 13:34:44.368989  8859 net.cpp:406] conv4 <- conv3
I0401 13:34:44.368993  8859 net.cpp:380] conv4 -> conv4
I0401 13:34:44.378180  8859 net.cpp:122] Setting up conv4
I0401 13:34:44.378197  8859 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:34:44.378201  8859 net.cpp:137] Memory required for data: 242380544
I0401 13:34:44.378207  8859 layer_factory.hpp:77] Creating layer relu4
I0401 13:34:44.378216  8859 net.cpp:84] Creating Layer relu4
I0401 13:34:44.378218  8859 net.cpp:406] relu4 <- conv4
I0401 13:34:44.378224  8859 net.cpp:367] relu4 -> conv4 (in-place)
I0401 13:34:44.378545  8859 net.cpp:122] Setting up relu4
I0401 13:34:44.378553  8859 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:34:44.378556  8859 net.cpp:137] Memory required for data: 250687232
I0401 13:34:44.378558  8859 layer_factory.hpp:77] Creating layer conv5
I0401 13:34:44.378571  8859 net.cpp:84] Creating Layer conv5
I0401 13:34:44.378573  8859 net.cpp:406] conv5 <- conv4
I0401 13:34:44.378578  8859 net.cpp:380] conv5 -> conv5
I0401 13:34:44.389262  8859 net.cpp:122] Setting up conv5
I0401 13:34:44.389286  8859 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:34:44.389290  8859 net.cpp:137] Memory required for data: 256225024
I0401 13:34:44.389305  8859 layer_factory.hpp:77] Creating layer relu5
I0401 13:34:44.389317  8859 net.cpp:84] Creating Layer relu5
I0401 13:34:44.389322  8859 net.cpp:406] relu5 <- conv5
I0401 13:34:44.389354  8859 net.cpp:367] relu5 -> conv5 (in-place)
I0401 13:34:44.390087  8859 net.cpp:122] Setting up relu5
I0401 13:34:44.390100  8859 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:34:44.390102  8859 net.cpp:137] Memory required for data: 261762816
I0401 13:34:44.390106  8859 layer_factory.hpp:77] Creating layer pool5
I0401 13:34:44.390118  8859 net.cpp:84] Creating Layer pool5
I0401 13:34:44.390122  8859 net.cpp:406] pool5 <- conv5
I0401 13:34:44.390130  8859 net.cpp:380] pool5 -> pool5
I0401 13:34:44.390177  8859 net.cpp:122] Setting up pool5
I0401 13:34:44.390185  8859 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0401 13:34:44.390188  8859 net.cpp:137] Memory required for data: 262942464
I0401 13:34:44.390192  8859 layer_factory.hpp:77] Creating layer fc6
I0401 13:34:44.390202  8859 net.cpp:84] Creating Layer fc6
I0401 13:34:44.390204  8859 net.cpp:406] fc6 <- pool5
I0401 13:34:44.390210  8859 net.cpp:380] fc6 -> fc6
I0401 13:34:44.781503  8859 net.cpp:122] Setting up fc6
I0401 13:34:44.781527  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.781528  8859 net.cpp:137] Memory required for data: 263466752
I0401 13:34:44.781538  8859 layer_factory.hpp:77] Creating layer relu6
I0401 13:34:44.781545  8859 net.cpp:84] Creating Layer relu6
I0401 13:34:44.781548  8859 net.cpp:406] relu6 <- fc6
I0401 13:34:44.781553  8859 net.cpp:367] relu6 -> fc6 (in-place)
I0401 13:34:44.782204  8859 net.cpp:122] Setting up relu6
I0401 13:34:44.782213  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.782215  8859 net.cpp:137] Memory required for data: 263991040
I0401 13:34:44.782218  8859 layer_factory.hpp:77] Creating layer drop6
I0401 13:34:44.782225  8859 net.cpp:84] Creating Layer drop6
I0401 13:34:44.782228  8859 net.cpp:406] drop6 <- fc6
I0401 13:34:44.782231  8859 net.cpp:367] drop6 -> fc6 (in-place)
I0401 13:34:44.782254  8859 net.cpp:122] Setting up drop6
I0401 13:34:44.782258  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.782260  8859 net.cpp:137] Memory required for data: 264515328
I0401 13:34:44.782263  8859 layer_factory.hpp:77] Creating layer fc7
I0401 13:34:44.782267  8859 net.cpp:84] Creating Layer fc7
I0401 13:34:44.782269  8859 net.cpp:406] fc7 <- fc6
I0401 13:34:44.782274  8859 net.cpp:380] fc7 -> fc7
I0401 13:34:44.938977  8859 net.cpp:122] Setting up fc7
I0401 13:34:44.939008  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.939013  8859 net.cpp:137] Memory required for data: 265039616
I0401 13:34:44.939024  8859 layer_factory.hpp:77] Creating layer relu7
I0401 13:34:44.939036  8859 net.cpp:84] Creating Layer relu7
I0401 13:34:44.939043  8859 net.cpp:406] relu7 <- fc7
I0401 13:34:44.939052  8859 net.cpp:367] relu7 -> fc7 (in-place)
I0401 13:34:44.939621  8859 net.cpp:122] Setting up relu7
I0401 13:34:44.939635  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.939640  8859 net.cpp:137] Memory required for data: 265563904
I0401 13:34:44.939644  8859 layer_factory.hpp:77] Creating layer drop7
I0401 13:34:44.939652  8859 net.cpp:84] Creating Layer drop7
I0401 13:34:44.939656  8859 net.cpp:406] drop7 <- fc7
I0401 13:34:44.939663  8859 net.cpp:367] drop7 -> fc7 (in-place)
I0401 13:34:44.939698  8859 net.cpp:122] Setting up drop7
I0401 13:34:44.939705  8859 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:34:44.939708  8859 net.cpp:137] Memory required for data: 266088192
I0401 13:34:44.939713  8859 layer_factory.hpp:77] Creating layer fc8
I0401 13:34:44.939723  8859 net.cpp:84] Creating Layer fc8
I0401 13:34:44.939728  8859 net.cpp:406] fc8 <- fc7
I0401 13:34:44.939733  8859 net.cpp:380] fc8 -> fc8
I0401 13:34:44.951412  8859 net.cpp:122] Setting up fc8
I0401 13:34:44.951442  8859 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:34:44.951445  8859 net.cpp:137] Memory required for data: 266113280
I0401 13:34:44.951457  8859 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0401 13:34:44.951468  8859 net.cpp:84] Creating Layer fc8_fc8_0_split
I0401 13:34:44.951473  8859 net.cpp:406] fc8_fc8_0_split <- fc8
I0401 13:34:44.951516  8859 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0401 13:34:44.951529  8859 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0401 13:34:44.951576  8859 net.cpp:122] Setting up fc8_fc8_0_split
I0401 13:34:44.951586  8859 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:34:44.951589  8859 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:34:44.951592  8859 net.cpp:137] Memory required for data: 266163456
I0401 13:34:44.951596  8859 layer_factory.hpp:77] Creating layer accuracy
I0401 13:34:44.951603  8859 net.cpp:84] Creating Layer accuracy
I0401 13:34:44.951607  8859 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0401 13:34:44.951612  8859 net.cpp:406] accuracy <- label_val-data_1_split_0
I0401 13:34:44.951620  8859 net.cpp:380] accuracy -> accuracy
I0401 13:34:44.951629  8859 net.cpp:122] Setting up accuracy
I0401 13:34:44.951634  8859 net.cpp:129] Top shape: (1)
I0401 13:34:44.951637  8859 net.cpp:137] Memory required for data: 266163460
I0401 13:34:44.951640  8859 layer_factory.hpp:77] Creating layer loss
I0401 13:34:44.951647  8859 net.cpp:84] Creating Layer loss
I0401 13:34:44.951650  8859 net.cpp:406] loss <- fc8_fc8_0_split_1
I0401 13:34:44.951655  8859 net.cpp:406] loss <- label_val-data_1_split_1
I0401 13:34:44.951660  8859 net.cpp:380] loss -> loss
I0401 13:34:44.951669  8859 layer_factory.hpp:77] Creating layer loss
I0401 13:34:44.952622  8859 net.cpp:122] Setting up loss
I0401 13:34:44.952636  8859 net.cpp:129] Top shape: (1)
I0401 13:34:44.952639  8859 net.cpp:132]     with loss weight 1
I0401 13:34:44.952654  8859 net.cpp:137] Memory required for data: 266163464
I0401 13:34:44.952659  8859 net.cpp:198] loss needs backward computation.
I0401 13:34:44.952664  8859 net.cpp:200] accuracy does not need backward computation.
I0401 13:34:44.952669  8859 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0401 13:34:44.952672  8859 net.cpp:198] fc8 needs backward computation.
I0401 13:34:44.952677  8859 net.cpp:198] drop7 needs backward computation.
I0401 13:34:44.952679  8859 net.cpp:198] relu7 needs backward computation.
I0401 13:34:44.952683  8859 net.cpp:198] fc7 needs backward computation.
I0401 13:34:44.952687  8859 net.cpp:198] drop6 needs backward computation.
I0401 13:34:44.952689  8859 net.cpp:198] relu6 needs backward computation.
I0401 13:34:44.952693  8859 net.cpp:198] fc6 needs backward computation.
I0401 13:34:44.952697  8859 net.cpp:198] pool5 needs backward computation.
I0401 13:34:44.952702  8859 net.cpp:198] relu5 needs backward computation.
I0401 13:34:44.952705  8859 net.cpp:198] conv5 needs backward computation.
I0401 13:34:44.952709  8859 net.cpp:198] relu4 needs backward computation.
I0401 13:34:44.952713  8859 net.cpp:198] conv4 needs backward computation.
I0401 13:34:44.952716  8859 net.cpp:198] relu3 needs backward computation.
I0401 13:34:44.952720  8859 net.cpp:198] conv3 needs backward computation.
I0401 13:34:44.952723  8859 net.cpp:198] pool2 needs backward computation.
I0401 13:34:44.952728  8859 net.cpp:198] norm2 needs backward computation.
I0401 13:34:44.952730  8859 net.cpp:198] relu2 needs backward computation.
I0401 13:34:44.952734  8859 net.cpp:198] conv2 needs backward computation.
I0401 13:34:44.952739  8859 net.cpp:198] pool1 needs backward computation.
I0401 13:34:44.952741  8859 net.cpp:198] norm1 needs backward computation.
I0401 13:34:44.952745  8859 net.cpp:198] relu1 needs backward computation.
I0401 13:34:44.952749  8859 net.cpp:198] conv1 needs backward computation.
I0401 13:34:44.952754  8859 net.cpp:200] label_val-data_1_split does not need backward computation.
I0401 13:34:44.952757  8859 net.cpp:200] val-data does not need backward computation.
I0401 13:34:44.952761  8859 net.cpp:242] This network produces output accuracy
I0401 13:34:44.952765  8859 net.cpp:242] This network produces output loss
I0401 13:34:44.952787  8859 net.cpp:255] Network initialization done.
I0401 13:34:44.952929  8859 solver.cpp:56] Solver scaffolding done.
I0401 13:34:44.953536  8859 caffe.cpp:248] Starting Optimization
I0401 13:34:44.953548  8859 solver.cpp:272] Solving
I0401 13:34:44.953580  8859 solver.cpp:273] Learning Rate Policy: fixed
I0401 13:34:44.956198  8859 solver.cpp:330] Iteration 0, Testing net (#0)
I0401 13:34:44.956213  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:34:45.070174  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:34:57.621374  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:34:57.678864  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00639764
I0401 13:34:57.678903  8859 solver.cpp:397]     Test net output #1: loss = 5.27706 (* 1 = 5.27706 loss)
I0401 13:34:57.808687  8859 solver.cpp:218] Iteration 0 (3.10257e+35 iter/s, 12.8549s/8 iters), loss = 5.28066
I0401 13:34:57.808732  8859 solver.cpp:237]     Train net output #0: loss = 5.28066 (* 1 = 5.28066 loss)
I0401 13:34:57.808741  8859 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0401 13:35:00.257938  8859 solver.cpp:218] Iteration 8 (3.26644 iter/s, 2.44915s/8 iters), loss = 5.27614
I0401 13:35:00.257987  8859 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0401 13:35:00.257995  8859 sgd_solver.cpp:105] Iteration 8, lr = 0.001
I0401 13:35:03.650782  8859 solver.cpp:218] Iteration 16 (2.35798 iter/s, 3.39274s/8 iters), loss = 5.29289
I0401 13:35:03.650833  8859 solver.cpp:237]     Train net output #0: loss = 5.29289 (* 1 = 5.29289 loss)
I0401 13:35:03.650841  8859 sgd_solver.cpp:105] Iteration 16, lr = 0.001
I0401 13:35:07.351735  8859 solver.cpp:218] Iteration 24 (2.16167 iter/s, 3.70085s/8 iters), loss = 5.28928
I0401 13:35:07.351776  8859 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0401 13:35:07.351783  8859 sgd_solver.cpp:105] Iteration 24, lr = 0.001
I0401 13:35:11.007580  8859 solver.cpp:218] Iteration 32 (2.18834 iter/s, 3.65575s/8 iters), loss = 5.30037
I0401 13:35:11.007627  8859 solver.cpp:237]     Train net output #0: loss = 5.30037 (* 1 = 5.30037 loss)
I0401 13:35:11.007632  8859 sgd_solver.cpp:105] Iteration 32, lr = 0.001
I0401 13:35:14.844357  8859 solver.cpp:218] Iteration 40 (2.08514 iter/s, 3.83668s/8 iters), loss = 5.28964
I0401 13:35:14.844509  8859 solver.cpp:237]     Train net output #0: loss = 5.28964 (* 1 = 5.28964 loss)
I0401 13:35:14.844517  8859 sgd_solver.cpp:105] Iteration 40, lr = 0.001
I0401 13:35:18.458454  8859 solver.cpp:218] Iteration 48 (2.21368 iter/s, 3.61389s/8 iters), loss = 5.28421
I0401 13:35:18.458506  8859 solver.cpp:237]     Train net output #0: loss = 5.28421 (* 1 = 5.28421 loss)
I0401 13:35:18.458516  8859 sgd_solver.cpp:105] Iteration 48, lr = 0.001
I0401 13:35:22.158324  8859 solver.cpp:218] Iteration 56 (2.1623 iter/s, 3.69977s/8 iters), loss = 5.28247
I0401 13:35:22.158363  8859 solver.cpp:237]     Train net output #0: loss = 5.28247 (* 1 = 5.28247 loss)
I0401 13:35:22.158370  8859 sgd_solver.cpp:105] Iteration 56, lr = 0.001
I0401 13:35:25.074381  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:35:25.376403  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_64.caffemodel
I0401 13:35:28.560178  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_64.solverstate
I0401 13:35:30.906286  8859 solver.cpp:330] Iteration 64, Testing net (#0)
I0401 13:35:30.906304  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:35:42.818362  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:35:42.934334  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:35:42.934367  8859 solver.cpp:397]     Test net output #1: loss = 5.27772 (* 1 = 5.27772 loss)
I0401 13:35:43.076304  8859 solver.cpp:218] Iteration 64 (0.382451 iter/s, 20.9177s/8 iters), loss = 5.28723
I0401 13:35:43.076355  8859 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0401 13:35:43.076362  8859 sgd_solver.cpp:105] Iteration 64, lr = 0.001
I0401 13:35:45.587920  8859 solver.cpp:218] Iteration 72 (3.18532 iter/s, 2.51152s/8 iters), loss = 5.27225
I0401 13:35:45.588093  8859 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0401 13:35:45.588101  8859 sgd_solver.cpp:105] Iteration 72, lr = 0.001
I0401 13:35:49.157995  8859 solver.cpp:218] Iteration 80 (2.24099 iter/s, 3.56985s/8 iters), loss = 5.29231
I0401 13:35:49.158037  8859 solver.cpp:237]     Train net output #0: loss = 5.29231 (* 1 = 5.29231 loss)
I0401 13:35:49.158043  8859 sgd_solver.cpp:105] Iteration 80, lr = 0.001
I0401 13:35:52.743332  8859 solver.cpp:218] Iteration 88 (2.23138 iter/s, 3.58523s/8 iters), loss = 5.28594
I0401 13:35:52.743391  8859 solver.cpp:237]     Train net output #0: loss = 5.28594 (* 1 = 5.28594 loss)
I0401 13:35:52.743398  8859 sgd_solver.cpp:105] Iteration 88, lr = 0.001
I0401 13:35:56.451297  8859 solver.cpp:218] Iteration 96 (2.15758 iter/s, 3.70786s/8 iters), loss = 5.28252
I0401 13:35:56.451336  8859 solver.cpp:237]     Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
I0401 13:35:56.451341  8859 sgd_solver.cpp:105] Iteration 96, lr = 0.001
I0401 13:36:00.069593  8859 solver.cpp:218] Iteration 104 (2.21105 iter/s, 3.6182s/8 iters), loss = 5.28252
I0401 13:36:00.069640  8859 solver.cpp:237]     Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
I0401 13:36:00.069646  8859 sgd_solver.cpp:105] Iteration 104, lr = 0.001
I0401 13:36:03.685190  8859 solver.cpp:218] Iteration 112 (2.2127 iter/s, 3.6155s/8 iters), loss = 5.2615
I0401 13:36:03.685236  8859 solver.cpp:237]     Train net output #0: loss = 5.2615 (* 1 = 5.2615 loss)
I0401 13:36:03.685245  8859 sgd_solver.cpp:105] Iteration 112, lr = 0.001
I0401 13:36:07.428525  8859 solver.cpp:218] Iteration 120 (2.13719 iter/s, 3.74324s/8 iters), loss = 5.28067
I0401 13:36:07.428565  8859 solver.cpp:237]     Train net output #0: loss = 5.28067 (* 1 = 5.28067 loss)
I0401 13:36:07.428570  8859 sgd_solver.cpp:105] Iteration 120, lr = 0.001
I0401 13:36:09.752481  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:36:10.449592  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_128.caffemodel
I0401 13:36:13.505051  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_128.solverstate
I0401 13:36:15.818226  8859 solver.cpp:330] Iteration 128, Testing net (#0)
I0401 13:36:15.818312  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:36:27.648036  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:36:27.806593  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00984252
I0401 13:36:27.806627  8859 solver.cpp:397]     Test net output #1: loss = 5.2793 (* 1 = 5.2793 loss)
I0401 13:36:27.942585  8859 solver.cpp:218] Iteration 128 (0.389981 iter/s, 20.5138s/8 iters), loss = 5.27535
I0401 13:36:27.942643  8859 solver.cpp:237]     Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
I0401 13:36:27.942653  8859 sgd_solver.cpp:105] Iteration 128, lr = 0.001
I0401 13:36:30.551812  8859 solver.cpp:218] Iteration 136 (3.06616 iter/s, 2.60913s/8 iters), loss = 5.28164
I0401 13:36:30.551860  8859 solver.cpp:237]     Train net output #0: loss = 5.28164 (* 1 = 5.28164 loss)
I0401 13:36:30.551867  8859 sgd_solver.cpp:105] Iteration 136, lr = 0.001
I0401 13:36:34.192926  8859 solver.cpp:218] Iteration 144 (2.1972 iter/s, 3.641s/8 iters), loss = 5.28341
I0401 13:36:34.192979  8859 solver.cpp:237]     Train net output #0: loss = 5.28341 (* 1 = 5.28341 loss)
I0401 13:36:34.192986  8859 sgd_solver.cpp:105] Iteration 144, lr = 0.001
I0401 13:36:38.064114  8859 solver.cpp:218] Iteration 152 (2.06661 iter/s, 3.87108s/8 iters), loss = 5.26905
I0401 13:36:38.064169  8859 solver.cpp:237]     Train net output #0: loss = 5.26905 (* 1 = 5.26905 loss)
I0401 13:36:38.064177  8859 sgd_solver.cpp:105] Iteration 152, lr = 0.001
I0401 13:36:41.483098  8859 solver.cpp:218] Iteration 160 (2.33995 iter/s, 3.41888s/8 iters), loss = 5.269
I0401 13:36:41.483146  8859 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0401 13:36:41.483153  8859 sgd_solver.cpp:105] Iteration 160, lr = 0.001
I0401 13:36:45.134305  8859 solver.cpp:218] Iteration 168 (2.19112 iter/s, 3.6511s/8 iters), loss = 5.27708
I0401 13:36:45.134366  8859 solver.cpp:237]     Train net output #0: loss = 5.27708 (* 1 = 5.27708 loss)
I0401 13:36:45.134375  8859 sgd_solver.cpp:105] Iteration 168, lr = 0.001
I0401 13:36:48.613210  8859 solver.cpp:218] Iteration 176 (2.29965 iter/s, 3.47879s/8 iters), loss = 5.27064
I0401 13:36:48.613377  8859 solver.cpp:237]     Train net output #0: loss = 5.27064 (* 1 = 5.27064 loss)
I0401 13:36:48.613386  8859 sgd_solver.cpp:105] Iteration 176, lr = 0.001
I0401 13:36:52.176863  8859 solver.cpp:218] Iteration 184 (2.24502 iter/s, 3.56344s/8 iters), loss = 5.27427
I0401 13:36:52.176913  8859 solver.cpp:237]     Train net output #0: loss = 5.27427 (* 1 = 5.27427 loss)
I0401 13:36:52.176918  8859 sgd_solver.cpp:105] Iteration 184, lr = 0.001
I0401 13:36:54.223428  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:36:55.317690  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_192.caffemodel
I0401 13:36:58.322075  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_192.solverstate
I0401 13:37:00.638451  8859 solver.cpp:330] Iteration 192, Testing net (#0)
I0401 13:37:00.638466  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:37:12.007889  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:37:12.222954  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:37:12.222992  8859 solver.cpp:397]     Test net output #1: loss = 5.28091 (* 1 = 5.28091 loss)
I0401 13:37:12.359827  8859 solver.cpp:218] Iteration 192 (0.396379 iter/s, 20.1827s/8 iters), loss = 5.2521
I0401 13:37:12.359887  8859 solver.cpp:237]     Train net output #0: loss = 5.2521 (* 1 = 5.2521 loss)
I0401 13:37:12.359895  8859 sgd_solver.cpp:105] Iteration 192, lr = 0.001
I0401 13:37:14.992508  8859 solver.cpp:218] Iteration 200 (3.03884 iter/s, 2.63258s/8 iters), loss = 5.28429
I0401 13:37:14.992550  8859 solver.cpp:237]     Train net output #0: loss = 5.28429 (* 1 = 5.28429 loss)
I0401 13:37:14.992555  8859 sgd_solver.cpp:105] Iteration 200, lr = 0.001
I0401 13:37:18.605229  8859 solver.cpp:218] Iteration 208 (2.21446 iter/s, 3.61262s/8 iters), loss = 5.23286
I0401 13:37:18.605271  8859 solver.cpp:237]     Train net output #0: loss = 5.23286 (* 1 = 5.23286 loss)
I0401 13:37:18.605278  8859 sgd_solver.cpp:105] Iteration 208, lr = 0.001
I0401 13:37:22.179586  8859 solver.cpp:218] Iteration 216 (2.23823 iter/s, 3.57425s/8 iters), loss = 5.29796
I0401 13:37:22.179726  8859 solver.cpp:237]     Train net output #0: loss = 5.29796 (* 1 = 5.29796 loss)
I0401 13:37:22.179735  8859 sgd_solver.cpp:105] Iteration 216, lr = 0.001
I0401 13:37:25.628022  8859 solver.cpp:218] Iteration 224 (2.32002 iter/s, 3.44824s/8 iters), loss = 5.27052
I0401 13:37:25.628082  8859 solver.cpp:237]     Train net output #0: loss = 5.27052 (* 1 = 5.27052 loss)
I0401 13:37:25.628091  8859 sgd_solver.cpp:105] Iteration 224, lr = 0.001
I0401 13:37:29.291779  8859 solver.cpp:218] Iteration 232 (2.18362 iter/s, 3.66365s/8 iters), loss = 5.28876
I0401 13:37:29.291828  8859 solver.cpp:237]     Train net output #0: loss = 5.28876 (* 1 = 5.28876 loss)
I0401 13:37:29.291837  8859 sgd_solver.cpp:105] Iteration 232, lr = 0.001
I0401 13:37:32.918787  8859 solver.cpp:218] Iteration 240 (2.20574 iter/s, 3.6269s/8 iters), loss = 5.26483
I0401 13:37:32.918840  8859 solver.cpp:237]     Train net output #0: loss = 5.26483 (* 1 = 5.26483 loss)
I0401 13:37:32.918848  8859 sgd_solver.cpp:105] Iteration 240, lr = 0.001
I0401 13:37:36.406199  8859 solver.cpp:218] Iteration 248 (2.29404 iter/s, 3.4873s/8 iters), loss = 5.26693
I0401 13:37:36.406255  8859 solver.cpp:237]     Train net output #0: loss = 5.26693 (* 1 = 5.26693 loss)
I0401 13:37:36.406263  8859 sgd_solver.cpp:105] Iteration 248, lr = 0.001
I0401 13:37:37.782706  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:37:39.365325  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_256.caffemodel
I0401 13:37:42.322435  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_256.solverstate
I0401 13:37:44.629257  8859 solver.cpp:330] Iteration 256, Testing net (#0)
I0401 13:37:44.629276  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:37:56.036412  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:37:56.292423  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:37:56.292448  8859 solver.cpp:397]     Test net output #1: loss = 5.28208 (* 1 = 5.28208 loss)
I0401 13:37:56.431418  8859 solver.cpp:218] Iteration 256 (0.399502 iter/s, 20.0249s/8 iters), loss = 5.25301
I0401 13:37:56.431483  8859 solver.cpp:237]     Train net output #0: loss = 5.25301 (* 1 = 5.25301 loss)
I0401 13:37:56.431493  8859 sgd_solver.cpp:105] Iteration 256, lr = 0.001
I0401 13:37:59.113217  8859 solver.cpp:218] Iteration 264 (2.98319 iter/s, 2.68169s/8 iters), loss = 5.2215
I0401 13:37:59.113263  8859 solver.cpp:237]     Train net output #0: loss = 5.2215 (* 1 = 5.2215 loss)
I0401 13:37:59.113271  8859 sgd_solver.cpp:105] Iteration 264, lr = 0.001
I0401 13:38:02.685129  8859 solver.cpp:218] Iteration 272 (2.23976 iter/s, 3.57181s/8 iters), loss = 5.26544
I0401 13:38:02.685173  8859 solver.cpp:237]     Train net output #0: loss = 5.26544 (* 1 = 5.26544 loss)
I0401 13:38:02.685178  8859 sgd_solver.cpp:105] Iteration 272, lr = 0.001
I0401 13:38:05.963647  8859 solver.cpp:218] Iteration 280 (2.4402 iter/s, 3.27842s/8 iters), loss = 5.25245
I0401 13:38:05.963688  8859 solver.cpp:237]     Train net output #0: loss = 5.25245 (* 1 = 5.25245 loss)
I0401 13:38:05.963694  8859 sgd_solver.cpp:105] Iteration 280, lr = 0.001
I0401 13:38:09.523772  8859 solver.cpp:218] Iteration 288 (2.24717 iter/s, 3.56003s/8 iters), loss = 5.28484
I0401 13:38:09.523818  8859 solver.cpp:237]     Train net output #0: loss = 5.28484 (* 1 = 5.28484 loss)
I0401 13:38:09.523823  8859 sgd_solver.cpp:105] Iteration 288, lr = 0.001
I0401 13:38:13.004490  8859 solver.cpp:218] Iteration 296 (2.29844 iter/s, 3.48062s/8 iters), loss = 5.26187
I0401 13:38:13.004545  8859 solver.cpp:237]     Train net output #0: loss = 5.26187 (* 1 = 5.26187 loss)
I0401 13:38:13.004552  8859 sgd_solver.cpp:105] Iteration 296, lr = 0.001
I0401 13:38:16.667846  8859 solver.cpp:218] Iteration 304 (2.18385 iter/s, 3.66325s/8 iters), loss = 5.24806
I0401 13:38:16.667902  8859 solver.cpp:237]     Train net output #0: loss = 5.24806 (* 1 = 5.24806 loss)
I0401 13:38:16.667910  8859 sgd_solver.cpp:105] Iteration 304, lr = 0.001
I0401 13:38:20.284309  8859 solver.cpp:218] Iteration 312 (2.21217 iter/s, 3.61636s/8 iters), loss = 5.25697
I0401 13:38:20.284354  8859 solver.cpp:237]     Train net output #0: loss = 5.25697 (* 1 = 5.25697 loss)
I0401 13:38:20.284360  8859 sgd_solver.cpp:105] Iteration 312, lr = 0.001
I0401 13:38:21.568784  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:38:23.114265  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_320.caffemodel
I0401 13:38:26.281880  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_320.solverstate
I0401 13:38:28.631160  8859 solver.cpp:330] Iteration 320, Testing net (#0)
I0401 13:38:28.631186  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:38:35.961549  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:38:40.113210  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:38:40.447793  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:38:40.447829  8859 solver.cpp:397]     Test net output #1: loss = 5.28252 (* 1 = 5.28252 loss)
I0401 13:38:40.587069  8859 solver.cpp:218] Iteration 320 (0.39404 iter/s, 20.3025s/8 iters), loss = 5.27423
I0401 13:38:40.587141  8859 solver.cpp:237]     Train net output #0: loss = 5.27423 (* 1 = 5.27423 loss)
I0401 13:38:40.587152  8859 sgd_solver.cpp:105] Iteration 320, lr = 0.001
I0401 13:38:43.030872  8859 solver.cpp:218] Iteration 328 (3.27374 iter/s, 2.44369s/8 iters), loss = 5.26132
I0401 13:38:43.030926  8859 solver.cpp:237]     Train net output #0: loss = 5.26132 (* 1 = 5.26132 loss)
I0401 13:38:43.030936  8859 sgd_solver.cpp:105] Iteration 328, lr = 0.001
I0401 13:38:46.363329  8859 solver.cpp:218] Iteration 336 (2.40071 iter/s, 3.33235s/8 iters), loss = 5.28332
I0401 13:38:46.363385  8859 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0401 13:38:46.363392  8859 sgd_solver.cpp:105] Iteration 336, lr = 0.001
I0401 13:38:49.885901  8859 solver.cpp:218] Iteration 344 (2.27114 iter/s, 3.52246s/8 iters), loss = 5.26161
I0401 13:38:49.885960  8859 solver.cpp:237]     Train net output #0: loss = 5.26161 (* 1 = 5.26161 loss)
I0401 13:38:49.885969  8859 sgd_solver.cpp:105] Iteration 344, lr = 0.001
I0401 13:38:53.491686  8859 solver.cpp:218] Iteration 352 (2.21873 iter/s, 3.60567s/8 iters), loss = 5.26043
I0401 13:38:53.491747  8859 solver.cpp:237]     Train net output #0: loss = 5.26043 (* 1 = 5.26043 loss)
I0401 13:38:53.491756  8859 sgd_solver.cpp:105] Iteration 352, lr = 0.001
I0401 13:38:57.006705  8859 solver.cpp:218] Iteration 360 (2.27602 iter/s, 3.51491s/8 iters), loss = 5.28445
I0401 13:38:57.006834  8859 solver.cpp:237]     Train net output #0: loss = 5.28445 (* 1 = 5.28445 loss)
I0401 13:38:57.006840  8859 sgd_solver.cpp:105] Iteration 360, lr = 0.001
I0401 13:39:00.552970  8859 solver.cpp:218] Iteration 368 (2.25601 iter/s, 3.54608s/8 iters), loss = 5.26356
I0401 13:39:00.553023  8859 solver.cpp:237]     Train net output #0: loss = 5.26356 (* 1 = 5.26356 loss)
I0401 13:39:00.553031  8859 sgd_solver.cpp:105] Iteration 368, lr = 0.001
I0401 13:39:04.073309  8859 solver.cpp:218] Iteration 376 (2.27258 iter/s, 3.52023s/8 iters), loss = 5.24204
I0401 13:39:04.073367  8859 solver.cpp:237]     Train net output #0: loss = 5.24204 (* 1 = 5.24204 loss)
I0401 13:39:04.073376  8859 sgd_solver.cpp:105] Iteration 376, lr = 0.001
I0401 13:39:05.047123  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:39:07.216564  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_384.caffemodel
I0401 13:39:10.266608  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_384.solverstate
I0401 13:39:14.677244  8859 solver.cpp:330] Iteration 384, Testing net (#0)
I0401 13:39:14.677264  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:39:26.029582  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:39:26.420938  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:39:26.420974  8859 solver.cpp:397]     Test net output #1: loss = 5.28203 (* 1 = 5.28203 loss)
I0401 13:39:26.562815  8859 solver.cpp:218] Iteration 384 (0.355726 iter/s, 22.4892s/8 iters), loss = 5.24237
I0401 13:39:26.562867  8859 solver.cpp:237]     Train net output #0: loss = 5.24237 (* 1 = 5.24237 loss)
I0401 13:39:26.562875  8859 sgd_solver.cpp:105] Iteration 384, lr = 0.001
I0401 13:39:29.248764  8859 solver.cpp:218] Iteration 392 (2.97858 iter/s, 2.68585s/8 iters), loss = 5.27856
I0401 13:39:29.248895  8859 solver.cpp:237]     Train net output #0: loss = 5.27856 (* 1 = 5.27856 loss)
I0401 13:39:29.248904  8859 sgd_solver.cpp:105] Iteration 392, lr = 0.001
I0401 13:39:32.897558  8859 solver.cpp:218] Iteration 400 (2.19261 iter/s, 3.64862s/8 iters), loss = 5.25592
I0401 13:39:32.897609  8859 solver.cpp:237]     Train net output #0: loss = 5.25592 (* 1 = 5.25592 loss)
I0401 13:39:32.897619  8859 sgd_solver.cpp:105] Iteration 400, lr = 0.001
I0401 13:39:36.423846  8859 solver.cpp:218] Iteration 408 (2.26874 iter/s, 3.52618s/8 iters), loss = 5.2657
I0401 13:39:36.423885  8859 solver.cpp:237]     Train net output #0: loss = 5.2657 (* 1 = 5.2657 loss)
I0401 13:39:36.423892  8859 sgd_solver.cpp:105] Iteration 408, lr = 0.001
I0401 13:39:40.114797  8859 solver.cpp:218] Iteration 416 (2.16752 iter/s, 3.69086s/8 iters), loss = 5.27266
I0401 13:39:40.114846  8859 solver.cpp:237]     Train net output #0: loss = 5.27266 (* 1 = 5.27266 loss)
I0401 13:39:40.114853  8859 sgd_solver.cpp:105] Iteration 416, lr = 0.001
I0401 13:39:43.720003  8859 solver.cpp:218] Iteration 424 (2.21908 iter/s, 3.6051s/8 iters), loss = 5.2792
I0401 13:39:43.720055  8859 solver.cpp:237]     Train net output #0: loss = 5.2792 (* 1 = 5.2792 loss)
I0401 13:39:43.720062  8859 sgd_solver.cpp:105] Iteration 424, lr = 0.001
I0401 13:39:47.314225  8859 solver.cpp:218] Iteration 432 (2.22586 iter/s, 3.59412s/8 iters), loss = 5.27056
I0401 13:39:47.314272  8859 solver.cpp:237]     Train net output #0: loss = 5.27056 (* 1 = 5.27056 loss)
I0401 13:39:47.314280  8859 sgd_solver.cpp:105] Iteration 432, lr = 0.001
I0401 13:39:50.670785  8859 solver.cpp:218] Iteration 440 (2.38346 iter/s, 3.35646s/8 iters), loss = 5.26325
I0401 13:39:50.670843  8859 solver.cpp:237]     Train net output #0: loss = 5.26325 (* 1 = 5.26325 loss)
I0401 13:39:50.670851  8859 sgd_solver.cpp:105] Iteration 440, lr = 0.001
I0401 13:39:51.265774  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:39:53.658932  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_448.caffemodel
I0401 13:39:56.663609  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_448.solverstate
I0401 13:39:59.014292  8859 solver.cpp:330] Iteration 448, Testing net (#0)
I0401 13:39:59.014309  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:40:10.164856  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:40:10.578400  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:40:10.578438  8859 solver.cpp:397]     Test net output #1: loss = 5.28002 (* 1 = 5.28002 loss)
I0401 13:40:10.716958  8859 solver.cpp:218] Iteration 448 (0.399084 iter/s, 20.0459s/8 iters), loss = 5.23559
I0401 13:40:10.717002  8859 solver.cpp:237]     Train net output #0: loss = 5.23559 (* 1 = 5.23559 loss)
I0401 13:40:10.717007  8859 sgd_solver.cpp:105] Iteration 448, lr = 0.001
I0401 13:40:13.247200  8859 solver.cpp:218] Iteration 456 (3.16186 iter/s, 2.53015s/8 iters), loss = 5.27225
I0401 13:40:13.247251  8859 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0401 13:40:13.247258  8859 sgd_solver.cpp:105] Iteration 456, lr = 0.001
I0401 13:40:16.663780  8859 solver.cpp:218] Iteration 464 (2.34159 iter/s, 3.41648s/8 iters), loss = 5.26408
I0401 13:40:16.663831  8859 solver.cpp:237]     Train net output #0: loss = 5.26408 (* 1 = 5.26408 loss)
I0401 13:40:16.663839  8859 sgd_solver.cpp:105] Iteration 464, lr = 0.001
I0401 13:40:20.259357  8859 solver.cpp:218] Iteration 472 (2.22502 iter/s, 3.59547s/8 iters), loss = 5.25685
I0401 13:40:20.259409  8859 solver.cpp:237]     Train net output #0: loss = 5.25685 (* 1 = 5.25685 loss)
I0401 13:40:20.259418  8859 sgd_solver.cpp:105] Iteration 472, lr = 0.001
I0401 13:40:23.735582  8859 solver.cpp:218] Iteration 480 (2.30142 iter/s, 3.47612s/8 iters), loss = 5.27534
I0401 13:40:23.735628  8859 solver.cpp:237]     Train net output #0: loss = 5.27534 (* 1 = 5.27534 loss)
I0401 13:40:23.735635  8859 sgd_solver.cpp:105] Iteration 480, lr = 0.001
I0401 13:40:27.268764  8859 solver.cpp:218] Iteration 488 (2.26431 iter/s, 3.53308s/8 iters), loss = 5.24397
I0401 13:40:27.268808  8859 solver.cpp:237]     Train net output #0: loss = 5.24397 (* 1 = 5.24397 loss)
I0401 13:40:27.268815  8859 sgd_solver.cpp:105] Iteration 488, lr = 0.001
I0401 13:40:30.830472  8859 solver.cpp:218] Iteration 496 (2.24618 iter/s, 3.56161s/8 iters), loss = 5.27221
I0401 13:40:30.830533  8859 solver.cpp:237]     Train net output #0: loss = 5.27221 (* 1 = 5.27221 loss)
I0401 13:40:30.830543  8859 sgd_solver.cpp:105] Iteration 496, lr = 0.001
I0401 13:40:34.500418  8859 solver.cpp:218] Iteration 504 (2.17994 iter/s, 3.66983s/8 iters), loss = 5.2541
I0401 13:40:34.500473  8859 solver.cpp:237]     Train net output #0: loss = 5.2541 (* 1 = 5.2541 loss)
I0401 13:40:34.500480  8859 sgd_solver.cpp:105] Iteration 504, lr = 0.001
I0401 13:40:34.661779  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:40:37.309473  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_512.caffemodel
I0401 13:40:40.391182  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_512.solverstate
I0401 13:40:42.717926  8859 solver.cpp:330] Iteration 512, Testing net (#0)
I0401 13:40:42.717949  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:40:53.883538  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:40:54.442338  8859 solver.cpp:397]     Test net output #0: accuracy = 0.00935039
I0401 13:40:54.442368  8859 solver.cpp:397]     Test net output #1: loss = 5.27552 (* 1 = 5.27552 loss)
I0401 13:40:54.584118  8859 solver.cpp:218] Iteration 512 (0.398338 iter/s, 20.0834s/8 iters), loss = 5.25533
I0401 13:40:54.584163  8859 solver.cpp:237]     Train net output #0: loss = 5.25533 (* 1 = 5.25533 loss)
I0401 13:40:54.584169  8859 sgd_solver.cpp:105] Iteration 512, lr = 0.001
I0401 13:40:57.283927  8859 solver.cpp:218] Iteration 520 (2.96327 iter/s, 2.69972s/8 iters), loss = 5.26406
I0401 13:40:57.283980  8859 solver.cpp:237]     Train net output #0: loss = 5.26406 (* 1 = 5.26406 loss)
I0401 13:40:57.283988  8859 sgd_solver.cpp:105] Iteration 520, lr = 0.001
I0401 13:41:00.678928  8859 solver.cpp:218] Iteration 528 (2.35648 iter/s, 3.3949s/8 iters), loss = 5.24702
I0401 13:41:00.678974  8859 solver.cpp:237]     Train net output #0: loss = 5.24702 (* 1 = 5.24702 loss)
I0401 13:41:00.678982  8859 sgd_solver.cpp:105] Iteration 528, lr = 0.001
I0401 13:41:04.151891  8859 solver.cpp:218] Iteration 536 (2.30357 iter/s, 3.47287s/8 iters), loss = 5.26901
I0401 13:41:04.151942  8859 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0401 13:41:04.151949  8859 sgd_solver.cpp:105] Iteration 536, lr = 0.001
I0401 13:41:07.667343  8859 solver.cpp:218] Iteration 544 (2.27574 iter/s, 3.51535s/8 iters), loss = 5.26685
I0401 13:41:07.667395  8859 solver.cpp:237]     Train net output #0: loss = 5.26685 (* 1 = 5.26685 loss)
I0401 13:41:07.667403  8859 sgd_solver.cpp:105] Iteration 544, lr = 0.001
I0401 13:41:11.212203  8859 solver.cpp:218] Iteration 552 (2.25686 iter/s, 3.54476s/8 iters), loss = 5.23016
I0401 13:41:11.212343  8859 solver.cpp:237]     Train net output #0: loss = 5.23016 (* 1 = 5.23016 loss)
I0401 13:41:11.212353  8859 sgd_solver.cpp:105] Iteration 552, lr = 0.001
I0401 13:41:14.749020  8859 solver.cpp:218] Iteration 560 (2.26204 iter/s, 3.53663s/8 iters), loss = 5.25005
I0401 13:41:14.749060  8859 solver.cpp:237]     Train net output #0: loss = 5.25005 (* 1 = 5.25005 loss)
I0401 13:41:14.749066  8859 sgd_solver.cpp:105] Iteration 560, lr = 0.001
I0401 13:41:17.856134  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:41:18.007352  8859 solver.cpp:218] Iteration 568 (2.45531 iter/s, 3.25824s/8 iters), loss = 5.25053
I0401 13:41:18.007407  8859 solver.cpp:237]     Train net output #0: loss = 5.25053 (* 1 = 5.25053 loss)
I0401 13:41:18.007416  8859 sgd_solver.cpp:105] Iteration 568, lr = 0.001
I0401 13:41:21.052443  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_576.caffemodel
I0401 13:41:24.107765  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_576.solverstate
I0401 13:41:26.425498  8859 solver.cpp:330] Iteration 576, Testing net (#0)
I0401 13:41:26.425518  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:41:37.366639  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:41:37.918493  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0105807
I0401 13:41:37.918529  8859 solver.cpp:397]     Test net output #1: loss = 5.26354 (* 1 = 5.26354 loss)
I0401 13:41:38.057653  8859 solver.cpp:218] Iteration 576 (0.399002 iter/s, 20.05s/8 iters), loss = 5.24316
I0401 13:41:38.057706  8859 solver.cpp:237]     Train net output #0: loss = 5.24316 (* 1 = 5.24316 loss)
I0401 13:41:38.057714  8859 sgd_solver.cpp:105] Iteration 576, lr = 0.001
I0401 13:41:40.681651  8859 solver.cpp:218] Iteration 584 (3.04889 iter/s, 2.6239s/8 iters), loss = 5.25242
I0401 13:41:40.681699  8859 solver.cpp:237]     Train net output #0: loss = 5.25242 (* 1 = 5.25242 loss)
I0401 13:41:40.681707  8859 sgd_solver.cpp:105] Iteration 584, lr = 0.001
I0401 13:41:44.146493  8859 solver.cpp:218] Iteration 592 (2.30897 iter/s, 3.46475s/8 iters), loss = 5.25398
I0401 13:41:44.146617  8859 solver.cpp:237]     Train net output #0: loss = 5.25398 (* 1 = 5.25398 loss)
I0401 13:41:44.146623  8859 sgd_solver.cpp:105] Iteration 592, lr = 0.001
I0401 13:41:47.606679  8859 solver.cpp:218] Iteration 600 (2.31213 iter/s, 3.46001s/8 iters), loss = 5.25395
I0401 13:41:47.606724  8859 solver.cpp:237]     Train net output #0: loss = 5.25395 (* 1 = 5.25395 loss)
I0401 13:41:47.606729  8859 sgd_solver.cpp:105] Iteration 600, lr = 0.001
I0401 13:41:51.149353  8859 solver.cpp:218] Iteration 608 (2.25824 iter/s, 3.54258s/8 iters), loss = 5.26389
I0401 13:41:51.149402  8859 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0401 13:41:51.149410  8859 sgd_solver.cpp:105] Iteration 608, lr = 0.001
I0401 13:41:54.524729  8859 solver.cpp:218] Iteration 616 (2.37018 iter/s, 3.37528s/8 iters), loss = 5.24502
I0401 13:41:54.524773  8859 solver.cpp:237]     Train net output #0: loss = 5.24502 (* 1 = 5.24502 loss)
I0401 13:41:54.524780  8859 sgd_solver.cpp:105] Iteration 616, lr = 0.001
I0401 13:41:59.295589  8859 solver.cpp:218] Iteration 624 (1.67689 iter/s, 4.77074s/8 iters), loss = 5.19627
I0401 13:41:59.295650  8859 solver.cpp:237]     Train net output #0: loss = 5.19627 (* 1 = 5.19627 loss)
I0401 13:41:59.295660  8859 sgd_solver.cpp:105] Iteration 624, lr = 0.001
I0401 13:42:04.471312  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:42:05.196941  8859 solver.cpp:218] Iteration 632 (1.35966 iter/s, 5.88383s/8 iters), loss = 5.21093
I0401 13:42:05.197003  8859 solver.cpp:237]     Train net output #0: loss = 5.21093 (* 1 = 5.21093 loss)
I0401 13:42:05.197011  8859 sgd_solver.cpp:105] Iteration 632, lr = 0.001
I0401 13:42:09.535594  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_640.caffemodel
I0401 13:42:13.571410  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_640.solverstate
I0401 13:42:17.413655  8859 solver.cpp:330] Iteration 640, Testing net (#0)
I0401 13:42:17.413743  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:42:32.799084  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:42:33.598661  8859 solver.cpp:397]     Test net output #0: accuracy = 0.011565
I0401 13:42:33.598695  8859 solver.cpp:397]     Test net output #1: loss = 5.22742 (* 1 = 5.22742 loss)
I0401 13:42:33.755465  8859 solver.cpp:218] Iteration 640 (0.28013 iter/s, 28.5582s/8 iters), loss = 5.24944
I0401 13:42:33.755522  8859 solver.cpp:237]     Train net output #0: loss = 5.24944 (* 1 = 5.24944 loss)
I0401 13:42:33.755529  8859 sgd_solver.cpp:105] Iteration 640, lr = 0.001
I0401 13:42:37.130081  8859 solver.cpp:218] Iteration 648 (2.37072 iter/s, 3.37451s/8 iters), loss = 5.211
I0401 13:42:37.130139  8859 solver.cpp:237]     Train net output #0: loss = 5.211 (* 1 = 5.211 loss)
I0401 13:42:37.130147  8859 sgd_solver.cpp:105] Iteration 648, lr = 0.001
I0401 13:42:41.590721  8859 solver.cpp:218] Iteration 656 (1.79351 iter/s, 4.46052s/8 iters), loss = 5.20259
I0401 13:42:41.590777  8859 solver.cpp:237]     Train net output #0: loss = 5.20259 (* 1 = 5.20259 loss)
I0401 13:42:41.590785  8859 sgd_solver.cpp:105] Iteration 656, lr = 0.001
I0401 13:42:46.026722  8859 solver.cpp:218] Iteration 664 (1.80347 iter/s, 4.43588s/8 iters), loss = 5.2585
I0401 13:42:46.026779  8859 solver.cpp:237]     Train net output #0: loss = 5.2585 (* 1 = 5.2585 loss)
I0401 13:42:46.026787  8859 sgd_solver.cpp:105] Iteration 664, lr = 0.001
I0401 13:42:49.363739  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:42:50.543784  8859 solver.cpp:218] Iteration 672 (1.77111 iter/s, 4.51694s/8 iters), loss = 5.24086
I0401 13:42:50.543843  8859 solver.cpp:237]     Train net output #0: loss = 5.24086 (* 1 = 5.24086 loss)
I0401 13:42:50.543851  8859 sgd_solver.cpp:105] Iteration 672, lr = 0.001
I0401 13:42:54.917953  8859 solver.cpp:218] Iteration 680 (1.82897 iter/s, 4.37405s/8 iters), loss = 5.17068
I0401 13:42:54.918009  8859 solver.cpp:237]     Train net output #0: loss = 5.17068 (* 1 = 5.17068 loss)
I0401 13:42:54.918017  8859 sgd_solver.cpp:105] Iteration 680, lr = 0.001
I0401 13:42:59.203083  8859 solver.cpp:218] Iteration 688 (1.86697 iter/s, 4.28501s/8 iters), loss = 5.19549
I0401 13:42:59.203148  8859 solver.cpp:237]     Train net output #0: loss = 5.19549 (* 1 = 5.19549 loss)
I0401 13:42:59.203158  8859 sgd_solver.cpp:105] Iteration 688, lr = 0.001
I0401 13:43:02.778309  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:43:03.763533  8859 solver.cpp:218] Iteration 696 (1.75426 iter/s, 4.56032s/8 iters), loss = 5.13619
I0401 13:43:03.763595  8859 solver.cpp:237]     Train net output #0: loss = 5.13619 (* 1 = 5.13619 loss)
I0401 13:43:03.763604  8859 sgd_solver.cpp:105] Iteration 696, lr = 0.001
I0401 13:43:07.565902  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_704.caffemodel
I0401 13:43:12.732551  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_704.solverstate
I0401 13:43:16.261920  8859 solver.cpp:330] Iteration 704, Testing net (#0)
I0401 13:43:16.261950  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:43:32.041219  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:43:32.912007  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0113189
I0401 13:43:32.912041  8859 solver.cpp:397]     Test net output #1: loss = 5.18152 (* 1 = 5.18152 loss)
I0401 13:43:33.063681  8859 solver.cpp:218] Iteration 704 (0.27304 iter/s, 29.2998s/8 iters), loss = 5.16237
I0401 13:43:33.063740  8859 solver.cpp:237]     Train net output #0: loss = 5.16237 (* 1 = 5.16237 loss)
I0401 13:43:33.063748  8859 sgd_solver.cpp:105] Iteration 704, lr = 0.001
I0401 13:43:36.341651  8859 solver.cpp:218] Iteration 712 (2.44062 iter/s, 3.27786s/8 iters), loss = 5.17029
I0401 13:43:36.347805  8859 solver.cpp:237]     Train net output #0: loss = 5.17029 (* 1 = 5.17029 loss)
I0401 13:43:36.347827  8859 sgd_solver.cpp:105] Iteration 712, lr = 0.001
I0401 13:43:40.862746  8859 solver.cpp:218] Iteration 720 (1.77191 iter/s, 4.51489s/8 iters), loss = 5.14727
I0401 13:43:40.862816  8859 solver.cpp:237]     Train net output #0: loss = 5.14727 (* 1 = 5.14727 loss)
I0401 13:43:40.862825  8859 sgd_solver.cpp:105] Iteration 720, lr = 0.001
I0401 13:43:45.198881  8859 solver.cpp:218] Iteration 728 (1.84502 iter/s, 4.336s/8 iters), loss = 5.13302
I0401 13:43:45.198936  8859 solver.cpp:237]     Train net output #0: loss = 5.13302 (* 1 = 5.13302 loss)
I0401 13:43:45.198945  8859 sgd_solver.cpp:105] Iteration 728, lr = 0.001
I0401 13:43:49.739321  8859 solver.cpp:218] Iteration 736 (1.76199 iter/s, 4.54032s/8 iters), loss = 5.23771
I0401 13:43:49.739387  8859 solver.cpp:237]     Train net output #0: loss = 5.23771 (* 1 = 5.23771 loss)
I0401 13:43:49.739395  8859 sgd_solver.cpp:105] Iteration 736, lr = 0.001
I0401 13:43:53.929508  8859 solver.cpp:218] Iteration 744 (1.90928 iter/s, 4.19007s/8 iters), loss = 5.15109
I0401 13:43:53.929548  8859 solver.cpp:237]     Train net output #0: loss = 5.15109 (* 1 = 5.15109 loss)
I0401 13:43:53.929553  8859 sgd_solver.cpp:105] Iteration 744, lr = 0.001
I0401 13:43:57.505998  8859 solver.cpp:218] Iteration 752 (2.23689 iter/s, 3.5764s/8 iters), loss = 5.14017
I0401 13:43:57.506039  8859 solver.cpp:237]     Train net output #0: loss = 5.14017 (* 1 = 5.14017 loss)
I0401 13:43:57.506044  8859 sgd_solver.cpp:105] Iteration 752, lr = 0.001
I0401 13:43:59.804567  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:44:01.021946  8859 solver.cpp:218] Iteration 760 (2.27541 iter/s, 3.51585s/8 iters), loss = 5.12566
I0401 13:44:01.021991  8859 solver.cpp:237]     Train net output #0: loss = 5.12566 (* 1 = 5.12566 loss)
I0401 13:44:01.021997  8859 sgd_solver.cpp:105] Iteration 760, lr = 0.001
I0401 13:44:04.082350  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_768.caffemodel
I0401 13:44:07.550899  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_768.solverstate
I0401 13:44:11.243335  8859 solver.cpp:330] Iteration 768, Testing net (#0)
I0401 13:44:11.243353  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:44:22.214197  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:44:23.009083  8859 solver.cpp:397]     Test net output #0: accuracy = 0.011811
I0401 13:44:23.009116  8859 solver.cpp:397]     Test net output #1: loss = 5.16269 (* 1 = 5.16269 loss)
I0401 13:44:23.150694  8859 solver.cpp:218] Iteration 768 (0.361525 iter/s, 22.1285s/8 iters), loss = 5.21524
I0401 13:44:23.152257  8859 solver.cpp:237]     Train net output #0: loss = 5.21524 (* 1 = 5.21524 loss)
I0401 13:44:23.152269  8859 sgd_solver.cpp:105] Iteration 768, lr = 0.001
I0401 13:44:25.736819  8859 solver.cpp:218] Iteration 776 (3.09535 iter/s, 2.58452s/8 iters), loss = 5.12329
I0401 13:44:25.736889  8859 solver.cpp:237]     Train net output #0: loss = 5.12329 (* 1 = 5.12329 loss)
I0401 13:44:25.736899  8859 sgd_solver.cpp:105] Iteration 776, lr = 0.001
I0401 13:44:29.238279  8859 solver.cpp:218] Iteration 784 (2.28484 iter/s, 3.50135s/8 iters), loss = 5.19309
I0401 13:44:29.238337  8859 solver.cpp:237]     Train net output #0: loss = 5.19309 (* 1 = 5.19309 loss)
I0401 13:44:29.238345  8859 sgd_solver.cpp:105] Iteration 784, lr = 0.001
I0401 13:44:32.834144  8859 solver.cpp:218] Iteration 792 (2.22485 iter/s, 3.59575s/8 iters), loss = 5.19029
I0401 13:44:32.834201  8859 solver.cpp:237]     Train net output #0: loss = 5.19029 (* 1 = 5.19029 loss)
I0401 13:44:32.834210  8859 sgd_solver.cpp:105] Iteration 792, lr = 0.001
I0401 13:44:36.403566  8859 solver.cpp:218] Iteration 800 (2.24133 iter/s, 3.56931s/8 iters), loss = 5.14475
I0401 13:44:36.403693  8859 solver.cpp:237]     Train net output #0: loss = 5.14475 (* 1 = 5.14475 loss)
I0401 13:44:36.403703  8859 sgd_solver.cpp:105] Iteration 800, lr = 0.001
I0401 13:44:40.032774  8859 solver.cpp:218] Iteration 808 (2.20445 iter/s, 3.62903s/8 iters), loss = 5.13707
I0401 13:44:40.032821  8859 solver.cpp:237]     Train net output #0: loss = 5.13707 (* 1 = 5.13707 loss)
I0401 13:44:40.032828  8859 sgd_solver.cpp:105] Iteration 808, lr = 0.001
I0401 13:44:43.777573  8859 solver.cpp:218] Iteration 816 (2.13635 iter/s, 3.7447s/8 iters), loss = 5.09445
I0401 13:44:43.777617  8859 solver.cpp:237]     Train net output #0: loss = 5.09445 (* 1 = 5.09445 loss)
I0401 13:44:43.777623  8859 sgd_solver.cpp:105] Iteration 816, lr = 0.001
I0401 13:44:45.858506  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:44:47.328974  8859 solver.cpp:218] Iteration 824 (2.2527 iter/s, 3.5513s/8 iters), loss = 5.13788
I0401 13:44:47.329027  8859 solver.cpp:237]     Train net output #0: loss = 5.13788 (* 1 = 5.13788 loss)
I0401 13:44:47.329033  8859 sgd_solver.cpp:105] Iteration 824, lr = 0.001
I0401 13:44:50.318317  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_832.caffemodel
I0401 13:44:53.417001  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_832.solverstate
I0401 13:44:55.744937  8859 solver.cpp:330] Iteration 832, Testing net (#0)
I0401 13:44:55.744958  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:45:06.765286  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:45:07.484678  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0140256
I0401 13:45:07.484710  8859 solver.cpp:397]     Test net output #1: loss = 5.15372 (* 1 = 5.15372 loss)
I0401 13:45:07.627450  8859 solver.cpp:218] Iteration 832 (0.394123 iter/s, 20.2982s/8 iters), loss = 5.12388
I0401 13:45:07.627493  8859 solver.cpp:237]     Train net output #0: loss = 5.12388 (* 1 = 5.12388 loss)
I0401 13:45:07.627499  8859 sgd_solver.cpp:105] Iteration 832, lr = 0.001
I0401 13:45:10.171787  8859 solver.cpp:218] Iteration 840 (3.14434 iter/s, 2.54425s/8 iters), loss = 5.12175
I0401 13:45:10.171828  8859 solver.cpp:237]     Train net output #0: loss = 5.12175 (* 1 = 5.12175 loss)
I0401 13:45:10.171834  8859 sgd_solver.cpp:105] Iteration 840, lr = 0.001
I0401 13:45:13.770539  8859 solver.cpp:218] Iteration 848 (2.22306 iter/s, 3.59865s/8 iters), loss = 5.14802
I0401 13:45:13.770592  8859 solver.cpp:237]     Train net output #0: loss = 5.14802 (* 1 = 5.14802 loss)
I0401 13:45:13.770601  8859 sgd_solver.cpp:105] Iteration 848, lr = 0.001
I0401 13:45:17.239404  8859 solver.cpp:218] Iteration 856 (2.3063 iter/s, 3.46876s/8 iters), loss = 5.14569
I0401 13:45:17.239454  8859 solver.cpp:237]     Train net output #0: loss = 5.14569 (* 1 = 5.14569 loss)
I0401 13:45:17.239462  8859 sgd_solver.cpp:105] Iteration 856, lr = 0.001
I0401 13:45:20.817049  8859 solver.cpp:218] Iteration 864 (2.23617 iter/s, 3.57754s/8 iters), loss = 5.14875
I0401 13:45:20.817121  8859 solver.cpp:237]     Train net output #0: loss = 5.14875 (* 1 = 5.14875 loss)
I0401 13:45:20.817131  8859 sgd_solver.cpp:105] Iteration 864, lr = 0.001
I0401 13:45:24.399749  8859 solver.cpp:218] Iteration 872 (2.23303 iter/s, 3.58257s/8 iters), loss = 5.1429
I0401 13:45:24.399806  8859 solver.cpp:237]     Train net output #0: loss = 5.1429 (* 1 = 5.1429 loss)
I0401 13:45:24.399813  8859 sgd_solver.cpp:105] Iteration 872, lr = 0.001
I0401 13:45:28.156515  8859 solver.cpp:218] Iteration 880 (2.12955 iter/s, 3.75665s/8 iters), loss = 5.05003
I0401 13:45:28.156565  8859 solver.cpp:237]     Train net output #0: loss = 5.05003 (* 1 = 5.05003 loss)
I0401 13:45:28.156575  8859 sgd_solver.cpp:105] Iteration 880, lr = 0.001
I0401 13:45:29.957688  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:45:31.823160  8859 solver.cpp:218] Iteration 888 (2.18189 iter/s, 3.66654s/8 iters), loss = 5.06024
I0401 13:45:31.823220  8859 solver.cpp:237]     Train net output #0: loss = 5.06024 (* 1 = 5.06024 loss)
I0401 13:45:31.823230  8859 sgd_solver.cpp:105] Iteration 888, lr = 0.001
I0401 13:45:34.842555  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_896.caffemodel
I0401 13:45:39.410888  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_896.solverstate
I0401 13:45:43.454187  8859 solver.cpp:330] Iteration 896, Testing net (#0)
I0401 13:45:43.454208  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:45:54.567358  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:45:55.351553  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0152559
I0401 13:45:55.351588  8859 solver.cpp:397]     Test net output #1: loss = 5.13324 (* 1 = 5.13324 loss)
I0401 13:45:55.487730  8859 solver.cpp:218] Iteration 896 (0.338062 iter/s, 23.6643s/8 iters), loss = 5.03946
I0401 13:45:55.487771  8859 solver.cpp:237]     Train net output #0: loss = 5.03946 (* 1 = 5.03946 loss)
I0401 13:45:55.487776  8859 sgd_solver.cpp:105] Iteration 896, lr = 0.001
I0401 13:45:58.102488  8859 solver.cpp:218] Iteration 904 (3.05966 iter/s, 2.61467s/8 iters), loss = 5.12366
I0401 13:45:58.102545  8859 solver.cpp:237]     Train net output #0: loss = 5.12366 (* 1 = 5.12366 loss)
I0401 13:45:58.102552  8859 sgd_solver.cpp:105] Iteration 904, lr = 0.001
I0401 13:46:01.469578  8859 solver.cpp:218] Iteration 912 (2.37601 iter/s, 3.36698s/8 iters), loss = 5.10154
I0401 13:46:01.469626  8859 solver.cpp:237]     Train net output #0: loss = 5.10154 (* 1 = 5.10154 loss)
I0401 13:46:01.469635  8859 sgd_solver.cpp:105] Iteration 912, lr = 0.001
I0401 13:46:05.123872  8859 solver.cpp:218] Iteration 920 (2.18927 iter/s, 3.65419s/8 iters), loss = 5.08203
I0401 13:46:05.123917  8859 solver.cpp:237]     Train net output #0: loss = 5.08203 (* 1 = 5.08203 loss)
I0401 13:46:05.123924  8859 sgd_solver.cpp:105] Iteration 920, lr = 0.001
I0401 13:46:08.714215  8859 solver.cpp:218] Iteration 928 (2.22826 iter/s, 3.59024s/8 iters), loss = 5.07402
I0401 13:46:08.714258  8859 solver.cpp:237]     Train net output #0: loss = 5.07402 (* 1 = 5.07402 loss)
I0401 13:46:08.714263  8859 sgd_solver.cpp:105] Iteration 928, lr = 0.001
I0401 13:46:12.298944  8859 solver.cpp:218] Iteration 936 (2.23175 iter/s, 3.58463s/8 iters), loss = 5.07969
I0401 13:46:12.299122  8859 solver.cpp:237]     Train net output #0: loss = 5.07969 (* 1 = 5.07969 loss)
I0401 13:46:12.299131  8859 sgd_solver.cpp:105] Iteration 936, lr = 0.001
I0401 13:46:15.818405  8859 solver.cpp:218] Iteration 944 (2.27322 iter/s, 3.51923s/8 iters), loss = 5.05293
I0401 13:46:15.818456  8859 solver.cpp:237]     Train net output #0: loss = 5.05293 (* 1 = 5.05293 loss)
I0401 13:46:15.818465  8859 sgd_solver.cpp:105] Iteration 944, lr = 0.001
I0401 13:46:17.112679  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:46:19.261312  8859 solver.cpp:218] Iteration 952 (2.32369 iter/s, 3.4428s/8 iters), loss = 5.02793
I0401 13:46:19.261355  8859 solver.cpp:237]     Train net output #0: loss = 5.02793 (* 1 = 5.02793 loss)
I0401 13:46:19.261361  8859 sgd_solver.cpp:105] Iteration 952, lr = 0.001
I0401 13:46:22.173166  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_960.caffemodel
I0401 13:46:25.198745  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_960.solverstate
I0401 13:46:27.833102  8859 solver.cpp:330] Iteration 960, Testing net (#0)
I0401 13:46:27.833124  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:46:38.427784  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:46:39.384174  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0169783
I0401 13:46:39.384208  8859 solver.cpp:397]     Test net output #1: loss = 5.11666 (* 1 = 5.11666 loss)
I0401 13:46:39.522858  8859 solver.cpp:218] Iteration 960 (0.394842 iter/s, 20.2613s/8 iters), loss = 5.01072
I0401 13:46:39.522902  8859 solver.cpp:237]     Train net output #0: loss = 5.01072 (* 1 = 5.01072 loss)
I0401 13:46:39.522907  8859 sgd_solver.cpp:105] Iteration 960, lr = 0.001
I0401 13:46:42.078613  8859 solver.cpp:218] Iteration 968 (3.1303 iter/s, 2.55567s/8 iters), loss = 5.0511
I0401 13:46:42.078666  8859 solver.cpp:237]     Train net output #0: loss = 5.0511 (* 1 = 5.0511 loss)
I0401 13:46:42.078673  8859 sgd_solver.cpp:105] Iteration 968, lr = 0.001
I0401 13:46:45.414244  8859 solver.cpp:218] Iteration 976 (2.39842 iter/s, 3.33552s/8 iters), loss = 5.03106
I0401 13:46:45.414356  8859 solver.cpp:237]     Train net output #0: loss = 5.03106 (* 1 = 5.03106 loss)
I0401 13:46:45.414366  8859 sgd_solver.cpp:105] Iteration 976, lr = 0.001
I0401 13:46:48.931429  8859 solver.cpp:218] Iteration 984 (2.27465 iter/s, 3.51703s/8 iters), loss = 5.09406
I0401 13:46:48.931471  8859 solver.cpp:237]     Train net output #0: loss = 5.09406 (* 1 = 5.09406 loss)
I0401 13:46:48.931478  8859 sgd_solver.cpp:105] Iteration 984, lr = 0.001
I0401 13:46:52.266489  8859 solver.cpp:218] Iteration 992 (2.39883 iter/s, 3.33496s/8 iters), loss = 5.116
I0401 13:46:52.266531  8859 solver.cpp:237]     Train net output #0: loss = 5.116 (* 1 = 5.116 loss)
I0401 13:46:52.266536  8859 sgd_solver.cpp:105] Iteration 992, lr = 0.001
I0401 13:46:55.882464  8859 solver.cpp:218] Iteration 1000 (2.21246 iter/s, 3.61588s/8 iters), loss = 4.96902
I0401 13:46:55.882504  8859 solver.cpp:237]     Train net output #0: loss = 4.96902 (* 1 = 4.96902 loss)
I0401 13:46:55.882509  8859 sgd_solver.cpp:105] Iteration 1000, lr = 0.001
I0401 13:46:59.312288  8859 solver.cpp:218] Iteration 1008 (2.33255 iter/s, 3.42973s/8 iters), loss = 5.06687
I0401 13:46:59.312341  8859 solver.cpp:237]     Train net output #0: loss = 5.06687 (* 1 = 5.06687 loss)
I0401 13:46:59.312350  8859 sgd_solver.cpp:105] Iteration 1008, lr = 0.001
I0401 13:47:00.353502  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:47:02.857605  8859 solver.cpp:218] Iteration 1016 (2.25656 iter/s, 3.54521s/8 iters), loss = 5.08208
I0401 13:47:02.857651  8859 solver.cpp:237]     Train net output #0: loss = 5.08208 (* 1 = 5.08208 loss)
I0401 13:47:02.857659  8859 sgd_solver.cpp:105] Iteration 1016, lr = 0.001
I0401 13:47:05.869077  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1024.caffemodel
I0401 13:47:08.953588  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1024.solverstate
I0401 13:47:11.248132  8859 solver.cpp:330] Iteration 1024, Testing net (#0)
I0401 13:47:11.248150  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:47:15.242534  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:47:22.208127  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:47:23.085472  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0174705
I0401 13:47:23.085502  8859 solver.cpp:397]     Test net output #1: loss = 5.10431 (* 1 = 5.10431 loss)
I0401 13:47:23.227185  8859 solver.cpp:218] Iteration 1024 (0.392748 iter/s, 20.3693s/8 iters), loss = 5.04089
I0401 13:47:23.227239  8859 solver.cpp:237]     Train net output #0: loss = 5.04089 (* 1 = 5.04089 loss)
I0401 13:47:23.227247  8859 sgd_solver.cpp:105] Iteration 1024, lr = 0.001
I0401 13:47:25.801255  8859 solver.cpp:218] Iteration 1032 (3.10803 iter/s, 2.57398s/8 iters), loss = 4.9902
I0401 13:47:25.801306  8859 solver.cpp:237]     Train net output #0: loss = 4.9902 (* 1 = 4.9902 loss)
I0401 13:47:25.801314  8859 sgd_solver.cpp:105] Iteration 1032, lr = 0.001
I0401 13:47:29.068944  8859 solver.cpp:218] Iteration 1040 (2.44829 iter/s, 3.26758s/8 iters), loss = 5.02185
I0401 13:47:29.069022  8859 solver.cpp:237]     Train net output #0: loss = 5.02185 (* 1 = 5.02185 loss)
I0401 13:47:29.069031  8859 sgd_solver.cpp:105] Iteration 1040, lr = 0.001
I0401 13:47:32.590377  8859 solver.cpp:218] Iteration 1048 (2.27187 iter/s, 3.52133s/8 iters), loss = 5.11289
I0401 13:47:32.590436  8859 solver.cpp:237]     Train net output #0: loss = 5.11289 (* 1 = 5.11289 loss)
I0401 13:47:32.590445  8859 sgd_solver.cpp:105] Iteration 1048, lr = 0.001
I0401 13:47:36.051172  8859 solver.cpp:218] Iteration 1056 (2.31168 iter/s, 3.46068s/8 iters), loss = 5.04277
I0401 13:47:36.051215  8859 solver.cpp:237]     Train net output #0: loss = 5.04277 (* 1 = 5.04277 loss)
I0401 13:47:36.051221  8859 sgd_solver.cpp:105] Iteration 1056, lr = 0.001
I0401 13:47:39.627372  8859 solver.cpp:218] Iteration 1064 (2.23707 iter/s, 3.5761s/8 iters), loss = 5.05309
I0401 13:47:39.627427  8859 solver.cpp:237]     Train net output #0: loss = 5.05309 (* 1 = 5.05309 loss)
I0401 13:47:39.627436  8859 sgd_solver.cpp:105] Iteration 1064, lr = 0.001
I0401 13:47:43.231632  8859 solver.cpp:218] Iteration 1072 (2.21966 iter/s, 3.60415s/8 iters), loss = 5.00223
I0401 13:47:43.231685  8859 solver.cpp:237]     Train net output #0: loss = 5.00223 (* 1 = 5.00223 loss)
I0401 13:47:43.231694  8859 sgd_solver.cpp:105] Iteration 1072, lr = 0.001
I0401 13:47:43.880518  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:47:46.763666  8859 solver.cpp:218] Iteration 1080 (2.26505 iter/s, 3.53193s/8 iters), loss = 5.01278
I0401 13:47:46.763717  8859 solver.cpp:237]     Train net output #0: loss = 5.01278 (* 1 = 5.01278 loss)
I0401 13:47:46.763726  8859 sgd_solver.cpp:105] Iteration 1080, lr = 0.001
I0401 13:47:49.757928  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1088.caffemodel
I0401 13:47:52.957286  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1088.solverstate
I0401 13:47:55.259218  8859 solver.cpp:330] Iteration 1088, Testing net (#0)
I0401 13:47:55.259241  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:48:05.841955  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:48:06.803709  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0177165
I0401 13:48:06.803747  8859 solver.cpp:397]     Test net output #1: loss = 5.08844 (* 1 = 5.08844 loss)
I0401 13:48:06.952549  8859 solver.cpp:218] Iteration 1088 (0.396263 iter/s, 20.1886s/8 iters), loss = 5.10939
I0401 13:48:06.954133  8859 solver.cpp:237]     Train net output #0: loss = 5.10939 (* 1 = 5.10939 loss)
I0401 13:48:06.954149  8859 sgd_solver.cpp:105] Iteration 1088, lr = 0.001
I0401 13:48:09.720554  8859 solver.cpp:218] Iteration 1096 (2.89186 iter/s, 2.76639s/8 iters), loss = 5.11154
I0401 13:48:09.720611  8859 solver.cpp:237]     Train net output #0: loss = 5.11154 (* 1 = 5.11154 loss)
I0401 13:48:09.720620  8859 sgd_solver.cpp:105] Iteration 1096, lr = 0.001
I0401 13:48:13.267242  8859 solver.cpp:218] Iteration 1104 (2.25569 iter/s, 3.54658s/8 iters), loss = 5.07452
I0401 13:48:13.267284  8859 solver.cpp:237]     Train net output #0: loss = 5.07452 (* 1 = 5.07452 loss)
I0401 13:48:13.267290  8859 sgd_solver.cpp:105] Iteration 1104, lr = 0.001
I0401 13:48:16.758096  8859 solver.cpp:218] Iteration 1112 (2.29176 iter/s, 3.49076s/8 iters), loss = 4.99979
I0401 13:48:16.758134  8859 solver.cpp:237]     Train net output #0: loss = 4.99979 (* 1 = 4.99979 loss)
I0401 13:48:16.758141  8859 sgd_solver.cpp:105] Iteration 1112, lr = 0.001
I0401 13:48:20.122526  8859 solver.cpp:218] Iteration 1120 (2.37788 iter/s, 3.36434s/8 iters), loss = 5.07174
I0401 13:48:20.122565  8859 solver.cpp:237]     Train net output #0: loss = 5.07174 (* 1 = 5.07174 loss)
I0401 13:48:20.122570  8859 sgd_solver.cpp:105] Iteration 1120, lr = 0.001
I0401 13:48:23.743714  8859 solver.cpp:218] Iteration 1128 (2.20928 iter/s, 3.6211s/8 iters), loss = 5.12151
I0401 13:48:23.743844  8859 solver.cpp:237]     Train net output #0: loss = 5.12151 (* 1 = 5.12151 loss)
I0401 13:48:23.743851  8859 sgd_solver.cpp:105] Iteration 1128, lr = 0.001
I0401 13:48:27.473374  8859 solver.cpp:218] Iteration 1136 (2.14507 iter/s, 3.72948s/8 iters), loss = 4.99267
I0401 13:48:27.473431  8859 solver.cpp:237]     Train net output #0: loss = 4.99267 (* 1 = 4.99267 loss)
I0401 13:48:27.473440  8859 sgd_solver.cpp:105] Iteration 1136, lr = 0.001
I0401 13:48:27.742533  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:48:31.113170  8859 solver.cpp:218] Iteration 1144 (2.19799 iter/s, 3.63969s/8 iters), loss = 5.01252
I0401 13:48:31.113209  8859 solver.cpp:237]     Train net output #0: loss = 5.01252 (* 1 = 5.01252 loss)
I0401 13:48:31.113215  8859 sgd_solver.cpp:105] Iteration 1144, lr = 0.001
I0401 13:48:34.440260  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1152.caffemodel
I0401 13:48:37.670433  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1152.solverstate
I0401 13:48:40.034960  8859 solver.cpp:330] Iteration 1152, Testing net (#0)
I0401 13:48:40.034983  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:48:51.854085  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:48:53.100579  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0177165
I0401 13:48:53.100616  8859 solver.cpp:397]     Test net output #1: loss = 5.07645 (* 1 = 5.07645 loss)
I0401 13:48:53.251754  8859 solver.cpp:218] Iteration 1152 (0.361365 iter/s, 22.1383s/8 iters), loss = 5.11696
I0401 13:48:53.251812  8859 solver.cpp:237]     Train net output #0: loss = 5.11696 (* 1 = 5.11696 loss)
I0401 13:48:53.251821  8859 sgd_solver.cpp:105] Iteration 1152, lr = 0.001
I0401 13:48:59.444020  8859 solver.cpp:218] Iteration 1160 (1.29196 iter/s, 6.19212s/8 iters), loss = 5.07973
I0401 13:48:59.456964  8859 solver.cpp:237]     Train net output #0: loss = 5.07973 (* 1 = 5.07973 loss)
I0401 13:48:59.456979  8859 sgd_solver.cpp:105] Iteration 1160, lr = 0.001
I0401 13:49:06.302889  8859 solver.cpp:218] Iteration 1168 (1.16859 iter/s, 6.84585s/8 iters), loss = 5.02533
I0401 13:49:06.302951  8859 solver.cpp:237]     Train net output #0: loss = 5.02533 (* 1 = 5.02533 loss)
I0401 13:49:06.302960  8859 sgd_solver.cpp:105] Iteration 1168, lr = 0.001
I0401 13:49:11.190682  8859 solver.cpp:218] Iteration 1176 (1.63677 iter/s, 4.88766s/8 iters), loss = 5.0266
I0401 13:49:11.190740  8859 solver.cpp:237]     Train net output #0: loss = 5.0266 (* 1 = 5.0266 loss)
I0401 13:49:11.190748  8859 sgd_solver.cpp:105] Iteration 1176, lr = 0.001
I0401 13:49:17.920933  8859 solver.cpp:218] Iteration 1184 (1.19195 iter/s, 6.71167s/8 iters), loss = 4.95921
I0401 13:49:17.920986  8859 solver.cpp:237]     Train net output #0: loss = 4.95921 (* 1 = 4.95921 loss)
I0401 13:49:17.920996  8859 sgd_solver.cpp:105] Iteration 1184, lr = 0.001
I0401 13:49:23.128933  8859 solver.cpp:218] Iteration 1192 (1.53672 iter/s, 5.20591s/8 iters), loss = 5.02893
I0401 13:49:23.128984  8859 solver.cpp:237]     Train net output #0: loss = 5.02893 (* 1 = 5.02893 loss)
I0401 13:49:23.128993  8859 sgd_solver.cpp:105] Iteration 1192, lr = 0.001
I0401 13:49:27.604530  8859 solver.cpp:218] Iteration 1200 (1.78752 iter/s, 4.47548s/8 iters), loss = 5.04355
I0401 13:49:27.604593  8859 solver.cpp:237]     Train net output #0: loss = 5.04355 (* 1 = 5.04355 loss)
I0401 13:49:27.604602  8859 sgd_solver.cpp:105] Iteration 1200, lr = 0.001
I0401 13:49:27.661989  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:49:32.516989  8859 solver.cpp:218] Iteration 1208 (1.62856 iter/s, 4.91233s/8 iters), loss = 4.92101
I0401 13:49:32.525918  8859 solver.cpp:237]     Train net output #0: loss = 4.92101 (* 1 = 4.92101 loss)
I0401 13:49:32.525933  8859 sgd_solver.cpp:105] Iteration 1208, lr = 0.001
I0401 13:49:36.463712  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1216.caffemodel
I0401 13:49:40.494189  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1216.solverstate
I0401 13:49:43.340528  8859 solver.cpp:330] Iteration 1216, Testing net (#0)
I0401 13:49:43.340550  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:49:58.325328  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:49:59.898039  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0187008
I0401 13:49:59.898079  8859 solver.cpp:397]     Test net output #1: loss = 5.06851 (* 1 = 5.06851 loss)
I0401 13:50:00.063575  8859 solver.cpp:218] Iteration 1216 (0.290514 iter/s, 27.5374s/8 iters), loss = 5.07747
I0401 13:50:00.063624  8859 solver.cpp:237]     Train net output #0: loss = 5.07747 (* 1 = 5.07747 loss)
I0401 13:50:00.063632  8859 sgd_solver.cpp:105] Iteration 1216, lr = 0.001
I0401 13:50:03.100935  8859 solver.cpp:218] Iteration 1224 (2.63931 iter/s, 3.03109s/8 iters), loss = 5.10878
I0401 13:50:03.101059  8859 solver.cpp:237]     Train net output #0: loss = 5.10878 (* 1 = 5.10878 loss)
I0401 13:50:03.101068  8859 sgd_solver.cpp:105] Iteration 1224, lr = 0.001
I0401 13:50:07.452140  8859 solver.cpp:218] Iteration 1232 (1.83865 iter/s, 4.35102s/8 iters), loss = 5.00898
I0401 13:50:07.452199  8859 solver.cpp:237]     Train net output #0: loss = 5.00898 (* 1 = 5.00898 loss)
I0401 13:50:07.452208  8859 sgd_solver.cpp:105] Iteration 1232, lr = 0.001
I0401 13:50:11.754758  8859 solver.cpp:218] Iteration 1240 (1.85939 iter/s, 4.3025s/8 iters), loss = 5.08734
I0401 13:50:11.754813  8859 solver.cpp:237]     Train net output #0: loss = 5.08734 (* 1 = 5.08734 loss)
I0401 13:50:11.754822  8859 sgd_solver.cpp:105] Iteration 1240, lr = 0.001
I0401 13:50:16.323107  8859 solver.cpp:218] Iteration 1248 (1.75123 iter/s, 4.56823s/8 iters), loss = 5.02191
I0401 13:50:16.329275  8859 solver.cpp:237]     Train net output #0: loss = 5.02191 (* 1 = 5.02191 loss)
I0401 13:50:16.329308  8859 sgd_solver.cpp:105] Iteration 1248, lr = 0.001
I0401 13:50:20.876379  8859 solver.cpp:218] Iteration 1256 (1.75938 iter/s, 4.54706s/8 iters), loss = 4.95876
I0401 13:50:20.876431  8859 solver.cpp:237]     Train net output #0: loss = 4.95876 (* 1 = 4.95876 loss)
I0401 13:50:20.876441  8859 sgd_solver.cpp:105] Iteration 1256, lr = 0.001
I0401 13:50:24.732244  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:50:25.164762  8859 solver.cpp:218] Iteration 1264 (1.86556 iter/s, 4.28827s/8 iters), loss = 4.99203
I0401 13:50:25.164937  8859 solver.cpp:237]     Train net output #0: loss = 4.99203 (* 1 = 4.99203 loss)
I0401 13:50:25.164947  8859 sgd_solver.cpp:105] Iteration 1264, lr = 0.001
I0401 13:50:29.821847  8859 solver.cpp:218] Iteration 1272 (1.7179 iter/s, 4.65684s/8 iters), loss = 5.01587
I0401 13:50:29.821908  8859 solver.cpp:237]     Train net output #0: loss = 5.01587 (* 1 = 5.01587 loss)
I0401 13:50:29.821919  8859 sgd_solver.cpp:105] Iteration 1272, lr = 0.001
I0401 13:50:33.475975  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1280.caffemodel
I0401 13:50:39.227062  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1280.solverstate
I0401 13:50:42.111057  8859 solver.cpp:330] Iteration 1280, Testing net (#0)
I0401 13:50:42.111081  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:50:54.964990  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:50:56.139503  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0187008
I0401 13:50:56.139554  8859 solver.cpp:397]     Test net output #1: loss = 5.06444 (* 1 = 5.06444 loss)
I0401 13:50:56.271450  8859 solver.cpp:218] Iteration 1280 (0.302466 iter/s, 26.4493s/8 iters), loss = 5.04824
I0401 13:50:56.271502  8859 solver.cpp:237]     Train net output #0: loss = 5.04824 (* 1 = 5.04824 loss)
I0401 13:50:56.271508  8859 sgd_solver.cpp:105] Iteration 1280, lr = 0.001
I0401 13:50:58.887665  8859 solver.cpp:218] Iteration 1288 (3.05796 iter/s, 2.61613s/8 iters), loss = 5.04207
I0401 13:50:58.887704  8859 solver.cpp:237]     Train net output #0: loss = 5.04207 (* 1 = 5.04207 loss)
I0401 13:50:58.887709  8859 sgd_solver.cpp:105] Iteration 1288, lr = 0.001
I0401 13:51:02.411543  8859 solver.cpp:218] Iteration 1296 (2.27029 iter/s, 3.52378s/8 iters), loss = 5.00559
I0401 13:51:02.411599  8859 solver.cpp:237]     Train net output #0: loss = 5.00559 (* 1 = 5.00559 loss)
I0401 13:51:02.411608  8859 sgd_solver.cpp:105] Iteration 1296, lr = 0.001
I0401 13:51:06.103768  8859 solver.cpp:218] Iteration 1304 (2.16678 iter/s, 3.69211s/8 iters), loss = 5.12664
I0401 13:51:06.103919  8859 solver.cpp:237]     Train net output #0: loss = 5.12664 (* 1 = 5.12664 loss)
I0401 13:51:06.103929  8859 sgd_solver.cpp:105] Iteration 1304, lr = 0.001
I0401 13:51:09.776525  8859 solver.cpp:218] Iteration 1312 (2.17832 iter/s, 3.67255s/8 iters), loss = 4.93892
I0401 13:51:09.776585  8859 solver.cpp:237]     Train net output #0: loss = 4.93892 (* 1 = 4.93892 loss)
I0401 13:51:09.776594  8859 sgd_solver.cpp:105] Iteration 1312, lr = 0.001
I0401 13:51:13.693189  8859 solver.cpp:218] Iteration 1320 (2.04261 iter/s, 3.91655s/8 iters), loss = 5.03496
I0401 13:51:13.693243  8859 solver.cpp:237]     Train net output #0: loss = 5.03496 (* 1 = 5.03496 loss)
I0401 13:51:13.693250  8859 sgd_solver.cpp:105] Iteration 1320, lr = 0.001
I0401 13:51:16.514387  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:51:17.280387  8859 solver.cpp:218] Iteration 1328 (2.23022 iter/s, 3.58709s/8 iters), loss = 4.92914
I0401 13:51:17.280443  8859 solver.cpp:237]     Train net output #0: loss = 4.92914 (* 1 = 4.92914 loss)
I0401 13:51:17.280452  8859 sgd_solver.cpp:105] Iteration 1328, lr = 0.001
I0401 13:51:20.984974  8859 solver.cpp:218] Iteration 1336 (2.15955 iter/s, 3.70448s/8 iters), loss = 5.03061
I0401 13:51:20.985020  8859 solver.cpp:237]     Train net output #0: loss = 5.03061 (* 1 = 5.03061 loss)
I0401 13:51:20.985026  8859 sgd_solver.cpp:105] Iteration 1336, lr = 0.001
I0401 13:51:24.076059  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1344.caffemodel
I0401 13:51:27.085760  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1344.solverstate
I0401 13:51:30.087152  8859 solver.cpp:330] Iteration 1344, Testing net (#0)
I0401 13:51:30.087179  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:51:40.570844  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:51:41.094435  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:51:41.810714  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0199311
I0401 13:51:41.810750  8859 solver.cpp:397]     Test net output #1: loss = 5.0553 (* 1 = 5.0553 loss)
I0401 13:51:41.959271  8859 solver.cpp:218] Iteration 1344 (0.381424 iter/s, 20.974s/8 iters), loss = 4.92923
I0401 13:51:41.959312  8859 solver.cpp:237]     Train net output #0: loss = 4.92923 (* 1 = 4.92923 loss)
I0401 13:51:41.959318  8859 sgd_solver.cpp:105] Iteration 1344, lr = 0.001
I0401 13:51:44.541541  8859 solver.cpp:218] Iteration 1352 (3.09815 iter/s, 2.58218s/8 iters), loss = 4.99309
I0401 13:51:44.541584  8859 solver.cpp:237]     Train net output #0: loss = 4.99309 (* 1 = 4.99309 loss)
I0401 13:51:44.541590  8859 sgd_solver.cpp:105] Iteration 1352, lr = 0.001
I0401 13:51:47.824813  8859 solver.cpp:218] Iteration 1360 (2.43666 iter/s, 3.28318s/8 iters), loss = 4.9068
I0401 13:51:47.824858  8859 solver.cpp:237]     Train net output #0: loss = 4.9068 (* 1 = 4.9068 loss)
I0401 13:51:47.824864  8859 sgd_solver.cpp:105] Iteration 1360, lr = 0.001
I0401 13:51:51.409345  8859 solver.cpp:218] Iteration 1368 (2.23188 iter/s, 3.58443s/8 iters), loss = 5.09757
I0401 13:51:51.409402  8859 solver.cpp:237]     Train net output #0: loss = 5.09757 (* 1 = 5.09757 loss)
I0401 13:51:51.409411  8859 sgd_solver.cpp:105] Iteration 1368, lr = 0.001
I0401 13:51:54.626221  8859 solver.cpp:218] Iteration 1376 (2.48697 iter/s, 3.21677s/8 iters), loss = 4.97736
I0401 13:51:54.626272  8859 solver.cpp:237]     Train net output #0: loss = 4.97736 (* 1 = 4.97736 loss)
I0401 13:51:54.626282  8859 sgd_solver.cpp:105] Iteration 1376, lr = 0.001
I0401 13:51:58.096343  8859 solver.cpp:218] Iteration 1384 (2.30546 iter/s, 3.47002s/8 iters), loss = 4.95653
I0401 13:51:58.096382  8859 solver.cpp:237]     Train net output #0: loss = 4.95653 (* 1 = 4.95653 loss)
I0401 13:51:58.096388  8859 sgd_solver.cpp:105] Iteration 1384, lr = 0.001
I0401 13:52:00.798203  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:52:01.904927  8859 solver.cpp:218] Iteration 1392 (2.10057 iter/s, 3.80848s/8 iters), loss = 5.00867
I0401 13:52:01.904991  8859 solver.cpp:237]     Train net output #0: loss = 5.00867 (* 1 = 5.00867 loss)
I0401 13:52:01.904999  8859 sgd_solver.cpp:105] Iteration 1392, lr = 0.001
I0401 13:52:05.423854  8859 solver.cpp:218] Iteration 1400 (2.27349 iter/s, 3.51881s/8 iters), loss = 4.89387
I0401 13:52:05.423894  8859 solver.cpp:237]     Train net output #0: loss = 4.89387 (* 1 = 4.89387 loss)
I0401 13:52:05.423900  8859 sgd_solver.cpp:105] Iteration 1400, lr = 0.001
I0401 13:52:08.427989  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1408.caffemodel
I0401 13:52:11.413249  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1408.solverstate
I0401 13:52:13.710888  8859 solver.cpp:330] Iteration 1408, Testing net (#0)
I0401 13:52:13.710911  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:52:24.402985  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:52:25.639804  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0211614
I0401 13:52:25.639844  8859 solver.cpp:397]     Test net output #1: loss = 5.04833 (* 1 = 5.04833 loss)
I0401 13:52:25.775836  8859 solver.cpp:218] Iteration 1408 (0.393087 iter/s, 20.3517s/8 iters), loss = 4.91231
I0401 13:52:25.775874  8859 solver.cpp:237]     Train net output #0: loss = 4.91231 (* 1 = 4.91231 loss)
I0401 13:52:25.775879  8859 sgd_solver.cpp:105] Iteration 1408, lr = 0.001
I0401 13:52:28.350356  8859 solver.cpp:218] Iteration 1416 (3.10747 iter/s, 2.57444s/8 iters), loss = 4.93204
I0401 13:52:28.350394  8859 solver.cpp:237]     Train net output #0: loss = 4.93204 (* 1 = 4.93204 loss)
I0401 13:52:28.350399  8859 sgd_solver.cpp:105] Iteration 1416, lr = 0.001
I0401 13:52:31.873317  8859 solver.cpp:218] Iteration 1424 (2.27088 iter/s, 3.52286s/8 iters), loss = 5.04059
I0401 13:52:31.873373  8859 solver.cpp:237]     Train net output #0: loss = 5.04059 (* 1 = 5.04059 loss)
I0401 13:52:31.873381  8859 sgd_solver.cpp:105] Iteration 1424, lr = 0.001
I0401 13:52:35.353503  8859 solver.cpp:218] Iteration 1432 (2.2988 iter/s, 3.48008s/8 iters), loss = 5.05123
I0401 13:52:35.353564  8859 solver.cpp:237]     Train net output #0: loss = 5.05123 (* 1 = 5.05123 loss)
I0401 13:52:35.353575  8859 sgd_solver.cpp:105] Iteration 1432, lr = 0.001
I0401 13:52:38.995971  8859 solver.cpp:218] Iteration 1440 (2.19638 iter/s, 3.64236s/8 iters), loss = 4.98357
I0401 13:52:38.996023  8859 solver.cpp:237]     Train net output #0: loss = 4.98357 (* 1 = 4.98357 loss)
I0401 13:52:38.996031  8859 sgd_solver.cpp:105] Iteration 1440, lr = 0.001
I0401 13:52:42.520642  8859 solver.cpp:218] Iteration 1448 (2.26979 iter/s, 3.52456s/8 iters), loss = 4.84895
I0401 13:52:42.520797  8859 solver.cpp:237]     Train net output #0: loss = 4.84895 (* 1 = 4.84895 loss)
I0401 13:52:42.520807  8859 sgd_solver.cpp:105] Iteration 1448, lr = 0.001
I0401 13:52:44.772841  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:52:46.088066  8859 solver.cpp:218] Iteration 1456 (2.24264 iter/s, 3.56722s/8 iters), loss = 5.00638
I0401 13:52:46.088107  8859 solver.cpp:237]     Train net output #0: loss = 5.00638 (* 1 = 5.00638 loss)
I0401 13:52:46.088112  8859 sgd_solver.cpp:105] Iteration 1456, lr = 0.001
I0401 13:52:49.779886  8859 solver.cpp:218] Iteration 1464 (2.16701 iter/s, 3.69172s/8 iters), loss = 4.96737
I0401 13:52:49.779934  8859 solver.cpp:237]     Train net output #0: loss = 4.96737 (* 1 = 4.96737 loss)
I0401 13:52:49.779940  8859 sgd_solver.cpp:105] Iteration 1464, lr = 0.001
I0401 13:52:52.631295  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1472.caffemodel
I0401 13:52:55.759582  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1472.solverstate
I0401 13:52:58.050869  8859 solver.cpp:330] Iteration 1472, Testing net (#0)
I0401 13:52:58.050892  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:53:08.351212  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:53:09.658572  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0226378
I0401 13:53:09.658612  8859 solver.cpp:397]     Test net output #1: loss = 5.04274 (* 1 = 5.04274 loss)
I0401 13:53:09.800530  8859 solver.cpp:218] Iteration 1472 (0.399593 iter/s, 20.0204s/8 iters), loss = 4.92243
I0401 13:53:09.802091  8859 solver.cpp:237]     Train net output #0: loss = 4.92243 (* 1 = 4.92243 loss)
I0401 13:53:09.802103  8859 sgd_solver.cpp:105] Iteration 1472, lr = 0.001
I0401 13:53:12.420609  8859 solver.cpp:218] Iteration 1480 (3.05521 iter/s, 2.61848s/8 iters), loss = 4.96272
I0401 13:53:12.420661  8859 solver.cpp:237]     Train net output #0: loss = 4.96272 (* 1 = 4.96272 loss)
I0401 13:53:12.420670  8859 sgd_solver.cpp:105] Iteration 1480, lr = 0.001
I0401 13:53:15.954501  8859 solver.cpp:218] Iteration 1488 (2.26386 iter/s, 3.53379s/8 iters), loss = 5.0287
I0401 13:53:15.954588  8859 solver.cpp:237]     Train net output #0: loss = 5.0287 (* 1 = 5.0287 loss)
I0401 13:53:15.954596  8859 sgd_solver.cpp:105] Iteration 1488, lr = 0.001
I0401 13:53:19.387162  8859 solver.cpp:218] Iteration 1496 (2.33065 iter/s, 3.43252s/8 iters), loss = 4.9827
I0401 13:53:19.387213  8859 solver.cpp:237]     Train net output #0: loss = 4.9827 (* 1 = 4.9827 loss)
I0401 13:53:19.387221  8859 sgd_solver.cpp:105] Iteration 1496, lr = 0.001
I0401 13:53:22.829134  8859 solver.cpp:218] Iteration 1504 (2.32432 iter/s, 3.44187s/8 iters), loss = 4.94878
I0401 13:53:22.829190  8859 solver.cpp:237]     Train net output #0: loss = 4.94878 (* 1 = 4.94878 loss)
I0401 13:53:22.829198  8859 sgd_solver.cpp:105] Iteration 1504, lr = 0.001
I0401 13:53:26.300664  8859 solver.cpp:218] Iteration 1512 (2.30453 iter/s, 3.47142s/8 iters), loss = 4.90806
I0401 13:53:26.300705  8859 solver.cpp:237]     Train net output #0: loss = 4.90806 (* 1 = 4.90806 loss)
I0401 13:53:26.300711  8859 sgd_solver.cpp:105] Iteration 1512, lr = 0.001
I0401 13:53:28.064136  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:53:29.610095  8859 solver.cpp:218] Iteration 1520 (2.4174 iter/s, 3.30934s/8 iters), loss = 4.95718
I0401 13:53:29.610138  8859 solver.cpp:237]     Train net output #0: loss = 4.95718 (* 1 = 4.95718 loss)
I0401 13:53:29.610143  8859 sgd_solver.cpp:105] Iteration 1520, lr = 0.001
I0401 13:53:33.076468  8859 solver.cpp:218] Iteration 1528 (2.30795 iter/s, 3.46628s/8 iters), loss = 4.81523
I0401 13:53:33.076517  8859 solver.cpp:237]     Train net output #0: loss = 4.81523 (* 1 = 4.81523 loss)
I0401 13:53:33.076522  8859 sgd_solver.cpp:105] Iteration 1528, lr = 0.001
I0401 13:53:36.294037  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1536.caffemodel
I0401 13:53:41.135290  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1536.solverstate
I0401 13:53:44.212865  8859 solver.cpp:330] Iteration 1536, Testing net (#0)
I0401 13:53:44.212891  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:53:54.599979  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:53:55.897583  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0228839
I0401 13:53:55.897619  8859 solver.cpp:397]     Test net output #1: loss = 5.03436 (* 1 = 5.03436 loss)
I0401 13:53:56.039615  8859 solver.cpp:218] Iteration 1536 (0.348389 iter/s, 22.9629s/8 iters), loss = 4.93348
I0401 13:53:56.039680  8859 solver.cpp:237]     Train net output #0: loss = 4.93348 (* 1 = 4.93348 loss)
I0401 13:53:56.039686  8859 sgd_solver.cpp:105] Iteration 1536, lr = 0.001
I0401 13:53:58.783180  8859 solver.cpp:218] Iteration 1544 (2.91603 iter/s, 2.74345s/8 iters), loss = 4.98547
I0401 13:53:58.783236  8859 solver.cpp:237]     Train net output #0: loss = 4.98547 (* 1 = 4.98547 loss)
I0401 13:53:58.783243  8859 sgd_solver.cpp:105] Iteration 1544, lr = 0.001
I0401 13:54:02.322850  8859 solver.cpp:218] Iteration 1552 (2.26017 iter/s, 3.53956s/8 iters), loss = 4.9319
I0401 13:54:02.329007  8859 solver.cpp:237]     Train net output #0: loss = 4.9319 (* 1 = 4.9319 loss)
I0401 13:54:02.329027  8859 sgd_solver.cpp:105] Iteration 1552, lr = 0.001
I0401 13:54:06.077805  8859 solver.cpp:218] Iteration 1560 (2.13404 iter/s, 3.74876s/8 iters), loss = 4.86927
I0401 13:54:06.077843  8859 solver.cpp:237]     Train net output #0: loss = 4.86927 (* 1 = 4.86927 loss)
I0401 13:54:06.077848  8859 sgd_solver.cpp:105] Iteration 1560, lr = 0.001
I0401 13:54:09.377251  8859 solver.cpp:218] Iteration 1568 (2.42472 iter/s, 3.29936s/8 iters), loss = 4.95882
I0401 13:54:09.377302  8859 solver.cpp:237]     Train net output #0: loss = 4.95882 (* 1 = 4.95882 loss)
I0401 13:54:09.377310  8859 sgd_solver.cpp:105] Iteration 1568, lr = 0.001
I0401 13:54:12.856855  8859 solver.cpp:218] Iteration 1576 (2.29918 iter/s, 3.4795s/8 iters), loss = 4.88371
I0401 13:54:12.856920  8859 solver.cpp:237]     Train net output #0: loss = 4.88371 (* 1 = 4.88371 loss)
I0401 13:54:12.856925  8859 sgd_solver.cpp:105] Iteration 1576, lr = 0.001
I0401 13:54:14.396867  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:54:16.458503  8859 solver.cpp:218] Iteration 1584 (2.22128 iter/s, 3.60153s/8 iters), loss = 4.7539
I0401 13:54:16.458550  8859 solver.cpp:237]     Train net output #0: loss = 4.7539 (* 1 = 4.7539 loss)
I0401 13:54:16.458556  8859 sgd_solver.cpp:105] Iteration 1584, lr = 0.001
I0401 13:54:20.211189  8859 solver.cpp:218] Iteration 1592 (2.13186 iter/s, 3.75258s/8 iters), loss = 4.83851
I0401 13:54:20.211230  8859 solver.cpp:237]     Train net output #0: loss = 4.83851 (* 1 = 4.83851 loss)
I0401 13:54:20.211236  8859 sgd_solver.cpp:105] Iteration 1592, lr = 0.001
I0401 13:54:23.465106  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1600.caffemodel
I0401 13:54:27.887764  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1600.solverstate
I0401 13:54:31.282529  8859 solver.cpp:330] Iteration 1600, Testing net (#0)
I0401 13:54:31.282553  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:54:41.731336  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:54:43.103052  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0238681
I0401 13:54:43.103081  8859 solver.cpp:397]     Test net output #1: loss = 5.0346 (* 1 = 5.0346 loss)
I0401 13:54:43.238102  8859 solver.cpp:218] Iteration 1600 (0.347424 iter/s, 23.0266s/8 iters), loss = 4.90579
I0401 13:54:43.238160  8859 solver.cpp:237]     Train net output #0: loss = 4.90579 (* 1 = 4.90579 loss)
I0401 13:54:43.238168  8859 sgd_solver.cpp:105] Iteration 1600, lr = 0.001
I0401 13:54:45.863353  8859 solver.cpp:218] Iteration 1608 (3.04745 iter/s, 2.62514s/8 iters), loss = 4.87657
I0401 13:54:45.863410  8859 solver.cpp:237]     Train net output #0: loss = 4.87657 (* 1 = 4.87657 loss)
I0401 13:54:45.863418  8859 sgd_solver.cpp:105] Iteration 1608, lr = 0.001
I0401 13:54:49.360672  8859 solver.cpp:218] Iteration 1616 (2.28753 iter/s, 3.49721s/8 iters), loss = 4.96685
I0401 13:54:49.360730  8859 solver.cpp:237]     Train net output #0: loss = 4.96685 (* 1 = 4.96685 loss)
I0401 13:54:49.360740  8859 sgd_solver.cpp:105] Iteration 1616, lr = 0.001
I0401 13:54:52.802621  8859 solver.cpp:218] Iteration 1624 (2.32434 iter/s, 3.44184s/8 iters), loss = 4.98095
I0401 13:54:52.802685  8859 solver.cpp:237]     Train net output #0: loss = 4.98095 (* 1 = 4.98095 loss)
I0401 13:54:52.802692  8859 sgd_solver.cpp:105] Iteration 1624, lr = 0.001
I0401 13:54:56.260275  8859 solver.cpp:218] Iteration 1632 (2.31378 iter/s, 3.45754s/8 iters), loss = 4.79092
I0401 13:54:56.260318  8859 solver.cpp:237]     Train net output #0: loss = 4.79092 (* 1 = 4.79092 loss)
I0401 13:54:56.260324  8859 sgd_solver.cpp:105] Iteration 1632, lr = 0.001
I0401 13:54:59.685541  8859 solver.cpp:218] Iteration 1640 (2.33565 iter/s, 3.42518s/8 iters), loss = 4.9148
I0401 13:54:59.685665  8859 solver.cpp:237]     Train net output #0: loss = 4.9148 (* 1 = 4.9148 loss)
I0401 13:54:59.685672  8859 sgd_solver.cpp:105] Iteration 1640, lr = 0.001
I0401 13:55:00.691118  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:55:02.919059  8859 solver.cpp:218] Iteration 1648 (2.47422 iter/s, 3.23335s/8 iters), loss = 4.98366
I0401 13:55:02.919103  8859 solver.cpp:237]     Train net output #0: loss = 4.98366 (* 1 = 4.98366 loss)
I0401 13:55:02.919109  8859 sgd_solver.cpp:105] Iteration 1648, lr = 0.001
I0401 13:55:06.615319  8859 solver.cpp:218] Iteration 1656 (2.16441 iter/s, 3.69616s/8 iters), loss = 4.83088
I0401 13:55:06.615367  8859 solver.cpp:237]     Train net output #0: loss = 4.83088 (* 1 = 4.83088 loss)
I0401 13:55:06.615373  8859 sgd_solver.cpp:105] Iteration 1656, lr = 0.001
I0401 13:55:09.634959  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1664.caffemodel
I0401 13:55:12.764760  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1664.solverstate
I0401 13:55:18.188613  8859 solver.cpp:330] Iteration 1664, Testing net (#0)
I0401 13:55:18.188635  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:55:28.127516  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:55:29.498157  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0265748
I0401 13:55:29.498195  8859 solver.cpp:397]     Test net output #1: loss = 5.02622 (* 1 = 5.02622 loss)
I0401 13:55:29.646766  8859 solver.cpp:218] Iteration 1664 (0.347356 iter/s, 23.0312s/8 iters), loss = 4.87688
I0401 13:55:29.648329  8859 solver.cpp:237]     Train net output #0: loss = 4.87688 (* 1 = 4.87688 loss)
I0401 13:55:29.648340  8859 sgd_solver.cpp:105] Iteration 1664, lr = 0.001
I0401 13:55:32.269934  8859 solver.cpp:218] Iteration 1672 (3.05161 iter/s, 2.62156s/8 iters), loss = 4.90564
I0401 13:55:32.270057  8859 solver.cpp:237]     Train net output #0: loss = 4.90564 (* 1 = 4.90564 loss)
I0401 13:55:32.270066  8859 sgd_solver.cpp:105] Iteration 1672, lr = 0.001
I0401 13:55:35.601473  8859 solver.cpp:218] Iteration 1680 (2.4042 iter/s, 3.32752s/8 iters), loss = 4.98557
I0401 13:55:35.601521  8859 solver.cpp:237]     Train net output #0: loss = 4.98557 (* 1 = 4.98557 loss)
I0401 13:55:35.601528  8859 sgd_solver.cpp:105] Iteration 1680, lr = 0.001
I0401 13:55:39.236471  8859 solver.cpp:218] Iteration 1688 (2.20089 iter/s, 3.63489s/8 iters), loss = 4.91213
I0401 13:55:39.236531  8859 solver.cpp:237]     Train net output #0: loss = 4.91213 (* 1 = 4.91213 loss)
I0401 13:55:39.236539  8859 sgd_solver.cpp:105] Iteration 1688, lr = 0.001
I0401 13:55:42.944140  8859 solver.cpp:218] Iteration 1696 (2.15775 iter/s, 3.70756s/8 iters), loss = 4.95993
I0401 13:55:42.944177  8859 solver.cpp:237]     Train net output #0: loss = 4.95993 (* 1 = 4.95993 loss)
I0401 13:55:42.944182  8859 sgd_solver.cpp:105] Iteration 1696, lr = 0.001
I0401 13:55:46.916983  8859 solver.cpp:218] Iteration 1704 (2.01372 iter/s, 3.97275s/8 iters), loss = 4.80503
I0401 13:55:46.917026  8859 solver.cpp:237]     Train net output #0: loss = 4.80503 (* 1 = 4.80503 loss)
I0401 13:55:46.917032  8859 sgd_solver.cpp:105] Iteration 1704, lr = 0.001
I0401 13:55:47.625583  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:55:50.537086  8859 solver.cpp:218] Iteration 1712 (2.20995 iter/s, 3.62s/8 iters), loss = 4.84634
I0401 13:55:50.537147  8859 solver.cpp:237]     Train net output #0: loss = 4.84634 (* 1 = 4.84634 loss)
I0401 13:55:50.537156  8859 sgd_solver.cpp:105] Iteration 1712, lr = 0.001
I0401 13:55:54.405230  8859 solver.cpp:218] Iteration 1720 (2.06824 iter/s, 3.86803s/8 iters), loss = 4.90026
I0401 13:55:54.405297  8859 solver.cpp:237]     Train net output #0: loss = 4.90026 (* 1 = 4.90026 loss)
I0401 13:55:54.405308  8859 sgd_solver.cpp:105] Iteration 1720, lr = 0.001
I0401 13:55:57.676043  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1728.caffemodel
I0401 13:56:00.832985  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1728.solverstate
I0401 13:56:03.258078  8859 solver.cpp:330] Iteration 1728, Testing net (#0)
I0401 13:56:03.258198  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:56:04.001194  8859 blocking_queue.cpp:49] Waiting for data
I0401 13:56:14.404951  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:56:15.953910  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0263287
I0401 13:56:15.953949  8859 solver.cpp:397]     Test net output #1: loss = 5.01721 (* 1 = 5.01721 loss)
I0401 13:56:16.089717  8859 solver.cpp:218] Iteration 1728 (0.368932 iter/s, 21.6842s/8 iters), loss = 4.83805
I0401 13:56:16.089776  8859 solver.cpp:237]     Train net output #0: loss = 4.83805 (* 1 = 4.83805 loss)
I0401 13:56:16.089787  8859 sgd_solver.cpp:105] Iteration 1728, lr = 0.001
I0401 13:56:18.944118  8859 solver.cpp:218] Iteration 1736 (2.8028 iter/s, 2.85429s/8 iters), loss = 4.89408
I0401 13:56:18.944185  8859 solver.cpp:237]     Train net output #0: loss = 4.89408 (* 1 = 4.89408 loss)
I0401 13:56:18.944195  8859 sgd_solver.cpp:105] Iteration 1736, lr = 0.001
I0401 13:56:22.425622  8859 solver.cpp:218] Iteration 1744 (2.29794 iter/s, 3.48139s/8 iters), loss = 4.90903
I0401 13:56:22.425673  8859 solver.cpp:237]     Train net output #0: loss = 4.90903 (* 1 = 4.90903 loss)
I0401 13:56:22.425679  8859 sgd_solver.cpp:105] Iteration 1744, lr = 0.001
I0401 13:56:26.389156  8859 solver.cpp:218] Iteration 1752 (2.01846 iter/s, 3.96342s/8 iters), loss = 4.93176
I0401 13:56:26.389221  8859 solver.cpp:237]     Train net output #0: loss = 4.93176 (* 1 = 4.93176 loss)
I0401 13:56:26.389230  8859 sgd_solver.cpp:105] Iteration 1752, lr = 0.001
I0401 13:56:30.152499  8859 solver.cpp:218] Iteration 1760 (2.12584 iter/s, 3.76322s/8 iters), loss = 4.8899
I0401 13:56:30.152545  8859 solver.cpp:237]     Train net output #0: loss = 4.8899 (* 1 = 4.8899 loss)
I0401 13:56:30.152551  8859 sgd_solver.cpp:105] Iteration 1760, lr = 0.001
I0401 13:56:34.067430  8859 solver.cpp:218] Iteration 1768 (2.04352 iter/s, 3.91482s/8 iters), loss = 4.80616
I0401 13:56:34.067549  8859 solver.cpp:237]     Train net output #0: loss = 4.80616 (* 1 = 4.80616 loss)
I0401 13:56:34.067559  8859 sgd_solver.cpp:105] Iteration 1768, lr = 0.001
I0401 13:56:34.538892  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:56:37.500180  8859 solver.cpp:218] Iteration 1776 (2.33061 iter/s, 3.43258s/8 iters), loss = 4.94987
I0401 13:56:37.500224  8859 solver.cpp:237]     Train net output #0: loss = 4.94987 (* 1 = 4.94987 loss)
I0401 13:56:37.500229  8859 sgd_solver.cpp:105] Iteration 1776, lr = 0.001
I0401 13:56:41.318341  8859 solver.cpp:218] Iteration 1784 (2.09531 iter/s, 3.81806s/8 iters), loss = 4.98643
I0401 13:56:41.318392  8859 solver.cpp:237]     Train net output #0: loss = 4.98643 (* 1 = 4.98643 loss)
I0401 13:56:41.318400  8859 sgd_solver.cpp:105] Iteration 1784, lr = 0.001
I0401 13:56:44.573925  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1792.caffemodel
I0401 13:56:47.662978  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1792.solverstate
I0401 13:56:50.009227  8859 solver.cpp:330] Iteration 1792, Testing net (#0)
I0401 13:56:50.009244  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:57:00.986122  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:02.698107  8859 solver.cpp:397]     Test net output #0: accuracy = 0.027313
I0401 13:57:02.698148  8859 solver.cpp:397]     Test net output #1: loss = 4.99617 (* 1 = 4.99617 loss)
I0401 13:57:02.836797  8859 solver.cpp:218] Iteration 1792 (0.371779 iter/s, 21.5182s/8 iters), loss = 4.91416
I0401 13:57:02.838366  8859 solver.cpp:237]     Train net output #0: loss = 4.91416 (* 1 = 4.91416 loss)
I0401 13:57:02.838382  8859 sgd_solver.cpp:105] Iteration 1792, lr = 0.001
I0401 13:57:05.321317  8859 solver.cpp:218] Iteration 1800 (3.22202 iter/s, 2.48292s/8 iters), loss = 4.90226
I0401 13:57:05.321458  8859 solver.cpp:237]     Train net output #0: loss = 4.90226 (* 1 = 4.90226 loss)
I0401 13:57:05.321468  8859 sgd_solver.cpp:105] Iteration 1800, lr = 0.001
I0401 13:57:08.945519  8859 solver.cpp:218] Iteration 1808 (2.2075 iter/s, 3.62402s/8 iters), loss = 4.82701
I0401 13:57:08.945562  8859 solver.cpp:237]     Train net output #0: loss = 4.82701 (* 1 = 4.82701 loss)
I0401 13:57:08.945569  8859 sgd_solver.cpp:105] Iteration 1808, lr = 0.001
I0401 13:57:12.621397  8859 solver.cpp:218] Iteration 1816 (2.17641 iter/s, 3.67578s/8 iters), loss = 4.79303
I0401 13:57:12.621462  8859 solver.cpp:237]     Train net output #0: loss = 4.79303 (* 1 = 4.79303 loss)
I0401 13:57:12.621471  8859 sgd_solver.cpp:105] Iteration 1816, lr = 0.001
I0401 13:57:16.418170  8859 solver.cpp:218] Iteration 1824 (2.10712 iter/s, 3.79665s/8 iters), loss = 4.87026
I0401 13:57:16.418217  8859 solver.cpp:237]     Train net output #0: loss = 4.87026 (* 1 = 4.87026 loss)
I0401 13:57:16.418226  8859 sgd_solver.cpp:105] Iteration 1824, lr = 0.001
I0401 13:57:20.168473  8859 solver.cpp:218] Iteration 1832 (2.13322 iter/s, 3.7502s/8 iters), loss = 4.86282
I0401 13:57:20.168525  8859 solver.cpp:237]     Train net output #0: loss = 4.86282 (* 1 = 4.86282 loss)
I0401 13:57:20.168535  8859 sgd_solver.cpp:105] Iteration 1832, lr = 0.001
I0401 13:57:20.268148  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:23.958204  8859 solver.cpp:218] Iteration 1840 (2.11103 iter/s, 3.78962s/8 iters), loss = 4.87645
I0401 13:57:23.958256  8859 solver.cpp:237]     Train net output #0: loss = 4.87645 (* 1 = 4.87645 loss)
I0401 13:57:23.958264  8859 sgd_solver.cpp:105] Iteration 1840, lr = 0.001
I0401 13:57:27.614874  8859 solver.cpp:218] Iteration 1848 (2.18785 iter/s, 3.65656s/8 iters), loss = 4.86563
I0401 13:57:27.614928  8859 solver.cpp:237]     Train net output #0: loss = 4.86563 (* 1 = 4.86563 loss)
I0401 13:57:27.614936  8859 sgd_solver.cpp:105] Iteration 1848, lr = 0.001
I0401 13:57:30.755362  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1856.caffemodel
I0401 13:57:33.781255  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1856.solverstate
I0401 13:57:36.172773  8859 solver.cpp:330] Iteration 1856, Testing net (#0)
I0401 13:57:36.172834  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:57:47.242023  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:48.937551  8859 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0401 13:57:48.937588  8859 solver.cpp:397]     Test net output #1: loss = 4.99391 (* 1 = 4.99391 loss)
I0401 13:57:49.076977  8859 solver.cpp:218] Iteration 1856 (0.372755 iter/s, 21.4618s/8 iters), loss = 4.96635
I0401 13:57:49.077036  8859 solver.cpp:237]     Train net output #0: loss = 4.96635 (* 1 = 4.96635 loss)
I0401 13:57:49.077045  8859 sgd_solver.cpp:105] Iteration 1856, lr = 0.001
I0401 13:57:51.955191  8859 solver.cpp:218] Iteration 1864 (2.7796 iter/s, 2.87811s/8 iters), loss = 4.72076
I0401 13:57:51.955232  8859 solver.cpp:237]     Train net output #0: loss = 4.72076 (* 1 = 4.72076 loss)
I0401 13:57:51.955238  8859 sgd_solver.cpp:105] Iteration 1864, lr = 0.001
I0401 13:57:55.795732  8859 solver.cpp:218] Iteration 1872 (2.08309 iter/s, 3.84044s/8 iters), loss = 4.98507
I0401 13:57:55.795774  8859 solver.cpp:237]     Train net output #0: loss = 4.98507 (* 1 = 4.98507 loss)
I0401 13:57:55.795780  8859 sgd_solver.cpp:105] Iteration 1872, lr = 0.001
I0401 13:57:59.614480  8859 solver.cpp:218] Iteration 1880 (2.09498 iter/s, 3.81865s/8 iters), loss = 4.81812
I0401 13:57:59.614535  8859 solver.cpp:237]     Train net output #0: loss = 4.81812 (* 1 = 4.81812 loss)
I0401 13:57:59.614545  8859 sgd_solver.cpp:105] Iteration 1880, lr = 0.001
I0401 13:58:03.400553  8859 solver.cpp:218] Iteration 1888 (2.11307 iter/s, 3.78596s/8 iters), loss = 4.81318
I0401 13:58:03.400612  8859 solver.cpp:237]     Train net output #0: loss = 4.81318 (* 1 = 4.81318 loss)
I0401 13:58:03.400621  8859 sgd_solver.cpp:105] Iteration 1888, lr = 0.001
I0401 13:58:06.988952  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:58:07.257953  8859 solver.cpp:218] Iteration 1896 (2.074 iter/s, 3.85729s/8 iters), loss = 4.93502
I0401 13:58:07.257998  8859 solver.cpp:237]     Train net output #0: loss = 4.93502 (* 1 = 4.93502 loss)
I0401 13:58:07.258004  8859 sgd_solver.cpp:105] Iteration 1896, lr = 0.001
I0401 13:58:11.151074  8859 solver.cpp:218] Iteration 1904 (2.05496 iter/s, 3.89302s/8 iters), loss = 4.88776
I0401 13:58:11.151113  8859 solver.cpp:237]     Train net output #0: loss = 4.88776 (* 1 = 4.88776 loss)
I0401 13:58:11.151121  8859 sgd_solver.cpp:105] Iteration 1904, lr = 0.001
I0401 13:58:14.939558  8859 solver.cpp:218] Iteration 1912 (2.11172 iter/s, 3.78838s/8 iters), loss = 4.89384
I0401 13:58:14.945742  8859 solver.cpp:237]     Train net output #0: loss = 4.89384 (* 1 = 4.89384 loss)
I0401 13:58:14.945767  8859 sgd_solver.cpp:105] Iteration 1912, lr = 0.001
I0401 13:58:18.471702  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1920.caffemodel
I0401 13:58:23.534075  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1920.solverstate
I0401 13:58:26.820363  8859 solver.cpp:330] Iteration 1920, Testing net (#0)
I0401 13:58:26.820389  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:58:53.021284  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:58:56.852176  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0334646
I0401 13:58:56.852213  8859 solver.cpp:397]     Test net output #1: loss = 4.97789 (* 1 = 4.97789 loss)
I0401 13:58:57.040936  8859 solver.cpp:218] Iteration 1920 (0.190078 iter/s, 42.088s/8 iters), loss = 4.81663
I0401 13:58:57.040997  8859 solver.cpp:237]     Train net output #0: loss = 4.81663 (* 1 = 4.81663 loss)
I0401 13:58:57.041005  8859 sgd_solver.cpp:105] Iteration 1920, lr = 0.001
I0401 13:59:02.250869  8859 solver.cpp:218] Iteration 1928 (1.53557 iter/s, 5.20979s/8 iters), loss = 4.77794
I0401 13:59:02.250931  8859 solver.cpp:237]     Train net output #0: loss = 4.77794 (* 1 = 4.77794 loss)
I0401 13:59:02.250938  8859 sgd_solver.cpp:105] Iteration 1928, lr = 0.001
I0401 13:59:07.586812  8859 solver.cpp:218] Iteration 1936 (1.4993 iter/s, 5.33581s/8 iters), loss = 4.87584
I0401 13:59:07.586866  8859 solver.cpp:237]     Train net output #0: loss = 4.87584 (* 1 = 4.87584 loss)
I0401 13:59:07.587587  8859 sgd_solver.cpp:105] Iteration 1936, lr = 0.001
I0401 13:59:11.942791  8859 solver.cpp:218] Iteration 1944 (1.83661 iter/s, 4.35586s/8 iters), loss = 4.77466
I0401 13:59:11.942849  8859 solver.cpp:237]     Train net output #0: loss = 4.77466 (* 1 = 4.77466 loss)
I0401 13:59:11.942857  8859 sgd_solver.cpp:105] Iteration 1944, lr = 0.001
I0401 13:59:16.903048  8859 solver.cpp:218] Iteration 1952 (1.61286 iter/s, 4.96013s/8 iters), loss = 4.8457
I0401 13:59:16.903100  8859 solver.cpp:237]     Train net output #0: loss = 4.8457 (* 1 = 4.8457 loss)
I0401 13:59:16.903108  8859 sgd_solver.cpp:105] Iteration 1952, lr = 0.001
I0401 13:59:20.688047  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:59:21.560581  8859 solver.cpp:218] Iteration 1960 (1.71769 iter/s, 4.65742s/8 iters), loss = 4.72519
I0401 13:59:21.560632  8859 solver.cpp:237]     Train net output #0: loss = 4.72519 (* 1 = 4.72519 loss)
I0401 13:59:21.560640  8859 sgd_solver.cpp:105] Iteration 1960, lr = 0.001
I0401 13:59:26.298545  8859 solver.cpp:218] Iteration 1968 (1.68853 iter/s, 4.73784s/8 iters), loss = 4.71243
I0401 13:59:26.304946  8859 solver.cpp:237]     Train net output #0: loss = 4.71243 (* 1 = 4.71243 loss)
I0401 13:59:26.304971  8859 sgd_solver.cpp:105] Iteration 1968, lr = 0.001
I0401 13:59:30.851107  8859 solver.cpp:218] Iteration 1976 (1.75984 iter/s, 4.54586s/8 iters), loss = 4.77845
I0401 13:59:30.851161  8859 solver.cpp:237]     Train net output #0: loss = 4.77845 (* 1 = 4.77845 loss)
I0401 13:59:30.851168  8859 sgd_solver.cpp:105] Iteration 1976, lr = 0.001
I0401 13:59:34.876583  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1984.caffemodel
I0401 13:59:38.767357  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1984.solverstate
I0401 13:59:41.767671  8859 solver.cpp:330] Iteration 1984, Testing net (#0)
I0401 13:59:41.767696  8859 net.cpp:676] Ignoring source layer train-data
I0401 13:59:56.696017  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:59:59.269106  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0332185
I0401 13:59:59.269150  8859 solver.cpp:397]     Test net output #1: loss = 4.96943 (* 1 = 4.96943 loss)
I0401 13:59:59.444936  8859 solver.cpp:218] Iteration 1984 (0.279795 iter/s, 28.5923s/8 iters), loss = 4.82867
I0401 13:59:59.444990  8859 solver.cpp:237]     Train net output #0: loss = 4.82867 (* 1 = 4.82867 loss)
I0401 13:59:59.444999  8859 sgd_solver.cpp:105] Iteration 1984, lr = 0.001
I0401 14:00:03.018555  8859 solver.cpp:218] Iteration 1992 (2.2387 iter/s, 3.57351s/8 iters), loss = 4.74197
I0401 14:00:03.018613  8859 solver.cpp:237]     Train net output #0: loss = 4.74197 (* 1 = 4.74197 loss)
I0401 14:00:03.018621  8859 sgd_solver.cpp:105] Iteration 1992, lr = 0.001
I0401 14:00:07.306838  8859 solver.cpp:218] Iteration 2000 (1.8656 iter/s, 4.28816s/8 iters), loss = 4.91365
I0401 14:00:07.306892  8859 solver.cpp:237]     Train net output #0: loss = 4.91365 (* 1 = 4.91365 loss)
I0401 14:00:07.306900  8859 sgd_solver.cpp:105] Iteration 2000, lr = 0.001
I0401 14:00:11.747864  8859 solver.cpp:218] Iteration 2008 (1.80301 iter/s, 4.43703s/8 iters), loss = 4.65685
I0401 14:00:11.747923  8859 solver.cpp:237]     Train net output #0: loss = 4.65685 (* 1 = 4.65685 loss)
I0401 14:00:11.747932  8859 sgd_solver.cpp:105] Iteration 2008, lr = 0.001
I0401 14:00:16.395789  8859 solver.cpp:218] Iteration 2016 (1.72125 iter/s, 4.6478s/8 iters), loss = 4.77963
I0401 14:00:16.402034  8859 solver.cpp:237]     Train net output #0: loss = 4.77963 (* 1 = 4.77963 loss)
I0401 14:00:16.402056  8859 sgd_solver.cpp:105] Iteration 2016, lr = 0.001
I0401 14:00:19.225905  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:00:20.240826  8859 solver.cpp:218] Iteration 2024 (2.08401 iter/s, 3.83876s/8 iters), loss = 4.70133
I0401 14:00:20.240880  8859 solver.cpp:237]     Train net output #0: loss = 4.70133 (* 1 = 4.70133 loss)
I0401 14:00:20.240898  8859 sgd_solver.cpp:105] Iteration 2024, lr = 0.001
I0401 14:00:24.050024  8859 solver.cpp:218] Iteration 2032 (2.10024 iter/s, 3.80909s/8 iters), loss = 4.80649
I0401 14:00:24.050071  8859 solver.cpp:237]     Train net output #0: loss = 4.80649 (* 1 = 4.80649 loss)
I0401 14:00:24.050077  8859 sgd_solver.cpp:105] Iteration 2032, lr = 0.001
I0401 14:00:27.723114  8859 solver.cpp:218] Iteration 2040 (2.17807 iter/s, 3.67298s/8 iters), loss = 4.83866
I0401 14:00:27.723255  8859 solver.cpp:237]     Train net output #0: loss = 4.83866 (* 1 = 4.83866 loss)
I0401 14:00:27.723265  8859 sgd_solver.cpp:105] Iteration 2040, lr = 0.001
I0401 14:00:31.034042  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2048.caffemodel
I0401 14:00:34.178086  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2048.solverstate
I0401 14:00:36.509016  8859 solver.cpp:330] Iteration 2048, Testing net (#0)
I0401 14:00:36.509040  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:00:45.094318  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:00:47.629472  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:00:49.393338  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0322343
I0401 14:00:49.393369  8859 solver.cpp:397]     Test net output #1: loss = 4.97095 (* 1 = 4.97095 loss)
I0401 14:00:49.534837  8859 solver.cpp:218] Iteration 2048 (0.366781 iter/s, 21.8114s/8 iters), loss = 4.6997
I0401 14:00:49.534889  8859 solver.cpp:237]     Train net output #0: loss = 4.6997 (* 1 = 4.6997 loss)
I0401 14:00:49.534898  8859 sgd_solver.cpp:105] Iteration 2048, lr = 0.001
I0401 14:00:52.351562  8859 solver.cpp:218] Iteration 2056 (2.84028 iter/s, 2.81662s/8 iters), loss = 4.74967
I0401 14:00:52.351619  8859 solver.cpp:237]     Train net output #0: loss = 4.74967 (* 1 = 4.74967 loss)
I0401 14:00:52.351629  8859 sgd_solver.cpp:105] Iteration 2056, lr = 0.001
I0401 14:00:55.980118  8859 solver.cpp:218] Iteration 2064 (2.2048 iter/s, 3.62845s/8 iters), loss = 4.73854
I0401 14:00:55.980165  8859 solver.cpp:237]     Train net output #0: loss = 4.73854 (* 1 = 4.73854 loss)
I0401 14:00:55.980171  8859 sgd_solver.cpp:105] Iteration 2064, lr = 0.001
I0401 14:00:59.785343  8859 solver.cpp:218] Iteration 2072 (2.10243 iter/s, 3.80512s/8 iters), loss = 4.81347
I0401 14:00:59.785570  8859 solver.cpp:237]     Train net output #0: loss = 4.81347 (* 1 = 4.81347 loss)
I0401 14:00:59.785579  8859 sgd_solver.cpp:105] Iteration 2072, lr = 0.001
I0401 14:01:03.482717  8859 solver.cpp:218] Iteration 2080 (2.16386 iter/s, 3.69709s/8 iters), loss = 4.68174
I0401 14:01:03.482772  8859 solver.cpp:237]     Train net output #0: loss = 4.68174 (* 1 = 4.68174 loss)
I0401 14:01:03.482780  8859 sgd_solver.cpp:105] Iteration 2080, lr = 0.001
I0401 14:01:05.850769  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:01:07.233446  8859 solver.cpp:218] Iteration 2088 (2.13298 iter/s, 3.75062s/8 iters), loss = 4.77844
I0401 14:01:07.233486  8859 solver.cpp:237]     Train net output #0: loss = 4.77844 (* 1 = 4.77844 loss)
I0401 14:01:07.233492  8859 sgd_solver.cpp:105] Iteration 2088, lr = 0.001
I0401 14:01:10.988597  8859 solver.cpp:218] Iteration 2096 (2.13046 iter/s, 3.75505s/8 iters), loss = 4.80251
I0401 14:01:10.988642  8859 solver.cpp:237]     Train net output #0: loss = 4.80251 (* 1 = 4.80251 loss)
I0401 14:01:10.988648  8859 sgd_solver.cpp:105] Iteration 2096, lr = 0.001
I0401 14:01:14.884698  8859 solver.cpp:218] Iteration 2104 (2.05339 iter/s, 3.896s/8 iters), loss = 4.66901
I0401 14:01:14.884759  8859 solver.cpp:237]     Train net output #0: loss = 4.66901 (* 1 = 4.66901 loss)
I0401 14:01:14.884768  8859 sgd_solver.cpp:105] Iteration 2104, lr = 0.001
I0401 14:01:17.996281  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2112.caffemodel
I0401 14:01:22.477990  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2112.solverstate
I0401 14:01:25.920395  8859 solver.cpp:330] Iteration 2112, Testing net (#0)
I0401 14:01:25.920418  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:01:36.872037  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:01:39.010684  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0344488
I0401 14:01:39.010720  8859 solver.cpp:397]     Test net output #1: loss = 4.97332 (* 1 = 4.97332 loss)
I0401 14:01:39.152648  8859 solver.cpp:218] Iteration 2112 (0.329657 iter/s, 24.2676s/8 iters), loss = 4.74493
I0401 14:01:39.152694  8859 solver.cpp:237]     Train net output #0: loss = 4.74493 (* 1 = 4.74493 loss)
I0401 14:01:39.152700  8859 sgd_solver.cpp:105] Iteration 2112, lr = 0.001
I0401 14:01:42.005400  8859 solver.cpp:218] Iteration 2120 (2.8044 iter/s, 2.85266s/8 iters), loss = 4.78127
I0401 14:01:42.005447  8859 solver.cpp:237]     Train net output #0: loss = 4.78127 (* 1 = 4.78127 loss)
I0401 14:01:42.005453  8859 sgd_solver.cpp:105] Iteration 2120, lr = 0.001
I0401 14:01:45.403138  8859 solver.cpp:218] Iteration 2128 (2.35458 iter/s, 3.39764s/8 iters), loss = 4.80131
I0401 14:01:45.403193  8859 solver.cpp:237]     Train net output #0: loss = 4.80131 (* 1 = 4.80131 loss)
I0401 14:01:45.403201  8859 sgd_solver.cpp:105] Iteration 2128, lr = 0.001
I0401 14:01:49.030369  8859 solver.cpp:218] Iteration 2136 (2.20561 iter/s, 3.62712s/8 iters), loss = 4.72612
I0401 14:01:49.030428  8859 solver.cpp:237]     Train net output #0: loss = 4.72612 (* 1 = 4.72612 loss)
I0401 14:01:49.030437  8859 sgd_solver.cpp:105] Iteration 2136, lr = 0.001
I0401 14:01:52.712574  8859 solver.cpp:218] Iteration 2144 (2.17268 iter/s, 3.6821s/8 iters), loss = 4.66321
I0401 14:01:52.712620  8859 solver.cpp:237]     Train net output #0: loss = 4.66321 (* 1 = 4.66321 loss)
I0401 14:01:52.712630  8859 sgd_solver.cpp:105] Iteration 2144, lr = 0.001
I0401 14:01:54.849619  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:01:56.558869  8859 solver.cpp:218] Iteration 2152 (2.07998 iter/s, 3.84619s/8 iters), loss = 4.74803
I0401 14:01:56.558928  8859 solver.cpp:237]     Train net output #0: loss = 4.74803 (* 1 = 4.74803 loss)
I0401 14:01:56.558938  8859 sgd_solver.cpp:105] Iteration 2152, lr = 0.001
I0401 14:02:00.452436  8859 solver.cpp:218] Iteration 2160 (2.05473 iter/s, 3.89345s/8 iters), loss = 4.69151
I0401 14:02:00.452486  8859 solver.cpp:237]     Train net output #0: loss = 4.69151 (* 1 = 4.69151 loss)
I0401 14:02:00.452494  8859 sgd_solver.cpp:105] Iteration 2160, lr = 0.001
I0401 14:02:04.059767  8859 solver.cpp:218] Iteration 2168 (2.21777 iter/s, 3.60723s/8 iters), loss = 4.59019
I0401 14:02:04.059820  8859 solver.cpp:237]     Train net output #0: loss = 4.59019 (* 1 = 4.59019 loss)
I0401 14:02:04.059829  8859 sgd_solver.cpp:105] Iteration 2168, lr = 0.001
I0401 14:02:07.358311  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2176.caffemodel
I0401 14:02:10.412478  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2176.solverstate
I0401 14:02:12.906842  8859 solver.cpp:330] Iteration 2176, Testing net (#0)
I0401 14:02:12.906868  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:02:23.580183  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:02:25.530649  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0359252
I0401 14:02:25.530687  8859 solver.cpp:397]     Test net output #1: loss = 4.95524 (* 1 = 4.95524 loss)
I0401 14:02:25.669075  8859 solver.cpp:218] Iteration 2176 (0.370216 iter/s, 21.609s/8 iters), loss = 4.74516
I0401 14:02:25.669117  8859 solver.cpp:237]     Train net output #0: loss = 4.74516 (* 1 = 4.74516 loss)
I0401 14:02:25.669122  8859 sgd_solver.cpp:105] Iteration 2176, lr = 0.001
I0401 14:02:28.435495  8859 solver.cpp:218] Iteration 2184 (2.89191 iter/s, 2.76633s/8 iters), loss = 4.89761
I0401 14:02:28.435539  8859 solver.cpp:237]     Train net output #0: loss = 4.89761 (* 1 = 4.89761 loss)
I0401 14:02:28.435544  8859 sgd_solver.cpp:105] Iteration 2184, lr = 0.001
I0401 14:02:32.180718  8859 solver.cpp:218] Iteration 2192 (2.13611 iter/s, 3.74513s/8 iters), loss = 4.64576
I0401 14:02:32.180771  8859 solver.cpp:237]     Train net output #0: loss = 4.64576 (* 1 = 4.64576 loss)
I0401 14:02:32.180779  8859 sgd_solver.cpp:105] Iteration 2192, lr = 0.001
I0401 14:02:35.811251  8859 solver.cpp:218] Iteration 2200 (2.2036 iter/s, 3.63043s/8 iters), loss = 4.73303
I0401 14:02:35.811305  8859 solver.cpp:237]     Train net output #0: loss = 4.73303 (* 1 = 4.73303 loss)
I0401 14:02:35.811313  8859 sgd_solver.cpp:105] Iteration 2200, lr = 0.001
I0401 14:02:39.458612  8859 solver.cpp:218] Iteration 2208 (2.19343 iter/s, 3.64726s/8 iters), loss = 4.75725
I0401 14:02:39.458739  8859 solver.cpp:237]     Train net output #0: loss = 4.75725 (* 1 = 4.75725 loss)
I0401 14:02:39.458746  8859 sgd_solver.cpp:105] Iteration 2208, lr = 0.001
I0401 14:02:41.078713  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:02:43.053462  8859 solver.cpp:218] Iteration 2216 (2.22552 iter/s, 3.59467s/8 iters), loss = 4.39201
I0401 14:02:43.053514  8859 solver.cpp:237]     Train net output #0: loss = 4.39201 (* 1 = 4.39201 loss)
I0401 14:02:43.053521  8859 sgd_solver.cpp:105] Iteration 2216, lr = 0.001
I0401 14:02:46.959905  8859 solver.cpp:218] Iteration 2224 (2.04795 iter/s, 3.90634s/8 iters), loss = 4.49637
I0401 14:02:46.959957  8859 solver.cpp:237]     Train net output #0: loss = 4.49637 (* 1 = 4.49637 loss)
I0401 14:02:46.959966  8859 sgd_solver.cpp:105] Iteration 2224, lr = 0.001
I0401 14:02:50.569811  8859 solver.cpp:218] Iteration 2232 (2.21619 iter/s, 3.6098s/8 iters), loss = 4.55951
I0401 14:02:50.569864  8859 solver.cpp:237]     Train net output #0: loss = 4.55951 (* 1 = 4.55951 loss)
I0401 14:02:50.569872  8859 sgd_solver.cpp:105] Iteration 2232, lr = 0.001
I0401 14:02:53.678669  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2240.caffemodel
I0401 14:02:56.818202  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2240.solverstate
I0401 14:02:59.136804  8859 solver.cpp:330] Iteration 2240, Testing net (#0)
I0401 14:02:59.136823  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:03:09.690850  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:11.657950  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0371555
I0401 14:03:11.657989  8859 solver.cpp:397]     Test net output #1: loss = 4.93301 (* 1 = 4.93301 loss)
I0401 14:03:11.793184  8859 solver.cpp:218] Iteration 2240 (0.376948 iter/s, 21.2231s/8 iters), loss = 4.57539
I0401 14:03:11.793241  8859 solver.cpp:237]     Train net output #0: loss = 4.57539 (* 1 = 4.57539 loss)
I0401 14:03:11.793249  8859 sgd_solver.cpp:105] Iteration 2240, lr = 0.001
I0401 14:03:14.519155  8859 solver.cpp:218] Iteration 2248 (2.93484 iter/s, 2.72587s/8 iters), loss = 4.65452
I0401 14:03:14.519202  8859 solver.cpp:237]     Train net output #0: loss = 4.65452 (* 1 = 4.65452 loss)
I0401 14:03:14.519210  8859 sgd_solver.cpp:105] Iteration 2248, lr = 0.001
I0401 14:03:18.272047  8859 solver.cpp:218] Iteration 2256 (2.13175 iter/s, 3.75278s/8 iters), loss = 4.60659
I0401 14:03:18.278221  8859 solver.cpp:237]     Train net output #0: loss = 4.60659 (* 1 = 4.60659 loss)
I0401 14:03:18.278249  8859 sgd_solver.cpp:105] Iteration 2256, lr = 0.001
I0401 14:03:21.978926  8859 solver.cpp:218] Iteration 2264 (2.16177 iter/s, 3.70068s/8 iters), loss = 4.61514
I0401 14:03:21.978976  8859 solver.cpp:237]     Train net output #0: loss = 4.61514 (* 1 = 4.61514 loss)
I0401 14:03:21.978983  8859 sgd_solver.cpp:105] Iteration 2264, lr = 0.001
I0401 14:03:25.805274  8859 solver.cpp:218] Iteration 2272 (2.09082 iter/s, 3.82624s/8 iters), loss = 4.70719
I0401 14:03:25.805331  8859 solver.cpp:237]     Train net output #0: loss = 4.70719 (* 1 = 4.70719 loss)
I0401 14:03:25.805338  8859 sgd_solver.cpp:105] Iteration 2272, lr = 0.001
I0401 14:03:26.946924  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:29.392524  8859 solver.cpp:218] Iteration 2280 (2.23019 iter/s, 3.58714s/8 iters), loss = 4.66417
I0401 14:03:29.392567  8859 solver.cpp:237]     Train net output #0: loss = 4.66417 (* 1 = 4.66417 loss)
I0401 14:03:29.392575  8859 sgd_solver.cpp:105] Iteration 2280, lr = 0.001
I0401 14:03:33.253123  8859 solver.cpp:218] Iteration 2288 (2.07227 iter/s, 3.8605s/8 iters), loss = 4.65079
I0401 14:03:33.253160  8859 solver.cpp:237]     Train net output #0: loss = 4.65079 (* 1 = 4.65079 loss)
I0401 14:03:33.253166  8859 sgd_solver.cpp:105] Iteration 2288, lr = 0.001
I0401 14:03:37.036396  8859 solver.cpp:218] Iteration 2296 (2.11463 iter/s, 3.78317s/8 iters), loss = 4.62307
I0401 14:03:37.036453  8859 solver.cpp:237]     Train net output #0: loss = 4.62307 (* 1 = 4.62307 loss)
I0401 14:03:37.036463  8859 sgd_solver.cpp:105] Iteration 2296, lr = 0.001
I0401 14:03:40.205965  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2304.caffemodel
I0401 14:03:43.375407  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2304.solverstate
I0401 14:03:45.756232  8859 solver.cpp:330] Iteration 2304, Testing net (#0)
I0401 14:03:45.756253  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:03:56.330509  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:58.440110  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0369094
I0401 14:03:58.440147  8859 solver.cpp:397]     Test net output #1: loss = 4.93659 (* 1 = 4.93659 loss)
I0401 14:03:58.579025  8859 solver.cpp:218] Iteration 2304 (0.371362 iter/s, 21.5423s/8 iters), loss = 4.68825
I0401 14:03:58.579077  8859 solver.cpp:237]     Train net output #0: loss = 4.68825 (* 1 = 4.68825 loss)
I0401 14:03:58.579085  8859 sgd_solver.cpp:105] Iteration 2304, lr = 0.001
I0401 14:04:01.530272  8859 solver.cpp:218] Iteration 2312 (2.71081 iter/s, 2.95115s/8 iters), loss = 4.49799
I0401 14:04:01.530316  8859 solver.cpp:237]     Train net output #0: loss = 4.49799 (* 1 = 4.49799 loss)
I0401 14:04:01.530321  8859 sgd_solver.cpp:105] Iteration 2312, lr = 0.001
I0401 14:04:05.119088  8859 solver.cpp:218] Iteration 2320 (2.22921 iter/s, 3.58872s/8 iters), loss = 4.7801
I0401 14:04:05.125259  8859 solver.cpp:237]     Train net output #0: loss = 4.7801 (* 1 = 4.7801 loss)
I0401 14:04:05.125278  8859 sgd_solver.cpp:105] Iteration 2320, lr = 0.001
I0401 14:04:08.703673  8859 solver.cpp:218] Iteration 2328 (2.23565 iter/s, 3.57838s/8 iters), loss = 4.59288
I0401 14:04:08.703722  8859 solver.cpp:237]     Train net output #0: loss = 4.59288 (* 1 = 4.59288 loss)
I0401 14:04:08.703730  8859 sgd_solver.cpp:105] Iteration 2328, lr = 0.001
I0401 14:04:12.450147  8859 solver.cpp:218] Iteration 2336 (2.1354 iter/s, 3.74637s/8 iters), loss = 4.51555
I0401 14:04:12.450246  8859 solver.cpp:237]     Train net output #0: loss = 4.51555 (* 1 = 4.51555 loss)
I0401 14:04:12.450253  8859 sgd_solver.cpp:105] Iteration 2336, lr = 0.001
I0401 14:04:13.453146  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:04:16.081374  8859 solver.cpp:218] Iteration 2344 (2.2032 iter/s, 3.63108s/8 iters), loss = 4.52388
I0401 14:04:16.081410  8859 solver.cpp:237]     Train net output #0: loss = 4.52388 (* 1 = 4.52388 loss)
I0401 14:04:16.081415  8859 sgd_solver.cpp:105] Iteration 2344, lr = 0.001
I0401 14:04:20.093282  8859 solver.cpp:218] Iteration 2352 (1.99411 iter/s, 4.01181s/8 iters), loss = 4.65578
I0401 14:04:20.093334  8859 solver.cpp:237]     Train net output #0: loss = 4.65578 (* 1 = 4.65578 loss)
I0401 14:04:20.093343  8859 sgd_solver.cpp:105] Iteration 2352, lr = 0.001
I0401 14:04:23.897593  8859 solver.cpp:218] Iteration 2360 (2.10294 iter/s, 3.8042s/8 iters), loss = 4.64642
I0401 14:04:23.897637  8859 solver.cpp:237]     Train net output #0: loss = 4.64642 (* 1 = 4.64642 loss)
I0401 14:04:23.897642  8859 sgd_solver.cpp:105] Iteration 2360, lr = 0.001
I0401 14:04:27.156154  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2368.caffemodel
I0401 14:04:30.243094  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2368.solverstate
I0401 14:04:32.629956  8859 solver.cpp:330] Iteration 2368, Testing net (#0)
I0401 14:04:32.629976  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:04:43.239831  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:04:45.385792  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0393701
I0401 14:04:45.385829  8859 solver.cpp:397]     Test net output #1: loss = 4.92769 (* 1 = 4.92769 loss)
I0401 14:04:45.538365  8859 solver.cpp:218] Iteration 2368 (0.369677 iter/s, 21.6405s/8 iters), loss = 4.67567
I0401 14:04:45.538424  8859 solver.cpp:237]     Train net output #0: loss = 4.67567 (* 1 = 4.67567 loss)
I0401 14:04:45.538431  8859 sgd_solver.cpp:105] Iteration 2368, lr = 0.001
I0401 14:04:48.321214  8859 solver.cpp:218] Iteration 2376 (2.87486 iter/s, 2.78274s/8 iters), loss = 4.60414
I0401 14:04:48.321272  8859 solver.cpp:237]     Train net output #0: loss = 4.60414 (* 1 = 4.60414 loss)
I0401 14:04:48.321280  8859 sgd_solver.cpp:105] Iteration 2376, lr = 0.001
I0401 14:04:52.128273  8859 solver.cpp:218] Iteration 2384 (2.10142 iter/s, 3.80695s/8 iters), loss = 4.62829
I0401 14:04:52.128314  8859 solver.cpp:237]     Train net output #0: loss = 4.62829 (* 1 = 4.62829 loss)
I0401 14:04:52.128319  8859 sgd_solver.cpp:105] Iteration 2384, lr = 0.001
I0401 14:04:55.923386  8859 solver.cpp:218] Iteration 2392 (2.10803 iter/s, 3.79501s/8 iters), loss = 4.57171
I0401 14:04:55.923441  8859 solver.cpp:237]     Train net output #0: loss = 4.57171 (* 1 = 4.57171 loss)
I0401 14:04:55.923449  8859 sgd_solver.cpp:105] Iteration 2392, lr = 0.001
I0401 14:04:59.736912  8859 solver.cpp:218] Iteration 2400 (2.09785 iter/s, 3.81342s/8 iters), loss = 4.57168
I0401 14:04:59.736960  8859 solver.cpp:237]     Train net output #0: loss = 4.57168 (* 1 = 4.57168 loss)
I0401 14:04:59.736968  8859 sgd_solver.cpp:105] Iteration 2400, lr = 0.001
I0401 14:05:00.278968  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:05:01.026937  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:05:03.538586  8859 solver.cpp:218] Iteration 2408 (2.10439 iter/s, 3.80157s/8 iters), loss = 4.64917
I0401 14:05:03.538635  8859 solver.cpp:237]     Train net output #0: loss = 4.64917 (* 1 = 4.64917 loss)
I0401 14:05:03.538640  8859 sgd_solver.cpp:105] Iteration 2408, lr = 0.001
I0401 14:05:07.223119  8859 solver.cpp:218] Iteration 2416 (2.1713 iter/s, 3.68442s/8 iters), loss = 4.72046
I0401 14:05:07.223186  8859 solver.cpp:237]     Train net output #0: loss = 4.72046 (* 1 = 4.72046 loss)
I0401 14:05:07.223196  8859 sgd_solver.cpp:105] Iteration 2416, lr = 0.001
I0401 14:05:11.132079  8859 solver.cpp:218] Iteration 2424 (2.04664 iter/s, 3.90884s/8 iters), loss = 4.66207
I0401 14:05:11.132117  8859 solver.cpp:237]     Train net output #0: loss = 4.66207 (* 1 = 4.66207 loss)
I0401 14:05:11.132123  8859 sgd_solver.cpp:105] Iteration 2424, lr = 0.001
I0401 14:05:14.269567  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2432.caffemodel
I0401 14:05:17.389240  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2432.solverstate
I0401 14:05:19.810520  8859 solver.cpp:330] Iteration 2432, Testing net (#0)
I0401 14:05:19.810539  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:05:30.562516  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:05:32.756707  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0393701
I0401 14:05:32.756748  8859 solver.cpp:397]     Test net output #1: loss = 4.89894 (* 1 = 4.89894 loss)
I0401 14:05:32.894285  8859 solver.cpp:218] Iteration 2432 (0.367614 iter/s, 21.7619s/8 iters), loss = 4.68162
I0401 14:05:32.894348  8859 solver.cpp:237]     Train net output #0: loss = 4.68162 (* 1 = 4.68162 loss)
I0401 14:05:32.894357  8859 sgd_solver.cpp:105] Iteration 2432, lr = 0.001
I0401 14:05:35.795837  8859 solver.cpp:218] Iteration 2440 (2.75725 iter/s, 2.90144s/8 iters), loss = 4.53256
I0401 14:05:35.795881  8859 solver.cpp:237]     Train net output #0: loss = 4.53256 (* 1 = 4.53256 loss)
I0401 14:05:35.795887  8859 sgd_solver.cpp:105] Iteration 2440, lr = 0.001
I0401 14:05:39.450817  8859 solver.cpp:218] Iteration 2448 (2.18885 iter/s, 3.65488s/8 iters), loss = 4.66605
I0401 14:05:39.450868  8859 solver.cpp:237]     Train net output #0: loss = 4.66605 (* 1 = 4.66605 loss)
I0401 14:05:39.450875  8859 sgd_solver.cpp:105] Iteration 2448, lr = 0.001
I0401 14:05:43.151633  8859 solver.cpp:218] Iteration 2456 (2.16175 iter/s, 3.70071s/8 iters), loss = 4.74756
I0401 14:05:43.151682  8859 solver.cpp:237]     Train net output #0: loss = 4.74756 (* 1 = 4.74756 loss)
I0401 14:05:43.151691  8859 sgd_solver.cpp:105] Iteration 2456, lr = 0.001
I0401 14:05:46.983249  8859 solver.cpp:218] Iteration 2464 (2.08795 iter/s, 3.83152s/8 iters), loss = 4.57649
I0401 14:05:46.983383  8859 solver.cpp:237]     Train net output #0: loss = 4.57649 (* 1 = 4.57649 loss)
I0401 14:05:46.983390  8859 sgd_solver.cpp:105] Iteration 2464, lr = 0.001
I0401 14:05:47.146204  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:05:50.703559  8859 solver.cpp:218] Iteration 2472 (2.15047 iter/s, 3.72012s/8 iters), loss = 4.53477
I0401 14:05:50.703609  8859 solver.cpp:237]     Train net output #0: loss = 4.53477 (* 1 = 4.53477 loss)
I0401 14:05:50.703617  8859 sgd_solver.cpp:105] Iteration 2472, lr = 0.001
I0401 14:05:54.153704  8859 solver.cpp:218] Iteration 2480 (2.31881 iter/s, 3.45005s/8 iters), loss = 4.60684
I0401 14:05:54.153757  8859 solver.cpp:237]     Train net output #0: loss = 4.60684 (* 1 = 4.60684 loss)
I0401 14:05:54.153766  8859 sgd_solver.cpp:105] Iteration 2480, lr = 0.001
I0401 14:05:57.904532  8859 solver.cpp:218] Iteration 2488 (2.13293 iter/s, 3.75072s/8 iters), loss = 4.65252
I0401 14:05:57.904587  8859 solver.cpp:237]     Train net output #0: loss = 4.65252 (* 1 = 4.65252 loss)
I0401 14:05:57.904594  8859 sgd_solver.cpp:105] Iteration 2488, lr = 0.001
I0401 14:06:01.130661  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2496.caffemodel
I0401 14:06:04.202960  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2496.solverstate
I0401 14:06:06.786478  8859 solver.cpp:330] Iteration 2496, Testing net (#0)
I0401 14:06:06.786499  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:06:16.742177  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:06:19.145202  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0442913
I0401 14:06:19.145292  8859 solver.cpp:397]     Test net output #1: loss = 4.87338 (* 1 = 4.87338 loss)
I0401 14:06:19.294926  8859 solver.cpp:218] Iteration 2496 (0.374005 iter/s, 21.3901s/8 iters), loss = 4.45113
I0401 14:06:19.296483  8859 solver.cpp:237]     Train net output #0: loss = 4.45113 (* 1 = 4.45113 loss)
I0401 14:06:19.296497  8859 sgd_solver.cpp:105] Iteration 2496, lr = 0.001
I0401 14:06:21.980085  8859 solver.cpp:218] Iteration 2504 (2.98111 iter/s, 2.68357s/8 iters), loss = 4.6169
I0401 14:06:21.980147  8859 solver.cpp:237]     Train net output #0: loss = 4.6169 (* 1 = 4.6169 loss)
I0401 14:06:21.980157  8859 sgd_solver.cpp:105] Iteration 2504, lr = 0.001
I0401 14:06:25.708114  8859 solver.cpp:218] Iteration 2512 (2.14597 iter/s, 3.72791s/8 iters), loss = 4.58901
I0401 14:06:25.708170  8859 solver.cpp:237]     Train net output #0: loss = 4.58901 (* 1 = 4.58901 loss)
I0401 14:06:25.708178  8859 sgd_solver.cpp:105] Iteration 2512, lr = 0.001
I0401 14:06:29.255369  8859 solver.cpp:218] Iteration 2520 (2.25533 iter/s, 3.54715s/8 iters), loss = 4.51054
I0401 14:06:29.255424  8859 solver.cpp:237]     Train net output #0: loss = 4.51054 (* 1 = 4.51054 loss)
I0401 14:06:29.255432  8859 sgd_solver.cpp:105] Iteration 2520, lr = 0.001
I0401 14:06:32.579694  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:06:32.794286  8859 solver.cpp:218] Iteration 2528 (2.26065 iter/s, 3.53881s/8 iters), loss = 4.49521
I0401 14:06:32.794344  8859 solver.cpp:237]     Train net output #0: loss = 4.49521 (* 1 = 4.49521 loss)
I0401 14:06:32.794353  8859 sgd_solver.cpp:105] Iteration 2528, lr = 0.001
I0401 14:06:36.493505  8859 solver.cpp:218] Iteration 2536 (2.16268 iter/s, 3.69911s/8 iters), loss = 4.40173
I0401 14:06:36.493546  8859 solver.cpp:237]     Train net output #0: loss = 4.40173 (* 1 = 4.40173 loss)
I0401 14:06:36.493551  8859 sgd_solver.cpp:105] Iteration 2536, lr = 0.001
I0401 14:06:40.300652  8859 solver.cpp:218] Iteration 2544 (2.10136 iter/s, 3.80705s/8 iters), loss = 4.6216
I0401 14:06:40.300689  8859 solver.cpp:237]     Train net output #0: loss = 4.6216 (* 1 = 4.6216 loss)
I0401 14:06:40.300695  8859 sgd_solver.cpp:105] Iteration 2544, lr = 0.001
I0401 14:06:44.043366  8859 solver.cpp:218] Iteration 2552 (2.13754 iter/s, 3.74263s/8 iters), loss = 4.57936
I0401 14:06:44.043406  8859 solver.cpp:237]     Train net output #0: loss = 4.57936 (* 1 = 4.57936 loss)
I0401 14:06:44.043411  8859 sgd_solver.cpp:105] Iteration 2552, lr = 0.001
I0401 14:06:47.101426  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2560.caffemodel
I0401 14:06:50.268627  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2560.solverstate
I0401 14:06:52.634827  8859 solver.cpp:330] Iteration 2560, Testing net (#0)
I0401 14:06:52.634852  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:07:03.037932  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:07:05.303999  8859 solver.cpp:397]     Test net output #0: accuracy = 0.046752
I0401 14:07:05.304039  8859 solver.cpp:397]     Test net output #1: loss = 4.85785 (* 1 = 4.85785 loss)
I0401 14:07:05.446808  8859 solver.cpp:218] Iteration 2560 (0.373776 iter/s, 21.4032s/8 iters), loss = 4.33699
I0401 14:07:05.446866  8859 solver.cpp:237]     Train net output #0: loss = 4.33699 (* 1 = 4.33699 loss)
I0401 14:07:05.446873  8859 sgd_solver.cpp:105] Iteration 2560, lr = 0.001
I0401 14:07:08.325690  8859 solver.cpp:218] Iteration 2568 (2.77896 iter/s, 2.87878s/8 iters), loss = 4.43684
I0401 14:07:08.325755  8859 solver.cpp:237]     Train net output #0: loss = 4.43684 (* 1 = 4.43684 loss)
I0401 14:07:08.325764  8859 sgd_solver.cpp:105] Iteration 2568, lr = 0.001
I0401 14:07:12.238597  8859 solver.cpp:218] Iteration 2576 (2.04458 iter/s, 3.91279s/8 iters), loss = 4.51127
I0401 14:07:12.238648  8859 solver.cpp:237]     Train net output #0: loss = 4.51127 (* 1 = 4.51127 loss)
I0401 14:07:12.238657  8859 sgd_solver.cpp:105] Iteration 2576, lr = 0.001
I0401 14:07:16.325467  8859 solver.cpp:218] Iteration 2584 (1.95754 iter/s, 4.08676s/8 iters), loss = 4.58881
I0401 14:07:16.325520  8859 solver.cpp:237]     Train net output #0: loss = 4.58881 (* 1 = 4.58881 loss)
I0401 14:07:16.325528  8859 sgd_solver.cpp:105] Iteration 2584, lr = 0.001
I0401 14:07:19.665516  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:07:20.227051  8859 solver.cpp:218] Iteration 2592 (2.05051 iter/s, 3.90147s/8 iters), loss = 4.52747
I0401 14:07:20.233220  8859 solver.cpp:237]     Train net output #0: loss = 4.52747 (* 1 = 4.52747 loss)
I0401 14:07:20.233237  8859 sgd_solver.cpp:105] Iteration 2592, lr = 0.001
I0401 14:07:23.901022  8859 solver.cpp:218] Iteration 2600 (2.18117 iter/s, 3.66776s/8 iters), loss = 4.58025
I0401 14:07:23.901124  8859 solver.cpp:237]     Train net output #0: loss = 4.58025 (* 1 = 4.58025 loss)
I0401 14:07:23.901130  8859 sgd_solver.cpp:105] Iteration 2600, lr = 0.001
I0401 14:07:27.582716  8859 solver.cpp:218] Iteration 2608 (2.17301 iter/s, 3.68154s/8 iters), loss = 4.31054
I0401 14:07:27.582763  8859 solver.cpp:237]     Train net output #0: loss = 4.31054 (* 1 = 4.31054 loss)
I0401 14:07:27.582770  8859 sgd_solver.cpp:105] Iteration 2608, lr = 0.001
I0401 14:07:31.204100  8859 solver.cpp:218] Iteration 2616 (2.20916 iter/s, 3.62128s/8 iters), loss = 4.47165
I0401 14:07:31.204144  8859 solver.cpp:237]     Train net output #0: loss = 4.47165 (* 1 = 4.47165 loss)
I0401 14:07:31.204150  8859 sgd_solver.cpp:105] Iteration 2616, lr = 0.001
I0401 14:07:34.312136  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2624.caffemodel
I0401 14:07:37.534085  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2624.solverstate
I0401 14:07:41.884598  8859 solver.cpp:330] Iteration 2624, Testing net (#0)
I0401 14:07:41.884625  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:07:51.990093  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:07:54.412230  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0474902
I0401 14:07:54.412346  8859 solver.cpp:397]     Test net output #1: loss = 4.84677 (* 1 = 4.84677 loss)
I0401 14:07:54.560338  8859 solver.cpp:218] Iteration 2624 (0.342525 iter/s, 23.3559s/8 iters), loss = 4.48055
I0401 14:07:54.561908  8859 solver.cpp:237]     Train net output #0: loss = 4.48055 (* 1 = 4.48055 loss)
I0401 14:07:54.561921  8859 sgd_solver.cpp:105] Iteration 2624, lr = 0.001
I0401 14:07:57.302459  8859 solver.cpp:218] Iteration 2632 (2.91916 iter/s, 2.74052s/8 iters), loss = 4.5564
I0401 14:07:57.302521  8859 solver.cpp:237]     Train net output #0: loss = 4.5564 (* 1 = 4.5564 loss)
I0401 14:07:57.302528  8859 sgd_solver.cpp:105] Iteration 2632, lr = 0.001
I0401 14:08:00.820417  8859 solver.cpp:218] Iteration 2640 (2.27412 iter/s, 3.51784s/8 iters), loss = 4.20736
I0401 14:08:00.820479  8859 solver.cpp:237]     Train net output #0: loss = 4.20736 (* 1 = 4.20736 loss)
I0401 14:08:00.820487  8859 sgd_solver.cpp:105] Iteration 2640, lr = 0.001
I0401 14:08:04.644234  8859 solver.cpp:218] Iteration 2648 (2.09221 iter/s, 3.8237s/8 iters), loss = 4.66748
I0401 14:08:04.644274  8859 solver.cpp:237]     Train net output #0: loss = 4.66748 (* 1 = 4.66748 loss)
I0401 14:08:04.644280  8859 sgd_solver.cpp:105] Iteration 2648, lr = 0.001
I0401 14:08:07.639169  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:08:08.459874  8859 solver.cpp:218] Iteration 2656 (2.09669 iter/s, 3.81553s/8 iters), loss = 4.31726
I0401 14:08:08.459935  8859 solver.cpp:237]     Train net output #0: loss = 4.31726 (* 1 = 4.31726 loss)
I0401 14:08:08.459944  8859 sgd_solver.cpp:105] Iteration 2656, lr = 0.001
I0401 14:08:12.166340  8859 solver.cpp:218] Iteration 2664 (2.15846 iter/s, 3.70635s/8 iters), loss = 4.41924
I0401 14:08:12.166400  8859 solver.cpp:237]     Train net output #0: loss = 4.41924 (* 1 = 4.41924 loss)
I0401 14:08:12.166409  8859 sgd_solver.cpp:105] Iteration 2664, lr = 0.001
I0401 14:08:15.829298  8859 solver.cpp:218] Iteration 2672 (2.1841 iter/s, 3.66284s/8 iters), loss = 4.55954
I0401 14:08:15.829355  8859 solver.cpp:237]     Train net output #0: loss = 4.55954 (* 1 = 4.55954 loss)
I0401 14:08:15.829365  8859 sgd_solver.cpp:105] Iteration 2672, lr = 0.001
I0401 14:08:19.624589  8859 solver.cpp:218] Iteration 2680 (2.10794 iter/s, 3.79518s/8 iters), loss = 4.4246
I0401 14:08:19.624629  8859 solver.cpp:237]     Train net output #0: loss = 4.4246 (* 1 = 4.4246 loss)
I0401 14:08:19.624634  8859 sgd_solver.cpp:105] Iteration 2680, lr = 0.001
I0401 14:08:23.036453  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2688.caffemodel
I0401 14:08:27.574385  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2688.solverstate
I0401 14:08:29.891577  8859 solver.cpp:330] Iteration 2688, Testing net (#0)
I0401 14:08:29.891597  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:08:40.126284  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:08:42.832710  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0440453
I0401 14:08:42.832747  8859 solver.cpp:397]     Test net output #1: loss = 4.8274 (* 1 = 4.8274 loss)
I0401 14:08:42.970487  8859 solver.cpp:218] Iteration 2688 (0.342677 iter/s, 23.3456s/8 iters), loss = 4.23045
I0401 14:08:42.970538  8859 solver.cpp:237]     Train net output #0: loss = 4.23045 (* 1 = 4.23045 loss)
I0401 14:08:42.970546  8859 sgd_solver.cpp:105] Iteration 2688, lr = 0.001
I0401 14:08:45.719313  8859 solver.cpp:218] Iteration 2696 (2.91044 iter/s, 2.74873s/8 iters), loss = 4.40203
I0401 14:08:45.719377  8859 solver.cpp:237]     Train net output #0: loss = 4.40203 (* 1 = 4.40203 loss)
I0401 14:08:45.719386  8859 sgd_solver.cpp:105] Iteration 2696, lr = 0.001
I0401 14:08:49.390419  8859 solver.cpp:218] Iteration 2704 (2.17925 iter/s, 3.67098s/8 iters), loss = 4.50528
I0401 14:08:49.390482  8859 solver.cpp:237]     Train net output #0: loss = 4.50528 (* 1 = 4.50528 loss)
I0401 14:08:49.390491  8859 sgd_solver.cpp:105] Iteration 2704, lr = 0.001
I0401 14:08:53.005210  8859 solver.cpp:218] Iteration 2712 (2.2132 iter/s, 3.61467s/8 iters), loss = 4.26024
I0401 14:08:53.005270  8859 solver.cpp:237]     Train net output #0: loss = 4.26024 (* 1 = 4.26024 loss)
I0401 14:08:53.005278  8859 sgd_solver.cpp:105] Iteration 2712, lr = 0.001
I0401 14:08:55.662760  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:08:56.890424  8859 solver.cpp:218] Iteration 2720 (2.05915 iter/s, 3.8851s/8 iters), loss = 4.29703
I0401 14:08:56.890477  8859 solver.cpp:237]     Train net output #0: loss = 4.29703 (* 1 = 4.29703 loss)
I0401 14:08:56.890484  8859 sgd_solver.cpp:105] Iteration 2720, lr = 0.001
I0401 14:09:00.765789  8859 solver.cpp:218] Iteration 2728 (2.06438 iter/s, 3.87526s/8 iters), loss = 4.44552
I0401 14:09:00.765913  8859 solver.cpp:237]     Train net output #0: loss = 4.44552 (* 1 = 4.44552 loss)
I0401 14:09:00.765920  8859 sgd_solver.cpp:105] Iteration 2728, lr = 0.001
I0401 14:09:04.484035  8859 solver.cpp:218] Iteration 2736 (2.15166 iter/s, 3.71807s/8 iters), loss = 4.44064
I0401 14:09:04.484088  8859 solver.cpp:237]     Train net output #0: loss = 4.44064 (* 1 = 4.44064 loss)
I0401 14:09:04.484097  8859 sgd_solver.cpp:105] Iteration 2736, lr = 0.001
I0401 14:09:08.257850  8859 solver.cpp:218] Iteration 2744 (2.11993 iter/s, 3.77371s/8 iters), loss = 4.31613
I0401 14:09:08.257901  8859 solver.cpp:237]     Train net output #0: loss = 4.31613 (* 1 = 4.31613 loss)
I0401 14:09:08.257910  8859 sgd_solver.cpp:105] Iteration 2744, lr = 0.001
I0401 14:09:11.595024  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2752.caffemodel
I0401 14:09:14.678422  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2752.solverstate
I0401 14:09:17.005558  8859 solver.cpp:330] Iteration 2752, Testing net (#0)
I0401 14:09:17.005580  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:09:21.915024  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:09:27.249094  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:09:29.866636  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0462598
I0401 14:09:29.866672  8859 solver.cpp:397]     Test net output #1: loss = 4.84518 (* 1 = 4.84518 loss)
I0401 14:09:30.008240  8859 solver.cpp:218] Iteration 2752 (0.367814 iter/s, 21.7501s/8 iters), loss = 4.53786
I0401 14:09:30.008303  8859 solver.cpp:237]     Train net output #0: loss = 4.53786 (* 1 = 4.53786 loss)
I0401 14:09:30.008312  8859 sgd_solver.cpp:105] Iteration 2752, lr = 0.001
I0401 14:09:32.778760  8859 solver.cpp:218] Iteration 2760 (2.88766 iter/s, 2.77041s/8 iters), loss = 4.4724
I0401 14:09:32.778882  8859 solver.cpp:237]     Train net output #0: loss = 4.4724 (* 1 = 4.4724 loss)
I0401 14:09:32.778892  8859 sgd_solver.cpp:105] Iteration 2760, lr = 0.001
I0401 14:09:36.415791  8859 solver.cpp:218] Iteration 2768 (2.1997 iter/s, 3.63686s/8 iters), loss = 4.33162
I0401 14:09:36.415843  8859 solver.cpp:237]     Train net output #0: loss = 4.33162 (* 1 = 4.33162 loss)
I0401 14:09:36.415850  8859 sgd_solver.cpp:105] Iteration 2768, lr = 0.001
I0401 14:09:40.309334  8859 solver.cpp:218] Iteration 2776 (2.05474 iter/s, 3.89343s/8 iters), loss = 4.42351
I0401 14:09:40.309391  8859 solver.cpp:237]     Train net output #0: loss = 4.42351 (* 1 = 4.42351 loss)
I0401 14:09:40.309401  8859 sgd_solver.cpp:105] Iteration 2776, lr = 0.001
I0401 14:09:42.342774  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:09:43.959587  8859 solver.cpp:218] Iteration 2784 (2.19169 iter/s, 3.65014s/8 iters), loss = 4.16959
I0401 14:09:43.959641  8859 solver.cpp:237]     Train net output #0: loss = 4.16959 (* 1 = 4.16959 loss)
I0401 14:09:43.959651  8859 sgd_solver.cpp:105] Iteration 2784, lr = 0.001
I0401 14:09:47.851529  8859 solver.cpp:218] Iteration 2792 (2.05559 iter/s, 3.89184s/8 iters), loss = 4.3927
I0401 14:09:47.851573  8859 solver.cpp:237]     Train net output #0: loss = 4.3927 (* 1 = 4.3927 loss)
I0401 14:09:47.851578  8859 sgd_solver.cpp:105] Iteration 2792, lr = 0.001
I0401 14:09:51.442018  8859 solver.cpp:218] Iteration 2800 (2.22817 iter/s, 3.59039s/8 iters), loss = 4.32985
I0401 14:09:51.442063  8859 solver.cpp:237]     Train net output #0: loss = 4.32985 (* 1 = 4.32985 loss)
I0401 14:09:51.442070  8859 sgd_solver.cpp:105] Iteration 2800, lr = 0.001
I0401 14:09:55.204051  8859 solver.cpp:218] Iteration 2808 (2.12657 iter/s, 3.76193s/8 iters), loss = 4.35078
I0401 14:09:55.204092  8859 solver.cpp:237]     Train net output #0: loss = 4.35078 (* 1 = 4.35078 loss)
I0401 14:09:55.204098  8859 sgd_solver.cpp:105] Iteration 2808, lr = 0.001
I0401 14:09:58.549798  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2816.caffemodel
I0401 14:10:01.712285  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2816.solverstate
I0401 14:10:04.481213  8859 solver.cpp:330] Iteration 2816, Testing net (#0)
I0401 14:10:04.481333  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:10:14.341869  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:10:17.035881  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0501968
I0401 14:10:17.035924  8859 solver.cpp:397]     Test net output #1: loss = 4.82439 (* 1 = 4.82439 loss)
I0401 14:10:17.172267  8859 solver.cpp:218] Iteration 2816 (0.364167 iter/s, 21.9679s/8 iters), loss = 4.36317
I0401 14:10:17.173878  8859 solver.cpp:237]     Train net output #0: loss = 4.36317 (* 1 = 4.36317 loss)
I0401 14:10:17.173892  8859 sgd_solver.cpp:105] Iteration 2816, lr = 0.001
I0401 14:10:19.981303  8859 solver.cpp:218] Iteration 2824 (2.84962 iter/s, 2.80739s/8 iters), loss = 4.30057
I0401 14:10:19.981349  8859 solver.cpp:237]     Train net output #0: loss = 4.30057 (* 1 = 4.30057 loss)
I0401 14:10:19.981355  8859 sgd_solver.cpp:105] Iteration 2824, lr = 0.001
I0401 14:10:23.632022  8859 solver.cpp:218] Iteration 2832 (2.19141 iter/s, 3.65061s/8 iters), loss = 4.4726
I0401 14:10:23.632086  8859 solver.cpp:237]     Train net output #0: loss = 4.4726 (* 1 = 4.4726 loss)
I0401 14:10:23.632094  8859 sgd_solver.cpp:105] Iteration 2832, lr = 0.001
I0401 14:10:27.312008  8859 solver.cpp:218] Iteration 2840 (2.17399 iter/s, 3.67987s/8 iters), loss = 4.37354
I0401 14:10:27.312062  8859 solver.cpp:237]     Train net output #0: loss = 4.37354 (* 1 = 4.37354 loss)
I0401 14:10:27.312068  8859 sgd_solver.cpp:105] Iteration 2840, lr = 0.001
I0401 14:10:28.956660  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:10:31.143692  8859 solver.cpp:218] Iteration 2848 (2.08792 iter/s, 3.83157s/8 iters), loss = 4.10564
I0401 14:10:31.143755  8859 solver.cpp:237]     Train net output #0: loss = 4.10564 (* 1 = 4.10564 loss)
I0401 14:10:31.143767  8859 sgd_solver.cpp:105] Iteration 2848, lr = 0.001
I0401 14:10:34.953627  8859 solver.cpp:218] Iteration 2856 (2.09984 iter/s, 3.80982s/8 iters), loss = 4.29898
I0401 14:10:34.953728  8859 solver.cpp:237]     Train net output #0: loss = 4.29898 (* 1 = 4.29898 loss)
I0401 14:10:34.953737  8859 sgd_solver.cpp:105] Iteration 2856, lr = 0.001
I0401 14:10:38.678229  8859 solver.cpp:218] Iteration 2864 (2.14797 iter/s, 3.72445s/8 iters), loss = 4.29154
I0401 14:10:38.678284  8859 solver.cpp:237]     Train net output #0: loss = 4.29154 (* 1 = 4.29154 loss)
I0401 14:10:38.678292  8859 sgd_solver.cpp:105] Iteration 2864, lr = 0.001
I0401 14:10:42.358783  8859 solver.cpp:218] Iteration 2872 (2.17365 iter/s, 3.68044s/8 iters), loss = 4.24854
I0401 14:10:42.358839  8859 solver.cpp:237]     Train net output #0: loss = 4.24854 (* 1 = 4.24854 loss)
I0401 14:10:42.358847  8859 sgd_solver.cpp:105] Iteration 2872, lr = 0.001
I0401 14:10:45.462641  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2880.caffemodel
I0401 14:10:48.550309  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2880.solverstate
I0401 14:10:50.968533  8859 solver.cpp:330] Iteration 2880, Testing net (#0)
I0401 14:10:50.968557  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:11:01.108888  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:11:03.843578  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0590551
I0401 14:11:03.843618  8859 solver.cpp:397]     Test net output #1: loss = 4.81603 (* 1 = 4.81603 loss)
I0401 14:11:03.986217  8859 solver.cpp:218] Iteration 2880 (0.369905 iter/s, 21.6271s/8 iters), loss = 4.27321
I0401 14:11:03.986275  8859 solver.cpp:237]     Train net output #0: loss = 4.27321 (* 1 = 4.27321 loss)
I0401 14:11:03.986284  8859 sgd_solver.cpp:105] Iteration 2880, lr = 0.001
I0401 14:11:06.902747  8859 solver.cpp:218] Iteration 2888 (2.74309 iter/s, 2.91642s/8 iters), loss = 4.20851
I0401 14:11:06.902901  8859 solver.cpp:237]     Train net output #0: loss = 4.20851 (* 1 = 4.20851 loss)
I0401 14:11:06.902911  8859 sgd_solver.cpp:105] Iteration 2888, lr = 0.001
I0401 14:11:10.638031  8859 solver.cpp:218] Iteration 2896 (2.14186 iter/s, 3.73508s/8 iters), loss = 4.17667
I0401 14:11:10.638084  8859 solver.cpp:237]     Train net output #0: loss = 4.17667 (* 1 = 4.17667 loss)
I0401 14:11:10.638093  8859 sgd_solver.cpp:105] Iteration 2896, lr = 0.001
I0401 14:11:14.540971  8859 solver.cpp:218] Iteration 2904 (2.0498 iter/s, 3.90283s/8 iters), loss = 4.33262
I0401 14:11:14.541019  8859 solver.cpp:237]     Train net output #0: loss = 4.33262 (* 1 = 4.33262 loss)
I0401 14:11:14.541025  8859 sgd_solver.cpp:105] Iteration 2904, lr = 0.001
I0401 14:11:15.860525  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:11:17.948644  8859 solver.cpp:218] Iteration 2912 (2.34772 iter/s, 3.40757s/8 iters), loss = 4.27973
I0401 14:11:17.948712  8859 solver.cpp:237]     Train net output #0: loss = 4.27973 (* 1 = 4.27973 loss)
I0401 14:11:17.948722  8859 sgd_solver.cpp:105] Iteration 2912, lr = 0.001
I0401 14:11:21.553275  8859 solver.cpp:218] Iteration 2920 (2.21945 iter/s, 3.6045s/8 iters), loss = 3.99638
I0401 14:11:21.553333  8859 solver.cpp:237]     Train net output #0: loss = 3.99638 (* 1 = 3.99638 loss)
I0401 14:11:21.553341  8859 sgd_solver.cpp:105] Iteration 2920, lr = 0.001
I0401 14:11:25.197046  8859 solver.cpp:218] Iteration 2928 (2.19559 iter/s, 3.64367s/8 iters), loss = 4.14441
I0401 14:11:25.197094  8859 solver.cpp:237]     Train net output #0: loss = 4.14441 (* 1 = 4.14441 loss)
I0401 14:11:25.197103  8859 sgd_solver.cpp:105] Iteration 2928, lr = 0.001
I0401 14:11:28.903282  8859 solver.cpp:218] Iteration 2936 (2.15858 iter/s, 3.70613s/8 iters), loss = 4.24872
I0401 14:11:28.903335  8859 solver.cpp:237]     Train net output #0: loss = 4.24872 (* 1 = 4.24872 loss)
I0401 14:11:28.903344  8859 sgd_solver.cpp:105] Iteration 2936, lr = 0.001
I0401 14:11:32.169544  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2944.caffemodel
I0401 14:11:35.267459  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2944.solverstate
I0401 14:11:37.637022  8859 solver.cpp:330] Iteration 2944, Testing net (#0)
I0401 14:11:37.637089  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:11:48.138764  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:11:51.001628  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0565945
I0401 14:11:51.001657  8859 solver.cpp:397]     Test net output #1: loss = 4.84547 (* 1 = 4.84547 loss)
I0401 14:11:51.143144  8859 solver.cpp:218] Iteration 2944 (0.359719 iter/s, 22.2396s/8 iters), loss = 4.21284
I0401 14:11:51.143204  8859 solver.cpp:237]     Train net output #0: loss = 4.21284 (* 1 = 4.21284 loss)
I0401 14:11:51.143211  8859 sgd_solver.cpp:105] Iteration 2944, lr = 0.001
I0401 14:11:53.965502  8859 solver.cpp:218] Iteration 2952 (2.83462 iter/s, 2.82225s/8 iters), loss = 4.35366
I0401 14:11:53.965548  8859 solver.cpp:237]     Train net output #0: loss = 4.35366 (* 1 = 4.35366 loss)
I0401 14:11:53.965554  8859 sgd_solver.cpp:105] Iteration 2952, lr = 0.001
I0401 14:11:57.570720  8859 solver.cpp:218] Iteration 2960 (2.21907 iter/s, 3.60511s/8 iters), loss = 4.20819
I0401 14:11:57.572933  8859 solver.cpp:237]     Train net output #0: loss = 4.20819 (* 1 = 4.20819 loss)
I0401 14:11:57.572949  8859 sgd_solver.cpp:105] Iteration 2960, lr = 0.001
I0401 14:12:01.343623  8859 solver.cpp:218] Iteration 2968 (2.12165 iter/s, 3.77065s/8 iters), loss = 4.17497
I0401 14:12:01.343674  8859 solver.cpp:237]     Train net output #0: loss = 4.17497 (* 1 = 4.17497 loss)
I0401 14:12:01.343681  8859 sgd_solver.cpp:105] Iteration 2968, lr = 0.001
I0401 14:12:02.480250  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:12:05.240037  8859 solver.cpp:218] Iteration 2976 (2.05322 iter/s, 3.89631s/8 iters), loss = 4.11792
I0401 14:12:05.240080  8859 solver.cpp:237]     Train net output #0: loss = 4.11792 (* 1 = 4.11792 loss)
I0401 14:12:05.240087  8859 sgd_solver.cpp:105] Iteration 2976, lr = 0.001
I0401 14:12:08.982213  8859 solver.cpp:218] Iteration 2984 (2.13785 iter/s, 3.74207s/8 iters), loss = 4.32519
I0401 14:12:08.982369  8859 solver.cpp:237]     Train net output #0: loss = 4.32519 (* 1 = 4.32519 loss)
I0401 14:12:08.982379  8859 sgd_solver.cpp:105] Iteration 2984, lr = 0.001
I0401 14:12:12.778877  8859 solver.cpp:218] Iteration 2992 (2.10723 iter/s, 3.79646s/8 iters), loss = 4.17548
I0401 14:12:12.778914  8859 solver.cpp:237]     Train net output #0: loss = 4.17548 (* 1 = 4.17548 loss)
I0401 14:12:12.778920  8859 sgd_solver.cpp:105] Iteration 2992, lr = 0.001
I0401 14:12:16.378166  8859 solver.cpp:218] Iteration 3000 (2.22272 iter/s, 3.5992s/8 iters), loss = 4.2008
I0401 14:12:16.378216  8859 solver.cpp:237]     Train net output #0: loss = 4.2008 (* 1 = 4.2008 loss)
I0401 14:12:16.378224  8859 sgd_solver.cpp:105] Iteration 3000, lr = 0.001
I0401 14:12:19.671178  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3008.caffemodel
I0401 14:12:22.811435  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3008.solverstate
I0401 14:12:25.146384  8859 solver.cpp:330] Iteration 3008, Testing net (#0)
I0401 14:12:25.146404  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:12:35.012159  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:12:37.846052  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0462598
I0401 14:12:37.846091  8859 solver.cpp:397]     Test net output #1: loss = 4.8473 (* 1 = 4.8473 loss)
I0401 14:12:37.999593  8859 solver.cpp:218] Iteration 3008 (0.370008 iter/s, 21.6211s/8 iters), loss = 4.15705
I0401 14:12:38.001173  8859 solver.cpp:237]     Train net output #0: loss = 4.15705 (* 1 = 4.15705 loss)
I0401 14:12:38.001194  8859 sgd_solver.cpp:105] Iteration 3008, lr = 0.001
I0401 14:12:40.618755  8859 solver.cpp:218] Iteration 3016 (3.05628 iter/s, 2.61756s/8 iters), loss = 4.27869
I0401 14:12:40.618896  8859 solver.cpp:237]     Train net output #0: loss = 4.27869 (* 1 = 4.27869 loss)
I0401 14:12:40.618907  8859 sgd_solver.cpp:105] Iteration 3016, lr = 0.001
I0401 14:12:44.250836  8859 solver.cpp:218] Iteration 3024 (2.20271 iter/s, 3.63189s/8 iters), loss = 4.38662
I0401 14:12:44.250883  8859 solver.cpp:237]     Train net output #0: loss = 4.38662 (* 1 = 4.38662 loss)
I0401 14:12:44.250888  8859 sgd_solver.cpp:105] Iteration 3024, lr = 0.001
I0401 14:12:47.810863  8859 solver.cpp:218] Iteration 3032 (2.24724 iter/s, 3.55993s/8 iters), loss = 4.22693
I0401 14:12:47.810905  8859 solver.cpp:237]     Train net output #0: loss = 4.22693 (* 1 = 4.22693 loss)
I0401 14:12:47.810914  8859 sgd_solver.cpp:105] Iteration 3032, lr = 0.001
I0401 14:12:48.457638  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:12:51.528506  8859 solver.cpp:218] Iteration 3040 (2.15196 iter/s, 3.71755s/8 iters), loss = 4.15774
I0401 14:12:51.528540  8859 solver.cpp:237]     Train net output #0: loss = 4.15774 (* 1 = 4.15774 loss)
I0401 14:12:51.528545  8859 sgd_solver.cpp:105] Iteration 3040, lr = 0.001
I0401 14:12:55.431001  8859 solver.cpp:218] Iteration 3048 (2.05002 iter/s, 3.9024s/8 iters), loss = 4.22728
I0401 14:12:55.431051  8859 solver.cpp:237]     Train net output #0: loss = 4.22728 (* 1 = 4.22728 loss)
I0401 14:12:55.431059  8859 sgd_solver.cpp:105] Iteration 3048, lr = 0.001
I0401 14:12:59.092604  8859 solver.cpp:218] Iteration 3056 (2.1849 iter/s, 3.6615s/8 iters), loss = 4.46257
I0401 14:12:59.092662  8859 solver.cpp:237]     Train net output #0: loss = 4.46257 (* 1 = 4.46257 loss)
I0401 14:12:59.092672  8859 sgd_solver.cpp:105] Iteration 3056, lr = 0.001
I0401 14:13:02.858489  8859 solver.cpp:218] Iteration 3064 (2.1244 iter/s, 3.76577s/8 iters), loss = 4.32304
I0401 14:13:02.858536  8859 solver.cpp:237]     Train net output #0: loss = 4.32304 (* 1 = 4.32304 loss)
I0401 14:13:02.858541  8859 sgd_solver.cpp:105] Iteration 3064, lr = 0.001
I0401 14:13:06.216660  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3072.caffemodel
I0401 14:13:12.072538  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3072.solverstate
I0401 14:13:17.063081  8859 solver.cpp:330] Iteration 3072, Testing net (#0)
I0401 14:13:17.063102  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:13:27.214681  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:13:29.939752  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:13:30.132030  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0457677
I0401 14:13:30.132067  8859 solver.cpp:397]     Test net output #1: loss = 4.85771 (* 1 = 4.85771 loss)
I0401 14:13:30.284663  8859 solver.cpp:218] Iteration 3072 (0.291696 iter/s, 27.4258s/8 iters), loss = 4.18477
I0401 14:13:30.286238  8859 solver.cpp:237]     Train net output #0: loss = 4.18477 (* 1 = 4.18477 loss)
I0401 14:13:30.286259  8859 sgd_solver.cpp:105] Iteration 3072, lr = 0.001
I0401 14:13:32.868572  8859 solver.cpp:218] Iteration 3080 (3.09801 iter/s, 2.5823s/8 iters), loss = 4.28866
I0401 14:13:32.868631  8859 solver.cpp:237]     Train net output #0: loss = 4.28866 (* 1 = 4.28866 loss)
I0401 14:13:32.868640  8859 sgd_solver.cpp:105] Iteration 3080, lr = 0.001
I0401 14:13:36.627022  8859 solver.cpp:218] Iteration 3088 (2.1286 iter/s, 3.75835s/8 iters), loss = 4.08814
I0401 14:13:36.627070  8859 solver.cpp:237]     Train net output #0: loss = 4.08814 (* 1 = 4.08814 loss)
I0401 14:13:36.627077  8859 sgd_solver.cpp:105] Iteration 3088, lr = 0.001
I0401 14:13:40.216954  8859 solver.cpp:218] Iteration 3096 (2.22852 iter/s, 3.58983s/8 iters), loss = 4.01614
I0401 14:13:40.217007  8859 solver.cpp:237]     Train net output #0: loss = 4.01614 (* 1 = 4.01614 loss)
I0401 14:13:40.217015  8859 sgd_solver.cpp:105] Iteration 3096, lr = 0.001
I0401 14:13:40.472407  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:13:43.900254  8859 solver.cpp:218] Iteration 3104 (2.17203 iter/s, 3.68319s/8 iters), loss = 4.25214
I0401 14:13:43.900365  8859 solver.cpp:237]     Train net output #0: loss = 4.25214 (* 1 = 4.25214 loss)
I0401 14:13:43.900374  8859 sgd_solver.cpp:105] Iteration 3104, lr = 0.001
I0401 14:13:47.410249  8859 solver.cpp:218] Iteration 3112 (2.27931 iter/s, 3.50983s/8 iters), loss = 3.98582
I0401 14:13:47.410310  8859 solver.cpp:237]     Train net output #0: loss = 3.98582 (* 1 = 3.98582 loss)
I0401 14:13:47.410317  8859 sgd_solver.cpp:105] Iteration 3112, lr = 0.001
I0401 14:13:51.267462  8859 solver.cpp:218] Iteration 3120 (2.0741 iter/s, 3.85709s/8 iters), loss = 4.06457
I0401 14:13:51.267519  8859 solver.cpp:237]     Train net output #0: loss = 4.06457 (* 1 = 4.06457 loss)
I0401 14:13:51.267527  8859 sgd_solver.cpp:105] Iteration 3120, lr = 0.001
I0401 14:13:55.179284  8859 solver.cpp:218] Iteration 3128 (2.04514 iter/s, 3.91171s/8 iters), loss = 4.24278
I0401 14:13:55.179337  8859 solver.cpp:237]     Train net output #0: loss = 4.24278 (* 1 = 4.24278 loss)
I0401 14:13:55.179343  8859 sgd_solver.cpp:105] Iteration 3128, lr = 0.001
I0401 14:13:58.343425  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3136.caffemodel
I0401 14:14:01.504606  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3136.solverstate
I0401 14:14:03.903570  8859 solver.cpp:330] Iteration 3136, Testing net (#0)
I0401 14:14:03.903594  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:14:13.469652  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:14:16.400650  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0558563
I0401 14:14:16.400758  8859 solver.cpp:397]     Test net output #1: loss = 4.84404 (* 1 = 4.84404 loss)
I0401 14:14:16.542627  8859 solver.cpp:218] Iteration 3136 (0.374478 iter/s, 21.3631s/8 iters), loss = 4.37697
I0401 14:14:16.542677  8859 solver.cpp:237]     Train net output #0: loss = 4.37697 (* 1 = 4.37697 loss)
I0401 14:14:16.542685  8859 sgd_solver.cpp:105] Iteration 3136, lr = 0.001
I0401 14:14:19.431653  8859 solver.cpp:218] Iteration 3144 (2.76919 iter/s, 2.88893s/8 iters), loss = 4.23868
I0401 14:14:19.431713  8859 solver.cpp:237]     Train net output #0: loss = 4.23868 (* 1 = 4.23868 loss)
I0401 14:14:19.431722  8859 sgd_solver.cpp:105] Iteration 3144, lr = 0.001
I0401 14:14:23.225553  8859 solver.cpp:218] Iteration 3152 (2.10871 iter/s, 3.79379s/8 iters), loss = 4.18678
I0401 14:14:23.225598  8859 solver.cpp:237]     Train net output #0: loss = 4.18678 (* 1 = 4.18678 loss)
I0401 14:14:23.225605  8859 sgd_solver.cpp:105] Iteration 3152, lr = 0.001
I0401 14:14:26.971197  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:14:27.128877  8859 solver.cpp:218] Iteration 3160 (2.04959 iter/s, 3.90322s/8 iters), loss = 4.15998
I0401 14:14:27.128924  8859 solver.cpp:237]     Train net output #0: loss = 4.15998 (* 1 = 4.15998 loss)
I0401 14:14:27.128931  8859 sgd_solver.cpp:105] Iteration 3160, lr = 0.001
I0401 14:14:30.852316  8859 solver.cpp:218] Iteration 3168 (2.14861 iter/s, 3.72334s/8 iters), loss = 3.85351
I0401 14:14:30.852370  8859 solver.cpp:237]     Train net output #0: loss = 3.85351 (* 1 = 3.85351 loss)
I0401 14:14:30.852377  8859 sgd_solver.cpp:105] Iteration 3168, lr = 0.001
I0401 14:14:34.411939  8859 solver.cpp:218] Iteration 3176 (2.2475 iter/s, 3.55952s/8 iters), loss = 3.82562
I0401 14:14:34.411988  8859 solver.cpp:237]     Train net output #0: loss = 3.82562 (* 1 = 3.82562 loss)
I0401 14:14:34.411995  8859 sgd_solver.cpp:105] Iteration 3176, lr = 0.001
I0401 14:14:38.151278  8859 solver.cpp:218] Iteration 3184 (2.13947 iter/s, 3.73924s/8 iters), loss = 3.97253
I0401 14:14:38.151335  8859 solver.cpp:237]     Train net output #0: loss = 3.97253 (* 1 = 3.97253 loss)
I0401 14:14:38.151342  8859 sgd_solver.cpp:105] Iteration 3184, lr = 0.001
I0401 14:14:41.798547  8859 solver.cpp:218] Iteration 3192 (2.19349 iter/s, 3.64715s/8 iters), loss = 3.90288
I0401 14:14:41.798609  8859 solver.cpp:237]     Train net output #0: loss = 3.90288 (* 1 = 3.90288 loss)
I0401 14:14:41.798619  8859 sgd_solver.cpp:105] Iteration 3192, lr = 0.001
I0401 14:14:44.954640  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3200.caffemodel
I0401 14:14:48.074579  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3200.solverstate
I0401 14:14:50.388945  8859 solver.cpp:330] Iteration 3200, Testing net (#0)
I0401 14:14:50.388967  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:15:00.361996  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:15:03.297999  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0575787
I0401 14:15:03.298030  8859 solver.cpp:397]     Test net output #1: loss = 4.85325 (* 1 = 4.85325 loss)
I0401 14:15:03.429394  8859 solver.cpp:218] Iteration 3200 (0.369847 iter/s, 21.6306s/8 iters), loss = 4.13458
I0401 14:15:03.429455  8859 solver.cpp:237]     Train net output #0: loss = 4.13458 (* 1 = 4.13458 loss)
I0401 14:15:03.429466  8859 sgd_solver.cpp:105] Iteration 3200, lr = 0.001
I0401 14:15:06.226223  8859 solver.cpp:218] Iteration 3208 (2.86049 iter/s, 2.79673s/8 iters), loss = 4.26392
I0401 14:15:06.226279  8859 solver.cpp:237]     Train net output #0: loss = 4.26392 (* 1 = 4.26392 loss)
I0401 14:15:06.226287  8859 sgd_solver.cpp:105] Iteration 3208, lr = 0.001
I0401 14:15:09.821535  8859 solver.cpp:218] Iteration 3216 (2.22519 iter/s, 3.5952s/8 iters), loss = 4.16232
I0401 14:15:09.827798  8859 solver.cpp:237]     Train net output #0: loss = 4.16232 (* 1 = 4.16232 loss)
I0401 14:15:09.827824  8859 sgd_solver.cpp:105] Iteration 3216, lr = 0.001
I0401 14:15:13.114744  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:15:13.487298  8859 solver.cpp:218] Iteration 3224 (2.18611 iter/s, 3.65947s/8 iters), loss = 3.87275
I0401 14:15:13.493463  8859 solver.cpp:237]     Train net output #0: loss = 3.87275 (* 1 = 3.87275 loss)
I0401 14:15:13.493482  8859 sgd_solver.cpp:105] Iteration 3224, lr = 0.001
I0401 14:15:17.364877  8859 solver.cpp:218] Iteration 3232 (2.06645 iter/s, 3.87138s/8 iters), loss = 3.94035
I0401 14:15:17.364931  8859 solver.cpp:237]     Train net output #0: loss = 3.94035 (* 1 = 3.94035 loss)
I0401 14:15:17.364938  8859 sgd_solver.cpp:105] Iteration 3232, lr = 0.001
I0401 14:15:21.092674  8859 solver.cpp:218] Iteration 3240 (2.14611 iter/s, 3.72768s/8 iters), loss = 3.9231
I0401 14:15:21.104959  8859 solver.cpp:237]     Train net output #0: loss = 3.9231 (* 1 = 3.9231 loss)
I0401 14:15:21.104974  8859 sgd_solver.cpp:105] Iteration 3240, lr = 0.001
I0401 14:15:24.716779  8859 solver.cpp:218] Iteration 3248 (2.21498 iter/s, 3.61178s/8 iters), loss = 4.20478
I0401 14:15:24.716838  8859 solver.cpp:237]     Train net output #0: loss = 4.20478 (* 1 = 4.20478 loss)
I0401 14:15:24.716850  8859 sgd_solver.cpp:105] Iteration 3248, lr = 0.001
I0401 14:15:28.473675  8859 solver.cpp:218] Iteration 3256 (2.12948 iter/s, 3.75679s/8 iters), loss = 4.21759
I0401 14:15:28.473721  8859 solver.cpp:237]     Train net output #0: loss = 4.21759 (* 1 = 4.21759 loss)
I0401 14:15:28.473727  8859 sgd_solver.cpp:105] Iteration 3256, lr = 0.001
I0401 14:15:31.724309  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0401 14:15:34.915652  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0401 14:15:37.229275  8859 solver.cpp:330] Iteration 3264, Testing net (#0)
I0401 14:15:37.229295  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:15:46.945334  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:15:50.048768  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0531496
I0401 14:15:50.048804  8859 solver.cpp:397]     Test net output #1: loss = 4.86521 (* 1 = 4.86521 loss)
I0401 14:15:50.186005  8859 solver.cpp:218] Iteration 3264 (0.368459 iter/s, 21.712s/8 iters), loss = 4.30408
I0401 14:15:50.186059  8859 solver.cpp:237]     Train net output #0: loss = 4.30408 (* 1 = 4.30408 loss)
I0401 14:15:50.186069  8859 sgd_solver.cpp:105] Iteration 3264, lr = 0.001
I0401 14:15:53.060997  8859 solver.cpp:218] Iteration 3272 (2.78272 iter/s, 2.87489s/8 iters), loss = 4.034
I0401 14:15:53.061110  8859 solver.cpp:237]     Train net output #0: loss = 4.034 (* 1 = 4.034 loss)
I0401 14:15:53.061120  8859 sgd_solver.cpp:105] Iteration 3272, lr = 0.001
I0401 14:15:56.717922  8859 solver.cpp:218] Iteration 3280 (2.18773 iter/s, 3.65676s/8 iters), loss = 4.22634
I0401 14:15:56.717974  8859 solver.cpp:237]     Train net output #0: loss = 4.22634 (* 1 = 4.22634 loss)
I0401 14:15:56.717983  8859 sgd_solver.cpp:105] Iteration 3280, lr = 0.001
I0401 14:15:59.535615  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:00.318677  8859 solver.cpp:218] Iteration 3288 (2.22182 iter/s, 3.60065s/8 iters), loss = 3.86606
I0401 14:16:00.318717  8859 solver.cpp:237]     Train net output #0: loss = 3.86606 (* 1 = 3.86606 loss)
I0401 14:16:00.318722  8859 sgd_solver.cpp:105] Iteration 3288, lr = 0.001
I0401 14:16:04.075034  8859 solver.cpp:218] Iteration 3296 (2.12978 iter/s, 3.75626s/8 iters), loss = 3.93521
I0401 14:16:04.075079  8859 solver.cpp:237]     Train net output #0: loss = 3.93521 (* 1 = 3.93521 loss)
I0401 14:16:04.075085  8859 sgd_solver.cpp:105] Iteration 3296, lr = 0.001
I0401 14:16:07.740913  8859 solver.cpp:218] Iteration 3304 (2.18235 iter/s, 3.66577s/8 iters), loss = 3.91971
I0401 14:16:07.747074  8859 solver.cpp:237]     Train net output #0: loss = 3.91971 (* 1 = 3.91971 loss)
I0401 14:16:07.747090  8859 sgd_solver.cpp:105] Iteration 3304, lr = 0.001
I0401 14:16:11.630878  8859 solver.cpp:218] Iteration 3312 (2.05986 iter/s, 3.88375s/8 iters), loss = 3.96945
I0401 14:16:11.630926  8859 solver.cpp:237]     Train net output #0: loss = 3.96945 (* 1 = 3.96945 loss)
I0401 14:16:11.630934  8859 sgd_solver.cpp:105] Iteration 3312, lr = 0.001
I0401 14:16:15.392686  8859 solver.cpp:218] Iteration 3320 (2.12669 iter/s, 3.76171s/8 iters), loss = 3.76992
I0401 14:16:15.392731  8859 solver.cpp:237]     Train net output #0: loss = 3.76992 (* 1 = 3.76992 loss)
I0401 14:16:15.392737  8859 sgd_solver.cpp:105] Iteration 3320, lr = 0.001
I0401 14:16:18.442029  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3328.caffemodel
I0401 14:16:21.587278  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3328.solverstate
I0401 14:16:23.958473  8859 solver.cpp:330] Iteration 3328, Testing net (#0)
I0401 14:16:23.958566  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:16:33.708341  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:36.623047  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0536417
I0401 14:16:36.623083  8859 solver.cpp:397]     Test net output #1: loss = 4.87114 (* 1 = 4.87114 loss)
I0401 14:16:36.761982  8859 solver.cpp:218] Iteration 3328 (0.374374 iter/s, 21.369s/8 iters), loss = 4.13816
I0401 14:16:36.762037  8859 solver.cpp:237]     Train net output #0: loss = 4.13816 (* 1 = 4.13816 loss)
I0401 14:16:36.762044  8859 sgd_solver.cpp:105] Iteration 3328, lr = 0.001
I0401 14:16:39.527909  8859 solver.cpp:218] Iteration 3336 (2.89245 iter/s, 2.76582s/8 iters), loss = 3.98985
I0401 14:16:39.527957  8859 solver.cpp:237]     Train net output #0: loss = 3.98985 (* 1 = 3.98985 loss)
I0401 14:16:39.527966  8859 sgd_solver.cpp:105] Iteration 3336, lr = 0.001
I0401 14:16:43.546106  8859 solver.cpp:218] Iteration 3344 (1.991 iter/s, 4.01809s/8 iters), loss = 4.05336
I0401 14:16:43.552268  8859 solver.cpp:237]     Train net output #0: loss = 4.05336 (* 1 = 4.05336 loss)
I0401 14:16:43.552291  8859 sgd_solver.cpp:105] Iteration 3344, lr = 0.001
I0401 14:16:46.067049  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:47.269713  8859 solver.cpp:218] Iteration 3352 (2.15204 iter/s, 3.71741s/8 iters), loss = 4.04261
I0401 14:16:47.269769  8859 solver.cpp:237]     Train net output #0: loss = 4.04261 (* 1 = 4.04261 loss)
I0401 14:16:47.269780  8859 sgd_solver.cpp:105] Iteration 3352, lr = 0.001
I0401 14:16:50.949065  8859 solver.cpp:218] Iteration 3360 (2.17436 iter/s, 3.67924s/8 iters), loss = 3.77602
I0401 14:16:50.949116  8859 solver.cpp:237]     Train net output #0: loss = 3.77602 (* 1 = 3.77602 loss)
I0401 14:16:50.949126  8859 sgd_solver.cpp:105] Iteration 3360, lr = 0.001
I0401 14:16:54.552273  8859 solver.cpp:218] Iteration 3368 (2.22031 iter/s, 3.6031s/8 iters), loss = 3.97546
I0401 14:16:54.552376  8859 solver.cpp:237]     Train net output #0: loss = 3.97546 (* 1 = 3.97546 loss)
I0401 14:16:54.552383  8859 sgd_solver.cpp:105] Iteration 3368, lr = 0.001
I0401 14:16:58.207504  8859 solver.cpp:218] Iteration 3376 (2.18883 iter/s, 3.65493s/8 iters), loss = 3.73253
I0401 14:16:58.207552  8859 solver.cpp:237]     Train net output #0: loss = 3.73253 (* 1 = 3.73253 loss)
I0401 14:16:58.207559  8859 sgd_solver.cpp:105] Iteration 3376, lr = 0.001
I0401 14:17:02.064070  8859 solver.cpp:218] Iteration 3384 (2.07444 iter/s, 3.85646s/8 iters), loss = 4.14359
I0401 14:17:02.064129  8859 solver.cpp:237]     Train net output #0: loss = 4.14359 (* 1 = 4.14359 loss)
I0401 14:17:02.064138  8859 sgd_solver.cpp:105] Iteration 3384, lr = 0.001
I0401 14:17:05.242156  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3392.caffemodel
I0401 14:17:08.315799  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3392.solverstate
I0401 14:17:10.632612  8859 solver.cpp:330] Iteration 3392, Testing net (#0)
I0401 14:17:10.632633  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:17:20.212798  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:17:23.359186  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0511811
I0401 14:17:23.359220  8859 solver.cpp:397]     Test net output #1: loss = 4.92029 (* 1 = 4.92029 loss)
I0401 14:17:23.505261  8859 solver.cpp:218] Iteration 3392 (0.373119 iter/s, 21.4409s/8 iters), loss = 3.98952
I0401 14:17:23.505316  8859 solver.cpp:237]     Train net output #0: loss = 3.98952 (* 1 = 3.98952 loss)
I0401 14:17:23.505324  8859 sgd_solver.cpp:105] Iteration 3392, lr = 0.001
I0401 14:17:26.406855  8859 solver.cpp:218] Iteration 3400 (2.7572 iter/s, 2.90149s/8 iters), loss = 3.81837
I0401 14:17:26.406996  8859 solver.cpp:237]     Train net output #0: loss = 3.81837 (* 1 = 3.81837 loss)
I0401 14:17:26.407006  8859 sgd_solver.cpp:105] Iteration 3400, lr = 0.001
I0401 14:17:29.958349  8859 solver.cpp:218] Iteration 3408 (2.2527 iter/s, 3.5513s/8 iters), loss = 3.89786
I0401 14:17:29.958407  8859 solver.cpp:237]     Train net output #0: loss = 3.89786 (* 1 = 3.89786 loss)
I0401 14:17:29.958415  8859 sgd_solver.cpp:105] Iteration 3408, lr = 0.001
I0401 14:17:32.072098  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:17:33.812927  8859 solver.cpp:218] Iteration 3416 (2.07552 iter/s, 3.85446s/8 iters), loss = 3.82713
I0401 14:17:33.812975  8859 solver.cpp:237]     Train net output #0: loss = 3.82713 (* 1 = 3.82713 loss)
I0401 14:17:33.812983  8859 sgd_solver.cpp:105] Iteration 3416, lr = 0.001
I0401 14:17:37.735620  8859 solver.cpp:218] Iteration 3424 (2.03948 iter/s, 3.92258s/8 iters), loss = 3.78193
I0401 14:17:37.735687  8859 solver.cpp:237]     Train net output #0: loss = 3.78193 (* 1 = 3.78193 loss)
I0401 14:17:37.735695  8859 sgd_solver.cpp:105] Iteration 3424, lr = 0.001
I0401 14:17:41.282783  8859 solver.cpp:218] Iteration 3432 (2.2554 iter/s, 3.54704s/8 iters), loss = 3.63694
I0401 14:17:41.282841  8859 solver.cpp:237]     Train net output #0: loss = 3.63694 (* 1 = 3.63694 loss)
I0401 14:17:41.282850  8859 sgd_solver.cpp:105] Iteration 3432, lr = 0.001
I0401 14:17:44.941949  8859 solver.cpp:218] Iteration 3440 (2.18636 iter/s, 3.65906s/8 iters), loss = 3.73826
I0401 14:17:44.941998  8859 solver.cpp:237]     Train net output #0: loss = 3.73826 (* 1 = 3.73826 loss)
I0401 14:17:44.942005  8859 sgd_solver.cpp:105] Iteration 3440, lr = 0.001
I0401 14:17:48.796408  8859 solver.cpp:218] Iteration 3448 (2.07557 iter/s, 3.85436s/8 iters), loss = 3.72491
I0401 14:17:48.796455  8859 solver.cpp:237]     Train net output #0: loss = 3.72491 (* 1 = 3.72491 loss)
I0401 14:17:48.796463  8859 sgd_solver.cpp:105] Iteration 3448, lr = 0.001
I0401 14:17:52.114506  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3456.caffemodel
I0401 14:17:55.236969  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3456.solverstate
I0401 14:17:59.335276  8859 solver.cpp:330] Iteration 3456, Testing net (#0)
I0401 14:17:59.335350  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:18:00.436857  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:18:08.257256  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:18:11.516289  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0553642
I0401 14:18:11.516317  8859 solver.cpp:397]     Test net output #1: loss = 4.89315 (* 1 = 4.89315 loss)
I0401 14:18:11.652909  8859 solver.cpp:218] Iteration 3456 (0.350014 iter/s, 22.8562s/8 iters), loss = 3.81873
I0401 14:18:11.652952  8859 solver.cpp:237]     Train net output #0: loss = 3.81873 (* 1 = 3.81873 loss)
I0401 14:18:11.652957  8859 sgd_solver.cpp:105] Iteration 3456, lr = 0.001
I0401 14:18:14.494771  8859 solver.cpp:218] Iteration 3464 (2.81514 iter/s, 2.84177s/8 iters), loss = 4.01574
I0401 14:18:14.494817  8859 solver.cpp:237]     Train net output #0: loss = 4.01574 (* 1 = 4.01574 loss)
I0401 14:18:14.494822  8859 sgd_solver.cpp:105] Iteration 3464, lr = 0.001
I0401 14:18:18.080123  8859 solver.cpp:218] Iteration 3472 (2.23136 iter/s, 3.58525s/8 iters), loss = 4.09115
I0401 14:18:18.080170  8859 solver.cpp:237]     Train net output #0: loss = 4.09115 (* 1 = 4.09115 loss)
I0401 14:18:18.080176  8859 sgd_solver.cpp:105] Iteration 3472, lr = 0.001
I0401 14:18:19.937191  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:18:21.674844  8859 solver.cpp:218] Iteration 3480 (2.22555 iter/s, 3.59461s/8 iters), loss = 3.74185
I0401 14:18:21.674894  8859 solver.cpp:237]     Train net output #0: loss = 3.74185 (* 1 = 3.74185 loss)
I0401 14:18:21.674902  8859 sgd_solver.cpp:105] Iteration 3480, lr = 0.001
I0401 14:18:25.421514  8859 solver.cpp:218] Iteration 3488 (2.13529 iter/s, 3.74656s/8 iters), loss = 3.96111
I0401 14:18:25.421569  8859 solver.cpp:237]     Train net output #0: loss = 3.96111 (* 1 = 3.96111 loss)
I0401 14:18:25.421577  8859 sgd_solver.cpp:105] Iteration 3488, lr = 0.001
I0401 14:18:29.368638  8859 solver.cpp:218] Iteration 3496 (2.02685 iter/s, 3.94701s/8 iters), loss = 3.75828
I0401 14:18:29.368791  8859 solver.cpp:237]     Train net output #0: loss = 3.75828 (* 1 = 3.75828 loss)
I0401 14:18:29.368803  8859 sgd_solver.cpp:105] Iteration 3496, lr = 0.001
I0401 14:18:32.916610  8859 solver.cpp:218] Iteration 3504 (2.25494 iter/s, 3.54777s/8 iters), loss = 3.655
I0401 14:18:32.920976  8859 solver.cpp:237]     Train net output #0: loss = 3.655 (* 1 = 3.655 loss)
I0401 14:18:32.920995  8859 sgd_solver.cpp:105] Iteration 3504, lr = 0.001
I0401 14:18:36.671959  8859 solver.cpp:218] Iteration 3512 (2.13279 iter/s, 3.75095s/8 iters), loss = 3.81244
I0401 14:18:36.672001  8859 solver.cpp:237]     Train net output #0: loss = 3.81244 (* 1 = 3.81244 loss)
I0401 14:18:36.672008  8859 sgd_solver.cpp:105] Iteration 3512, lr = 0.001
I0401 14:18:39.983064  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3520.caffemodel
I0401 14:18:43.049007  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3520.solverstate
I0401 14:18:45.410610  8859 solver.cpp:330] Iteration 3520, Testing net (#0)
I0401 14:18:45.410637  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:18:54.839231  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:18:57.888695  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0511811
I0401 14:18:57.888731  8859 solver.cpp:397]     Test net output #1: loss = 4.89121 (* 1 = 4.89121 loss)
I0401 14:18:58.030431  8859 solver.cpp:218] Iteration 3520 (0.374563 iter/s, 21.3582s/8 iters), loss = 3.84387
I0401 14:18:58.030486  8859 solver.cpp:237]     Train net output #0: loss = 3.84387 (* 1 = 3.84387 loss)
I0401 14:18:58.030494  8859 sgd_solver.cpp:105] Iteration 3520, lr = 0.001
I0401 14:19:00.853221  8859 solver.cpp:218] Iteration 3528 (2.83418 iter/s, 2.82269s/8 iters), loss = 3.81321
I0401 14:19:00.853314  8859 solver.cpp:237]     Train net output #0: loss = 3.81321 (* 1 = 3.81321 loss)
I0401 14:19:00.853322  8859 sgd_solver.cpp:105] Iteration 3528, lr = 0.001
I0401 14:19:04.476075  8859 solver.cpp:218] Iteration 3536 (2.2083 iter/s, 3.6227s/8 iters), loss = 3.89421
I0401 14:19:04.476140  8859 solver.cpp:237]     Train net output #0: loss = 3.89421 (* 1 = 3.89421 loss)
I0401 14:19:04.476150  8859 sgd_solver.cpp:105] Iteration 3536, lr = 0.001
I0401 14:19:05.965453  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:19:08.082420  8859 solver.cpp:218] Iteration 3544 (2.21839 iter/s, 3.60622s/8 iters), loss = 3.77856
I0401 14:19:08.082474  8859 solver.cpp:237]     Train net output #0: loss = 3.77856 (* 1 = 3.77856 loss)
I0401 14:19:08.082480  8859 sgd_solver.cpp:105] Iteration 3544, lr = 0.001
I0401 14:19:12.027850  8859 solver.cpp:218] Iteration 3552 (2.02772 iter/s, 3.94532s/8 iters), loss = 3.85349
I0401 14:19:12.027895  8859 solver.cpp:237]     Train net output #0: loss = 3.85349 (* 1 = 3.85349 loss)
I0401 14:19:12.027902  8859 sgd_solver.cpp:105] Iteration 3552, lr = 0.001
I0401 14:19:15.733705  8859 solver.cpp:218] Iteration 3560 (2.15881 iter/s, 3.70575s/8 iters), loss = 3.54665
I0401 14:19:15.733763  8859 solver.cpp:237]     Train net output #0: loss = 3.54665 (* 1 = 3.54665 loss)
I0401 14:19:15.733772  8859 sgd_solver.cpp:105] Iteration 3560, lr = 0.001
I0401 14:19:19.553886  8859 solver.cpp:218] Iteration 3568 (2.0942 iter/s, 3.82007s/8 iters), loss = 4.00784
I0401 14:19:19.553941  8859 solver.cpp:237]     Train net output #0: loss = 4.00784 (* 1 = 4.00784 loss)
I0401 14:19:19.553946  8859 sgd_solver.cpp:105] Iteration 3568, lr = 0.001
I0401 14:19:23.227988  8859 solver.cpp:218] Iteration 3576 (2.17747 iter/s, 3.67399s/8 iters), loss = 3.92686
I0401 14:19:23.228036  8859 solver.cpp:237]     Train net output #0: loss = 3.92686 (* 1 = 3.92686 loss)
I0401 14:19:23.228044  8859 sgd_solver.cpp:105] Iteration 3576, lr = 0.001
I0401 14:19:26.263550  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3584.caffemodel
I0401 14:19:30.764511  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3584.solverstate
I0401 14:19:34.264802  8859 solver.cpp:330] Iteration 3584, Testing net (#0)
I0401 14:19:34.264911  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:19:42.776692  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:19:45.665694  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0479823
I0401 14:19:45.665730  8859 solver.cpp:397]     Test net output #1: loss = 4.94633 (* 1 = 4.94633 loss)
I0401 14:19:45.807214  8859 solver.cpp:218] Iteration 3584 (0.354313 iter/s, 22.5789s/8 iters), loss = 4.1042
I0401 14:19:45.807262  8859 solver.cpp:237]     Train net output #0: loss = 4.1042 (* 1 = 4.1042 loss)
I0401 14:19:45.807268  8859 sgd_solver.cpp:105] Iteration 3584, lr = 0.001
I0401 14:19:48.464151  8859 solver.cpp:218] Iteration 3592 (3.01109 iter/s, 2.65684s/8 iters), loss = 3.88002
I0401 14:19:48.464201  8859 solver.cpp:237]     Train net output #0: loss = 3.88002 (* 1 = 3.88002 loss)
I0401 14:19:48.464210  8859 sgd_solver.cpp:105] Iteration 3592, lr = 0.001
I0401 14:19:51.843717  8859 solver.cpp:218] Iteration 3600 (2.36724 iter/s, 3.37947s/8 iters), loss = 3.94944
I0401 14:19:51.843760  8859 solver.cpp:237]     Train net output #0: loss = 3.94944 (* 1 = 3.94944 loss)
I0401 14:19:51.843766  8859 sgd_solver.cpp:105] Iteration 3600, lr = 0.001
I0401 14:19:52.781473  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:19:55.348567  8859 solver.cpp:218] Iteration 3608 (2.28262 iter/s, 3.50475s/8 iters), loss = 3.77679
I0401 14:19:55.348616  8859 solver.cpp:237]     Train net output #0: loss = 3.77679 (* 1 = 3.77679 loss)
I0401 14:19:55.348623  8859 sgd_solver.cpp:105] Iteration 3608, lr = 0.001
I0401 14:19:58.893838  8859 solver.cpp:218] Iteration 3616 (2.25659 iter/s, 3.54517s/8 iters), loss = 3.83477
I0401 14:19:58.893879  8859 solver.cpp:237]     Train net output #0: loss = 3.83477 (* 1 = 3.83477 loss)
I0401 14:19:58.893884  8859 sgd_solver.cpp:105] Iteration 3616, lr = 0.001
I0401 14:20:02.509078  8859 solver.cpp:218] Iteration 3624 (2.21292 iter/s, 3.61514s/8 iters), loss = 3.75225
I0401 14:20:02.509133  8859 solver.cpp:237]     Train net output #0: loss = 3.75225 (* 1 = 3.75225 loss)
I0401 14:20:02.509141  8859 sgd_solver.cpp:105] Iteration 3624, lr = 0.001
I0401 14:20:06.082775  8859 solver.cpp:218] Iteration 3632 (2.23865 iter/s, 3.57359s/8 iters), loss = 3.8262
I0401 14:20:06.082911  8859 solver.cpp:237]     Train net output #0: loss = 3.8262 (* 1 = 3.8262 loss)
I0401 14:20:06.082921  8859 sgd_solver.cpp:105] Iteration 3632, lr = 0.001
I0401 14:20:09.747359  8859 solver.cpp:218] Iteration 3640 (2.18317 iter/s, 3.6644s/8 iters), loss = 3.88856
I0401 14:20:09.747400  8859 solver.cpp:237]     Train net output #0: loss = 3.88856 (* 1 = 3.88856 loss)
I0401 14:20:09.747407  8859 sgd_solver.cpp:105] Iteration 3640, lr = 0.001
I0401 14:20:12.682471  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3648.caffemodel
I0401 14:20:15.867740  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3648.solverstate
I0401 14:20:18.185873  8859 solver.cpp:330] Iteration 3648, Testing net (#0)
I0401 14:20:18.185894  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:20:26.670521  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:20:29.619009  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0526575
I0401 14:20:29.619038  8859 solver.cpp:397]     Test net output #1: loss = 4.91014 (* 1 = 4.91014 loss)
I0401 14:20:29.757427  8859 solver.cpp:218] Iteration 3648 (0.399804 iter/s, 20.0098s/8 iters), loss = 3.90823
I0401 14:20:29.757481  8859 solver.cpp:237]     Train net output #0: loss = 3.90823 (* 1 = 3.90823 loss)
I0401 14:20:29.757488  8859 sgd_solver.cpp:105] Iteration 3648, lr = 0.001
I0401 14:20:32.476342  8859 solver.cpp:218] Iteration 3656 (2.94246 iter/s, 2.71882s/8 iters), loss = 4.09008
I0401 14:20:32.476393  8859 solver.cpp:237]     Train net output #0: loss = 4.09008 (* 1 = 4.09008 loss)
I0401 14:20:32.476402  8859 sgd_solver.cpp:105] Iteration 3656, lr = 0.001
I0401 14:20:35.940709  8859 solver.cpp:218] Iteration 3664 (2.30929 iter/s, 3.46427s/8 iters), loss = 3.92457
I0401 14:20:35.940748  8859 solver.cpp:237]     Train net output #0: loss = 3.92457 (* 1 = 3.92457 loss)
I0401 14:20:35.940753  8859 sgd_solver.cpp:105] Iteration 3664, lr = 0.001
I0401 14:20:36.521071  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:20:39.521425  8859 solver.cpp:218] Iteration 3672 (2.23425 iter/s, 3.58062s/8 iters), loss = 3.84893
I0401 14:20:39.521481  8859 solver.cpp:237]     Train net output #0: loss = 3.84893 (* 1 = 3.84893 loss)
I0401 14:20:39.521492  8859 sgd_solver.cpp:105] Iteration 3672, lr = 0.001
I0401 14:20:43.280300  8859 solver.cpp:218] Iteration 3680 (2.12836 iter/s, 3.75877s/8 iters), loss = 3.90509
I0401 14:20:43.280350  8859 solver.cpp:237]     Train net output #0: loss = 3.90509 (* 1 = 3.90509 loss)
I0401 14:20:43.280359  8859 sgd_solver.cpp:105] Iteration 3680, lr = 0.001
I0401 14:20:46.677610  8859 solver.cpp:218] Iteration 3688 (2.35487 iter/s, 3.39721s/8 iters), loss = 3.89606
I0401 14:20:46.677651  8859 solver.cpp:237]     Train net output #0: loss = 3.89606 (* 1 = 3.89606 loss)
I0401 14:20:46.677657  8859 sgd_solver.cpp:105] Iteration 3688, lr = 0.001
I0401 14:20:50.048460  8859 solver.cpp:218] Iteration 3696 (2.37336 iter/s, 3.37075s/8 iters), loss = 3.58239
I0401 14:20:50.048527  8859 solver.cpp:237]     Train net output #0: loss = 3.58239 (* 1 = 3.58239 loss)
I0401 14:20:50.048537  8859 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0401 14:20:53.562949  8859 solver.cpp:218] Iteration 3704 (2.27637 iter/s, 3.51437s/8 iters), loss = 3.82701
I0401 14:20:53.563011  8859 solver.cpp:237]     Train net output #0: loss = 3.82701 (* 1 = 3.82701 loss)
I0401 14:20:53.563020  8859 sgd_solver.cpp:105] Iteration 3704, lr = 0.001
I0401 14:20:56.577366  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3712.caffemodel
I0401 14:20:59.677165  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3712.solverstate
I0401 14:21:01.989830  8859 solver.cpp:330] Iteration 3712, Testing net (#0)
I0401 14:21:01.989851  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:21:10.926409  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:14.145102  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0536417
I0401 14:21:14.145129  8859 solver.cpp:397]     Test net output #1: loss = 4.89544 (* 1 = 4.89544 loss)
I0401 14:21:14.286764  8859 solver.cpp:218] Iteration 3712 (0.386034 iter/s, 20.7235s/8 iters), loss = 4.03627
I0401 14:21:14.286809  8859 solver.cpp:237]     Train net output #0: loss = 4.03627 (* 1 = 4.03627 loss)
I0401 14:21:14.286815  8859 sgd_solver.cpp:105] Iteration 3712, lr = 0.001
I0401 14:21:16.870820  8859 solver.cpp:218] Iteration 3720 (3.09601 iter/s, 2.58397s/8 iters), loss = 3.85331
I0401 14:21:16.870867  8859 solver.cpp:237]     Train net output #0: loss = 3.85331 (* 1 = 3.85331 loss)
I0401 14:21:16.870875  8859 sgd_solver.cpp:105] Iteration 3720, lr = 0.001
I0401 14:21:20.264930  8859 solver.cpp:218] Iteration 3728 (2.35709 iter/s, 3.39401s/8 iters), loss = 3.71828
I0401 14:21:20.264976  8859 solver.cpp:237]     Train net output #0: loss = 3.71828 (* 1 = 3.71828 loss)
I0401 14:21:20.264982  8859 sgd_solver.cpp:105] Iteration 3728, lr = 0.001
I0401 14:21:20.525310  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:23.783576  8859 solver.cpp:218] Iteration 3736 (2.27366 iter/s, 3.51855s/8 iters), loss = 3.72748
I0401 14:21:23.783619  8859 solver.cpp:237]     Train net output #0: loss = 3.72748 (* 1 = 3.72748 loss)
I0401 14:21:23.783625  8859 sgd_solver.cpp:105] Iteration 3736, lr = 0.001
I0401 14:21:27.511356  8859 solver.cpp:218] Iteration 3744 (2.14611 iter/s, 3.72768s/8 iters), loss = 3.66658
I0401 14:21:27.511410  8859 solver.cpp:237]     Train net output #0: loss = 3.66658 (* 1 = 3.66658 loss)
I0401 14:21:27.511420  8859 sgd_solver.cpp:105] Iteration 3744, lr = 0.001
I0401 14:21:31.177775  8859 solver.cpp:218] Iteration 3752 (2.18203 iter/s, 3.66631s/8 iters), loss = 3.60183
I0401 14:21:31.177822  8859 solver.cpp:237]     Train net output #0: loss = 3.60183 (* 1 = 3.60183 loss)
I0401 14:21:31.177829  8859 sgd_solver.cpp:105] Iteration 3752, lr = 0.001
I0401 14:21:34.969094  8859 solver.cpp:218] Iteration 3760 (2.11014 iter/s, 3.79122s/8 iters), loss = 3.79717
I0401 14:21:34.969133  8859 solver.cpp:237]     Train net output #0: loss = 3.79717 (* 1 = 3.79717 loss)
I0401 14:21:34.969138  8859 sgd_solver.cpp:105] Iteration 3760, lr = 0.001
I0401 14:21:38.369113  8859 solver.cpp:218] Iteration 3768 (2.35299 iter/s, 3.39993s/8 iters), loss = 3.82096
I0401 14:21:38.369153  8859 solver.cpp:237]     Train net output #0: loss = 3.82096 (* 1 = 3.82096 loss)
I0401 14:21:38.369158  8859 sgd_solver.cpp:105] Iteration 3768, lr = 0.001
I0401 14:21:41.490013  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3776.caffemodel
I0401 14:21:44.544777  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3776.solverstate
I0401 14:21:46.853368  8859 solver.cpp:330] Iteration 3776, Testing net (#0)
I0401 14:21:46.853389  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:21:55.034523  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:21:55.314743  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:58.568281  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0629921
I0401 14:21:58.568315  8859 solver.cpp:397]     Test net output #1: loss = 4.89317 (* 1 = 4.89317 loss)
I0401 14:21:58.710014  8859 solver.cpp:218] Iteration 3776 (0.393301 iter/s, 20.3406s/8 iters), loss = 3.85738
I0401 14:21:58.710054  8859 solver.cpp:237]     Train net output #0: loss = 3.85738 (* 1 = 3.85738 loss)
I0401 14:21:58.710060  8859 sgd_solver.cpp:105] Iteration 3776, lr = 0.001
I0401 14:22:01.487071  8859 solver.cpp:218] Iteration 3784 (2.88084 iter/s, 2.77697s/8 iters), loss = 3.57949
I0401 14:22:01.487112  8859 solver.cpp:237]     Train net output #0: loss = 3.57949 (* 1 = 3.57949 loss)
I0401 14:22:01.487118  8859 sgd_solver.cpp:105] Iteration 3784, lr = 0.001
I0401 14:22:05.013253  8859 solver.cpp:218] Iteration 3792 (2.2688 iter/s, 3.52609s/8 iters), loss = 3.72805
I0401 14:22:05.013298  8859 solver.cpp:237]     Train net output #0: loss = 3.72805 (* 1 = 3.72805 loss)
I0401 14:22:05.013303  8859 sgd_solver.cpp:105] Iteration 3792, lr = 0.001
I0401 14:22:05.048458  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:22:08.375526  8859 solver.cpp:218] Iteration 3800 (2.37941 iter/s, 3.36218s/8 iters), loss = 3.45154
I0401 14:22:08.375568  8859 solver.cpp:237]     Train net output #0: loss = 3.45154 (* 1 = 3.45154 loss)
I0401 14:22:08.375574  8859 sgd_solver.cpp:105] Iteration 3800, lr = 0.001
I0401 14:22:11.862695  8859 solver.cpp:218] Iteration 3808 (2.29419 iter/s, 3.48707s/8 iters), loss = 3.49494
I0401 14:22:11.862797  8859 solver.cpp:237]     Train net output #0: loss = 3.49494 (* 1 = 3.49494 loss)
I0401 14:22:11.862804  8859 sgd_solver.cpp:105] Iteration 3808, lr = 0.001
I0401 14:22:15.483573  8859 solver.cpp:218] Iteration 3816 (2.20951 iter/s, 3.62072s/8 iters), loss = 3.62182
I0401 14:22:15.483629  8859 solver.cpp:237]     Train net output #0: loss = 3.62182 (* 1 = 3.62182 loss)
I0401 14:22:15.483639  8859 sgd_solver.cpp:105] Iteration 3816, lr = 0.001
I0401 14:22:18.949096  8859 solver.cpp:218] Iteration 3824 (2.30852 iter/s, 3.46542s/8 iters), loss = 3.59016
I0401 14:22:18.949132  8859 solver.cpp:237]     Train net output #0: loss = 3.59016 (* 1 = 3.59016 loss)
I0401 14:22:18.949137  8859 sgd_solver.cpp:105] Iteration 3824, lr = 0.001
I0401 14:22:22.601872  8859 solver.cpp:218] Iteration 3832 (2.19017 iter/s, 3.65269s/8 iters), loss = 3.44581
I0401 14:22:22.601920  8859 solver.cpp:237]     Train net output #0: loss = 3.44581 (* 1 = 3.44581 loss)
I0401 14:22:22.601927  8859 sgd_solver.cpp:105] Iteration 3832, lr = 0.001
I0401 14:22:25.651072  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3840.caffemodel
I0401 14:22:29.539412  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3840.solverstate
I0401 14:22:33.417809  8859 solver.cpp:330] Iteration 3840, Testing net (#0)
I0401 14:22:33.417831  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:22:42.270886  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:22:45.369443  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0615157
I0401 14:22:45.369478  8859 solver.cpp:397]     Test net output #1: loss = 4.94954 (* 1 = 4.94954 loss)
I0401 14:22:45.510497  8859 solver.cpp:218] Iteration 3840 (0.349218 iter/s, 22.9083s/8 iters), loss = 3.69082
I0401 14:22:45.510548  8859 solver.cpp:237]     Train net output #0: loss = 3.69082 (* 1 = 3.69082 loss)
I0401 14:22:45.510557  8859 sgd_solver.cpp:105] Iteration 3840, lr = 0.001
I0401 14:22:48.139911  8859 solver.cpp:218] Iteration 3848 (3.04261 iter/s, 2.62932s/8 iters), loss = 3.49381
I0401 14:22:48.139956  8859 solver.cpp:237]     Train net output #0: loss = 3.49381 (* 1 = 3.49381 loss)
I0401 14:22:48.139961  8859 sgd_solver.cpp:105] Iteration 3848, lr = 0.001
I0401 14:22:51.265480  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:22:51.586349  8859 solver.cpp:218] Iteration 3856 (2.3213 iter/s, 3.44634s/8 iters), loss = 3.50697
I0401 14:22:51.586390  8859 solver.cpp:237]     Train net output #0: loss = 3.50697 (* 1 = 3.50697 loss)
I0401 14:22:51.586396  8859 sgd_solver.cpp:105] Iteration 3856, lr = 0.001
I0401 14:22:54.970135  8859 solver.cpp:218] Iteration 3864 (2.36428 iter/s, 3.3837s/8 iters), loss = 3.60665
I0401 14:22:54.970177  8859 solver.cpp:237]     Train net output #0: loss = 3.60665 (* 1 = 3.60665 loss)
I0401 14:22:54.970183  8859 sgd_solver.cpp:105] Iteration 3864, lr = 0.001
I0401 14:22:58.515316  8859 solver.cpp:218] Iteration 3872 (2.25665 iter/s, 3.54508s/8 iters), loss = 3.42303
I0401 14:22:58.515367  8859 solver.cpp:237]     Train net output #0: loss = 3.42303 (* 1 = 3.42303 loss)
I0401 14:22:58.515377  8859 sgd_solver.cpp:105] Iteration 3872, lr = 0.001
I0401 14:23:01.902567  8859 solver.cpp:218] Iteration 3880 (2.36187 iter/s, 3.38715s/8 iters), loss = 3.71823
I0401 14:23:01.902628  8859 solver.cpp:237]     Train net output #0: loss = 3.71823 (* 1 = 3.71823 loss)
I0401 14:23:01.902637  8859 sgd_solver.cpp:105] Iteration 3880, lr = 0.001
I0401 14:23:05.351385  8859 solver.cpp:218] Iteration 3888 (2.31971 iter/s, 3.44871s/8 iters), loss = 3.4371
I0401 14:23:05.351440  8859 solver.cpp:237]     Train net output #0: loss = 3.4371 (* 1 = 3.4371 loss)
I0401 14:23:05.351449  8859 sgd_solver.cpp:105] Iteration 3888, lr = 0.001
I0401 14:23:08.796140  8859 solver.cpp:218] Iteration 3896 (2.32244 iter/s, 3.44466s/8 iters), loss = 3.43789
I0401 14:23:08.796175  8859 solver.cpp:237]     Train net output #0: loss = 3.43789 (* 1 = 3.43789 loss)
I0401 14:23:08.796180  8859 sgd_solver.cpp:105] Iteration 3896, lr = 0.001
I0401 14:23:11.793407  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3904.caffemodel
I0401 14:23:14.782330  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3904.solverstate
I0401 14:23:17.075423  8859 solver.cpp:330] Iteration 3904, Testing net (#0)
I0401 14:23:17.075444  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:23:25.558569  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:23:28.949879  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0654528
I0401 14:23:28.949914  8859 solver.cpp:397]     Test net output #1: loss = 4.96257 (* 1 = 4.96257 loss)
I0401 14:23:29.091974  8859 solver.cpp:218] Iteration 3904 (0.394174 iter/s, 20.2956s/8 iters), loss = 3.64573
I0401 14:23:29.092027  8859 solver.cpp:237]     Train net output #0: loss = 3.64573 (* 1 = 3.64573 loss)
I0401 14:23:29.092036  8859 sgd_solver.cpp:105] Iteration 3904, lr = 0.001
I0401 14:23:31.617208  8859 solver.cpp:218] Iteration 3912 (3.16814 iter/s, 2.52514s/8 iters), loss = 3.7768
I0401 14:23:31.617254  8859 solver.cpp:237]     Train net output #0: loss = 3.7768 (* 1 = 3.7768 loss)
I0401 14:23:31.617259  8859 sgd_solver.cpp:105] Iteration 3912, lr = 0.001
I0401 14:23:34.432943  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:23:35.178622  8859 solver.cpp:218] Iteration 3920 (2.24636 iter/s, 3.56132s/8 iters), loss = 3.30572
I0401 14:23:35.178663  8859 solver.cpp:237]     Train net output #0: loss = 3.30572 (* 1 = 3.30572 loss)
I0401 14:23:35.178668  8859 sgd_solver.cpp:105] Iteration 3920, lr = 0.001
I0401 14:23:38.813310  8859 solver.cpp:218] Iteration 3928 (2.20107 iter/s, 3.63459s/8 iters), loss = 3.62163
I0401 14:23:38.813351  8859 solver.cpp:237]     Train net output #0: loss = 3.62163 (* 1 = 3.62163 loss)
I0401 14:23:38.813357  8859 sgd_solver.cpp:105] Iteration 3928, lr = 0.001
I0401 14:23:42.366583  8859 solver.cpp:218] Iteration 3936 (2.25151 iter/s, 3.55318s/8 iters), loss = 3.22844
I0401 14:23:42.366636  8859 solver.cpp:237]     Train net output #0: loss = 3.22844 (* 1 = 3.22844 loss)
I0401 14:23:42.366644  8859 sgd_solver.cpp:105] Iteration 3936, lr = 0.001
I0401 14:23:46.034315  8859 solver.cpp:218] Iteration 3944 (2.18125 iter/s, 3.66762s/8 iters), loss = 3.42413
I0401 14:23:46.034456  8859 solver.cpp:237]     Train net output #0: loss = 3.42413 (* 1 = 3.42413 loss)
I0401 14:23:46.034466  8859 sgd_solver.cpp:105] Iteration 3944, lr = 0.001
I0401 14:23:49.614998  8859 solver.cpp:218] Iteration 3952 (2.23433 iter/s, 3.58049s/8 iters), loss = 3.27476
I0401 14:23:49.615041  8859 solver.cpp:237]     Train net output #0: loss = 3.27476 (* 1 = 3.27476 loss)
I0401 14:23:49.615046  8859 sgd_solver.cpp:105] Iteration 3952, lr = 0.001
I0401 14:23:53.246443  8859 solver.cpp:218] Iteration 3960 (2.20304 iter/s, 3.63135s/8 iters), loss = 3.62231
I0401 14:23:53.246493  8859 solver.cpp:237]     Train net output #0: loss = 3.62231 (* 1 = 3.62231 loss)
I0401 14:23:53.246500  8859 sgd_solver.cpp:105] Iteration 3960, lr = 0.001
I0401 14:23:56.276232  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3968.caffemodel
I0401 14:23:59.303009  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3968.solverstate
I0401 14:24:01.589800  8859 solver.cpp:330] Iteration 3968, Testing net (#0)
I0401 14:24:01.589821  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:24:10.420997  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:24:13.939185  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0615157
I0401 14:24:13.939229  8859 solver.cpp:397]     Test net output #1: loss = 4.9523 (* 1 = 4.9523 loss)
I0401 14:24:14.075803  8859 solver.cpp:218] Iteration 3968 (0.384078 iter/s, 20.8291s/8 iters), loss = 3.66042
I0401 14:24:14.075863  8859 solver.cpp:237]     Train net output #0: loss = 3.66042 (* 1 = 3.66042 loss)
I0401 14:24:14.075872  8859 sgd_solver.cpp:105] Iteration 3968, lr = 0.001
I0401 14:24:16.720592  8859 solver.cpp:218] Iteration 3976 (3.02493 iter/s, 2.64469s/8 iters), loss = 3.43147
I0401 14:24:16.720685  8859 solver.cpp:237]     Train net output #0: loss = 3.43147 (* 1 = 3.43147 loss)
I0401 14:24:16.720693  8859 sgd_solver.cpp:105] Iteration 3976, lr = 0.001
I0401 14:24:19.220216  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:24:20.269836  8859 solver.cpp:218] Iteration 3984 (2.25409 iter/s, 3.54911s/8 iters), loss = 3.44703
I0401 14:24:20.269877  8859 solver.cpp:237]     Train net output #0: loss = 3.44703 (* 1 = 3.44703 loss)
I0401 14:24:20.269883  8859 sgd_solver.cpp:105] Iteration 3984, lr = 0.001
I0401 14:24:23.651511  8859 solver.cpp:218] Iteration 3992 (2.36576 iter/s, 3.38158s/8 iters), loss = 3.79248
I0401 14:24:23.651554  8859 solver.cpp:237]     Train net output #0: loss = 3.79248 (* 1 = 3.79248 loss)
I0401 14:24:23.651559  8859 sgd_solver.cpp:105] Iteration 3992, lr = 0.001
I0401 14:24:27.027609  8859 solver.cpp:218] Iteration 4000 (2.36967 iter/s, 3.376s/8 iters), loss = 3.51315
I0401 14:24:27.027673  8859 solver.cpp:237]     Train net output #0: loss = 3.51315 (* 1 = 3.51315 loss)
I0401 14:24:27.027685  8859 sgd_solver.cpp:105] Iteration 4000, lr = 0.001
I0401 14:24:30.680016  8859 solver.cpp:218] Iteration 4008 (2.19041 iter/s, 3.65229s/8 iters), loss = 3.46801
I0401 14:24:30.680060  8859 solver.cpp:237]     Train net output #0: loss = 3.46801 (* 1 = 3.46801 loss)
I0401 14:24:30.680066  8859 sgd_solver.cpp:105] Iteration 4008, lr = 0.001
I0401 14:24:34.242807  8859 solver.cpp:218] Iteration 4016 (2.24549 iter/s, 3.56269s/8 iters), loss = 3.31073
I0401 14:24:34.242858  8859 solver.cpp:237]     Train net output #0: loss = 3.31073 (* 1 = 3.31073 loss)
I0401 14:24:34.242867  8859 sgd_solver.cpp:105] Iteration 4016, lr = 0.001
I0401 14:24:37.667927  8859 solver.cpp:218] Iteration 4024 (2.33575 iter/s, 3.42502s/8 iters), loss = 3.65898
I0401 14:24:37.667984  8859 solver.cpp:237]     Train net output #0: loss = 3.65898 (* 1 = 3.65898 loss)
I0401 14:24:37.667994  8859 sgd_solver.cpp:105] Iteration 4024, lr = 0.001
I0401 14:24:40.613924  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4032.caffemodel
I0401 14:24:43.703756  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4032.solverstate
I0401 14:24:46.005707  8859 solver.cpp:330] Iteration 4032, Testing net (#0)
I0401 14:24:46.005731  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:24:54.269704  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:24:57.628463  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0661909
I0401 14:24:57.628502  8859 solver.cpp:397]     Test net output #1: loss = 4.95548 (* 1 = 4.95548 loss)
I0401 14:24:57.770265  8859 solver.cpp:218] Iteration 4032 (0.397969 iter/s, 20.1021s/8 iters), loss = 3.36846
I0401 14:24:57.770323  8859 solver.cpp:237]     Train net output #0: loss = 3.36846 (* 1 = 3.36846 loss)
I0401 14:24:57.770331  8859 sgd_solver.cpp:105] Iteration 4032, lr = 0.001
I0401 14:25:00.325795  8859 solver.cpp:218] Iteration 4040 (3.13059 iter/s, 2.55543s/8 iters), loss = 3.31428
I0401 14:25:00.325842  8859 solver.cpp:237]     Train net output #0: loss = 3.31428 (* 1 = 3.31428 loss)
I0401 14:25:00.325850  8859 sgd_solver.cpp:105] Iteration 4040, lr = 0.001
I0401 14:25:02.369868  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:25:03.722865  8859 solver.cpp:218] Iteration 4048 (2.35504 iter/s, 3.39697s/8 iters), loss = 3.61495
I0401 14:25:03.722920  8859 solver.cpp:237]     Train net output #0: loss = 3.61495 (* 1 = 3.61495 loss)
I0401 14:25:03.722929  8859 sgd_solver.cpp:105] Iteration 4048, lr = 0.001
I0401 14:25:07.277840  8859 solver.cpp:218] Iteration 4056 (2.25044 iter/s, 3.55486s/8 iters), loss = 3.67898
I0401 14:25:07.277910  8859 solver.cpp:237]     Train net output #0: loss = 3.67898 (* 1 = 3.67898 loss)
I0401 14:25:07.277920  8859 sgd_solver.cpp:105] Iteration 4056, lr = 0.001
I0401 14:25:10.848176  8859 solver.cpp:218] Iteration 4064 (2.24076 iter/s, 3.57021s/8 iters), loss = 3.50696
I0401 14:25:10.848240  8859 solver.cpp:237]     Train net output #0: loss = 3.50696 (* 1 = 3.50696 loss)
I0401 14:25:10.848249  8859 sgd_solver.cpp:105] Iteration 4064, lr = 0.001
I0401 14:25:14.398804  8859 solver.cpp:218] Iteration 4072 (2.2532 iter/s, 3.55051s/8 iters), loss = 3.62776
I0401 14:25:14.398859  8859 solver.cpp:237]     Train net output #0: loss = 3.62776 (* 1 = 3.62776 loss)
I0401 14:25:14.398866  8859 sgd_solver.cpp:105] Iteration 4072, lr = 0.001
I0401 14:25:17.857057  8859 solver.cpp:218] Iteration 4080 (2.31338 iter/s, 3.45815s/8 iters), loss = 3.8343
I0401 14:25:17.857106  8859 solver.cpp:237]     Train net output #0: loss = 3.8343 (* 1 = 3.8343 loss)
I0401 14:25:17.857115  8859 sgd_solver.cpp:105] Iteration 4080, lr = 0.001
I0401 14:25:21.373030  8859 solver.cpp:218] Iteration 4088 (2.2754 iter/s, 3.51587s/8 iters), loss = 3.52002
I0401 14:25:21.373075  8859 solver.cpp:237]     Train net output #0: loss = 3.52002 (* 1 = 3.52002 loss)
I0401 14:25:21.373081  8859 sgd_solver.cpp:105] Iteration 4088, lr = 0.001
I0401 14:25:24.470423  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4096.caffemodel
I0401 14:25:28.924499  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4096.solverstate
I0401 14:25:32.500368  8859 solver.cpp:330] Iteration 4096, Testing net (#0)
I0401 14:25:32.500393  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:25:40.725173  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:25:44.200349  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0607776
I0401 14:25:44.200381  8859 solver.cpp:397]     Test net output #1: loss = 4.9707 (* 1 = 4.9707 loss)
I0401 14:25:44.341913  8859 solver.cpp:218] Iteration 4096 (0.348302 iter/s, 22.9686s/8 iters), loss = 4.0815
I0401 14:25:44.341964  8859 solver.cpp:237]     Train net output #0: loss = 4.0815 (* 1 = 4.0815 loss)
I0401 14:25:44.341971  8859 sgd_solver.cpp:105] Iteration 4096, lr = 0.001
I0401 14:25:47.070498  8859 solver.cpp:218] Iteration 4104 (2.93203 iter/s, 2.72849s/8 iters), loss = 3.50585
I0401 14:25:47.070554  8859 solver.cpp:237]     Train net output #0: loss = 3.50585 (* 1 = 3.50585 loss)
I0401 14:25:47.070564  8859 sgd_solver.cpp:105] Iteration 4104, lr = 0.001
I0401 14:25:48.851581  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:25:50.492695  8859 solver.cpp:218] Iteration 4112 (2.33775 iter/s, 3.42209s/8 iters), loss = 3.74984
I0401 14:25:50.492736  8859 solver.cpp:237]     Train net output #0: loss = 3.74984 (* 1 = 3.74984 loss)
I0401 14:25:50.492743  8859 sgd_solver.cpp:105] Iteration 4112, lr = 0.001
I0401 14:25:53.876914  8859 solver.cpp:218] Iteration 4120 (2.36398 iter/s, 3.38413s/8 iters), loss = 3.36335
I0401 14:25:53.876955  8859 solver.cpp:237]     Train net output #0: loss = 3.36335 (* 1 = 3.36335 loss)
I0401 14:25:53.876961  8859 sgd_solver.cpp:105] Iteration 4120, lr = 0.001
I0401 14:25:57.462848  8859 solver.cpp:218] Iteration 4128 (2.231 iter/s, 3.58584s/8 iters), loss = 3.38465
I0401 14:25:57.462973  8859 solver.cpp:237]     Train net output #0: loss = 3.38465 (* 1 = 3.38465 loss)
I0401 14:25:57.462982  8859 sgd_solver.cpp:105] Iteration 4128, lr = 0.001
I0401 14:26:01.032510  8859 solver.cpp:218] Iteration 4136 (2.24122 iter/s, 3.56949s/8 iters), loss = 3.82599
I0401 14:26:01.032573  8859 solver.cpp:237]     Train net output #0: loss = 3.82599 (* 1 = 3.82599 loss)
I0401 14:26:01.032583  8859 sgd_solver.cpp:105] Iteration 4136, lr = 0.001
I0401 14:26:01.032848  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:26:04.769758  8859 solver.cpp:218] Iteration 4144 (2.14068 iter/s, 3.73714s/8 iters), loss = 3.52128
I0401 14:26:04.769798  8859 solver.cpp:237]     Train net output #0: loss = 3.52128 (* 1 = 3.52128 loss)
I0401 14:26:04.769802  8859 sgd_solver.cpp:105] Iteration 4144, lr = 0.001
I0401 14:26:08.303826  8859 solver.cpp:218] Iteration 4152 (2.26374 iter/s, 3.53397s/8 iters), loss = 3.6091
I0401 14:26:08.303875  8859 solver.cpp:237]     Train net output #0: loss = 3.6091 (* 1 = 3.6091 loss)
I0401 14:26:08.303882  8859 sgd_solver.cpp:105] Iteration 4152, lr = 0.001
I0401 14:26:11.233101  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4160.caffemodel
I0401 14:26:14.371590  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4160.solverstate
I0401 14:26:16.731582  8859 solver.cpp:330] Iteration 4160, Testing net (#0)
I0401 14:26:16.731606  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:26:24.921866  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:26:28.339125  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0647146
I0401 14:26:28.339272  8859 solver.cpp:397]     Test net output #1: loss = 4.93211 (* 1 = 4.93211 loss)
I0401 14:26:28.479692  8859 solver.cpp:218] Iteration 4160 (0.396519 iter/s, 20.1756s/8 iters), loss = 3.51694
I0401 14:26:28.479737  8859 solver.cpp:237]     Train net output #0: loss = 3.51694 (* 1 = 3.51694 loss)
I0401 14:26:28.479741  8859 sgd_solver.cpp:105] Iteration 4160, lr = 0.001
I0401 14:26:31.181299  8859 solver.cpp:218] Iteration 4168 (2.96129 iter/s, 2.70152s/8 iters), loss = 3.26586
I0401 14:26:31.181342  8859 solver.cpp:237]     Train net output #0: loss = 3.26586 (* 1 = 3.26586 loss)
I0401 14:26:31.181347  8859 sgd_solver.cpp:105] Iteration 4168, lr = 0.001
I0401 14:26:32.523927  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:26:34.643397  8859 solver.cpp:218] Iteration 4176 (2.3108 iter/s, 3.462s/8 iters), loss = 3.25859
I0401 14:26:34.643453  8859 solver.cpp:237]     Train net output #0: loss = 3.25859 (* 1 = 3.25859 loss)
I0401 14:26:34.643462  8859 sgd_solver.cpp:105] Iteration 4176, lr = 0.001
I0401 14:26:38.116698  8859 solver.cpp:218] Iteration 4184 (2.30336 iter/s, 3.47319s/8 iters), loss = 3.08792
I0401 14:26:38.116744  8859 solver.cpp:237]     Train net output #0: loss = 3.08792 (* 1 = 3.08792 loss)
I0401 14:26:38.116750  8859 sgd_solver.cpp:105] Iteration 4184, lr = 0.001
I0401 14:26:41.503703  8859 solver.cpp:218] Iteration 4192 (2.36204 iter/s, 3.3869s/8 iters), loss = 3.53598
I0401 14:26:41.503758  8859 solver.cpp:237]     Train net output #0: loss = 3.53598 (* 1 = 3.53598 loss)
I0401 14:26:41.503767  8859 sgd_solver.cpp:105] Iteration 4192, lr = 0.001
I0401 14:26:45.074153  8859 solver.cpp:218] Iteration 4200 (2.24069 iter/s, 3.57033s/8 iters), loss = 3.42081
I0401 14:26:45.074209  8859 solver.cpp:237]     Train net output #0: loss = 3.42081 (* 1 = 3.42081 loss)
I0401 14:26:45.074218  8859 sgd_solver.cpp:105] Iteration 4200, lr = 0.001
I0401 14:26:48.543244  8859 solver.cpp:218] Iteration 4208 (2.30615 iter/s, 3.46898s/8 iters), loss = 3.28579
I0401 14:26:48.543305  8859 solver.cpp:237]     Train net output #0: loss = 3.28579 (* 1 = 3.28579 loss)
I0401 14:26:48.543314  8859 sgd_solver.cpp:105] Iteration 4208, lr = 0.001
I0401 14:26:52.090400  8859 solver.cpp:218] Iteration 4216 (2.2554 iter/s, 3.54705s/8 iters), loss = 3.40435
I0401 14:26:52.090441  8859 solver.cpp:237]     Train net output #0: loss = 3.40435 (* 1 = 3.40435 loss)
I0401 14:26:52.090446  8859 sgd_solver.cpp:105] Iteration 4216, lr = 0.001
I0401 14:26:54.988898  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4224.caffemodel
I0401 14:26:59.438642  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4224.solverstate
I0401 14:27:03.034138  8859 solver.cpp:330] Iteration 4224, Testing net (#0)
I0401 14:27:03.034163  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:27:11.226828  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:14.911098  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0652067
I0401 14:27:14.911132  8859 solver.cpp:397]     Test net output #1: loss = 5.01466 (* 1 = 5.01466 loss)
I0401 14:27:15.052843  8859 solver.cpp:218] Iteration 4224 (0.348399 iter/s, 22.9622s/8 iters), loss = 3.47558
I0401 14:27:15.052923  8859 solver.cpp:237]     Train net output #0: loss = 3.47558 (* 1 = 3.47558 loss)
I0401 14:27:15.052932  8859 sgd_solver.cpp:105] Iteration 4224, lr = 0.001
I0401 14:27:17.599735  8859 solver.cpp:218] Iteration 4232 (3.14124 iter/s, 2.54677s/8 iters), loss = 3.28704
I0401 14:27:17.599800  8859 solver.cpp:237]     Train net output #0: loss = 3.28704 (* 1 = 3.28704 loss)
I0401 14:27:17.599810  8859 sgd_solver.cpp:105] Iteration 4232, lr = 0.001
I0401 14:27:18.652751  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:20.931459  8859 solver.cpp:218] Iteration 4240 (2.40124 iter/s, 3.33161s/8 iters), loss = 3.22135
I0401 14:27:20.931514  8859 solver.cpp:237]     Train net output #0: loss = 3.22135 (* 1 = 3.22135 loss)
I0401 14:27:20.931522  8859 sgd_solver.cpp:105] Iteration 4240, lr = 0.001
I0401 14:27:24.427103  8859 solver.cpp:218] Iteration 4248 (2.28863 iter/s, 3.49554s/8 iters), loss = 3.48178
I0401 14:27:24.427148  8859 solver.cpp:237]     Train net output #0: loss = 3.48178 (* 1 = 3.48178 loss)
I0401 14:27:24.427156  8859 sgd_solver.cpp:105] Iteration 4248, lr = 0.001
I0401 14:27:28.173255  8859 solver.cpp:218] Iteration 4256 (2.13558 iter/s, 3.74605s/8 iters), loss = 3.53975
I0401 14:27:28.173302  8859 solver.cpp:237]     Train net output #0: loss = 3.53975 (* 1 = 3.53975 loss)
I0401 14:27:28.173308  8859 sgd_solver.cpp:105] Iteration 4256, lr = 0.001
I0401 14:27:31.633744  8859 solver.cpp:218] Iteration 4264 (2.31188 iter/s, 3.46039s/8 iters), loss = 3.23138
I0401 14:27:31.633859  8859 solver.cpp:237]     Train net output #0: loss = 3.23138 (* 1 = 3.23138 loss)
I0401 14:27:31.633865  8859 sgd_solver.cpp:105] Iteration 4264, lr = 0.001
I0401 14:27:35.298636  8859 solver.cpp:218] Iteration 4272 (2.18298 iter/s, 3.66472s/8 iters), loss = 3.17469
I0401 14:27:35.298686  8859 solver.cpp:237]     Train net output #0: loss = 3.17469 (* 1 = 3.17469 loss)
I0401 14:27:35.298693  8859 sgd_solver.cpp:105] Iteration 4272, lr = 0.001
I0401 14:27:38.923061  8859 solver.cpp:218] Iteration 4280 (2.20731 iter/s, 3.62432s/8 iters), loss = 3.59454
I0401 14:27:38.923105  8859 solver.cpp:237]     Train net output #0: loss = 3.59454 (* 1 = 3.59454 loss)
I0401 14:27:38.923111  8859 sgd_solver.cpp:105] Iteration 4280, lr = 0.001
I0401 14:27:41.997742  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4288.caffemodel
I0401 14:27:45.537559  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4288.solverstate
I0401 14:27:47.841564  8859 solver.cpp:330] Iteration 4288, Testing net (#0)
I0401 14:27:47.841588  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:27:55.829144  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:59.459239  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0578248
I0401 14:27:59.459273  8859 solver.cpp:397]     Test net output #1: loss = 5.05848 (* 1 = 5.05848 loss)
I0401 14:27:59.591063  8859 solver.cpp:218] Iteration 4288 (0.387077 iter/s, 20.6677s/8 iters), loss = 3.61621
I0401 14:27:59.591110  8859 solver.cpp:237]     Train net output #0: loss = 3.61621 (* 1 = 3.61621 loss)
I0401 14:27:59.591118  8859 sgd_solver.cpp:105] Iteration 4288, lr = 0.001
I0401 14:28:02.149945  8859 solver.cpp:218] Iteration 4296 (3.12647 iter/s, 2.5588s/8 iters), loss = 2.91904
I0401 14:28:02.150058  8859 solver.cpp:237]     Train net output #0: loss = 2.91904 (* 1 = 2.91904 loss)
I0401 14:28:02.150066  8859 sgd_solver.cpp:105] Iteration 4296, lr = 0.001
I0401 14:28:02.805984  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:28:05.609023  8859 solver.cpp:218] Iteration 4304 (2.31287 iter/s, 3.45891s/8 iters), loss = 3.06594
I0401 14:28:05.609076  8859 solver.cpp:237]     Train net output #0: loss = 3.06594 (* 1 = 3.06594 loss)
I0401 14:28:05.609086  8859 sgd_solver.cpp:105] Iteration 4304, lr = 0.001
I0401 14:28:09.241447  8859 solver.cpp:218] Iteration 4312 (2.20245 iter/s, 3.63232s/8 iters), loss = 3.2184
I0401 14:28:09.241497  8859 solver.cpp:237]     Train net output #0: loss = 3.2184 (* 1 = 3.2184 loss)
I0401 14:28:09.241503  8859 sgd_solver.cpp:105] Iteration 4312, lr = 0.001
I0401 14:28:12.826838  8859 solver.cpp:218] Iteration 4320 (2.23134 iter/s, 3.58529s/8 iters), loss = 3.28725
I0401 14:28:12.826885  8859 solver.cpp:237]     Train net output #0: loss = 3.28725 (* 1 = 3.28725 loss)
I0401 14:28:12.826892  8859 sgd_solver.cpp:105] Iteration 4320, lr = 0.001
I0401 14:28:16.228444  8859 solver.cpp:218] Iteration 4328 (2.3519 iter/s, 3.40151s/8 iters), loss = 3.07803
I0401 14:28:16.228492  8859 solver.cpp:237]     Train net output #0: loss = 3.07803 (* 1 = 3.07803 loss)
I0401 14:28:16.228500  8859 sgd_solver.cpp:105] Iteration 4328, lr = 0.001
I0401 14:28:19.754633  8859 solver.cpp:218] Iteration 4336 (2.2688 iter/s, 3.52609s/8 iters), loss = 3.34989
I0401 14:28:19.754683  8859 solver.cpp:237]     Train net output #0: loss = 3.34989 (* 1 = 3.34989 loss)
I0401 14:28:19.754691  8859 sgd_solver.cpp:105] Iteration 4336, lr = 0.001
I0401 14:28:23.425089  8859 solver.cpp:218] Iteration 4344 (2.17963 iter/s, 3.67035s/8 iters), loss = 3.45416
I0401 14:28:23.425135  8859 solver.cpp:237]     Train net output #0: loss = 3.45416 (* 1 = 3.45416 loss)
I0401 14:28:23.425141  8859 sgd_solver.cpp:105] Iteration 4344, lr = 0.001
I0401 14:28:26.303615  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4352.caffemodel
I0401 14:28:29.381470  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4352.solverstate
I0401 14:28:31.710542  8859 solver.cpp:330] Iteration 4352, Testing net (#0)
I0401 14:28:31.710562  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:28:39.734197  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:28:43.660405  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0573327
I0401 14:28:43.660434  8859 solver.cpp:397]     Test net output #1: loss = 5.08403 (* 1 = 5.08403 loss)
I0401 14:28:43.798429  8859 solver.cpp:218] Iteration 4352 (0.392675 iter/s, 20.3731s/8 iters), loss = 3.90052
I0401 14:28:43.798470  8859 solver.cpp:237]     Train net output #0: loss = 3.90052 (* 1 = 3.90052 loss)
I0401 14:28:43.798475  8859 sgd_solver.cpp:105] Iteration 4352, lr = 0.001
I0401 14:28:46.300451  8859 solver.cpp:218] Iteration 4360 (3.19752 iter/s, 2.50194s/8 iters), loss = 3.28749
I0401 14:28:46.300534  8859 solver.cpp:237]     Train net output #0: loss = 3.28749 (* 1 = 3.28749 loss)
I0401 14:28:46.300541  8859 sgd_solver.cpp:105] Iteration 4360, lr = 0.001
I0401 14:28:46.768903  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:28:49.551717  8859 solver.cpp:218] Iteration 4368 (2.46065 iter/s, 3.25118s/8 iters), loss = 2.99849
I0401 14:28:49.551761  8859 solver.cpp:237]     Train net output #0: loss = 2.99849 (* 1 = 2.99849 loss)
I0401 14:28:49.551767  8859 sgd_solver.cpp:105] Iteration 4368, lr = 0.001
I0401 14:28:53.273137  8859 solver.cpp:218] Iteration 4376 (2.14978 iter/s, 3.72132s/8 iters), loss = 3.43386
I0401 14:28:53.273190  8859 solver.cpp:237]     Train net output #0: loss = 3.43386 (* 1 = 3.43386 loss)
I0401 14:28:53.273198  8859 sgd_solver.cpp:105] Iteration 4376, lr = 0.001
I0401 14:28:56.901589  8859 solver.cpp:218] Iteration 4384 (2.20486 iter/s, 3.62834s/8 iters), loss = 3.26447
I0401 14:28:56.901636  8859 solver.cpp:237]     Train net output #0: loss = 3.26447 (* 1 = 3.26447 loss)
I0401 14:28:56.901644  8859 sgd_solver.cpp:105] Iteration 4384, lr = 0.001
I0401 14:29:00.506855  8859 solver.cpp:218] Iteration 4392 (2.21904 iter/s, 3.60517s/8 iters), loss = 3.07729
I0401 14:29:00.506911  8859 solver.cpp:237]     Train net output #0: loss = 3.07729 (* 1 = 3.07729 loss)
I0401 14:29:00.506919  8859 sgd_solver.cpp:105] Iteration 4392, lr = 0.001
I0401 14:29:04.143333  8859 solver.cpp:218] Iteration 4400 (2.19999 iter/s, 3.63637s/8 iters), loss = 2.98478
I0401 14:29:04.143379  8859 solver.cpp:237]     Train net output #0: loss = 2.98478 (* 1 = 2.98478 loss)
I0401 14:29:04.143386  8859 sgd_solver.cpp:105] Iteration 4400, lr = 0.001
I0401 14:29:07.784250  8859 solver.cpp:218] Iteration 4408 (2.19731 iter/s, 3.64082s/8 iters), loss = 3.32503
I0401 14:29:07.784289  8859 solver.cpp:237]     Train net output #0: loss = 3.32503 (* 1 = 3.32503 loss)
I0401 14:29:07.784294  8859 sgd_solver.cpp:105] Iteration 4408, lr = 0.001
I0401 14:29:10.733546  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4416.caffemodel
I0401 14:29:13.760008  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4416.solverstate
I0401 14:29:16.121655  8859 solver.cpp:330] Iteration 4416, Testing net (#0)
I0401 14:29:16.121675  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:29:24.006738  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:29:27.655150  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0607776
I0401 14:29:27.655182  8859 solver.cpp:397]     Test net output #1: loss = 5.06607 (* 1 = 5.06607 loss)
I0401 14:29:27.796685  8859 solver.cpp:218] Iteration 4416 (0.399756 iter/s, 20.0122s/8 iters), loss = 3.37109
I0401 14:29:27.796731  8859 solver.cpp:237]     Train net output #0: loss = 3.37109 (* 1 = 3.37109 loss)
I0401 14:29:27.796737  8859 sgd_solver.cpp:105] Iteration 4416, lr = 0.001
I0401 14:29:30.351874  8859 solver.cpp:218] Iteration 4424 (3.13099 iter/s, 2.5551s/8 iters), loss = 3.18338
I0401 14:29:30.351927  8859 solver.cpp:237]     Train net output #0: loss = 3.18338 (* 1 = 3.18338 loss)
I0401 14:29:30.351934  8859 sgd_solver.cpp:105] Iteration 4424, lr = 0.001
I0401 14:29:30.456809  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:29:33.832839  8859 solver.cpp:218] Iteration 4432 (2.29828 iter/s, 3.48086s/8 iters), loss = 3.03953
I0401 14:29:33.832913  8859 solver.cpp:237]     Train net output #0: loss = 3.03953 (* 1 = 3.03953 loss)
I0401 14:29:33.832922  8859 sgd_solver.cpp:105] Iteration 4432, lr = 0.001
I0401 14:29:37.386456  8859 solver.cpp:218] Iteration 4440 (2.25131 iter/s, 3.55349s/8 iters), loss = 3.14603
I0401 14:29:37.386516  8859 solver.cpp:237]     Train net output #0: loss = 3.14603 (* 1 = 3.14603 loss)
I0401 14:29:37.386525  8859 sgd_solver.cpp:105] Iteration 4440, lr = 0.001
I0401 14:29:41.119534  8859 solver.cpp:218] Iteration 4448 (2.14307 iter/s, 3.73297s/8 iters), loss = 2.84719
I0401 14:29:41.119721  8859 solver.cpp:237]     Train net output #0: loss = 2.84719 (* 1 = 2.84719 loss)
I0401 14:29:41.119730  8859 sgd_solver.cpp:105] Iteration 4448, lr = 0.001
I0401 14:29:44.719764  8859 solver.cpp:218] Iteration 4456 (2.22223 iter/s, 3.59999s/8 iters), loss = 2.94763
I0401 14:29:44.719822  8859 solver.cpp:237]     Train net output #0: loss = 2.94763 (* 1 = 2.94763 loss)
I0401 14:29:44.719831  8859 sgd_solver.cpp:105] Iteration 4456, lr = 0.001
I0401 14:29:48.374539  8859 solver.cpp:218] Iteration 4464 (2.18898 iter/s, 3.65467s/8 iters), loss = 2.7188
I0401 14:29:48.374579  8859 solver.cpp:237]     Train net output #0: loss = 2.7188 (* 1 = 2.7188 loss)
I0401 14:29:48.374585  8859 sgd_solver.cpp:105] Iteration 4464, lr = 0.001
I0401 14:29:51.989084  8859 solver.cpp:218] Iteration 4472 (2.21334 iter/s, 3.61444s/8 iters), loss = 3.26818
I0401 14:29:51.989138  8859 solver.cpp:237]     Train net output #0: loss = 3.26818 (* 1 = 3.26818 loss)
I0401 14:29:51.989146  8859 sgd_solver.cpp:105] Iteration 4472, lr = 0.001
I0401 14:29:55.101737  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4480.caffemodel
I0401 14:29:58.240212  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4480.solverstate
I0401 14:30:00.658017  8859 solver.cpp:330] Iteration 4480, Testing net (#0)
I0401 14:30:00.658035  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:30:05.758509  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:30:08.764796  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:30:12.624672  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0634843
I0401 14:30:12.624761  8859 solver.cpp:397]     Test net output #1: loss = 5.11326 (* 1 = 5.11326 loss)
I0401 14:30:12.766518  8859 solver.cpp:218] Iteration 4480 (0.385038 iter/s, 20.7772s/8 iters), loss = 3.27703
I0401 14:30:12.766577  8859 solver.cpp:237]     Train net output #0: loss = 3.27703 (* 1 = 3.27703 loss)
I0401 14:30:12.766585  8859 sgd_solver.cpp:105] Iteration 4480, lr = 0.001
I0401 14:30:15.333007  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:30:15.599643  8859 solver.cpp:218] Iteration 4488 (2.82383 iter/s, 2.83303s/8 iters), loss = 3.5812
I0401 14:30:15.599686  8859 solver.cpp:237]     Train net output #0: loss = 3.5812 (* 1 = 3.5812 loss)
I0401 14:30:15.599691  8859 sgd_solver.cpp:105] Iteration 4488, lr = 0.001
I0401 14:30:18.979385  8859 solver.cpp:218] Iteration 4496 (2.36711 iter/s, 3.37965s/8 iters), loss = 2.75466
I0401 14:30:18.979434  8859 solver.cpp:237]     Train net output #0: loss = 2.75466 (* 1 = 2.75466 loss)
I0401 14:30:18.979441  8859 sgd_solver.cpp:105] Iteration 4496, lr = 0.001
I0401 14:30:22.362174  8859 solver.cpp:218] Iteration 4504 (2.36498 iter/s, 3.38269s/8 iters), loss = 3.2749
I0401 14:30:22.362219  8859 solver.cpp:237]     Train net output #0: loss = 3.2749 (* 1 = 3.2749 loss)
I0401 14:30:22.362224  8859 sgd_solver.cpp:105] Iteration 4504, lr = 0.001
I0401 14:30:25.986934  8859 solver.cpp:218] Iteration 4512 (2.20711 iter/s, 3.62465s/8 iters), loss = 2.9632
I0401 14:30:25.986994  8859 solver.cpp:237]     Train net output #0: loss = 2.9632 (* 1 = 2.9632 loss)
I0401 14:30:25.987002  8859 sgd_solver.cpp:105] Iteration 4512, lr = 0.001
I0401 14:30:29.521211  8859 solver.cpp:218] Iteration 4520 (2.26362 iter/s, 3.53416s/8 iters), loss = 2.97705
I0401 14:30:29.521268  8859 solver.cpp:237]     Train net output #0: loss = 2.97705 (* 1 = 2.97705 loss)
I0401 14:30:29.521276  8859 sgd_solver.cpp:105] Iteration 4520, lr = 0.001
I0401 14:30:33.137733  8859 solver.cpp:218] Iteration 4528 (2.21214 iter/s, 3.61642s/8 iters), loss = 2.78091
I0401 14:30:33.137776  8859 solver.cpp:237]     Train net output #0: loss = 2.78091 (* 1 = 2.78091 loss)
I0401 14:30:33.137782  8859 sgd_solver.cpp:105] Iteration 4528, lr = 0.001
I0401 14:30:36.553356  8859 solver.cpp:218] Iteration 4536 (2.34225 iter/s, 3.41551s/8 iters), loss = 2.78681
I0401 14:30:36.553404  8859 solver.cpp:237]     Train net output #0: loss = 2.78681 (* 1 = 2.78681 loss)
I0401 14:30:36.553411  8859 sgd_solver.cpp:105] Iteration 4536, lr = 0.001
I0401 14:30:39.315073  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4544.caffemodel
I0401 14:30:43.761387  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4544.solverstate
I0401 14:30:46.060948  8859 solver.cpp:330] Iteration 4544, Testing net (#0)
I0401 14:30:46.060976  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:30:53.964331  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:30:57.629091  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0693898
I0401 14:30:57.629129  8859 solver.cpp:397]     Test net output #1: loss = 5.12278 (* 1 = 5.12278 loss)
I0401 14:30:57.764365  8859 solver.cpp:218] Iteration 4544 (0.377168 iter/s, 21.2107s/8 iters), loss = 3.00455
I0401 14:30:57.764425  8859 solver.cpp:237]     Train net output #0: loss = 3.00455 (* 1 = 3.00455 loss)
I0401 14:30:57.764432  8859 sgd_solver.cpp:105] Iteration 4544, lr = 0.001
I0401 14:30:59.627466  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:31:00.177248  8859 solver.cpp:218] Iteration 4552 (3.31567 iter/s, 2.41279s/8 iters), loss = 2.94874
I0401 14:31:00.177294  8859 solver.cpp:237]     Train net output #0: loss = 2.94874 (* 1 = 2.94874 loss)
I0401 14:31:00.177300  8859 sgd_solver.cpp:105] Iteration 4552, lr = 0.001
I0401 14:31:03.595564  8859 solver.cpp:218] Iteration 4560 (2.3404 iter/s, 3.41822s/8 iters), loss = 2.83013
I0401 14:31:03.595620  8859 solver.cpp:237]     Train net output #0: loss = 2.83013 (* 1 = 2.83013 loss)
I0401 14:31:03.595629  8859 sgd_solver.cpp:105] Iteration 4560, lr = 0.001
I0401 14:31:07.014199  8859 solver.cpp:218] Iteration 4568 (2.34019 iter/s, 3.41853s/8 iters), loss = 2.70692
I0401 14:31:07.014250  8859 solver.cpp:237]     Train net output #0: loss = 2.70692 (* 1 = 2.70692 loss)
I0401 14:31:07.014257  8859 sgd_solver.cpp:105] Iteration 4568, lr = 0.001
I0401 14:31:10.684348  8859 solver.cpp:218] Iteration 4576 (2.17981 iter/s, 3.67005s/8 iters), loss = 2.88806
I0401 14:31:10.684391  8859 solver.cpp:237]     Train net output #0: loss = 2.88806 (* 1 = 2.88806 loss)
I0401 14:31:10.684397  8859 sgd_solver.cpp:105] Iteration 4576, lr = 0.001
I0401 14:31:14.319756  8859 solver.cpp:218] Iteration 4584 (2.20063 iter/s, 3.63532s/8 iters), loss = 2.74282
I0401 14:31:14.324952  8859 solver.cpp:237]     Train net output #0: loss = 2.74282 (* 1 = 2.74282 loss)
I0401 14:31:14.324965  8859 sgd_solver.cpp:105] Iteration 4584, lr = 0.001
I0401 14:31:17.907442  8859 solver.cpp:218] Iteration 4592 (2.23311 iter/s, 3.58245s/8 iters), loss = 2.9892
I0401 14:31:17.907483  8859 solver.cpp:237]     Train net output #0: loss = 2.9892 (* 1 = 2.9892 loss)
I0401 14:31:17.907488  8859 sgd_solver.cpp:105] Iteration 4592, lr = 0.001
I0401 14:31:21.489794  8859 solver.cpp:218] Iteration 4600 (2.23323 iter/s, 3.58225s/8 iters), loss = 2.99345
I0401 14:31:21.489850  8859 solver.cpp:237]     Train net output #0: loss = 2.99345 (* 1 = 2.99345 loss)
I0401 14:31:21.489857  8859 sgd_solver.cpp:105] Iteration 4600, lr = 0.001
I0401 14:31:24.490010  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4608.caffemodel
I0401 14:31:27.828107  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4608.solverstate
I0401 14:31:33.336637  8859 solver.cpp:330] Iteration 4608, Testing net (#0)
I0401 14:31:33.336659  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:31:40.930399  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:31:44.870640  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0612697
I0401 14:31:44.870771  8859 solver.cpp:397]     Test net output #1: loss = 5.12664 (* 1 = 5.12664 loss)
I0401 14:31:45.008533  8859 solver.cpp:218] Iteration 4608 (0.340159 iter/s, 23.5184s/8 iters), loss = 3.20539
I0401 14:31:45.008597  8859 solver.cpp:237]     Train net output #0: loss = 3.20539 (* 1 = 3.20539 loss)
I0401 14:31:45.008607  8859 sgd_solver.cpp:105] Iteration 4608, lr = 0.001
I0401 14:31:46.641602  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:31:47.642767  8859 solver.cpp:218] Iteration 4616 (3.03706 iter/s, 2.63413s/8 iters), loss = 3.15273
I0401 14:31:47.642812  8859 solver.cpp:237]     Train net output #0: loss = 3.15273 (* 1 = 3.15273 loss)
I0401 14:31:47.642817  8859 sgd_solver.cpp:105] Iteration 4616, lr = 0.001
I0401 14:31:51.177143  8859 solver.cpp:218] Iteration 4624 (2.26354 iter/s, 3.53428s/8 iters), loss = 3.10659
I0401 14:31:51.177183  8859 solver.cpp:237]     Train net output #0: loss = 3.10659 (* 1 = 3.10659 loss)
I0401 14:31:51.177189  8859 sgd_solver.cpp:105] Iteration 4624, lr = 0.001
I0401 14:31:54.841110  8859 solver.cpp:218] Iteration 4632 (2.18348 iter/s, 3.66387s/8 iters), loss = 2.87813
I0401 14:31:54.841166  8859 solver.cpp:237]     Train net output #0: loss = 2.87813 (* 1 = 2.87813 loss)
I0401 14:31:54.841174  8859 sgd_solver.cpp:105] Iteration 4632, lr = 0.001
I0401 14:31:58.393796  8859 solver.cpp:218] Iteration 4640 (2.25189 iter/s, 3.55257s/8 iters), loss = 2.64749
I0401 14:31:58.393852  8859 solver.cpp:237]     Train net output #0: loss = 2.64749 (* 1 = 2.64749 loss)
I0401 14:31:58.393862  8859 sgd_solver.cpp:105] Iteration 4640, lr = 0.001
I0401 14:32:01.948559  8859 solver.cpp:218] Iteration 4648 (2.25057 iter/s, 3.55465s/8 iters), loss = 2.62993
I0401 14:32:01.948621  8859 solver.cpp:237]     Train net output #0: loss = 2.62993 (* 1 = 2.62993 loss)
I0401 14:32:01.948632  8859 sgd_solver.cpp:105] Iteration 4648, lr = 0.001
I0401 14:32:05.448078  8859 solver.cpp:218] Iteration 4656 (2.28611 iter/s, 3.4994s/8 iters), loss = 2.80129
I0401 14:32:05.448163  8859 solver.cpp:237]     Train net output #0: loss = 2.80129 (* 1 = 2.80129 loss)
I0401 14:32:05.448181  8859 sgd_solver.cpp:105] Iteration 4656, lr = 0.001
I0401 14:32:09.020586  8859 solver.cpp:218] Iteration 4664 (2.23941 iter/s, 3.57238s/8 iters), loss = 3.22354
I0401 14:32:09.020643  8859 solver.cpp:237]     Train net output #0: loss = 3.22354 (* 1 = 3.22354 loss)
I0401 14:32:09.020653  8859 sgd_solver.cpp:105] Iteration 4664, lr = 0.001
I0401 14:32:11.992571  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4672.caffemodel
I0401 14:32:15.004209  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4672.solverstate
I0401 14:32:17.284695  8859 solver.cpp:330] Iteration 4672, Testing net (#0)
I0401 14:32:17.284715  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:32:25.009232  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:32:28.869222  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0625
I0401 14:32:28.869285  8859 solver.cpp:397]     Test net output #1: loss = 5.12977 (* 1 = 5.12977 loss)
I0401 14:32:29.011087  8859 solver.cpp:218] Iteration 4672 (0.400196 iter/s, 19.9902s/8 iters), loss = 3.18322
I0401 14:32:29.011143  8859 solver.cpp:237]     Train net output #0: loss = 3.18322 (* 1 = 3.18322 loss)
I0401 14:32:29.011152  8859 sgd_solver.cpp:105] Iteration 4672, lr = 0.001
I0401 14:32:30.416746  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:32:31.671869  8859 solver.cpp:218] Iteration 4680 (3.00675 iter/s, 2.66068s/8 iters), loss = 3.00895
I0401 14:32:31.671926  8859 solver.cpp:237]     Train net output #0: loss = 3.00895 (* 1 = 3.00895 loss)
I0401 14:32:31.671934  8859 sgd_solver.cpp:105] Iteration 4680, lr = 0.001
I0401 14:32:35.225546  8859 solver.cpp:218] Iteration 4688 (2.25126 iter/s, 3.55357s/8 iters), loss = 3.21906
I0401 14:32:35.225592  8859 solver.cpp:237]     Train net output #0: loss = 3.21906 (* 1 = 3.21906 loss)
I0401 14:32:35.225601  8859 sgd_solver.cpp:105] Iteration 4688, lr = 0.001
I0401 14:32:38.828368  8859 solver.cpp:218] Iteration 4696 (2.22054 iter/s, 3.60273s/8 iters), loss = 2.96576
I0401 14:32:38.828411  8859 solver.cpp:237]     Train net output #0: loss = 2.96576 (* 1 = 2.96576 loss)
I0401 14:32:38.828418  8859 sgd_solver.cpp:105] Iteration 4696, lr = 0.001
I0401 14:32:42.334308  8859 solver.cpp:218] Iteration 4704 (2.2819 iter/s, 3.50584s/8 iters), loss = 2.75462
I0401 14:32:42.334353  8859 solver.cpp:237]     Train net output #0: loss = 2.75462 (* 1 = 2.75462 loss)
I0401 14:32:42.334359  8859 sgd_solver.cpp:105] Iteration 4704, lr = 0.001
I0401 14:32:45.884291  8859 solver.cpp:218] Iteration 4712 (2.2536 iter/s, 3.54988s/8 iters), loss = 2.80622
I0401 14:32:45.884469  8859 solver.cpp:237]     Train net output #0: loss = 2.80622 (* 1 = 2.80622 loss)
I0401 14:32:45.884479  8859 sgd_solver.cpp:105] Iteration 4712, lr = 0.001
I0401 14:32:49.511765  8859 solver.cpp:218] Iteration 4720 (2.20553 iter/s, 3.62724s/8 iters), loss = 2.75951
I0401 14:32:49.511808  8859 solver.cpp:237]     Train net output #0: loss = 2.75951 (* 1 = 2.75951 loss)
I0401 14:32:49.511816  8859 sgd_solver.cpp:105] Iteration 4720, lr = 0.001
I0401 14:32:53.067698  8859 solver.cpp:218] Iteration 4728 (2.24982 iter/s, 3.55584s/8 iters), loss = 3.18032
I0401 14:32:53.067736  8859 solver.cpp:237]     Train net output #0: loss = 3.18032 (* 1 = 3.18032 loss)
I0401 14:32:53.067742  8859 sgd_solver.cpp:105] Iteration 4728, lr = 0.001
I0401 14:32:56.252508  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4736.caffemodel
I0401 14:32:59.203630  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4736.solverstate
I0401 14:33:01.498883  8859 solver.cpp:330] Iteration 4736, Testing net (#0)
I0401 14:33:01.498903  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:33:09.027603  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:12.971323  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0649606
I0401 14:33:12.971364  8859 solver.cpp:397]     Test net output #1: loss = 5.03959 (* 1 = 5.03959 loss)
I0401 14:33:13.114706  8859 solver.cpp:218] Iteration 4736 (0.399067 iter/s, 20.0467s/8 iters), loss = 3.22902
I0401 14:33:13.114766  8859 solver.cpp:237]     Train net output #0: loss = 3.22902 (* 1 = 3.22902 loss)
I0401 14:33:13.114775  8859 sgd_solver.cpp:105] Iteration 4736, lr = 0.001
I0401 14:33:14.057730  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:15.666028  8859 solver.cpp:218] Iteration 4744 (3.13575 iter/s, 2.55122s/8 iters), loss = 2.99654
I0401 14:33:15.666080  8859 solver.cpp:237]     Train net output #0: loss = 2.99654 (* 1 = 2.99654 loss)
I0401 14:33:15.666088  8859 sgd_solver.cpp:105] Iteration 4744, lr = 0.001
I0401 14:33:19.161893  8859 solver.cpp:218] Iteration 4752 (2.28849 iter/s, 3.49576s/8 iters), loss = 3.23502
I0401 14:33:19.162058  8859 solver.cpp:237]     Train net output #0: loss = 3.23502 (* 1 = 3.23502 loss)
I0401 14:33:19.162068  8859 sgd_solver.cpp:105] Iteration 4752, lr = 0.001
I0401 14:33:22.532685  8859 solver.cpp:218] Iteration 4760 (2.37348 iter/s, 3.37058s/8 iters), loss = 2.91707
I0401 14:33:22.532734  8859 solver.cpp:237]     Train net output #0: loss = 2.91707 (* 1 = 2.91707 loss)
I0401 14:33:22.532742  8859 sgd_solver.cpp:105] Iteration 4760, lr = 0.001
I0401 14:33:25.969974  8859 solver.cpp:218] Iteration 4768 (2.32748 iter/s, 3.43719s/8 iters), loss = 2.65567
I0401 14:33:25.970017  8859 solver.cpp:237]     Train net output #0: loss = 2.65567 (* 1 = 2.65567 loss)
I0401 14:33:25.970023  8859 sgd_solver.cpp:105] Iteration 4768, lr = 0.001
I0401 14:33:29.582361  8859 solver.cpp:218] Iteration 4776 (2.21466 iter/s, 3.61229s/8 iters), loss = 2.73165
I0401 14:33:29.582406  8859 solver.cpp:237]     Train net output #0: loss = 2.73165 (* 1 = 2.73165 loss)
I0401 14:33:29.582412  8859 sgd_solver.cpp:105] Iteration 4776, lr = 0.001
I0401 14:33:33.152206  8859 solver.cpp:218] Iteration 4784 (2.24106 iter/s, 3.56974s/8 iters), loss = 2.82124
I0401 14:33:33.152269  8859 solver.cpp:237]     Train net output #0: loss = 2.82124 (* 1 = 2.82124 loss)
I0401 14:33:33.152278  8859 sgd_solver.cpp:105] Iteration 4784, lr = 0.001
I0401 14:33:36.737746  8859 solver.cpp:218] Iteration 4792 (2.23125 iter/s, 3.58543s/8 iters), loss = 3.02381
I0401 14:33:36.737792  8859 solver.cpp:237]     Train net output #0: loss = 3.02381 (* 1 = 3.02381 loss)
I0401 14:33:36.737798  8859 sgd_solver.cpp:105] Iteration 4792, lr = 0.001
I0401 14:33:39.790997  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4800.caffemodel
I0401 14:33:43.764732  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4800.solverstate
I0401 14:33:47.527621  8859 solver.cpp:330] Iteration 4800, Testing net (#0)
I0401 14:33:47.527638  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:33:55.189930  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:59.261279  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0696358
I0401 14:33:59.261312  8859 solver.cpp:397]     Test net output #1: loss = 5.0723 (* 1 = 5.0723 loss)
I0401 14:33:59.403470  8859 solver.cpp:218] Iteration 4800 (0.35296 iter/s, 22.6654s/8 iters), loss = 2.92632
I0401 14:33:59.403522  8859 solver.cpp:237]     Train net output #0: loss = 2.92632 (* 1 = 2.92632 loss)
I0401 14:33:59.403530  8859 sgd_solver.cpp:105] Iteration 4800, lr = 0.001
I0401 14:34:00.009454  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:34:00.629393  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:34:02.081960  8859 solver.cpp:218] Iteration 4808 (2.98687 iter/s, 2.67839s/8 iters), loss = 2.928
I0401 14:34:02.082020  8859 solver.cpp:237]     Train net output #0: loss = 2.928 (* 1 = 2.928 loss)
I0401 14:34:02.082029  8859 sgd_solver.cpp:105] Iteration 4808, lr = 0.001
I0401 14:34:05.525310  8859 solver.cpp:218] Iteration 4816 (2.32339 iter/s, 3.44324s/8 iters), loss = 2.71455
I0401 14:34:05.525362  8859 solver.cpp:237]     Train net output #0: loss = 2.71455 (* 1 = 2.71455 loss)
I0401 14:34:05.525370  8859 sgd_solver.cpp:105] Iteration 4816, lr = 0.001
I0401 14:34:08.828662  8859 solver.cpp:218] Iteration 4824 (2.42186 iter/s, 3.30325s/8 iters), loss = 2.63661
I0401 14:34:08.828718  8859 solver.cpp:237]     Train net output #0: loss = 2.63661 (* 1 = 2.63661 loss)
I0401 14:34:08.828727  8859 sgd_solver.cpp:105] Iteration 4824, lr = 0.001
I0401 14:34:12.355239  8859 solver.cpp:218] Iteration 4832 (2.26856 iter/s, 3.52647s/8 iters), loss = 2.54663
I0401 14:34:12.355283  8859 solver.cpp:237]     Train net output #0: loss = 2.54663 (* 1 = 2.54663 loss)
I0401 14:34:12.355288  8859 sgd_solver.cpp:105] Iteration 4832, lr = 0.001
I0401 14:34:15.994088  8859 solver.cpp:218] Iteration 4840 (2.19856 iter/s, 3.63875s/8 iters), loss = 2.26825
I0401 14:34:15.994138  8859 solver.cpp:237]     Train net output #0: loss = 2.26825 (* 1 = 2.26825 loss)
I0401 14:34:15.994164  8859 sgd_solver.cpp:105] Iteration 4840, lr = 0.001
I0401 14:34:19.529829  8859 solver.cpp:218] Iteration 4848 (2.26267 iter/s, 3.53564s/8 iters), loss = 2.67194
I0401 14:34:19.529866  8859 solver.cpp:237]     Train net output #0: loss = 2.67194 (* 1 = 2.67194 loss)
I0401 14:34:19.529871  8859 sgd_solver.cpp:105] Iteration 4848, lr = 0.001
I0401 14:34:23.096607  8859 solver.cpp:218] Iteration 4856 (2.24298 iter/s, 3.56669s/8 iters), loss = 2.80228
I0401 14:34:23.096649  8859 solver.cpp:237]     Train net output #0: loss = 2.80228 (* 1 = 2.80228 loss)
I0401 14:34:23.096655  8859 sgd_solver.cpp:105] Iteration 4856, lr = 0.001
I0401 14:34:26.069299  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4864.caffemodel
I0401 14:34:29.231758  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4864.solverstate
I0401 14:34:31.536348  8859 solver.cpp:330] Iteration 4864, Testing net (#0)
I0401 14:34:31.536372  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:34:38.938558  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:34:42.927670  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0775098
I0401 14:34:42.927708  8859 solver.cpp:397]     Test net output #1: loss = 5.0569 (* 1 = 5.0569 loss)
I0401 14:34:43.064049  8859 solver.cpp:218] Iteration 4864 (0.400657 iter/s, 19.9672s/8 iters), loss = 2.94331
I0401 14:34:43.065626  8859 solver.cpp:237]     Train net output #0: loss = 2.94331 (* 1 = 2.94331 loss)
I0401 14:34:43.065639  8859 sgd_solver.cpp:105] Iteration 4864, lr = 0.001
I0401 14:34:43.285022  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:34:45.630813  8859 solver.cpp:218] Iteration 4872 (3.11874 iter/s, 2.56514s/8 iters), loss = 2.89671
I0401 14:34:45.630877  8859 solver.cpp:237]     Train net output #0: loss = 2.89671 (* 1 = 2.89671 loss)
I0401 14:34:45.630887  8859 sgd_solver.cpp:105] Iteration 4872, lr = 0.001
I0401 14:34:49.133221  8859 solver.cpp:218] Iteration 4880 (2.28422 iter/s, 3.50229s/8 iters), loss = 3.03202
I0401 14:34:49.133275  8859 solver.cpp:237]     Train net output #0: loss = 3.03202 (* 1 = 3.03202 loss)
I0401 14:34:49.133283  8859 sgd_solver.cpp:105] Iteration 4880, lr = 0.001
I0401 14:34:52.467128  8859 solver.cpp:218] Iteration 4888 (2.39966 iter/s, 3.3338s/8 iters), loss = 2.91004
I0401 14:34:52.467173  8859 solver.cpp:237]     Train net output #0: loss = 2.91004 (* 1 = 2.91004 loss)
I0401 14:34:52.467178  8859 sgd_solver.cpp:105] Iteration 4888, lr = 0.001
I0401 14:34:56.060482  8859 solver.cpp:218] Iteration 4896 (2.22639 iter/s, 3.59326s/8 iters), loss = 2.35122
I0401 14:34:56.060523  8859 solver.cpp:237]     Train net output #0: loss = 2.35122 (* 1 = 2.35122 loss)
I0401 14:34:56.060528  8859 sgd_solver.cpp:105] Iteration 4896, lr = 0.001
I0401 14:34:59.568873  8859 solver.cpp:218] Iteration 4904 (2.28031 iter/s, 3.5083s/8 iters), loss = 2.07835
I0401 14:34:59.568974  8859 solver.cpp:237]     Train net output #0: loss = 2.07835 (* 1 = 2.07835 loss)
I0401 14:34:59.568980  8859 sgd_solver.cpp:105] Iteration 4904, lr = 0.001
I0401 14:35:03.105572  8859 solver.cpp:218] Iteration 4912 (2.2621 iter/s, 3.53654s/8 iters), loss = 2.95948
I0401 14:35:03.105621  8859 solver.cpp:237]     Train net output #0: loss = 2.95948 (* 1 = 2.95948 loss)
I0401 14:35:03.105628  8859 sgd_solver.cpp:105] Iteration 4912, lr = 0.001
I0401 14:35:06.825173  8859 solver.cpp:218] Iteration 4920 (2.15083 iter/s, 3.71949s/8 iters), loss = 2.55945
I0401 14:35:06.825223  8859 solver.cpp:237]     Train net output #0: loss = 2.55945 (* 1 = 2.55945 loss)
I0401 14:35:06.825232  8859 sgd_solver.cpp:105] Iteration 4920, lr = 0.001
I0401 14:35:09.579396  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4928.caffemodel
I0401 14:35:12.557902  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4928.solverstate
I0401 14:35:14.878896  8859 solver.cpp:330] Iteration 4928, Testing net (#0)
I0401 14:35:14.878916  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:35:22.567472  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:26.688474  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0777559
I0401 14:35:26.688514  8859 solver.cpp:397]     Test net output #1: loss = 5.04364 (* 1 = 5.04364 loss)
I0401 14:35:26.824453  8859 solver.cpp:218] Iteration 4928 (0.40002 iter/s, 19.999s/8 iters), loss = 2.83247
I0401 14:35:26.824512  8859 solver.cpp:237]     Train net output #0: loss = 2.83247 (* 1 = 2.83247 loss)
I0401 14:35:26.824519  8859 sgd_solver.cpp:105] Iteration 4928, lr = 0.001
I0401 14:35:26.840595  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:29.398329  8859 solver.cpp:218] Iteration 4936 (3.10828 iter/s, 2.57377s/8 iters), loss = 2.46851
I0401 14:35:29.398386  8859 solver.cpp:237]     Train net output #0: loss = 2.46851 (* 1 = 2.46851 loss)
I0401 14:35:29.398393  8859 sgd_solver.cpp:105] Iteration 4936, lr = 0.001
I0401 14:35:32.950995  8859 solver.cpp:218] Iteration 4944 (2.2519 iter/s, 3.55256s/8 iters), loss = 3.1184
I0401 14:35:32.951128  8859 solver.cpp:237]     Train net output #0: loss = 3.1184 (* 1 = 3.1184 loss)
I0401 14:35:32.951138  8859 sgd_solver.cpp:105] Iteration 4944, lr = 0.001
I0401 14:35:36.383285  8859 solver.cpp:218] Iteration 4952 (2.33093 iter/s, 3.43211s/8 iters), loss = 2.73992
I0401 14:35:36.383322  8859 solver.cpp:237]     Train net output #0: loss = 2.73992 (* 1 = 2.73992 loss)
I0401 14:35:36.383327  8859 sgd_solver.cpp:105] Iteration 4952, lr = 0.001
I0401 14:35:40.034292  8859 solver.cpp:218] Iteration 4960 (2.19123 iter/s, 3.65092s/8 iters), loss = 2.47501
I0401 14:35:40.034333  8859 solver.cpp:237]     Train net output #0: loss = 2.47501 (* 1 = 2.47501 loss)
I0401 14:35:40.034338  8859 sgd_solver.cpp:105] Iteration 4960, lr = 0.001
I0401 14:35:43.681699  8859 solver.cpp:218] Iteration 4968 (2.19339 iter/s, 3.64732s/8 iters), loss = 2.30546
I0401 14:35:43.681737  8859 solver.cpp:237]     Train net output #0: loss = 2.30546 (* 1 = 2.30546 loss)
I0401 14:35:43.681743  8859 sgd_solver.cpp:105] Iteration 4968, lr = 0.001
I0401 14:35:47.293303  8859 solver.cpp:218] Iteration 4976 (2.21514 iter/s, 3.61151s/8 iters), loss = 2.97088
I0401 14:35:47.293359  8859 solver.cpp:237]     Train net output #0: loss = 2.97088 (* 1 = 2.97088 loss)
I0401 14:35:47.293367  8859 sgd_solver.cpp:105] Iteration 4976, lr = 0.001
I0401 14:35:50.660928  8859 solver.cpp:218] Iteration 4984 (2.37564 iter/s, 3.36751s/8 iters), loss = 2.74619
I0401 14:35:50.660985  8859 solver.cpp:237]     Train net output #0: loss = 2.74619 (* 1 = 2.74619 loss)
I0401 14:35:50.660995  8859 sgd_solver.cpp:105] Iteration 4984, lr = 0.001
I0401 14:35:53.641355  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4992.caffemodel
I0401 14:35:54.582319  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:56.757133  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4992.solverstate
I0401 14:36:00.043496  8859 solver.cpp:330] Iteration 4992, Testing net (#0)
I0401 14:36:00.043514  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:36:07.521814  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:36:11.691974  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0794783
I0401 14:36:11.692005  8859 solver.cpp:397]     Test net output #1: loss = 5.06154 (* 1 = 5.06154 loss)
I0401 14:36:11.828925  8859 solver.cpp:218] Iteration 4992 (0.378027 iter/s, 21.1625s/8 iters), loss = 3.1382
I0401 14:36:11.828979  8859 solver.cpp:237]     Train net output #0: loss = 3.1382 (* 1 = 3.1382 loss)
I0401 14:36:11.828987  8859 sgd_solver.cpp:105] Iteration 4992, lr = 0.001
I0401 14:36:14.355561  8859 solver.cpp:218] Iteration 5000 (3.16638 iter/s, 2.52654s/8 iters), loss = 2.55651
I0401 14:36:14.355608  8859 solver.cpp:237]     Train net output #0: loss = 2.55651 (* 1 = 2.55651 loss)
I0401 14:36:14.355616  8859 sgd_solver.cpp:105] Iteration 5000, lr = 0.001
I0401 14:36:17.631762  8859 solver.cpp:218] Iteration 5008 (2.44192 iter/s, 3.2761s/8 iters), loss = 2.85413
I0401 14:36:17.631814  8859 solver.cpp:237]     Train net output #0: loss = 2.85413 (* 1 = 2.85413 loss)
I0401 14:36:17.631821  8859 sgd_solver.cpp:105] Iteration 5008, lr = 0.001
I0401 14:36:21.257997  8859 solver.cpp:218] Iteration 5016 (2.20621 iter/s, 3.62612s/8 iters), loss = 2.47869
I0401 14:36:21.258059  8859 solver.cpp:237]     Train net output #0: loss = 2.47869 (* 1 = 2.47869 loss)
I0401 14:36:21.258067  8859 sgd_solver.cpp:105] Iteration 5016, lr = 0.001
I0401 14:36:24.594647  8859 solver.cpp:218] Iteration 5024 (2.3977 iter/s, 3.33653s/8 iters), loss = 2.46743
I0401 14:36:24.594705  8859 solver.cpp:237]     Train net output #0: loss = 2.46743 (* 1 = 2.46743 loss)
I0401 14:36:24.594714  8859 sgd_solver.cpp:105] Iteration 5024, lr = 0.001
I0401 14:36:28.259377  8859 solver.cpp:218] Iteration 5032 (2.18304 iter/s, 3.66461s/8 iters), loss = 2.37519
I0401 14:36:28.259436  8859 solver.cpp:237]     Train net output #0: loss = 2.37519 (* 1 = 2.37519 loss)
I0401 14:36:28.259445  8859 sgd_solver.cpp:105] Iteration 5032, lr = 0.001
I0401 14:36:31.752830  8859 solver.cpp:218] Iteration 5040 (2.29007 iter/s, 3.49334s/8 iters), loss = 2.51874
I0401 14:36:31.752871  8859 solver.cpp:237]     Train net output #0: loss = 2.51874 (* 1 = 2.51874 loss)
I0401 14:36:31.752877  8859 sgd_solver.cpp:105] Iteration 5040, lr = 0.001
I0401 14:36:35.355715  8859 solver.cpp:218] Iteration 5048 (2.2205 iter/s, 3.60279s/8 iters), loss = 2.89424
I0401 14:36:35.355772  8859 solver.cpp:237]     Train net output #0: loss = 2.89424 (* 1 = 2.89424 loss)
I0401 14:36:35.355780  8859 sgd_solver.cpp:105] Iteration 5048, lr = 0.001
I0401 14:36:38.421574  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5056.caffemodel
I0401 14:36:38.991161  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:36:41.852386  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5056.solverstate
I0401 14:36:44.151226  8859 solver.cpp:330] Iteration 5056, Testing net (#0)
I0401 14:36:44.151245  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:36:51.564612  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:36:55.925348  8859 solver.cpp:397]     Test net output #0: accuracy = 0.081939
I0401 14:36:55.925380  8859 solver.cpp:397]     Test net output #1: loss = 5.12296 (* 1 = 5.12296 loss)
I0401 14:36:56.067083  8859 solver.cpp:218] Iteration 5056 (0.386266 iter/s, 20.7111s/8 iters), loss = 2.4583
I0401 14:36:56.067147  8859 solver.cpp:237]     Train net output #0: loss = 2.4583 (* 1 = 2.4583 loss)
I0401 14:36:56.067157  8859 sgd_solver.cpp:105] Iteration 5056, lr = 0.001
I0401 14:36:58.766458  8859 solver.cpp:218] Iteration 5064 (2.96377 iter/s, 2.69927s/8 iters), loss = 2.53986
I0401 14:36:58.766512  8859 solver.cpp:237]     Train net output #0: loss = 2.53986 (* 1 = 2.53986 loss)
I0401 14:36:58.766520  8859 sgd_solver.cpp:105] Iteration 5064, lr = 0.001
I0401 14:37:02.245576  8859 solver.cpp:218] Iteration 5072 (2.2995 iter/s, 3.47901s/8 iters), loss = 2.58508
I0401 14:37:02.245630  8859 solver.cpp:237]     Train net output #0: loss = 2.58508 (* 1 = 2.58508 loss)
I0401 14:37:02.245638  8859 sgd_solver.cpp:105] Iteration 5072, lr = 0.001
I0401 14:37:05.583663  8859 solver.cpp:218] Iteration 5080 (2.39666 iter/s, 3.33798s/8 iters), loss = 2.44822
I0401 14:37:05.583736  8859 solver.cpp:237]     Train net output #0: loss = 2.44822 (* 1 = 2.44822 loss)
I0401 14:37:05.583747  8859 sgd_solver.cpp:105] Iteration 5080, lr = 0.001
I0401 14:37:08.866192  8859 solver.cpp:218] Iteration 5088 (2.43724 iter/s, 3.2824s/8 iters), loss = 2.13576
I0401 14:37:08.866320  8859 solver.cpp:237]     Train net output #0: loss = 2.13576 (* 1 = 2.13576 loss)
I0401 14:37:08.866328  8859 sgd_solver.cpp:105] Iteration 5088, lr = 0.001
I0401 14:37:12.545815  8859 solver.cpp:218] Iteration 5096 (2.17424 iter/s, 3.67945s/8 iters), loss = 2.07323
I0401 14:37:12.545855  8859 solver.cpp:237]     Train net output #0: loss = 2.07323 (* 1 = 2.07323 loss)
I0401 14:37:12.545861  8859 sgd_solver.cpp:105] Iteration 5096, lr = 0.001
I0401 14:37:15.912840  8859 solver.cpp:218] Iteration 5104 (2.37605 iter/s, 3.36693s/8 iters), loss = 2.6201
I0401 14:37:15.912897  8859 solver.cpp:237]     Train net output #0: loss = 2.6201 (* 1 = 2.6201 loss)
I0401 14:37:15.912905  8859 sgd_solver.cpp:105] Iteration 5104, lr = 0.001
I0401 14:37:19.611109  8859 solver.cpp:218] Iteration 5112 (2.16323 iter/s, 3.69817s/8 iters), loss = 2.58041
I0401 14:37:19.611160  8859 solver.cpp:237]     Train net output #0: loss = 2.58041 (* 1 = 2.58041 loss)
I0401 14:37:19.611169  8859 sgd_solver.cpp:105] Iteration 5112, lr = 0.001
I0401 14:37:22.820092  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5120.caffemodel
I0401 14:37:23.161872  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:37:25.912281  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5120.solverstate
I0401 14:37:30.215909  8859 solver.cpp:330] Iteration 5120, Testing net (#0)
I0401 14:37:30.215930  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:37:37.449728  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:37:41.500571  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0809547
I0401 14:37:41.500710  8859 solver.cpp:397]     Test net output #1: loss = 5.17114 (* 1 = 5.17114 loss)
I0401 14:37:41.642305  8859 solver.cpp:218] Iteration 5120 (0.363126 iter/s, 22.0309s/8 iters), loss = 2.97677
I0401 14:37:41.642364  8859 solver.cpp:237]     Train net output #0: loss = 2.97677 (* 1 = 2.97677 loss)
I0401 14:37:41.642372  8859 sgd_solver.cpp:105] Iteration 5120, lr = 0.001
I0401 14:37:44.269050  8859 solver.cpp:218] Iteration 5128 (3.04572 iter/s, 2.62664s/8 iters), loss = 2.25524
I0401 14:37:44.269116  8859 solver.cpp:237]     Train net output #0: loss = 2.25524 (* 1 = 2.25524 loss)
I0401 14:37:44.269126  8859 sgd_solver.cpp:105] Iteration 5128, lr = 0.001
I0401 14:37:47.637979  8859 solver.cpp:218] Iteration 5136 (2.37472 iter/s, 3.36882s/8 iters), loss = 2.37104
I0401 14:37:47.638020  8859 solver.cpp:237]     Train net output #0: loss = 2.37104 (* 1 = 2.37104 loss)
I0401 14:37:47.638027  8859 sgd_solver.cpp:105] Iteration 5136, lr = 0.001
I0401 14:37:50.873572  8859 solver.cpp:218] Iteration 5144 (2.47257 iter/s, 3.2355s/8 iters), loss = 2.33494
I0401 14:37:50.873616  8859 solver.cpp:237]     Train net output #0: loss = 2.33494 (* 1 = 2.33494 loss)
I0401 14:37:50.873622  8859 sgd_solver.cpp:105] Iteration 5144, lr = 0.001
I0401 14:37:54.282465  8859 solver.cpp:218] Iteration 5152 (2.34687 iter/s, 3.4088s/8 iters), loss = 1.94514
I0401 14:37:54.282511  8859 solver.cpp:237]     Train net output #0: loss = 1.94514 (* 1 = 1.94514 loss)
I0401 14:37:54.282517  8859 sgd_solver.cpp:105] Iteration 5152, lr = 0.001
I0401 14:37:57.888748  8859 solver.cpp:218] Iteration 5160 (2.21841 iter/s, 3.60618s/8 iters), loss = 2.07193
I0401 14:37:57.888799  8859 solver.cpp:237]     Train net output #0: loss = 2.07193 (* 1 = 2.07193 loss)
I0401 14:37:57.888804  8859 sgd_solver.cpp:105] Iteration 5160, lr = 0.001
I0401 14:38:01.157989  8859 solver.cpp:218] Iteration 5168 (2.44713 iter/s, 3.26913s/8 iters), loss = 2.36285
I0401 14:38:01.158048  8859 solver.cpp:237]     Train net output #0: loss = 2.36285 (* 1 = 2.36285 loss)
I0401 14:38:01.158056  8859 sgd_solver.cpp:105] Iteration 5168, lr = 0.001
I0401 14:38:04.698246  8859 solver.cpp:218] Iteration 5176 (2.25979 iter/s, 3.54015s/8 iters), loss = 2.29502
I0401 14:38:04.698302  8859 solver.cpp:237]     Train net output #0: loss = 2.29502 (* 1 = 2.29502 loss)
I0401 14:38:04.698312  8859 sgd_solver.cpp:105] Iteration 5176, lr = 0.001
I0401 14:38:07.702356  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:07.776722  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5184.caffemodel
I0401 14:38:10.753224  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5184.solverstate
I0401 14:38:13.046854  8859 solver.cpp:330] Iteration 5184, Testing net (#0)
I0401 14:38:13.046978  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:38:14.617784  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:38:20.045581  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:24.172693  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0831693
I0401 14:38:24.172729  8859 solver.cpp:397]     Test net output #1: loss = 5.22728 (* 1 = 5.22728 loss)
I0401 14:38:24.312741  8859 solver.cpp:218] Iteration 5184 (0.407867 iter/s, 19.6142s/8 iters), loss = 2.11074
I0401 14:38:24.312800  8859 solver.cpp:237]     Train net output #0: loss = 2.11074 (* 1 = 2.11074 loss)
I0401 14:38:24.312808  8859 sgd_solver.cpp:105] Iteration 5184, lr = 0.001
I0401 14:38:26.777655  8859 solver.cpp:218] Iteration 5192 (3.24568 iter/s, 2.46482s/8 iters), loss = 2.20343
I0401 14:38:26.777694  8859 solver.cpp:237]     Train net output #0: loss = 2.20343 (* 1 = 2.20343 loss)
I0401 14:38:26.777700  8859 sgd_solver.cpp:105] Iteration 5192, lr = 0.001
I0401 14:38:30.042305  8859 solver.cpp:218] Iteration 5200 (2.45056 iter/s, 3.26455s/8 iters), loss = 2.06094
I0401 14:38:30.042371  8859 solver.cpp:237]     Train net output #0: loss = 2.06094 (* 1 = 2.06094 loss)
I0401 14:38:30.042379  8859 sgd_solver.cpp:105] Iteration 5200, lr = 0.001
I0401 14:38:33.415841  8859 solver.cpp:218] Iteration 5208 (2.37148 iter/s, 3.37342s/8 iters), loss = 1.99683
I0401 14:38:33.415894  8859 solver.cpp:237]     Train net output #0: loss = 1.99683 (* 1 = 1.99683 loss)
I0401 14:38:33.415901  8859 sgd_solver.cpp:105] Iteration 5208, lr = 0.001
I0401 14:38:36.932718  8859 solver.cpp:218] Iteration 5216 (2.27481 iter/s, 3.51677s/8 iters), loss = 2.04955
I0401 14:38:36.932775  8859 solver.cpp:237]     Train net output #0: loss = 2.04955 (* 1 = 2.04955 loss)
I0401 14:38:36.932781  8859 sgd_solver.cpp:105] Iteration 5216, lr = 0.001
I0401 14:38:40.476775  8859 solver.cpp:218] Iteration 5224 (2.25737 iter/s, 3.54395s/8 iters), loss = 2.05182
I0401 14:38:40.476821  8859 solver.cpp:237]     Train net output #0: loss = 2.05182 (* 1 = 2.05182 loss)
I0401 14:38:40.476827  8859 sgd_solver.cpp:105] Iteration 5224, lr = 0.001
I0401 14:38:43.895107  8859 solver.cpp:218] Iteration 5232 (2.34039 iter/s, 3.41824s/8 iters), loss = 2.31244
I0401 14:38:43.895201  8859 solver.cpp:237]     Train net output #0: loss = 2.31244 (* 1 = 2.31244 loss)
I0401 14:38:43.895207  8859 sgd_solver.cpp:105] Iteration 5232, lr = 0.001
I0401 14:38:47.295645  8859 solver.cpp:218] Iteration 5240 (2.35267 iter/s, 3.4004s/8 iters), loss = 2.36362
I0401 14:38:47.295689  8859 solver.cpp:237]     Train net output #0: loss = 2.36362 (* 1 = 2.36362 loss)
I0401 14:38:47.295696  8859 sgd_solver.cpp:105] Iteration 5240, lr = 0.001
I0401 14:38:49.797251  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:50.100474  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5248.caffemodel
I0401 14:38:53.570710  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5248.solverstate
I0401 14:38:57.338135  8859 solver.cpp:330] Iteration 5248, Testing net (#0)
I0401 14:38:57.338160  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:39:04.291792  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:39:08.488852  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0831693
I0401 14:39:08.488898  8859 solver.cpp:397]     Test net output #1: loss = 5.27245 (* 1 = 5.27245 loss)
I0401 14:39:08.624065  8859 solver.cpp:218] Iteration 5248 (0.375091 iter/s, 21.3281s/8 iters), loss = 2.10716
I0401 14:39:08.625625  8859 solver.cpp:237]     Train net output #0: loss = 2.10716 (* 1 = 2.10716 loss)
I0401 14:39:08.625639  8859 sgd_solver.cpp:105] Iteration 5248, lr = 0.001
I0401 14:39:11.226449  8859 solver.cpp:218] Iteration 5256 (3.07599 iter/s, 2.60079s/8 iters), loss = 2.11091
I0401 14:39:11.226493  8859 solver.cpp:237]     Train net output #0: loss = 2.11091 (* 1 = 2.11091 loss)
I0401 14:39:11.226500  8859 sgd_solver.cpp:105] Iteration 5256, lr = 0.001
I0401 14:39:14.538208  8859 solver.cpp:218] Iteration 5264 (2.4157 iter/s, 3.31167s/8 iters), loss = 2.10158
I0401 14:39:14.538331  8859 solver.cpp:237]     Train net output #0: loss = 2.10158 (* 1 = 2.10158 loss)
I0401 14:39:14.538338  8859 sgd_solver.cpp:105] Iteration 5264, lr = 0.001
I0401 14:39:17.880911  8859 solver.cpp:218] Iteration 5272 (2.3934 iter/s, 3.34253s/8 iters), loss = 2.09547
I0401 14:39:17.880965  8859 solver.cpp:237]     Train net output #0: loss = 2.09547 (* 1 = 2.09547 loss)
I0401 14:39:17.880975  8859 sgd_solver.cpp:105] Iteration 5272, lr = 0.001
I0401 14:39:21.225625  8859 solver.cpp:218] Iteration 5280 (2.39191 iter/s, 3.34461s/8 iters), loss = 1.69712
I0401 14:39:21.225670  8859 solver.cpp:237]     Train net output #0: loss = 1.69712 (* 1 = 1.69712 loss)
I0401 14:39:21.225677  8859 sgd_solver.cpp:105] Iteration 5280, lr = 0.001
I0401 14:39:24.758194  8859 solver.cpp:218] Iteration 5288 (2.2647 iter/s, 3.53247s/8 iters), loss = 1.78096
I0401 14:39:24.758237  8859 solver.cpp:237]     Train net output #0: loss = 1.78096 (* 1 = 1.78096 loss)
I0401 14:39:24.758244  8859 sgd_solver.cpp:105] Iteration 5288, lr = 0.001
I0401 14:39:28.230700  8859 solver.cpp:218] Iteration 5296 (2.30388 iter/s, 3.47241s/8 iters), loss = 2.2915
I0401 14:39:28.230762  8859 solver.cpp:237]     Train net output #0: loss = 2.2915 (* 1 = 2.2915 loss)
I0401 14:39:28.230772  8859 sgd_solver.cpp:105] Iteration 5296, lr = 0.001
I0401 14:39:31.727548  8859 solver.cpp:218] Iteration 5304 (2.28784 iter/s, 3.49675s/8 iters), loss = 2.21332
I0401 14:39:31.727594  8859 solver.cpp:237]     Train net output #0: loss = 2.21332 (* 1 = 2.21332 loss)
I0401 14:39:31.727602  8859 sgd_solver.cpp:105] Iteration 5304, lr = 0.001
I0401 14:39:33.957309  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:39:34.558090  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5312.caffemodel
I0401 14:39:37.542464  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5312.solverstate
I0401 14:39:39.831843  8859 solver.cpp:330] Iteration 5312, Testing net (#0)
I0401 14:39:39.831862  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:39:46.751003  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:39:51.033759  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0812008
I0401 14:39:51.033797  8859 solver.cpp:397]     Test net output #1: loss = 5.26042 (* 1 = 5.26042 loss)
I0401 14:39:51.172470  8859 solver.cpp:218] Iteration 5312 (0.411424 iter/s, 19.4447s/8 iters), loss = 2.28349
I0401 14:39:51.172519  8859 solver.cpp:237]     Train net output #0: loss = 2.28349 (* 1 = 2.28349 loss)
I0401 14:39:51.172526  8859 sgd_solver.cpp:105] Iteration 5312, lr = 0.001
I0401 14:39:53.679450  8859 solver.cpp:218] Iteration 5320 (3.1912 iter/s, 2.50689s/8 iters), loss = 2.13362
I0401 14:39:53.679488  8859 solver.cpp:237]     Train net output #0: loss = 2.13362 (* 1 = 2.13362 loss)
I0401 14:39:53.679493  8859 sgd_solver.cpp:105] Iteration 5320, lr = 0.001
I0401 14:39:57.072741  8859 solver.cpp:218] Iteration 5328 (2.35766 iter/s, 3.3932s/8 iters), loss = 1.75473
I0401 14:39:57.072784  8859 solver.cpp:237]     Train net output #0: loss = 1.75473 (* 1 = 1.75473 loss)
I0401 14:39:57.072790  8859 sgd_solver.cpp:105] Iteration 5328, lr = 0.001
I0401 14:40:00.419931  8859 solver.cpp:218] Iteration 5336 (2.39013 iter/s, 3.34709s/8 iters), loss = 1.57844
I0401 14:40:00.419986  8859 solver.cpp:237]     Train net output #0: loss = 1.57844 (* 1 = 1.57844 loss)
I0401 14:40:00.419996  8859 sgd_solver.cpp:105] Iteration 5336, lr = 0.001
I0401 14:40:03.857174  8859 solver.cpp:218] Iteration 5344 (2.32752 iter/s, 3.43714s/8 iters), loss = 2.03068
I0401 14:40:03.857229  8859 solver.cpp:237]     Train net output #0: loss = 2.03068 (* 1 = 2.03068 loss)
I0401 14:40:03.857236  8859 sgd_solver.cpp:105] Iteration 5344, lr = 0.001
I0401 14:40:07.304257  8859 solver.cpp:218] Iteration 5352 (2.32087 iter/s, 3.44698s/8 iters), loss = 1.81382
I0401 14:40:07.304304  8859 solver.cpp:237]     Train net output #0: loss = 1.81382 (* 1 = 1.81382 loss)
I0401 14:40:07.304311  8859 sgd_solver.cpp:105] Iteration 5352, lr = 0.001
I0401 14:40:10.754206  8859 solver.cpp:218] Iteration 5360 (2.31894 iter/s, 3.44985s/8 iters), loss = 2.19435
I0401 14:40:10.754256  8859 solver.cpp:237]     Train net output #0: loss = 2.19435 (* 1 = 2.19435 loss)
I0401 14:40:10.754261  8859 sgd_solver.cpp:105] Iteration 5360, lr = 0.001
I0401 14:40:14.338028  8859 solver.cpp:218] Iteration 5368 (2.23232 iter/s, 3.58372s/8 iters), loss = 2.11629
I0401 14:40:14.338074  8859 solver.cpp:237]     Train net output #0: loss = 2.11629 (* 1 = 2.11629 loss)
I0401 14:40:14.338080  8859 sgd_solver.cpp:105] Iteration 5368, lr = 0.001
I0401 14:40:16.279475  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:40:17.327023  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5376.caffemodel
I0401 14:40:21.811934  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5376.solverstate
I0401 14:40:25.201057  8859 solver.cpp:330] Iteration 5376, Testing net (#0)
I0401 14:40:25.201081  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:40:31.981933  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:40:36.340793  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0760335
I0401 14:40:36.340829  8859 solver.cpp:397]     Test net output #1: loss = 5.32516 (* 1 = 5.32516 loss)
I0401 14:40:36.470494  8859 solver.cpp:218] Iteration 5376 (0.361464 iter/s, 22.1322s/8 iters), loss = 2.14698
I0401 14:40:36.470541  8859 solver.cpp:237]     Train net output #0: loss = 2.14698 (* 1 = 2.14698 loss)
I0401 14:40:36.470547  8859 sgd_solver.cpp:105] Iteration 5376, lr = 0.001
I0401 14:40:39.052740  8859 solver.cpp:218] Iteration 5384 (3.09818 iter/s, 2.58216s/8 iters), loss = 2.31742
I0401 14:40:39.052781  8859 solver.cpp:237]     Train net output #0: loss = 2.31742 (* 1 = 2.31742 loss)
I0401 14:40:39.052788  8859 sgd_solver.cpp:105] Iteration 5384, lr = 0.001
I0401 14:40:42.437062  8859 solver.cpp:218] Iteration 5392 (2.36391 iter/s, 3.38423s/8 iters), loss = 2.02878
I0401 14:40:42.437108  8859 solver.cpp:237]     Train net output #0: loss = 2.02878 (* 1 = 2.02878 loss)
I0401 14:40:42.437114  8859 sgd_solver.cpp:105] Iteration 5392, lr = 0.001
I0401 14:40:45.918061  8859 solver.cpp:218] Iteration 5400 (2.29826 iter/s, 3.4809s/8 iters), loss = 1.99068
I0401 14:40:45.918120  8859 solver.cpp:237]     Train net output #0: loss = 1.99068 (* 1 = 1.99068 loss)
I0401 14:40:45.918128  8859 sgd_solver.cpp:105] Iteration 5400, lr = 0.001
I0401 14:40:49.512064  8859 solver.cpp:218] Iteration 5408 (2.226 iter/s, 3.59389s/8 iters), loss = 1.91113
I0401 14:40:49.512181  8859 solver.cpp:237]     Train net output #0: loss = 1.91113 (* 1 = 1.91113 loss)
I0401 14:40:49.512190  8859 sgd_solver.cpp:105] Iteration 5408, lr = 0.001
I0401 14:40:52.945598  8859 solver.cpp:218] Iteration 5416 (2.33007 iter/s, 3.43337s/8 iters), loss = 1.92163
I0401 14:40:52.945649  8859 solver.cpp:237]     Train net output #0: loss = 1.92163 (* 1 = 1.92163 loss)
I0401 14:40:52.945657  8859 sgd_solver.cpp:105] Iteration 5416, lr = 0.001
I0401 14:40:56.330384  8859 solver.cpp:218] Iteration 5424 (2.36359 iter/s, 3.38468s/8 iters), loss = 2.40813
I0401 14:40:56.330430  8859 solver.cpp:237]     Train net output #0: loss = 2.40813 (* 1 = 2.40813 loss)
I0401 14:40:56.330435  8859 sgd_solver.cpp:105] Iteration 5424, lr = 0.001
I0401 14:40:59.776840  8859 solver.cpp:218] Iteration 5432 (2.32129 iter/s, 3.44636s/8 iters), loss = 2.225
I0401 14:40:59.776878  8859 solver.cpp:237]     Train net output #0: loss = 2.225 (* 1 = 2.225 loss)
I0401 14:40:59.776887  8859 sgd_solver.cpp:105] Iteration 5432, lr = 0.001
I0401 14:41:01.342700  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:02.676983  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5440.caffemodel
I0401 14:41:05.672314  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5440.solverstate
I0401 14:41:07.986307  8859 solver.cpp:330] Iteration 5440, Testing net (#0)
I0401 14:41:07.986331  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:41:14.727427  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:19.066280  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0834154
I0401 14:41:19.066314  8859 solver.cpp:397]     Test net output #1: loss = 5.20032 (* 1 = 5.20032 loss)
I0401 14:41:19.203980  8859 solver.cpp:218] Iteration 5440 (0.4118 iter/s, 19.4269s/8 iters), loss = 1.99028
I0401 14:41:19.204026  8859 solver.cpp:237]     Train net output #0: loss = 1.99028 (* 1 = 1.99028 loss)
I0401 14:41:19.204035  8859 sgd_solver.cpp:105] Iteration 5440, lr = 0.001
I0401 14:41:21.715185  8859 solver.cpp:218] Iteration 5448 (3.18584 iter/s, 2.51111s/8 iters), loss = 1.98036
I0401 14:41:21.715344  8859 solver.cpp:237]     Train net output #0: loss = 1.98036 (* 1 = 1.98036 loss)
I0401 14:41:21.715354  8859 sgd_solver.cpp:105] Iteration 5448, lr = 0.001
I0401 14:41:25.210597  8859 solver.cpp:218] Iteration 5456 (2.28885 iter/s, 3.4952s/8 iters), loss = 1.88122
I0401 14:41:25.210657  8859 solver.cpp:237]     Train net output #0: loss = 1.88122 (* 1 = 1.88122 loss)
I0401 14:41:25.210666  8859 sgd_solver.cpp:105] Iteration 5456, lr = 0.001
I0401 14:41:28.697366  8859 solver.cpp:218] Iteration 5464 (2.29445 iter/s, 3.48667s/8 iters), loss = 1.92151
I0401 14:41:28.697405  8859 solver.cpp:237]     Train net output #0: loss = 1.92151 (* 1 = 1.92151 loss)
I0401 14:41:28.697410  8859 sgd_solver.cpp:105] Iteration 5464, lr = 0.001
I0401 14:41:32.001173  8859 solver.cpp:218] Iteration 5472 (2.42152 iter/s, 3.30371s/8 iters), loss = 1.62522
I0401 14:41:32.001221  8859 solver.cpp:237]     Train net output #0: loss = 1.62522 (* 1 = 1.62522 loss)
I0401 14:41:32.001230  8859 sgd_solver.cpp:105] Iteration 5472, lr = 0.001
I0401 14:41:35.390806  8859 solver.cpp:218] Iteration 5480 (2.36021 iter/s, 3.38953s/8 iters), loss = 1.69116
I0401 14:41:35.390853  8859 solver.cpp:237]     Train net output #0: loss = 1.69116 (* 1 = 1.69116 loss)
I0401 14:41:35.390859  8859 sgd_solver.cpp:105] Iteration 5480, lr = 0.001
I0401 14:41:38.754447  8859 solver.cpp:218] Iteration 5488 (2.37844 iter/s, 3.36354s/8 iters), loss = 2.19857
I0401 14:41:38.754493  8859 solver.cpp:237]     Train net output #0: loss = 2.19857 (* 1 = 2.19857 loss)
I0401 14:41:38.754498  8859 sgd_solver.cpp:105] Iteration 5488, lr = 0.001
I0401 14:41:42.170020  8859 solver.cpp:218] Iteration 5496 (2.34228 iter/s, 3.41547s/8 iters), loss = 2.64282
I0401 14:41:42.170084  8859 solver.cpp:237]     Train net output #0: loss = 2.64282 (* 1 = 2.64282 loss)
I0401 14:41:42.170094  8859 sgd_solver.cpp:105] Iteration 5496, lr = 0.001
I0401 14:41:43.491811  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:45.102983  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5504.caffemodel
I0401 14:41:48.268893  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5504.solverstate
I0401 14:41:50.577303  8859 solver.cpp:330] Iteration 5504, Testing net (#0)
I0401 14:41:50.577322  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:41:57.210906  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:58.749698  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:42:01.633096  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0799705
I0401 14:42:01.633126  8859 solver.cpp:397]     Test net output #1: loss = 5.35071 (* 1 = 5.35071 loss)
I0401 14:42:01.772096  8859 solver.cpp:218] Iteration 5504 (0.408126 iter/s, 19.6018s/8 iters), loss = 2.18147
I0401 14:42:01.772158  8859 solver.cpp:237]     Train net output #0: loss = 2.18147 (* 1 = 2.18147 loss)
I0401 14:42:01.772167  8859 sgd_solver.cpp:105] Iteration 5504, lr = 0.001
I0401 14:42:04.452795  8859 solver.cpp:218] Iteration 5512 (2.98441 iter/s, 2.68059s/8 iters), loss = 1.90783
I0401 14:42:04.452845  8859 solver.cpp:237]     Train net output #0: loss = 1.90783 (* 1 = 1.90783 loss)
I0401 14:42:04.452852  8859 sgd_solver.cpp:105] Iteration 5512, lr = 0.001
I0401 14:42:07.960332  8859 solver.cpp:218] Iteration 5520 (2.28087 iter/s, 3.50744s/8 iters), loss = 2.11634
I0401 14:42:07.960374  8859 solver.cpp:237]     Train net output #0: loss = 2.11634 (* 1 = 2.11634 loss)
I0401 14:42:07.960381  8859 sgd_solver.cpp:105] Iteration 5520, lr = 0.001
I0401 14:42:11.363044  8859 solver.cpp:218] Iteration 5528 (2.35113 iter/s, 3.40262s/8 iters), loss = 1.76386
I0401 14:42:11.363096  8859 solver.cpp:237]     Train net output #0: loss = 1.76386 (* 1 = 1.76386 loss)
I0401 14:42:11.363106  8859 sgd_solver.cpp:105] Iteration 5528, lr = 0.001
I0401 14:42:14.902621  8859 solver.cpp:218] Iteration 5536 (2.26022 iter/s, 3.53947s/8 iters), loss = 1.63115
I0401 14:42:14.902674  8859 solver.cpp:237]     Train net output #0: loss = 1.63115 (* 1 = 1.63115 loss)
I0401 14:42:14.902684  8859 sgd_solver.cpp:105] Iteration 5536, lr = 0.001
I0401 14:42:18.556685  8859 solver.cpp:218] Iteration 5544 (2.18941 iter/s, 3.65396s/8 iters), loss = 1.76323
I0401 14:42:18.556748  8859 solver.cpp:237]     Train net output #0: loss = 1.76323 (* 1 = 1.76323 loss)
I0401 14:42:18.556758  8859 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0401 14:42:22.053645  8859 solver.cpp:218] Iteration 5552 (2.28777 iter/s, 3.49685s/8 iters), loss = 2.23061
I0401 14:42:22.053687  8859 solver.cpp:237]     Train net output #0: loss = 2.23061 (* 1 = 2.23061 loss)
I0401 14:42:22.053694  8859 sgd_solver.cpp:105] Iteration 5552, lr = 0.001
I0401 14:42:25.455986  8859 solver.cpp:218] Iteration 5560 (2.35139 iter/s, 3.40224s/8 iters), loss = 1.92097
I0401 14:42:25.456035  8859 solver.cpp:237]     Train net output #0: loss = 1.92097 (* 1 = 1.92097 loss)
I0401 14:42:25.456043  8859 sgd_solver.cpp:105] Iteration 5560, lr = 0.001
I0401 14:42:26.370292  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:42:28.320607  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5568.caffemodel
I0401 14:42:33.083869  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5568.solverstate
I0401 14:42:35.397970  8859 solver.cpp:330] Iteration 5568, Testing net (#0)
I0401 14:42:35.397994  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:42:41.993232  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:42:46.519198  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0794783
I0401 14:42:46.519237  8859 solver.cpp:397]     Test net output #1: loss = 5.37221 (* 1 = 5.37221 loss)
I0401 14:42:46.666733  8859 solver.cpp:218] Iteration 5568 (0.377172 iter/s, 21.2105s/8 iters), loss = 2.22016
I0401 14:42:46.666774  8859 solver.cpp:237]     Train net output #0: loss = 2.22016 (* 1 = 2.22016 loss)
I0401 14:42:46.666780  8859 sgd_solver.cpp:105] Iteration 5568, lr = 0.001
I0401 14:42:49.151077  8859 solver.cpp:218] Iteration 5576 (3.22028 iter/s, 2.48425s/8 iters), loss = 1.94282
I0401 14:42:49.151136  8859 solver.cpp:237]     Train net output #0: loss = 1.94282 (* 1 = 1.94282 loss)
I0401 14:42:49.151145  8859 sgd_solver.cpp:105] Iteration 5576, lr = 0.001
I0401 14:42:52.521733  8859 solver.cpp:218] Iteration 5584 (2.3735 iter/s, 3.37055s/8 iters), loss = 1.91064
I0401 14:42:52.521785  8859 solver.cpp:237]     Train net output #0: loss = 1.91064 (* 1 = 1.91064 loss)
I0401 14:42:52.521792  8859 sgd_solver.cpp:105] Iteration 5584, lr = 0.001
I0401 14:42:55.775264  8859 solver.cpp:218] Iteration 5592 (2.45894 iter/s, 3.25343s/8 iters), loss = 1.61119
I0401 14:42:55.775306  8859 solver.cpp:237]     Train net output #0: loss = 1.61119 (* 1 = 1.61119 loss)
I0401 14:42:55.775311  8859 sgd_solver.cpp:105] Iteration 5592, lr = 0.001
I0401 14:42:59.149325  8859 solver.cpp:218] Iteration 5600 (2.3711 iter/s, 3.37397s/8 iters), loss = 1.63104
I0401 14:42:59.149412  8859 solver.cpp:237]     Train net output #0: loss = 1.63104 (* 1 = 1.63104 loss)
I0401 14:42:59.149420  8859 sgd_solver.cpp:105] Iteration 5600, lr = 0.001
I0401 14:43:02.461277  8859 solver.cpp:218] Iteration 5608 (2.41559 iter/s, 3.31182s/8 iters), loss = 1.71374
I0401 14:43:02.461328  8859 solver.cpp:237]     Train net output #0: loss = 1.71374 (* 1 = 1.71374 loss)
I0401 14:43:02.461338  8859 sgd_solver.cpp:105] Iteration 5608, lr = 0.001
I0401 14:43:05.916503  8859 solver.cpp:218] Iteration 5616 (2.3154 iter/s, 3.45512s/8 iters), loss = 1.97724
I0401 14:43:05.916548  8859 solver.cpp:237]     Train net output #0: loss = 1.97724 (* 1 = 1.97724 loss)
I0401 14:43:05.916553  8859 sgd_solver.cpp:105] Iteration 5616, lr = 0.001
I0401 14:43:09.494246  8859 solver.cpp:218] Iteration 5624 (2.23611 iter/s, 3.57764s/8 iters), loss = 1.87272
I0401 14:43:09.494305  8859 solver.cpp:237]     Train net output #0: loss = 1.87272 (* 1 = 1.87272 loss)
I0401 14:43:09.494314  8859 sgd_solver.cpp:105] Iteration 5624, lr = 0.001
I0401 14:43:10.072147  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:12.488940  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5632.caffemodel
I0401 14:43:16.148077  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5632.solverstate
I0401 14:43:19.183409  8859 solver.cpp:330] Iteration 5632, Testing net (#0)
I0401 14:43:19.183429  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:43:25.903904  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:30.433660  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0713583
I0401 14:43:30.433802  8859 solver.cpp:397]     Test net output #1: loss = 5.5297 (* 1 = 5.5297 loss)
I0401 14:43:30.571118  8859 solver.cpp:218] Iteration 5632 (0.379568 iter/s, 21.0766s/8 iters), loss = 2.05304
I0401 14:43:30.572695  8859 solver.cpp:237]     Train net output #0: loss = 2.05304 (* 1 = 2.05304 loss)
I0401 14:43:30.572705  8859 sgd_solver.cpp:105] Iteration 5632, lr = 0.001
I0401 14:43:33.126693  8859 solver.cpp:218] Iteration 5640 (3.13239 iter/s, 2.55396s/8 iters), loss = 1.85112
I0401 14:43:33.126755  8859 solver.cpp:237]     Train net output #0: loss = 1.85112 (* 1 = 1.85112 loss)
I0401 14:43:33.126765  8859 sgd_solver.cpp:105] Iteration 5640, lr = 0.001
I0401 14:43:36.546891  8859 solver.cpp:218] Iteration 5648 (2.33912 iter/s, 3.42009s/8 iters), loss = 1.97225
I0401 14:43:36.546952  8859 solver.cpp:237]     Train net output #0: loss = 1.97225 (* 1 = 1.97225 loss)
I0401 14:43:36.546962  8859 sgd_solver.cpp:105] Iteration 5648, lr = 0.001
I0401 14:43:40.063975  8859 solver.cpp:218] Iteration 5656 (2.27468 iter/s, 3.51697s/8 iters), loss = 1.70098
I0401 14:43:40.064023  8859 solver.cpp:237]     Train net output #0: loss = 1.70098 (* 1 = 1.70098 loss)
I0401 14:43:40.064030  8859 sgd_solver.cpp:105] Iteration 5656, lr = 0.001
I0401 14:43:43.593333  8859 solver.cpp:218] Iteration 5664 (2.26677 iter/s, 3.52926s/8 iters), loss = 1.48576
I0401 14:43:43.593389  8859 solver.cpp:237]     Train net output #0: loss = 1.48576 (* 1 = 1.48576 loss)
I0401 14:43:43.593397  8859 sgd_solver.cpp:105] Iteration 5664, lr = 0.001
I0401 14:43:46.926981  8859 solver.cpp:218] Iteration 5672 (2.39984 iter/s, 3.33355s/8 iters), loss = 1.79109
I0401 14:43:46.927016  8859 solver.cpp:237]     Train net output #0: loss = 1.79109 (* 1 = 1.79109 loss)
I0401 14:43:46.927021  8859 sgd_solver.cpp:105] Iteration 5672, lr = 0.001
I0401 14:43:50.279369  8859 solver.cpp:218] Iteration 5680 (2.38642 iter/s, 3.3523s/8 iters), loss = 2.26219
I0401 14:43:50.279424  8859 solver.cpp:237]     Train net output #0: loss = 2.26219 (* 1 = 2.26219 loss)
I0401 14:43:50.279433  8859 sgd_solver.cpp:105] Iteration 5680, lr = 0.001
I0401 14:43:53.757616  8859 solver.cpp:218] Iteration 5688 (2.30008 iter/s, 3.47814s/8 iters), loss = 1.86337
I0401 14:43:53.757658  8859 solver.cpp:237]     Train net output #0: loss = 1.86337 (* 1 = 1.86337 loss)
I0401 14:43:53.757664  8859 sgd_solver.cpp:105] Iteration 5688, lr = 0.001
I0401 14:43:53.962486  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:56.515524  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5696.caffemodel
I0401 14:43:59.480785  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5696.solverstate
I0401 14:44:02.737028  8859 solver.cpp:330] Iteration 5696, Testing net (#0)
I0401 14:44:02.737147  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:44:09.493618  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:44:13.981556  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0802165
I0401 14:44:13.981590  8859 solver.cpp:397]     Test net output #1: loss = 5.49025 (* 1 = 5.49025 loss)
I0401 14:44:14.126058  8859 solver.cpp:218] Iteration 5696 (0.392769 iter/s, 20.3682s/8 iters), loss = 2.12067
I0401 14:44:14.127645  8859 solver.cpp:237]     Train net output #0: loss = 2.12067 (* 1 = 2.12067 loss)
I0401 14:44:14.127658  8859 sgd_solver.cpp:105] Iteration 5696, lr = 0.001
I0401 14:44:16.737006  8859 solver.cpp:218] Iteration 5704 (3.06592 iter/s, 2.60933s/8 iters), loss = 1.95221
I0401 14:44:16.737043  8859 solver.cpp:237]     Train net output #0: loss = 1.95221 (* 1 = 1.95221 loss)
I0401 14:44:16.737049  8859 sgd_solver.cpp:105] Iteration 5704, lr = 0.001
I0401 14:44:20.152956  8859 solver.cpp:218] Iteration 5712 (2.34202 iter/s, 3.41586s/8 iters), loss = 1.91014
I0401 14:44:20.152994  8859 solver.cpp:237]     Train net output #0: loss = 1.91014 (* 1 = 1.91014 loss)
I0401 14:44:20.153000  8859 sgd_solver.cpp:105] Iteration 5712, lr = 0.001
I0401 14:44:23.489419  8859 solver.cpp:218] Iteration 5720 (2.39781 iter/s, 3.33637s/8 iters), loss = 1.66809
I0401 14:44:23.489460  8859 solver.cpp:237]     Train net output #0: loss = 1.66809 (* 1 = 1.66809 loss)
I0401 14:44:23.489465  8859 sgd_solver.cpp:105] Iteration 5720, lr = 0.001
I0401 14:44:26.965540  8859 solver.cpp:218] Iteration 5728 (2.30148 iter/s, 3.47603s/8 iters), loss = 1.4045
I0401 14:44:26.965582  8859 solver.cpp:237]     Train net output #0: loss = 1.4045 (* 1 = 1.4045 loss)
I0401 14:44:26.965587  8859 sgd_solver.cpp:105] Iteration 5728, lr = 0.001
I0401 14:44:30.398427  8859 solver.cpp:218] Iteration 5736 (2.33046 iter/s, 3.43279s/8 iters), loss = 1.85953
I0401 14:44:30.398478  8859 solver.cpp:237]     Train net output #0: loss = 1.85953 (* 1 = 1.85953 loss)
I0401 14:44:30.398486  8859 sgd_solver.cpp:105] Iteration 5736, lr = 0.001
I0401 14:44:33.847206  8859 solver.cpp:218] Iteration 5744 (2.31973 iter/s, 3.44868s/8 iters), loss = 2.41946
I0401 14:44:33.847313  8859 solver.cpp:237]     Train net output #0: loss = 2.41946 (* 1 = 2.41946 loss)
I0401 14:44:33.847321  8859 sgd_solver.cpp:105] Iteration 5744, lr = 0.001
I0401 14:44:37.370122  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:44:37.548619  8859 solver.cpp:218] Iteration 5752 (2.16143 iter/s, 3.70125s/8 iters), loss = 2.43282
I0401 14:44:37.548674  8859 solver.cpp:237]     Train net output #0: loss = 2.43282 (* 1 = 2.43282 loss)
I0401 14:44:37.548683  8859 sgd_solver.cpp:105] Iteration 5752, lr = 0.001
I0401 14:44:40.353791  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5760.caffemodel
I0401 14:44:43.564388  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5760.solverstate
I0401 14:44:45.902114  8859 solver.cpp:330] Iteration 5760, Testing net (#0)
I0401 14:44:45.902135  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:44:52.603086  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:44:57.218571  8859 solver.cpp:397]     Test net output #0: accuracy = 0.074311
I0401 14:44:57.218602  8859 solver.cpp:397]     Test net output #1: loss = 5.39347 (* 1 = 5.39347 loss)
I0401 14:44:57.347275  8859 solver.cpp:218] Iteration 5760 (0.404073 iter/s, 19.7984s/8 iters), loss = 2.21414
I0401 14:44:57.347319  8859 solver.cpp:237]     Train net output #0: loss = 2.21414 (* 1 = 2.21414 loss)
I0401 14:44:57.347326  8859 sgd_solver.cpp:105] Iteration 5760, lr = 0.001
I0401 14:44:59.875771  8859 solver.cpp:218] Iteration 5768 (3.16405 iter/s, 2.52841s/8 iters), loss = 2.03718
I0401 14:44:59.875818  8859 solver.cpp:237]     Train net output #0: loss = 2.03718 (* 1 = 2.03718 loss)
I0401 14:44:59.875823  8859 sgd_solver.cpp:105] Iteration 5768, lr = 0.001
I0401 14:45:03.303146  8859 solver.cpp:218] Iteration 5776 (2.33422 iter/s, 3.42727s/8 iters), loss = 1.69949
I0401 14:45:03.303207  8859 solver.cpp:237]     Train net output #0: loss = 1.69949 (* 1 = 1.69949 loss)
I0401 14:45:03.303216  8859 sgd_solver.cpp:105] Iteration 5776, lr = 0.001
I0401 14:45:06.739534  8859 solver.cpp:218] Iteration 5784 (2.3281 iter/s, 3.43628s/8 iters), loss = 1.30354
I0401 14:45:06.739701  8859 solver.cpp:237]     Train net output #0: loss = 1.30354 (* 1 = 1.30354 loss)
I0401 14:45:06.739712  8859 sgd_solver.cpp:105] Iteration 5784, lr = 0.001
I0401 14:45:10.078052  8859 solver.cpp:218] Iteration 5792 (2.39643 iter/s, 3.3383s/8 iters), loss = 1.35612
I0401 14:45:10.078095  8859 solver.cpp:237]     Train net output #0: loss = 1.35612 (* 1 = 1.35612 loss)
I0401 14:45:10.078100  8859 sgd_solver.cpp:105] Iteration 5792, lr = 0.001
I0401 14:45:13.486176  8859 solver.cpp:218] Iteration 5800 (2.3474 iter/s, 3.40803s/8 iters), loss = 1.84863
I0401 14:45:13.486222  8859 solver.cpp:237]     Train net output #0: loss = 1.84863 (* 1 = 1.84863 loss)
I0401 14:45:13.486227  8859 sgd_solver.cpp:105] Iteration 5800, lr = 0.001
I0401 14:45:16.981631  8859 solver.cpp:218] Iteration 5808 (2.28875 iter/s, 3.49536s/8 iters), loss = 1.98478
I0401 14:45:16.981678  8859 solver.cpp:237]     Train net output #0: loss = 1.98478 (* 1 = 1.98478 loss)
I0401 14:45:16.981684  8859 sgd_solver.cpp:105] Iteration 5808, lr = 0.001
I0401 14:45:19.935951  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:45:20.339368  8859 solver.cpp:218] Iteration 5816 (2.38262 iter/s, 3.35764s/8 iters), loss = 1.95631
I0401 14:45:20.339414  8859 solver.cpp:237]     Train net output #0: loss = 1.95631 (* 1 = 1.95631 loss)
I0401 14:45:20.339421  8859 sgd_solver.cpp:105] Iteration 5816, lr = 0.001
I0401 14:45:23.148408  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5824.caffemodel
I0401 14:45:26.148128  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5824.solverstate
I0401 14:45:28.461815  8859 solver.cpp:330] Iteration 5824, Testing net (#0)
I0401 14:45:28.461833  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:45:34.938889  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:45:39.630826  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0816929
I0401 14:45:39.630942  8859 solver.cpp:397]     Test net output #1: loss = 5.41535 (* 1 = 5.41535 loss)
I0401 14:45:39.767947  8859 solver.cpp:218] Iteration 5824 (0.41177 iter/s, 19.4283s/8 iters), loss = 1.88501
I0401 14:45:39.768002  8859 solver.cpp:237]     Train net output #0: loss = 1.88501 (* 1 = 1.88501 loss)
I0401 14:45:39.768009  8859 sgd_solver.cpp:105] Iteration 5824, lr = 0.001
I0401 14:45:42.353941  8859 solver.cpp:218] Iteration 5832 (3.09371 iter/s, 2.58589s/8 iters), loss = 1.58123
I0401 14:45:42.354003  8859 solver.cpp:237]     Train net output #0: loss = 1.58123 (* 1 = 1.58123 loss)
I0401 14:45:42.354012  8859 sgd_solver.cpp:105] Iteration 5832, lr = 0.001
I0401 14:45:45.781966  8859 solver.cpp:218] Iteration 5840 (2.33378 iter/s, 3.42791s/8 iters), loss = 1.61516
I0401 14:45:45.782021  8859 solver.cpp:237]     Train net output #0: loss = 1.61516 (* 1 = 1.61516 loss)
I0401 14:45:45.782029  8859 sgd_solver.cpp:105] Iteration 5840, lr = 0.001
I0401 14:45:49.144141  8859 solver.cpp:218] Iteration 5848 (2.37949 iter/s, 3.36207s/8 iters), loss = 1.53749
I0401 14:45:49.144201  8859 solver.cpp:237]     Train net output #0: loss = 1.53749 (* 1 = 1.53749 loss)
I0401 14:45:49.144210  8859 sgd_solver.cpp:105] Iteration 5848, lr = 0.001
I0401 14:45:52.635605  8859 solver.cpp:218] Iteration 5856 (2.29138 iter/s, 3.49135s/8 iters), loss = 1.29609
I0401 14:45:52.635653  8859 solver.cpp:237]     Train net output #0: loss = 1.29609 (* 1 = 1.29609 loss)
I0401 14:45:52.635659  8859 sgd_solver.cpp:105] Iteration 5856, lr = 0.001
I0401 14:45:56.018894  8859 solver.cpp:218] Iteration 5864 (2.36463 iter/s, 3.38319s/8 iters), loss = 1.62342
I0401 14:45:56.018939  8859 solver.cpp:237]     Train net output #0: loss = 1.62342 (* 1 = 1.62342 loss)
I0401 14:45:56.018944  8859 sgd_solver.cpp:105] Iteration 5864, lr = 0.001
I0401 14:45:58.223767  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:45:59.543378  8859 solver.cpp:218] Iteration 5872 (2.2699 iter/s, 3.52438s/8 iters), loss = 1.75775
I0401 14:45:59.543423  8859 solver.cpp:237]     Train net output #0: loss = 1.75775 (* 1 = 1.75775 loss)
I0401 14:45:59.543429  8859 sgd_solver.cpp:105] Iteration 5872, lr = 0.001
I0401 14:46:02.355130  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:46:03.133574  8859 solver.cpp:218] Iteration 5880 (2.22835 iter/s, 3.5901s/8 iters), loss = 2.1074
I0401 14:46:03.133620  8859 solver.cpp:237]     Train net output #0: loss = 2.1074 (* 1 = 2.1074 loss)
I0401 14:46:03.133625  8859 sgd_solver.cpp:105] Iteration 5880, lr = 0.001
I0401 14:46:06.001937  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5888.caffemodel
I0401 14:46:09.016265  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5888.solverstate
I0401 14:46:11.327230  8859 solver.cpp:330] Iteration 5888, Testing net (#0)
I0401 14:46:11.327337  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:46:17.680794  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:46:22.482203  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0816929
I0401 14:46:22.482231  8859 solver.cpp:397]     Test net output #1: loss = 5.38642 (* 1 = 5.38642 loss)
I0401 14:46:22.623440  8859 solver.cpp:218] Iteration 5888 (0.410475 iter/s, 19.4896s/8 iters), loss = 1.5954
I0401 14:46:22.623487  8859 solver.cpp:237]     Train net output #0: loss = 1.5954 (* 1 = 1.5954 loss)
I0401 14:46:22.623495  8859 sgd_solver.cpp:105] Iteration 5888, lr = 0.001
I0401 14:46:25.028357  8859 solver.cpp:218] Iteration 5896 (3.32665 iter/s, 2.40482s/8 iters), loss = 1.81331
I0401 14:46:25.028415  8859 solver.cpp:237]     Train net output #0: loss = 1.81331 (* 1 = 1.81331 loss)
I0401 14:46:25.028425  8859 sgd_solver.cpp:105] Iteration 5896, lr = 0.001
I0401 14:46:28.337787  8859 solver.cpp:218] Iteration 5904 (2.41741 iter/s, 3.30933s/8 iters), loss = 1.6303
I0401 14:46:28.337843  8859 solver.cpp:237]     Train net output #0: loss = 1.6303 (* 1 = 1.6303 loss)
I0401 14:46:28.337853  8859 sgd_solver.cpp:105] Iteration 5904, lr = 0.001
I0401 14:46:31.760413  8859 solver.cpp:218] Iteration 5912 (2.33746 iter/s, 3.42252s/8 iters), loss = 1.42067
I0401 14:46:31.760460  8859 solver.cpp:237]     Train net output #0: loss = 1.42067 (* 1 = 1.42067 loss)
I0401 14:46:31.760466  8859 sgd_solver.cpp:105] Iteration 5912, lr = 0.001
I0401 14:46:35.248505  8859 solver.cpp:218] Iteration 5920 (2.29359 iter/s, 3.48799s/8 iters), loss = 1.66459
I0401 14:46:35.248553  8859 solver.cpp:237]     Train net output #0: loss = 1.66459 (* 1 = 1.66459 loss)
I0401 14:46:35.248559  8859 sgd_solver.cpp:105] Iteration 5920, lr = 0.001
I0401 14:46:38.662794  8859 solver.cpp:218] Iteration 5928 (2.34316 iter/s, 3.41419s/8 iters), loss = 1.6239
I0401 14:46:38.662842  8859 solver.cpp:237]     Train net output #0: loss = 1.6239 (* 1 = 1.6239 loss)
I0401 14:46:38.662847  8859 sgd_solver.cpp:105] Iteration 5928, lr = 0.001
I0401 14:46:41.970263  8859 solver.cpp:218] Iteration 5936 (2.41884 iter/s, 3.30737s/8 iters), loss = 1.7195
I0401 14:46:41.970373  8859 solver.cpp:237]     Train net output #0: loss = 1.7195 (* 1 = 1.7195 loss)
I0401 14:46:41.970386  8859 sgd_solver.cpp:105] Iteration 5936, lr = 0.001
I0401 14:46:44.144088  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:46:45.286132  8859 solver.cpp:218] Iteration 5944 (2.41276 iter/s, 3.31571s/8 iters), loss = 1.8575
I0401 14:46:45.286188  8859 solver.cpp:237]     Train net output #0: loss = 1.8575 (* 1 = 1.8575 loss)
I0401 14:46:45.286196  8859 sgd_solver.cpp:105] Iteration 5944, lr = 0.001
I0401 14:46:48.075801  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5952.caffemodel
I0401 14:46:50.997257  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5952.solverstate
I0401 14:46:53.302259  8859 solver.cpp:330] Iteration 5952, Testing net (#0)
I0401 14:46:53.302276  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:46:59.578889  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:47:04.258323  8859 solver.cpp:397]     Test net output #0: accuracy = 0.078002
I0401 14:47:04.258374  8859 solver.cpp:397]     Test net output #1: loss = 5.36885 (* 1 = 5.36885 loss)
I0401 14:47:04.398262  8859 solver.cpp:218] Iteration 5952 (0.418588 iter/s, 19.1119s/8 iters), loss = 1.72244
I0401 14:47:04.399837  8859 solver.cpp:237]     Train net output #0: loss = 1.72244 (* 1 = 1.72244 loss)
I0401 14:47:04.399847  8859 sgd_solver.cpp:105] Iteration 5952, lr = 0.001
I0401 14:47:06.984584  8859 solver.cpp:218] Iteration 5960 (3.09513 iter/s, 2.58471s/8 iters), loss = 1.31874
I0401 14:47:06.984634  8859 solver.cpp:237]     Train net output #0: loss = 1.31874 (* 1 = 1.31874 loss)
I0401 14:47:06.984642  8859 sgd_solver.cpp:105] Iteration 5960, lr = 0.001
I0401 14:47:10.421452  8859 solver.cpp:218] Iteration 5968 (2.32777 iter/s, 3.43677s/8 iters), loss = 1.3559
I0401 14:47:10.421507  8859 solver.cpp:237]     Train net output #0: loss = 1.3559 (* 1 = 1.3559 loss)
I0401 14:47:10.421515  8859 sgd_solver.cpp:105] Iteration 5968, lr = 0.001
I0401 14:47:13.843235  8859 solver.cpp:218] Iteration 5976 (2.33803 iter/s, 3.42168s/8 iters), loss = 1.35518
I0401 14:47:13.843329  8859 solver.cpp:237]     Train net output #0: loss = 1.35518 (* 1 = 1.35518 loss)
I0401 14:47:13.843336  8859 sgd_solver.cpp:105] Iteration 5976, lr = 0.001
I0401 14:47:17.360265  8859 solver.cpp:218] Iteration 5984 (2.27474 iter/s, 3.51688s/8 iters), loss = 1.13204
I0401 14:47:17.360319  8859 solver.cpp:237]     Train net output #0: loss = 1.13204 (* 1 = 1.13204 loss)
I0401 14:47:17.360328  8859 sgd_solver.cpp:105] Iteration 5984, lr = 0.001
I0401 14:47:20.803748  8859 solver.cpp:218] Iteration 5992 (2.3233 iter/s, 3.44338s/8 iters), loss = 1.34203
I0401 14:47:20.803791  8859 solver.cpp:237]     Train net output #0: loss = 1.34203 (* 1 = 1.34203 loss)
I0401 14:47:20.803797  8859 sgd_solver.cpp:105] Iteration 5992, lr = 0.001
I0401 14:47:24.290298  8859 solver.cpp:218] Iteration 6000 (2.2946 iter/s, 3.48645s/8 iters), loss = 2.05596
I0401 14:47:24.290347  8859 solver.cpp:237]     Train net output #0: loss = 2.05596 (* 1 = 2.05596 loss)
I0401 14:47:24.290355  8859 sgd_solver.cpp:105] Iteration 6000, lr = 0.001
I0401 14:47:26.258509  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:47:27.766685  8859 solver.cpp:218] Iteration 6008 (2.30131 iter/s, 3.47629s/8 iters), loss = 2.12441
I0401 14:47:27.766727  8859 solver.cpp:237]     Train net output #0: loss = 2.12441 (* 1 = 2.12441 loss)
I0401 14:47:27.766733  8859 sgd_solver.cpp:105] Iteration 6008, lr = 0.001
I0401 14:47:30.632601  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6016.caffemodel
I0401 14:47:37.281272  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6016.solverstate
I0401 14:47:40.583402  8859 solver.cpp:330] Iteration 6016, Testing net (#0)
I0401 14:47:40.583425  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:47:46.966094  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:47:51.774561  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0853839
I0401 14:47:51.774595  8859 solver.cpp:397]     Test net output #1: loss = 5.51559 (* 1 = 5.51559 loss)
I0401 14:47:51.916123  8859 solver.cpp:218] Iteration 6016 (0.331275 iter/s, 24.1491s/8 iters), loss = 1.66555
I0401 14:47:51.916180  8859 solver.cpp:237]     Train net output #0: loss = 1.66555 (* 1 = 1.66555 loss)
I0401 14:47:51.916188  8859 sgd_solver.cpp:105] Iteration 6016, lr = 0.001
I0401 14:47:54.568979  8859 solver.cpp:218] Iteration 6024 (3.01573 iter/s, 2.65275s/8 iters), loss = 1.46022
I0401 14:47:54.569022  8859 solver.cpp:237]     Train net output #0: loss = 1.46022 (* 1 = 1.46022 loss)
I0401 14:47:54.569028  8859 sgd_solver.cpp:105] Iteration 6024, lr = 0.001
I0401 14:47:57.830075  8859 solver.cpp:218] Iteration 6032 (2.45323 iter/s, 3.261s/8 iters), loss = 1.49083
I0401 14:47:57.830118  8859 solver.cpp:237]     Train net output #0: loss = 1.49083 (* 1 = 1.49083 loss)
I0401 14:47:57.830124  8859 sgd_solver.cpp:105] Iteration 6032, lr = 0.001
I0401 14:48:01.243624  8859 solver.cpp:218] Iteration 6040 (2.34367 iter/s, 3.41345s/8 iters), loss = 1.41228
I0401 14:48:01.243670  8859 solver.cpp:237]     Train net output #0: loss = 1.41228 (* 1 = 1.41228 loss)
I0401 14:48:01.243677  8859 sgd_solver.cpp:105] Iteration 6040, lr = 0.001
I0401 14:48:04.623950  8859 solver.cpp:218] Iteration 6048 (2.3667 iter/s, 3.38023s/8 iters), loss = 1.24474
I0401 14:48:04.623991  8859 solver.cpp:237]     Train net output #0: loss = 1.24474 (* 1 = 1.24474 loss)
I0401 14:48:04.623996  8859 sgd_solver.cpp:105] Iteration 6048, lr = 0.001
I0401 14:48:08.015062  8859 solver.cpp:218] Iteration 6056 (2.35917 iter/s, 3.39102s/8 iters), loss = 1.31168
I0401 14:48:08.015118  8859 solver.cpp:237]     Train net output #0: loss = 1.31168 (* 1 = 1.31168 loss)
I0401 14:48:08.015127  8859 sgd_solver.cpp:105] Iteration 6056, lr = 0.001
I0401 14:48:11.334265  8859 solver.cpp:218] Iteration 6064 (2.4103 iter/s, 3.31909s/8 iters), loss = 1.78655
I0401 14:48:11.334316  8859 solver.cpp:237]     Train net output #0: loss = 1.78655 (* 1 = 1.78655 loss)
I0401 14:48:11.334324  8859 sgd_solver.cpp:105] Iteration 6064, lr = 0.001
I0401 14:48:12.907619  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:48:14.717905  8859 solver.cpp:218] Iteration 6072 (2.36439 iter/s, 3.38354s/8 iters), loss = 1.69269
I0401 14:48:14.717943  8859 solver.cpp:237]     Train net output #0: loss = 1.69269 (* 1 = 1.69269 loss)
I0401 14:48:14.717948  8859 sgd_solver.cpp:105] Iteration 6072, lr = 0.001
I0401 14:48:17.724077  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6080.caffemodel
I0401 14:48:20.731040  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6080.solverstate
I0401 14:48:23.037941  8859 solver.cpp:330] Iteration 6080, Testing net (#0)
I0401 14:48:23.037967  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:48:29.516045  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:48:34.437815  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0807087
I0401 14:48:34.437845  8859 solver.cpp:397]     Test net output #1: loss = 5.57004 (* 1 = 5.57004 loss)
I0401 14:48:34.577983  8859 solver.cpp:218] Iteration 6080 (0.402823 iter/s, 19.8598s/8 iters), loss = 1.93414
I0401 14:48:34.578030  8859 solver.cpp:237]     Train net output #0: loss = 1.93414 (* 1 = 1.93414 loss)
I0401 14:48:34.578035  8859 sgd_solver.cpp:105] Iteration 6080, lr = 0.001
I0401 14:48:37.151757  8859 solver.cpp:218] Iteration 6088 (3.10838 iter/s, 2.57369s/8 iters), loss = 1.62463
I0401 14:48:37.151800  8859 solver.cpp:237]     Train net output #0: loss = 1.62463 (* 1 = 1.62463 loss)
I0401 14:48:37.151808  8859 sgd_solver.cpp:105] Iteration 6088, lr = 0.001
I0401 14:48:40.579327  8859 solver.cpp:218] Iteration 6096 (2.33408 iter/s, 3.42748s/8 iters), loss = 1.43119
I0401 14:48:40.579377  8859 solver.cpp:237]     Train net output #0: loss = 1.43119 (* 1 = 1.43119 loss)
I0401 14:48:40.579385  8859 sgd_solver.cpp:105] Iteration 6096, lr = 0.001
I0401 14:48:43.823848  8859 solver.cpp:218] Iteration 6104 (2.46577 iter/s, 3.24442s/8 iters), loss = 1.65579
I0401 14:48:43.823891  8859 solver.cpp:237]     Train net output #0: loss = 1.65579 (* 1 = 1.65579 loss)
I0401 14:48:43.823896  8859 sgd_solver.cpp:105] Iteration 6104, lr = 0.001
I0401 14:48:47.114944  8859 solver.cpp:218] Iteration 6112 (2.43087 iter/s, 3.291s/8 iters), loss = 1.49679
I0401 14:48:47.114985  8859 solver.cpp:237]     Train net output #0: loss = 1.49679 (* 1 = 1.49679 loss)
I0401 14:48:47.114992  8859 sgd_solver.cpp:105] Iteration 6112, lr = 0.001
I0401 14:48:50.498448  8859 solver.cpp:218] Iteration 6120 (2.36448 iter/s, 3.38341s/8 iters), loss = 1.40788
I0401 14:48:50.498584  8859 solver.cpp:237]     Train net output #0: loss = 1.40788 (* 1 = 1.40788 loss)
I0401 14:48:50.498590  8859 sgd_solver.cpp:105] Iteration 6120, lr = 0.001
I0401 14:48:54.056191  8859 solver.cpp:218] Iteration 6128 (2.24874 iter/s, 3.55755s/8 iters), loss = 1.48138
I0401 14:48:54.056262  8859 solver.cpp:237]     Train net output #0: loss = 1.48138 (* 1 = 1.48138 loss)
I0401 14:48:54.056270  8859 sgd_solver.cpp:105] Iteration 6128, lr = 0.001
I0401 14:48:55.429354  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:48:57.538769  8859 solver.cpp:218] Iteration 6136 (2.29723 iter/s, 3.48246s/8 iters), loss = 1.61557
I0401 14:48:57.538822  8859 solver.cpp:237]     Train net output #0: loss = 1.61557 (* 1 = 1.61557 loss)
I0401 14:48:57.538830  8859 sgd_solver.cpp:105] Iteration 6136, lr = 0.001
I0401 14:49:00.541441  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6144.caffemodel
I0401 14:49:03.559451  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6144.solverstate
I0401 14:49:05.921447  8859 solver.cpp:330] Iteration 6144, Testing net (#0)
I0401 14:49:05.921466  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:49:12.055400  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:49:16.896169  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0846457
I0401 14:49:16.896210  8859 solver.cpp:397]     Test net output #1: loss = 5.61119 (* 1 = 5.61119 loss)
I0401 14:49:17.033442  8859 solver.cpp:218] Iteration 6144 (0.410374 iter/s, 19.4944s/8 iters), loss = 1.66771
I0401 14:49:17.033488  8859 solver.cpp:237]     Train net output #0: loss = 1.66771 (* 1 = 1.66771 loss)
I0401 14:49:17.033493  8859 sgd_solver.cpp:105] Iteration 6144, lr = 0.001
I0401 14:49:19.729730  8859 solver.cpp:218] Iteration 6152 (2.96714 iter/s, 2.6962s/8 iters), loss = 1.29373
I0401 14:49:19.729774  8859 solver.cpp:237]     Train net output #0: loss = 1.29373 (* 1 = 1.29373 loss)
I0401 14:49:19.729779  8859 sgd_solver.cpp:105] Iteration 6152, lr = 0.001
I0401 14:49:23.167604  8859 solver.cpp:218] Iteration 6160 (2.32708 iter/s, 3.43778s/8 iters), loss = 1.5451
I0401 14:49:23.167701  8859 solver.cpp:237]     Train net output #0: loss = 1.5451 (* 1 = 1.5451 loss)
I0401 14:49:23.167706  8859 sgd_solver.cpp:105] Iteration 6160, lr = 0.001
I0401 14:49:26.608625  8859 solver.cpp:218] Iteration 6168 (2.32499 iter/s, 3.44087s/8 iters), loss = 1.38068
I0401 14:49:26.608669  8859 solver.cpp:237]     Train net output #0: loss = 1.38068 (* 1 = 1.38068 loss)
I0401 14:49:26.608673  8859 sgd_solver.cpp:105] Iteration 6168, lr = 0.001
I0401 14:49:30.085992  8859 solver.cpp:218] Iteration 6176 (2.30065 iter/s, 3.47727s/8 iters), loss = 1.45814
I0401 14:49:30.086035  8859 solver.cpp:237]     Train net output #0: loss = 1.45814 (* 1 = 1.45814 loss)
I0401 14:49:30.086042  8859 sgd_solver.cpp:105] Iteration 6176, lr = 0.001
I0401 14:49:33.618319  8859 solver.cpp:218] Iteration 6184 (2.26486 iter/s, 3.53223s/8 iters), loss = 1.51994
I0401 14:49:33.618358  8859 solver.cpp:237]     Train net output #0: loss = 1.51994 (* 1 = 1.51994 loss)
I0401 14:49:33.618363  8859 sgd_solver.cpp:105] Iteration 6184, lr = 0.001
I0401 14:49:37.035586  8859 solver.cpp:218] Iteration 6192 (2.34112 iter/s, 3.41717s/8 iters), loss = 1.62722
I0401 14:49:37.035645  8859 solver.cpp:237]     Train net output #0: loss = 1.62722 (* 1 = 1.62722 loss)
I0401 14:49:37.035652  8859 sgd_solver.cpp:105] Iteration 6192, lr = 0.001
I0401 14:49:38.121687  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:49:40.665154  8859 solver.cpp:218] Iteration 6200 (2.20419 iter/s, 3.62945s/8 iters), loss = 1.52665
I0401 14:49:40.665208  8859 solver.cpp:237]     Train net output #0: loss = 1.52665 (* 1 = 1.52665 loss)
I0401 14:49:40.665217  8859 sgd_solver.cpp:105] Iteration 6200, lr = 0.001
I0401 14:49:43.472498  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6208.caffemodel
I0401 14:49:47.144234  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6208.solverstate
I0401 14:49:51.004719  8859 solver.cpp:330] Iteration 6208, Testing net (#0)
I0401 14:49:51.004747  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:49:56.167874  8859 blocking_queue.cpp:49] Waiting for data
I0401 14:49:57.190606  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:50:02.108821  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0829232
I0401 14:50:02.108860  8859 solver.cpp:397]     Test net output #1: loss = 5.63435 (* 1 = 5.63435 loss)
I0401 14:50:02.243062  8859 solver.cpp:218] Iteration 6208 (0.370754 iter/s, 21.5776s/8 iters), loss = 1.5027
I0401 14:50:02.243120  8859 solver.cpp:237]     Train net output #0: loss = 1.5027 (* 1 = 1.5027 loss)
I0401 14:50:02.243129  8859 sgd_solver.cpp:105] Iteration 6208, lr = 0.001
I0401 14:50:04.755148  8859 solver.cpp:218] Iteration 6216 (3.18473 iter/s, 2.51199s/8 iters), loss = 1.17108
I0401 14:50:04.755187  8859 solver.cpp:237]     Train net output #0: loss = 1.17108 (* 1 = 1.17108 loss)
I0401 14:50:04.755195  8859 sgd_solver.cpp:105] Iteration 6216, lr = 0.001
I0401 14:50:08.308531  8859 solver.cpp:218] Iteration 6224 (2.25143 iter/s, 3.5533s/8 iters), loss = 1.20669
I0401 14:50:08.308573  8859 solver.cpp:237]     Train net output #0: loss = 1.20669 (* 1 = 1.20669 loss)
I0401 14:50:08.308579  8859 sgd_solver.cpp:105] Iteration 6224, lr = 0.001
I0401 14:50:11.805445  8859 solver.cpp:218] Iteration 6232 (2.28779 iter/s, 3.49682s/8 iters), loss = 1.41723
I0401 14:50:11.805486  8859 solver.cpp:237]     Train net output #0: loss = 1.41723 (* 1 = 1.41723 loss)
I0401 14:50:11.805491  8859 sgd_solver.cpp:105] Iteration 6232, lr = 0.001
I0401 14:50:15.215653  8859 solver.cpp:218] Iteration 6240 (2.34596 iter/s, 3.41012s/8 iters), loss = 1.49863
I0401 14:50:15.215693  8859 solver.cpp:237]     Train net output #0: loss = 1.49863 (* 1 = 1.49863 loss)
I0401 14:50:15.215699  8859 sgd_solver.cpp:105] Iteration 6240, lr = 0.001
I0401 14:50:18.403321  8859 solver.cpp:218] Iteration 6248 (2.50975 iter/s, 3.18757s/8 iters), loss = 1.21295
I0401 14:50:18.403369  8859 solver.cpp:237]     Train net output #0: loss = 1.21295 (* 1 = 1.21295 loss)
I0401 14:50:18.403375  8859 sgd_solver.cpp:105] Iteration 6248, lr = 0.001
I0401 14:50:21.959637  8859 solver.cpp:218] Iteration 6256 (2.24958 iter/s, 3.55621s/8 iters), loss = 1.66491
I0401 14:50:21.959684  8859 solver.cpp:237]     Train net output #0: loss = 1.66491 (* 1 = 1.66491 loss)
I0401 14:50:21.959692  8859 sgd_solver.cpp:105] Iteration 6256, lr = 0.001
I0401 14:50:22.588068  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:50:25.277760  8859 solver.cpp:218] Iteration 6264 (2.41107 iter/s, 3.31803s/8 iters), loss = 1.47825
I0401 14:50:25.277801  8859 solver.cpp:237]     Train net output #0: loss = 1.47825 (* 1 = 1.47825 loss)
I0401 14:50:25.277806  8859 sgd_solver.cpp:105] Iteration 6264, lr = 0.001
I0401 14:50:28.324879  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6272.caffemodel
I0401 14:50:31.337929  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6272.solverstate
I0401 14:50:33.635871  8859 solver.cpp:330] Iteration 6272, Testing net (#0)
I0401 14:50:33.635891  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:50:39.740478  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:50:44.731676  8859 solver.cpp:397]     Test net output #0: accuracy = 0.082185
I0401 14:50:44.731714  8859 solver.cpp:397]     Test net output #1: loss = 5.63152 (* 1 = 5.63152 loss)
I0401 14:50:44.869869  8859 solver.cpp:218] Iteration 6272 (0.408333 iter/s, 19.5919s/8 iters), loss = 1.47569
I0401 14:50:44.869916  8859 solver.cpp:237]     Train net output #0: loss = 1.47569 (* 1 = 1.47569 loss)
I0401 14:50:44.869922  8859 sgd_solver.cpp:105] Iteration 6272, lr = 0.001
I0401 14:50:47.532141  8859 solver.cpp:218] Iteration 6280 (3.00506 iter/s, 2.66218s/8 iters), loss = 1.32819
I0401 14:50:47.532188  8859 solver.cpp:237]     Train net output #0: loss = 1.32819 (* 1 = 1.32819 loss)
I0401 14:50:47.532194  8859 sgd_solver.cpp:105] Iteration 6280, lr = 0.001
I0401 14:50:50.943871  8859 solver.cpp:218] Iteration 6288 (2.34492 iter/s, 3.41163s/8 iters), loss = 1.21672
I0401 14:50:50.943934  8859 solver.cpp:237]     Train net output #0: loss = 1.21672 (* 1 = 1.21672 loss)
I0401 14:50:50.943944  8859 sgd_solver.cpp:105] Iteration 6288, lr = 0.001
I0401 14:50:54.321139  8859 solver.cpp:218] Iteration 6296 (2.36886 iter/s, 3.37716s/8 iters), loss = 1.46669
I0401 14:50:54.321192  8859 solver.cpp:237]     Train net output #0: loss = 1.46669 (* 1 = 1.46669 loss)
I0401 14:50:54.321200  8859 sgd_solver.cpp:105] Iteration 6296, lr = 0.001
I0401 14:50:57.785022  8859 solver.cpp:218] Iteration 6304 (2.30961 iter/s, 3.46378s/8 iters), loss = 1.38726
I0401 14:50:57.785061  8859 solver.cpp:237]     Train net output #0: loss = 1.38726 (* 1 = 1.38726 loss)
I0401 14:50:57.785066  8859 sgd_solver.cpp:105] Iteration 6304, lr = 0.001
I0401 14:51:01.268584  8859 solver.cpp:218] Iteration 6312 (2.29656 iter/s, 3.48347s/8 iters), loss = 1.57544
I0401 14:51:01.268702  8859 solver.cpp:237]     Train net output #0: loss = 1.57544 (* 1 = 1.57544 loss)
I0401 14:51:01.268709  8859 sgd_solver.cpp:105] Iteration 6312, lr = 0.001
I0401 14:51:04.598237  8859 solver.cpp:218] Iteration 6320 (2.40277 iter/s, 3.32948s/8 iters), loss = 1.39915
I0401 14:51:04.598279  8859 solver.cpp:237]     Train net output #0: loss = 1.39915 (* 1 = 1.39915 loss)
I0401 14:51:04.598285  8859 sgd_solver.cpp:105] Iteration 6320, lr = 0.001
I0401 14:51:04.869776  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:51:08.067832  8859 solver.cpp:218] Iteration 6328 (2.3058 iter/s, 3.46951s/8 iters), loss = 1.27682
I0401 14:51:08.067870  8859 solver.cpp:237]     Train net output #0: loss = 1.27682 (* 1 = 1.27682 loss)
I0401 14:51:08.067876  8859 sgd_solver.cpp:105] Iteration 6328, lr = 0.001
I0401 14:51:11.088305  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6336.caffemodel
I0401 14:51:14.110402  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6336.solverstate
I0401 14:51:16.431628  8859 solver.cpp:330] Iteration 6336, Testing net (#0)
I0401 14:51:16.431651  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:51:22.437543  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:51:27.450048  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0851378
I0401 14:51:27.450083  8859 solver.cpp:397]     Test net output #1: loss = 5.571 (* 1 = 5.571 loss)
I0401 14:51:27.591375  8859 solver.cpp:218] Iteration 6336 (0.409767 iter/s, 19.5233s/8 iters), loss = 1.18898
I0401 14:51:27.591424  8859 solver.cpp:237]     Train net output #0: loss = 1.18898 (* 1 = 1.18898 loss)
I0401 14:51:27.591430  8859 sgd_solver.cpp:105] Iteration 6336, lr = 0.001
I0401 14:51:30.116617  8859 solver.cpp:218] Iteration 6344 (3.16813 iter/s, 2.52515s/8 iters), loss = 1.18708
I0401 14:51:30.116663  8859 solver.cpp:237]     Train net output #0: loss = 1.18708 (* 1 = 1.18708 loss)
I0401 14:51:30.116669  8859 sgd_solver.cpp:105] Iteration 6344, lr = 0.001
I0401 14:51:33.352092  8859 solver.cpp:218] Iteration 6352 (2.47266 iter/s, 3.23538s/8 iters), loss = 1.18343
I0401 14:51:33.352185  8859 solver.cpp:237]     Train net output #0: loss = 1.18343 (* 1 = 1.18343 loss)
I0401 14:51:33.352191  8859 sgd_solver.cpp:105] Iteration 6352, lr = 0.001
I0401 14:51:36.696452  8859 solver.cpp:218] Iteration 6360 (2.39219 iter/s, 3.34422s/8 iters), loss = 1.45887
I0401 14:51:36.696498  8859 solver.cpp:237]     Train net output #0: loss = 1.45887 (* 1 = 1.45887 loss)
I0401 14:51:36.696504  8859 sgd_solver.cpp:105] Iteration 6360, lr = 0.001
I0401 14:51:40.214068  8859 solver.cpp:218] Iteration 6368 (2.27434 iter/s, 3.51751s/8 iters), loss = 1.50677
I0401 14:51:40.214123  8859 solver.cpp:237]     Train net output #0: loss = 1.50677 (* 1 = 1.50677 loss)
I0401 14:51:40.214133  8859 sgd_solver.cpp:105] Iteration 6368, lr = 0.001
I0401 14:51:43.590662  8859 solver.cpp:218] Iteration 6376 (2.36933 iter/s, 3.37649s/8 iters), loss = 1.55193
I0401 14:51:43.590713  8859 solver.cpp:237]     Train net output #0: loss = 1.55193 (* 1 = 1.55193 loss)
I0401 14:51:43.590723  8859 sgd_solver.cpp:105] Iteration 6376, lr = 0.001
I0401 14:51:47.104094  8859 solver.cpp:218] Iteration 6384 (2.27704 iter/s, 3.51333s/8 iters), loss = 1.49265
I0401 14:51:47.104138  8859 solver.cpp:237]     Train net output #0: loss = 1.49265 (* 1 = 1.49265 loss)
I0401 14:51:47.104143  8859 sgd_solver.cpp:105] Iteration 6384, lr = 0.001
I0401 14:51:47.134766  8888 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:51:50.405586  8859 solver.cpp:218] Iteration 6392 (2.42322 iter/s, 3.30139s/8 iters), loss = 1.68063
I0401 14:51:50.405643  8859 solver.cpp:237]     Train net output #0: loss = 1.68063 (* 1 = 1.68063 loss)
I0401 14:51:50.405652  8859 sgd_solver.cpp:105] Iteration 6392, lr = 0.001
I0401 14:51:53.476855  8859 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6400.caffemodel
I0401 14:51:58.655674  8859 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6400.solverstate
I0401 14:52:03.192134  8859 solver.cpp:310] Iteration 6400, loss = 1.3268
I0401 14:52:03.192157  8859 solver.cpp:330] Iteration 6400, Testing net (#0)
I0401 14:52:03.192160  8859 net.cpp:676] Ignoring source layer train-data
I0401 14:52:09.179996  8952 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:52:14.223222  8859 solver.cpp:397]     Test net output #0: accuracy = 0.0841535
I0401 14:52:14.223259  8859 solver.cpp:397]     Test net output #1: loss = 5.58867 (* 1 = 5.58867 loss)
I0401 14:52:14.223265  8859 solver.cpp:315] Optimization Done.
I0401 14:52:14.223268  8859 caffe.cpp:259] Optimization Done.