Deep learning Caffe:培训时出现空文件异常
在训练数据集时。我得到以下错误：Deep learning Caffe:培训时出现空文件异常,deep-learning,caffe,pycaffe,Deep Learning,Caffe,Pycaffe,在训练数据集时。我得到以下错误： I0614 19:07:11.271327 30865 layer_factory.hpp:77] Creating layer data I0614 19:07:11.271596 30865 net.cpp:84] Creating Layer data I0614 19:07:11.271848 30865 net.cpp:380] data -> data I0614 19:07:11.271896 30865 net.cpp:380] data
I0614 19:07:11.271327 30865 layer_factory.hpp:77] Creating layer data
I0614 19:07:11.271596 30865 net.cpp:84] Creating Layer data
I0614 19:07:11.271848 30865 net.cpp:380] data -> data
I0614 19:07:11.271896 30865 net.cpp:380] data -> label
I0614 19:07:11.271941 30865 data_transformer.cpp:25] Loading mean file from: train_mean
I0614 19:07:11.275465 30865 image_data_layer.cpp:38] Opening file
F0614 19:07:11.275923 30865 image_data_layer.cpp:49] Check failed: !lines_.empty() File is empty
*** Check failure stack trace: ***
@ 0x7fba518d25cd google::LogMessage::Fail()
@ 0x7fba518d4433 google::LogMessage::SendToLog()
@ 0x7fba518d215b google::LogMessage::Flush()
@ 0x7fba518d4e1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7fba51ce9509 caffe::ImageDataLayer<>::DataLayerSetUp()
@ 0x7fba51d1f62e caffe::BasePrefetchingDataLayer<>::LayerSetUp()
@ 0x7fba51de7897 caffe::Net<>::Init()
@ 0x7fba51de9fde caffe::Net<>::Net()
@ 0x7fba51df24e5 caffe::Solver<>::InitTrainNet()
@ 0x7fba51df3925 caffe::Solver<>::Init()
@ 0x7fba51df3c4f caffe::Solver<>::Solver()
@ 0x7fba51dc8bb1 caffe::Creator_SGDSolver<>()
@ 0x40a4b8 train()
@ 0x406fa0 main
@ 0x7fba50843830 __libc_start_main
@ 0x4077c9 _start
@ (nil) (unknown)
Aborted (core dumped)
../build/tools/caffe train --solver=my_solver_val.prototxt
I0614 19:32:54.634418 31048 caffe.cpp:211] Use CPU.
I0614 19:32:54.635144 31048 solver.cpp:44] Initializing solver from parameters:
test_iter: 1000
test_interval: 1000
base_lr: 0.01
display: 20
max_iter: 50000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 10000
snapshot: 10000
snapshot_prefix: "models/mymodel/caffenet_train"
solver_mode: CPU
net: "my_train_val.prototxt"
train_state {
level: 0
stage: ""
}
I0614 19:32:54.639066 31048 solver.cpp:87] Creating training net from net file: my_train_val.prototxt
I0614 19:32:54.640214 31048 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0614 19:32:54.640645 31048 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0614 19:32:54.641345 31048 net.cpp:51] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 256
mean_file: "train_mean"
}
data_param {
source: "train_files.txt"
batch_size: 2
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: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
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: 1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
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: 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: 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: 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: 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: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0614 19:32:54.644022 31048 layer_factory.hpp:77] Creating layer data
I0614 19:32:54.644239 31048 net.cpp:84] Creating Layer data
I0614 19:32:54.644256 31048 net.cpp:380] data -> data
I0614 19:32:54.644280 31048 net.cpp:380] data -> label
I0614 19:32:54.644448 31048 data_transformer.cpp:25] Loading mean file from: train_mean
I0614 19:32:54.646653 31048 image_data_layer.cpp:38] Opening file
F0614 19:32:54.646975 31048 image_data_layer.cpp:49] Check failed: !lines_.empty() File is empty
*** Check failure stack trace: ***
@ 0x7f83c21c95cd google::LogMessage::Fail()
@ 0x7f83c21cb433 google::LogMessage::SendToLog()
@ 0x7f83c21c915b google::LogMessage::Flush()
@ 0x7f83c21cbe1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f83c25e0509 caffe::ImageDataLayer<>::DataLayerSetUp()
@ 0x7f83c261662e caffe::BasePrefetchingDataLayer<>::LayerSetUp()
@ 0x7f83c26de897 caffe::Net<>::Init()
@ 0x7f83c26e0fde caffe::Net<>::Net()
@ 0x7f83c26e94e5 caffe::Solver<>::InitTrainNet()
@ 0x7f83c26ea925 caffe::Solver<>::Init()
@ 0x7f83c26eac4f caffe::Solver<>::Solver()
@ 0x7f83c26bfbb1 caffe::Creator_SGDSolver<>()
@ 0x40a4b8 train()
@ 0x406fa0 main
@ 0x7f83c113a830 __libc_start_main
@ 0x4077c9 _start
@ (nil) (unknown)
Aborted (core dumped)
I0614 19:32:54.634418 31048 caffe.cpp:211]使用CPU。
I0614 19:32:54.635144 31048解算器。cpp:44]从参数初始化解算器：
试验堆：1000
测试间隔：1000
基准线：0.01
显示器：20
最高温度：50000
lr_政策：“步骤”
伽马：0.1
动量：0.9
重量衰减：0.0005
步长：10000
快照：10000
快照前缀：“型号/mymodel/caffenet\u列车”
解算器模式：CPU
net:“我的火车”prototxt
列车状态{
级别：0
阶段：“
}
I0614 19:32:54.639066 31048 solver.cpp:87]从网络文件创建训练网络：my_train_val.prototxt
I0614 19:32:54.640214 31048 net.cpp:294]NetState阶段（0）与层数据中规则指定的阶段（1）不同
I0614 19:32:54.640645 31048 net.cpp:294]网络状态阶段（0）与层精度规则指定的阶段（1）不同
I0614 19:32:54.641345 31048 net.cpp:51]从参数初始化网络：
名称：“卡菲内”
陈述{
阶段：列车
级别：0
阶段：“
}
层{
名称：“数据”
类型：“ImageData”
顶部：“数据”
顶部：“标签”
包括{
阶段：列车
}
变换参数{
镜子：是的
裁剪尺寸：256
平均值文件：“训练平均值”
}
数据参数{
来源：“train_files.txt”
批量：2
后端：LMDB
}
}
层{
名称：“conv1”
类型：“卷积”
底部：“数据”
顶部：“conv1”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
卷积参数{
输出数量：96
内核大小：11
步幅：4
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
数值：0
}
}
}
层{
名称：“relu1”
类型：“ReLU”
底部：“conv1”
顶部：“conv1”
}
层{
名称：“池1”
类型：“池”
底部：“conv1”
顶部：“池1”
池参数{
游泳池：马克斯
内核大小：3
步幅：2
}
}
层{
名称：“norm1”
类型：“LRN”
底部：“池1”
顶部：“正常1”
lrn_参数{
本地大小：5
α：0.0001
贝塔系数：0.75
}
}
层{
名称：“conv2”
类型：“卷积”
底部：“正常1”
顶部：“conv2”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
卷积参数{
输出数量：256
pad:2
内核大小：5
组别:2
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
价值：1
}
}
}
层{
名称：“relu2”
类型：“ReLU”
底部：“conv2”
顶部：“conv2”
}
层{
名称：“池2”
类型：“池”
底部：“conv2”
顶部：“池2”
池参数{
游泳池：马克斯
内核大小：3
步幅：2
}
}
层{
名称：“norm2”
类型：“LRN”
底部：“池2”
顶部：“norm2”
lrn_参数{
本地大小：5
α：0.0001
贝塔系数：0.75
}
}
层{
名称：“conv3”
类型：“卷积”
底部：“norm2”
顶部：“conv3”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
卷积参数{
输出数量：384
pad:1
内核大小：3
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
数值：0
}
}
}
层{
名称：“relu3”
类型：“ReLU”
底部：“conv3”
顶部：“conv3”
}
层{
名称：“conv4”
类型：“卷积”
底部：“conv3”
顶部：“conv4”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
卷积参数{
输出数量：384
pad:1
内核大小：3
组别:2
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
价值：1
}
}
}
层{
名称：“relu4”
类型：“ReLU”
底部：“conv4”
顶部：“conv4”
}
层{
名称：“conv5”
类型：“卷积”
底部：“conv4”
顶部：“conv5”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
卷积参数{
输出数量：256
pad:1
内核大小：3
组别:2
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
价值：1
}
}
}
层{
名称：“relu5”
类型：“ReLU”
底部：“conv5”
顶部：“conv5”
}
层{
名称：“池5”
类型：“池”
底部：“conv5”
顶部：“池5”
池参数{
游泳池：马克斯
内核大小：3
步幅：2
}
}
层{
名称：“fc6”
类型：“内部产品”
底部：“池5”
顶部：“fc6”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
内积参数{
输出数量：4096
重量填料{
类型：“高斯”
标准：0.005
}
偏压填料{
类型：“常量”
价值：1
}
}
}
层{
名称：“relu6”
类型：“ReLU”
底部：“fc6”
顶部：“fc6”
}
层{
名称：“drop6”
类型：“辍学”
底部：“fc6”
顶部：“fc6”
辍学参数{
辍学率：0.5
}
}
层{
名称：“fc7”
类型：“内部产品”
底部：“fc6”
顶部：“fc7”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
内积参数{
输出数量：4096
重量填料{
类型：“高斯”
标准：0.005
}
偏压填料{
类型：“常量”
价值：1
}
}
}
层{
名称：“relu7”
类型：“ReLU”
底部：“fc7”
顶部：“fc7”
}
层{
名称：“drop7”
类型：“辍学”
底部：“fc7”
顶部：“fc7”
辍学参数{
辍学率：0.5
}
}
层{
名称：“fc8”
类型：“内部产品”
底部：“fc7”
顶部：“fc8”
param{
lr_mult：1
衰变倍数：1
}
param{
lr_mult:2
衰减倍数：0
}
内积参数{
输出数量：2
重量填料{
类型：“高斯”
标准：0.01
}
偏压填料{
类型：“常量”
数值：0
}
}
}
层{
名称：“损失”
类型：“SoftmaxWithLoss”
底部：“fc8”
底部：“标签”
顶部：“损失”
}
I0614 19:32:54.64
I0614 19:32:54.634418 31048 caffe.cpp:211] Use CPU.
I0614 19:32:54.635144 31048 solver.cpp:44] Initializing solver from parameters:
test_iter: 1000
test_interval: 1000
base_lr: 0.01
display: 20
max_iter: 50000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 10000
snapshot: 10000
snapshot_prefix: "models/mymodel/caffenet_train"
solver_mode: CPU
net: "my_train_val.prototxt"
train_state {
level: 0
stage: ""
}
I0614 19:32:54.639066 31048 solver.cpp:87] Creating training net from net file: my_train_val.prototxt
I0614 19:32:54.640214 31048 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0614 19:32:54.640645 31048 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0614 19:32:54.641345 31048 net.cpp:51] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 256
mean_file: "train_mean"
}
data_param {
source: "train_files.txt"
batch_size: 2
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: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
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: 1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm2"
type: "LRN"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
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: 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: 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: 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: 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: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0614 19:32:54.644022 31048 layer_factory.hpp:77] Creating layer data
I0614 19:32:54.644239 31048 net.cpp:84] Creating Layer data
I0614 19:32:54.644256 31048 net.cpp:380] data -> data
I0614 19:32:54.644280 31048 net.cpp:380] data -> label
I0614 19:32:54.644448 31048 data_transformer.cpp:25] Loading mean file from: train_mean
I0614 19:32:54.646653 31048 image_data_layer.cpp:38] Opening file
F0614 19:32:54.646975 31048 image_data_layer.cpp:49] Check failed: !lines_.empty() File is empty
*** Check failure stack trace: ***
@ 0x7f83c21c95cd google::LogMessage::Fail()
@ 0x7f83c21cb433 google::LogMessage::SendToLog()
@ 0x7f83c21c915b google::LogMessage::Flush()
@ 0x7f83c21cbe1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f83c25e0509 caffe::ImageDataLayer<>::DataLayerSetUp()
@ 0x7f83c261662e caffe::BasePrefetchingDataLayer<>::LayerSetUp()
@ 0x7f83c26de897 caffe::Net<>::Init()
@ 0x7f83c26e0fde caffe::Net<>::Net()
@ 0x7f83c26e94e5 caffe::Solver<>::InitTrainNet()
@ 0x7f83c26ea925 caffe::Solver<>::Init()
@ 0x7f83c26eac4f caffe::Solver<>::Solver()
@ 0x7f83c26bfbb1 caffe::Creator_SGDSolver<>()
@ 0x40a4b8 train()
@ 0x406fa0 main
@ 0x7f83c113a830 __libc_start_main
@ 0x4077c9 _start
@ (nil) (unknown)
Aborted (core dumped)