Can not find typo in my net file. Caffe report me:
0216 17:26:44.361912 6254 caffe.cpp:184] Using GPUs 0
I0216 17:26:44.667951 6254 solver.cpp:47] Initializing solver from parameters:
test_iter: 200
test_interval: 200
base_lr: 0.01
display: 200
max_iter: 450000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 400
solver_mode: GPU
device_id: 0
net: "/home/ashkanaev/Notebook/CNN/autorobot_cnn.prototxt"
I0216 17:26:44.668064 6254 solver.cpp:90] Creating training net from net file: /home/ashkanaev/Notebook/CNN/autorobot_cnn.prototxt
[libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.NetParameter: 29:9: Expected string.
F0216 17:26:44.668236 6254 upgrade_proto.cpp:68] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: /home/ashkanaev/Notebook/CNN/autorobot_cnn.prototxt
*** Check failure stack trace: ***
@ 0x7fa1625d8daa (unknown)
@ 0x7fa1625d8ce4 (unknown)
@ 0x7fa1625d86e6 (unknown)
@ 0x7fa1625db687 (unknown)
@ 0x7fa162ce16de caffe::ReadNetParamsFromTextFileOrDie()
@ 0x7fa162bdcfeb caffe::Solver<>::InitTrainNet()
@ 0x7fa162bde1fc caffe::Solver<>::Init()
@ 0x7fa162bde509 caffe::Solver<>::Solver()
@ 0x7fa162bb41f3 caffe::Creator_SGDSolver<>()
@ 0x40e78e caffe::SolverRegistry<>::CreateSolver()
@ 0x4079cb train()
@ 0x4058c1 main
@ 0x7fa1618e6ec5 (unknown)
@ 0x405fd1 (unknown)
@ (nil) (unknown)
Aborted
Net file:
name: "Autorobot_cnn"
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
hdf5_data_param {
source: "/home/ashkanaev/Notebook/autorobot/train_data.txt"
batch_size: 256
}
include: { phase: TRAIN }
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
hdf5_data_param {
source: "/home/ashkanaev/Notebook/autorobot/test_data.txt"
batch_size: 256
}
include: { phase: TEST }
}
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: 20
kernel_size: 5
stride: 1
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: 2
stride: 2
}
}
layer {
name: "norm1"
type: "LNR"
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: 60
kernel_size: 5
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: 2
stride: 1
}
}
layer {
name: "norm2"
type: "LNR"
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "fc1"
type: "InnerProduct"
bottom: "norm2"
top: "fc1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 500
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "fc1"
top: "fc1"
}
layer {
name: "drop1"
type: "Dropout"
bottom: "fc1"
top: "fc1"
dropout_param {
dropout_ratio: 0.3
}
}
layer {
name: "fc2"
type: "InnerProduct"
bottom: "fc1"
top: "fc2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "fc2"
top: "fc2"
}
layer {
name: "drop1"
type: "Dropout"
bottom: "fc2"
top: "fc2"
dropout_param {
dropout_ratio: 0.3
}
}
layer {
name: "fc3"
type: "InnerProduct"
bottom: "fc2"
top: "fc3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 28
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc3"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "Softmax"
bottom: "fc3"
bottom: "label"
top: "loss"
}
someone knows how to fix it?