Srujana Gattupalli
scripts/train.sh AlexNet
scripts/train.sh: line 19: 61298 Segmentation fault: 11 $caffe_dir/python/draw_net.py train_test.prototxt net.png
Srujanas-MacBook-Pro:deeppose srujanagattupalli$ appending output to nohup.out
In nohup.out -
I0624 17:05:10.054033 1961857792 net.cpp:192] loss needs backward computation.
I0624 17:05:10.054040 1961857792 net.cpp:192] sigmoid8 needs backward computation.
I0624 17:05:10.054049 1961857792 net.cpp:192] fc8 needs backward computation.
I0624 17:05:10.054057 1961857792 net.cpp:192] drop7 needs backward computation.
I0624 17:05:10.054066 1961857792 net.cpp:192] relu7 needs backward computation.
I0624 17:05:10.054074 1961857792 net.cpp:192] fc7 needs backward computation.
I0624 17:05:10.054082 1961857792 net.cpp:192] drop6 needs backward computation.
I0624 17:05:10.054090 1961857792 net.cpp:192] relu6 needs backward computation.
I0624 17:05:10.054098 1961857792 net.cpp:192] fc6 needs backward computation.
I0624 17:05:10.054106 1961857792 net.cpp:192] pool5 needs backward computation.
I0624 17:05:10.054136 1961857792 net.cpp:192] relu5 needs backward computation.
I0624 17:05:10.054162 1961857792 net.cpp:192] conv5 needs backward computation.
I0624 17:05:10.054172 1961857792 net.cpp:192] relu4 needs backward computation.
I0624 17:05:10.054179 1961857792 net.cpp:192] conv4 needs backward computation.
I0624 17:05:10.054188 1961857792 net.cpp:192] relu3 needs backward computation.
I0624 17:05:10.054196 1961857792 net.cpp:192] conv3 needs backward computation.
I0624 17:05:10.054204 1961857792 net.cpp:192] norm2 needs backward computation.
I0624 17:05:10.054214 1961857792 net.cpp:192] pool2 needs backward computation.
I0624 17:05:10.054221 1961857792 net.cpp:192] relu2 needs backward computation.
I0624 17:05:10.054229 1961857792 net.cpp:192] conv2 needs backward computation.
I0624 17:05:10.054239 1961857792 net.cpp:192] norm1 needs backward computation.
I0624 17:05:10.054246 1961857792 net.cpp:192] pool1 needs backward computation.
I0624 17:05:10.054255 1961857792 net.cpp:192] relu1 needs backward computation.
I0624 17:05:10.054262 1961857792 net.cpp:192] conv1 needs backward computation.
I0624 17:05:10.054271 1961857792 net.cpp:194] label does not need backward computation.
I0624 17:05:10.054278 1961857792 net.cpp:194] data does not need backward computation.
I0624 17:05:10.054286 1961857792 net.cpp:235] This network produces output loss
I0624 17:05:10.054306 1961857792 net.cpp:482] Collecting Learning Rate and Weight Decay.
I0624 17:05:10.054319 1961857792 net.cpp:247] Network initialization done.
I0624 17:05:10.054327 1961857792 net.cpp:248] Memory required for data: 1756238852
I0624 17:05:10.054608 1961857792 solver.cpp:42] Solver scaffolding done.
I0624 17:05:10.054688 1961857792 solver.cpp:250] Solving AlexNet
I0624 17:05:10.054698 1961857792 solver.cpp:251] Learning Rate Policy: step
I0624 17:05:10.584841 1961857792 solver.cpp:294] Iteration 0, Testing net (#0)
The training is done successfully. The test phase stops at Iteration 0.
train_test.prototext contatins
name: "AlexNet"
layers {
name: "data"
type: DATA
top: "data"
data_param {
backend: LMDB
source: "../../data/image_train.lmdb"
batch_size: 256
}
transform_param {
mean_file: "../../data/image_mean.binaryproto"
}
}
layers {
name: "label"
type: DATA
top: "label"
data_param {
backend: LMDB
source: "../../data/joint_train.lmdb"
batch_size: 256
}
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 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
}
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 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
}
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 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
}
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 14
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "sigmoid8"
type: SIGMOID
bottom: "fc8"
top: "predict"
}
layers {
name: "loss"
type: EUCLIDEAN_LOSS
bottom: "predict"
bottom: "label"
top: "loss"
}
Please Help! Thanks.