root@ankit:/home/ankit/caffe# ./examples/_temp/train_lenet.sh
I0425 22:01:22.738116 10225 caffe.cpp:185] Using GPUs 0
I0425 22:01:22.773546 10225 caffe.cpp:190] GPU 0: GeForce 920M
I0425 22:01:22.973305 10225 solver.cpp:48] Initializing solver from parameters:
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "examples/_temp/lenet"
solver_mode: GPU
device_id: 0
net: "examples/_temp/2.prototxt"
I0425 22:01:22.973559 10225 solver.cpp:91] Creating training net from net file: examples/_temp/2.prototxt
I0425 22:01:22.973896 10225 net.cpp:49] Initializing net from parameters:
name: "Tester"
state {
phase: TRAIN
}
layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
hdf5_data_param {
source: "examples/_temp/train_h5_list.txt"
}
}
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: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
}
}
layer {
name: "fullyCon1"
type: "InnerProduct"
bottom: "pool1"
top: "fullyCon1"
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: "prob"
type: "Softmax"
bottom: "fullyCon1"
top: "prob"
}
I0425 22:01:22.974227 10225 layer_factory.hpp:77] Creating layer data
I0425 22:01:22.974256 10225 net.cpp:91] Creating Layer data
I0425 22:01:22.974269 10225 net.cpp:399] data -> data
I0425 22:01:22.974293 10225 net.cpp:399] data -> label
I0425 22:01:22.974334 10225 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/_temp/train_h5_list.txt
I0425 22:01:22.974364 10225 hdf5_data_layer.cpp:93] Number of HDF5 files: 1
I0425 22:01:22.975319 10225 hdf5.cpp:32] Datatype class: H5T_FLOAT
*** Aborted at 1461601882 (unix time) try "date -d @1461601882" if you are using GNU date ***
PC: @ 0x7fe2880cf76e caffe::Blob<>::Reshape()
*** SIGFPE (@0x7fe2880cf76e) received by PID 10225 (TID 0x7fe288803a40) from PID 18446744071697135470; stack trace: ***
@ 0x7fe2866312f0 (unknown)
@ 0x7fe2880cf76e caffe::Blob<>::Reshape()
@ 0x7fe2880fecea caffe::HDF5DataLayer<>::LayerSetUp()
@ 0x7fe2880baee3 caffe::Net<>::Init()
@ 0x7fe2880bc138 caffe::Net<>::Net()
@ 0x7fe28808fc2a caffe::Solver<>::InitTrainNet()
@ 0x7fe288091221 caffe::Solver<>::Init()
@ 0x7fe2880915aa caffe::Solver<>::Solver()
@ 0x7fe2881ddfe3 caffe::Creator_SGDSolver<>()
@ 0x41366c caffe::SolverRegistry<>::CreateSolver()
@ 0x40aef3 train()
@ 0x4080ed main
@ 0x7fe28661ca40 (unknown)
@ 0x408969 _start
@ 0x0 (unknown)
Floating point exception (core dumped)
import h5py, os
import sys
import caffe
import numpy as np
SIZE = 256 # fixed size to all images
with open( 'file_list.txt', 'r' ) as T :
lines = T.readlines()
# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
X = np.zeros( (len(lines), 3, SIZE, SIZE), dtype='f4' )
y = np.zeros( (len(lines), 1), dtype='f4' )
for i,l in enumerate(lines):
sp = l.split(' ')
#print 'iter: ', i
#print sp[0], sp[1]
img = caffe.io.load_image( sp[0] )
img = caffe.io.resize( img, (SIZE, SIZE, 3) ) # resize to fixed size
img = np.transpose(img, (2, 0, 1))
#print img.shape
# you may apply other input transformations here...
X[i] = img
y[i] = int(sp[1])
with h5py.File('train.h5','w') as H:
H.create_dataset( 'data', data=X ) # note the name X given to the dataset!
H.create_dataset( 'label', data=y ) # note the name y given to the dataset!
with open('train_h5_list.txt','w') as L:
L.write( 'examples/_temp/train.h5' ) # list all h5 files you are going to use
print 'Done'
/home/ankit/caffe/examples/images/cat.jpg 0/home/ankit/caffe/examples/images/fish-bike.jpg 1/home/ankit/caffe/examples/images/cat_gray.jpg 0