[Caffe] How to use Caffe to solve the regression problem?

There is a question coming up to my mind recently. How to use Caffe to solve the regression problem? We used to see a bunch of examples related to image recognition with labels and they are classification problem. In my experience, I have done this problem using TensorFlow, not Caffe. But, I think in theory they are both the same. The key point is using EuclideanLossLayer as the final Loss Layer and it's the detail from the official web site:
"This can be used for least-squares regression tasks. An InnerProductLayer input to a EuclideanLossLayer exactly formulates a linear least squares regression problem. With non-zero weight decay the problem becomes one of ridge regression – see src/caffe/test/test_sgd_solver.cpp for a concrete example wherein we check that the gradients computed for a Net with exactly this structure match hand-computed gradient formulas for ridge regression. (Note: Caffe, a…

[Raspberry Pi] Use Wireless and Ethernet together

The following content is my Raspberry Pi 3's setting in /etc/network/interface as follows. In my case, I both use wireless and ethernet device at the same time.
# Include files from /etc/network/interfaces.d: source-directory /etc/network/interfaces.d auto lo iface lo inet loopback auto wlan0 allow-hotplug wlan0 iface wlan0 inet manual Wpa-conf /etc/wpa_supplicant/wpa_supplicant.conf allow-hotplug eth0 iface eth0 inet static address netmask up ip route add via dev eth0 up ip route add via dev eth0 up ip route add via dev eth0
But, this situation will encounter routing problem like this ( There are two default gateways):
I only want the 2nd one.
pi@raspberrypi:~ $ route Kernel IP routing table Destination Gateway Genmask Flags Metric Ref Use Iface default UG 202 0 0 eth0…

[Debug] Debugging Python and C++ exposed by boost together

During the studying of Caffe, I was curious about how Caffe provides Python interface and what kind of tool uses for wrapping. Then, the answer is Boost.Python. I think for C++ developer, it is worth time to learn and I will study it sooner. In this post, I want to introduce the debugging skill which I found in this post and I believe these are very useful such as debugging Caffe with Python Layer. Here is the link:

And it provides a useful approach to deal with this kind of debugging Python and C++ together as follows:

I use its way to try debugging a little bit and it works well. Here is a simple example using Caffe Python.

$ gdb python (gdb) target exec python (gdb) run >>> import caffe >>> [C-c] # Ctrl + C (gdb) break caffe::set_mode_cpu() # Please refer to $(CAFFE_ROO…

[PCIe] lspci command and the PCIe devices in my server

The following content is about my PCIe devices/drivers and the lspci command results.
$ cd /sys/bus/pci_express/drivers $ ls -al drwxr-xr-x 2 root root 0  7月  6 15:33 aer/ drwxr-xr-x 2 root root 0  7月  6 15:33 pciehp/ drwxr-xr-x 2 root root 0  7月  6 15:33 pcie_pme/

$ cd  pcie_pme $ ls -al

$ lspci | grep 00:1c

Or $ cd /sys/bus/pci_express/devices $ ls -al

$ cd 0000:00:1c.0:pcie01 $ ls -al total 0 drwxr-xr-x 3 root root    0  7月  5 08:56 ./ drwxr-xr-x 6 root root    0  7月  5 08:56 ../ lrwxrwxrwx 1 root root    0  7月  6 15:51 driver -> ../../../../bus/pci_express/drivers/pcie_pme/ drwxr-xr-x 2 root root    0  7月  6 15:51 power/ lrwxrwxrwx 1 root root    0  7月  6 15:51 subsystem -> ../../../../bus/pci_express/

[Caffe] Install Caffe and the depended packages

This article is just for me to quickly record the all the steps to install the depended packages for Caffe. So, be careful that it maybe is not good for you to walk through them in your environment. ^_^

# Install CCMAKE $ sudo apt-get install cmake-curses-gui
# Build my own installation location $ mkdir ~/local_install # ProtoBuffer $ tar zxvf protobuf-2.5.0.tar.gz
$ cd protobuf-2.5.0
$ ./configure --prefix=/home/liudanny/local_install/
$ make -j2
$ make install
#Boost $ tar xvf boost_1_56_0.tar.bz2
$ cd boost_1_56_0
### ./ --show-libraries ###
$ ./ --with-libraries=program_options,filesystem,system,exception,thread
$ ./b2
$ cp -r boost/ /home/liudanny/local_install/include
$ cp stage/lib/* /home/liudanny/local_install/lib/
# Gflags $ unzip
$ cd gflags-2.1.1
$ mkdir build
$ cd build
$ cmake ..
$ ccmake ..

$ make -j2
$ make install
# Glog $ tar zxvf glog-0.3.3.tar.gz
$ cd glog-0.3.3
$ ./configure --prefix=/home/liudanny/local_install
$ make -j2

[NCCL] Build and run the test of NCCL

NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. Best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.

Note: NCCL may also work with CUDA 6.5, but this is an untested configuration.

Build & run To build the library and tests.

$ cd nccl
$ make CUDA_HOME=<cuda install path> test
Test binaries are located in the subdirectories nccl/build/test/{single,mpi}.

$ ~/git/nccl$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./build/lib
$ ~/git/nccl$ ./build/test/single/all_reduce_test 100000000
# Using devices
#   Rank  0 uses device  0 [0x04] GeForce GTX 1080 Ti
#   Rank  1 uses device  1 [0x05] GeForce GTX 1080 Ti
#   Rank  2 uses device  2 [0x08] GeForce GTX 1080 Ti
#   Rank  3 uses device  3 [0x09] GeForce GTX 1080 Ti
#   Rank  4 uses device  4 [0x83] GeForce GTX 1080 Ti
#   Rank  5 uses device  5 [0x84] GeForce GTX 1080 Ti
#   Rank  6 uses device  6 [0x87] GeForce GTX 1080 Ti
#   Rank  7 uses de…

[Mpld3] Render Matplotlib chart to web using Mpld3

The following example is about rendering a matplotlib chart on web, which is based on Django framework to build up. I encountered some problems before, such as, not able to see chart on the web page or having a run-time error after reloading the page. But, all the problems are solved.

<< demo/>> import matplotlib.pyplot as plt
import numpy as np
import mpld3

def plot_test1(request):
context = {}
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100

Demo about using matplotlib and mpld3 to rendor charts
scatter = ax.scatter(np.random.normal(size=N),
s=1000 * np.random.random(size=N),
ax.grid(color='white', linestyle='solid')

ax.set_title("Scatter Plot (with tooltips!)", size=20)

labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=label…