Getting Started With Caffe For Deep Learning

In your home directory:

wget https://github.com/BVLC/caffe/archive/master.zip
unzip master.zip
cd caffe-master/
cp Makefile.config.example Makefile.config

 

To compile the caffe:

make all -j4
make test -j4
make runtest

if everything goes well, inside caffe-master directory :

./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh

 

To run mnist sample network in the caffe_root:

./examples/mnist/train_lenet.sh 2> examples/mnist/name_of_your_file.log.txt

 

 

This will run the caffe and write the log in the file examples/mnist/name_of_your_file.log.txt. To see your logs, open another SSH in another tab:

cd caffe-master/
tail -f examples/mnist/name_of_your_file.log.txt

 

When your job is done, press Ctrl+C to exit tail output and then you can:

cd examples/mnist/
../../tools/extra/parse_log.sh name_of_your_file.log.txt

This will generate a readable format of name_of_your_file.log.txt.test and name_of_your_file.log.txt.train. You can use scp to copy these files into your local machine and use Matlab/octav/python etc. to plot your training and test.
If by accident you logged off the machine, you can see your job list (something like taskmanager in windows) by using command:
top

if you want to call your job, use commmand:
kill -9 <PID>
You can change solver prototxt to change your network architecture, parameters etc. We will go over it in the class.

Find more information about training mnist in caffe:

http://caffe.berkeleyvision.org/gathered/examples/mnist.html (Links to an external site.)