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Usually I train with Caffe from Python, since it allows me to initiate layers with just a bit of Python code.


python solve.py <solverstate file> 2>&1 | tee -a solve.log

Caffe prints everything out to stderr instead of stdout, this is why I redirect it using 2>&1. Tee splits stdout to both a file (which is appended thanks to -a) and also displays it to the screen. If I didn't use tee I would just end up with another terminal for tail -f.

1.Command to parse training log

$python /path/to/caffe/tools/extra/parse_log.py /logfile_path/logfile.log /output_dir

Ouput example:
# my_model.log.train
NumIters,Seconds,LearningRate,loss
6000.0,10.468114,1e-06,0.0476156
6020.0,17.372427,1e-06,0.0195639
6040.0,24.237645,1e-06,0.0556274
6060.0,31.084703,1e-06,0.0244656
6080.0,37.927866,1e-06,0.0325582
6100.0,44.778659,1e-06,0.0131274
6120.0,51.62342,1e-06,0.0607449

2. Command to visualize log

python ~/caffe/tools/extra/parse_log.py my_model.log .
gnuplot -persist gnuplot_commands

where gnuplot_commands is a file that stores a set of gnuplot commands.
# gnuplot_commands
set datafile separator ','
set term x11 0
plot '../my_model.log.train' using 1:4  with line,\
     '../my_model.log.test' using 1:5 with line
set term x11 1
plot '../my_model.log.test' using 1:4 with line