# These snippets serve only as basic examples.

# Customization is a must.

# You can copy, paste, edit them in whatever way you want.

# Be warned that the fields in the training log may change in the future.

# You had better check the data files before designing your own plots.

# Please generate the necessary data files with

# /path/to/caffe/tools/extra/parse_log.sh before plotting.

# Example usage:

# ./parse_log.sh mnist.log

# Now you have mnist.log.train and mnist.log.test.

# gnuplot mnist.gnuplot

# The fields present in the data files that are usually proper to plot along

# the y axis are test accuracy, test loss, training loss, and learning rate.

# Those should plot along the x axis are training iterations and seconds.

# Possible combinations:

# 1. Test accuracy (test score 0) vs. training iterations / time;

# 2. Test loss (test score 1) time;

# 3. Training loss vs. training iterations / time;

# 4. Learning rate vs. training iterations / time;

# A rarer one: Training time vs. iterations.

# What is the difference between plotting against iterations and time?

# If the overhead in one iteration is too high, one algorithm might appear

# to be faster in terms of progress per iteration and slower when measured

# against time. And the reverse case is not entirely impossible. Thus, some

# papers chose to only publish the more favorable type. It is your freedom

# to decide what to plot.

reset

set terminal png

set output "your_chart_name.png"

set style data lines

set key right

###### Fields in the data file your_log_name.log.train are

###### Iters Seconds TrainingLoss LearningRate

# Training loss vs. training iterations

set title "Training loss vs. training iterations"

set xlabel "Training iterations"

set ylabel "Training loss"

plot "mnist.log.train" using 1:3 title "mnist"

# Training loss vs. training time

# plot "mnist.log.train" using 2:3 title "mnist"

# Learning rate vs. training iterations;

# plot "mnist.log.train" using 1:4 title "mnist"

# Learning rate vs. training time;

# plot "mnist.log.train" using 2:4 title "mnist"

###### Fields in the data file your_log_name.log.test are

###### Iters Seconds TestAccuracy TestLoss

# Test loss vs. training iterations

# plot "mnist.log.test" using 1:4 title "mnist"

# Test accuracy vs. training iterations

# plot "mnist.log.test" using 1:3 title "mnist"

# Test loss vs. training time

# plot "mnist.log.test" using 2:4 title "mnist"

# Test accuracy vs. training time

# plot "mnist.log.test" using 2:3 title "mnist"