Add gnuplot example to plot the training log

diff --git a/scripts/plot_log.gnuplot.example b/scripts/plot_log.gnuplot.example
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+# 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 neccessary data files with 
+# /path/to/caffe/scripts/parselog.sh before plotting.
+# Example usage: 
+#     ./parselog.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 loss"
+set ylabel "Training iterations"
+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"