70 lines
2.5 KiB
Plaintext
70 lines
2.5 KiB
Plaintext
# These snippets serve only as basic examples.
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# Customization is a must.
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# You can copy, paste, edit them in whatever way you want.
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# Be warned that the fields in the training log may change in the future.
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# You had better check the data files before designing your own plots.
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# Please generate the necessary data files with
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# /path/to/caffe/tools/extra/parse_log.sh before plotting.
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# Example usage:
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# ./parse_log.sh mnist.log
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# Now you have mnist.log.train and mnist.log.test.
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# gnuplot mnist.gnuplot
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# The fields present in the data files that are usually proper to plot along
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# the y axis are test accuracy, test loss, training loss, and learning rate.
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# Those should plot along the x axis are training iterations and seconds.
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# Possible combinations:
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# 1. Test accuracy (test score 0) vs. training iterations / time;
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# 2. Test loss (test score 1) time;
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# 3. Training loss vs. training iterations / time;
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# 4. Learning rate vs. training iterations / time;
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# A rarer one: Training time vs. iterations.
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# What is the difference between plotting against iterations and time?
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# If the overhead in one iteration is too high, one algorithm might appear
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# to be faster in terms of progress per iteration and slower when measured
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# against time. And the reverse case is not entirely impossible. Thus, some
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# papers chose to only publish the more favorable type. It is your freedom
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# to decide what to plot.
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reset
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set terminal png
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set output "your_chart_name.png"
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set style data lines
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set key right
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###### Fields in the data file your_log_name.log.train are
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###### Iters Seconds TrainingLoss LearningRate
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# Training loss vs. training iterations
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set title "Training loss vs. training iterations"
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set xlabel "Training iterations"
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set ylabel "Training loss"
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plot "mnist.log.train" using 1:3 title "mnist"
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# Training loss vs. training time
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# plot "mnist.log.train" using 2:3 title "mnist"
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# Learning rate vs. training iterations;
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# plot "mnist.log.train" using 1:4 title "mnist"
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# Learning rate vs. training time;
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# plot "mnist.log.train" using 2:4 title "mnist"
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###### Fields in the data file your_log_name.log.test are
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###### Iters Seconds TestAccuracy TestLoss
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# Test loss vs. training iterations
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# plot "mnist.log.test" using 1:4 title "mnist"
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# Test accuracy vs. training iterations
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# plot "mnist.log.test" using 1:3 title "mnist"
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# Test loss vs. training time
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# plot "mnist.log.test" using 2:4 title "mnist"
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# Test accuracy vs. training time
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# plot "mnist.log.test" using 2:3 title "mnist"
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