serhii.net

In the middle of the desert you can say anything you want

23 Apr 2020

Day 479

Vim exclamation mark to switch parameters

A ! either forces the action or toggles the action:

:set cursorline, to turn off: :set nocursorline

Is equivalent to:

:set cursorline! 1

python tabulate module

tabulate2 generates nice tables of various formats! Things like:

print(tabulate.tabulate(db,headers=db.columns))

      epoch    loss    val_loss    val f-score
--  -------  ------  ----------  -------------
 0        1    4.31        4.62          0.579
 1        2    3.72        3.61          0.705
 2        3    3.54        3.25          0.722
 3        4    3.31        3.06          0.737
 4        5    3.19        2.93          0.736
 5        1    4.31        4.62          0.581
 6        2    3.72        3.61          0.72
 7        3    3.54        3.25          0.755
 8        4    3.31        3.06          0.755
 9        5    3.19        2.93          0.764
10        6    3.12        2.83          0.798
11        7    2.95        2.76          0.779
12        8    2.91        2.69          0.757
13        9    2.84        2.64          0.816
14       10    2.68        2.63          0.835
15       11    2.71        2.56          0.83
16       12    2.69        2.52          0.825
17       13    2.62        2.49          0.826
18       14    2.6         2.46          0.845
19       15    2.56        2.44          0.84

tabulate · PyPI is the basic documentation with visualizations of each tablefmt. It even supports jira! And pipe is the usual markdown format. Let’s try:

epoch loss val_loss val f-score
0 1 4.31 4.62 0.579
1 2 3.72 3.61 0.705
2 3 3.54 3.25 0.722
3 4 3.31 3.06 0.737
4 5 3.19 2.93 0.736
5 1 4.31 4.62 0.581
6 2 3.72 3.61 0.72
7 3 3.54 3.25 0.755
8 4 3.31 3.06 0.755
9 5 3.19 2.93 0.764
10 6 3.12 2.83 0.798
11 7 2.95 2.76 0.779
12 8 2.91 2.69 0.757
13 9 2.84 2.64 0.816
14 10 2.68 2.63 0.835
15 11 2.71 2.56 0.83
16 12 2.69 2.52 0.825
17 13 2.62 2.49 0.826
18 14 2.6 2.46 0.845
19 15 2.56 2.44 0.84

Tensorflow how does training happen with nan? TODO

How does Tensorflow train stuff when loss is nan? It keeps doing something, accuracy changes, etc etc etc. - is the gradient calculated per batch as normal,

Note

Einstein / Netzah “do your own thing”

Nel mezzo del deserto posso dire tutto quello che voglio.
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