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์ „์ฒด ๊ธ€ 177

Python print ์˜ต์…˜ 'sep'

Python์˜ print ํ•จ์ˆ˜์—๋Š” ์ถœ๋ ฅํ•  ๋‚ด์šฉ ์™ธ์—๋„ ๋‹ค๋ฅธ ์ธ์ž๋ฅผ ์ „๋‹ฌํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ์ค‘ ํ•˜๋‚˜์ธ 'sep'์€ ์ถœ๋ ฅํ•  ๋‚ด์šฉ๋“ค์„ ์–ด๋–ป๊ฒŒ ๋ถ„๋ฆฌํ•  ๊ฒƒ์ธ์ง€๋ฅผ ์ง€์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ ' ' ์œผ๋กœ ์„ค์ •๋˜์–ด ์žˆ๋‹ค. ๋งŒ์•ฝ '\n'์œผ๋กœ sep ์˜ต์…˜์„ ์„ค์ •ํ•˜๋ฉด, ์ž…๋ ฅ๋ฐ›์€ ์ธ์ž๋“ค์„ ์ค„๋ฐ”๊ฟˆ์œผ๋กœ ๋ถ„๋ฆฌํ•˜์—ฌ ์ถœ๋ ฅํ•ด ์ค€๋‹ค.

[pip/์—๋Ÿฌ ํ•ด๊ฒฐ] inplace-abn ์„ค์น˜ ์‹œ ์˜ค๋ฅ˜

pip๋ฅผ ์ด์šฉํ•ด inplace-abn์„ ์„ค์น˜ํ•˜๋ ค๊ณ  ํ–ˆ๋Š”๋ฐ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์˜ค๋ฅ˜๊ฐ€ ๋ฐœ์ƒํ–ˆ๋‹ค. >> pip install inplace-abn Collecting inplace-abn Using cached inplace-abn-1.1.0.tar.gz (137 kB) Building wheels for collected packages: inplace-abn Building wheel for inplace-abn (setup.py) ... error ERROR: Command errored out with exit status 1: command: /home/user/anaconda3/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'..

์œˆ๋„์šฐ ํ™”๋ฉด๋ถ„ํ•  ํ”„๋กœ๊ทธ๋žจ PowerToys (+zoom ์˜ˆ๊ธฐ์น˜ ์•Š๊ฒŒ ์ข…๋ฃŒ)

๋‚˜๋Š” ๋ชจ๋‹ˆํ„ฐ ๋‘ ๋Œ€ ์ค‘ ํ•œ ๋Œ€๋ฅผ ์„ธ๋กœ๋กœ ์„ธ์›Œ์„œ ์‚ฌ์šฉํ•˜๋Š”๋ฐ, ๊ฐ€๋กœ ๋ชจ๋‹ˆํ„ฐ์˜ ๊ฒฝ์šฐ Win + ←/→ ๋‹จ์ถ•ํ‚ค๋ฅผ ์ด์šฉํ•ด ํ™”๋ฉด์„ ๋ถ„ํ• ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฉด, ์„ธ๋กœ ๋ชจ๋‹ˆํ„ฐ๋Š” ์œ„์•„๋ž˜๋กœ ๋ถ„ํ• ํ•˜๋Š” ๋‹จ์ถ•ํ‚ค๊ฐ€ ์—†์–ด์„œ ๋ถˆํŽธํ•˜๋”๋ผ.. ์ฐพ์•„๋ณด๋‹ˆ PowerToys๋ผ๋Š” ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•ด์„œ ์œˆ๋„์šฐ์—์„œ ํ™”๋ฉด์„ ์ƒํ•˜ ๋ถ„ํ• ํ•  ์ˆ˜ ์žˆ๊ธธ๋ž˜ ์†Œ๊ฐœํ•˜๊ณ ์ž ํ•œ๋‹ค. 1. ์„ค์น˜ ๋‹ค์Œ ๋งํฌ์—์„œ ์„ค์น˜ํŒŒ์ผ์„ ๋‹ค์šด๋กœ๋“œํ•  ์ˆ˜ ์žˆ๋‹ค. https://github.com/microsoft/PowerToys/releases/tag/v0.49.1 Release Release v0.49.1 · microsoft/PowerToys This is a patch release to fix issues in v0.49.0 we deemed important for stability b..

[PyTorch] Scheduler ์‹œ๊ฐํ™”ํ•˜๊ธฐ (Visualize scheduler)

๋‹ค์Œ ํ•จ์ˆ˜๋ฅผ ์ด์šฉํ•ด PyTorch scheduler์„ ์‹œ๊ฐํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. import matplotlib.pyplot as plt def visualize_scheduler(optimizer, scheduler, epochs): lrs = [] for _ in range(epochs): optimizer.step() lrs.append(optimizer.param_groups[0]['lr']) scheduler.step() plt.plot(lrs) plt.show() scheduler.get_lr()๋กœ learning rate๋ฅผ ์–ป์–ด์˜ค์ง€ ์•Š๊ณ  optimizer.param_groups[0]['lr']๋กœ ์–ป์–ด์˜ค๋Š” ์ด์œ ๋Š”, ReduceLROnPlateau ๋“ฑ์˜ scheduler์˜ ๊ฒฝ์šฐ get_lr() meth..

๋‚ด๊ฐ€ ๋ณด๋ ค๊ณ  ์ •๋ฆฌํ•˜๋Š” LaTex ์ž์ฃผ ์“ฐ๋Š” ์ˆ˜์‹ ์ •๋ฆฌ

LaTex ํŠน์ˆ˜๋ฌธ์ž, ์ˆ˜์‹์„ ๋˜‘๊ฐ™์€ ๊ฑฐ ๋งค๋ฒˆ ๊ตฌ๊ธ€๋งํ•˜๊ธฐ ๊ท€์ฐฎ์•„์„œ ๋งŒ๋“œ๋Š” ์ž์ฃผ ์“ฐ๋Š” LaTex ์ˆ˜์‹ ๋ชจ์Œ ๐ŸŽจ Font typefaces - ์‹ค์ˆ˜ ์ง‘ํ•ฉ (R), ๋ฐ์ดํ„ฐ์…‹ (D) ๋“ฑ์—์„œ ์ž์ฃผ ์“ฐ๋Š” ๋ฌธ์ž. \mathcal{RQSZ} \mathbb{RQSZ} ๊ธฐํ˜ธ (Symbols) LaTex ์ˆ˜์‹ ํ‘œ์‹œ ์„ค๋ช… ์—ฐ์‚ฐ์ž (Operators) \cdot · ๊ณฑ์…ˆ๊ธฐํ˜ธ / ๋‚ด์  (inner product) \circ โˆ˜ circle / ์› (*degree ๋“ฑ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•œ ์œ„ ์ฒจ์ž๋กœ ์‚ฌ์šฉํ•˜๋ ค๋ฉด ^\circ๋ฅผ ์ด์šฉํ•˜๋ฉด ๋œ๋‹ค.) \times × ๊ณฑ์…ˆ๊ธฐํ˜ธ / ์™ธ์  (cross product) \pm ± ํ”Œ๋Ÿฌ์Šค ๋งˆ์ด๋„ˆ์Šค (plus minus) \circledast โŠ› convolution / ์› ์•ˆ์˜ ๋ณ„ํ‘œ (asterisk) \..

T test ์™€ P value

T test๋ž€? T test๋Š” ๋‘ group๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์–ผ๋งˆ๋‚˜ "significant"ํ•œ์ง€๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ์ฆ‰, ๋‘ group๊ฐ„์˜ ์ฐจ์ด๊ฐ€ "์šฐ์—ฐํžˆ" ์ผ์–ด๋‚œ ์ผ์ผ ํ™•๋ฅ ์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณดํ†ต ๋ฐ์ดํ„ฐ ์ˆ˜๊ฐ€ ์ ์€ ๊ฒฝ์šฐ์— ์‚ฌ์šฉํ•œ๋‹ค. ํ•˜๋‚˜์˜ ์˜ˆ์‹œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ œ์•ฝํšŒ์‚ฌ์—์„œ ์ƒˆ๋กœ์šด ํ•ญ์•”์ œ๋ฅผ ๊ฐœ๋ฐœํ•ด ์ด๊ฒƒ์ด ๊ธฐ๋Œ€์ˆ˜๋ช…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜๊ณ  ์‹ถ๋‹ค๊ณ  ํ•˜์ž. ์ด๋Ÿฌํ•œ ์‹คํ—˜์„ ์ง„ํ–‰ํ•  ๋•Œ์—๋Š”, ํ•ญ์ƒ ๋Œ€์กฐ๊ตฐ(placebo ๋ณต์šฉ)์ด ์กด์žฌํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ๋Œ€์กฐ๊ตฐ์˜ ๊ธฐ๋Œ€์ˆ˜๋ช…์ด ํ‰๊ท ์ ์œผ๋กœ 5๋…„ ์ฆ๊ฐ€ํ–ˆ๊ณ , ์‹คํ—˜๊ตฐ(์‹ค์ œ ํ•ญ์•”์ œ ๋ณต์šฉ)์˜ ๊ธฐ๋Œ€์ˆ˜๋ช…์ด ํ‰๊ท ์ ์œผ๋กœ 6๋…„ ์ฆ๊ฐ€ํ–ˆ๋‹ค๊ณ  ํ•œ๋‹ค๋ฉด, ์–ธ๋œป ๋ณด๊ธฐ์—๋Š” ํ•ญ์•”์ œ๊ฐ€ ์‹ค์ œ๋กœ ๊ธฐ๋Œ€์ˆ˜๋ช…์„ ๋Š˜๋ ค์ฃผ๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ์–ด ๋ณด์ธ๋‹ค. ์‹ค์ œ๋กœ ์ด๊ฒƒ์ด ์šฐ์—ฐ์— ์˜ํ•ด ๋ฐœ์ƒํ•œ ์ผ์ธ์ง€, ์•„๋‹Œ์ง€๋ฅผ T test๋Š” ํ™•๋ฅ ์„ ํ†ตํ•ด ์•Œ๋ ค์ค„ ์ˆ˜..

[PyTorch] ReduceLROnPlateau

ReduceLROnPlateau๋Š” ๋”์ด์ƒ ํ•™์Šต์ด ์ง„ํ–‰๋˜์ง€ ์•Š์„ ๋•Œ learning rate๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š” scheduler์ด๋‹ค. scheduler์— input์œผ๋กœ metric์„ ์ฃผ๋ฉด, ์ผ์ • epoch ๋™์•ˆ ๋ณ€ํ™”๊ฐ€ ์—†์„ ๋•Œ learning rate๋ฅผ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ์ฃผ์š” Parameter์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. mode: [min, max] ์ค‘ ํ•˜๋‚˜. Input์œผ๋กœ ์ฃผ๋Š” metric์ด ๋‚ฎ์„์ˆ˜๋ก ์ข‹์€์ง€, ๋†’์„์ˆ˜๋ก ์ข‹์€์ง€๋ฅผ ์˜๋ฏธํ•œ๋‹ค. 'min' option์„ ์ฃผ๋ฉด, metric์˜ ๊ฐ์†Œ๊ฐ€ ๋ฉˆ์ถœ ๋•Œ๋งˆ๋‹ค learning rate๋ฅผ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. factor: Learning rate๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๋น„์œจ. new_lr = lr * factor์ด ๋œ๋‹ค. patience: Metric์ด ์–ผ๋งˆ ๋™์•ˆ ๋ณ€ํ™”๊ฐ€ ์—†์„ ๋•Œ learning rat..

Transformer์˜ positional encoding (PE)

Transformer์„ ๊ตฌ์„ฑํ•˜๋Š” Multi-Head Self-Attention layer๋Š” permutation equivariantํ•œ ํŠน์„ฑ์„ ๊ฐ–๊ธฐ ๋•Œ๋ฌธ์—, postitional encoding์ด ํ•„์ˆ˜์ ์œผ๋กœ ํ•„์š”ํ•˜๋‹ค. Transformer์—์„œ ์‚ฌ์šฉํ•˜๋Š” positional encoding ์šฐ์„ , Transformer์—์„œ ์‚ฌ์šฉํ•˜๋Š” positional encoding์˜ ์‹์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. $PE_{(pos,2i)}=sin(pos/10000^{2i/d_{model}})$ $PE_{(pos,2i+1)}=cos(pos/10000^{2i/d_{model}})$ ์ด๋ฅผ ํ’€์–ด ์“ฐ๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ˜•ํƒœ๋ฅผ ๊ฐ–๊ฒŒ ๋˜๊ณ , ์ด๋ฅผ ์‹œ๊ฐํ™”ํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋ณธ ๊ธ€์—์„œ๋Š” ์™œ transformer์˜ positional encoding์ด ์ด..

[ML] Kernel Density Estimation (KDE)์™€ Kernel Regression (KR)

I. Kernel Density Estimation (KDE) KDE๋Š” kernel ํ•จ์ˆ˜๋ฅผ ์ด์šฉํ•ด ํ™•๋ฅ ๋ฐ€๋„ํ•จ์ˆ˜๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ํ•˜๋‚˜์˜ ์˜ˆ์‹œ๋กœ, ๊ธธ๊ฑฐ๋ฆฌ์—์˜ ๋ฒ”์ฃ„ ๋ฐœ์ƒ๋Ÿ‰์„ ๋‚˜ํƒ€๋‚ธ ๋ฐ์ดํ„ฐ๊ฐ€ ์žˆ๋‹ค๊ณ  ํ•˜์ž. Crime Location 1 15 2 12 3 10 ... ... ์ด ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•ด, ๊ธธ๊ฑฐ๋ฆฌ ๊ฐ ์ง€์ ์—์„œ์˜ ๋ฒ”์ฃ„ ๋ฐœ์ƒ ๊ฐ€๋Šฅ์„ฑ์„ ์ถ”์ •ํ•˜๊ณ  ์‹ถ๋‹ค๊ณ  ํ•˜์ž. ํŠน์ • ์ง€์ ์—์„œ ๋ฒ”์ฃ„๊ฐ€ ๋ฐœ์ƒํ–ˆ๋‹ค๋ฉด, ๊ทธ ๊ทผ์ฒ˜์—๋„ ๋ฒ”์ฃ„๊ฐ€ ๋ฐœ์ƒํ–ˆ์„ ํ™•๋ฅ ์ด ๋†’๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์šฐ๋ฆฌ๋Š” ๊ณผ๊ฑฐ์— ๋ฒ”์ฃ„๊ฐ€ ๋ฐœ์ƒํ–ˆ๋˜ ์œ„์น˜๋งˆ๋‹ค kernel์„ ์Œ“๊ณ , ์ด๋ฅผ ๋ชจ๋‘ ๋”ํ•˜์—ฌ ๋ฒ”์ฃ„์œจ์— ๋Œ€ํ•œ ๋ฐ€๋„ํ•จ์ˆ˜(KDE)๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. $\hat{f}_h(x)=\frac{q}{nh}\sum_{i-1}^nK(\frac{x-x_i}{h})$ Kernel ํ•จ์ˆ˜ $K..

[PyTorch/์—๋Ÿฌ ํ•ด๊ฒฐ] torchaudio.transforms.SpectralCentroid output์ด nan์ด ๋˜๋Š” ๋ฌธ์ œ

ํ›ˆ๋ จ์„ ์‹œํ‚ค๋‹ค๊ฐ€ loss๊ฐ€ ์ž๊พธ nan์ด ๋˜๋Š” ํ˜„์ƒ์ด ๋ฐœ์ƒํ–ˆ๋‹ค. ๋””๋ฒ„๊น…์„ ํ•ด๋ณด๋‹ˆ loss๊ฐ€ ์ž˜ ์ค„์–ด๋“ค๋‹ค๊ฐ€ ๊ฐ‘์ž๊ธฐ nan์œผ๋กœ ๋ฐ”๋€Œ์–ด์„œ ํ•™์Šต ๊ณผ์ • ์ž์ฒด์˜ ๋ฌธ์ œ๋Š” ์•„๋‹Œ ๊ฒƒ ๊ฐ™์•˜๊ณ , ์‚ดํŽด๋ณด๋‹ˆ ๋ชจ๋ธ ๋‚ด๋ถ€์—์„œ ๊ฐ’์ด nan์ด ๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ชจ๋ธ์—์„œ torchaudio.transforms.SpectralCentroid๋ฅผ ์ด์šฉํ•ด ๋ฐ์ดํ„ฐ์˜ spectral centroid๋ฅผ ์ถ”์ถœํ•˜๋Š” ๊ณผ์ •์ด ์žˆ์—ˆ๋Š”๋ฐ, ์ผ๋ถ€ ๋ฐ์ดํ„ฐ์˜ spectral centroid๊ฐ€ nan์„ ํฌํ•จํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ฝ”๋“œ๋ฅผ ์‚ดํŽด๋ณด๋‹ˆ spectral centroid๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ณผ์ •์—์„œ spectrogram์˜ sum์œผ๋กœ ๊ฐ’์„ ๋‚˜๋ˆ ์ฃผ๋Š” ๊ณผ์ •์ด ์žˆ์—ˆ๋Š”๋ฐ, (๋งˆ์ง€๋ง‰ ์ค„) ์ด ๊ฐ’์ด 0์ด ๋˜์–ด์„œ nan๊ฐ’์ด ๋‚˜์˜จ ๊ฒƒ์ด์—ˆ๋‹ค. Solution torch.nan_to_num์„ ..

๋ฐ˜์‘ํ˜•