It's 2021 now. Not too different from 2020 but people are more optimistic.
Investing
A new calendar year is a chance to take a look at the year I started actively investing. Etrade provides a helpful comparison to indexes, but it isn't particularly useful due to the inseparable inclusion of ESPP.
It's probably not useful to look at long-term holds, so I didn't include those.
As I suspected at various points throughout the year, individual bond investments have given me the best gains. And this is just the ones I bought and sold - bonds that gained ~10% and had a low coupon value. It also doesn't account for interest payments (can't find that in etrade for the life of me). ETFs were something of a mixed bag since I traded regular market index-types and inverses. My ETF bottom line is dominated by the Barclay's oil fund that I bought just before crude bottomed out. I went in with the intent of holding it until the inevitable rebound (petrol and plastics simply aren't going away). Unfortunately, Barclay's decided to liquidate the fund and I was effectively forced to sell low.
I guess SPY finished 15% up from the start of the year, so if that's the benchmark I am a terrible investor. Then again, if you told me in March that I could net 10% on the year safely or 15% while risking a pandemic market, I'd have taken the former. Since the year was about cashing out long positions and learning how to stonks, it's been one for setting a baseline rather than being compared to one.
Should I just hold SPY until retirement because stonks always go up? Well:
Sunset session
Black's is supposed to be firing tomorrow, but I took the 500mm down this evening to get some sunset shots. I'm not super into the silhouettes and focus was troublesome (backlit haze), but it was worth experimenting.
Krieg and Zane
On the Borderlands 3 front, me and J finished the Krieg DLC and proceeded to switch to our alt characters (Zane and Moze, respectively) to level them, play through the DLC, and check out their new skill tree.
Having a fourth skill tree is somewhere between 'awesome' and 'game-changing', simply because there are so many ways to create synergy between skills. Really the only disappointment was a lack of class mod and relic diversity.
The new(ish) takedown isn't easy. Like, squishies take minutes to kill with builds that can rip through Penn and Teller with relative ease. Aside from the difficulty, the fatal flaw for me was the platform jumping. This really sucks when there are no revives. Still, J's Amara build made short work of the bosses after I fell to my death.
We're going to take a second pass at Krieg in a bit, but have since switched to...
Bloodborne
Continuing a saga of Souls-like games that started with Nioh, we're roaming a monster-filled gothic metropolis.
It's a very atmospheric game. Dark and magical isn't my preferred genre, but I can still appreciate the style.
Like Nioh, the maps are meant to be played through multiple times. Instead of save points, you unlock shortcuts so that when the boss beats you, it's not a 45-minute return trip.
The combat is... not easy. One of the main combat mechanics involves blasting enemies with your pistol when they do a strong attack to stagger them. Tens of levels in, I still can't get the timing - or really recognize the incoming attack. With insta-death hits and combos, I probably would not last very long in Bloodborne without my coop buddy. Even with him (and sometimes an internet rando/guru), the game can be pretty frustrating but that's only because I haven't played Battletoads in a long time.
Socially distant
It's mostly video games, coding, streaming, and house projects. Still. But there was some golf.
Style non-transfer
Continuing down the road of photo augmentation that doesn't rely on that one style transfer algorithm, I tried to train a network to mimic the posterize artistic filter. My dataset amounted to a sampling of images from the archive with the posterize filter run on them en masse. Starting with a monochrome network, I pretty much just stacked some convolution and dense layers, omitting any maxpooling or transpose convolution.
For each of these I ran a set number of epochs that would be 4-6 hours, spitting out sample tiles along the way.
Input / actual / prediction
Applying the trained network to hard-edged tiles of a complete image gives an output that isn't too far off from the answer key (input, actual, prediction below):
In principle, a machine learning algorithm has the potential to be more content-sensitive in its application of lame instagram-y filters. The key to this, however, may rest on properly training it to identify when the hard algorithm does things well and poorly. And what about training it on orthogonal output data and seeing if it can find a happy medium between, say, posterizing and crappy HDRization? Well, a lot of this rests on moving out of monochrome.
Color space
I'd been thinking about color spaces for a while; one of the training examples I'd seen used YCbCr (luma/brightness, red-delta, blue-delta, implied green-delta). Notionally, when you treat a loss as the RGB distance from the desired output, you're penalizing color difference. This approach isn't unreasonable, but in many cases one of the most critical things to get right is the brightness of the output. So using YCbCr or HSV (hue, saturation, value) seems like good way to prioritize getting brightness correct. In talking image stylization, oftentimes style is derived from differences in hue or saturation so there's a case to be made for writing a loss function that emphasizes the brightness component even more.
Here's the posterize 'answer key'.
RGB
RGB isn't too far off, but isn't guessing colors very well.
YCbCr
YCbCr similarly finds the contour, but wants everything to look puke green.
HSV
HSV seems to replicate contrast the best.
Images/numpy
Trying various ml applications left me with a utils file that grew like mold and was as organized. Finally understanding all(?) the needs I might have for preprocessing an image file into a numpy array, I wrote the following that probably demonstrates how unwilling I am to look up proper pythonese:
def file_to_np_array(size, infile, outfile=None, color='HSV', mode='none'):
input_image = Image.open(infile)
if outfile is not None:
output_image = Image.open(outfile)
return image_to_np_array(size, input_image, outimage=output_image,
color=color, mode=mode)
else:
return image_to_np_array(size, input_image, color=color, mode=mode)
def image_to_np_array(size, inimage, outimage=None, color='HSV', mode=
'none'):
if mode == 'none':
if inimage.width != size or inimage.height != size:
print('None mode using image size: ',inimage.width,'x',inimage.
height,' for size length ',size)
raise
i = inimage.convert(color)
if outimage is not None:
if outimage.width != size or outimage.height != size:
print('None mode using image size [truncated]')
raise
o = outimage.convert(color)
else:
o = None
elif mode == 'scale':
print('Scale not yet implemented')
raise
elif mode == 'sample':
if outimage is not None:
if inimage.width != outimage.width or inimage.height !=
outimage.height:
print('Input/output image different size [truncated]')
raise
box = get_random_crop_dimensions(inimage, size, size)
i = inimage.crop(box)
i = i.convert(color)
if outimage is not None:
o = outimage.crop(box)
o = o.convert(color)
else:
o = None
else:
print('Undefined mode: ',mode)
raise
if (o is not None):
return (np.array(i) / 255.0, np.array(o) / 255.0)
else:
return (np.array(i) / 255.0, None)
def np_array_to_image(array, incolor='HSV', outcolor='RGB'):
array = array * 255.0
array = array.astype(np.uint8)
if incolor == 'L':
return Image.fromarray(array[:,:], incolor).convert(outcolor)
else:
return Image.fromarray(array, incolor).convert(outcolor)
Beyond posterizing
I tried that model on a hodgepodge of other filter effects, it seems like it might work for an arbitrary transform. I still need to refine the stitch though.
Vignettes de Battlegrounds Pt II
In this episode: a drive in the gold Mirado gone wrong, opponents feeling the bite of the circle, an am-bush, more creative physics, and a glider drive-by.
Fantasy finale: Tannehilled
Medieval Gridiron: After a bye week, I benched CeeDee and Gio while Ryan got almost 40 from Tannehill. I did poorly in the consolation match.
Password is Taco: Another bye, but actually a victory in the semi-finals. Then I unwisely played the Indy defense over Pittsburgh - in retrospect the home field advantage should have been more of a consideration than the respective teams' recent form. It came down to Rodgers getting me within a point in the third quarter of the SNF game. Then he threw a pick and Tennesee stalled, so he had no reason to overcome the less-than-one point gap.
Siren Fantasy Football: My bye week rolled into another matchup against Tannehill, this one also had three 20+ point RB/WRs I sputtered in the consolation game.
Week
d'san andreas da bears
- Medieval Gridiron -
Covid-20
- Password is Taco -
Dominicas
- Siren -
1
Danville Isotopes
110.8 - 72.5 W (1-0)
Black Cat Cowboys
155.66 - 78.36 W (1-0)
TeamNeverSkipLegDay
136.24 - 107.50 W (1-0)
2
Screaming Goat Battering Rams
119.9 - 105.9 W (2-0)
[Random UTF characters resembling an EQ]
115.50 - 115.74 L (1-1)
Dem' Arby's Boyz
94.28 - 102.02 L (1-1)
3
Nogales Chicle
106.5 - 117.8 L (2-1)
Circle the Wagons
100.42 - 90.02 W (2-1)
JoeExotic'sPrisonOil
127.90 - 69.70 W (2-1)
4
Britons Longbowmen
122.9 - 105.1 W (3-1)
Staying at Mahomes
123.28 - 72.90 W (3-1)
Daaaaaaaang
138.10 - 108.00 W (3-1)
5
Toronto Tanto
105.0 - 108.2 L (3-2)
Robocop's Posse
111.32 - 134.26 L (3-2)
Alpha Males
86.20 - 76.12 W (4-1)
6
Only Those Who Stand
108.2 - 66.7 W (4-2)
KickAssGreenNinja
65.10 - 84.02 L (3-3)
SlideCode #Jab
71.60 - 53.32 W (5-1)
7
San Francisco Seduction
121.7 - 126.4 L (4-3)
Ma ma ma my Corona
118.22 - 84.20 W (4-3)
G's Unit
109.20 - 92.46 W (6-1)
8
LA Boiling Hot Tar
116.2 - 59.4 W (5-3)
Kamaravirus
118.34 - 109.94 W (5-3)
WeaponX
113.14 - 85.40 W (7-1)
9
SD The Rapier
135.0 - 90.8 W (6-3)
C. UNONEUVE
117.80 - 90.16 W (6-3)
Chu Fast Chu Furious
128.28 - 59.06 W (8-1)
10
West Grove Wankers
72.9 - 122.8 L (6-4)
Pug Runners
98.90 - 77.46 W (7-3)
NY Giants LARP
75.24 - 75.06 W (9-1)
11
SF Lokovirus
127.9 - 87.1 W (7-4)
Bravo Zulus
116.34 - 45.50 W (8-3)
HitMeBradyOneMoTime
107.42 - 89.22 W (10-1)
12
Danville Isotopes
154.7 - 98.9 W (8-4)
Forget the Titans
92.84 - 125.14 L (8-4)
TeamNeverSkipLegDay
132.78 - 140.84 L (10-2)
13
Screaming Goat Battering Rams
136.9 - 84.5 W (9-4)
[Random UTF characters resembling an EQ]
135.20 - 72.52 W (9-4)
Taking the 500mm out for some surf shots. Tweaking neural style transfer.
Related / external
Risky click advisory: these links are produced algorithmically from a crawl of the subsurface web (and some select mainstream web). I haven't personally looked at them or checked them for quality, decency, or sanity. None of these links are promoted, sponsored, or affiliated with this site. For more information, see this post.
A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library. Keras is a collectionof high-level APIs in Python for creating and training neural networks, using either Theano or TensorFlow as the underlying engine. Given my previous posts on implementing an...
Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow.
Follow this tutorial to learn what attention in deep learning is, and why attention is so important in image classification tasks. We then follow up with a demo on implementing attention from scratch with VGG.
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