Infopost | 2021.01.02
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Type Cost Proceeds P/L P/L % Notes -------------------------------------------------------------------------- Stocks: 67,731.58 -> 70,417.55 = 2,685.97 3.9% Options: 54,759.95 -> 56,314.35 = 1,554.40 2.8% ETFs: 105,840.48 -> 100,117.95 = -5,722.53 -5.4% FU Barclays Bonds: 83,766.56 -> 93,714.51 = 9,947.95 11.8% Doesn't include divs Mutuals: [All long term]
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 192, 192, 16) 800 _________________________________________________________________ gaussian_noise (GaussianNois (None, 192, 192, 16) 0 _________________________________________________________________ dense (Dense) (None, 192, 192, 96) 1632 _________________________________________________________________ dropout (Dropout) (None, 192, 192, 96) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 192, 192, 16) 38416 _________________________________________________________________ dense_1 (Dense) (None, 192, 192, 96) 1632 _________________________________________________________________ dropout_1 (Dropout) (None, 192, 192, 96) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 192, 192, 16) 13840 _________________________________________________________________ batch_normalization (BatchNo (None, 192, 192, 16) 64 _________________________________________________________________ conv2d_3 (Conv2D) (None, 192, 192, 16) 2320 _________________________________________________________________ conv2d_4 (Conv2D) (None, 192, 192, 1) 145 _________________________________________________________________ conv2d_5 (Conv2D) (None, 192, 192, 1) 2 ================================================================= Total params: 58,851 Trainable params: 58,819 Non-trainable params: 32 _________________________________________________________________
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)
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) |
Dem Arby's Boyz
97.62 - 63.52 W (11-2) |
P-1 |
Bye
99.2 |
Bye
129.30 |
Bye
94.12 |
P-2 |
Screaming Goat Battering Rams
112.0 - 125.4 L |
Ma ma ma my Corona
127.42 - 104.46 W |
G's Unit
118.56 - 142.52 L |
P-3 |
Britons Longbowmen
86.4 - 125.8 L (4th) |
Forget the Titans
114.84 - 115.72 L (2nd) |
TeamNeverSkipLegDay
78.62 - 94.44 L (4th) |
2020.12.12
On lockThe covid surge that everyone expected after Thanksgiving has hit. Jes is busy at work. I can get by with games, streaming, jogging, and taking the dog out. |
2020.11.29
ModsSince it was just the two-ish of us, Jes and I went to the Lodge for Thanksgiving lunch. |
2020.12.06
Edges and cornersTaking the 500mm out for some surf shots. Tweaking neural style transfer. |
timdettmers.com
Machine Learning PhD Applications Everything You Need to Know Tim DettmersThis blog post explains how to proceed in your PhD applications from A to Z and how to get admitted to top school in deep learning and machine learning. |
polukhin.tech
Lightweight Neural Network Architectures | Andrii PolukhinAs the field of Deep Learning continues to grow, the demand for efficient and lightweight neural networks becomes increasingly important. In this blog post, we will explore six lightweight neural network architectures. |
coen.needell.org
ResMem and M3MIn my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. M3M, a... |