Zafar z-a-f

## index.html

<!doctype html>
<html>
<head>
  <title>Zafar's Test Page</title>

  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0">
  <meta name="mobile-web-app-capable" content="yes">
  <meta name="apple-mobile-web-app-capable" content="yes">

## material_design_colors.xml
<?xml version="1.0" encoding="utf-8"?>

<!-- https://material.google.com/style/color.html -->

<resources>
	<color name="colorPrimary">@color/indigo_primary</color>
	<color name="colorPrimaryDark">@color/indigo_700</color>
	<color name="colorAccent">@color/pink_A200</color>

	<!-- Materials Themes -->

## .gitconfig
[alias]
	a = add .
	ai = add -i
	#############
	ap = apply
	as = apply --stat
	ac = apply --check
	#############
	ama = am --abort
	amr = am --resolved

## 00-virtualenv.py
## Special thanks to http://stackoverflow.com/a/30650831/3606192
## Place this file under '~/.ipython/profile_default/startup/'

import os
import sys

if 'VIRTUAL_ENV' in os.environ:
  py_version = sys.version_info[:2] # formatted as X.Y
  py_infix = os.path.join('lib', ('python%d.%d' % py_version))
  virtual_site = os.path.join(os.environ.get('VIRTUAL_ENV'), py_infix, 'site-packages')

## htmlize.py
#!/usr/bin/env python

import sys

"""
Convert any text to a set of HTML codes.
You can use it against email and phone number HTML parsers
"""
def htmlize(text):
  assert(isinstance(text, str))

## .gitconfig
[include]
  path = .gitconfig_user
  path = .gitconfig_alias

## fetch_all_assets.py
#!/usr/bin/env python
from lxml import html, etree
import requests
import urllib
import re
from tqdm import tqdm

import sys, os

page = requests.get('http://www.roboticsconference.org/program/detailed/index.html')

## Ians_gans_02_2018.md

      
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                z-a-f
                / Ians_gans_02_2018.md
            
            
              Last active
              April 14, 2019 02:07
            
              
                Ian's GAN list 02/2018
              
          
    The list by @goodfellow_ian, as per Twitter discussion with @timnitGebru: https://twitter.com/timnitGebru/status/968242968007200769

Progressive GANs:

Video: https://www.youtube.com/watch?v=XOxxPcy5Gr4
Link: https://arxiv.org/abs/1710.10196
Description: Probably the highest quality images so far


Spectral normalization:

Link: https://openreview.net/forum?id=B1QRgziT-&noteId=BkxnM1TrM
Description: Got GANs working on lots of classes, which has been hard


Projection discriminator


## simple_model_for_mnist.py
import keras

def preprocess_images(images, normalization=255):
  """Normalizes the inputs"""
  dims = images.shape
  return images.reshape((dims[0], dims[1]*dims[2])).astype('float32') / normalization

def preprocess_labels(labels):
  """Preprocesses the labels into categorical"""
  return keras.utils.to_categorical(labels)

## simple_model_for_mnist_run.py
import keras
# Get the data
(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()
train_images = preprocess_images(train_images)
test_images = preprocess_images(test_images)
train_labels = preprocess_labels(train_labels)
test_labels = preprocess_labels(test_labels)
# Get the model
network = get_model()
# Fit the model

	<!doctype html>
	<html>
	<head>
	<title>Zafar's Test Page</title>

	<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0">
	<meta name="mobile-web-app-capable" content="yes">
	<meta name="apple-mobile-web-app-capable" content="yes">
	<?xml version="1.0" encoding="utf-8"?>

	<!-- https://material.google.com/style/color.html -->

	<resources>
	<color name="colorPrimary">@color/indigo_primary</color>
	<color name="colorPrimaryDark">@color/indigo_700</color>
	<color name="colorAccent">@color/pink_A200</color>

	<!-- Materials Themes -->
	[alias]
	a = add .
	ai = add -i
	#############
	ap = apply
	as = apply --stat
	ac = apply --check
	#############
	ama = am --abort
	amr = am --resolved
	## Special thanks to http://stackoverflow.com/a/30650831/3606192
	## Place this file under '~/.ipython/profile_default/startup/'

	import os
	import sys

	if 'VIRTUAL_ENV' in os.environ:
	py_version = sys.version_info[:2] # formatted as X.Y
	py_infix = os.path.join('lib', ('python%d.%d' % py_version))
	virtual_site = os.path.join(os.environ.get('VIRTUAL_ENV'), py_infix, 'site-packages')
	#!/usr/bin/env python

	import sys

	"""
	Convert any text to a set of HTML codes.
	You can use it against email and phone number HTML parsers
	"""
	def htmlize(text):
	assert(isinstance(text, str))
	#!/usr/bin/env python
	from lxml import html, etree
	import requests
	import urllib
	import re
	from tqdm import tqdm

	import sys, os

	page = requests.get('http://www.roboticsconference.org/program/detailed/index.html')
	import keras

	def preprocess_images(images, normalization=255):
	"""Normalizes the inputs"""
	dims = images.shape
	return images.reshape((dims[0], dims[1]*dims[2])).astype('float32') / normalization

	def preprocess_labels(labels):
	"""Preprocesses the labels into categorical"""
	return keras.utils.to_categorical(labels)
	import keras
	# Get the data
	(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()
	train_images = preprocess_images(train_images)
	test_images = preprocess_images(test_images)
	train_labels = preprocess_labels(train_labels)
	test_labels = preprocess_labels(test_labels)
	# Get the model
	network = get_model()
	# Fit the model