| <!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)) |
| [include] | |
| path = .gitconfig_user | |
| path = .gitconfig_alias |
| #!/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') |
The list by @goodfellow_ian, as per Twitter discussion with @timnitGebru: https://twitter.com/timnitGebru/status/968242968007200769
| 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 |
