- Texture Synthesis Using Convolutional Neural Networks
- A Neural Algorithm of Artistic Style
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
- Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Texture Synthesis
| #!/usr/bin/awk -f | |
| # This program is a copy of guff, a plot device. https://github.com/silentbicycle/guff | |
| # My copy here is written in awk instead of C, has no compelling benefit. | |
| # Public domain. @thingskatedid | |
| # Run as awk -v x=xyz ... or env variables for stuff? | |
| # Assumptions: the data is evenly spaced along the x-axis | |
| # TODO: moving average |
This was created years ago; at the time I'd been a Shibboleth admin for nearly a decade but we needed something that could handle OIDC/OAuth and that explicitly supported OpenJDK. After a lot of investigation, I really liked Keycloak/Red Hat Single Sign-On. More details here: Gluu vs keycloack vs wso2 identity management
(Items in bold indicate possible concerns)
| # coding: utf-8 | |
| import logging | |
| import re | |
| from collections import Counter | |
| import numpy as np | |
| import torch | |
| from sklearn.datasets import fetch_20newsgroups | |
| from torch.autograd import Variable |
| %%----------------------------------------------------------------------- | |
| %% Make your own quadrille, graph, hex, etc paper! | |
| %% Uses the pgf/TikZ package for LaTeX, which should be part of | |
| %% any modern TeX installation. | |
| %% Email: mcnees@gmail.com | |
| %% Twitter: @mcnees | |
| %%----------------------------------------------------------------------- | |
| \documentclass[11pt]{article} |
Disclaimer 1: Type classes are great but they are not the right tool for every job. Enjoy some balance and balance to your balance.
Disclaimer 2: I should tidy this up but probably won’t.
Disclaimer 3: Yeah called it, better to be realistic.
Type classes are a language of their own, this is an attempt to document features and give a name to them.
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| # Working example for my blog post at: | |
| # http://danijar.com/variable-sequence-lengths-in-tensorflow/ | |
| import functools | |
| import sets | |
| import tensorflow as tf | |
| from tensorflow.models.rnn import rnn_cell | |
| from tensorflow.models.rnn import rnn | |
| def lazy_property(function): |