oeway

import numpy as np
import scipy
import scipy.ndimage
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import map_coordinates
import collections
from PIL import Image
import numbers

__author__ = "Wei OUYANG"

gngdb

benjamintd

# coding: utf-8

# Imports

import os
import cPickle
import numpy as np
import theano
import theano.tensor as T

fogcity89

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toolness

Adventures in Python Core Dumping

After watching Bryan Cantrill's presentation on [Running Aground: Debugging Docker in Production][aground] I got all excited (and strangely nostalgic) about the possibility of core-dumping server-side Python apps whenever they go awry. This would theoretically allow me to fully inspect the state of the program at the point it exploded, rather than relying solely on the information of a stack trace.

samim23

# "Colorizing B/W Movies with Neural Nets", 
# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
# BACKGROUND: http://tinyclouds.org/colorize/
# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
# USAGE:
# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/
# 2. Use FFMPEG or such to extract frames from video.
# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
# mogrify -resize 224x224 *.jpg
# mogrify -gravity center -background black -extent 224x224 *.jpg

Hello1024

#!/bin/bash

# Run this on This AMI on AWS:
# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8

# You should get yourself a fully working GPU enabled tensorflow installation.

cd ~

# grab cuda 7.0

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

a1k0n

#include <math.h>
#include <stdio.h>
#include <unistd.h>

// Each character encodes an angle of a plane we are checking
const char plane_angles[] = "O:85!fI,wfO8!yZfO8!f*hXK3&fO;:O;#hP;\"i[";
// and these encode an offset from the origin s.t. (x, y) dot (cos(a), sin(a)) < offset
const char plane_offsets[] = "<[\\]O=IKNAL;KNRbF8EbGEROQ@BSXXtG!#t3!^";

// this table encodes the offsets within the above tables of each polygon

JadenGeller

// In languages like Python and Haskell, we can write list comprehension syntax like
// super simply to generate complex lists. Below, for example, we find all numbers that are
// the product of two sides of a triangle.
//   [a * b | a <- [1..10], b <- [1..10], c <- [1..10], a * a + b * b == c * c]

// Why is this called nondeterministic computation? Because we essentially try ALL possible combinations
// of these values (a,b) and--you can imagine--run them all simultanously and get the result that matches the predicate.
// Now, obviously, this doesn't all happen at the same time, but that's the idea behind the nondeterminism.

// Let's examine how we can get a similiar result in Swift! We'll start super simple and work