L0SG

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim


# toy feed-forward net
class Net(nn.Module):
    def __init__(self):

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

grimzy

#!/usr/bin/env bash

git branch -r | grep -v '\->' | while read remote; do git branch --track "${remote#origin/}" "$remote"; done
git fetch --all
git pull --all

harv

#! /bin/sh

# update glibc to 2.17 for CentOS 6

wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-common-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-devel-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-headers-2.17-55.el6.x86_64.rpm

sudo rpm -Uvh glibc-2.17-55.el6.x86_64.rpm \

chenerg

This re-styles your sublime text sidebar to be dark, it fits default Monokai theme.

Save the Default.sublime-theme file into packages/Theme - Default, make a backup of your original if you want to be able to go back easily.

Based on:

• https://gist.github.com/olivierlacan/1857652
• https://gist.github.com/larrybotha/2935636

dboyliao

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
## References
- [Umeyama's paper](http://edge.cs.drexel.edu/Dmitriy/Matching_and_Metrics/Umeyama/um.pdf)
- [CarloNicolini's python implementation](https://gist.github.com/CarloNicolini/7118015)
"""
from __future__ import print_function
import numpy as np

lornajane

Keyboard-only Mac Cheatsheet

Hi, I'm Lorna and I don't use a mouse. I have had RSI issues since a bad workstation setup at work in 2006. I've tried a number of extra hardware modifications but what works best for me is to use the keyboard and only the keyboard, so I'm in a good position and never reaching for anything else (except my coffee cup!). I rather unwisely took a job which required me to use a mac (I've been a linux user until now and also had the ability to choose my tools carefully) so here is my cheatsheet of the apps, tricks and keyboard shortcuts I'm using, mostly for my own reference. Since keyboard-only use is also great for productivity, you may also find some of these ideas useful, in which case at least something good has come of this :)

Apps List

There's more detail on a few of these apps but here is a quick overview of the tools I've installed and found helpful

Tool	Link	Comments

ryerh

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表 & 简明教程

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

kenzo0107

sudo yum -y install gcc gcc-c++ kernel-devel
sudo yum -y install python-devel libxslt-devel libffi-devel openssl-devel
pip install cryptography