MatthieuBizien

# 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

hyqneuron

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

farrajota

-- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters.

require 'torch'
require 'nn'
require 'optim'
torch.setdefaulttensortype('torch.FloatTensor')

-- (1) Define a model for this example.
local model = nn.Sequential()
model:add(nn.Linear(10,20))

vsouza

# Set variables in .bashrc file

# don't forget to change your path correctly!

export GOPATH=$HOME/golang
export GOROOT=/usr/local/opt/go/libexec
export PATH=$PATH:$GOPATH/bin
export PATH=$PATH:$GOROOT/bin

aras-p

// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

perky

--[[
	ProFi v1.3, by Luke Perkin 2012. MIT Licence http://www.opensource.org/licenses/mit-license.php.
	
	Example:
		ProFi = require 'ProFi'
		ProFi:start()
		some_function()
		another_function()
		coroutine.resume( some_coroutine )
		ProFi:stop()