eerwitt

# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

whophil

import os
import fnmatch

def recursive_glob(rootdir='.', pattern='*'):
	"""Search recursively for files matching a specified pattern.
	
	Adapted from http://stackoverflow.com/questions/2186525/use-a-glob-to-find-files-recursively-in-python
	"""

	matches = []

chris-jamieson

• Download Franz for your distribution from MeetFranz.com
• change into the same directory as the downloaded file, then sudo tar -xf Franz-linux-x64-0.9.10.tgz -C /opt/franz
• (optional) wget "https://cdn-images-1.medium.com/max/360/1*v86tTomtFZIdqzMNpvwIZw.png" -O franz-icon.png then sudo cp franz-icon.png /opt/franz
• (optional) sudo touch /usr/share/applications/franz.desktop then sudo vim /usr/share/applications/franz.desktop

paste the following lines into the file, then save the file:

[Desktop Entry]
Name=Franz
Comment=

evanwill

How to add more to Git Bash on Windows

Git for Windows comes bundled with the "Git Bash" terminal which is incredibly handy for unix-like commands on a windows machine. It is missing a few standard linux utilities, but it is easy to add ones that have a windows binary available.

The basic idea is that C:\Program Files\Git\mingw64\ is your / directory according to Git Bash (note: depending on how you installed it, the directory might be different. from the start menu, right click on the Git Bash icon and open file location. It might be something like C:\Users\name\AppData\Local\Programs\Git, the mingw64 in this directory is your root. Find it by using pwd -W). If you go to that directory, you will find the typical linux root folder structure (bin, etc, lib and so on).

If you are missing a utility, such as wget, track down a binary for windows and copy the files to the corresponding directories. Sometimes the windows binary have funny prefixes, so

tomokishii

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

rajarsheem

import numpy as np
import tensorflow as tf

# Newton's optimization method for multivariate function in tensorflow

def cons(x):
    return tf.constant(x, dtype=tf.float32)

def compute_hessian(fn, vars):
    mat = []

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

harpone

import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np

# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
    
    # Need to generate a unique name to avoid duplicates:
    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
    

gelim

Installing ShareLaTex on Ubuntu 16.04

Use this as-is without any warranty, and don't bug ShareLatex project members if there are problems with my instructions. They officialy support docker installation only.

This gist summarize the actions I did to setup a pure ShareLaTex community edition on a bare linux ubuntu 16.04 server (no docker). I intentionnaly tried to stick to distribution practice for running daemons (via runit, and not via a custom my_init + setuser wrapper).

I basically took ShareLaTeX receipt to build their docker and adapted it.

• Install

tdeboissiere

import torch
from torch.autograd import Variable
import torch.nn as nn
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
import time
from torchvision.models import vgg

# Initialize network