ksopyla

# This is shorthened version of blog post 
#  http://ksopyla.com/2017/02/tensorflow-gpu-virtualenv-python3/

# update packages
sudo apt-get update
sudo apt-get upgrade

#Add the ppa repo for NVIDIA graphics driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

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))
    

bartvm

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

Differentiation

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.

Pretz

#!/usr/bin/python

import wave
import numpy
import struct
import sys
import csv
from scikits.samplerate import resample

def write_wav(data, filename, framerate, amplitude):