mieitza

# @Author: xiewenqian <int>
# @Date:   2016-11-28T20:35:09+08:00
# @Email:  wixb50@gmail.com
# @Last modified by:   int
# @Last modified time: 2016-12-01T19:32:48+08:00


import pandas as pd
from pymongo import MongoClient

ledmaster

r9y9

omimo

# Create a simple TF Graph 
# By Omid Alemi - Jan 2017
# Works with TF <r1.0

import tensorflow as tf

I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

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

protrolium

Install ImageMagick

brew install ImageMagick

Pull specific region of frames from video file w/ ffmpeg

ffmpeg -ss 14:55 -i video.mkv -t 5 -s 480x270 -f image2 %04d.png

• -ss 14:55 gives the timestamp where I want FFmpeg to start, as a duration string.
  
• -t 5 says how much I want FFmpeg to decode, using the same duration syntax as for -ss.
  
• -s 480x270 tells FFmpeg to resize the video output to 480 by 270 pixels.
  
• -f image2 selects the output format, a series of still images — make sure there are leading zeros in filename.
  

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

zoltanctoth

from pyspark.sql.types import StringType
from pyspark.sql.functions import udf

maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
df.withColumn("maturity", maturity_udf(df.age))

df.show()

ChunMinChang

#!/usr/bin/python
import re
import sys

def removeComments(text):
    """ remove c-style comments.
        text: blob of text with comments (can include newlines)
        returns: text with comments removed
    """
    pattern = r"""

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman