alshell7

##Basically AsyncTask uses generic types in its parameters The three types used by an asynchronous task are the following:

• Params, the type of the parameters sent to the task upon execution.
• Progress, the type of the progress units published during the background computation.
• Result, the type of the result of the background computation.

Not all types are always used by an asynchronous task. To mark a type as unused, simply use the type Void: private class MyTask extends AsyncTask<Void, Void, Void> { ... }

##Taking a very known and common example..

hbcafe

package com.ibm.sentimentsensitiveapp;

import android.os.AsyncTask;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.view.View;
import android.widget.Button;
import android.widget.EditText;
import android.widget.TextView;

xalexchen

import android.app.Activity;
import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.graphics.drawable.AnimationDrawable;
import android.graphics.drawable.BitmapDrawable;
import android.os.Bundle;
import android.view.View;
import android.widget.ImageView;

sureshdsk

# http://www.idiotinside.com/2015/02/13/get-number-of-likes-of-a-facebook-page-using-graph-api-in-python/

import urllib2
import json

def get_page_data(page_id,access_token):
    api_endpoint = "https://graph.facebook.com/v2.4/"
    fb_graph_url = api_endpoint+page_id+"?fields=id,name,likes,link&access_token="+access_token
    try:
        api_request = urllib2.Request(fb_graph_url)

fbchow

I always forget the exacty syntax for copying & moving files, especially when dealing with a remote Linux server. And I had wrote some documentation years ago but lost it (so much for persistent storage!). So here's some notes:

Change into the directory you want to download the files
cd /home/dir_to_dump_file_in 

Log onto server using Safe File Transfer Protocol--just like how you would "ssh" into the Linux server
sftp homersimpson@sprinfielduniversity.edu
password: ilovemarge

Obtain the files from the Linux server on to your local server

grantwwoodford

# Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
import threading as t
import tensorflow as tf

graph = tf.get_default_graph()

def t_thread():

dusenberrymw

TensorFlow Tips & Tricks

GPU Memory Issues

• nvidia-smi to check for current memory usage.
• watch -n 1 nvidia-smi to monitor memory usage every second.
• Often, extra Python processes can stay running in the background, maintaining a hold on the GPU memory, even if nvidia-smi doesn't show it.
  • Probably due to running Keras in a notebook, and then running the cell that starts the processes again, since this will fork the current process, which has a hold on GPU memory. In the future, restart the kernel first, and stop all process before exiting (even though they are daemons and should stop automatically when the parent process ends).

bamthomas

from multiprocessing.process import Process
import time
import redis

def pub(myredis):
    for n in range(10):
        myredis.publish('channel','blah %d' % n)
        time.sleep(5)

def sub(myredis, name):

markemus

"""Easily save tf.data.Datasets as tfrecord files, and restore tfrecords as Datasets.

The goal of this module is to create a SIMPLE api to tfrecords that can be used without
learning all of the underlying mechanics.

Users only need to deal with 2 functions:
save(dataset)
dataset = load(tfrecord, header)

To make this work, we create a .header file for each tfrecord which encodes metadata

xalexchen

import android.graphics.Bitmap;

/**
 * Created by paveld on 3/6/14.
 */
public class FastBlur {

    public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {

        // Stack Blur v1.0 from