ankitshekhawat

import numpy as np
from PIL import Image, ImageDraw
import colorsys, random
from pylab import plot,show
from numpy import vstack,array
from numpy.random import rand
from scipy.cluster.vq import kmeans,vq
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

ankitshekhawat

from keras import backend as K
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
sess = K.get_session()
export_path ="."  
export_version = 1

saver = tf.train.Saver(sharded=True)
### Code if you want to only export .meta files

ankitshekhawat

function product() {
  var args = Array.prototype.slice.call(arguments); // makes array from arguments
  return args.reduce(function tl (accumulator, value) {
    var tmp = [];
    accumulator.forEach(function (a0) {
      value.forEach(function (a1) {
        tmp.push(a0.concat(a1));
      });
    });
    return tmp;

ankitshekhawat

from subprocess import check_output
import re

def scan_networks():
	airport = '/System/Library/PrivateFrameworks/Apple80211.framework/Versions/A/Resources/airport -s'
	rc  = check_output(airport, shell=True).split('\n')[1:-1]

	networks =[]
	for i in rc:
		i =  i.lstrip().rstrip()

ankitshekhawat

// ==UserScript==
// @name         Moonraider
// @namespace    http://moonraft.com/
// @version      0.2
// @description  Chanege the mail logo to one you want
// @author       You
// @match        https://mail.google.com/mail/*
// @grant        none
// ==/UserScript==

ankitshekhawat

Verifying that "ankitshekhawat.id" is my Blockstack ID. https://onename.com/ankitshekhawat

ankitshekhawat

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

ankitshekhawat

#python3

def pil2datauri(img):
    #converts PIL image to datauri
    data = BytesIO()
    img.save(data, "JPEG")
    data64 = base64.b64encode(data.getvalue())
    return u'data:img/jpeg;base64,'+data64.decode('utf-8')

ankitshekhawat

//
//  GIF2MP4.swift
//
//  Created by PowHu Yang on 2017/1/24.
//  Copyright © 2017 PowHu Yang. All rights reserved.
//

ankitshekhawat

#PIL method
from PIL import Image

def letterbox(image, size=299, bg=(0,0,0)):
    (w,h) = image.size
    ratio = size / max([w,h])
    image = image.resize((int(ratio*w),int(ratio*h)))
    background = Image.new('RGB', (size, size), bg)
    background.paste(image, [int((size-s)/2) for s in image.size])
    return background