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//
//  NSObject.swift
//  QBDemo01
//
//  Created by 默司 on 2016/12/2.
//  Copyright © 2016年 默司. All rights reserved.
//

import Foundation

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Heroku cli
> https://devcenter.heroku.com/articles/heroku-cli

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What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Element

Box metrics

• elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent
• elem.clientLeft, elem.clientTop, elem.clientWidth, elem.clientHeight
• elem.getClientRects(), elem.getBoundingClientRect()

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Calling Functions without Apply

function doStuff (x, y, z) { }
var args = [0, 1, 2];
doStuff(...args);

function doStuff(x, y, z) {}
var args = [0, 1, 2];

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If you want a run-down of the 1.3 changes and the design decisions behidn those changes, check out the LonestarElixir Phoenix 1.3 keynote: https://www.youtube.com/watch?v=tMO28ar0lW8

To use the new phx.new project generator, you can install the archive with the following command:

$ mix archive.install https://github.com/phoenixframework/archives/raw/master/phx_new.ez

Bump your phoenix dep

Phoenix v1.3.0 is a backwards compatible release with v1.2.x. To upgrade your existing 1.2.x project, simply bump your phoenix dependency in mix.exs:

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docker ps -a | grep "Exited" | awk '{print $1 }'|xargs docker stop
docker ps -a | grep "Exited" | awk '{print $1 }'|xargs docker rm
docker images|grep none|awk '{print $3 }'|xargs docker rmi

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{
  "name": "test template",
  "ecommerceId": 657,
  "width": 800,
  "height": 800,
  "style": 2,
  "isAnimation": false,
  "interval": 0,
  "frames": [
    {

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str1 = "http://www.google.tw/search?q=中文";
obj = {
    get a(){return '1';},
    set b(value){this.c = value;},
    c:1
};
class Cls{
    constructor(name){
        console.log("hello",name);
        if (typeof(new.target)==='function')

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##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

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Lesson 1 - Iterations
- BinaryGap - https://codility.com/demo/results/trainingU2FQPQ-7Y4/

Lesson 2 - Arrays
- OddOccurrencesInArray - https://codility.com/demo/results/trainingFN5RVT-XQ4/
- CyclicRotation - https://codility.com/demo/results/trainingSH2W5R-RP5/

Lesson 3 - Time Complexity
- FrogJmp - https://codility.com/demo/results/training6KKWUD-BXJ/
- PermMissingElem - https://codility.com/demo/results/training58W4YJ-VHA/