type below:
brew update
brew install redis
To have launchd start redis now and restart at login:
brew services start redis
const ffmpeg = require('fluent-ffmpeg') | |
const parseUrl = require('url').parse | |
// Polyfill, modifying the global Object | |
require('es6-object-assign').polyfill() | |
global.Promise = require('es6-promise').Promise | |
const aws4 = require('aws4') | |
const axios = require('axios') | |
const CancelToken = axios.CancelToken | |
const fs = require('fs') | |
const PipeViewer = require('pv') |
type below:
brew update
brew install redis
To have launchd start redis now and restart at login:
brew services start redis
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
var install_hook_to = function(obj) { | |
if (obj.hook || obj.unhook) { | |
throw new Error('Object already has properties hook and/or unhook'); | |
} | |
obj.hook = function(_meth_name, _fn, _is_async) { | |
var self = this, | |
meth_ref; | |