Toshiaki Toyama manboubird

##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

Generating CloudFormation Templates from YAML Dictionaries

The security group and Network ACLs parts of CloudFormation templates can be difficult to read. This template containing Jinja macros converts easier-to-read YAML dictionaries of security group and NACL rules into JSON.

Here ais a generic macro template and some example files showing how to use it.

cloudformation.macros.j2 - the macros
cf_vars.yml - YAML dictionaly of security groups and NACLs, including some complex rules for both
test.template.j2 - a simple CloudFormation template that uses the macro file
cf_test.yml - a simple Ansible playbook that generates CloudFormation JSON from thr previous file.

Installing SSHPASS

SSHPass is a tiny utility, which allows you to provide the ssh password without using the prompt. This will very helpful for scripting. SSHPass is not good to use in multi-user environment. If you use SSHPass on your development machine, it don't do anything evil.

Installing on Ubuntu

apt-get install sshpass

Installing on OS X

Install python 2.7.11

command line

$ PYTHON_CONFIGURE_OPTS="--enable-shared" \
    LDSHARED="clang -bundle" \
    LDCXXSHARED="clang++ -bundle" \
    BLDSHARED="clang -bundle -lpython2.7" \
    pyenv install 2.7.11

Paddington

A small library for padding strings in JavaScript. Marmalade-free.

![NPM version][shield-npm] ![Node.js version support][shield-node] ![Build status][shield-build] ![Code coverage][shield-coverage]

Set up Django, Nginx and Gunicorn in a Virtualenv controled by Supervisor

Steps with explanations to set up a server using:

Virtualenv
Virtualenvwrapper
Django
Gunicorn

	'''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/

	#!groovy
	# Best of Jenkinsfile
	# `Jenkinsfile` is a groovy script DSL for defining CI/CD workflows for Jenkins
	node {
	}

	{
	"variables": {
	"aws_access_key": "{{env `AWS_ACCESS_KEY`}}",
	"aws_secret_key": "{{env `AWS_SECRET_KEY`}}"
	},
	"builders": [
	{
	"type": "amazon-ebs",
	"ssh_pty": true,
	"access_key": "{{user `aws_access_key`}}",

	#!/bin/bash
	SOURCEINSTANCE=${SOURCEINSTANCE:-''}
	SOURCEUSER=${SOURCEUSER:-'admin'}
	SOURCEPORT=${SOURCEPORT:-'5439'}
	SOURCEDB=${SOURCEDB:-'db'}
	SOURCESCHEMA=${SOURCESCHEMA:-'public'}
	SCHEMA=${SCHEMA:-'public'}
	echo "CREATE TABLE ${SCHEMA}.${TABLE} ("
	psql -h ${SOURCEINSTANCE} -U ${SOURCEUSER} -p ${SOURCEPORT} ${SOURCEDB} -t -c "select (\"column\" \|\| ' ' \|\| type \|\| ' ENCODE ' \|\| encoding \|\| ',' ) from pg_table_def where schemaname='$SCHEMA' and tablename = '$TABLE'" \| sed 's/ENCODE none/ENCODE RAW/' \| sed '$d' \| sed '$ s/,$//'
	echo ")"