Graham Duncan duncangh

AWS CLI Cheatsheet

##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

brew install git bash-completion

Configure things:

git config --global user.name "Your Name"

git config --global user.email "[email protected]"

	import smtplib
	import os

	def send_email(host, port, username, password, subject, body, mail_to, mail_from = None, reply_to = None):
	if mail_from is None: mail_from = username
	if reply_to is None: reply_to = mail_to

	message = """From: %s\nTo: %s\nReply-To: %s\nSubject: %s\n\n%s""" % (mail_from, mail_to, reply_to, subject, body)
	print (message)
	try:

	[user]
	name = Pavan Kumar Sunkara
	email = [email protected]
	username = pksunkara
	[core]
	editor = vim
	whitespace = fix,-indent-with-non-tab,trailing-space,cr-at-eol
	excludesfile = ~/.gitignore
	[sendemail]
	smtpencryption = tls

	import subprocess

	def write_to_clipboard(output):
	process = subprocess.Popen(
	'pbcopy', env={'LANG': 'en_US.UTF-8'}, stdin=subprocess.PIPE)
	process.communicate(output.encode('utf-8'))

	#How to read a Unicode string from the Mac clipboard:

	import subprocess

	-- show running queries (pre 9.2)
	SELECT procpid, age(clock_timestamp(), query_start), usename, current_query
	FROM pg_stat_activity
	WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%'
	ORDER BY query_start desc;

	-- show running queries (9.2)
	SELECT pid, age(clock_timestamp(), query_start), usename, query
	FROM pg_stat_activity
	WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%'

	<html lang="en">
	<head>
	<script src="https://d3js.org/d3.v5.min.js"></script>
	</head>
	<body>
	<div>
	<h1>Example SVG Shapes</h1>
	<p>Circle
	<svg>
	<circle class="circle" style="fill: #69b3a2" stroke="black" cx=50 cy=50 r=40 opacity="0.25"></circle>

	import streamlit as st
	import pandas as pd

	# Reuse this data across runs!
	read_and_cache_csv = st.cache(pd.read_csv)

	BUCKET = "https://streamlit-self-driving.s3-us-west-2.amazonaws.com/"
	data = read_and_cache_csv(BUCKET + "labels.csv.gz", nrows=1000)
	desired_label = st.selectbox('Filter to:', ['car', 'truck'])
	st.write(data[data.label == desired_label])


	def reduce_namespace_to_public_only(namespace_directory : list) -> list:
	"""
	returns: list of public attribute and or method names
	"""
	_is_public_facing = lambda x: not x.startswith('_')

	public_namespace_only : list = sorted(
	filter(_is_public_facing, namespace_directory)
	)