Merghadi Abdelaziz aminevsaziz

title	date	tags	categories
How to get started to build your own Spacemacs	2018-10-12 12:59	emacs	Tools

I have been a Vim user for quite a while. I love it, so the very first thing when I set up a new server is installing VIM. I heard a lot about Emacs and tried it. It's better at almost everything except text editing. So I stayed with Vim until Spacemacs was released. The Vim key bindings of Spacemacs is so excellent that I switched.

However, Spacemacs has its problem: boots up slowly and crashes frequently. Upgrading packages became very challenging. Finally, I have to use the develop branch because the master branch is not working anymore. People began to complain and requested to speed up the release from the master branch. Considering its dependencies and numerous open issues, I have no hope for this.

	---
	title: "Decouple Code and Output in xaringan slides"
	subtitle: "Demo Slides for <a href='https://garrickadenbuie.com/blog/2018/08/14/decouple-code-and-output-in-xaringan-slides/'>Related Blog Post</a>"
	author: "Garrick Aden-Buie"
	date: "`r Sys.Date()`"
	output:
	xaringan::moon_reader:
	lib_dir: libs
	nature:
	ratio: 16:9

	#--------------------------
	# USER-SPECIFIED DATA
	#--------------------------

	# Tune these parameters

	num_classes = 2
	image_shape = (160, 576)
	EPOCHS = 40
	BATCH_SIZE = 16

	"""An example of how to use tf.Dataset in Keras Model"""
	import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after

	_EPOCHS = 5
	_NUM_CLASSES = 10
	_BATCH_SIZE = 128

	def training_pipeline():
	# #############
	# Load Dataset

	# Set Seed
	set.seed(12345)

	# AirlineDemoSmall XDF File
	airline <- RxXdfData(file.path(rxGetOption("sampleDataDir"), "AirlineDemoSmall.xdf"))

	# Define a split function which splits the dataset into 75% train and 25% test using rxSplit
	".split" <- function(keys, data)
	{
	dataSplit <- rxSplit(data,

	FROM ubuntu

	MAINTAINER Guilherme Louro "guipclouro@gmail.com"

	# Update aptitude with new repo
	RUN apt-get update

	# Install software
	RUN apt-get install -y git
	# Make ssh dir

	First install pip for Python2. Download the get-pip.py file from https://bootstrap.pypa.io/get-pip.py
	$ cd <download location>
	$ sudo -H python ./get-pip.py

	Installing pip also installs Python3
	To run Python3
	$ python3

	Install pip3 by just executing the same file as in the step above, but this time using Python3
	$ sudo -H python3 ./get-pip.py

	# Typical setup to include TensorFlow.
	import tensorflow as tf

	# Make a queue of file names including all the JPEG images files in the relative
	# image directory.
	filename_queue = tf.train.string_input_producer(
	tf.train.match_filenames_once("./images/*.jpg"))

	# Read an entire image file which is required since they're JPEGs, if the images
	# are too large they could be split in advance to smaller files or use the Fixed

	%%file _chi2.c
	#include <Python.h>
	#include <numpy/arrayobject.h>
	#include "chi2.h"

	static char module_docstring[] =
	"This module provides an interface for calculating chi-squared using C.";
	static char chi2_docstring[] =
	"Calculate the chi-squared of some data given a model.";