obins obinsc

1. Python style

Generally follow PEP8 Python style guide

But! Try to use Tensorflow wherever it useful (or possible...)

2. Tensors

Operations that deal with batches may assume that the first dimension of a Tensor is the batch dimension.

	# Install script of Caffe2 and Detectron on AWS EC2
	#
	# Tested environment:
	# - AMI: Deep Learning Base AMI (Ubuntu) Version 6.0 - ami-ce3673b6 (CUDA is already installed)
	# - Instance: p3.2xlarge (V100 * 1)
	# - Caffe2: https://github.com/pytorch/pytorch/commit/731273b8d61dfa2aa8b2909f27c8810ede103952
	# - Detectron: https://github.com/facebookresearch/Detectron/commit/cd447c77c96f5752d6b37761d30bbdacc86989a2
	#
	# Usage:
	# Launch a fresh EC2 instance, put this script on the /home/ubuntu/, and run the following command.

	import tensorflow as tf
	import numpy as np

	corpus_raw = 'He is the king . The king is royal . She is the royal queen '

	# convert to lower case
	corpus_raw = corpus_raw.lower()

	words = []
	for word in corpus_raw.split():

	# Clone the Tensorflow Serving source
	git clone https://github.com/tensorflow/serving
	cd serving && git checkout <commit_hash>

	# Build the docker image (time to go get yourself a coffee, maybe a meal as well, this will take a while.)
	docker build -t some_user_namespace/tensorflow-serving:latest -f ./serving/tensorflow_serving/tools/docker/Dockerfile.devel .

	# Run up the Docker container in terminal
	docker run -ti some_user_namespace/tensorflow-serving:latest


	mkdir -p /work/

	# Clone the source from Github
	cd /work/ && git clone — recurse-submodules https://github.com/tensorflow/serving

	# Pin the version of Tensorflow Serving and its submodule
	TENSOR_SERVING_COMMIT_HASH=85db9d3
	TENSORFLOW_COMMIT_HASH=dbe5e17
	cd /work/serving && git checkout $TENSOR_SERVING_COMMIT_HASH

	# required tensorflow 0.12
	# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

	from gensim.models import Word2Vec
	import tensorflow as tf
	from tensorflow.contrib.tensorboard.plugins import projector

	# loading your gensim
	model = Word2Vec.load("YOUR-MODEL")

	'''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


	// npm install telegraf telegraf-wit

	var Telegraf = require('telegraf')
	var TelegrafWit = require('telegraf-wit')

	var app = new Telegraf(process.env.BOT_TOKEN)
	var wit = new TelegrafWit(process.env.WIT_TOKEN)

	app.use(Telegraf.memorySession())

	import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
	import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
	import org.apache.spark.mllib.linalg.Vector

	import sqlContext.implicits._

	val numTopics: Int = 100
	val maxIterations: Int = 100
	val vocabSize: Int = 10000