Sungju Kim goddoe

##VGG19 model for Keras

This is the Keras model of the 19-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

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주([email protected])

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

##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

[공통] 마크다운 markdown 작성법

영어지만, 조금 더 상세하게 마크다운 사용법을 안내하고 있는
"Markdown Guide (https://www.markdownguide.org/)" 를 보시는 것을 추천합니다. ^^

아, 그리고 마크다운만으로 표현이 부족하다고 느끼신다면, HTML 태그를 활용하시는 것도 좋습니다.

1. 마크다운에 관하여

Docker 치트 시트

한국어 번역(초벌): nacyot
같이 읽으면 좋은 문서들

왜 Docker를 사용해야하는가?

Performance of Flask, Tornado, GEvent, and their combinations

Wensheng Wang, 10/1/11

Source: http://blog.wensheng.org/2011/10/performance-of-flask-tornado-gevent-and.html

When choosing a web framework, I pretty much have eyes set on Tornado. But I heard good things about Flask and Gevent. So I tested the performance of each and combinations of the three. I chose something just a little more advanced than a "Hello World" program to write - one that use templates. Here are the codes:

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#
	# [2016-03-14] Challenge #258 [Easy] IRC: Making a Connection
	# https://www.reddit.com/r/dailyprogrammer/comments/4ad23z/20160314_challenge_258_easy_irc_making_a/
	#

	import socket

	input = """chat.freenode.net:6667
	dude1267
	dude1267

	'''This scripts implements Kim's paper "Convolutional Neural Networks for Sentence Classification"
	with a very small embedding size (20) than the commonly used values (100 - 300) as it gives better
	result with much less parameters.

	Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py

	Get to 0.853 test accuracy after 5 epochs. 13s/epoch on Nvidia GTX980 GPU.
	'''

	from __future__ import print_function