Jaemin Cho j-min

## tensorboard_inline.py
from IPython.display import clear_output, Image, display, HTML
import numpy as np

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add()
        n.MergeFrom(n0)
        if n.op == 'Const':

## TF_MDN.ipynb

      
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                j-min
                / TF_MDN.ipynb
            
            
              Created
              November 15, 2016 10:46
            
              
                Mixture Density Network in TensorFlow
              
          
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## matplotlib_plot_demo.py
import matplotlib

# font configuration
matplotlib.rc('font', family='NanumGothic', size=22)

## .zshrc
ZSH=$HOME/.zsh

HISTFILE=$HOME/.history
HISTSIZE=10000
SAVEHIST=10000

export TERM=xterm-256color
export LANG=en_US.UTF-8
# added by Anaconda3 4.1.1 installer
export PATH=$HOME/anaconda3/bin:$PATH

## convertSize.py
import math

def convertSize(size):
	"""
	Return filesize (in Bytes) in human-readable format
	"""
	if (size == 0):
		return '0B'
	units = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
	i = int(math.floor(math.log(size,1024)))

## hangul.py
# -*- coding: utf-8 -*-

class Hangul:
    BASE_CODE = 44032
    CHOSUNG = 588
    JUNGSUNG = 28

    # 초성 리스트. 00 ~ 18
    CHOSUNG_LIST = [
            'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ',

## tokenize_dparser.py
import json
import requests

def tokenize_dparser(text):
  dparser_link = 'http://parser.datanada.com/parse?version=1&string='

  url = dparser_link+text

  response = requests.get(url)

## 2_3.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

## CSVFileIO.py
import csv
import os

def get_csv_writer(filename, rows, delimiter):
    with open(filename, 'w') as csvfile:
        fieldnames = rows[0].keys()
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=delimiter)
        writer.writeheader()
        for row in rows:
            try:

## install-tensorflow.sh
# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update
	from IPython.display import clear_output, Image, display, HTML
	import numpy as np

	def strip_consts(graph_def, max_const_size=32):
	"""Strip large constant values from graph_def."""
	strip_def = tf.GraphDef()
	for n0 in graph_def.node:
	n = strip_def.node.add()
	n.MergeFrom(n0)
	if n.op == 'Const':
	import matplotlib

	# font configuration
	matplotlib.rc('font', family='NanumGothic', size=22)
	ZSH=$HOME/.zsh

	HISTFILE=$HOME/.history
	HISTSIZE=10000
	SAVEHIST=10000

	export TERM=xterm-256color
	export LANG=en_US.UTF-8
	# added by Anaconda3 4.1.1 installer
	export PATH=$HOME/anaconda3/bin:$PATH
	import math

	def convertSize(size):
	"""
	Return filesize (in Bytes) in human-readable format
	"""
	if (size == 0):
	return '0B'
	units = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
	i = int(math.floor(math.log(size,1024)))
	# -- coding: utf-8 --

	class Hangul:
	BASE_CODE = 44032
	CHOSUNG = 588
	JUNGSUNG = 28

	# 초성 리스트. 00 ~ 18
	CHOSUNG_LIST = [
	'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ',
	import json
	import requests

	def tokenize_dparser(text):
	dparser_link = 'http://parser.datanada.com/parse?version=1&string='

	url = dparser_link+text

	response = requests.get(url)
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function
	import csv
	import os

	def get_csv_writer(filename, rows, delimiter):
	with open(filename, 'w') as csvfile:
	fieldnames = rows[0].keys()
	writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=delimiter)
	writer.writeheader()
	for row in rows:
	try:
	# Note – this is not a bash script (some of the steps require reboot)
	# I named it .sh just so Github does correct syntax highlighting.
	#
	# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
	#
	# The CUDA part is mostly based on this excellent blog post:
	# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

	# Install various packages
	sudo apt-get update