jkang jaekookang

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',

	- Open Automator
	- File -> New -> Service
	- Change "Service Receives" to "files or folders" in "Finder"
	- Add a "Run Shell Script" action
	- Change "Pass input" to "as arguments"
	- Paste the following in the shell script box: open -n -b "com.microsoft.VSCode" --args "$*"
	- Save it as something like "Open in Visual Studio Code"

	# Working example for my blog post at:
	# https://danijar.github.io/structuring-your-tensorflow-models
	import functools
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data


	def doublewrap(function):
	"""
	A decorator decorator, allowing to use the decorator to be used without

	#We can check for Ctrl-C with KeyboardInterrupt exception as follows:

	try:
	while True:
	print "Echo ", raw_input(">")
	except KeyboardInterrupt:
	print "Good bye"

	#When python process was killed, we will not get KeyboardInterrupt. But we can instead catch SIGTERM sent by kill command.
	#In order to catch SIGTERM, we can do:

	{
	"cmd": ["pandoc --latex-engine=xelatex --filter=pandoc-citeproc -o '$file_base_name.pdf' '$file_name'"],
	"selector": "text.html.markdown",
	"shell": true
	}

	#
	# mnist_cnn_bn.py date. 5/21/2016
	# date. 6/2/2017 check TF 1.1 compatibility
	#

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import os

	# References
	# - https://www.tensorflow.org/versions/r0.10/tutorials/word2vec/index.html
	# - https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/word2vec/word2vec_basic.py

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import collections
	import math

	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation: