| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| FROM python:3.5 | |
| MAINTAINER Yusuke Nemoto <[email protected]> | |
| RUN apt-get update && apt-get install -y build-essential \ | |
| cmake \ | |
| wget \ | |
| git \ | |
| unzip \ | |
| yasm \ |
| FROM kaakaa/opencv-contrib-python3 | |
| RUN pip install --upgrade pip && \ | |
| pip install jupyter scipy numpy matplotlib Pillow scikit-image | |
| CMD unset XDG_RUNTIME_DIR && \ | |
| jupyter notebook --notebook-dir=/project --port=9999 --ip=0.0.0.0 --no-browser --allow-root |
| /** | |
| * plotly.js v1.39.4 | |
| * Copyright 2012-2018, Plotly, Inc. | |
| * All rights reserved. | |
| * Licensed under the MIT license | |
| */ | |
| !function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){var r=e[o][1][t];return i(r||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":"direction:ltr;font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;","X i |
| '''read a csv file representing a table and write a restructured text simple | |
| table''' | |
| import sys | |
| import csv | |
| def get_out(out=None): | |
| ''' | |
| return a file like object from different kinds of values | |
| None: returns stdout | |
| string: returns open(path) |
These codes are TensorFlow Autoencoder implementation examples. They are inspired by very educational Keras Blog article.
http://blog.keras.io/building-autoencoders-in-keras.html
Building Autoencodes in Keras
"Autoencoding" is a data compression algorithm where the compression and decompression
AppleVolume.default
# The line below sets some DEFAULT, starting with Netatalk 2.1.
:DEFAULT: options:upriv,usedots
# By default all users have access to their home directories.
#~/ "Home Directory"
#/l/parks1_2 "lparks1_2's Time Machine" options:tm allow:parks1
#/l/parks1 "lparks1's Time Machine" options:tm allow:parks1
#/timemachine_backup_zpool/jkotimak "jkotimak's Time Machine" options:tm allow:jkotimak
For better debugging I will elaborate my process and status. I installed Netatalk 3.1.11 by self-build from source. We DO NOT have Avahi installed as the module is prohibited in the system.
sudo apt-get install autoconf \
libtool-bin \
libtool \
automake \
build-essential \
libssl-dev \
| ; | |
| ; Netatalk 3.x configuration file | |
| ; http://netatalk.sourceforge.net/3.0/htmldocs/afp.conf.5.html | |
| ; | |
| [Global] | |
| ; Global server settings | |
| vol preset = default_for_all_vol | |
| log file = /var/log/netatalk.log | |
| uam list = uams_dhx.so,uams_dhx2.so |