Jiho Lee DPS0340
DPS0340
/ coursera-subtitle.js
Last active
September 15, 2024 04:23
— forked from jizusun/coursera-subtitle.js
Tampermonkey script - Coursera subtitles(outside the video)
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| // ==UserScript== | |
| // @name Coursera subtitles(outside the video) | |
| // @description Coursera subtitles outside the video. | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.2 | |
| // @author 木杉, [email protected] | |
| // @include http://www.coursera.org/* | |
| // @include https://www.coursera.org/* | |
| // @grant none | |
| // @run-at document-end |
DPS0340
/ nginx.conf
Created
January 5, 2019 14:06
— forked from plentz/nginx.conf
Best nginx configuration for improved security(and performance). Complete blog post here http://tautt.com/best-nginx-configuration-for-security/
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| # to generate your dhparam.pem file, run in the terminal | |
| openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048 |
DPS0340
/ nginx.conf
Created
January 5, 2019 14:06
— forked from plentz/nginx.conf
Best nginx configuration for improved security(and performance). Complete blog post here http://tautt.com/best-nginx-configuration-for-security/
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| # to generate your dhparam.pem file, run in the terminal | |
| openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048 |
DPS0340
/ inceptionv3_retrain.py
Created
August 9, 2018 11:21
— forked from solaris33/inceptionv3_retrain.py
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| # -*- coding: utf-8 -*- | |
| """Inception v3 architecture 모델을 이용한 간단한 Transfer Learning (TensorBoard 포함) | |
| This example shows how to take a Inception v3 architecture model trained on | |
| ImageNet images, and train a new top layer that can recognize other classes of | |
| images. | |
| The top layer receives as input a 2048-dimensional vector for each image. We | |
| train a softmax layer on top of this representation. Assuming the softmax layer |
DPS0340
/ retrain_run_inference.py
Created
August 9, 2018 11:20
— forked from solaris33/retrain_run_inference.py
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| # -*- coding: utf-8 -*- | |
| """Inception v3 architecture 모델을 retraining한 모델을 이용해서 이미지에 대한 추론(inference)을 진행하는 예제""" | |
| import numpy as np | |
| import tensorflow as tf | |
| imagePath = '/tmp/test_chartreux.jpg' # 추론을 진행할 이미지 경로 | |
| modelFullPath = '/tmp/output_graph.pb' # 읽어들일 graph 파일 경로 | |
| labelsFullPath = '/tmp/output_labels.txt' # 읽어들일 labels 파일 경로 |