haje01

텐서플로우 시작하기

글쓴이: 김정주(haje01@gmail.com)

이 문서는 텐서플로우 공식 페이지 내용을 바탕으로 만들어졌습니다.

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소개

텐서플로우(TensorFlow)는 기계 학습과 딥러닝을 위해 구글에서 만든 오픈소스 라이브러리입니다. 데이터 플로우 그래프(Data Flow Graph) 방식을 사용하였습니다.

Geoyi

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

SSARCandy

Setting up OpenCV with Cmake GUI

1. Download OpenCV and Cmake
2. Build opencv with cmake

• Press configure, choose visual studio 2015, finish
• Then press generate

3. Open OpenCV.sln under build/
4. Build it using Debug, Release
   

jcheong0428

iamtekeste

Download Google Drive files with WGET

Example Google Drive download link:

https://docs.google.com/open?id=[ID]
To download the file with WGET you need to use this link:

https://googledrive.com/host/[ID]

Example WGET command:

kde713

const INTERVAL = 10000;

var getQueryVariable = function (key) {
  let query = window.location.search.substring(1);
  let vars = query.split('&');
  for (let i = 0; i < vars.length; i++) {
      let pair = vars[i].split('=');
      if (decodeURIComponent(pair[0]) == key) {
          return decodeURIComponent(pair[1]);
      }

bearpaw

./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000001.jpg 1
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000002.jpg 1823
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000003.jpg 2903
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000004.jpg 4470
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000005.jpg 4698
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000006.jpg 4797
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000007.jpg 4917
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000008.jpg 5072
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000009.jpg 5177
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000010.jpg 5271

lantiga

Indexed convolutions

A convolution operator over a 1D tensor (BxCxL), where a list of neighbors for each element is provided through a indices tensor (LxK), where K is the size of the convolution kernel. Each row of indices specifies the indices of the K neighbors of the corresponding element in the input. A -1 is handled like for zero padding.

Note that the neighbors specified in indices are not relative, but rather absolute. They have to be specified for each of the elements of the output.

A use case is for convolutions over non-square lattices, such as images on hexagonal lattices coming from Cherenkov telescopes (http://www.isdc.unige.ch/%7Elyard/FirstLight/FirstLight_slowHD.mov).

Example:

sofyan-ahmad

# Make sure you grab the latest version
curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip

# Unzip
unzip protoc-3.2.0-linux-x86_64.zip -d protoc3

# Move protoc to /usr/local/bin/
sudo mv protoc3/bin/* /usr/local/bin/

# Move protoc3/include to /usr/local/include/

eknight7

// Copyright 2019 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,