// jQuery
$(document).ready(function() {
// code
})
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#!/usr/bin/php | |
<?php | |
printf("%sGit pre-commit hook %1\$s", PHP_EOL); | |
$projectName = basename(getcwd()); | |
exec('phpunit --configuration phpunit.xml', $output, $returnCode); // Assuming cwd here | |
if ($returnCode !== 0) { | |
$minimalTestSummary = array_pop($output); | |
printf("Test suite for %s failed: ", $projectName); | |
printf("( %s ) %s%2\$s", $minimalTestSummary, PHP_EOL); | |
return false; // exit(1); |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
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#!/bin/bash | |
# Install dependencies | |
# older ubuntus | |
#apt-get install build-essential libsqlite3-dev ruby1.9.1-dev | |
# xenial | |
apt install build-essential libsqlite3-dev ruby-dev | |
# Install the gem | |
gem install mailcatcher --no-ri --no-rdoc |
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/* converts Bencoded data on stdin to JSON on stdout */ | |
#include <stdio.h> | |
#include <ctype.h> | |
#include <stdlib.h> | |
int pos = 0; | |
int c; | |
char stack[1024], *sp = stack; |
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variables: | |
REGISTRY_URL: registry.lekode.com:5000 | |
IMAGE_NAME: app | |
BUILD_CODE_IMAGE: $REGISTRY_URL/$IMAGE_NAME:$CI_COMMIT_REF_NAME | |
LATEST_CODE_IMAGE: $REGISTRY_URL/$IMAGE_NAME:latest | |
stages: | |
- test | |
- install | |
- build |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
# Python script to update CDMon dynamic DNS | |
# | |
# @author = @jovimon | |
# @version = 0.1.20200930 | |
# | |
# Prerequisites: sudo pip install requests / sudo apt install python-requests | |
# |