| # See this wiki page for more info: | |
| # https://github.com/dylanaraps/neofetch/wiki/Customizing-Info | |
| print_info() { | |
| #info title | |
| #info underline | |
| info "OS" distro | |
| #info "Host" model | |
| info "Kernel" kernel | |
| info "Uptime" uptime |
| while true; do neofetch --backend off > /tmp/neofetch_file; clear; cat /tmp/neofetch_file; sleep 60; done | |
| # or | |
| watch -ctn 1 'neofetch | sed "s/25[hl]//g;s/7[hl]//"' |
| #!/usr/env python3 | |
| ######################################################################## | |
| # | |
| # Simple HTTP server that supports file upload for moving data around | |
| # between boxen on HTB. Based on a gist by bones7456, but mangled by me | |
| # as I've tried (badly) to port it to Python 3, code golf it, and make | |
| # It a little more robust. I was also able to strip out a lot of the | |
| # code trivially because Python3 SimpleHTTPServer is a thing, and the | |
| # cgi module handles multipart data nicely. | |
| # |
| const originalFunctionWithPromise = (num) => new Promise((resolve, reject) => { | |
| try { | |
| if (typeof num !== 'number') { | |
| return reject(new Error('Invalid input')); | |
| } | |
| return resolve(5 * num); | |
| } catch (err) { | |
| return reject(err); | |
| } | |
| }); |
| /** | |
| * Knick Knack Steam Bot | |
| * | |
| * Consumes all knick-knacks in Your steam inventory | |
| * | |
| * - Go to https://steamcommunity.com/my/inventory#753_6 | |
| * - Wait for inventory to load | |
| * - Press F12 | |
| * - Paste the code in console tab | |
| * |
| # Instructions for 4.14 and cuda 9.1 | |
| # If upgrading from 4.13 and cuda 9.0 | |
| $ sudo apt-get purge --auto-remove libcud* | |
| $ sudo apt-get purge --auto-remove cuda* | |
| $ sudo apt-get purge --auto-remove nvidia* | |
| # also remove the container directory direcotory at /usr/local/cuda-9.0/ | |
| # Important libs required with 4.14.x with Cuda 9.X | |
| $ sudo apt install libelf1 libelf-dev |
| #!/bin/bash | |
| # Usage: dl-lession.sh https://egghead.io/courses/react-fundamentals | |
| let n=1 | |
| for link in $(curl -s $1 | pup '.series-lessons-list a attr{href}'); do | |
| filename=$(echo $link | sed "s/https:\/\/egghead.io\/lessons\///g" | sed "s/\?.*$//g") | |
| filename_numbered=$(echo "$n $filename" | awk '{ printf "%02i-%s.mp4\n", $1, $2 }') | |
| youtube-dl -o "$filename_numbered" "$link" | |
| let n++ | |
| done |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def show_images(images, cols = 1, titles = None): | |
| """Display a list of images in a single figure with matplotlib. | |
| Parameters | |
| --------- | |
| images: List of np.arrays compatible with plt.imshow. | |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
##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