DocGarbanzo

Creating training pipelines in tensorflow using generators

Here we show how using generators allows to use large data sets that don't fit into computer memory for training. We focus on models with multiple input and output arrays, where the generator has to provide the data in form of dictionaries to the tf.dataset. This data pipeline is used in the Donkey Car training. The notebook is simply extracting the pipeline mechanics to give a simplified view on the functionality.

nahakiole

const request = require('request');

exports.Flatastic = function Flatastic(apikey)  {

    this.apikey = apikey;



    Flatastic.prototype.request = function (url, option, cb){
        const options = {

DocGarbanzo

Mock up of alternative donkeycar parts and car infrastructure.

Here we share a global data bus which allows to remove the explicit input and output arguments for current parts when added to the Vehicle. Each part runs in its own thread with its own frequency. Parts can write to and read from the data bus asynchronously and there is no global vehicle loop any longer. There is some resemblance to ROS whereby parts subscribe to topics. There is currently no control about type safety or allowed types in the data bus but that could be added easily.

obax

# Source: https://weworkweplay.com/play/rebooting-the-raspberry-pi-when-it-loses-wireless-connection-wifi/
# Save under /usr/local/bin/checkwifi.sh
# The Raspberry Pi tends to drop network connection (especially wireless wifi) rather fast, which is a real pain when you're trying to do anything that has the RPi running constantly from a remote location (like our RaspEye does).
# However, it's possible to detect wifi connection loss and perform upon it. It's easiest to just do a full system reboot.
# Change the IP on the first line to the IP of your router, or some other device on your network that you can assume will be always online.

# First step is to ping your IP.

# On line three, the $? represents the exit code of the previous command, in this case the ping.

anantbahuguna

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

pansapiens

okajasto

"""
Script to augment teaching data

Usage:
    augment.py --path=<source-dir> [--out=<target_dir>] --aug=<name> [--prob=<PROBABILITY>] [--add-prefix] [--start-id=<id>]

Options:
    -h --help           Show this screen.
    --path TUBPATH      Path of the record directory
    --out TARGETPATH    Path to directory where augmented data is placed, directory is created if it doesn't exist

parulnith

carry0987

Auto reconnect to wifi when lost connection

Before you start, make sure ip command is available on your system. In modern Linux distributions, ip replaces older ifconfig command. If net-tools package (that includes ifconfig) is not installed and you prefer using it, you can install it via sudo apt-get install net-tools.

Create script file

Use touch /home/pi/wifi-reconnect.sh to create a shell script file, with the following content:

#!/bin/bash

iannase

from selenium import webdriver
import requests
import bs4
import os

# new, top, or mix url
top_url = "https://soundcloud.com/charts/top"
new_url = "https://soundcloud.com/charts/new"
track_url = "https://soundcloud.com/search/sounds?q="
artist_url = "https://soundcloud.com/search/people?q="