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#######################################################
#! /bin/sh
# . /etc/rc.d/init.d/functions  # uncomment/modify for your killproc
case "$1" in
    start)
        echo "Starting noip2."
        /usr/local/bin/noip2
    ;;
    stop)
        echo -n "Shutting down noip2."

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import tensorflow as tf
import time

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# Parameters
learning_rate = 0.001
training_iters = 200000

EKami

#! /bin/sh
# . /etc/rc.d/init.d/functions  # uncomment/modify for your killproc
case "$1" in
    start)
        echo "Starting noip2."
        /usr/local/bin/noip2
    ;;
    stop)
        echo -n "Shutting down noip2."
        killproc -TERM /usr/local/bin/noip2

EKami

EKami

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

EKami

Building TensorFlow 1.3.0-rc1 for Raspberry Pi/Ubuntu 16.04: a Step-By-Step Guide

Here you'll learn how to build Tensorflow for the raspberry pi 3 with either the Python API or as a standalone shared library which can be interfaced from the C++ API and eventually as a library which can be used in other languages.

For the C++ library this tutorial will show you how extract tensorflow library and headers to use in any environment you want.

(This tutorial couldn't be possible without the help of the people from the References section)

What You Need

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import gym
from gym import wrappers

env = gym.make('CartPole-v0')
env = wrappers.Monitor(env, '/tmp/cartpole-experiment-1', force=True)

for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
        env.render()

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import os
import requests
from io import BytesIO
import Algorithmia
from Algorithmia.acl import ReadAcl
from torchlite.eval import eval
from pathlib import Path
from PIL import Image
import uuid