remarkablemark

How to add an image to a gist

1. Create or find a gist that you own.
2. Clone your gist (replace <hash> with your gist's hash):

   # with ssh
   git clone [email protected]:<hash>.git mygist
   
   # with https

git clone https://gist.github.com/.git mygist

Ashwinning

How To Open Another Terminal/Bash Instance In A Running Docker Container

Add the following to your bashrc.

#Add another docker window
function dock()
{
  if [ "$1" == "-h" ]
 then

stefanbuck

#!/usr/bin/env bash
#
# Author: Stefan Buck
# License: MIT
# https://gist.github.com/stefanbuck/ce788fee19ab6eb0b4447a85fc99f447
#
#
# This script accepts the following parameters:
#
# * owner

fm4dd

GCC compiler optimization for ARM-based systems

2017-03-03 fm4dd

The gcc compiler can optimize code by taking advantage of CPU specific features. Especially for ARM CPU's, this can have impact on application performance. ARM CPU's, even under the same architecture, could be implemented with different versions of floating point units (FPU). Utilizing full FPU potential improves performance of heavier operating systems such as full Linux distributions.

-mcpu, -march: Defining the CPU type and architecture

These flags can both be used to set the CPU type. Setting one or the other is sufficient.

sunsided

import argparse
import os
import sys
from typing import Iterable
import tensorflow as tf


parser = argparse.ArgumentParser()
parser.add_argument('file', type=str, help='The file name of the frozen graph.')
args = parser.parse_args()

stared

CarlosGS

# Fast reading from the raspberry camera with Python, Numpy, and OpenCV
# Allows to process grayscale video up to 124 FPS (tested in Raspberry Zero Wifi with V2.1 camera)
#
# Made by @CarlosGS in May 2017
# Club de Robotica - Universidad Autonoma de Madrid
# http://crm.ii.uam.es/
# License: Public Domain, attribution appreciated

import cv2
import numpy as np

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

ByungSunBae

# from : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/graph_editor/examples/edit_graph_example.py
import numpy as np

import tensorflow as tf
from tensorflow.contrib import graph_editor as ge

# create a graph
g = tf.Graph()

with g.as_default():

benjaminmgross

def press_statistic(y_true, y_pred, xs):
    """
    Calculation of the `Press Statistics <https://www.otexts.org/1580>`_
    """
    res = y_pred - y_true
    hat = xs.dot(np.linalg.pinv(xs))
    den = (1 - np.diagonal(hat))
    sqr = np.square(res/den)
    return sqr.sum()