Andrews Cordolino Sobral andrewssobral

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

1. Copy m3u8 link

2. Run command

echo "Enter m3u8 link:";read link;echo "Enter output filename:";read filename;ffmpeg -i "$link" -bsf:a aac_adtstoasc -vcodec copy -c copy -crf 50 $filename.mp4

Revert the full commit

Sometimes you may want to undo a whole commit with all changes. Instead of going through all the changes manually, you can simply tell git to revert a commit, which does not even have to be the last one. Reverting a commit means to create a new commit that undoes all changes that were made in the bad commit. Just like above, the bad commit remains there, but it no longer affects the the current master and any future commits on top of it.

git revert {commit_id}

About History Rewriting

Delete the last commit

Deleting the last commit is the easiest case. Let's say we have a remote origin with branch master that currently points to commit dd61ab32. We want to remove the top commit. Translated to git terminology, we want to force the master branch of the origin remote repository to the parent of dd61ab32:


	#Evolution Strategies with Keras
	#Based off of: https://blog.openai.com/evolution-strategies/
	#Implementation by: Nicholas Samoray

	#README
	#Meant to be run on a single machine
	#APPLY_BIAS is currently not working, keep to False
	#Solves Cartpole as-is in about 50 episodes
	#Solves BipedalWalker-v2 in about 1000

	"""
	A bare bones examples of optimizing a black-box function (f) using
	Natural Evolution Strategies (NES), where the parameter distribution is a
	gaussian of fixed standard deviation.
	"""

	import numpy as np
	np.random.seed(0)

	# the function we want to optimize

	#!/bin/bash
	#
	# EDIT: this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
	#
	sudo apt-get install python-pip

	# upgrade pip
	pip install -U pip
	pip --version
	# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

	-- Xception model
	-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
	-- E. Culurciello, October 2016

	require 'nn'
	local nClasses = 1000

	function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
	local block = nn.Sequential()
	block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))

	from keras.models import Sequential
	from keras.layers import Dense

	x, y = ...
	x_val, y_val = ...

	# 1-dimensional MSE linear regression in Keras
	model = Sequential()
	model.add(Dense(1, input_dim=x.shape[1]))
	model.compile(optimizer='rmsprop', loss='mse')

	'''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