Mehdi Cherti mehdidc

Pixel Recurrent Neural Network

Introduction

Problem: Building an expressive, tractable and scalable image model which can be used in downstream tasks like image generation, reconstruction, compression etc.
Link to the paper

Model

Scan the image, one row at a time and one pixel at a time (within each row).

With the release of Vivaldi 2.2, this page is now obsolete and unmaintained. Widevine is fetched automatically on post install of our official packages. The information below and the script are left for historical reasons but will not be updated.

If you are using something newer than Vivaldi 2.2, you should not be using this script as there is simply no need. Any need you think you have for it would be a bug IMHO and thus should be logged in a bug report. Before you do so however, you should also checkout the Vivaldi help page on Widevine, on Linux

Summary

A bunch of people asked how they could use this script with pure Chromium on Ubuntu. The following is a quick guide. Though I still suggest you at least try Vivaldi. Who knows, you might like it. Worried about proprietary componants? Remember that libwidevinecdm.so is a b

	from keras.layers import Recurrent
	import keras.backend as K
	from keras import activations
	from keras import initializers
	from keras import regularizers
	from keras import constraints
	from keras.engine import Layer
	from keras.engine import InputSpec

	"""
	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

	Code Complete by Steve McConnell

	Jeff Atwood (Coding Horror)
	https://blog.codinghorror.com/code-reviews-just-do-it/

	Measuring Defect Potentials and Defect Removal Efficiency
	http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

	Expectations, Outcomes, and Challenges Of Modern Code Review
	https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

	#!/usr/bin/env python
	"""
	A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
	The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
	instance). TensorFlow takes ~35 ms. The PyTorch implementation takes ~25 ms, an improvement
	over TensorFlow, but worse than the original Torch.
	"""

	from __future__ import absolute_import

	from tf_logger import TFLogger


	""" Example of using TFLogger to save train & dev statistics. To visualize
	in tensorboard simply do:

	tensorboard --logdir /path/to/summaries

	This code does depend on Tensorflow, but does not require that your model
	is built using Tensorflow. For instance, could build a model in Chainer, then

	"""
	Example of iterating Bach Chorales and getting individual voice parts
	In this case, want specifically 4 voice pieces only
	Also transpose to key of C (major or minor depending on piece)
	Also shows how to write out all the xml as midi
	"""
	# Author: Kyle Kastner
	# License: BSD 3-Clause
	# Based on StackOverflow answer
	# http://stackoverflow.com/questions/36647054/music21-getting-all-notes-with-durations

	import numpy as np
	from keras.layers import GRU, initializations, K
	from collections import OrderedDict

	class GRULN(GRU):
	'''Gated Recurrent Unit with Layer Normalization
	Current impelemtation only works with consume_less = 'gpu' which is already
	set.

	# Arguments