Frank Denis jedisct1

Image-to-Image Translation with Conditional Adversarial Networks

Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
GANs learn a loss function rather than using an existing one.
GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
Unlike an unconditional GAN, both th

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

duplicates = multiple editions

	#define KBUILD_MODNAME "load_balancer"
	#include <uapi/linux/bpf.h>
	#include <linux/in.h>
	#include <linux/if_ether.h>
	#include <linux/if_packet.h>
	#include <linux/if_vlan.h>
	#include <linux/ip.h>
	#include <linux/ipv6.h>

	BPF_HASH(counter, uint32_t, long);

	class AttentionLSTM(LSTM):
	"""LSTM with attention mechanism

	This is an LSTM incorporating an attention mechanism into its hidden states.
	Currently, the context vector calculated from the attended vector is fed
	into the model's internal states, closely following the model by Xu et al.
	(2016, Sec. 3.1.2), using a soft attention model following
	Bahdanau et al. (2014).

	The layer expects two inputs instead of the usual one:

	#!/bin/bash

	# Raspberry Pi ZRAM script
	# Tuned for quad core, 1 GB RAM models
	# put me in /etc/init.d/zram.sh and make me executable
	# then run "sudo update-rc.d zram.sh defaults"

	modprobe zram
	echo 3 >/sys/devices/virtual/block/zram0/max_comp_streams
	echo lz4 >/sys/devices/virtual/block/zram0/comp_algorithm

	* Install VPS with Debian/Archinux/whatever you're comfortable with
	* Get the bsd.rd file and put it in / for instance
	wget ftp://ftp.openbsd.org/pub/OpenBSD/5.8/amd64/bsd.rd
	* Add something along these lines in your grub.cfg (or /etc/grub.d/40_custom)
	Make sure you adapt the (hd0,1) line to your config
	menuentry "OpenBSD installer" {
	set root=(hd0,1)
	kopenbsd /bsd.rd
	}
	* Update the main grub.cfg: