gpchelkin

### NOT A SCRIPT, JUST A REFERENCE!

# install dante-server
sudo apt update
sudo apt install dante-server

# or download latest dante-server deb for Ubuntu, works for 16.04 / 18.04 / 20.04:
wget http://archive.ubuntu.com/ubuntu/pool/universe/d/dante/dante-server_1.4.2+dfsg-7build5_amd64.deb
# or older version:
wget http://ppa.launchpad.net/dajhorn/dante/ubuntu/pool/main/d/dante/dante-server_1.4.1-1_amd64.deb

nicksam112

#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

shamatar

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

wassname

"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

udibr

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

knzm

#!/bin/sh

sudo apt-get update
sudo apt-get upgrade -y
sudo apt-get dist-upgrade -y

sudo apt-get install -y git subversion unzip
sudo apt-get install -y build-essential gfortran
sudo apt-get install -y python-virtualenv
sudo apt-get install -y python-dev

paulrouget

Document moved to: https://github.com/servo/servo/blob/master/HACKING_QUICKSTART.md

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

tomaka

This code is under the MIT license.
The GLSL code was taken from https://github.com/mattdesl/glsl-fxaa and is also under the MIT license.


Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions: