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/** | |
* POST to create a new user. | |
*/ | |
exports.create = function *(){ | |
var body = yield parse(this); | |
// password | |
var pass = body.password; | |
assert(pass, 400, 'password is required'); |
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var _log = console.log; | |
window.console.log = function(log){ | |
_log.call(console, log.reverse ? log.reverse() : typeof log === 'string' ? log.split('').reverse().join('') : typeof log === 'number' ? log.toString().split('').reverse().join('') : typeof log === 'boolean' ? !log : log); | |
}; |
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#!/bin/bash | |
# Run this on This AMI on AWS: | |
# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8 | |
# You should get yourself a fully working GPU enabled tensorflow installation. | |
cd ~ | |
# grab cuda 7.0 |
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julia> using JET, JET.JETInterfaces | |
julia> get_module(sv::Core.Compiler.InferenceState) = sv.mod | |
get_module (generic function with 2 methods) | |
julia> function get_module(linfo::Core.MethodInstance) | |
def = linfo.def | |
return isa(def, Module) ? def : def.module | |
end | |
get_module (generic function with 2 methods) |
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#!/bin/bash -e | |
echo ' | |
PATH=$HOME/go/bin:$PATH | |
export GOPATH=$HOME | |
export CDPATH=.:$HOME/src/golang.org/x:$HOME/go/src:$HOME/src/github.com:$HOME/src/github.com/nf:$HOME/src/github.com/adg | |
export EDITOR=vim | |
' >> ~/.profile | |
sudo apt-get update |
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# WARNING: These steps seem to not work anymore! | |
#!/bin/bash | |
# Purge existign CUDA first | |
sudo apt --purge remove "cublas*" "cuda*" | |
sudo apt --purge remove "nvidia*" | |
# Install CUDA Toolkit 10 | |
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb |
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""" | |
A deep neural network with or w/o dropout in one file. | |
License: Do What The Fuck You Want to Public License http://www.wtfpl.net/ | |
""" | |
import numpy, theano, sys, math | |
from theano import tensor as T | |
from theano import shared | |
from theano.tensor.shared_randomstreams import RandomStreams |
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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
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