philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

philipperemy

from __future__ import print_function

import os

import numpy as np
from keras.layers import RepeatVector
from keras.layers.core import Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.models import load_model

philipperemy

from __future__ import print_function

import os

import numpy as np
from keras.layers import RepeatVector
from keras.layers.core import Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.models import load_model

philipperemy

from __future__ import print_function

import numpy as np
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

USE_SEQUENCES = False
USE_STATELESS_MODEL = False

philipperemy

#!/usr/bin/env python


import pickle
import sys

if __name__ == '__main__':
    argv = sys.argv
    if len(argv) <= 1:
        print 'Specify pickle file as parameter.'

philipperemy

from __future__ import print_function

import numpy as np
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

USE_SEQUENCES = False
USE_STATELESS_MODEL = False

philipperemy

var https = require('https'),
        user = process.argv[2],
            opts = parseOpts(process.argv.slice(3))

    request('/users/' + user, function (res) {
            if (!res.public_repos) {
                        console.log(res.message)
                return
            }
                var pages = Math.ceil(res.public_repos / 100),

philipperemy

# Ubuntu:14.04 - 64 bits
# MINIMUM IS 8GB OF RAM.

curl -sk https://raw.githubusercontent.com/torch/ezinstall/master/install-deps | bash
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; ./install.sh
. ~/.bashrc  # or: . ~/.zshrc

luarocks install nn
luarocks install nngraph

philipperemy

#!/usr/bin/env python
"""
Produces load on all available CPU cores
"""

from multiprocessing import Pool
from multiprocessing import cpu_count

def f(x):
    while True:

philipperemy

# On Mac OSX

# Will run AIRODUMP scan of all BSSIDs
# sudo airport -s

# Will run AIRODUMP sniff on channel 11
# sudo airport en0 sniff 11
# Atm, I can't find how to sniff on all channels at the same time.

if [ $# -eq 0 ]