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[Unit]
Description=Description of this service

[Service]
Type=simple
ExecStart=/usr/bin/nohup /path/to/server.sh
Restart=always
RestartSec=30
StandardOutput=/path/to/log
StandardError=/path/to/log

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Specific Papers

• https://arxiv.org/abs/1505.07376

  Texture Synthesis Using Convolutional Neural Networks
• https://arxiv.org/abs/1508.06576

  A Neural Algorithm of Artistic Style
• https://arxiv.org/abs/1603.03417

  Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
• https://arxiv.org/abs/1701.02096

  Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Texture Synthesis

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The file page.st goes in the templates/ directory in the Gitit wiki home directory. You'll put the Ace JavaScript and CSS files in static/.

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{-# LANGUAGE FlexibleInstances #-}

-- | An implementation of Section 3, Local Type Argument Synthesis, from the
-- paper /Local Type Inference/ by Pierce and Turner.
module Infer where

import Control.Monad (foldM, join, zipWithM)
import Data.Function (on)
import Data.List (foldl', groupBy, intercalate, intersect, nub)

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class BayesianModel(object):
    samples = 2000
    def __init__(self, cache_model=True):
        self.cached_model = None
        self.cached_start = None
        self.cached_sampler = None
        self.shared_vars = {}

    def cache_model(self, **inputs):
        self.shared_vars = self._create_shared_vars(**inputs)

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# Update to 4.9 kernel do not delete the old kernel as it will be your failsafe if something happens to this one

# Install KabyLake graphics patches
cd /tmp;
wget https://01.org/sites/default/files/downloads/intelr-graphics-linux/kbldmcver101.tar.bz2;
tar xjvf kbldmcver101.tar.bz2; cd kbl_dmc_ver1_01/; sudo ./install.sh

cd /tmp;
wget https://01.org/sites/default/files/downloads/intelr-graphics-linux/kblgucver914.tar.gz;
tar xvzf kblgucver914.tar.gz; cd firmware/kbl/guc/kbl_guc_ver/; sudo ./install.sh

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from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np 

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

model = Sequential()
model.add(Dense(8, input_dim=2))

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