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"""
Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
"""
from __future__ import print_function
FROM ubuntu:16.04
MAINTAINER Dockerfiles
# Install required packages and remove the apt packages cache when done.
RUN apt-get update && \
apt-get upgrade -y && \
apt-get install -y \
autoconf \
autoconf-archive \
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>AdventCalendar</title>
<link rel="stylesheet" href="https://img.homepagemodules.de/ds/static/aktionen/adventskal/css/colorbox_1.css" />
<link href="css/style.css" rel="stylesheet"/>
</head>
<body>
/****************************************************************************************\
* Polygonal Approximation *
\****************************************************************************************/
/* Ramer-Douglas-Peucker algorithm for polygon simplification */
namespace cv
{
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE AllowAmbiguousTypes #-}
module FindContours
(
findingContours
) where
import Utils
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE AllowAmbiguousTypes #-}
module CropImage
(
cropImage
) where
import Utils