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import numpy as np
from scipy import signal

def conv2d_simplified(input, weight, bias=None, padding=0):
    # This is an implemention of torch's conv2d using scipy correlate2d. Only
    # limited options are supported for simplicity.
    # Inspired by https://github.com/99991/NumPyConv2D/
    c_out, c_in_by_groups, kh, kw = weight.shape
    if not isinstance(padding, int):
        raise NotImplementedError()

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import csv

def MergeUpdate(dataDict, update):

    for li in update:

        #print (li['ElectorNumber'], li['ElectorCreatedMonth'], li['ElectorChangedMonth'], li['ElectorDeletedMonth'])
        electNum = li['ElectorNumber'].strip()

        if int(li['ElectorCreatedMonth']) or int(li['ElectorChangedMonth']):

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# A script to group delivery areas into routes of roughly similar size.

import csv
from sklearn import cluster
from pyproj import Proj, transform
import numpy as np
from matplotlib import pyplot as plt

class EqualWeightClustering(object):

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# https://askubuntu.com/a/1337205/294281
import os
import stat
import glob
from pathlib import Path
import shutil

if __name__=="__main__":

    ver = "495.29.05"

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#pyellipse by Tim Sheerman-Chase (C) 2021
#Calculate circumference of an ellipse in python (both exact and approximate approaches) 
#This source code may be used under the CC0 license https://creativecommons.org/publicdomain/zero/1.0/
#
#For the best exact circumference, use EllipseCircumAdlaj2012
#
#For good approximations that are faster to compute, see EllipseCircumRamanujan2ndApprox and EllipseCircumJacobsenWaadeland1985
#
#Incidentally, scipy has a function to exactly calculate it:
#	C = 4.0*a*special.ellipe(e*e)

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#Using Keras to tackle the Inria aerial image labeling dataset
# https://project.inria.fr/aerialimagelabeling/

import os
#Work around for https://github.com/tensorflow/tensorflow/issues/24496
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# Work around for https://github.com/tensorflow/tensorflow/issues/33024
import tensorflow.compat as compat
compat.v1.disable_eager_execution()

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import os
#Work around for https://github.com/tensorflow/tensorflow/issues/24496
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# Work around for https://github.com/tensorflow/tensorflow/issues/33024
import tensorflow.compat as compat
compat.v1.disable_eager_execution()

import imageio
from sklearn.model_selection import KFold, train_test_split

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#Based on https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-from-scratch-for-mnist-handwritten-digit-classification/

import os
#Work around for https://github.com/tensorflow/tensorflow/issues/24496
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# baseline cnn model for mnist
from numpy import mean
from numpy import std
from matplotlib import pyplot

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import os
#Work around for https://github.com/tensorflow/tensorflow/issues/24496
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

# Work around for https://github.com/tensorflow/tensorflow/issues/33024
import tensorflow.compat as compat
compat.v1.disable_eager_execution()

# baseline cnn model for mnist
import numpy as np

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Working environments (Ubuntu 18.04 with Geforce RTX 2060 super)

tensorflow-gpu-1.13 and after require TF_FORCE_GPU_ALLOW_GROWTH work around tensorflow/tensorflow#24496

Keras-2.2.5
tensorflow-gpu-1.12.0 
libcudnn7 7.6.5.32-1+cuda10.0
cuda-libraries-10-0