jamesjjcondon

We start with the imports:

import dicom
import os
import numpy

The pydicom package can be installed through pip and can be found in https://pypi.python.org/pypi/pydicom/

	import keras
	from keras.models import Sequential
	from keras.models import load_model
	from keras.layers import Dense
	from keras.optimizers import Adam

	import numpy as np
	import random
	from collections import deque

	# -- coding: utf-8 --
	from __future__ import print_function

	import os
	import sys
	import argparse
	import tqdm

	import tensorflow as tf

	#!/bin/bash

	# install CUDA Toolkit v9.0
	# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
	CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
	wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
	sudo dpkg -i ${CUDA_REPO_PKG}
	sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
	sudo apt-get update
	sudo apt-get -y install cuda-9-0

	import io
	import os
	import numpy as np
	from PIL import Image
	from pymongo import MongoClient
	from torch.utils.data import Dataset, DataLoader
	from torchvision import transforms


	def pil_loader(f):

	import torch
	import torchvision as tv


	class ImagesDataset(torch.utils.data.Dataset):
	def __init__(self, df, transform=None,
	loader=tv.datasets.folder.default_loader):
	self.df = df
	self.transform = transform
	self.loader = loader

	import torch
	import torch.nn as nn
	import numpy as np
	import torch.optim as optim
	from torch.autograd import Variable

	# (1, 0) => target labels 0+2
	# (0, 1) => target labels 1
	# (1, 1) => target labels 3
	train = []

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	#!/usr/local/bin/python

	#######################################################
	# Otsu's Method
	# Author: Samuel Jackson (samueljackson@outlook.com)
	# Date: 21/07/2013
	# Description: Performs Otsu's method of thresholding
	# using the between class variance.
	#######################################################