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data = open('./input.txt').read().splitlines()
data = list(map(lambda i: (i[0], int(i[1:])), data))

def turn(d,x,y,n):
    while n:
        if d == 'R':
            x, y = y, -x
        else:
            x, y = -y, x
        n -=1

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from itertools import product

def neigh(row,col, part2=True):
    out = []
    for (i,j) in product((-1,0,1), (-1,0,1)):
        if not i and not j: continue
        r,c = row,col
        while not (r+i)//row_len and not (c+j)//col_len:
            if d[r+i][c+j] == '.' and part2:
                r+=i

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//Adi
(function() {
    'use strict';
    document.getElementsByTagName("body")[0].setAttribute("style", "margin:auto; max-width: 1500px;");
})();

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python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib() + "/python/_pywrap_tensorflow_internal.so")' | xargs ldd

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import json
import sys

data_type = sys.argv[1]

t = json.load(open('./captions_' + data_type + '2014.json'))
ta = t[u'annotations']

ids = {}
    

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import torch
assert torch.backends.cudnn.enabled == True
print(torch.backends.cudnn.version())

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#Step1:

mkdir /hdd/ImageNet

cd /hdd/ImageNet

#Step2: Download ImageNet data

#Download training images (about 50GB) 
wget -c http://www.image-net.org/challenges/LSVRC/2012/nonpub/ILSVRC2012_img_train.tar &

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apt list --installed | grep latex | cut -d '/' -f 1 | xargs -L 1 -P 1 dpkg --force-all -P

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{
    "notebook:hide-cell-ouputs": {
      "keys": [
        "L"
      ],
      "command": "notebook:hide-cell-ouputs",
      "selector": ".jp-Notebook:focus",
    }
      "notebook:show-cell-ouputs": {
      "keys": [

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import numpy as np
import pandas as pd
import json
from glob import glob
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline 
import seaborn as sns
sns.set(style="ticks" , palette='Set2')
sns.despine()