RomanSteinberg

import pandas as pd
import json

# input parameters
quiz_res_file = '/home/roman/temp/pre_survey/quiz_result.csv'
quiz_descr_file = '/home/roman/temp/pre_survey/quiz_description.json'

# main
df = pd.read_csv(quiz_res_file)
description = json.load(open(quiz_descr_file))

RomanSteinberg

class Heap {
  constructor() {
    this.container = [null];
  }
  
  isRoot(ind) {
    return ind == 1;
  }
  
  swap(ind1, ind2) {

RomanSteinberg

import git
repo = git.Repo(search_parent_directories=True)
opt_file.write('branch: %s\n' % repo.active_branch)
opt_file.write('sha: %s\n' % repo.head.object.hexsha)

RomanSteinberg

import numpy as np

import tensorflow as tf

experiment_folder = '/output/'
input_shape = [299, 299, 3]


def imgs_input_fn(filenames, labels=None, perform_shuffle=False, repeat_count=1, batch_size=1):
    """

RomanSteinberg

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Input, Dropout 


def imgs_input_fn(filenames, labels=None, perform_shuffle=False, repeat_count=1, batch_size=1):
    """
    Creates tf.data.Dataset object.
    Args:
        filenames (list: 
        labels (list): 

RomanSteinberg

const Promise = require('bluebird');
const mongoose = require('mongoose');
const _ = require('lodash');

const Answer = require('./answer');
const GooGl = require('./goo-gl');
const Meta = require('./meta');
const Recommendations = require('./recommendations');

const config = require('../config');

RomanSteinberg

function fk(n) {
	return (n*(n + 1)) / 2;
}

var data = readline().split(' ').map(function(x) { return parseInt(x); });
var n = data[0];
var m = data[1];
var k = data[2];

var nL = k - 1;

RomanSteinberg

import tensorflow as tf
from imageio import imsave, imread


def read_and_preproc():
    inp_img_op = tf.placeholder(tf.float32, shape=[None, None, 3])
    image_size_before_crop, IMG_HEIGHT, IMG_WIDTH = 286, 256, 256
    # Preprocessing:
    out = tf.image.resize_images(inp_img_op, [image_size_before_crop, image_size_before_crop])
    out = tf.random_crop(out, [IMG_HEIGHT, IMG_WIDTH, 3])

RomanSteinberg

import tensorflow as tf
from glob import glob

folder = './data/*.jpg'

def create_graph():
    filename_queue = tf.train.string_input_producer(list(glob(folder)))

    reader = tf.TextLineReader()
    key, value = reader.read(filename_queue)

RomanSteinberg

# -*- coding: utf-8 -*-

import numpy as np
from glob import glob
import tensorflow.contrib.keras as K
from skimage.util import view_as_windows, pad

resnet = K.applications.resnet50
image = K.preprocessing.image