gpleiss

import os
import csv
from zipfile import ZipFile
from contextlib import contextmanager

import torch
from PIL import Image


class _ImageZipDataset(torch.utils.data.Dataset):

gpleiss

import torch
import numpy as np
from matplotlib import pyplot as plt

def make_model_diagrams(outputs, labels, n_bins=10):
    """
    outputs - a torch tensor (size n x num_classes) with the outputs from the final linear layer
    - NOT the softmaxes
    labels - a torch tensor (size n) with the labels
    """

gpleiss

import fs from 'fs-promise';
import DAG from 'dag-map';
import glob from 'glob';
import promisify from 'es6-promisify';

const promisedGlob = promisify(glob);

(async function() {
  const dag = new DAG();
  const allPackageFilePaths = await promisedGlob('**/node_modules/*/package.json');

gpleiss

def has_analytics_message?(msg)
  document.synchronize do |variable|
    gaq = evaluate_script('window._gaq')
    raise Capybara::ElementNotFound, "#{msg} was not fired.\nActual analytics events triggered:\n\n#{gaq}" unless gaq.include?(msg)
  end

  true
end